Transmission system multi-body dynamics and structure bearing model confidence evaluation method and system

Abstract

The application relates to the technical field of digital design verification of a transmission system, and discloses a confidence evaluation method and system for multi-body dynamics and structure bearing models of a transmission system. The method comprises the following steps: based on design data of the transmission system, a multi-body dynamics model and a structure bearing model are built, simulation analysis is carried out, and simulation results are obtained; characteristic parameters in the simulation results are extracted, dimensionless similarity criteria and similarity elements are constructed; a multi-layer evaluation system is built based on the constructed dimensionless similarity criteria and similarity elements, and the confidence of each index is obtained in combination with qualitative and quantitative methods; the confidence of each index is normalized, the overall confidence of the multi-body dynamics model and the structure bearing model is calculated by weighting, and the corresponding confidence interval is solved. The application effectively solves the problems of insufficient joint propagation means of multi-source uncertainty and weak fusion mechanism of qualitative and quantitative evaluation in the prior art.

Description

Technical Field

[0001] This invention relates to the field of digital design verification technology for transmission systems, specifically to a method and system for evaluating the confidence level of multibody dynamics and structural load-bearing models of transmission systems. Background Technology

[0002] With the rapid development of industrial robots, aerospace equipment, and high-end defense weaponry, composite transmission systems have found increasingly widespread engineering applications. These systems typically consist of a motor drive unit, a power conversion and control unit, a mechanical transmission chain (including planetary gear sets, shaft systems, bearings, and couplings), and a load-bearing structure, offering significant advantages such as high power density, a wide speed range, and precise torque control.

[0003] To shorten the R&D cycle and reduce the cost of prototype production, engineering practice increasingly relies on computer simulation to establish multibody dynamics and structural load-bearing models of electromechanical composite transmission systems. On the one hand, multibody dynamics software is used to establish a transmission chain dynamic model that includes rigid-flexible coupling shaft systems and time-varying meshing stiffness to analyze the inherent characteristics and dynamic response of the system. On the other hand, finite element software is used to establish structural models of key load-bearing components to evaluate the load-bearing capacity and fatigue life under extreme working conditions.

[0004] While existing simulation model verification technologies have established certain standardized systems in high-reliability fields such as aerospace and precision machining, the following prominent shortcomings still exist in the application of composite transmission systems: Simulation models of transmission systems involve multiple sources of error, including geometric tolerances, material property discrepancies, load spectrum fluctuations, and uncertainties in damping parameters. Existing methods often analyze a single source of uncertainty, lacking a systematic approach to integrate these multiple uncertainties into a confidence assessment framework. This makes it difficult to objectively reflect the overall reliability of the model's predictions. Furthermore, in engineering practice, qualitative factors such as the rationality of structural design, the applicability of material models, and the realism of boundary conditions have a significant impact on model confidence. However, existing methods often rely solely on quantitative error indicators for evaluation, lacking a systematic assessment mechanism that integrates expert experience with quantitative calculation results. Consequently, the evaluation conclusions fail to fully reflect the actual reliability level of the model.

[0005] Therefore, there is an urgent need for a confidence assessment method suitable for the characteristics of multibody dynamics and structural load-bearing models of transmission systems, to cover core physical domains such as mechanical dynamics and structural load-bearing, to achieve quantitative propagation of parameter uncertainties, and thus maximize the potential of transmission system simulation models to replace physical experiments while ensuring the safety and reliability of system design. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for evaluating the confidence of multibody dynamics and structural bearing models of transmission systems, so as to solve the technical problems of insufficient means of joint propagation of multi-source uncertainties and weak integration mechanism of qualitative and quantitative evaluation in the existing technology.

