A global dynamic response prediction method based on limited monitoring points

Abstract

The present invention discloses a method for predicting global dynamic responses based on a limited number of monitoring points, belonging to the field of dynamic monitoring and inversion technology. The method comprises the following steps: establishing a three-dimensional simulation calculation model based on the actual external dimensions of the monitored object and structures such as local ribs and plates, including a finite element / boundary element mesh model; establishing a coupling model involving internal and external environmental loads; optimizing the selection of monitoring points; processing, real-time storage, and data transmission of monitoring point signals; establishing a prediction model through a transfer function, and inverting the dynamic excitation and global dynamic response under the current working conditions. By rationally selecting monitoring points and accurately establishing transfer functions and prediction models, the present invention overcomes the pathological nature of the load inversion matrix inversion process, and can achieve rapid and accurate prediction of the global vibration distribution response of the entire system.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic monitoring and inversion, and in particular relates to a global dynamic response prediction method based on limited monitoring points. Background Art

[0002] Identifying the dynamic responses of systems such as automobiles, ships, and aircraft is particularly complex due to their inherent structural complexity and the complex environments they operate in. To ensure structural design reliability and to provide real-time operational status and in-depth analysis, obtaining the global dynamic response of the structure is a prerequisite. In many practical projects, direct monitoring of the global dynamic response of an engineering entity is difficult and expensive due to inherent structural complexity or harsh testing environments. For example, wind loads on offshore platforms or high-rise buildings are difficult to measure directly using sensors because the locations of the dynamic loads on the structure are unknown. Similarly, during navigation, the bottom structure of a ship is exposed to water on the outside and ballast water and oil on the inside, making it difficult to secure sensors to the surface. Furthermore, for some delicate structures, installing too many sensors can compromise their inherent characteristics. However, local structural responses are often easier to obtain. Deducing the global dynamic response and distribution using limited monitoring points is of great theoretical and practical significance.

[0003] Based on the sensor monitoring data of limited monitoring points, through the technical approach of load identification and inversion, it is possible to achieve rapid prediction of global vibration response distribution, break through the fast, accurate, energy-saving and low-consumption global dynamic monitoring technology, solve the problem that traditional monitoring systems cannot obtain global environmental parameters based on limited sensor data, and ultimately achieve real-time intelligent monitoring of global dynamic responses.

[0004] The monitoring method of global dynamic inversion has many technical difficulties that need to be overcome in actual engineering:

[0005] 1. How to optimize the selection of unlimited monitoring points and obtain the global dynamic characteristics as much as possible through fewer monitoring points. The monitoring points of existing technical solutions are mostly evenly arranged;

[0006] 2. How to establish accurate structural transfer functions and prediction models involving environmental loads;

[0007] 3. The instability of the structural matrix inversion process will lead to serious distortion of the load inversion and poor stability of the inversion system.

[0008] CN103308157A discloses a method for estimating the low-frequency sound power radiated by a structure under the condition of undersampling of the vibration distribution. It proposes a method for evaluating the low-frequency sound power radiated by a structure under the condition of undersampling of the vibration distribution. By testing the vibration velocity distribution of certain parts of the structure surface, the vibration data distribution of other parts of the structure is estimated, and the sound power is estimated. In this invention, the vibration sensors are arranged at equal intervals and are only applicable to geometrically simple structures. They cannot be applied to geometrically complex models and the influence of their internal reinforcing ribs. The interpolation scheme proposed is essentially a numerical processing method that cannot take into account the physical properties and boundary conditions of the structure itself.

[0009] CN106599387A discloses a comprehensive method for constructing an engine casing vibration profile. The method proposes using a uniform speed and steady-state method to measure vibration information in various regions of the device under test. The surface vibration matrices of these regions are then stitched together to form a complete vibration profile. This invention uses a laser vibrometer to scan each region sequentially, requiring consistent and stable device operating conditions throughout the measurement process. The entire scanning and data processing process is time-consuming. Most devices experience rapidly changing external environments and operating conditions during normal operation, making them unstable. Furthermore, manual, sequential measurements of large-scale devices are time-consuming and labor-intensive, resulting in significant cumulative errors. Summary of the Invention

[0010] Purpose of the invention: The present invention provides a global dynamic response prediction method based on a limited number of monitoring points. By rationally selecting the monitoring points and accurately establishing the transfer function and the prediction model, the morbidity in the inversion process of the load inversion matrix can be overcome, and the global vibration distribution response of the entire system can be quickly and accurately predicted.

