Large deformation plate state parameter cross convergence mapping causal test method and system

Abstract

The invention provides a large deformation plate state parameter cross convergence mapping causal test method and system, and belongs to the technical field of power system nonlinear time sequence analysis, and the method comprises the steps: constructing a large deformation plate vibration control equation, and carrying out the iterative solution to obtain two state parameters of a large deformation plate; obtaining two constant parameter phase space reconstruction attractors according to the two state parameters by adopting a constant parameter phase space reconstruction method; taking the two constant parameter phase space reconstruction attractors as shadow manifolds, and solving a cross convergence mapping correlation coefficient of a true value and a predicted value between system state parameters through a cross convergence mapping algorithm; and a causal test index based on a cross convergence mapping correlation coefficient difference value is constructed, and the causal relationship between the state parameters of the large deformation plate is tested according to the sign symbol. According to the method, the problems of parameter uncertainty, low test efficiency and weak big data processing capability caused by dependence on traditional phase-space reconstruction in the prior art are solved.

Description

Technical Field

[0001] The invention relates to the technical field of nonlinear time series analysis of dynamic systems, and in particular to a causal testing method and system for cross-convergence mapping of state parameters of a large deformation plate. Background Art

[0002] Nonlinear time series often come from real dynamic systems, such as nonlinear vibration systems of large deformation plates. The internal evolution mechanism of the dynamic system will lead to complex causal relationships between system variables. Causal testing can explain the causal laws of the dynamic evolution of system variables and provide reliable scientific support for the prediction of system dynamic behavior and the detection of abnormal dynamic behavior. The cross-convergence mapping method is one of the common causal testing methods. Its core element is the shadow manifold based on the phase space reconstruction attractor, which can effectively characterize the asymmetric causal relationship between system state variables.

[0003] However, traditional phase space reconstruction methods require complex optimization selection of embedding parameters (delay time and embedding dimension). Due to the lack of a unified optimization standard, the reconstructed attractors under different optimized embedding parameters are non-unique, which reduces the efficiency and reliability of the causal test of the cross-convergence mapping of state parameters of large deformation plates. Summary of the invention

[0004] To solve the above problems, the present invention proposes a causal test method for cross-convergence mapping of state parameters of large deformation plates improved by constant parameter phase space reconstruction on the basis of a constant parameter phase space reconstruction method. Compared with the existing test methods, the method of the present invention is based on the constant parameter phase space reconstruction attractor, and can quickly obtain the high-precision shadow manifold required for cross-convergence mapping causal test, thereby improving the efficiency and accuracy of causal test of state parameters of large deformation plates.

[0005] To achieve the above object, the present invention provides the following technical solutions.

[0006] The present invention provides a causal verification method for cross-convergence mapping of state parameters of a large deformation plate, comprising the following steps: The vibration control equation of the large deformation plate is constructed and the two state parameters of the large deformation plate are obtained by iterative solution. Using the constant parameter phase space reconstruction method, two constant parameter phase space reconstruction attractors are obtained according to two state parameters; The two constant parameter phase space reconstructed attractors are used as shadow manifolds, and the cross-convergence mapping correlation coefficient between the real value and the predicted value of the system state parameters is solved by the cross-convergence mapping algorithm. A causal test index based on the difference of cross-convergence mapping correlation coefficients is constructed. The ability of the constant parameter phase space to reconstruct attractors to cross-predict state parameters is determined according to the positive and negative values ​​of the causal test index, and the causal relationship between different state parameters is tested.

[0007] Preferably, constructing the vibration control equation of the large deformation plate and determining the two state parameters of the large deformation plate comprises the following steps: The vibration control equation of the large deformation plate in the form of a Duffing system is constructed, that is, the vibration control equation is a second-order constant coefficient nonlinear differential equation with cubic nonlinear terms, as shown in the following formula: ; in, is the damping coefficient, and are the coefficients of the first and third terms, respectively, and are the amplitude and frequency of the simple harmonic load respectively; for x Second derivative with respect to time; The vibration control equation of the large deformation plate is rewritten as a set of first-order differential equations that are easy to solve numerically. x and y The solution value is the state parameter, and They are x and y The first-order derivative with respect to time has the mathematical form: ; Determine the coefficients of the vibration control equations of the large deformation plate, use the fourth-order Runge-Kutta method to solve the first-order differential equations, and obtain the state parameters X ( t )and Y ( t ) time course curve.

