An iterative coupling method and system of equivalent environmental parameters and structural parameters

Abstract

The embodiment of the present application provides an equivalent environmental parameter and structural parameter iterative coupling method and system, the method comprises the following steps: S2, obtaining monitoring data of a target structure, and establishing an overall model and a structure model of the target structure; S4, determining a probability model of environmental data, simulating the overall model, and calculating initial equivalent environmental load parameters; S6, updating parameters of a second numerical model; S8, updating a first numerical model; S10, judging whether the first numerical model meets a convergence condition, if yes, entering S12, and if not, using the first numerical model for iteration, calculating equivalent environmental load parameters and judging convergence, and the convergence condition comprises the following steps: based on the first numerical model, calculating equivalent environmental load parameters, and judging whether convergence is achieved by comparing previous results; S12, determining final equivalent environmental load parameters after convergence, and performing safety evaluation on the target structure.

Description

Technical Field

[0001] This invention relates to the field of marine engineering structure safety assessment technology, and in particular to an iterative coupling method and system for equivalent environmental parameters and structural parameters. Background Technology

[0002] Large marine engineering structures such as offshore wind turbines are subjected to complex and random dynamic loads such as wind, waves, and currents throughout their decades-long design life. Extreme weather events such as typhoons pose a severe challenge to their structural safety. Therefore, establishing high-fidelity numerical simulation models to accurately predict the dynamic response and damage accumulation of structures under various working conditions is a core technical means for structural design optimization, safety assessment, and operation and maintenance decision-making.

[0003] The reliability of numerical simulation analysis systems fundamentally depends on the accuracy of two cornerstones: the accurate representation of external environmental load inputs and the accurate modeling of the physical properties of the structural system itself. In current technological practice, the construction of these two cornerstones usually unfolds along two parallel but independent paths, each facing insurmountable bottlenecks, thus generating a systemic uncertainty transmission.

[0004] Based on the aforementioned problems, in existing technologies, the characterization of environmental load inputs typically relies on historical meteorological data or numerical weather prediction to establish joint probability distribution models describing multiple environmental factors such as wind speed and wave height. These models can statistically depict the long-term recurrence characteristics of extreme events. However, a key challenge lies in how to transform this probabilistic, high-dimensional environmental description into deterministic, engineering-grade load inputs that can be directly applied to the structural numerical model. A common approach is to rapidly calculate the structural response under numerous random environmental combinations using a simplified overall dynamic model (such as a beam-based model), and then derive a set of equivalent design load parameters through the principle of response consistency. The inherent limitation of this method is that the parameters of the simplified overall model it relies on (e.g., foundation stiffness representing pile-soil interaction, modal damping ratio of the structure, etc.) are based on initial design assumptions or empirical formulas. Because the actual pile-soil interaction mechanism is extremely complex and may change over time (e.g., foundation stiffness degradation due to scouring), there are inevitably significant differences between the a priori assumed model parameters and the actual physical state of the structure. Therefore, the accuracy of the calculation results based on a model with significant uncertainties in its physical properties will inevitably be limited.

[0005] On the other hand, in terms of structural systems, existing model update techniques aim to adjust the uncertain parameters in the model through optimization algorithms, so that the model's predicted outputs (such as natural frequencies and mode shapes) closely match the measured data. However, this process also faces its own challenges. When performing modal parameter identification and model updates, external environmental excitations are usually assumed to be stationary white noise, or only their statistical characteristics are considered. However, for extreme events such as typhoons, environmental loads have strong non-stationarity and non-Gaussian characteristics, and their complex time-frequency domain characteristics directly affect the dynamic response of the structure, thus interfering with the accuracy of modal parameter identification. More importantly, when inverting model parameters, due to the lack of precise understanding of the time history of specific external loads, it is difficult to effectively decouple the load effects from the effects caused by changes in the structure's own characteristics. This limits the application accuracy of model update methods under real and complex working conditions.

[0006] In summary, existing technological approaches present a disconnect between "load identification" and "model updating." The former relies on an uncalibrated structural model, while the latter lacks an understanding of accurate loads. This creates a logical "circular dependency": using an uncalibrated structural model to estimate environmental loads, and then using this potentially inaccurate set of loads to calibrate the structural model. This unidirectional, decoupled analytical method allows errors to be solidified and propagated between the two stages, ultimately limiting the fidelity and reliability of the entire structural safety assessment system.

[0007] Therefore, there is a technical need in this field to develop a new method that can break through this technical barrier and achieve bidirectional coupling and collaborative optimization of environmental load identification and structural model updating. Summary of the Invention

[0008] To address the problems existing in the prior art, embodiments of the present invention provide an iterative coupling method and system for equivalent environmental parameters and structural parameters.

