Structure global dynamic intensity high-fidelity evaluation method and system based on multi-source graph fusion

Abstract

The invention discloses a multi-source image fusion-based structure global dynamic intensity high-fidelity evaluation method and system. The method comprises the following steps of: obtaining actual measurement excitation data and limited sensor measurement point response data; establishing a finite element model of the target structure, and calculating simulation frequency response data of global nodes of the structure based on the actually measured excitation data; based on a finite element model, exporting a finite element mesh division result as a global node communication undirected graph, and determining corresponding nodes in the global node communication undirected graph according to the measurement point arrangement position of the finite sensor; calculating a weighting coefficient based on the global node communication undirected graph; carrying out load inversion by combining simulation frequency response data on the basis of limited sensor measuring point response data to obtain equivalent excitation data at limited sensor measuring points, and calculating weighted equivalent excitation data of global nodes by combining weighting coefficients; and based on the weighted equivalent excitation data, calculation is carried out in combination with simulation frequency response data, global node response data is finally obtained, and structural global dynamic strength evaluation is realized.

Description

Technical Field

[0001] The present invention relates to the field of engineering structure mechanics analysis, and in particular to a high-fidelity evaluation method and system for global dynamic strength of a structure based on multi-source image fusion. Background Art

[0002] In the research and development process of aerospace equipment structures, the traditional numerical models constructed based on finite element methods have significant defects. When dealing with large-scale and highly complex equipment structures, the consumption of computing resources is extremely huge, and the calculation time is extremely long, which seriously delays the rapid iteration and optimization process of the design scheme. At the same time, the interaction and coordination between traditional models and test data is poor, and the test data is difficult to effectively integrate and verify the model, resulting in a significant reduction in the accuracy and reliability of the model, which greatly weakens the effectiveness of mechanical performance analysis and optimization design based on the model.

[0003] In modern engineering application scenarios, it is of vital importance to accurately evaluate the dynamic mechanical response of the test device, especially to fully understand the mechanical response of the test device in the entire domain. However, in the actual test operation process, the layout of sensors faces many limitations. Their number is usually limited, and the layout positions are relatively sparse, which makes it difficult to effectively cover some key areas. Even in some specific areas, due to space or technical conditions, sensors cannot be directly installed. In this case, relying only on limited measured data from measurement points to infer global response data has become a key problem that needs to be solved in the current engineering field. Summary of the invention

[0004] The purpose of the present invention is to provide a high-fidelity evaluation method and system for the global dynamic strength of a structure based on multi-source graph fusion, so as to overcome the deficiencies in the mechanical response analysis of structures in the prior art. The present invention realizes data fusion through graph theory, achieves the goal of improving the analysis accuracy of large-scale numerical models and global mechanical response analysis, and provides strong support for system status evaluation, design optimization, fault diagnosis, etc.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: The high-fidelity evaluation method of the global dynamic strength of a structure based on multi-source image fusion includes the following steps: S1. Dynamic test plan for the design target structure under working condition; S2, conducting a vibration test based on the dynamic test scheme under the working state of the target structure obtained in step S1 to obtain measured excitation data and response data of limited sensor measurement points; S3, establishing a finite element model of the target structure, and calculating the simulation frequency response data of the global nodes of the structure based on the measured excitation data in step S2; S4. Based on the finite element model in step S3, export the finite element mesh division result as an undirected graph with all nodes connected, and determine the corresponding nodes in the undirected graph with all nodes connected according to the arrangement positions of the finite sensor measurement points in the dynamic test. S5. Based on the undirected graph with all nodes connected obtained in step S4, calculate the shortest paths from all nodes to all sensor measurement points and convert them into weighting coefficients. S6. Based on the response data of the finite sensor measurement points obtained in step S2, combined with the simulated frequency response data obtained in step S3, perform load inversion to obtain the equivalent excitation data at the finite sensor measurement points. Then, combined with the weighting coefficients obtained in step S5, calculate the weighted equivalent excitation data of all nodes. S7. Based on the weighted equivalent excitation data obtained in step S6, combined with the simulated frequency response data obtained in step S3, perform calculations to finally obtain the response data of all nodes, realizing the evaluation of the dynamic strength of the entire structure.