[0008] Preferably, the coefficient of the vibration control equation of the large deformation plate is , , , , ; The fourth-order Runge-Kutta method is used to numerically solve the first-order differential equations with the initial conditions , time step h =0.0125 s, the total calculation time is 750 s, the data length N =60000; Among them, the numerical solution based on the fourth-order Runge-Kutta method is x and y The iteration formula is: ; in, , , , , , , and is the intermediate variable, is the time, and the calculation formula is: .

[0009] Preferably, the constant parameter phase space reconstruction method is used to obtain two constant parameter phase space reconstruction attractors according to two state parameters, including the following steps: Select the phase space reconstruction constant parameters, where the delay time and embedding dimension ; According to the state parameter X Constructing and reconstructing attractors , as shown below: ; in, Indicates i Phase point coordinates, M Indicates the number of phase points; Computational reconstruction attractor The autocorrelation matrix of is: ; Perform eigenvalue decomposition on the autocorrelation matrix: ; in, is the eigenvalue matrix, represents the feature vector; Project the reconstructed attractor along the direction of the main eigenvector to obtain the constant parameter phase space reconstructed attractor : ; Normalize the reconstructed attractor to the interval , and obtain the final normalized constant parameter reconstruction attractor M x ; For state parameters Y Solve according to the above steps to obtain the normalized constant parameter phase space reconstruction attractor M y .

[0010] Preferably, the method of using two constant parameter phase space reconstruction attractors as shadow manifolds and solving the cross-convergence mapping correlation coefficients between the real values ​​and predicted values ​​of the system state parameters by a cross-convergence mapping algorithm comprises the following steps: Solving the normalized constant parameter phase space to reconstruct the attractor M yThe Euler distance between each phase point is sorted from small to large. M y Middle i The phase point is ,and The smallest distance m +1=3 points ,in Indicates phase point The corresponding time is calculated according to the following formula: and Distance : ; Among them, the operator Representation vector , The Euler distance between Calculate phase point The weight coefficient of: ; in, , is the standardized coefficient; Extraction by reconstruction of attractors M y The obtained state parameters X In time Predicted value: ; Further solve the state parameters X The cross-convergence mapping correlation coefficient between the true value and the predicted value is: ; Among them, the operator Representation variables A , B The standard Pearson correlation coefficient between Reconstructing the attractor M x Repeat the above steps to obtain the state parameters Y The cross-convergence mapping correlation coefficient between the true value and the predicted value is: .

[0011] Preferably, the calculation formula of the causality test index is as follows: ; in, It is a causal test indicator.

[0012] Preferably, the method of determining the ability of the constant parameter phase space to reconstruct the attractor cross-prediction state parameter according to the positive and negative values ​​of the causal test index, and testing the causal relationship between different state parameters, comprises the following steps: If the causal test index , indicating that the reconstructed attractor M x Can accurately predict state parameters Y , that is, the state parameter Y Drive status parameters X ; If the causal test index , indicating that the reconstructed attractor M y Can accurately predict state parameters X , that is, the state parameter X Drive status parameters Y ; If the causal test index , indicating that the reconstructed attractor M y Prediction state parameters X The ability to reconstruct the attractor M y Prediction state parameters X The ability is consistent, that is, the state parameter X With state parameters Y No significant causal relationship was found.

[0013] The present invention also provides a large deformation plate state parameter cross-convergence mapping causal verification system, comprising: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the above-mentioned large deformation plate state parameter cross-convergence mapping causal verification method are implemented.

[0014] The present invention also provides a computer-readable storage medium, on which a data processing program is stored. When the data processing program is executed by a processor, the steps of the large deformation plate state parameter cross-convergence mapping causal verification method are implemented.

