Metal roof damage detection method and system based on multi-environment coupling loading

Abstract

The invention relates to the technical field of constructional engineering structure tests, and discloses a metal roof damage detection method and system based on multi-environment coupling loading, and the method comprises the steps: determining a six-dimensional boundary constraint condition of a metal roof through a structure parameter in roof parameters; constructing a digital twinborn model of the metal roof according to attribute parameters in the roof parameters and six-dimensional boundary constraint conditions; the environment simulation unit is used for generating multi-dimensional environment factors corresponding to the digital twin model; generating a coupling load spectrum corresponding to the environment simulation unit; performing graded loading simulation operation on an environment simulation unit in the digital twinborn model based on the coupled load spectrum, and collecting real-time acoustic emission signals and real-time full-field strain data of the metal roof in the simulation operation; the damage energy entropy of the metal roof is calculated through the real-time acoustic emission signals and the real-time full-field strain data, and the damage stage of the metal roof is judged according to the damage energy entropy. According to the invention, the accuracy of metal roof damage detection can be improved.

Description

Technical Field

[0001] This invention relates to the field of structural testing technology in building engineering, and in particular to a method and system for detecting damage to metal roofs based on multi-environment coupling loading. Background Technology

[0002] In the current field of metal roof performance testing, existing metal roof simulation tests have many significant limitations, which greatly restrict the accurate evaluation and research of the performance of metal roofs under complex actual working conditions.

[0003] Most existing metal roof performance testing devices primarily use single-factor loading, simulating only the effects of individual environmental factors such as wind, rain, temperature, or snow on the roof. However, in actual engineering projects, metal roofs are often subjected to the coupled effects of multiple environmental factors simultaneously. For example, during typhoons, roofs may experience strong winds, torrential rain, and sudden temperature changes. This coupling effect of multiple environmental loads has a complex impact on the structural performance of the roof. The inability to effectively simulate this coupling effect leads to significant discrepancies between test results and actual conditions, making it difficult to accurately predict the roof's performance during actual use. Consequently, the accuracy of metal roof damage detection is low. Summary of the Invention

[0004] This invention provides a method and system for detecting damage to metal roofs based on multi-environment coupling loading, the main purpose of which is to solve the problem of low accuracy in detecting damage to metal roofs.

[0005] To achieve the above objectives, the present invention provides a method for detecting damage to metal roofs based on multi-environment coupled loading, comprising:

[0006] The six-dimensional boundary constraints of the metal roof are determined by using the structural parameters from the pre-acquired roof parameters.

[0007] A digital twin model of the metal roof is constructed based on the attribute parameters in the roof parameters and the six-dimensional boundary constraints.

[0008] Based on the preset engineering test requirements, an environmental simulation unit for multi-dimensional environmental factors corresponding to the digital twin model is generated.

[0009] Generate the coupled load spectrum corresponding to the environmental simulation unit according to the preset actual engineering requirements;

[0010] Based on the coupled load spectrum, a graded loading simulation operation is performed on the environmental simulation unit in the digital twin model, and real-time acoustic emission signals and real-time full-field strain data of the metal roof are collected during the simulation operation.

[0011] Calculate a damage energy entropy value of the metal roof by using the real-time acoustic emission signal and the real-time full-field strain data, and determine a damage stage of the metal roof according to the damage energy entropy value.

[0012] Optionally, the six-dimensional boundary constraint condition of the metal roof is determined by a structure parameter in the pre-acquired roof parameter, and the structure parameter comprises:

[0013] Extract a support geometric coordinate and a constraint type in the structure parameter;

[0014] Calculate a spatial position matrix of the metal roof support based on the support geometric coordinate;

[0015] Generate a degree of freedom constraint vector of the metal roof support according to the constraint type;

[0016] Adjust the spatial position matrix and the degree of freedom constraint vector by a motion parameter of a preset six-dimensional force feedback mechanical arm, so as to obtain a spatial position matrix and a degree of freedom constraint vector conforming to the metal roof structure;

[0017] Fuse the spatial position matrix and the degree of freedom constraint vector conforming to the metal roof structure into the six-dimensional boundary constraint condition.

[0018] Optionally, the digital twin model of the metal roof is constructed according to an attribute parameter in the roof parameter and the six-dimensional boundary constraint condition, and the construction comprises:

[0019] Analyze a material constitutive relation in the attribute parameter, and generate an elastic modulus matrix of the metal roof according to the material constitutive relation;

[0020] Convert the six-dimensional boundary constraint condition into a finite element node constraint equation;

[0021] Generate an initial roof topological grid of the metal roof by inverse parameterization mapping based on a preset geometric model and the elastic modulus matrix;

[0022] Embed the finite element node constraint equation into a boundary node of the initial roof topological grid, so as to generate a roof dynamics model of the metal roof;

[0023] Correct material parameters of the roof dynamics model by a preset freeze-thaw cycle coefficient, and output the digital twin model of the metal roof.

[0024] Optionally, an environmental simulation unit of a multi-dimensional environmental factor corresponding to the digital twin model is generated according to a preset engineering test requirement, and the environmental simulation unit comprises:

[0025] Analyze an environmental factor type in the engineering test requirement;

[0026] Match the environmental factor type with a preset physical field simulation rule to obtain a rule mapping relationship;

[0027] Configure loading logic of wind load, water load, temperature load and snow load in the multi-dimensional environmental factor based on the rule mapping relationship;

[0028] Associate the loading logic to the digital twin model to generate an environment simulation unit.

[0029] Optionally, the generation of the coupling load spectrum corresponding to the environment simulation unit according to the preset engineering actual demand comprises:

[0030] Extract the load time sequence relationship corresponding to the environment simulation unit in the engineering actual demand;

[0031] Phase align the wind load, water load, temperature load and snow load in the multi-dimensional environmental factor based on the load time sequence relationship to obtain an aligned load;

[0032] Fuse the intensity variation curve of the aligned load using a preset convolution algorithm to obtain an intensity fusion curve;

[0033] Extract the coupling load spectrum corresponding to the environment simulation unit according to the intensity fusion curve.

[0034] Optionally, the performing of the hierarchical loading simulation operation on the environment simulation unit in the digital twin model based on the coupling load spectrum comprises:

[0035] Decompose the coupling load spectrum into incremental load stages corresponding to different simulation units;

[0036] Gradually activate different environment simulation units according to the incremental load stages;

[0037] Dynamically adjust the loading rate of the load in the activated environment simulation unit through a preset closed-loop control algorithm;

[0038] Synchronously couple and load simulate different environment simulation units according to the loading rate.

