Existing building safety monitoring method and system based on BIM

Abstract

The invention discloses a BIM-based existing building safety monitoring method and system, relates to the technical field of building safety monitoring, and realizes multi-dimensional abnormal state fusion and quantitative judgment based on original sensing data through an abnormal index calculation module and a risk level judgment module. The problems of misjudgment and missed judgment caused by a traditional mode of giving priority to single-parameter threshold judgment are avoided, risk identification is more detailed, and hierarchical management can be achieved. Through a BIM component graph layer dynamic updating module, the system can directly bind a judged risk level to a component number, and render a risk hot area in real time in a three-dimensional model in a graph layer mode, so that conversion from a data result to space visualization is realized; through the component attribute acquisition and processing module, high-frequency acquisition and standardized processing of real-time physical data such as strain, displacement and temperature of each component in the BIM model are realized, and the problem that sensor data in an existing system is difficult to accurately bind with BIM components is solved.

Description

Technical Field

[0001] This invention relates to the field of building safety monitoring technology, specifically to a BIM-based method and system for monitoring the safety of existing buildings. Background Technology

[0002] With the widespread adoption of Building Information Modeling (BIM) technology in the construction engineering field, smart construction and intelligent operation and maintenance are gradually becoming emerging directions in project management. Especially in the niche area of ​​structural health monitoring of existing buildings, BIM is no longer merely a design modeling tool, but is gradually evolving into a key link connecting "physical components and digital twins." Against this backdrop, component-level safety status perception and intelligent assessment based on BIM platforms have become key research directions for improving the operational safety of older buildings. Specific applications such as structural component strain monitoring, thermal expansion anomaly analysis, and real-time risk visualization are driving traditional monitoring methods towards a new stage of data-driven, layer-visible, and dynamically evolving approaches.

[0003] In current structural monitoring practices for existing buildings, distributed sensors are commonly used to collect physical quantities such as strain, displacement, and temperature of components, and anomaly alarms and manual verification are performed through local systems. However, this approach has several key drawbacks: first, the correspondence between sensor data and spatial components is unclear, making it difficult to pinpoint the specific location of risks; second, monitoring results are often presented in tables, numerical values, or two-dimensional plan views, lacking intuitive three-dimensional visualization feedback.

[0004] The core reason for the above-mentioned defects is that most current systems are based on a separate data analysis architecture, meaning that sensor data is only processed in isolation at the acquisition end or within the local system, failing to achieve deep integration with the BIM model. In addition, the lack of an anomaly trend mapping mechanism based on component number makes it impossible to form a sustainable "risk hot zone" expression in the BIM model. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a BIM-based method and system for monitoring the safety of existing buildings, solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a BIM-based safety monitoring system for existing buildings, comprising a component attribute acquisition and processing module, an anomaly index calculation module, a risk level determination module, a risk hot zone mapping module, a trend evolution analysis module, and an inspection scheduling and decision-making module; The component attribute acquisition and processing module collects component data from the BIM model through sensors, performs preprocessing, and obtains the component data group GW. The anomaly index calculation module analyzes the component data group GW within a fixed period and calculates and obtains the anomaly index AU. The risk level determination module classifies the anomaly index AU, outputs the risk level R, and maps it to the BIM model layer. The BIM component layer dynamic update module matches the acquired risk level R with the component number in the BIM model and dynamically marks it to generate a risk hotspot layer. The trend evolution analysis module statistically analyzes the changing trend of risk level R within a fixed window W, constructs a trend index ZB, and combines it with a risk hotspot layer to determine the risk status. The inspection scheduling and decision-making module combines the risk level R and the trend indicator ZB to generate inspection tasks and transform them into an inspection scheduling priority suggestion table.

[0007] Preferably, the component attribute acquisition and processing module includes a component data acquisition and component mapping unit and a data cleaning and normalization processing unit; The component data acquisition and component mapping unit collects data on all components in the BIM model through sensors, including micro-displacement Dm, low-frequency strain Ea, and interface thermal disturbance gradient Tg. Among them, the micro-displacement Dm is acquired by a laser displacement sensor; Low-frequency strain Ea was acquired using a resistance strain gauge. The interface thermal perturbation gradient Tg was acquired using a thermocouple array. Map the data of each component to the unique component ID in the BIM to obtain the original data group ZW; The original data set ZW is obtained using the following formula: ZWi(t)={Dmi(t),Eai(t),Tgi(t)}; In the formula, ZWi(t) represents the original data set of component i at time t, Dmi(t) represents the micro-displacement of component i at time t, Eai(t) represents the low-frequency strain of component i at time t, and Tgi(t) represents the interface thermal disturbance gradient of component i at time t.

[0008] Preferably, the data cleaning and normalization processing unit cleans and normalizes the original data group ZW to obtain the component data group GW; Cleaning includes dynamic interference removal and frequency domain interference resampling; Dynamic error elimination uses nonlinear weighted slope identification to eliminate isolated anomalies caused by short-term external disturbances, such as impacts, wind vibrations, and voltage jumps. The method for identifying isolated outliers is as follows: First, the information of the component is analyzed through the original data group ZW, and the dynamic threshold value Qrs of the component is calculated and obtained. The dynamic threshold value Qrs is obtained using the following formula: ; In the formula, Qrsi(t) represents the dynamic threshold value of component i at time t, Dmi(t-1) represents the micro-displacement of component i at time t-1, Eai(t-1) represents the low-frequency strain of component i at time t-1, and Tgi(t-1) represents the interface thermal disturbance gradient of component i at time t-1. When the dynamic threshold value Qrs is greater than the preset threshold value TQr, it indicates that component i has been subjected to external disturbance, is marked as an isolated outlier, and undergoes forward smooth interpolation in the time domain. Frequency domain interference resampling uses FFT transformation to process the data in the original data group ZW, calculates the frequency band energy mutation E from 1 to 5 Hz, and if the frequency band energy mutation E > 80% fluctuation or the energy difference between adjacent windows > 20%, the data is reprocessed using window function low-pass resampling to avoid frequency band pollution caused by transient excitation of mechanical microcracks. Normalization is performed by processing the original data set ZW using the max-min normalization method to obtain the component data set GW.

[0009] The component data group GW is obtained using the following formula: ; In the formula, GWo represents the o-th data item in the component data group GW, ZWo represents the o-th data item in the original data group ZW, min represents the valley value of the o-th data item in the original data group ZW, and max represents the peak value of the o-th data item in the original data group ZW.