[0015] Beneficial effects of the present invention: The present invention proposes a causal verification method and system for cross-convergence mapping of state parameters of a large deformation plate. The method obtains a reconstructed attractor with high topological similarity to the nonlinear vibration system of a large deformation plate through a constant parameter phase space reconstruction method, and uses it as a shadow manifold to improve the traditional cross-convergence mapping algorithm, calculates the cross-convergence mapping correlation coefficient based on different state variable shadow manifolds, and then constructs a causal verification index to quantitatively characterize the causal relationship between state parameters of the large deformation plate, providing reliable scientific support for the prediction of dynamic behavior of the large deformation plate and the detection of abnormal dynamic behavior. Compared with the existing methods, the method breaks through the problems of high dependence on phase space embedding parameters and low efficiency of causal verification under large data samples in the prior art of cross-convergence mapping, and effectively improves the accuracy and efficiency of causal verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a method flow chart of an embodiment of the present invention; Figure 2 is the state parameter of the large deformation plate of the embodiment of the present invention X and Y Time course graph; Figure 3 is a graph showing a trend of the convergence of the cross-convergence mapping correlation coefficient according to an embodiment of the present invention as a function of the data length; Figure 4 is the state parameter of the embodiment of the present invention X The cross-convergence mapping correlation image of the true value and the predicted value; Figure 5 is the state parameter of the embodiment of the present invention Y The cross-convergence mapping correlation image of the true value and the predicted value; Figure 6 is the state parameter of the embodiment of the present invention X and Y The constant parameter phase space reconstruction attractor shadow manifold diagram and its cross-convergence mapping diagram; among them, Figure 6 (a) is M x arrive M y Cross-convergence mapping of ; Figure 6 (b) is M y arrive M x Cross-convergence mapping. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] Example 1 Traditional phase space reconstruction methods require complex optimization and selection of embedding parameters (delay time and embedding dimension). Due to the lack of a unified optimization standard, the reconstructed attractors under different optimized embedding parameters are non-unique, which reduces the efficiency and reliability of the causal test of the cross-convergence mapping of state parameters. Studies have found that the constant parameter phase space reconstruction method can map the reconstructed attractor to the main direction of the phase trajectory through singular value decomposition under fixed embedding parameters, and can obtain high-precision reconstructed attractors for low-dimensional dynamic systems such as nonlinear vibration systems of large deformation plates, thereby overcoming the shortcomings of traditional phase space reconstruction methods that rely on embedding parameter optimization. Therefore, based on the constant parameter phase space reconstruction method, the present invention proposes a cross-convergence mapping causal test method for state parameters of large deformation plates improved by constant parameter phase space reconstruction. Compared with the existing cross-convergence mapping causal test method, the method of the present invention is based on the constant parameter phase space reconstruction attractor, and can quickly obtain the high-precision shadow manifold required for the cross-convergence mapping causal test, thereby improving the efficiency and accuracy of the causal test of state parameters of large deformation plates. Specifically, Figure 1 As shown, Figure 1 The flowchart of the causal verification method of the cross-convergence mapping of state parameters of large deformation plates includes the following steps: S1: Construct the vibration control equation of the large deformation plate and solve iteratively to obtain the two state parameters of the large deformation plate.

[0019] Specific: S1.1: Construct the vibration control equation of the large deformation plate in the form of a Duffing system, that is, the vibration control equation is a second-order constant coefficient nonlinear differential equation with cubic nonlinear terms, as shown in the following formula: ; in, is the damping coefficient, and are the coefficients of the first and third terms, respectively, and are the amplitude and frequency of the simple harmonic load respectively; for x Second derivative with respect to time.

[0020] S1.2: Rewrite the vibration control equations of large deformation plates into a set of first-order differential equations that are easy to solve numerically. x and y The solution value is the state parameter, expressed as X and Y , its mathematical form is: ; Determine the coefficients of the vibration control equations of the large deformation plate, use the fourth-order Runge-Kutta method to solve the first-order differential equations, and obtain the state parameters X (t )and Y ( t ) time course curve.

[0021] S1.3: The coefficients of the vibration control equations for large deformation plates are taken as , , , , .

[0022] S1.4: Use the fourth-order Runge-Kutta method to numerically solve the first-order differential equations with the initial conditions , time step h =0.0125 s, the total calculation time is 750 s, the data length N =60000; Among them, the numerical solution based on the fourth-order Runge-Kutta method is x and y The iteration formula is: ; in, , , , , , , and is the intermediate variable, is the time, and the calculation formula is: ; S2: Using the constant parameter phase space reconstruction method, two constant parameter phase space reconstruction attractors are obtained according to the two state parameters.

[0023] Specific: S2.1: Select the phase space reconstruction constant parameters, where the delay time and embedding dimension .

[0024] S2.2: According to the state parameters X Constructing and reconstructing attractors , as shown below: ; in, Indicates i Phase point coordinates, M Indicates the number of phase points.