[0039] Optionally, the collection of the real-time acoustic emission signal and the real-time full-field strain data of the metal roof in the simulation operation comprises:

[0040] Deploy an acoustic emission sensor array at the panel joint of the metal roof, and configure a speckle marker point array on the surface of the metal roof;

[0041] Capture the stress wave signal of the metal roof in the simulation operation through the sampling frequency of the acoustic emission sensor array, and extract the characteristic frequency component of the stress wave signal;

[0042] filtering the characteristic frequency component from the ambient noise to obtain a real-time acoustic emission signal of the metal roof in the simulation operation;

[0043] tracking displacement of the marker points in the speckle marker point array to obtain a displacement trajectory;

[0044] calculating a local strain concentration degree of the metal roof in the simulation operation according to the displacement trajectory;

[0045] mapping the local strain concentration degree to the digital twin model to generate the real-time full-field strain data.

[0046] Optionally, the calculating the damage energy entropy value of the metal roof by using the real-time acoustic emission signal and the real-time full-field strain data comprises:

[0047] decomposing the real-time acoustic emission signal into a characteristic frequency offset, and normalizing the characteristic frequency offset into a frequency domain damage factor;

[0048] extracting a local strain gradient at a roof panel joint in the real-time full-field strain data;

[0049] dynamically coupling the frequency domain damage factor and the local strain gradient by a preset material coefficient of the metal roof to obtain a weight coefficient of a load mode in each environmental simulation unit;

[0050] when the weight coefficient exceeds a preset critical value, taking the weight coefficient as a failure threshold;

[0051] calculating the damage energy entropy value of the metal roof by using the weight coefficient, a real-time cycle number of the load mode, and the failure threshold.

[0052] Optionally, the determining the damage stage of the metal roof according to the damage energy entropy value comprises:

[0053] constructing a mapping relationship between the damage energy entropy value and a preset metal material yield strength;

[0054] determining a two-stage early warning threshold of the metal roof according to the mapping relationship;

[0055] when the damage energy entropy value exceeds a primary threshold in the two-stage early warning threshold, triggering a microscopic crack early warning, and determining the damage stage of the metal roof as a microscopic damage stage according to the microscopic crack early warning;

[0056] when the damage energy entropy value exceeds a secondary threshold in the two-stage early warning threshold, triggering a macroscopic damage early warning, and determining the damage stage of the metal roof as a macroscopic damage stage according to the macroscopic damage early warning.

[0057] To solve the above problems, the application also provides a metal roof damage detection system based on multi-environment coupling loading, which comprises:

[0058] A six-dimensional boundary constraint condition analysis module is configured to determine the six-dimensional boundary constraint condition of the metal roof by the structural parameters in the roof parameters.

[0059] A digital twin model construction module is configured to construct a digital twin model of the metal roof according to the attribute parameters in the roof parameters and the six-dimensional boundary constraint condition.

[0060] An environment simulation unit generation module is configured to generate environment simulation units of multi-dimensional environmental factors corresponding to the digital twin model according to preset engineering test requirements.

[0061] A coupling load spectrum generation module is configured to generate a coupling load spectrum corresponding to the environment simulation units according to preset engineering actual requirements.

[0062] A hierarchical loading simulation operation module is configured to perform hierarchical loading simulation operation on the environment simulation units in the digital twin model based on the coupling load spectrum, and collect real-time acoustic emission signals and real-time full-field strain data of the metal roof in the simulation operation.

[0063] A damage stage determination module is configured to calculate the damage energy entropy value of the metal roof by using the real-time acoustic emission signals and the real-time full-field strain data, and determine the damage stage of the metal roof according to the damage energy entropy value.

[0064] The embodiment of the application determines the six-dimensional boundary constraint condition of the metal roof accurately, constructs a digital twin model in combination with attribute parameters, and then generates multi-dimensional environment simulation units and coupling load spectrum, collects multi-source data through hierarchical loading simulation, calculates the damage energy entropy value to determine the damage stage, effectively solves the problems of model distortion, isolated environment factor simulation, and lagging damage evaluation in traditional metal roof monitoring, realizes the whole-process accurate management of the metal roof from modeling to damage determination, improves the accuracy and reliability of structural safety evaluation, provides efficient support for engineering design optimization and operation decision, and helps to prolong the service life of the metal roof. Therefore, the metal roof damage detection method and system based on multi-environment coupling loading can solve the problem of low accuracy in metal roof damage detection. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The flowchart of the metal roof damage detection method based on multi-environment coupling loading provided by an embodiment of the application is shown.

[0066] Figure 2A functional module diagram of a metal roof damage detection system based on multi-environment coupling loading provided by an embodiment of the present application is shown.

[0067] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

[0069] Embodiments of the present application provide a metal roof damage detection method based on multi-environment coupling loading. The execution subject of the metal roof damage detection method based on multi-environment coupling loading includes but is not limited to at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the metal roof damage detection method based on multi-environment coupling loading can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms, etc. basic cloud computing services.

[0070] Reference Figure 1 A flowchart of a metal roof damage detection method based on multi-environment coupling loading provided by an embodiment of the present application is shown. In this embodiment, the metal roof damage detection method based on multi-environment coupling loading includes:

[0071] S1, determining the six-dimensional boundary constraint condition of the metal roof by the structure parameter in the pre-acquired roof parameter.

[0072] In the embodiments of the present application, the structure parameter is a data set describing the characteristics of the metal roof support structure, covering the geometric position of the support, the constraint type, and other information related to the roof structure support; the six-dimensional boundary constraint condition refers to the use of a six-dimensional force feedback mechanical arm that can dynamically simulate the actual constraint state of the roof support under different load combinations. Under the action of complex environmental loads, the constraint state of the roof support has an important influence on the stress performance and damage evolution of the roof.

