CN119205067A

CN119205067A - A method, system, device and storage medium for monitoring and managing building structures

Info

Publication number: CN119205067A
Application number: CN202411144789.8A
Authority: CN
Inventors: 徐磊; 李子乔; 周堃野; 余光远; 王少纯
Original assignee: Shanghai Construction No 1 Group Co Ltd
Current assignee: Shanghai Construction No 1 Group Co Ltd
Priority date: 2024-08-20
Filing date: 2024-08-20
Publication date: 2024-12-27

Abstract

A method, system, device and storage medium for monitoring and managing building structures, relating to the field of civil engineering. In the method, three-dimensional data of a building structure is obtained, and a digital twin model of the building structure is constructed based on the three-dimensional data; real-time status data of the building structure is obtained from a preset sensor network, and the real-time status data is compared with the reference status data in the digital twin model; when the difference between the real-time status data and the reference status data in the digital twin model is less than a first threshold, the performance prediction and life evaluation of the building structure are performed based on the real-time status data combined with influencing factors. The implementation of the technical solution provided by the present application realizes the information and intelligent management of the entire life cycle of the project.

Description

Building structure monitoring and managing method, system, equipment and storage medium

Technical Field

The application relates to the technical field of civil engineering, in particular to a method, a system, equipment and a storage medium for monitoring and managing a building structure.

Background

With the development of science and technology, super high-rise buildings are more and more, and the long-term guarantee of structural safety and use functions of the super high-rise buildings becomes an important problem.

At present, the structure monitoring method is often limited to detection at a specific time point, management of a full life cycle cannot be achieved, and the processing and application modes of monitoring data are relatively single, so that the advantages of the digital twin technology cannot be fully exerted.

Therefore, a method for implementing full-cycle building structure monitoring and management by digital twinning technology is needed.

Disclosure of Invention

The application provides a method, a system, equipment and a storage medium for monitoring and managing a building structure, which are used for realizing real-time monitoring and continuous monitoring, improving the monitoring efficiency and accuracy, realizing high-efficiency data processing and visual result display through a digital twin technology, realizing informatization and intelligent management of the whole life cycle of engineering by combining a whole life cycle management concept, considering factors such as material aging, damage accumulation and the like, and improving the accuracy of structure performance prediction and life assessment.

In a first aspect of the present application, there is provided a method for building structure monitoring and management, applied to a building management platform, the method comprising:

Acquiring three-dimensional data of a building structure, and constructing a digital twin model of the building structure according to the three-dimensional data;

acquiring real-time state data of the building structure from a preset sensor network, and comparing the real-time state data with reference state data in the digital twin model;

When the difference between the real-time state data and the reference state data in the digital twin model is smaller than a first threshold value, carrying out performance prediction and life assessment on the building structure according to the real-time state data and combining influencing factors, wherein the influencing factors comprise physical factors, environmental factors, using factors and maintenance factors, the physical factors comprise material properties and structural dimensions, the environmental factors comprise temperature, humidity, wind power, precipitation amount and precipitation pH value, the using factors comprise a load mode and a service life, the maintenance factors comprise maintenance history and maintenance quality,

The performance prediction and life assessment of the building structure according to the real-time state data and the influence factors comprises the following steps:

acquiring physical factors of the building structure, and acquiring environmental factors, use factors and maintenance factors in a preset time period, and performing first aging prediction on building materials of the building structure according to the physical factors, the environmental factors, the use factors and the maintenance factors;

and adjusting the first aging prediction by combining the real-time state data to obtain a second aging prediction, and predicting the service life of the building structure according to the second aging prediction, wherein the real-time state data comprises displacement, deformation, vibration and stress strain.

By adopting the technical scheme, the three-dimensional data of the building structure is obtained, and the digital twin model is constructed, so that the physical state of the building structure can be restored to a high degree, and an accurate virtual mirror image is provided for subsequent monitoring, evaluation and prediction. The digital twin model allows the manager to perform various simulations and tests in a virtual environment without disturbing the actual building structure. The state data of the building structure is obtained from a preset sensor network in real time and is compared with the reference state data in the digital twin model, so that the tiny change of the state of the building structure can be found in time. The real-time monitoring mechanism is beneficial to quick response to potential safety hazards and prevention of accidents. And comprehensively considering various influencing factors such as physical factors, environmental factors, use factors, maintenance factors and the like, carrying out ageing prediction on the building materials of the building structure, and carrying out performance prediction and service life assessment according to the ageing prediction. The method not only considers the attribute of the material, but also fully considers the influence of the external environment and the actual use condition on the building structure, so that the prediction result is more comprehensive and accurate. The first aging prediction is adjusted in combination with the real-time status data to obtain a more accurate second aging prediction. The real-time state data such as displacement, deformation, vibration, stress strain and the like can directly reflect the current state of the building structure, and the actual aging condition and the residual life of the building structure can be more accurately estimated through combination with the prediction result. Optionally, the constructing the digital twin model of the building structure according to the three-dimensional data includes:

and constructing a geometric model according to the design drawing of the building structure and the building information model, constructing a physical model according to the mechanical properties and the structural connection mode of the geometric model combined material, and constructing a performance model according to the physical model combined with the historical performance data of each component.

By adopting the technical scheme, the geometric model is constructed according to the design drawing of the building structure and the building information model, so that the integrity and the accuracy of the digital twin model in geometric form are ensured. The method is helpful to truly reflect the appearance and the size of the building structure, and provides a solid foundation for the subsequent physical model and performance model construction. And a physical model is established by combining the mechanical properties of the materials and the structural connection mode, so that the digital twin model can simulate the physical properties and behaviors of the building structure. The method is favorable for predicting the stress condition and the deformation condition of the building structure under different conditions, and provides important basis for the optimal design and the safety evaluation of the structure. The performance model is constructed based on the physical model and in combination with historical performance data of each component, so that the digital twin model can more accurately predict the performance of the building structure. By comparing and analyzing the historical data with the real-time data, the performance deviation can be found and corresponding optimization measures can be taken, so that the running efficiency and the safety of the building structure are improved. The complete digital twin model provides a powerful decision support tool for building managers. The manager can simulate the building structure performance under different scenes through the model, and evaluate the influence of different decision schemes, so that the optimal scheme is selected. Meanwhile, the model can also help to identify potential risks and make corresponding risk management measures, so that the possibility of accidents is reduced. The digital twin model is a data driven platform that can receive and process data from the sensor network in real time. This enables building managers to make management and maintenance decisions based on real-time data, improving management efficiency and reducing unnecessary maintenance costs.