[0007] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for evaluating the confidence level of a multibody dynamics and structural load-bearing model of a transmission system, comprising: Based on the transmission system design data, a multibody dynamics model and a structural load-bearing model were built and simulation analysis was carried out to obtain simulation results. Extract feature parameters from simulation results to construct dimensionless similarity criteria and similarity elements; A multi-layered evaluation system was built based on the constructed dimensionless similarity criteria and similarity elements, and the confidence levels of each indicator were obtained by combining qualitative and quantitative methods. The confidence levels of each indicator are normalized, and the overall confidence levels of the multibody dynamics model and the structural bearing model are calculated by weighting and solving for the corresponding confidence intervals.

[0008] Preferably, the multibody dynamics model is imported into the finite element flexible body using the modal synthesis method, the bearing adopts the equivalent spring damping element model, and the gear pair adopts the time-varying meshing stiffness model. The structural load-bearing model constructs a finite element mesh model for key load-bearing components, refines the mesh in areas of stress concentration and performs mesh convergence verification.

[0009] Preferably, a multibody dynamics model and a structural bearing model are built and simulation analysis is carried out. Specifically, the power flow and load spectrum under actual working conditions are used as boundary inputs to the multibody dynamics model to carry out system inherent characteristic analysis and transient impact analysis; the dynamic load spectrum under actual working conditions is used as load inputs to the structural bearing model to carry out static analysis, transient dynamic analysis and thermo-mechanical coupling analysis under extreme temperatures.

[0010] Preferably, the specific process of extracting feature parameters from simulation results and constructing dimensionless similarity criteria and similarity elements includes: Extract multibody dynamic characteristic parameters and structural load-bearing characteristic parameters; calculate the characteristic proportionality coefficients of geometric dimensions, material properties, operating parameters, and damping parameters; use Buckingham's π theorem to complete dimensional consistency verification, construct a dimensionless similarity criterion, and derive the similarity element correction and conversion formula for response quantities.

[0011] Preferably, the multi-layer evaluation system built based on the constructed dimensionless similarity criteria and similarity elements is a three-layer evaluation system; the target layer is the overall confidence of the multibody dynamics and structural bearing model of the transmission system, the criterion layer includes the accuracy of the mechanical dynamics model and the credibility of the structural bearing model, and the index layer includes qualitative and quantitative evaluation indicators for each dimension.

[0012] Furthermore, in the process of obtaining the confidence levels of various indicators by combining qualitative and quantitative methods, the confidence levels of qualitative indicators are determined through multiple rounds of expert evaluation using the Delphi method; the confidence levels of quantitative indicators are obtained by quantifying uncertainty using Monte Carlo simulation or interval analysis methods.

[0013] Furthermore, the Monte Carlo simulation method constructs a random variable model using elastic modulus, load, and geometric parameters, and determines the confidence level based on the output response probability density distribution, the matching of measured values ​​with confidence intervals, and the deviation factor. The interval analysis method is suitable for scenarios where the precise probability distribution of parameters cannot be obtained, and determines the confidence level based on the matching of measured values ​​with intervals and the relative width of the intervals.

[0014] Preferably, the confidence levels of each indicator are normalized, the overall confidence levels of the multibody dynamics model and the structural bearing model are calculated by weighting, and the corresponding confidence intervals are solved by constructing a judgment matrix using the analytic hierarchy process to solve the initial weights, and a consistency ratio test is carried out. Furthermore, the indicator weights can be adjusted according to the evaluation purpose.

[0015] Furthermore, maximum and minimum confidence scores of indicators within the historical evaluation database are used as the benchmark for max-min normalization; the normalized indicator confidence scores are weighted and summed to obtain the overall confidence score, and the confidence interval of the overall confidence score is calculated using the weighted error propagation formula.