[0010] Preferably, the anomaly index calculation module includes a reference baseline construction unit and an anomaly index calculation unit; The reference baseline construction unit calculates the reference mean and fluctuation range of three types of parameters in the component data set GW within the time window Tw, and obtains baseline data, including the micro-displacement reference mean μDm, low-frequency strain fluctuation σEa, and thermal disturbance reference gradient θTg. The method for obtaining the reference mean value of micro-displacement μDm is as follows: First, select a time window Tw and obtain the micro-displacement data of component i at N time points within the time window Tw; then, add up all the micro-displacement values ​​at the N time points and sum them; finally, divide by N to obtain the reference mean value of micro-displacement μDm of component i within the time window Tw. The low-frequency strain fluctuation σEa is obtained as follows: Within a time window Tw, low-frequency strain data of component i are collected at N time points; first, the N low-frequency strain data are averaged to calculate the average strain value within the time window Tw; then, the average strain value is subtracted from the low-frequency strain data at each time point to obtain the difference; next, each difference is squared, and the deviation square values ​​corresponding to all time points are accumulated to obtain the sum; finally, the sum is divided by N-1 to obtain the quotient value, and the square root of the quotient value is calculated to obtain the low-frequency strain fluctuation σEa. The thermal perturbation reference gradient θTg is obtained as follows: Within the time window Tw, the interface thermal perturbation gradient of component i is collected at N time points. First, the rate of change of the interface thermal perturbation gradient over time is calculated: the thermal perturbation gradient values ​​of two adjacent time points are taken in sequence, the difference between them is calculated, and then divided by the time interval between the two time points. Then, the rates of change of all interface thermal perturbation gradients over time are summed and divided by the number of sampling points N to obtain the thermal perturbation reference gradient θTg of component i within the time window Tw.

[0011] Preferably, the anomaly index calculation unit calculates the anomaly index AU based on the obtained micro-displacement reference mean μDm, low-frequency strain fluctuation σEa, and thermal disturbance reference gradient θTg. The anomaly index AU is obtained using the following formula: ; In the formula, AUi(t) represents the anomaly index of component i at time t, μDmi represents the reference mean value of micro-displacement of component i, σEai represents the low-frequency strain fluctuation of component i, θTgi represents the thermal disturbance reference gradient of component i, ln represents the logarithmic function, and d represents the derivative sign.

[0012] The formula for obtaining the anomaly index AU is used to construct an anomaly index AU for evaluating whether a building component has a multidimensional anomaly state at a certain moment. Its core idea is: to use historical reference values ​​as a benchmark; to perform component analysis on three types of risk characteristics: deviation of the current value, strain rate fluctuation, and thermal disturbance trend change; and to use classical mathematical methods such as square terms, logarithmic terms, and denominator reduction to one term to improve anomaly sensitivity and numerical stability. The formula is designed based on principles from multiple disciplines, including Euclidean distance, the confidence function in information theory, and the rate of temperature change in the law of heat conduction. The first sub-item comes from the Euclidean distance calculation idea and is used to measure the strength of the deviation between the current value and the mean. It is widely used in mathematical analysis, cluster discrimination, signal anomaly detection and other fields. The second sub-item comes from the self-information formula I=log(1+x) in information theory, and the logarithmic compression of the fluctuation ratio in signal processing, which is used to enhance the abnormal response under small fluctuations. The third sub-item is referenced from Fourier's law of heat conduction and differential physics modeling theory: the rate of change of thermal disturbance in a system is an important factor affecting the redistribution of stress within the structure; using the derivative to reflect the speed of the disturbance is more effective in reflecting trend anomalies than a simple value. Preferably, the risk level determination module includes a risk level calculation and classification unit and a level binding and spatial mapping unit; The risk level calculation and classification unit determines the interval of the abnormal index AU and introduces a non-uniform dynamic distribution threshold mechanism to map the abnormal index AU with quantile statistics to obtain the risk level R. The risk level R is obtained using the following formula: ; In the formula, TH represents the high-risk threshold, corresponding to the 95th percentile of the anomaly index AU; TM represents the medium-risk threshold, corresponding to the 75th percentile of the anomaly index AU; and TL represents the low-risk threshold, corresponding to the 55th percentile of the anomaly index AU. The risk level binding and spatial mapping unit maps the risk level R to the corresponding 3D instance of the component in the BIM model, and assigns color labels and interactive attributes to construct BIM spatial mapping rules. When the risk level R=3, it indicates high risk and is mapped to red; When the risk level R=2, it represents a medium risk and is mapped to orange. When the risk level R=1, it indicates a slight risk, which is represented by yellow. When the risk level R=0, it indicates a safe state, which is mapped to green.

[0013] Preferably, the BIM component layer dynamic update module includes a component status binding and layer update unit and a time sliding window layer backtracking unit; The component status binding and layer update unit maps the risk level R to the component number in the BIM model; The component matching formula is as follows: Fbind(IDi) → {Ri(t), Pi(t)}; In the formula, Pi(t) represents the set of visualization parameters of component i at time t, including color, transparency and flashing frequency, IDi represents the unique identifier of BIM component, and Fbind represents the component state binding function; ; Colors visually represent risk levels; Transparency is used to prioritize warnings for risk areas; The flashing frequency is used to guide users to pay attention to hot topics, with red 2Hz indicating the most urgent alert; The time-sliding window layer backtracking unit constructs a risk hotspot layer based on the selected arbitrary time point by invoking the state of risk level R, including the layer snapshot recording function and the sliding window backtracking function; The formula for the layer snapshot recording function is as follows: Si(t) = {IDi, Ri(t), Pi(t)}; In the formula, Si(t) represents a snapshot of the visual state of component i at time t, and all snapshots are stored in the layer version library; The formula for the sliding window backtracking function is as follows: ; In the formula, L(t) represents the risk hot zone layer at time t, which includes the status of all components, M represents the total number of components, which means that the status of all M components in the system at time t is summarized into the total layer L, and Si(t) represents the status snapshot of component i at time t, which includes component number, risk level and visualization parameters (color, transparency and flashing frequency).

