A steel structure inclination monitoring method, system, device and medium

Abstract

The application discloses a kind of steel structure inclination monitoring method, system, equipment and medium, including according to distribution map installation sensing device;According to the result of structural damage analysis, adjust the arrangement position of sensing device;Carry out inclination analysis and eigenvalue buckling analysis;Carry out stability analysis;Early warning.The application is analyzed according to the shape of steel structure electric power facility, structural defect, determines the arrangement position of sensing device, improves the precision of steel structure electric power facility information acquisition, can also identify deformation in time when structure appears deformation, and through the steel structure data obtained by sensing device, inclination analysis and stability analysis are carried out, and the collapse risk caused by the inclination of steel structure electric power facility is identified in time by inclination monitoring method and early warning is carried out.Solve the problem that existing steel structure inclination monitoring method cannot find the inclination problem of steel structure electric power facility in time and affect the operation safety of power system.

Description

Technical Field

[0001] This invention relates to the field of steel structure tilt monitoring technology, specifically to a steel structure tilt monitoring method, system, equipment, and medium. Background Technology

[0002] Steel structures, as a type of prefabricated structure with high strength, excellent plasticity and toughness, and a high degree of industrialization, are gradually becoming a key development area in the construction industry.

[0003] Steel structures are also widely used in power grid construction. Taking common power towers as an example, the use of steel structures in the construction of power towers allows for the filling of the outer surface with fiberglass, which plays a positive role in fire prevention, heat transfer, and effectively reduces the propagation of solid-borne sound. Therefore, steel structures are commonly chosen as the raw material for building power towers. In recent years, steel structures have also been gradually applied to the construction of substations: the first 220 kV steel structure substation in Quankou is expected to be put into operation in mid-December 2021; on November 4, 2022, Shenzhen's first steel structure substation, the 220 kV Yangfan Substation, was officially capped.

[0004] Power towers and substations, as crucial power transmission facilities, are increasingly important in terms of engineering construction and daily management. Monitoring the condition of power facilities is a vital part of power grid construction and forms the foundation for its safety and stability. Steel structures, a key material in the construction of power towers and substations, are constantly exposed to the external environment, and their working condition is affected by weather and environmental factors. Initially, the deformation range of the steel structure is small and cannot be detected by the naked eye. However, when the deformation reaches a certain extent, it causes the steel structure to tilt, which in turn causes power towers, substations, and other power facilities to tilt, thus posing a potential threat to the safe operation of the power grid.

[0005] Currently, the tilt of steel structures is typically monitored using instruments such as laser positioning devices, theodolites, triaxial positioning devices, or plumb bobs. These instruments are used to measure the tilt degree and rate of tilt from the top observation point relative to the bottom fixed point or from the upper layer relative to the lower observation point. However, these methods require manual operation. Given the large number of steel structure power facilities and the limited resources of the power industry, it is impossible to obtain timely information on the tilt status of these facilities, nor can the tilt be evaluated or addressed. When the tilt of a steel structure power facility reaches a certain degree, it may collapse, thereby affecting the power transmission system.

[0006] Therefore, long-term and stable monitoring of the status changes of steel structure power facilities to promptly identify and resolve problems and ensure the safe operation of the power system is an urgent issue that needs to be addressed. Summary of the Invention

[0007] The technical problem to be solved by this application is that existing steel structure tilt monitoring methods cannot monitor the state changes of steel structure power facilities in a long-term and stable manner, which leads to the inability to detect the tilting problem of steel structure power facilities in a timely manner, affecting the operational safety of the power system. The purpose is to provide a steel structure tilt monitoring method, system, equipment and medium, which solves the problem that existing steel structure tilt monitoring methods cannot detect the tilting problem of steel structure power facilities in a timely manner, thus affecting the operational safety of the power system.

[0008] This invention is achieved through the following technical solution:

[0009] The first aspect of this invention provides a method for monitoring the tilt of a steel structure, comprising the following steps:

[0010] S1. Obtain the distribution map of the sensing device based on the improved average modal kinetic energy model, install the sensing device according to the distribution map, and collect the first steel structure data through the sensing device;

[0011] S2. Based on the theory of redistribution of internal forces in steel structures, perform structural damage analysis on the first steel structure data, adjust the arrangement of the sensing device according to the results of the structural damage analysis, and collect the second steel structure data.

