Accurate configuration method and device for curved surface of engine blade, electronic equipment and storage medium

Abstract

The invention discloses an engine blade curved surface accurate registration method and device, electronic equipment and a storage medium, the method and device are applied to the electronic equipment, specifically, a distribution sensitivity matrix is constructed based on an initial registration point set, the initial registration point set comprises m initial registration points selected from an engine blade, and m is a positive integer greater than 2; constructing a registration transformation error based on the distributed sensitivity matrix and a measurement error for measuring the engine blade; estimating an upper bound of a registration transformation error, wherein the upper bound is used for framing optimal registration point distribution; and calculating a minimum value of a registration transformation error in the optimal registration point distribution through D-optimization iteration to obtain a blade curved surface fine registration point set corresponding to the minimum value. Through the blade curved surface precise registration point set, the problem of how to realize precise control of the angle and the distance between the detection sensor and the engine blade during nondestructive detection of the engine blade can be solved.

Description

Technical Field

[0001] The present application relates to the field of engine technology, and more specifically, to a method, device, electronic device and storage medium for precise alignment of the curved surface of an engine blade. Background Art

[0002] Non-destructive testing is a necessary process in the production of aircraft engine blades and an important means of product quality control. Unlike sampling testing and destructive sampling testing, non-destructive testing can be carried out directly on blades without causing damage to the inspected samples.

[0003] In all aspects of non-destructive testing of engine blades, positioning deviation is involved, because positioning deviation has a significant impact on the test results. For example, when ultrasonically measuring the wall thickness of engine blades, a slight angle deviation between the probe and the blade can cause abnormal signal attenuation or disappearance, resulting in wall thickness measurement deviation; in eddy current testing of engine blades, slight angle and distance deviations between the probe and the blade can generate interference signals, and slight distance changes between the probe and the edge of the structure can cause false defect signals; when performing X-ray testing on engine blades, the angle and distance between the X-ray source and the blade will have a significant impact on the imaging quality, etc. In order to avoid the above situation, it is necessary to ensure that the angle and distance between the detection sensor and the engine blade are accurately controllable.

[0004] Usually, robots can be used to achieve precise positioning, but there is still a problem of mounting deviation that needs to be solved for non-destructive testing of aircraft engine blade profiling. This is because non-destructive testing of engine blades requires that the detection sensor have high accessibility to the blades, resulting in fewer available mounting wedging surfaces on the engine blades, especially for blank blades, which are more difficult to find an effective mounting reference. In practice, the actual position and theoretical position of the blade mounting often deviate, making it difficult to establish an accurate position relationship between the blade and the robot's programmed motion trajectory through mounting, making it difficult to meet the requirements of precise control of the angle and distance between the detection sensor and the engine blade. Summary of the invention

[0005] In view of this, the present application provides a precise alignment method, device, electronic device and storage medium for engine blades, which are used to solve the problem of how to achieve precise control of the angle and distance between the detection sensor and the engine blades when performing non-destructive testing on the engine blades.

[0006] In order to achieve the above objectives, the proposed solution is as follows:

[0007] A method for accurately aligning the curved surface of an engine blade is applied to electronic equipment. The method comprises the following steps:

[0008] Constructing a distribution sensitivity matrix based on an initial registration point set, wherein the initial registration point set includes m initial registration points selected from the engine blade, where m is a positive integer greater than 2;

[0009] constructing a registration transformation error based on the distributed sensitivity matrix and a measurement error of measuring the engine blade;

[0010] estimating an upper bound of the registration transformation error, wherein the upper bound is used to define an optimal registration point distribution;

[0011] The minimum value of the registration transformation error is calculated through D-optimization iteration within the optimal registration point distribution to obtain a blade surface precise registration point set corresponding to the minimum value.

