Efficient analysis method for safety of components and parts in BIM model

Abstract

The invention relates to the technical field of component safety analysis, in particular to an efficient analysis method for component safety in a BIM model, and the method comprises the steps: obtaining a vibration frequency sequence, a temperature sequence and a tension sequence of each component at each collection moment; clustering the vibration frequency sequence of each component at the current acquisition moment to obtain an abnormal cluster therein; according to the random degree of the vibration frequency sequence, the local density of all abnormal clusters and the joint action degree among the vibration frequency, the temperature and the tension at all the collection moments, the coupling field intensity risk degree of each component at the current collection moment is obtained, and then the resource weight of each component at the current collection moment is obtained; and classifying the components at the current acquisition moment, and selecting a corresponding safety analysis model for the components. According to the method, risk assessment is carried out on each component and the appropriate safety analysis model is selected for each component, so that the safety analysis efficiency of the components is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction component safety analysis, in particular to a high-efficiency analysis method for construction component safety in a BIM model. BACKGROUND

[0002] In traditional construction engineering, construction component safety analysis highly depends on two-dimensional drawings, which is inefficient and prone to errors. The collision and stress state of complex structures are difficult to verify intuitively, and safety hazards are often exposed late. With the rapid development of BIM technology and computer capabilities, construction component safety analysis is undergoing a revolution. Early BIM mainly realizes three-dimensional visualization and basic collision detection. Subsequently, parameterized modeling and Industry Foundation Classes (IFC) deepened the integration of component properties, enabling early exposure of design conflicts and structural risks, significantly reducing site changes and accidents, and ensuring life and property safety. It promotes collaborative decision-making and fine safety management throughout the design, construction, and operation chain, laying the foundation for intelligent review and automated compliance inspection.

[0003] In the safety analysis of construction components in a BIM model, the construction state changes require real-time updating of model geometry and boundary conditions, while the safety analysis of construction components relies on high-precision physical simulation. The combination of the two leads to an exponential increase in computational complexity. In existing technologies, if a high-precision safety analysis model is used to analyze the safety of construction components, although the analysis accuracy can be guaranteed, the complex calculation process will affect the efficiency of safety analysis, resulting in a lag in safety analysis results. If a simplified model is used to analyze the safety of construction components, the implicit correlation of high-risk construction components under the influence of different environmental fields will be ignored, which will affect the accuracy of risk assessment of construction components, and further reduce the accuracy of early warning of high-risk construction components. Therefore, there is an urgent need for a safety analysis method that balances accuracy and speed. SUMMARY

[0004] To solve the above technical problems, the present application provides a high-efficiency analysis method for construction component safety in a BIM model to solve the existing problems.

[0005] The high-efficiency analysis method for construction component safety in a BIM model provided by the present application adopts the following technical solutions: One embodiment of the present application provides a high-efficiency analysis method for construction component safety in a BIM model, which includes the following steps: Obtain the vibration frequency sequence, temperature sequence, and tension sequence of each construction component at each collection time; The data of each component in the vibration frequency sequence at the current collection time is clustered, the local density of each cluster is obtained according to the total number of data of each cluster and the corresponding area, and compared with a preset density threshold to obtain the abnormal cluster of each component at the current collection time; the random degree of the vibration frequency sequence of each component at the current collection time and the average level of the local density of all abnormal clusters of each component are obtained to obtain the implicit risk index of each component at the current collection time. According to the mutual dependence degree between the vibration frequency sequence and the temperature sequence of each component at the current collection time and all collection times before the current collection time, and the correlation degree between the vibration frequency sequence and the tension sequence, the coupling strength factor of each component at the current collection time is obtained, and the coupling field strength risk degree of each component at the current collection time is obtained in combination with the implicit risk index of each component at the current collection time, and the resource weight of each component at the current collection time is obtained in combination with the accumulation of the coupling field strength risk degrees of all components at the current collection time, and then the components at the current collection time are classified; a corresponding safety analysis model is selected according to the category of each component at the current collection time.

[0006] Preferably, the local density of each cluster is obtained by obtaining the cluster area of each cluster, and the ratio of the total number of data points in each cluster to the cluster area is taken as the local density of each cluster.

