Process parameter coupling relation analysis method for component butt joint process

Abstract

The invention discloses a process parameter coupling relation analysis method for a component butt joint process, which comprises the following steps of: defining a measurement point representing component butt joint quality characteristics as a quality characteristic point, defining a process equipment positioning position in the component butt joint process as a process monitoring point, and defining a spatial position coordinate of the quality characteristic point as a dependent variable Y, defining a spatial position coordinate of the process monitoring point as an independent variable X; respectively collecting spatial position coordinates of the independent variable X at different moments and spatial position coordinates of the dependent variable Y corresponding to each moment in the component butt joint process; performing data standardization processing on the independent variable X and the dependent variable Y to obtain a standard vertebral independent variable matrix X0 and a standard vertebral dependent variable matrix Y0 after standardization processing of the dependent variable Y and the independent variable X; based on the standard vertebral independent variable matrix X0 and the standard vertebral dependent variable matrix Y0, constructing a regression model of the independent variable X and the dependent variable Y by adopting an improved partial least square algorithm; and predicting spatial position coordinates of the docking quality characteristics according to the regression model. According to the technical scheme provided by the invention, the problem that in the existing docking process of the aircraft parts, the docking assembly process of the whole parts is slow in the current posture adjusting process due to the fact that the data utilization rate in the posture adjusting process is low and mining research on the technological parameter relation of the posture adjusting process is lacked at present is solved.

Description

Technical Field

[0001] The present invention relates to the field of, but is not limited to, aircraft component docking assembly process monitoring technology, and in particular to a method for analyzing the correlation between quality characteristics and process equipment for docking assembly. Background Art

[0002] Aircraft component docking is a key link in aircraft manufacturing, and its quality directly affects flight safety and overall aircraft performance. The current attitude adjustment principle of the component docking process is: 1) Use a laser tracker to measure the spatial coordinates of the horizontal measurement point of the current position of the component, and display the current position of the attitude adjustment locator on the attitude adjustment industrial control computer interface; 2) Calculate the attitude adjustment target position of the attitude adjustment locator based on the measured horizontal measurement point position and the theoretical position fit; 3) Drive the attitude adjustment locator to the target position, use the laser tracker to measure the spatial coordinates of the horizontal measurement point again, and evaluate whether the quality parameters are qualified; 4) Repeat the above three steps until the docking attitude quality parameters are within a reasonable tolerance range.

[0003] From the above existing attitude adjustment principle and process, it can be seen that the position of the product horizontal measurement point and the position of the attitude adjustment locator during the component docking process are key process parameters. The attitude adjustment locator is driven to move based on the product horizontal measurement point, and the position of the attitude adjustment locator also represents the position of the horizontal measurement point to a certain extent. However, due to the low utilization rate of attitude adjustment process data, there is little research on the relationship between process parameters of the attitude adjustment process, which leads to the slow assembly process of the overall component docking in the current attitude adjustment process. Summary of the invention

[0004] Purpose of the present invention: In order to solve the above-mentioned technical problems, an embodiment of the present invention provides a method for analyzing the coupling relationship of process parameters in a component docking process, so as to solve the problem that in the existing docking process of aircraft components, the overall component docking and assembly process is slow due to the low utilization rate of attitude adjustment process data and the current lack of research on the mining of process parameter relationships in the attitude adjustment process.

[0005] Technical solution of the present invention: In a first aspect, an embodiment of the present invention provides a method for analyzing the coupling relationship of process parameters in a component docking process, comprising the following steps:

[0006] Step 1: define the measurement points that characterize the quality characteristics of the component docking as quality characteristic points, define the positioning positions of the process equipment in the component docking process as process monitoring points, and define the spatial position coordinates of the quality characteristic points as dependent variables Y, and define the spatial position coordinates of the process monitoring points as independent variables X;

[0007] Step 2: respectively collect the spatial position coordinates of the independent variable X at different times during the component docking process and the spatial position coordinates of the dependent variable Y corresponding to each time;

[0008] Step 3: Perform data standardization on the independent variable X and the dependent variable Y to obtain the standardized independent variable matrix X0 and the standardized dependent variable matrix Y0 after the dependent variable Y and the independent variable X are standardized;

[0009] Step 4: Based on the labeled independent variable matrix X0 and the labeled dependent variable matrix Y0, an improved partial least squares algorithm is used to construct a regression model of the independent variable X and the dependent variable Y;

[0010] Step 5: Predict the spatial position coordinates of the docking quality characteristics based on the regression model.

