A three-dimensional visualized steel roof monitoring system and method

Abstract

The present application relates to the technical field of building operation and maintenance management, and particularly relates to a three-dimensional visual steel roof monitoring system and method, comprising obtaining historical monitoring data and finite element analysis results of the steel roof, and performing clustering analysis to identify risk structure nodes; for any risk structure node, environmental data and load data obtained in real time are fused and input into a preset structure state safety response model to obtain stress threshold values and deformation displacement threshold values; the stress threshold values and the deformation displacement threshold values are corrected based on a preset structure aging and weakening correction mechanism; for any risk structure node, in response to a real-time node stress parameter being greater than the corrected stress threshold value and / or a real-time deformation displacement parameter being greater than the corrected deformation displacement threshold value, the risk structure node is marked through a three-dimensional visualization platform and a multi-channel early warning is triggered. The present application can dynamically adjust the safety threshold value and reduce the possibility of false positives and false negatives.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building operation and maintenance, and particularly relates to a three-dimensional visual steel roof monitoring system and method. BACKGROUND

[0002] As the core load-bearing structure of large traffic hubs and public buildings, the safety and reliability of steel roof are crucial to the overall performance of the building. With the wide application of large-span steel structures in high-speed railway stations, stadiums and other buildings, steel roofs are facing complex load conditions and problems such as material aging and fatigue damage during long-term service, and therefore, a precise health monitoring system is needed to realize real-time evaluation and early warning of the structure state.

[0003] The existing steel roof monitoring system usually uses a fixed threshold to monitor the stress, deformation and other parameters of the structure, for example, by comparing the data collected by the sensor with the preset static safety threshold, and triggering an alarm if the threshold is exceeded. The existing system does not dynamically adjust the safety threshold in combination with the actual working conditions of the steel roof (such as environmental temperature, load change frequency) and the degree of structural aging, resulting in false alarms or missed alarms, for example, without considering the impact of steel strength decay on the threshold during long-term service, or without integrating dynamic parameters such as real-time wind load and train operation load, making it difficult to achieve accurate evaluation and real-time warning of the structure health state. SUMMARY

[0004] The present application provides a three-dimensional visual steel roof monitoring system and method that can dynamically adjust the safety threshold and reduce the possibility of false alarms and missed alarms, effectively solving the problems in the background art.

[0005] To achieve the above purpose, in a first aspect, the present application provides a three-dimensional visual steel roof monitoring method, comprising:

[0006] Obtain historical monitoring data and finite element analysis results of the steel roof, and perform cluster analysis to identify risk structure nodes;

[0007] For any of the risk structure nodes, integrate real-time acquired environmental data and load data, and input them into a preset structure state safety response model to obtain stress threshold and deformation displacement threshold;

[0008] Based on a preset structure aging decay correction mechanism, correct the stress threshold and the deformation displacement threshold;

[0009] For any of the risk structure nodes, in response to the real-time node stress parameter being greater than the corrected stress threshold and / or the real-time deformation displacement parameter being greater than the corrected deformation displacement threshold, mark the risk structure node through a three-dimensional visualization platform and trigger a multi-channel alarm;

[0010] In response to the real-time node stress parameter and the real-time deformation displacement parameter not being greater than the respective corrected threshold values, the real-time node stress parameter and the real-time deformation displacement parameter are comprehensively evaluated, and it is determined whether the risk structure node needs to be warned based on the evaluation result.

[0011] In combination with the first aspect, in a possible design, the environmental data includes temperature, wind speed and humidity.

[0012] The load data includes train dynamic load, crowd load and wind load.

[0013] In combination with the first aspect, in a possible design, the structure aging weakening correction mechanism corrects the threshold value by monitoring the fatigue damage degree of steel, the corrosion rate of coating and the node loosening amount, and combining a material aging model.

[0014] In combination with the first aspect, in a possible design, the real-time node stress parameter and the real-time deformation displacement parameter are obtained by a sensor deployed at the risk structure node; the sensor includes a strain gauge and a displacement gauge.

[0015] In combination with the first aspect, in a possible design, the risk structure node at least includes a support node, a rod intersection node and a cantilever part node.

[0016] In combination with the first aspect, in a possible design, the structure state safety response model considers the material strength degradation with the environment and the load combination, calculates the stress threshold value, and the formula is:

[0017] ;

[0018] wherein, represents the stress threshold value, represents the real-time yield strength, which is calculated by a material constitutive model; represents a safety factor; represents a load combination coefficient, which considers the coupling effect of dynamic load and wind load.

[0019] In combination with the first aspect, in a possible design, the load combination coefficient is calculated by considering the coupling effect of dynamic load and wind load, and the formula is:

[0020] ;

[0021] wherein, represents the train dynamic load; represents the wind load, , is air density, is a wind load shape coefficient, A is a wind receiving area, and V is a real-time wind speed.

[0022] In combination with the first aspect, in a possible design, considering the superposition effect under multiple loads, the deformation displacement threshold calculation formula is:

[0023] ;

[0024] denotes the deformation displacement threshold; L denotes the component calculation length; denotes the dynamic load influence coefficient, which is fitted through historical data a, b and c are regression coefficients, which are determined through correlation analysis of measured displacement data and loads.

[0025] In combination with the first aspect, in a possible design, in the three-dimensional visualization platform, the stress overrun node is displayed using a first preset color and is prompted by flashing;

[0026] The deformation overrun node is displayed using a second preset color and is dynamically labeled with a deformation vector, wherein the arrow direction indicates the deformation direction and the length reflects the deformation amount.

[0027] Clicking the labeled node pops up an information box, which displays at least the real-time parameter, the corrected threshold, the overrun amplitude and the historical trend curve.