[0006] Further, step S1 is specifically as follows: S101. Analyze the functional characteristics, operating environment, and types of mechanical loads that the target structure can withstand, apply random excitation at the vibration source position to ensure that the stress state of the target structure in actual operation can be simulated. S102. Considering the limitations of the test space and cost, according to the geometric shape, size, and material properties of the target structure, plan the preliminary arrangement positions of sensors and control points to ensure that the key parts of the target structure and the sensitive areas where mechanical response changes occur can be covered, and use the control points to obtain accurate excitation data.

[0007] Further, step S2 is specifically as follows: S201. Install and fix the target structure and build a vibration test system. S202. Debug and calibrate the vibration test system. S203. Conduct a dynamic test, apply random excitation, measure the excitation data through the control points, and obtain the response data through the finite sensor measurement points. Among them, the excitation data and the response data are analyzed in terms of power spectral density and the power spectral density of the response data and the power spectral density of the excitation data are calculated through the frequency response data . The specific formula is: .

[0008] Further, step S3 is specifically as follows: S301. Based on the kinetic test scheme obtained in step S1, as well as the geometric shape, size, and material properties of the target structure, establish a finite element model of the target structure in finite element software. Use multiple element types for mesh division to improve the analysis accuracy, and at the same time set the material properties and boundary conditions; S302. Based on the measured excitation data obtained in step S2, apply random excitation to the target structure and conduct finite element analysis to obtain frequency response data.

[0009] Further, step S4 is specifically as follows: S401. Based on the finite element model of the target structure established in step S3, export the mesh division result information as a global node-connected undirected graph; S402. Write different node connection rules according to different element types ; S403. Add the element numbers of the target structure finite element model to the global node-connected undirected graph according to the node connection rules in step S402. Each node in the global node-connected undirected graph represents an actual node in the finite element model, and the edge represents the connection between nodes; S404. Based on the spatial positions of the sensors arranged in the kinetic test scheme designed in step S1, combined with the finite element model of the target structure, determine the node numbers in the finite element model corresponding to the spatial positions of the sensors arranged; Among them, in step S401, exporting the mesh division result information as a global node-connected undirected graph is specifically as follows: Export the element numbers, element types, and node numbers contained in the elements of the target structure to form a global node-connected undirected graph, where the nodes of the finite element mesh correspond to the nodes in the graph, and the connection relationships of the elements form the edges of the graph; Step S402 is specifically as follows: According to the node number order of different types of elements, use the node connection rules to determine the connection order of the element numbers at different positions.

[0010] Further, step S5 is specifically as follows: S501. Use the Dijkstra algorithm for each node in the global node-connected undirected graph to calculate the shortest path length from it to all sensor measurement points; S502. According to the shortest path lengths of the sensor measurement points obtained in step S501, represent the degree of association between the nodes and the sensor measurement points with a weighting coefficient.

[0011] Further, step S501 is specifically as follows: Taking each node in the undirected graph with all nodes connected as the source node, and the nodes corresponding to all sensor measurement points as the target nodes, use the Dijkstra algorithm to calculate the shortest path length between the source node and the target node, continuously select the node with the smallest current distance, and gradually calculate the shortest path from the source node to the target node , where represents the distance of the edge from node to node , is the estimated value of the shortest path of the current node ; Step S502 is specifically as follows: Assume that the shortest path from node to the sensor measurement point is , then the weighting coefficient is the reciprocal of the square of the distance: .

[0012] Furthermore, step S6 is specifically as follows: S601. Use the power spectral density of the response data at the dynamic test measurement points , combined with the simulated frequency response data at the sensor measurement points obtained in step S3, perform load inversion to calculate the power spectral density of the equivalent excitation data at a single measurement point;

[0013] S602. According to the equivalent excitation data at a single measurement point obtained in step S601, combined with the weighting coefficient obtained in step S5, calculate the power spectral density of the equivalent excitation data after the combined action of all sensor measurement points on node :

[0014] where represents the reciprocal of the square of the shortest path from node to the sensor measurement point , represents the power spectral density of the equivalent excitation data of the sensor measurement point , represents the power spectral density of the equivalent excitation data of node .