[0014] Preferably, the trend evolution analysis module includes a risk level time series statistics unit and a trend status layer overlay and anomaly annotation unit; The risk level time series statistics unit is based on components, extracting the risk level R sequence within a fixed window W, and constructing a trend indicator ZB by calculating the time difference sequence; The trend indicator ZB is obtained using the following formula: ; In the formula, ZBi represents the trend index ZB of component i, Ri(tk) represents the risk level of component i at time tk, and Ri(tk) represents the risk level of component i at time tk-1. The trend status layer overlay and anomaly annotation unit combine the trend index ZB with the risk hot zone layer L, and adjust the additional visual attributes of the components in the BIM layer according to the trend index ZB to achieve enhanced visualization of trend annotation. The formula for enhancing trend label visualization is as follows: nPi(t)=Pi(t)+TrendColor(ZBi)+EdgeStyle(ZBi); In the formula, nPi(t) represents the set of visualization parameters of component i at time t after the superimposed trend, TrendColor(ZBi) represents the color adjustment based on the trend index, and EdgeStyle(ZBi) represents the flashing frequency adjustment based on the trend index.

[0015] Preferably, the inspection scheduling and decision-making module includes a scheduling priority scoring calculation unit and a task generation and scheduling suggestion output unit; The scheduling priority scoring calculation unit combines the risk level R and the trend indicator ZB to construct the inspection scheduling priority score QA; The inspection dispatch priority score (QA) is obtained using the following formula: QAi(t)=Ri(t)(1+q1×max(0,ZBi)); In the formula, QAi(t) represents the inspection scheduling priority score of component i at time t, q1 represents the trend amplification coefficient, and max(0, ZBi) represents the non-negative trend function. If the trend is positive, the value is retained; if the trend is negative, it is regarded as 0 to prevent the inspection priority from being reduced. The scheduling suggestion output unit sorts the obtained inspection scheduling priority scores (QA) in descending order, and generates an inspection task suggestion table by combining the component location, component number, and risk level information. The formula for sorting sequences is as follows: Order = Sort(descQAi(t)); In the formula, Order represents the order of inspection task scheduling, Sort represents the sorting function, and desc represents descending order sorting; The inspection task suggestion form includes component number IDi, risk level R, trend indicator ZB, and inspection scheduling priority score QA.

[0016] A BIM-based method for safety monitoring of existing buildings includes the following steps: Step 1: The component attribute acquisition and processing module collects component data from the BIM model through sensors, performs preprocessing, and obtains the component data group GW. Step 2: The anomaly index calculation module analyzes the component data group GW within a fixed period and calculates and obtains the anomaly index AU. Step 3: The risk level determination module classifies the anomaly index AU, outputs the risk level R, and maps it to the BIM model layer. Step 4: The BIM component layer dynamic update module matches the acquired risk level R with the component number in the BIM model and dynamically marks it to generate a risk hotspot layer. Step 5: The trend evolution analysis module statistically analyzes the changing trend of risk level R within a fixed window W, constructs trend index ZB, and combines it with the risk hot zone layer to determine the risk status. Step Six: The inspection scheduling and decision-making module combines the risk level R and the trend indicator ZB to generate inspection tasks and transform them into an inspection scheduling priority suggestion table.

[0017] This invention provides a BIM-based method and system for safety monitoring of existing buildings, which has the following beneficial effects: (1) During system operation, the anomaly index calculation module and risk level determination module realize the fusion and quantitative determination of multi-dimensional anomaly states based on the original sensor data, avoiding the misjudgment and omission problems caused by the traditional method of mainly judging by single parameter thresholds, making risk identification more detailed and hierarchical management. Through the BIM component layer dynamic update module, the system can directly bind the determined risk level to the component number and render the "risk hot zone" in the three-dimensional model in real time as a layer, realizing the transformation from "data results" to "spatial visibility", breaking through the limitation of the traditional system that only outputs two-dimensional alarm reports.

[0018] (2) By binding the collected data to the unique component ID in the BIM model in real time through the component data acquisition and component mapping unit, the problem of difficult spatial positioning of sensor data in traditional systems is effectively solved. This mechanism realizes the seamless connection from "time series data" to "spatial three-dimensional model data", making subsequent risk visualization and layer labeling possible, and greatly improving the decision-making assistance value and interpretability of the BIM model.

[0019] (3) Through a precise binding mechanism between risk level and BIM component ID, this module establishes a clear data-component-visual status mapping relationship, ensuring that risk assessment results can be accurately located in the BIM 3D model and that the degree of risk is intuitively reflected through visualization methods such as color, flashing, and transparency. This mechanism significantly improves the perception efficiency and spatial identification of building status, enabling managers to locate high-risk components in seconds and achieve graded response and handling, thereby enhancing the direct practicality of monitoring data in the operation and maintenance management process.

[0020] (4) By calculating the offset trend and fluctuation characteristics in the component data set within a fixed period, an anomaly index integrating displacement, strain and thermal disturbance characteristics is constructed. This effectively breaks through the limitations of traditional methods that rely on "single index judgment and are prone to misjudgment and omission," and improves the stability and scientific nature of anomaly perception. This index can serve as a sensitive trigger point for changes in structural health status, providing a highly reliable input basis for subsequent risk levels and evolution trends.

[0021] By employing a tiered classification mechanism in the risk level assessment module, the safety status of each component is mapped to a color-coded visualization layer. This allows for real-time rendering and display of the status within the BIM spatial model, bridging the gap between "data" and "graphics." This mechanism significantly improves the response efficiency and intuitiveness for maintenance personnel, avoiding the inefficiency of relying solely on tables or logs to understand component status. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the block flow of a BIM-based safety monitoring system for existing buildings according to the present invention. Figure 2 This is a schematic diagram illustrating the steps of a BIM-based safety monitoring method for existing buildings according to the present invention. Figure 3 This is a flowchart illustrating the trend indicator of the present invention; Figure 4 This is a trend chart of the abnormal index of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Example 1 This invention provides a BIM-based safety monitoring system for existing buildings. Please refer to [link / reference]. Figures 1 to 4 It includes a component attribute acquisition and processing module, an anomaly index calculation module, a risk level determination module, a risk hot zone mapping module, a trend evolution analysis module, and an inspection scheduling and decision-making module; The component attribute acquisition and processing module collects component data from the BIM model through sensors, performs preprocessing, and obtains the component data group GW. The anomaly index calculation module analyzes the component data group GW within a fixed period and calculates and obtains the anomaly index AU. The risk level determination module classifies the anomaly index AU, outputs the risk level R, and maps it to the BIM model layer. The BIM component layer dynamic update module matches the acquired risk level R with the component number in the BIM model and dynamically marks it to generate a risk hotspot layer. The trend evolution analysis module statistically analyzes the changing trend of risk level R within a fixed window W, constructs a trend index ZB, and combines it with a risk hotspot layer to determine the risk status. The inspection scheduling and decision-making module combines the risk level R and the trend indicator ZB to generate inspection tasks and transform them into an inspection scheduling priority suggestion table.