[0012] S3. Construct a steel structure model, input the second steel structure data into the steel structure model to perform tilt analysis and eigenvalue buckling analysis, and obtain the tilt value and buckling load of the steel structure;

[0013] S4. Combine the tilt value and buckling load of the steel structure to perform stability analysis and obtain the bearing capacity of the steel structure;

[0014] S5. Assess the load-bearing capacity of the steel structure and issue an early warning when the load-bearing capacity of the steel structure exceeds the load-bearing capacity threshold.

[0015] In the above technical solution, since there are many types of steel structures with different uses, the static and dynamic loads they are subjected to are also different. In order to reduce measurement errors under various factors, the steel structure is analyzed based on the improved average modal kinetic energy model. The distribution map of the sensing devices is obtained according to the characteristics of the steel structure, and the sensing devices are arranged according to the distribution map, so that the monitoring of the steel structure power facilities is more comprehensive and reasonable. The first steel structure data collected by the sensing devices improves the accuracy of steel structure information acquisition.

[0016] Because structural defects inevitably occur in steel structures during use, such as deviations in the curved shape of the steel structure, these defects can lead to deviations in the data from the sensing devices at various nodes. By analyzing the data of the first steel structure based on the theory of redistribution of internal forces in steel structures, damage to members sensitive to initial geometric defects in the structure can be identified, so that the sensing devices can promptly identify deformations in the structure.

[0017] Since not all tilting will cause the collapse of steel structure power facilities, when the sensor detects tilting of the steel structure, tilt analysis and eigenvalue buckling analysis are performed on the second steel structure data collected by the sensor to obtain the tilt value and buckling load of the steel structure. By combining the tilt value and buckling load of the steel structure for stability analysis, the bearing capacity of the steel structure is obtained, and thus the bearing capacity of the steel structure is judged. An early warning is only issued when the bearing capacity of the steel structure exceeds the bearing capacity threshold. This technical solution allows for long-term stable monitoring of the state changes of steel structure power facilities, timely detection and early warning of collapse risks caused by tilting, and avoids over-warning due to slight tilting.

[0018] In one alternative embodiment, the method for obtaining the distribution map of the sensing device based on the improved average modal kinetic energy model is as follows:

[0019] S11. Select the pre-installation node of the sensing device and calculate the modal kinetic energy of the pre-installation node;

[0020] S12. Construct a unit average modal kinetic energy model, and input the modal kinetic energy into the unit average modal kinetic energy model to perform kinetic energy contribution analysis, and obtain the arrangement scheme of the sensing device.

[0021] S13. Construct a stability model for the sensing device, input the arrangement scheme into the stability model for stability analysis, sort the arrangement schemes according to the stability analysis results, and draw a distribution diagram of the sensing device based on the arrangement scheme ranked first according to the stability analysis results.

[0022] In one alternative embodiment, after obtaining the arrangement of the sensing devices, the method further includes:

[0023] Signal-to-noise ratio analysis of the sensor arrangement scheme:

[0024]

[0025] Where W(i) is the signal-to-noise ratio of the i-th arrangement scheme, and C MKE MAC is the average modal kinetic energy of the unit. i Let be the modal guarantee criterion value for the i-th layout scheme;

[0026] Normalize the signal-to-noise ratio:

[0027]

[0028] Where S(i) represents the degree of visualization of the trend change of the i-th layout scheme, and W min To achieve the minimum signal-to-noise ratio, W max This represents the maximum signal-to-noise ratio.

[0029] In one alternative embodiment, the sensor device optimization model is as follows:

[0030] f MAC =min(MAC) i,j (i≠j)

[0031]

[0032] f y =max(Y i )

[0033]

[0034] f g =max(g(x))g(x)={h1,h2,...,h n-1}

[0035] Among them, f MAC The objective function for optimizing the model of the sensing device. f is the element-average modal kinetic energy function. y For the comprehensive information function, Y i For comprehensive information, h i f is the modal matrix function. g It is a robustness function.