[0016] Secondly, the present invention also provides a confidence assessment system for a multibody dynamics and structural load-bearing model of a transmission system, used to implement the aforementioned confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system, comprising: The modeling and simulation module is used to build a multibody dynamics model and a structural load-bearing model based on the transmission system design data, and to carry out simulation analysis to obtain simulation results. The similarity construction module is used to extract feature parameters from simulation results and construct dimensionless similarity criteria and similarity elements. The indicator evaluation module is used to build a multi-layer evaluation system based on the constructed dimensionless similarity criteria and similarity elements, and to obtain the confidence level of each indicator by combining qualitative and quantitative methods. The confidence calculation module is used to normalize the confidence levels of various indicators, calculate the overall confidence level of the multibody dynamics model and the structural bearing model by weighting, and solve for the corresponding confidence intervals.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a confidence assessment method for multibody dynamics and structural load-bearing models of transmission systems. By combining Monte Carlo simulation and interval analysis, it integrates multi-source uncertainties into the confidence assessment framework, objectively quantifying the overall credibility of model prediction results. This effectively overcomes the limitations of existing methods that only analyze a single source of uncertainty, achieving joint quantitative propagation of multi-source uncertainties. Simultaneously, it utilizes the analytic hierarchy process (AHP) framework to systematically integrate qualitative experience and quantitative calculation, compensating for the shortcomings of existing assessment methods that rely solely on quantitative error indicators and ignore expert experience. Furthermore, it normalizes the confidence levels of various indicators and uses a weighted error propagation formula to obtain the confidence interval corresponding to the overall confidence level, giving the assessment results a quantitative uncertainty representation capability. Moreover, it can continuously revise the assessment reference benchmark based on accumulated data, promoting dynamic iterative optimization of the entire assessment system. This effectively solves the problems of insufficient joint propagation methods for multi-source uncertainties and weak integration mechanisms of qualitative and quantitative assessments in existing technologies. Attached Figure Description

[0018] Figure 1 This is a flowchart of the confidence assessment method for the multibody dynamics and structural load-bearing model of the transmission system of the present invention; Figure 2 This is a flowchart of the confidence assessment method for the multibody dynamics and structural load-bearing model of the transmission system in an embodiment of the present invention; Figure 3 This is a schematic diagram of the confidence evaluation system for the multibody dynamics and structural load-bearing model of the transmission system in an embodiment of the present invention. In the diagram: 1. Modeling and simulation module; 2. Similarity construction module; 3. Index evaluation module; 4. Confidence calculation module. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] The purpose of this invention is to provide a method and system for evaluating the confidence level of multibody dynamics and structural load-bearing models of transmission systems, so as to solve the technical problems of insufficient means of joint propagation of multi-source uncertainties and weak integration mechanism of qualitative and quantitative evaluation in the prior art.

[0021] The present invention will now be described in further detail with reference to the accompanying drawings: See Figure 1 In one embodiment of the present invention, a method for evaluating the confidence level of a multibody dynamics and structural load-bearing model of a transmission system is provided, comprising: Step 1: Based on the transmission system design data, build a multibody dynamics model and a structural load-bearing model and conduct simulation analysis to obtain simulation results; Specifically, based on the design data of the transmission system to be evaluated, a multibody dynamics model and a structural load-bearing model are established. The multibody dynamics model is a transmission chain model containing a rigid-flexible coupled shaft system built in multibody dynamics software. The finite element flexible body is imported using modal synthesis. Bearings are modeled using equivalent spring-damped elements, and gear pairs use a time-varying meshing stiffness model. Power flow and load spectrum parameters from actual operating conditions are used as the input boundaries of the multibody dynamics model to solve for the system's dynamic response. The structural load-bearing model focuses on key load-bearing components (output shaft, planetary carrier, housing, etc.). A high-fidelity mesh model is built in the finite element software. Mesh refinement is implemented in stress concentration zones, and mesh convergence is verified. The dynamic load spectrum from actual operating conditions is used as the load input for the structural model, and static analysis, transient dynamic analysis, and thermo-mechanical coupling analysis in extreme temperature scenarios are performed.

