Structural service performance evaluation method, device, storage medium and product

Abstract

The application discloses a structure service performance evaluation method and device, a storage medium and a product, relates to the technical field of structure engineering, and is based on the correlation fusion of multi-source heterogeneous data of a target structure in a whole life cycle to obtain a digital twin database; an initial simulation model is constructed based on the digital twin database, real-time monitoring data of the target structure is acquired, the initial simulation model is dynamically calibrated based on the real-time monitoring data, and a target digital twin body is obtained; a future service environment load spectrum is constructed, the service performance of the target structure is safely evaluated based on the target digital twin body and the future service environment load spectrum, and a safety evaluation result is obtained. The application realizes accurate evaluation of the service performance of the structure based on dynamic updating of whole life cycle data.

Description

Technical Field

[0001] This application relates to the field of structural engineering technology, and in particular to a method, device, storage medium and product for evaluating the service performance of structures. Background Technology

[0002] In the field of structural engineering, performance evaluation and redesign of existing structures are crucial for ensuring their long-term safety and achieving scientific maintenance. Currently, traditional techniques typically employ static model correction based on phased monitoring data. This involves establishing an initial finite element model based on as-built data and then adjusting the parameters once to match the model response to the measured data.

[0003] However, static model correction based on phased monitoring data relies on a limited data source, primarily consisting of as-built documentation and phased monitoring data. Data from different sources and phases are isolated, making it difficult to form a complete description of the structural state evolution. Furthermore, the model correction process is typically static; the corrected model cannot be continuously updated as the structural state changes and new monitoring data accumulates, causing the model to gradually deviate from the actual state of the structure and resulting in a gradual decrease in the accuracy of structural service performance assessment. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, storage medium and product for evaluating the service performance of structures, which aims to solve the technical problem that traditional technologies rely on static models and phased monitoring data and cannot achieve accurate evaluation.

[0005] To achieve the above objectives, this application proposes a structural service performance evaluation method, which includes: A digital twin database is obtained by associating and fusing multi-source heterogeneous data throughout the entire lifecycle of the target structure. An initial simulation model is constructed based on the digital twin database, real-time monitoring data of the target structure is obtained, and the initial simulation model is dynamically calibrated based on the real-time monitoring data to obtain the target digital twin. A future service environment load spectrum is constructed, and the service performance of the target structure is safely assessed based on the target digital twin and the future service environment load spectrum to obtain the safety assessment results.

[0006] In one embodiment, the step of associating and fusing multi-source heterogeneous data based on the target structure throughout its entire lifecycle to obtain a digital twin database includes: The static attribute data and historical event data generated by the target structure throughout its entire lifecycle are obtained, and the static attribute data and historical event data are semantically aligned and associated with each other to obtain associated fused data. The associated and fused data is structured and stored to obtain the digital twin database.

[0007] In one embodiment, the step of acquiring real-time monitoring data of the target structure includes: Raw monitoring data is collected in real time by sensors deployed on the target structure; The raw monitoring data is preprocessed to obtain the real-time monitoring data; The real-time monitoring data is associated and bound with the corresponding components in the target structure in terms of time and space, and stored in the digital twin database.

[0008] In one embodiment, the step of dynamically calibrating the initial simulation model based on the real-time monitoring data to obtain the target digital twin includes: Based on at least one key parameter in the initial simulation model, construct a set of parameters to be calibrated; Based on the real-time monitoring data, an objective function is constructed to quantify the difference between the simulation output and the real-time monitoring data; The set of parameters to be calibrated is iteratively optimized based on the optimization algorithm to obtain the optimal parameter solution, wherein the optimal parameter solution is used to make the function value of the objective function satisfy the preset convergence condition; The optimal parameter solution is input into the initial simulation model to obtain the target digital twin.

[0009] In one embodiment, the step of constructing the future service environment load spectrum includes: Acquire historical environmental monitoring data and climate prediction information of the target structure; Based on the historical environmental monitoring data and the climate prediction information, a probabilistic load model is constructed, which is used as the future service environment load spectrum. The probabilistic load model is used to describe the changes and frequency of environmental load intensity of the target structure during its future service period.

[0010] In one embodiment, the step of conducting a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum, and obtaining the safety assessment result, includes: The future service environment load spectrum is used as input load conditions and applied to the target digital twin; Based on the material properties of the target structure, a time-varying degradation model for material properties is set up; Under the input load conditions, the time-varying degradation model is coupled to perform simulation analysis on the target digital twin, simulating the mechanical response analysis results of the target structure at different time stages during its future service life; The safety assessment is performed on the service performance of the target structure based on the mechanical response analysis results, and the safety assessment results are obtained.

[0011] In one embodiment, after the steps of constructing the future service environment load spectrum, performing a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum, and obtaining the safety assessment result, the method further includes: The security assessment results are compared with preset performance thresholds; If the security assessment result does not meet the preset performance threshold, at least one design variable is defined in the target digital twin; Using the design variables as the optimization object and the future service environment load spectrum as the input condition, the target digital twin is optimized and iterated to obtain the optimal redesign scheme.

[0012] In addition, to achieve the above objectives, this application also proposes an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the structural service performance evaluation method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the structural service performance evaluation method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the structural service performance evaluation method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application obtains a digital twin database by fusing multi-source heterogeneous data from a target structure throughout its entire lifecycle. An initial simulation model is constructed based on this database, and real-time monitoring data of the target structure is acquired. The initial simulation model is then dynamically calibrated based on this real-time monitoring data to obtain a target digital twin. A future service environment load spectrum is constructed, and the service performance of the target structure is assessed using both the target digital twin and the future service environment load spectrum to obtain a safety assessment result. In other words, this application's embodiment obtains a digital twin database by fusing multi-source heterogeneous data throughout the entire lifecycle of the structure. This systematic integration and fusion of multi-source heterogeneous data throughout the structure's lifecycle provides a complete and reliable data source for model construction, improving the initial matching degree between the initial simulation model and the physical entity. An initial simulation model is constructed based on a digital twin database. Real-time monitoring data of the target structure is acquired, and the initial simulation model is dynamically calibrated based on the real-time monitoring data to obtain a target digital twin. The initial simulation model is then dynamically modified using real-time monitoring data as constraints, enabling it to automatically update as the structural state evolves and monitoring data accumulates. This achieves a shift in the evaluation model from static to dynamic synchronization. By constructing a load spectrum for the future service environment, a safety assessment of the target structure's service performance is performed based on the target digital twin and the future service environment load spectrum, yielding a safety assessment result. By constructing a load spectrum characterizing future service environment excitations, and using the dynamically calibrated target digital twin as the simulation carrier, quantitative simulation analysis of performance changes during the future service phase of the structure and accurate assessment of the structure are achieved. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the structural service performance evaluation method of this application. Figure 2 A schematic diagram of the process for obtaining safety assessment results provided by the structural service performance assessment method of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the structural service performance evaluation method of this application. Figure 4A simplified flowchart illustrating the structural service performance evaluation method of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the structural service performance evaluation method in the embodiments of this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is as follows: Based on the correlation and fusion of multi-source heterogeneous data of the target structure throughout its entire life cycle, a digital twin database is obtained; based on the digital twin database, an initial simulation model is constructed, real-time monitoring data of the target structure is obtained, and the initial simulation model is dynamically calibrated based on the real-time monitoring data to obtain a target digital twin; a future service environment load spectrum is constructed, and the service performance of the target structure is safely assessed based on the target digital twin and the future service environment load spectrum to obtain a safety assessment result.