Optionally, the comparing the real-time state data with reference state data in the digital twin model includes:

Observing the change condition of the real-time state data along with time through time sequence analysis, and judging whether mutation exists, wherein the mutation means that the difference between the real-time state data at the current moment and the real-time state data at the previous moment is larger than a second threshold value;

when no mutation exists, extracting characteristic frequency in a structural vibration signal through spectrum analysis to evaluate the mechanical property of the building structure;

and comparing the mechanical property data obtained by evaluation with the reference state data in the digital twin model.

By adopting the technical scheme, the change condition of the real-time state data along with time is observed according to time sequence analysis, so that the mutation phenomenon in the data can be rapidly discovered. Such abrupt changes may mean that an abnormal or potential risk of the building structure occurs, such as suddenly increasing stress or displacement. The mutations are found and processed in time, so that potential safety accidents are avoided, and the stability of the building structure is ensured. And when no mutation exists, extracting the characteristic frequency in the structural vibration signal by utilizing frequency spectrum analysis, so as to evaluate the mechanical property of the building structure. The method can be used for deeply knowing the dynamic behavior of the building structure and providing accurate information about performance parameters such as structural rigidity, damping and the like. And then, comparing the mechanical property data obtained by the evaluation with reference state data in the digital twin model, and accurately judging whether the performance of the building structure meets the design requirement or is degraded. By comparing the real-time state data with the reference state data, not only can the current performance problem be found, but also future performance trends can be predicted. Once the performance deviation or the descending trend is found, an early warning mechanism can be formulated in time and corresponding preventive strategies such as reinforcing monitoring, adjusting the use condition or carrying out necessary maintenance and reinforcement can be adopted, so that further performance deterioration is avoided. Based on the comparison result of the real-time state data and the reference state data, optimal management and decision support can be provided for building managers. The manager can adjust maintenance plans, optimize resource allocation or improve building design according to the comparison result so as to improve the safety, durability and economic benefit of the building structure.

Optionally, the performing a first aging prediction on the building material of the building structure according to the physical factor, the environmental factor, the usage factor, and the maintenance factor, and adjusting the first aging prediction in combination with the real-time status data to obtain a second aging prediction includes:

predicting building materials of the building structure by using a preset model according to the physical factors, the environmental factors, the use factors and the maintenance factors to obtain a first ageing prediction;

Comparing the first aging prediction with the real-time state data to determine a difference result, and adjusting parameters of the preset model according to the difference result, wherein the parameters comprise a material aging rate, an environmental erosion coefficient and a load factor;

and predicting building materials of the building structure by using the adjusted preset model according to the physical factors, the environmental factors, the use factors, the maintenance factors and the real-time state data to obtain a second ageing prediction.

By adopting the technical scheme, the preset model comprehensively considers various variables such as physical factors, environmental factors, use factors, maintenance factors and the like to predict the aging of the building material, and the multi-factor comprehensive analysis method is more comprehensive and accurate than single-factor prediction. It can reflect the aging process of building materials under different conditions more truly. And comparing the first aging prediction with the real-time state data, and adjusting parameters (such as material aging rate, environmental erosion coefficient, load factor and the like) of a preset model according to the comparison, so that dynamic optimization of the model is realized. The method can ensure that the prediction result is continuously adjusted along with the change of the actual condition, and improves the timeliness and the accuracy of the prediction. And using the adjusted preset model, and combining the real-time state data and other influencing factors to perform second aging prediction. The comprehensive prediction not only considers the influence of the property and the external condition of the material, but also combines the feedback of real-time data, so that the prediction result is closer to the actual situation, and a more reliable basis is provided for the performance evaluation and the service life prediction of the building structure. By means of real-time data feedback and dynamic model adjustment, prediction errors are effectively reduced, and prediction accuracy is improved. This is critical to maintenance and management of the building structure, as it can help the manager to more accurately determine the actual condition of the building structure, and to formulate a more scientific and reasonable maintenance plan. Optionally, the performance prediction and life assessment of the building structure according to the real-time status data in combination with the influencing factors further includes:

acquiring a surface image of the building structure, and determining surface damage according to the surface image;

Acquiring mechanical property data from the real-time state, wherein the mechanical property data comprise elastic modulus, yield strength, impact toughness, residual stress and strain;

and adjusting the second aging prediction according to the surface damage and the mechanical property data to obtain a third aging prediction, and predicting the service life of the building structure according to the third aging prediction.

By adopting the technical scheme, the surface image of the building structure is obtained, and the damage condition of the surface can be accurately determined. The non-contact detection method is efficient and accurate, can timely find out damage such as surface cracks and flaking, and provides important basis for subsequent performance prediction and service life evaluation. The mechanical property data in the real-time state data, such as elastic modulus, yield strength, impact toughness, residual stress, strain and the like, are key indexes for evaluating the performance of the building structure. By acquiring the data, the mechanical properties of the building structure can be comprehensively known, and a quantitative basis is provided for performance prediction and service life evaluation. Based on the existing second aging prediction, the surface damage and mechanical property data are combined to adjust the second aging prediction, so that a more accurate third aging prediction is obtained. The adjustment can more comprehensively consider the ageing factors of the building structure, and improves the prediction accuracy. Based on the life prediction performed by the third aging prediction, various information such as surface damage, mechanical properties, environmental factors and the like of the building structure are comprehensively considered, so that the residual life of the building structure can be predicted more accurately. This helps building manager to make reasonable maintenance plan, prolongs building structure's life. By comprehensively considering information in many aspects such as surface damage, mechanical properties, environmental factors and the like, a building manager can more scientifically make decisions, optimize resource allocation and reduce potential risks. This helps promote the overall security and the reliability of building structure, ensure personnel's and property's safety.

Optionally, the method further comprises:

and when the difference between the real-time state data and the reference state data in the digital twin model is greater than or equal to a threshold value, positioning an abnormal position according to the real-time state data, and generating warning information according to the abnormal position.