[0025] In this embodiment, the component attribute acquisition and processing module enables high-frequency acquisition and standardized processing of real-time physical data such as strain, displacement, and temperature of each component in the BIM model. This solves the problem of difficulty in accurately binding sensor data with BIM components in existing systems, and significantly improves the spatial resolution and data adaptability of component-level risk identification.

[0026] Through the anomaly index calculation module and risk level determination module, the system achieves multi-dimensional anomaly state fusion and quantitative determination based on raw sensor data. This avoids the misjudgment and omission problems caused by the traditional method of mainly relying on single-parameter threshold judgment, making risk identification more detailed and hierarchical management possible. Through the BIM component layer dynamic update module, the system can directly bind the determined risk level to the component number and render "risk hot zones" in real time as layers in the 3D model, realizing the transformation from "data results" to "spatial visualization" and breaking through the limitation of traditional systems that only output two-dimensional alarm reports.

[0027] The trend evolution analysis module uses a sliding time window to statistically analyze the changing trends of risk levels, constructing a trend index ZB to dynamically track changes in risk status over time. This mechanism effectively addresses the shortcomings of existing technologies, such as the inability to trace risk evolution and the lack of visual records of abnormal changes, providing a time-dimensional reference for subsequent maintenance. By combining the risk level R with the trend index ZB, the inspection scheduling and decision-making module generates a structured inspection task suggestion table, enabling maintenance personnel to prioritize high-risk, upward-trending component areas, thereby improving the accuracy and efficiency of inspection tasks and avoiding resource waste and redundant operations.

[0028] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the component attribute acquisition and processing module includes a component data acquisition and component mapping unit and a data cleaning and normalization processing unit; The component data acquisition and component mapping unit collects data on all components in the BIM model through sensors, including micro-displacement Dm, low-frequency strain Ea, and interface thermal disturbance gradient Tg. Among them, the micro-displacement Dm is acquired by a laser displacement sensor; Low-frequency strain Ea was acquired using a resistance strain gauge. The interface thermal perturbation gradient Tg was acquired using a thermocouple array. Map the data of each component to the unique component ID in the BIM to obtain the original data group ZW; The original data set ZW is obtained using the following formula: ZWi(t)={Dmi(t),Eai(t),Tgi(t)}; In the formula, ZWi(t) represents the original data set of component i at time t, Dmi(t) represents the micro-displacement of component i at time t, Eai(t) represents the low-frequency strain of component i at time t, and Tgi(t) represents the interface thermal disturbance gradient of component i at time t.

[0029] The data cleaning and normalization unit cleans and normalizes the original data group ZW to obtain the component data group GW; Cleaning includes dynamic interference removal and frequency domain interference resampling; Dynamic false disturbance elimination uses nonlinear weighted slope identification to eliminate isolated outliers caused by short-term external disturbances. The method for identifying isolated outliers is as follows: First, the information of the component is analyzed through the original data group ZW, and the dynamic threshold value Qrs of the component is calculated and obtained. The dynamic threshold value Qrs is obtained using the following formula: ; In the formula, Qrsi(t) represents the dynamic threshold value of component i at time t, Dmi(t-1) represents the micro-displacement of component i at time t-1, Eai(t-1) represents the low-frequency strain of component i at time t-1, and Tgi(t-1) represents the interface thermal disturbance gradient of component i at time t-1. When the dynamic threshold value Qrs is greater than the preset threshold value TQr, it indicates that component i has been subjected to external disturbance, is marked as an isolated outlier, and undergoes forward smooth interpolation in the time domain. Frequency domain interference resampling uses FFT transformation to process the data in the original data group ZW, calculates the frequency band energy mutation E from 1 to 5 Hz, and if the frequency band energy mutation E > 80% fluctuation or the energy difference between adjacent windows > 20%, then the data is reprocessed by window function low-pass resampling. Normalization is performed by processing the original data set ZW using the max-min normalization method to obtain the component data set GW.

[0030] In this embodiment, this module explicitly employs three high-precision physical sensors: a laser displacement sensor, a resistance strain gauge, and a thermocouple array. These sensors respectively collect three key parameters: micro-displacement Dm, low-frequency strain Ea, and interface thermal disturbance gradient Tg. This allows for a comprehensive reflection of the stress state, material response, and heat conduction process of structural components at the microscopic scale, far superior to traditional monitoring systems that rely solely on stress or displacement as a single dimension. This multi-dimensional, multi-physical quantity fusion acquisition mode makes the state perception of each component more three-dimensional and detailed, laying a solid foundation for subsequent evaluation.

[0031] By binding the collected data to a unique component ID in the BIM model in real time through the component data acquisition and component mapping unit, the problem of spatial positioning of sensor data in traditional systems is effectively solved. This mechanism achieves seamless integration from "time series data" to "spatial three-dimensional model data," making subsequent risk visualization and layer annotation possible, and greatly enhancing the decision-making support value and interpretability of the BIM model.

[0032] In the data preprocessing stage, this module employs a nonlinear weighted slope identification method to dynamically calculate threshold values, accurately identifying "isolated outliers." Time-domain smoothing interpolation is then used for correction, preventing short-term disturbances from misleading long-term state assessments. After data cleaning, a unified maximum-minimum normalization method is used for normalization, ensuring that all component indicators are compared and analyzed on the same numerical scale. This avoids the impact of different physical unit dimensions on algorithm calculations, improving the adaptability and operability of subsequent anomaly index calculations and trend judgments. This mechanism enables the system to be portable for cross-project and cross-regional deployment.