[0011] Optionally, in the above-mentioned method for analyzing the coupling relationship of process parameters in the component docking process, in step 2, the spatial position coordinates of the independent variable X collected at different times are:

[0012]

[0013] The spatial position coordinates of the dependent variable Y corresponding to each moment collected are:

[0014]

[0015] Where n represents the number of samples of the independent variable X, p represents the number of independent variables X in each sample; m represents the number of samples of the dependent variable Y, and l represents the number of dependent variables Y in each sample.

[0016] Optionally, in the above-mentioned method for analyzing the coupling relationship of process parameters for the component docking process, the formulas of the scaled independent variable matrix X0 and the scaled dependent variable matrix Y0 obtained by the scaled processing in step 3 are respectively expressed as follows:

[0017]

[0018] Among them, any element in the labeled independent variable matrix X0 represents the average value of the data in the jth column of the independent variable X, Represents the standard deviation of the data in the jth column of the independent variable X;

[0019] Scaling any element in the dependent variable matrix Y0 represents the average value of the data in the jth column of matrix Y, Represents the standard deviation of the data in the jth column of matrix Y.

[0020] Optionally, in the above-mentioned method for analyzing the coupling relationship of process parameters in the component docking process, the method of constructing the regression model of the independent variable X and the dependent variable Y in the step 4 is:

[0021] Adopt the partial least squares regression modeling method based on Renyi entropy to construct a regression model for independent variable X and dependent variable Y.

[0022] Optionally, in the process parameter coupling relationship analysis method for component docking as described above, the calculation process of Renyi entropy in step 4 is as follows:

[0023] Step 41, calculate the corresponding independent variable covariance matrix K of the standardized independent variable matrix X0 and the standardized dependent variable matrix Y0 x and the dependent variable covariance matrix K y , and the calculation formula is as follows:

[0024]

[0025] Step 42, solve the corresponding eigenvalues x of the independent variable covariance matrix K y and the dependent variable covariance matrix K and the eigenvectors

[0026] Step 43, calculate the Renyi entropy corresponding to each eigenvalue in the independent variable covariance matrix K x , and the calculation formula is:

[0027]

[0028] where E x represents the unit vector with the same dimension as the eigenvector , and k represents the number of eigenvalues of the independent variable covariance matrix K x .

[0029] Optionally, in the process of constructing the regression model using the partial least squares regression modeling method based on Renyi entropy in step 4 of the process parameter coupling relationship analysis method for component docking as described above, the methods for principal component extraction and determination of the number of principal components include:

[0030] Step 44, arrange Renyi entropy in descending order, and calculate the cumulative information rate ω corresponding to the eigenvalues and eigenvectors of the independent variable covariance matrix K x , and the calculation formula is:

[0031]

[0032] Step 45, solve the maximum cumulative information rate and the corresponding position coordinate j, which requires 1 < j < k, and calculate the information attenuation rate corresponding to the first j eigenvalues and eigenvectors , and the calculation formula is as follows:

[0033]

[0034] Step 46, calculate the information decay rate when the cumulative information rate ω is maximum When the maximum number of principal components k′ is greater than the preset decay rate, k′ satisfies 0 <k′<p;

[0035] Step 47, according to the determined number of principal components k′, a regression model of the independent variable X and the dependent variable Y is fitted using a partial least squares fitting method, and a fitting coefficient matrix A and a constant term C of the regression model are determined.