[0015] Furthermore, step S7 is specifically as follows: S701. According to node Equivalent excitation data power spectral density and the simulated frequency response data obtained in step S3 calculate the response data power spectral density of all-domain nodes :

[0016] wherein represents the transpose of the simulated frequency response data ; S702. According to the response data power spectral density of the nodes obtained in step S701 calculate the root mean square value of the response data power spectral density as an evaluation index of the structural dynamic strength, and complete the evaluation of the structural all-domain dynamic strength.

[0017] The high-fidelity evaluation system for the structural all-domain dynamic strength based on multi-source graph fusion includes: Design module: used to design the dynamic test scheme for the target structure under the working state; Data acquisition module: used to carry out vibration tests based on the dynamic test scheme for the target structure under the working state, and obtain the measured excitation data and the response data of finite sensor measurement points; Calculation module: used to establish the finite element model of the target structure, and calculate the simulated frequency response data of all-domain nodes of the structure based on the measured excitation data; Node determination module: used to export the finite element mesh division result as an all-domain node connected undirected graph based on the finite element model, and determine the corresponding nodes in the all-domain node connected undirected graph according to the arrangement positions of the finite sensor measurement points in the dynamic test; Weighting coefficient calculation module: used to calculate the shortest paths from all nodes to all sensor measurement points based on the all-domain node connected undirected graph and convert them into weighting coefficients; Weighted equivalent excitation data calculation module: used to perform load inversion based on the response data of finite sensor measurement points, combine with the simulated frequency response data to obtain the equivalent excitation data at the finite sensor measurement points, and combine with the weighting coefficients to calculate the weighted equivalent excitation data of all-domain nodes; Evaluation module: used to perform calculations based on the weighted equivalent excitation data, combine with the simulated frequency response data, and finally obtain the all-domain node response data to realize the evaluation of the structural all-domain dynamic strength.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention makes full use of limited measurement point data and combines the advantages of graph theory and finite element analysis, enabling more accurate evaluation of the mechanical response of a structure within the entire domain. Through the quantitative analysis of the correlation degree between nodes and sensor measurement points and the load inversion process, the accuracy of the analysis results is effectively improved, providing strong support for system state evaluation, design optimization, fault diagnosis, etc., effectively improving the problem of limited sensor arrangement in actual tests. Even in the case of limited measurement points, accurate analysis of the global mechanical response can still be achieved, contributing to improving the safety and reliability of engineering structures.

[0019] In addition, the present invention converts the finite element mesh division result of the target structure into an undirected graph of global node connectivity, and converts the graph distance information into a weighting coefficient. The correlation degree between nodes is reflected in the form of a weighting coefficient. This method based on graph theory can effectively integrate limited measurement point data and structural model information, breaking through the limitations of traditional methods that only rely on limited measurement points or simple finite element analysis; during the analysis process, load inversion is performed by combining the measured response data of dynamic test measurement points and the frequency response data at sensor measurement points to obtain equivalent excitation data, and further combining the weighting coefficient to calculate the equivalent excitation data of global nodes, and finally obtaining global response data. This process realizes a data fusion method, combining measured response data and finite element simulation data, making the analysis results more accurate and reliable, and being able to more accurately reflect the global mechanical response of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings in the specification are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0021] Figure 1 is a flowchart of the method of the present invention; Figure 2 is a schematic diagram of the structure of the engine compartment section; Figure 3 is a diagram of the acceleration measurement point positions of the engine compartment section structure; Figure 4 is a schematic diagram of the simulation analysis results of the mechanical response of the engine compartment section structure; Figure 5 is a schematic diagram of the analysis results of the global mechanical response integrating measured data. DETAILED DESCRIPTION OF THE INVENTION