[0009] This invention provides an iterative coupling method for equivalent environmental parameters and structural parameters, the method comprising:

[0010] S2. Acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole machine model and the second numerical model is a structural model.

[0011] S4. Based on the statistical results of the environmental data, construct a probability model, simulate the first numerical model, train the surrogate model, and combine the probability model and the surrogate model to calculate the initial equivalent environmental load parameters.

[0012] S6. Based on the structural response data, update the parameters of the second numerical model and output the updated parameter set;

[0013] S8. Feed the parameter set back to the first numerical model for updating, and obtain the updated first numerical model;

[0014] S10. Determine whether the updated first numerical model meets the convergence condition. If yes, proceed to S12. If no, use the updated first numerical model from S8 for the next iteration to recalculate the equivalent environmental load parameters and perform convergence judgment again to continue iterating until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters of the previous iteration. Determine whether convergence is achieved based on the comparison result.

[0015] S12. Based on the first and second numerical models updated after convergence, determine the corresponding final equivalent environmental load parameters, and perform a safety assessment on the target structure based on the final equivalent environmental load parameters.

[0016] In one embodiment, the method further includes:

[0017] Determine the boundary conditions of the second numerical model;

[0018] The steps for determining the boundary conditions include:

[0019] Obtain the acceleration time history signals of multiple measurement points corresponding to the structural response data;

[0020] The acceleration time history signal is processed to reconstruct the first-order displacement time history signal at each measuring point;

[0021] A polynomial curve is obtained by fitting the first-order displacement values ​​and spatial coordinates of all measuring points at any given time using a polynomial. The polynomial curve represents the first-order deformation shape of the structure at that time.

[0022] Extend the polynomial curve toward the structural foundation and solve for the intersection point of the curve with the central axis of the structure. The intersection point is the equivalent constraint position of the second numerical model.

[0023] In one embodiment, the first-order displacement time history signal is obtained by performing joint complex exponential decomposition on the acceleration time history signal, extracting the first-order modal response component, and integrating the response component.

[0024] In one embodiment, the method further includes:

[0025] Update the physical parameters of the second numerical model;

[0026] The updating of the physical parameters includes:

[0027] Identify modal parameters from the structural response data;

[0028] Based on the Bayesian inference method, the uncertain physical parameters in the second numerical model are probabilistically updated according to the modal parameters.

[0029] In one embodiment, the method further includes:

[0030] The first numerical model is an integrated simulation model of the whole machine based on multibody dynamics and beam element theory;

[0031] The second numerical model is a high-fidelity finite element model based on continuum mechanics, using shell elements or solid elements.

[0032] In one embodiment, the surrogate model is constructed using Gaussian process regression, multinomial chaotic expansion, or artificial neural networks.

[0033] In one embodiment, the structural response data includes at least one of acceleration, dynamic strain, and tilt angle.

[0034] This invention provides an iterative coupling system of equivalent environmental parameters and structural parameters, the system comprising:

[0035] The monitoring module is used to acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole machine model and the second numerical model is a structural model.

[0036] The initial module is used to construct a probabilistic model based on the statistical results of the environmental data, simulate the first numerical model, train the surrogate model, and calculate the initial equivalent environmental load parameters by combining the probabilistic model and the surrogate model.

[0037] The second numerical model update module is used to update the parameters of the second numerical model based on the structural response data and output the updated parameter set.

[0038] The first numerical model update module is used to feed the parameter set back to the first numerical model for updating, so as to obtain the updated first numerical model.

[0039] The convergence judgment module is used to determine whether the updated first numerical model meets the convergence condition. If yes, it enters the evaluation module; if no, it instructs the initial module to recalculate the load parameters based on the updated model of the first numerical model update module and perform the next convergence judgment until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters of the previous iteration, and judging whether convergence is achieved based on the comparison result.

[0040] The evaluation module is used to determine the corresponding final equivalent environmental load parameters based on the first and second numerical models updated after convergence, and to perform a safety assessment on the target structure based on the final equivalent environmental load parameters.

[0041] This invention provides an electronic device, including a processor and a memory;

[0042] The processor is connected to the memory;

[0043] The memory is used to store executable program code;

[0044] The processor runs a program corresponding to the executable program code stored in the memory to perform the methods described in one or more embodiments.

[0045] This invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described iterative coupling method of equivalent environmental parameters and structural parameters.

