Methods, devices and equipment for monitoring vibration of airport buildings

Abstract

This application discloses a method, device, and equipment for monitoring vibration in airport buildings, relating to the field of structural health monitoring. The method includes acquiring vibration data from multiple vibration sensing nodes in an airport building complex; performing frequency domain analysis and energy integration on the vibration data to obtain the vibration energy index of each vibration sensing node; generating a vibration energy isosurface cloud map on a digital twin model of the airport building complex using a spatial interpolation algorithm based on the spatial coordinates of the vibration sensing nodes and their corresponding vibration energy indices; calculating the spatial gradient field of the vibration energy isosurface cloud map and determining anomaly zones in the energy gradient based on the spatial gradient field; determining structurally weak areas in the airport building complex based on the anomaly zones in the energy gradient; and generating safety early warning information based on the structurally weak areas. This application has the advantage of identifying and assessing structurally weak areas based on the spatial distribution characteristics of vibration energy, thereby achieving early warning of structural damage.

Description

Technical Field

[0001] This application relates to the field of building structural health monitoring, and in particular to a method, device and equipment for monitoring vibration of airport buildings. Background Technology

[0002] With the rapid development of the air transport industry, modern airports have evolved into complex building clusters consisting of various individual buildings with different functions and structural forms, such as terminals, control towers, jet bridges, and hangars. During long-term operation, these buildings continuously bear complex vibration loads generated by aircraft takeoffs and landings, ground traffic, equipment operation, and natural factors. Long-term or severe vibrations not only affect user comfort but may also induce structural fatigue damage, threatening the safety and durability of the buildings. Therefore, implementing accurate and efficient vibration monitoring and health assessments is particularly important.

[0003] Currently, the mainstream approach involves deploying a limited number of sensors at critical structural locations to collect vibration signals and analyze their time-domain or frequency-domain characteristics to determine whether the vibration level exceeds a safety threshold. However, this method has the following limitations when dealing with complex airport building complexes with large spatial scales: The monitoring points are relatively isolated, and the data spatial coverage is insufficient, making it difficult to fully capture the distribution and transmission patterns of vibration energy in the complex spatial network of the entire building complex. The analysis methods mostly focus on the time or frequency domain characteristics of single-point signals, lacking the means to deeply integrate discrete point measurement data with the three-dimensional spatial morphology of the building complex, and failing to intuitively and globally display the spatial accumulation and diffusion of vibration energy. Existing technologies often rely on empirical thresholds for alarms, and cannot actively identify local accumulation or transmission anomalies of vibration energy caused by structural abnormalities, that is, it is difficult to accurately locate the weak dynamic areas, resulting in delayed early warning.

[0004] Therefore, there is an urgent need for a vibration monitoring method for airport buildings to overcome the limitations of traditional point-based monitoring and achieve more accurate and timely safety warnings. Summary of the Invention

[0005] This application provides a method, device, and equipment for monitoring vibration of airport buildings, which can dynamically identify and quantitatively assess structurally weak areas based on the spatial distribution characteristics of vibration energy, thereby achieving early warning of structural damage.

[0006] In a first aspect, this application provides a method for monitoring vibration of airport buildings, including: Vibration data of multiple vibration sensing nodes in the airport building complex are acquired, wherein the vibration sensing nodes are set on key nodes and key transmission paths of each individual structure in the airport building complex. Frequency domain analysis and energy integration are performed on the vibration data to obtain the vibration energy index of each vibration sensing node; Based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, a vibration energy isosurface cloud map is generated on the digital twin model of the airport building complex using a spatial interpolation algorithm. Calculate the spatial gradient field of the vibration energy isosurface cloud map, and determine the energy gradient anomaly region based on the spatial gradient field; Based on the energy gradient anomaly region, the structurally and dynamically weak areas of the airport building complex are determined; Safety warning information is generated based on the structurally weak areas.

[0007] Preferably, acquiring vibration data from multiple vibration sensing nodes within the airport building complex includes: If the vibration data acquired by any vibration sensing node exceeds a preset threshold or a collaborative wake-up message is received, the vibration sensing node will be switched from a low-power sleep mode to a high sampling rate working mode and will serve as the initial trigger node. Based on the three-dimensional structural topology map of the airport building complex and the location information of the initial trigger node, the direction of vibration energy transmission is determined, and coordinated wake-up information is sent sequentially to the adjacent vibration sensing nodes in the transmission direction. The vibration sensing node that receives the coordinated wake-up information enters a high sampling rate working mode and acts as a relay node, repeatedly sending coordinated wake-up information to adjacent vibration sensing nodes in the transmission direction to form a relay wake-up chain on the force transmission path of the airport building complex.

[0008] Preferred options also include: When any vibration sensing node in the relay wake-up chain is woken up and enters the high sampling rate working mode, the vibration data is compared with the preset threshold in real time, and the local comparison time when the vibration data is greater than the preset threshold is obtained. The local comparison time is compared with the reference time in the collaborative wake-up information obtained from the upstream vibration sensing node to obtain the actual propagation time difference of vibration on the structural path between adjacent nodes. The actual propagation time difference is correlated with the corresponding spatial coordinates to analyze the propagation velocity distribution of vibrations within the airport building complex.

[0009] Preferably, the step of performing frequency domain analysis and energy integration on the vibration data to obtain the vibration energy index of each vibration sensing node includes: Based on the actual propagation time difference, a dynamic transmission path network diagram is constructed to show the transmission of vibration energy from the initial triggering node to each vibration sensing node. Modal decomposition is performed on the vibration data of each vibration sensing node to obtain multiple intrinsic mode functions; For each intrinsic mode function, the path attenuation effect of the frequency component propagating to the corresponding vibration sensing node is estimated based on the center frequency and the dynamic transmission path network graph. The vibration energy of each intrinsic mode function is compensated based on the path attenuation effect, and the compensated vibration energy is integrated in the time domain to obtain the vibration energy index corresponding to each vibration sensing node. The vibration energy index is used to characterize the local structural dynamic characteristics after the influence of the propagation path is removed.

