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

Abstract

The invention discloses a building structure monitoring and management method, system and equipment and a storage medium, and relates to the field of civil engineering. The method comprises the following steps: acquiring three-dimensional data of a building structure, and constructing a digital twinborn 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; and 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 evaluation on the building structure according to the real-time state data in combination with influence factors. By implementing the technical scheme provided by the invention, informatization and intelligent management of the whole life cycle of the project is realized.

Description

Technical Field

[0001] The present application relates to the technical field of civil engineering, and in particular to a method, system, device and storage medium for monitoring and managing building structures. Background Art

[0002] With the development of science and technology, there are more and more super high-rise buildings, and the long-term guarantee of their structural safety and functional use has become an important issue.

[0003] At present, structural monitoring methods are often limited to detection at specific time points and cannot achieve full life cycle management. In addition, the processing and application methods of monitoring data are relatively simple and cannot fully utilize the advantages of digital twin technology.

[0004] Therefore, a method is needed to achieve full-cycle building structure monitoring and management through digital twin technology. Summary of the invention

[0005] The present application provides a method, system, equipment and storage medium for monitoring and managing building structures, which realize real-time and continuous monitoring and improve monitoring efficiency and accuracy; realize efficient data processing and intuitive result display through digital twin technology; combine the concept of full life cycle management to realize information and intelligent management of the entire life cycle of the project; consider factors such as material aging and damage accumulation to improve the accuracy of structural performance prediction and life assessment.

[0006] In a first aspect of the present application, a method for monitoring and managing a building structure is provided, which is applied to a building management platform. 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, the real-time status data including displacement, deformation, vibration, stress and strain.

[0007] By adopting the above technical solutions, the three-dimensional data of the building structure is obtained and the digital twin model is constructed, which can highly restore the physical state of the building structure and provide an accurate virtual mirror for subsequent monitoring, evaluation and prediction. The digital twin model allows managers to perform various simulations and tests in a virtual environment without interfering with the actual building structure. The state data of the building structure is obtained in real time from the preset sensor network and compared with the baseline state data in the digital twin model, so that slight changes in the state of the building structure can be discovered in time. This real-time monitoring mechanism helps to quickly respond to potential safety hazards and prevent accidents. Taking into account a variety of influencing factors such as physical factors, environmental factors, usage factors and maintenance factors, the aging of the building materials of the building structure is predicted, and performance prediction and life evaluation are carried out accordingly. This method not only takes into account the properties of the material itself, but also fully considers the impact of the external environment and actual usage on the building structure, making the prediction results more comprehensive and accurate. The first aging prediction is adjusted in combination with the real-time state data to obtain a more accurate second aging prediction. Real-time status data such as displacement, deformation, vibration, stress and strain can directly reflect the current status of the building structure. By combining with the prediction results, the actual aging and remaining life of the building structure can be more accurately evaluated. Optionally, constructing a digital twin model of the building structure based on the three-dimensional data includes: 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.

[0008] By adopting the above technical solutions, the geometric model is constructed according to the design drawings and building information model of the building structure, ensuring the integrity and accuracy of the digital twin model in terms of geometric form. This helps to truly reflect the appearance and size of the building structure, and provides a solid foundation for the subsequent construction of the physical model and performance model. The physical model is established in combination with the mechanical properties of the material and the structural connection method, so that the digital twin model can simulate the physical properties and behavior of the building structure. This helps to predict the stress and deformation of the building structure under different conditions, and provides an important basis for the optimization design and safety assessment of the structure. The performance model is constructed based on the physical model and combined with the 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 historical data and real-time data, performance deviations can be found and corresponding optimization measures can be taken to improve the operating efficiency and safety of the building structure. The complete digital twin model provides a powerful decision support tool for building managers. Managers can use the model to simulate the performance of the building structure in different scenarios, evaluate the impact of different decision-making options, and then select the best option. At the same time, the model can also help identify potential risks and formulate corresponding risk management measures to reduce the possibility of accidents. The digital twin model is a data-driven platform that can receive and process data from sensor networks in real time. This enables building managers to make management and maintenance decisions based on real-time data, improve management efficiency and reduce unnecessary maintenance costs.

[0009] Optionally, comparing the real-time status data with the reference status data in the digital twin model includes: 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.