[0012] Optionally, constructing a distribution sensitivity matrix based on an initial registration point set comprises the steps of:

[0013] The CAD model of the engine blade is uniformly discretized to obtain a plurality of discrete points, and points in the measurement information of n engine blades that are close to the normal vectors and curvatures of the discrete points are selected as the initial registration points to obtain n initial registration point sets, where n is a positive integer greater than or equal to 2;

[0014] Performing normal distribution determination on the n initial registration point sets, if they do not conform to the normal distribution, removing the initial registration points that do not conform to the normal distribution, and returning to the previous step to select the corresponding initial registration points again;

[0015] If the n initial registration point sets conform to a normal distribution, the n initial registration point sets are constructed as the distribution sensitivity matrix.

[0016] Optionally, the registration transformation error The upper bound of is:

[0017]

[0018] Among them, the norm is the product of the variance and eigenvalue of the distribution of the initial registration point set, λ I and λ j is the eigenvalue of the initial registration point set.

[0019] Optionally, the iterative calculation of the minimum value of the registration transformation error by D-optimization within the optimal registration point distribution to obtain a blade surface precise registration point set corresponding to the minimum value comprises the steps of:

[0020] Calculating the distribution sensitivity matrix for different numbers of initial registration points to obtain a series of registration transformation errors;

[0021] Determining the minimum value by comparing two adjacent registration transformation errors;

[0022] The initial registration point set corresponding to the minimum value is determined as the blade surface precise registration point set.

[0023] A curved surface precision registration device for an engine blade, applied to electronic equipment, the curved surface precision registration device comprising:

[0024] A first construction module is configured to construct a distribution sensitivity matrix based on an initial registration point set, wherein the initial registration point set includes m initial registration points selected from the engine blade, where m is a positive integer greater than 2;

[0025] A second construction module is configured to construct a registration transformation error based on the distributed sensitivity matrix and a measurement error of measuring the engine blade;

[0026] An upper bound estimation module is configured to estimate an upper bound of the registration transformation error, wherein the upper bound is used to define an optimal registration point distribution;

[0027] The iterative optimization module is configured to iteratively calculate the minimum value of the registration transformation error through D-optimization within the optimal registration point distribution, and obtain a blade surface precise registration point set corresponding to the minimum value.

[0028] Optionally, the first building block includes:

[0029] A first construction unit is configured to uniformly discretize the CAD model of the engine blade to obtain a plurality of discrete points, select points in the measurement information of n engine blades that are close to the normal vectors and curvatures of the discrete points as the initial registration points, and obtain n initial registration point sets, where n is a positive integer greater than or equal to 2;

[0030] A judgment execution unit is configured to perform normal distribution judgment on the n initial registration point sets, and if they do not conform to the normal distribution, the initial registration points that do not conform to the normal distribution are removed, and the discrete processing unit is enabled to select the corresponding initial registration points again;

[0031] The second construction unit is configured to construct the n initial registration point sets into the distribution sensitivity matrix if the n initial registration point sets conform to the normal distribution.

[0032] Optionally, the registration transformation error The upper bound of is:

[0033] or

[0034] Among them, the norm is the product of the variance and eigenvalue of the distribution of the initial registration point set, λ I and λ j is the eigenvalue of the initial registration point set.

[0035] Optionally, the iterative optimization module is configured to calculate the distribution sensitivity matrix for different numbers of initial registration points to obtain a series of the registration transformation errors; determine the minimum value by comparing two adjacent registration transformation errors; and determine the initial registration point set corresponding to the minimum value as the blade surface precise registration point set.

[0036] 9. An electronic device, optionally comprising at least one processor and a memory connected to the processor, wherein:

[0037] The memory is used to store computer programs or instructions;

[0038] The processor is used to execute the computer program or instruction so that the electronic device implements the surface registration method according to any one of claims 1 to 4.

[0039] 10. A storage medium, applied to an electronic device, optionally, the storage medium carries one or more computer programs, the one or more computer programs can be executed by the electronic device, so that the electronic device implements the surface registration method as described in any one of claims 1 to 4.