[0025] S2.3: Computational reconstruction attractor The autocorrelation matrix of is: ; S2.4: Perform eigenvalue decomposition on the autocorrelation matrix: ; in, is the eigenvalue matrix, represents a feature vector.

[0026] S2.5: Project the reconstructed attractor along the direction of the main eigenvector to obtain the constant parameter phase space reconstructed attractor : ; S2.6: Normalize the reconstructed attractor to the interval , and obtain the final normalized constant parameter reconstruction attractor M x .

[0027] S2.7: State parameters Y Solve according to S2.1~S2.5 to obtain the normalized constant parameter phase space reconstructed attractor M y .

[0028] S3: Reconstruct the attractors of the two constant parameter phase spaces as shadow manifolds and solve the state parameters through the cross-convergence mapping algorithm X and Y The cross-convergence mapping correlation coefficient between the true value and the predicted value.

[0029] Specific: S3.1: Solving the normalized constant parameter phase space to reconstruct the attractor M y The Euler distance between each phase point is sorted from small to large. M y Middle i The phase point is ,and The first m+1=3 points with the smallest distance are ,in Indicates phase point The corresponding time is calculated according to the following formula: and Distance : ; Among them, the operator Representation vector , The Euler distance between .

[0030] S3.2: Calculate phase points The weight coefficient of: ; in, , is the standardized coefficient.

[0031] S3.3: Extracting attractors by reconstruction M y The obtained state parameters X In time Predicted value: ; S3.4: Further solving state parameters X The cross-convergence mapping correlation coefficient between the true value and the predicted value is: ; Among them, the operator Representation variables A , B The standard Pearson correlation coefficient between .

[0032] S3.5: Reconstructing the attractor M x Repeat S3.1~S3.4 to obtain the state parameters Y The cross-convergence mapping correlation coefficient between the true value and the predicted value is: ; S4: Construct a causal test indicator based on the difference of the cross-convergence mapping correlation coefficient, determine the ability of the constant parameter phase space to reconstruct the attractor to cross-predict state parameters according to the positive and negative values ​​of the causal test indicator, and test the causal relationship between different state parameters.

[0033] Causality test index , the specific calculation formula is: ; According to the causal test index The sign check state parameter X and Y The causal relationship between them is as follows: (1) If , indicating that the reconstructed attractor M x Can accurately predict state parameters Y , that is, the state parameter Y Driven state parameters X , abbreviated as Y → X ; (2) If , indicating that the reconstructed attractor M y Can accurately predict state parameters X , that is, the state parameterX Driven state parameters Y , abbreviated as X → Y ; (3) If , indicating that the reconstructed attractor M y Prediction state parameters X The ability to reconstruct the attractor M y Prediction state parameters X The ability is consistent, that is, the state parameter X With state parameters Y No significant causal relationship was found.

[0034] In this embodiment, in order to verify the effectiveness of the causal test method of the large deformation plate state parameter cross convergence mapping improved by constant parameter phase space reconstruction of the present invention, a numerical simulation experiment was carried out to extract two state parameters of the large deformation plate nonlinear vibration system. X and Y Conduct analysis.

[0035] The overall process of the method of the present invention is shown in Figure 1 In this embodiment, the coefficients of the nonlinear vibration control equation of the large deformation plate are: , , , , ; The initial condition is , time step h = 0.0125s, total calculation time is 750s, data length N =60000.

[0036] The classic fourth-order Runge-Kutta algorithm is used to iteratively solve the state parameters of the large deformation plate. X and Y The time course curve of Figure 2 As shown. The constant parameter phase space reconstruction method is used to obtain the real state parameters. X and Y Reconstructed attractor M x and M y , using it as the shadow manifold in the cross-convergence mapping algorithm to calculate the state parameters X and Y The cross-convergence mapping correlation coefficient C YX and C XY , and its convergence curves with data length are as follows Figure 3 Shown in solid and dotted lines.

[0037] Depend on Figure 3 It can be seen that when the data length is greater than 2000, the correlation coefficient C YX That is, convergence, and the correlation coefficient C XY Then it gradually converges after the data length is greater than 10000, so in this embodiment, the data length of 60000 meets the convergence requirement of the correlation coefficient. X and Y The cross-convergence mapping correlation images of the true value and the predicted value are as follows Figure 4 and Figure 5 shown.

