Notch position blade profile tolerance point taking device, system and method

Abstract

The invention discloses a notch position blade profile tolerance point taking device, system and method, and the method comprises the steps: enabling an actual measurement point cloud to be aligned with a theoretical model through progressive spatial registration, carrying out the initial coarse registration, and dynamically adjusting a root mean square error threshold value and a curvature change rate threshold value based on curvature sensitivity analysis, so as to achieve the self-adaptive segmented interruption control; a three-dimensional Hash mapping engine is constructed to store original point cloud data, an incremental data access mode and an iterative data structure are combined, a historical transformation matrix stack, a B spline control point and an 8-bit state register are included, and data processing integrity and high efficiency are ensured; a genetic algorithm is adopted to optimize segmented B-spline surface fitting, and contours of an air inlet edge and an air outlet edge are synchronously optimized through transverse segmentation and longitudinal segmentation; and finally, composite convergence verification is passed. According to the method, through dynamic threshold regulation and control and a hybrid optimization strategy, the detection precision and the calculation efficiency of the complex curved surface are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of precision manufacturing of aero-engines, and in particular to a device, system and method for taking points of blade profile at a notch position. Background Art

[0002] Precision-cast blades are core components in equipment such as aircraft engines and gas turbines, and their profile directly impacts the performance and efficiency of the equipment. This is particularly true at the blade exhaust edge notch location, where the complex structure and large curvature variations make traditional blade profile measurement methods difficult to meet high-precision measurement requirements. Existing technologies for this location suffer from insufficient fitting accuracy, low data processing efficiency, and an inability to effectively address curvature-sensitive areas. These methods result in significant measurement errors, impacting the quality of subsequent blade processing and assembly. Traditional measurement methods for blade exhaust edge notches face three technical bottlenecks: First, the measurement point distribution strategy relies on manual experience, which can easily create blind spots in areas of sudden curvature changes, leading to the loss of key feature points during profile fitting. Second, when using cubic spline interpolation or polynomial fitting, the response to changes in the second-order derivative of curvature in curvature-sensitive areas is delayed, resulting in systematic deviations between the fitted curve and the true profile. Third, the data processing pipeline lacks a curvature-adaptive sampling density mechanism, resulting in segmented fitting distortion in transition regions with curvature radii less than 2 mm. These defects not only cause the blade profile error to exceed the process standard of ±0.03mm, but also lead to a decrease in aerodynamic performance, directly affecting the thermal efficiency of the combustion chamber and the power output of the turbine machinery. Therefore, a new method for determining the blade profile point at the notch position has become a problem urgently needed by those skilled in the art. Summary of the Invention

[0003] In view of this, the present invention provides a device, system and method for determining the blade profile at the notch position, so as to solve the problems raised in the background art.

[0004] The technical solutions adopted are as follows: The blade profile point measurement device at the notch position has: A processor and a memory, wherein the memory has a contour point acquisition program, which enables the processor to perform the following operations: Registration unit: Install a progressive spatial registration model to align the measured point cloud with the theoretical model. Specifically, it uses an improved iterative closest point algorithm to perform coarse registration on the measured point cloud, and constructs a KD-Tree to accelerate the nearest neighbor search through privileged weight allocation. The curvature field of the point cloud is calculated in real time through the curvature sensitive area recognition unit, and the registration parameters are adjusted dynamically; Segmentation processing unit: Based on the alignment data output by the registration unit, the dynamically adjusted root mean square error threshold and curvature change rate threshold are used to implement segmentation interruption control and generate classified segmented contours. The specific trigger conditions are: When the root mean square error exceeds 0.5% of the theoretical curvature radius, lateral segmentation is triggered, and the segmentation boundary is dynamically adjusted by the interruption coupling controller; The curvature change rate of adjacent point clouds exceeds 0.01 mm -1 The longitudinal segmentation is triggered when the curvature mutation interval is reconstructed by the B-spline surface segmentation through the progressive segmentation fitting unit; Optimization and fitting part: Genetic algorithm is used to optimize the segmented B-spline surface fitting, and the contour data of the intake and exhaust edges are optimized synchronously according to the segmented contours. The configuration is: Encode segment boundary points into binary gene sequences; The fitting accuracy and curvature continuity are comprehensively evaluated through the fitness function; Verification output unit: Using the data obtained by the optimization fitting unit, the geometric accuracy and surface smoothness are verified by the spatial residual monitoring unit and the curvature continuity analysis unit, and then the final contour point result set is output; Non-destructive data management module: used to build a three-dimensional hash index structure to store original point cloud data; Iteration state memory: used to store historical transformation matrix stacks, B-spline control points, and convergence flags.

[0005] Furthermore, the coarse registration operation performed by the registration unit includes: During the iteration process, a dynamic interruption strategy is adopted to adaptively adjust the segment length according to the curvature and error distribution. When the curvature or error is large, the segment length is shortened, and vice versa. The incremental fitting retains the Jacobian matrix condition number of the previous fitting, and the LM algorithm is used to update the parameters. The iteration termination condition is that the error is ≤ 0.001 mm.