[0040] It can be seen from the above technical solutions that the present application discloses a method, device, electronic device and storage medium for precise registration of the curved surface of an engine blade. The method and device are applied to electronic devices, specifically, constructing a distribution sensitivity matrix based on an initial registration point set, the initial registration point set includes m initial registration points selected from the engine blade, and m is a positive integer greater than 2; constructing a registration transformation error based on the distribution sensitivity matrix and the measurement error of measuring the engine blade; estimating the upper bound of the registration transformation error, which is used to frame the optimal registration point distribution; calculating the minimum value of the registration transformation error through D-optimization iteration within the optimal registration point distribution, and obtaining a blade curved surface precise registration point set corresponding to the minimum value. The blade curved surface precise registration point set can be used to solve the problem of how to achieve precise control of the angle and distance between the detection sensor and the engine blade when performing non-destructive testing on the engine blade. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A flowchart of a method for precise registration of engine blades according to an embodiment of the present application;

[0043] Figure 2 A flow chart of a method for constructing a distributed sensitivity matrix according to an embodiment of the present application;

[0044] Figure 3 A schematic diagram of constructing the initial registration points in an embodiment of the present application;

[0045] Figure 4 A block diagram of a precise alignment device for engine blades according to an embodiment of the present application;

[0046] Figure 5 A block diagram of another precise alignment device for engine blades according to an embodiment of the present application;

[0047] Figure 6 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] Surface precision registration is one of the effective technical approaches to solve the problem of blade clamping deviation. For the clamped blade, optical, mechanical and other measurement methods are used to obtain the blade surface point cloud data, which is then registered with the point cloud data corresponding to the ideal blade clamping posture. The clamping error correction is achieved by calculating the coordinate transformation parameters between the two point cloud data. In precision registration, the data points involved in the calculation are called registration points, and the statistics are the accumulation of historical measurement data of the same blade. Based on this, the following registration scheme is proposed.

[0050] Figure 1 This is a flow chart of a method for precise alignment of engine blades according to an embodiment of the present application.

[0051] like Figure 1As shown, this embodiment provides a precise registration method, which is applied to an electronic device and is used to achieve precise registration between a detection sensor and a blade surface when detecting an engine blade. The electronic device can be understood as a computer, server or cloud platform with data computing and information processing capabilities. The precise registration method includes the following steps:

[0052] S1. Construct a distribution sensitivity matrix based on the initial registration point set.

[0053] The initial registration point set includes m initial registration points selected from the engine blades, where m is a positive integer relative to 2. Specifically, the construction of the distribution sensitivity matrix is ​​achieved through the following steps. Figure 2 As shown:

[0054] S101, constructing an initial registration point set of n engine blades.

[0055] The CAD model of the engine blade is uniformly discretized to obtain multiple discrete points. For a single engine blade, the point in the measurement information obtained by measuring it that is close to the normal vector and curvature of each discrete point is selected as the initial registration point, thereby obtaining multiple initial registration points for a single engine blade, such as Figure 3 As shown, each initial registration point is recorded as:

[0056]

[0057] A plurality of initial configuration points constitute an initial registration point set of the engine blade. The initial registration point set is represented in this application as:

[0058] P{P i ,i=1,…,m}

[0059] Among them, Pi is the i-th initial registration point.

[0060] S102: Determine whether each initial registration point conforms to a normal distribution.

[0061] That is, a normal distribution determination is performed for each initial registration point in the set of all initial registration points. If, after sequential determination, it is found that an initial registration point does not conform to the normal distribution, the process returns to the previous step, the initial registration point is reselected, and then the normal distribution determination is performed again.

[0062] S103, construct a distribution sensitivity matrix.

[0063] Finally, after the normal distribution judgment, if it is determined that each initial registration point conforms to the normal distribution, the n initial registration point sets are constructed as the distribution sensitivity matrix

[0064]

[0065] a is the number of points with similar curvature, θ is the distribution parameter of the measuring points, k is the sample of the extracted variable, F is the expression of the sampling probability density, and N is the matrix The number of initial registration points in .

[0066] S2. Construct the registration transformation error based on the distribution sensitivity matrix.