[0023] In this embodiment, for ease of description, the following description uses an electronic device as the execution subject.

[0024] In the field of structural engineering, performance evaluation and redesign of existing structures are crucial for ensuring their long-term safety and achieving scientific maintenance. Currently, traditional techniques typically employ static model correction based on phased monitoring data. This involves establishing an initial finite element model based on as-built data and then adjusting the parameters once to match the model response to the measured data.

[0025] However, static model correction based on phased monitoring data relies on a limited data source, primarily consisting of as-built documentation and phased monitoring data. Data from different sources and phases are isolated, making it difficult to form a complete description of the structural state evolution. Furthermore, the model correction process is typically static; the corrected model cannot be continuously updated as the structural state changes and new monitoring data accumulates, causing the model to gradually deviate from the actual state of the structure and resulting in a gradual decrease in the accuracy of structural service performance assessment.

[0026] This application obtains a digital twin database by fusing multi-source heterogeneous data from a target structure throughout its entire lifecycle. An initial simulation model is constructed based on this database, and real-time monitoring data of the target structure is acquired. The initial simulation model is then dynamically calibrated based on this real-time monitoring data to obtain a target digital twin. A future service environment load spectrum is constructed, and the service performance of the target structure is assessed using both the target digital twin and the future service environment load spectrum to obtain a safety assessment result. In other words, this application's embodiment obtains a digital twin database by fusing multi-source heterogeneous data throughout the entire lifecycle of the structure. This systematic integration and fusion of multi-source heterogeneous data throughout the structure's lifecycle provides a complete and reliable data source for model construction, improving the initial matching degree between the initial simulation model and the physical entity. An initial simulation model is constructed based on a digital twin database. Real-time monitoring data of the target structure is acquired, and the initial simulation model is dynamically calibrated based on the real-time monitoring data to obtain a target digital twin. The initial simulation model is then dynamically modified using real-time monitoring data as constraints, enabling it to automatically update as the structural state evolves and monitoring data accumulates. This achieves a shift in the evaluation model from static to dynamic synchronization. By constructing a load spectrum for the future service environment, a safety assessment of the target structure's service performance is performed based on the target digital twin and the future service environment load spectrum, yielding a safety assessment result. By constructing a load spectrum characterizing future service environment excitations, and using the dynamically calibrated target digital twin as the simulation carrier, quantitative simulation analysis of performance changes during the future service phase of the structure and accurate assessment of the structure are achieved.

[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses an electronic device as an example to illustrate this embodiment and the subsequent embodiments.

[0028] Based on this, embodiments of this application provide a method for evaluating the service performance of a structure, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the structural service performance evaluation method of this application.

[0029] In this embodiment, the structural service performance evaluation method includes steps S10 to S30: Step S10: Based on the multi-source heterogeneous data of the target structure throughout its entire life cycle, perform correlation and fusion to obtain a digital twin database; It should be noted that the target structure refers to the physical entity structure that requires service performance evaluation. The target structure can be infrastructure such as building structures, bridge structures, tunnel structures, dam structures, and wind turbine towers. It can also be the fuselage structure or wing structure of an aircraft. Furthermore, it can be large cranes, wind turbine blades, etc.—any physical structure requiring long-term service performance monitoring and evaluation is eligible for the technical solution of this application. The entire life cycle refers to the complete time span of the target structure from design, construction, operation and maintenance to decommissioning. The entire life cycle includes the design phase (e.g., schematic design, construction drawing design, design changes), the construction phase (e.g., material arrival, component installation, final acceptance), the operation and maintenance phase (e.g., daily use, periodic inspection, repair and reinforcement, disaster events), and the monitoring phase (e.g., sensor deployment and data acquisition). The types, formats, and frequencies of data generated at different stages vary and require unified integration. Multi-source heterogeneous data refers to heterogeneous datasets generated by the target structure throughout its entire life cycle, originating from different acquisition channels, storage formats, and data types. Association fusion refers to the process of integrating fragmented, isolated data into a structured dataset with unified semantics, traceability, and interoperability, addressing the problems of inconsistent formats, semantic mismatches, spatial mismatches, and asynchronous temporal dimensions inherent in multi-source heterogeneous data. This is achieved through data cleaning, format standardization, semantic alignment, spatiotemporal binding, and entity association mapping. The digital twin database, after association fusion, is the core of structured data management, carrying full-lifecycle, full-dimensional data of the target structure. It serves as a reliable data source for the entire process, from initial simulation model construction and dynamic model calibration to service performance simulation analysis, providing standardized data query, retrieval, and update services for the entire analysis process.

[0030] Additionally, it should be noted that the electronic equipment, through pre-defined standardized data interfaces, connects to the design unit, construction unit, operation and maintenance management unit, and structural health monitoring system corresponding to the target structure, respectively, to collect multi-source heterogeneous data throughout the entire lifecycle of the target structure. The collected multi-source heterogeneous data undergoes standardized preprocessing, filtering and cleaning invalid values, outliers, and duplicate data. For data sources with different formats such as drawings, documents, point clouds, and time series data, format standardization conversion is performed to unify them into a recognizable and processable structured data format. Furthermore, the units of measurement, coordinate systems, and time bases of data from different sources are uniformly calibrated to eliminate reference deviations between data sources. The preprocessed multi-source heterogeneous data undergoes association and fusion operations. A unique identifier (ID) is assigned to each physical load-bearing component of the target structure, spanning the entire structure's lifecycle and serving as the sole anchor point for data association. Based on a standard ontology library in structural engineering, the semantics of data from different sources are uniformly aligned, binding each data item to the unique ID of its corresponding component. This establishes a mapping relationship between data and physical components. For time-series monitoring data and historical event data, dual binding across time and spatial dimensions is achieved, establishing a complete traceability link from a single data point to its corresponding component, and then to the component's entire lifecycle historical data. The fused full lifecycle dataset is then structured and stored according to a pre-defined entity relationship model, constructing a digital twin database. This database is configured with standardized access interfaces to support on-demand data access, real-time updates, and traceability queries in subsequent steps.