[0036] Optionally, in the above-mentioned method for analyzing the coupling relationship of process parameters in the component docking process, the regression model for fitting the independent variable X and the dependent variable Y in step 47 is:

[0037] Y T =AX T +C;

[0038] Wherein, A is the coefficient of the independent variable in the regression model, and C is the constant term.

[0039] Optionally, in the above-mentioned method for analyzing the coupling relationship of process parameters in the component docking process,

[0040] The preset attenuation rate is between [0.1, 0.2].

[0041] An embodiment of the present invention further provides a computer-readable storage medium, including: a memory and a processor;

[0042] The memory is configured to store executable instructions;

[0043] The processor is specifically configured to implement the process parameter coupling relationship analysis method for component docking process as described in any one of the above items when executing the executable instructions stored in the memory.

[0044] The beneficial effects of the present invention are as follows: in view of the unclear influencing mechanism of process parameters in the process of component docking assembly attitude adjustment, which leads to low efficiency of repeated measurements in the process of attitude adjustment, the embodiment of the present invention provides a method for analyzing the coupling relationship of process parameters in the process of component docking, which collects the position data of the attitude locator and the horizontal measurement point position of the product in the process of component docking assembly attitude adjustment, constructs a process parameter regression model based on improved partial least squares, and optimizes the principal component extraction and the number of principal components in partial least squares by Reiny entropy, thereby solving the problem of multiple iterations of high-dimensional data regression caused by cross-validation in the construction of traditional partial least squares regression model, and improves the computational efficiency of the regression fitting model construction of high-dimensional data. By improving the regression model constructed by partial least squares, the spatial position of the horizontal measurement point of the product can be predicted in real time based on the data of the attitude locator, which effectively reduces the number of repeated measurements in the process of attitude adjustment, improves the attitude adjustment efficiency, and provides theoretical support for attitude adjustment trajectory planning and product attitude evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0046] Figure 1 A flow chart of a method for analyzing process parameter coupling relationships in a component docking process provided by an embodiment of the present invention;

[0047] Figure 2 Schematic diagram of a curve showing a change in the accumulated information rate and information decay rate of Renyi entropy with the number of principal components in an embodiment of the present invention;

[0048] Figure 3 Schematic diagram of the deviation between the predicted value and the actual value based on partial least squares fitting in an embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the deviation between the relative positions of the predicted points and the actual points based on partial least squares fitting in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clear, the embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily without conflict.

[0051] The above background technology has explained the importance of aircraft component docking in aircraft manufacturing, based on the existing aircraft component docking process and attitude adjustment principle; however, the following specific problems still exist in the current attitude adjustment process: 1) After the attitude adjustment is completed, although the position of the attitude locator is known, the position of the horizontal measurement point needs to be remeasured; 2) The position of the horizontal measurement point needs to be measured once for each attitude adjustment, and there are multiple repeated measurements. At the same time, due to the low measurement efficiency of the laser tracker, the overall component docking assembly process is slow.

[0052] In view of the above problems, how to explore the correlation between the position of the attitude locator and the horizontal measurement point during the docking assembly process, explore the law of the horizontal measurement point changing with the attitude position during the attitude adjustment process, and use the attitude locator position data to characterize the position of the horizontal measurement point, so as to achieve the purpose of reducing multiple measurements and improve the attitude adjustment efficiency, is a key problem that needs to be solved in the component docking assembly process. Based on the above analysis, an embodiment of the present invention provides a method for analyzing the coupling relationship of process parameters for the component docking process.

[0053] The present invention provides the following specific embodiments which can be combined with each other. The same or similar concepts or processes may not be described in detail in some embodiments.