[0010] By adopting the above technical solution, the mutation phenomenon in the data can be quickly discovered by observing the change of real-time status data over time according to time series analysis. This mutation may mean that the building structure has an abnormality or potential risk, such as a sudden increase in stress or displacement. Timely discovery and processing of these mutations will help avoid potential safety accidents and ensure the stability of the building structure. When there is no mutation, the characteristic frequency in the structural vibration signal is extracted by spectrum analysis to evaluate the mechanical properties of the building structure. This method can deeply understand the dynamic behavior of the building structure and provide accurate information on performance parameters such as structural stiffness and damping. Subsequently, the mechanical performance data obtained by these evaluations are compared with the baseline state data in the digital twin model to accurately determine whether the performance of the building structure meets the design requirements or whether it has degraded. By comparing the real-time status data with the baseline status data, not only can the current performance problems be discovered, but also the future performance trends can be predicted. Once a performance deviation or downward trend is found, an early warning mechanism can be formulated in time and corresponding preventive strategies can be taken, such as strengthening monitoring, adjusting the use conditions, or performing necessary repairs and reinforcements, so as to avoid further deterioration of performance. Based on the comparison results of the real-time status data and the baseline status data, optimization management and decision support can be provided for building managers. Managers can adjust maintenance plans, optimize resource allocation or improve building design based on the comparison results to improve the safety, durability and economic benefits of building structures.

[0011] Optionally, 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: 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.

[0012] By adopting the above technical solution, the preset model comprehensively considers multiple variables such as physical factors, environmental factors, usage factors and maintenance factors to predict the aging of building materials. This multi-factor comprehensive analysis method is more comprehensive and accurate than single factor prediction. It can more realistically reflect the aging process of building materials under different conditions. The first aging prediction is compared with the real-time status data, and the parameters of the preset model (such as material aging rate, environmental erosion coefficient, load factor, etc.) are adjusted accordingly to achieve dynamic optimization of the model. This method can ensure that the prediction results are constantly adjusted as the actual conditions change, thereby improving the timeliness and accuracy of the prediction. The adjusted preset model is used in combination with real-time status data and other influencing factors to make the second aging prediction. This comprehensive prediction not only takes into account the properties of the material itself and the influence of external conditions, but also combines the feedback of real-time data, so that the prediction results are closer to the actual situation, providing a more reliable basis for the performance evaluation and life prediction of building structures. Through the feedback of real-time data and the dynamic adjustment of the model, the prediction error is effectively reduced and the prediction accuracy is improved. This is crucial for the maintenance and management of building structures, because it can help managers more accurately judge the actual condition of building structures and formulate more scientific and reasonable maintenance plans. Optionally, the performing performance prediction and life assessment on the building structure according to the real-time status data in combination with 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, 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.

[0013] By adopting the above technical solution, the surface image of the building structure is obtained, and the surface damage can be accurately determined. This non-contact detection method is both efficient and accurate, and can detect surface cracks, peeling and other damage in time, providing an important basis for subsequent performance prediction and life assessment. The mechanical properties data in the real-time status data, such as elastic modulus, yield strength, impact toughness, residual stress and strain, are key indicators for evaluating the performance of building structures. By obtaining these data, the mechanical properties of the building structure can be fully understood, providing a quantitative basis for performance prediction and life assessment. On the basis of the existing second aging prediction, it is adjusted in combination with surface damage and mechanical properties data to obtain a more accurate third aging prediction. This adjustment can more comprehensively consider the aging factors of the building structure and improve the accuracy of the prediction. The life prediction based on the third aging prediction comprehensively considers the surface damage, mechanical properties and environmental factors of the building structure, so it can more accurately predict the remaining life of the building structure. This helps building managers to formulate reasonable maintenance plans and extend the service life of the building structure. By comprehensively considering various information such as surface damage, mechanical properties and environmental factors, building managers can make decisions more scientifically, optimize resource allocation and reduce potential risks. This helps improve the overall safety and reliability of the building structure and ensures the safety of people and property.

[0014] Optionally, the method further includes: 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.

[0015] By adopting the above technical solution, the real-time status data is compared with the reference status data. Once the difference is detected to exceed the threshold, the location of the abnormality can be quickly located. This rapid positioning capability is crucial for timely discovery and handling of problems in building structures, and helps to avoid the expansion of problems or more serious consequences. After locating the abnormal location, the building management platform can automatically generate warning information. These warning messages can contain information such as the specific location, nature, and severity of the abnormality, which helps managers to quickly understand the situation and take corresponding countermeasures. At the same time, the warning information can be transmitted in a variety of ways, such as mobile phone text messages, e-mails, system interface prompts, etc., to ensure that managers can receive and respond in time. By timely locating the abnormal location and generating warning information, it is helpful for building managers to take preventive measures before risks occur and avoid potential safety accidents. This is of great significance for ensuring the stability of building structures, extending their service life, and protecting the safety of personnel and property. Through automated anomaly detection and warning generation, the workload of managers can be reduced and management efficiency can be improved. At the same time, based on the abnormal location and warning information of real-time data, managers can make decisions more accurately, optimize resource allocation, and improve the maintenance and management level of building structures.

[0016] Optionally, locating the abnormal position according to the real-time status data includes: 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.