[0031] Understandably, by systematically integrating and semantically associating structural data scattered across different stages, systems, and formats, a comprehensive, accurate, and traceable digital twin database has been constructed. This solves the problems of fragmented data foundations and inherent discrepancies between the initial model state and physical entities in traditional technologies, laying a solid data foundation for subsequent model building, dynamic calibration, and performance prediction. It also realizes the transformation of structural data management from isolated stages to full lifecycle integration, providing a complete data view for accurate assessment.

[0032] In one feasible implementation, the step of associating and fusing multi-source heterogeneous data based on the target structure throughout its entire lifecycle to obtain a digital twin database includes steps S11-S12: Step S11: Obtain the static attribute data and historical event data generated by the target structure throughout its entire lifecycle, and perform semantic alignment and association mapping on the static attribute data and historical event data to obtain associated fusion data; It should be noted that static attribute data refers to data describing the inherent properties of the target structure that, under normal circumstances, do not change or change very slowly over time. For example, static attribute data can include drawings, calculation sheets, and design change notices from the design phase; as-built drawings, building information models, material inspection reports, component certificates of conformity, concrete test block strength reports, and weld flaw detection reports from the construction phase; and acceptance records and measured data from the completion and acceptance phase. Historical event data refers to data recording important events experienced by the target structure during its service life. For example, historical event data can include periodic inspection reports, maintenance and reinforcement records (such as reinforcement time, reinforcement scheme, and materials used), records of changes in usage function (such as changes in building use and load conditions), records of disaster events (such as the occurrence time of extreme events such as earthquakes, typhoons, and fires), and records of structural damage (such as the time, location, and width of cracks). Semantic alignment and association mapping refers to standardizing heterogeneous data from different sources, with varying formats and inconsistent semantics, according to unified semantic rules, and establishing logical relationships between the data.

[0033] Step S12: The associated fused data is stored in a structured manner to obtain the digital twin database.

[0034] It should be noted that structured storage refers to organizing data, after semantic alignment and association mapping, into persistent storage media according to a predefined data model.

[0035] Specifically, regarding the storage model, this application adopts an entity-relationship model or knowledge graph as the underlying data model. Entities correspond to physical objects in the target structure, such as the structure itself, floors, components, nodes, materials, and measuring points. Relationships describe the connections and mappings between entities, such as belonging to, connected to, monitored, and experienced. Attributes record various characteristic parameters of the entities, such as geometric dimensions, material strength, and installation time. Through this model, the semantic relationships of the data are preserved and can be efficiently utilized during queries. In terms of storage implementation, this application adopts a hybrid storage architecture. For entity and relational data, a graph database is used for storage to support complex relational queries and reasoning. For time-series data such as monitoring data, a time-series database is used for storage to support high-throughput writes and efficient time-range retrieval. For document-type data such as drawings and reports, object storage is used, and file indexes and metadata are retained in the graph database. Through this hierarchical storage strategy, different types of data can be managed in the most suitable storage engine, balancing query performance and storage efficiency.

[0036] Understandably, by acquiring static attribute data and historical event data generated throughout the target structure's entire lifecycle, and performing semantic alignment and association mapping on these data, a fused and correlated data is obtained. This fused and correlated data is then stored in a structured manner to create a digital twin database. This achieves the collection, fusion, and structured management of multi-source heterogeneous data throughout the target structure's entire lifecycle. Through semantic alignment and association mapping, the problems of semantic ambiguity, data fragmentation, and lack of inherent correlation in multi-source data, inherent in traditional technologies, are completely eliminated. The digital twin database constructed through structured storage provides a unified data source, solving the analytical bias problems caused by isolated data sources and inconsistent data across multiple stages in traditional technologies. It also supports continuous synchronous updates of data as the structure serves, providing a data foundation for subsequent dynamic model calibration and dynamic forward-looking assessment of service performance.

[0037] Step S20: Construct an initial simulation model based on the digital twin database, obtain real-time monitoring data of the target structure, and dynamically calibrate the initial simulation model based on the real-time monitoring data to obtain the target digital twin; It should be noted that an initial simulation model corresponding to the physical structure is generated through a digital twin database, and the initial simulation model is dynamically calibrated using real-time monitoring data to obtain a target digital twin that accurately reflects the current actual state of the target structure. The specific implementation process includes model construction, data acquisition, and dynamic calibration.

[0038] Specifically, in the model building phase, the as-built geometric information of the target structure is first extracted from the digital twin database. It should be noted that this application uses 3D laser scanning point cloud data to compare and analyze with the original design building information model. Through point cloud registration, feature extraction, and model fitting algorithms, construction deviations are automatically identified and quantified to generate a high-precision real-world 3D geometric model reflecting the structure's completed state—that is, the as-built digital model. Based on this model, by assigning a unique code to each physical component, the measured strength of materials and weld quality reports from the database are precisely associated with the corresponding components in the model, completing the digital twin mapping from design intent to the construction entity. Furthermore, maintenance and reinforcement events recorded during the operation and maintenance phase (such as reinforcement time, scheme, and materials) and detected damage information (such as crack location and size) are read from the database. Based on the corresponding mechanical principles, component attributes are modified in the model (such as increasing equivalent thickness or reducing local stiffness) or damage representations are implanted, enabling the model to reflect all known state changes up to the current moment. Finally, the integrated physical model is discretized using the finite element method. Based on the structural characteristics and accuracy requirements, appropriate element types are selected, and the mesh is refined in key areas. Key uncertain parameters affecting the structural response (such as the elastic modulus of concrete, connection stiffness, damping ratio, etc.) are defined as an adjustable set of input parameters to reserve an interface for subsequent calibration. This completes the construction of the initial simulation model.

[0039] Specifically, in the data acquisition phase, raw monitoring data is collected in real time through a sensing system deployed on the target structure. It should be noted that the sensor deployment scheme is based on the preliminary analysis results of the initial simulation model. Specifically, by performing modal or static analysis on the initial simulation model, the dynamic characteristics and mechanical weaknesses of the structure are identified. Sensors are preferentially deployed near points with large modal displacements, key force transmission path components, theoretically weak areas, and areas near existing damage to ensure that the monitoring data is highly sensitive to model corrections. The collected raw monitoring data undergoes real-time preprocessing through a data transmission channel established by an IoT platform or edge computing gateway, including outlier filtering, signal noise reduction, and data compression. The processed data, with timestamps and spatial tags, is injected into the digital twin database in real-time or near real-time as a data stream, and is associated with corresponding components and locations to achieve state synchronization of the digital twin.