[0010] Preferably, the step of generating a vibration energy isosurface cloud map on the digital twin model of the airport building complex based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, using a spatial interpolation algorithm, includes: Based on the timing of the digital twin model and the relay wake-up chain, a digital twin simulation is performed to obtain the theoretical energy distribution field; For each vibration sensing node, the vibration energy index and the theoretical energy distribution field are fused at the spatial coordinates of the vibration sensing node to obtain multiple assimilated data points, wherein each assimilated data point includes spatial coordinates and the fused optimized energy value. Based on the spatial coordinates and optimized energy values ​​of all the assimilated data points, a continuous vibration energy isosurface cloud map is generated by fitting the structural surface of the digital twin model using a spatial interpolation algorithm.

[0011] Preferably, calculating the spatial gradient field of the vibration energy isosurface contour map includes: Based on the Monte Carlo random walk algorithm, energy diffusion simulation was performed on the vibration energy isosurface cloud map to obtain the probability flux density vector field. Obtain the theoretical stress streamline field corresponding to the digital twin model; The probability flux density vector field is coupled with the theoretical stress streamline field to obtain a scalar coupled field; The spatial gradient field is obtained by performing gradient calculation on the scalar coupled field.

[0012] Preferably, determining the energy gradient anomaly region based on the spatial gradient field includes: The spatial gradient field, the vibration energy isosurface cloud map, and the structural topology of the digital twin model are input into the trained neural network model to obtain the anomaly probability value of each structural component. The energy gradient anomaly region is determined based on the aforementioned anomaly probability value.

[0013] Preferably, determining the structurally weak areas of the airport complex based on the energy gradient anomaly area includes: Structural components corresponding to the energy gradient anomaly region are extracted from the digital twin model, and the extracted structural components are used as components to be evaluated. Based on the vibration transmission sequence of the relay-type wake-up chain, the vibration wave propagation sequence from the initial trigger node through the force transmission path to the component to be evaluated is extracted. Based on the vibration wave propagation sequence, the dynamic characteristic parameters of the component to be evaluated in the vibration event are calculated. The dynamic characteristic parameters include at least the natural frequency, damping ratio, and mode participation factor. Based on the vibration source type, intensity, and location of the vibration event, structural dynamic response simulation is performed in a digital twin model to obtain the theoretical dynamic characteristic parameters of the corresponding location of the component to be evaluated. The dynamic characteristic parameters are compared with the theoretical dynamic characteristic parameters to obtain the comparison results; Based on the comparison results and the gradient anomaly intensity and spatial continuity of the energy gradient anomaly zone, the structural region where vibration energy accumulates due to the degradation of dynamic characteristics is determined, and the determined structural region is regarded as the structural dynamic weak zone.

[0014] Secondly, this application provides an airport building vibration monitoring device, the device comprising: The acquisition module is used to acquire vibration data from multiple vibration sensing nodes in the airport building complex, wherein the vibration sensing nodes are set on key nodes and key transmission paths of each individual structure in the airport building complex. The analysis module is used to perform frequency domain analysis and energy integration on the vibration data to obtain the vibration energy index of each vibration sensing node. The first generation module is used to generate a vibration energy isosurface cloud map on the digital twin model of the airport building complex based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, using a spatial interpolation algorithm. The calculation module is used to calculate the spatial gradient field of the vibration energy isosurface cloud map and determine the energy gradient anomaly region based on the spatial gradient field. The determination module is used to determine the structurally weak areas of the airport building complex based on the energy gradient anomaly areas; The second generation module is used to generate safety early warning information based on the structurally weak areas.

[0015] Thirdly, this application also provides an electronic device, the device comprising: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements any one of the above-described airport building vibration monitoring methods.

[0016] This application discloses a vibration monitoring method, device, and equipment for airport buildings. Based on a digital twin model of the airport building complex, it performs spatial interpolation of vibration energy indices at discrete sensing nodes to obtain a continuous isosurface cloud map reflecting the distribution and transmission characteristics of vibration energy across the entire field. This transforms traditional isolated single-point vibration analysis into a comprehensive visualization of the entire field situation deeply integrated with three-dimensional spatial morphology, overcoming the limitation that limited measuring points cannot fully capture the energy distribution patterns in complex spatial networks. Furthermore, by calculating the spatial gradient field and identifying anomalous areas from the vibration energy isosurface cloud map, it enables proactive detection of abnormal energy transmission patterns in the structural dynamic response. The identified energy gradient anomalous areas directly correspond to potential abrupt changes in structural force transmission paths or local stiffness weaknesses, solving the problem of relying on fixed thresholds and failing to provide early warnings of abnormal structural behavior. Based on this, combined with the spatial location of energy gradient anomalous areas and their correlation analysis with the overall distribution pattern, it can locate the weakest areas where dynamic characteristics change earliest, even when the absolute value of vibration energy has not yet exceeded the standard but abnormal characteristics have already appeared in the local transmission pattern. This provides a technical basis for issuing early warnings at the stages of early structural performance degradation, local energy accumulation, or the onset of transmission anomalies. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the airport building vibration monitoring method in the embodiments of this application; Figure 2 This is a structural block diagram illustrating the airport building vibration monitoring device in the embodiments of this application; Figure 3 This is a structural block diagram illustrating the electronic device in the embodiments of this application. Detailed Implementation

[0018] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0020] Existing vibration monitoring methods for airport building complexes suffer from relatively isolated monitoring points and insufficient spatial data coverage, making it difficult to comprehensively capture the distribution and transmission patterns of vibration energy within the complex spatial network of the entire building complex. Analysis methods often focus on the time or frequency domain characteristics of single-point signals, lacking the means to deeply integrate discrete point measurement data with the three-dimensional spatial morphology of the building complex, thus failing to intuitively and comprehensively demonstrate the spatial accumulation and diffusion of vibration energy. Furthermore, existing technologies often rely on empirical thresholds for alarms, failing to proactively identify localized accumulation or transmission anomalies of vibration energy caused by structural abnormalities, making it difficult to accurately locate weak dynamic areas and resulting in delayed early warnings.