[0011] Technical solution: A global dynamic response prediction method based on limited monitoring points of the present invention comprises the following steps:

[0012] 1) Establish a three-dimensional simulation model based on the actual dimensions of the monitored object and the local rib structure, including a finite element / boundary element mesh model;

[0013] 2) Establish a coupling model involving internal and external environmental loads;

[0014] 3) Optimal selection of monitoring points;

[0015] 4) Monitoring point signal processing, real-time storage and data transmission;

[0016] 5) Establish a prediction model through transfer function to invert the dynamic excitation and global dynamic response under the current working conditions.

[0017] In some embodiments, the step 2) specifically includes:

[0018] A coupling model involving internal and external environmental loads is established, and the virtual mass method is used to establish the transfer function involving internal and external environmental loads. The dynamic equation is described as follows:

[0019] [M+M A ]·[ü]+[K+K A ]·[u]={F} (1)

[0020] Where: M and M A are the mass matrix and the additional mass matrix generated by the environmental load, K and K A are the stiffness matrix and the additional stiffness matrix generated by the environmental load, ü and u are the acceleration vector and velocity vector, respectively, and F is the generalized force.

[0021] In some embodiments, in step 2), the corresponding relationship between the excitation and the response is determined by the transfer function, and the vibration velocity of the structural vibration monitoring point and the acoustic reconstruction point obtained by the modal superposition method is:

[0022]

[0023]

[0024] Where: v l (ω), v l (ω) is the vibration velocity of the monitoring point and the acoustic reconstruction point, ω is the circular frequency, M r is the modal mass, C r is the modal damping, K r is the modal stiffness, f p (ω) is the modal load, is the modal vector, p is the load application point, l is the monitoring point on the structure, k is the acoustic reconstruction point on the structure, r is the rth node, and j represents the imaginary part.

[0025] In some embodiments, the response of the monitored object in step 2) is affected by the boundaries of the internal and external environments. The ballast water tanks and oil tanks in the ship have free liquid surface boundary conditions. According to the source-sink method, it is found that there is a point sink with equal source strength and opposite direction on the symmetric surface of the free liquid surface. The velocity potential of any point in the internal and external environments is calculated using a simple Green's function:

[0026]

[0027] Where, Green's function

[0028] are the distances from the actual point source and virtual point sink to any point in the fluid, x p 、y p 、z pis the coordinate of the actual point source, x s 、y s 、z s is the coordinate of the virtual point source, σ(x s ,y s ,z s ) is the virtual point source intensity.

[0029] In some embodiments, the step 3) specifically includes:

[0030] 1.1) Rough selection

[0031] The center cluster method or the effective independent-driving point residual method is used to preliminarily select the measuring points;

[0032] 1.2) Selected measuring points (N1 is the preset number of selected measuring points)

[0033] Remove inappropriate measuring points from the rough selection points;

[0034] 1.3) Determine the final number and location of measurement points (N is the final number of measurement points, N2 is the number of excitation source devices)

[0035] The final number (N) and positions of the measuring points are determined according to the above method. The final number (N) of measuring points is the larger value between the number of selected measuring points (N1) and three times the number of excitation source devices (3N2).

[0036] In some embodiments, in step 1.2), the principles of verification include:

[0037] S1. Transfer function pathology: Evaluate the independence of each measurement point using the effective independence method;

[0038] S2. Degree of linear correlation of mode shapes: Evaluate the modal characterization of each measurement point according to the modal confidence criterion method;

[0039] S3. Correlation of acoustic vibration function: Evaluate the correlation between the vibration of each measuring point and the final physical parameter of interest based on the correlation method.

[0040] In some embodiments, in step 4), the monitoring point signal is processed, stored in real time, and the data is transmitted, the data is stored in a storage medium, and the data is transmitted through a wired or wireless network.

[0041] In some embodiments, step 5) specifically includes the following steps:

[0042] Load inversion is the dynamic excitation under the current load condition, based on the first kind integral equation of the structural dynamics system:

[0043]

[0044] Where f(x, t) represents the load function acting at position x at time t; h(x, t) represents the structural system operator function at position x at time t; y(x, t) represents the structural response at position x at time t, and the structural response is displacement, velocity, acceleration, and strain.