[0040] Specifically, in the dynamic calibration phase, this application employs data assimilation technology to drive model updates. First, at least one set of key parameters sensitive to structural response is selected from the initial simulation model to form a parameter vector to be calibrated, along with a set of target response quantities obtainable through monitoring. Second, model calibration is formulated as a multi-objective optimization problem, constructing an objective function to quantify the difference between simulation output and measured data, for example, using weighted least squares. Then, optimization algorithms such as genetic algorithms and particle swarm optimization are used for parameter inversion and solution. An initial population is randomly generated, and finite element analysis is performed on each individual to calculate the objective function value. Iterative optimization is achieved through selection, crossover, and mutation operations until convergence criteria are met, obtaining the optimal parameter solution. This process is data assimilation, which inversely integrates observed information from the physical world into the digital model. Finally, the optimized parameter vector is updated to the attribute fields of the corresponding component in the digital twin database, and the parameters in the initial simulation model are updated simultaneously, thereby obtaining a calibrated digital twin whose mechanical behavior highly matches the current physical structure in a statistical sense—the target digital twin.

[0041] Understandably, by constructing an initial simulation model based on a digital twin database, acquiring real-time monitoring data of the target structure, and dynamically calibrating the initial simulation model based on this real-time monitoring data, a target digital twin is obtained. This achieves the calibration from a static initial simulation model to a dynamically calibrated target digital twin. It solves the problem in traditional technologies where model correction relies solely on limited as-built data and interim monitoring data, resulting in a static, open-loop correction process. By employing data assimilation technology, and using real-time monitoring data as a constraint to drive dynamic model updates, the target digital twin can continuously and automatically calibrate as the structural state changes and new monitoring data is added, always maintaining synchronous evolution with the physical entity. This transforms the evaluation model from a static snapshot with one-time correction to a continuously dynamically updated high-fidelity mirror, providing a benchmark model that accurately reproduces the current mechanical behavior and performance state of the physical structure for subsequent performance simulation analysis, thereby fundamentally ensuring the timeliness and accuracy of the evaluation results.

[0042] In one feasible implementation, the step of acquiring real-time monitoring data of the target structure includes steps S21-S23: Step S21: Collect raw monitoring data in real time using sensors deployed on the target structure; It should be noted that the physical sensing layer is used to acquire real-time response information of the target structure during service. The sensor deployment scheme is not randomly arranged, but optimized based on the preliminary analysis results of the initial simulation model in step S20. Specifically, modal or static analysis is first performed on the initial simulation model to identify the dynamic characteristics of the structure (such as low-order vibration modes and natural frequencies) and mechanical weaknesses (such as stress concentration areas and critical force transmission paths). Based on the analysis results, sensors are preferentially deployed at points with large modal displacements (sensitive to the overall dynamic response), key components (such as main load-bearing beams and columns), theoretically weak parts (such as nodal areas and abrupt changes in cross-section), and near existing damage (such as crack monitoring points) to ensure that the collected monitoring data is highly sensitive to subsequent model corrections.

[0043] Step S22: Preprocess the raw monitoring data to obtain the real-time monitoring data; It should be noted that raw monitoring data typically includes noise, outliers, and redundant information, making it unsuitable for direct model calibration. Therefore, preprocessing is necessary for data cleaning and transformation. For example, outlier filtering removes data points that significantly deviate from the true values ​​due to environmental interference or sensor malfunction. Signal denoising is also performed on the raw monitoring data, using low-pass filtering and wavelet denoising to process the sensor signals and retain the effective frequency range.

[0044] Step S23: Associate and bind the real-time monitoring data with the corresponding components in the target structure in terms of time and space, and store it in the digital twin database.

[0045] It should be noted that the preprocessed real-time monitoring data is integrated into the digital twin database, establishing a connection between it and the geometric model and historical data of the target structure, achieving synchronization between the physical and digital worlds. In the time dimension, each monitoring data point carries a precise timestamp, recording the moment of data acquisition. The real-time monitoring data is sorted chronologically to obtain a time series, which is then stored in the time series database. In the spatial dimension, each sensor is bound to a specific physical component during deployment, and its precise installation location is recorded. Through a mapping relationship between sensor identifiers and unique component codes, the real-time monitoring data is associated with the corresponding component in the digital twin database. After completing the temporal and spatial association, the real-time monitoring data is stored in the digital twin database.

[0046] Understandably, raw monitoring data is collected in real time by sensors deployed on the target structure. This raw data is preprocessed to obtain real-time monitoring data, which is then linked to corresponding components within the target structure in both time and space, and stored in a digital twin database. This establishes a complete data synchronization link from physical sensors to the digital twin database. Because the sensor deployment is based on model analysis and optimization, the sensitivity of the collected data is ensured. Real-time preprocessing guarantees data quality and availability. Spatiotemporal linkage transforms the real-time monitoring data from isolated numerical sequences into structured information closely associated with specific components and historical events. This achieves the transformation of real-time monitoring data from raw acquisition to semantic integration, providing high-quality input data for subsequent dynamic calibration.

[0047] In one feasible implementation, the step of dynamically calibrating the initial simulation model based on the real-time monitoring data to obtain the target digital twin includes steps S31-S34: Step S31: Based on at least one key parameter in the initial simulation model, construct a set of parameters to be calibrated; It should be noted that key parameters refer to parameters that significantly affect the mechanical behavior of the structure in the initial simulation model, but whose actual values ​​deviate from the design values. Key parameters can be material performance parameters (such as the elastic modulus and compressive strength of concrete, the yield strength and elastic modulus of steel, and the constitutive parameters of composite materials). In actual engineering applications, the measured strength of materials fluctuates to some extent from the design values ​​and degrades over service time. Geometric dimensional parameters (such as component cross-sectional dimensions, plate thickness, and member length) are also important. Construction errors can lead to deviations between the actual geometric dimensions and the design drawings, which can affect the stiffness and load-bearing capacity of the structure. Connection and boundary parameters (such as the rotational stiffness of beam-column joints, support connection stiffness, foundation constraint stiffness, and damping ratio) are also crucial. The connection state of nodes and supports may not conform to the ideal rigid or hinged assumptions during actual construction and use, and may loosen or be damaged over service time.

[0048] Specifically, for a concrete bridge that has been in service for many years, sensitivity analysis revealed that the elastic modulus of the main beam concrete and the horizontal stiffness of the pier supports have the greatest impact on the bridge's vertical frequency and lateral displacement. Therefore, these two parameters were selected as the set of parameters to be calibrated.