By adopting the technical scheme, the real-time state data and the reference state data are compared, and once the difference is detected to exceed the threshold value, the abnormal position can be rapidly positioned. This rapid positioning capability is critical to the timely discovery and management of problems in the building structure, helping to avoid problem escalation or causing more serious consequences. After the building management platform is positioned at the abnormal position, the building management platform can automatically generate warning information. The warning information can contain information of specific positions, properties, severity and the like of the anomalies, so that management staff can quickly know the conditions and take corresponding countermeasures. Meanwhile, the warning information can be transmitted in various modes, such as mobile phone short messages, emails, system interface prompts and the like, so that management staff can be ensured to receive and react in time. By locating the abnormal position and generating the warning information in time, the building manager is helped to take preventive measures before the risk occurs, and potential safety accidents are avoided. The method has important significance for guaranteeing the stability of the building structure, prolonging the service life and protecting the safety of personnel and property. Through automatic anomaly detection and warning generation, the workload of management staff can be lightened, and the management efficiency is improved. Meanwhile, based on abnormal positioning and warning information of the real-time data, a manager can more accurately make decisions, optimize resource allocation and improve maintenance and management level of a building structure.

Optionally, the locating the abnormal position according to the real-time status data includes:

Determining sub-time state data, determining the corresponding position of the sub-time state data in the digital twin model according to a mapping relation, and determining the actual position of the building structure according to the corresponding position, wherein the sub-time state data is data in which the difference between the real-time state data and the reference state data in the digital twin model is greater than or equal to a threshold value.

By adopting the technical scheme, the real-time state data and the reference state data in the digital twin model are compared, so that the data with the difference larger than or equal to the threshold value, namely the sub-time state data, can be rapidly identified. These data reflect anomalies or problems that may exist with the building structure, providing critical information for further anomaly localization. The mapping relation is utilized to determine the corresponding position of the sub-time state data in the digital twin model, so that the method is efficient and accurate. The digital twin model is used as a virtual replica of the building structure and has a one-to-one correspondence with the actual structure. Through the mapping relation, the position matched with the state data in the sub-time can be quickly found in the digital twin model, and an important basis is provided for determining the actual position. The actual abnormal position of the building structure is determined according to the corresponding position in the digital twin model, so that a manager can intuitively and accurately know the position of the problem. The positioning mode not only improves the efficiency of exception handling, but also is beneficial to reducing the possibility of misjudgment and missed judgment, and ensures the safety and stability of the building structure.

In a second aspect of the present application, a system for monitoring and managing a building structure is provided, including a model module, a comparison module, and a prediction module, wherein:

The model module is configured to acquire three-dimensional data of a building structure, and construct a digital twin model of the building structure according to the three-dimensional data;

The comparison module is configured to acquire real-time state data of the building structure from a preset sensor network and compare the real-time state data with reference state data in the digital twin model;

A prediction module configured to predict performance and evaluate life of the building structure based on the real-time status data in combination with influencing factors when a difference between the real-time status data and reference status data in the digital twin model is less than a first threshold, the influencing factors including physical factors including material properties and structural dimensions, environmental factors including temperature, humidity, wind power, precipitation and precipitation ph, the using factors including load pattern and age, and the maintaining factors including maintenance history and maintenance quality,

In a third aspect the application provides an electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface, both for communicating with other devices, the processor being for executing instructions stored in the memory to cause the electronic device to perform a method as claimed in any one of the preceding claims.

In a fourth aspect of the application there is provided a computer readable storage medium storing instructions which, when executed, perform a method as claimed in any one of the preceding claims.

In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. By acquiring three-dimensional data of the building structure and constructing a digital twin model, virtual replication and simulation of the building structure can be realized. The method enables a manager to monitor and evaluate the running state of the building structure in real time in a virtual environment, avoids the complicated process of on-site inspection in the traditional method, and improves the monitoring efficiency;

2. The comparison and analysis of the real-time state data and the reference state data in the digital twin model can find the difference between the real-time state data and the reference state data in time, and when the difference is smaller than a first threshold value, the building structure is indicated to be in a normal running state, and at the moment, the performance prediction and the service life evaluation of the building structure can be carried out by combining the influence factors. The method is beneficial to managers to know potential problems of the building structure in advance, a reasonable maintenance plan is formulated, and the service life of the building structure is prolonged;

3. And comprehensively considering various influencing factors such as physical factors, environmental factors, use factors, maintenance factors and the like, carrying out ageing prediction on the building materials of the building structure, and carrying out performance prediction and service life assessment according to the ageing prediction. The method not only considers the attribute of the material, but also fully considers the influence of the external environment and the actual use condition on the building structure, so that the prediction result is more comprehensive and accurate;

4. The first aging prediction is adjusted in combination with the real-time status data to obtain a more accurate second aging prediction. The real-time state data such as displacement, deformation, vibration, stress strain and the like can directly reflect the current state of the building structure, and the actual aging condition and the residual life of the building structure can be more accurately estimated through combination with the prediction result.

Drawings

FIG. 1 is a flow chart of a method for monitoring and managing a building structure according to an embodiment of the present application;

FIG. 2 is a block diagram of a system for monitoring and managing a building structure according to an embodiment of the present application;

Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Reference numerals illustrate 201, model module, 202, comparison module, 203, prediction module, 301, processor, 302, communication bus, 303, user interface, 304, network interface, 305, memory.

Detailed Description

In order that those skilled in the art will better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.

In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.

In the description of embodiments of the application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The embodiment discloses a method for monitoring and managing a building structure, fig. 1 is a flow chart of the method for monitoring and managing a building structure disclosed by the embodiment of the application, as shown in fig. 1, the method comprises the following steps:

S110, acquiring three-dimensional data of a building structure, and constructing a digital twin model of the building structure according to the three-dimensional data;

Acquisition of three-dimensional data can be accomplished in a number of ways, including but not limited to 1, laser scanning, a technique that projects a laser beam onto a building surface and acquires the shape and structure of the building surface by measuring reflected laser signals received by a scanner. The technology has the characteristics of high precision and high efficiency, can quickly acquire geometric information of a building, a laser scanning system mainly comprises a three-dimensional laser scanner and system software, can quickly, conveniently and accurately map a close-range static object, acquire space fine three-dimensional coordinates and provide high-precision data for three-dimensional modeling work, 2, a photogrammetry technology, a three-dimensional image of the building is acquired through a photogrammetry instrument, then a three-dimensional model of the building is drawn through image processing and measurement, the technology is suitable for data acquisition outside the building, the technology has higher precision and comprehensiveness, the aerophotography measurement technology is used for loading a photographic platform on an airplane, such as a digital aerial camera (DLG) and a Digital Elevation Model (DEM), processing and synthesizing the data, and establishing space three-dimensional data such as a Digital Orthophotograph (DOM), the method is suitable for acquiring large-area space three-dimensional data, 3, an unmanned aerial vehicle remote sensing technology is utilized for carrying a camera or a laser scanner to acquire aerial and data of the building, and the unmanned aerial vehicle can acquire flexible data in a large-scale and quick flight and relatively low-cost.