[0028] The second aspect, the present application also provides a kind of three-dimensional visualization steel roof monitoring system, comprising:

[0029] Risk node identification module, for obtaining steel roof historical monitoring data and finite element analysis result, and carries out cluster analysis to identify risk structure node.

[0030] Data acquisition module, deployment is at risk structure node, and real-time acquisition node stress parameter and deformation displacement parameter.

[0031] Threshold calculation module, for any risk structure node, fusion real-time acquisition environmental data and load data, and input to the preset structure state safety response model, obtains stress threshold and deformation displacement threshold.

[0032] Threshold correction module, based on the preset structure aging weakening correction mechanism, corresponding stress threshold and deformation displacement threshold are corrected.

[0033] Early warning triggering module, for any risk structure node, when real-time node stress parameter is greater than corrected stress threshold, and / or real-time deformation displacement parameter is greater than corrected deformation displacement threshold, the risk structure node is labeled through three-dimensional visualization platform, and multi-channel early warning is triggered;

[0034] When the real-time node stress parameter and the real-time deformation displacement parameter are both not greater than the respective corresponding corrected threshold values, the real-time node stress parameter and the real-time node displacement are comprehensively evaluated, and whether the risk structure node needs to be warned is judged according to the evaluation result.

[0035] The technical scheme of the present application can achieve the following technical effects: by fusing real-time environmental data and load data and combining a structure aging weakening correction mechanism, the system can dynamically adjust the safety threshold, overcome the limitations of traditional fixed threshold monitoring, reduce the possibility of false positives and false negatives, and improve the accuracy and reliability of monitoring; the system not only triggers a warning when real-time data exceeds the corrected threshold, but also comprehensively evaluates when the data does not exceed the threshold, and the double monitoring mechanism ensures that the system can comprehensively evaluate the structure state under complex working conditions and issue a warning when necessary, thereby improving the comprehensiveness and timeliness of monitoring; by marking risk structure nodes on the three-dimensional visualization platform, the system can intuitively display the state changes of the structure, which not only facilitates managers to quickly identify problem nodes, but also provides intuitive display of the structure health state; the system combines historical monitoring data and real-time collected data to identify risk nodes through clustering analysis and finite element analysis results, which can more accurately identify potential risks and provide more comprehensive health evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical schemes in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0037] Figure 1 The logic flow chart of the three-dimensional visualization steel roof monitoring method in the present application;

[0038] Figure 2 The structural block diagram of the three-dimensional visualization steel roof monitoring system in the present application. DETAILED DESCRIPTION

[0039] The technical schemes in the embodiments of the present application will be described clearly and completely in the following by combining the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments.

[0040] The following will combine the drawings in the present application Figure 1 The present application is described.

[0041] As Figure 1 shown, the three-dimensional visualization steel roof monitoring method of the present application specifically includes the following steps:

[0042] Step S1, obtaining steel roof historical monitoring data and finite element analysis results, and performing cluster analysis to identify risk structure nodes;

[0043] Step S2, for any risk structure node, fusing real-time acquired environmental data and load data, inputting into a preset structure state safety response model to obtain stress threshold and deformation displacement threshold; the environmental data at least includes temperature, wind speed and humidity; the load data at least includes train dynamic load, crowd load and wind load;

[0044] Step S3, based on a preset structure aging and weakening correction mechanism, correcting the stress threshold and the deformation displacement threshold; the structure aging and weakening correction mechanism corrects the threshold by monitoring steel fatigue damage degree, coating corrosion rate and node loosening amount, and combining a material aging model;

[0045] Step S4, for any risk structure node, in response to that real-time node stress parameter is greater than the corrected stress threshold, and / or real-time deformation displacement parameter is greater than the corrected deformation displacement threshold, the risk structure node is marked through a three-dimensional visualization platform and multi-channel early warning is triggered; the real-time node stress parameter and the real-time deformation displacement parameter are obtained through a sensor deployed at the risk structure node; the sensor includes a strain gauge and a displacement meter;

[0046] In response to that the real-time node stress parameter and the real-time deformation displacement parameter are not greater than the respective corrected thresholds, the real-time node stress parameter and the real-time deformation displacement parameter are comprehensively evaluated, and whether the risk structure node needs early warning is judged based on the evaluation result.

[0047] In the embodiment, the steel roof historical monitoring data and the finite element analysis results are obtained through step S1, the risk structure nodes such as stress concentrated truss nodes and fatigue prone support connection places are identified by using cluster analysis; the defects of traditional monitoring system such as "uniform point distribution and blind monitoring" are changed, the high risk areas are accurately positioned based on data driving and mechanical analysis, the monitoring resources are concentrated on the key nodes where safety hidden dangers are truly possible to occur, the monitoring efficiency and pertinence are improved, the invalid data interference is avoided, and the possibility of false alarm and missed alarm is reduced from the source;

[0048] Step S2 fuses real-time environmental data and load data, inputs a structure state safety response model to calculate stress and deformation threshold, breaks through the limitation of traditional fixed threshold, and makes the safety threshold dynamically adjust with real-time working conditions; for example, the stress early warning threshold is automatically reduced under strong wind working condition, the threshold is corrected considering the steel strength attenuation in high temperature environment, the threshold is matched with the actual stress state, the early warning lag or excessive sensitivity caused by working condition change is avoided, and "accurate early warning" is realized;