[0017] By adopting the above technical solution, the real-time status data is compared with the reference status data in the digital twin model, and the data with a difference greater than or equal to the threshold, namely the sub-real-time status data, can be quickly identified. These data reflect the possible anomalies or problems of the building structure and provide key information for further anomaly positioning. It is an efficient and accurate method to determine the corresponding position of the sub-real-time status data in the digital twin model using the mapping relationship. As a virtual replica of the building structure, the digital twin model has a one-to-one correspondence with the actual structure. Through the mapping relationship, the position matching the sub-real-time status data can be quickly found in the digital twin model, which provides an important basis 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 the management personnel can intuitively and accurately understand the problem. This positioning method not only improves the efficiency of exception handling, but also helps to reduce the possibility of misjudgment and missed judgment, and ensure the safety and stability of the building structure.

[0018] In a second aspect of the present application, a system for monitoring and managing building structures is provided, including 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 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, the real-time status data including displacement, deformation, vibration, stress and strain.

[0019] In the third aspect of the present application, an electronic device is provided, including 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 any one of the methods described above.

[0020] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0021] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring the three-dimensional data of the building structure and constructing a digital twin model, the virtual replication and simulation of the building structure can be achieved. This enables managers to monitor and evaluate the operating status of the building structure in real time in a virtual environment, avoiding the tedious process of on-site inspections required in traditional methods and improving monitoring efficiency; 2. Comparison and analysis of real-time status data with the baseline status data in the digital twin model can timely discover the difference between the two. When the difference is less than the first threshold, it indicates that the building structure is in normal operation. At this time, the performance prediction and life assessment of the building structure can be combined with the influencing factors. This helps managers understand the potential problems of the building structure in advance, formulate reasonable maintenance plans, and extend the service life of the building structure; 3. Comprehensively consider various influencing factors such as physical factors, environmental factors, usage factors and maintenance factors, predict the aging of building materials of building structures, and make performance predictions and life assessments based on this. This method not only considers the properties of the material itself, but also fully considers the impact of the external environment and actual usage on the building structure, making the prediction results more comprehensive and accurate; 4. Adjust the first aging prediction in combination with real-time status data to obtain a more accurate second aging prediction. Real-time status data such as displacement, deformation, vibration, stress and strain can directly reflect the current state of the building structure. By combining with the prediction results, the actual aging and remaining life of the building structure can be more accurately evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the method for monitoring and managing building structures disclosed in the embodiment of the present application; Figure 2 It is a module schematic diagram of a system for monitoring and managing building structures disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0023] Explanation of the reference numerals: 201, model module; 202, comparison module; 203, prediction module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0024] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0025] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0026] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0027] This embodiment discloses a method for monitoring and managing building structures. Figure 1 is a flow chart of the method for monitoring and managing building structures disclosed in the embodiment of the present application, such as Figure 1 As shown, 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; The acquisition of three-dimensional data can be achieved in many ways, including but not limited to the following technologies: 1. Laser scanning technology. Laser scanning is a technology that projects a laser beam onto the surface of a building and obtains the surface shape and structure of the building by measuring the reflected laser signal received by the scanner. This technology has the characteristics of high precision and high efficiency, and can quickly obtain the geometric information of buildings. The laser scanning system is mainly composed of a 3D laser scanner and system software. It can quickly, conveniently and accurately map static objects at close range, and obtain fine spatial 3D coordinates to provide high-precision data for 3D modeling. 2. Photogrammetry technology uses a photogrammeter to obtain a stereoscopic image of a building, and then draws a 3D model of the building through image processing and measurement. This technology is suitable for data collection on the outside of a building and has high precision and comprehensiveness. Aerial photogrammetry technology uses a photographic platform loaded on an aircraft, such as a digital aerial camera and a LIDAR shadow measurement system, to process and synthesize data, obtain digital line graphs (DLGs) and digital elevation models (DEMs), and establish digital orthophoto maps (DOMs) and other spatial 3D data. This method is suitable for macroscopic and large-area acquisition of spatial 3D data. 3. UAV remote sensing technology uses a drone equipped with a camera or laser scanner to take aerial photos of buildings and collect data. UAVs can fly flexibly and quickly obtain data on buildings over a large area at a relatively low cost.

[0028] After acquiring the three-dimensional data, the next step is to build a digital twin model. The digital twin model is a virtual model that integrates the geometric and non-geometric information and their relationships of all model elements such as components and parts of buildings and facilities. It is constantly deepened and improved as the construction process progresses. The key steps to build a digital twin model include: data processing, pre-processing the acquired three-dimensional data, including denoising, filtering, registration and other steps to ensure the accuracy and consistency of the data; model construction, using professional three-dimensional modeling software to build a digital twin model of the building structure based on the processed three-dimensional data. This process may involve processing of multiple aspects such as the building's geometry, texture mapping, and lighting effects; model optimization, in order to improve the rendering efficiency and realism of the model, the model can be optimized by multi-level and multi-level instantiation, non-instantiated caching, etc., to generate pyramid cache data after scene data scheduling optimization, thereby realizing lightweight model data. The constructed digital twin model can truly reflect the appearance, location, height and other attributes of the building structure, with surveying and mapping-level accuracy, providing a solid foundation for subsequent performance prediction, life assessment, and anomaly detection applications.

