35 results about "Structural risk" patented technology

The invention discloses an overhead layer climbing frame wall-attached support steel truss stand column safety monitoring system fused with big data analysis, relates to the technical field of construction monitoring, and is used for solving the problem of recognition misjudgment caused by construction disturbance. According to construction behavior control signals, behavior labels are extracted, a semantic mapping relation between construction behaviors and structure responses is constructed, a behavior-driven data set is generated, a disturbance change trend is extracted through node response tensor and time difference analysis, the behavior labels are fused to construct a structure state vector, and a structure behavior distribution model is established. Analyzing the deviation degree between the current state and the model response, identifying the propagation path of the anomaly in space by combining a structure layout diagram, extracting a high-risk abnormal block mass region by adopting a diagram clustering method, analyzing the structural state change and construction behavior of the abnormal region, triggering different monitoring response strategies, and obtaining a high-risk abnormal block mass region; the structural risk identification and control in the construction process are realized, and the structural safety monitoring efficiency in the floor construction stage is improved.

The invention discloses a water conservancy project building full life cycle management method based on BIM technical analysis, and relates to the technical field of building full life cycle management. And respectively constructing three structural indexes, namely a microcrack expansion rate index F1, a damp-heat permeation combined degradation index F2 and a dynamic stiffness phase deviation index F3. The multi-dimensional feature system not only covers key risk sources such as material microcosmic degradation, environmental coupling effect and mechanical response mutation, but also breaks through a traditional extensive method which depends on single sensing data to perform health judgment, and provides a digital expression model with physical interpretation force for structural risks. According to the method, a unified health data standard system is established, and the health data standard system can be used as a core data source of upper-layer applications such as dynamic structure evaluation, an early warning model and visual mapping.

The invention provides a bridge structure risk early warning method based on Bayesian decision and information value. According to the method, Bayesian prior analysis is carried out through structure monitoring data, a cost model comprises structure failure cost, maintenance cost and management and control cost, and a structure state model is obtained through structure fatigue reliability analysis. Bayesian prognosis test analysis is carried out in the next step, short-term future monitoring data distribution is predicted based on current monitoring data, then structural fatigue reliability analysis is carried out on the possibility of each kind of monitoring data, corresponding failure risk indexes are calculated, and information values are calculated based on a cost model. And finally, through maximizing the information value, obtaining an optimal structure early warning threshold value under Bayesian prognosis test analysis so as to determine the optimal early warning time. According to the self-adaptive structure risk early warning method, the early warning cost and the structure performance degradation are comprehensively considered, and rapid early warning decisions with safety guarantee and economic benefits can be achieved.

The invention discloses a building construction safety management method and system based on artificial intelligence, and relates to the technical field of building construction safety, the system automatically identifies structure information in a CAD drawing through artificial intelligence, and generates a layout point in combination with point cloud data; a sensor group is installed at a layout point to collect state data of a supporting structure in real time and transmit the state data to a building construction safety management system for preprocessing, and a geometric state data set and a stress effect data set are formed; performing structure geometric state evaluation by constructing a structure geometric instability index jgs, executing a dynamic load impact analysis instruction when the support structure is evaluated to be normal, and performing structure impact stability evaluation by constructing a comprehensive steady state evolution index wty; the risk level is generated according to the two evaluation results, and a multi-level response strategy is executed, so that the structural risk dynamic perception, evaluation and intervention in the construction process are realized, and the construction safety intelligent level is improved.

The invention discloses a house structure safety Internet of Things remote monitoring system based on multi-source data fusion, and relates to the technical field of building structure monitoring. A target house is divided into different structure modules, and data acquisition terminals are arranged in the structure modules to acquire multi-dimensional data information; analyzing and judging whether the position of each data acquisition terminal is a structural risk point or not by utilizing the obtained multi-dimensional data information; on the basis of the incidence relation between the abnormal data item causing the occurrence of the structural risk point and other parameter items and other data acquisition terminals having the incidence relation with the data acquisition terminal at the position of the structural risk point, generating a corresponding simulation stress nephogram by using the simulation numerical value corresponding to the abnormal data item; therefore, while the possible risks of the house structure are monitored, other positions possibly influenced by the structure risk point can be evaluated according to the appearing structure risk point, so that the house safety monitoring efficiency is improved.