[0049] Furthermore, for a super high-rise building, considering the complexity of wind vibration response, the overall structural damping ratio, the elastic modulus of the core tube concrete, the connection stiffness of the outer frame columns, and the floor slab stiffness reduction factor of several key floors (due to the possible presence of cracks) are combined to form a set of parameters to be calibrated, which includes a total of 5 parameters.

[0050] Step S32: Based on the real-time monitoring data, construct an objective function to quantify the difference between the simulation output and the real-time monitoring data; It should be noted that by constructing an objective function, which measures the difference between the calculated results of the current simulation model and the measured data, the calibration problem is transformed into an optimization problem. Target response quantities are selected from real-time monitoring data. Target response quantities refer to physical quantities that reflect the structural mechanical behavior and can be reliably obtained from real-time monitoring data. Target response quantities can be modal parameters, static responses, dynamic time-history responses, etc. The form of the objective function depends on the type and number of target response quantities. This embodiment uses a weighted least squares form, for example, Where θ is the parameter vector to be calibrated, M is the number of target response quantities, Si(θ) is the simulated value of the i-th target response quantity under the parameter vector θ to be calibrated, Mi is the measured value of the i-th target response quantity, and wi is the weighting coefficient of the i-th target response quantity.

[0051] Step S33: Iteratively optimize the set of parameters to be calibrated based on the optimization algorithm to obtain the optimal parameter solution, wherein the optimal parameter solution is used to make the function value of the objective function satisfy the preset convergence condition; It should be noted that by automatically searching for the parameter combination that minimizes the objective function through optimization algorithms, the reverse fusion from physical observations (measured data) to digital models can be achieved. The selection of optimization algorithms needs to comprehensively consider factors such as the number of parameters and the properties of the objective function.

[0052] Preferably, the optimization algorithm can be a genetic algorithm, particle swarm optimization, or quadratic programming. When using a genetic algorithm to iteratively optimize the set of parameters to be calibrated, the process begins with initialization, randomly generating a set of parameter vectors (population). Then, for each individual (the set of parameters to be calibrated), finite element analysis is performed to calculate the function value of the objective function (fitness). Based on the fitness, superior individuals are selected, and a new generation of population is generated through crossover and mutation operations. The optimization steps are repeated until the convergence criterion is met (e.g., the objective function value is less than the convergence threshold or the maximum number of iterations is reached).

[0053] Step S34: Input the optimal parameter solution into the initial simulation model to obtain the target digital twin.

[0054] It should be noted that the optimal parameter solution obtained from the optimization is input into the initial simulation model to complete the dynamic update of the model, generating a target digital twin that highly matches the current state of the physical structure. The obtained target digital twin has high fidelity, meaning that its key mechanical parameters have been inversely calibrated using measured data, and the simulation output highly matches the real-time monitoring data, accurately reproducing the mechanical behavior of the physical structure at the current moment. It also has synchronicity, meaning that based on the latest real-time monitoring data, the target digital twin reflects the true state of the physical structure up to the current moment, including changes in material properties and degradation of connection stiffness.

[0055] Understandably, data assimilation technology solves the problems of isolated data foundations, static correction processes, and inability to dynamically update with changes in structural state in traditional model correction. The constructed target digital twin is not only a precise mirror of the current state but also an evolving and traceable dynamic model, providing a highly reliable digital foundation for subsequent long-term performance prediction and redesign optimization. When new monitoring data arrives in the future, simply repeating this calibration process ensures that the model remains continuously synchronized with the physical entity. This elevates the structural assessment model from a static snapshot with one-time correction to a continuously dynamically updated high-fidelity mirror, providing technical support for intelligent management throughout the entire structural lifecycle.

[0056] Step S30: Construct a future service environment load spectrum, and conduct a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum to obtain the safety assessment results.

[0057] It should be noted that by utilizing the target digital twin and combining it with scientific predictions of future environmental loads, a safety assessment is conducted on the performance evolution of the target structure during its remaining service life, ultimately outputting a quantitative safety assessment result. The future service environmental load spectrum refers to an input condition model used for safety performance assessment. Based on statistical analysis of long-term online monitoring environmental data of the structure, combined with regional climate data and extreme event prediction information, it constructs a probabilistic model describing the intensity, frequency, and temporal changes of environmental excitations such as wind, earthquakes, and temperature that the structure may experience during its future service life. The construction of the future service environmental load spectrum requires two types of basic data: first, historical environmental monitoring data extracted from the digital twin database, including wind speed and direction time-series records (such as hourly wind speeds over the past 10 years), temperature and humidity variation curves, seismic ground motion records, and rainfall; second, climate prediction information, which can come from the output data of global or regional climate models, containing predicted time series or probability distributions of key climate parameters such as wind speed and temperature for the next few decades. During the construction of the future service environmental load spectrum, extreme value analysis and spectral characteristic analysis are performed on the historical monitoring data. Extreme value analysis uses generalized extreme value distributions or Gumbel distributions to fit annual maximum wind speed and annual extreme temperature, estimating design values ​​for different return periods (e.g., 10-year, 50-year, 100-year). Spectral feature analysis is used to extract frequency domain characteristic parameters such as fluctuating wind speed spectra and seismic response spectra. Future prediction data from climate models are transformed into location-scale prediction information for the target structure using statistical downscaling or dynamic downscaling methods. Statistical downscaling establishes statistical relationships between large-scale climate variables and local meteorological elements, while dynamic downscaling uses nested high-resolution regional climate models for simulation. The fusion yields predicted time series or probability distribution trends for parameters such as wind speed and temperature over the next few decades.

[0058] Additionally, it's important to note that a time-varying degradation model of material properties needs to be incorporated into the safety assessment. A time-varying degradation model is a mathematical model used to characterize the decay of material strength or stiffness over service time. The remaining service life of the target structure is divided into multiple time periods (e.g., one year or five years per analysis step). For each time period, the possible load intensity and probability of occurrence are determined based on the load spectrum of future service stages. Using the time-varying degradation model, the material property parameters at the end of each time period are calculated, and the corresponding material properties in the target digital twin are updated. Static or dynamic analyses considering material nonlinearity and geometric nonlinearity are performed on each time period to calculate the mechanical response of the target structure under that time period and load condition. Key response indicators for each time period are recorded, forming a time-varying trajectory of mechanical performance evolution. This yields the mechanical response evolution trajectory of the target structure under different time stages and load conditions during its future service life. The safety assessment result refers to the quantitative assessment result extracted from the mechanical response evolution trajectory, used to characterize the safety status of the target structure. The types of safety assessment results can include overall deformation indicators, component stress indicators, damage indicators, bearing capacity indicators, performance reserve indicators, etc. By comparing the safety assessment results with the limits specified in the current design specifications or the pre-set performance targets, a quantitative current service performance verification report and a future performance prediction report can be generated, clarifying whether the target structure currently meets the safety requirements, and under what time points or load conditions the risk of performance inadequacy may occur.

