Self-adaptive processing method and device for geometric data of blade

Abstract

The invention provides a self-adaptive processing method and device for geometric data of a blade, and relates to the field of computer-aided engineering. The method comprises the steps of obtaining original blade geometric data, and performing format analysis and density adaptive cleaning processing on the original blade geometric data to obtain target blade section data; performing geometric feature point identification based on the target blade section data to obtain a leading edge vertex and a trailing edge vertex; determining a corresponding boundary critical point by using the front edge vertex and the rear edge vertex, and performing structure division on the target blade section data by combining the boundary critical point to obtain blade section partition data; and performing curve fitting on the blade section partition data to obtain a fitting curve of a corresponding blade area, and constructing a blade geometric model based on the fitting curve. According to the method and the device, full-process automation and self-adaptive processing of blade geometric data from import to modeling can be realized, and the data processing efficiency and geometric reconstruction precision are remarkably improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer-aided engineering, and in particular to a method and device for adaptively processing blade geometry data. Background Art

[0002] In the entire process of design, simulation and manufacturing of aircraft engine blades, blade geometry data is usually recorded in txt (Text File) format, which is an important data basis for CNC modeling and machining programming. This type of data is based on cross-sections and contains a large number of spatial coordinate points, which is directly related to the integrity of geometric modeling and the accuracy of tool path generation. With the continuous improvement of machining accuracy and design complexity, higher requirements are placed on the processing capabilities of blade geometry data. However, related technologies still have many deficiencies when facing the task of processing blade data in txt format, which has become a key bottleneck limiting modeling efficiency and machining consistency.

[0003] On the one hand, in the data analysis stage, related methods mostly use general programming tools or simple scripts to identify and read the txt format blade data, lacking robust structural design for the characteristics of blade data. When there are problems such as irregular format, missing values or noise interference in the data, traditional processing methods are often difficult to detect and correct in time, resulting in reduced analysis accuracy, and geometric anomalies or defects are prone to occur in the subsequent modeling process. In severe cases, it is even necessary to rely on manual verification one by one, which is inefficient.

[0004] On the other hand, in the process of geometric structure division, engineers usually need to rely on manual interaction to divide the blade cross-section data into geometric areas to meet the needs of subsequent modeling and processing path planning. However, this processing method is highly dependent on the operator's experience, which is time-consuming and labor-intensive, and it is difficult to ensure the consistency of processing accuracy.

[0005] Therefore, how to break through the efficiency and accuracy bottlenecks in the process of blade geometry data analysis and geometric structure division has become a technical problem that needs to be solved urgently in the current field of blade CNC modeling.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the present disclosure is to provide a method and device for adaptively processing blade geometric data, thereby at least to a certain extent solving the problems of low efficiency and insufficient precision in the analysis and geometric structure division process of blade geometric data in the related art.

[0008] According to a first aspect of the present disclosure, there is provided a method for adaptively processing blade geometric data, including: Obtain the original blade geometric data, and perform format parsing and density adaptive cleaning processing on the original blade geometric data to obtain target blade cross-section data; Based on the target blade cross-section data, identify geometric feature points to obtain leading edge vertices and trailing edge vertices; Use the leading edge vertices and trailing edge vertices to determine corresponding boundary critical points, and combine the boundary critical points to divide the structure of the target blade cross-section data to obtain blade cross-section partition data; Perform curve fitting on the blade cross-section partition data to obtain a fitting curve for the corresponding blade region, and construct a blade geometric model based on the fitting curve.

[0009] In an exemplary embodiment of the present disclosure, the performing format parsing and density adaptive cleaning processing on the original blade geometric data to obtain target blade cross-section data includes: Extract the coordinate data segments of each cross-section of the blade from the original blade geometric data; Use a preset regular expression to match the three-dimensional coordinate format of the coordinate data segments, and perform anomaly detection on the coordinate data rows that meet the three-dimensional coordinate format. The initial blade cross-section data is composed of the coordinate data rows for which the format matching is successful and there is no abnormal data; Calculate the local density of the data points included in each cross-section in the initial blade cross-section data, and based on the calculated local density, in combination with a dynamically updated density threshold factor, screen out the target blade cross-section data from the initial blade cross-section data.

[0010] In an exemplary embodiment of the present disclosure, the calculating the local density of the data points included in each cross-section in the initial blade cross-section data includes: Traverse all the data points in each cross-section, and calculate the first distance between each data point and other data points in the same cross-section; Determine the number of data points falling within the corresponding neighborhood range according to the first distance and a preset neighborhood radius, and determine the local density of the data point according to the number of data points.

[0011] In an exemplary embodiment of the present disclosure, the screening out the target blade cross-section data from the initial blade cross-section data based on the calculated local density, in combination with a dynamically updated density threshold factor, includes: Initialize the density threshold factor and the threshold factor adjustment step size; Screening step: Based on the density threshold factor and the local density of all data points in each cross-section, calculate the density threshold corresponding to each cross-section; determine the target data points with local density less than the density threshold from all data points in each cross-section, and use the other data points in each cross-section after removing the target data points to perform cross-section line closure analysis; If the cross-section line is closed, determine the target blade cross-section data according to the other data points in each cross-section after removing the target data points; If the cross-section line is not closed, adjust the density threshold factor according to the threshold factor adjustment step size, and repeat the screening step using the adjusted density threshold factor until the target blade cross-section data screened out meets the cross-section line closure condition.

[0012] In an exemplary embodiment of the present disclosure, the performing cross-section line closure analysis using the other data points in each cross-section after removing the target data points includes: Calculate the second distance between the preset starting point in each cross-section and the other unsorted data points in the same cross-section, and sort the data points according to the second distance; For the sorted data point sequence, calculate the third distance between the head and tail data points in the data point sequence and the maximum distance between any adjacent data points; If the third distance is less than or equal to the maximum distance, determine that the cross-section line is closed; If the third distance is greater than the maximum distance, determine that the cross-section line is not closed.

[0013] In an exemplary embodiment of the present disclosure, the identifying geometric feature points based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex includes: Fit each cross-section according to the target blade cross-section data, and calculate the curvature of each data point based on the cross-section line obtained by fitting; Identify the data points corresponding to the maximum curvature as candidate feature points according to the curvature of each data point, and respectively determine the leading edge vertex and the trailing edge vertex in combination with the relative positions of the candidate feature points in the cross-section geometric structure.

