Adaptive processing method and device for blade geometric data

Abstract

The present disclosure provides an adaptive processing method and device for blade geometric data, which relates to the field of computer-aided engineering. The method includes: obtaining original blade geometric data, and performing format parsing and density adaptive cleaning processing on the original blade geometric data to obtain target blade cross-sectional data; performing geometric feature point identification based on the target blade cross-sectional data to obtain leading edge vertices and trailing edge vertices; using the leading edge vertices and trailing edge vertices to determine the corresponding boundary critical points, and combining the boundary critical points to perform structural division on the target blade cross-sectional data to obtain blade cross-sectional partition data; performing curve fitting on the blade cross-sectional partition data to obtain a fitting curve for the corresponding blade area, and constructing a blade geometric model based on the fitting curve. The present disclosure can realize the full process automation and adaptive processing of blade geometric data from import to modeling, significantly improving data processing efficiency and geometric reconstruction accuracy.

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 geometric data. Background Art

[0002] Throughout the design, simulation, and manufacturing process of aircraft engine blades, blade geometry data is typically recorded in the txt (Text File) format, forming a crucial data foundation for CNC modeling and machining programming. This data, organized in cross-sections and containing numerous spatial coordinate points, is directly related to the integrity of geometric modeling and the accuracy of toolpath generation. With the continuous improvement of machining accuracy and design complexity, higher requirements are being placed on the processing capabilities of blade geometry data. However, relevant technologies still face numerous deficiencies when processing blade data in txt format, becoming a critical bottleneck limiting modeling efficiency and machining consistency.

[0003] On the one hand, during the data analysis phase, related methods often use general programming tools or simple scripts to identify and read the text-based blade data, lacking robust structural design tailored to the specific characteristics of blade data. When data contain issues such as irregular formats, missing values, or noise, traditional processing methods often struggle to detect and correct them promptly, resulting in reduced analysis accuracy and the potential for geometric anomalies or defects in the subsequent modeling process. In severe cases, manual verification of each item is even required, which is inefficient.

[0004] On the other hand, during the geometric structure division process, engineers often rely on manual interaction to divide the blade cross-section data into geometric regions to meet the needs of subsequent modeling and machining path planning. However, this method is highly dependent on operator experience, is time-consuming and labor-intensive, and cannot guarantee consistent machining accuracy.

[0005] Therefore, how to break through the efficiency and accuracy bottlenecks in the process of blade geometric 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 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 adaptive processing of blade geometric data, thereby at least to a certain extent solving the problems of low efficiency and insufficient accuracy in the analysis and geometric structure division of blade geometric data in related technologies.

[0008] According to a first aspect of the present disclosure, a method for adaptively processing blade geometric data is provided, comprising:

[0009] Acquire original blade geometric data, and perform format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-sectional data;

[0010] Performing geometric feature point recognition based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex;

[0011] Determine corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and perform structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data;

[0012] Curve fitting is performed on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and a blade geometric model is constructed based on the fitting curve.

[0013] In an exemplary embodiment of the present disclosure, performing format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-sectional data includes:

[0014] Extracting coordinate data segments of each blade cross section from the original blade geometric data;

[0015] Using a preset regular expression to match the three-dimensional coordinate format of the coordinate data segment, and performing anomaly detection on the coordinate data rows that meet the three-dimensional coordinate format, the coordinate data rows that successfully match the format and do not contain abnormal data constitute the initial blade cross-section data;

[0016] A local density calculation is performed on the data points contained in each section in the initial blade section data, and based on the calculated local density and in combination with a dynamically updated density threshold factor, the target blade section data is screened out from the initial blade section data.

[0017] In an exemplary embodiment of the present disclosure, performing local density calculation on the data points contained in each section of the initial blade cross-section data includes:

[0018] Traverse all data points in each cross section and calculate the first distance between each data point and other data points in the same cross section;

[0019] The number of data points falling within the corresponding neighborhood range is determined according to the first distance and a preset neighborhood radius, and the local density of the data points is determined according to the number of data points.

[0020] In an exemplary embodiment of the present disclosure, the step of filtering out the target blade cross-sectional data from the initial blade cross-sectional data based on the calculated local density and in combination with a dynamically updated density threshold factor includes:

[0021] Initialize the density threshold factor and threshold factor adjustment step size;

[0022] Screening step: Calculate the density threshold corresponding to each section based on the density threshold factor and the local density of all data points in each section; Determine the target data point whose local density is less than the density threshold from all data points in each section, and perform section line closure analysis using the remaining data points in each section after removing the target data point;

[0023] If the cross-section line is closed, the target blade cross-section data is determined based on other data points after removing the target data point in each cross-section;

[0024] If the cross-section line is not closed, the density threshold factor is adjusted according to the threshold factor adjustment step, and the screening step is repeated using the adjusted density threshold factor until the screened target blade cross-section data meets the cross-section line closing condition.

[0025] In an exemplary embodiment of the present disclosure, performing section line closure analysis using other data points after excluding the target data point in each section includes:

[0026] Calculating a second distance between a preset starting point in each cross section and other unsorted data points in the same cross section, and sorting the data points according to the second distance;

[0027] For the sorted data point sequence, calculating the third distance between the first and last data points in the data point sequence and the maximum distance between any adjacent data points;

[0028] If the third distance is less than or equal to the maximum distance, determining that the cross-section line is closed;

[0029] If the third distance is greater than the maximum distance, it is determined that the cross-section line is not closed.

[0030] In an exemplary embodiment of the present disclosure, the identifying of geometric feature points based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex includes:

[0031] Fitting each cross section according to the target blade cross section data, and calculating the curvature of each data point based on the cross section line obtained by fitting;

[0032] According to the curvature of each data point, the data point corresponding to the maximum curvature is identified as a candidate feature point, and the leading edge vertex and the trailing edge vertex are determined respectively in combination with the relative position of the candidate feature point in the cross-sectional geometric structure.