[0022] Step 2: Extract the feature parameters from the simulation results and construct the dimensionless similarity criterion and similarity element; Specifically, multibody dynamics characteristic parameters, such as the natural frequency of the transmission system, the amplitude of shaft vibration displacement, the shaft end torsional angle, and the dynamic coefficient of gear meshing force, are extracted from the simulation results. Structural load-bearing characteristic parameters, such as the peak stress at critical sections and the deformation of key components, are also extracted. Then, the characteristic scaling factors between the model and the actual component in terms of geometric dimensions, material properties, and operating parameters are calculated. Buckingham's π theorem is used to verify the dimensionality consistency of each scaling factor, and a dimensionless similarity criterion is constructed based on this, establishing similarity elements.

[0023] Step 3: Based on the constructed dimensionless similarity criteria and similarity elements, a multi-layer evaluation system is built, and the confidence levels of each indicator are obtained by combining qualitative and quantitative methods. Specifically, a three-tiered evaluation system is established using the Analytic Hierarchy Process (AHP). The target tier is the overall confidence level of the multibody dynamics and structural load-bearing models of the transmission system. The criterion tier sets the accuracy of the mechanical dynamics model and the reliability of the structural load-bearing model. The indicator tier contains specific qualitative and quantitative evaluation indicators for each dimension. The confidence level of qualitative indicators is determined through multiple rounds of Delphi method expert evaluation. Quantitative indicators are quantified using Monte Carlo simulation or interval analysis methods to obtain the probability distribution or interval range of the model output parameters. These are then compared and judged using techniques such as similarity analysis or confidence interval discrimination to ultimately provide the confidence level of the indicators.

[0024] Step 4: Normalize the confidence levels of each indicator, calculate the overall confidence levels of the multibody dynamics model and the structural bearing model using weighted averages, and solve for the corresponding confidence intervals.

[0025] Specifically, using the maximum and minimum confidence scores of each indicator in the historical evaluation database as a reference, the confidence scores of each domain indicator are normalized to eliminate the interference of inconsistent value ranges of each indicator on the effectiveness of the weights. The normalized confidence scores of each indicator are then weighted and summed to calculate the overall confidence score of the model; the confidence interval of the overall confidence score is obtained through the weighted error propagation formula.

[0026] Example 1 This embodiment is based on Figure 2 As shown, a method for evaluating the confidence level of a multibody dynamics and structural load-bearing model of a transmission system is provided, including the following steps: Specific implementation of step S1: 1.1 Establishment of Multibody Dynamics Model A whole-machine dynamic model including flexible parts was established in multibody dynamics software. The Craig-Bampton modal synthesis method was used to extract the low-order modes of the flexible parts, which were then imported into the multibody dynamics environment to achieve rigid-flexible coupling simulation. Support components (such as bearings) adopted an equivalent spring-damped model, with reasonable values ​​for elastic stiffness and damping coefficients. Modal analysis results should verify the resonance margin between the natural frequency and the operating excitation frequency. Transient impact analysis should focus on the difference between the peak dynamic stress and static results under typical operating conditions (such as start-up acceleration) to confirm whether fatigue assessment based on dynamic stress is necessary.

[0027] 1.2 Establishment of Structural Load-Bearing Model A finite element model of the part is established in finite element analysis software based on the manufacturing drawings. The mesh size of key areas is systematically refined and scanned, with the change in the main evaluation response (such as peak stress) as the mesh size decreases serving as the convergence criterion. When the rate of change of response between adjacent refinement levels is lower than a preset threshold, the current mesh size is considered to have converged. The elastic coefficient, density, and strength parameters are determined according to the relevant material standard handbook. Boundary conditions are then applied, and the constraint method at the support location should, as far as possible, replicate the support form in the actual structure. Loads should be applied according to the actual working conditions. Under rated combined load conditions, static analysis should focus on the maximum equivalent stress level of the critical section and its ratio to the yield strength. Fatigue life assessment is based on the standard SN curve of the part material, combined with the local stress concentration factor, converting the nominal stress amplitude into the effective stress amplitude to obtain the predicted fatigue life, which is then compared with the design requirements.