[0014] In an exemplary embodiment of the present disclosure, the determining corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and partitioning the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data includes: Based on the coordinate positions of the leading edge vertex and the trailing edge vertex, traverse adjacent data points point by point along the cross-section direction, and determine the corresponding boundary critical points by means of dynamic curvature comparison; Based on the leading edge vertex, trailing edge vertex, and boundary critical points, the target blade cross-section data is structurally divided to obtain the blade cross-section partition data.

[0015] In an exemplary embodiment of the present disclosure, the curve fitting of the blade cross-section partition data to obtain a fitting curve for the corresponding blade region, and constructing a blade geometric model based on the fitting curve includes: Determine whether the blade cross-section partition data within the same partition satisfies a preset curve fitting condition; If the curve fitting condition is not satisfied, interpolate and supplement adjacent data points in the blade cross-section partition data until the blade cross-section partition data after supplementation satisfies the curve fitting condition, and then perform curve fitting processing to obtain a fitting curve for the corresponding blade region; Determine whether the fitting curves of each blade region satisfy a preset surface lofting condition; If the surface lofting condition is satisfied, perform surface lofting processing on the fitting curve along the cross-section direction to generate a geometric surface corresponding to each blade region, and construct the blade geometric model based on each geometric surface.

[0016] In an exemplary embodiment of the present disclosure, the method further includes: Based on the blade geometric model, determine the tool type and machining strategy parameters required for machining; Based on the tool type and machining strategy parameters, generate blade tool path trajectory data, and post-process the blade tool path trajectory data to obtain corresponding machining instruction data.

[0017] According to a second aspect of the present disclosure, there is provided an adaptive processing device for blade geometric data, including: A cross-section data preprocessing module for acquiring original blade geometric data, and performing format parsing and density adaptive cleaning processing on the original blade geometric data to obtain target blade cross-section data; A blade feature point recognition module for performing geometric feature point recognition based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex; A blade structure division module for using the leading edge vertex and the trailing edge vertex to determine corresponding boundary critical points, and combining the boundary critical points to structurally divide the target blade cross-section data to obtain blade cross-section partition data; A blade geometry reconstruction module for performing curve fitting on the blade cross-section partition data to obtain a fitting curve for the corresponding blade region, and constructing a blade geometric model based on the fitting curve.

[0018] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program, which when executed by a processing unit implements the adaptive processing method for blade geometric data described in any one of the above.

[0019] According to a fourth aspect of the present disclosure, there is provided an electronic device, including: a processing unit; and a storage unit for storing executable instructions of the processing unit; wherein the processing unit is configured to execute the adaptive processing method for blade geometric data described in any one of the above by executing the executable instructions.

[0020] The exemplary embodiments of the present disclosure may have some or all of the following beneficial effects: In the adaptive processing method for blade geometric data provided by the exemplary embodiment of the present disclosure, on the one hand, by introducing format parsing and density adaptive cleaning operations in the initial stage of the processing flow, it is possible to structurally clean the interference information such as inconsistent formats, invalid numerical values, and duplicate records that may exist in the original blade geometric data, thereby ensuring that the data for subsequent processing has good standardization and integrity, helping to reduce the geometric reconstruction deviation caused by data anomalies, and improving the processing efficiency and stability of the original data parsing process without relying on manual identification and intervention; on the other hand, based on the cleaned target cross-section data, by combining curvature information to extract leading-edge vertices and trailing-edge vertices, it is possible to identify representative spatial boundary points from geometric features, and further determine boundary critical points by means of dynamic curvature comparison, realizing the automatic structural division of the blade cross-section, making the traditional geometric partitioning process that relies on manual drawing of dividing lines systematic and standardized, reducing geometric errors caused by human operation differences, and thus improving the efficiency and consistency of the geometric structure division process; on the further hand, performing curve fitting operations on the various cross-section partition data after division and constructing a three-dimensional geometric model of the blade based on the fitting results can establish an effective mapping relationship from two-dimensional cross-section data to a spatial model, ensuring the geometric continuity of the model and the connection coordination between regions, and meeting the accuracy requirements of subsequent processes such as modeling, simulation, or manufacturing.

[0021] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0023] Figure 1 The schematic diagram of the system architecture to which the adaptive processing method of blade geometry data according to the embodiments of the present disclosure can be applied is shown.

[0024] Figure 2 The schematic flowchart of an adaptive processing method for blade geometry data according to the embodiments of the present disclosure is shown.

[0025] Figure 3 The schematic flowchart of a process for determining target blade cross-section data according to the embodiments of the present disclosure is shown.

[0026] Figure 4 The schematic diagram of the curvature curve corresponding to a certain cross-section according to the embodiments of the present disclosure is shown.

[0027] Figure 5 The schematic diagram of the contour of a certain cross-section according to the embodiments of the present disclosure is shown.

[0028] Figure 6 The schematic diagram of the distribution of an original blade cross-section data set according to the embodiments of the present disclosure is shown.

[0029] Figure 7 The schematic diagram of the distribution of another original blade cross-section data set according to the embodiments of the present disclosure is shown.

[0030] Figure 8 The schematic diagram of the intermediate result of blade cross-section data cleaning iteration according to the embodiments of the present disclosure is shown.

[0031] Figure 9 The schematic diagram of the distribution of the cleaned blade cross-section data set according to the embodiments of the present disclosure is shown.

[0032] Figure 10 The schematic diagram of an adaptive processing device for blade geometry data according to the embodiments of the present disclosure is shown.

[0033] Figure 11 The schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0034] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed implementation manners

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will recognize that one or more of the specific details may be omitted, or other methods, components, devices, steps, etc. may be used. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0036] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0037] Figure 1 The system architecture diagram of an adaptive processing method for a kind of blade geometric data to which the embodiments of the present disclosure can be applied is shown.

[0038] As Figure 1 shown, the system architecture 100 may include one or more of terminal devices such as a smart phone 101, a portable computer 102, a desktop computer 103, etc., a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc. The terminal device may be various electronic devices having data processing functions, and a display screen is provided on the electronic device, and the display screen may be used to display original blade geometric data, target blade cross-section data, leading edge vertices and trailing edge vertices, blade geometric models, etc. The electronic device includes, but is not limited to, the above-mentioned smart phone 101, portable computer 102, desktop computer 103, etc.

[0039] It should be understood that Figure 1 the numbers of the terminal devices, network, and server in

[0040] The adaptive processing method for blade geometric data provided by the embodiments of the present disclosure is generally executed by a terminal device. Correspondingly, the adaptive processing device for blade geometric data is generally set in the terminal device. However, those skilled in the art can easily understand that the adaptive processing method for blade geometric data provided by the embodiments of the present disclosure can also be executed by the server 105. Correspondingly, the adaptive processing device for blade geometric data can also be set in the server 105. No special limitation is made in this exemplary embodiment.