[0046] In view of the above, in one or more embodiments of this specification, S2: acquire monitoring data of the target structure, and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole-machine model, and the second numerical model is a structural model. S4: based on the statistical results of the environmental data, construct a probabilistic model, simulate the first numerical model, train a surrogate model, and combine the probabilistic model and the surrogate model to calculate the initial equivalent environmental load parameters. S6: based on the structural response data, update the parameters of the second numerical model and output the updated parameter set. S8: feed the parameter set back to the first numerical model for updating, and obtain the updated first numerical model. The numerical model is described in S10. The first numerical model is then evaluated to determine if it meets the convergence criteria. If yes, proceed to S12; otherwise, use the updated first numerical model from S8 for the next iteration to recalculate the equivalent environmental load parameters and perform convergence again. The iteration continues until convergence. The convergence criteria include: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters from the previous iteration; determining convergence based on the comparison results. The second numerical model is then evaluated to determine the corresponding final equivalent environmental load parameters based on the updated first and second numerical models after convergence. A safety assessment of the target structure is then performed based on these final equivalent environmental load parameters. This closed-loop feedback mechanism overcomes the limitation of independent load identification and model updating in the background technology, systematically eliminating the cyclical error of inaccurate loads caused by inaccurate models or inaccurate model updates caused by inaccurate loads. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of an iterative coupling method for equivalent environmental parameters and structural parameters provided in one embodiment of this specification.

[0049] Figure 2 This is a flowchart of another equivalent method for iterative coupling of environmental parameters and structural parameters provided in one embodiment of this specification.

[0050] Figure 3 This is a schematic diagram of an iterative coupling system of equivalent environmental parameters and structural parameters provided in one embodiment of this specification.

[0051] Figure 4This is a schematic diagram of the structure of an electronic device provided in one embodiment of this specification. Detailed Implementation

[0052] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0053] As used herein, the term "comprising" and its variations are open terms meaning "including but not limited to". The term "based on" means "at least partially based on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. Unless explicitly indicated by the context, the definition of a term shall remain consistent throughout the specification.

[0054] like Figure 1 As shown, this embodiment of the invention provides an iterative coupling method for equivalent environmental parameters and structural parameters, including:

[0055] Step S2: Obtain monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole machine model and the second numerical model is a structural model.

[0056] Specifically, this involves acquiring on-site monitoring data, which can be categorized into two main types. The first type is environmental data, such as time-series data continuously recorded by meteorological and oceanographic sensors deployed and installed on the structure itself or in the surrounding marine environment. Specific parameters include time-history data such as wind speed V(t), wind direction Dir(t), significant wave height Hs(t), and wave period Tp(t). The primary acquisition methods include ultrasonic anemometers installed in the nacelle or tower top (wind-related parameters that describe the main time-varying characteristics of wind loads) and wave radar or buoy measurements (wave-related parameters that describe the key statistical characteristics of wave loads). Direct quantification of external environmental data serves as the data basis for reconstructing and parameterizing the environmental load model in subsequent steps. The second type is (structural) response data, which involves monitoring the dynamic behavior of the structure under environmental excitation through sensors deployed at key structural locations. Specific parameters include acceleration response ã_i(t), tilt signal θ(t) (used to monitor the overall stiffness of the structure and foundation settlement), and dynamic strain signal ε(t) (used by users to assess local damage accumulation and fatigue state). The main way to obtain this information is by measuring the acceleration response using accelerometers placed at different heights of the tower (such as the top, middle, and bottom).

[0057] Furthermore, two initial numerical models are established. The first numerical model (M0) is built using relevant model-building software, creating a coupled dynamic model encompassing all components, including the tower, foundation, nacelle, and rotor. This model typically employs beam elements and multibody dynamics principles to simplify the structure, resulting in high computational efficiency. It also integrates aerodynamic and hydrodynamic load modules. The initial parameters of this model are based on empirical and theoretical assumptions, leading to errors compared to actual values. The second numerical model (F0) uses general-purpose finite element software to create a detailed model of the structure. Shell or solid elements are used for more refined modeling of the tower, transition sections, etc., to more realistically capture bending, shear, and local effects at the cross-sections, but this approach is computationally more expensive. Compared to M0 (relatively simpler but faster, overall), F0 (relatively refined but slower, structural) can more accurately calculate the inherent properties of the structure. Although the initial data is also based on design parameters and theoretical assumptions, it offers higher precision. However, it is not suitable for inversion calculations involving large amounts of load.

[0058] Step S4: Based on the statistical results of the environmental data, construct a probability model, simulate the first numerical model, train the surrogate model, and combine the probability model and the surrogate model to calculate the initial equivalent environmental load parameters.

[0059] Specifically, long-term environmental data corresponding to the environmental data in the above steps are obtained, and statistical analysis is performed on the long-term environmental data (including wind speed V(t), wind direction Dir(t), significant wave height Hs(t), and wave period Tp(t)). Through data statistical methods, a joint probability distribution model p(V, Hs, ...) among environmental parameters is constructed. Thus, through joint statistics, the probability space between different environmental data is determined, ensuring the true environmental statistical characteristics.