[0007] Preferably, the abnormal cluster of each component at the current collection time refers to the cluster whose local density is greater than the preset density threshold among all clusters of each component at the current collection time.

[0008] Preferably, the implicit risk index of each component at the current collection time is obtained by: According to the random degree of the vibration frequency sequence of each component at the current collection time, the marginal spectral entropy value of each component at the current collection time is obtained. The implicit risk index of each component at the current collection time is calculated as: ; wherein, is the implicit risk index of the i th component at the current collection time, is the marginal spectral entropy value of the i th component at the current collection time, is the local density of the j th abnormal cluster of the i th component at the current collection time, is the total number of abnormal clusters of the i th component at the current collection time.

[0009] Preferably, the process of obtaining the marginal spectrum entropy value of each component at the current acquisition time is: taking the vibration frequency sequence of each component at the current acquisition time as the input of the Hilbert-Huang transform marginal spectrum entropy analysis algorithm, using the empirical mode decomposition stopping criterion, and outputting the marginal spectrum entropy value of each component at the current acquisition time.

[0010] Preferably, the process of obtaining the coupling strength factor of each component at the current acquisition time is: taking the vibration frequency sequence and the temperature sequence of each component at each acquisition time as the input of the mutual information algorithm, outputting the mutual information value of each component at each acquisition time; taking the vibration frequency sequence and the tension sequence of each component at each acquisition time as the input of the covariance analysis algorithm, outputting the covariance value of each component at each acquisition time; taking the mutual information values of each component at the current acquisition time and all previous acquisition times to form a sequence in ascending order of time, denoted as the mutual information sequence of each component at the current acquisition time; taking the covariance values of each component at the current acquisition time and all previous acquisition times to form a sequence in ascending order of time, denoted as the covariance sequence of each component at the current acquisition time; taking the mutual information sequence and the covariance sequence of each component at the current acquisition time as the input of the weighted moving average algorithm, and taking the output fusion value as the coupling strength factor of each component at the current acquisition time.

[0011] Preferably, the calculation formula of the coupling field strength risk degree of each component at the current acquisition time is: ; in the formula, is the coupling field strength risk degree of the i th component at the current acquisition time, is the hidden risk index of the i th component at the current acquisition time, is the coupling strength factor of the i th component at the current acquisition time.

[0012] Preferably, the calculation formula of the resource weight of each component at the current acquisition time is: ; in the formula, is the resource weight of the i th component at the current acquisition time, is the coupling field strength risk degree of the i th component at the current acquisition time, is the sum of the coupling field strength risk degrees of all components at the current acquisition time.

[0013] Preferably, the specific process of classifying the components at the current acquisition time is: when the resource weight of any component at the current acquisition time is greater than a preset monitoring threshold, the component at the current acquisition time is classified as a high-risk component; otherwise, the component at the current acquisition time is classified as a low-risk component.

[0014] Preferably, the specific process of selecting the corresponding safety analysis model according to the category of each component at the current collection time is as follows: if any component at the current collection time is a high-risk component, a high-precision full-order coupling simulation model is selected as the safety analysis model of the component at the current collection time; otherwise, a reduced-order surrogate model is selected as the safety analysis model of the component at the current collection time.

[0015] The present application has at least the following beneficial effects: 1. By analyzing the marginal spectrum entropy value of the vibration frequency sequence and the stress concentration risk characteristics, the implicit risk index of each component at the current collection time is constructed, directly representing the degree of cooperative harm of stress concentration and vibration randomness, so that the subsequent risk assessment of each component is more accurate.

[0016] 2. In view of the defect that the implicit risk index is difficult to capture the time-varying cumulative effect, the mutual information algorithm is used to quantify the temperature-vibration nonlinear influence with vibration, temperature and tension sequences as input, the covariance analysis is used to evaluate the tension-vibration diffusion correlation degree, and the coupling strength is further judged by weighted moving average fusion, so as to reflect the joint action strength of tension-temperature-vibration field in real time, eliminate the random interference in the dynamic interaction of environmental load, and evaluate the failure risk of each component from the overall trend of risk evolution, thereby improving the accuracy of risk assessment.