[0045] In some embodiments, in step 5), when the load position is known, x is omitted, and the entire time history is discretized into Q time units. The dynamic load identification system model is

[0046] y load =H load f load (9)

[0047] where y load =(y load (Δt),…,y load (QΔt)) T is the response vector,

[0048] f load =(f load (0),…,f load ((Q-1)Δt)) T Input vector for dynamic loads;

[0049] Equation (9) is the discrete system model under the single-input single-output (SISO) system;

[0050] For a more general case, the dynamic load identification system model of the MIMO system is:

[0051]

[0052] where f o (o=1,…,q) is the dynamic load vector, y b (b=1,…,p) is the structural response vector, q and p are the number of dynamic load vectors and structural response vectors respectively, H ob Represents the impulse response function matrix from the oth node to the bth node in the structure.

[0053] In some embodiments, in step 5), during load inversion, a regularization method is selected to solve the ill-posed problem in the load identification problem; the regularization method examines the optimization problem:

[0054]

[0055] Where α>0 is the regularization parameter, which represents the Euclidean norm;

[0056] The matrix H loadThe SVD decomposition of is brought in and the Tikhonov regularization solution is obtained as

[0057]

[0058] where u i , v i are the left singular value matrix elements and the right singular value matrix elements after matrix decomposition;

[0059] where ξ i (α) is the Tikhonov regularized filter operator and satisfies

[0060]

[0061] The L-curved edge criterion or the generalized intersection test criterion GCV is used to reasonably select the value of the regularization parameter α so that the filter operator ξ i (α) to achieve the best filtering effect.

[0062] In some embodiments, in step 5), the global vibration distribution prediction is processed based on the product of the pre-stored global transfer function relationship involving internal and external environmental loads and the dynamic excitation obtained by inversion.

[0063] In some embodiments, in-depth analysis such as integrated control / evaluation may include fatigue analysis, acoustic prediction, fault diagnosis, and decision support.

[0064] Beneficial Effects: Compared with the prior art, the present invention's global dynamic response prediction method based on a limited number of monitoring points establishes a prediction model through transfer functions, inverting the dynamic excitation and global dynamic response under the current operating conditions. By rationally selecting monitoring points and accurately establishing transfer functions and prediction models, the present invention overcomes the pathological nature of the load inversion matrix inversion process, enabling rapid and accurate prediction of the global vibration distribution response of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The technical solutions and other beneficial effects of the present invention will be made apparent by describing in detail the specific embodiments of the present invention in conjunction with the accompanying drawings.

[0066] Figure 1 Flowchart for optimizing the selection of measuring points;

[0067] Figure 2 This is a flow chart of the global dynamic response prediction method based on limited monitoring points;

[0068] Figure 3 It is a cloud map for predicting the global dynamic response of the model;

[0069] Figure 4 This is a comparison chart of the forecast accuracy of monitoring points;

[0070] Figure 5 This is the vibration distribution cloud diagram of the monitoring object at a frequency of 10Hz;

[0071] Figure 6 This is a comparison chart of actual test data and forecast inversion data. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0073] In the marine industry, hull structures are complex, with intricate internal reinforcement plates. Furthermore, the main engine, auxiliary engines, pumps, and propellers all have distinct excitation characteristics, and under most operating conditions, multiple devices are simultaneously active. Monitoring every node in the hull model is prohibitively expensive. Furthermore, due to the aquatic environment and the presence of oil and water inside, some monitoring points cannot be fixed. Therefore, it is crucial to monitor the operating status of the entire ship system by monitoring data from a limited number of nodes within the system.

[0074] A global dynamic response prediction method based on limited monitoring points includes the following steps:

[0075] (1) Establish a three-dimensional simulation model based on the actual dimensions of the monitored object and the structure of the local ribs, including the finite element / boundary element mesh model;

[0076] (2) Establish a coupling model involving internal and external environmental loads;

[0077] (3) Optimal selection of monitoring points;

[0078] (4) Monitoring point signal processing, real-time storage and data transmission;

[0079] (5) Establish a prediction model through transfer function to inverse the dynamic excitation and global dynamic response under the current working conditions;

[0080] (6) Conduct in-depth analysis such as comprehensive control / evaluation.

[0081] A coupling model involving internal and external environmental loads is established, and the virtual mass method is used to establish the transfer function involving internal and external environmental loads. The dynamic equation is described as follows:

[0082] [M+M A ]·[ü]+[K+K A ]·[u]={F} (1)

[0083] Where: M and M A are the mass matrix and the additional mass matrix generated by the environmental load, K and K A are the stiffness matrix and the additional stiffness matrix generated by the environmental load, ü and u are the acceleration vector and velocity vector, respectively, and F is the generalized force.