[0060] Furthermore, model M0 was used to perform multiple simulations using methods such as Latin hypercube to obtain sample pairs of different environmental inputs [V, Hs, ...] and key structural responses (such as the tower base bending moment R). Based on these sample pairs, an efficient surrogate model R=f_proxy(V, Hs, ...) was trained.

[0061] The specific surrogate model can be constructed using Gaussian process regression, multinomial chaotic expansion, or artificial neural networks. The training steps involve generating multiple environmental combination points covering the multidimensional parameter space using methods such as Latin hypercube sampling within the possible range of environmental parameters during sampling. Each sampled point is then used as input to drive the initial model M0 to perform a complete dynamic time history analysis and calculate one or more structural response indices (such as the tower base bending moment R). Then, the input and output (environmental parameters and structural response) sample pairs are collected as a training dataset, and the surrogate model R = f_proxy(V, Hs, ...) is trained using machine learning regression. Compared to directly calling the first numerical model M0 or the second numerical model F0 for extensive computation, the evaluation efficiency of the surrogate model is significantly improved. Taking the tower base bending moment R as an example, the surrogate model f_proxy can quickly predict the response R based on any input [V, Hs, ...], and its accuracy can replace the original M0 in subsequent massive integral calculations, thus improving computational efficiency.

[0062] Furthermore, using the joint probability distribution model p(V, Hs, ...) and the surrogate model f_proxy established in the above steps, the long-term expected value E[R] of the key structural response index (e.g., R) is calculated. E[R] = ∫R*pdVdHs... Then, an inverse solution is performed. E[R] represents the statistical average response of the structure under long-term environmental influences. A set of definite, equivalent environmental parameters E_eq_1 needs to be found such that when E_eq_1 is input into the surrogate model, the calculated response equals the long-term expected value E[R]. That is, f_proxy(E_eq_1) = E[R] is solved to obtain E_eq_1. E_eq_1 is the equivalent environmental input calculated to achieve the target response; it is a specific set of parameters that can be used to drive a deterministic dynamic analysis. It represents the load conditions that are "equivalent" to the actual random environment in a long-term statistical sense.

[0063] Step S6: Based on the structural response data, update the parameters of the second numerical model and output the updated parameter set.

[0064] Specifically, in this step, the parameters of the second numerical model are updated using structural response data, and the updated parameter set is output. The detailed update steps can include two levels, focusing on solving the basic boundary problems and the calibration of the overall physical parameters in marine structural modeling.

[0065] First, there is the fundamental boundary problem:

[0066] A joint complex exponential decomposition is performed on the acceleration signal ã_i(t) at measurement point i of all structural response data. Using a multi-input multi-output modal parameter identification method, first-order modal response components can be simultaneously identified from a dataset, such as the shared system pole λ (containing frequency and damping information) and the residue γ_i corresponding to each measurement point (related to mode amplitude). The joint analysis ensures that the modal frequencies and damping identified at all measurement points are consistent, achieving higher accuracy than single-point analysis. Furthermore, after identifying the pole λ_c of the first-order bending mode, the portion a_ci(t) purely contributed by the first-order mode in the acceleration signal of each measurement point can be reconstructed. This is equivalent to performing a modal filtering operation, filtering out interference from higher-order modes and noise, and obtaining the most important overall structural deformation information.

[0067] Furthermore, by integrating twice or directly using the relation x_ci(t) = a_ci(t) / (λ_c^2) in the frequency domain, the first-order acceleration time history a_ci(t) can be transformed into the first-order displacement time history x_ci(t). Given the precise frequency λ_c, this type of transformation is more accurate than direct integration and avoids integration drift. At any given time t_k, the displacement values ​​x_ci(t_k) at a series of discrete points z_i (sensor mounting height) on the structure can be determined. These points (z_i, x_ci(t_k)) are fitted into a smooth polynomial curve y(z). This curve represents the pure first-order deformation shape of the structure at that time.

[0068] Solve the polynomial equation y(z)=0. Extend the polynomial curve towards the foundation and find the intersection point with the central axis of the structure. Determine the position of this intersection point as the equivalent constraint position of the second numerical model, and apply fixed or elastic constraints at this position. This determines the point where the displacement is zero under the current deformation state of the structure, i.e., the theoretical "fixed point". This point is the equivalent consolidation position z_eff(t_k) at that moment. Here, z_eff is the equivalent embedment depth of the pile-soil interaction. In the above treatment of the foundation boundary problem, this embodiment bypasses the complex soil mechanics model and directly infers the system boundary from the system output (deformation). If the foundation is eroded or the soil softens, the effective embedment depth of the structure will become shallower, and the z_eff point will move upward. By averaging over multiple moments or analyzing its trend, a stable and reliable equivalent constraint position can be obtained.