After the three-dimensional data is acquired, the process of constructing a digital twin model follows. The digital twin model is a virtual model integrating geometric and non-geometric information and relations of model elements such as all building and facility components and parts. It is deepened and perfected continuously along with the gradual progress of engineering construction process. The method comprises the key steps of constructing a digital twin model, namely, data processing, preprocessing acquired three-dimensional data, including denoising, filtering, registering and the like, so as to ensure the accuracy and consistency of the data, constructing the digital twin model of a building structure according to the processed three-dimensional data by using professional three-dimensional modeling software, optimizing the model, and optimizing the model to improve the rendering efficiency and the sense of reality of the model, wherein the process possibly involves the processing of multiple aspects of geometric shapes, texture mapping, illumination effects and the like of the building, and optimizing operations such as multilevel, multilevel instantiation, non-instantiation caching and the like can be performed on the model to generate pyramid cache data after the scheduling optimization of scene data, so that the light weight of the model data is realized. The constructed digital twin model can truly reflect the appearance, position, height and other attributes of the building structure, has mapping-level precision, and provides a solid foundation for subsequent performance prediction, life assessment, anomaly detection and other applications.

Optionally, the constructing the digital twin model of the building structure according to the three-dimensional data includes:

The embodiment of the application is illustrated by taking super high-rise buildings as an example. And obtaining design drawings of the super high-rise building, including a plan view, an elevation view, a section view and the like, and Building Information Model (BIM). This information provides the basic geometry and structure of the building. And (3) accurately constructing a geometric model of the super high-rise building according to the design drawing and BIM data by utilizing professional three-dimensional modeling software. The model includes information about the appearance of the building, the internal space layout, the location and size of the individual elements, etc. Through continuous adjustment and optimization, the high consistency of the model and the actual building structure is ensured. On the basis of the geometric model, the establishment of the physical model is started. The physical model mainly focuses on the mechanical properties and connection modes of the building structure. Mechanical properties of various materials used for the building, such as modulus of elasticity, poisson's ratio, yield strength, etc., are obtained from the materials database. Meanwhile, structural connection modes such as welding, bolting and the like are analyzed. And combining the information and the geometric model, setting corresponding materials and connection properties in software, and constructing a physical model of the super high-rise building. The model can simulate the response and deformation of the building structure under the stress condition, and provides a basis for subsequent performance prediction. Then, a performance model is constructed based on the physical model and historical performance data of each component. Historical performance data, including data on strength, durability, deformation, etc., of various components (e.g., beams, columns, panels, etc.) of the super high-rise building is collected. These data may be derived from actual operating data of similar buildings, or may be obtained through experimentation and simulation. The model is further optimized and calibrated using data analysis tools and algorithms in combination with the physical model and the historical performance data. By continuously adjusting parameters and simulation conditions, a digital twin model capable of reflecting the actual performance of the super high-rise building is obtained.

By constructing a geometric model by combining a design drawing of the building structure and a building information model, the appearance, the size and the spatial layout of the building structure can be accurately reflected. The method provides an accurate geometric data basis for subsequent model analysis and application, and ensures the consistency of the digital twin model and the actual building structure. And a physical model is established according to the mechanical characteristics of the geometric model combined material and the structural connection mode, so that the response and deformation of the building structure under the stress condition can be further simulated. The physical model takes the characteristics of elasticity, plasticity, strength and the like of materials, and the connection mode and constraint conditions between the components into consideration, so that the model can reflect the mechanical behavior of the building structure more truly. And constructing a performance model according to the physical model and combining the historical performance data of each component, so that the performance change trend of the building structure under different environmental conditions can be predicted. The performance model takes into account not only the static performance of the structure but also its dynamic properties such as durability, shock resistance, fatigue life, etc. By introducing historical performance data, the model can more accurately reflect the performance characteristics of the structure in actual operation, and powerful support is provided for subsequent monitoring, early warning and maintenance management.

S120, acquiring real-time state data of the building structure from a preset sensor network, and comparing the real-time state data with reference state data in the digital twin model;

The preset sensor network is the basis for acquiring real-time state data. These sensors are deployed at various critical locations of the building structure for monitoring and collecting various data related to the performance and safety of the building structure. The sensor types are various and may include displacement sensors, stress sensors, temperature sensors, humidity sensors, etc., which are capable of measuring and recording various parameter changes of the building structure in real time during operation. After the real-time state data is acquired, the real-time state data is compared with reference state data in the digital twin model. The reference state data is determined according to design parameters, material characteristics, expected operation environment and other factors of the building structure in the digital twin model construction process. It represents the performance of the building structure in an ideal state and is a reference standard for evaluating whether the actual state data are normal or not. The alignment process is typically accomplished by data analysis software or a platform. The software can receive real-time data from the sensor network and automatically match and compare the real-time data with reference data in the digital twin model. The content of the alignment may include various aspects of structural displacement, stress distribution, temperature variation, etc. By comparing the difference between the real-time data and the reference data, whether the building structure is in a normal state or whether an abnormality or potential risk exists can be judged. In the comparison process, accuracy and reliability of data also need to be considered. Because the sensor itself may have errors or because of interference from environmental factors, data cleaning and preprocessing is required to eliminate outliers and noise. In addition, a reasonable comparison threshold value is required to be set according to the characteristics of the building structure and the operation environment.