[0059] Understandably, by constructing a load spectrum for the future service environment, coupling it with a time-varying material degradation model, and conducting a safety assessment based on a target digital twin, the final safety assessment results represent a shift in structural performance evaluation from post-event verification to pre-event prediction, from static fixed values ​​to dynamic probabilities, and from overall indicators to multi-scale quantitative indicators. When the safety assessment results indicate that the target structure may have insufficient performance at some point in the future, subsequent redesign and optimization steps can be triggered, proactively seeking solutions in the digital space and achieving preventative structural safety management. This provides a scientific and forward-looking decision-making basis for preventative maintenance and precise management throughout the entire lifecycle of the target structure.

[0060] In one feasible implementation, the step of constructing the future service environment load spectrum includes steps S41-S42: Step S41: Obtain historical environmental monitoring data and climate prediction information for the target structure; It should be noted that historical environmental monitoring data refers to time-series data collected over a long period by environmental sensors deployed on or near the target structure, reflecting the environmental characteristics of the target structure's service life. Climate prediction information refers to forecast data based on global or regional climate models, reflecting climate evolution trends over the next few decades.

[0061] Step S42: Based on the historical environmental monitoring data and the climate prediction information, construct a probabilistic load model and use the probabilistic load model as the future service environment load spectrum. The probabilistic load model is used to describe the changes and frequency of environmental load intensity of the target structure during its future service period.

[0062] It should be noted that the probabilistic load model is a mathematical model used to describe the probability of loads of different intensities occurring at different points in time during the future service life. The construction of the probabilistic load model first requires statistical analysis of the extreme values ​​of historical environmental monitoring data. This involves preprocessing the historical environmental monitoring data to extract the maximum value sequence for each time period (e.g., annual maximum wind speed, annual extreme temperature), and fitting the maximum value sequence using extreme value statistical theory, such as the generalized extreme value distribution or the Günbel distribution. Through parameter estimation, the extreme value distribution parameters of the historical environmental monitoring data are obtained. Then, climate prediction information is extracted and fused. For climate prediction information, the result is typically a sequence of predicted values ​​for each year (e.g., annual maximum wind speed prediction). Since the output of the climate model itself has uncertainty, it needs to be transformed into information that can be used to update the extreme value distribution parameters. Based on the above analysis, a complete probabilistic load model is generated. The future service environment load spectrum can be presented in various forms to adapt to different simulation analysis needs, such as a time series format, directly used as input for annual simulation analysis; or a probabilistic distribution format, available for probabilistic simulations.

[0063] Understandably, based on historical environmental monitoring data and climate prediction information, a probabilistic load model is constructed. This model serves as the future service environment load spectrum, bridging the current state assessment and future performance prediction. This allows subsequent simulation analyses to move beyond snapshot-style verification based on static assumptions to proactive simulations based on time-varying probabilistic inputs. When the future service environment load spectrum is combined with a material time-varying degradation model and a target digital twin, it becomes possible to simulate the structure's gradual deterioration and the application of gradually changing loads over the next few decades, thus realistically reflecting the evolution trajectory of structural performance. This elevates structural assessment from a compliance judgment of whether it meets regulations to a risk warning of when it might fail to meet requirements, providing a scientific timeline reference for preventative maintenance.

[0064] In one feasible implementation, see Figure 2 , Figure 2 This is a schematic diagram of the safety assessment result acquisition process provided by the structural service performance assessment method of this application. The step of conducting a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum to obtain the safety assessment result includes steps S51 to S54: Step S51: The future service environment load spectrum is applied to the target digital twin as input load conditions; It should be noted that the input load conditions refer to the external actions used to drive the simulation model to perform calculations, including information such as the type, magnitude, direction, location, and duration of the load.

[0065] Specifically, different loading strategies are adopted based on the expression form of the future service environment load spectrum and the needs of simulation analysis. If the future service environment load spectrum is presented in the form of a time series, the time series data is directly used as the input for simulation analysis. If the future service environment load spectrum is presented in the form of a probability distribution, loading needs to be performed through sampling or equivalent load transformation.

[0066] Step S52: Based on the material properties of the target structure, set a time-varying degradation model for material properties; It should be noted that by introducing a time-varying degradation model of material properties, the simulation analysis can reflect the performance degradation of the target structure due to factors such as aging, corrosion, and fatigue. A time-varying degradation model of material properties refers to a mathematical model used to characterize the decay of material strength, stiffness, or other mechanical property parameters over service time. During long-term service, the mechanical properties of the target structure will undergo irreversible degradation due to environmental erosion, repeated loading, and material aging. Establishing a time-varying degradation model of material properties requires selecting an appropriate degradation mechanism and mathematical model based on the material characteristics of the target structure. For example, for steel structures in marine environments, a uniform corrosion model of steel is selected. After the time-varying degradation model of material properties is determined, the model and its parameters need to be stored in a digital twin database and associated with the corresponding components in the target digital twin so that the material property parameters are updated at time steps during the simulation analysis.

[0067] Step S53: Under the input load conditions, the time-varying degradation model is coupled to perform simulation analysis on the target digital twin, simulating the mechanical response analysis results of the target structure at different time stages during its future service life; It should be noted that coupling refers to incorporating both the load application and material degradation processes into the analysis framework simultaneously, allowing them to interact and jointly influence the structural response. The analysis of the mechanical response of the simulated target at different time stages requires determining the time step. An excessively large step step will miss details of the degradation process, while an excessively small step step will result in excessive computation. At the end of each time step, the state variables affecting subsequent performance from the current step's calculation results need to be recorded and passed to the next time step. These state variables include cumulative damage, residual strain, geometric changes, and updated material parameters. Within the same time period, multiple load combinations are involved. Based on the combination information provided by the load spectrum, the combination of load conditions to be analyzed for that period needs to be determined and calculated separately or combined according to specifications. During long-term service, the target structure may enter a plastic state or undergo large deformations. Therefore, the simulation analysis should consider material nonlinearity and geometric nonlinearity. For extreme loads such as earthquakes, elastoplastic time history analysis should be used; for long-term deformation, time-varying effects such as creep and shrinkage should be considered.

[0068] Step S54: Based on the mechanical response analysis results, perform the safety assessment on the service performance of the target structure to obtain the safety assessment results.