[0033] Example 3 This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 3 and Figure 4 Specifically: the anomaly index calculation module includes a reference baseline construction unit and an anomaly index calculation unit; The reference baseline construction unit calculates the reference mean and fluctuation range of three types of parameters in the component data set GW within the time window Tw, and obtains baseline data, including the micro-displacement reference mean μDm, low-frequency strain fluctuation σEa, and thermal disturbance reference gradient θTg. The method for obtaining the reference mean value of micro-displacement μDm is as follows: First, select a time window Tw and obtain the micro-displacement data of component i at N time points within the time window Tw; then, add up all the micro-displacement values ​​at the N time points and sum them; finally, divide by N to obtain the reference mean value of micro-displacement μDm of component i within the time window Tw. The low-frequency strain fluctuation σEa is obtained as follows: Within a time window Tw, low-frequency strain data of component i are collected at N time points; first, the N low-frequency strain data are averaged to calculate the average strain value within the time window Tw; then, the average strain value is subtracted from the low-frequency strain data at each time point to obtain the difference; next, each difference is squared, and the deviation square values ​​corresponding to all time points are accumulated to obtain the sum; finally, the sum is divided by N-1 to obtain the quotient value, and the square root of the quotient value is calculated to obtain the low-frequency strain fluctuation σEa. The thermal perturbation reference gradient θTg is obtained as follows: Within the time window Tw, the interface thermal perturbation gradient of component i is collected at N time points. First, the rate of change of the interface thermal perturbation gradient over time is calculated: the thermal perturbation gradient values ​​of two adjacent time points are taken in sequence, the difference between them is calculated, and then divided by the time interval between the two time points. Then, the rates of change of all interface thermal perturbation gradients over time are summed and divided by the number of sampling points N to obtain the thermal perturbation reference gradient θTg of component i within the time window Tw.

[0034] The anomaly index calculation unit calculates the anomaly index AU based on the obtained micro-displacement reference mean μDm, low-frequency strain fluctuation σEa, and thermal disturbance reference gradient θTg. The anomaly index AU is obtained using the following formula: ; In the formula, AUi(t) represents the anomaly index of component i at time t, μDmi represents the reference mean value of micro-displacement of component i, σEai represents the low-frequency strain fluctuation of component i, θTgi represents the thermal disturbance reference gradient of component i, ln represents the logarithmic function, and d represents the derivative sign.

[0035] In this embodiment, a reference baseline construction unit is introduced to dynamically generate reference mean values ​​and fluctuation amplitudes of three types of parameters—micro-displacement, low-frequency strain, and thermal disturbance—in real time within a time window Tw. This enables adaptive modeling of the "reasonable range of the current component state." This method effectively avoids false positives caused by environmental temperature changes and short-term loading variations, significantly improving the accuracy and stability of anomaly identification.

[0036] In the calculation of the anomaly index AU, the micro-displacement part adopts the mean balance judgment mechanism, the low-frequency strain part models the structural stability fluctuation through the standard deviation, and the thermal disturbance is assessed by the gradient rate to evaluate the degree of temperature change. A nonlinear aggregation index is constructed using logarithmic functions and derivative operations. This multi-layer superposition algorithm structure of statistics + differentiation + logarithms can keenly capture small, slowly changing, or even lagging anomaly trends, avoiding the problem of "slow exponential response" and providing accurate early warning before irreversible damage to components is about to occur.

[0037] Traditional safety monitoring often relies heavily on manual experience to set fixed safety values ​​or fluctuating warning lines, which have poor adaptability to different building types, climates, and operating conditions. In this module, all parameter extraction and judgment logic is based entirely on statistical and physical law modeling, achieving a fully automated processing flow from "benchmark generation → risk identification → index output," significantly reducing human error and omissions, and improving system deployment efficiency and reliability.

[0038] Example 4 This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 and Figure 3 Specifically: the risk level determination module includes a risk level calculation and classification unit and a level binding and spatial mapping unit; The risk level calculation and classification unit determines the interval of the abnormal index AU and introduces a non-uniform dynamic distribution threshold mechanism to map the abnormal index AU with quantile statistics to obtain the risk level R. The risk level R is obtained using the following formula: ; In the formula, TH represents the high-risk threshold, corresponding to the 95th percentile of the anomaly index AU; TM represents the medium-risk threshold, corresponding to the 75th percentile of the anomaly index AU; and TL represents the low-risk threshold, corresponding to the 55th percentile of the anomaly index AU. The risk level binding and spatial mapping unit maps the risk level R to the corresponding 3D instance of the component in the BIM model, and assigns color labels and interactive attributes to construct BIM spatial mapping rules. When the risk level R=3, it indicates high risk and is mapped to red; When the risk level R=2, it represents a medium risk and is mapped to orange. When the risk level R=1, it indicates a slight risk, which is represented by yellow. When the risk level R=0, it indicates a safe state, which is mapped to green.

[0039] The BIM spatial mapping rule table is as follows: The BIM component layer dynamic update module includes a component status binding and layer update unit and a time sliding window layer backtracking unit; The component status binding and layer update unit maps the risk level R to the component number in the BIM model; The component matching formula is as follows: Fbind(IDi) → {Ri(t), Pi(t)}; In the formula, Pi(t) represents the set of visualization parameters of component i at time t, including color, transparency and flashing frequency, IDi represents the unique identifier of BIM component, and Fbind represents the component state binding function; The time-sliding window layer backtracking unit constructs a risk hotspot layer based on the selected arbitrary time point by invoking the state of risk level R, including the layer snapshot recording function and the sliding window backtracking function; The formula for the layer snapshot recording function is as follows: Si(t) = {IDi, Ri(t), Pi(t)}; In the formula, Si(t) represents a snapshot of the visual state of component i at time t, and all snapshots are stored in the layer version library; The formula for the sliding window backtracking function is as follows: ; In the formula, L(t) represents the risk hot zone layer at time t, which includes the status of all components, and M represents the total number of components.

[0040] In this embodiment, the method of classifying risk levels using fixed thresholds, commonly used in traditional monitoring systems, is abandoned. Instead, a quantile statistical modeling approach is adopted, which maps the anomaly index AU to segments according to dynamic percentiles, automatically adjusting the risk level classification intervals. This mechanism can adapt to the differences in "data fluctuation patterns" under different structural states and operating conditions, avoiding the one-size-fits-all setting of high and low risk boundaries. It greatly improves the scientificity, flexibility, and timeliness of risk level classification, and is particularly suitable for dealing with the evolution of building conditions under the complex influence of factors such as structural aging, environmental disturbances, and load fluctuations.

[0041] By establishing a precise binding mechanism between risk levels and BIM component IDs, this module creates a clear data-component-visualization mapping relationship. This ensures that risk assessment results are accurately positioned within the BIM 3D model and that the degree of risk is intuitively reflected through visualization techniques such as color, flashing, and transparency. This mechanism significantly improves the efficiency of building status perception and spatial identification, enabling managers to locate high-risk components within seconds and implement tiered response measures, thus enhancing the direct usability of monitoring data in the operation and maintenance management process.