The invention provides a bridge supporting column stress measurement method based on structure collaborative stress analysis, and belongs to the technical field of stress measurement, and the method comprises the steps: collecting the stress, strain and displacement data of each bridge supporting column in real time; calculating a local stress index of each bridge support column based on the collected real-time data; according to the connection relation between the bridge overall structure parameters and the bridge supporting columns, a structure collaborative stress model is established; calculating a cooperative stress correction index of each bridge support column; and comparing the cooperative stress correction index with the dynamic stress evaluation interval, performing trend analysis in combination with historical data and environmental factors, judging whether a structural risk exists or not, and sending out an early warning signal when the structural risk exceeds a threshold value. According to the method, a structure collaborative stress analysis mechanism is introduced, the influence of the overall stress state of the bridge on a single bridge supporting column is comprehensively considered, key structure risk points can be recognized more accurately, and the reliability and early warning capacity of bridge safety monitoring are improved.

This invention belongs to the field of data processing technology, specifically relating to a method and system for generating maintenance strategies for bridge collision avoidance facilities based on knowledge graphs. The method includes: constructing a physical topology map of the collision avoidance facilities, obtaining the stiffness weights of connecting edges and initializing node parameters; collecting strain and stress data sequences of the collision avoidance units, and calculating the rheological damage index of the collision avoidance units using a dynamic time warping algorithm; constructing a force transmission probability matrix, and obtaining the comprehensive structural risk value of the collision avoidance units through iterative calculation; and calculating the maintenance urgency index by combining the position weight coefficients and service duration of the collision avoidance units to generate a dynamic maintenance strategy. This invention, by constructing a physical topology map reflecting the physical connection relationships between collision avoidance units, and combining the calculation of hysteresis energy consumption and stress transmission deduction of viscoelastic materials, can accurately identify latent transmitted damage and assess the degree of material rheological aging, thus achieving intelligent maintenance of bridge collision avoidance facilities.

The invention discloses a medical insurance fraud detection method and system based on a block chain and artificial intelligence, and belongs to the technical field of information detection, and the method comprises the steps: carrying out the embedded coding of original data, obtaining a structure embedded vector, and evaluating a structure risk score; extracting a medical insurance object list behavior sequence, calculating a behavior template coding vector, and calculating a behavior deviation score through a deviation score function; constructing a payment path diagram, outputting a payment jump index, and calculating to obtain a consistency scoring index; performing structure alignment detection on the payment path sequence and a behavior template coding vector, and outputting a structure offset score; outputting a final risk score; and carrying out risk grade division according to the interval to which the final risk score belongs, and executing a corresponding preset strategy. According to the method, a potential risk list is identified from a field structure, and abnormal combination and frequency characteristics are identified based on historical behavior modeling and rule offset analysis, so that early discovery of complex and evolutionary fraudulent behaviors is realized.

The present invention discloses a monitoring and early warning method and system for a container gantry crane, which relates to the technical field of crane monitoring and early warning. The monitoring and early warning method for the container gantry crane continuously obtains the monitoring time-series data of the gantry crane, and respectively performs risk extraction processing on the monitoring time-series data of the gantry crane to obtain a risk assessment index set for each time period of the gantry crane, including a structural risk aggregation index, a lifting behavior risk index, and an operating environment interference aggregation index; comprehensively analyzes the risk assessment index set for each time period of the gantry crane to obtain a risk coupling determination index for each time period of the gantry crane. The present invention takes preset early warning measures for the gantry crane based on the risk coupling determination index for each time period, thereby significantly improving the prediction and early warning capabilities for potential safety hazards, further effectively guaranteeing the safety during the crane operation process, and then avoiding misjudgment and missed judgment.

The invention relates to the technical field of structural engineering and computational mechanics, in particular to a method and system for determining the axial pressure bearing capacity of a concrete filled steel tube at negative temperature, which responds to a bearing capacity calculation request, obtains working condition parameters of a target component and constructs a finite element simulation model for simulation analysis to obtain simulation bearing capacity data; the simulation bearing capacity data is compared with a standard theoretical value obtained through calculation according to a preset standard formula, a deviation coefficient is obtained, when the deviation coefficient is larger than a preset deviation threshold value, coefficient fitting is conducted through a preset size effect-negative temperature coupling correction model, a size effect correction coefficient and a negative temperature damage correction coefficient are obtained, and the size effect correction coefficient and the negative temperature damage correction coefficient are obtained. And based on the size effect correction coefficient and the negative temperature damage correction coefficient, the standard theoretical value is corrected, and the final axial compression bearing capacity is obtained. The problem of poor negative temperature adaptability caused by size effect and large-diameter deviation in the prior art is solved, and the structural risk caused by misjudgment of the bearing capacity is avoided.