[0006] The present invention provides a device for determining the blade profile at a notch position, comprising: Contour point acquisition module, used to obtain measured point cloud data of precision casting blades; The processing feedback module is used to dynamically adjust the blade processing parameters according to the contour point selection result set to form a detection-feedback-processing closed loop; The evaluation module forms a closed-loop interaction with the processing feedback module; The dynamic fitting execution module is used to interact with the non-destructive data management module through a high-speed data bus, including: The curvature sensitive area recognition unit is used to calculate the point cloud curvature field in real time, mark the curvature mutation interval, and send segmentation signals to the progressive segmentation fitting unit; The progressive piecewise fitting unit is used to perform B-spline surface piecewise reconstruction on the marked interval and call the genetic algorithm to optimize the control point weights to ensure the continuity of the second-order derivative of the surface; The interrupt coupling controller is used for bidirectional communication with the progressive segment fitting unit, dynamically adjusting the segment boundaries based on the set error threshold and curvature threshold, and triggering the incremental data access interface to load the corresponding hash unit point cloud; Non-destructive data management modules include: A three-dimensional hash mapping engine is used to divide the point cloud according to the spatial hash value and transmit the curvature field data to the curvature sensitive area recognition unit in real time; Incremental data access interface, used to dynamically push local point cloud data streams based on segmentation requests, and supports fast retrieval based on hash key values; Iteration state memory, used to store IterationData structure, providing transformation data and fitting parameters for the dual convergence criterion evaluation module; The dual convergence criterion evaluation module includes: The spatial residual monitoring unit is used to read the transformation matrix from the iterative state memory and calculate the distance between the actual point and the fitting surface; Curvature continuity analysis unit, used to receive curvature field data and verify the continuity of curvature change rate and second-order derivative of adjacent segments; The composite conditional decision maker is used to trigger the threshold adjustment through the interrupt mechanism and output a convergence signal when both the double criteria are met; otherwise, a new transformation matrix is generated and written into the iterative state memory.

[0007] Furthermore, the collaborative mechanism between the dynamic fitting execution module and the non-destructive data management module is: The 3D hash mapping engine divides the original point cloud into hash units, and each unit stores the curvature value of the corresponding point cloud; The curvature sensitive area identification unit quickly locates the curvature mutation unit through the hash key value, triggering the progressive segmented fitting unit to call the incremental data access interface to load the unit point cloud, and the data loading delay is ≤5ms.

[0008] Furthermore, the closed-loop control path of the dual-convergence criterion evaluation module is: The composite conditional decision maker outputs a convergence signal → interrupts the coupling controller → adjusts the segment boundaries and fitting parameters; If a single condition is not met, the historical transformation matrix stack is updated through the iterative state memory and a new round of genetic algorithm optimization is started.

[0009] Furthermore, the interaction between the processing feedback module and the contour point taking device includes: Receive and verify the contour error report of the output unit and automatically generate machining compensation instructions; The measured point cloud data after processing is fed back to the evaluation module to form an iterative optimization link between detection and processing.

[0010] The present invention also provides a method for obtaining a point of blade profile at a notch position, comprising the following steps: Step S1: Dynamic fitting-interruption coupling stage: Based on the progressive spatial registration of the theoretical model and the measured point cloud, a curvature-driven adaptive segmentation strategy is adopted, and segmentation interruption control is implemented through dynamically adjusted root mean square error thresholds and curvature change rate thresholds, including: The coarse registration step is used to project the measured point cloud into the theoretical model coordinate system and use the improved iterative closest point algorithm for initial registration; The curvature sensitivity analysis step is used to calculate the curvature value of each point cloud through the parametric surface differential geometry method to dynamically adjust the registration parameters until the root mean square error of each sub-segment meets the set conditions; Segment trigger step, used to trigger lateral segmentation when the root mean square error of the current segment fitting exceeds 0.5% of the theoretical curvature radius, and when the curvature change rate of adjacent point clouds exceeds 0.01 mm -1 When the longitudinal segmentation is triggered, the segmentation boundary is dynamically adjusted by the interruption coupling controller; Step S2: Non-destructive data architecture phase: Create a 3D hash map engine to store the original point cloud data, use incremental data access mode to preserve data integrity, and simultaneously establish an iterative data structure, including: The original point cloud is divided into spatial hash units using a three-dimensional hash mapping function, and only the local point cloud data associated with the current segment is loaded; Establish an iterative data structure including original data pointer, historical transformation matrix stack, B-spline control point set and 8-bit convergence flag register; Step S3: Dual-objective optimization fitting stage: Using a genetic algorithm to optimize the segmented B-spline surface fitting, synchronously controlling the horizontal and vertical segmentation of the intake and exhaust sides, including: The chromosome encoding step is used to encode the segment boundary point index number into a binary gene sequence, and an elite retention strategy is used to prevent the optimal solution from being lost; The fitness function optimization step is used to comprehensively evaluate the fitting accuracy and curvature continuity through the fitness function, perform selection, crossover, and mutation operations, and the maximum number of iterations is 1000; The segmented reconstruction step is used to perform segmented reconstruction of the B-spline surface on the curvature mutation interval to ensure that the fitting surface has second-order derivative continuity; Step S4: Composite convergence verification stage: When both the geometric accuracy and surface smoothness conditions are met, the final contour point result set is output, including: The geometric accuracy verification step is used to use the spatial residual threshold ≤ 0.03 mm as the accuracy verification standard; Surface smoothness verification step: the curvature change rate between adjacent segments is ≤0.01 mm -1 And the second-order derivative is continuous; If a single condition is not met, the segment boundary redefinition and fitting parameter iterative optimization are automatically triggered until both criteria are met.