[0067] Specifically, the registration transformation error is constructed based on the distribution sensitivity matrix and the measurement error of the engine blade. and registration transformation error The relationship between the distribution and sensitivity matrix Eigenvalue λ i (i=1,…,I), the expression is:

[0068]

[0069] Among them, u i is a standard normal variable, v i is the variable distribution parameter, when λ I >0, measurement point registration transformation error for:

[0070]

[0071] After the registration, the error distribution of the n blade points on each section is obtained, which can be used to select a minimum number of measurements to achieve the best fitting accuracy. j >0,λ j+1 =λ j+2 =…λ I =0, then the transformation error of the above formula can be rewritten as:

[0072]

[0073] Among them, ω i is an invalid transformation coefficient, and the important sampling probability density function F(k,θ) of the measurement point concentration registration point is introduced. According to the important sampling method, the measurement point distribution parameter W(F) can be obtained as follows:

[0074]

[0075] Where D(k,θ) is the joint probability density function of k.

[0076] S3. Estimate the upper bound of the registration transformation boundary.

[0077] In the process of registration point selection, there is an optimal registration point distribution, which is used to reflect the geometric properties of the number of measurement points and the measurement position on the registration transformation error. The upper bound of the distribution error of the two can be expressed as the upper bound of the transformation error, which defines the distribution range of the optimal registration point.

[0078] In this application, the transformation error The upper bound is expressed as:

[0079] or

[0080] Among them, the norm is the variance S of the P distribution 2 The product of (P) and the eigenvalue is shown in the following formula:

[0081] or

[0082] Among them, the definition point distribution is f k,θ (k,θ|F), the actual measurement point distribution is g k,θ (k,θ|F), the two can be expressed as:

[0083]

[0084] Among them, the P distribution conforms to the chi-square distribution, and the expectation of the P distribution is expressed as:

[0085]

[0086] Then, S 2 (P) = 2E(P)

[0087] S4. Calculate the blade surface precise registration point set based on the optimal registration point distribution.

[0088] Specifically, the minimum value of the registration transformation error is calculated by D-optimization iteration within the optimal registration point distribution. On the basis of the minimum value, the blade surface precise registration point set corresponding to the minimum value is obtained. Generally speaking, P is related to the sensitivity matrix and variance. With the optimization of the P position, D-optimization is used to iterate the registration points, optimize the distribution of registration points and obtain Minimum value, stop D-optimization of P position.

[0089] The specific process is to increase the number of initial registration points in sequence. As the number of P increases, that is, when m=m+1; Minimum value ≥ When the minimum value is reached, the D-optimization of the number P is stopped, P{P i ,i=1,…,m} is the blade surface precise registration point set based on statistical analysis. When m=M, and Minimum value ≥ When the value is the minimum, the P distribution of the sampled n blades fails to satisfy the chi-square distribution. At this time, let n = n + 1, n ≤ N, and perform iterative optimization until the blade surface precise registration point set P {P i ,i=1,…,m}.

[0090] After obtaining the above blade surface precise registration point set, when inspecting the engine blade, the test can be performed based on the point set to obtain minimum value, thus making the test result more accurate.

[0091] It can be seen from the above technical scheme that the present application provides a method for precise registration of the surface of an engine blade, which is applied to electronic equipment, specifically, constructing a distribution sensitivity matrix based on an initial registration point set, the initial registration point set includes m initial registration points selected from the engine blade, and m is a positive integer greater than 2; constructing a registration transformation error based on the distribution sensitivity matrix and the measurement error of measuring the engine blade; estimating the upper bound of the registration transformation error, which is used to frame the optimal registration point distribution; calculating the minimum value of the registration transformation error through D-optimization iteration within the optimal registration point distribution, and obtaining a blade surface precise registration point set corresponding to the minimum value. The blade surface precise registration point set can be used to solve the problem of how to achieve precise control of the angle and distance between the detection sensor and the engine blade when performing non-destructive testing on the engine blade.

[0092] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0094] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0095] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0096] Figure 4 This is a block diagram of a precise alignment device for engine blades according to an embodiment of the present application.