[0029] Optionally, constructing the digital twin model of the building structure according to the three-dimensional data includes: 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.

[0030] The embodiment of the present application is described by taking a super high-rise building as an example. The design drawings of the super high-rise building are obtained, including plan views, elevations, sections, etc., as well as building information models (BIM). This information provides the basic geometry and structure of the building. Using professional 3D modeling software, the geometric model of the super high-rise building is accurately constructed based on the design drawings and BIM data. This model includes information such as the appearance of the building, the internal space layout, the position and size of each component. Through continuous adjustment and optimization, the high consistency between the model and the actual building structure is ensured. On the basis of the geometric model, the physical model is established. The physical model mainly focuses on the mechanical properties and connection methods of the building structure. The mechanical properties of various materials used in the building, such as elastic modulus, Poisson's ratio, yield strength, etc., are obtained from the material database. At the same time, the structural connection methods, such as welding, bolt connection, etc., are also analyzed. Combined with this information and the geometric model, the corresponding material and connection properties are set in the software to construct a physical model of the super high-rise building. This model can simulate the response and deformation of the building structure under stress, providing a basis for subsequent performance prediction. Then, a performance model is constructed based on the physical model and the historical performance data of each component. The historical performance data of various components of super high-rise buildings (such as beams, columns, and plates) were collected, including data on strength, durability, deformation, etc. These data can come from the actual operation data of similar buildings, or they can be obtained through experiments and simulations. Combining the physical model with historical performance data, the model was further optimized and calibrated using data analysis tools and algorithms. By continuously adjusting parameters and simulation conditions, a digital twin model that can reflect the actual performance of super high-rise buildings was obtained.

[0031] By combining the design drawings of the building structure with the building information model to build a geometric model, the appearance, size and spatial layout of the building structure can be accurately reflected. This provides an accurate geometric data basis for subsequent model analysis and application, ensuring the consistency between the digital twin model and the actual building structure. The physical model is established based on the geometric model combined with the mechanical properties of the material and the structural connection method, which can further simulate the response and deformation of the building structure under stress. The physical model takes into account the elasticity, plasticity, strength and other properties of the material, as well as the connection method and constraints between the components, which enables the model to more realistically reflect the mechanical behavior of the building structure. The performance model is constructed based on the physical model combined with the historical performance data of each component, which can predict the performance change trend of the building structure under different environmental conditions. The performance model not only considers the static performance of the structure, but also its dynamic performance, such as durability, seismic resistance, fatigue life, etc. By introducing historical performance data, the model can more accurately reflect the performance characteristics of the structure in actual operation, providing strong support for subsequent monitoring, early warning and maintenance management.

[0032] S120, acquiring real-time status data of the building structure from a preset sensor network, and comparing the real-time status data with reference status data in the digital twin model; The preset sensor network is the basis for obtaining real-time status data. These sensors are deployed at various key parts of the building structure to monitor and collect various data related to the performance and safety of the building structure. There are various types of sensors, which may include displacement sensors, stress sensors, temperature sensors, humidity sensors, etc. They can measure and record the changes of various parameters of the building structure in real time during operation. After obtaining the real-time status data, it is compared with the baseline status data in the digital twin model. The baseline status data is determined during the construction of the digital twin model based on factors such as the design parameters, material properties and expected operating environment of the building structure. It represents the performance of the building structure under ideal conditions and is a reference standard for evaluating whether the actual status data is normal. The comparison process is usually completed through data analysis software or platforms. These software can receive real-time data from the sensor network and automatically match and compare it with the baseline data in the digital twin model. The comparison content may include structural displacement, stress distribution, temperature change and other aspects. By comparing the difference between real-time data and baseline data, it can be judged whether the building structure is in a normal state or whether there are abnormalities or potential risks. In the comparison process, the accuracy and reliability of the data also need to be considered. Since the sensor itself may have errors or data fluctuations due to interference from environmental factors, data cleaning and preprocessing are required to eliminate outliers and noise. In addition, it is necessary to set a reasonable comparison threshold based on the characteristics of the building structure and the operating environment.

[0033] Optionally, comparing the real-time status data with the reference status data in the digital twin model includes: 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.