[0054] Figure 1 A flowchart of a method for analyzing the process parameter coupling relationship of a component docking process provided by an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a method for analyzing the coupling relationship of process parameters for component docking, which includes the following steps:

[0055] Step 1: define the measurement points that characterize the quality characteristics of the component docking as quality characteristic points, define the positioning positions of the process equipment in the component docking process as process monitoring points, and define the spatial position coordinates of the quality characteristic points as dependent variables Y, and define the spatial position coordinates of the process monitoring points as independent variables X;

[0056] Step 2: respectively collect the spatial position coordinates of the independent variable X at different times during the component docking process and the spatial position coordinates of the dependent variable Y corresponding to each time;

[0057] Step 3: Perform data standardization on the independent variable X and the dependent variable Y to obtain the standardized independent variable matrix X0 and the standardized dependent variable matrix Y0 after the dependent variable Y and the independent variable X are standardized;

[0058] Step 4: Based on the labeled independent variable matrix X0 and the labeled dependent variable matrix Y0, an improved partial least squares algorithm is used to construct a regression model of the independent variable X and the dependent variable Y;

[0059] Step 5: Predict the spatial position coordinates of the docking quality characteristics based on the regression model.

[0060] In one implementation of the embodiment of the present invention, the spatial position coordinates of the independent variable X collected in the above step 2 at different times are:

[0061]

[0062] The spatial position coordinates of the dependent variable Y corresponding to each moment collected are:

[0063]

[0064] Among them, n represents the number of samples of the independent variable X, p represents the number of independent variables X in each sample; m represents the number of samples of the dependent variable Y, and l represents the number of dependent variables Y in each sample.

[0065] In one implementation of the embodiment of the present invention, the formulas of the scaled independent variable matrix X0 and the scaled dependent variable matrix Y0 obtained by the scaled processing in the above step 3 are respectively expressed as follows:

[0066]

[0067] Among them, any element in the above-mentioned normalized independent variable matrix X0 represents the average value of the data in the jth column of the independent variable X, Represents the standard deviation of the data in the jth column of the independent variable X;

[0068] Scaling any element in the dependent variable matrix Y0 represents the average value of the data in the jth column of matrix Y, Represents the standard deviation of the data in the jth column of matrix Y.

[0069] In one implementation of the embodiment of the present invention, the method of constructing the regression model of the independent variable X and the dependent variable Y in the above step 4 is:

[0070] The partial least squares regression modeling method based on Renyi entropy is used to construct the regression model of independent variable X and dependent variable Y.

[0071] In this implementation, the calculation process of Renyi entropy in step 4 is as follows:

[0072] Step 41, respectively calculate the independent variable covariance matrix K corresponding to the scaled independent variable matrix X0 and the scaled dependent variable matrix Y0 x and the dependent variable covariance matrix K y , the calculation formula is as follows:

[0073]

[0074] Step 42: Solve the covariance matrix \(K\) of the independent variables respectively x and the covariance matrix \(K\) of the dependent variables y for their corresponding eigenvalues and eigenvectors

[0075] Step 43: Calculate the Renyi entropy corresponding to each eigenvalue in the covariance matrix \(K\) of the independent variables. The calculation formula is: x

[0076]

[0077] where \(E\) x represents a unit vector with the same dimension as the eigenvector and \(k\) represents the number of eigenvalues of the covariance matrix \(K\) of the independent variables x .

[0078] Furthermore, in the process of constructing the regression model using the partial least squares regression modeling method based on Renyi entropy in Step 4, the methods for principal component extraction and determination of the number of principal components include:

[0079] Step 44: Arrange the Renyi entropy in descending order, and calculate the cumulative information rate \(\omega\) corresponding to the eigenvalues and their eigenvectors of the covariance matrix \(K\) of the independent variables. The calculation formula is: x

[0080]

[0081]

[0081] Step 45: Solve for the maximum cumulative information rate and the corresponding position coordinate \(j\), where \(1 < j < k\), and calculate the information attenuation rate corresponding to the first \(j\) eigenvalues and eigenvectors The calculation formula is as follows:

[0082]

[0083] Step 46: Calculate the maximum number of principal components \(k'\) corresponding to when the cumulative information rate \(\omega\) is the largest and the information attenuation rate is greater than the preset attenuation rate, then \(k'\) satisfies \(0 < k' < p\);

[0084] Step 47: According to the determined number of principal components \(k'\), use the partial least squares fitting method to fit the regression model of the independent variable \(X\) and the dependent variable \(Y\), and determine the fitting coefficient matrix \(A\) and the constant term \(C\) of the regression model.