[0011] Furthermore, in the iterative process of the progressive spatial registration, a dynamic interruption strategy and incremental fitting are adopted: Dynamically adjust the segment length, shortening the segment length when the curvature or error is large, and extending it otherwise; The incremental fitting retains the condition number of the previous Jacobian matrix, and the LM algorithm is used to update the parameters. The iteration termination condition is that the error is ≤ 0.001 mm.

[0012] Furthermore, curvature continuity analysis and segmented reconstruction are achieved through the following collaborative mechanism: The curvature sensitive area recognition unit marks the curvature mutation interval in real time; The progressive piecewise fitting unit calls the point cloud data of the corresponding hash unit according to the marked interval, performs B-spline surface piecewise reconstruction, and optimizes the control point weights through genetic algorithm.

[0013] Furthermore, the closed-loop interaction process between the evaluation module and the processing feedback module is as follows: Generate blade processing parameter adjustment instructions based on the final contour point selection result set; After receiving the measured point cloud data after processing, progressive spatial registration and segmented fitting are re-executed to form an iterative optimization link of detection-processing-redetection.

[0014] The beneficial effects of the present application are as follows: the method and system for taking points for blade profile contour at the notch position described in the present invention significantly improves the contour detection accuracy and efficiency of complex structure blades through multi-stage collaborative control and intelligent algorithm optimization. The specific technical advantages are as follows: In the dynamic fitting-interruption coupling stage, based on the curvature-driven adaptive segmentation strategy and progressive spatial registration technology, the dual mechanism of dynamically adjusting the root mean square error threshold and the curvature change rate threshold is used to achieve accurate identification and segmentation control of curvature-sensitive areas such as the notch on the exhaust edge of the blade. Compared with the traditional fixed threshold segmentation method, this solution dynamically optimizes the segmentation boundary division based on the actual geometric features of the point cloud data, effectively avoiding the fitting distortion caused by sudden changes in curvature, and is particularly suitable for local geometric distortion areas caused by the casting process at the notch on the exhaust edge of precision-cast blades, ensuring that the segmented fitting process can accurately characterize the microscopic features of the blade profile.

[0015] The non-destructive data architecture stage integrates a three-dimensional hash index structure with an incremental data access mechanism to build an efficient data retrieval and iterative update system while maintaining the topological integrity of the original point cloud data. By designing a three-dimensional hash mapping function, rapid spatial partitioning of point cloud data is achieved. Combined with the incremental access mode, only the local point cloud data associated with the current segment needs to be loaded during the dynamic fitting process, reducing data processing by 60%-75% and significantly improving system response efficiency. The historical transformation matrix stack and B-spline control point set configured in the iterative data structure provide a parameter traceability benchmark for multiple rounds of fitting iterations, ensuring the verifiable stability of the fitting process.

[0016] During the dual-objective optimization fitting phase, a genetic algorithm is used to achieve global optimization of the segmented B-spline surface fitting. By encoding segment boundary points through binary gene sequences and constructing a comprehensive fitness function that integrates fitting accuracy and curvature continuity indicators, the coordinated optimization of both horizontal and vertical segments is achieved. Compared to fitting methods guided by a single geometric error, this dual-objective optimization mechanism simultaneously meets the geometric tolerance requirements of the blade profile and the surface smoothness indicators. When segment control is performed simultaneously on the intake and exhaust edges, it effectively eliminates the surface continuity mutation caused by unidimensional threshold-triggered segmentation, ensures the second-order derivative continuity of the fitting surface in the gap area, and significantly enhances the engineering adaptability of the contour point selection results.

[0017] The composite convergence verification phase establishes a joint verification standard for both the spatial residual threshold and the curvature continuity criterion. Final results are output only when both criteria meet the accuracy specifications, completely eliminating the risk of false convergence associated with single-condition judgments. In a typical scenario, when the spatial residual meets the standard but the curvature change rate exceeds the limit, the system automatically triggers a dynamic adjustment mechanism for the fitting parameters. Iterative optimization is implemented by redefining segmented boundaries and redistributing fitting weights until both criteria meet engineering standards, mathematically guaranteeing the geometric accuracy and surface quality of the contour point extraction results.

[0018] The supporting point acquisition system, through closed-loop collaboration among a dynamic fitting execution module, a non-destructive data management module, and a dual-convergence criterion evaluation module, establishes a fully automated process from curvature-sensitive area identification to segmented fitting to convergence verification. A high-speed data bus architecture and priority interrupt mechanism ensure inter-module data transmission latency of ≤3ms. The collaborative design of the 3D hash mapping engine and incremental data access interface enables the system to process point cloud datasets of ≥10^7. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram showing a comparison of the contours provided by the present invention; Figure 2 This is a connection diagram of the notch position blade profile point taking system provided by the present invention; Figure 3This is a schematic diagram of the module structure of the notch position blade profile point taking device provided by the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] The notch position blade profile point taking device comprises: a processor and a memory, wherein the memory has a profile point taking program, which enables the processor to perform the following operations: a registration part: installing a progressive spatial registration model for aligning the measured point cloud with the theoretical model, specifically including: using an improved iterative nearest point algorithm to perform coarse registration on the measured point cloud, constructing a KD-Tree to accelerate the nearest neighbor search through privileged weight allocation; calculating the point cloud curvature field in real time through a curvature sensitive area recognition unit, and dynamically adjusting the registration parameters; a segmentation processing part: implementing segmentation interruption control based on the alignment data output by the registration part using a dynamically adjusted root mean square error threshold and a curvature change rate threshold to generate a classified segmented contour, and the specific triggering conditions are: when the root mean square error exceeds 0.5% of the theoretical curvature radius, lateral segmentation is triggered, and the segmentation boundary is dynamically adjusted by the interruption coupling controller; when the curvature change rate of adjacent point clouds exceeds 0.01 mm -1 The longitudinal segmentation is triggered when the curvature mutation interval is reconstructed by the B-spline surface segmentation through the progressive segmentation fitting unit; Optimization and fitting section: A genetic algorithm is used to optimize the segmented B-spline surface fitting, and the contour data of the intake and exhaust edges are simultaneously optimized according to the segmented contour. The configuration is as follows: the segmented boundary points are encoded as binary gene sequences; the fitting accuracy and curvature continuity are comprehensively evaluated through the fitness function; the verification output section: Using the data obtained from the optimization and fitting section, the geometric accuracy and surface smoothness are verified through the spatial residual monitoring unit and the curvature continuity analysis unit, and then the final contour point result set is output.