[0033] In an exemplary embodiment of the present disclosure, determining corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and performing structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data includes:

[0034] Based on the coordinate positions of the leading edge vertex and the trailing edge vertex, traverse the adjacent data points point by point along the cross-sectional direction, and determine the corresponding boundary critical points by dynamic curvature comparison;

[0035] Based on the leading edge vertex, the trailing edge vertex and the boundary critical point, the target blade cross-section data is structurally divided to obtain the blade cross-section partition data.

[0036] In an exemplary embodiment of the present disclosure, performing curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and constructing a blade geometric model based on the fitting curve includes:

[0037] Determine whether the blade cross-section partition data within the same partition meets the preset curve fitting conditions;

[0038] If the curve fitting condition is not met, interpolation is performed on adjacent data points in the blade cross-section partition data until the blade cross-section partition data after interpolation meets the curve fitting condition, and then curve fitting processing is performed to obtain a fitting curve for the corresponding blade area;

[0039] Determine whether the fitting curve of each blade area meets the preset surface lofting conditions;

[0040] If the surface lofting condition is met, the fitting curve is subjected to surface lofting processing along the cross-sectional direction to generate geometric surfaces corresponding to each blade region, and the blade geometric model is constructed based on each geometric surface.

[0041] In an exemplary embodiment of the present disclosure, the method further includes:

[0042] Based on the blade geometric model, determining the tool type and machining strategy parameters required for machining;

[0043] Based on the tool type and machining strategy parameters, blade tool path trajectory data is generated, and the blade tool path trajectory data is post-processed to obtain corresponding machining instruction data.

[0044] According to a second aspect of the present disclosure, there is provided an adaptive processing device for blade geometric data, comprising:

[0045] A cross-sectional data preprocessing module is used to obtain original blade geometric data, and perform format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-sectional data;

[0046] A blade feature point recognition module is used to perform geometric feature point recognition based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex;

[0047] a blade structure division module, configured to determine corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and perform structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data;

[0048] The blade geometry reconstruction module is used to perform curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and construct a blade geometry model based on the fitting curve.

[0049] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processing unit, the method for adaptively processing blade geometric data described in any one of the above items is implemented.

[0050] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0051] A processing unit; and a storage unit for storing executable instructions of the processing unit; wherein the processing unit is configured to execute any one of the above-mentioned methods for adaptive processing of blade geometric data by executing the executable instructions.

[0052] The exemplary embodiments of the present disclosure may have some or all of the following beneficial effects:

[0053] In the adaptive processing method of blade geometric data provided in 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 perform structured cleaning on interference information such as inconsistent formats, invalid values ​​and repeated records that may exist in the original blade geometric data, thereby ensuring that the data based on which subsequent processing is based has good standardization and integrity, helping to reduce geometric reconstruction deviations 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, the leading edge vertices and the trailing edge vertices are extracted in combination with the curvature information, which can be used to extract the geometric characteristics. The representative spatial boundary points are identified based on the characteristics of the features, and the critical points of the boundaries are further determined through dynamic curvature comparison to realize the automatic structural division of the blade section. This makes the traditional geometric partitioning process that relies on manual drawing of dividing lines systematized and standardized, reduces the geometric errors caused by human operation differences, and thus improves the efficiency and consistency of the geometric structure division processing; on the other hand, curve fitting operations are performed on the various cross-sectional partition data after division, and a three-dimensional geometric model of the blade is constructed based on the fitting results. It can establish an effective mapping relationship from two-dimensional cross-sectional data to the spatial model, ensure the geometric continuity of the model and the connection coordination between each area, and meet the accuracy requirements of subsequent modeling, simulation or manufacturing processes.

[0054] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, serve to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0056] Figure 1 A schematic diagram of a system architecture is shown to which the adaptive processing method for blade geometry data according to an embodiment of the present disclosure can be applied.

[0057] Figure 2 A flow chart of an adaptive processing method for blade geometric data in an embodiment of the present disclosure is shown.

[0058] Figure 3 A schematic diagram of a process for determining target blade cross-sectional data in an embodiment of the present disclosure is shown.

[0059] Figure 4 A schematic diagram of a curvature curve corresponding to a certain cross section in an embodiment of the present disclosure is shown.

[0060] Figure 5 A schematic outline diagram of a cross section in an embodiment of the present disclosure is shown.

[0061] Figure 6 A distribution diagram of an original blade cross-section data set in an embodiment of the present disclosure is shown.

[0062] Figure 7 A distribution diagram of another original blade cross-section data set in an embodiment of the present disclosure is shown.

[0063] Figure 8 A schematic diagram of an intermediate result of a blade cross-section data cleaning iteration in an embodiment of the present disclosure is shown.

[0064] Figure 9 A distribution diagram of a blade cross-section data set after cleaning in an embodiment of the present disclosure is shown.

[0065] Figure 10 A schematic diagram of an adaptive processing device for blade geometric data in an embodiment of the present disclosure is shown.

[0066] Figure 11 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0067] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0068] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0069] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0070] Figure 1 A system architecture diagram is shown to which an adaptive processing method for blade geometry data according to an embodiment of the present disclosure can be applied.

[0071] like Figure 1 As shown, system architecture 100 may include one or more terminal devices such as a smartphone 101, a portable computer 102, and a desktop computer 103, a network 104, and a server 105. Network 104 is used to provide a medium for a communication link between the terminal device and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. The terminal device may be any electronic device with data processing capabilities, having a display screen that can be used to display raw blade geometry data, target blade cross-section data, leading edge vertices and trailing edge vertices, blade geometry models, etc. to the user. Such electronic devices include, but are not limited to, the aforementioned smartphone 101, portable computer 102, and desktop computer 103.