[0021] To address the problems of the prior art, this application provides a method, apparatus, and equipment for monitoring vibration of airport buildings. The method for monitoring vibration of airport buildings provided in this application will be described first.

[0022] Figure 1 A schematic flowchart of an airport building vibration monitoring method according to an embodiment of this application is shown. Figure 1 As shown, the airport building vibration monitoring method provided in this application includes the following steps: S101. Obtain vibration data from multiple vibration sensing nodes in the airport building complex, wherein the vibration sensing nodes are set on key nodes and key transmission paths of each individual structure in the airport building complex.

[0023] The vibration sensing nodes are physical sensor units used to collect vibration signals from the airport building complex. Multiple vibration sensing nodes are deployed at key nodes and key transmission paths of each individual structure in the complex. For example, vibration sensors are deployed at beam-column joints of the terminal building, the top structure of the control tower, the connection points of the jet bridges, and the large-span roof of the hangar. Each vibration sensor is a vibration sensing node. The vibration sensors can be piezoelectric accelerometers, fiber optic grating sensors, or MEMS sensors. The vibration sensors transmit the collected vibration data to electronic devices via wired or wireless means. The electronic devices can be computers or mobile phones, without specific limitations. The vibration data are the original physical signals collected by the vibration sensors that reflect the dynamic response of the airport building structure under external and internal excitations. The vibration data includes, but is not limited to, continuous sequences of acceleration, velocity, displacement, and strain changing over time.

[0024] The acquisition of vibration data specifically includes: when the vibration data acquired by any vibration sensing node exceeds a preset threshold or a collaborative wake-up message is received, the vibration sensing node is switched from a low-power sleep mode to a high sampling rate working mode and acts as an initial trigger node; the direction of vibration energy transmission is determined based on the three-dimensional structural topology map of the airport building complex and the location information of the initial trigger node, and collaborative wake-up messages are sent sequentially to adjacent vibration sensing nodes in the transmission direction; wherein, the vibration sensing node that receives the collaborative wake-up message enters the high sampling rate working mode and acts as a relay node, repeating the step of sending collaborative wake-up messages to adjacent vibration sensing nodes in the transmission direction, so as to form a relay wake-up chain on the force transmission path of the airport building complex.

[0025] The preset threshold can be a fixed vibration amplitude or a threshold that is dynamically adjusted based on environmental noise or historical vibration data. The collaborative wake-up message is a message sent by other vibration sensing nodes. The collaborative wake-up message is used to indicate the existence of a significant vibration event and the need for collaborative monitoring. The low-power sleep mode is an energy-saving working state for the vibration sensor. For example, the vibration sensor operates at an extremely low sampling rate to detect only coarse vibration changes, and the main processor is turned off. The high sampling rate working mode is when the vibration sensor actively collects detailed vibration data at a higher frequency to capture complete vibration waveform information. For example, continuous data acquisition and processing are performed at a sampling rate of 100Hz to 1000Hz. The initial trigger node is the first vibration sensing node to detect a significant vibration event and send a collaborative wake-up message. The vibration event is a structural dynamic phenomenon that propagates in space and time and contains rich physical information. The vibration sensing nodes send and receive the collaborative wake-up message wirelessly or via wired means.

[0026] In this embodiment, the three-dimensional structural topology diagram adopts a detailed finite element model or building information model, which includes the geometry, material properties and connection details of the structural components. The location information is the spatial coordinates of the vibration sensing nodes within the airport building complex. The location information can be obtained through GPS positioning and associated with the corresponding vibration sensing nodes. The direction of vibration energy transmission is the path of vibration energy propagating from the initial triggering node through the structure. The transmission direction can be determined by the structural connectivity in the three-dimensional structural topology diagram, such as identifying the structural connection path with the least resistance or the most direct connection.

[0027] In this embodiment, the relay node is activated by the collaborative wake-up information and is responsible for waking up the downstream vibration sensing nodes. After receiving the collaborative wake-up information, the relay node switches to a high sampling rate working mode, then identifies the next one or more vibration sensing nodes on the force transmission path and sends collaborative wake-up information to them. The relay wake-up chain is a sequence of vibration sensing nodes that are activated step by step along the vibration energy propagation path to form a dynamic, self-organizing sensor network, ensuring that vibration events can be continuously and comprehensively monitored when they propagate in the structure. The force transmission path is the main channel or trajectory for the propagation and diffusion of vibration energy within the three-dimensional structure when a vibration event occurs on the airport building complex structure.

[0028] In this embodiment, the vibration sensing node adopts a low-power sleep mode during normal operation. Only when the vibration data exceeds a preset threshold or a coordinated wake-up message is received will the corresponding vibration sensing node switch to a high sampling rate working mode, thereby avoiding energy waste caused by continuously using the high sampling rate mode for monitoring. Furthermore, by using the three-dimensional structural topology map of the airport building complex and the location information of the initial trigger node to determine the direction of vibration energy transmission, and sequentially activating adjacent vibration sensing nodes along the force transmission path, a relay wake-up chain is formed, thereby ensuring that the vibration is fully and dynamically covered during the propagation process.

[0029] After the vibration sensing node is awakened, the process also includes: when any vibration sensing node in the relay wake-up chain is awakened and enters a high sampling rate working mode, comparing the vibration data with a preset threshold in real time and obtaining the local comparison time when the vibration data is greater than the preset threshold; comparing the local comparison time with the reference time in the collaborative wake-up information obtained from the upstream vibration sensing node to obtain the actual propagation time difference of the vibration on the structural path between adjacent nodes; and associating the actual propagation time difference with the corresponding spatial coordinates to analyze the propagation speed distribution of the vibration in the airport building complex.