[0017] 3. Based on the real-time coupling field strength risk degree, the resource weight of the component is calculated, and each component is classified, so that the high-risk component executes the high-precision full-order coupling simulation model, and the low-risk component executes the simplified model, thereby breaking through the zero-sum game of precision-efficiency of traditional lightweight model, focusing the limited resources on the components with high risk, avoiding redundant calculation on non-critical components, and improving the efficiency of safety analysis while ensuring the accuracy of safety analysis of components. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 A step flow chart of the efficient analysis method of component safety in a BIM model provided by the present application; Figure 2 A flow chart of obtaining the resource weight of each component at the current collection time provided by the present application. DETAILED DESCRIPTION

[0020] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an efficient analysis method for the safety of components in a BIM model proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0022] The following, in conjunction with the accompanying drawings, details the specific scheme of an efficient analysis method for the safety of components in a BIM model provided in this application.

[0023] This application provides an efficient method for analyzing the safety of components in a BIM model, specifically, the following efficient method for analyzing the safety of components in a BIM model is provided. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps: Step 1: Obtain the vibration frequency sequence, temperature sequence, and tension sequence of each component at each acquisition time.

[0024] In the BIM model, all components in the BIM model are obtained by parsing the hierarchical structure and attribute set of IFC standard components, and extracting all physical entity instances containing geometric topology and physical parameters through the hierarchical structure and attribute set of IFC standard components.

[0025] Virtual triaxial accelerometers are attached to the center-of-gravity coordinates of each component to extract vibration frequency data in real time from the structural dynamics simulation module, which is used to quantify the hidden fatigue risk caused by vortex-induced resonance. For each component, virtual infrared temperature sensors are embedded in the thermally coupled mesh nodes on its surface to synchronously acquire surface temperature data of the component, which is used to monitor the nonlinear effect of thermal stress on material properties. Virtual tension monitoring points are loaded at the finite element nodes of each component to acquire conductor tension data in real time, which is used to characterize the dynamic mechanical load under wind vibration and icing conditions.

[0026] The data collection frequencies of the three types of virtual sensors are all set to 10 Hz, and the three types of data are subjected to noise reduction processing by using a moving average filtering algorithm, and the vibration frequency, temperature and tension of each component are subjected to normalization processing. In this embodiment, the minimum-maximum value normalization method is used to normalize each type of data to eliminate dimensional differences. The moving average filtering algorithm and the minimum-maximum value normalization method are both known technologies, and the specific process will not be described again.

[0027] For the denoised and normalized data finally obtained for each component, the data of each component at all collection time points from the start collection time point to the current collection time point are sorted in ascending order of time to obtain the vibration frequency sequence, temperature sequence and tension sequence of each component at the current collection time point, thereby providing high signal-to-noise ratio input for subsequent multi-field coupling analysis.

[0028] Similarly, the vibration frequency sequence, temperature sequence and tension sequence of each component at each collection time point are obtained.

[0029] Step two: the data in the vibration frequency sequence of each component at the current collection time point are clustered, the local density of each cluster is obtained according to the total number of data of each cluster and the corresponding area, and compared with the preset density threshold to obtain the abnormal cluster of each component at the current collection time point; the implicit risk index of each component at the current collection time point is obtained according to the randomness of the vibration frequency sequence of each component at the current collection time point and the average level of the local density of all abnormal clusters of each component.

[0030] Because the building components are easily subjected to implicit stress concentration and nonlinear deformation under the action of complex environmental loads such as wind vibration, temperature change and multi-physical field coupling of structural stress. The traditional simplified model often ignores or weakens the nonlinear correlation mechanism between these physical fields, making it difficult to effectively identify and warn potential safety hazards and increasing the risk of structural failure.