[0034] Collect real-time status data of super high-rise buildings over a period of time, especially data on displacement and stress. These data are presented in the form of time series, reflecting the changes in the status of super high-rise buildings over time. Use time series analysis technology to observe the change trend between these data points. In particular, pay attention to whether there is a mutation point, that is, the difference between the real-time status data at the current moment and the data at the previous moment significantly exceeds the preset second threshold. These mutation points may indicate a sudden change or abnormality in the structure of a super high-rise building. In an embodiment of the present application, if it is found that the displacement data suddenly increases at a certain moment and exceeds the preset threshold. This suggests that there may be some abnormal situation, which requires further analysis. Vibration is one of the important indicators for evaluating the mechanical properties of super high-rise buildings. Perform spectral analysis on the vibration signal and decompose it into components of different frequencies. By analyzing these frequency components, the characteristic frequencies of the super high-rise building structure can be extracted, and these frequencies are closely related to the inherent vibration characteristics of the super high-rise building. In an embodiment of the present application, the vibration signal is spectrally analyzed using methods such as fast Fourier transform (FFT) and several key characteristic frequencies are identified. Once the real-time mechanical performance data of the super high-rise building is obtained (obtained through spectral analysis), these data are compared with the baseline state data in the digital twin model. The baseline state data in the digital twin model is determined during the model construction process based on factors such as the design parameters, material properties and expected operating environment of the super high-rise building. It represents the performance of the super high-rise building under ideal conditions. The real-time mechanical performance data is compared with the baseline data, and the difference or ratio between them is calculated. These differences or ratios can 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 present application, it was found through comparison that the characteristic frequency of the real-time vibration data was offset to a certain extent compared with the baseline data. This may mean that the stiffness or damping of the super high-rise building structure has changed, which requires further analysis and evaluation.

[0035] By observing the changes of real-time status data over time through time series analysis and judging whether there are mutations, this process helps to timely discover abnormal changes in the state of the building structure. Mutations usually mean that the structure may have suffered external impact, damage or performance degradation. By setting a reasonable second threshold, the building management platform can automatically detect and mark these mutation points, thereby reminding managers to pay attention to and deal with potential risks in a timely manner. When there is no mutation, the characteristic frequency in the structural vibration signal is further extracted through spectrum analysis to evaluate the mechanical properties of the building structure. Spectral analysis can decompose complex vibration signals into a series of simple frequency components, thereby revealing the inherent vibration characteristics of the structure. By extracting the characteristic frequency and comparing it with the benchmark data, it can be determined whether the structure has changed in performance parameters such as stiffness and damping. This method not only improves the accuracy of the evaluation, but also can discover some problems that are difficult to find through intuitive observation or simple measurement. By comparing the mechanical performance data obtained by the evaluation with the benchmark state data in the digital twin model, the gap between the actual performance and the expected performance of the building structure can be comprehensively evaluated. By calculating the difference or ratio, the performance status of the structure can be quantitatively understood, providing a decision-making basis for subsequent maintenance and management. This comparison method not only improves the accuracy and efficiency of monitoring, but also helps predict the future performance trends of the structure, thereby formulating a more scientific and reasonable maintenance plan.

[0036] S130: 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, 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, the real-time status data including displacement, deformation, vibration, stress and strain.

[0037] When the difference between the real-time status data and the baseline status data in the digital twin model is less than the first threshold, it means that the current performance of the building structure is basically consistent with the expected performance and is in normal operation. However, in order to have a more comprehensive understanding of the performance of the building structure and predict its future development trend, it is also necessary to combine the influencing factors to predict the performance and life of the building structure. Material properties: including basic physical properties such as strength, toughness, corrosion resistance, and thermal conductivity of building materials. These properties directly affect the durability and performance of building materials. Structural size: The geometric characteristics of the building structure, such as size, shape, and layout, determine the force performance and stability of the structure. Temperature: Temperature changes will affect the thermal expansion and contraction of building materials, and long-term high or low temperature environments may also accelerate the aging of materials. Humidity: Too high humidity will cause the material to absorb moisture and expand, increasing the risk of corrosion; too low humidity may cause the material to dry and crack. Wind force: Wind force will cause the building structure to produce dynamic response, and long-term wind force may cause structural fatigue damage. Precipitation and precipitation pH: Precipitation affects the wetness of the building, while precipitation pH directly determines the corrosiveness of rainwater and causes erosion of building materials. Load mode: The type, size, and distribution of loads borne on a building structure directly affect the stress state and safety of the structure. Service life: As the service life increases, building materials and structures will gradually age and their performance will gradually decline. Maintenance history: This includes historical records of repairs, reinforcements, and renovations of building structures in the past, reflecting the maintenance status and level of the building structure. Maintenance quality: The quality of maintenance work directly affects the service life and performance of the building structure. High-quality maintenance can delay material aging and improve structural safety. Obtain physical factor data of the building structure, including material properties and structural dimensions. Collect environmental factors, usage factors, and maintenance factor data within a preset time period. Use a preset aging prediction model (such as a physical model, a statistical model, or a machine learning model) and combine the above factors to make a first aging prediction of building materials. This prediction takes into account the aging laws and speeds of materials under different environmental conditions. Adjust the first aging prediction in combination with real-time status data (such as displacement, deformation, vibration, stress and strain, etc.). Real-time status data reflects the current actual stress state and performance of the building structure, which can provide important verification and correction basis for aging prediction. Based on the adjusted aging prediction results (i.e., the second aging prediction), the life of the building structure is predicted. Life prediction usually considers factors such as the remaining strength of the material, the overall stability and safety of the structure, etc., to evaluate the service life and reliability of the building structure in the future.