[0097] like Figure 4 As shown, this embodiment provides a precise alignment device, which is applied to an electronic device and is used to achieve precise alignment between a detection sensor and a blade surface when detecting an engine blade. The electronic device can be understood as a computer, a server or a cloud platform with data computing and information processing capabilities. The precise alignment device includes a first building module 10, a second building module 20, an upper bound estimation module 30 and an iterative optimization module 40.

[0098] The first construction module is used to construct a distribution sensitivity matrix based on an initial registration point set.

[0099] The initial registration point set includes m initial registration points selected from the engine blades, where m is a positive integer relative to 2. Specifically, the module includes a first construction unit 11, a judgment execution unit 12, and a second construction unit 13. Figure 5 As shown:

[0100] The first construction unit is used to construct an initial registration point set of n engine blades.

[0101] The CAD model of the engine blade is uniformly discretized to obtain multiple discrete points. For a single engine blade, the point in the measurement information obtained by measuring it that is close to the normal vector and curvature of each discrete point is selected as the initial registration point, thereby obtaining multiple initial registration points for a single engine blade, such as Figure 3 As shown, each initial registration point is recorded as:

[0102]

[0103] A plurality of initial configuration points constitute an initial registration point set of the engine blade. The initial registration point set is represented in this application as:

[0104] P{P i ,i=1,…,m}

[0105] Among them, Pi is the i-th initial registration point.

[0106] The judgment execution unit is used to judge whether each initial registration point conforms to the normal distribution.

[0107] That is, a normal distribution determination is performed for each initial registration point in the set of all initial registration points. If, after sequential determination, it is found that an initial registration point does not conform to the normal distribution, the process returns to the previous step, the initial registration point is reselected, and then the normal distribution determination is performed again.

[0108] The second construction unit is used to construct a distribution sensitivity matrix.

[0109] Finally, after the normal distribution judgment, if it is determined that each initial registration point conforms to the normal distribution, the n initial registration point sets are constructed as the distribution sensitivity matrix

[0110]

[0111] a is the number of points with similar curvature, θ is the distribution parameter of the measuring points, k is the sample of the extracted variable, F is the expression of the sampling probability density, and N is the matrix The number of initial registration points in .

[0112] The second building module is used to build the registration transformation error based on the distribution sensitivity matrix.

[0113] Specifically, the registration transformation error is constructed based on the distribution sensitivity matrix and the measurement error of the engine blade. and registration transformation error The relationship between the distribution and sensitivity matrix Eigenvalue λ i (i=1,…,I), the expression is:

[0114]

[0115] Among them, u i is a standard normal variable, v i is the variable distribution parameter, when λI >0, measurement point registration transformation error for:

[0116]

[0117] After the registration, the error distribution of the n blade points on each section is obtained, which can be used to select a minimum number of measurements to achieve the best fitting accuracy. j >0,λ j+1 =λ j+2 =…λ I =0, then the transformation error of the above formula can be rewritten as:

[0118]

[0119] Among them, ω i is an invalid transformation coefficient, and the important sampling probability density function F(k,θ) of the measurement point concentration registration point is introduced. According to the important sampling method, the measurement point distribution parameter W(F) can be obtained as follows:

[0120]

[0121] Where D(k,θ) is the joint probability density function of k.

[0122] The upper bound estimation module is used to estimate the upper bound of the registration transformation boundary.

[0123] In the process of registration point selection, there is an optimal registration point distribution, which is used to reflect the geometric properties of the number of measurement points and the measurement position on the registration transformation error. The upper bound of the distribution error of the two can be expressed as the upper bound of the transformation error, which defines the distribution range of the optimal registration point.

