Method for calculating gasket adjustment amount in coaxial adjustment of helicopter tail transmission shaft

Abstract

The application relates to the technical field of aviation assembly, in particular to a method for calculating the adjusting amount of a gasket in the process of adjusting the coaxiality of a helicopter tail transmission shaft, which comprises the following steps: constructing target key points and a target virtual camera of the helicopter tail transmission shaft to determine a first coaxiality error representation vector of the helicopter tail transmission shaft; obtaining target measurement data of the helicopter tail transmission shaft and equivalently mapping the target measurement data to the first coaxiality error representation vector to obtain a second coaxiality error representation vector meeting a preset format; and inputting the second coaxiality error representation vector into a pre-calibrated mathematical solution model to output the adjusting amount of each gasket of the helicopter tail transmission shaft. Thus, the problems that in the related art, only two single circular targets are measured, the coaxiality of the helicopter tail transmission shaft cannot be effectively observed, and the adjusting amount of the installed gasket is estimated only according to artificial experience, thereby increasing the artificial cost and reducing the accuracy of the gasket adjusting amount are solved.

Description

Technical Field

[0001] This application relates to the field of aviation assembly technology, and in particular to a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft. Background Technology

[0002] In the field of aerospace manufacturing, during the assembly of helicopter transmission systems, it is necessary to control the coaxiality between the various transmission shafts at the helicopter tail to meet process requirements. Currently, the main method is to evaluate the coaxiality status of the helicopter tail transmission shafts using a sensor (laser tracker, laser alignment instrument, collimating telescope, etc.), and then add or remove shims on multiple mounting supports of the intermediate gear reducer to solve the coaxiality compensation problem of the input / output shafts of the main gear reducer, intermediate gear reducer, and tail gear reducer.

[0003] Within the helicopter manufacturing plant, the existing tail drive shaft coaxiality adjustment technology utilizes collimating telescopes. Based on worker experience, coaxiality checks and compensations for each shaft are performed. Specifically, as follows... Figure 1 As shown, the main process is as follows: (1) Install a mid-reduction simulation fixture at the mid-reduction mounting base. The fixture is equipped with two dedicated collimating telescopes. The angle between the axes of the two collimating telescopes is 145°±2'. (2) Install a Φ2.0mm circular target at the center of the output shaft flange of the main reducer tail shaft and install a Φ2.0mm circular target at the center of the input shaft flange of the tail reducer model. (3) Use the collimating telescopes to check and repeatedly adjust the thickness of the adjustment pad between the mid-reduction and the mid-reduction mounting support according to manual experience so that the two Φ2.0mm circular targets appear simultaneously in the field of view of the two collimating telescopes.

[0004] However, the existing methods for manually measuring and adjusting the coaxiality of the tail drive shaft have several problems: (1) Measuring only two sets of single circular targets cannot effectively observe coaxiality. For example, observing only one target on the main reducer can only characterize the center position of the starting point of the main reducer output shaft, but cannot characterize the coaxiality between the main reducer output shaft and the intermediate reducer input shaft; observing only one target on the tail reducer can only characterize the center position of the ending point of the tail reducer output shaft, but cannot characterize the coaxiality between the tail reducer output shaft and the intermediate reducer input shaft. (2) It is impossible to obtain a quantitative mathematical relationship between the observation data and the adjustment amount of the mounting shims: The adjustment amount of the mounting shims is estimated based only on manual experience, so it may be necessary to repeatedly adjust and observe.

[0005] To address the aforementioned issues, researchers hope to utilize digital measurement methods to assist in coaxiality measurement and shim adjustment analysis. Some scholars have used laser trackers to detect the coaxiality of ultra-large astronomical telescopes. They simultaneously used collimating telescopes and laser trackers to obtain coaxiality measurements of two widely spaced holes on ultra-large components. These studies have inspired methods for measuring the coaxiality of the tail beam's axes. While coaxiality measurement methods are crucial, obtaining the coaxiality between axes still makes it difficult to accurately determine the shim system's adjustment amount.

[0006] Therefore, the relevant technology only measures two sets of single circular targets, which cannot effectively observe the coaxiality of the helicopter tail drive shaft. Furthermore, it relies solely on manual experience to estimate the adjustment amount of the installation shims, which increases labor costs and results in low accuracy of the estimated shim adjustment amount, thus reducing the accuracy and reliability of the shim adjustment amount. This issue urgently needs to be addressed. Summary of the Invention

[0007] This application provides a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft. This method addresses the problems in related technologies where only two sets of single circular targets are measured, which cannot effectively observe the coaxiality of the helicopter tail drive shaft. Furthermore, the method relies solely on manual experience to estimate the shim adjustment amount, increasing labor costs and resulting in low accuracy of the estimated shim adjustment amount, thus reducing the accuracy and reliability of the shim adjustment amount.

[0008] The first aspect of this application provides a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft, comprising the following steps: constructing target key points and a target virtual camera for the helicopter tail drive shaft, and using the target virtual camera and the target key points to determine a first coaxiality error representation vector for the helicopter tail drive shaft; acquiring target measurement data of the helicopter tail drive shaft, and equivalently mapping the target measurement data to the first coaxiality error representation vector to obtain a second coaxiality error representation vector that satisfies a preset format; and inputting the second coaxiality error representation vector into a pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft.