Real-time status data, particularly data concerning displacement and stress, is collected over a period of time for the super high-rise building. These data are presented in time series reflecting the change in the status of the super high-rise building over time. Using time series analysis techniques, the trend of variation between these data points was observed. In particular, it is noted whether there is a mutation point, i.e. the real-time status data at the current moment differs significantly from the data at the previous moment by more than a preset second threshold. These abrupt points may be indicative of abrupt changes or anomalies in the super high-rise building structure. In the embodiment of the application, if the displacement data is found to suddenly increase at a certain moment, the preset threshold value is exceeded. This suggests that there may be some kind of anomaly that requires further analysis. Vibration is one of the important indexes for evaluating the mechanical properties of super high-rise buildings. The vibration signal is subjected to spectrum analysis and is decomposed into components with different frequencies, and the characteristic frequencies of the super high-rise building structure can be extracted by analyzing the frequency components, and the frequencies are closely related to the inherent vibration characteristics of the super high-rise building. In the embodiment of the application, the vibration signal is subjected to spectrum analysis by using a Fast Fourier Transform (FFT) method and the like, and several key characteristic frequencies are identified. Once the real-time mechanical property data (obtained by spectrum analysis) of the super high-rise building are obtained, the data are compared with the reference state data in the digital twin model. The reference state data in the digital twin model is determined based on design parameters, material characteristics, expected operating environment and other factors of the super high-rise building in the model construction process. It represents the performance of super high-rise buildings in an ideal state. And comparing the real-time mechanical property data with the reference data, and calculating the difference or the ratio between the real-time mechanical property data and the reference data. These differences or ratios may determine whether the actual performance of the super high-rise building meets expectations and whether there is potential performance degradation or damage. In the embodiment of the application, the characteristic frequency of the real-time vibration data is found to have a certain degree of deviation compared with the reference data through comparison. This may mean that the stiffness or damping of the super high-rise building structure is changed, requiring further analysis and evaluation.

The time sequence analysis is used for observing the change condition of the real-time state data along with time and judging whether mutation exists, and the process is helpful for timely finding out abnormal change of the building structure state. Abrupt changes generally mean that the structure may suffer from external impact, damage, or performance degradation. By setting a reasonable second threshold value, the building management platform can automatically detect and mark the mutation points, so that management staff is reminded to pay attention to and process potential risks in time. And when no mutation exists, further extracting the characteristic frequency in the structural vibration signal through spectrum analysis so as to evaluate the mechanical property of the building structure. Spectral analysis can decompose a complex vibration signal into a series of simple frequency components, revealing the natural vibration characteristics of the structure. By extracting the characteristic frequency and comparing the characteristic frequency with the reference data, whether the structure has the change of performance parameters such as rigidity, damping and the like can be judged. This approach not only improves the accuracy of the assessment, but also finds some problems that are difficult to find by visual observation or simple measurement. And comparing the mechanical property data obtained by evaluation with the reference state data in the digital twin model, so that the gap between the actual performance and the expected performance of the building structure can be comprehensively evaluated. By calculating the difference or the ratio, the performance state of the structure can be known quantitatively, and a decision basis is provided for subsequent maintenance and management. The comparison method not only improves the monitoring precision and efficiency, but also is beneficial to predicting the future performance trend of the structure, thereby making a more scientific and reasonable maintenance plan.

S130, when the difference between the real-time state data and the reference state data in the digital twin model is smaller than a first threshold value, performing performance prediction and life assessment on the building structure according to the real-time state data and combining influence factors, wherein the influence factors comprise physical factors, environmental factors, use factors and maintenance factors, the physical factors comprise material properties and structural dimensions, the environmental factors comprise temperature, humidity, wind power, precipitation and precipitation pH value, the use factors comprise a load mode and a service life, the maintenance factors comprise maintenance history and maintenance quality,

When the difference between the real-time state data and the reference state data in the digital twin model is smaller than a first threshold value, the current performance of the building structure is basically consistent with the expected performance, and the building structure is in a normal running state. However, in order to more fully understand the performance of a building structure and predict its future development trend, it is also necessary to perform performance prediction and life assessment on the building structure in combination with influencing factors. Material properties include basic physical properties of building materials such as strength, toughness, corrosion resistance, thermal conductivity, etc. These properties directly affect the durability and performance of the building material. The structural dimension, namely the geometric characteristics of the building structure, such as the dimension, the shape, the layout and the like, determine the stress performance and the stability of the structure. The temperature change can influence the expansion and contraction of the building material, and the aging of the material can be accelerated in a long-term high-temperature or low-temperature environment. Too high a humidity may lead to hygroscopic expansion of the material and increase the risk of corrosion, while too low a humidity may lead to material cracking. Wind force, the wind force action can lead the building structure to generate dynamic response, and the long-term wind force action can lead to structural fatigue damage. Precipitation and precipitation pH value, wherein precipitation affects the wetting degree of a building, and precipitation pH value directly determines the corrosiveness of rainwater and erodes building materials. Load mode, namely load type, size, distribution and the like born by the building structure, directly influences the stress state and the safety of the structure. Years of use as the years of use increase, building materials and structures can age gradually, and the performance gradually decreases. The maintenance history comprises historical records of maintenance, reinforcement, reconstruction and the like of the building structure in the past, and reflects the maintenance condition and level of the building structure. Maintenance quality-the quality of maintenance work directly affects the service life and performance of the building structure. The high-quality maintenance can delay the aging of materials and improve the structural safety. Physical factor data of the building structure is acquired, including material properties, structure dimensions, and the like. And collecting environmental factor, usage factor and maintenance factor data within a preset time period. The first aging prediction of the building material is performed by using a preset aging prediction model (such as a physical model, a statistical model or a machine learning model) and combining the factors. The predictions take into account the aging laws and speeds of the material under different environmental conditions. The first aging prediction is adjusted in conjunction with real-time status data (e.g., displacement, deformation, vibration, stress strain, etc.). The real-time state data reflects the current actual stress state and performance of the building structure, and can provide important verification and correction basis for ageing prediction. And predicting the service life of the building structure according to the adjusted aging prediction result (namely, the second aging prediction). Life prediction generally considers factors such as the remaining strength of the material, the overall stability and safety of the structure, etc. to assess the service life and reliability of the building structure over a period of time in the future.