[0041] The exemplary embodiment of the present disclosure also provides an adaptive processing method for blade geometric data. Refer to Figure 2 As shown, the method may include the following steps S210 to S240: Step S210: Obtain the original blade geometric data, and perform format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-section data; Step S220: Identify geometric feature points based on the target blade cross-section data to obtain leading edge vertices and trailing edge vertices; Step S230: Determine corresponding boundary critical points by using the leading edge vertices and trailing edge vertices, and combine the boundary critical points to divide the structure of the target blade cross-section data to obtain blade cross-section partition data; Step S240: Perform curve fitting on the blade cross-section partition data to obtain a fitting curve for the corresponding blade region, and construct a blade geometric model based on the fitting curve.

[0042] By executing format parsing and density adaptive cleaning at the initial stage of data processing when implementing the adaptive processing method for blade geometric data provided by the exemplary embodiment of the present disclosure, format anomalies and redundant information in the original blade geometric data can be effectively avoided, data standardization can be improved, and parsing efficiency and stability can be enhanced. On this basis, by extracting the leading edge and trailing edge vertices in combination with curvature information and using a dynamic curvature comparison method to locate the boundary critical points, automatic division of the cross-section structure can be achieved, which helps to improve the consistency and processing efficiency of geometric partitioning. Further, performing curve fitting on the cross-section data of each partition and constructing a three-dimensional model can ensure geometric continuity and connection coordination between regions, meeting the requirements of high-precision modeling and machining.

[0043] Next, the adaptive processing method for blade geometric data in this exemplary embodiment will be described in detail.

[0044] In step S210, the original blade geometric data is obtained, and format parsing and density adaptive cleaning are performed on the original blade geometric data to obtain target blade cross-section data.

[0045] In the exemplary embodiments of the present disclosure, the original blade geometric data is usually stored in the txt format. The discrete coordinate data is stored in plain text, which has the advantages of small file size and fast transmission speed, facilitating efficient reading and preprocessing. Moreover, the txt file does not depend on a dedicated 3D modeling software and has good cross-platform compatibility. Importantly, the txt file can retain the geometric information in the form of original point data, avoiding the accuracy loss and boundary errors caused by the parametric surface format during cross-software parsing.

[0046] It can be understood that the original blade geometric data stored in the txt file contains multiple three-dimensional coordinate point data representing blade cross-sections. Since the original blade geometric data comes from the actual measurement or simulation output process, there may be problems such as non-standard format, data noise interference, missing values, and duplicates. Directly using such data will affect the subsequent modeling accuracy and processing stability.

[0047] To improve the accuracy and robustness of data processing, the format of the original blade geometric data can be parsed first. Exemplarily, parsing the format of the original blade geometric data means extracting the coordinate data segments of each blade cross-section from the txt file and, based on a preset three-dimensional coordinate recognition rule, such as regular expression matching, determining whether each line of coordinate data meets the specified three-dimensional coordinate expression format. Then, only the coordinate data lines that meet the format specification are retained, and invalid data such as null values (NaN, Not a Number), missing fields, or syntax errors are removed, thereby constructing the initial blade cross-section data.

[0048] As shown in Table 1, each row of data in Table 1 corresponds to a sampling point on a certain blade cross-section.

[0049] Table 1 In Table 1, "# Profile l at 0.0000%" is a comment statement in the txt file of the blade geometry data, which is used to indicate the blade section to which the three-dimensional data point belongs. Among them, "Profile l" represents section 1, and "at 0.0000%" represents the position of section 1, such as the percentage position of section 1 along the span length of the blade. In this case, it means that section 1 is located at the beginning of the blade, such as near the root of the blade or the starting span position. For the first row of data (253.999759, 0.349947171, 50.2493955), the three values are the coordinate values of the section point in the X direction, Y direction and Z direction, respectively, indicating the position of the section point in the axial direction of the blade geometry, the position of the section point in the radial direction of the blade geometry, and the position of the section point in the rotation direction of the blade. It can be seen that the second row of data is abnormal data that cannot be parsed for the Y coordinate value, the third row of data is abnormal data that lacks the Z coordinate value, and the fourth row of data is abnormal data with all coordinate values connected together. It should be noted that the content shown in Table 1 is only for illustrative purposes and is used to display various data structures and abnormal situations in the blade geometry data in txt format.

[0050] After reading all the lines of data in the txt file, you can first perform a blank line filtering operation to remove invalid content and avoid introducing interference in the subsequent parsing process. Then, identify all the lines of data containing comments and determine whether there are identical comments. If multiple data segments are found to correspond to the same comment, such as "# Profile 1 at 0.0000%", it can be determined that these data segments all belong to the data of section 1, and the corresponding multiple data segments are merged to ensure that the point data corresponding to each section is complete and unified, providing a standardized input structure for subsequent geometric processing steps.

[0051] After the format parsing is completed, the initial blade section data is further subjected to density adaptive cleaning processing. In the example implementation of the present disclosure, density adaptive cleaning processing refers to identifying and removing isolated points or abnormal points whose local density is significantly lower than the average level based on the local point density distribution characteristics of each data point in the section to which it belongs. This process does not rely on a fixed judgment threshold, but combines the spatial distribution characteristics of each cross-sectional data to dynamically adjust the density discrimination conditions to adapt to the geometric complexity and point cloud density of different data areas, thereby improving the accuracy of noise point recognition and enhancing the adaptability of the data processing process to diverse input data.

[0052] In some example implementations, reference Figure 3 As shown, step S210 may further include steps S310 to S330: Step S310: extracting coordinate data segments of each cross section of the blade from the original blade geometric data.

[0053] Specifically, all line data in the txt file are read line by line. Based on identifying the annotation statements for marking the cross-section positions, the grouped coordinate data segments corresponding to each annotation statement are extracted, providing a basis for subsequent structural analysis and geometric processing.

[0054] Step S320: Use a preset regular expression to perform three-dimensional coordinate format matching on the coordinate data segments, and perform anomaly detection on the coordinate data lines that meet the three-dimensional coordinate format. The coordinate data lines with successful format matching and no abnormal data constitute the initial blade cross-section data.

[0055] First, for the coordinate data segments of each cross-section, call the preset regular expression to perform three-dimensional coordinate format matching. Among them, the regular expression can be preset as a matching pattern that conforms to the standard three-dimensional coordinate format. For example, it can be a structure of "floating point number + delimiter + floating point number + delimiter + floating point number", which is used to identify whether each coordinate data line completely represents the three-dimensional coordinate information of a blade cross-section point.