[0009] Optionally, in one embodiment of this application, the step of constructing target key points and target virtual cameras for the helicopter tail drive shaft, and using the target virtual cameras and target key points to determine the first coaxiality error representation vector of the helicopter tail drive shaft, includes: extracting first key points, second key points, third key points, and fourth key points on the axes of the main reducer output shaft, the intermediate reducer input shaft, the intermediate reducer output shaft, and the tail reducer input shaft, respectively; constructing a first virtual camera and a second virtual camera on the axes of the intermediate reducer input shaft and the intermediate reducer output shaft, respectively; determining a first coaxiality relationship between the main reducer output shaft and the intermediate reducer input shaft based on the first virtual camera, the first key points, and the second key points; determining a second coaxiality relationship between the tail reducer input shaft and the intermediate reducer output shaft based on the second virtual camera, the third key points, and the fourth key points; and determining the first coaxiality error representation vector of the helicopter tail drive shaft according to the first coaxiality relationship and the second coaxiality relationship.

[0010] Optionally, in one embodiment of this application, obtaining the target measurement data of the helicopter tail drive shaft includes: obtaining first measurement data of the helicopter tail drive shaft based on a laser tracker; obtaining second measurement data of the helicopter tail drive shaft based on a laser alignment instrument; obtaining third measurement data of the helicopter tail drive shaft based on a collimating telescope; and composing the target measurement data based on the first measurement data, the second measurement data, and the third measurement data.

[0011] Optionally, in one embodiment of this application, before inputting the second coaxiality error characterization vector into the pre-calibrated mathematical solution model, the method further includes: collecting multiple sets of pre-calibrated data using the target measurement method; performing equivalent processing on the multiple sets of pre-calibrated data to obtain equivalent processed data; and using the equivalent processed data to calibrate the parameters of the pre-constructed initial mathematical solution model to determine the pre-calibrated mathematical solution model.

[0012] Optionally, in one embodiment of this application, the step of outputting the adjustment amount of each shim of the helicopter tail drive shaft includes: constructing a target approximate linear Jacobian matrix relationship; calibrating each coefficient of the Jacobian matrix based on the target approximate linear Jacobian matrix relationship and at least one set of data in the target measurement data to obtain a calibrated Jacobian matrix; and calculating the adjustment amount of each shim of the helicopter tail drive shaft using the calibrated Jacobian matrix.

[0013] A second aspect of this application provides a device for calculating the adjustment amount of shims during the coaxiality adjustment of a helicopter tail drive shaft, comprising: a construction module for constructing target key points and a target virtual camera for the helicopter tail drive shaft, and using the target virtual camera and the target key points to determine a first coaxiality error representation vector of the helicopter tail drive shaft; an acquisition module for acquiring target measurement data of the helicopter tail drive shaft, and equivalently mapping the target measurement data to the first coaxiality error representation vector to obtain a second coaxiality error representation vector that satisfies a preset format; and a calculation module for inputting the second coaxiality error representation vector into a pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft.

[0014] Optionally, in one embodiment of this application, the construction module includes: a first construction unit, configured to extract a first key point, a second key point, a third key point, and a fourth key point on the axes of the main reducer output shaft, the intermediate reducer input shaft, the intermediate reducer output shaft, and the tail reducer input shaft, respectively, and construct a first virtual camera and a second virtual camera on the axes of the intermediate reducer input shaft and the intermediate reducer output shaft, respectively; a first determination unit, configured to determine a first coaxiality relationship between the main reducer output shaft and the intermediate reducer input shaft based on the first virtual camera, the first key point, and the second key point; a second determination unit, configured to determine a second coaxiality relationship between the tail reducer input shaft and the intermediate reducer output shaft based on the second virtual camera, the third key point, and the fourth key point; and a third determination unit, configured to determine the first coaxiality error representation vector of the helicopter tail drive shaft according to the first coaxiality relationship and the second coaxiality relationship.

[0015] Optionally, in one embodiment of this application, the acquisition module includes: a first acquisition unit, configured to acquire first measurement data of the helicopter tail drive shaft based on a laser tracker; a second acquisition unit, configured to acquire second measurement data of the helicopter tail drive shaft based on a laser alignment instrument; a third acquisition unit, configured to acquire third measurement data of the helicopter tail drive shaft based on a collimating telescope; and a fourth acquisition unit, configured to compose the target measurement data based on the first measurement data, the second measurement data, and the third measurement data.

[0016] Optionally, in one embodiment of this application, the apparatus further includes: a data acquisition module, configured to acquire multiple sets of pre-calibrated data using the target measurement method before inputting the second coaxiality error representation vector into the pre-calibrated mathematical solution model; a determination module, configured to perform equivalent processing on the multiple sets of pre-calibrated data to obtain equivalent processed data before inputting the second coaxiality error representation vector into the pre-calibrated mathematical solution model; and a processing module, configured to perform parameter calibration on a pre-constructed initial mathematical solution model using the equivalent processed data to determine the pre-calibrated mathematical solution model before inputting the second coaxiality error representation vector into the pre-calibrated mathematical solution model.

[0017] Optionally, in one embodiment of this application, the calculation module includes: a second construction unit for constructing a target approximate linear Jacobian matrix relationship; a fifth acquisition unit for calibrating each coefficient of the Jacobian matrix based on the target approximate linear Jacobian matrix relationship and at least one set of data from the target measurement data to obtain a calibrated Jacobian matrix; and a calculation unit for calculating the adjustment amount of each shim of the helicopter tail drive shaft using the calibrated Jacobian matrix.

[0018] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft.

[0020] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft as described above.

[0021] This application embodiment can construct target key points and a target virtual camera for the helicopter tail drive shaft to determine the first coaxiality error representation vector of the helicopter tail drive shaft. It then acquires target measurement data of the helicopter tail drive shaft and maps it equivalently to the first coaxiality error representation vector to obtain a second coaxiality error representation vector that meets a certain format. This second coaxiality error representation vector is then input into a pre-calibrated mathematical solution model to output the adjustment amounts of each shim on the helicopter tail drive shaft, effectively improving the accuracy and reliability of the shim adjustment amounts. This solves the problems in related technologies where only two sets of single circular targets are measured, making it impossible to effectively observe the coaxiality of the helicopter tail drive shaft. Furthermore, relying solely on manual experience to estimate the adjustment amounts of the installed shims increases labor costs and reduces the accuracy and reliability of the shim adjustment amounts.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 A schematic diagram illustrating the manual measurement of the coaxiality of the tail drive shaft in related technologies;

[0025] Figure 2 This is a flowchart illustrating a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft according to an embodiment of this application.