[0036] In one optional embodiment, the method for performing structural damage analysis on the first steel structure data based on the theory of internal force redistribution in steel structures is as follows:

[0037] S21. Perform buckling analysis on the steel structure based on the first steel structure data to obtain the distribution of defects in the steel structure;

[0038] S22. Construct a sensitivity index model, and analyze the steel structure defect distribution by inputting the sensitivity index model to obtain the sensitive steel structure members;

[0039] S23. A damage model of the steel structure is constructed by simulating the damage of the sensitive steel structure members in the form of stiffness reduction.

[0040] S24. Based on the theory of redistribution of internal forces in steel structures, the damage model of the steel structure is simulated under load conditions to obtain a set of several sets of multi-overlapping members.

[0041] S25. Select the group of multiple overlapping members with a repetition count greater than the repetition threshold as the damage placement location of the sensing device.

[0042] In one alternative embodiment, after collecting the second steel structure data, the process further includes filtering the second steel structure data.

[0043] In one optional embodiment, the method for filtering the second steel structure data is as follows:

[0044] Collect wind load, wind speed, and noise from the sensing device;

[0045] When the wind load wind speed is greater than or equal to 15 m / s or the noise is greater than or equal to 1%, the second steel structure data collected by the sensing device is discarded.

[0046] A second aspect of this application provides a steel structure tilt monitoring system, comprising:

[0047] A sensor arrangement module is used to obtain a distribution map of the sensor devices based on an improved average modal kinetic energy model, install the sensor devices according to the distribution map, and collect first steel structure data through the sensor devices.

[0048] The structural damage analysis module is used to perform structural damage analysis on the first steel structure data based on the theory of redistribution of internal forces in steel structures, adjust the arrangement of the sensing device according to the results of the structural damage analysis, and collect the second steel structure data.

[0049] The steel structure model analysis module is used to construct a steel structure model, input the second steel structure data into the steel structure model to perform tilt analysis and eigenvalue buckling analysis, and obtain the tilt value and buckling load of the steel structure.

[0050] A stability analysis module is used to perform stability analysis by combining the tilt value and buckling load of the steel structure to obtain the load-bearing capacity of the steel structure.

[0051] The early warning module is used to determine the load-bearing capacity of the steel structure and issue an early warning when the load-bearing capacity of the steel structure exceeds the load-bearing capacity threshold.

[0052] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for monitoring the tilt of a steel structure.

[0053] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements a method for monitoring the tilt of a steel structure.

[0054] Compared with the prior art, this application has the following advantages and beneficial effects:

[0055] This application analyzes the shape and structural defects of steel structure power facilities to determine the placement of sensing devices, improving the accuracy of information acquisition for steel structure power facilities. It also enables the sensing devices to promptly identify deformation when it occurs. Furthermore, by performing tilt and stability analyses on the steel structure data acquired by the sensing devices, the application uses tilt monitoring methods to promptly identify and issue early warnings of collapse risks caused by tilting of steel structure power facilities. This solves the problem that existing steel structure tilt monitoring methods cannot promptly detect tilting issues in steel structure power facilities that affect the operational safety of the power system. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0057] Figure 1 A flowchart illustrating a steel structure tilt monitoring method provided in one embodiment of this application;

[0058] Figure 2 This is a schematic diagram of a steel structure tilt monitoring system provided in one embodiment of this application;

[0059] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0061] Example 1

[0062] Figure 1 The flowchart of a steel structure tilt monitoring method provided in Embodiment 1 of the present invention is as follows: Figure 1 As shown, the monitoring method includes the following steps:

[0063] Step S1: Obtain the distribution map of the sensing device based on the improved average modal kinetic energy model, install the sensing device according to the distribution map, and collect the first steel structure data through the sensing device.

[0064] In this embodiment, the sensing devices include, but are not limited to, tilt sensors, hydrostatic levels, inclinometers, surface strain gauges, force-balanced vibration sensors, load sensors, and displacement sensors. The monitoring method provided in this embodiment first requires installing the aforementioned sensing devices at suitable locations on the steel structure electrical facility. The first steel structure data consists of displacement data, load data, etc., collected through the aforementioned sensing devices.