[0069] It should be noted that safety assessment results refer to the evaluation results extracted from mechanical response analysis results that characterize the structural safety, serviceability, and durability. Depending on the assessment objectives, the types of safety assessment results can include overall performance indicators, such as inter-story drift angle, which is a fundamental indicator for measuring whether the overall stiffness of the target structure meets requirements for building structures; vertex displacement, the horizontal displacement of the top of the target structure, which reflects the overall deformation of the structure; local performance indicators, such as member stress ratio, which is the ratio of the actual stress of a member to its material strength; and time-related indicators, such as remaining life, time to first exceedance, and performance degradation rate.

[0070] Understandably, when a safety assessment indicates that a structure may be underperforming at some point in the future, subsequent redesign and optimization steps can be triggered to proactively seek solutions in the digital space, thus shifting the structural safety management model from passive analysis to proactive prediction. This enables full lifecycle health management of the target structure.

[0071] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3This is a flowchart illustrating Embodiment 2 of the structural service performance evaluation method of this application. After the steps of constructing the future service environment load spectrum, performing a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum, and obtaining the safety assessment result, steps A11 to A13 are further included: Step A11: Compare the security assessment result with a preset performance threshold; It should be noted that comparing the safety assessment results with preset performance thresholds determines whether the current or future performance of the target structure meets safety standards, thereby deciding whether a redesign optimization process needs to be initiated. Preset performance thresholds are pre-defined thresholds used to measure whether structural performance meets requirements. These thresholds can be set based on performance limits explicitly specified in industry design standards, or based on the structure's importance and operational needs. The comparison can be a direct comparison of the current safety assessment results with the threshold. For example, if the target structure's current inter-story drift angle is 1 / 600, comparing it to the preset performance threshold of 1 / 550, 1 / 600 is less than 1 / 550, thus meeting safety standards. Alternatively, the comparison can be a comparison of predicted indicators at future time points with the preset performance thresholds to identify the first point of exceedance. For example, if the predicted inter-story drift angle sequence is 1 / 600 in the first year, 1 / 580 in the fifth year, and 1 / 490 in the tenth year, comparing it to the preset performance threshold, the first point of exceedance is the tenth year. Therefore, it is determined that the target structure's performance will not meet requirements after the tenth year.

[0072] Step A12: If the security assessment result does not meet the preset performance threshold, define at least one design variable in the target digital twin; It should be noted that when structural performance is deemed insufficient, adjustable design parameters are identified as variables for subsequent optimization iterations. Design variables represent possible reinforcement or modification measures, which will be virtually adjusted and their effects verified within the target digital twin. Design variables refer to parameters that can be modified within the digital twin, corresponding to reinforcement or modification measures that can be implemented in the physical world. Design variables can be component geometric dimensions, material properties, and structural system variables. Defining design variables in the target digital twin first requires determining the range of adjustable parameters; that is, each design variable needs a reasonable value range to ensure that the optimized structure is physically feasible. Then, parametric modeling is required, meaning the target digital twin should possess parametric capabilities. Finally, the design variables are associated with the digital twin database, that is, the definitions and value ranges of the design variables are stored in the digital twin database and associated with the corresponding components of the target digital twin.

[0073] Step A13: Using the design variables as the optimization object and the future service environment load spectrum as the input condition, optimize and iterate the target digital twin to obtain the optimal redesign scheme.

[0074] It should be noted that through automatic iterative search, the optimal reinforcement scheme that meets both performance requirements and is economically reasonable is found in the digital space. Optimization iteration refers to a cyclical process that uses design variables as the adjustment object, structural performance requirements as the constraint, and preset objectives (such as lowest cost, simplest construction, and minimal impact on use) as the optimization direction. This process involves modifying design variables, re-performing performance simulations, evaluating results, and then modifying again to obtain the optimal solution. The optimal solution is then subjected to final simulation verification to ensure it meets safety performance requirements, and the optimal redesign scheme is output. The optimal redesign scheme may include design variable values, a comparison of performance indicators after reinforcement, cost estimation, and construction recommendations.

[0075] Furthermore, based on the optimal redesign scheme, a decision report is generated, clearly proposing recommendations such as continued use, preventative maintenance, or immediate reinforcement. If the decision recommendation is adopted and physical intervention measures (such as reinforcement construction) are implemented, the reinforced structure will serve as the new initial state, and its monitoring data will be fed back to the digital twin database, triggering a new round of model calibration and analysis, thus realizing the dynamic evolution of the digital twin and the physical structure throughout their entire lifecycle.

[0076] Understandably, when performance predictions indicate potential structural deficiencies in the future, the system can automatically identify the optimal intervention plan in the digital space and output the plan for user reference. This enables proactive structural management, allowing for anticipation of problems and early reinforcement, achieving intelligent governance throughout the entire lifecycle based on digital twins.

[0077] For example, to help understand the implementation process of the structural service performance evaluation method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 4 , Figure 4 A simplified flowchart illustrating the structural service performance evaluation method of this application.

[0078] Specifically, on the physical world side, the target physical structure is in long-term service. Sensors deployed on the structure collect real-time data on structural response (such as vibration, deformation, and strain) and environmental loads (such as wind, earthquakes, temperature, and humidity). This real-time monitoring data is continuously fed back to the digital twin world through data transmission channels. When the redesign scheme in subsequent steps is implemented on the physical structure, the reinforced structural state will serve as a new input, again fed back to the digital twin world through monitoring data, forming a closed loop.