[0084] The corresponding relationship between excitation and response is determined by the transfer characteristic of the system, that is, the transfer function. The vibration velocity of the structural vibration monitoring point and the acoustic reconstruction point is obtained by the modal superposition method:

[0085]

[0086]

[0087] Where: v l (ω), v l (ω) is the vibration velocity of the monitoring point and the acoustic reconstruction point, ω is the circular frequency, M r is the modal mass, C r is the modal damping, K r is the modal stiffness, f p (ω) is the modal load, is the modal vector, p is the load application point, l is the monitoring point on the structure, k is the acoustic reconstruction point on the structure, r is the rth node, and j represents the imaginary part.

[0088] The ballast water tank and oil tank in the ship have free surface boundary conditions. According to the source-sink method, it is assumed that there is a point sink with equal source strength and opposite direction on the symmetric surface of the free surface. The velocity potential of any point in the internal and external environment is calculated using a simple Green's function:

[0089]

[0090] Where, Green's function

[0091] are the distances from the actual point source and virtual point sink to any point in the fluid, x p 、y p 、z p is the coordinate of the actual point source, x s 、y s 、z s is the coordinate of the virtual point source, σ(x s ,y s ,z s ) is the virtual point source intensity.

[0092] The measuring point optimization method specifically includes the following steps:

[0093] 1. Rough selection

[0094] The center cluster method or the effective independent-driving point residual method is used to preliminarily select the measuring points;

[0095] 2. Number of selected measuring points (N1)

[0096] Remove inappropriate measuring points from the rough selection points;

[0097] There are three principles for verification:

[0098] S1. Transfer function pathology: Evaluate the independence of each measurement point using the effective independence method;

[0099] S2. Degree of linear correlation of mode shapes: Evaluate the modal characterization of each measurement point according to the modal confidence criterion method;

[0100] S3. Correlation of acoustic vibration function: Evaluate the correlation between the vibration of each measuring point and the final physical parameter of interest based on the correlation method.

[0101] 3. Determine the number (N) and location of measuring points

[0102] Determine the number and location of measuring points according to the above method. The final number of measuring points cannot be less than the number of devices (N2), and preferably more than three times the number of devices.

[0103] The measurement point optimization layout is performed based on the effective independent driving point residual value method. This method achieves the optimal layout of the sensor by weighting the effective independent driving point residual value coefficient with the effective independent distribution matrix. The unit stiffness modal motion energy is defined as:

[0104]

[0105] Where, φ s Represents the system modal vibration shape, E represents the unit stiffness modal motion energy, N a Represents the modal order, K a Represents the system stiffness matrix, M a represents the system mass matrix, ω mn represents the nth order target modal frequency of the mth unit, φ mn Represents the nth mode shape of the mth unit. Define the effective independent driving point residual coefficient C DPR is the modal motion energy of the unit stiffness, that is

[0106]

[0107] Use C DPR The weighted effective independent assignment matrix is:

[0108]

[0109] Monitoring point signal processing, real-time storage and data transmission. Data can be stored in a database or local disk or other storage media, and data transmission is via wired or wireless network.

[0110] Load inversion is the dynamic excitation under the current load condition, based on the first kind integral equation of the structural dynamics system:

[0111]

[0112] Where f(x, t) represents the load function acting at position x at time t; h(x, t) represents the structural system operator function at position x at time t; y(x, t) represents the structural response at position x at time t. The structural response can be displacement, velocity, acceleration, strain, etc.

[0113] When the load position is known, x can be omitted and the entire time history is discretized into Q time units. The dynamic load identification system model is:

[0114] y load =H load f load (9)

[0115] where y load =(y load (Δt),…,y load (QΔt)) T is the response vector,

[0116] f load =(f load (0),…,f load ((Q-1)Δt)) T Enter the vector for the dynamic load.

[0117] Equation (9) is the discrete system model under the single-input single-output (SISO) system.

[0118] For a more general case, the dynamic load identification system model of the multiple-input multiple-output (MIMO) system is:

[0119]

[0120] where f o (o=1,…,q) is the dynamic load vector, y b (b=1,…,p) is the structural response vector, q and p are the number of dynamic load vectors and structural response vectors respectively, H ob Represents the impulse response function matrix from the oth node to the bth node in the structure.