[0042] This module innovatively introduces layer snapshot recording and sliding window backtracking functions to archive the component status at each moment and supports constructing a complete risk hotspot layer evolution trajectory starting from any point in time. This mechanism not only serves as technical support for post-event review and fault tracing, but also provides an objective and reliable data foundation for AI prediction model training, inspection task root cause analysis, and regulatory accountability, greatly compensating for the shortcomings of traditional monitoring systems that "only see the present and not the past."

[0043] By introducing a composite set of visualization parameters such as color, transparency, and flashing frequency for each component, this module not only achieves multi-level display of risk status but also provides highly flexible parameter interfaces for subsequent interactive operations, intelligent queries, and path planning functions. This rich visualization method can be customized according to the focus of different users (operation and maintenance personnel, managers, and designers), enhancing the intelligent interactive capabilities of the BIM model and connecting the cognitive chain of "risk data - decision-making behavior".

[0044] All calculations and state mappings are based on unique component numbers and directly interface with modeling units in the BIM platform. They do not rely on hardware modifications or component structural alterations, exhibiting high compatibility and deployability. They can be deployed on existing BIM platforms as plug-ins or extension modules, greatly expanding the intelligent sensing and dynamic visualization capabilities of the BIM system during operation and maintenance, providing a practical path for the construction industry to move towards intelligent and digital development.

[0045] Example 5 This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 3 Specifically: the trend evolution analysis module includes a risk level time series statistics unit and a trend status layer overlay and anomaly annotation unit; The risk level time series statistics unit is based on components, extracting the risk level R sequence within a fixed window W, and constructing a trend indicator ZB by calculating the time difference sequence; The trend indicator ZB is obtained using the following formula: ; In the formula, ZBi represents the trend index ZB of component i, Ri(tk) represents the risk level of component i at time tk, and Ri(tk) represents the risk level of component i at time tk-1. The trend status layer overlay and anomaly annotation unit combine the trend index ZB with the risk hot zone layer L, and adjust the additional visual attributes of the components in the BIM layer according to the trend index ZB to achieve enhanced visualization of trend annotation. Layer state enhancement mapping logic: The final appearance of a component in the layer is determined by a combination of the risk level R and the trend indicator ZB, with the following mapping rules: The formula for enhancing trend label visualization is as follows: nPi(t)=Pi(t)+TrendColor(ZBi)+EdgeStyle(ZBi); In the formula, nPi(t) represents the set of visualization parameters of component i at time t after the superimposed trend, TrendColor(ZBi) represents the color adjustment based on the trend index, and EdgeStyle(ZBi) represents the flashing frequency adjustment based on the trend index.

[0046] The inspection scheduling and decision-making module includes a scheduling priority scoring calculation unit and a task generation and scheduling suggestion output unit; The scheduling priority scoring calculation unit combines the risk level R and the trend indicator ZB to construct the inspection scheduling priority score QA; The inspection dispatch priority score (QA) is obtained using the following formula: QAi(t)=Ri(t)(1+q1×max(0,ZBi)); In the formula, QAi(t) represents the inspection scheduling priority score of component i at time t, q1 represents the trend amplification coefficient, and max(0, ZBi) represents the non-negative trend function; The scheduling suggestion output unit sorts the obtained inspection scheduling priority scores (QA) in descending order, and generates an inspection task suggestion table by combining the component location, component number, and risk level information. The formula for sorting sequences is as follows: Order = Sort(descQAi(t)); In the formula, Order represents the order of inspection task scheduling, Sort represents the sorting function, and desc represents descending order sorting; The inspection task suggestion form includes component number IDi, risk level R, trend indicator ZB, and inspection scheduling priority score QA.

[0047] In this embodiment, by setting a fixed time window, time difference calculation is performed on the risk level sequence of components to construct the trend index ZB, realizing the quantitative perception and dynamic extraction of the speed and direction of structural state changes. This mechanism no longer relies on static state judgment, but focuses on whether the risk is intensifying or slowing down, giving the system the ability to provide early warning and dynamic identification, providing a technical foundation for predictive operation and maintenance, and completely solving the core defect of traditional monitoring systems that "can only see the present and cannot see the future."

[0048] This invention utilizes a combined mapping logic between the risk level (R) of a component and the trend indicator (ZB) to overlay the trend status onto the BIM 3D component layer in real time through color highlighting, flashing frequency, and border styles, greatly enhancing the visualization layer's ability to express the direction of risk evolution. Traditional inspection scheduling is mostly based on risk level ranking, neglecting the urgency of risk development. This embodiment, by integrating risk level and trend indicator to construct a scheduling priority score, achieves a shift from "static ranking" to "dynamic urgency ranking." In particular, for components with a medium risk level but a rapidly rising trend, the system can automatically increase their scheduling score and prioritize their inclusion in inspection tasks, effectively avoiding the blind spot problem of "risk escalation not being addressed in a timely manner."

[0049] This embodiment introduces a trend amplification adjustment parameter, which allows for flexible adjustment of the trend influence weight according to the actual monitoring scenario. It can be used to increase the trend weight in sensitive scenarios or to reduce the response intensity to trends in general scenarios, thereby avoiding over-response. This mechanism improves the system's versatility and deployment robustness across various building types, making the solution more valuable for industry promotion.

[0050] Example 6 A BIM-based method for safety monitoring of existing buildings; please refer to [reference needed]. Figure 2 Specifically, it includes the following steps: Step 1: The component attribute acquisition and processing module collects component data from the BIM model through sensors, performs preprocessing, and obtains the component data group GW. Step 2: The anomaly index calculation module analyzes the component data group GW within a fixed period and calculates and obtains the anomaly index AU. Step 3: The risk level determination module classifies the anomaly index AU, outputs the risk level R, and maps it to the BIM model layer. Step 4: The BIM component layer dynamic update module matches the acquired risk level R with the component number in the BIM model and dynamically marks it to generate a risk hotspot layer. Step 5: The trend evolution analysis module statistically analyzes the changing trend of risk level R within a fixed window W, constructs trend index ZB, and combines it with the risk hot zone layer to determine the risk status. Step Six: The inspection scheduling and decision-making module combines the risk level R and the trend indicator ZB to generate inspection tasks and transform them into an inspection scheduling priority suggestion table.

[0051] In this embodiment, the component attribute acquisition and processing module, based on unconventional monitoring data such as micro-displacement, low-frequency strain, and interface thermal disturbance gradient, completes real-time status perception of each component in the BIM and establishes a unique component number mapping relationship. This mechanism does not rely on modifications to the original building hardware structure, achieving high-granularity data mapping and perception capabilities under existing building conditions, significantly improving the deployment compatibility and detail sensitivity of the monitoring system.