This invention provides a method for safely controlling the demolition sequence of building components that integrates genetic algorithms, Bayesian reasoning, and machine learning. This method uses topological sorting to establish a directed acyclic graph of component dependencies. Mechanical simulation and genetic algorithm optimization are combined to generate feasible, low-risk dismantling sequence paths. Bayesian reasoning is used to dynamically perceive structural risks, supplemented by machine learning models to enhance the stability of risk identification, achieving a coordinated linkage between dismantling sequence path generation and risk control. The invention's data interaction and decision-making linkage form a closed-loop control chain driven by risk assessment to regulate the dismantling sequence path. By integrating genetic algorithms, Bayesian reasoning, and machine learning, this method achieves dynamic optimization and full-process safety assurance for building structure demolition.

A Beidou-based structural deformation monitoring method, system, and storage medium relate to the field of deformation monitoring. The method comprises: acquiring the engineering facility structure of a target monitoring object and performing structural analysis on its key components to accurately obtain dynamic characteristic parameters of the engineering facility structure. These dynamic characteristic parameters reflect the response characteristics of the engineering facility structure under different environmental parameters, thereby enabling the establishment of an accurate physical model of the engineering facility. By acquiring and inputting real-time monitoring data, state prediction is performed in combination with preset environmental parameters to obtain predicted monitoring data, which is then compared with the real-time monitoring data. Abnormal changes in the engineering facility structure can be detected in a timely manner. When the monitoring difference exceeds a preset change threshold, an early warning prompt message is sent to a client. This can improve the accuracy and timeliness of deformation monitoring, prevent potential structural risks, and thus ensure the safety and stability of the engineering facility.

The application provides a digital twin method and a predictive operation and maintenance system for a rectangular melting and separating furnace, equipment and a medium, which comprehensively considers the multidisciplinary complexity of the rectangular melting and separating furnace system by using the calculation results of the thermal coupling finite element model, is high in engineering precision, and is reliable in results; and can realize real-time forward and reverse calculation by using a statistical proxy model, can obtain the result state of the rectangular melting and separating furnace by forward process operation setting, and can perform fault detection and risk assessment, and can obtain the recommended value of the operation item setting by reverse presetting results, thereby effectively guiding the on-site production of the rectangular melting and separating furnace. Therefore, the application can efficiently and accurately predict the running state of the rectangular melting and separating furnace equipment under different process conditions, dynamically analyze the erosion and life trend of the furnace lining, identify abnormal deformation and structural risks of the furnace body in advance, guide the accurate operation and maintenance on site, significantly improve the safety, reliability and production efficiency of the furnace body, and thus has significant industrial application value and economic benefits.

The invention relates to the technical field of structural engineering and mechanics, in particular to a staggered stacked container structure risk assessment method and device, and the method comprises the steps: S1, obtaining the structural information of a container, the information of a cabinet in the container, and the information of predicted carrying personnel; s2, constructing a placement structure model of the container according to the structure information, the cabinet information and the predicted bearing personnel information; s3, simplifying the placement structure model, and performing finite element modeling according to the simplified placement structure model to obtain a simulation model; s4, performing static analysis on the simulation model, and obtaining the maximum structural stress of the staggered containers and the maximum structural deflection of the top plate of the related container at the lowest layer according to an analysis result; and S5, matching the structure maximum stress and the structure maximum deflection with a preset safety range, and performing structure risk assessment according to a matching result. According to the method, the accuracy of structural risk assessment is improved by constructing the simulation model of the staggered stacked container.

The invention provides an electricity selling company bidding robust optimization method and system for wind power uncertainty, relates to the technical field of electricity market transaction and information processing, and is used for improving the problem that an electricity selling company bidding strategy cannot be effectively quantified and internal structural risks cannot be avoided in related technologies. And therefore, the technical problem of insufficient robustness under the extreme fluctuation of the market can be solved. Comprising the following steps: constructing a set comprising a plurality of discrete quotation units; generating a plurality of uncertain scene impacts; for a to-be-evaluated bidding strategy combination composed of a plurality of discrete quotation units, calculating a pore risk potential of the bidding strategy combination under the impact of the uncertain scene; executing an iterative optimization process, iteratively disturbing the bidding strategy combination and accepting or abandoning the bidding strategy combination according to a preset acceptance criterion by taking the pore risk potential and the expected income as optimization targets until a convergence condition is met, so as to obtain a final robust bidding strategy combination; and outputting the robust bidding strategy combination.