[0085] In specific implementation, the regression model for fitting the independent variable \(X\) and the dependent variable \(Y\) in Step 47 is:

[0086] YT =AX T +C;

[0087] Wherein, A is the coefficient of the independent variable in the regression model, and C is the constant term.

[0088] The preset attenuation rate in the above step 46 is between [0.1, 0.2].

[0089] In view of the unclear influencing mechanism of process parameters in the component docking assembly posture adjustment process, which leads to low efficiency of repeated measurements in the posture adjustment process, an embodiment of the present invention provides a process parameter coupling relationship analysis method for the component docking process. The method collects the position data of the posture locator and the horizontal measurement point position of the product in the component docking assembly posture adjustment process, constructs a process parameter regression model based on improved partial least squares, and optimizes the principal component extraction and the number of principal components in the partial least squares through Reiny entropy. It solves the problem of multiple iterations of high-dimensional data regression caused by cross-validation in the construction of traditional partial least squares regression model, improves the computational efficiency of the regression fitting model construction of high-dimensional data, and through the regression model constructed by improving the partial least squares, it can predict the spatial position of the horizontal measurement point of the product in real time based on the data of the posture locator, effectively reducing the number of repeated measurements in the posture adjustment process, improving the posture adjustment efficiency, and providing theoretical support for posture adjustment trajectory planning and product posture evaluation.

[0090] Based on the method for analyzing the process parameter coupling relationship of the component docking process provided by the above-mentioned embodiment of the present invention, the embodiment of the present invention further provides a computer-readable storage medium, characterized in that it includes: a memory and a processor;

[0091] Wherein, the memory is configured to store executable instructions;

[0092] The processor is specifically configured to implement the method for analyzing the coupling relationship of process parameters for component docking process provided in any of the above embodiments when executing the executable instructions stored in the memory.

[0093] Application Cases

[0094] This embodiment provides a method for analyzing the coupling relationship of process parameters in the component docking process. In this implementation application case, the coupling relationship analysis of process parameters in the attitude adjustment process of large aircraft wings is taken as an example. In this application case, the wing docking assembly attitude adjustment process is taken as the object, and the attitude adjustment locator group position and horizontal measurement point position data are used as the basis. By using data mining knowledge, a partial least squares process coupling relationship model based on Renyi entropy is constructed, and the horizontal measurement point position data of each attitude adjustment is used to predict the position of the horizontal measurement point, providing theoretical support for the next attitude adjustment direction and size, thereby improving the attitude adjustment. The specific algorithm includes the following steps:

[0095] Step 1: define the horizontal measurement point of the product during the wing docking attitude adjustment process as the dependent variable, and the corresponding attitude adjustment positioner group position data as the independent variable;

[0096] Step 2: Collect the position data of the posture locator group and the corresponding horizontal measurement point position data at the same time. The posture locator group position data contains 12 monitoring data and 15 sample groups, forming a 15×12 independent variable X. The horizontal measurement point data contains 36 monitoring data and 15 sample groups. In this calculation, 3 monitoring data are taken from the horizontal measurement point data to form a 15×3 dependent variable matrix Y. The calculation method of other dependent variable points is the same and will not be repeated.

[0097] Step 3: Perform data standardization on the independent variable X and the dependent variable Y to obtain the standardized independent variable matrix X0 and the standardized dependent variable matrix Y0 after the dependent variable and the independent variable are standardized, which can be expressed as:

[0098]

[0099] Step 4: Based on the standardized independent variable matrix X0 and the standardized dependent variable matrix Y0, the partial least squares method of Renyi entropy is used to construct a regression model of independent variable X and dependent variable Y;

[0100] First, calculate the Renyi entropy of the independent variable and dependent variable matrix. The calculation process is as follows:

[0101] 4-1 Calculate the independent variable covariance matrix K corresponding to the independent variable X and the dependent variable Y respectively x and the dependent variable covariance matrix K y , which is expressed as follows:

[0102]