[0049] Step S3 corrects the mechanism of structural aging deterioration, monitors parameters such as fatigue damage of steel materials, corrosion rate of coating, and node loosening amount, corrects the threshold value such as Paris law in combination with a material aging model, makes up for the defects of the existing system that "does not consider the performance degradation of long-term service materials", so that the threshold value can dynamically reflect the actual bearing capacity of the steel roof after aging; for example, the steel member that has been in service for many years will have a corresponding decrease in the corrected threshold value, avoiding "false negatives" caused by not updating the threshold value, and improving the monitoring reliability of long-term service structures;

[0050] Step S4 sets double early warning conditions, when the real-time stress / strain parameters exceed the corrected threshold value, the nodes are marked through a three-dimensional visualization platform and multi-channel early warning (SMS, sound and light alarm, etc.), the abnormality is directly located and quickly responded; when the parameters do not exceed the threshold value, the potential risks (such as fatigue accumulation of stress continuously close to the threshold value) are evaluated through trend analysis and multi-parameter coupling effect, avoiding "single threshold value judgment" leading to false negatives; a double mechanism of "explicit over-standard early warning + implicit risk assessment" is built, which not only deals with immediate dangers such as stress over-limit caused by sudden load, but also captures gradual hidden dangers such as node loosening caused by long-term vibration, covers the "normal parameters but potential safety hazards" scenario that cannot be identified by traditional systems, and improves the comprehensiveness and forward-looking of early warning;

[0051] Through the three-dimensional visualization platform, the stress / strain data of the risk nodes are mapped in real time, and the structure state is displayed in combination with multi-source data such as environment and load; the traditional monitoring system changes the problem of "data fragmentation and non-intuitive display", and the overall stress state and risk distribution of the steel roof are directly presented by the three-dimensional model, such as red highlight display of stress over-limit nodes, which assists the operation and maintenance personnel to quickly locate the problem and develop countermeasures; at the same time, multi-channel early warning (SMS, on-site alarm) ensures timely response in different scenarios and shortens the safety hazard processing cycle;

[0052] The above method solves the core problems of the traditional monitoring system such as "fixed threshold value, ignoring aging, and extensive early warning" through the technical chain of "accurately locating risks, dynamically generating threshold values, aging correction, and multi-dimensional early warning", realizes real-time and accurate evaluation, whole-cycle safety monitoring, and multi-scenario effective early warning of the steel roof structure state, and provides scientific and reliable technical support for long-term safety operation and maintenance of large steel structures.

[0053] In some embodiments of the present application, the specific implementation of identifying the risk structure node is as follows:

[0054] Step S11, collect historical monitoring data, obtain historical monitoring data from sensors installed at different positions of the steel roof and of different types, including strain gauges, displacement meters, accelerometers, etc., for recording changes in various physical parameters of the steel roof during past operation; for example, strain gauges can measure the strain of the steel roof structure under load, and displacement meters can record the displacement of the structure; the collected data needs to cover a long time span so as to comprehensively reflect the performance of the steel roof under different working conditions, including different seasons, different time periods (such as day, night, holidays, etc.), and different load conditions (such as empty load, full load, dynamic load, etc.).

[0055] Step S12, obtain finite element analysis results, model and analyze the steel roof using finite element analysis software; in the modeling process, detailed information such as the structural form, material properties, and connection method of the steel roof needs to be accurately considered to ensure that the model can accurately reflect the mechanical properties of the actual structure; through finite element analysis, simulate the stress distribution, deformation, and other mechanical responses of the steel roof under various load combinations, and obtain the corresponding analysis results, including the stress size and displacement value at different positions, etc., to provide theoretical calculation data support for subsequent clustering analysis.

[0056] Step S13, perform clustering analysis on the above data and identify risk structure nodes, specifically;

[0057] Step S131, preprocess the obtained historical monitoring data and finite element analysis results, including data cleaning, data normalization, etc.; data cleaning is used to remove noise, outliers, and missing values in the data to ensure data quality and reliability; data normalization is to standardize different types of data according to certain rules to make them comparable so that subsequent clustering analysis can proceed smoothly;

[0058] Step S132, select appropriate clustering algorithms according to the characteristics of the data and the analysis target; common clustering algorithms include K-Means clustering, DBSCAN clustering, hierarchical clustering, etc.; in this method, one or more clustering algorithms may be combined according to the characteristics of the steel roof structure and the distribution of the monitoring data; for example, the K-Means clustering algorithm is suitable for data with uniform distribution and spherical clusters; while the DBSCAN clustering algorithm is more suitable for handling data with complex shapes and noise; by considering various factors, selecting appropriate clustering algorithms can improve the accuracy and reliability of the clustering results;

[0059] Step S133, after selecting the clustering algorithm, the relevant parameters of clustering need to be determined; for different clustering algorithms, the parameter settings are also different; for example, in the K-Means clustering algorithm, the number of clusters K needs to be determined; in the DBSCAN clustering algorithm, the neighborhood radius and minimum point number and other parameters need to be set; the determination of clustering parameters needs to be adjusted in combination with domain knowledge and experimental results; different parameter combinations can be tried several times, and the optimal parameter setting can be selected according to the evaluation index (such as the silhouette coefficient, Calinski-Harabasz index, etc.) of the clustering result to obtain the best clustering effect;

[0060] Step S134, input the preprocessed historical monitoring data and finite element analysis results into the selected clustering algorithm to perform clustering analysis; the clustering algorithm will divide the data points into different clusters according to the similarity between the data; in the steel roof monitoring, similar data points represent that the steel roof structure has similar characteristics or response modes in mechanical properties; through clustering analysis, the steel roof structure is divided into different regions or node categories, and each cluster represents a structure part with similar characteristics, and the nodes in the cluster are the potential risk structure nodes.