[0026] Figure 3 This is a key point of a specific embodiment of the present application and a schematic diagram of the arrangement of the virtual camera in the coaxiality adjustment system of the tail beam drive shaft;

[0027] Figure 4 This is a schematic diagram illustrating an equivalent mapping method from various sensor measurement data to the coaxiality error representation vector in a mathematical model in a specific embodiment of this application.

[0028] Figure 5 This is a schematic diagram illustrating the approximate linear influence of shim adjustment on the pixel coordinates of key points in an image captured by a virtual camera and the position of the reducer tooling in a specific embodiment of this application.

[0029] Figure 6 A schematic diagram of the combined operation process of a general calculation method for gasket adjustment amount according to a specific embodiment of this application;

[0030] Figure 7This is a schematic diagram of a device for calculating the shim adjustment amount during the coaxiality adjustment process of a helicopter tail drive shaft according to an embodiment of this application.

[0031] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0033] The following describes a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft, referring to the accompanying drawings. Addressing the issues raised in the background section regarding the related technologies that only measure two sets of single circular targets, failing to effectively observe the coaxiality of the helicopter tail drive shaft, and relying solely on manual experience to estimate the shim adjustment amount, increasing labor costs and reducing the accuracy of shim adjustment, this application provides a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft. In this method, target key points and a target virtual camera of the helicopter tail drive shaft can be constructed to determine the first coaxiality error representation vector of the helicopter tail drive shaft. Target measurement data of the helicopter tail drive shaft is obtained and equivalently mapped to the first coaxiality error representation vector to obtain a second coaxiality error representation vector satisfying a certain format. This second coaxiality error representation vector is then input into a pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft, effectively improving the accuracy and reliability of the shim adjustment amount. This solves the problems in related technologies, such as the inability to effectively observe the coaxiality of the helicopter tail drive shaft by measuring only two sets of single circular targets, and the increased labor costs and reduced accuracy and reliability of shim adjustment by estimating the adjustment amount of the installation shims based solely on manual experience.

[0034] Specifically, Figure 2 This is a flowchart illustrating a method for calculating the shim adjustment amount during the coaxiality adjustment of a helicopter tail drive shaft, as provided in an embodiment of this application.

[0035] like Figure 2 As shown, the calculation method for the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft includes the following steps:

[0036] In step S201, target key points and target virtual camera of helicopter tail drive shaft are constructed, and the first coaxiality error representation vector of helicopter tail drive shaft is determined using target virtual camera and target key points.

[0037] In this embodiment of the application, the first coaxiality error representation vector is a vector that abstractly represents the coaxiality error based on the constructed key points and the virtual camera.

[0038] It is understood that the embodiments of this application can construct target key points and target virtual cameras for the helicopter tail drive shaft. For example, firstly, in order to characterize the coaxiality relationship, key points are extracted on the axes of the main reducer output shaft, intermediate reducer input shaft, intermediate reducer output shaft, and tail reducer input shaft of the helicopter tail drive shaft to form two sets of spatial point groups to be aligned (each set contains two key points), which are used to characterize the coaxiality relationship between the main reducer output shaft and the intermediate reducer input shaft, and between the intermediate reducer output shaft and the tail reducer input shaft, respectively. In addition, two monocular cameras are virtually constructed on the intermediate reducer fixture. According to the perspective projection equation of the monocular camera, the coordinates of the aforementioned key points in the 2-D pixel coordinate system can be calculated, and then the image coordinate error vector in the following steps, namely the first coaxiality error characterization vector, can be constructed to realize the indirect observation and characterization of the coaxiality error, effectively improving the feasibility of shim adjustment during the calculation of the coaxiality adjustment of the helicopter tail drive shaft.

[0039] Optionally, in one embodiment of this application, target key points and a target virtual camera are constructed for the helicopter tail drive shaft, and the first coaxiality error representation vector of the helicopter tail drive shaft is determined using the target virtual camera and the target key points. This includes: extracting a first key point, a second key point, a third key point, and a fourth key point on the axes of the main reducer output shaft, the intermediate reducer input shaft, the intermediate reducer output shaft, and the tail reducer input shaft, respectively; constructing a first virtual camera and a second virtual camera on the axes of the intermediate reducer input shaft and the intermediate reducer output shaft, respectively; determining the first coaxiality relationship between the main reducer output shaft and the intermediate reducer input shaft based on the first virtual camera, the first key point, and the second key point; determining the second coaxiality relationship between the tail reducer input shaft and the intermediate reducer output shaft based on the second virtual camera, the third key point, and the fourth key point; and determining the first coaxiality error representation vector of the helicopter tail drive shaft according to the first coaxiality relationship and the second coaxiality relationship.

[0040] For example, when characterizing the relative pose of two axes, it is necessary to know the positional relationship of at least two different points on one axis relative to the other axis. Specifically, in the tail beam assembly system, if it is necessary to characterize the coaxiality of the main reducer output shaft and the intermediate reducer input shaft, it is necessary to observe at least two spatial points on the intermediate reducer input shaft axis using a fixed sensor on the main reducer output shaft, or observe two spatial points on the main reducer output shaft axis using a fixed sensor on the intermediate reducer input shaft. The same principle applies to the tail reducer input shaft and the intermediate reducer output shaft.