An appropriate pre-set model needs to be selected to make a prediction of the aging of the building material. The model can be a model based on physical principles (such as a model considering the microstructure change of a material), a statistical model (such as a regression analysis model based on historical data), or a machine learning model (such as a neural network, random forest and the like). The model should be selected based on the characteristics of the building material, the type of data available, and the accuracy of the predictions. Physical factors, collecting attribute data of building materials, such as strength, toughness, corrosion resistance and the like, and structural dimension data. And collecting environmental data such as temperature, humidity, wind power, precipitation amount, precipitation pH value and the like in a preset time period. Such data may be obtained through weather stations, sensor networks, and the like. And determining the load mode and service life of the building structure. Load patterns may include static load, dynamic load, impact load, etc., and the age refers to the length of time a building structure is built up to the present. And (5) collecting maintenance history and maintenance quality data of the building structure. Such data may include time, content, performance assessment, etc. of past repairs. And inputting the collected data of the physical factors, the environmental factors, the use factors and the maintenance factors into a preset model, and calculating to obtain a first ageing prediction result. This prediction is typically indicative of the degree of aging or remaining life of the building material under given conditions. And acquiring current state data of the building structure, such as displacement, deformation, vibration, stress strain and the like, through a sensor network and other real-time monitoring systems. These data reflect the actual stress state and performance behavior of the building structure in a real-time environment. And comparing the first aging prediction result with the real-time state data, and analyzing the difference between the first aging prediction result and the real-time state data. Differences may result from limitations of the preset model, inaccuracy of the input data, or abrupt changes in real-time environmental conditions, etc. And adjusting parameters of the preset model according to the comparison result and the difference analysis. These parameters typically include material aging rate, environmental erosion coefficient, loading factor, etc., which directly affect the prediction of the model. The purpose of adjusting parameters is to enable the prediction result of the model to be more approximate to real-time state data, and to improve the accuracy and reliability of prediction. And adjusting the parameter value of the material aging rate according to the material aging condition reflected by the real-time state data. And adjusting parameter values of the environmental erosion coefficients according to the monitoring data of the real-time environmental conditions so as to reflect the influence degree of different environmental conditions on material aging. And adjusting parameter values of the load factors according to parameters such as stress and strain in the real-time state data so as to reflect the influence of the actual load condition on material aging. And predicting the building materials of the building structure again according to the physical factors, the environmental factors, the use factors, the maintenance factors and the real-time state data by using the adjusted preset model to obtain a second aging prediction result. The prediction result is verified by real-time data and the model parameters are adjusted, so that the prediction result is closer to the actual situation, and has higher accuracy and reliability.

The first aging prediction is performed by combining a plurality of variables such as physical factors, environmental factors, use factors, maintenance factors and the like and using the preset model, so that the aging process of the building material under different conditions can be more comprehensively considered. And then, comparing the first aging prediction with the real-time state data, and adjusting model parameters according to the difference result, so that the accuracy of the prediction is further improved. The dynamic adjustment mechanism ensures that the prediction result can be closer to the actual situation, and reduces errors. Through the feedback of real-time data, the model can continuously learn and adapt to the change of the building structure in actual operation, so that the adaptability and the robustness of the model are improved. The model with strong adaptability can better cope with various complex conditions, and provides more reliable basis for performance prediction and service life evaluation of building structures. And performing second aging prediction based on the adjusted preset model, so that the obtained prediction result is more accurate and reliable. The prediction results not only can help the manager to know the current state and the future development trend of the building structure, but also can provide powerful support for making scientific and reasonable maintenance plans, optimizing structural design, prolonging service life and other decisions. The whole prediction and adjustment process realizes automation and intellectualization, and reduces the influence of manual intervention and subjective judgment. This not only improves the working efficiency, but also reduces the risk of human error. Meanwhile, the intelligent management means provides a more convenient and efficient mode for long-term monitoring and maintenance of the building structure. Optionally, the performance prediction and life assessment of the building structure according to the real-time status data in combination with the influencing factors further includes:

The unmanned plane or the wall climbing robot is used for acquiring surface images of the super high-rise building, and the images can clearly show damage conditions such as cracks, rust, peeling and the like on the surface of the super high-rise building. These images are analyzed using image processing techniques to identify and quantify surface damage. The damage degree and the distribution condition of different areas can be compared, so that the overall damage condition of the super high-rise building can be primarily judged. Real-time status data is obtained from sensors installed on super high-rise buildings and mechanical property data is extracted therefrom. These data include modulus of elasticity, yield strength, impact toughness, residual stress and strain, etc., which can reflect the intrinsic properties of the super high-rise building structure. After the surface damage and mechanical property data are obtained, the previous second aging prediction is adjusted in combination with these data. Likewise, parameters of the preset model can be adjusted according to the surface damage and the mechanical property data. The surface damage data provides direct information about the external state of the structure, while the mechanical property data reflects the property changes inside the structure. By comprehensively considering the data of the two aspects, the aging condition of the super high-rise building can be estimated more accurately. For example, if the surface damage is severe and the mechanical property data also shows a significant decrease, this may mean that the structural properties of the super high-rise building have been severely degraded. Based on this comprehensive consideration, the second aging prediction is adjusted to obtain a third aging prediction. This prediction is more closely related to the actual aging of the super high-rise building. And predicting the service life of the super high-rise building according to the third aging prediction. And the remaining service life of the super high-rise building is estimated by combining the factors such as the design service life, the service condition, the safety standard and the like of the super high-rise building. If the prediction result shows that the residual life of the super high-rise building is short, or the performance of the super high-rise building cannot meet the current use requirement, corresponding maintenance and management strategies such as reinforcing monitoring, reinforcing repair and the like can be formulated.

By acquiring a surface image of the building structure and determining the surface damage, the state of the exterior of the building structure can be intuitively understood. Surface damage such as cracks, corrosion and the like is a direct representation of the performance degradation of the building structure, and can find potential safety hazards in time by accurately identifying and analyzing the surface damage and the corrosion, thereby providing important basis for subsequent performance prediction and life assessment. Mechanical property data such as elastic modulus, yield strength, impact toughness, residual stress, strain and the like are extracted from the real-time state data, so that the intrinsic performance of the building structure can be deeply known. The mechanical property data reflect the response and deformation conditions of the building structure under the action of stress, and have important significance for evaluating the bearing capacity and safety of the structure. The second aging prediction is adjusted by combining the surface damage and the mechanical property data, so that the aging condition of the building structure can be considered more comprehensively. The surface damage and mechanical property data reflect the performance states of the building structure from the external aspect and the internal aspect respectively, and the comprehensive analysis of the surface damage and the mechanical property data can evaluate the aging degree and the trend of the structure more accurately. And according to the third aging prediction, the service life of the building structure is predicted, so that more scientific and reliable results can be obtained. The third aging prediction is obtained based on comprehensive analysis of surface damage and mechanical property data, so that the third aging prediction is closer to the actual condition of the building structure, and the residual service life of the structure can be predicted more accurately.