[0022] This application analyzes the curvature distribution characteristics of the blade exhaust edge notch area and recognizes the necessity of dynamically adjusting the registration parameters and adaptive segmentation strategies. Based on the iterative closest point algorithm to optimize registration efficiency, a segmentation trigger mechanism is established in conjunction with real-time calculation of the curvature field. It also proposes the introduction of a genetic algorithm into the surface reconstruction process to form a closed-loop quality control system. Among them, the progressive spatial registration model refers to a coordinate transformation model that implements point cloud alignment in stages, which can be implemented using an improved iterative nearest point algorithm combined with a privileged weight allocation mechanism to improve the efficiency of initial registration. The curvature-sensitive area recognition unit refers to a processing module that calculates the curvature of the point cloud based on the differential geometry method of parametric surfaces. For example, it identifies mutation areas by calculating the principal curvature value, which is used to dynamically adjust the registration weight. The dynamically adjusted root mean square error threshold refers to an error trigger value that is dynamically set according to the theoretical curvature radius. Binary gene sequence encoding refers to converting the spatial position information of the segmented boundary points into a binary string that can be processed by the genetic algorithm, such as using Gray code encoding, to optimize the surface reconstruction parameters; Specifically, the registration unit uses the KD-Tree acceleration structure to quickly match the point cloud correspondence, and the curvature-sensitive area identification unit updates the registration weight coefficient in real time, giving priority to ensuring the registration accuracy of high curvature areas. The segmentation processing unit dynamically adjusts the horizontal and vertical segment boundaries based on the error distribution and curvature gradient calculated in real time, and interrupts the coupling controller to coordinate segment reconstruction and data loading. The optimization fitting unit iteratively optimizes the control point position through a genetic algorithm, and the fitness function simultaneously evaluates the geometric deviation and curvature continuity index to ensure that the reconstructed surface meets both measurement accuracy and smoothness requirements. The verification output unit adopts a double verification mechanism. The spatial residual monitoring unit detects the distance deviation between the point cloud and the surface, and the curvature continuity analysis unit verifies the curvature transition characteristics of adjacent segments.

[0023] The notch position blade profile point taking device also includes: a non-destructive data management module for constructing a three-dimensional hash mapping engine to store original point cloud data; an iterative state memory for storing historical transformation matrix stacks, B-spline control points and convergence marks.

[0024] The 3D hash mapping engine divides the 3D space into cubic units of equal size, mapping each unit to a unique key value via a hash function. This can be implemented using a spatial meshing algorithm, generating the hash key by calculating the quotient of the point cloud coordinates and the mesh resolution. This structure optimizes data access paths based on the principle of spatial locality, addressing the efficiency bottleneck of traditional linear storage structures when retrieving complex surface data. The historical transformation matrix stack stores the sequence of spatial transformation parameters generated by each iteration of the registration process. This can be implemented using a dynamic array structure, saving the historical state of the transformation matrix through a push operation, providing a backtracking basis for parameter optimization. B-spline control points store the coordinate data of the control points used in piecewise surface reconstruction. This can be implemented using a sparse matrix storage format, preserving the control point parameters to ensure geometric continuity during piecewise reconstruction. The convergence flag is a binary status indicator that records whether the iterative process has reached the termination condition. This can be implemented using an iteration status register, enabling adaptive iteration termination control through status bit judgment.

[0025] The non-destructive data management module uses a three-dimensional hash mapping engine to cluster and store the original point cloud according to spatial location. When accessing data in a specific area, the corresponding cube unit is quickly located through the hash key value to avoid delays caused by full data scanning. The iterative state memory continuously updates the historical transformation matrix stack during the alignment process. The new transformation matrix generated by each iteration is pushed to the top of the stack to form a traceable parameter evolution trajectory. The B-spline control points are dynamically updated during the surface reconstruction stage, and the control point parameters generated by each segment fitting are incrementally stored to ensure geometric continuity between adjacent segments. The convergence mark is updated in real time by the dual convergence criterion evaluation module. When the spatial residual and curvature continuity meet the standards at the same time, the state bit flip is triggered, terminating the current iteration process.

