A method and device for monitoring deformation of a two-way large-span spatial combined steel grid

Abstract

The application discloses a kind of deformation monitoring method and device of two-way large-span space combined steel net rack, it is related to structural monitoring technical field, the method includes: obtaining the basic structure data and environmental action parameter of steel net rack, constructs combined steel net rack twin model, simulates stress-strain response under two-way load coupling.Identify abnormal deformation characteristics and generate deformation correction coefficient.Based on correction coefficient adjustment model boundary condition, iterative optimization dynamic monitoring threshold, establish hierarchical early warning mechanism, realize the structure health of steel net rack reminds.Solve the technical problems that the precision of existing large-span steel net rack deformation monitoring method is limited under complex environment coupling load, dynamic threshold poor adaptability, by constructing twin model and dynamic monitoring area division, combined with multi-source sensor data fusion and iterative optimization algorithm, reach the stress-strain response high-precision real-time monitoring under two-way load coupling, realize the technical effect of dynamic threshold hierarchical early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structure monitoring, and particularly relates to a deformation monitoring method and device for a bidirectional large-span spatial combined steel net rack. BACKGROUND

[0002] The existing large-span spatial steel net rack structure is widely applied in large public buildings such as stadiums and airport terminals due to its light and excellent bearing performance. However, such a structure is prone to problems such as stress concentration of a rod, displacement out of limit of a node or sudden change of deflection in the middle of a span due to the coupling of temperature gradient, wind load and vibration and other factors during construction and operation. Traditional monitoring methods rely on contact sensors and periodic detection, and have defects such as poor real-time performance and insufficient environmental adaptability. For example, the vertical direction accuracy of GPS is low, and a laser vibration meter needs strict vibration isolation conditions. In addition, existing structure health evaluation systems are mostly based on specification limits, and it is difficult to integrate finite element simulation and real-time monitoring data to realize dynamic threshold optimization, resulting in delayed abnormal deformation warning. Therefore, there is an urgent need for a deformation monitoring method that takes into account real-time performance, accuracy and environmental robustness.

[0003] In the related art, the deformation monitoring method for a large-span steel net rack has the technical problems of limited accuracy under complex environmental coupling loads (temperature, wind and vibration) and poor adaptability of dynamic thresholds. SUMMARY

[0004] The present application provides a deformation monitoring method and device for a bidirectional large-span spatial combined steel net rack, which solves the technical problems of limited accuracy of the existing deformation monitoring method for a large-span steel net rack under complex environmental coupling loads (temperature, wind and vibration) and poor adaptability of dynamic thresholds.

[0005] The present application provides a deformation monitoring method for a bidirectional large-span spatial combined steel net rack, comprising:

[0006] The basic structure data of the steel net frame component is acquired, including the geometric parameters of the rod, the connection type of the node, and the material mechanics performance parameters; the environmental action parameters including the temperature gradient distribution, the wind load time history data, and the vibration frequency spectrum are collected, and combined with the basic structure data, a combined steel net frame twin model is constructed to simulate the stress-strain response under the coupling action of the two-way load; at the same time, a dynamic monitoring area is divided in the combined steel net frame twin model; the collected real-time strain sensor data is compared and analyzed with the simulated stress-strain response, the abnormal deformation characteristics of the steel net frame component are identified, and a deformation correction coefficient is generated; according to the abnormal deformation characteristics and the deformation correction coefficient, the boundary conditions of the combined steel net frame twin model are adjusted, and the dynamic monitoring threshold of the steel net frame component is iteratively optimized; a hierarchical early warning mechanism is established with the optimized dynamic monitoring threshold to remind the structure health of the steel net frame component.

[0007] The application provides a deformation monitoring device for a bidirectional large-span spatial combined steel net frame, comprising:

[0008] The basic structure data acquisition module is used to acquire the basic structure data of the steel net frame component, including the geometric parameters of the rod, the connection type of the node, and the material mechanics performance parameters; the environmental action parameter acquisition module is used to collect the environmental action parameters including the temperature gradient distribution, the wind load time history data, and the vibration frequency spectrum, and combined with the basic structure data, a combined steel net frame twin model is constructed to simulate the stress-strain response under the coupling action of the two-way load; the dynamic monitoring area division module is used to divide a dynamic monitoring area in the combined steel net frame twin model at the same time; the abnormal deformation characteristic failure module is used to compare and analyze the collected real-time strain sensor data with the simulated stress-strain response, identify the abnormal deformation characteristics of the steel net frame component, and generate a deformation correction coefficient; the boundary condition adjustment module is used to adjust the boundary conditions of the combined steel net frame twin model according to the abnormal deformation characteristics and the deformation correction coefficient, and iteratively optimize the dynamic monitoring threshold of the steel net frame component; the hierarchical early warning mechanism construction module is used to establish a hierarchical early warning mechanism with the optimized dynamic monitoring threshold to remind the structure health of the steel net frame component.

[0009] The application provides a deformation monitoring method and device for a bidirectional large-span spatial combined steel net rack. First, basic structure data and environmental action parameters of the steel net rack are acquired, a combined steel net rack twin model is constructed, stress-strain responses under bidirectional load coupling are simulated, and a dynamic monitoring area is divided. By comparing real-time strain sensor data with the simulated responses, abnormal deformation features are identified and deformation correction coefficients are generated. Based on the correction coefficients, model boundary conditions are adjusted, dynamic monitoring thresholds are iteratively optimized, a hierarchical early warning mechanism is constructed, structural health of the steel net rack is reminded, and through new material detection technology (steel yield strength probability distribution analysis) combined with a standardized process (dynamic threshold calibration), high-precision real-time monitoring of stress-strain responses under bidirectional load coupling is achieved, and the technical effect of hierarchical early warning of dynamic thresholds is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, according to the needs, various steps can be processed in reverse order or at the same time. At the same time, other operations can be added to these processes, or one or more steps of operation can be removed from these processes.

[0011] Figure 1 A flowchart of a deformation monitoring method for a bidirectional large-span spatial combined steel net rack provided by the embodiments of the present application;

[0012] Figure 2 A structural schematic diagram of a deformation monitoring device for a bidirectional large-span spatial combined steel net rack provided by the embodiments of the present application.