[0041] Next, considering that the reducers in the tail drive shaft system are arranged in the order of main reducer, intermediate reducer, and tail reducer, the intermediate reducer is used as a reference. Two coordinate points on the output shaft axis of the main reducer and two coordinate points on the input shaft axis of the tail reducer are observed from the intermediate reducer to the front and rear sides respectively, resulting in two sets of spatial points to be aligned, forming an indirect characterization of the coaxiality of each shaft. For example... Figure 3 As shown, let P m,1 P m,2 P represents two spatial points on the output shaft axis of the main reducer that need to be aligned. t,1 P t,2 This represents two spatial points on the input shaft axis of the tail reducer that need to be aligned, and the coordinate system OX of the main reducer. m Y m Z m Coordinate system OX of intermediate reducer z Y z Z z , Tail reducer coordinate system OX t Y t Z t They are respectively fixed to the main reducer simulation component, the intermediate reducer simulation component, and the tail reducer simulation component.

[0042] Subsequently, as Figure 3 As shown, a virtual camera, i.e., a virtual monocular camera, is generated on both the input and output axes of the reducer; its coordinate system is OX. z Y z Z z The relative pose remains unchanged, and the optical axes of the two virtual cameras coincide with the input / output axes of the intermediate reducer, respectively.

[0043] Furthermore, based on the perspective projection equation of a monocular camera, P can be obtained. m,1 P m,2 The coordinates of the virtual image 1 captured by virtual camera 1 are p m,1 p m,2 ;P t,1 P t,2 The coordinates of the virtual image 2 captured by virtual camera 2 are p t,1 p t,2 In this way, the relative pose relationships of each axis are ultimately indirectly characterized by two sets of four key points to be aligned.

[0044] Secondly, based on the four key points and two virtual cameras constructed, the coaxiality error can be abstractly represented. The coaxiality relationship between the output shaft of the main reducer and the input shaft of the intermediate reducer will be represented by p in virtual image 1. m,1 p m,2The error in the center coordinates of virtual image 1 is characterized by the coaxiality relationship between the input shaft of the tail reducer and the output shaft of the intermediate reducer, which will be represented by p in virtual image 2. t,1 p t,2 The error is characterized by the center coordinates of virtual image 2. Assume the image coordinates of the four spatial points to be aligned in two sets are: p m,1 =(u m,1 ,v m,1 ), p m,2 =(u m,2 ,v m,2 ), p t,1 =(u t,1 ,v t,1 ), p t,2 =(u t,2 ,v t,2 The image coordinate vectors u and v are jointly constructed as follows:

[0045] u = [u m,1 ,u m,2 ,u t,1 ,u t,2 ] T

[0046] v = [v m,1 ,v m,2 ,v t,1 ,v t,2 ] T

[0047] Where, p m,1 p m,2 For P m,1 P m,2 The coordinates p under the virtual image 1 captured by virtual camera 1 t,1 p t,2 For P t,1 P t,2 The coordinates of virtual image 2 captured by virtual camera 2; u and v are image coordinate vectors.

[0048] Let the center coordinates of virtual image 1 be (u m ,v m The center coordinates of virtual image 2 are (u t ,v t Construct the image coordinate error vector e u e v :

[0049] e u =[u m,1 -u m ,u m,2 -u m ,u t,1 -ut ,u t,2 -u t ] T

[0050] e v =[v m,1 -v m ,v m,2 -v m ,v t,1 -v t ,v t,2 -v t ] T

[0051] Among them, e u e v This is the image coordinate error vector.

[0052] Therefore, this application embodiment abstracts two sets of key points to be aligned: the main reducer coordinate system, the intermediate reducer coordinate system, the tail reducer coordinate system, and two virtual cameras, and accurately establishes their poses; and designs a virtual image coordinate error vector with a unified format, thereby providing a unified and indirect representation of coaxiality error.

[0053] In step S202, target measurement data of the helicopter tail drive shaft is obtained, and the target measurement data is equivalently mapped to the first coaxiality error characterization vector to obtain a second coaxiality error characterization vector that meets the preset format.

[0054] In this embodiment of the application, the preset format is a coordinate format in a 2-D pixel coordinate system; the second coaxiality error characterization vector may include the coaxiality error characterization vector of the collimating telescope, the coaxiality error characterization vector of the laser alignment instrument or the laser tracker.

[0055] It is understood that the embodiments of this application can acquire target measurement data of the helicopter tail drive shaft. For example, it can acquire raw measurement data based on various measurement methods such as laser alignment instruments, laser trackers, or collimating telescopes, and uniformly convert it into the coordinates of each key point in the above steps in the 2-D pixel coordinate system. Specifically, the collimating telescope can directly capture images, and then use image processing algorithms to calculate the 2-D pixel coordinates of the key points; the laser alignment instrument and laser tracker can measure the relative pose relationship of each reducer's corresponding axis, calculate the three-dimensional coordinates of each key point, and then calculate the 2-D pixel coordinates of the key points according to the perspective projection equation of the monocular camera. Thus, the coaxiality error characterization vector of the collimating telescope and the coaxiality error characterization vector of the laser alignment instrument or laser tracker can be obtained, effectively improving the accuracy of shim adjustment amount in the process of calculating the coaxiality adjustment of the helicopter tail drive shaft.

[0056] Optionally, in one embodiment of this application, acquiring target measurement data of the helicopter tail drive shaft includes: acquiring first measurement data of the helicopter tail drive shaft based on a laser tracker; acquiring second measurement data of the helicopter tail drive shaft based on a laser alignment instrument; acquiring third measurement data of the helicopter tail drive shaft based on a collimating telescope; and composing target measurement data based on the first measurement data, the second measurement data, and the third measurement data.