[0030] In this embodiment, after the vibration sensing node is woken up and enters the high sampling rate working mode, the vibration data is compared with a preset threshold in real time. When the amplitude or energy of the vibration data exceeds the preset threshold, the current time is obtained and used as the local comparison time of the vibration sensing node. Then, the time difference between the local comparison time and the reference time of the upstream vibration sensing node is calculated. The time difference is the actual propagation time difference on the structural path between adjacent nodes. When the vibration sensing node sends the collaborative wake-up information, it adds the local comparison time as a reference time to the collaborative wake-up information. The preset threshold is set as needed.

[0031] In this embodiment, the local propagation speed of vibration waves on a specific structural path is calculated by the actual propagation time difference and spatial distance. For example, each actual propagation time difference is stored together with a tuple containing the three-dimensional coordinates of the start node and the end node to form a propagation path database; or, the actual propagation time difference is directly mapped to the corresponding position in the digital twin model, and the propagation speed in different areas is displayed through visualization tools, thereby revealing the speed and directionality characteristics of vibration energy propagation in the airport building complex.

[0032] In this embodiment, in the relay wake-up chain, the local comparison time of the vibration sensing node is compared with the reference time of the upstream vibration sensing node to obtain the actual propagation time difference of the vibration wave on the structural path between adjacent nodes. This solves the problem that the traditional method cannot accurately obtain the vibration propagation time. The actual propagation time difference is associated with the spatial coordinates of the vibration sensing node, which facilitates the construction of a detailed distribution map of the propagation speed of the vibration wave in the airport building complex.

[0033] S102. Perform frequency domain analysis and energy integration on the vibration data to obtain the vibration energy index of each vibration sensing node.

[0034] Specifically, a dynamic transmission path network diagram of vibration energy from the initial trigger node to each vibration sensing node is constructed based on the actual propagation time difference. Modal decomposition is performed on the vibration data of each vibration sensing node to obtain multiple intrinsic mode functions (EMFs). For each EMF, the path attenuation effect of the frequency component propagating to the corresponding vibration sensing node is estimated based on the center frequency and the dynamic transmission path network diagram. The vibration energy of each EMF is compensated based on the path attenuation effect, and the compensated vibration energy is integrated in the time domain to obtain the vibration energy index corresponding to each vibration sensing node. The vibration energy index is used to characterize the local structural dynamic characteristics after removing the influence of the propagation path.

[0035] The construction of a dynamic propagation path network diagram involves establishing a dynamically changing graph based on the path and time relationship of vibration energy propagation from the initial trigger node to each vibration sensing node within the airport building complex. This dynamic propagation path network diagram reflects the actual propagation trajectory of vibration energy within the structure. For example, based on the actual propagation time difference and the spatial topological relationship of the vibration sensing nodes, a directed weighted graph can be formed using graph theory algorithms, such as Dijkstra's algorithm. Alternatively, by utilizing structural connection information and material properties from a digital twin model through finite element analysis or discrete element simulation, the propagation path of the vibration wave can be tracked in real time after a vibration event occurs, and calibrated according to the actual propagation time difference, thereby dynamically generating a dynamic propagation path network diagram.

[0036] In this embodiment, modal decomposition is performed on the vibration data of each vibration sensing node to obtain multiple intrinsic mode functions (EMFs). This decomposes the complex vibration signal into multiple simple vibration modes with specific frequencies, damping, and mode shapes. For example, signal processing techniques such as empirical mode decomposition, variational mode decomposition, or wavelet packet decomposition can be used to decompose the original time-domain vibration signal into multiple intrinsic mode functions (EMFs), each corresponding to a primary frequency component. Alternatively, frequency domain analysis methods can be used, such as determining the primary vibration frequency components through fast Fourier transform combined with peak identification, and then extracting the structural modal parameters from the vibration response through random subspace identification or frequency domain decomposition to construct the intrinsic mode functions.

[0037] In this embodiment, the path attenuation effect of frequency components propagating to the corresponding vibration sensing nodes is estimated based on the center frequency and the dynamic transmission path network diagram. This quantifies the energy loss caused by factors such as material damping, geometric diffusion, and structural connection losses during the propagation of vibration energy from the source to the monitoring point. The center frequency is the main frequency of each intrinsic mode function after modal decomposition. For example, an empirical attenuation model based on structural material properties, geometric dimensions, and propagation distance can be established. The empirical attenuation model can adopt an exponential attenuation model or a power-law attenuation model, where the attenuation coefficient is related to the frequency. Combined with the path length and material information in the dynamic transmission path network diagram, the attenuation of each frequency component is estimated. Alternatively, a digital twin model can be used for simulation. By simulating vibration propagation in the digital twin model and comparing the energy response at different locations, the attenuation law of a specific frequency component on a specific path can be inverted.

[0038] In this embodiment, the vibration energy of each intrinsic mode function is compensated based on the path attenuation effect, and the compensated vibration energy is integrated in the time domain to obtain the vibration energy index corresponding to each vibration sensing node. This is achieved by eliminating energy loss along the propagation path, so that the measured vibration energy can more accurately reflect the dynamic characteristics of the local structure itself, rather than the attenuation value affected by the propagation path. The time domain integration is to accumulate the vibration energy of each frequency component after compensation in the time dimension to obtain the total vibration energy index. For example, for each intrinsic mode function, the vibration energy measured at the vibration sensing node is multiplied by a compensation factor to obtain the compensated vibration energy, where the compensation factor is the reciprocal of the attenuation effect. Then, the vibration energy of all compensated intrinsic mode functions is accumulated or integrated over the entire monitoring period to obtain the final vibration energy index.