[0013] Explanation of reference signs: basic structure data acquisition module 10, environmental action parameter acquisition module 20, dynamic monitoring area division module 30, abnormal deformation feature failure module 40, boundary condition adjustment module 50, hierarchical early warning mechanism construction module 60. DETAILED DESCRIPTION

[0014] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0015] In order to make the purposes, technical solutions and advantages of the present application clearer, the following will further describe the present application in conjunction with the accompanying drawings, the described embodiments should not be regarded as limitations to the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0016] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict, the term "first\second" referred to only distinguishes similar objects, and does not represent a specific order for the objects. The terms "include" and "have" and any variations, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices, unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art belonging to the technical field of the present application. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0017] The embodiments of the present application provide a deformation monitoring method for a two-way large-span spatial combined steel grid structure, as shown in Figure 1 The method comprises the following steps.

[0018] In step S100, the basic structure data of the steel grid structure member is acquired, including the geometric parameters of the rod, the connection type of the node and the material mechanics performance parameters. Specifically, when acquiring the basic structure data of the steel grid structure member, the process is planned first, and the acquisition order, tools, personnel arrangement and time estimation are determined. For the geometric parameters of the rod, the length is measured by a total station, a laser range finder, etc., the cross-sectional size is measured by a caliper and a three-dimensional laser scanner, etc., the shape is recorded by taking a picture and drawing a sketch, and the long rod is measured multiple times, the special-shaped rod is segmented or processed by using professional software. When determining the connection type of the node, the welding seam, bolt, rivet, etc. are observed on site, the design data is consulted to compare the actual and design requirements, and non-destructive testing is used to assist in judging the node that is difficult to observe. The material mechanics performance parameters are obtained by consulting the quality certificate, sampling and sending to a professional laboratory for tensile, compression, hardness, etc. test, and finally the parameters are sorted to establish a database containing the information of material type, specification, batch, performance and corresponding structure member position, etc., thereby providing support for subsequent analysis and monitoring.

[0019] In a possible implementation, the base structure data of the steel net rack component is acquired, including the geometric parameters of the rod, the connection type of the node, and the material mechanical property parameters, and step S100 further includes step S110 of acquiring the geometric topology structure of the combined steel net rack and extracting the rod length deviation and the node coordinate offset. Specifically, in the acquisition of the geometric topology structure of the combined steel net rack and the extraction of the related deviation data, a professional team is first established, and calibrated measurement tools such as a total station, a three-dimensional laser scanner, and a steel tape are prepared, and design drawings and other materials are collected. The geometric topology structure can be measured by a total station, and the total station is reasonably arranged around the steel net rack to accurately measure the three-dimensional coordinates of the nodes, the distances and angles between the nodes, and the average values are obtained by multiple measurements, and the data is arranged to construct the structure. The three-dimensional laser scanning method can also be used, and the scanning route and station are planned, and the point cloud data is scanned and obtained, and the structure is constructed by denoising, splicing, and segmentation. Then, the data constructed by field measurement is compared with the design drawings, the rod length deviation (actual length minus design length) and the coordinate offset (actual coordinate minus design coordinate) of the node in the X, Y, and Z directions are calculated, and finally the deviation data is arranged and analyzed to make charts to evaluate the compliance of the structure and the design.

[0020] In step S120, based on the geometric topology structure, the rod length deviation, and the node coordinate offset, the yield strength probability distribution function in the material mechanical property parameters is calibrated. Specifically, the geometric topology structure is analyzed, the connection relationship between each rod and node is determined, the rod length deviation and the node coordinate offset are corresponded to the atlas position, the spatial distribution rule and the correlation with the stress characteristics are analyzed, the influence mechanism of the structure deviation on the material mechanical property is evaluated, and the key area and parameter combination are determined. Then, based on the mechanical principle and the structure analysis method, the mechanical response of the steel net rack under different structure parameter deviation combinations is simulated by using the finite element software, and a preliminary mathematical relationship model of the yield strength and the structure parameter is established. Then, representative material samples are selected from the key area of the steel net rack, a tensile test is carried out, the stress-strain curve is accurately measured to determine the yield strength, the corresponding structure parameter data is recorded, and enough data points are obtained by repeating the experiment multiple times. Subsequently, statistical methods and data fitting techniques are used to fit the preliminary model, the form and parameters of the yield strength probability distribution function are determined by comparing the fitting degrees of different distribution functions, and after optimization such as residual analysis, the yield strength probability distribution function is applied to the actual steel net rack structure analysis, compared and verified with the field monitoring or actual cases, if there is deviation, it is rechecked and corrected, and finally used for structure reliability evaluation and the like.

[0021] In a possible implementation, based on the geometric topology structure, the rod length deviation, and the node coordinate offset, the yield strength probability distribution function in the material mechanical property parameters is calibrated, and step S120 further includes step S121 of calibrating the yield strength probability distribution function ; wherein, is the probability distribution function of yield strength, S is the yield strength of steel grid structure member, is the mean value of yield strength, is the standard deviation of yield strength. Specifically, from different batches, locations and stress states of steel grid structure members, the yield strength values of each sample are obtained through standard tensile test, and the mean value of yield strength is calculated (Using formula ) and the standard deviation (calculated according to ). Then through data fitting test, the parameters are substituted into the function by statistical software, the theoretical curve is compared with the actual data frequency histogram, and the chi-square test and other hypothesis testing methods are used to judge whether the function is suitable for describing the yield strength distribution of steel grid structure member. Finally, in the structural reliability analysis, the probability of the yield strength of the member being less than the critical value under the given load is calculated by using the function, and the reasonable safety factor is determined according to the function in the design stage, combining the importance and stress characteristics of the member, to realize the economic and safe balance of steel grid design.

[0022] Step S200, collect environmental action parameters including temperature gradient distribution, wind load time history data and vibration frequency spectrum, and combine the basic structure data to build a twin model of composite steel grid, and simulate the stress-strain response under the coupling action of two-way load. Specifically, when building a twin model of composite steel grid to simulate the stress-strain response under the coupling action of two-way load, first, carry out the environmental action parameter collection work. In terms of temperature gradient distribution collection, select appropriate sensors and arrange reasonably, record and store data in real time at a certain sampling frequency, and calibrate the sensors regularly; wind load time history data collection needs to install anemometer and wind vane, collect data at certain intervals, and obtain key parameters through spectrum analysis and statistical processing; vibration frequency spectrum collection needs to arrange vibration sensors at key nodes and members, collect and analyze signals at high sampling frequency to build frequency spectrum. At the same time, the obtained basic structure data of steel grid member is sorted and verified and organized according to the format. Then enter the model building link, select professional finite element software, create three-dimensional geometric model according to geometric topology, assign material properties and reasonably divide grid, then apply two-way load according to environmental parameters, set boundary conditions according to actual support, submit calculation task, draw cloud chart for analysis, compare with actual experience or specification, if there is deviation, then modify and recalculate, until the result is reasonable.