[0124] In this application, the transformation error The upper bound is expressed as:

[0125] or

[0126] Among them, the norm is the variance S of the P distribution 2 The product of (P) and the eigenvalue is shown in the following formula:

[0127] or

[0128] Among them, the definition point distribution is f k,θ (k,θ|F), the actual measurement point distribution is g k,θ (k,θ|F), the two can be expressed as:

[0129]

[0130]

[0131] Among them, the P distribution conforms to the chi-square distribution, and the expectation of the P distribution is expressed as:

[0132]

[0133] Then, S 2 (P) = 2E(P)

[0134] The iterative optimization module is used to calculate the blade surface precise registration point set based on the optimal registration point distribution.

[0135] Specifically, the minimum value of the registration transformation error is calculated by D-optimization iteration within the optimal registration point distribution. On the basis of the minimum value, the blade surface precise registration point set corresponding to the minimum value is obtained. Generally speaking, P is related to the sensitivity matrix and variance. With the optimization of the P position, D-optimization is used to iterate the registration points, optimize the distribution of registration points and obtain Minimum value, stop D-optimization of P position.

[0136] The specific process is to increase the number of initial registration points in sequence. As the number of P increases, that is, when m=m+1; Minimum value ≥ When the minimum value is reached, the D-optimization of the number P is stopped, P{P i ,i=1,…,m} is the blade surface precise registration point set based on statistical analysis. When m=M, and Minimum value ≥ When the value is the minimum, the P distribution of the sampled n blades fails to satisfy the chi-square distribution. At this time, let n = n + 1, n ≤ N, and perform iterative optimization until the blade surface precise registration point set P {P i ,i=1,…,m}.

[0137] After obtaining the above blade surface precise registration point set, when inspecting the engine blade, the test can be performed based on the point set to obtain minimum value, thus making the test result more accurate.

[0138] It can be seen from the above technical scheme that the present application provides a device for precise registration of the curved surface of an engine blade, which is applied to electronic equipment, specifically, constructing a distribution sensitivity matrix based on an initial registration point set, the initial registration point set includes m initial registration points selected from the engine blade, and m is a positive integer greater than 2; constructing a registration transformation error based on the distribution sensitivity matrix and the measurement error of measuring the engine blade; estimating the upper bound of the registration transformation error, which is used to frame the optimal registration point distribution; calculating the minimum value of the registration transformation error through D-optimization iteration within the optimal registration point distribution, and obtaining a blade curved surface precise registration point set corresponding to the minimum value. The blade curved surface precise registration point set can be used to solve the problem of how to achieve precise control of the angle and distance between the detection sensor and the engine blade when performing non-destructive testing on the engine blade.

[0139] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0140] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0141] Figure 6 A block diagram of an electronic device according to an embodiment of the present application.

[0142] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0143] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 602 or a program loaded from an input device 606 into a random access memory RAM 603. In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0144] Typically, the following devices may be connected to the I / O interface: input devices including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 609. The communication device 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0145] The present application also provides a computer-readable storage medium embodiment.

[0146] The computer-readable storage medium is applied to an electronic device and carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device constructs a distribution sensitivity matrix based on an initial registration point set, wherein the initial registration point set includes m initial registration points selected from the engine blade, where m is a positive integer greater than 2; constructs a registration transformation error based on the distribution sensitivity matrix and the measurement error of the engine blade; estimates the upper bound of the registration transformation error, which is used to frame the optimal registration point distribution; and calculates the minimum value of the registration transformation error through D-optimization iteration within the optimal registration point distribution to obtain a blade surface precision registration point set corresponding to the minimum value. The blade surface precision registration point set can be used to solve the problem of how to achieve precise control of the angle and distance between the detection sensor and the engine blade when performing non-destructive testing on the engine blade.

[0147] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer 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 of the above.

[0148] In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-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 of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0149] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0150] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0151] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0152] The technical solution provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for accurate registration of the curved surface of an engine blade, applied to electronic equipment, characterized in that: The surface precise registration method comprises the steps of: Constructing a distribution sensitivity matrix based on an initial registration point set, wherein the initial registration point set includes m initial registration points selected from the engine blade, where m is a positive integer greater than 2; constructing a registration transformation error based on the distributed sensitivity matrix and a measurement error of measuring the engine blade; estimating an upper bound of the registration transformation error, wherein the upper bound is used to define an optimal registration point distribution; The minimum value of the registration transformation error is calculated through D-optimization iteration within the optimal registration point distribution to obtain a blade surface precise registration point set corresponding to the minimum value.