[0056] During the matching process, the content in the coordinate data segments is parsed line by line, and the coordinate data lines with standardized formats and complete structures are screened out. If a certain coordinate data line does not conform to this matching rule, such as missing coordinate values, having illegal characters, or non-standard delimiters, it is determined that the format matching of this coordinate data line fails, and the abnormal line data that does not conform to the format is deleted.

[0057] Then, further anomaly detection is performed on the coordinate data lines with successful format matching. For example, it is detected whether there are extreme values or obviously incorrect numerical values that exceed the physically reasonable range of the coordinates, such as abnormal data points that exceed the physical size of the blade, and the coordinate data lines with abnormal data are deleted to further improve the physical rationality and engineering applicability of the data. At the same time, to prevent data redundancy from affecting the accuracy of subsequent curve fitting and structure reconstruction, for the coordinate data segments of each cross-section, the duplicate coordinate data lines except the first and last points can also be deleted to ensure the effectiveness and representativeness of the cross-section point distribution.

[0058] That is to say, only the coordinate data lines with successful format matching and no detected abnormal data are recognized as valid data and finally constitute the initial blade cross-section data, serving as the basic input for subsequent density calculation and geometric analysis.

[0059] Step S330: Calculate the local density of the data points included in each cross-section in the initial blade cross-section data, and based on the calculated local density, combined with the dynamically updated density threshold factor, screen out the target blade cross-section data from the initial blade cross-section data.

[0060] In some exemplary embodiments, for each data point in each cross-section, the number of neighboring points included within a given radius neighborhood range is counted, and this number is the local density of the data point. By traversing all the data points in each cross-section, the local densities of all the data points in each cross-section can be obtained.

[0061] Subsequently, based on the density distribution characteristics of each cross-section and the initialized density threshold factor, it is determined which data points have significantly low density. By iteratively adjusting the density threshold factor and combining with the judgment of the closure of the cross-section line, the points that do not meet the density requirements are gradually excluded, and finally, the target blade cross-section data with spatial continuity and geometric rationality is selected, providing high-quality data support for subsequent feature recognition and fitting.

[0062] Exemplarily, when calculating the local density of the data points included in each cross-section of the initial blade cross-section data, all the data points in each cross-section can be traversed, and the first distance between each data point and other data points in the same cross-section is calculated, denoted as d , the first distance can be Euclidean distance, Manhattan distance, cosine distance, etc., and the present disclosure does not limit this. After calculating the first distance, the number of data points falling within the corresponding neighborhood range can be determined according to the first distance and the preset neighborhood radius, and finally, the local density of the data point is determined according to the number of data points. For example, the preset neighborhood radius r = 0.1mm, if the first distance d is less than or equal to the neighborhood radius r , then it is considered that the data point is within the neighborhood range. By counting the number of data points falling within the neighborhood range, that is, counting the number of data points whose first distance is less than or equal to the neighborhood radius, this number of data points is the local density of the data point.

[0063] Furthermore, based on the calculated local density and in combination with the dynamically updated density threshold factor, the target blade cross-section data is selected from the initial blade cross-section data. Specifically, first, the density threshold factor and the threshold factor adjustment step size are initialized. For example, the density threshold factor is initialized to 0.8 to guide the subsequent data screening process, and the density threshold factor adjustment step size is set to 0.02, so that when the cross-section line does not meet the closure requirement, the density screening condition can be gradually adjusted, thereby realizing the adaptive regulation of the local density screening accuracy.

[0064] Then the screening step is executed. The screening step specifically includes: calculating the density threshold corresponding to each cross-section based on the density threshold factor and the local density of all the data points in each cross-section.

[0065] For example, by counting the local density of all the data points in each cross-section, the average value and standard deviation of the local density of all the data points are obtained, and in combination with the current density threshold factor, the density threshold corresponding to each cross-section is calculated, as follows: Among them, θ is the density threshold corresponding to a certain cross-section, μ is the average value of the local densities of all data points within a certain cross-section, σ is the standard deviation of the local densities of all data points within a certain cross-section, α is the density threshold factor, which is an adjustable parameter.

[0066] It should be noted that in the exemplary embodiments of the present disclosure, the density threshold can be used to determine whether the density of each data point reaches the screening standard, so as to identify target data points with relatively low local density that may constitute noise points or discontinuous boundaries, providing a basis for subsequent cross-section line closure analysis and data cleaning.

[0067] For example, target data points with local density less than the density threshold are determined from all data points within each cross-section, and cross-section line closure analysis is performed using the other data points in each cross-section after removing the target data points. If the cross-section line is closed, the target blade cross-section data is determined according to the other data points in each cross-section after removing the target data points. If the cross-section line is not closed, the density threshold factor is adjusted according to the threshold factor adjustment step size, and the screening steps are repeated using the adjusted density threshold factor until the target blade cross-section data selected satisfies the cross-section line closure condition.

[0068] Specifically, from all data points within each cross-section, it is traversed to determine whether the local density of each data point is greater than or equal to the corresponding density threshold. If the local density of a certain data point is less than the corresponding density threshold, then the data point is determined to be a noise point or a low-confidence data point, and it is removed from the data point set of the current cross-section. After removing the noise points, the remaining high-density data points form a new cross-section data subset. Subsequently, based on this data subset, cross-section line closure analysis is performed to evaluate whether the geometric continuity and closure of the cross-section contour are retained in this screening process, providing a basis for determining whether to update the density threshold factor and perform iterative optimization in the next step.

[0069] In some exemplary embodiments, using the other data points in each cross-section after removing the target data points for cross-section line closure analysis specifically means calculating the second distance between a preset starting point within each cross-section and the other unordered data points in the same cross-section, and sorting the data points according to the second distance. For the sorted data point sequence, calculate the third distance between the first and last data points in the data point sequence and the maximum distance between any adjacent data points. If the third distance is less than or equal to the maximum distance, it is determined that the cross-section line is closed; if the third distance is greater than the maximum distance, it is determined that the cross-section line is not closed.

[0070] Taking the second distance, the third distance, and the maximum distance as Euclidean distances as an example, for each cross-sectional data to be processed, the first data point in the cross-sectional coordinate data segment can be selected as the starting point. Subsequently, starting from this starting point, the Euclidean distances between the current data point and other unordered data points are calculated in sequence, and in each step, the data point with the closest distance to the current data point is preferentially selected to be added to the data point sequence, thereby completing the iterative sorting of all data points within the cross-section.