[0023] In one possible implementation, environmental action parameters including temperature gradient distribution, wind load time history data and vibration frequency spectrum are collected, and combined with the foundation structure data to construct a combined steel grid twin model to simulate the stress-strain response under the coupling action of two-way load. Step S200 further includes step S210 of arranging a distributed temperature sensor array to record the change rule of temperature gradient distribution over time. Specifically, first, suitable equipment such as fiber distributed temperature sensors is selected according to the monitoring requirements of the steel grid and the environment, the number is calculated according to the size of the grid, and then purchased from reliable suppliers, and the quality is carefully checked. Then study the grid structure drawing, plan in combination with gridding and key areas, arrange at a certain interval in the horizontal and vertical directions, especially for parts prone to temperature concentration or large gradient, draw detailed arrangement drawing. Clean the surface of the rod, prepare tools and train personnel, carefully lay the optical fiber according to the drawing, fix it with clamps, straps or glue, fuse the connection site, connect the data line and test. Then build a data acquisition system, select a compatible and powerful acquisition unit, install it in the right place, set the parameters, build a transmission network and debug. Finally, the system records temperature data in real time after starting, checks regularly, processes with data analysis software, constructs a temperature distribution matrix, calculates the temperature gradient, draws a curve over time, compares data at different times, seasons and weather, and summarizes the change rule to support steel grid structure analysis and maintenance.

[0024] Step S220, capture wind load time history data, and extract the dominant vibration frequency component through spectrum analysis. Specifically, first, select equipment such as ultrasonic anemometer that can accurately measure different wind speed ranges, has high precision and covers the maximum possible wind speed, and match high-sensitivity electronic wind vane. According to the height and shape of the steel grid, deploy at different height layers and multiple directions, such as setting points at high, medium and low parts and east, south, west and north directions. Connect the anemometer and wind vane to the multichannel data acquisition device with data line, equip with stable power supply, set the sampling frequency, storage format and other parameters, install data acquisition software and build transmission network. After starting the system, the device monitors the change of wind speed and direction in real time, the data acquisition device collects and stores at a certain frequency, checks the completeness and accuracy regularly, and compares with the data of the surrounding weather station. Then export the data, remove noise, outliers and standardize with professional software, select spectrum analysis methods such as fast Fourier transform, input the data into the software to generate a frequency spectrum graph combined with a suitable window function, find the frequency with the largest energy ratio, and if necessary, use frequency refinement technology to accurately identify the dominant vibration frequency, and then judge its rationality combined with experience and knowledge.

[0025] Step S230, based on the monitoring sensor network, synchronously collect the multi-point vibration acceleration signals of the combined steel grid structure, and construct a vibration frequency spectrum. Specifically, first, optimize the monitoring sensor network, determine the acceleration sensor installation position at the key nodes and main force members of the steel grid structure, check and eliminate interference with other sensors, and ensure stable and clear lines. Then, select wideband response and calibrated acceleration sensors, firmly install them with special bases, etc., ensure the correct direction of the sensitive axis, and check the connection. Then, build a system containing a multi-channel synchronous data acquisition card, configure the driver and software in the industrial control computer, set the synchronization trigger and channel parameters, and make good power and shielding. Start the system to collect data in real time, pay attention to data anomalies, back up regularly, and record environmental information. Finally, import the data into professional software for preprocessing, convert it to a frequency domain signal using fast Fourier transform algorithm, construct a vibration frequency spectrum and visualize it, and analyze the peak frequency to evaluate the steel grid structure condition.

[0026] In one possible implementation, environmental action parameters including temperature gradient distribution, wind load time history data and vibration frequency spectrum are collected, and combined with the basic structure data, a twin model of the combined steel grid structure is constructed to simulate the stress-strain response under the coupling action of bidirectional load. Step S200 further includes step S240, according to the geometric parameters of the members and the connection type of the nodes, an initial model architecture is established. Specifically, first, use total station, laser range finder, caliper and other equipment to accurately measure the geometric parameters of the steel grid members, calculate the length of the straight members by measuring the coordinates of the two endpoints, take the average of the cross-sectional dimensions by measuring multiple times, and fit the segmented measurements of curved or special-shaped members; determine the connection type of the nodes by observing and consulting the data, record the related details such as welds and bolts, and organize the data into detailed tables. Then, select modeling software according to the complexity of the steel grid structure and the analysis requirements, such as Tekla_Structures for conventional structures, ANSYS, ABAQUS, etc. for complex structures, open the software and set the unit system, display range, precision and coordinate system. Then create nodes according to the organized data, model according to the connection type, create members and set cross-sectional properties, check the geometric shape and topological relationship of the model from multiple angles, and make simple mechanical analysis if necessary, and repeatedly correct until the initial model architecture accurately reflects the actual structure of the steel grid.

[0027] Step S250, based on the initial model architecture, import the material mechanical property parameters, define the nonlinear contact boundary conditions and time-varying load parameters. Specifically, first obtain the steel grid material mechanical property parameters from the material supplier file, standard specification or laboratory test, such as the elastic modulus of Q345 steel, etc., and after sorting, create a material definition in the modeling software material library management module, and assign properties to the corresponding members and nodes according to the model architecture. Then analyze the steel grid to determine the nonlinear contact area, such as member connection, node and support, define the contact pair in the software contact analysis module, estimate the contact stiffness according to the formula, set the friction coefficient according to the surface condition, and select the appropriate algorithm. Finally, collect meteorological data to define temperature load parameters, create amplitude curves using the "Amplitude" module of the software to apply; obtain wind load data from meteorological departments, etc., combine with specification calculation parameters, and measured time history data is applied according to the formula; after determining the vibration source, define the vibration load in the corresponding module of the software according to its frequency, amplitude, etc., such as simple harmonic vibration or input seismic wave to simulate earthquake action.