[0069] Secondly, there is the issue of calibrating the overall physical parameters:

[0070] The calculated z_eff is applied to the second numerical model F0. Specifically, this involves cutting the structure at a height of z = z_eff and applying fixed constraints or a set of equivalent spring constraints to this section (stiffness can be further adjusted through subsequent optimization). This yields a model F0' with updated boundary conditions. The z_eff identified in the data is then incorporated into model F0, correcting the model's biggest source of erroneous assumptions at its origin. Through this boundary condition update, the accuracy of model F0' in simulating the overall bending behavior of the structure is significantly improved compared to the initial model F0.

[0071] Then, Operational Modal Analysis (OMA) technology can be used to process the measured acceleration signal ã_i(t). The OMA method performs well in handling broadband random excitations (such as conventional wind and waves), and its theoretical basis usually assumes that the input is white noise. However, the colored noise characteristics of typhoon loads pose a challenge to the identification accuracy, which is why this embodiment makes improvements. To achieve this improvement, this embodiment no longer treats the load spectrum as known white noise when using the Bayesian inference method. Instead, the power spectral density (PSD) of the load is modeled as a parameterized form, such as the exponential form S_F(f)=C*(f / f_m)^(-b), where the load spectrum exponent b is the parameter to be determined. Subsequently, this parameterized load spectrum model is embedded into the likelihood function of Bayesian inference. In the iterative framework, the prior information of this parameterized load spectrum model (such as the initial value and range of the exponent b) will be provided by the load spectrum L_spec identified in the previous step (load characteristic identification). By solving the joint posterior probability distribution of structural parameters and load spectrum parameters, a more accurate estimate of structural modal parameters can be achieved. Multiple natural frequencies ω_measured and mode shapes φ_measured are identified as targets. Using ω_measured and φ_measured as observation targets, Bayesian inference methods (such as Markov chain Monte Carlo MCMC) are employed to optimize and adjust uncertain physical parameters (such as material elastic modulus E, structural damping ratio ζ, and added mass) in model F0'. This method can find a set of optimal parameters and also provide the probability distribution of the parameters (e.g., the elastic modulus may be 210 GPa, but with a 90% confidence level between 205-215 GPa). This quantifies the updated uncertainty, providing accurate information for subsequent risk assessment. Furthermore, since the boundary conditions have already been significantly modified in the preceding steps, optimizing the physical parameters at this stage avoids the "compensation effect" between parameters.

[0072] Step S8: Feed the parameter set back to the first numerical model for updating, and obtain the updated first numerical model.

[0073] Specifically, the structural parameter z_eff and the parameter set P_struct_1 from the above steps are systematically assigned to the initial first numerical model M0, thereby generating a revised new model M1. The reparameterization process of the new model M1 includes:

[0074] 1) Boundary Condition Update: Adjust the parameters representing pile-soil interaction in the first numerical model M0 based on the change in the global equivalent constraint location z_eff. For z_eff, in M0, pile-soil interaction is typically simulated by a set of equivalent springs (py, tz, Qz curves) set at the mudline. z_eff is a global equivalent result. Now, it is necessary to convert the global equivalent result into parameters that M0 can understand. This can be done by: keeping the spring properties unchanged, but moving the spring setpoints from the initially assumed mudline depth to the z_eff depth. Or, more precisely, adjusting the stiffness values ​​of the equivalent springs inversely according to the change in z_eff, so that the M0 model is consistent with the updated high-fidelity model F0' in terms of overall stiffness (especially first-order frequencies).

[0075] 2) Physical Parameter Update: For P_struct_1, the parameters in P_struct_1 are directly assigned to M1. For example, the updated material elastic modulus E is input into the properties of the beam element, and the updated damping ratio ζ is input into the damping definition of the model. Compared with M0, M1 significantly improves physical realism. Furthermore, the structural parameters of the inverted load model (M1) are closer to the actual state, so the environmental load parameters calculated by it will be more accurate than the results in M0.

[0076] Step S10: Determine whether the updated first numerical model meets the convergence condition. If yes, proceed to S12. If no, use the updated first numerical model from S8 for the next iteration to recalculate the equivalent environmental load parameters and perform convergence judgment again. Continue iterating until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters from the previous iteration. Determine whether convergence is achieved based on the comparison results.

[0077] Specifically, after updating the parameters of the initial model, a further iterative process is performed to determine whether the entire system has reached a stable and self-consistent optimal state. This involves replacing the initial model M0 with the updated, more accurate model M1, and then executing step S4. This includes:

[0078] Establish a joint probability distribution p(V, Hs, ...) using the same measured environmental data (this distribution remains unchanged);

[0079] Latin hypercube sampling and extensive simulations were performed using M1 to generate new sample pairs;

[0080] Train a new proxy model f_proxy_M1 based on M1;

[0081] By performing probability integration and solving the equation, the equivalent environmental parameter E_eq_2 is finally obtained.