[0079] On one side of the digital twin world, a digital twin database was first built (i.e. Figure 4(The digital twin data hub in the context of digital twins). This involves the correlation and fusion of multi-source heterogeneous data throughout the entire lifecycle of the target structure to obtain a digital twin database. For example... Figure 4 As shown, the digital twin database is the core of the entire system. It employs a structured data model (such as ontology or knowledge graph) to systematically integrate and associate multi-source heterogeneous data generated throughout the entire lifecycle of the structure. This includes static attribute data such as geometric models and material properties from the design and construction phases, as well as time-series monitoring data streams from the operation and maintenance and monitoring phases. By establishing events and relationships between data, a traceable data source is formed, providing data services for all subsequent analytical applications. Then, an initial simulation model is constructed based on the digital twin database, and a target digital twin is obtained through dynamic calibration. Correspondingly, based on the initial simulation model constructed from the digital twin database, real-time monitoring data of the target structure is acquired, and the initial simulation model is dynamically calibrated based on this real-time monitoring data to obtain the target digital twin. Based on the as-built geometric information, material properties, and historical damage records provided by the digital twin database, a parameterized initial simulation model is constructed. This model integrates known damage information and reinforcement history, and defines key parameters as an adjustable set of parameters to be calibrated. Real-time monitoring data from the physical world is acquired, and using this measured data as a benchmark, an objective function is constructed to quantify the difference between the simulation output and the measured data. The data assimilation and calibration loop begins, using real-time monitoring data as input. An optimization algorithm engine (such as genetic algorithm, particle swarm optimization, or sequential quadratic programming) iteratively optimizes the parameter set to be calibrated, finding the optimal parameter solution that satisfies the convergence condition of the objective function. This optimal parameter solution is then updated into the initial simulation model, yielding a target digital twin (i.e., a digital twin that highly matches the current state of the physical structure) Figure 4(A calibrated digital twin is used). Current performance verification and long-term performance prediction are performed using the calibrated digital twin to obtain a current state diagnostic report and future performance degradation trajectory for the target structure. This is used to determine whether the target structure currently meets safety requirements and to predict when and under what operating conditions performance deficiencies may occur. When the safety assessment results do not meet preset performance thresholds, redesign optimization is performed to obtain the optimal redesign scheme. Adjustable design variables are defined in the target digital twin. Then, using these design variables as optimization objects and the future service environment load spectrum as input conditions, iterative simulation analysis is performed on the target digital twin until the optimal design parameters that meet future performance requirements are obtained, generating a redesign digital twin. Based on the current state diagnostic report and future performance prediction trajectory, a decision report is generated, explicitly proposing decisions such as continued use, preventative maintenance, or immediate reinforcement. If the decision recommendation is adopted and physical intervention measures (such as reinforcement construction) are implemented, the reinforced structure will serve as the new initial state, and its monitoring data will be fed back to the digital twin database, triggering a new round of model calibration and analysis, thereby realizing the dynamic evolution of the digital twin and the physical structure throughout their entire life cycle.

[0080] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the structural service performance evaluation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0081] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the structural service performance evaluation method in Embodiment 1 above.

[0082] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The electronic devices in these embodiments may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0083] like Figure 5 As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. While electronic devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0084] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0085] The electronic device provided in this application, employing the structural service performance evaluation method described in the above embodiments, can solve the technical problem that traditional technologies, relying on static models and phased monitoring data, cannot achieve accurate evaluation. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the structural service performance evaluation method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0086] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0088] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the structural service performance evaluation method described in the above embodiments.

[0089] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0090] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0091] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by an electronic device, the electronic device causes the following: it performs correlation and fusion of multi-source heterogeneous data of the target structure throughout its entire lifecycle to obtain a digital twin database; it constructs an initial simulation model based on the digital twin database, acquires real-time monitoring data of the target structure, dynamically calibrates the initial simulation model based on the real-time monitoring data, and obtains a target digital twin; it constructs a future service environment load spectrum, and performs a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum, obtaining a safety assessment result.

[0092] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0095] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described structural service performance evaluation method. This solves the technical problem that traditional technologies, which rely on static models and periodic monitoring data, cannot achieve accurate evaluation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the structural service performance evaluation method provided in the above embodiments, and will not be repeated here.

[0096] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the structural service performance evaluation method described above.

[0097] The computer program product provided in this application can solve the technical problem that traditional technologies rely on static models and phased monitoring data, making accurate evaluation impossible. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the structural service performance evaluation method provided in the above embodiments, and will not be repeated here.

[0098] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for evaluating the service performance of a structure, characterized in that, The structural service performance evaluation method includes: A digital twin database is obtained by associating and fusing multi-source heterogeneous data throughout the entire lifecycle of the target structure. An initial simulation model is constructed based on the digital twin database, real-time monitoring data of the target structure is obtained, and the initial simulation model is dynamically calibrated based on the real-time monitoring data to obtain the target digital twin. Construct a future service environment load spectrum and apply the future service environment load spectrum as input load conditions to the target digital twin; Based on the material properties of the target structure, a time-varying degradation model for material properties is set up; Under the input load conditions, the time-varying degradation model is coupled to perform simulation analysis on the target digital twin, simulating the mechanical response analysis results of the target structure at different time stages during its future service life; Based on the mechanical response analysis results, a safety assessment of the service performance of the target structure is performed, and the safety assessment results are obtained.

2. The structural service performance evaluation method as described in claim 1, characterized in that, The steps for obtaining a digital twin database by associating and fusing multi-source heterogeneous data based on the target structure throughout its entire lifecycle include: The static attribute data and historical event data generated by the target structure throughout its entire lifecycle are obtained, and the static attribute data and historical event data are semantically aligned and associated with each other to obtain associated fused data. The associated and fused data is structured and stored to obtain the digital twin database.

3. The structural service performance evaluation method as described in claim 1, characterized in that, The step of acquiring real-time monitoring data of the target structure includes: Raw monitoring data is collected in real time by sensors deployed on the target structure; The raw monitoring data is preprocessed to obtain the real-time monitoring data; The real-time monitoring data is associated and bound with the corresponding components in the target structure in terms of time and space, and stored in the digital twin database.

4. The structural service performance evaluation method as described in claim 1, characterized in that, The step of dynamically calibrating the initial simulation model based on the real-time monitoring data to obtain the target digital twin includes: Based on at least one key parameter in the initial simulation model, construct a set of parameters to be calibrated; Based on the real-time monitoring data, an objective function is constructed to quantify the difference between the simulation output and the real-time monitoring data; The set of parameters to be calibrated is iteratively optimized based on the optimization algorithm to obtain the optimal parameter solution, wherein the optimal parameter solution is used to make the function value of the objective function satisfy the preset convergence condition; The optimal parameter solution is input into the initial simulation model to obtain the target digital twin.

5. The structural service performance evaluation method as described in claim 1, characterized in that, The steps for constructing the load spectrum of the future service environment include: Acquire historical environmental monitoring data and climate prediction information of the target structure; Based on the historical environmental monitoring data and the climate prediction information, a probabilistic load model is constructed, which is used as the future service environment load spectrum. The probabilistic load model is used to describe the changes and frequency of environmental load intensity of the target structure during its future service period.

6. The structural service performance evaluation method as described in claim 1, characterized in that, After the steps of constructing the future service environment load spectrum and conducting a safety assessment of the service performance of the target structure based on the target digital twin and the future service environment load spectrum, the method further includes: The security assessment results are compared with preset performance thresholds; If the security assessment result does not meet the preset performance threshold, at least one design variable is defined in the target digital twin; Using the design variables as the optimization object and the future service environment load spectrum as the input condition, the target digital twin is optimized and iterated to obtain the optimal redesign scheme.

7. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the structural service performance evaluation method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the structural service performance evaluation method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the structural service performance evaluation method as described in any one of claims 1 to 6.

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