[0038] Depend on Figure 4 It can be seen that the state parameter X The predicted value is not completely consistent with its true value, indicating that the state variable X Not driven M y all the reasons for Figure 5 It can be seen that the state parameter Y The predicted value and the true value correspond consistently, indicating that the state variable Y Is driven by M x Specifically, Figure 6 Based on the state parameter X and Y The constant parameter phase space reconstruction attractor shadow manifold diagram and its cross-convergence mapping diagram. The line from the black square to the white five-pointed star in the figure represents a mapping of the shadow manifold. The narrower the mapping range, the stronger the causal relationship between the two. Figure 6 (b) It can be seen that M y arrive M x The cross-convergence mapping range is narrower, indicating that the state parameters Y Driven state parameters X .

[0039] Table 1 Quantitative comparison results of the efficiency and accuracy of causal test of state variables of large deformation plates

[0040] Table 1 is a quantitative comparison of the efficiency and accuracy of the causal test of state variables of large deformation plates between the proposed method and the original method. In terms of computational efficiency, the efficiency of the improved cross-convergence mapping method based on the constant parameter phase space reconstruction method has been improved by more than 14%, up to 55%; in terms of the cross-convergence mapping correlation coefficient, the efficiency of the improved cross-convergence mapping method has been improved by more than 14%, up to 55%. , mutually verified state parameters Y Driven state parameters XThe results of the analysis are shown in Figure 2. In particular, the improved cross-convergence mapping method obtains C YX = 1.0, which is consistent with the large deformation plate vibration control equation The results are more consistent, verifying the accuracy and reliability of the cross-convergence mapping method based on the constant parameter phase space reconstruction method in the causal test of state parameters of large deformation plates. Therefore, a cross-convergence mapping causal test method for state parameters of large deformation plates improved by constant parameter phase space reconstruction breaks through the problems of high dependence on phase space embedding parameters and low causal test efficiency under large data samples in the cross-convergence mapping in the prior art, and provides an accurate and efficient cross-convergence mapping causal test method for state parameters of large deformation plates.

[0041] The above is a causal verification method for the cross-convergence mapping of state parameters of a large deformation plate provided by one embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding causal verification system for the cross-convergence mapping of state parameters of a large deformation plate. The specific limitations of the causal verification system for the cross-convergence mapping of state parameters of a large deformation plate can be found in the limitations of the causal verification method for the cross-convergence mapping of state parameters of a large deformation plate mentioned above, which will not be repeated here. Each module in the above-mentioned causal verification system for the cross-convergence mapping of state parameters of a large deformation plate can be fully or partially implemented through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0042] This embodiment also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A causal verification method for cross-convergence mapping of state parameters of large deformation plates is provided.

[0043] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0044] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A causal verification method for cross-convergence mapping of state parameters of large deformation plates, characterized in that: The following steps are involved: The vibration control equation of the large deformation plate is constructed and the two state parameters of the large deformation plate are obtained by iterative solution. Using the constant parameter phase space reconstruction method, two constant parameter phase space reconstruction attractors are obtained according to two state parameters; The two constant parameter phase space reconstructed attractors are used as shadow manifolds, and the cross-convergence mapping correlation coefficient between the real value and the predicted value of the system state parameters is solved by the cross-convergence mapping algorithm. A causal test index based on the difference of cross-convergence mapping correlation coefficients is constructed. The ability of the constant parameter phase space to reconstruct attractors to cross-predict state parameters is determined according to the positive and negative values ​​of the causal test index, and the causal relationship between different state parameters is tested.

2. The causal verification method for cross-convergence mapping of state parameters of large deformation plates according to claim 1 is characterized in that: The construction of the vibration control equation of the large deformation plate and the determination of two state parameters of the large deformation plate include the following steps: The vibration control equation of the large deformation plate in the form of a Duffing system is constructed, that is, the vibration control equation is a second-order constant coefficient nonlinear differential equation with cubic nonlinear terms, as shown in the following formula: ； in, is the damping coefficient, and are the coefficients of the first and third terms, respectively, and are the amplitude and frequency of the simple harmonic load respectively; for x Second derivative with respect to time; The vibration control equation of the large deformation plate is rewritten as a set of first-order differential equations that are easy to solve numerically. x and y The solution value is the state parameter, and They are x and y The first-order derivative with respect to time has the mathematical form: ； Determine the coefficients of the vibration control equations of the large deformation plate, use the fourth-order Runge-Kutta method to solve the first-order differential equations, and obtain the state parameters X ( t )and Y ( t ) time course curve.