[0072] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0073] The adaptive processing method for blade geometric data provided in the embodiments of the present disclosure is generally executed by a terminal device, and accordingly, the adaptive processing device for blade geometric data is generally disposed in the terminal device. However, it will be readily understood by those skilled in the art that the adaptive processing method for blade geometric data provided in the embodiments of the present disclosure may also be executed by the server 105, and accordingly, the adaptive processing device for blade geometric data may also be disposed in the server 105, and this exemplary embodiment does not specifically limit this.

[0074] The exemplary embodiment of the present disclosure also provides a method for adaptively processing blade geometric data, referring to Figure 2 As shown, the method may include the following steps S210 to S240:

[0075] Step S210, obtaining original blade geometric data, and performing format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-sectional data;

[0076] Step S220 , performing geometric feature point recognition based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex;

[0077] Step S230, using the leading edge vertex and the trailing edge vertex to determine corresponding boundary critical points, and performing structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data;

[0078] Step S240 , performing curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and constructing a blade geometric model based on the fitting curve.

[0079] The adaptive processing method for blade geometry data provided by the exemplary embodiments of the present disclosure effectively avoids format anomalies and redundant information in the original blade geometry data by performing format parsing and density adaptive cleaning at the initial stage of data processing, improving data standardization and enhancing parsing efficiency and stability. Furthermore, by extracting leading and trailing edge vertices based on curvature information and locating boundary critical points using dynamic curvature comparison, automatic cross-sectional structure segmentation can be achieved, helping to improve the consistency and processing efficiency of geometric partitioning. Furthermore, curve fitting is performed on the cross-sectional data of each partition and a three-dimensional model is constructed to ensure geometric continuity and connection coordination between regions, meeting the requirements of high-precision modeling and processing.

[0080] The adaptive processing method of blade geometric data in this exemplary embodiment will be described in detail below.

[0081] In step S210 , original blade geometric data is acquired, and format parsing and density adaptive cleaning are performed on the original blade geometric data to obtain target blade cross-sectional data.

[0082] In the example embodiments of this disclosure, raw blade geometry data is typically stored in txt format. Using plain text to store discrete coordinate data offers advantages such as small file size and fast transmission, facilitating efficient reading and preprocessing. Furthermore, txt files do not require dedicated 3D modeling software, offering excellent cross-platform compatibility. Importantly, txt files preserve geometric information in the form of raw point data, avoiding the precision loss and boundary errors often associated with cross-software parsing using parametric surface formats.

[0083] Understandably, the raw blade geometry data stored in the txt file contains multiple 3D coordinate points representing the blade cross-section. Because this raw blade geometry data originates from actual measurements or simulation output, it may contain issues such as non-standard formatting, data noise, missing values, and duplicates. Directly using this data will affect subsequent modeling accuracy and processing stability.

[0084] To improve the accuracy and robustness of data processing, the raw blade geometry data can first be formatted. For example, formatting the raw blade geometry data involves extracting the coordinate data segments for each blade section from a txt file and determining whether each row of coordinate data meets the specified 3D coordinate expression format based on preset 3D coordinate recognition rules, such as regular expression matching. Only rows of coordinate data that meet the formatting specifications are retained, and invalid data such as null values ​​(NaN, Not a Number), missing fields, or syntax errors are eliminated to construct the initial blade cross-section data.

[0085] As shown in Table 1, each row of data in Table 1 corresponds to a sampling point on a blade cross section.

[0086] Table 1

[0087]

[0088] In Table 1, "# Profile 1 at 0.0000%" is a comment statement in the txt file of the blade geometry data, indicating the blade section to which the 3D data point belongs. "Profile 1" represents section 1, and "at 0.0000%" indicates the location of section 1. For example, it can be a percentage of section 1 along the blade's spanwise length, meaning that section 1 is located at the beginning of the blade, such as near the blade root or at the start of the span. For the first row of data (253.999759, 0.349947171, 50.2493955), the three values ​​are the X, Y, and Z coordinates of the section point, respectively, representing the section point's position in the axial direction of the blade geometry, the section point's position in the radial direction of the blade geometry, and the section point's position in the blade's rotational direction. It can be seen that the second row of data contains anomalous data for which the Y coordinate value could not be parsed, the third row contains anomalous data for which the Z coordinate value is missing, and the fourth row contains anomalous data for which all coordinate values ​​are concatenated. It should be noted that the content shown in Table 1 is only for illustrative purposes, and is used to demonstrate various data structures and abnormal situations in the blade geometry data in txt format.

[0089] After reading all the lines of data from the txt file, you can first perform a blank line filter to remove invalid content and avoid interference during the subsequent parsing process. Next, identify all lines of data containing comments and determine whether there are duplicate comments. If multiple data segments correspond to the same comment, such as "# Profile 1 at 0.0000%", it can be determined that these data segments all belong to Section 1. The corresponding data segments are then merged to ensure the integrity and uniformity of the point data corresponding to each section, providing a standardized input structure for subsequent geometric processing steps.

[0090] After format parsing is complete, density-adaptive cleaning is further performed on the initial blade cross-section data. In the exemplary embodiments of the present disclosure, density-adaptive cleaning involves identifying and removing isolated or outliers whose local density is significantly below the average level based on the local point density distribution characteristics of each data point within its corresponding cross-section. This process does not rely on a fixed judgment threshold, but rather dynamically adjusts the density discrimination criteria based on the spatial distribution characteristics of each cross-section data to accommodate the geometric complexity and point cloud density of different data regions. This improves the accuracy of noise point identification and enhances the data processing process's adaptability to diverse input data.

[0091] In some example implementations, reference Figure 3 As shown, step S210 may further include steps S310 to S330:

[0092] Step S310: extracting coordinate data segments of each blade cross section from the original blade geometric data.

[0093] Specifically, all the line data in the txt file are read line by line. Based on the identification of the comment statements used to mark the cross-section position, the grouped coordinate data segments corresponding to each comment statement are extracted to provide a basis for subsequent structural analysis and geometric processing.