[0052] By calculating the offset trends and fluctuation characteristics of component data sets within a fixed period, an anomaly index integrating displacement, strain, and thermal disturbance characteristics is constructed. This effectively overcomes the limitations of traditional methods, such as "single-index judgment, prone to misjudgment and omission," and improves the stability and scientific rigor of anomaly detection. This index can serve as a sensitive trigger point for changes in structural health status, providing a highly reliable input basis for subsequent risk levels and evolution trends.

[0053] By employing a tiered classification mechanism in the risk level assessment module, the safety status of each component is mapped to a color-coded visualization layer. This allows for real-time rendering and display of the status within the BIM spatial model, bridging the gap between "data" and "graphics." This mechanism significantly improves the response efficiency and intuitiveness for maintenance personnel, avoiding the inefficiency of relying solely on tables or logs to understand component status.

[0054] Based on a dual-factor input of risk level and trend indicators, this invention constructs a scheduling priority scoring mechanism that automatically evaluates and ranks component inspection needs, and outputs a task list containing number, level, trend, and scheduling suggestions, achieving closed-loop linkage from perception to execution. Compared with traditional manual screening and task assignment methods, this mechanism greatly improves the efficiency of manpower allocation and reduces the risk of resource waste.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A BIM-based safety monitoring system for existing buildings, characterized in that: It includes a component attribute acquisition and processing module, an anomaly index calculation module, a risk level determination module, a risk hot zone mapping module, a trend evolution analysis module, and an inspection scheduling and decision-making module; The component attribute acquisition and processing module collects component data from the BIM model through sensors, performs preprocessing, and obtains component data sets. The anomaly index calculation module analyzes the component data set within a fixed period and calculates the anomaly index. The risk level determination module classifies and determines the abnormal index, outputs the risk level, and maps it to the BIM model layer. The BIM component layer dynamic update module matches the acquired risk level with the component number in the BIM model and dynamically marks it to generate a risk hotspot layer. The trend evolution analysis module statistically analyzes the changing trend of risk levels within a fixed window W, constructs trend indicators, and combines them with the risk hot zone layer to determine the risk status. The inspection scheduling and decision-making module combines risk levels and trend indicators to generate inspection tasks and transform them into an inspection scheduling priority suggestion table.

2. The BIM-based safety monitoring system for existing buildings according to claim 1, characterized in that: The component attribute acquisition and processing module includes a component data acquisition and component mapping unit and a data cleaning and normalization processing unit; The component data acquisition and component mapping unit collects data on all components in the BIM model through sensors, including micro-displacement Dm, low-frequency strain Ea, and interface thermal disturbance gradient Tg. Among them, the micro-displacement Dm is acquired by a laser displacement sensor; Low-frequency strain Ea was acquired using a resistance strain gauge. The interface thermal perturbation gradient Tg was acquired using a thermocouple array. Map the data of each component to the unique component ID in the BIM to obtain the original data group ZW; The original data set ZW is obtained using the following formula: ZWi(t)={Dmi(t),Eai(t),Tgi(t)}; In the formula, ZWi(t) represents the original data set of component i at time t, Dmi(t) represents the micro-displacement of component i at time t, Eai(t) represents the low-frequency strain of component i at time t, and Tgi(t) represents the interface thermal disturbance gradient of component i at time t.

3. The existing building safety monitoring system based on BIM according to claim 2, characterized in that: The data cleaning and normalization unit cleans and normalizes the original data group ZW to obtain the component data group GW; Cleaning includes dynamic interference removal and frequency domain interference resampling; Dynamic false disturbance elimination uses nonlinear weighted slope identification to eliminate isolated outliers caused by short-term external disturbances. The method for identifying isolated outliers is as follows: First, the information of the component is analyzed through the original data group ZW, and the dynamic threshold value Qrs of the component is calculated and obtained. The dynamic threshold value Qrs is obtained using the following formula: ； In the formula, Qrsi(t) represents the dynamic threshold value of component i at time t, Dmi(t-1) represents the micro-displacement of component i at time t-1, Eai(t-1) represents the low-frequency strain of component i at time t-1, and Tgi(t-1) represents the interface thermal disturbance gradient of component i at time t-1. When the dynamic threshold value Qrs is greater than the preset threshold value TQr, it indicates that component i has been subjected to external disturbance, is marked as an isolated outlier, and undergoes forward smooth interpolation in the time domain. Frequency domain interference resampling uses FFT transformation to process the data in the original data group ZW, calculates the frequency band energy mutation E from 1 to 5 Hz, and if the frequency band energy mutation E > 80% fluctuation or the energy difference between adjacent windows > 20%, then the data is reprocessed by window function low-pass resampling. Normalization is performed by processing the original data set ZW using the max-min normalization method to obtain the component data set GW.

4. The BIM-based safety monitoring system for existing buildings according to claim 3, characterized in that: The anomaly index calculation module includes a reference baseline construction unit and an anomaly index calculation unit; The reference baseline construction unit calculates the reference mean and fluctuation range of three types of parameters in the component data set GW within the time window Tw, and obtains baseline data, including the micro-displacement reference mean μDm, low-frequency strain fluctuation σEa, and thermal disturbance reference gradient θTg. The method for obtaining the reference mean value of micro-displacement μDm is as follows: First, select a time window Tw and obtain the micro-displacement data of component i at N time points within the time window Tw; then, add up all the micro-displacement values ​​at the N time points and sum them; finally, divide by N to obtain the reference mean value of micro-displacement μDm of component i within the time window Tw. The low-frequency strain fluctuation σEa is obtained as follows: within the time window Tw, low-frequency strain data of component i are collected at N time points; firstly, the N low-frequency strain data are averaged to calculate the average strain value within the time window Tw. Then, the low-frequency strain data at each time point is subtracted from the mean strain value to obtain the difference; next, each difference is squared, and the deviation square values ​​corresponding to all time points are accumulated to obtain the sum; finally, the sum is divided by N-1 to obtain the quotient, and the square root of the quotient is calculated to obtain the low-frequency strain fluctuation σEa. The thermal perturbation reference gradient θTg is obtained as follows: within the time window Tw, the interface thermal perturbation gradient of component i is collected at N time points; first, the rate of change of the interface thermal perturbation gradient over time is calculated: the thermal perturbation gradient values ​​of two adjacent time points are taken in sequence, the difference between them is calculated, and then divided by the time interval between the two time points. Then, the rates of change of all interface thermal perturbation gradients over time are summed and divided by the number of sampling points N to obtain the thermal perturbation reference gradient θTg of component i within the time window Tw.