[0026] The coarse registration operation performed by the registration unit includes: using a dynamic interruption strategy during the iteration process, adaptively adjusting the segment length according to the curvature and error distribution, shortening the segment length when the curvature or error is large, and extending it otherwise; incremental fitting retains the Jacobian matrix condition number of the previous fit, using the LM algorithm for parameter update, and the iteration termination condition is an error ≤ 0.001 mm.

[0027] The dynamic interruption strategy is a control mechanism that dynamically adjusts the length of point cloud segments based on the curvature field and error distribution calculated in real time. This is achieved through the collaborative work of a curvature-sensitive region recognition unit and an error space residual monitoring unit. When a curvature value exceeds a preset threshold or a local root mean square error (RMS) increases, segment boundary adjustments are immediately triggered. This strategy achieves dynamic matching of segment length and surface complexity through a dual feedback mechanism of curvature and error.

[0028] Incremental fitting is an optimization method that retains the Jacobian matrix condition number from the previous iteration for parameter updates. This can be achieved by using a matrix condition number cache mechanism in conjunction with the LM algorithm. Each iteration reuses the previously calculated matrix condition number as the initial value, and only updates the incremental changes. This method inherits the state of previous iterations, avoiding resource waste caused by repeated calculations.

[0029] The present invention also provides a blade profile point-taking system at a notch position, comprising any of the above-mentioned blade profile point-taking devices at a notch position, including: a contour point-taking module for acquiring measured point cloud data of a precision-cast blade; a processing feedback module for dynamically adjusting blade processing parameters according to a contour point-taking result set to form a detection-feedback-processing closed loop; and an evaluation module for forming a closed-loop interaction with the processing feedback module; The dynamic fitting execution module is used to interact with the non-destructive data management module through a high-speed data bus, including: a curvature sensitive area identification unit, which is used to calculate the point cloud curvature field in real time, mark the curvature mutation interval, and send a segmentation signal to the progressive segmentation fitting unit; the progressive segmentation fitting unit is used to perform B-spline surface segmentation reconstruction on the marked interval, call the genetic algorithm to optimize the control point weights, and ensure the continuity of the second-order derivative of the surface; the interruption coupling controller is used to communicate bidirectionally with the progressive segmentation fitting unit, dynamically adjust the segmentation boundary based on the set error threshold and curvature threshold, and trigger the incremental data access interface to load the corresponding hash unit point cloud; the non-destructive data management module includes: a three-dimensional hash mapping engine, which is used to divide the point cloud according to the spatial hash value, and transmit the curvature field data to the curvature sensitive area identification unit in real time; the incremental data access interface is used to dynamically push the local point cloud data stream according to the segmentation request, and support fast retrieval based on the hash key value; the iteration state memory is used to save IterationData The structure provides transformation data and fitting parameters for the dual convergence criterion evaluation module; the dual convergence criterion evaluation module includes: a spatial residual monitoring unit, which is used to read the transformation matrix from the iterative state memory and calculate the distance between the actual point and the fitted surface; a curvature continuity analysis unit, which is used to receive curvature field data and verify the curvature change rate of adjacent segments and the continuity of the second-order derivative; a composite conditional decision maker, which is used to trigger the threshold adjustment through the interrupt mechanism and output a convergence signal when both dual criteria are met, otherwise a new transformation matrix is generated and written into the iterative state memory.

[0030] The contour point taking module refers to a device used to collect and process the three-dimensional coordinate data of the blade surface. Specifically, it can be implemented by using a laser scanner in conjunction with a point cloud preprocessing algorithm, and a standardized measured point cloud data set is generated through filtering, noise reduction, and coordinate transformation. The processing feedback module refers to a control unit that connects the measurement system and the processing equipment. Specifically, it can be implemented using the industrial Ethernet communication protocol, and converts the contour error vector into a tool path compensation instruction for the CNC machine tool. The evaluation module and the processing feedback module form a closed-loop interaction, and generate blade processing parameter adjustment instructions based on the final contour point taking result set; receive the measured point cloud data after processing, and re-execute progressive spatial registration and segmented fitting to form an iterative optimization link of detection-processing-re-detection; The curvature-sensitive region identification unit in the dynamic fitting execution module is a real-time computational unit based on differential geometry principles. Specifically, it can be implemented using a Gaussian curvature algorithm coupled with a sliding window mechanism. It identifies regions of sudden curvature changes by calculating the local surface curvature values of the point cloud. The progressive piecewise fitting unit is an operational unit that performs surface reconstruction. Specifically, it can be implemented using a non-uniform rational B-spline algorithm coupled with a genetic algorithm optimizer. The continuity of the second-order derivatives of the surface is ensured by adjusting the weights of control points. The three-dimensional hash mapping engine in the non-destructive data management module is a spatial data indexing device. Specifically, it can be implemented using a three-dimensional meshing algorithm coupled with a hash function. It divides point cloud data into quickly retrievable storage units based on spatial location. The composite conditional decision maker in the dual-convergence criterion evaluation module is a multi-conditional logic judgment device. Specifically, it can be implemented using a combination of a threshold comparator and logic gate circuits. It triggers iterative optimization instructions by concurrently verifying geometric accuracy and surface smoothness indicators.

[0031] The collaborative mechanism between the dynamic fitting execution module and the non-destructive data management module is as follows: the three-dimensional hash mapping engine divides the original point cloud into hash units, each unit stores the curvature value of the corresponding point cloud; the curvature-sensitive area identification unit quickly locates the curvature mutation unit through the hash key value, triggering the progressive segmented fitting unit to call the incremental data access interface to load the unit point cloud, and the data loading delay is ≤5ms.