[0061] More specifically, in the steel roof structure, the risk structure nodes are usually located at the parts with complex stress, stress concentration or easy deformation, which are more likely to have safety problems in long-term use; mainly including the following categories:

[0062] Support node: as the key part of supporting the steel roof, the support bears huge load and transmits it to the lower structure; for example, the support node of the station building steel roof not only bears the gravity load of the roof itself, but also resists horizontal forces such as wind load and earthquake action; under the action of long-term load, the support node is prone to stress concentration, causing fatigue damage of steel; moreover, since the support node is connected with the lower structure, if the connection part is not handled properly or loosens after long-term use, it will affect the stability of the entire steel roof; especially under the influence of load difference of different platform layers and train running vibration, the stress of the support node is more complex;

[0063] Beam intersection node: Steel roof is usually composed of a large number of beams, and the intersection node of the beams is the key position for force transmission and conversion. Take the large-span special-shaped space truss structure as an example. At the node of the truss, multiple beams intersect, and the direction and size of force transmission are complex. When subjected to external loads, these nodes are prone to large stress and deformation. For example, under the combined action of wind load and temperature change, the stress state of some truss nodes is complex and changeable, and long-term accumulation may cause loosening and fracture of the connecting parts of the beams. The same is true for the nodes of roof trusses. Different axis trusses intersect at the nodes and bear forces from multiple directions, which are typical positions of risk structure nodes.

[0064] Overhanging part node: For the overhanging part of the steel roof, the nodes at the end and support are under special stress. For example, some areas of the station house have overhanging structures. The nodes of the overhanging part not only bear the gravity of the overhanging part itself, but also resist the bending moment and torque caused by overhanging. Under the action of crowd load, wind load, and other loads, the deformation and stress of these nodes are large. If the overhanging length is large, the load effect borne by the nodes is more significant, and safety hazards are prone to occur. The same risk structure nodes exist in the overhanging structure parts such as station canopies, which need to be monitored.

[0065] Temperature change sensitive node: Due to the large area of the steel roof, temperature changes will cause large internal forces and deformations in the structure. At the interface of different materials, areas with strong constraints, and the middle part of large-span steel roofs, temperature stress concentration is obvious. For example, at the node of the steel roof, the thermal expansion coefficients of steel and concrete are different, and large temperature stresses will occur when the temperature changes, which can easily cause cracks or other damage at the node. In the middle part of the large-span steel roof, the nodes are also prone to large temperature stresses due to the constraint of expansion caused by temperature changes, which are risk structure nodes.

[0066] In the present embodiment, historical monitoring data of different positions and types of sensors under long-term multi-working conditions is collected to comprehensively reflect the performance of the steel roof; different types of sensors monitor the steel roof from multiple dimensions, and different working condition data ensures to cover various actual situations, so that the data has wide representativeness; step S12 accurately models through finite element analysis, comprehensively considers information such as the structure, material and connection mode of the steel roof, can accurately simulate the mechanical response, provides theoretical calculation data support, makes up for the limitations of monitoring data, reveals the potential risks of the steel roof from the theoretical level, and mutually supplements the historical monitoring data, improves the accuracy and reliability of risk node identification; step S132 selects a suitable clustering algorithm according to the data characteristics and analysis target, and reasonably determines the related parameters in S133; the combination of multiple clustering algorithms and parameter optimization can better adapt to the complex structure of the steel roof and the diverse data distribution, improve the accuracy and reliability of the clustering results, and avoid misjudgment or omission of risk nodes due to improper algorithm selection; step S134 inputs the preprocessed data into the clustering algorithm, divides the regions and node categories according to the data similarity, focuses on the potential risk structure nodes, and effectively distinguishes different performance regions based on the clustering method of similar features, helps to identify the risk area, and improves the monitoring efficiency and pertinence.

[0067] In some embodiments of the present application, the collection of environmental data and load data is implemented as follows:

[0068] A variety of sensors are used to collect environmental data around the risk structure node in real time, and these environmental data at least include temperature, wind speed and humidity; the change of temperature will cause the thermal expansion and contraction of steel, and then change the internal stress distribution of the steel roof; wind speed not only directly acts on the steel roof to generate wind load, but also may cause structure vibration; humidity may affect the corrosion rate of steel and indirectly affect the structure performance; for example, in the monitoring of the steel roof of a high-speed railway station building, the temperature of the environment where the steel roof is located is monitored in real time by a temperature sensor, the wind speed is measured by a wind speed and direction instrument, and the air humidity is obtained by a humidity sensor, so as to comprehensively grasp the environmental conditions;

[0069] A variety of load data is collected, including train dynamic load, crowd load and wind load; the train generates vibration and impact force in the running process, forming train dynamic load acting on the steel roof; a large number of passengers generate crowd load when the high-speed railway station building is in operation, which also affects the steel roof; wind load changes according to different weather conditions and building surrounding environment, and is one of the important factors affecting the force of the steel roof; by arranging sensors at corresponding positions, such as setting acceleration sensors near the track to monitor train dynamic load, setting pressure sensors on the platform to estimate crowd load, and setting wind speed and direction instruments at high places of the steel roof to measure wind load, accurate real-time load data can be ensured to be obtained.