[0031] Because vibration can cause dynamic stress inside the component, abnormal vibration often causes stress to concentrate, which in turn causes the component to fail. Therefore, taking the i-th component as an example, the vibration frequency sequence of the i-th component at the current collection time point is taken as the input of the DBSCAN clustering algorithm, the neighborhood radius is set to 15 and the minimum sample number is set to 5 to ensure that the abnormal point is not noise, and all clusters after clustering are output. The DBSCAN clustering algorithm is a known technology, and the specific process will not be described again. Subsequently, the local density of all clusters is calculated Specifically, the local density calculation method is: using the convex hull algorithm to calculate the cluster area of each clustering cluster, and taking the ratio of the total number of data points in each clustering cluster to the cluster area as the local density of each clustering cluster. The clustering cluster with large local density represents that the vibration frequency data of the clustering cluster is highly concentrated in the spatial distribution, indicating the risk hotspot area of the stress resonance of the assembly. Therefore, a preset density threshold is set, and in an embodiment of the present application, the preset density threshold is the average value of the local densities of all clustering clusters of the i th assembly at the current acquisition time; the clustering cluster with a local density greater than the preset density threshold among all clustering clusters of the i th assembly at the current acquisition time is taken as an abnormal clustering cluster of the i th assembly at the current acquisition time, representing the stress overrun risk area of the i th assembly caused by vibration anomaly.

[0032] The vibration frequency sequence of the i th assembly at the current acquisition time is taken as the input of the Hilbert-Huang Transform (HHT) marginal spectrum entropy analysis algorithm, the standard deviation threshold is set to 0.2 to avoid over-decomposition, and the marginal spectrum frequency resolution is set to 0.5 Hz, and the marginal spectrum entropy value of the i th assembly at the current acquisition time is output, thereby representing the randomness of the vibration energy distribution in the frequency domain. The Hilbert-Huang Transform (HHT) marginal spectrum entropy analysis algorithm is a known technology, and the specific process will not be described here.

[0033] As a preferred embodiment, according to the randomness degree of the vibration frequency sequence of each assembly at the current acquisition time and the average level of the local density of all abnormal clustering clusters of each assembly, the implicit risk index of each assembly at the current acquisition time is obtained, which is used to represent the synergistic damage degree of stress concentration and vibration energy randomness of each assembly under the action of multiple field coupling.

[0034] In this embodiment, the implicit risk index of the i th assembly at the current acquisition time is denoted as , and the specific calculation relationship is: ; in the formula, , the implicit risk index of the i th assembly at the current acquisition time is , the marginal spectrum entropy value of the i th assembly at the current acquisition time, which directly reflects the structural response state of the assembly under dynamic load by capturing the nonlinear characteristics of vibration behavior, the greater the value, the stronger the randomness of vibration energy distribution, the more likely it is caused by complex multi-field coupling, and the more likely the structure is in an unstable state, which is easy to cause implicit fatigue or stress diffusion; , the local density of the j th abnormal clustering cluster of the i th assembly at the current acquisition time, which quantifies the severity of the stress concentration area of the assembly, the greater the value, the higher the stress concentration, and the greater the risk of the structure; , the total number of abnormal clustering clusters of the i th assembly at the current acquisition time.

[0035] The implicit risk index is used to quantify the comprehensive degree of the implicit failure risk of each component, represents the synergistic hazard degree of stress concentration and vibration energy randomness of each component under the coupling of multiple fields, and the value thereof maps the failure risk grade of the component in real time. The greater the value is, the higher the implicit failure risk of the component is.

[0036] Step three: According to the mutual dependence degree between the vibration frequency sequence and the temperature sequence of each component at the current collection time and all collection times before the current collection time, and the correlation degree between the vibration frequency sequence and the tension sequence, the coupling strength factor of each component at the current collection time is obtained, and the coupling field strength risk degree of each component at the current collection time is obtained in combination with the implicit risk index of each component at the current collection time. The resource weight of each component at the current collection time is obtained in combination with the accumulation of the coupling field strength risk degrees of all components at the current collection time, and then the components at the current collection time are classified; the corresponding safety analysis model is selected according to the category of each component at the current collection time.