[0032] A three-dimensional hash mapping engine refers to an indexing engine that generates hash key values based on spatial coordinates. Specifically, a three-dimensional grid partitioning algorithm can be used to divide the point cloud space into equal-volume cubic units. Each unit stores the curvature value of the point cloud in the corresponding space, and rapid retrieval is achieved by establishing a mapping relationship between spatial position and hash key value. A curvature-sensitive area identification unit refers to a feature detection module based on curvature threshold judgment. Specifically, a Gaussian curvature calculation method can be used to analyze the curvature distribution of the point cloud in real time, and hash key value matching can be used to quickly locate the spatial unit corresponding to the curvature mutation area. An incremental data access interface refers to a transmission channel for loading local data on demand. Specifically, memory mapping technology can be used to establish a direct mapping between the hash unit address and the physical storage location, and only the point cloud data blocks associated with the current processing area are loaded.

[0033] The closed-loop control path of the dual-convergence criterion evaluation module is as follows: the composite conditional decision maker outputs a convergence signal → interrupts the coupling controller → adjusts the segment boundaries and fitting parameters; if a single condition is not met, the historical transformation matrix stack is updated through the iterative state memory to start a new round of genetic algorithm optimization.

[0034] A composite conditional decision maker is a logic unit that makes convergence decisions based on the dual indicators of geometric accuracy and surface smoothness. It can be implemented using a multi-threaded parallel computing architecture and is used to compare spatial residual thresholds and curvature continuity indicators in real time. An iterative state memory is a data storage unit that stores a stack of historical transformation matrices. It can be implemented using non-volatile memory and is used to preserve the parameter trajectory of the previous optimization process. Genetic algorithm optimization is a global optimization method based on the principles of biological evolution. It can be implemented using an elite retention strategy and an adaptive crossover rate mechanism to overcome the limitations of local optimal solutions.

[0035] The interaction between the processing feedback module and the contour point device includes: receiving the contour error report from the verification output part and automatically generating processing compensation instructions; feeding back the measured point cloud data after processing to the evaluation module to form a detection-processing iterative optimization link.

[0036] The profile error report refers to a set of quantized error data verified by the spatial residual monitoring unit and the curvature continuity analysis unit. Specifically, it can be generated by combining a three-dimensional coordinate deviation matrix and a curvature gradient change matrix to accurately characterize the degree of deviation between the actual machined surface and the theoretical model. Machining compensation instructions refer to process parameter correction instructions based on the reverse derivation of the error vector field. They can be converted into machine tool motion trajectory compensation through the CNC system interface protocol to achieve online compensation of machining errors. The detection-machining iterative optimization link refers to a two-way feedback channel for measurement data and machining parameters. Specifically, a high-speed industrial bus is used to establish a real-time data pipeline to ensure that the machining compensation instructions complete a closed-loop response within a single production cycle.

[0037] The present invention also provides a method for obtaining a point of blade profile at a notch position, comprising the following steps: Step S1: Dynamic fitting-interruption coupling stage: Based on the progressive spatial registration of the theoretical model and the measured point cloud, a curvature-driven adaptive segmentation strategy is adopted, and segmentation interruption control is implemented through dynamically adjusted root mean square error thresholds and curvature change rate thresholds, including: a coarse registration step, which is used to project the measured point cloud into the theoretical model coordinate system and use the improved iterative closest point algorithm for initial registration; a curvature sensitivity analysis step, which is used to calculate the curvature value of each point cloud through the parametric surface differential geometry method to dynamically adjust the registration parameters until the root mean square error of each sub-segment meets the set conditions; a segmentation triggering step, which is used to trigger lateral segmentation when the root mean square error of the current segment fitting exceeds 0.5% of the theoretical curvature radius, and when the curvature change rate of adjacent point clouds exceeds 0.01 mm -1 When the longitudinal segmentation is triggered, the segmentation boundary is dynamically adjusted by the interruption coupling controller; Step S2: Non-destructive data architecture phase: Create a 3D hash mapping engine to store the original point cloud data, use an incremental data access mode to preserve data integrity, and simultaneously establish an iterative data structure, including: using a 3D hash mapping function to divide the original point cloud into spatial hash units, loading only the local point cloud data associated with the current segment; establish an iterative data structure containing the original data pointer, the historical transformation matrix stack, the B-spline control points, and the 8-bit convergence flag register; Step S3: Dual-objective optimization fitting phase: A genetic algorithm is used to optimize the segmented B-spline surface fitting, and the lateral and longitudinal segmentation of the intake and exhaust edges are simultaneously controlled. This includes: a chromosome encoding step for encoding the segment boundary point index number into a binary gene sequence, and an elite retention strategy is used to prevent the loss of the optimal solution; a fitness function optimization step for comprehensively evaluating the fitting accuracy and curvature continuity through the fitness function, performing selection, crossover, and mutation operations, with an upper limit of 1000 iterations; and a segmented reconstruction step for performing segmented B-spline surface reconstruction on the curvature mutation interval to ensure that the fitted surface has second-order derivative continuity. Step S4: Composite convergence verification stage: When both the geometric accuracy and surface smoothness conditions are met, the final contour point selection result set is output, including: a geometric accuracy verification step, which uses a spatial residual threshold of ≤0.03 mm as an accuracy verification standard; Surface smoothness verification step: the curvature change rate between adjacent segments is ≤0.01 mm -1 And the second-order derivative is continuous; If a single condition is not met, the segment boundary redefinition and fitting parameter iterative optimization are automatically triggered until both criteria are met.