[0028] Step S260, based on the nonlinear contact boundary conditions and time-varying load parameters, perform transient dynamics analysis and output the dynamic response cloud of the steel grid under the coupling action of temperature, wind load and vibration. Specifically, when performing transient dynamics analysis and outputting the dynamic response cloud of the steel grid based on nonlinear contact boundary conditions and time-varying load parameters, first carefully check the model to confirm that the member, node and material parameters are accurate, and the nonlinear contact and load parameter settings are reasonable, then select the transient dynamics analysis module of professional software such as ANSYS and ABAQUS. Next, set appropriate time steps according to the natural period of the steel grid and the load frequency, such as 0.01 seconds for a grid with a natural period of 0.5 seconds, set the total time according to the load duration, such as 35 minutes for a 30 minute strong wind action. Then select Newmark method or Wilson-θ method for solving algorithm and set related parameters, such as force convergence tolerance 0.001, displacement convergence tolerance 0.0001, maximum iteration 50-100 times for simple model, and 200-500 times or more for complex model. After completing the parameter setting, submit the task, pay attention to software running, log and computing resources during calculation, and extract displacement, stress and other dynamic response results from the software after analysis, and generate displacement, stress and strain dynamic response cloud map using post-processing function, which can be displayed as animation to evaluate the mechanical properties and safety of the steel grid.

[0029] Step S300, at the same time, the dynamic monitoring area is divided in the combined steel grid structure twin model. Specifically, when the dynamic monitoring area is divided in the combined steel grid structure twin model, first, the mechanical analysis and risk assessment of the steel grid structure under various working conditions are carried out by combining finite element analysis with engineering cases and industry standards, so as to determine the weak links and the risks of each part. According to this, the key monitoring parameters such as stress and strain and the accuracy requirements are determined, then the grid structure is divided into areas such as roof bearing area according to the structure function zoning, and the monitoring area is divided according to the risk level, while the spatial distribution uniformity is taken into account. Then, the monitoring points are reasonably set in each area, and appropriate sensors such as strain gauges and laser displacement are selected according to the parameters and accuracy. The wired or wireless data transmission network is built, the data processing software with functions such as filtering and storage is developed, and finally the sensors are calibrated regularly according to the standard, the maintenance system is established, the sensor installation, line and software operation are checked, and the long-term reliability of the monitoring system is ensured.

[0030] In one possible implementation, at the same time, the dynamic monitoring area is divided in the combined steel grid structure twin model, step S300 further includes step S310, the dynamic monitoring area includes high stress concentration area, node displacement sensitive area and mid-span deflection change area. Specifically, the dynamic monitoring area is determined in the combined steel grid structure twin model, first, the high stress concentration area is determined, the modeling is carried out by means of finite element software such as ANSYS and ABAQUS, the parameters are input and various load working conditions such as self weight, wind, snow and earthquake are applied, the high stress area is preliminarily locked through the stress nephogram, and then the judgment and correction are carried out by combining with similar engineering case data at home and abroad and expert experience, and the node is the key object of attention. Then, the node displacement sensitive area is determined, the node displacement under different loads is calculated by using the finite element software, the nodes with large displacement are analyzed and found out, such as the edge, the node bearing concentrated load and the key deformation position of the structure, and the influence of environmental factors such as surrounding vibration source and actual use conditions such as equipment installation on the node displacement is considered. Finally, the mid-span deflection change area is determined, the mid-span area of the large-span member is determined according to the principle of structural mechanics, the deflection is calculated by using formula theory, the simulation verification is carried out by means of finite element model, and the range near the mid-span is appropriately expanded considering the actual factors such as material non-uniformity.

[0031] Step S320, based on the stress distribution characteristics of the high stress concentration area, the node displacement sensitive area and the mid-span deflection change area in the dynamic monitoring area, a monitoring sensor network is laid out, which includes strain sensors, displacement sensors and acceleration sensors. Specifically, when laying out the monitoring sensor network, for the high stress concentration area, foil strain gauge sensors are selected, 3-4 are uniformly arranged every 5-10 cm around the weld seam of the welded node, 5 cm within the bolt hole near the bolt connection node, and every 8-10 cm along the edge of the node plate and the bar connection, and temperature self-compensating strain gauges or double-bridge measurement circuits are used to compensate the temperature influence. In the node displacement sensitive area, at least one laser displacement sensor or LVDT is installed for each node with large displacement change, which is mainly arranged at the edge, the key position of the structure deformation and the node bearing concentrated load, and is matched with a piezoelectric acceleration sensor installed at a suitable position near the node to monitor the vibration acceleration. In the mid-span deflection change area, displacement sensors are arranged every 1-2 meters along the mid-span area of the large-span bar, and acceleration sensors are installed at a suitable position nearby. When laying out, ensure that the sensors are installed firmly and the lines are reliable, and number and mark them, so as to build a comprehensive and efficient monitoring sensor network.

[0032] Step S400, the collected real-time strain sensor data is compared and analyzed with the simulated stress-strain response, the abnormal deformation characteristics of the steel grid structure member are identified, and the deformation correction coefficient is generated. Specifically, first, collect real-time strain sensor data and pre-process, and at the same time, obtain simulated stress-strain response data under the same working condition from the finite element model. Then match them in time and space, take strain difference, ratio, etc. as comparison indexes, calculate and draw comparison curves, and set threshold to screen abnormal data points. Then judge the abnormal deformation type combined with the characteristics of the steel grid structure, and determine the abnormal deformation position according to the sensor position. Then calculate the deformation correction coefficient according to the abnormal situation, such as correction based on material elastic modulus or structural stiffness, apply the coefficient to the model for reanalysis, adjust the coefficient to acceptable difference after comparison, and finally record the collected data, analysis results, abnormal characteristics and correction coefficient, etc. to generate a report containing analysis process, abnormal description, coefficient calculation and structure evaluation suggestion, which provides support for steel grid health monitoring and maintenance.

[0033] In a possible implementation, the collected real-time strain sensor data is compared and analyzed with the simulated stress-strain response, the abnormal deformation characteristics of the steel grid structure member are identified, and the deformation correction coefficient is generated, and step S400 further includes step S410, wavelet packet decomposition is performed on the real-time strain sensor data, and the energy distribution characteristics of different frequency bands are extracted. Specifically, wavelet packet decomposition is performed on the real-time strain sensor data, and the energy distribution characteristics of different frequency bands are extracted, and data preparation needs to be done in advance. The strain sensor needs to be accurately installed at the key position of the steel grid, and the data is collected by a stable collection system at an appropriate sampling frequency, and then the noise is removed by filtering, and the measurement difference and the influence of the order are eliminated by minimum-maximum or Z-score normalization. Then, wavelet packet decomposition is performed, appropriate wavelet basis functions such as Daubechies are selected according to the data characteristics and analysis purposes, the decomposition layer number is determined according to the data frequency range and analysis requirements, and then the decomposition operation is completed. Then, the energy distribution characteristics are extracted, the frequency bands are divided according to the equal bandwidth or logarithmic bandwidth, the energy values are calculated by summing the square of the wavelet packet coefficients of each frequency band, and then the energy values are normalized. Finally, the results need to be verified, cross-validation or comparison with other methods can be used, the characteristics need to be analyzed, and whether there is an abnormality in the energy distribution is observed, which provides a basis for steel grid health monitoring and fault diagnosis.