[0057] In this application embodiment, the digital equipment for measuring the shaft of the helicopter tail drive system mainly includes the following three types: laser tracker, laser alignment instrument, and collimating telescope. Their specific measurement principles fall into two categories:

[0058] (1) Through rotational scanning, the laser tracker and laser alignment instrument will provide the relative pose relationship between the axes of the intermediate reducer simulation fixture, the main reducer output shaft simulation component, and the tail reducer input shaft simulation component, which corresponds to the main reducer coordinate system OX we defined above. m Y m Z m Coordinate system OX of intermediate reducer z Y z Z z , Tail reducer coordinate system OX t Y t Z t The homogeneous transformation matrix T between them;

[0059] (2) By designing an image target, the image information captured by the collimating telescope is used, and the image processing algorithm is combined to directly give the center coordinates of the actual system observation target.

[0060] Therefore, it is necessary to uniformly map the measurement data of different types of actual sensors to the aforementioned coaxiality indirect characterization parameter, i.e., the image coordinate error vector e. u e v This helps in the unified calculation of subsequent shim adjustment amounts.

[0061] For example, such as Figure 4 As shown, the embodiments of this application design data mapping methods for different types of measuring equipment, as detailed below:

[0062] (1) Laser tracker or laser alignment instrument: Combining the CAD models of the intermediate reducer simulation fixture, the main reducer output shaft simulation component, and the tail reducer input shaft simulation component, these two measurement methods can provide the coordinate system OX of the main reducer. m Y m Z m Coordinate system OX of intermediate reducer z Y z Z z , Tail reducer coordinate system OXt Y t Z t The homogeneous transformation matrix between them. Let the coordinate system in the system be OX. z Y z Z z Relative to the virtual camera 1 coordinate system OX C1 Y C1 Z C1 The homogeneous transformation matrix is ​​T C1 Z The measured coordinate system OX m Y m Z m Relative to coordinate system OX z Y z Z z The homogeneous transformation matrix is ​​T z m P m,1 P m,2 In coordinate system OX m Y m Z m The three-dimensional coordinates below are and Then P can be calculated using the following formula. m,1 P m,2 In coordinate system OX C1 Y C1 Z C1 The coordinates below are and p can be obtained using the perspective projection equation of a monocular camera. m,1 and p m,2 , in which, i.e. and It can be represented as:

[0063]

[0064] in, and For P m,1 P m,2 In coordinate system OX C1 Y C1 Z C1 The coordinates below; T C1 Z Let the coordinate system be OX z Y z Z z Relative to the virtual camera 1 coordinate system OX C1 Y C1 Z C1 The homogeneous transformation matrix T; z m Let the coordinate system be OXm Y m Z m Relative to coordinate system OX z Y z Z z The homogeneous transformation matrix, and For P m,1 P m,2 In coordinate system OX m Y m Z m The three-dimensional coordinates below.

[0065] Similarly, let the coordinate system in the system be OX. z Y z Z z Relative to the virtual camera 2 coordinate system OX C2 Y C2 Z C2 The homogeneous transformation matrix is ​​T C2 Z The measured coordinate system OX t Y t Z t Relative to coordinate system OX z Y z Z z The homogeneous transformation matrix is ​​T z t P t,1 P t,2 In coordinate system OX t Y t Z t The three-dimensional coordinates below are and Then P can be calculated using the following formula. t,1 P t,2 In coordinate system OX C2 Y C2 Z C2 The coordinates below are and p can be obtained using the perspective projection equation of a monocular camera. t,1 and p t,2 ,in, and It can be represented as:

[0066]

[0067] Among them, T C2 Z Let the coordinate system be OX z Y z Z z Relative to the virtual camera 2 coordinate system OX C2 YC2 Z C2 The homogeneous transformation matrix T; z t Let the coordinate system be OX t Y t Z t Relative to coordinate system OX z Y z Z z The homogeneous transformation matrix; and For P t,1 P t,2 In coordinate system OX t Y t Z t The three-dimensional coordinates below; and For P t,1 P t,2 In coordinate system OX C2 Y C2 Z C2 The coordinates below.

[0068] Therefore, the actual measurement data of the laser tracker or laser alignment instrument in this application embodiment can be equivalent to e. u e v .

[0069] (2) Collimating telescope: A corresponding P needs to be installed on the main reducer simulator of the actual system. m,1 P m,2 An output shaft simulator with two targets is installed at the position, corresponding to P on the tail reducer simulator. t,1 P t,2 An input shaft simulator with two targets is installed at the designated location. A collimating telescope is installed at each of the virtual cameras 1 and 2 on the intermediate reducer simulator. Two industrial 2D cameras are installed at the ends of the collimating telescopes. This replaces manual data acquisition with digital acquisition equipment, allowing the coordinates p of the targets on the simulator to be directly obtained from the digitally acquired images using image processing algorithms. m,1 p m,2 p t,1 and p t,2 .

[0070] It should be noted that the core of this equivalent method is to accurately map the positions of the four observation targets in the actual system to the four key points in the constructed mathematical system. Therefore, it is necessary to measure the two axial distances L of the shaft simulation component in the actual system. m L t In mathematical systems, when selecting key points, care should be taken to ensure that: dist(P) m,1 P m,2 ) = Lm ;dist(P t,1 P t,2 ) = L t Secondly, it is necessary to ensure that the internal and external parameters of the real camera in the actual system and the virtual camera in the mathematical system are matched, specifically including camera pixels, imaging plane size, focal length, and camera position. After completing these two steps of matching, the actual system image can be directly processed to obtain p. m,1 p m,2 p t,1 and p t,2 Therefore, the actual measurement data of the collimating telescope in this embodiment can be equivalent to e. u and e v .

[0071] It should be noted that the embodiments of this application can equivalently map different types of actual sensor measurement data to the coaxiality error representation vector in the aforementioned constructed mathematical model; and for measurement data obtained from two different measurement principles, two equivalent methods are designed respectively, so that the multi-source sensor data collected by the actual system generates the same type of error representation in the constructed mathematical model.