[0038] During the iterative process of progressive spatial registration, a dynamic interruption strategy and incremental fitting were adopted: the segment length was dynamically adjusted to shorten the segment length when the curvature was large or the error was large, and to extend it otherwise; the incremental fitting retained the condition number of the previous Jacobian matrix and used the LM algorithm to update the parameters; the iteration termination condition was an error ≤ 0.001 mm.

[0039] Curvature continuity analysis and segmented reconstruction are achieved through the following collaborative mechanism: the curvature sensitive area recognition unit marks the curvature mutation interval in real time; the progressive segmented fitting unit calls the point cloud data of the corresponding hash unit according to the marked interval, performs B-spline surface segmented reconstruction, and optimizes the control point weights through genetic algorithm.

[0040] The above descriptions are merely a few embodiments of the present application and do not constitute any form of limitation to the present application. Although the present application discloses the preferred embodiments as above, they are not intended to limit the present application. Any technical personnel familiar with the present profession, without departing from the scope of the technical solution of the present application, using the technical content disclosed above to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. The blade profile point measuring device at the notch position is characterized by: have: A processor; and a memory, wherein the memory has a contour point acquisition program, which causes the processor to perform the following operations: Registration unit: is configured to: install a progressive spatial registration model to align the measured point cloud with the theoretical model, specifically including: using an improved iterative closest point algorithm to perform coarse registration on the measured point cloud, and constructing a KD-Tree to accelerate the nearest neighbor search through privileged weight allocation; The curvature field of the point cloud is calculated in real time through the curvature sensitive area recognition unit, and the registration parameters are adjusted dynamically; Segmentation processing unit: Based on the alignment data output by the registration unit, the dynamically adjusted root mean square error threshold and curvature change rate threshold are used to implement segmentation interruption control and generate classified segmented contours. The specific trigger conditions are: When the root mean square error exceeds 0.5% of the theoretical curvature radius, lateral segmentation is triggered, and the segmentation boundary is dynamically adjusted by the interruption coupling controller; The curvature change rate of adjacent point clouds exceeds 0.01 mm -1 The longitudinal segmentation is triggered when the curvature mutation interval is reconstructed by the B-spline surface segmentation through the progressive segmentation fitting unit; Optimization and fitting part: Genetic algorithm is used to optimize the segmented B-spline surface fitting, and the contour data of the intake and exhaust edges are optimized synchronously according to the segmented contours. The configuration is: Encode segment boundary points into binary gene sequences; The fitting accuracy and curvature continuity are comprehensively evaluated through the fitness function; Verification output unit: Using the data obtained by the optimization fitting unit, the geometric accuracy and surface smoothness are verified by the spatial residual monitoring unit and the curvature continuity analysis unit, and then the final contour point result set is output; Non-destructive data management module: used to build a three-dimensional hash index structure to store original point cloud data; Iteration state memory: used to store historical transformation matrix stacks, B-spline control points, and convergence flags.

2. The notch position blade profile point taking device according to claim 1, characterized in that: The coarse registration operation performed by the registration unit includes: During the iteration process, a dynamic interruption strategy is adopted to adaptively adjust the segment length according to the curvature and error distribution. When the curvature or error is large, the segment length is shortened, and vice versa. The incremental fitting retains the Jacobian matrix condition number of the previous fitting, and the LM algorithm is used to update the parameters. The iteration termination condition is that the error is ≤ 0.001 mm.

3. A notch position blade profile point taking system, comprising a notch position blade profile point taking device according to any one of claims 1 or 2, characterized in that: include: Contour point acquisition module, used to obtain measured point cloud data of precision casting blades; The processing feedback module is used to dynamically adjust the blade processing parameters according to the contour point selection result set to form a detection-feedback-processing closed loop; The evaluation module forms a closed-loop interaction with the processing feedback module; The dynamic fitting execution module is used to interact with the non-destructive data management module through a high-speed data bus, including: The curvature sensitive area recognition unit is used to calculate the point cloud curvature field in real time, mark the curvature mutation interval, and send segmentation signals to the progressive segmentation fitting unit; The progressive piecewise fitting unit is used to perform B-spline surface piecewise reconstruction on the marked interval and call the genetic algorithm to optimize the control point weights to ensure the continuity of the second-order derivative of the surface; The interrupt coupling controller is used for bidirectional communication with the progressive segment fitting unit, dynamically adjusting the segment boundaries based on the set error threshold and curvature threshold, and triggering the incremental data access interface to load the corresponding hash unit point cloud; Non-destructive data management modules include: A three-dimensional hash mapping engine is used to divide the point cloud according to the spatial hash value and transmit the curvature field data to the curvature sensitive area recognition unit in real time; Incremental data access interface, used to dynamically push local point cloud data streams based on segmentation requests, and supports fast retrieval based on hash key values; Iteration state memory, used to store IterationData structure, providing transformation data and fitting parameters for the dual convergence criterion evaluation module; The dual convergence criterion evaluation module includes: The spatial residual monitoring unit is used to read the transformation matrix from the iterative state memory and calculate the distance between the actual point and the fitting surface; Curvature continuity analysis unit, used to receive curvature field data and verify the continuity of curvature change rate and second-order derivative of adjacent segments; The composite conditional decision maker is used to trigger the threshold adjustment through the interrupt mechanism and output a convergence signal when both the double criteria are met; otherwise, a new transformation matrix is generated and written into the iterative state memory.