[0037] Since the safety of the components in the BIM model is affected by the complex coupling of multiple physical fields such as structural load, temperature, vibration, etc., and the risk has time-varying accumulation and spatial correlation, it is difficult to accurately predict the risk evolution trend and realize real-time early warning of high-risk scenarios only by relying on the constructed implicit risk index, which has the risk of early warning lag or misjudgment.

[0038] The greater the mutual dependence degree between the temperature data and the vibration frequency data is, the greater the influence of temperature on vibration frequency is, and the greater the possibility of failure risk of the component is. Therefore, the mutual information (Mutual Information) algorithm is used to set the dynamic sliding window to 120s to cover the typical wind vibration period, and the histogram bin number is set to 20 to balance the accuracy and real-time performance, and finally the mutual information value of the ith component at each collection time is output, so as to quantify the mutual dependence degree between the temperature data and the vibration frequency data.

[0039] In order to evaluate the influence strength of tension change on vibration diffusion, the correlation degree between vibration data and tension data can be analyzed. The greater the correlation degree is, the greater the influence of tension change on vibration diffusion is, and the higher the risk of the component is. Based on this, the covariance analysis algorithm is used to set the attenuation factor to 0.9, and the covariance value of the ith component at each collection time is output, which is used to represent the correlation degree between the tension and the vibration frequency of the component at each collection time.

[0040] The sequence of the mutual information value of the ith component assembly at all collection time points before and including the current collection time point in ascending order of time is denoted as the mutual information sequence of the ith component assembly at the current collection time point; and the sequence of the covariance value of the ith component assembly at all collection time points before and including the current collection time point in ascending order of time is denoted as the covariance sequence of the ith component assembly at the current collection time point.

[0041] Subsequently, for each component assembly at each collection time point, the mutual information sequence and the covariance sequence of the ith component assembly at the current collection time point are used as input, the mutual information sequence and the covariance sequence are smoothed and fused by using a weighted moving average (WMA) algorithm, the time window is set to n seconds, the weight distribution of the two parameters in this embodiment is 0.5, and the output fusion value is denoted as the coupling strength factor of the ith component assembly at the current collection time point, which is used to represent the strength of the multi-field coupling synergistic effect at the current collection time point, which analyzes the dynamic interaction of the environmental load, and the greater the value, the stronger the synergistic effect of the multi-field coupling, and the greater the potential of risk acceleration evolution. In this embodiment, n is 60.

[0042] It should be noted that the mutual information algorithm, the covariance analysis algorithm and the weighted moving average algorithm are all known technologies in the art, and the specific process will not be described again.

[0043] As a preferred embodiment, the coupling field strength risk degree of each component assembly at the current collection time point is obtained according to the coupling strength factor and the implicit risk index of each component assembly at the current collection time point, which is used to represent the comprehensive failure risk degree of each component assembly at the current collection time point.

[0044] In this embodiment, the coupling field strength risk degree of the ith component assembly at the current collection time point is denoted as , and the specific expression is: ; in the formula, is the coupling field strength risk degree of the ith component assembly at the current collection time point, is the implicit risk index of the ith component assembly at the current collection time point, which captures the quasi-static risk characteristics of the implicit failure of the component assembly under dynamic load, and the greater the value, the stronger the synergistic hazards of stress concentration and vibration randomness in the structural response, and the higher the probability of fatigue failure or stress diffusion; is the coupling strength factor of the ith component assembly at the current collection time point.

[0045] The coupling field strength risk degree represents the comprehensive failure risk strength of the component assembly under the complex multi-field coupling effect, and quantifies the comprehensive effect of the structural implicit risk and the dynamic coupling strength of the environment in real time, thereby solving the nonlinear, time-varying cumulative and spatial correlation risk that the traditional model cannot capture, The greater the value of the ith component at the current acquisition time, the greater the risk of failure of the ith component at the current acquisition time, and the vicious cycle of multiple field interactions is strengthened.

[0046] Since the BIM model needs to respond to construction state changes in real time in component safety analysis, and synchronously process multi-field coupling simulation of structural thermal vibration, the demand for computing resources increases exponentially. The incremental update strategy adopted by the traditional lightweight model is prone to error accumulation in complex interaction chains, and resource allocation is limited by the game between high-precision simulation in key areas and simplification in non-key areas, which cannot guarantee the timeliness of early warning in high-risk scenarios and the global calculation accuracy at the same time.