[0082] Then, by comparing the results of two adjacent iterations of E_eq_2 and E_eq_1, a convergence test is performed, including: calculating the relative rate of change of key components (such as equivalent wind speed V_eq and equivalent wave height Hs_eq) between E_eq_2 and E_eq_1.

[0083] Rate of change δ = |(E_eq_2-E_eq_1) / E_eq_1|*100%

[0084] The value δ is then compared with a preset, extremely small convergence threshold ε (ε can be dynamically adjusted according to project requirements and computational load, such as 1% or 0.5%). If the rate of change δ is less than ε, it means that the load derived from the latest model M1 is very similar to that derived from the previous model M0. This indicates that model updates can no longer cause significant changes in load estimates. On the other hand, from the system's perspective, the environmental load parameters and structural model parameters have reached a balance, i.e., they are mutually synergistic, and the model's predictions are highly consistent with the measured data. At this point, the system is considered to have converged.

[0085] Conversely, when the rate of change δ is greater than ε, it indicates that the system has not yet converged and further iterations are needed to correct the estimation of the load parameters. At this point, the updated, higher-fidelity first numerical model M1 from step S8 is used as the input for the next round of calculations, and the complete process of step S4 is repeated, i.e.:

[0086] Latin hypercube sampling and extensive simulations were performed using M1; a new surrogate model f_proxy_M1 based on M1 was trained; probability integration was performed and solved to finally obtain a new set of equivalent environment parameters E_eq_2.

[0087] Then, return to this step (step S10) and compare the newly calculated E_eq_2 with the previous E_eq_1. If the relative rate of change is still greater than the threshold ε, continue this iterative process until the difference between the equivalent environmental load parameters obtained from two consecutive iterations is less than the convergence threshold.

[0088] In theory, this is a process of continuously decreasing error. As the first numerical model M becomes closer to physical reality due to a one-time accurate calibration, the load parameters E_eq it inverses will also tend to be stable and accurate, eventually converging and outputting a self-consistent solution.

[0089] In addition, the detailed process of the above iterative steps can also be described as follows: Figure 2 As shown, Figure 2The actual data iteration process is not described. This embodiment is divided into several major steps, including: (data) initialization, loop start, environmental parameter estimation (kth round), structural model update (kth round), convergence judgment, if convergence is achieved, final analysis and application are performed without liability, otherwise model feedback and update are performed.

[0090] Step S12: Determine the corresponding final equivalent environmental load parameters based on the first and second numerical models updated after convergence, and conduct a safety assessment of the target structure based on the final equivalent environmental load parameters.

[0091] Specifically, after the iterative process converges, the final equivalent environmental load parameter E_eq_final is output. Furthermore, the boundary conditions in the second numerical model, defined by z_eff_final derived from the data, accurately reflect the true state of pile-soil interaction. The physical parameters, defined by P_struct_final obtained through Bayesian inference, accurately reflect the constitutive relation and damping characteristics of the material.

[0092] Furthermore, E_eq_final can be used as input to the updated first numerical model to generate the corresponding wind, wave, and other load time histories, and then applied to the updated second numerical model for refined structural safety assessment, such as stress analysis under extreme conditions or fatigue damage analysis over the entire life cycle.

[0093] The ultimate strength analysis can use the load time history corresponding to E_eq_final (which may also need to be multiplied by an appropriate safety factor) to drive the updated model for nonlinear dynamic analysis. The maximum stress / strain response of the structure over the entire time history is extracted, especially the stress distribution in key areas (such as the tower-foundation transition section and the mud surface). Because both the model and loads have been accurately calibrated, the calculated maximum stress results can more accurately reflect the actual stress level that the structure may experience under real extreme events.

[0094] Fatigue damage analysis can utilize the load time history corresponding to E_eq_final to drive the updated model for dynamic analysis. Stress time histories at key fatigue details (such as welds) are extracted, and stress cycles are statistically analyzed using methods such as rainflow counting. Then, combined with the material's SN curve and Miner's linear cumulative damage rule, the fatigue damage caused by the equivalent load event is calculated. The updated model (especially accurate z_eff and E) ensures the accuracy of local stress concentration calculations.