4. The notch position blade profile point determination system according to claim 3, characterized in that: The collaborative mechanism between the dynamic fitting execution module and the non-destructive data management module is: The 3D hash mapping engine divides the original point cloud into hash units, and each unit stores the curvature value of the corresponding point cloud; The curvature sensitive area identification unit quickly locates the curvature mutation unit through the hash key value, triggering the progressive segmented fitting unit to call the incremental data access interface to load the unit point cloud, and the data loading delay is ≤5ms.

5. The notch position blade profile point taking system according to claim 3, characterized in that: The closed-loop control path of the dual-convergence criterion evaluation module is: The composite conditional decision maker outputs a convergence signal → interrupts the coupling controller → adjusts the segment boundaries and fitting parameters; If a single condition is not met, the historical transformation matrix stack is updated through the iterative state memory and a new round of genetic algorithm optimization is started.

6. The notch position blade profile point determination system according to claim 3, characterized in that: The interaction between the processing feedback module and the contour point taking device includes: Receive and verify the contour error report of the output unit and automatically generate machining compensation instructions; The measured point cloud data after processing is fed back to the evaluation module to form an iterative optimization link between detection and processing.

7. The notch position blade profile point determination system according to claim 6, characterized in that: The closed-loop interaction between the evaluation module and the processing feedback module is as follows: generating blade processing parameter adjustment instructions based on the final contour point selection results, receiving the measured point cloud data after processing, and re-executing progressive spatial registration and segmented fitting to form an iterative optimization link of detection-processing-redetection.

8. The method for selecting the point of blade profile at the notch position is characterized by: The following steps are involved: Step S1: Dynamic fitting-interruption coupling stage: Based on the progressive spatial registration of the theoretical model and the measured point cloud, a curvature-driven adaptive segmentation strategy is adopted, and segmentation interruption control is implemented through dynamically adjusted root mean square error thresholds and curvature change rate thresholds, including: The coarse registration step is used to project the measured point cloud into the theoretical model coordinate system and use the improved iterative closest point algorithm for initial registration; The curvature sensitivity analysis step is used to calculate the curvature value of each point cloud through the parametric surface differential geometry method to dynamically adjust the registration parameters until the root mean square error of each sub-segment meets the set conditions; Segment trigger step, used to trigger lateral segmentation when the root mean square error of the current segment fitting exceeds 0.5% of the theoretical curvature radius, and when the curvature change rate of adjacent point clouds exceeds 0.01 mm -1 When the longitudinal segmentation is triggered, the segmentation boundary is dynamically adjusted by the interruption coupling controller; Step S2: Non-destructive data architecture phase: Create a 3D hash map engine to store the original point cloud data, use incremental data access mode to preserve data integrity, and simultaneously establish an iterative data structure, including: The original point cloud is divided into spatial hash units using a three-dimensional hash mapping function, and only the local point cloud data associated with the current segment is loaded; Establish an iterative data structure including original data pointer, historical transformation matrix stack, B-spline control points and 8-bit convergence flag register; Step S3: Dual-objective optimization fitting stage: Using a genetic algorithm to optimize the segmented B-spline surface fitting, synchronously controlling the horizontal and vertical segmentation of the intake and exhaust sides, including: The chromosome encoding step is used to encode the segment boundary point index number into a binary gene sequence, and an elite retention strategy is used to prevent the optimal solution from being lost; The fitness function optimization step is used to comprehensively evaluate the fitting accuracy and curvature continuity through the fitness function, perform selection, crossover, and mutation operations, and the maximum number of iterations is 1000; The segmented reconstruction step is used to perform segmented reconstruction of the B-spline surface on the curvature mutation interval to ensure that the fitting surface has second-order derivative continuity; Step S4: Composite convergence verification stage: When both the geometric accuracy and surface smoothness conditions are met, the final contour point result set is output, including: The geometric accuracy verification step is used to use the spatial residual threshold ≤ 0.03 mm as the accuracy verification standard; Surface smoothness verification step: the curvature change rate between adjacent segments is ≤0.01 mm -1 And the second-order derivative is continuous; If a single condition is not met, the segment boundary redefinition and fitting parameter iterative optimization are automatically triggered until both criteria are met.

9. The method for determining blade profile points at notch positions according to claim 8, characterized in that: In the iterative process of the progressive spatial registration, a dynamic interruption strategy and incremental fitting are adopted: Dynamically adjust the segment length, shortening the segment length when the curvature or error is large, and extending it otherwise; The incremental fitting retains the condition number of the previous Jacobian matrix, and the LM algorithm is used to update the parameters. The iteration termination condition is that the error is ≤ 0.001 mm.

10. The method for determining blade profile points at notch positions according to claim 8, characterized in that: Curvature continuity analysis and segmented reconstruction are achieved through the following collaborative mechanisms: The curvature sensitive area recognition unit marks the curvature mutation interval in real time; The progressive piecewise fitting unit calls the point cloud data of the corresponding hash unit according to the marked interval, performs B-spline surface piecewise reconstruction, and optimizes the control point weights through genetic algorithm.

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