[0039] In this embodiment, a dynamic transmission path network diagram is constructed using the actual propagation time difference to dynamically capture the true transmission trajectory of vibration energy within the airport building complex. Modal decomposition is performed on the vibration data of each vibration sensing node, decomposing the complex vibration signal into intrinsic mode functions (EMFs) of different frequencies, thus enabling differentiated attenuation analysis for different frequency components. Furthermore, the path attenuation effect of frequency components propagating to the corresponding vibration sensing node is estimated based on the center frequency and the dynamic transmission path network diagram. This combination of frequency characteristics and actual transmission path information facilitates the quantification of energy loss during vibration propagation. The vibration energy of each EMF is compensated based on the path attenuation effect, and the compensated vibration energy is integrated in the time domain. This eliminates the interference of the propagation path on the vibration energy, allowing the obtained vibration energy index to accurately and realistically characterize the local structural dynamic characteristics after stripping away the influence of the propagation path.

[0040] S103. Based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, a vibration energy isosurface cloud map is generated on the digital twin model of the airport building complex using a spatial interpolation algorithm.

[0041] Specifically, digital twin simulation is performed based on the timing of the digital twin model and the relay wake-up chain to obtain the theoretical energy distribution field. For each vibration sensing node, the vibration energy index and the theoretical energy distribution field are fused at the spatial coordinates of the vibration sensing node to obtain multiple assimilated data points. Each assimilated data point includes spatial coordinates and the fused optimized energy value. Based on the spatial coordinates and optimized energy values ​​of all assimilated data points, a spatial interpolation algorithm is used to fit the structural surface of the digital twin model to generate a continuous vibration energy isosurface cloud map.

[0042] Among them, by using the digital twin model of the airport building complex and combining the vibration transmission timing information reflected by the relay-style wake-up chain when a vibration event occurs, the theoretical propagation and distribution of vibration energy in the structure are simulated, thereby providing an energy distribution benchmark based on physical model and theoretical calculation, which can be compared and integrated with vibration energy indicators.

[0043] In this embodiment, the digital twin model can be imported into finite element analysis software. Based on the time sequence information of the relay-style wake-up chain, corresponding dynamic loads are applied to perform transient dynamic simulation, thereby calculating the theoretical vibration energy density or energy flow at different times and locations, and thus obtaining the theoretical energy distribution field. The time sequence information includes, but is not limited to, the vibration source location, excitation time, propagation path, and velocity. A weighted average method can be used to fuse the vibration energy index and the theoretical energy distribution field. For example, a Bayesian estimation method can be used, with the vibration energy index as the observed data and the theoretical energy distribution field as the prior information. The posterior probability distribution can be calculated using the Bayesian formula to obtain the most likely optimized energy value at each vibration sensing node.

[0044] In this embodiment, the spatial interpolation algorithm can be the Kriging interpolation algorithm, a statistical method that considers spatial autocorrelation, predicts the energy value of unknown points by weighted averaging of assimilated data points, and provides an interpolation error estimate, thereby generating a smooth and statistically significant isosurface cloud map; or, the radial basis function interpolation algorithm can be used, which fits the data points by constructing a linear combination of radial basis functions, can handle irregularly distributed data, and generate a smooth interpolation surface, thereby generating a continuous vibration energy isosurface cloud map on the structural surface of the digital twin model.

[0045] In this embodiment, simulation is performed using a digital twin model of the airport building complex and the timing information of a relay-style wake-up chain to generate a theoretical energy distribution field, providing a benchmark for vibration energy indicators. The vibration energy indicators of each vibration sensing node are fused with the theoretical energy distribution field at their corresponding spatial coordinates to correct local deviations in the vibration energy indicators and obtain more reliable optimized energy values. Furthermore, based on the optimized assimilated data points, a continuous and accurate vibration energy isosurface cloud map is generated by fitting the structural surface of the digital twin model using a spatial interpolation algorithm. This ensures that the generated vibration energy isosurface cloud map accurately reflects the actual structural dynamic behavior of the airport building complex and the spatial accumulation and diffusion patterns of vibration energy.

[0046] S104. Calculate the spatial gradient field of the vibration energy isosurface cloud map, and determine the energy gradient anomaly zone based on the spatial gradient field.

[0047] The calculation of the spatial gradient field of the vibration energy isosurface contour map specifically includes: performing energy diffusion simulation on the vibration energy isosurface contour map based on the Monte Carlo random walk algorithm to obtain the probability flux density vector field; obtaining the theoretical stress streamline field corresponding to the digital twin model; coupling the probability flux density vector field with the theoretical stress streamline field to obtain the scalar coupled field; and performing gradient calculation on the scalar coupled field to obtain the spatial gradient field.

[0048] Among them, the Monte Carlo random walk algorithm is a computational method based on random sampling and statistical simulation. It solves complex problems through simulation of a large number of random samples. For example, at each grid point of the vibration energy isosurface contour map, a large number of virtual particles are randomly released to simulate the random motion and energy transfer process of these particles inside the structure. The dwell time or path of the particles at different positions is recorded, thereby statistically determining the probability distribution of energy diffusion. Energy diffusion simulation aims to simulate the propagation and attenuation process of vibration energy in the structure, considering its randomness and uncertainty. For example, the vibration energy isosurface contour map is discretized into a grid. At each grid point, according to the preset diffusion coefficient and random perturbation term, the random diffusion process of energy from high-energy regions to low-energy regions is simulated until a steady state is reached. The probability flux density vector field describes the probability distribution of energy flow through a unit area per unit time. Its direction represents the main trend of energy diffusion, and its magnitude represents the intensity of diffusion. It quantifies the trend and intensity of random diffusion of vibration energy in the structure. For example, the probability flux density vector of each region can be calculated by statistically analyzing the frequency and direction of virtual particles crossing a specific cross section in the Monte Carlo simulation.