[0121] For load inversion, regularization methods are preferred to solve the ill-posed problem in load identification.

[0122] The main idea of ​​the regularization method is to examine the optimization problem:

[0123]

[0124] Where α>0 is the regularization parameter, which represents the Euclidean norm.

[0125] The matrix H load The SVD decomposition of is brought in and the Tikhonov regularization solution is obtained as

[0126]

[0127] where u i , v i are the left singular value matrix elements and right singular value matrix elements after matrix decomposition.

[0128] where ξ i (α) is the Tikhonov regularized filter operator and satisfies

[0129]

[0130] The regularization parameter α is reasonably selected by the L-curved edge criterion or the generalized intersection test criterion (GCV) to make the filter operator ξ i (α) to achieve the best filtering effect.

[0131] The global vibration distribution prediction steps are as follows: the global transfer function relationship involving internal and external environmental loads stored in step 1 (2) is multiplied by the dynamic excitation product obtained by inversion in step 1 (5).

[0132] In-depth analysis such as integrated control / evaluation may include fatigue analysis, acoustic prediction, fault diagnosis and decision support.

[0133] Example

[0134] For a small or medium-sized cabin model, the finite element software was used to model and divide the mesh, with 10035 elements and 9560 nodes. The internal and external non-structural masses and the attached water mass were taken into account, and the transfer function of the system was calculated. The measurement point selection algorithm was used to obtain the actual test point layout and number. Based on the sensor data, the algorithm inversion was used to obtain the global dynamic response of the system, such as Figure 3 Shown is the vibration distribution cloud diagram of the cabin model at a frequency of 50 Hz. Figure 4 The geometric center of the compartment's machinery deck was used as the monitoring point. Comparison of the actual test data and the predicted inversion data shows that the trends in the medium and low frequency bands are basically consistent, with the vibration velocity level error within 3dB. This shows that this dynamic prediction method is feasible for small and medium-scale monitoring objects.

[0135] For the bow area of ​​a large-scale ship model, the number of units is 16065 and the number of nodes is 12860. The same monitoring and prediction method is used, such as Figure 5 Shown is the vibration distribution cloud diagram of the monitored object at a frequency of 10 Hz.

[0136] Figure 6 The geometric center point of the forecastle deck is used as the monitoring point. Comparison between the actual test data and the forecast inversion data shows that the trends in the medium and low frequency bands are basically consistent, indicating that this dynamic prediction method is also feasible for large-scale monitoring objects.

[0137] The inverted velocity distribution cloud map can guide instrument placement and structural optimization and reinforcement design. The frequency response curves for specific locations obtained through inversion can be compared with regulatory limits to identify frequencies at risk of exceeding limits, allowing for appropriate optimization measures to be implemented before construction.

[0138] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] The above is a detailed introduction to a global dynamic response prediction method based on limited monitoring points provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the technical solutions and core ideas of the present invention. Ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A global dynamic response prediction method based on limited monitoring points, characterized in that: The following steps are involved: 1) Establish a three-dimensional simulation model based on the actual dimensions of the monitored object and the local rib structure, including a finite element / boundary element mesh model; 2) Establish a coupling model involving internal and external environmental loads; 3) Optimal selection of monitoring points; 4) Monitoring point signal processing, real-time storage and data transmission; 5) Establish a prediction model through transfer function to inverse the dynamic excitation and global dynamic response under the current working conditions; Said step 2) specifically comprises: A coupling model involving internal and external environmental loads is established, and the virtual mass method is used to establish the transfer function involving internal and external environmental loads. The dynamic equation is described as follows: Where: M and M A are the mass matrix and the additional mass matrix generated by the environmental load, K and K A are the stiffness matrix and the additional stiffness matrix generated by the environmental load, and u are acceleration vector and velocity vector respectively, F is the generalized force; In step 2), the corresponding relationship between the excitation and the response is determined by the transfer function, and the vibration velocity of the structural vibration monitoring point and the acoustic reconstruction point is obtained by the modal superposition method: Where: v l (ω), v k (ω) is the vibration velocity of the monitoring point and the acoustic reconstruction point, ω is the circular frequency, M r is the modal mass, C r is the modal damping, K r is the modal stiffness, f p (ω) is the modal load, is the modal vector, p is the load application point, l is the monitoring point on the structure, k is the acoustic reconstruction point on the structure, r is the rth node, and j represents the imaginary part.