[0073] In the embodiments of the present application, the six-dimensional boundary constraint condition of the metal roof is determined by the structure parameter in the pre-acquired roof parameter, including:

[0074] extracting support geometric coordinates and constraint types in the structure parameters;

[0075] calculating a spatial position matrix of the metal roof support based on the support geometric coordinates;

[0076] generating a degree of freedom constraint vector of the metal roof support according to the constraint types;

[0077] adjusting the spatial position matrix and the degree of freedom constraint vector by presetting the motion parameters of the six-dimensional force feedback mechanical arm, to obtain a spatial position matrix and a degree of freedom constraint vector conforming to the metal roof structure;

[0078] fusing the spatial position matrix and the degree of freedom constraint vector conforming to the metal roof structure into six-dimensional boundary constraint conditions.

[0079] In detail, the support geometric coordinates are the specific position coordinates of the support in space, and the constraint types refer to the restriction of the support on the roof in different directions, such as fixed constraint or hinged support constraint, etc., that is, the three-dimensional space coordinates of the support mounting point are read from the structure parameters and the constraint types (for example, fixed / hinged / slid), which can accurately position the support space position and provide input data for the constraint equation, solving the boundary distortion problem caused by positioning error in traditional methods; the spatial position matrix is a matrix form obtained by mathematical calculation of the three-dimensional coordinates of the support, which can reflect the spatial distribution relationship of the support, that is, a 6x6 transformation matrix is constructed based on the support geometric coordinates, including translation and rotation components, through the homogeneous coordinate transformation formula: wherein is a rotation matrix, is a translation vector, that is, for each support, the rotation state of the support in space is determined, and the rotation state is represented by the rotation matrix , the rotation matrix is a 3x3 matrix, and its elements are calculated according to the rotation angle of the support around axis, and by multiplying the rotation matrices of each axis, a comprehensive rotation matrix is obtained; the translation vector is a 3x1 vector, and its components are the translation amounts of the support in axis direction relative to the reference point, that is, the three-dimensional coordinates of the support are directly determined, , then the spatial position matrix is obtained according to the rotation matrix and the translation vector.

[0080] Specifically, the degree of freedom constraint vector is used to represent the restriction of the support to the roof in six degrees of freedom (translation along the x, y, z axes and rotation around the x, y, z axes) in the form of a vector, for example, a certain support is fixed constraint, and the constraint vector components in the six degrees of freedom are all 1, indicating complete restriction; the six-dimensional force feedback mechanical arm has high-precision repeat positioning capability, and the repeat positioning accuracy can reach ±0.05mm, can accurately simulate the displacement, rotation and stress of the roof support, and by adjusting the motion parameters of the mechanical arm, the device can meet the installation requirements of flat roof and curved roof, and ensure that the test device can adapt to different types of metal roof structures, for example, the mechanical arm feeds back the actual displacement deviation , then the modified translation vector is obtained to eliminate the installation error and make the virtual model consistent with the physical entity, and the boundary conditions of the roof structure in actual use can be truly reflected; the modified spatial position matrix is combined with the degree of freedom constraint vector to form complete constraint conditions.

[0081] Further, under the action of complex environmental loads, the constraint state of the roof support has an important influence on the stress performance and damage evolution of the roof. By dynamically simulating the actual constraint state of the support, the performance of the metal roof under actual working conditions can be more accurately evaluated, high-precision boundary input is provided for the digital twin model, and the reliability of subsequent multi-environment coupling loading is supported.

[0082] S2, constructing a digital twin model of the metal roof according to the attribute parameters in the roof parameters and the six-dimensional boundary constraint conditions.

[0083] In the embodiment of the application, the attribute parameters are data describing the material characteristics and physical properties of the metal roof, such as the elastic modulus and Poisson's ratio of the material; the digital twin model refers to a digital replica constructed in a virtual space, which is highly consistent with the actual metal roof in physical properties, structural characteristics, behavior performance and the like, and is a dynamic model integrating material characteristics, structural parameters, boundary constraint conditions and response laws under the action of multi-environment loads and the like.

[0084] In the embodiment of the application, the digital twin model of the metal roof is constructed according to the attribute parameters in the roof parameters and the six-dimensional boundary constraint conditions, including:

[0085] Analyzing the material constitutive relation in the attribute parameters, and generating an elastic modulus matrix of the metal roof according to the material constitutive relation;

[0086] Converting the six-dimensional boundary constraint conditions into finite element node constraint equations;

[0087] Based on the preset geometric model and the elastic modulus matrix, an initial roof topology grid of the metal roof is generated through reverse parameterization mapping;

[0088] The finite element node constraint equation is embedded into the boundary nodes of the initial roof topology grid to generate a roof dynamics model of the metal roof;

[0089] The material parameters of the roof dynamics model are corrected by using a preset freeze-thaw cycle coefficient, and a digital twin model of the metal roof is output.

[0090] In detail, the material constitutive relation is a mathematical expression describing the relationship between stress and strain of the material. By analyzing the material constitutive relation in the attribute parameters (such as aluminum alloy: elastic modulus 70 GPa, Poisson's ratio 0.33), the elastic modulus, Poisson's ratio, and shear model in the material constitutive relation are extracted. Based on the elastic modulus, Poisson's ratio, and shear model, an orthotropic matrix is constructed. The orthotropic matrix is determined as the elastic modulus matrix, i.e., the elastic modulus matrix is a matrix used to describe the elastic properties of the material in different directions, which can reflect the deformation characteristics of the material under stress, and digitize the mechanical properties of the material, solving the problem of ignoring material anisotropy in traditional models. In finite element analysis, boundary conditions need to be applied to the nodes of the model in the form of equations. The six-dimensional boundary constraint condition contains spatial position and degree of freedom constraint information. Through mathematical conversion method, these information is converted into constraint equation of each node in corresponding degree of freedom, for example, the displacement of a node in x direction is fixed, the corresponding constraint equation is that the x direction displacement of the node is equal to 0, for example, the finite element node equation is wherein is the stiffness matrix, is the node displacement vector, is the node force vector, realizing direct conversion of physical constraints to numerical model and guaranteeing the authenticity of model boundary.