[0049] In this embodiment, the digital twin model is a virtual mapping of a physical entity in the digital world, containing information such as geometry, materials, loads, and boundary conditions. It can perform simulation analysis to provide stress distribution information of the structure under theoretical loads, thus serving as a benchmark for evaluating the stress state of the structure. For example, based on the CAD and BIM models of an airport building complex, combined with material mechanics parameters and preset typical loads, static or dynamic simulations can be performed using finite element analysis software to obtain the stress distribution inside the structure. The theoretical stress streamline field is a vector field describing the direction and magnitude of the stress distribution inside the structure. The streamline direction is consistent with the principal stress direction, and the streamline density reflects the stress magnitude, characterizing the stress path and stress concentration area of ​​the structure under ideal conditions. For example, the principal stress tensor can be extracted from the finite element analysis results, and stress streamlines can be drawn according to the principal stress direction to form the theoretical stress streamline field.

[0050] In this embodiment, coupling combines two or more fields with different properties to form a more comprehensive evaluation index that reflects their interaction or combined effect. For example, a dot product operation is used to perform a vector dot product between the probability flux density vector field and the theoretical stress streamline field at each spatial point to obtain a scalar field. This scalar field reflects the consistency between the energy diffusion direction and the theoretical stress direction, as well as the product of their intensities. The scalar coupling field is a field with a single value at each point in space. This value comprehensively reflects the information of the probability flux density and the theoretical stress streamline, providing a comprehensive index that can simultaneously consider the trend of energy diffusion and the rationality of structural stress. For example, the result of the dot product operation can be directly output to form a numerical distribution map.

[0051] In this embodiment, gradient calculation involves spatially differentiating the scalar field to obtain a vector field. The direction of this vector field points to the direction of the fastest numerical growth of the scalar field, and its magnitude represents the rate of growth. This reveals the rate of change and direction of the scalar coupled field in space, thereby highlighting the anomalous region of the combined effect of energy diffusion and stress distribution. For example, using the finite difference method, the partial derivatives in the X, Y, and Z directions are calculated at each discrete point of the scalar coupled field, and then the gradient vector is synthesized. The spatial gradient field is a vector field describing the rate of change and direction of the scalar coupled field in space, which can quantify the spatial variation trend of the coupling effect of energy diffusion and theoretical stress. For example, the calculated gradient vector can be visualized on the digital twin model in the form of arrows or color codes, where the direction of the arrow indicates the gradient direction, and the color or length indicates the gradient magnitude.

[0052] In this embodiment, the vibration energy isosurface cloud map is simulated using the Monte Carlo random walk algorithm to obtain the probability flux density vector field, which characterizes the randomness and uncertainty of vibration energy propagation. Combined with the theoretical stress streamline field provided by the digital twin model, the two are coupled to generate a scalar coupled field, thereby integrating the random characteristics of actual propagation with the deterministic information of theoretical force. Based on this, gradient calculation is performed to obtain a high-precision spatial gradient field, which reveals the changing trend of the energy-stress combined effect, identifies energy gradient anomaly areas, and provides data support for the determination of structural dynamic weak areas, thereby improving the accuracy of vibration monitoring and the timeliness of early warning to a certain extent.

[0053] Determining energy gradient anomaly regions based on spatial gradient fields specifically includes: inputting the spatial gradient field, vibration energy isosurface cloud map, and structural topology of the digital twin model into a trained neural network model to obtain the anomaly probability value of each structural component; and determining energy gradient anomaly regions based on the anomaly probability values.

[0054] In this embodiment, the neural network model can be a convolutional neural network, which extracts local and global features in the spatial gradient field, vibration energy isosurface cloud map, and structural topology through multiple convolutional kernels. During the training process, the neural network model uses labeled normal and abnormal vibration data to adjust the model parameters through the backpropagation algorithm, so that it can distinguish the energy gradient features under different states.

[0055] In this embodiment, after inputting the spatial gradient field, vibration energy isosurface cloud map, and digital twin model into the neural network model, the anomaly probability value of each structural component is obtained. After obtaining the anomaly probability value of each structural component, various strategies can be used to determine the energy gradient anomaly region. For example, a preset probability threshold can be set, and all structural components with anomaly probability values ​​higher than the probability threshold can be regarded as energy gradient anomaly regions; or, a clustering algorithm can be combined to cluster adjacent components with high anomaly probability values ​​to form continuous anomaly regions, thereby more accurately locating the structural dynamic weak area.

[0056] S105. Determine the structurally weak areas of the airport building complex based on the energy gradient anomaly area.

[0057] Specifically, structural components corresponding to energy gradient anomaly regions are extracted from the digital twin model, and these extracted components are used as the components to be evaluated. Based on the vibration transmission sequence of the relay-style wake-up chain, the vibration wave propagation sequence from the initial trigger node through the force transmission path to the component to be evaluated is extracted. Based on the vibration wave propagation sequence, the dynamic characteristic parameters of the component to be evaluated in the vibration event are calculated. The dynamic characteristic parameters include at least the natural frequency, damping ratio, and mode participation factor. Based on the vibration source type, intensity, and location of the vibration event, the structural dynamic response simulation is performed in the digital twin model to obtain the theoretical dynamic characteristic parameters of the corresponding location of the component to be evaluated. The dynamic characteristic parameters are compared with the theoretical dynamic characteristic parameters to obtain the comparison results. Based on the comparison results and the gradient anomaly intensity and spatial continuity of the energy gradient anomaly region, the structural region where vibration energy accumulation is caused by dynamic characteristic degradation is determined, and the determined structural region is used as the structural dynamic weak zone.

[0058] In this embodiment, all structural components are numbered and attribute-marked in advance in the digital twin model. Once the energy gradient anomaly region is determined, the structural component corresponding to the energy gradient anomaly region can be quickly extracted by querying the structural component ID.