[0022] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] Embodiment 1 The present invention provides a high-fidelity evaluation method for the structural global dynamic strength based on multi-source graph fusion, including the following steps: S1. Design a dynamic test plan for the target structure under the working state; S2. Conduct a vibration test based on the dynamic test plan for the target structure under the working state obtained in step S1, and obtain the measured excitation data and the response data of finite sensor measurement points; S3. Establish a finite element model of the target structure, and calculate the simulated frequency response data of all nodes in the structure domain based on the measured excitation data in step S2; S4. Based on the finite element model in step S3, export the finite element mesh division result as an undirected graph connected by all nodes in the domain, and determine the corresponding nodes in the undirected graph connected by all nodes in the domain according to the arrangement positions of the finite sensor measurement points in the dynamic test; S5. Based on the undirected graph connected by all nodes in the domain obtained in step S4, calculate the shortest paths from all nodes to all sensor measurement points and convert them into weighting coefficients; S6. Based on the response data of the finite sensor measurement points obtained in step S2, combine the simulated frequency response data obtained in step S3 to perform load inversion to obtain the equivalent excitation data at the finite sensor measurement points, and combine the weighting coefficients obtained in step S5 to calculate the weighted equivalent excitation data of all nodes in the domain; S7. Based on the global node weighted equivalent excitation data obtained in step S6, calculate in combination with the global node simulation frequency response data obtained in step S3, and finally obtain the global node response data to achieve the evaluation of the structural global dynamic strength.

[0025] Based on the known input excitation, the present invention makes full use of the measured dynamic response data of a finite number of measuring points, and through a scientific and reasonable inference method, realizes the efficient calculation of the global response data, and further completes the analysis of the global mechanical response of the test device.

[0026] Embodiment 2 A high-fidelity evaluation method for the structural global dynamic strength based on multi-source graph fusion, as Figure 1 shown, includes the following steps: S1. Design a dynamic test scheme for the target structure under the working state.

[0027] The specific implementation of step S1 in this embodiment includes the following sub-steps: Figure 2 It is a schematic diagram of the structure of an engine compartment section. The overall height of the engine compartment section structure is 600 mm, and the outer diameter is 430 mm. The materials of the compartment section structure are all 2A12 aluminum alloy, with a density about , Poisson's ratio , Young's modulus , yield strength . Based on the analysis of the actual operating conditions, the vibration test of the engine compartment section structure is fixed and constrained on the vibration table through a fixture and the bottom bolt holes, and 36 acceleration sensors are evenly arranged on the compartment section structure. The measuring point positions are shown in Figure 3 (A1 - A36).

[0028] S2. Carry out a vibration test based on the dynamic test scheme of the target structure under the working state obtained in step S1, and obtain the measured excitation and finite measuring point response data.

[0029] The specific implementation of step S2 in this embodiment includes the following sub-steps: According to the engine compartment section structure dynamic test scheme in step S1, fix it on the vibration table through a fixture, and perform random excitations of different magnitudes to obtain the acceleration response data at the sensor measuring points.

[0030] S3. Use commercial finite element software to establish a finite element model of the target structure, and calculate the simulation frequency response data of the entire structure based on the measured excitation data in step S2.

[0031] The specific implementation of step S3 in this embodiment includes the following sub-steps: Based on the kinetic test scheme obtained in step S1 and data such as the geometric shape, size, and material properties of the target structure, a finite element model of the engine compartment structure is established in the commercial finite element software HyperMesh. There are a total of 105,680 nodes, and the element types include CHEXA8 element, CPYRA element, CQUAD4 element, CTETRA4 element, CTRIA3 element, and RBE2 element. The total number of elements is 105,680. At the same time, material properties and boundary conditions are set, and the excitation is the measured random excitation at the control points. The OptiStruct solver is used for solution to obtain the frequency response functions of all nodes and the random response results. The frequency response results are as Figure 4 shown

[0032] S4. Based on the finite element model in step S3, export the finite element mesh division result as an undirected graph with all nodes connected globally, and determine the corresponding nodes in the graph according to the sensor layout positions in the kinetic test