[0065] The power system covers a large area and involves numerous steel-structured power facilities with power lines. Due to the physical properties of steel structures, a single sensor can only provide partial status information and cannot obtain the status information of the entire steel-structured power facility. To meet the requirements of tilt monitoring of the steel structure, multiple sensors are needed for status monitoring of the entire steel-structured power facility. However, too many sensors would lead to resource waste and increased costs. Therefore, it is necessary to analyze the steel-structured power facility to determine the optimal locations for installing the sensors.

[0066] Current methods for optimizing the location of sensors mainly include the effective independent method, modal kinetic energy method, and particle swarm optimization algorithm. However, sensors may fail during use, and traditional methods for optimizing the location of sensors cannot guarantee that the sensor network composed of the remaining monitoring points can still perform monitoring after a sensor fails.

[0067] This application improves upon the modal kinetic energy method by employing an improved average modal kinetic energy model to perform location and data analysis on steel structure power facilities, thereby obtaining a distribution map of the sensing devices. This approach not only acquires accurate data on steel structure power facilities but also saves costs and resources.

[0068] Furthermore, the specific steps for obtaining the distribution map of the sensing device based on the improved average modal kinetic energy model in step S1 are as follows:

[0069] Step S11: Select the pre-installation node of the sensing device and calculate the modal kinetic energy of the pre-installation node.

[0070] Since locations with larger amplitudes have higher modal kinetic energies, multiple pre-installed nodes are pre-selected from the steel structure power facility. The modal kinetic energies of these pre-installed nodes are calculated, and monitoring points with lower modal kinetic energies are deleted until the number of pre-installed nodes is the same as the number of nodes to be tested. The number of nodes to be tested can be determined based on data such as the volume and density of the steel structure used in the steel structure power facility; this application does not impose further restrictions.

[0071] The modal kinetic energy of the pre-installed node is calculated as follows:

[0072]

[0073] In the above formula, MKE is the modal kinetic energy, M is the mass matrix, and φ fsIt is an n*N modal matrix.

[0074] Step S12: Construct a unit average modal kinetic energy model, input the modal kinetic energy into the unit average modal kinetic energy model to analyze the kinetic energy contribution, and obtain the arrangement scheme of the sensing device.

[0075] To reduce interference between various sensors and obtain comprehensive monitoring data for steel structure electrical facilities using a limited number of sensors, it is necessary to ensure that the angle between modal vectors of each order is as large as possible. Therefore, the element-average modal kinetic energy model includes a modal guarantee criterion constructed by calculating the modal matrix. The expression of the modal guarantee criterion is as follows:

[0076]

[0077] In the above formula, Denotes the i-th mode vector. Let represent the j-th mode vector. To improve the accuracy of monitoring data, the mode guarantee criterion value needs to be minimized as much as possible.

[0078] The element-averaged modal kinetic energy model also includes modal kinetic energy contribution and element-averaged modal kinetic energy, expressed as follows:

[0079] GM = (MKE1, MKE2, MKE3, ..., MKE n )

[0080]

[0081] In the above formula, GM represents the modal kinetic energy contribution of the i-th selected node, and C... MKE It represents the average modal kinetic energy of the unit.

[0082] By judging the modal kinetic energy contribution of the selected measurement point layout scheme and calculating it, the unit average modal kinetic energy of the calculation result is used as the basis for evaluating the merits of the sensor layout scheme. The larger the unit average modal kinetic energy, the better the layout scheme.

[0083] By constructing a unit-average modal kinetic energy model using modal guarantee criteria, modal kinetic energy contribution, and unit-average modal kinetic energy, an optimal sensor arrangement can be obtained through modal kinetic energy calculation of the sensing device. Substituting the modal kinetic energy into the unit-average modal kinetic energy model for kinetic energy contribution analysis yields the optimal sensor arrangement.

[0084] In one alternative embodiment, after obtaining the arrangement scheme of the sensing devices, the method further includes: performing signal-to-noise ratio analysis on the arrangement scheme of the sensing devices and normalizing the signal-to-noise ratio.