[0072] In step S203, the second coaxiality error characterization vector is input into the pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft.

[0073] It is understood that the embodiments of this application can input the second coaxiality error characterization vector into a pre-calibrated mathematical solution model. For example, based on the image coordinate error vector obtained by the measurement data of each sensor in the above steps, the adjustment amount of each shim of the helicopter tail drive shaft can be obtained. In other words, its essence is to obtain the optimal shim adjustment amount by means of a mathematical model based on the deviation, which effectively improves the accuracy and reliability of the shim adjustment amount.

[0074] Optionally, in one embodiment of this application, outputting the adjustment amount of each shim of the helicopter tail drive shaft includes: constructing a target approximate linear Jacobian matrix relationship; calibrating each coefficient of the Jacobian matrix based on the target approximate linear Jacobian matrix relationship and at least one set of data from the target measurement data to obtain a calibrated Jacobian matrix; and calculating the adjustment amount of each shim of the helicopter tail drive shaft using the calibrated Jacobian matrix.

[0075] In this embodiment, adjusting the shim thickness can indirectly adjust the pitch and yaw angles of the intermediate reducer, as follows: Figure 5As shown, adjusting the thickness of the two shims distributed along the axial direction adjusts the pitch angle of the intermediate gear reducer. Similarly, changing the thickness of the two shims distributed perpendicular to the axial direction will produce the yaw angle of the intermediate gear reducer. Furthermore, simultaneously increasing or decreasing the thickness of all shims will cause the intermediate gear reducer to move as a whole.

[0076] For example, when the virtual camera's z-axis is far from the observed keypoint, the pitch, yaw, and movement of the mid-reducer will approximately cause a linear shift in the keypoint's coordinates in the pixel coordinate system. When the shim adjustment is far from the distances of the various mounting supports, the shim adjustment will approximately linearly cause the pitch, yaw, and movement of the mid-reducer. Therefore, by combining these two linear relationships, it can be concluded that the shim adjustment will approximately linearly affect the pixel coordinates of the keypoint to be aligned.

[0077] Here, there are n sets of shims on the mounting support, with adjustment amounts of x1, x2, ..., xn respectively. n , forming a vector:

[0078] x = [x1, x2, ..., x n ] T

[0079] The vectors then satisfy an approximately linear image Jacobian matrix relationship:

[0080]

[0081] Next, the Jacobian coefficient matrix J of the model is obtained. u J v Then, the influence of the shim adjustment amount on the virtual image coordinate values ​​can be characterized, and the Jacobian coefficient matrix of the model can be accurately obtained using the calibration method in the following steps.

[0082] In this embodiment, the acquisition and calculation of detailed parameters for each component are not required, thus reducing the difficulty of system application. Applying the Jacobian coefficient matrix of the above model, the image coordinate error vector e is obtained based on the equivalent data from the aforementioned sensor measurements. u e v The shim adjustment amount is obtained according to the following formula. Essentially, it is based on the deviation and uses a mathematical model to find the optimal shim adjustment amount using an optimization method:

[0083]

[0084] The calibration method for the key parameters in the Jacobian coefficient matrix of the above model is as follows: First, install the main reducer, intermediate reducer, and tail reducer in the actual system without adding shims, and record the sensor measurement data (relative position and attitude measured by a laser alignment instrument or laser tracker, or images measured by a collimating telescope). Then, perform equivalent processing on the sensor measurement data and record a set of data u1 and v1. Next, add multiple sets of standard shims of known thickness, and perform equivalent processing on the sensor measurement data, recording multiple sets of data u1 and v1. i v i Thus, m sets of coaxiality characterization quantities are obtained, which correspond to the actual system shim adjustment state:

[0085]

[0086] Where m is the number of samples in the calibration data. Then, the Jacobian coefficient matrix of the model is obtained according to the best-fit method:

[0087] J u =UX T (X T X) -1 J v =VX T (X T X) -1

[0088] Where X = [x1 x2 ... x m ], U=[u1 u2...u m V = [v1 v2 ... v] m ].

[0089] In other words, the embodiments of this application can construct an approximately linear Jacobian matrix relationship between the shim adjustment amount and the 2-D pixel coordinates, calculate the coefficients of the Jacobian matrix through model parameter calibration, and finally solve the shim adjustment amount through the inverse of the Jacobian matrix, effectively improving the accuracy and reliability of the shim adjustment amount.

[0090] Optionally, in one embodiment of this application, before inputting the second coaxiality error characterization vector into the pre-calibrated mathematical solution model, the method further includes: collecting multiple sets of pre-calibrated data using a target measurement method; performing equivalent processing on the multiple sets of pre-calibrated data to obtain equivalent processed data; and using the equivalent processed data to calibrate the parameters of the pre-constructed initial mathematical solution model to determine the pre-calibrated mathematical solution model.

[0091] As one possible way to achieve this, such as Figure 6As shown, when constructing the model, it is necessary to consider the type of sensor used in the actual system and, based on the corresponding data equivalence method, to convert the actual system measurement data into the coaxiality indirect representation in the mathematical model. Specifically, when calibrating the model, m sets of data combinations need to be collected from the actual system and converted into the unified error data format defined in the mathematical model to calibrate the model parameters, thereby obtaining a pre-calibrated mathematical solution model. When calculating the model, only one set of actual system measurement data needs to be collected, which is equivalent to the coaxiality indirect representation in the mathematical model. The pre-calibrated mathematical solution model is used to calculate the shim adjustment amount required by the actual system, thereby making the coaxiality error approach 0, effectively improving the uniformity, universality, and efficiency of calculating the shim adjustment amount.