The application discloses a crack-resistant reinforcement configuration optimization method for high concrete face slab dam panels, S1: acquiring first data; S2: generating second data based on a geometric model of dam panels; S3: generating an indication signal based on the first data and the second data through a Pareto frontier solver; S4: generating a reinforcement network optimization instruction based on a selected working point on a Pareto frontier curve; and S5: adjusting an initial reinforcement topology network in response to the reinforcement network optimization instruction, and outputting a final reinforcement configuration scheme; the application can realize significant synergistic gain of design safety and economic benefits by deep quantization of structural risk through probability inference, construction of a double-target optimization framework of efficiency and robustness, generation of a Pareto frontier performance boundary, and output of a reinforcement scheme with higher safety under the same cost or lower cost under the same safety standard through an interactive decision tool.

The invention discloses an integrated intelligent monitoring system and method for an intelligent construction site, and relates to the technical field of construction site monitoring, and the method comprises the steps: arranging a monitoring camera based on a construction scene of construction site construction, carrying out the image collection of the construction site based on the arranged monitoring camera, and obtaining the collected image data; performing image recognition on the acquired image data based on an image recognition technology to obtain article data, performing structural risk judgment on the article data, and marking an article with risk to obtain first article data; determining position information of the article data based on the acquired image data, performing state risk assessment on the article according to the position information, and marking the article with risk as second article data; according to the first article data and the second article data, determining the dangerousness of the article, and marking the article with the dangerousness as a dangerous object; and when the dangerous object is judged to exist, performing alarm marking on the dangerous object. The construction site safety monitoring method has the effect of improving the construction site safety monitoring accuracy.

The present invention relates to the technical field of intelligent manufacturing equipment industry, specifically an online pressure vessel deformation detection system, the system comprising: a working condition data acquisition module, a boundary adjustment calculation module, a fluid-solid stress transfer module, a stress deformation update module, and a deformation risk assessment module. In the present invention, by real-time acquisition of wall temperature, local strain, external load, and shear stress gradient data, a multi-physical quantity correlation model is constructed to improve the integrity of deformation data, optimize stress state calibration, accurately adjust the stress boundary of the shell connection area, calculate the fluid-solid interface stress distribution, enhance stress change perception, optimize deformation adjustment strategy, improve prediction accuracy, locate deformation over-limit areas, enhance structural risk warning capabilities, realize data collaborative analysis, dynamically adjust monitoring parameters, improve deformation detection stability and adaptability, and ensure the safe operation of pressure vessels.

The present application relates to the technical field of image processing, in particular to a cable tower concrete crack identification method and system based on machine vision, comprising the following steps: based on the difference between the crack and non-crack areas of the cable tower surface image, identifying consistent fracture zones and marking texture levels, analyzing the gradient direction consistency and gray mutation response rate, screening mismatched areas, screening composite abnormal areas, evaluating the gray change trend, calibrating the crack response deviation, screening areas that need to adjust the coverage range, and outputting the cable tower crack identification linkage adjustment instruction set. In the present application, by extracting the local gradient direction and gray response characteristics of the image pixels, combining spatial matching and connectivity analysis, the abnormal areas with disorderly direction and response mismatch are accurately identified, and then the gray deviation of the crack area is evaluated, the crack level calibration and risk positioning are realized, the multi-scale information extraction and heterogeneous area adaptation capability are improved, and the structural risk evaluation effect is optimized.

This invention belongs to the field of data processing and risk management technology. Specifically, it discloses an environmental risk assessment method and system based on topological data analysis, comprising: acquiring multi-source real-time monitoring data; identifying dense data clusters and sparse bridge nodes through topological clustering; dynamically adjusting monitoring resources; performing dynamic feature calculations on the data to generate multi-level dynamic field data; performing continuous coherence analysis to extract stable topological patterns and encode them as structural fingerprint vectors; using these vectors to dynamically activate and adjust the environmental risk knowledge graph to generate a dynamic risk logic graph; separately deriving parametric risk scores and structural risk scores, and calculating their conflict degree; making decisions based on the conflict degree, outputting a comprehensive risk level or triggering structural risk warnings and explanatory information. This invention aims to overcome the limitations of traditional assessments in terms of lag and insufficient interpretability, achieving predictive management of environmental risks and self-optimization of the knowledge base.