[0047] Based on the coupling field strength risk degree, the resource weight of each component at the current acquisition time is obtained, and in this embodiment, the resource weight of the ith component at the current acquisition time is denoted as , and the specific expression is: ; in the formula, is the resource weight of the ith component at the current acquisition time, is the coupling field strength risk degree of the ith component at the current acquisition time, is the sum of the coupling field strength risk degrees of all components at the current acquisition time. The greater the value of the ith component at the current acquisition time, the greater the risk of failure of the ith component at the current acquisition time relative to other components at the current acquisition time. The process of obtaining the resource weight of each component at the current acquisition time is shown in Figure 2 .

[0048] Further, by calculating the resource weight of each component in real time, each component at the current acquisition time is classified, specifically, a preset monitoring threshold is set, and in this embodiment, the preset monitoring threshold is 0.1. When , the ith component at the current acquisition time is regarded as a high-risk component; when , the ith component at the current acquisition time is regarded as a low-risk component. Further, according to the category of each component, a millisecond-level model switching is performed on the safety analysis model of each component. When the ith component at the current acquisition time is a high-risk component, a high-precision full-order coupling simulation model is used as the safety analysis model of the component at the current acquisition time; when the ith component at the current acquisition time is a low-risk component, a reduced-order surrogate model is used as the safety analysis model of the component at the current acquisition time. In the process of model switching, an incremental variable transfer is used to ensure the continuity of the physical field.

[0049] It should be noted that the high-precision full-order coupling simulation model adopts full-order multi-physical field coupling simulation for high-risk components, performs millisecond time step transient finite element analysis based on real-time updated boundary conditions, completely solves the structure dynamics equation, heat conduction equation and nonlinear material constitutive model, and synchronously iteratively calculates the thermal stress distribution and vibration response spectrum, so as to accurately capture the implicit crack propagation and fatigue accumulation effect. The reduced order model is a simplified model of the high-precision full-order coupling simulation model, which has the following differences compared with the high-precision full-order coupling simulation model: the finite element node freedom is compressed to 5% of the original model, only the key modal parameters are reserved, and the non-main freedom is eliminated by static condensation method, combined with linear equivalent stiffness matrix instead of nonlinear calculation, and the non-key physical field is frozen, so that the calculation load is reduced. The construction of the above high-precision full-order coupling simulation model and the reduced order model are known to the skilled person, and will not be described here.

[0050] The failure risk intensity of each component is dynamically quantified by the coupling field strength calculated in real time, and each component is classified accordingly, so that the calculation resources are inclined to high-risk components; the simulation accuracy and early warning timeliness of components in high stress concentration and multi-field coupling region are preferentially guaranteed, while redundant calculation of low-risk components is avoided, so as to effectively suppress error accumulation and optimize global model analysis efficiency under limited resources, and overcome the defects of traditional lightweight model in high-risk scene response lag or resource allocation imbalance.

[0051] It should be noted that the above-mentioned embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. Moreover, the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or may be advantageous.

[0052] Each embodiment in the present specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

[0053] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; modifying the technical solutions described in the above embodiments, or equivalently replacing some technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An efficient method for analyzing the safety of components in a BIM model, characterized in that, The method includes the following steps: Obtain the vibration frequency sequence, temperature sequence, and tension sequence of each component at each acquisition time; The data of each component in the vibration frequency sequence at the current acquisition time are clustered. The local density of each cluster is obtained based on the total number of data in each cluster and its corresponding area. This density is then compared with a preset density threshold to obtain the abnormal clusters of each component at the current acquisition time. Based on the randomness of the vibration frequency sequence of each component at the current acquisition time and the average level of the local density of all abnormal clusters of each component, the latent risk index of each component at the current acquisition time is obtained. Based on the interdependence between the vibration frequency sequence and temperature sequence of each component at the current acquisition time and all previous acquisition times, as well as the correlation between the vibration frequency sequence and tension sequence, the coupling strength factor of each component at the current acquisition time is obtained. Combined with the implicit risk index of each component at the current acquisition time, the coupling field strength risk degree of each component at the current acquisition time is obtained. By combining the sum of the coupling field strength risk degrees of all components at the current acquisition time, the resource weight of each component at the current acquisition time is obtained, and then the components at the current acquisition time are classified. The corresponding safety analysis model is selected according to the category of each component at the current acquisition time.

2. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The process of obtaining the local density of each cluster is as follows: obtain the cluster area of ​​each cluster, and take the ratio of the total number of data points in each cluster to the cluster area as the local density of each cluster.

3. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The abnormal clusters of each component at the current acquisition time refer to the clusters among all clusters of each component at the current acquisition time whose local density is greater than a preset density threshold.

4. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The process for obtaining the latent risk index of each component at the current data collection time is as follows: Based on the randomness of the vibration frequency sequence of each component at the current acquisition time, the marginal spectral entropy value of each component at the current acquisition time is obtained. Calculate the latent risk index of each component at the current data acquisition moment: In the formula, Let be the implicit risk index of the i-th component at the current data collection moment. Let be the marginal spectral entropy value of the i-th component at the current acquisition time. Let be the local density of the j-th anomalous cluster of the i-th component at the current acquisition time. This represents the total number of abnormal clusters for the i-th component at the current acquisition time.

5. The efficient analysis method for the safety of components in a BIM model as described in claim 4, characterized in that, The process of obtaining the marginal spectral entropy value of each component at the current acquisition time is as follows: the vibration frequency sequence of each component at the current acquisition time is used as the input of the Hilbert-Huang transform marginal spectral entropy analysis algorithm, and the empirical mode decomposition stopping criterion is used to output the marginal spectral entropy value of each component at the current acquisition time.

6. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The process for obtaining the coupling strength factor of each component at the current acquisition time is as follows: The vibration frequency sequence and temperature sequence of each component at each acquisition time are used as input to the mutual information algorithm, and the mutual information value of each component at each acquisition time is output; the vibration frequency sequence and tension sequence of each component at each acquisition time are used as input to the covariance analysis algorithm, and the covariance value of each component at each acquisition time is output. The sequence of mutual information values ​​of each component at the current acquisition time and all previous acquisition times, arranged in ascending order of time, is denoted as the mutual information sequence of each component at the current acquisition time; the sequence of covariance values ​​of each component at the current acquisition time and all previous acquisition times, arranged in ascending order of time, is denoted as the covariance sequence of each component at the current acquisition time. The mutual information sequence and covariance sequence of each component at the current acquisition time are used as inputs to the weighted moving average algorithm, and the output fusion value is recorded as the coupling strength factor of each component at the current acquisition time.

7. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The formula for calculating the coupling field strength risk of each component at the current acquisition time is as follows: In the formula, Let represent the coupling field strength risk level of the i-th component at the current acquisition moment. Let be the implicit risk index of the i-th component at the current data collection moment. Let be the coupling strength factor of the i-th component at the current acquisition time.

8. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The formula for calculating the resource weight of each component at the current acquisition time is as follows: In the formula, Let i be the resource weight of the i-th component at the current acquisition time. Let represent the coupling field strength risk level of the i-th component at the current acquisition moment. This represents the sum of the coupling field strength risk of all components at the current acquisition moment.

9. The efficient analysis method for the safety of components in a BIM model as described in claim 1, characterized in that, The specific process of classifying components at the current acquisition time is as follows: when the resource weight of any component at the current acquisition time is greater than the preset monitoring threshold, the component at the current acquisition time is regarded as a high-risk component. Conversely, the component at the current acquisition time will be considered a low-risk component.

10. The efficient analysis method for the safety of components in a BIM model as described in claim 9, characterized in that, The specific process of selecting the corresponding safety analysis model according to the category of each component at the current acquisition time is as follows: if any component at the current acquisition time is a high-risk component, then the high-precision full-order coupled simulation model is used as the safety analysis model of the component at the current acquisition time. Otherwise, the downgraded proxy model will be used as the security analysis model for this component at the current acquisition time.

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