[0071] After completing the sorting, first calculate the Euclidean distance between the head and tail data points of the data point sequence, that is, the head-to-tail distance between the starting point and the ending point, denoted as d start-end At the same time, traverse all adjacent point pairs in the data point sequence, calculate the Euclidean distances between each adjacent point pair respectively, and determine the maximum distance among all adjacent point pairs, denoted as d max Subsequently, by comparing d start-end with d max to judge whether the cross-sectional line is closed.

[0072] For example, if d start-end ≤ d max , it can be determined that the current data point sequence forms a closed curve, and it is considered that the cross-sectional data meets the closure requirement. If d start-end > d max , it can be determined that the current data point sequence does not form a closed curve. At this time, the screening parameters need to be continuously adjusted to optimize the closure of the point set. For example, automatically update the density threshold factor, such as increasing a preset threshold factor adjustment step, and re-perform the density screening and cross-sectional closure judgment process based on the updated density threshold factor. It can be understood that the above screening steps will be sequentially executed for all cross-sectional data until all cross-sections meet the preset closure conditions.

[0073] In this density - adaptive manner, the cleaning criteria can be flexibly adjusted according to the distribution characteristics of different cross - section data, effectively removing measurement errors or noise points such as isolated points, jump points, and duplicate points. In addition, during the cleaning process, a geometric structure constraint mechanism based on the closedness of the cross - section line is introduced as the criterion for cleaning quality. After a cross - section data is cleaned once, sorting and closedness judgment are performed based on the remaining data points. If the result cannot form a continuous closed contour, the density threshold factor is automatically adjusted, and data cleaning and closedness judgment are performed again until the target blade cross - section data that meets geometric integrity is output. In this example, the structured processing and quality optimization of the original blade geometric data can be completed without manual intervention, laying a data foundation for subsequent geometric feature recognition and model construction.

[0074] In step S220, geometric feature points are identified based on the target blade cross - section data to obtain leading - edge vertices and trailing - edge vertices.

[0075] Exemplarily, each cross - section can be fitted according to the target blade cross - section data, and the curvature of each data point is calculated based on the fitted cross - section line. Then, the data points corresponding to the maximum curvature are identified as candidate feature points according to the curvature of each data point, and the leading - edge vertices and trailing - edge vertices are respectively determined in combination with the relative positions of the candidate feature points in the cross - section geometric structure. The leading - edge vertices and trailing - edge vertices are usually located in the starting - end and ending - end regions of the cross - section line, corresponding to the directions in which the fluid enters and leaves the blade cross - section respectively, and are important geometric landmark points characterizing the aerodynamic shape of the blade cross - section.

[0076] Specifically, curve fitting processing can be first performed on the target blade cross - section data within each cross - section to obtain a smooth and continuous cross - section line. The fitting method can adopt polynomial fitting, B - spline fitting, or other types of geometric curve fitting algorithms to ensure that the fitting result has good smoothness and representativeness. Then, on the fitted cross - section line, for each data point on the cross - section line, its corresponding curvature is calculated. Among them, curvature, as a parameter measuring the degree of local shape change, can effectively reflect the geometric mutation degree of the curve at this point.

[0077] For example, according to: Calculate the curvature corresponding to each data point k ; where is the first - order derivative of the cross - section line, representing the tangent vector of the cross - section line at point t, is the second - order derivative of the cross - section line, is the vector cross - product of the first - order derivative and the second - order derivative, is the modulus of the vector cross - product, is the modulus of the tangent vector.

[0078] Subsequently, according to the calculated curvature information, data points corresponding to the maximum curvature are screened out from the target blade cross-section data as candidate feature points. Considering that there may be multiple local maximum curvature points in some cross-sections, in order to ensure the structural stability and geometric representativeness of the finally selected leading-edge vertex and trailing-edge vertex, further judgment can also be made by combining the relative distribution positions of the candidate feature points in the overall geometric structure of the cross-section line. For example, the leading-edge vertex and the trailing-edge vertex are located at both ends of the blade, the curvature of the leading-edge vertex and the trailing-edge vertex is relatively large, and the curvature of the leading-edge vertex is greater than that of the trailing-edge vertex. Therefore, the maximum curvature point near the front side of the geometric center of the cross-section line can be preferentially determined as the leading-edge vertex, while the maximum curvature point in the tail region of the cross-section line can be used as the trailing-edge vertex.

[0079] Determining the leading-edge vertex and the trailing-edge vertex corresponding to each blade cross-section can provide an accurate geometric basis for subsequent boundary critical point identification and blade partition structure division.

[0080] In addition, before performing curve fitting processing, it is also necessary to check whether there are duplicate data in each cross-section data. If no duplicate data is detected, the first row of data can be copied and pasted to the end of the coordinate data segment to construct a closed sequence of cross-section data points. This processing method helps to ensure that the cross-section contour line forms a complete closed loop geometrically, thereby providing a continuous and stable input data basis for subsequent geometric feature extraction and structure division operations, and avoiding fitting errors or processing interruptions caused by curve breaks.

[0081] In step S230, the corresponding boundary critical points are determined using the leading-edge vertex and the trailing-edge vertex, and the target blade cross-section data is structurally divided in combination with the boundary critical points to obtain blade cross-section partition data.

[0082] After identifying the leading-edge vertex and the trailing-edge vertex, in order to achieve automatic structural division of the target blade cross-section data, it is necessary to dynamically determine boundary critical points in their neighborhoods based on the leading-edge vertex and the trailing-edge vertex as initial geometric reference points to define the division positions between different functional regions.

[0083] Exemplarily, based on the coordinate positions of the leading-edge vertex and the trailing-edge vertex, adjacent data points can be traversed point by point along the cross-section direction, and the corresponding boundary critical points can be determined by means of dynamic curvature comparison. Then, based on the leading-edge vertex, the trailing-edge vertex, and the boundary critical points, the target blade cross-section data is structurally divided to obtain blade cross-section partition data.

[0084] Specifically, the identified leading edge vertex or trailing edge vertex can be used as the starting point for traversal. Combining the relative distribution relationship of each cross-section data point in the cross-section line, adjacent data points are traversed point by point along the cross-section direction. By calculating the curvature of each data point and comparing it one by one with the dynamically updated curvature threshold, the position where the curvature change trend undergoes a significant turning point is identified. This turning point usually corresponds to the boundary transition zone where the blade geometry changes from a sharp change to a gentle region and can be used as the boundary critical point for dividing different blade partitions.