[0103] 4-2 Solve the independent variable covariance matrix K separately x and the dependent variable covariance matrix K y The corresponding eigenvalue and the eigenvector The calculation results are shown as follows: x =diag(-2.9360×10 -16 ,-2.3271×10 -16 ,1.3748×10 -16 ,5.54355×10 -16 ,2.6464×10 -6 ,0.0023,0.1088,0.3900,0.6906,1.3666,2.4731,6.1686);

[0104]

[0105] D y = diag(0.2902, 0.9043, 1.6056)

[0106]

[0107] 4-3 Calculate the independent variable covariance matrix K according to the formula x The Renyi entropy corresponding to each eigenvalue, and the calculation results are as follows: H x = [-1.48×10 -18 , -2.9142×10 -17 , 5.8467×10 -19 , 1.8441×10 -17 , 9.4898×10 -07 , 0.0126, 0.0113, 0.4384, 0.0342, 4.0609, 4.5243, 0.2127];

[0108] 4-4 Arrange in descending order, calculate the cumulative information rate ω corresponding to the eigenvalues and their eigenvectors, and the calculation results are as follows:

[0109] ω x = [0.4868, 0.9237, 0.9709, 0.9938, 0.9974, 0.9988, 1, 1, 1, 1, 1, 1];

[0110] 4-5 Solve for the maximum cumulative information rate and the corresponding position coordinate j, which requires 1 < j < k, and calculate the information attenuation rate corresponding to the first j eigenvalues and eigenvectors The calculation results are as follows:

[0111] η x = [0.1024, 0.8920, 0.5148, 0.8392, 0.6323, 0.1027];

[0112] 4-6 Calculate the principal component number k' = 5 when the cumulative information rate ω is the largest and the information attenuation rate is greater than 0.15. The curves of its cumulative information rate and information attenuation rate changing with the principal component number are as Figure 1 shown.

[0113] 4-7 According to the determined principal component number k' = 5, use partial least squares method to fit the regression model of independent variable and dependent variable, and obtain the fitting coefficient matrix A and the constant term C, and construct the regression model as follows:

[0114] ​

[0115] Y T =AX T +C.

[0116] Step 5: According to the regression model determined in step 4, the spatial coordinate position of the horizontal measurement point during the 10 posture adjustments is predicted. The error between the spatial coordinate position and the actual measurement value is as follows: Figure 2 shown.

[0117] In this application implementation case, the large aircraft wing docking attitude adjustment process is taken as the object. Based on the attitude adjustment locator position data in the attitude adjustment process and its corresponding product horizontal measurement point position data, the coupling influence relationship between the attitude adjustment locator position data and the product horizontal measurement point position data in the attitude adjustment process is explored through data collection, analysis and mining. A partial least squares regression modeling method based on Renyi entropy is proposed, and the algorithm is used to fit the attitude adjustment process data of 15 times of large aircraft wing docking attitude adjustment process for regression modeling, and the 1# horizontal measurement point position data of 10 times of wing attitude adjustment process is predicted. The deviation between the predicted spatial position and the actual measured spatial position is shown in the figure. Figure 3 As shown, it can be seen that the maximum deviation between the spatial position of the product measurement point calculated by this algorithm and the actual position is about 0.5mm, and the minimum can reach about 0.16mm. It is completely possible to predict the current attitude of the wing in real time through the attitude locator during the attitude adjustment process, which can effectively reduce multiple measurements during the attitude adjustment process, and only require inspection measurements at the final attitude.