[0028] Specific implementation of step S2: Precision measuring equipment (coordinate measuring machine, laser displacement sensor, etc.) and dynamic measurement methods (accelerometer, angle encoder, strain gauge, etc.) are used to systematically measure the parts, constructing a comparison dataset of simulation and actual measurements. Geometric characteristic parameters such as axial position and eccentricity of the critical section are extracted, compared with the measurement results of the precision measuring equipment, and the relative deviation is calculated. Dynamic characteristic parameters such as vibration displacement amplitude and torsional angle are extracted and compared with the sensor measurement results. When the relative deviation of a parameter exceeds a set threshold, its main error sources should be analyzed, such as geometry, materials, boundary conditions, or damping.

[0029] For four dimensions—geometric dimensions, material properties, operating parameters, and damping parameters—the ratios of the digital model values ​​to the corresponding values ​​of the actual component obtained through experimental methods are calculated to obtain the characteristic proportionality coefficients for each dimension. Taking each response quantity as the analysis object, the variables involved and their dimensions are listed, and the number of independent dimensionless parameter sets is determined according to Buckingham's π theorem, constructing the dimensionless similarity criterion number. Under the similarity element framework, the model and the actual object are required to be consistent in each π number. The similarity element correction conversion formula for each response quantity is derived, and the simulation prediction values ​​are corrected to more accurate prediction values.

[0030] Specific implementation of step S3: 3.1 Expert Evaluation of Qualitative Indicators An evaluation system based on the Analytic Hierarchy Process (AHP) was established, and experts were organized to evaluate the qualitative indicators (structural design rationality, material model applicability, boundary condition authenticity, etc.) under each dimension of the criteria layer. Several experts with relevant engineering experience were invited to independently score the indicators according to a unified scoring criterion (a 0-1 point scale is recommended).

[0031] The Delphi method is used for multiple iterations: after each round, the mean and dispersion of the overall scores are calculated, and the statistical information is fed back to the experts, who then decide whether to adjust their scores based on the statistical information. The iteration ends when the dispersion (e.g., coefficient of variation) of the expert scores converges to below a preset threshold, and the final round mean is used as the confidence level of the qualitative indicator.

[0032] 3.2 Quantification of Uncertainty in Quantitative Indicators A random variable model is established using key input parameters such as elastic modulus, load, and geometric dimensions (the parameter distribution type is determined based on experimental data). The probability density distribution of the output response is obtained through Monte Carlo simulation, and its mean, standard deviation, and 95% confidence interval are calculated. The confidence level of the index is determined primarily by whether the measured value falls within the confidence interval and the multiple of the deviation between the measured value and the predicted mean.

[0033] For parameters whose precise probability distribution is difficult to give (such as damping ratio), the interval analysis method is adopted. Based on the physical mechanism and experimental experience, a reasonable range of parameters is determined. The response output range is calculated through the interval model. The confidence level is determined by whether the measured value falls within the output range and the relative width of the range.

[0034] Specific implementation of step S4: The Analytic Hierarchy Process (AHP) was used to construct judgment matrices at both the criterion and indicator levels. Eigenvectors were calculated as initial weights, and the logical consistency of the judgment matrices was confirmed through a consistency ratio test. Based on this, the weights of each relevant indicator were appropriately adjusted according to the evaluation purpose (e.g., whether the digital prototype is mainly used for dynamic response analysis or static load prediction) to reflect the differences in the accuracy requirements of each dimension for the application scenario.

[0035] Using historical extreme values ​​of the confidence levels of each indicator in a database of similar historical evaluation cases as a reference, the current confidence levels of each indicator are normalized by maximum and minimum values ​​to make each indicator comparable within the range of [0,1]. When a historical database has not yet been established, theoretical extreme values ​​can be used as a substitute, and gradually corrected as cases are accumulated.