[0070] Further, the real-time acquired environmental data and load data are fused to form a comprehensive data set, and the purpose of data fusion is to integrate different types of data together so as to more comprehensively describe the actual working condition of the steel roof; for example, the temperature, wind speed, humidity and train dynamic load, crowd load, wind load and other data are combined according to certain rules to form a multi-dimensional input vector;

[0071] The fused comprehensive data set is input into a preset structure state safety response model; the structure state safety response model is obtained based on structural mechanics theory, material constitutive relation and a large amount of historical data, and can calculate the stress threshold and deformation displacement threshold of each risk structure node under the current working condition according to the input environmental data and load data;

[0072] The stress threshold represents the upper limit of stress that the risk structure node can withstand under the current working condition; the deformation displacement threshold represents the maximum deformation amount allowed by the node; the above threshold is not fixed but dynamically generated according to the real-time collected data; compared with the traditional fixed threshold, the actual environmental changes and load fluctuations of the steel roof are fully considered, which is more suitable for the actual working state of the structure; for example, under strong wind weather, the model can calculate more reasonable stress threshold and deformation displacement threshold of the risk structure node according to real-time environmental data such as wind speed and wind direction and wind load data, combined with the structural characteristics of the steel roof, to provide a scientific basis for subsequent accurate assessment of structural safety.

[0073] More specifically, the mathematical model of the structure state safety response model is illustrated, combined with the structural mechanics theory and the material constitutive relation, and the following takes the steel truss node as an example to illustrate the mathematical modeling idea of the structure state safety response model:

[0074] The comprehensive environmental and load data are used to construct an input vector: ;

[0075] T represents the real-time temperature, which affects the elastic modulus of steel ; is a temperature correction coefficient;

[0076] V represents the real-time wind speed, which is used to calculate the wind load , is the air density, is the wind load shape coefficient, and A is the wind receiving area;

[0077] H represents the humidity, which affects the corrosion rate of steel;

[0078] represents the train dynamic load, which varies with the speed and axle load, and is represented as , k is the dynamic load coefficient, and v is the vehicle speed.

[0079] The stress threshold and the deformation displacement threshold are calculated based on a structural mechanics threshold calculation model, specifically;

[0080] The stress threshold is expressed as:

[0081] ;

[0082] The stress threshold is expressed as: The real-time yield strength is calculated by a material constitutive model, such as: ; The yield strength in a dry environment at room temperature is: and The humidity and temperature degradation coefficients are: The safety factor is expressed as: The load combination coefficient is expressed as: .

[0083] Based on structural deformation theory, such as the truss joint displacement formula, the superposition effect under multiple loads is considered, and the deformation displacement threshold calculation formula is:

[0084] ;

[0085] The deformation displacement threshold is expressed as: L represents the calculated length of the component; The dynamic load influence coefficient is expressed as: ; a, b, and c are regression coefficients determined by correlation analysis of measured displacement data and loads.

[0086] In this embodiment, based on the material constitutive relationship and structural mechanics formula, the threshold value is ensured to meet the principles of engineering mechanics, avoiding the "black box" defect of pure data-driven models; the model parameters are trained by historical data to correct the differences between the theoretical model and the actual structure, making the threshold value closer to the real stress behavior of the steel roof; the combination of the two makes the model not only scientific and rigorous, but also adaptable to the complex uncertainty in actual engineering; the mechanical response of the steel roof is the result of the joint action of multiple factors, such as temperature change, which not only affects the material strength, but also causes structural thermal deformation, which is superimposed with load deformation, and the traditional fixed threshold cannot quantify this coupling effect; the integrated model can accurately calculate the synergistic effect between parameters;

[0087] Step S2 builds a comprehensive working condition description through multi-source data fusion, dynamically generates threshold values by using theoretical and data-driven models, and solves the core problem of "fixed threshold and disconnection of working conditions" in traditional monitoring. The core value lies in changing the safety threshold from a "static value based on design specifications" to a "dynamic solution based on real-time working conditions", significantly improving the real-time, accuracy and reliability of steel roof structure health assessment, and providing scientific and effective technical means for structural safety warning under complex loads and environments.

[0088] In some embodiments of the present application, step S3 corrects the stress threshold and deformation displacement threshold generated in step S2 by a pre-set structure aging and weakening correction mechanism, to reflect the structural performance degradation caused by factors such as material aging and fatigue damage during the long-term service of the steel roof, make up for the defects of the traditional monitoring system "fixed threshold without considering aging", make the safety threshold dynamically adapt to the service life and aging degree of the structure, and improve the long-term reliability of the warning; the core content of the structure aging and weakening correction mechanism includes:

[0089] Steel fatigue damage degree: by monitoring the stress cycle number, stress amplitude and other parameters of the risk structure node for a long time through strain gauges, combining Paris law (fatigue crack propagation model) or Miner linear cumulative damage theory, the fatigue damage degree D of the steel is calculated; for example, when the stress cycle number of a certain steel truss node under train dynamic load exceeds the design value, the fatigue damage degree D rises, indicating that the fatigue resistance of the steel decreases;

[0090] Coating corrosion rate: using corrosion sensors or periodically detecting coating thickness, steel corrosion potential and other parameters to quantify the corrosion degree of the steel surface; for example, after the coating is damaged in a high humidity area, the corrosion rate of the steel increases, resulting in a decrease in the effective cross-sectional area of the steel and a decrease in the strength of the steel;

[0091] Node loosening amount: the relative displacement or rotation angle of the node connection part (such as bolted joint, welded joint) is monitored by a displacement meter or an inclination sensor to determine the degree of node loosening; node loosening will cause a decrease in structural stiffness and an increase in deformation under the same load.

[0092] The material aging model and threshold correction logic are as follows:

[0093] Based on the monitored aging parameters, an attenuation function of steel strength with time is established:

[0094] ;

[0095] Where f0 is the initial strength of the steel; λ1, λ2, and λ3 are the influence coefficients of fatigue damage, corrosion degree, and node loosening; D is the fatigue damage degree, C is the corrosion rate, and L is the node loosening amount, reflecting the comprehensive weakening effect of aging factors on the strength of the steel;

[0096] The stress threshold correction formula is:

[0097] ；

[0098] Wherein is the current working condition stress threshold generated in step S2, is the aging strength attenuation coefficient;

[0099] The deformation displacement threshold is corrected, considering the stiffness degradation caused by node loosening. The deformation threshold needs to be adjusted according to the node stiffness reduction coefficient The formula is:

[0100] ;

[0101] The more serious the node loosening is, the greater the node stiffness reduction coefficient is, and the smaller the allowed deformation displacement threshold is. Due to the decrease in stiffness, the deformation under the same load increases.