This invention discloses a big data-based intelligent management method and system for water conservancy projects, belonging to the field of intelligent management technology for water conservancy projects. It collects multi-source operational data such as water flow rate, capillary permeability, freezing depth, and structural stress fluctuations, reconstructing them into spatiotemporally coupled data. Through tensor decomposition, it extracts leading factors of operational anomalies, constructs a freeze-thaw model, and infers cumulative structural risk factors. These risk factors are embedded in a graph structure, and a graph neural network is used to predict future local degradation sensitive points. Fuzzy logic reasoning is applied to these sensitive points to output reinforcement suggestions. A regional-level freezing environment vulnerability map is constructed across multiple projects, generating maintenance schedules and inspection paths. This invention achieves proactive identification and refined maintenance of freeze-thaw risks in hydraulic structures, improving project operational safety and resource allocation efficiency.

This invention relates to the field of civil engineering structural safety monitoring and aircraft image detection technology, and discloses a method and system for quantitative non-destructive monitoring of concrete structure safety based on aircraft. The method includes: constructing a multi-objective path optimization model to generate a flight path point sequence; the aircraft acquiring images and calculating image reliability scores; using a deep learning model to identify cracks and spalling defects; calculating the structural risk index (SFI) based on a partial least squares regression model; and generating a monitoring report and uploading it to the cloud. Compared to existing technologies that rely on manual inspection or static image recognition, especially in irregular curved surfaces, complex occlusion areas, or conditions with drastic lighting changes, this invention addresses the technical problem of failing to achieve quantitative assessment of structural safety based on in-flight images. By integrating three-dimensional path optimization, image reliability scoring mechanisms, and AI defect recognition, this invention achieves intelligent identification of structural surface coverage and crack spalling, improving the automation level of concrete structure monitoring.

The present invention provides a bridge support column stress measurement method based on structural collaborative force analysis, which belongs to the field of stress measurement technology. It collects stress, strain, and displacement data of each bridge support column in real time; calculates the local stress index of each bridge support column based on the collected real-time data; establishes a structural collaborative force model based on the connection relationship between the overall structural parameters of the bridge and the bridge support columns; calculates the collaborative stress correction index of each bridge support column; compares the collaborative stress correction index with the dynamic stress assessment interval, combines historical data and environmental factors to perform trend analysis, determines whether there is a structural risk, and issues a warning signal when the threshold is exceeded. By introducing a structural collaborative force analysis mechanism, the present invention comprehensively considers the impact of the overall stress state of the bridge on a single bridge support column, can more accurately identify key structural risk points, and improve the reliability and early warning capabilities of bridge safety monitoring.

According to the digital twinning method for the rectangular melt separation furnace, the predictive operation and maintenance system, the predictive operation and maintenance equipment and the medium, the multidisciplinary complexity of the rectangular melt separation furnace system is comprehensively considered by utilizing the calculation result of the thermal-mechanical coupling finite element model, the engineering precision is high, and the result is reliable; and forward and reverse real-time calculation can be realized by utilizing a statistical agent model, the result state of the rectangular melt separation furnace can be obtained through process operation setting in the forward direction, fault detection and risk assessment can be carried out, and recommended values of operation item setting can be obtained through a preset result in the reverse direction, so that field production of the rectangular melt separation furnace is effectively guided. Therefore, the method can efficiently and accurately predict the operation state of the rectangular melt separation furnace equipment under different process conditions, dynamically analyze the erosion and life trend of the furnace lining, identify the abnormal deformation and structural risk of the furnace body in advance, guide the precise operation and maintenance operation on site, remarkably improve the safety, reliability and production efficiency of the furnace body, and reduce the production cost. Therefore, the method has remarkable industrial application value and economic benefit.

This invention provides a method and related equipment for predicting the global dynamic response of a structure, comprising: acquiring a real-world dataset of the structure in its current state; processing multiple sparse sensor data based on a preset finite element model to output a simulation dataset, the simulation dataset including multiple pairs of sparse input-global output data; pre-training a blank neural network model based on the simulation dataset to obtain a virtual model; performing physical correction processing on the virtual model to obtain a real-world model; processing the sensor data of the current structure using the real-world model to determine the global dynamic response field data of the current structure; and predicting the risk deformation of the current structure based on the global dynamic response field data and executing corresponding risk warning operations. Through the above method steps, global dynamic response prediction of large and complex structures can be achieved with only a small amount of sensor data, reducing sensor deployment costs and improving the accuracy and precision of structural risk monitoring.