[0085] In order to ensure that the sensor arrangement scheme is more adapted to the overall structure of the steel structure power facility, it is necessary to improve the signal-to-noise ratio of the sensor arrangement scheme so that the overall arrangement effect is better and can more comprehensively reflect the complete information data of the overall structure of the steel structure power facility.

[0086] Furthermore, the expression for signal-to-noise ratio analysis of the sensor arrangement scheme is as follows:

[0087]

[0088] Where W(i) is the signal-to-noise ratio of the i-th arrangement scheme, and C MKE MAC is the average modal kinetic energy of the unit. i Let be the modal guarantee criterion value for the i-th arrangement scheme.

[0089] Furthermore, the expression for normalizing the signal-to-noise ratio is as follows:

[0090]

[0091] Where S(i) represents the degree of visualization of the trend change of the i-th layout scheme, and W min To achieve the minimum signal-to-noise ratio, W max This represents the maximum signal-to-noise ratio.

[0092] Step S13: Construct a stability model for the sensing device, input the layout scheme into the stability model for stability analysis, sort the layout schemes according to the stability analysis results, and draw a distribution diagram of the sensing device based on the layout scheme ranked first according to the stability analysis results.

[0093] The placement of sensors affects the accuracy of monitoring data from steel structure power facilities. Even if a sensor malfunctions during operation, the remaining monitoring points must still function normally. To ensure the continued operation of the monitoring network when a sensor malfunctions, a sensor stability model is constructed, and the placement scheme is then incorporated into this model for stability analysis.

[0094] If the sensing device malfunctions during operation, the monitoring network composed of the remaining monitoring points will be unable to operate, and the arrangement scheme will be judged as unstable, with a low stability analysis result.

[0095] Based on the stability analysis results, the sensor layout is sorted. The layout scheme ranked first in the stability analysis results is used to draw a distribution diagram of the sensor devices. This distribution diagram meets the stability requirements, and the monitoring position in the distribution diagram is the optimal monitoring position.

[0096] Furthermore, the sensor optimization model is as follows:

[0097] fMAC =min(MAC) i,j (i≠j)

[0098]

[0099] f y =max(Y i )

[0100]

[0101] f g =max(g(x))g(x)={h1,h2,...,h n-1}

[0102] In the above formula, f MAC The objective function for optimizing the model of the sensing device. f is the contribution function of the unit average modal kinetic energy. y For the comprehensive information function, Y i For comprehensive information, h i f is the modal matrix function. g For robustness function

[0103] Among them, f MAC The higher the value, the better the stability analysis result, and the higher its ranking.

[0104] Step S2: Perform structural damage analysis on the first steel structure data based on the theory of redistribution of internal forces in steel structures, adjust the arrangement of the sensing device according to the results of the structural damage analysis, and collect the second steel structure data.

[0105] Since structural defects inevitably occur in steel structures during use, such as deviations in the curved shape of the steel structure, these defects can lead to deviations in the data from the sensing devices at various nodes. By analyzing the data of the first steel structure based on the theory of redistribution of internal forces in steel structures, damage to members sensitive to initial geometric defects in the structure can be identified, so that the sensing devices can promptly identify the failure of the structure in the event of progressive collapse.

[0106] In this application, structural damage analysis of the first steel structure data is performed based on the theory of redistribution of internal forces in steel structures, and the arrangement of the sensing device is adjusted according to the results of the structural damage analysis.

[0107] The second steel structure data obtained after adjusting the sensing device takes into account the damage to the steel structure more closely than the first steel structure data, making it closer to the actual state of the steel structure.

[0108] Furthermore, the structural damage analysis of the first steel structure data based on the theory of internal force redistribution in steel structures specifically includes the following steps:

[0109] Step S21: Perform buckling analysis on the steel structure based on the first steel structure data to obtain the distribution of defects in the steel structure.

[0110] Since steel-structured power facilities are defect-sensitive structures, initial geometric defects can degrade the stability or reduce the load-bearing capacity of a well-maintained steel-structured power facility. Even a minor initial failure can cause significant changes in the structural stress performance. To identify structural defects in steel-structured power facilities caused by stress, this application employs buckling analysis of the steel structure to obtain the defect distribution.