[0092] According to the calculation method for shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft proposed in the embodiments of this application, the target key points and target virtual camera of the helicopter tail drive shaft can be constructed to determine the first coaxiality error representation vector of the helicopter tail drive shaft. The target measurement data of the helicopter tail drive shaft can be obtained and equivalently mapped to the first coaxiality error representation vector to obtain a second coaxiality error representation vector that meets a certain format. Then, the second coaxiality error representation vector is input into a pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft, effectively improving the accuracy and reliability of the shim adjustment amount. This solves the problems in related technologies where only two sets of single circular targets are measured, making it impossible to effectively observe the coaxiality of the helicopter tail drive shaft, and where the adjustment amount of the installed shims is estimated based solely on manual experience, increasing labor costs and reducing the accuracy and reliability of the shim adjustment amount.

[0093] Next, referring to the accompanying drawings, a device for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft according to an embodiment of this application is described.

[0094] Figure 7 This is a block diagram of a device for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft according to an embodiment of this application.

[0095] like Figure 7 As shown, the calculation device 10 for the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft includes: a construction module 100, an acquisition module 200, and a calculation module 300.

[0096] Specifically, module 100 is used to construct target key points and target virtual camera for the helicopter tail drive shaft, and to determine the first coaxiality error representation vector of the helicopter tail drive shaft using the target virtual camera and target key points.

[0097] The acquisition module 200 is used to acquire target measurement data of the helicopter tail drive shaft and map the target measurement data to the first coaxiality error characterization vector to obtain a second coaxiality error characterization vector that meets the preset format.

[0098] The calculation module 300 is used to input the second coaxiality error characterization vector into a pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft.

[0099] Optionally, in one embodiment of this application, the construction module 100 includes: a first construction unit, a first determination unit, a second determination unit, and a third determination unit.

[0100] The first construction unit is used to extract the first key point, the second key point, the third key point and the fourth key point on the axis of the main reducer output shaft, the middle reducer input shaft, the middle reducer output shaft and the tail reducer input shaft respectively, and to construct the first virtual camera and the second virtual camera on the axis of the middle reducer input shaft and the middle reducer output shaft respectively.

[0101] The first determining unit is used to determine the first coaxiality relationship between the output shaft of the main reducer and the input shaft of the intermediate reducer based on the first virtual camera, the first key point and the second key point.

[0102] The second determining unit is used to determine the second coaxiality relationship between the input shaft of the tail reducer and the output shaft of the intermediate reducer based on the second virtual camera, the third key point and the fourth key point.

[0103] The third determining unit is used to determine the first coaxiality error characterization vector of the helicopter tail drive shaft based on the first coaxiality relationship and the second coaxiality relationship.

[0104] Optionally, in one embodiment of this application, the acquisition module 200 includes: a first acquisition unit, a second acquisition unit, a third acquisition unit, and a fourth acquisition unit.

[0105] The first acquisition unit is used to acquire first measurement data of the helicopter tail drive shaft based on a laser tracker.

[0106] The second acquisition unit is used to acquire second measurement data of the helicopter tail drive shaft based on the laser alignment instrument.

[0107] The third acquisition unit is used to acquire third measurement data of the helicopter tail drive shaft based on the collimating telescope.

[0108] The fourth acquisition unit is used to compose target measurement data based on the first measurement data, the second measurement data, and the third measurement data.

[0109] Optionally, in one embodiment of this application, the apparatus 10 of this application embodiment further includes: a data acquisition module, a determination module, and a processing module.

[0110] The acquisition module is used to acquire multiple sets of pre-calibrated data using a target measurement method before inputting the second coaxiality error characterization vector into the pre-calibrated mathematical solution model.

[0111] The determination module is used to perform equivalent processing on multiple sets of pre-calibrated data before inputting the second coaxiality error characterization vector into the pre-calibrated mathematical solution model, so as to obtain equivalent processed data.

[0112] The processing module is used to calibrate the parameters of the pre-constructed initial mathematical solution model using equivalently processed data before inputting the second coaxiality error characterization vector into the pre-calibrated mathematical solution model, so as to determine the pre-calibrated mathematical solution model.

[0113] Optionally, in one embodiment of this application, the calculation module 300 includes: a second construction unit, a fifth acquisition unit, and a calculation unit.

[0114] The second building unit is used to construct the target approximate linear Jacobian matrix relationship.

[0115] The fifth acquisition unit is used to calibrate the coefficients of the Jacobian matrix based on the target approximate linear Jacobian matrix relationship and at least one set of data from the target measurement data, so as to obtain the calibrated Jacobian matrix.

[0116] The calculation unit is used to calculate the adjustment amount of each shim of the helicopter tail drive shaft using a calibrated Jacobian matrix.

[0117] It should be noted that the explanation of the above-mentioned method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft also applies to the shim adjustment amount calculation device during the coaxiality adjustment of the helicopter tail drive shaft in this embodiment, and will not be repeated here.

[0118] The calculation device for shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft, as proposed in the embodiments of this application, can construct the target key points and target virtual camera of the helicopter tail drive shaft to determine the first coaxiality error representation vector of the helicopter tail drive shaft, acquire the target measurement data of the helicopter tail drive shaft, and equivalently map it to the first coaxiality error representation vector to obtain a second coaxiality error representation vector that meets a certain format. Then, the second coaxiality error representation vector is input into a pre-calibrated mathematical solution model to output the adjustment amount of each shim of the helicopter tail drive shaft, effectively improving the accuracy and reliability of the shim adjustment amount. This solves the problems in related technologies where only two sets of single circular targets are measured, making it impossible to effectively observe the coaxiality of the helicopter tail drive shaft, and where the adjustment amount of the installed shims is estimated based solely on manual experience, increasing labor costs and reducing the accuracy and reliability of the shim adjustment amount.