[0034] Step S420, the energy distribution characteristics of different frequency bands are matched and analyzed with the frequency components in the simulated stress-strain response, and the abnormal deformation characteristic matrix is configured. Specifically, the energy distribution characteristics of different frequency bands are matched and analyzed with the frequency components in the simulated stress-strain response, and the abnormal deformation characteristic matrix is configured. First, data preparation needs to be done. The energy distribution characteristic data of different frequency bands obtained by wavelet packet decomposition is arranged in order according to the frequency band; the finite element model of the composite steel grid is analyzed, the frequency components and corresponding energy values of the simulated stress-strain response are extracted by Fourier transform and sorted. Then, matching analysis is performed, the frequency ranges of the two are aligned first, and the range is made consistent by cutting or interpolation processing; then the similarity is calculated by selecting methods such as Euclidean distance and cosine similarity, and whether the difference is significant is judged according to the set threshold value and the related data is recorded. Finally, the abnormal deformation characteristic matrix is configured, the matrix structure is designed, the rows represent the monitoring positions, and the columns represent the frequency bands; the matching analysis results are filled in the matrix, and a specific value is set for the places where the difference is not significant; the matrix is optimized, such as data smoothing and outlier removal, and verified by methods such as comparison with known abnormalities and cross-validation. If the result is not good, adjust the parameters and configuration method to ensure that the matrix can effectively identify the abnormal deformation characteristics and provide a basis for steel grid health monitoring and fault diagnosis.

[0035] In step S430, based on the abnormal deformation feature matrix, the deformation correction coefficient is determined, and the deformation correction coefficient is used to adjust the boundary condition of the combined steel grid structure twin model. Specifically, the energy distribution characteristics of different frequency bands and the frequency components in the simulated stress-strain response are matched and analyzed, and the abnormal deformation feature matrix is configured. First, data acquisition and preprocessing are performed. Strain sensors are arranged at key positions of the steel grid structure to collect real-time strain data. After wavelet packet decomposition and normalization processing, energy distribution characteristics are obtained. At the same time, a finite element model is constructed to simulate the actual working condition. The frequency components and energy values of the time domain signal of the simulated stress-strain response are extracted by Fourier transform. Then, matching analysis is carried out. The frequency range and resolution of the two are unified. The Euclidean distance, cosine similarity and other methods are used to calculate the similarity. Whether it is abnormal is judged according to the set threshold value, and the information is recorded. Finally, the abnormal deformation feature matrix is configured. The number of rows corresponds to the number of monitoring positions, and the number of columns corresponds to the number of frequency bands. The element is the similarity index value. The matching analysis results are filled into the matrix, and then data smoothing, outlier removal and other optimization processing are performed. The matrix is evaluated by comparison with known abnormalities or cross-validation. If the result is not good, the parameters and configuration method are adjusted to ensure that the matrix effectively identifies abnormal deformation features and provides support for structural health monitoring and fault diagnosis.

[0036] In one possible implementation, the energy distribution characteristics of different frequency bands and the frequency components in the simulated stress-strain response are matched and analyzed, and the abnormal deformation feature matrix is configured. Step S420 further includes step S421. The energy distribution characteristics of different frequency bands and the frequency components in the simulated stress-strain response are matched and analyzed, and the abnormal deformation is identified in combination with the preset matching degree threshold. Specifically, to match and analyze the energy distribution characteristics of different frequency bands and the frequency components in the simulated stress-strain response and identify abnormal deformation in combination with the preset matching degree threshold, data preparation is needed first. Real-time strain data of the steel grid structure is collected by strain sensors, and energy distribution characteristics are obtained by wavelet packet decomposition, energy calculation and normalization. A finite element model is constructed to simulate stress, and frequency component data of the simulated stress-strain response are obtained by Fourier transform. Then, matching analysis is carried out. The frequency range and resolution of the two are unified. The Euclidean distance, cosine similarity and other methods are used to calculate the similarity and organize the results. The preset matching degree threshold is determined by historical data statistics or simulation test. The similarity obtained by matching analysis is compared with the threshold value. If the threshold value is exceeded, it is determined that the corresponding part of the steel grid structure has abnormal deformation and is identified and recorded. Finally, the results are verified by field inspection or comparison with other monitoring methods. If there is a false positive or false negative, the threshold value is adjusted and optimized to achieve accurate abnormal deformation identification and provide a basis for steel grid structure health monitoring.

[0037] Step S422, determine the spatio-temporal distribution characteristics of the abnormal deformation region through the abnormal deformation identification result. Specifically, by determining the spatio-temporal distribution characteristics of the abnormal deformation region through the abnormal deformation identification result, first, data integration and preprocessing are performed, abnormal deformation identification information is collected, sensor position data is associated, and an ordered time sequence is arranged. Then analyze the spatial distribution characteristics, use GIS or three-dimensional modeling software to visualize the abnormal position, use clustering and correlation analysis to determine the high-risk area and spatial relationship. Then carry out time distribution characteristic analysis, count the number of abnormal occurrences and frequency, use time series analysis to predict trends, and analyze the duration of abnormality. Then perform spatio-temporal comprehensive analysis, construct a spatio-temporal cube, mine spatio-temporal association rules, and analyze spatio-temporal evolution trends. Finally, verify the results, compare them with the results of field inspection or other monitoring methods, ensure accuracy, and generate a detailed report containing spatial, temporal, spatio-temporal comprehensive characteristics and verification results, to support steel grid structure health monitoring and maintenance decision-making.

[0038] Step S423, generate an abnormal deformation feature matrix containing location code, deformation level and evolution trend according to the spatio-temporal distribution characteristics of the abnormal deformation region. Specifically, by determining the spatio-temporal distribution characteristics of the abnormal deformation region through the abnormal deformation identification result, the following steps are required. First, data collection and integration, summarize abnormal deformation identification data, supplement steel grid structure, environment and use related data, clean and standardize data. Then analyze the spatial distribution characteristics, use GIS or visualization tools to display abnormal positions, and find out high-risk areas and spatial correlations through clustering and autocorrelation analysis. Then carry out time distribution characteristic analysis, draw time series chart, use clustering and trend analysis method to judge the time law and trend of abnormal deformation. Then perform spatio-temporal comprehensive analysis, construct a spatio-temporal cube, mine spatio-temporal association rules, and analyze spatio-temporal evolution trends. Finally, verify the results, use cross-validation or compare with other data sources to ensure accuracy, and explain the results in combination with the actual situation of steel grid to provide targeted suggestions for maintenance and management.