[0091] Specifically, the preset geometric model refers to a rough geometric shape model constructed according to the design drawings or actual size of the metal roof, and the reverse parameterization mapping is a method of generating detailed grid by known parameters and model relationship. Combined with the material properties reflected by the elastic modulus matrix, the preset geometric model is refined into the initial roof topology grid by using this method. The density of the grid can be set according to the material properties and analysis requirements to ensure the accuracy and efficiency of subsequent analysis, i.e., based on the BIM geometric model (such as Rhino curved surface model), the elastic modulus matrix is bound to the grid element through reverse parameterization mapping, NURBS curved surface reconstruction algorithm is adopted to ensure that the grid nodes correspond to the material properties one by one, and the grid distortion problem of complex curved surface roof is solved. Further, constraint equations are applied to the boundary nodes of the topology grid, for example, the fixed hinge support node is applied , realize "geometric-material-constraint" full coupling, improve the accuracy of the dynamic model, and the boundary node refers to the node located at the edge of the metal roof and subjected to constraint, and the obtained finite element node constraint equation is applied to the boundary node, so that the model can correctly reflect the boundary restriction condition of the metal roof. The roof dynamic model is a model capable of describing the motion and stress state of the metal roof under dynamic load, which comprehensively considers material properties, geometric shape and boundary conditions and the like.

[0092] Further, in actual engineering, the metal roof will be affected by environmental factors such as freeze-thaw cycle, resulting in changes in material properties. The preset freeze-thaw cycle coefficient is a coefficient for correcting material parameters determined according to previous experimental data and engineering experience. Then the elastic modulus is dynamically corrected by the formula: Dynamic correction of elastic modulus, wherein is the number of freeze-thaw cycles, is calibrated by -40 DEG C to 80 DEG C temperature control experiment, is a material parameter, is the corrected material parameter, so that the model can more truly reflect the performance changes of the metal roof in the actual environment, and the finally generated digital twin model can accurately simulate various behaviors of the metal roof.

[0093] S3, generating an environmental simulation unit of multi-dimensional environmental factors corresponding to the digital twin model according to the preset engineering test requirements.

[0094] In the embodiment of the application, the engineering test requirements refer to the environmental condition requirements that need to be simulated according to the actual engineering situation and test purposes; the multi-dimensional environmental factors refer to various environmental load factors that may act on the metal roof, such as wind load, water load, temperature load and snow load; and the environmental simulation unit is a unit capable of simulating various environmental load actions in the digital twin model, for example, a wind load simulation unit, a water load simulation unit, a temperature load simulation unit and a snow load simulation unit.

[0095] In the embodiment of the application, the environmental simulation unit of multi-dimensional environmental factors corresponding to the digital twin model is generated according to the preset engineering test requirements, and includes:

[0096] Analyzing the environmental factor types in the engineering test requirements;

[0097] Matching the environmental factor types with preset physical field simulation rules to obtain a rule mapping relationship;

[0098] Configuring the loading logic of wind load, water load, temperature load and snow load in the multi-dimensional environmental factors based on the rule mapping relationship;

[0099] The loading logic is associated to the digital twin model to generate an environment simulation unit.

[0100] In detail, the environment type code (such as typhoon working condition code TY01) in the engineering test requirement is identified, and the load types defined in the specification are matched: wind (W), rain (R), temperature (T), and snow (S), that is, by analyzing the engineering test requirement, the specific environmental factors that need to be considered are determined, for example, in some areas, the wind load and temperature load have greater influence on the metal roof, so the environment factor type mainly includes the two; the preset physical field simulation rule refers to the rule and method prepared in advance for simulating the physical action process of different environmental factors, for example, the simulation rule of wind load may involve the setting method of wind speed, wind direction, and wind pressure distribution parameters, the identified environment factor type is corresponded to these rules one by one, the simulation rule applicable to each environment factor is determined, a rule mapping relationship is formed, for example, wind load: k-ε turbulence model is adopted, rain load: SPH particle injection algorithm is adopted.

[0101] Specifically, the loading logic refers to the rules of the way, time sequence, and intensity change of various environmental loads applied to the metal roof. For example, the loading logic of wind load can be applied by a variable frequency fan according to a certain wind speed time curve through PID control, the water load can simulate the change of roof water during the rainfall process; the loading logic of temperature load can adjust the temperature change by adjusting the angle of liquid nitrogen injection through infrared feedback, and then the specific loading logic of each load is determined according to the rule mapping relationship, and the loading logic is associated to the digital twin model to generate an independently callable simulation unit instance, for example, wind load unit = {control object: variable frequency fan, control algorithm: PID, target parameter: wind speed 45 m / s}. By establishing the relationship between the loading logic and the digital twin model, the environmental load can act on the model according to the set logic, and the environment simulation unit is a unit that can simulate the action of multiple environmental factors on the metal roof, and can reproduce the influence of the metal roof under different environmental conditions in the digital twin model.

[0102] Exemplarily, the wind load simulation unit: adopt programmable wind tunnel, wind speed range is 0-60m / s, through PID algorithm adjusts variable frequency fan, wind speed control precision can reach ±0.8m / s.Water load simulation unit: including rotatable spray matrix, particle size controllable hail generator and drainage slope self-adaptive adjustment platform.Spray matrix can simulate the rainstorm condition of rainfall intensity 0-300mm / h, hail generator can generate particle size controllable hail, and drainage slope adjustment platform can adapt to the roof drainage demand of different slopes.Temperature load unit: adopt liquid nitrogen circulating temperature control box, temperature range is-40℃ to 80℃, through infrared thermal imager feedback adjusts liquid nitrogen injection angle, and temperature gradient control precision can reach ±1.2℃ / m.Snow load simulation unit: adopt hydraulic snow load simulator, load range is 0-2.5kPa, and different thickness and density of snow can be simulated to the pressure of roof.

[0103] Further, based on the digital twin model constructed above, an environment simulation function is added to solve the problem that multiple environmental factors cannot be comprehensively simulated in the background technology, and to provide a basis for subsequent load analysis.

[0104] S4, generate a coupling load spectrum corresponding to the environment simulation unit according to the preset engineering actual demand.

[0105] In the embodiment of the application, the coupling load spectrum refers to a spectrum that can comprehensively reflect the interaction relationship between multiple environmental loads in time and intensity, and embodies the coupling effect between different loads, which is an effective description of the complex load conditions of the metal roof in actual engineering.

[0106] In the embodiment of the application, the coupling load spectrum corresponding to the environment simulation unit is generated according to the preset engineering actual demand, comprising:

[0107] Extracting the load time sequence relationship corresponding to the environment simulation unit in the engineering actual demand;

[0108] Phase aligning wind load, water load, temperature load and snow load in the multi-dimensional environmental factors based on the load time sequence relationship, to obtain aligned load;

[0109] Fusing the intensity variation curve of the aligned load using a preset convolution algorithm to obtain an intensity fusion curve;

[0110] Extracting the coupling load spectrum corresponding to the environment simulation unit according to the intensity fusion curve.