[0095] This invention provides an iterative coupling method for equivalent environmental parameters and structural parameters. The method comprises the following steps: S2: Acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole-machine model, and the second numerical model is a structural model. S4: Based on the statistical results of the environmental data, construct a probabilistic model, simulate the first numerical model, train a surrogate model, and combine the probabilistic model and the surrogate model to calculate the initial equivalent environmental load parameters. S6: Based on the structural response data, update the parameters of the second numerical model and output the updated parameter set. S8: Invert the parameter set... The first numerical model is updated by feeding back the data to obtain the updated first numerical model; S10: Determine whether the updated first numerical model meets the convergence condition. If yes, proceed to S12. If no, continue iterating with S6 and S8 as loop steps until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters of the previous iteration. Determine whether convergence has occurred based on the comparison results; S12: Determine the corresponding final equivalent environmental load parameters based on the updated first and second numerical models after convergence, and conduct a safety assessment of the target structure based on the final equivalent environmental load parameters.

[0096] In this embodiment, the beneficial effect of an iterative coupling method for equivalent environmental parameters and structural parameters is as follows:

[0097] It can solve the problem of circular dependency: Through the designed closed-loop feedback mechanism, it breaks the dilemma of load identification and model update being independent of each other in the background technology, and systematically eliminates the circular error of inaccurate load due to inaccurate model, or inaccurate model update due to inaccurate load.

[0098] It achieves multi-level, high-precision model updates: It creatively combines two advanced update techniques, which physically locate the true boundary through the "equivalent constraint position" method and probabilistically calibrate the intrinsic structural parameters through the "Bayesian method", making the model update process more comprehensive and reliable.

[0099] It enhances practicality and feasibility: the framework is clear, and specific, complementary implementation paths are specified in the key structural model update stage, making the entire solution highly operable. The final results can be directly and seamlessly applied to engineering safety assessments, providing a more reliable basis for decision-making.

[0100] Please see Figure 3 , Figure 3 This is a schematic diagram of an iteratively coupled system of equivalent environmental parameters and structural parameters provided in an embodiment of this application. For example... Figure 3 As shown, the system includes:

[0101] The monitoring module S302 is used to acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole machine model and the second numerical model is a structural model.

[0102] The initial module S304 is used to construct a probability model based on the statistical results of the environmental data, simulate the first numerical model, train the surrogate model, and calculate the initial equivalent environmental load parameters by combining the probability model and the surrogate model.

[0103] The second numerical model update module S306 is used to update the parameters of the second numerical model based on the structural response data and output the updated parameter set.

[0104] The first numerical model update module S308 is used to feed the parameter set back to the first numerical model for updating, so as to obtain the updated first numerical model.

[0105] The convergence judgment module S310 is used to determine whether the updated first numerical model meets the convergence condition. If yes, it enters the evaluation module; if no, it instructs the initial module S304 to recalculate the load parameters based on the model updated by the first numerical model update module S308, and to perform the next convergence judgment until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model, comparing them with the equivalent environmental load parameters of the previous iteration, and judging whether convergence is achieved based on the comparison result.

[0106] The evaluation module S312 is used to determine the corresponding final equivalent environmental load parameters based on the first and second numerical models updated after convergence, and to perform a safety assessment on the target structure based on the final equivalent environmental load parameters.

[0107] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0108] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0109] See Figure 4It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 4 As shown, the electronic device 400 may include: at least one processor 401, at least one network interface 404, user interface 403, memory 405, and at least one communication bus 402.

[0110] The communication bus 402 is used to enable communication between these components.

[0111] The user interface 403 may include a display screen and a camera. Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0112] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0113] The processor 401 may include one or more processing cores. The processor 401 connects to various parts within the electronic device 400 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling data stored in the memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 401 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, without being integrated into the processor 401.

[0114] The memory 405 may include random access memory (RAM) or read-only memory. Optionally, the memory 405 may include a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. Figure 4 As shown, the memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0115] exist Figure 4 In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and acquire user input data; while the processor 401 can be used to call the image-based interactive application stored in the memory 405 and specifically perform the following operations: S2, acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole-machine model and the second numerical model is a structural model; S4, based on the statistical results of the environmental data, construct a probability model, simulate the first numerical model, train the surrogate model, and combine the probability model and the surrogate model to calculate the initial equivalent environmental load parameters; S6, based on the structural response data, update the parameters of the second numerical model and output the updated parameters. S8. Feed the parameter set back to the first numerical model for updating, and obtain the updated first numerical model; S10. Determine whether the updated first numerical model meets the convergence condition. If yes, proceed to S12. If no, use the updated first numerical model in step S8 for the next iteration to recalculate the equivalent environmental load parameters and perform convergence judgment again. Continue iterating until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters of the previous iteration. Determine whether convergence is achieved based on the comparison results; S12. Determine the corresponding final equivalent environmental load parameters based on the updated first and second numerical models after convergence, and conduct a safety assessment of the target structure based on the final equivalent environmental load parameters.