The invention relates to the technical field of financial science and technology and risk monitoring, in particular to an intelligent financial risk sensing method based on deep learning, and the method comprises the steps: regularly collecting the multi-dimensional time series data of an asset end, a liability end, economic operation indexes and the like of a target region through accessing a financial data system; constructing a cross feature representing a dynamic coupling relationship between indexes based on the cleaned data; processing by using a risk perception model comprising a time sequence feature extraction layer and a multi-head attention layer, and outputting a risk occurrence probability predicted based on a historical time sequence rule and a structure anomaly index calculated based on attention weight distribution; and finally, judging a risk level according to the double threshold values and outputting an early warning signal. According to the method, the structural discretization degree of the financial system can be quantified by introducing the structural anomaly index and sparsity regularization training, the deep structural risk under numerical value coverage is effectively identified, and automatic attribution of the risk source is realized.

The invention discloses a tower construction management system and method based on a three-dimensional digital simulation model, and relates to the technical field of tower construction management. The tower assembling construction management method based on the three-dimensional digital simulation model comprises the following steps: S1, acquiring derrick data in a tower assembling construction process in real time, and performing data cleaning and standardization processing on the derrick data; s2, constructing a three-dimensional simulation model of the tower body and the derrick, calculating a structure risk assessment value, assessing a current structure state and configuring a structure adjustment strategy; s3, calculating an operation risk assessment value based on the derrick data, comparing the operation risk assessment value with an operation threshold value in real time, identifying an operation abnormal behavior and configuring an operation adjustment strategy; and S4, calculating an intervention evaluation value, and judging whether to enter a high-risk active intervention process or not based on the intervention evaluation value. The problem that safety accidents possibly occur due to the fact that the abnormal states of the holding pole and the tower section cannot be recognized in real time is solved.

This invention discloses an intelligent identification method for systemic financial risk based on causal networks and multi-model optimization. The method first reads and cleans financial market and corporate financial data to construct a quarterly logarithmic return series. Then, it calculates individual risk exposure based on the CoVaR mechanism and generates binary labels for risk states using a Gaussian mixture model (GMM). Further, it employs Granger causality testing to construct a risk transmission map between enterprises, uncovering structural risk relationships within the system. Next, it constructs a Long Short-Term Memory (LSTM) network and a Gradient Boosting Tree (XGBoost) model to predict risk states over multiple periods, and uses a Particle Swarm Optimization (PSO) algorithm to jointly fine-tune hyperparameters. Finally, it integrates the multi-model prediction results to output a systemic risk score and supports model interpretation and visualization based on SHAP values. This method is applicable to various scenarios such as financial supervision, bank risk control, and asset management, enabling efficient identification and accurate prediction of systemic risks.

This invention relates to the field of bridge monitoring technology, specifically disclosing a method, system, and device for early warning and discrimination of bridge structural risk status based on correlation network diagrams. The invention first constructs a correlation network diagram of measuring points in the bridge structure under a baseline state using preprocessed benchmark monitoring data. When a risk measuring point triggers an over-limit alarm during real-time monitoring, a risk correlation sub-network diagram is extracted centered on that risk measuring point and compared with the benchmark correlation network diagram. By analyzing the linkage anomaly characteristics of node data and changes in the correlation degree between nodes in the sub-network diagram, a true early warning (abnormal structural state) is distinguished from a false alarm (equipment failure, environmental interference, etc.). This invention significantly improves the accuracy and reliability of early warning of bridge structural risk status through multi-measuring point correlation linkage analysis.

The invention discloses a BIM (Building Information Modeling)-based existing building potential safety hazard detection and analysis method and system, relates to the technical field of building engineering, and is used for solving the problems of long response period, data lag, incomplete evaluation and strong subjective dependence caused by the fact that a manual inspection and periodic static detection mode is often adopted for potential safety hazard troubleshooting in the prior art. According to the method, structural stress monitoring, image recognition and analysis, historical use data and multi-dimensional data subjectively fed back by personnel are subjected to unified modeling, and a precisely quantified potential safety hazard assessment system is constructed in combination with a dynamic stress change trend and fluctuation degree, component apparent damage characteristics and structural risk index core parameters; according to the method, full-period and multi-dimensional evaluation and graded early warning of the existing building structure state are realized, the accuracy of structure risk identification and the real-time performance of response are remarkably improved, the structure leak detection and misjudgment risks are effectively reduced, and the structure safety in the long-term operation process of a building is guaranteed.