[0094] Step S320, using a preset regular expression to perform three-dimensional coordinate format matching on the coordinate data segment, and performing anomaly detection on the coordinate data rows that meet the three-dimensional coordinate format, and the coordinate data rows that successfully match the format and do not have abnormal data constitute the initial blade cross-section data.

[0095] First, a preset regular expression is used to match the 3D coordinate format of each cross-section's coordinate data segment. The regular expression can be preset to a matching pattern that conforms to a standard 3D coordinate format, for example, a "floating point number + separator + floating point number + separator + floating point number" structure, to determine whether each coordinate data row fully represents the 3D coordinate information of a blade cross-section point.

[0096] During the matching process, the coordinate data segment is parsed line by line to select coordinate data rows with a standardized format and complete structure. If a coordinate data row does not meet the matching rules, such as missing coordinate values, illegal characters, or non-standard separators, the format matching of the coordinate data row is determined to have failed, and the abnormal row data that does not meet the format is deleted.

[0097] The coordinate data rows that successfully match the format are then further checked for anomalies. For example, these rows are checked for extreme values ​​or clearly incorrect values ​​that exceed the physically reasonable range of the coordinates, such as abnormal data points that exceed the physical dimensions of the blade. These rows are then deleted to further improve the data's physical rationality and engineering applicability. Furthermore, to prevent data redundancy from affecting the accuracy of subsequent curve fitting and structural reconstruction, duplicate coordinate data rows, excluding the first and last points, are deleted for each cross-section's coordinate data segment to ensure the validity and representativeness of the cross-section point distribution.

[0098] In other words, only the coordinate data rows with successful format matching and no abnormal data detected are considered valid data and ultimately constitute the initial blade cross-section data, which serves as the basic input for subsequent density calculation and geometric analysis.

[0099] Step S330 , performing local density calculation on the data points contained in each section in the initial blade section data, and filtering out the target blade section data from the initial blade section data based on the calculated local density and the dynamically updated density threshold factor.

[0100] In some example implementations, for each data point in each cross section, the number of neighboring points within a given radius is counted, and this number is the local density of the data point. By traversing all data points in each cross section, the local density of all data points in each cross section can be obtained.

[0101] Subsequently, based on the density distribution characteristics of each cross-section and the initialized density threshold factor, the density of the data points with significantly low density is determined. By iteratively adjusting the density threshold factor and combining it with the closure judgment of the cross-section line, points that do not meet the density requirements are gradually eliminated. Ultimately, 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.

[0102] For example, when performing local density calculation on the data points contained in each section of the initial blade section data, all data points in each section can be traversed to calculate the first distance between each data point and other data points in the same section, which is recorded as dThe first distance can be Euclidean distance, Manhattan distance, cosine distance, etc., which is not limited in this disclosure. After calculating the first distance, the number of data points falling into the corresponding neighborhood range can be determined based on the first distance and the preset neighborhood radius, and finally the local density of the data points can be determined based on the number of data points. For example, the preset neighborhood radius r =0.1mm, if the first distance d Less than or equal to the neighborhood radius r , then the data point is considered to be within the neighborhood. By counting the number of data points that fall within the neighborhood, that is, counting the number of data points whose first distance is less than or equal to the neighborhood radius, the number of data points is the local density of the data point.

[0103] Furthermore, based on the calculated local density and the dynamically updated density threshold factor, the target blade cross-section data is filtered from the initial blade cross-section data. Specifically, the density threshold factor and threshold factor adjustment step size are first initialized. For example, the density threshold factor is initialized to 0.8 to guide the subsequent data screening process. The density threshold factor adjustment step size is set to 0.02 to gradually adjust the density screening conditions when the cross-section line does not meet the closure requirement, thereby achieving adaptive control of the local density screening accuracy.

[0104] Then, a screening step is performed, which specifically includes: calculating a density threshold corresponding to each cross section based on a density threshold factor and the local density of all data points in each cross section.

[0105] For example, the local density of all data points in each section can be counted to obtain the average value and standard deviation of the local density of all data points. Combined with the current density threshold factor, the density threshold corresponding to each section can be calculated as follows:

[0106]

[0107] in, θ is the density threshold corresponding to a certain section, μ is the average value of the local density of all data points in a certain cross section, σ is the standard deviation of the local density of all data points in a certain cross section, α is the density threshold factor, which is an adjustable parameter.

[0108] It should be noted that in the example implementation of the present disclosure, the density threshold can be used to determine whether the density of each data point meets the screening criteria, thereby identifying target data points with low local density that may constitute noise points or discontinuous boundaries, providing a basis for subsequent section line closure analysis and data cleaning.

[0109] For example, from all data points within each cross section, target data points with a local density less than a density threshold are identified. Section line closure analysis is then performed using the remaining data points within each cross section after removing the target data point. If the section line is closed, the target blade section data is determined based on the remaining data points within each cross section after removing the target data point. If the section line is not closed, the density threshold factor is adjusted based on the step size of the threshold factor, and the screening process is repeated using the adjusted density threshold factor until the selected target blade section data meets the section line closure condition.

[0110] Specifically, from all the data points in each 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 data point is less than the corresponding density threshold, the data point is determined to be a noise point or a low-confidence data point and is removed from the data point set of the current section. After the noise point removal is completed, the retained high-density data points constitute a new section data subset. The section line closure analysis is then performed based on this data subset to evaluate whether the screening process retains the geometric continuity and closure of the section profile, providing a basis for the next step to determine whether the density threshold factor needs to be updated and iterative optimization needs to be performed.

[0111] In some example embodiments, a cross-section line closure analysis is performed using the remaining data points within each cross-section after removing the target data point. Specifically, the second distance between a preset starting point within each cross-section and other unsorted data points within the same cross-section is calculated, and the data points are sorted according to the second distance. For the sorted data point sequence, the third distance between the first and last data points in the sequence, as well as the maximum distance between any adjacent data points, is calculated. If the third distance is less than or equal to the maximum distance, the cross-section line is determined to be closed; if the third distance is greater than the maximum distance, the cross-section line is determined to be open.