Optionally, the method further comprises:

And establishing a digital twin model of the super high-rise building. The model contains detailed information of the building structure, such as geometry, material properties, connection relations, etc., and sets reference state data based on historical data and simulation analysis. These baseline state data represent expected performance parameters of the building structure under normal operating conditions. And collecting state data of the super high-rise building in real time, including displacement, deformation, vibration, stress strain and the like. These data are transmitted through the sensor network and compared in real time with the digital twin model. On a certain day, a significant difference is found between the real-time state data and the reference state data in the digital twin model. Specifically, the vibration data of a certain floor exceeds a preset threshold. Based on this finding, the abnormality localization program is immediately started. By analyzing the spatial distribution and time series change of vibration data, it is determined that abnormality occurs at a specific floor and area of the super high-rise building. And then generating warning information according to the abnormal position. This information contains the specific location, nature, severity and possible cause of the anomaly. The warning information is sent to related personnel, such as building manager, maintenance personnel or safety monitoring personnel, through the early warning system. After receiving the warning information, the related personnel take action rapidly. They go to the abnormal location for field inspection and take emergency measures such as evacuating personnel, limiting use or temporary reinforcement as needed.

By comparing the real-time state data with the reference state data in the digital twin model in real time, the change of the performance of the building structure can be found in time. When the difference between the real-time state data and the reference state data exceeds a preset threshold value, it means that abnormal conditions such as damage, deformation or performance degradation of the building structure may occur. The real-time comparison method can find potential safety hazards in time and is beneficial to preventing accidents. The abnormal position is positioned according to the real-time state data, and the specific position of the problem in the building structure can be accurately determined. This is critical for subsequent maintenance and management. By locating the abnormal position, the inspection and repair work can be more pertinently carried out, the maintenance efficiency is improved, and unnecessary resource waste is reduced. The warning information is generated according to the abnormal position, so that related personnel can be timely notified, and the rapid response of the personnel and the corresponding measures can be ensured. The warning information contains specific information of the abnormal position, so that related personnel can quickly know the severity of the problem and make a correct decision. This helps to reduce the risk of accidents and ensure the safety of personnel. By the method, the digital twin model can be combined with real-time state data, so that comprehensive monitoring and early warning of the building structure can be realized. The digital twin model provides basic information and performance expectations of the building structure, while the real-time status data reflects the actual status of the building structure. The performance state of the building structure can be more comprehensively known through comparing and analyzing the data of the two aspects, and a scientific basis is provided for subsequent maintenance and management.

The real-time state data is compared with reference state data in the digital twin model. In this process, it is found that the difference between the state data at some sub-time and the reference state data exceeds a preset threshold. These sub-time state data are mainly from vibration sensors in a specific area of the super high-rise building. To locate the anomaly location, their corresponding locations in the digital twin model are found at the data processing center based on the sub-time state data. Since the mapping relationship between the sensor and a specific location in the model has already been established when the digital twin model is established, the exact location of these sub-time state data in the model can be determined. The digital twin model can intuitively see the position of the abnormal data and further analyze the structural characteristics and possible abnormal reasons of the position. Based on the analysis of the model, the preliminary judgment abnormality may be caused by the occurrence of a problem in the supporting structure of a certain area of the super high-rise building. And determining the actual abnormal position of the bridge according to the corresponding position in the digital twin model. This provides accurate guidance for our subsequent field inspection and repair.

By determining sub-temporal state data, i.e., screening out real-time state data that differs from reference state data in the digital twin model by greater than or equal to a threshold, data points that are abnormal or degraded in performance can be accurately focused. This avoids the processing of a large amount of normal state data, and improves the efficiency and accuracy of anomaly detection. And determining the corresponding position of the sub-real-time state data in the digital twin model according to the mapping relation, and realizing the mapping from the data space to the physical space. The mapping relation enables the specific position of the abnormal data in the building structure to be intuitively known, and convenience is provided for subsequent abnormal positioning and cause analysis. And determining the actual position of the building structure according to the corresponding position in the digital twin model, and further mapping the abnormal position in the virtual model to the actual building structure. This ensures the accuracy and reliability of the anomaly location so that the relevant personnel can go directly to the actual location for inspection and repair. By the method, the abnormal position in the building structure can be found in time and can be quickly positioned to a specific physical position. This helps to reduce potential safety hazards and improve the reliability and durability of the building structure. Meanwhile, an important reference basis is provided for subsequent maintenance and management, so that related personnel can carry out maintenance and service work more pertinently.

The embodiment also discloses a system for monitoring and managing a building structure, and fig. 2 is a schematic block diagram of the system for monitoring and managing a building structure disclosed in the embodiment of the application, as shown in fig. 2, the system includes a model module 201, a comparison module 202 and a prediction module 203, wherein:

a model module 201 configured to obtain three-dimensional data of a building structure, and construct a digital twin model of the building structure according to the three-dimensional data;

A comparison module 202 configured to obtain real-time state data of the building structure from a preset sensor network, and compare the real-time state data with reference state data in the digital twin model;

A prediction module 203 configured to predict performance and evaluate life of the building structure based on the real-time status data and the reference status data in the digital twin model when the difference between the real-time status data and the reference status data is less than a first threshold, the real-time status data in combination with influencing factors including physical factors including material properties and structural dimensions, environmental factors including temperature, humidity, wind power, precipitation amount, and precipitation ph, and maintenance factors including load patterns and age, the maintenance factors including maintenance history and maintenance quality,

Optionally, the model module 201 is configured to:

Optionally, the comparison module 202 is configured to:

Optionally, the prediction module 203 is configured to:

Optionally, the prediction module 203 is further configured to:

Optionally, the system further comprises a positioning module configured to:

Optionally, the positioning module is configured to:

It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be implemented by different functional modules, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.

The present embodiment also discloses an electronic device, referring to fig. 3, which may comprise at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, at least one memory 305.

Wherein the communication bus 302 is used to enable connected communication between these components.

The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.