[0085] Reference Figure 4 As shown, a schematic diagram of the curvature curve corresponding to a certain cross-section is shown. The horizontal axis of this curvature curve is the cross-section data of the target blade, and the vertical axis is the curvature. That is to say, this curvature curve is a contour curve fitted based on the cross-section data of the target blade, and is formed after calculating the curvature of each point on the contour curve, and is used to represent the curvature change trend of the cross-section along the contour direction. In Figure 4 , the leading edge vertex and the trailing edge vertex are points P i and point P j respectively. Taking the leading edge vertex P i as an example, starting from this point, adjacent data points are traversed point by point along the cross-section direction, such as including the forward adjacent points P i+1 , P i+2 …, P i+n and the backward adjacent points P i-1 , P i-2 …, P i-n . During the forward traversal, the curvature value of the leading edge vertex P i is compared with the curvature values of each forward adjacent point respectively. For example, if the curvature of the leading edge vertex P i is greater than the curvature of the forward adjacent point P i+1 , it indicates that the curvature shows a decreasing trend. At this time, the curvature threshold is dynamically adjusted to the curvature of the forward adjacent point P i+1 , and the forward adjacent point P i+1 is used as the new comparison reference point and continues to be compared with the subsequent adjacent points, and so on, until the curvature of a certain forward adjacent point no longer satisfies the relationship of decreasing in sequence. For example, if the curvature of the forward adjacent point P i+n-1 is less than the curvature of the forward adjacent point P i+n , it is considered that the forward adjacent point P i+n is located in the transition zone between the leading edge segment and the middle contour region, and it can be determined that the forward adjacent point P i+n is the boundary critical point at one end of the leading edge. Similarly, assuming that the backward adjacent point P i-n is determined as the boundary critical point at the other end of the leading edge through backward traversal. For the process of extracting the boundary critical point of the trailing edge vertex, dynamic curvature comparison and positioning can be carried out in the same way.

[0086] ReferenceFigure 5 As shown, a schematic contour diagram of a certain cross-section is shown. Figure 5 It includes the identified leading-edge vertex P i and boundary critical points P i at both ends of the leading-edge vertex P i+n and P i-n , the trailing-edge vertex P j and boundary critical points P j at both ends of the trailing-edge vertex P j+n and P j-n .

[0087] After completing the positioning of the boundary critical points, the relative positional relationship between the leading-edge vertex and the trailing-edge vertex can be combined to construct the corresponding boundary intervals for the leading-edge segment and the trailing-edge segment respectively. Exemplarily, the set of points between the leading-edge vertex and its two boundary critical points can be used as the leading-edge segment data, and the set of points between the trailing-edge vertex and its two boundary critical points can be used as the trailing-edge segment data. The remaining data points in the unincluded intermediate region can be further divided into suction surface segment data and pressure surface segment data. For example, based on the reference line connecting the leading-edge vertex and the trailing-edge vertex, combined with the spatial distribution position of the intermediate region data points relative to this reference line, the intermediate region data points can be divided into the suction surface segment and the pressure surface segment by means of symmetric division, sequential segmentation or normal direction, etc., thereby completing the structural partition processing of the target blade cross-section data.

[0088] It can be understood that the finally obtained blade cross-section partition data includes the leading-edge segment data, trailing-edge segment data, suction surface segment data and pressure surface segment data of each cross-section.

[0089] This process can achieve curvature-based adaptive boundary recognition, that is, the automatic segmentation of the target cross-section point data, avoid the inconsistencies brought by manual boundary drawing or interactive determination, help improve the accuracy and automation of cross-section structure division, and further enhance the structural rationality and partition accuracy of geometric processing, providing a good foundation for subsequent partition curve fitting and geometric modeling.

[0090] In step S240, curve fitting is performed on the blade cross-section partition data to obtain a fitting curve corresponding to the blade region, and a blade geometric model is constructed based on the fitting curve.

[0091] To accurately reconstruct the three-dimensional geometric model of the blade, a fitting modeling operation needs to be performed on the blade cross-section partition data that has completed the structural division. For each blade cross-section, after completing the structural division, discrete coordinate data points of multiple partition regions can be obtained. In this disclosure, fitting processing needs to be performed on these data points respectively to obtain fitting curves describing the contour shapes of each region.

[0092] In some exemplary embodiments, it is possible to first determine whether the blade cross-section partition data within the same partition meets a preset curve fitting condition. Among them, the preset curve fitting condition may include, but is not limited to, indicators such as the number of points threshold (e.g., more than 4 data points), point distribution density, and coordinate value continuity. The present disclosure does not limit this, as long as it can ensure that the fitting result will not be distorted or the contour will not be incoherent due to sparse data or abnormal arrangement during the fitting process.

[0093] If the curve fitting condition is not met, such as insufficient number of points or insufficient point distribution density within a certain partition, interpolation and point supplementation are performed on adjacent data points in the blade cross-section partition data to supplement the missing coordinate points in the middle, and the point supplementation is continuously iterated until the blade cross-section partition data after point supplementation meets the curve fitting condition, and then curve fitting processing is performed to obtain the fitting curve of the corresponding blade region. This fitting curve will accurately reflect the geometric contour characteristics of the blade partition and provide a basis for subsequent three-dimensional modeling. Among them, the curve fitting method can adopt polynomial fitting, B-spline fitting or other types of geometric curve fitting algorithms.

[0094] After the generation of the fitting curves within each blade cross-section is completed, a linkage analysis is performed on the fitting curves at corresponding positions between multiple cross-sections along the blade span direction, and it is determined whether the fitting curves of each blade region meet the preset surface lofting condition. Among them, the surface lofting condition may include criteria such as the number of cross-section line threshold (e.g., more than 2 cross-section lines), topological structure matching, and spatial continuity between the starting and ending curves. The present disclosure does not limit this.

[0095] If the surface lofting condition is met, surface lofting processing is performed on the fitting curves along the cross-section direction to generate the geometric surfaces corresponding to each blade region, and a blade geometric model is constructed based on each geometric surface, that is, a continuous spatial surface is generated for each partition, and multiple spatial surfaces together constitute the final blade geometric model.

[0096] Through the above curve fitting and surface lofting processes, the original discrete blade cross-section partition data can be accurately converted into a three-dimensional blade model with clear structure and geometric continuity, ensuring that the model structure is complete, the contour is smooth, and the boundary transition is natural, providing high-quality geometric support for subsequent simulation analysis and numerical control machining.