[0118] Although the embodiments disclosed in the present invention are as above, the contents are only embodiments adopted to facilitate understanding of the present invention and are not intended to limit the present invention. Any technician in the field to which the present invention belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for analyzing the coupling relationship of process parameters in a component docking process, characterized in that: include; Step 1: define the measurement points that characterize the quality characteristics of the component docking as quality characteristic points, define the positioning positions of the process equipment in the component docking process as process monitoring points, and define the spatial position coordinates of the quality characteristic points as dependent variables Y, and define the spatial position coordinates of the process monitoring points as independent variables X; Step 2: respectively collect the spatial position coordinates of the independent variable X at different times during the component docking process and the spatial position coordinates of the dependent variable Y corresponding to each time; Step 3: Perform data standardization on the independent variable X and the dependent variable Y to obtain the standardized independent variable matrix X0 and the standardized dependent variable matrix Y0 after the dependent variable Y and the independent variable X are standardized; Step 4: Based on the labeled independent variable matrix X0 and the labeled dependent variable matrix Y0, an improved partial least squares algorithm is used to construct a regression model of the independent variable X and the dependent variable Y; Step 5: Predict the spatial position coordinates of the docking quality characteristics based on the regression model.

2. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 1 is characterized in that: In step 2, the spatial position coordinates of the independent variable X collected at different times are: The spatial position coordinates of the dependent variable Y corresponding to each moment collected are: Where n represents the number of samples of the independent variable X, p represents the number of independent variables X in each sample; m represents the number of samples of the dependent variable Y, and l represents the number of dependent variables Y in each sample.

3. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 2, characterized in that: The formulas of the scaled independent variable matrix X0 and the scaled dependent variable matrix Y0 obtained by the scaled processing in step 3 are respectively: Among them, any element in the labeled independent variable matrix X0 represents the average value of the data in the jth column of the independent variable X, Represents the standard deviation of the data in the jth column of the independent variable X; Scaling any element in the dependent variable matrix Y0 represents the average value of the data in the jth column of matrix Y, Represents the standard deviation of the data in the jth column of matrix Y.

4. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 1, characterized in that: The method of constructing the regression model of the independent variable X and the dependent variable Y in step 4 is: The partial least squares regression modeling method based on Renyi entropy is used to construct the regression model of independent variable X and dependent variable Y.

5. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 4, characterized in that: The calculation process of Renyi entropy in step 4 is as follows: Step 41, respectively calculate the independent variable covariance matrix K corresponding to the scaled independent variable matrix X0 and the scaled dependent variable matrix Y0 x and the dependent variable covariance matrix K y , the calculation formula is as follows: Step 42, respectively solve the independent variable covariance matrix K x and the dependent variable covariance matrix K y The corresponding eigenvalue and the eigenvector Step 43, calculate the independent variable covariance matrix K x The Renyi entropy corresponding to each eigenvalue in is calculated as follows: Where E x Representation and eigenvectors Unit vectors of equal dimension, k represents the independent variable covariance matrix K x The number of eigenvalues.

6. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 5, characterized in that: In the process of constructing regression modeling using the partial least squares regression modeling method based on Renyi entropy adopted in step 4, the method of extracting principal components and determining the number of principal components includes: Step 44: convert the Renyi entropy Arrange in descending order and calculate the independent variable covariance matrix K x The cumulative information rate ω corresponding to the eigenvalue and its eigenvector is calculated as follows: Step 45: Solve for the maximum cumulative information rate and the corresponding position coordinate j, where 1 < j < k is required, and calculate the information decay rate corresponding to the eigenvalues and eigenvectors of the first j The calculation formula is as follows: Step 46, calculate the information decay rate when the cumulative information rate ω is maximum When the maximum number of principal components k′ is greater than the preset decay rate, k′ satisfies 0 <k′<p; Step 47, according to the determined number of principal components k′, a regression model of the independent variable X and the dependent variable Y is fitted using a partial least squares fitting method, and a fitting coefficient matrix A and a constant term C of the regression model are determined.

7. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 6, characterized in that: The regression model for fitting the independent variable X and the dependent variable Y in step 47 is: Y T =AX T +C; Wherein, A is the coefficient of the independent variable in the regression model, and C is the constant term.

8. The method for analyzing the process parameter coupling relationship of the component docking process according to claim 6, characterized in that: The preset attenuation rate is between [0.1, 0.2].

9. A computer-readable storage medium, characterized in that: include: Memory and processor; The memory is configured to store executable instructions; The processor is specifically configured to implement the method for analyzing the coupling relationship of process parameters for a component docking process according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.

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