The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall server, perform various functions of the server and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may be implemented in at least one hardware form of digital signal Processing (DIGITAL SIGNAL Processing, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processor 301 (Central Processing Unit, CPU), an image processor 301 (Graphics Processing Unit, GPU), a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like, the GPU is used for rendering and drawing contents required to be displayed by the display screen, and the modem is used for processing wireless communication. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.

The Memory 305 may include a random access Memory 305 (Random Access Memory, RAM), or may include a Read-Only Memory 305 (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area that may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the respective method embodiments described above, etc., and a stored data area that may store data, etc., involved in the respective method embodiments described above. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. As shown, the memory 305, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and applications for methods of building structure monitoring and management.

In the electronic device shown in fig. 3, the user interface 303 is mainly used as an interface for providing input to a user and obtaining data input by the user, while the processor 301 may be used to invoke an application program in the memory 305 for storing methods of building structure monitoring and management, which when executed by the one or more processors 301, causes the electronic device to perform the methods as in one or more of the embodiments described above.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory 305. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory 305, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. The memory 305 includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a magnetic disk, or an optical disk.

The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims

1. A method for monitoring and managing building structures, characterized in that it is applied to a building management platform, and the method comprises:

Acquire three-dimensional data of a building structure, and construct a digital twin model of the building structure according to the three-dimensional data;

Acquire real-time status data of the building structure from a preset sensor network, and compare the real-time status data with the reference status data in the digital twin model;

When the difference between the real-time status data and the reference status data in the digital twin model is less than a first threshold, the performance prediction and life assessment of the building structure are performed according to the real-time status data combined with influencing factors, wherein the influencing factors include physical factors, environmental factors, usage factors and maintenance factors, wherein the physical factors include material properties and structural dimensions, the environmental factors include temperature, humidity, wind force, precipitation and precipitation pH, the usage factors include load mode and service life, and the maintenance factors include maintenance history and maintenance quality.

The performing of performance prediction and life assessment on the building structure according to the real-time status data combined with influencing factors comprises:

Acquire physical factors of the building structure, and acquire environmental factors, usage factors, and maintenance factors within a preset time period, and perform a first aging prediction on building materials of the building structure according to the physical factors, the environmental factors, the usage factors, and the maintenance factors;

The first aging prediction is adjusted in combination with the real-time status data to obtain a second aging prediction, and the life of the building structure is predicted based on the second aging prediction, wherein the real-time status data includes displacement, deformation, vibration, stress and strain.

2. The method for monitoring and managing a building structure according to claim 1, wherein constructing a digital twin model of the building structure according to the three-dimensional data comprises:

A geometric model is constructed according to the design drawings and building information model of the building structure, a physical model is established according to the geometric model combined with the mechanical properties of the materials and the structural connection method, and a performance model is constructed according to the physical model combined with the historical performance data of each component.

3. The method for monitoring and managing a building structure according to claim 1, wherein comparing the real-time status data with the reference status data in the digital twin model comprises:

Observe the change of the real-time status data over time through time series analysis to determine whether there is a mutation, wherein the mutation refers to that the difference between the real-time status data at the current moment and the real-time status data at the previous moment is greater than a second threshold;

When there is no mutation, extracting characteristic frequencies in the structural vibration signal through spectrum analysis to evaluate the mechanical properties of the building structure;

The evaluated mechanical property data is compared with the baseline state data in the digital twin model.

4. The method for monitoring and managing a building structure according to claim 1, wherein the performing a first aging prediction on the building materials of the building structure according to the physical factors, the environmental factors, the usage factors and the maintenance factors, and adjusting the first aging prediction in combination with the real-time status data to obtain a second aging prediction comprises:

Using a preset model to predict the building material of the building structure according to the physical factors, the environmental factors, the usage factors and the maintenance factors to obtain a first aging prediction;

Comparing the first aging prediction with the real-time status data to determine a difference result, and adjusting the parameters of the preset model according to the difference result, wherein the parameters include material aging rate, environmental erosion coefficient, and load factor;

The adjusted preset model is used to predict the building materials of the building structure according to the physical factors, the environmental factors, the usage factors, the maintenance factors and the real-time status data to obtain a second aging prediction.

5. The method for monitoring and managing building structures according to claim 1, wherein the step of predicting the performance and evaluating the life of the building structure based on the real-time status data combined with influencing factors further comprises:

Acquiring mechanical property data from the real-time state, the mechanical property data including elastic modulus, yield strength, impact toughness, residual stress and strain;

The second aging prediction is adjusted according to the surface damage and the mechanical property data to obtain a third aging prediction, and the life of the building structure is predicted according to the third aging prediction.

6. The method for monitoring and managing building structures according to claim 1, characterized in that the method further comprises:

When the difference between the real-time status data and the reference status data in the digital twin model is greater than or equal to a threshold, the abnormal position is located according to the real-time status data, and warning information is generated according to the abnormal position.

7. The method for monitoring and managing building structures according to claim 1, wherein locating the abnormal position according to the real-time status data comprises:

Determine sub-real-time status data, determine the corresponding position of the sub-real-time status data in the digital twin model according to the mapping relationship, and determine the actual position of the building structure according to the corresponding position, wherein the sub-real-time status data is data in the real-time status data whose difference with the baseline status data in the digital twin model is greater than or equal to a threshold.

8. A system for monitoring and managing building structures, comprising a model module, a comparison module and a prediction module, wherein:

A model module is configured to obtain three-dimensional data of a building structure and construct a digital twin model of the building structure according to the three-dimensional data;

A comparison module, configured to obtain real-time status data of the building structure from a preset sensor network, and compare the real-time status data with the reference status data in the digital twin model;

a prediction module, configured to perform performance prediction and life assessment on the building structure according to the real-time status data in combination with influencing factors when the difference between the real-time status data and the reference status data in the digital twin model is less than a first threshold, wherein the influencing factors include physical factors, environmental factors, usage factors and maintenance factors, the physical factors include material properties and structural dimensions, the environmental factors include temperature, humidity, wind force, precipitation and precipitation pH, the usage factors include load mode and service life, and the maintenance factors include maintenance history and maintenance quality,

The first aging prediction is adjusted in combination with the real-time status data to obtain a second aging prediction, and the life of the building structure is predicted based on the second aging prediction, wherein the real-time status data includes displacement, deformation, vibration, and stress-strain.

9. An electronic device, characterized in that it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.