[0033] The specific implementation of step S4 in this embodiment includes the following sub-steps Export all element numbers, element types, and node numbers contained in the elements of the engine compartment structure finite element model in the commercial finite element software HyperMesh; for different types of elements, write node connection rules and save the nodes and the connection relationships between nodes to the graph. Each node in the graph represents an actual node in the finite element model, and the edge represents the connection between nodes; in the finite element model, find the node numbers in the finite element model corresponding to 36 measurement points according to the Figure 3 spatial position map of the acceleration sensors of the engine compartment structure

[0034] S5. Based on the undirected graph with all nodes connected globally obtained in step S4, calculate the shortest paths (i.e., graph distances) from all nodes to all sensor measurement points and convert them into weighting coefficients .

[0035] The specific implementation of step S5 in this embodiment includes the following sub-steps According to the undirected graph with all nodes connected globally obtained in step S4, use the 105,680 nodes in the graph as source nodes and the nodes corresponding to 36 sensor measurement points as target nodes, and use the Dijkstra algorithm to calculate the shortest path lengths between the source nodes and the target nodes. Continuously select the node with the smallest current distance and gradually calculate the shortest path from the source node to the target node; after obtaining the shortest path length data between a certain node and 36 sensor measurement points, calculate the weighting coefficient. Assume that the graph distance from node to measurement point is , then the weighting coefficient is the inverse square of the distance:

[0036] S6. Perform load inversion on the response data of the dynamic test measurement points to obtain the equivalent excitation power spectrum at the measurement points , combined with the weighting coefficient obtained in step S5 , calculate the power spectral density of the equivalent excitation data of all global nodes .

[0037] In this embodiment, the specific implementation of step S6 includes the following sub-steps: Utilize the power spectral density of the response data at 36 measurement points of the dynamic test , combined with the 36 simulation frequency response functions at the sensor measurement points obtained in step S3 , perform load inversion to calculate the power spectral density of the equivalent excitation data for each of the 36 measurement points ;

[0038] According to the weighting coefficient obtained in step S5 , calculate the power spectral density of the equivalent excitation data for all 105,680 nodes

[0039]

[0040] S7. Based on the simulation frequency response functions obtained in step S3 perform calculations to finally obtain the power spectral density of the global response data , and realize the calculation of the global mechanical response of the structure.

[0041] In this embodiment, the specific implementation of step S7 includes the following sub-steps: According to the equivalent excitation power spectra of all 105,680 nodes obtained in step S6 , and the simulation frequency response functions of all 105,680 nodes obtained in step S3 and its transpose , calculate the power spectral density of the response data of all nodes

[0042] Use the RMS (root mean square value) of the power spectral density of the response data as an evaluation index for the dynamic strength of the structure to complete the evaluation of the global dynamic strength of the structure.

[0043] Embodiment III The present invention also provides a high-fidelity evaluation system for the global dynamic strength of a structure based on multi-source graph fusion, including: Design module: used to design the dynamic test plan for the target structure in the working state; Data acquisition module: used to conduct vibration tests based on the dynamic test plan for the target structure in the working state, and obtain measured excitation data and finite sensor measurement point response data; Calculation module: used to establish a finite element model of the target structure, and calculate the simulated frequency response data of all nodes in the structure based on the measured excitation data; Node determination module: used to export the finite element mesh division result as an undirected graph connected by all nodes based on the finite element model, and determine the corresponding nodes in the undirected graph connected by all nodes according to the arrangement positions of the finite sensor measurement points in the dynamic test; Weighted coefficient calculation module: used to calculate the shortest paths from all nodes to all sensor measurement points based on the undirected graph connected by all nodes and convert them into weighted coefficients; Weighted equivalent excitation data calculation module: used to perform load inversion based on the finite sensor measurement point response data and in combination with the simulated frequency response data to obtain the equivalent excitation data at the finite sensor measurement points, and calculate the weighted equivalent excitation data of all nodes in combination with the weighted coefficients; Evaluation module: used to perform calculations based on the weighted equivalent excitation data and in combination with the simulated frequency response data, and finally obtain the response data of all nodes in the domain to achieve the dynamic strength evaluation of the entire structure.