2. The surface precise registration method according to claim 1, characterized in that: The method of constructing a distribution sensitivity matrix based on an initial registration point set comprises the following steps: The CAD model of the engine blade is uniformly discretized to obtain a plurality of discrete points, and points in the measurement information of n engine blades that are close to the normal vectors and curvatures of the discrete points are selected as the initial registration points to obtain n initial registration point sets, where n is a positive integer greater than or equal to 2; Performing normal distribution determination on the n initial registration point sets, if they do not conform to the normal distribution, removing the initial registration points that do not conform to the normal distribution, and returning to the previous step to select the corresponding initial registration points again; If the n initial registration point sets conform to a normal distribution, the n initial registration point sets are constructed as the distribution sensitivity matrix.

3. The surface precise registration method according to claim 1, characterized in that: The registration transformation error The upper bound of is: or Among them, the norm is the product of the variance and eigenvalue of the distribution of the initial registration point set, λ I and λ j is the eigenvalue of the initial registration point set.

4. The surface registration method according to claim 1, characterized in that: The method of calculating the minimum value of the registration transformation error by D-optimization iteration within the optimal registration point distribution to obtain a blade surface precise registration point set corresponding to the minimum value comprises the following steps: Calculating the distribution sensitivity matrix for different numbers of initial registration points to obtain a series of registration transformation errors; Determining the minimum value by comparing two adjacent registration transformation errors; The initial registration point set corresponding to the minimum value is determined as the blade surface precise registration point set.

5. A device for accurately aligning the curved surface of an engine blade, applied to electronic equipment, characterized in that: The curved surface precise registration device comprises: A first construction module is configured to construct a distribution sensitivity matrix based on an initial registration point set, wherein the initial registration point set includes m initial registration points selected from the engine blade, where m is a positive integer greater than 2; A second construction module is configured to construct a registration transformation error based on the distributed sensitivity matrix and a measurement error of measuring the engine blade; An upper bound estimation module is configured to estimate an upper bound of the registration transformation error, wherein the upper bound is used to define an optimal registration point distribution; The iterative optimization module is configured to iteratively calculate the minimum value of the registration transformation error through D-optimization within the optimal registration point distribution, and obtain a blade surface precise registration point set corresponding to the minimum value.

6. The curved surface precise registration device according to claim 5, characterized in that: The first building block comprises: A first construction unit is configured to uniformly discretize the CAD model of the engine blade to obtain a plurality of discrete points, select points in the measurement information of n engine blades that are close to the normal vectors and curvatures of the discrete points as the initial registration points, and obtain n initial registration point sets, where n is a positive integer greater than or equal to 2; A judgment execution unit is configured to perform normal distribution judgment on the n initial registration point sets, and if they do not conform to the normal distribution, the initial registration points that do not conform to the normal distribution are removed, and the discrete processing unit is enabled to select the corresponding initial registration points again; The second construction unit is configured to construct the n initial registration point sets into the distribution sensitivity matrix if the n initial registration point sets conform to the normal distribution.

7. The curved surface precise registration device according to claim 5, characterized in that: The registration transformation error The upper bound of is: or Among them, the norm is the product of the variance and eigenvalue of the distribution of the initial registration point set, λ I and λ j is the eigenvalue of the initial registration point set.

8. The curved surface registration device according to claim 5, characterized in that: The iterative optimization module is configured to calculate the distribution sensitivity matrix for different numbers of initial registration points to obtain a series of the registration transformation errors; determine the minimum value by comparing two adjacent registration transformation errors; and determine the initial registration point set corresponding to the minimum value as the blade surface precise registration point set.

9. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instruction so that the electronic device implements the surface registration method according to any one of claims 1 to 4.

10. A storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device implements the surface registration method according to any one of claims 1 to 4.

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