[0119] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0120] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0121] When the processor 802 executes the program, it implements the method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft provided in the above embodiments.

[0122] Furthermore, electronic devices also include:

[0123] Communication interface 803 is used for communication between memory 801 and processor 802.

[0124] The memory 801 is used to store computer programs that can run on the processor 802.

[0125] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0126] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0127] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0128] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0129] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft.

[0130] This embodiment also provides a computer program product, including a computer program that, when executed, is used to implement the above-mentioned method for calculating the shim adjustment amount during the coaxiality adjustment of the helicopter tail drive shaft.

[0131] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0133] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0135] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0136] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0137] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0138] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for calculating the amount of gasket adjustment during coaxiality adjustment of a tail drive shaft of a helicopter, characterized in that, The method comprises the following steps: constructing target key points and a target virtual camera of a tail transmission shaft of a helicopter, and determining a first coaxial error representation vector of the tail transmission shaft of the helicopter by using the target virtual camera and the target key points; acquiring target measurement data of the tail transmission shaft of the helicopter, and equivalently mapping the target measurement data into the first coaxial error representation vector to obtain a second coaxial error representation vector satisfying a preset format; inputting the second coaxial error representation vector into a pre-calibrated mathematical solution model to output adjustment amounts of each gasket of the tail transmission shaft of the helicopter; the constructing target key points and a target virtual camera of a tail transmission shaft of a helicopter, and determining a first coaxial error representation vector of the tail transmission shaft of the helicopter by using the target virtual camera and the target key points, comprises: respectively extracting first key points, second key points, third key points and fourth key points on the axes of a main reducer output shaft, a middle reducer input shaft, a middle reducer output shaft and a tail reducer input shaft, and constructing a first virtual camera and a second virtual camera on the axes of the middle reducer input shaft and the middle reducer output shaft respectively; determining a first coaxial relationship of the main reducer output shaft and the middle reducer input shaft based on the first virtual camera, the first key points and the second key points; determining a second coaxial relationship of the tail reducer input shaft and the middle reducer output shaft based on the second virtual camera, the third key points and the fourth key points; determining the first coaxial error representation vector of the tail transmission shaft of the helicopter according to the first coaxial relationship and the second coaxial relationship.

2. The method of claim 1, wherein, the acquiring target measurement data of the tail transmission shaft of the helicopter, comprises: acquiring first measurement data of the tail transmission shaft of the helicopter based on a laser tracker; acquiring second measurement data of the tail transmission shaft of the helicopter based on a laser aligning instrument; acquiring third measurement data of the tail transmission shaft of the helicopter based on a collimating telescope; composing the target measurement data based on the first measurement data, the second measurement data and the third measurement data.

3. The method of claim 1, wherein, before inputting the second coaxial error representation vector into a pre-calibrated mathematical solution model, further comprising: acquiring a plurality of groups of pre-calibrated data by using the target measurement data; equivalently processing the plurality of groups of pre-calibrated data to obtain equivalently processed data; performing parameter calibration on an initially constructed mathematical solution model by using the equivalently processed data to determine the pre-calibrated mathematical solution model.

4. The method of claim 1, wherein, the outputting adjustment amounts of each gasket of the tail transmission shaft of the helicopter, comprises: constructing a target approximate linear Jacobian matrix relationship; calibrating each coefficient of a Jacobian matrix based on at least one group of data in the target measurement data to obtain a calibrated Jacobian matrix based on the target approximate linear Jacobian matrix relationship and the at least one group of data; calculating the adjustment amounts of each gasket of the tail transmission shaft of the helicopter by using the calibrated Jacobian matrix.

5. A device for calculating the amount of gasket adjustment during coaxiality adjustment of a tail drive shaft of a helicopter, characterized in that, comprises: The construction module is configured to construct a target key point and a target virtual camera of the tail transmission shaft of the helicopter, and determine a first coaxial error representation vector of the tail transmission shaft of the helicopter by using the target virtual camera and the target key point. The acquisition module is configured to acquire target measurement data of the tail transmission shaft of the helicopter, and equivalently map the target measurement data into the first coaxial error representation vector to obtain a second coaxial error representation vector satisfying a preset format. The calculation module is configured to input the second coaxial error representation vector into a pre-calibrated mathematical solution model to output an adjustment amount of each gasket of the tail transmission shaft of the helicopter. The construction module comprises: The construction unit is configured to extract a first key point, a second key point, a third key point and a fourth key point on the axis of the main reducer output shaft, the intermediate reducer input shaft, the intermediate reducer output shaft and the tail reducer input shaft respectively, and construct a first virtual camera and a second virtual camera on the axis of the intermediate reducer input shaft and the intermediate reducer output shaft respectively. The first determination unit is configured to determine a first coaxial relationship between the main reducer output shaft and the intermediate reducer input shaft based on the first virtual camera, the first key point and the second key point. The second determination unit is configured to determine a second coaxial relationship between the tail reducer input shaft and the intermediate reducer output shaft based on the second virtual camera, the third key point and the fourth key point. The third determination unit is configured to determine the first coaxial error representation vector of the tail transmission shaft of the helicopter according to the first coaxial relationship and the second coaxial relationship.

6. An electronic device, comprising: The memory, the processor and the computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for calculating the gasket adjustment amount in the coaxial adjustment process of the tail transmission shaft of the helicopter according to any one of claims 1-4. The program is executed by the processor to implement the method for calculating the gasket adjustment amount in the coaxial adjustment process of the tail transmission shaft of the helicopter according to any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for calculating the gasket adjustment amount in the coaxial adjustment process of the tail transmission shaft of the helicopter according to any one of claims 1-4.

8. A computer program product comprising a computer program, characterized in that, ​

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