[0112] Taking the second distance, third distance and maximum distance as Euclidean distance as an example, for each section data to be processed, the first data point in the section coordinate data segment can be selected as the starting point. Then, starting from the starting point, the Euclidean distance between the current data point and other data points that have not been sorted is calculated in sequence, and in each step, the data point closest 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 in the section.

[0113] After the sorting is completed, the Euclidean distance between the first and last data points of the data point sequence is calculated, that is, the distance between the starting point and the end point, which is recorded as d start-end At the same time, traverse all adjacent point pairs in the data point sequence, calculate the Euclidean distance between each adjacent point pair, and determine the maximum distance among all adjacent point pairs, which is recorded as d maxThen, by comparing d start-end and d max The size relationship is used to determine whether the section line is closed.

[0114] For example, if d start-end ≤ d max , then it can be determined that the current data point sequence forms a closed curve, and the cross-section data meets the closure requirement. d start-end > d max , it can be determined that the current data point sequence does not form a closed curve. In this case, further adjustment of the screening parameters is required to optimize the closure of the point set. For example, the density threshold factor can be automatically updated, such as by adding a preset threshold factor adjustment step size, and the density screening and cross-sectional closure determination process is repeated based on the updated density threshold factor. It is understood that the above screening steps will be performed sequentially for all cross-sectional data until all cross-sectional areas meet the preset closure conditions.

[0115] Through this density adaptation method, the cleaning standard can be flexibly adjusted according to the distribution characteristics of different cross-sectional data, and measurement errors or noise points such as isolated points, jump points and repeated points can be effectively eliminated. In addition, during the cleaning process, a geometric structure constraint mechanism based on the closure of the cross-sectional line is introduced as a criterion for cleaning quality. After a certain cross-sectional data is cleaned once, sorting and closure 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 closure judgment are performed again until the target blade cross-sectional data that meets the geometric integrity is output. In this example, the structured processing and quality optimization of the original blade geometric data can be completed without human intervention, laying a data foundation for subsequent geometric feature recognition and model construction.

[0116] In step S220 , geometric feature points are identified based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex.

[0117] For example, each section can be fitted based on the target blade cross-sectional data, and the curvature of each data point can be calculated based on the fitted cross-sectional line. Then, based on the curvature of each data point, the data point corresponding to the maximum curvature value is identified as a candidate feature point. The leading and trailing edge vertices are determined based on the relative positions of the candidate feature points within the cross-sectional geometry. The leading and trailing edge vertices are typically located at the starting and ending points of the cross-sectional line, corresponding to the directions of fluid entering and exiting the blade cross-section, respectively. They are important geometric landmarks that characterize the aerodynamic shape of the blade cross-section.

[0118] Specifically, curve fitting can be performed on the target blade cross-sectional data within each section to obtain a smooth and continuous cross-sectional line. This fitting method can employ polynomial fitting, B-spline fitting, or other types of geometric curve fitting algorithms to ensure the smoothness and representativeness of the fitting result. Next, the curvature of each data point on the fitted cross-sectional line is calculated. Curvature, as a parameter that measures the degree of local shape change, effectively reflects the degree of geometric abruptness of the curve at that point.

[0119] For example, according to:

[0120]

[0121] Calculate the curvature corresponding to each data point k ;in, is the first-order derivative of the cross-section line, which represents 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 and second derivatives, is the modulus of the vector cross product, is the modulus of the tangent vector.

[0122] Subsequently, based on the calculated curvature information, the data points corresponding to the maximum curvature are screened out from the target blade cross-section data as candidate feature points. Taking into account that some cross-sections may have multiple local curvature maximum points, in order to ensure that the leading edge vertex and trailing edge vertex finally selected have structural stability and geometric representativeness, further judgment can be made based on the relative distribution position 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 large, and the curvature of the leading edge vertex is greater than that of the trailing edge vertex. Therefore, the curvature maximum point located near the front of the geometric center of the cross-section line can be preferentially judged as the leading edge vertex, and the curvature maximum point located in the tail area of ​​the cross-section line can be used as the trailing edge vertex.

[0123] Determining the leading edge vertex and trailing edge vertex corresponding to each blade section can provide an accurate geometric basis for subsequent boundary critical point identification and blade partition structure division.

[0124] In addition, before performing curve fitting, it is 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 cross-section data point sequence. This processing method helps to ensure that the cross-section contour line forms a complete closed loop in a geometric sense, thereby providing a continuous and stable input data foundation for subsequent geometric feature extraction and structural division operations, avoiding fitting errors or processing interruptions caused by curve breaks.

[0125] In step S230, the leading edge vertex and the trailing edge vertex are used to determine corresponding boundary critical points, and the target blade cross-section data is structurally divided in combination with the boundary critical points to obtain blade cross-section partition data.

[0126] After the leading edge vertex and the trailing edge vertex are identified, in order to realize the automatic structural division of the target blade cross-section data, it is necessary to use the leading edge vertex and the trailing edge vertex as the initial geometric reference points, and dynamically determine the boundary critical points within their neighborhood to define the segmentation positions between different functional areas.

[0127] For example, based on the coordinate positions of the leading and trailing edge vertices, adjacent data points can be traversed point by point along the cross-sectional direction, and the corresponding critical boundary points can be determined through dynamic curvature comparison. Then, based on the leading and trailing edge vertices and critical boundary points, the target blade cross-sectional data is structurally partitioned to obtain blade cross-sectional partition data.