[0039] In step S500, the boundary conditions of the combined steel grid structure twin model are adjusted according to the abnormal deformation characteristics and the deformation correction coefficient, and the dynamic monitoring threshold of the steel grid structure component is iteratively optimized. Specifically, according to the abnormal deformation characteristics and the deformation correction coefficient, the boundary conditions of the combined steel grid structure twin model are adjusted and the dynamic monitoring threshold of the steel grid structure component is iteratively optimized. First, data preparation and evaluation are done, the abnormal deformation characteristics and the deformation correction coefficient are sorted out, and the existing twin model and the monitoring threshold are evaluated. Then, the boundary condition adjustment strategy is determined and implemented according to the data, and the model is re-run to obtain the response result. Then, the model responses before and after adjustment are compared, the error after adjustment is evaluated, and if the error is large, further adjustment is analyzed. Then, the monitoring threshold is updated based on the new model result, and its effectiveness is verified. If it is not ideal, it is iteratively adjusted and optimized. Finally, the final adjusted twin model and the optimized monitoring threshold are verified by multiple methods, the whole process is summarized, and a report containing the analysis results, adjustment steps, verification results and suggestions is written, providing a reference for steel grid structure health monitoring and maintenance management.

[0040] In one possible implementation, according to the abnormal deformation characteristics and the deformation correction coefficient, the boundary conditions of the combined steel grid structure twin model are adjusted, and the dynamic monitoring threshold of the steel grid structure component is iteratively optimized. Step S500 further includes step S510, based on the deformation correction coefficient, locally refining the grid density in the abnormal deformation area. Specifically, based on the deformation correction coefficient, the grid density in the abnormal deformation area is locally refined. First, data preparation and analysis are done, the deformation correction coefficient reflecting the difference between actual and simulated deformation is collected, the abnormal area is determined according to the abnormal deformation identification and the spatial and temporal distribution characteristics, and the correlation between the two is analyzed. Then, the grid refinement strategy is developed, the refinement standard is set according to the deformation correction coefficient, the grid type is selected according to the area characteristics, and the reasonable transition mode is determined to ensure the continuity of the grid. Then, the model is imported into the finite element analysis software, the local grid refinement is implemented in the abnormal area, and the overall grid is checked. After the refinement is completed, the model is re-analyzed and calculated, the error between the calculation result and the actual monitoring data is evaluated and the reason is analyzed, if the error is large, the strategy is adjusted and optimized. Finally, the refinement process and result are recorded, the optimized refined model is applied to the actual monitoring and analysis of the steel grid structure, and the model is continuously updated and optimized according to the feedback.

[0041] Step S520, at the same time, the confidence interval corresponding to the dynamic monitoring threshold is updated. Specifically, to update the confidence interval corresponding to the dynamic monitoring threshold, the actual monitoring data of the abnormal deformation region, the deformation correction coefficient, and the existing dynamic monitoring threshold and confidence interval information need to be collected first, and the data is cleaned and integrated. Then, the characteristics of the monitoring data are analyzed, statistical analysis and time series analysis are performed, the data distribution is judged, and the correlation between the deformation correction coefficient and the monitoring data is investigated. Then, the updating method is determined, which can use a method based on statistical theory or machine learning. Then, according to the selected method, the new confidence interval is calculated and the dynamic monitoring threshold is adjusted. The updating result is verified and evaluated through historical data and actual monitoring, and if the effect is not good, it is adjusted again. Finally, the updating process and result are recorded in detail, and the updated confidence interval and threshold are applied to the actual monitoring system, and the updating is regularly evaluated to adapt to the structural state change.

[0042] Step S600, a hierarchical early warning mechanism is established with the optimized dynamic monitoring threshold to remind the structural health of the steel grid structure member. Specifically, to remind the structural health of the steel grid structure member with the hierarchical early warning mechanism established with the optimized dynamic monitoring threshold, the optimized dynamic monitoring threshold needs to be combed and confirmed first to ensure its accuracy and reliability. Then, the hierarchical early warning mechanism is designed, the warning levels such as level one (low risk), level two (medium risk), and level three (high risk) are divided, the triggering conditions of each level are clarified, and the corresponding response measures are formulated. Then, the early warning system is built and integrated, the hardware equipment is prepared, the software is developed and integrated with the existing monitoring management platform, and the system is tested and debugged. When implementing the structural health reminder, real-time monitoring is performed, the reminder containing detailed information is timely issued after triggering the early warning, and the corresponding measures are handled and tracked. Finally, the accuracy, timeliness, and effectiveness of the response measures of the early warning mechanism are regularly evaluated, the triggering conditions and response measures are optimized and adjusted according to the results, the dynamic monitoring threshold is updated, and the early warning mechanism is ensured to adapt to the actual situation.

[0043] The embodiment of the present application adopts the method of obtaining the basic structural data and environmental action parameters of the steel grid structure, constructing a combined steel grid structure twin model, simulating the stress-strain response under the coupling of bidirectional load, and dividing the dynamic monitoring area. By comparing the real-time strain sensor data with the simulated response, the abnormal deformation characteristics are identified and the deformation correction coefficient is generated. Based on the correction coefficient, the model boundary conditions are adjusted, the dynamic monitoring threshold is iteratively optimized, the hierarchical early warning mechanism is established, the structural health of the steel grid structure is reminded, and the technical effect of high-precision real-time monitoring of the stress-strain response under the coupling of bidirectional load is achieved through the new material detection technology (steel yield strength probability distribution analysis) combined with the standardized process (dynamic threshold calibration).

[0044] In the foregoing, with reference to Figure 1 A deformation monitoring method for a bidirectional large-span spatial combined steel grid structure according to an embodiment of the present application is described in detail. Next, with reference toFigure 2 The application discloses a deformation monitoring device for a bidirectional large-span spatial combined steel net rack.