[0111] In detail, the engineering actual demand refers to various working conditions and load conditions that the metal roof may encounter in actual use; the load time sequence relationship refers to the sequence and duration of different environmental loads in the action time, etc. For example, in actual engineering, wind load may occur first, followed by water load generated with rainfall, and temperature also changes constantly. By extracting these information, the time correlation of various loads is determined, for example, the time-load relationship curve is parsed from the engineering actual demand, such as the wind speed-rainfall time sequence data when a typhoon lands. Then, according to the load time sequence relationship, the time starting point and change rhythm of various loads are adjusted to keep consistent phase relationship on the time axis. For example, the action time starting points of wind load and water load are adjusted to the same position to ensure that they can act simultaneously or sequentially in the simulation process according to the actual situation, and the aligned load obtained can accurately reflect the synergistic effect of multiple loads in time.

[0112] Specifically, the intensity change curves of different loads are fused together, each load has its own intensity change curve with time, and the convolution operation is performed as follows: , wherein is the curve value of the intensity fusion curve at the moment, is the load value of the intensity change curve of the wind load at the moment, is the load value of the intensity change curve of the water load at the moment, is the load value of the intensity change curve of the temperature load at the moment, is the load value of the intensity change curve of the snow load at the moment, and then these curves are fused into an intensity fusion curve, which integrates the intensity information of multiple loads and reflects their coupled intensity change, and the envelope of can be extracted as a load spectrum and stored as a time-intensity two-dimensional array, thereby comprehensively describing the coupling effect of multiple environmental loads and providing accurate load basis for subsequent loading simulation of the metal roof.

[0113] Further, based on the generated environmental simulation unit, the coupled load spectrum is generated combined with the engineering actual demand, which can accurately describe the coupling effect of multiple loads and improve the accuracy of the stress analysis of the metal roof.

[0114] S5, based on the coupled load spectrum, performing hierarchical loading simulation operation on the environmental simulation unit in the digital twin model, and collecting real-time acoustic emission signals and real-time full-field strain data of the metal roof in the simulation operation.

[0115] In the embodiment of the present application, the hierarchical loading simulation operation refers to a simulation process of gradually applying loads to the digital twin model of the metal roof according to certain levels, by which the response of the metal roof under different load levels can be observed, and the stress change and damage evolution process thereof can be analyzed.

[0116] In the embodiment of the present application, the hierarchical loading simulation operation on the environment simulation unit in the digital twin model based on the coupled load spectrum comprises:

[0117] The coupled load spectrum is divided into incremental load stages corresponding to different simulation units;

[0118] Different environment simulation units are gradually activated according to the incremental load stages;

[0119] The loading rate of the activated environment simulation unit is dynamically adjusted by a preset closed-loop control algorithm;

[0120] The different environment simulation units are synchronously coupled and loaded according to the loading rate.

[0121] In detail, according to the load type and action characteristics corresponding to different environment simulation units, the coupled load spectrum is divided into multiple incremental load stages, each stage corresponding to a certain load intensity range, for example, for the wind load simulation unit, the load intensity can be divided into multiple stages from low to high, for example, 0-15 m / s is stage I, 15-30 m / s is stage II, and according to the divided incremental load stages, the corresponding environment simulation units are started in turn, so that each environment load acts on the digital twin model according to the set stage. For example, the first load stage of the wind load simulation unit is activated first, and the next load stage is activated after the simulation is stable, and the corresponding stages of other environment simulation units are activated as needed, that is, the modules are started and stopped by the PLC controller according to the stages, for example, the wind tunnel is started in stage I, and the spray matrix is started in stage II.

[0122] Specifically, the PID algorithm is used to adjust the load change rate in real time. The PID algorithm can adjust the control quantity in real time according to the feedback information of the system. During the loading process, the response of the model, such as strain, displacement, etc., is monitored, and the loading rate of the load is dynamically adjusted by using the PID algorithm. The set value is compared with the measured value, the loading rate is adjusted based on the deviation, the stability and accuracy of the loading process are ensured, and the simulation result is prevented from being distorted due to too fast or too slow loading. After adjusting the loading rate of each environmental simulation unit, they simultaneously apply loads to the digital twin model at their respective rates, realizing the synchronous coupling simulation of multiple loads. This simulation method can more realistically reflect the complex load conditions of the metal roof in actual engineering. The NTP protocol is used to synchronize the clocks of each module, and the wind-rain-temperature load is applied at the millisecond level.

[0123] For example, stage one: constant temperature 25℃, wind speed increases (0→45m / s, gradient 5m / s / 10min). The wind load is simulated by the wind tunnel, and the wind speed is gradually increased to 45m / s, while the temperature in the test cabin is kept constant at 25℃. Stage two: maintain 45m / s wind speed, simultaneously start rainstorm simulation (150mm / h) and temperature drop (25℃→-10℃, rate 5℃ / min). The rainstorm is simulated by the spraying matrix, and the temperature in the test cabin is lowered to-10℃ by the temperature control box. Stage three: under the action of the above wind, rain and temperature loads, the snow load simulator is started to apply a snow load of 0.5kPa to simulate the pressure of snow on the roof.

[0124] In the embodiment of the present application, during the test, the response data of the roof, including stress, strain, displacement and damage signals, are collected in real time by the acoustic emission sensor array and the DIC full-field strain measurement system. Based on the characteristic frequency, energy and frequency of the acoustic emission signal parameters, the damage state of the roof is judged in real time. The DIC system is used to monitor the full-field strain distribution of the roof in real time, and the deformation characteristics of the roof under the action of complex environmental loads are captured. When the test is terminated, the DIC system displays the misalignment at the panel joint of the roof. The real-time acoustic emission signal includes the characteristic frequency, energy and frequency of the emission signal, and the real-time full-field strain data refers to the strain distribution of the metal roof.

[0125] In the embodiment of the present application, the real-time acoustic emission signal and real-time full-field strain data of the metal roof in the simulation operation are collected, including:

[0126] The acoustic emission sensor array is arranged at the panel joint of the metal roof, and the speckle marker point array is arranged on the surface of the metal roof;

[0127] The stress wave signal of the metal roof in the simulation operation is captured by the sampling frequency of the acoustic emission sensor array, and the characteristic frequency component of the stress wave signal is extracted.