[0128] Specifically, the identified leading or trailing vertex can be used as the starting point for the traversal. Based on the relative distribution of each cross-sectional data point along the cross-sectional line, the adjacent data points are traversed point by point along the cross-sectional direction. The curvature of each data point is calculated and compared with the curvature of the current data point using a dynamically updated curvature threshold, thereby identifying the location where the curvature trend significantly changes. This turning point typically corresponds to the transition zone where the blade geometry changes from a sharp change to a gentle area, and can serve as the boundary critical point for dividing different blade partitions.

[0129] refer to Figure 4 As shown in the figure, a schematic diagram of the curvature curve corresponding to a certain section is shown. The horizontal axis of the curvature curve is the target blade section data, and the vertical axis is the curvature. That is to say, the curvature curve is a contour curve obtained by fitting the target blade section data, and the curvature of each point on the contour curve is calculated. It is used to represent the curvature change trend of the section along the contour direction. Figure 4 In the figure, the leading edge vertex and the trailing edge vertex are points P and i and point P j . At the front vertex P i For example, starting from this point, traverse the adjacent data points point by point along the cross-section direction, such as including the forward adjacent point P i+1 、P i+2 …、P i+n and the backward adjacent point P i-1 、P i-2 …、P i-n In the process of forward traversal, the front vertex P i The curvature value of is compared with the curvature value of each adjacent point in the front direction. For example, if the front vertex P i The curvature is greater than the forward adjacent point P i+1The curvature of the forward adjacent point P indicates that the curvature is decreasing. At this time, the curvature threshold is dynamically adjusted to i+1 curvature, and forward adjacent point P i+1 As a new reference point for comparison, continue to compare with subsequent adjacent points, and so on, until the curvature of a certain forward adjacent point no longer satisfies the decreasing relationship, such as the forward adjacent point P i+n-1 The curvature of the forward adjacent point P is less than i+n curvature, then it is considered that the forward adjacent point P i+n The transition zone between the leading edge segment and the middle contour area can be determined as the forward adjacent point P i+n is the critical point of the front edge. Similarly, suppose that the backward adjacent point P is determined by backward traversal i-n is the boundary critical point at the other end of the leading edge. For the boundary critical point extraction process of the trailing edge vertex, dynamic curvature comparison and positioning can be performed in the same way.

[0130] refer to Figure 5 As shown, a schematic outline of a cross section is shown. Figure 5 The identified front vertex P i , located at the leading edge vertex P i The boundary critical points P at both ends i+n 、P i-n , trailing edge vertex P j , located at the trailing edge vertex P j The boundary critical points P at both ends j+n 、P j-n .

[0131] After completing the positioning of the boundary critical points, the boundary intervals corresponding to the leading edge segment and the trailing edge segment can be constructed respectively in combination with the relative position relationship between the leading edge vertex and the trailing edge vertex. For example, the point set between the leading edge vertex and the boundary critical points at both ends can be used as the leading edge segment data, and the point set between the trailing edge vertex and the boundary critical points at both ends can be used as the trailing edge segment data. The remaining intermediate area data points that are not included can be further divided into suction surface segment data and pressure surface segment data. For example, based on the baseline connected by the leading edge vertex and the trailing edge vertex, combined with the spatial distribution position of the intermediate area data points relative to the baseline, the intermediate area data points can be divided into suction surface segments and pressure surface segments by symmetrical division, sequential segmentation or normal direction, thereby completing the structural partitioning processing of the target blade cross-section data.

[0132] It can be understood that the blade cross-section partition data finally obtained includes the leading edge segment data, trailing edge segment data, suction surface segment data and pressure surface segment data of each cross-section.

[0133] This process can realize curvature-based adaptive boundary recognition, that is, automatic segmentation of target section point data, avoiding the inconsistency caused by manual boundary drawing or interactive judgment, helping to improve the accuracy and automation of cross-sectional structure division, and thus improve the structural rationality and partition accuracy of geometric processing, providing a good foundation for subsequent partition curve fitting and geometric modeling.

[0134] In step S240, curve fitting is performed on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and a blade geometric model is constructed based on the fitting curve.

[0135] To accurately reconstruct the blade's 3D geometric model, a fitting modeling operation must be performed on the partitioned blade cross-section data. For each blade cross-section, after the structural partitioning is completed, the discrete coordinate data points of the partitioned regions are obtained. This disclosure requires fitting these data points individually to obtain fitting curves that describe the contour morphology of each region.

[0136] In some example implementations, it may be first determined whether the blade cross-section partition data within the same partition meets preset curve fitting conditions. Preset curve fitting conditions may include, but are not limited to, indicators such as a point count threshold (e.g., greater than 4 data points), point distribution density, and coordinate value continuity. This disclosure does not impose any restrictions on these conditions. The only requirement is that the fitting process ensures that the fitting result is not distorted or the contour is not discontinuous due to sparse data or abnormal data arrangement.

[0137] If the curve fitting conditions are not met, such as the number of points in a certain partition is insufficient or the point distribution density is insufficient, the adjacent data points in the blade cross-section partition data are interpolated to fill in the missing coordinate points in the middle, and the interpolation is continuously iterated until the blade cross-section partition data after filling in the points meets the curve fitting conditions. After that, the curve fitting process is performed to obtain the fitting curve of the corresponding blade area. This fitting curve will accurately reflect the geometric contour characteristics of the blade partition and provide a basis for subsequent 3D modeling. Among them, the curve fitting method can use polynomial fitting, B-spline fitting or other types of geometric curve fitting algorithms.

[0138] After generating the fitting curves for each blade section, the fitting curves for corresponding locations across multiple sections along the span of the blade are analyzed in a coordinated manner to determine whether the fitting curves for each blade region meet the preset surface lofting conditions. These surface lofting conditions may include criteria such as a threshold for the number of section lines (e.g., greater than two section lines), topological structure matching, and spatial continuity between the start and end curves, which are not limited in this disclosure.

[0139] If the surface lofting conditions are met, the fitting curve is lofted along the cross-sectional direction to generate geometric surfaces corresponding to each blade area, 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.