[0036] The overall confidence level is calculated by summing the products of the normalized confidence levels of each indicator and their corresponding weights. Simultaneously, the uncertainty at the indicator level is propagated to the overall confidence level using a weighted error propagation formula, resulting in the confidence interval for the overall confidence level.

[0037] Example 2 This embodiment also provides a confidence evaluation system for a multibody dynamics and structural load-bearing model of a transmission system, used to implement the aforementioned confidence evaluation method for a multibody dynamics and structural load-bearing model of a transmission system, including: Modeling and simulation module 1 is used to build a multibody dynamics model and a structural load-bearing model based on the transmission system design data, and to carry out simulation analysis to obtain simulation results. Similarity construction module 2 is used to extract feature parameters from simulation results and construct dimensionless similarity criteria and similarity elements; The indicator evaluation module 3 is used to build a multi-level evaluation system based on the constructed dimensionless similarity criteria and similarity elements, and to obtain the confidence level of each indicator by combining qualitative and quantitative methods. Confidence calculation module 4 is used to normalize the confidence levels of various indicators, calculate the overall confidence level of the multibody dynamics model and the structural bearing model by weighting, and solve for the corresponding confidence intervals.

[0038] In summary, this invention provides a method and system for evaluating the confidence level of a multibody dynamics and structural load-bearing model of a transmission system. First, a multibody dynamics model containing a rigid-flexible coupled shaft system and a structural load-bearing finite element model of key load-bearing components are established and simulated. Then, multibody dynamics characteristic parameters and structural load-bearing characteristic parameters are extracted from the simulation results, and the characteristic proportionality coefficients of each dimension are calculated. A dimensionless similarity criterion is constructed using Buckingham's π theorem, and similarity elements are established. Based on this, a three-layer evaluation index system is constructed using the analytic hierarchy process (AHP). The confidence level of qualitative indicators is determined through multiple rounds of expert evaluation using the Delphi method, while the confidence level of quantitative indicators is quantified using Monte Carlo simulation or interval analysis methods, and a confidence level is given. Finally, the confidence levels of each indicator are weighted and summed after max-min normalization to calculate the overall confidence level of the model, and the confidence interval of the overall confidence level is obtained through a weighted error propagation formula. This invention addresses multiple error sources in transmission system simulation models by combining Monte Carlo simulation with interval analysis. It integrates these multi-source uncertainties into a unified confidence assessment framework, objectively quantifying the overall reliability of model predictions. This overcomes the limitations of existing methods that analyze only a single source of uncertainty, achieving joint quantitative propagation of multi-source uncertainties. Within the framework of the Analytic Hierarchy Process (AHP), it systematically integrates qualitative experience with quantitative calculations, compensating for the shortcomings of existing methods that rely solely on quantitative error indicators while neglecting expert knowledge. Furthermore, it normalizes the confidence levels of each indicator and provides the overall confidence interval through a weighted error propagation formula. The assessment conclusion not only possesses a quantitative characterization of uncertainty but also allows for continuous correction of the reference benchmark as data accumulates, achieving dynamic iterative optimization of the assessment system.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the confidence level of a multibody dynamics and structural load-bearing model of a transmission system, characterized in that, include: Based on the transmission system design data, a multibody dynamics model and a structural load-bearing model were built and simulation analysis was carried out to obtain simulation results. Extract feature parameters from simulation results to construct dimensionless similarity criteria and similarity elements; A multi-layered evaluation system was built based on the constructed dimensionless similarity criteria and similarity elements, and the confidence levels of each indicator were obtained by combining qualitative and quantitative methods. The confidence levels of each indicator are normalized, and the overall confidence levels of the multibody dynamics model and the structural bearing model are calculated by weighting and solving for the corresponding confidence intervals.

2. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 1, characterized in that, The multibody dynamics model is introduced into the finite element flexible body using the modal synthesis method. The bearing adopts the equivalent spring damping element model, and the gear pair adopts the time-varying meshing stiffness model. The structural load-bearing model constructs a finite element mesh model for key load-bearing components, refines the mesh in areas of stress concentration and performs mesh convergence verification.

3. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 1, characterized in that, A multibody dynamics model and a structural bearing model are constructed and simulation analysis is carried out. Specifically, the power flow and load spectrum under actual working conditions are used as boundary inputs to the multibody dynamics model to carry out system inherent characteristic analysis and transient impact analysis; the dynamic load spectrum under actual working conditions is used as load inputs to the structural bearing model to carry out static analysis, transient dynamic analysis and thermo-mechanical coupling analysis under extreme temperatures.

4. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 1, characterized in that, The specific process of extracting feature parameters from simulation results and constructing dimensionless similarity criteria and similarity elements includes: Extract multibody dynamic characteristic parameters and structural load-bearing characteristic parameters; calculate the characteristic proportionality coefficients of geometric dimensions, material properties, operating parameters, and damping parameters; use Buckingham's π theorem to complete dimensional consistency verification, construct a dimensionless similarity criterion, and derive the similarity element correction and conversion formula for response quantities.

5. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 1, characterized in that, The multi-layer evaluation system built based on the constructed dimensionless similarity criteria and similarity elements is a three-layer evaluation system; the target layer is the overall confidence of the multibody dynamics and structural bearing capacity model of the transmission system; the criterion layer includes the accuracy of the mechanical dynamics model and the credibility of the structural bearing capacity model; and the index layer includes qualitative and quantitative evaluation indicators for each dimension.

6. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 5, characterized in that, The method of combining qualitative and quantitative approaches to obtain the confidence levels of various indicators involves determining the confidence levels of qualitative indicators through multiple rounds of Delphi method expert evaluation; and quantifying the uncertainty of quantitative indicators using Monte Carlo simulation or interval analysis methods to obtain the corresponding indicator confidence levels.

7. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 6, characterized in that, The Monte Carlo simulation method constructs a random variable model based on elastic modulus, load, and geometric parameters, and determines the confidence level based on the output response probability density distribution, the matching of measured values ​​with confidence intervals, and the deviation factor. The interval analysis method is suitable for scenarios where the precise probability distribution of parameters cannot be obtained, and determines the confidence level based on the matching of measured values ​​with intervals and the relative width of the intervals.

8. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 1, characterized in that, The confidence levels of each indicator are normalized, and the overall confidence levels of the multibody dynamics model and structural bearing model are calculated by weighting and solving the corresponding confidence intervals. The analytic hierarchy process is used to construct a judgment matrix to solve the initial weights, and a consistency ratio test is carried out. The indicator weights can be adjusted according to the evaluation purpose.

9. The confidence assessment method for a multibody dynamics and structural load-bearing model of a transmission system according to claim 8, characterized in that, Max-min normalization is performed based on the maximum and minimum confidence scores of indicators in the historical evaluation database. The weighted summation of the normalized indicator confidence scores is used to obtain the overall confidence score, and the confidence interval of the overall confidence score is calculated using the weighted error propagation formula.

10. A confidence evaluation system for multibody dynamics and structural load-bearing models of a transmission system, characterized in that, A method for evaluating the confidence level of a multibody dynamics and structural load-bearing model of a transmission system as described in any one of claims 1-9 includes: The modeling and simulation module is used to build a multibody dynamics model and a structural load-bearing model based on the transmission system design data, and to carry out simulation analysis to obtain simulation results. The similarity construction module is used to extract feature parameters from simulation results and construct dimensionless similarity criteria and similarity elements. The indicator evaluation module is used to build a multi-layer evaluation system based on the constructed dimensionless similarity criteria and similarity elements, and to obtain the confidence level of each indicator by combining qualitative and quantitative methods. The confidence calculation module is used to normalize the confidence levels of various indicators, calculate the overall confidence level of the multibody dynamics model and the structural bearing model by weighting, and solve for the corresponding confidence intervals.

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