[0044] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0046] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one process or a plurality of processes and / or one block or a plurality of blocks in the flow Figure 1 in one or more processes and / or Figure 1 the functions specified in one block or a plurality of blocks.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or one block or a plurality of blocks in the flow Figure 1 in one or more processes and / or Figure 1 the functions specified in one block or a plurality of blocks.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent replacements to the specific implementation manners of the invention, but these changes, modifications or equivalent replacements are all within the scope of protection of the pending claims of the invention.

Claims

1. A high-fidelity structural global dynamic strength assessment method based on multi-source image fusion, characterized in that: The steps include: S1. Dynamic test plan for the design target structure under working condition; S2, conducting a vibration test based on the dynamic test scheme under the working state of the target structure obtained in step S1 to obtain measured excitation data and response data of limited sensor measurement points; S3, establishing a finite element model of the target structure, and calculating the simulation frequency response data of the global nodes of the structure based on the measured excitation data in step S2; S4, based on the finite element model of step S3, export the finite element meshing result into a global node connected undirected graph, and determine the corresponding node in the global node connected undirected graph according to the arrangement position of the finite sensor measuring points in the dynamic test; S5, based on the global node connection undirected graph obtained in step S4, calculate the shortest paths from all nodes to all sensor measurement points and convert them into weighted coefficients; S6, based on the response data of the finite sensor measuring points obtained in step S2, combined with the simulation frequency response data obtained in step S3, load inversion is performed to obtain equivalent excitation data at the finite sensor measuring points, and combined with the weighting coefficients obtained in step S5, weighted equivalent excitation data of the global nodes are calculated; S7. Based on the weighted equivalent excitation data obtained in step S6, calculation is performed in combination with the simulation frequency response data obtained in step S3, and finally the global node response data is obtained to realize the global dynamic strength assessment of the structure.

2. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 1 is characterized in that: Step S1 is specifically as follows: S101. Analyze the functional characteristics, operating environment and the type of mechanical load that the target structure can withstand, and apply random excitation at the vibration source position to ensure that the stress state of the target structure in actual work can be simulated; S102. Considering the limitations of test space and cost, plan the preliminary layout of sensors and control points according to the geometric shape, size and material properties of the target structure to ensure that the key parts of the target structure and sensitive areas where mechanical response changes occur are covered, and use control points to obtain accurate excitation data.

3. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 2 is characterized in that: Step S2 is specifically as follows: S201. Install and fix the target structure and build the vibration test system; S202, debug and calibrate the vibration test system; S203, conducting a dynamic test, applying random excitation, measuring excitation data through control points, and obtaining response data through limited sensor measurement points; Among them, the excitation data and response data are expressed in terms of power spectral density The power spectral density of the response data is analyzed The power spectral density of the excitation data Frequency response data To calculate, the specific formula is: 。 4. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 2 is characterized in that: Step S3 is specifically as follows: S301, based on the dynamic test scheme obtained in step S1 and the geometric shape, size and material properties of the target structure, a finite element model of the target structure is established in a finite element software, a plurality of unit types are used for meshing to improve the analysis accuracy, and material properties and boundary conditions are set at the same time; S302. S302. Based on the measured excitation data obtained in step S2, random excitation is applied to the target structure, and finite element analysis is performed to obtain frequency response data.

5. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 4 is characterized in that: Step S4 is specifically as follows: S401, based on the finite element model of the target structure established in step S3, exporting the meshing result information as a global node connected undirected graph; S402. Write different node connection rules according to different unit types ; S403, adding the unit number of the target structure finite element model to the global node connection undirected graph according to the node connection rule of step S402, wherein each node in the global node connection undirected graph represents an actual node in the finite element model, and the edge represents the connection between the nodes; S404, based on the sensor arrangement spatial position in the dynamic test scheme designed in step S1, combined with the target structure finite element model, determine the finite element model node number corresponding to the sensor arrangement spatial position; Among them, in step S401, the grid division result information is exported as a global node connection undirected graph, specifically: The unit number, unit type, and node number data of the target structure are exported to form a global node-connected undirected graph, in which the nodes of the finite element mesh correspond to the nodes in the graph, and the connection relationship of the units constitutes the edges of the graph; Step S402 is specifically as follows: According to the node numbering order of different types of units, use the node connection rules , determine the connection order of unit numbers at different positions.

6. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 1 is characterized in that: Step S5 is specifically as follows: S501, using the Dijkstra algorithm to calculate the shortest path length from each node in the global node-connected undirected graph to all sensor measurement points; S502 , according to the shortest path length of the sensor measuring point obtained in step S501 , the association degree between the node and the sensor measuring point is expressed by a weighted coefficient.

7. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 6 is characterized in that: Step S501 is specifically as follows: Each node in the undirected graph connected by the global nodes is taken as the source node, and the corresponding nodes of all sensor measurement points are taken as the target nodes. The Dijkstra algorithm is used to calculate the shortest path length between the source node and the target node, and the node with the smallest current distance is continuously selected to gradually calculate the shortest path from the source node to the target node. ,in Representation Node To Node The distance from the edge, Is the current node The shortest path estimate of Step S502 is specifically as follows: Assume Node To sensor point The shortest path is , then the weighting coefficient is the inverse of the square of the distance: 。 8. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 1 is characterized in that: Step S6 is specifically as follows: S601, using the power spectrum density of the response data at the dynamic test point , combined with the simulated frequency response data at the sensor measurement point obtained in step S3 , perform load inversion and calculate the power spectrum density of the equivalent excitation data at a single measuring point ; S602, calculate all the equivalent excitation data at the single measuring point obtained in step S601 in combination with the weighting coefficient obtained in step S5 After the comprehensive effect of the sensor measurement points, the node The power spectral density of the equivalent excitation data : in, Representation Node To sensor point Shortest Path The square reciprocal of Indicates sensor measurement point The equivalent excitation data power spectral density is Representation Node The power spectral density of the equivalent excitation data.

9. The high-fidelity structural global dynamic strength assessment method based on multi-source image fusion according to claim 1 is characterized in that: Step S7 is specifically as follows: S701, nodes obtained according to step S6 Equivalent excitation data power spectral density , and the simulated frequency response data obtained in step S3 , calculate the power spectrum density of the response data of the global nodes : in, Represents the simulated frequency response data The transpose of S702: Power spectrum density of the node response data obtained in step S701 , calculate the power spectral density of the response data The root mean square value is used as the evaluation index of the structural dynamic strength to complete the global dynamic strength evaluation of the structure.

10. A high-fidelity structural global dynamic strength assessment system based on multi-source image fusion, characterized by: include: Design module: used to design the dynamic test plan under the working state of the target structure; Data acquisition module: used to carry out vibration tests based on the dynamic test plan under the working state of the target structure, and obtain the measured excitation data and the response data of the limited sensor measurement points; Calculation module: used to establish the finite element model of the target structure and calculate the simulation frequency response data of the global nodes of the structure based on the measured excitation data; Node determination module: used to export the finite element meshing results into a global node connection undirected graph based on the finite element model, and determine the corresponding nodes in the global node connection undirected graph according to the arrangement positions of the finite sensor measurement points in the dynamic test; Weighted coefficient calculation module: used to calculate the shortest paths from all nodes to all sensor points based on the global node connected undirected graph and convert them into weighted coefficients; Weighted equivalent excitation data calculation module: used to perform load inversion based on the response data of limited sensor measurement points combined with the simulation frequency response data, obtain the equivalent excitation data at the limited sensor measurement points, and calculate the weighted equivalent excitation data of the global nodes combined with the weighting coefficients; Evaluation module: It is used to perform calculations based on weighted equivalent excitation data combined with simulation frequency response data, and finally obtain global node response data to achieve global dynamic strength evaluation of the structure.

Images