[0111] In step S1, the first steel structure data is obtained, including but not limited to displacement data, stress data, and tilt data.

[0112] Buckling analysis was performed on the steel structure power facility using the above data, and the lowest order buckling mode of the steel structure was used as the distribution of defects in the steel structure power facility.

[0113] Step S22: Construct a sensitivity index model, input the steel structure defect distribution into the sensitivity index model for analysis, and obtain the sensitive steel structure members.

[0114] The sensitivity index model is as follows:

[0115]

[0116] In the above formula, S i As a sensitivity index, σ i , σ0, σ′ i Let be the axial stress of the steel structure member. Substituting the distribution of defects in the steel structure into the sensitivity index obtained from the above formula, and sorting the sensitivity indices from largest to smallest, the steel structure member with the larger sensitivity index is more sensitive, can thus identify the sensitive steel structure members.

[0117] Step S23: Perform damage simulation on sensitive steel structure members in the form of stiffness reduction to construct a steel structure damage model.

[0118] Damage simulation of sensitive steel structure members is performed by stiffness reduction method to obtain the corresponding structural parameters of the sensitive steel structure members before and after damage, and a steel structure damage model is constructed based on these parameters.

[0119] Step S24: Based on the theory of redistribution of internal forces in steel structures, the damage model of the steel structure is simulated under load conditions to obtain a set of several sets of multi-overlapping members.

[0120] According to the study of the redistribution law of internal forces under damage in steel structure internal force redistribution theory, the position distribution and number of multiple overlapping members determined by the method of determining the internal force redistribution law in steel structure under damage condition will be affected by the load condition.

[0121] Due to the inelastic nature of steel structures, the relationship between internal forces in each section no longer follows the linear elastic relationship. Therefore, the set of multiple overlapping members will change under different degrees of wind load. In the embodiments of this application, the redistribution of internal forces in the steel structure is used to set different degrees of wind load on the sensitive members of the steel structure to obtain the multiple overlapping members under a certain wind load condition when the sensitive members of the steel structure are damaged.

[0122] By setting multiple wind load parameters on the same sensitive steel structural member, several sets of overlapping members can be obtained.

[0123] Step S25: Select the location of the damage arrangement of the sensing device from the set of several sets of multi-overlapping rods with a repetition number greater than the repetition threshold.

[0124] When the number of repetitions of multiple overlapping members exceeds a certain threshold, the more repetitions of the steel structure members, the more applicable the placement of the sensing device can be under the influence of wind load factors.

[0125] In one alternative embodiment, after collecting the second steel structure data, the process further includes filtering the second steel structure data.

[0126] Furthermore, the method for filtering the second steel structure data is as follows:

[0127] Collect wind load, wind speed, and noise from the sensing device;

[0128] When the wind load wind speed is greater than or equal to 15 m / s or the noise is greater than or equal to 1%, the second steel structure data collected by the sensor is discarded.

[0129] Among them, since the effect of steel structure damage identification is unstable when the wind load and wind speed are greater than or equal to 15m / s or the noise is greater than or equal to 1%, it will have a certain impact on the data of steel structure tilt monitoring. Therefore, when the wind load and wind speed are greater than or equal to 15m / s or the noise is greater than or equal to 1%, the second steel structure data collected by the sensor is discarded.

[0130] Step S3: Construct a steel structure model, input the second steel structure data into the steel structure model for tilt analysis and eigenvalue buckling analysis, and obtain the tilt value and buckling load of the steel structure.

[0131] The second steel structure data includes, but is not limited to, the tilt angle monitored by the tilt sensor, the load value monitored by the load sensor, and the stress value monitored by the stress sensor.

[0132] A steel structure model is constructed using finite element analysis software. The data of the second steel structure is then input into the steel structure model. The buckling load is calculated by the formula "buckling load = eigenvalue * load value". The eigenvalue is the eigenvalue of the first mode of vibration of the steel structure, and the load value is the load value monitored by the load sensor. The tilt value and buckling load of the members in the steel structure model can be obtained.

[0133] The above analysis can be completed using the finite element analysis software SAP2000.