[0097] Reference Figure 6 As shown, a distribution diagram of an original blade cross-section data set is shown. Among them, the abscissa is the X-direction coordinate value of the blade cross-section data point in the cross-section plane, and the ordinate is the Y-direction coordinate value of the blade cross-section data point in the cross-section plane, reflecting the distribution position of the blade cross-section data point in the two-dimensional contour plane and used to describe the morphological structure of the cross-section geometry. As Figure 6 can be seen, the original point set before data cleaning is densely distributed along an approximate straight line and there are multiple outlier points, that is, noise points.

[0098] Reference Figure 7 As shown, a distribution schematic diagram of another original blade cross-section data set is shown. Compared with Figure 6 , by reducing the display range of the X-axis, the morphology of the point set arranged along the main contour can be presented more clearly, but there are still multiple noise points.

[0099] Reference Figure 8 As shown, a schematic diagram of the intermediate result of an iterative cleaning of blade cross-section data is shown. Figure 8 What is shown is the retention situation of local point sets during the data cleaning process, reflecting the dynamic process of density threshold adjustment and continuity judgment.

[0100] Reference Figure 9 As shown, a distribution schematic diagram of a cleaned blade cross-section data set is shown. Compared with Figures 6 to 8 , Figure 9 the point set in forms a complete closed contour, and the noise points are removed, and the contour line is more coherent.

[0101] Through density threshold judgment and point set continuity analysis, the present disclosure can effectively remove outlier abnormal points, restore contour closure and data consistency, and provide a stable geometric basis for subsequent curve fitting and structural modeling.

[0102] In some exemplary embodiments, after obtaining the blade geometric model, the preparation stage of numerical control machining can be entered. Exemplarily, based on the blade geometric model, machining instruction data that can be recognized and executed by a numerical control machine tool is automatically generated. For example, based on the blade geometric model, the tool type and machining strategy parameters required for machining can be determined. Specifically, by analyzing the spatial dimensions, surface distribution characteristics of the blade geometric model, and machining requirements of each region, the matching tool type and machining strategy parameters are automatically determined in combination with a preset process database.

[0103] Among them, the tool types include ball nose end mills, taper end mills or other tools suitable for free-form surface machining, etc. Tools with different shapes, diameters and parameters can be matched according to the curvature distribution and machining accessibility of different blade regions. The machining strategy parameters include machining path type, feed rate, spindle speed, machining direction, cutting layer height, etc., to ensure that the subsequent tool path trajectory covers all target regions and meets the machining accuracy and efficiency requirements.

[0104] Further, based on the tool type and machining strategy parameters, the blade tool path trajectory data can be generated, and the blade tool path trajectory data can be post-processed to obtain the corresponding machining instruction data. Among them, the blade tool path trajectory data includes a discrete coordinate sequence of the tool moving along a predetermined path in three-dimensional space. The post-processing can include steps such as format conversion, interpolation mode setting, tool compensation setting, feed speed and spindle speed setting, coordinate system and zero point setting, machining sequence and logic control, safety processing, file structure encapsulation, etc. The present disclosure does not limit the specific process of post-processing, as long as the blade tool path trajectory data can be converted into recognizable machining instruction data.

[0105] After the blade tool path trajectory is generated, the blade tool path trajectory data is post-processed to output machining instruction data that conforms to the control format of the specified numerical control system. The machining instruction data is used to drive the numerical control machine tool to perform the actual machining operation of the blade geometry, and can include G-code (standard instructions for controlling the machine tool movement mode) or NC (Numerical Control) program statements, including instruction parameters such as tool movement, cutting speed, and spindle control. Through this machining instruction data, the numerical control machine tool can accurately execute various milling operations to achieve the physical machining and manufacturing of the blade geometric model.

[0106] Further, in the present exemplary embodiment, an adaptive processing device for blade geometric data is also provided. Refer to Figure 10 As shown, the adaptive processing device 1000 for blade geometric data may include a cross-section data preprocessing module 1010, a blade feature point recognition module 1020, a blade structure division module 1030, and a blade geometry reconstruction module 1040, where: The cross-section data preprocessing module 1010 is configured to obtain the original blade geometric data and perform format parsing and density adaptive cleaning processing on the original blade geometric data to obtain the target blade cross-section data; The blade feature point recognition module 1020 is configured to perform geometric feature point recognition based on the target blade cross-section data to obtain the leading edge vertex and the trailing edge vertex; The blade structure division module 1030 is configured to determine the corresponding boundary critical points by using the leading edge vertex and the trailing edge vertex, and perform structure division on the target blade cross-section data in combination with the boundary critical points to obtain the blade cross-section partition data; The blade geometry reconstruction module 1040 is configured to perform curve fitting on the blade cross-section partition data to obtain the fitting curve of the corresponding blade region, and construct a blade geometric model based on the fitting curve.

[0107] The specific details of each module in the above-mentioned adaptive processing device for blade geometric data have been described in detail in the corresponding adaptive processing method for blade geometric data, and thus will not be elaborated here.

[0108] Exemplary embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon a program product capable of implementing the methods described above in this specification. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code that, when the program product runs on an electronic device, causes the electronic device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Methods" section above in this specification. The program product may be a portable compact disc read-only memory (CD-ROM) and include the program code, and may run on an electronic device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In the present disclosure, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0109] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0110] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0111] The program code contained on the readable medium may be transmitted by any suitable medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0112] Program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C#, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0113] Exemplary embodiments of the present disclosure also provide an electronic device capable of implementing the above method. The following will refer to Figure 11 to describe the electronic device 1100 according to such an exemplary embodiment of the present disclosure. Figure 11 The illustrated electronic device 1100 is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present disclosure.

[0114] As Figure 11 shown, the electronic device 1100 can be presented in the form of a general-purpose computing device. The components of the electronic device 1100 can include, but are not limited to: at least one processing unit 1110, at least one storage unit 1120, a bus 1130 connecting different system components (including the storage unit 1120 and the processing unit 1110), and a display unit 1140.

[0115] The storage unit 1120 stores program code, and the program code can be executed by the processing unit 1110, so that the processing unit 1110 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 1110 can execute Figure 2 the method steps in

[0116] The storage unit 1120 can include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 1121 and / or a cache storage unit (Cache) 1122, and can further include a read-only storage unit (ROM) 1123.