[0128] environmental noise filtering on the characteristic frequency component to obtain a real-time acoustic emission signal of the metal roof in the simulation operation;

[0129] displacement tracking on the marker points in the speckle marker point array to obtain a displacement trajectory;

[0130] calculating a local strain concentration degree of the metal roof in the simulation operation according to the displacement trajectory;

[0131] mapping the local strain concentration degree to the digital twin model to generate the real-time full-field strain data.

[0132] In detail, the plate joint is a part where the metal roof is prone to damage, and the acoustic emission sensor array can sensitively capture the stress wave signals generated at this part due to stress. The acoustic emission sensor array collects the stress wave signals generated by the metal roof during the loading process according to the set sampling frequency. The selection of the sampling frequency needs to be determined according to the possible stress wave frequency range to ensure that the signals can be completely captured. The collected stress wave signals are processed by methods such as fast Fourier transform to extract the characteristic frequency component that can reflect the damage condition of the metal roof. During the signal collection process, environmental noise will inevitably be mixed in. Through filtering algorithms such as wavelet transform filtering, the noise interference in the characteristic frequency component is removed to obtain a pure real-time acoustic emission signal. This signal can accurately reflect the damage evolution of the metal roof.

[0133] Specifically, the speckle marker point array is composed of a large number of randomly distributed micro markers, which facilitates tracking the displacement change of the metal roof surface by optical means. By shooting the speckle marker point array on the metal roof surface through a high-speed camera, the position change of each marker point is tracked by analyzing the images shot at different times by using a digital image recognition algorithm, so that the displacement trajectory of the marker point is obtained, which can reflect the deformation of the metal roof surface. Based on the digital image recognition algorithm, the strain values of each point on the metal roof surface are calculated by using the displacement trajectory of the marker point, and then by analyzing these strain values, the local strain concentration area is determined, and the local strain concentration degree is obtained, that is, based on the displacement data of each point, a local displacement matrix is constructed, according to the small deformation assumption in continuum mechanics, the strain components are calculated through the displacement gradient matrix, for the plane strain problem, the strain tensor is composed of the x-direction normal strain, the y-direction normal strain and the shear strain, the calculation formula is based on the partial derivative of displacement to coordinate (such as x-direction normal strain = (x-direction displacement-left point x-direction displacement) / two-point spacing), the strain components of each analysis unit are synthesized to obtain the principal strain value of the region, which is taken as the strain value of the point on the metal roof surface, and then the K-means clustering algorithm is used to cluster the strain values of all marker points, for example, the marker points with strain values > 1.5 times of the yield strain are classified into the same region, and the region is the local strain concentration area, which can reflect the stress concentration of the metal roof during loading, and is an important basis for judging whether the roof is damaged.

[0134] Further, by corresponding the calculated local strain concentration degree to the grid nodes of the digital twin model, the model can intuitively show the strain distribution of the metal roof during loading, and the generated real-time full-field strain data provides comprehensive strain information for subsequent analysis of the damage of the metal roof, and provides data support for accurately judging the damage of the roof.

[0135] S6, calculating a damage energy entropy value of the metal roof by using the real-time acoustic emission signal and the real-time full-field strain data, and determining the damage stage of the metal roof according to the damage energy entropy value.

[0136] In the embodiment of the present application, the damage energy entropy value is a physical quantity for quantitatively describing the damage degree of the metal roof, which comprehensively reflects the energy change and damage information reflected by the acoustic emission signal and strain data generated during the loading of the metal roof, and can effectively represent the damage state of the roof.

[0137] In the embodiment of the present application, the calculation of the damage energy entropy value of the metal roof by using the real-time acoustic emission signal and the real-time full-field strain data comprises:

[0138] decompose the real-time acoustic emission signal into a characteristic frequency offset, and normalize the characteristic frequency offset into a frequency domain damage factor;

[0139] extract a local strain gradient at a roof panel joint in the real-time full-field strain data;

[0140] dynamically couple the frequency domain damage factor and the local strain gradient by a preset material coefficient of the metal roof to obtain a weight coefficient of a load mode in each environmental simulation unit;

[0141] when the weight coefficient exceeds a preset critical value, take the weight coefficient as a failure threshold;

[0142] calculate a damage energy entropy value of the metal roof based on the weight coefficient, a real-time cycle number of the load mode, and the failure threshold.

[0143] In detail, the acoustic emission signal is subjected to FFT transformation to calculate a characteristic frequency offset rate, i.e., the real-time acoustic emission signal is decomposed by a signal processing method to obtain the characteristic frequency offset. Normalization processing is to convert the characteristic frequency offset to a fixed numerical range (such as between 0 and 1) to obtain a frequency domain damage factor, which can reflect the damage characteristics of the metal roof in the frequency domain. The roof panel joint is a sensitive area of damage, and the local strain gradient refers to the rate of strain change with position in the area. The local strain gradient at the roof panel joint is extracted by analyzing the real-time full-field strain data. The greater the gradient value, the more intense the strain change in the area, and the more likely the damage occurs.

[0144] Specifically, the material coefficient is a constant determined according to the characteristics of the metal roof material, which reflects the sensitivity of the material to acoustic emission signals and strain changes. Then, based on a dynamic coupling formula: wherein is the weight coefficient of the i-th load mode, is the frequency domain damage factor, is the local strain gradient, and is a material calibration coefficient, , , The material calibration coefficient makes the obtained weight coefficient reflect the contribution degree of different load modes to the damage of the metal roof. When the weight coefficient exceeds the preset critical value, the weight coefficient is taken as the failure threshold. The preset critical value is determined according to the ultimate bearing capacity of the metal roof material and the previous experimental data. When the weight coefficient exceeds the critical value, it indicates that the metal roof has approached or reached a failure state under the load mode. At this time, the weight coefficient is taken as the failure threshold for subsequent calculation of the damage energy entropy value. When the weight coefficient does not exceed the critical value, the failure threshold is set to 1.

[0145] Further, according to the acoustic emission signal and the DIC strain measurement result, a damage evolution model is established to calculate a damage energy entropy value : , wherein is the damage energy entropy value, is a weight coefficient of each load mode, is a current cycle number, is a failure threshold value, the model can comprehensively consider the influence of different environmental loads on the roof damage, and the damage stage and the remaining life of the roof can be accurately judged by monitoring the change of the damage degree in real time.