[0038] Optionally, 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: 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.

[0039] It is necessary to select a suitable preset model to predict the aging of building materials. This model can be a model based on physical principles (such as a model that considers the changes in the microstructure of the 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, etc.). The selection of the model should be based on the characteristics of the building materials, the type of data that can be obtained, and the requirements for prediction accuracy. Physical factors: Collect property data of building materials, such as strength, toughness, corrosion resistance, etc., as well as structural dimension data. Environmental factors: Collect environmental data such as temperature, humidity, wind force, precipitation, and precipitation pH within a preset time period. These data can be obtained through channels such as meteorological stations and sensor networks. Use factors: Determine the load mode and service life of the building structure. The load mode may include static load, dynamic load, impact load, etc., and the service life refers to the length of time from the completion of the building structure to the present. Maintenance factors: Collect the maintenance history and maintenance quality data of the building structure. These data may include the time, content, and effect evaluation of previous repairs. The collected physical factors, environmental factors, use factors, and maintenance factors data are input into the preset model and calculated to obtain the first aging prediction result. This prediction result usually indicates the aging degree or remaining life of the building material under given conditions. Through real-time monitoring systems such as sensor networks, the current state data of the building structure, such as displacement, deformation, vibration, stress and strain, etc., are obtained. These data reflect the actual stress state and performance of the building structure in a real-time environment. The first aging prediction result is compared with the real-time state data to analyze the difference between the two. The difference may come from the limitations of the preset model, the inaccuracy of the input data, or the sudden change of the real-time environmental conditions. According to the comparison results and difference analysis, the parameters of the preset model are adjusted. These parameters usually include material aging rate, environmental erosion coefficient, load factor, etc., which directly affect the prediction results of the model. The purpose of adjusting the parameters is to make the prediction results of the model closer to the real-time state data and improve the accuracy and reliability of the prediction. According to the material aging reflected by the real-time state data, the parameter value of the material aging rate is adjusted. According to the monitoring data of the real-time environmental conditions, the parameter value of the environmental erosion coefficient is adjusted to reflect the degree of influence of different environmental conditions on material aging. According to the parameters such as stress and strain in the real-time state data, the parameter value of the load factor is adjusted to reflect the influence of the actual load conditions on material aging. Using the adjusted preset model, the building materials of the building structure are predicted again based on physical factors, environmental factors, usage factors, maintenance factors and real-time status data to obtain the second aging prediction result. This prediction result has been verified by real-time data and the adjustment of model parameters, so it is closer to the actual situation and has higher accuracy and reliability.

[0040] By combining multiple variables such as physical factors, environmental factors, usage factors and maintenance factors, the first aging prediction is performed using a preset model, which can more comprehensively consider the aging process of building materials under different conditions. Subsequently, the first aging prediction is compared with the real-time status data, and the model parameters are adjusted according to the difference results, further improving the accuracy of the prediction. This dynamic adjustment mechanism ensures that the prediction results can be closer to the actual situation and reduce errors. Through the feedback of real-time data, the model can continuously learn and adapt to the changes in the building structure in actual operation, thereby improving the adaptability and robustness of the model. This highly adaptable model can better cope with various complex situations and provide a more reliable basis for the performance prediction and life assessment of the building structure. The second aging prediction based on the adjusted preset model can obtain more accurate and reliable prediction results. These prediction results can not only help managers understand the current status and future development trends of the building structure, but also provide strong support for making scientific and reasonable maintenance plans, optimizing structural design, and extending service life. The entire prediction and adjustment process is automated and intelligent, reducing the impact of manual intervention and subjective judgment. This not only improves work efficiency, but also reduces the risk of human error. At the same time, intelligent management methods also provide a more convenient and efficient way for long-term monitoring and maintenance of building structures. Optionally, the performing performance prediction and life assessment on the building structure according to the real-time status data in combination with 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, 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.