[0140] Through the above-mentioned curve fitting and surface lofting process, 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 the integrity of the model structure, smooth contours, and natural boundary transitions, providing high-quality geometric support for subsequent simulation analysis and CNC machining.

[0141] refer to Figure 6 As shown in the figure, a distribution diagram of an original blade cross-section data set is shown. The horizontal axis is the X-axis coordinate value of the blade cross-section data point in the cross-section plane, and the vertical axis is the Y-axis 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, which is used to describe the morphological structure of the cross-section geometric contour. Figure 6 It can be seen that the original point set before data cleaning is compactly distributed and densely arranged along an approximate straight line, and there are multiple outliers, also known as noise points.

[0142] refer to Figure 7 As shown in FIG, a distribution diagram of another original blade cross-section data set is shown. Figure 6 By reducing the display range of the X-axis, the point set shape arranged along the main contour can be more clearly displayed, but there are still many noise points.

[0143] refer to Figure 8 FIG. 1 shows a schematic diagram of an intermediate result of iterative cleaning of blade cross-section data. Figure 8 It shows the local point set retention during the data cleaning process, reflecting the dynamic process of density threshold adjustment and continuity judgment.

[0144] refer to Figure 9 As shown in FIG, a distribution diagram of a blade cross-section data set after cleaning is shown. Figures 6 to 8 , Figure 9 The point set in forms a complete closed contour, and the noise points are removed, making the contour line more coherent.

[0145] This paper uses density threshold judgment and point set continuity analysis to effectively remove outliers, restore contour closure and data consistency, and provide a stable geometric foundation for subsequent curve fitting and structural modeling.

[0146] In some example implementations, after obtaining the blade geometry model, the preparation phase for CNC machining can begin. For example, based on the blade geometry model, machining instruction data that can be recognized and executed by the CNC machine tool is automatically generated. For example, the blade geometry model can be used to determine the required tool type and machining strategy parameters. Specifically, by analyzing the blade geometry model's spatial dimensions, surface distribution characteristics, and machining requirements for each area, the matching tool type and machining strategy parameters are automatically determined based on a pre-defined process database.

[0147] Tool types include ball-end milling cutters, tapered milling cutters, and other tools suitable for free-form surface machining. Tools of varying shapes, diameters, and parameters can be matched based on the curvature distribution and machining accessibility of different blade regions. Machining strategy parameters include machining path type, feed rate, spindle speed, machining direction, and cutting depth, ensuring that subsequent toolpaths cover the entire target area while meeting machining accuracy and efficiency requirements.

[0148] Furthermore, based on the tool type and processing strategy parameters, blade tool path trajectory data can be generated, and the blade tool path trajectory data can be post-processed to obtain corresponding processing instruction data. The blade tool path trajectory data includes a discrete coordinate sequence of the tool moving along a predetermined path in three-dimensional space. Post-processing can include format conversion, interpolation mode setting, tool compensation setting, feed speed and spindle speed setting, coordinate system and zero point setting, processing sequence and logic control, safety processing, file structure packaging and other steps. The present disclosure does not limit the specific process of post-processing, and it is sufficient to be able to convert the blade tool path trajectory data into recognizable processing instruction data.

[0149] After the blade toolpath is generated, the data is post-processed to output machining instruction data that conforms to the specified CNC system control format. This machining instruction data is used to drive the CNC machine tool to perform machining operations on the actual blade geometry. It can include G-code (standard instructions for controlling machine tool motion patterns) or NC (Numerical Control) program statements, containing instruction parameters such as tool movement, cutting speed, and spindle control. Using this machining instruction data, the CNC machine tool can accurately execute various milling operations, achieving physical manufacturing of the blade geometry model.

[0150] Furthermore, in this exemplary embodiment, a device for adaptively processing blade geometric data is also provided. Figure 10 As shown, the adaptive processing device 1000 for blade geometric data may include a cross-sectional data preprocessing module 1010, a blade feature point recognition module 1020, a blade structure division module 1030, and a blade geometry reconstruction module 1040, wherein:

[0151] The cross-sectional data pre-processing module 1010 is used to obtain original blade geometric data, and perform format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-sectional data;

[0152] A blade feature point recognition module 1020 is configured to perform geometric feature point recognition based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex;

[0153] a blade structure division module 1030 for determining corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and performing structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data;

[0154] The blade geometry reconstruction module 1040 is configured to perform curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade region, and construct a blade geometry model based on the fitting curve.

[0155] 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, so they will not be repeated here.

[0156] The exemplary embodiments of the present disclosure also provide a computer-readable storage medium on which a program product capable of implementing the above-mentioned methods of this specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product is run on an electronic device, the program code is used to cause the electronic device to perform the steps described in the "Exemplary Methods" section above according to the various exemplary embodiments of the present disclosure. The program product can be a portable compact disc read-only memory (CD-ROM) and include program code, and can be run on an electronic device, such as a personal computer. However, the program product of the present disclosure is not limited to this. In the present disclosure, a readable storage medium can 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.

[0157] The program product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0158] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0159] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0160] 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++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user 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 computing device via 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., via the Internet using an Internet service provider).

[0161] The exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above method. Figure 11 1100 according to this exemplary embodiment of the present disclosure will be described. Figure 11 The electronic device 1100 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0162] like Figure 11 As shown, electronic device 1100 may be implemented as a general-purpose computing device. Components of electronic device 1100 may include, but are not limited to, at least one processing unit 1110, at least one storage unit 1120, a bus 1130 connecting various system components (including storage unit 1120 and processing unit 1110), and a display unit 1140.

[0163] The storage unit 1120 stores program codes, which can be executed by the processing unit 1110, so that the processing unit 1110 performs the steps described in the "Exemplary Method" section above according to various exemplary embodiments of the present disclosure. For example, the processing unit 1110 can execute Figure 2 The method steps in .