2. A global dynamic response prediction method based on limited monitoring points according to claim 1, characterized in that: In step 2), the response of the monitored object is affected by the boundaries of the internal and external environments. The ballast water tanks and oil tanks in the ship have free liquid surface boundary conditions. According to the source-sink method, it is found that there is a point sink with equal source strength and opposite direction on the symmetric surface of the free liquid surface. The velocity potential of any point in the internal and external environments is calculated using a simple Green's function: Where, Green's function are the distances from the actual point source and virtual point sink to any point in the fluid, x p 、y p 、z p is the coordinate of the actual point source, x s 、y s 、z s is the coordinate of the virtual point source, σ(x s ,y s , z s ) is the virtual point source intensity.

3. The global dynamic response prediction method based on limited monitoring points according to claim 1 is characterized in that: Said step 3) specifically comprises: 1.1) Rough selection The center cluster method or the effective independent-driving point residual method is used to preliminarily select the measuring points; 1.2) Selected measurement points Remove inappropriate measuring points from the rough selection points; 1.3) Determine the final number and location of measuring points.

4. The global dynamic response prediction method based on limited monitoring points according to claim 3 is characterized in that: In step 1.2), the principles of verification include: S1. Transfer function pathology: Evaluate the independence of each measurement point using the effective independence method; S2. Degree of linear correlation of mode shapes: Evaluate the modal characterization of each measurement point according to the modal confidence criterion method; S3. Correlation of acoustic vibration function: Evaluate the correlation between the vibration of each measuring point and the final physical parameter of interest based on the correlation method.

5. The global dynamic response prediction method based on limited monitoring points according to claim 3 is characterized in that: In the step 1.3), the final number and positions of the measuring points are determined according to the above method. The final number of measuring points is the larger value between the number of selected measuring points and three times the number of excitation source devices.

6. The global dynamic response prediction method based on limited monitoring points according to claim 1 is characterized in that: In the step 4), the monitoring point signal is processed, stored in real time and the data is transmitted, the data is stored in a storage medium, and the data is transmitted through a wired or wireless network.

7. The global dynamic response prediction method based on limited monitoring points according to claim 1 is characterized in that: The step 5) specifically includes the following steps: Load inversion is the dynamic excitation under the current load condition, based on the first kind integral equation of the structural dynamics system: Where f(x, τ) represents the load function acting at position x at time τ; h(x, t, τ) represents the structural system operator function at position x at time t; y(x, t) represents the structural response at position x at time t, and the structural response is displacement, velocity, acceleration, and strain.

8. The global dynamic response prediction method based on limited monitoring points according to claim 7 is characterized in that: In step 5), when the load position is known, x is omitted and the entire time history is discretized into Q time units. The dynamic load identification system model is: y load =H load ∫ load (9) where y load =(y load (Δt),…,y load (QΔt)) T is the response vector, f load =(f load (0),…,f load ((Q-1)Δt)) T Input vector for dynamic loads; Equation (9) is the discrete system model under the single-input single-output (SISO) system; For a more general case, the dynamic load identification system model of the MIMO system is: where f o (o=1,…,q) is the dynamic load vector, y b (b=1,…,p) is the structural response vector, q and p are the number of dynamic load vectors and structural response vectors respectively, H ob Represents the impulse response function matrix from the oth node to the bth node in the structure.

9. The global dynamic response prediction method based on limited monitoring points according to claim 8, characterized in that: In step 5): load inversion, a regularization method is used to solve the ill-posed problem in the load identification problem; the regularization method is used to examine the optimization problem: Where α>0 is the regularization parameter; The matrix H load The SVD decomposition of is brought in and the Tikhonov regularization solution is obtained as where u i , v i are the left singular value matrix elements and the right singular value matrix elements after matrix decomposition; where ξ i (α) is the Tikhonov regularized filter operator and satisfies The L-curved edge criterion or the generalized intersection test criterion GCV is used to reasonably select the value of the regularization parameter α so that the filter operator ξ i (α) to achieve the best filtering effect.

10. A global dynamic response prediction method based on limited monitoring points according to claim 9, characterized in that: In the step 5), the global vibration distribution prediction is processed based on the product of the pre-stored global transfer function relationship involving the internal and external environmental loads and the dynamic excitation obtained by inversion.

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