[0102] In the implementation process, the strain gauge continuously records the stress time history data for calculating the fatigue damage degree; the corrosion monitoring sensor regularly collects the steel surface corrosion data to calculate the corrosion rate; the high-precision displacement meter monitors the micro displacement of the node connection part to quantify the node loosening; the material aging model parameters are updated regularly (such as every month) according to the latest monitoring data, and the parameter calibration is realized through the inversion analysis of the historical aging data and the threshold correction effect.

[0103] After the current working condition threshold is generated in step S2 each time, the aging correction mechanism is immediately called, the aging strength attenuation coefficient and the node stiffness reduction coefficient are calculated in combination with the real-time aging parameters, and the threshold is corrected; for example: after a steel column node has served for 10 years, due to fatigue damage and coating corrosion, the strength of the steel material is attenuated by 8%, so the stress threshold is corrected from 250 MPa to 230 MPa; if the node bolt loosening leads to a 5% decrease in stiffness, the deformation displacement threshold is corrected from 50 mm to 52.5 mm.

[0104] The traditional monitoring system uses a fixed threshold value, without considering the attenuation of the strength of steel materials over time, such as a 10-15% decrease in the strength of steel materials after 20 years of service, resulting in a decrease in the actual bearing capacity while the threshold value remains unchanged, causing "missed reports"; step S3 corrects in real time, so that the threshold value decreases dynamically with the degradation of the structure performance, ensuring that the warning threshold value always matches the actual bearing capacity of the structure; at the same time, considering aging mechanisms such as fatigue damage, corrosion, and node loosening, to avoid the one-sidedness of single-factor correction; for example, corrosion may cause the cross-sectional area of steel to decrease, and fatigue may cause cracks to occur, and when the two act together, the corrected threshold value can more accurately reflect the combined aging effect; only high-risk nodes are corrected for aging, avoiding indiscriminate correction of the entire structure and improving efficiency; the current working condition threshold value is superimposed with an aging correction to form a "working condition + aging" dual-driven threshold system; the corrected threshold value serves as the final basis for warning, ensuring that the warning reflects real-time load working conditions and considers performance degradation caused by long-term aging.

[0105] In some embodiments of the present application, based on the dynamic safety threshold value corrected in step S3, the real-time monitoring data of the risk structure node is discriminated, the precise warning of the steel roof structure state is realized through the double warning mechanism (threshold value exceeding warning and comprehensive evaluation warning), and the effective transmission of warning information is ensured through the three-dimensional visualization platform and multi-channel notification, as follows:

[0106] Real-time node stress parameters and real-time deformation displacement parameters are collected in real time by sensors (strain gauges, displacement meters) deployed at risk structure nodes; the real-time node stress parameters are measured by strain gauges, reflecting the actual stress value of the node under the current load; the real-time deformation displacement parameters are measured by displacement meters, reflecting the deformation of the node;

[0107] The real-time collected stress parameters and deformation displacement parameters are compared in real time with the stress threshold value and the deformation displacement threshold value corrected in step S3, respectively, to form two triggering conditions:

[0108] Condition one: real-time node stress parameter > stress threshold value;

[0109] Condition two: real-time deformation displacement parameter > deformation displacement threshold value;

[0110] Any of the above conditions triggers a warning.

[0111] Through the three-dimensional visualization platform, the risk structure node triggering the warning is intuitively labeled in the steel roof model, for example: the stress over-limit node is displayed in red highlight and flashes; the deformation over-limit node is displayed in blue highlight and dynamically labels the deformation vector, with the arrow direction indicating the deformation direction and the length reflecting the deformation amount; at the same time, clicking on the labeled node can pop up a detailed information box, displaying real-time parameters, corrected threshold value, over-limit amplitude, historical trend curve, etc.

[0112] The early warning information is synchronously sent in the following ways to ensure that relevant personnel respond in time: on-site early warning, triggering sound and light alarms such as red warning light flashing and buzzer alarm in the monitoring room near the steel roof or at key positions; remote notification, sending a short message to the operation and maintenance person in charge, including node position, exceeding parameter, risk level and email of attached detailed data report; system integration early warning, connecting the early warning information to the building intelligent management system, linking the camera to focus on the risk area, or automatically generating a work order and pushing it to the maintenance system.

[0113] More specifically, if the real-time node stress parameter and the real-time deformation displacement parameter do not exceed the respective corrected threshold values, the monitoring will not be stopped immediately, but will enter a more in-depth analysis stage, that is, a comprehensive evaluation of these parameters will be carried out, not only relying on a single parameter to judge the safety of the structure, but also considering multiple factors for comprehensive consideration; for example, considering the mutual relationship between stress and displacement, as well as the influence of other environmental and load conditions, for example:

[0114] First, construct the real-time parameter feature vector:

[0115] ;

[0116] Where X represents the real-time parameter feature vector; represents the real-time stress; represents the real-time deformation displacement; represents the stress change per unit time, which is used to quickly identify sudden loads; represents the displacement change per unit time, which is used to identify the slip of the monitored structure such as support;

[0117] The calculation formula for comprehensive evaluation of the real-time node stress parameter and the real-time deformation displacement parameter is:

[0118]

[0119] Where μ represents the normal working condition mean; Σ represents the covariance matrix; T represents the transpose, converting the column vector to the row vector; when D M ≥ 2.5, that is, corresponding to 99% confidence interval, it is judged that the structure node at risk needs early warning.