3. The causal verification method for cross-convergence mapping of state parameters of large deformation plates according to claim 2 is characterized in that: The coefficients of the vibration control equation of the large deformation plate are taken as , , , , ; The fourth-order Runge-Kutta method is used to numerically solve the first-order differential equations with the initial conditions , time step h =0.0125 s, the total calculation time is 750 s, the data length N =60000; Among them, the numerical solution based on the fourth-order Runge-Kutta method is x and y The iteration formula is: ； in, , , , , , , and is the intermediate variable, is the time, and the calculation formula is: 。 4. The causal verification method for cross-convergence mapping of state parameters of large deformation plates according to claim 1 is characterized in that: The constant parameter phase space reconstruction method is used to obtain two constant parameter phase space reconstruction attractors according to two state parameters, including the following steps: Select the phase space reconstruction constant parameters, where the delay time and embedding dimension ; According to the state parameter X Constructing and reconstructing attractors , as shown below: ； in, Indicates i Phase point coordinates, M Indicates the number of phase points; Computational reconstruction attractor The autocorrelation matrix of is: ； Perform eigenvalue decomposition on the autocorrelation matrix: ； in, is the eigenvalue matrix, represents the feature vector; Project the reconstructed attractor along the direction of the main eigenvector to obtain the constant parameter phase space reconstructed attractor : ； Normalize the reconstructed attractor to the interval , and obtain the final normalized constant parameter reconstruction attractor M x ; For state parameters Y Solve according to the above steps to obtain the normalized constant parameter phase space reconstruction attractor M y .

5. The causal verification method for cross-convergence mapping of state parameters of large deformation plates according to claim 1 is characterized in that: The method uses two constant parameter phase space reconstructed attractors as shadow manifolds and solves the cross-convergence mapping correlation coefficient between the real value and the predicted value of the system state parameters through a cross-convergence mapping algorithm, including the following steps: Solving the normalized constant parameter phase space to reconstruct the attractor M y The Euler distance between each phase point is sorted from small to large. M y Middle i The phase point is ,and The smallest distance m +1=3 points ,in Indicates phase point The corresponding time is calculated as follows: and Distance : ； Among them, the operator Representation vector , The Euler distance between Calculate phase point The weight coefficient of: ； in, , is the standardized coefficient; Extraction by reconstruction of attractors M y The obtained state parameters X In time Predicted value: ； Further solve the state parameters X The cross-convergence mapping correlation coefficient between the true value and the predicted value is: ； Among them, the operator Representation variables A , B The standard Pearson correlation coefficient between Reconstructing the attractor M x Repeat the above steps to obtain the state parameters Y The cross-convergence mapping correlation coefficient between the true value and the predicted value is: 。 6. The causal verification method for cross-convergence mapping of state parameters of large deformation plates according to claim 5 is characterized in that: The calculation formula of the causal test index is as follows: ； in, It is a causal test indicator.

7. The causal verification method for cross-convergence mapping of state parameters of large deformation plates according to claim 6 is characterized in that: The method of determining the ability of the constant parameter phase space to reconstruct the attractor cross-prediction state parameter according to the positive and negative values ​​of the causal test index, and testing the causal relationship between different state parameters, includes the following steps: If the causal test index , indicating that the reconstructed attractor M x Can accurately predict state parameters Y , that is, the state parameter Y Drive status parameters X ; If the causal test index , indicating that the reconstructed attractor M y Can accurately predict state parameters X , that is, the state parameter X Drive status parameters Y ; If the causal test index , indicating that the reconstructed attractor M y Prediction state parameters X The ability to reconstruct the attractor M y Prediction state parameters X The ability is consistent, that is, the state parameter X With state parameters Y There is no significant causal relationship.

8. A causal verification system for cross-convergence mapping of state parameters of large deformation plates, characterized in that: include: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the causal verification method of cross-convergence mapping of state parameters of a large deformation plate as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the steps of the causal verification method for cross-convergence mapping of state parameters of a large deformation plate according to any one of claims 1 to 7.

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