[0146] In the embodiment of the application, the damage stage includes a microscopic damage stage and a macroscopic damage stage.

[0147] In the embodiment of the application, the damage stage of the metal roof is determined according to the damage energy entropy value, including:

[0148] A mapping relationship between the damage energy entropy value and a preset yield strength of a metal material is constructed;

[0149] A two-stage early warning threshold value of the metal roof is determined according to the mapping relationship;

[0150] When the damage energy entropy value exceeds a primary threshold value in the two-stage early warning threshold value, a microscopic crack early warning is triggered, and the damage stage of the metal roof is determined as the microscopic damage stage according to the microscopic crack early warning;

[0151] When the damage energy entropy value exceeds a secondary threshold value in the two-stage early warning threshold value, a macroscopic damage early warning is triggered, and the damage stage of the metal roof is determined as the macroscopic damage stage according to the macroscopic damage early warning.

[0152] In detail, the preset yield strength of the metal material is a strength value when the metal roof material starts to deform plastically, and is an important index for judging whether the material is damaged. Through experiments and data analysis, a corresponding relationship between the damage energy entropy value and the yield strength is established, that is, when the damage energy entropy value reaches a certain value, the corresponding yield strength of the metal material changes accordingly, and then the two-stage early warning threshold value of the metal roof is determined according to the mapping relationship. The two-stage early warning threshold value includes a primary threshold value and a secondary threshold value. The primary threshold value corresponds to the damage energy entropy value at which the metal roof starts to have microscopic cracks, and the secondary threshold value corresponds to the damage energy entropy value at which the metal roof has macroscopic damage. Through the mapping relationship between the damage energy entropy value and the yield strength, the damage energy entropy values corresponding to the microscopic cracks and the macroscopic damage are found, which are determined as the two-stage early warning threshold value.

[0153] Specifically, the primary threshold value: D >= 0.3, indicating triggering micro-crack warning, which is the micro-damage stage; the secondary threshold value: D >= 0.8, indicating triggering macro-damage warning, which is the macro-damage stage.

[0154] As Figure 2 shown in the figure is a functional module diagram of a metal roof damage detection system based on multi-environment coupling loading provided by an embodiment of the application.

[0155] The metal roof damage detection system based on multi-environment coupling loading 100 can be installed in an electronic device. According to the functions implemented, the metal roof damage detection system based on multi-environment coupling loading 100 can include a six-dimensional boundary constraint condition analysis module 101, a digital twin model construction module 102, an environment simulation unit generation module 103, a coupling load spectrum generation module 104, a hierarchical loading simulation operation module 105, and a damage stage determination module 106. The modules of the application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0156] In this embodiment, the functions of each module / unit are as follows:

[0157] The six-dimensional boundary constraint condition analysis module 101 is used to determine the six-dimensional boundary constraint conditions of the metal roof by the structure parameters in the roof parameters obtained in advance;

[0158] The digital twin model construction module 102 is used to construct a digital twin model of the metal roof according to the attribute parameters in the roof parameters and the six-dimensional boundary constraint conditions;

[0159] The environment simulation unit generation module 103 is used to generate environment simulation units of multi-dimensional environmental factors corresponding to the digital twin model according to the preset engineering test requirements;

[0160] The coupling load spectrum generation module 104 is used to generate a coupling load spectrum corresponding to the environment simulation units according to the preset engineering actual requirements;

[0161] The hierarchical loading simulation operation module 105 is used to perform hierarchical loading simulation operations on the environment simulation units in the digital twin model based on the coupling load spectrum, and collect real-time acoustic emission signals and real-time full-field strain data of the metal roof in the simulation operation;

[0162] The damage stage determination module 106 is used to calculate the damage energy entropy value of the metal roof by using the real-time acoustic emission signals and the real-time full-field strain data, and determine the damage stage of the metal roof according to the damage energy entropy value.

[0163] In detail, each module in the metal roof damage detection system 100 based on multi-environment coupling loading in the embodiments of the present application adopts the same technical means as the metal roof damage detection method based on multi-environment coupling loading in the above Figures 1 to 2 and can produce the same technical effects, which will not be described here.

[0164] In the several embodiments of the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical functional division, and actual implementation can have another division manner.

[0165] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, i.e., can be located in one place or distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0166] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0167] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0168] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is not limited only by the above description, and therefore all changes within the meaning and scope of equivalent elements falling within the scope of protection are intended to be included in the present application.

[0169] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (Artificial Intelligence, AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0170] Furthermore, the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural and vice-versa, unless the context clearly requires these exclusions. The conjunction "or" is used to link items in a list or a set of alternatives, and is not disjunctive, unless the context clearly requires it to be disjunctive. The conjunction "and" is used to link items in a list or a set of alternatives, and is not conjunctive, unless the context clearly requires it to be conjunctive. The prefix "first", "second", "third", etc. is used to identify similar items and does not require or imply any actual temporal or chronological order among the items, unless the context clearly requires it.

[0171] Finally, it should be noted that the above-mentioned embodiments illustrate rather than limit the application, since the scope of the application is determined by the appended claims.

Claims

1. A method for detecting damage to metal roofs based on multi-environment coupled loading, characterized in that, The method includes: The six-dimensional boundary constraints of the metal roof are determined by using the structural parameters from the pre-acquired roof parameters. A digital twin model of the metal roof is constructed based on the attribute parameters in the roof parameters and the six-dimensional boundary constraints. Based on the preset engineering test requirements, an environmental simulation unit for multi-dimensional environmental factors corresponding to the digital twin model is generated. Generate the coupled load spectrum corresponding to the environmental simulation unit according to the preset actual engineering requirements; Based on the coupled load spectrum, a graded loading simulation operation is performed on the environmental simulation unit in the digital twin model, and real-time acoustic emission signals and real-time full-field strain data of the metal roof are collected during the simulation operation. The damage energy entropy value of the metal roof is calculated using the real-time acoustic emission signal and the real-time full-field strain data, and the damage stage of the metal roof is determined based on the damage energy entropy value.

2. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 1, characterized in that, The process of determining the six-dimensional boundary constraints of the metal roof using structural parameters from pre-acquired roof parameters includes: Extract the support geometric coordinates and constraint types from the structural parameters; Calculate the spatial position matrix of the metal roof support based on the geometric coordinates of the support; Generate the degree-of-freedom constraint vector of the metal roof support according to the constraint type; By adjusting the motion parameters of the preset six-dimensional force feedback robotic arm, the spatial position matrix and the degree-of-freedom constraint vector are obtained to obtain a spatial position matrix and degree-of-freedom constraint vector that conform to the metal roof structure. The spatial position matrix and degree-of-freedom constraint vectors that conform to the metal roof structure are integrated into six-dimensional boundary constraint conditions.

3. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 1, characterized in that, The construction of a digital twin model of the metal roof based on the attribute parameters in the roof parameters and the six-dimensional boundary constraints includes: The material constitutive relation in the attribute parameters is analyzed, and the elastic modulus matrix of the metal roof is generated based on the material constitutive relation; The six-dimensional boundary constraints are converted into finite element nodal constraint equations. Based on the preset geometric model and the elastic modulus matrix, the initial roof topology mesh of the metal roof is generated through inverse parameterization mapping; The finite element node constraint equations are embedded into the boundary nodes of the initial roof topology mesh to generate a roof dynamics model of the metal roof. The material parameters of the roof dynamics model are corrected using a preset freeze-thaw cycle coefficient, and a digital twin model of the metal roof is output.

4. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 1, characterized in that, Based on preset engineering test requirements, an environmental simulation unit for multi-dimensional environmental factors corresponding to the digital twin model is generated, including: Analyze the types of environmental factors in the engineering test requirements; The environmental factor types are matched with preset physical field simulation rules to obtain rule mapping relationships; The loading logic for wind load, water load, temperature load and snow load in the multidimensional environmental factors is configured based on the rule mapping relationship. The loading logic is associated with the digital twin model to generate an environment simulation unit.

5. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 4, characterized in that, The step of generating the coupled load spectrum corresponding to the environmental simulation unit according to the preset actual engineering requirements includes: Extract the load time sequence relationship corresponding to the environmental simulation unit in the actual engineering requirements; Based on the load time sequence relationship, the wind load, water load, temperature load and snow load in the multidimensional environmental factors are phase aligned to obtain the aligned load; The intensity variation curves of the aligned load are fused using a preset convolution algorithm to obtain an intensity fusion curve; The coupled load spectrum corresponding to the environmental simulation unit is extracted based on the intensity fusion curve.

6. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 1, characterized in that, The step of performing a graded loading simulation operation on the environmental simulation unit in the digital twin model based on the coupled load spectrum includes: The coupled load spectrum is decomposed into incremental load stages corresponding to different simulation units; Different environmental simulation units are activated step by step according to the described incremental load stage; The loading rate of the load in the activated environment simulation unit is dynamically adjusted by a preset closed-loop control algorithm. Synchronous coupling loading simulation operations are performed on different environmental simulation units according to the loading rate.

7. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 1, characterized in that, The real-time acoustic emission signals and real-time full-field strain data of the metal roof collected during the simulation operation include: An array of acoustic emission sensors is deployed at the joints of the metal roof panels, and an array of speckle markers is configured on the surface of the metal roof. The stress wave signal of the metal roof during the simulation operation is captured by the sampling frequency of the acoustic emission sensor array, and the characteristic frequency components of the stress wave signal are extracted. Environmental noise filtering is applied to the characteristic frequency components to obtain the real-time acoustic emission signal of the metal roof during simulation operation; Displacement tracking is performed on the marker points in the speckle marker array to obtain the displacement trajectory; The local strain concentration of the metal roof during the simulation operation is calculated based on the displacement trajectory. The local strain concentration is mapped onto the digital twin model to generate the real-time full-field strain data.

8. The method for detecting metal roof damage based on multi-environment coupled loading as described in claim 1, characterized in that, The calculation of the damage energy entropy value of the metal roof using the real-time acoustic emission signal and the real-time full-field strain data includes: The real-time acoustic emission signal is decomposed into a characteristic frequency offset, and the characteristic frequency offset is normalized into a frequency domain impairment factor. Extract the local strain gradient at the roof panel joints from the real-time full-field strain data; By dynamically coupling the frequency domain damage factor with the local strain gradient using the preset material coefficient of the metal roof, the weighting coefficient of the load mode in each environmental simulation unit is obtained. When the weighting coefficient exceeds a preset threshold, the weighting coefficient is used as a failure threshold. The damage energy entropy value of the metal roof is calculated using the weighting coefficient, the real-time cycle number of the load mode, and the failure threshold.

9. The method for detecting damage to metal roofs based on multi-environment coupled loading as described in claim 1, characterized in that, The step of determining the damage stage of the metal roof based on the damage energy entropy value includes: Construct a mapping relationship between the damage energy entropy value and the preset yield strength of the metallic material; The dual-level early warning threshold for metal roofs is determined based on the mapping relationship. When the damage energy entropy value exceeds the primary threshold of the dual-level early warning threshold, a microcrack early warning is triggered, and the damage stage of the metal roof is determined to be the micro-damage stage based on the microcrack early warning. When the damage energy entropy value exceeds the secondary threshold of the dual-level early warning threshold, a macroscopic damage early warning is triggered, and the damage stage of the metal roof is determined to be the macroscopic damage stage based on the macroscopic damage early warning.

10. A metal roof damage detection system based on multi-environment coupled loading, characterized in that, The system is used to perform the metal roof damage detection method based on multi-environment coupled loading as described in any one of claims 1-9, the system comprising: The six-dimensional boundary constraint analysis module is used to determine the six-dimensional boundary constraint conditions of the metal roof using the structural parameters in the pre-acquired roof parameters. The digital twin model construction module is used to construct a digital twin model of the metal roof based on the attribute parameters in the roof parameters and the six-dimensional boundary constraints. The environmental simulation unit generation module is used to generate environmental simulation units of multi-dimensional environmental factors corresponding to the digital twin model according to the preset engineering test requirements. The coupled load spectrum generation module is used to generate the coupled load spectrum corresponding to the environmental simulation unit according to the preset actual engineering requirements. The graded loading simulation operation module is used to perform graded loading simulation operation on the environmental simulation unit in the digital twin model based on the coupled load spectrum, and to collect real-time acoustic emission signals and real-time full-field strain data of the metal roof during the simulation operation. The damage stage determination module is used to calculate the damage energy entropy value of the metal roof using the real-time acoustic emission signal and the real-time full-field strain data, and to determine the damage stage of the metal roof based on the damage energy entropy value.

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