[0120] In this embodiment, the double early warning mechanism covers explicit and implicit risks. The explicit risk, i.e. the over-limit early warning, quickly captures the instant danger caused by sudden load or serious aging, and achieves "second-level response" through visualization and multi-channel notification to avoid accident expansion. The implicit risk, i.e. comprehensive evaluation, identifies progressive hazards and solves the problem that the traditional system "cannot find the abnormal trend although the parameters are normal" by relying on a single threshold, for example, the stress of a certain node is maintained at 80%-90% of the threshold for a long time, and the traditional system does not respond, while the comprehensive evaluation can judge it as "fatigue early warning" through trend analysis.

[0121] The corrected threshold value serves as a "hard indicator" to ensure accurate identification of instant danger, and the comprehensive evaluation serves as a "soft indicator" to supplement the judgment of progressive risks. The combination of the two forms a "harmonious" early warning system. For example, the corrected threshold value of a certain node decreases due to steel aging, and even if the real-time stress does not exceed the limit, the comprehensive evaluation still triggers an early warning if it finds that the safety reserve is lower than the average value of the same type of node, prompting special detection.

[0122] The traditional monitoring system displays data in tables or two-dimensional curves, and the operation and maintenance personnel need to spend a lot of time locating abnormal positions. The three-dimensional visualization platform directly maps "model-data-early warning" to enable the operation and maintenance personnel to quickly lock the spatial position of the risk node and the surrounding associated structure, thereby shortening the decision-making time. For example, in the steel roof of a high-speed rail station building, the stress exceeding the limit of a certain truss rod can be immediately located through the three-dimensional model, and the stress relationship between the rod and the support and adjacent rods can be viewed to assist in judging the risk diffusion possibility.

[0123] In some schemes, multiple embodiments of the present application can be combined, and the combined scheme can be implemented. Optionally, some operations in the flow of each method embodiment are combined, and / or the order of some operations is changed. Moreover, the execution order between the steps of each flow is only exemplary, and does not constitute a limitation on the execution order between the steps, and other execution orders between the steps can also be used. The execution order is not intended to indicate the only execution order in which the operations can be performed. A person of ordinary skill in the art can think of various ways to reorder the operations described herein. In addition, it should be pointed out that the process details of one embodiment herein are also applicable in a similar manner to other embodiments, or different embodiments can be combined for use.

[0124] In addition, some steps in the method embodiment can be equivalently replaced by other possible steps. Alternatively, some steps in the method embodiment can be optional and can be deleted in some use scenarios. Alternatively, other possible steps can be added to the method embodiment. Moreover, each method embodiment can be implemented individually or in combination.

[0125] As Figure 2As shown, the application also provides a three-dimensional visualized steel roof monitoring system, specifically comprising the following modules;

[0126] A risk node identification module is configured to acquire historical monitoring data of the steel roof and finite element analysis results, and perform clustering analysis to identify risk structure nodes.

[0127] A data acquisition module is deployed at the risk structure nodes to acquire real-time node stress parameters and deformation displacement parameters.

[0128] A threshold calculation module is configured to, for any risk structure node, fuse real-time acquired environmental data and load data, and input them into a preset structure state safety response model to obtain stress threshold values and deformation displacement threshold values.

[0129] A threshold correction module is configured to correct the stress threshold values and the deformation displacement threshold values based on a preset structure aging and weakening correction mechanism.

[0130] An early warning triggering module is configured to, for any risk structure node, when the real-time node stress parameter is greater than the corrected stress threshold value and / or the real-time deformation displacement parameter is greater than the corrected deformation displacement threshold value, mark the risk structure node through a three-dimensional visualized platform and trigger a multi-channel early warning.

[0131] When neither the real-time node stress parameter nor the real-time deformation displacement parameter is greater than the respective corrected threshold value, the real-time node stress parameter and the real-time node displacement are comprehensively evaluated, and it is determined whether the risk structure node needs early warning according to the evaluation result.

[0132] In this embodiment, the risk node identification module can accurately find the risk structure nodes in the steel roof by performing clustering analysis on the historical monitoring data of the steel roof and the finite element analysis results, so that the monitoring is no longer blind and uniform monitoring of the entire steel roof, but focuses on the key parts that are most likely to have problems, improving the pertinence and efficiency of the monitoring; the threshold calculation module combines real-time environmental data and load data to calculate stress threshold values and deformation displacement threshold values through a preset structure state safety response model, overcoming the problem that the traditional fixed threshold values cannot adapt to actual working condition changes; and the threshold correction module further considers the factors of structure aging and weakening to correct the threshold values, so that the threshold values can more accurately reflect the actual bearing capacity of the steel roof in different service stages, effectively avoiding false positives and false negatives caused by unreasonable threshold values.

[0133] The early warning triggering module not only considers whether the real-time parameter exceeds the corrected threshold value, but also comprehensively evaluates when the parameter does not exceed the threshold value, so that the system can capture potential safety hazards, and even when the current data is not significantly over-standard, the system can discover possible problems in advance through analysis of parameter change trends and multi-parameter coupling effects, thereby improving the reliability and comprehensiveness of early warning; the risk structure node is marked on the three-dimensional visualization platform, so that the structural state and risk distribution of the steel roof can be intuitively displayed; the operation and maintenance personnel can more clearly understand the problem and make quick decisions, thereby improving the efficiency of emergency handling; meanwhile, the multi-channel early warning mode ensures that relevant personnel can obtain early warning information in a timely manner;

[0134] The various modules cooperate with each other to form a complete closed-loop monitoring system, the risk node identification module determines the monitoring focus, the data acquisition module provides real-time data, the threshold value calculation and correction module generates reasonable threshold values, and the early warning triggering module performs early warning judgment according to the data and the threshold values, so that the system can continuously and automatically monitor and evaluate the health state of the steel roof, and can respond in a timely manner once a problem is found, thereby realizing real-time and dynamic management of the structural state of the steel roof.