5. A BIM-based safety monitoring system for existing buildings according to claim 4, characterized in that: The anomaly index calculation unit calculates the anomaly index AU based on the obtained micro-displacement reference mean μDm, low-frequency strain fluctuation σEa, and thermal disturbance reference gradient θTg. The anomaly index AU is obtained using the following formula: ； In the formula, AUi(t) represents the anomaly index of component i at time t, μDmi represents the reference mean value of micro-displacement of component i, σEai represents the low-frequency strain fluctuation of component i, θTgi represents the thermal disturbance reference gradient of component i, ln represents the logarithmic function, and d represents the derivative sign.

6. A BIM-based safety monitoring system for existing buildings according to claim 5, characterized in that: The risk level determination module includes a risk level calculation and classification unit and a level binding and spatial mapping unit; The risk level calculation and classification unit determines the interval of the abnormal index AU and introduces a non-uniform dynamic distribution threshold mechanism to map the abnormal index AU with quantile statistics to obtain the risk level R. The risk level R is obtained using the following formula: ； In the formula, TH represents the high-risk threshold, corresponding to the 95th percentile of the anomaly index AU; TM represents the medium-risk threshold, corresponding to the 75th percentile of the anomaly index AU; and TL represents the low-risk threshold, corresponding to the 55th percentile of the anomaly index AU. The risk level binding and spatial mapping unit maps the risk level R to the corresponding 3D instance of the component in the BIM model, and assigns color labels and interactive attributes to construct BIM spatial mapping rules. When the risk level R=3, it indicates high risk and is mapped to red; When the risk level R=2, it represents a medium risk and is mapped to orange. When the risk level R=1, it indicates a slight risk, which is represented by yellow. When the risk level R=0, it indicates a safe state, which is mapped to green.

7. A BIM-based safety monitoring system for existing buildings according to claim 6, characterized in that: The BIM component layer dynamic update module includes a component status binding and layer update unit and a time sliding window layer backtracking unit; The component status binding and layer update unit maps the risk level R to the component number in the BIM model; The component matching formula is as follows: Fbind(IDi) → {Ri(t), Pi(t)}; In the formula, Pi(t) represents the set of visualization parameters of component i at time t, including color, transparency and flashing frequency, IDi represents the unique identifier of BIM component, and Fbind represents the component state binding function; The time-sliding window layer backtracking unit constructs a risk hotspot layer based on the selected arbitrary time point by invoking the state of risk level R, including the layer snapshot recording function and the sliding window backtracking function; The formula for the layer snapshot recording function is as follows: Si(t) = {IDi, Ri(t), Pi(t)}; In the formula, Si(t) represents a snapshot of the visual state of component i at time t, and all snapshots are stored in the layer version library; The formula for the sliding window backtracking function is as follows: ； In the formula, L(t) represents the risk hot zone layer at time t, which includes the status of all components, and M represents the total number of components.

8. A BIM-based safety monitoring system for existing buildings according to claim 7, characterized in that: The trend evolution analysis module includes a risk level time series statistics unit and a trend status layer overlay and anomaly annotation unit; The risk level time series statistics unit is based on components, extracting the risk level R sequence within a fixed window W, and constructing a trend indicator ZB by calculating the time difference sequence; The trend indicator ZB is obtained using the following formula: ； In the formula, ZBi represents the trend index ZB of component i, Ri(tk) represents the risk level of component i at time tk, and Ri(tk) represents the risk level of component i at time tk-1. The trend status layer overlay and anomaly annotation unit combine the trend index ZB with the risk hot zone layer L, and adjust the additional visual attributes of the components in the BIM layer according to the trend index ZB to achieve enhanced visualization of trend annotation. The formula for enhancing trend label visualization is as follows: nPi(t)=Pi(t)+TrendColor(ZBi)+EdgeStyle(ZBi); In the formula, nPi(t) represents the set of visualization parameters of component i at time t after the superimposed trend, TrendColor(ZBi) represents the color adjustment based on the trend index, and EdgeStyle(ZBi) represents the flashing frequency adjustment based on the trend index.

9. A BIM-based safety monitoring system for existing buildings according to claim 8, characterized in that: The inspection scheduling and decision-making module includes a scheduling priority scoring calculation unit and a task generation and scheduling suggestion output unit; The scheduling priority scoring calculation unit combines the risk level R and the trend indicator ZB to construct the inspection scheduling priority score QA; The inspection dispatch priority score (QA) is obtained using the following formula: QAi(t)=Ri(t)(1+q1×max(0,ZBi)); In the formula, QAi(t) represents the inspection scheduling priority score of component i at time t, q1 represents the trend amplification coefficient, and max(0, ZBi) represents the non-negative trend function; The scheduling suggestion output unit sorts the obtained inspection scheduling priority scores (QA) in descending order, and generates an inspection task suggestion table by combining the component location, component number, and risk level information. The formula for sorting sequences is as follows: Order = Sort(descQAi(t)); In the formula, Order represents the order of inspection task scheduling, Sort represents the sorting function, and desc represents descending order sorting; The inspection task suggestion form includes component number IDi, risk level R, trend indicator ZB, and inspection scheduling priority score QA.

10. A BIM-based method for safety monitoring of existing buildings, applied to the BIM-based safety monitoring system for existing buildings as described in any one of claims 1 to 9, characterized in that: Includes the following steps: Step 1: The component attribute acquisition and processing module collects component data from the BIM model through sensors, performs preprocessing, and obtains the component data group GW. Step 2: The anomaly index calculation module analyzes the component data group GW within a fixed period and calculates and obtains the anomaly index AU. Step 3: The risk level determination module classifies the anomaly index AU, outputs the risk level R, and maps it to the BIM model layer. Step 4: The BIM component layer dynamic update module matches the acquired risk level R with the component number in the BIM model and dynamically marks it to generate a risk hotspot layer. Step 5: The trend evolution analysis module statistically analyzes the changing trend of risk level R within a fixed window W, constructs trend index ZB, and combines it with the risk hot zone layer to determine the risk status. Step Six: The inspection scheduling and decision-making module combines the risk level R and the trend indicator ZB to generate inspection tasks and transform them into an inspection scheduling priority suggestion table.

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