[0045] The deformation monitoring device for the bidirectional large-span spatial combined steel net rack solves the technical problems of limited precision and poor dynamic threshold adaptability of the existing large-span steel net rack deformation monitoring method under complex environment coupling loads, realizes high-precision real-time monitoring of stress-strain responses under bidirectional load coupling through new material detection technology (steel yield strength probability distribution analysis) combined with a standardized process (dynamic threshold calibration), and achieves the technical effect of dynamic threshold grading early warning. The deformation monitoring device for the bidirectional large-span spatial combined steel net rack comprises a basic structure data acquisition module 10, an environment action parameter acquisition module 20, a dynamic monitoring area division module 30, an abnormal deformation feature failure module 40, a boundary condition adjustment module 50 and a grading early warning mechanism construction module 60.

[0046] The basic structure data acquisition module 10 is used for acquiring basic structure data of steel net rack components, including geometric parameters of rod members, node connection types and material mechanical performance parameters.

[0047] The environment action parameter acquisition module 20 is used for acquiring environment action parameters including temperature gradient distribution, wind load time history data and vibration frequency spectrum, and combining the basic structure data to construct a combined steel net rack twin model to simulate stress-strain responses under bidirectional load coupling.

[0048] The dynamic monitoring area division module 30 is used for simultaneously dividing dynamic monitoring areas in the combined steel net rack twin model.

[0049] The abnormal deformation feature failure module 40 is used for comparing and analyzing real-time strain sensor data collected with simulated stress-strain responses, identifying abnormal deformation features of the steel net rack components, and generating a deformation correction coefficient.

[0050] The boundary condition adjustment module 50 is used for adjusting boundary conditions of the combined steel net rack twin model according to the abnormal deformation features and the deformation correction coefficient, and iteratively optimizing dynamic monitoring thresholds of the steel net rack components.

[0051] The grading early warning mechanism construction module 60 is used for establishing a grading early warning mechanism with the optimized dynamic monitoring thresholds, and reminding of structure health of the steel net rack components.

[0052] Hereinafter, the specific configuration of the infrastructure data acquisition module 10 will be described in detail. As described above, the infrastructure data of the steel grid structure members, including the geometric parameters of the members, the connection types of the nodes, and the material mechanical property parameters, the infrastructure data acquisition module 10 further comprises: a geometric topology acquisition unit for acquiring the geometric topology of the composite steel grid structure and extracting the member length deviation and the node coordinate offset; a yield strength probability distribution function calibration unit for calibrating the yield strength probability distribution function in the material mechanical property parameters based on the geometric topology, the member length deviation, and the node coordinate offset.

[0053] wherein the yield strength probability distribution function calibration unit further comprises: a yield strength probability distribution function subunit for calibrating the yield strength probability distribution function ; wherein, is the probability distribution function of the yield strength, S is the yield strength of the steel grid structure member, is the mean value of the yield strength, is the standard deviation of the yield strength.

[0054] Hereinafter, the specific configuration of the environment action parameter acquisition module 20 will be described in detail. As described above, the environment action parameters including the temperature gradient distribution, the wind load time history data, and the vibration frequency spectrum are collected, and the composite steel grid structure twin model is constructed in combination with the infrastructure data to simulate the stress-strain response under the coupling action of the bidirectional load, the environment action parameter acquisition module 20 further comprises: a change law recording unit for arranging a distributed temperature sensor array to record the change law of the temperature gradient distribution over time; a dominant vibration frequency component extraction unit for capturing the wind load time history data and extracting the dominant vibration frequency component through frequency spectrum analysis; a vibration frequency spectrum construction unit for synchronously collecting the multi-point vibration acceleration signals of the composite steel grid structure based on the monitoring sensor network to construct the vibration frequency spectrum.

[0055] The environmental action parameter acquisition module 20 further comprises: an initial model architecture construction unit, which is configured to construct an initial model architecture according to the bar geometric parameters and the node connection type; a material mechanics performance parameter import unit, which is configured to import the material mechanics performance parameters based on the initial model architecture, define a nonlinear contact boundary condition and a time-varying load parameter; and a transient dynamics analysis unit, which is configured to perform transient dynamics analysis based on the nonlinear contact boundary condition and the time-varying load parameter, and output a dynamic response cloud chart of the steel net rack under the temperature-wind load-vibration coupling effect.

[0056] Next, the specific configuration of the dynamic monitoring area division module 30 will be described in detail. As described above, while the dynamic monitoring area is divided in the combined steel net rack twin model, the dynamic monitoring area division module 30 further comprises: a dynamic monitoring area composition unit, which is configured to divide the dynamic monitoring area into a high stress concentration area, a node displacement sensitive area and a mid-span deflection change area; and a monitoring sensor network layout unit, which is configured to layout a monitoring sensor network based on the stress distribution characteristics of the high stress concentration area, the node displacement sensitive area and the mid-span deflection change area in the dynamic monitoring area, wherein the monitoring sensor network comprises strain sensors, displacement sensors and acceleration sensors.

[0057] Next, the specific configuration of the abnormal deformation feature failure module 40 will be described in detail. As described above, the real-time strain sensor data collected is compared and analyzed with the simulated stress-strain response to identify the abnormal deformation features of the steel net rack member and generate a deformation correction coefficient, and the abnormal deformation feature failure module 40 further comprises: an energy distribution feature extraction unit, which is configured to perform wavelet packet decomposition on the real-time strain sensor data to extract energy distribution features of different frequency bands; a matching analysis unit, which is configured to perform matching analysis on the energy distribution features of different frequency bands and the frequency components in the simulated stress-strain response to configure an abnormal deformation feature matrix; and a deformation correction coefficient determination unit, which is configured to determine the deformation correction coefficient based on the abnormal deformation feature matrix, wherein the deformation correction coefficient is used to adjust the boundary conditions of the combined steel net rack twin model.

[0058] The matching analysis unit further comprises: an abnormal deformation identification sub-unit, configured to perform matching analysis on the energy distribution characteristics of different frequency bands and the frequency components in the simulated stress-strain response, and perform abnormal deformation identification in combination with a preset matching degree threshold; a spatio-temporal distribution feature determination sub-unit, configured to determine the spatio-temporal distribution features of the abnormal deformation region through the abnormal deformation identification result; and an abnormal deformation feature matrix generation sub-unit, configured to generate the abnormal deformation feature matrix containing position encoding, deformation magnitude and evolution trend according to the spatio-temporal distribution features of the abnormal deformation region.