[0041] Use drones or wall-climbing robots to obtain surface images of super high-rise buildings. These images can clearly show the damage conditions such as cracks, rust, and spalling on the surface of super high-rise buildings. Use image processing technology to analyze these images to identify and quantify surface damage. By comparing the degree and distribution of damage in different areas, the overall damage condition of super high-rise buildings can be preliminarily determined. Real-time status data is obtained from sensors installed on super high-rise buildings, and mechanical property data is extracted from them. These data include elastic modulus, yield strength, impact toughness, residual stress and strain, etc., which can reflect the intrinsic performance of super high-rise building structures. After obtaining surface damage and mechanical property data, the previous second aging prediction is adjusted in combination with these data. Similarly, the parameters of the preset model can be adjusted based on surface damage and mechanical property data. Surface damage data provides direct information about the external state of the structure, while mechanical property data reflects the performance changes inside the structure. By comprehensively considering these two aspects of data, the aging of super high-rise buildings can be more accurately assessed. For example, if the surface damage is severe and the mechanical property data also shows a significant decline, then this may mean that the structural performance of the super high-rise building has been seriously degraded. Based on this comprehensive consideration, the second aging prediction is adjusted to obtain the third aging prediction. This prediction is closer to the actual aging of super high-rise buildings. The life of super high-rise buildings is predicted based on the third aging prediction. The remaining service life of super high-rise buildings is evaluated by combining factors such as the design life, usage conditions, and safety standards of super high-rise buildings. If the prediction results show that the remaining life of super high-rise buildings is short, or their performance can no longer meet current usage requirements, then corresponding maintenance and management strategies can be formulated, such as strengthening monitoring and carrying out reinforcement and repair.

[0042] By acquiring the surface image of the building structure and determining the surface damage, the external state of the building structure can be intuitively understood. Surface damage such as cracks and rust are direct manifestations of the degradation of the performance of the building structure. Accurate identification and analysis of them can timely discover potential safety hazards and provide an important basis for subsequent performance prediction and life assessment. Extracting mechanical performance data such as elastic modulus, yield strength, impact toughness, residual stress and strain from real-time status data can provide an in-depth understanding of the intrinsic performance of the building structure. These mechanical performance data reflect the response and deformation of the building structure under stress, which is of great significance for evaluating the bearing capacity and safety of the structure. Adjusting the second aging prediction in combination with surface damage and mechanical performance data can more comprehensively consider the aging of the building structure. Surface damage and mechanical performance data reflect the performance status of the building structure from the external and internal aspects respectively. Their comprehensive analysis can more accurately evaluate the aging degree and trend of the structure. Predicting the life of the building structure based on the third aging prediction can produce more scientific and reliable results. Since the third aging prediction is based on the comprehensive analysis of surface damage and mechanical performance data, it is closer to the actual situation of the building structure and can more accurately predict the remaining service life of the structure.

[0043] Optionally, the method further includes: 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.

[0044] A digital twin model of a super high-rise building is established. This model contains detailed information about the building structure, such as geometry, material properties, connection relationships, etc., and sets baseline state data based on historical data and simulation analysis. These baseline state data represent the expected performance parameters of the building structure under normal operating conditions. Real-time state data of super high-rise buildings, including displacement, deformation, vibration, stress and strain, etc., are collected. These data are transmitted through a sensor network and compared with the digital twin model in real time. On a certain day, it was found that there was a significant difference between the real-time state data and the baseline state data in the digital twin model. Specifically, the vibration data of a certain floor exceeded the preset threshold. Based on this finding, the abnormality location procedure was immediately initiated. By analyzing the spatial distribution and time series changes of the vibration data, it was determined that the abnormality occurred on a specific floor and area of ​​the super high-rise building. Subsequently, an alert message was generated based on the abnormal location. This message contains the specific location, nature, severity and possible causes of the abnormality. The alert message is sent to relevant personnel, such as building managers, maintenance personnel or security monitoring personnel, through the early warning system. After receiving the alert message, the relevant personnel took prompt action. They conduct on-site inspections of abnormal locations and take emergency measures as needed, such as evacuating personnel, restricting use, or making temporary reinforcements.

[0045] By comparing the real-time status data with the baseline status data in the digital twin model in real time, changes in the performance of the building structure can be discovered in time. When the difference between the real-time status data and the baseline status data exceeds the preset threshold, it means that the building structure may have abnormal conditions, such as damage, deformation, or performance degradation. This real-time comparison method can detect potential safety hazards in a timely manner and help prevent accidents. By locating the abnormal position according to the real-time status data, the specific location of the problem in the building structure can be accurately determined. This is crucial for subsequent maintenance and management. By locating the abnormal position, inspection and repair work can be carried out more targeted, maintenance efficiency can be improved, and unnecessary waste of resources can be reduced. Generating warning information based on the abnormal position can notify relevant personnel in time to ensure that they can respond quickly and take appropriate measures. The warning information contains specific information about the abnormal location, allowing relevant personnel to quickly understand the severity of the problem and make correct decisions. This helps reduce the risk of accidents and ensure the safety of personnel. In this way, the digital twin model can be combined with the real-time status data to achieve comprehensive monitoring and early warning of the building structure. 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. By comparing and analyzing these two aspects of data, we can have a more comprehensive understanding of the performance status of the building structure and provide a scientific basis for subsequent maintenance and management.