[0116] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0117] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0118] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0123] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0124] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. An iterative coupling method for equivalent environmental parameters and structural parameters, the method comprising: S2. Acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole machine model and the second numerical model is a structural model. S4. Based on the statistical results of the environmental data, construct a probability model, simulate the first numerical model, train the surrogate model, and combine the probability model and the surrogate model to calculate the initial equivalent environmental load parameters. S6. Based on the structural response data, update the parameters of the second numerical model and output the updated parameter set; The step of updating the parameters of the second numerical model based on the structural response data includes: Determine the boundary conditions of the second numerical model; The steps for determining the boundary conditions include: Obtain the acceleration time history signals of multiple measurement points corresponding to the structural response data; The acceleration time history signal is processed to reconstruct the first-order displacement time history signal at each measuring point; A polynomial curve is obtained by fitting the first-order displacement values ​​and spatial coordinates of all measuring points at any given time using a polynomial. The polynomial curve represents the first-order deformation shape of the structure at that time. Extend the polynomial curve toward the structural foundation and solve for the intersection point of the curve with the central axis of the structure. The intersection point is the equivalent constraint position of the second numerical model. S8. Feed the parameter set back to the first numerical model for updating, and obtain the updated first numerical model; S10. Determine whether the updated first numerical model meets the convergence condition. If yes, proceed to S12. If no, use the updated first numerical model from S8 for the next iteration to recalculate the equivalent environmental load parameters and perform convergence judgment again. Continue iterating until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters of the previous iteration. Determine whether convergence is achieved based on the comparison result. S12. Based on the first and second numerical models updated after convergence, determine the corresponding final equivalent environmental load parameters, and perform a safety assessment on the target structure based on the final equivalent environmental load parameters.

2. The method according to claim 1, characterized in that, The first-order displacement time history signal is obtained by performing joint complex exponential decomposition on the acceleration time history signal, extracting the first-order modal response component, and integrating the response component.

3. The method according to claim 1, characterized in that, The step of updating the parameters of the second numerical model based on the structural response data further includes: Update the physical parameters of the second numerical model; The updating of the physical parameters includes: Identify modal parameters from the structural response data; Based on the Bayesian inference method, the uncertain physical parameters in the second numerical model are probabilistically updated according to the modal parameters.

4. The method according to claim 1, characterized in that, The method further includes: The first numerical model is an integrated simulation model of the whole machine based on multibody dynamics and beam element theory; The second numerical model is a high-fidelity finite element model based on continuum mechanics, using shell elements or solid elements.

5. The method according to claim 1, characterized in that, The proxy model is constructed using Gaussian process regression, multinomial chaotic expansion, or artificial neural network.

6. The method according to claim 5, characterized in that, The structural response data includes at least one of acceleration, dynamic strain, and tilt angle.

7. An iteratively coupled system of equivalent environmental parameters and structural parameters, characterized in that, The system includes; The monitoring module is used to acquire monitoring data of the target structure and establish a first numerical model and a second numerical model of the target structure. The monitoring data includes environmental data and structural response data. The first numerical model is a whole machine model and the second numerical model is a structural model. The initial module is used to construct a probabilistic model based on the statistical results of the environmental data, simulate the first numerical model, train the surrogate model, and calculate the initial equivalent environmental load parameters by combining the probabilistic model and the surrogate model. The second numerical model update module is used to update the parameters of the second numerical model based on the structural response data and output the updated parameter set. The step of updating the parameters of the second numerical model based on the structural response data includes: Determine the boundary conditions of the second numerical model; The steps for determining the boundary conditions include: Obtain the acceleration time history signals of multiple measurement points corresponding to the structural response data; The acceleration time history signal is processed to reconstruct the first-order displacement time history signal at each measuring point; A polynomial curve is obtained by fitting the first-order displacement values ​​and spatial coordinates of all measuring points at any given time using a polynomial. The polynomial curve represents the first-order deformation shape of the structure at that time. Extend the polynomial curve toward the structural foundation and solve for the intersection point of the curve with the central axis of the structure. The intersection point is the equivalent constraint position of the second numerical model. The first numerical model update module is used to feed the parameter set back to the first numerical model for updating, so as to obtain the updated first numerical model. The convergence judgment module is used to determine whether the updated first numerical model meets the convergence condition. If yes, it enters the evaluation module; if no, it instructs the initial module to recalculate the load parameters based on the updated model of the first numerical model update module and perform the next convergence judgment until convergence. The convergence condition includes: calculating the updated equivalent environmental load parameters based on the updated first numerical model and comparing them with the equivalent environmental load parameters of the previous iteration, and judging whether convergence is achieved based on the comparison result. The evaluation module is used to determine the corresponding final equivalent environmental load parameters based on the first and second numerical models updated after convergence, and to perform a safety assessment on the target structure based on the final equivalent environmental load parameters.

8. An electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, the computer program implementing the method as described in any one of claims 1-6 when executed by a processor.

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