[0059] Next, the specific configuration of the boundary condition adjustment module 50 will be described in detail. As described above, the boundary condition of the combined steel grid structure twin model is adjusted according to the abnormal deformation features and the deformation correction coefficient, and the dynamic monitoring threshold of the steel grid structure member is iteratively optimized. The boundary condition adjustment module 50 further comprises: a local refined grid density unit, configured to locally refine the grid density in the abnormal deformation region based on the deformation correction coefficient; and a confidence interval updating unit, configured to update the confidence interval corresponding to the dynamic monitoring threshold at the same time.

[0060] The deformation monitoring device of the bidirectional large-span spatial combined steel grid structure provided in the embodiments of the present application can perform the deformation monitoring method of the bidirectional large-span spatial combined steel grid structure provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0061] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy mutual differentiation, and do not limit the protection scope of the present application.

[0062] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring the deformation of a two-way large-span spatial composite steel space frame, characterized in that, The method includes: Obtain basic structural data for steel grid structure components, including member geometric parameters, node connection types, and material mechanical property parameters; Environmental parameters, including temperature gradient distribution, wind load time history data, and vibration frequency spectrum, were collected and combined with the foundation structure data to construct a combined steel space frame twin model to simulate the stress-strain response under bidirectional load coupling. Meanwhile, dynamic monitoring areas are defined in the combined steel space frame twin model; The collected real-time strain sensor data is compared and analyzed with the simulated stress-strain response to identify abnormal deformation characteristics of the steel grid structure components and generate deformation correction coefficients. Based on the abnormal deformation characteristics and deformation correction coefficients, the boundary conditions of the combined steel space frame twin model are adjusted, and the dynamic monitoring thresholds of the steel space frame components are iteratively optimized. A graded early warning mechanism is established based on the optimized dynamic monitoring threshold to provide structural health reminders for the steel grid structure components; The dynamic monitoring area includes a high stress concentration area, a nodal displacement sensitive area, and a mid-span deflection change area. Based on the stress distribution characteristics of the high stress concentration area, nodal displacement sensitive area, and mid-span deflection change area in the dynamic monitoring area, a monitoring sensor network is deployed, which includes strain sensors, displacement sensors, and acceleration sensors.

2. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 1, characterized in that, The method for collecting environmental parameters, including temperature gradient distribution, wind load time history data, and vibration frequency spectrum, includes: Deploy a distributed temperature sensor array to record the change pattern of temperature gradient distribution over time; Capture wind load time history data and extract dominant vibration frequency components through spectrum analysis; Based on the aforementioned monitoring sensor network, multi-point vibration acceleration signals of the combined steel space frame are collected synchronously to construct a vibration frequency spectrum.

3. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 1, characterized in that, The method for obtaining basic structural data of steel grid structure components includes: Obtain the geometric topology of the composite steel space frame and extract the member length deviation and node coordinate offset; Based on the aforementioned geometric topology, member length deviation, and node coordinate offset, the probability distribution function of yield strength in the material's mechanical property parameters is calibrated.

4. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 3, characterized in that, The yield strength probability distribution function ; in, It is the probability distribution function of yield strength, where S is the yield strength of the steel mesh structure member. It is the mean yield strength. It is the standard deviation of yield strength.

5. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 3, characterized in that, Based on the aforementioned basic structural data, a twin model of the composite steel space frame is constructed. The method includes: An initial model architecture is established based on the geometric parameters of the members and the types of node connections; Based on the initial model architecture, the material mechanical property parameters are imported, and nonlinear contact boundary conditions and time-varying load parameters are defined. Based on nonlinear contact boundary conditions and time-varying load parameters, transient dynamic analysis is performed, and dynamic response cloud maps of the steel space frame under temperature-wind load-vibration coupling are output.

6. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 5, characterized in that, The method further includes comparing and analyzing real-time strain sensor data with simulated stress-strain responses to identify abnormal deformation characteristics of steel grid structure components and generating deformation correction coefficients. Wavelet packet decomposition was performed on the real-time strain sensor data to extract energy distribution features in different frequency bands; Matching analysis was performed on the energy distribution characteristics of different frequency bands with the frequency components in the simulated stress-strain response to configure the abnormal deformation characteristic matrix; Based on the abnormal deformation feature matrix, the deformation correction coefficient is determined, and the deformation correction coefficient is used to adjust the boundary conditions of the combined steel space frame twin model.

7. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 6, characterized in that, The method involves matching the energy distribution characteristics of different frequency bands with the frequency components in the simulated stress-strain response to configure an abnormal deformation feature matrix. The energy distribution characteristics of different frequency bands are matched with the frequency components in the simulated stress-strain response, and abnormal deformation is identified by combining the preset matching degree threshold. The spatiotemporal distribution characteristics of the abnormal deformation region are determined by the abnormal deformation identification results; Based on the spatiotemporal distribution characteristics of the abnormal deformation region, an abnormal deformation feature matrix containing location encoding, deformation magnitude, and evolution trend is generated.

8. The deformation monitoring method for a two-way large-span spatial composite steel space frame as described in claim 6, characterized in that, The method for iteratively optimizing the dynamic monitoring thresholds of steel grid structure components includes: Based on the deformation correction coefficient, the mesh density is locally refined in the abnormal deformation region; At the same time, the confidence interval corresponding to the dynamic monitoring threshold is updated.

9. A deformation monitoring device for a two-way large-span spatial composite steel space frame, characterized in that, The device is used to implement the deformation monitoring method for a two-way large-span spatial composite steel space frame according to any one of claims 1-8, and the device comprises: The basic structure data acquisition module is used to acquire the basic structure data of the steel grid structure components, including the geometric parameters of the members, the node connection type, and the material mechanical property parameters. An environmental action parameter acquisition module is used to acquire environmental action parameters including temperature gradient distribution, wind load time history data, and vibration frequency spectrum, and combine them with the foundation structure data to construct a combined steel space frame twin model to simulate the stress-strain response under bidirectional load coupling. A dynamic monitoring area division module is used to simultaneously divide a dynamic monitoring area in the combined steel space frame twin model. An abnormal deformation feature failure module is used to compare and analyze the collected real-time strain sensor data with the simulated stress-strain response, identify the abnormal deformation features of the steel grid structure components, and generate deformation correction coefficients. A boundary condition adjustment module is used to adjust the boundary conditions of the combined steel space frame twin model according to the abnormal deformation characteristics and deformation correction coefficient, and to iteratively optimize the dynamic monitoring threshold of the steel space frame components. A graded early warning mechanism construction module is used to establish a graded early warning mechanism based on optimized dynamic monitoring thresholds to provide structural health reminders for the steel grid structure components.

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