[0059] For the extraction of vibration wave propagation sequences, a directed graph is constructed by analyzing the wake-up time and spatial location of each vibration sensing node in the relay wake-up chain and combining it with the structural topology information in the digital twin model. In this graph, nodes are the connection points between vibration sensing nodes and key structures, edges are structural components, and the weight of the edges is the vibration propagation time or distance. This allows the extraction of the vibration wave propagation sequence along the shortest or fastest propagation path from the initial trigger node to the component to be evaluated. Alternatively, the vibration data recorded by each vibration sensing node can be used to determine the propagation time of the vibration signal between adjacent nodes through cross-correlation analysis or waveform tracking algorithms. Combined with the order of the relay wake-up chain, the propagation path and timing of the vibration wave from the initial trigger node to the component to be evaluated can be traced in reverse or forward.

[0060] To calculate the dynamic characteristic parameters of the component to be evaluated in a vibration event, time-frequency analysis or modal identification can be performed on the vibration data at the component to be evaluated in the vibration wave propagation sequence, thereby extracting the natural frequency, damping ratio and mode participation coefficient of the component to be evaluated in the vibration event. Among them, time-frequency analysis includes wavelet transform, and modal identification includes random subspace identification method.

[0061] For structural dynamic response simulation, the theoretical dynamic response under healthy and undamaged conditions is calculated by simulating the same type, intensity, and location as the actual vibration event in a digital twin model. This yields the theoretical dynamic characteristic parameters of the component to be evaluated. The vibration sources include, but are not limited to, aircraft take-off and landing and vehicle traffic. In this embodiment, the digital twin model can be imported into finite element analysis software, and corresponding excitations can be applied based on the vibration source information of the actual vibration event to perform transient dynamic analysis. The theoretical natural frequency, damping ratio, and mode participation factor of the component to be evaluated can be extracted from the simulation results.

[0062] By comparing the actual dynamic characteristic parameters with the theoretical dynamic characteristic parameters, the percentage deviation between the actual dynamic characteristic parameters and the theoretical dynamic characteristic parameters can be calculated. For example, (actual value - theoretical value) / theoretical value × 100%. When the deviation exceeds the preset threshold, it is determined that there is a degradation in dynamic characteristics.

[0063] For structural regions where vibration energy accumulation is identified due to degradation of dynamic characteristics, multiple judgment rules can be set. For example, if the comparison results show that the natural frequency decreases by more than a preset percentage, the damping ratio increases or decreases by more than a preset percentage, and the energy gradient anomaly intensity of the region is higher than a preset threshold, and the anomaly region has a certain degree of spatial continuity (e.g., covering at least N adjacent components), the region is judged as a structurally weak area. Alternatively, a trained neural network model can be used, taking the comparison results (such as parameter deviation vector), energy gradient anomaly intensity, spatial continuity, etc., as input, and outputting the probability value of the region being a structurally weak area. The final structurally weak area can be determined by setting a probability threshold.

[0064] S106. Generate safety early warning information based on structurally weak areas.

[0065] In this embodiment, the safety warning information includes, but is not limited to, the location of the structurally weak area, the degree of abnormality, and recommended inspection measures. After obtaining the safety alarm information, the safety warning information is sent to the mobile terminal of the corresponding staff so that timely intervention measures can be taken.

[0066] The location of the structural dynamic weakness zone is determined by its position in the digital twin model. The degree of anomaly is determined based on the magnitude of the probability value. For example, multiple probability intervals are set, each corresponding to an anomaly level. The anomaly level, i.e. the degree of anomaly, is determined by the probability interval to which the probability value belongs. Multiple suggested inspection measures are set in the database. Each structural dynamic weakness zone is divided into multiple anomaly levels, and each anomaly level corresponds to a suggested inspection measure. The corresponding suggested inspection measures are determined by querying the database.

[0067] Based on the airport building vibration monitoring method provided in the above embodiments, this application also provides a specific implementation of an airport building vibration monitoring device. Please refer to the following embodiments.

[0068] See Figure 2 The airport building vibration monitoring device 200 provided in this application embodiment includes the following modules: The acquisition module 201 is used to acquire vibration data from multiple vibration sensing nodes in the airport building complex. The vibration sensing nodes are set on key nodes and key transmission paths of each individual structure in the airport building complex. Analysis module 202 is used to perform frequency domain analysis and energy integration on vibration data to obtain the vibration energy index of each vibration sensing node; The first generation module 203 is used to generate a vibration energy isosurface cloud map on the digital twin model of the airport building complex based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, using a spatial interpolation algorithm. The calculation module 204 is used to calculate the spatial gradient field of the vibration energy isosurface cloud map and determine the energy gradient anomaly area based on the spatial gradient field. Module 205 is used to determine the structurally and dynamically weak areas of the airport building complex based on the energy gradient anomaly area; The second generation module 206 is used to generate safety early warning information based on the structurally weak areas.

[0069] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0070] The electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0071] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0072] The memory 302 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, which is used to store application code that executes the scheme of this application and is controlled to execute by the processor 301.

[0073] In this embodiment, the electronic device may further include a communication interface 303 and a bus 304. Wherein, as... Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0074] Specifically, the communication interface 303 is mainly used to realize communication between various modules, devices, units, and / or equipment in the embodiments of this application. The bus 304 includes hardware, software, or both, coupling the components of the electronic device together. The bus 304 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0075] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0076] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for monitoring vibration of airport buildings, characterized in that, include: Vibration data of multiple vibration sensing nodes in the airport building complex are acquired, wherein the vibration sensing nodes are set on key nodes and key transmission paths of each individual structure in the airport building complex. Frequency domain analysis and energy integration are performed on the vibration data to obtain the vibration energy index of each vibration sensing node; Based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, a vibration energy isosurface cloud map is generated on the digital twin model of the airport building complex using a spatial interpolation algorithm. Calculate the spatial gradient field of the vibration energy isosurface cloud map, and determine the energy gradient anomaly region based on the spatial gradient field; Based on the energy gradient anomaly region, the structurally and dynamically weak areas of the airport building complex are determined; Safety warning information is generated based on the structurally weak areas.