[0117] The storage unit 1120 may also include a program / utilities 1124 having a set (at least one) of program modules 1125. Such program modules 1125 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0118] The bus 1130 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0119] The electronic device 1100 may also communicate with one or more external devices 1200 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 1100, and / or communicate with any device that enables the electronic device 1100 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface 1150. Moreover, the electronic device 1100 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 1160. As shown in the figure, the network adapter 1160 communicates with other modules of the electronic device 1100 through the bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Array of Independent Disks) systems, magnetic tape drives, and data backup storage systems, etc.

[0120] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the exemplary embodiments of the present disclosure.

[0121] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0122] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-mentioned modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] Those skilled in the art will readily think of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0124] It should be understood that the present disclosure is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. An adaptive processing method for blade geometric data, characterized in that Including: Obtain the original blade geometric data, perform format parsing and density adaptive cleaning processing on the original blade geometric data to obtain target blade cross-section data; Based on the target blade cross-section data, identify geometric feature points to obtain leading-edge vertices and trailing-edge vertices; Use the leading-edge vertices and trailing-edge vertices to determine corresponding boundary critical points, and combine the boundary critical points to divide the structure of the target blade cross-section data to obtain blade cross-section partition data; Perform curve fitting on the blade cross-section partition data to obtain a fitting curve for the corresponding blade area, and construct a blade geometric model based on the fitting curve.

2. The adaptive processing method for blade geometric data according to claim 1, characterized in that The performing format parsing and density adaptive cleaning processing on the original blade geometric data to obtain target blade cross-section data includes: Extract the coordinate data segments of each cross-section of the blade from the original blade geometric data; Use a preset regular expression to match the three-dimensional coordinate format of the coordinate data segments, and perform anomaly detection on the coordinate data rows that meet the three-dimensional coordinate format. The coordinate data rows with successful format matching and no abnormal data constitute the initial blade cross-section data; Calculate the local density of the data points included in each cross-section of the initial blade cross-section data, and based on the calculated local density, combined with a dynamically updated density threshold factor, screen out the target blade cross-section data from the initial blade cross-section data.

3. The adaptive processing method for blade geometric data according to claim 2, characterized in that The calculating the local density of the data points included in each cross-section of the initial blade cross-section data includes: Traverse all the data points in each cross-section and calculate the first distance between each data point and other data points in the same cross-section; Determine the number of data points falling within the corresponding neighborhood range according to the first distance and a preset neighborhood radius, and determine the local density of the data point according to the number of data points.

4. The adaptive processing method for blade geometric data according to claim 2, characterized in that, The screening out the target blade cross-section data from the initial blade cross-section data based on the calculated local density, combined with a dynamically updated density threshold factor, includes: Initialize the density threshold factor and the threshold factor adjustment step size; Screening step: Based on the density threshold factor and the local density of all data points in each cross-section, calculate the density threshold corresponding to each cross-section; Determine the target data points with local density less than the density threshold from all the data points in each cross-section, and perform cross-section line closing analysis using the other data points in each cross-section after removing the target data points; If the cross-section line is closed, determine the target blade cross-section data according to the other data points in each cross-section after removing the target data points; If the cross-section line is not closed, adjust the density threshold factor according to the threshold factor adjustment step size, and repeat the screening step using the adjusted density threshold factor until the target blade cross-section data screened out meets the cross-section line closing condition.

5. The adaptive processing method for blade geometric data according to claim 4, characterized in that The performing cross-section line closing analysis using the other data points in each cross-section after removing the target data points includes: Calculate the second distance between a preset starting point in each cross-section and the other unsorted data points in the same cross-section, and sort each data point according to the second distance; For the sorted data point sequence, calculate the third distance between the first and last data points in the data point sequence and the maximum distance between any adjacent data points; If the third distance is less than or equal to the maximum distance, determine that the section line is closed; If the third distance is greater than the maximum distance, determine that the section line is not closed.

6. The adaptive processing method for blade geometric data according to claim 1, characterized in that The geometric feature point recognition based on the target blade section data to obtain the leading edge vertex and the trailing edge vertex includes: Fitting each section according to the target blade section data and calculating the curvature of each data point based on the section line obtained by fitting; Identify the data points corresponding to the maximum curvature values according to the curvatures of the data points as candidate feature points, and determine the leading edge vertex and the trailing edge vertex respectively in combination with the relative positions of the candidate feature points in the section geometric structure.

7. The adaptive processing method for blade geometric data according to claim 1, characterized in that, The determination of the corresponding boundary critical points using the leading edge vertex and the trailing edge vertex and the structural division of the target blade section data in combination with the boundary critical points to obtain the blade section partition data includes: Based on the coordinate positions of the leading edge vertex and the trailing edge vertex, traverse adjacent data points point by point along the section direction and determine the corresponding boundary critical points by means of dynamic curvature comparison; Based on the leading edge vertex, the trailing edge vertex and the boundary critical points, perform a structural division on the target blade section data to obtain the blade section partition data.

8. The adaptive processing method for blade geometric data according to claim 1, characterized in that The curve fitting of the blade section partition data to obtain the fitting curve of the corresponding blade region and the construction of the blade geometric model based on the fitting curve includes: Judge whether the blade section partition data in the same partition meets the preset curve fitting conditions; If the curve fitting conditions are not met, perform interpolation to supplement points for adjacent data points in the blade section partition data until the blade section partition data after point supplementation meets the curve fitting conditions and then perform curve fitting processing to obtain the fitting curve of the corresponding blade region; Judge whether the fitting curves of each blade region meet the preset surface lofting conditions; If the surface lofting conditions are met, perform surface lofting processing on the fitting curves along the section direction to generate the geometric surfaces corresponding to each blade region, and construct the blade geometric model based on each geometric surface.

9. The adaptive processing method for blade geometry data according to claim 1, wherein The method further includes: Based on the blade geometric model, determine the tool type and machining strategy parameters required for machining; Based on the tool type and machining strategy parameters, generate blade tool path trajectory data and post-process the blade tool path trajectory data to obtain the corresponding machining instruction data.

10. An adaptive processing device for blade geometric data, characterized in that Including: A section data preprocessing module for obtaining the original blade geometric data and performing format parsing and density adaptive cleaning processing on the original blade geometric data to obtain the target blade section data; A blade feature point recognition module for performing geometric feature point recognition based on the target blade section data to obtain the leading edge vertex and the trailing edge vertex; A blade structure division module for determining the corresponding boundary critical points using the leading edge vertex and the trailing edge vertex and performing a structural division on the target blade section data in combination with the boundary critical points to obtain the blade section partition data; The blade geometry reconstruction module is used to perform curve fitting on the sectional partition data of the blade to obtain a fitting curve corresponding to the blade region, and construct a blade geometry model based on the fitting curve.

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