[0164] The storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 1121 and / or a cache memory unit (Cache) 1122 , and may further include a read-only memory unit (ROM) 1123 .

[0165] The storage unit 1120 may also include a program / utility 1124 having a set (at least one) of program modules 1125, such program modules 1125 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0166] 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 architectures.

[0167] Electronic device 1100 can also communicate with one or more external devices 1200 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 1100, and / or any device that enables electronic device 1100 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via input / output (I / O) interface 1150. Furthermore, electronic device 1100 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via network adapter 1160. As shown, network adapter 1160 communicates with other modules of electronic device 1100 via bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with 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, tape drives, and data backup storage systems.

[0168] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described herein can be implemented through software or through 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 (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (such as 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.

[0169] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0170] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0171] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0172] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for adaptive processing of blade geometric data, characterized in that: include: Acquiring original blade geometric data, and extracting coordinate data segments of each blade cross section from the original blade geometric data; Using a preset regular expression to match the three-dimensional coordinate format of the coordinate data segment, and performing anomaly detection on the coordinate data rows that meet the three-dimensional coordinate format, the coordinate data rows that successfully match the format and do not contain abnormal data constitute the initial blade cross-section data; Performing local density calculation on the data points contained in each section of the initial blade section data, and screening target blade section data from the initial blade section data based on the calculated local density and a dynamically updated density threshold factor; Performing geometric feature point recognition based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex; Determining corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and performing structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data; Performing curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and constructing a blade geometric model based on the fitting curve; The performing of local density calculation on the data points contained in each section of the initial blade cross-section data includes: Traverse all data points in each cross section and calculate the first distance between each data point and other data points in the same cross section; The number of data points falling within the corresponding neighborhood range is determined according to the first distance and a preset neighborhood radius, and the local density of the data points is determined according to the number of data points.

2. The adaptive processing method for blade geometric data according to claim 1, characterized in that: The step of screening the target blade cross-section data from the initial blade cross-section data based on the calculated local density and in combination with a dynamically updated density threshold factor includes: Initialize the density threshold factor and threshold factor adjustment step size; Screening step: Calculate the density threshold corresponding to each section based on the density threshold factor and the local density of all data points in each section; Determine the target data point whose local density is less than the density threshold from all data points in each section, and perform section line closure analysis using the remaining data points in each section after removing the target data point; If the cross-section line is closed, the target blade cross-section data is determined based on other data points after removing the target data point in each cross-section; If the cross-section line is not closed, the density threshold factor is adjusted according to the threshold factor adjustment step, and the screening step is repeated using the adjusted density threshold factor until the screened target blade cross-section data meets the cross-section line closing condition.

3. The adaptive processing method for blade geometric data according to claim 2, characterized in that: The cross-section line closure analysis is performed using other data points after removing the target data point in each cross-section, including: Calculating a second distance between a preset starting point in each cross section and other unsorted data points in the same cross section, and sorting the data points according to the second distance; For the sorted data point sequence, calculating 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, determining 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.

4. The adaptive processing method for blade geometric data according to claim 1, characterized in that: The step of identifying geometric feature points based on the target blade cross-section data to obtain a leading edge vertex and a trailing edge vertex includes: Fitting each cross section according to the target blade cross section data, and calculating the curvature of each data point based on the cross section line obtained by fitting; According to the curvature of each data point, the data point corresponding to the maximum curvature is identified as a candidate feature point, and the leading edge vertex and the trailing edge vertex are determined respectively in combination with the relative position of the candidate feature point in the cross-sectional geometric structure.

5. The adaptive processing method for blade geometric data according to claim 1, characterized in that: The method of determining corresponding boundary critical points by using the leading edge vertex and the trailing edge vertex, and performing structural division on 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 the adjacent data points point by point along the cross-sectional direction, and determine the corresponding boundary critical points by dynamic curvature comparison; Based on the leading edge vertex, the trailing edge vertex and the boundary critical point, the target blade cross-section data is structurally divided to obtain the blade cross-section partition data.

6. The adaptive processing method for blade geometric data according to claim 1, characterized in that: The performing curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and constructing a blade geometric model based on the fitting curve, includes: Determine whether the blade cross-section partition data within the same partition meets the preset curve fitting conditions; If the curve fitting condition is not met, interpolation is performed on adjacent data points in the blade cross-section partition data until the blade cross-section partition data after interpolation meets the curve fitting condition, and then curve fitting processing is performed to obtain a fitting curve for the corresponding blade area; Determine whether the fitting curve of each blade area meets the preset surface lofting conditions; If the surface lofting condition is met, the fitting curve is subjected to surface lofting processing along the cross-sectional direction to generate geometric surfaces corresponding to each blade region, and the blade geometric model is constructed based on each geometric surface.

7. The adaptive processing method for blade geometric data according to claim 1, characterized in that: The method further comprises: Based on the blade geometric model, determining the tool type and machining strategy parameters required for machining; Based on the tool type and machining strategy parameters, blade tool path trajectory data is generated, and the blade tool path trajectory data is post-processed to obtain corresponding machining instruction data.

8. An adaptive processing device for blade geometric data, characterized in that: The method for adaptively processing blade geometric data according to any one of claims 1 to 7 is applied, wherein the device comprises: A cross-sectional data preprocessing module is used to obtain original blade geometric data, and perform format parsing and density adaptive cleaning on the original blade geometric data to obtain target blade cross-sectional data; A blade feature point recognition module is used to perform 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, configured to determine corresponding boundary critical points using the leading edge vertex and the trailing edge vertex, and perform structural division on the target blade cross-section data in combination with the boundary critical points to obtain blade cross-section partition data; The blade geometry reconstruction module is used to perform curve fitting on the blade cross-section partition data to obtain a fitting curve corresponding to the blade area, and construct a blade geometry model based on the fitting curve.

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