[0135] In this embodiment, the division of the functional modules is performed according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated in one processing module. The integrated module can be realized in the form of hardware. It should be noted that the division of the modules in this embodiment is illustrative, and is only a logical functional division. In actual implementation, another division method can be used.

[0136] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited to the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of three-dimensional visualizing steel roof monitoring, characterized by, The method comprises the following steps: Obtain historical monitoring data of the steel roof and finite element analysis results, and perform clustering analysis to identify risk structure nodes; For any of the risk structure nodes, fuse real-time acquired environmental data and load data, input into a preset structure state safety response model, obtain stress threshold and deformation displacement threshold; the environmental data includes temperature, wind speed and humidity; the load data includes train dynamic load, crowd load and wind load; Based on the preset structure aging and weakening correction mechanism, the stress threshold and the deformation displacement threshold are corrected; For any of the risk structure nodes, in response to the real-time node stress parameter being greater than the corrected stress threshold, and / or the real-time deformation displacement parameter being greater than the corrected deformation displacement threshold, the risk structure node is marked through a three-dimensional visualization platform and a multi-channel early warning is triggered; In response to the real-time node stress parameter and the real-time deformation displacement parameter not being greater than the respective corresponding corrected thresholds, the real-time node stress parameter and the real-time deformation displacement parameter are comprehensively evaluated, and whether the risk structure node needs early warning is judged based on the evaluation result; The structure state safety response model considers the material strength degradation with the environment and load combination, calculates the stress threshold, and the formula is: ； wherein, represents a stress threshold value, represents a real-time yield strength, calculated by a material constitutive model; represents a safety factor; represents a load combination factor, considering the coupling effect of dynamic load and wind load; Considering the coupling effect of dynamic load and wind load, the load combination coefficient formula is: ； wherein, represents the train dynamic load; represents the wind load, , is the air density, is the wind load shape coefficient, A is the wind area, and V is the real-time wind speed; The comprehensive evaluation of the real-time node stress parameter and the real-time deformation displacement parameter comprises: Constructing a real-time parameter feature vector: ； X represents a real-time parameter feature vector; represents a real-time stress; represents a real-time deformation displacement; represents a stress change amount per unit time, and is used to identify a sudden load; represents a displacement change amount per unit time, and is used to identify a slip of the monitored structure; The comprehensive evaluation calculation formula of the real-time node stress parameter and the real-time deformation displacement parameter is: Wherein, μ represents the historical normal condition mean; Σ represents the covariance matrix; T represents the transpose, converting the column vector to the row vector; when D M ≥ 2.5, it is judged that the risk structure node needs to be warned.

2. The method of claim 1, wherein, The structure aging and weakening correction mechanism corrects the threshold by monitoring the fatigue damage degree of steel, the corrosion rate of coating and the node loosening amount, and combining a material aging model.

3. The method of claim 1, wherein, The real-time node stress parameter and the real-time deformation displacement parameter are obtained by sensors deployed at the risk structure nodes; the sensors include strain gauges and displacement meters.

4. The method of claim 1, wherein, The risk structure nodes at least include support nodes, rod intersection nodes and cantilever part nodes.

5. The method of claim 1, wherein, Considering the superposition effect under multiple loads, the deformation displacement threshold calculation formula is: ； where L is the computed length of the member and δ is the threshold deformation displacement; where K is the dynamic load effect factor, fitted by historical data where a, b and c are regression coefficients determined by correlation analysis of measured displacement data with load.

6. A method of three-dimensional visualized monitoring of a steel roof according to claim 5, characterized in that, In the three-dimensional visualization platform, the stress overrun node is displayed using a first preset color and is prompted by flashing; The deformation overrun node is displayed using a second preset color and is dynamically labeled with a deformation vector, the arrow direction indicating the deformation direction and the length reflecting the deformation amount; Clicking the labeled node pops up an information box, which at least displays real-time parameters, corrected thresholds, overrun amplitudes and historical trend curves.

7. A three-dimensional visualized steel roof monitoring system, which is applied to the three-dimensional visualized steel roof monitoring method according to claim 1, characterized in that, The method comprises the following steps: A risk node identification module is configured to obtain historical monitoring data of the steel roof and finite element analysis results, and perform clustering analysis to identify risk structure nodes; A data acquisition module is deployed at the risk structure nodes to acquire real-time node stress parameters and deformation displacement parameters; A threshold calculation module is configured to fuse real-time acquired environmental data and load data for any of the risk structure nodes, and input into a preset structure state safety response model to obtain stress threshold and deformation displacement threshold; A threshold correction module is configured to correct the stress threshold and the deformation displacement threshold based on a preset structure aging and weakening correction mechanism; The early warning triggering module, for any risk structure node, when the real-time node stress parameter is greater than the corrected stress threshold value, and / or the real-time deformation displacement parameter is greater than the corrected deformation displacement threshold value, labels the risk structure node through the three-dimensional visualization platform and triggers multi-channel early warning; When the real-time node stress parameter and the real-time deformation displacement parameter are not greater than the respective corrected threshold values, the real-time node stress parameter and the real-time node displacement are comprehensively evaluated, and whether the risk structure node needs early warning is judged according to the evaluation result.

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