[0046] Optionally, locating the abnormal position according to the real-time status data includes: 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.

[0047] The real-time status data is compared with the baseline status data in the digital twin model. In this process, it is found that the difference between some sub-real-time status data and the baseline status data exceeds the preset threshold. These sub-real-time status data mainly come from vibration sensors in a specific area of ​​the super high-rise building. In order to locate the abnormal position, the corresponding positions of these sub-real-time status data in the digital twin model are found in the data processing center. Since the mapping relationship between the sensor and the specific position in the model has been established when the digital twin model is established, the precise position of these sub-real-time status data in the model can be determined. Through the digital twin model, the location of the abnormal data can be intuitively seen, and the structural characteristics and possible causes of the abnormality at that location can be further analyzed. Based on the analysis of the model, it is preliminarily judged that the abnormality may be caused by a problem with the supporting structure in a certain area of ​​the super high-rise building. According to the corresponding position in the digital twin model, the actual abnormal position of the bridge is determined. This provides accurate guidance for our subsequent field inspection and maintenance.

[0048] By determining the sub-real-time status data, that is, screening out the real-time status data whose difference from the baseline status data in the digital twin model is greater than or equal to the threshold, it is possible to accurately focus on those data points with abnormalities or performance degradation. This avoids the processing of a large amount of normal state data and improves the efficiency and accuracy of anomaly detection. The corresponding position of the sub-real-time status data in the digital twin model is determined according to the mapping relationship, and the mapping from data space to physical space is realized. This mapping relationship enables intuitive understanding of the specific location of abnormal data in the building structure, which facilitates subsequent abnormal location and cause analysis. The actual location of the building structure is determined according to the corresponding position in the digital twin model, and the abnormal location in the virtual model is further mapped to the actual building structure. This ensures the accuracy and reliability of abnormal location, allowing relevant personnel to go directly to the actual location for inspection and maintenance. Through the above method, the abnormal location in the building structure can be discovered in time and quickly located to the specific physical location. This helps to reduce potential safety hazards and improve the reliability and durability of the building structure. At the same time, it also provides an important reference basis for subsequent maintenance and management, allowing relevant personnel to carry out repair and maintenance work more targeted.

[0049] This embodiment also discloses a system for monitoring and managing building structures. Figure 2Schematic diagram of the module of the system for monitoring and managing building structures disclosed in the embodiment of the present application. Figure 2 As shown, the system includes a model module 201, a comparison module 202 and a prediction module 203, wherein: A model module 201 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 202 is 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; The prediction module 203 is configured to perform performance prediction and life assessment on the building structure according to the real-time status data combined 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 value, 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 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, the real-time status data including displacement, deformation, vibration, stress and strain.

[0050] Optionally, the model module 201 is configured to: 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.

[0051] Optionally, the comparison module 202 is configured to: 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.

[0052] Optionally, the prediction module 203 is configured to: 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.

[0053] Optionally, the prediction module 203 is further configured to: 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, 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.

[0054] Optionally, the system further comprises a positioning module, wherein the positioning module is configured to: 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.

[0055] Optionally, the positioning module is configured to: 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.

[0056] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0057] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0058] The communication bus 302 is used to realize the connection and communication between these components.

[0059] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0060] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0061] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can 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), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit 301 (Central Processing Unit, CPU), an image processor 301 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0062] The memory 305 may include a random access memory 305 (RAM) or a read-only memory 305 (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be at least one storage device located away from the aforementioned processor 301. As shown in the figure, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a method for monitoring and managing a building structure.

[0063] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program storing the method of building structure monitoring and management in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0064] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0065] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0066] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0067] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0068] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0070] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for monitoring and managing building structures, characterized in that: Applied to a building management platform, 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, the real-time status data including displacement, deformation, vibration, stress and strain.

2. The method for monitoring and managing building structures according to claim 1, characterized in that: The step of constructing the 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 building structures according to claim 1, characterized in that: The comparing the real-time status data with the reference status data in the digital twin model includes: 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 building structures according to claim 1, characterized in that: The performing a first aging prediction on the building material 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, characterized in that: The performing of performance prediction and life assessment of the building structure according to the real-time status data combined with influencing factors also 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, 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, characterized in that: The 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, characterized in that: It includes model module, comparison module and prediction module, among which: 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 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, the real-time status data including displacement, deformation, vibration, stress and 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 as described in any one of claims 1-7.

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 performed.

Images