2. The method according to claim 1, characterized in that, The acquisition of vibration data from multiple vibration sensing nodes within the airport building complex includes: If the vibration data acquired by any vibration sensing node exceeds a preset threshold or a collaborative wake-up message is received, the vibration sensing node will be switched from a low-power sleep mode to a high sampling rate working mode and will serve as the initial trigger node. Based on the three-dimensional structural topology map of the airport building complex and the location information of the initial trigger node, the direction of vibration energy transmission is determined, and coordinated wake-up information is sent sequentially to the adjacent vibration sensing nodes in the transmission direction. The vibration sensing node that receives the coordinated wake-up information enters a high sampling rate working mode and acts as a relay node, repeatedly sending coordinated wake-up information to adjacent vibration sensing nodes in the transmission direction to form a relay wake-up chain on the force transmission path of the airport building complex.

3. The method according to claim 2, characterized in that, Also includes: When any vibration sensing node in the relay wake-up chain is woken up and enters the high sampling rate working mode, the vibration data is compared with the preset threshold in real time, and the local comparison time when the vibration data is greater than the preset threshold is obtained. The local comparison time is compared with the reference time in the collaborative wake-up information obtained from the upstream vibration sensing node to obtain the actual propagation time difference of vibration on the structural path between adjacent nodes. The actual propagation time difference is correlated with the corresponding spatial coordinates to analyze the propagation velocity distribution of vibrations within the airport building complex.

4. The method according to claim 3, characterized in that, The process of performing frequency domain analysis and energy integration on the vibration data to obtain the vibration energy index of each vibration sensing node includes: Based on the actual propagation time difference, a dynamic transmission path network diagram is constructed to show the transmission of vibration energy from the initial triggering node to each vibration sensing node. Modal decomposition is performed on the vibration data of each vibration sensing node to obtain multiple intrinsic mode functions; For each intrinsic mode function, the path attenuation effect of the frequency component propagating to the corresponding vibration sensing node is estimated based on the center frequency and the dynamic transmission path network graph. The vibration energy of each intrinsic mode function is compensated based on the path attenuation effect, and the compensated vibration energy is integrated in the time domain to obtain the vibration energy index corresponding to each vibration sensing node. The vibration energy index is used to characterize the local structural dynamic characteristics after the influence of the propagation path is removed.

5. The method according to claim 2, characterized in that, The process of generating a vibration energy isosurface cloud map on the digital twin model of the airport building complex based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy indices using a spatial interpolation algorithm includes: Based on the timing of the digital twin model and the relay wake-up chain, a digital twin simulation is performed to obtain the theoretical energy distribution field; For each vibration sensing node, the vibration energy index and the theoretical energy distribution field are fused at the spatial coordinates of the vibration sensing node to obtain multiple assimilated data points, wherein each assimilated data point includes spatial coordinates and the fused optimized energy value. Based on the spatial coordinates and optimized energy values ​​of all the assimilated data points, a continuous vibration energy isosurface cloud map is generated by fitting the structural surface of the digital twin model using a spatial interpolation algorithm.

6. The method according to claim 1 or 5, characterized in that, The calculation of the spatial gradient field of the vibration energy isosurface contour map includes: Based on the Monte Carlo random walk algorithm, energy diffusion simulation was performed on the vibration energy isosurface cloud map to obtain the probability flux density vector field. Obtain the theoretical stress streamline field corresponding to the digital twin model; The probability flux density vector field is coupled with the theoretical stress streamline field to obtain a scalar coupled field; The spatial gradient field is obtained by performing gradient calculation on the scalar coupled field.

7. The method according to claim 1, characterized in that, Determining energy gradient anomaly regions based on the spatial gradient field includes: The spatial gradient field, the vibration energy isosurface cloud map, and the structural topology of the digital twin model are input into the trained neural network model to obtain the anomaly probability value of each structural component. The energy gradient anomaly region is determined based on the aforementioned anomaly probability value.

8. The method according to claim 2, characterized in that, The determination of the structurally weak areas of the airport complex based on the energy gradient anomaly areas includes: Structural components corresponding to the energy gradient anomaly region are extracted from the digital twin model, and the extracted structural components are used as components to be evaluated. Based on the vibration transmission sequence of the relay-type wake-up chain, the vibration wave propagation sequence from the initial trigger node through the force transmission path to the component to be evaluated is extracted. Based on the vibration wave propagation sequence, the dynamic characteristic parameters of the component to be evaluated in the vibration event are calculated. The dynamic characteristic parameters include at least the natural frequency, damping ratio, and mode participation factor. Based on the vibration source type, intensity, and location of the vibration event, structural dynamic response simulation is performed in a digital twin model to obtain the theoretical dynamic characteristic parameters of the corresponding location of the component to be evaluated. The dynamic characteristic parameters are compared with the theoretical dynamic characteristic parameters to obtain the comparison results; Based on the comparison results and the gradient anomaly intensity and spatial continuity of the energy gradient anomaly zone, the structural region where vibration energy accumulates due to the degradation of dynamic characteristics is determined, and the determined structural region is regarded as the structural dynamic weak zone.

9. A vibration monitoring device for airport buildings, characterized in that, The device includes: The acquisition module is used to acquire vibration data from multiple vibration sensing nodes in the airport building complex, wherein the vibration sensing nodes are set on key nodes and key transmission paths of each individual structure in the airport building complex. The analysis module is used to perform frequency domain analysis and energy integration on the vibration data to obtain the vibration energy index of each vibration sensing node. The first generation module is used to generate a vibration energy isosurface cloud map on the digital twin model of the airport building complex based on the spatial coordinates of the vibration sensing nodes and the corresponding vibration energy index, using a spatial interpolation algorithm. The calculation module is used to calculate the spatial gradient field of the vibration energy isosurface cloud map and determine the energy gradient anomaly region based on the spatial gradient field. The determination module is used to determine the structurally weak areas of the airport building complex based on the energy gradient anomaly areas; The second generation module is used to generate safety early warning information based on the structurally weak areas.

10. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the airport building vibration monitoring method as described in any one of claims 1-8.

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