[0134] Step S4: Combine the tilt value and buckling load of the steel structure to perform stability analysis and obtain the bearing capacity of the steel structure.

[0135] The stability of a steel structure does not increase with the increase of the tilt angle of the members. When the tilt angle of the members increases to a certain value, the steel structure is in an unstable state. Therefore, it is necessary to combine the tilt value and buckling load of the steel structure for comprehensive analysis to calculate the bearing capacity of the steel structure.

[0136] By plotting the load-displacement curve of the steel structure using its tilt value and buckling load, it can be seen that as the tilt value of the steel structure increases, its load displacement also increases, and the stiffness of the steel structure decreases as the load displacement increases.

[0137] The load-bearing capacity of a steel structure is directly proportional to its stiffness; therefore, calculating the stiffness of a steel structure is equivalent to calculating its load-bearing capacity.

[0138] The stiffness of the steel structure is calculated whenever the tilt value and buckling load change.

[0139] Step S5: Determine the load-bearing capacity of the steel structure and issue an early warning when the load-bearing capacity of the steel structure exceeds the load-bearing capacity threshold.

[0140] The load-bearing capacity threshold is determined comprehensively based on data such as the material, structure, and density of the specific steel structure power facility, and this application does not impose further restrictions on it.

[0141] Example 2

[0142] Figure 2 This is a structural schematic diagram of a steel structure tilt monitoring system provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown, the system includes: a sensor arrangement module, a structural damage analysis module, a steel structure model analysis module, a stability analysis module, and an early warning module.

[0143] The sensor arrangement module is used to obtain a distribution map of the sensor devices based on the improved average modal kinetic energy model, install the sensor devices according to the distribution map, and collect the first steel structure data through the sensor devices.

[0144] The structural damage analysis module is used to perform structural damage analysis on the first steel structure data based on the theory of redistribution of internal forces in steel structures, adjust the arrangement of the sensing devices according to the results of the structural damage analysis, and collect the second steel structure data.

[0145] The steel structure model analysis module is used to construct a steel structure model. The second steel structure data is then input into the steel structure model to perform tilt analysis and eigenvalue buckling analysis, thereby obtaining the tilt value and buckling load of the steel structure.

[0146] The stability analysis module is used to perform stability analysis by combining the tilt value and buckling load of the steel structure to obtain the load-bearing capacity of the steel structure.

[0147] The early warning module is used to determine the load-bearing capacity of the steel structure and to issue an early warning when the load-bearing capacity of the steel structure exceeds the load-bearing capacity threshold.

[0148] Example 3

[0149] Figure 3 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention, as shown below. Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the computer device can be one or more. Figure 3 Taking a processor 21 as an example; the processor 21, memory 22, input device 23, and output device 24 in an electronic device can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0150] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, thereby implementing the steel structure tilt monitoring method of Embodiment 1.

[0151] The memory 22 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 22 may further include memory remotely located relative to the processor 21, which can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0152] Input device 23 can be used to receive user input such as ID and password. Output device 24 is used to output the network configuration page.

[0153] Example 3

[0154] Embodiment 3 of the present invention also provides a computer-readable storage medium, wherein computer-executable instructions, when executed by a computer processor, are used to implement a steel structure tilt monitoring method as provided in Embodiment 1.

[0155] The storage medium containing computer-executable instructions provided in the embodiments of the present invention is not limited to the method operation provided in Embodiment 1, but can also perform related operations in the steel structure tilt monitoring method provided in any embodiment of the present invention.

[0156] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring the tilt of steel structures, characterized in that, Includes the following steps: S1. Obtain the distribution map of the sensing device based on the improved average modal kinetic energy model, install the sensing device according to the distribution map, and collect the first steel structure data through the sensing device; S2. Based on the theory of redistribution of internal forces in steel structures, structural damage analysis is performed on the first steel structure data. The arrangement of the sensing device is adjusted according to the results of the structural damage analysis, and the second steel structure data is collected through the sensing device. S3. Construct a steel structure model, input the second steel structure data into the steel structure model to perform tilt analysis and eigenvalue buckling analysis, and obtain the tilt value and buckling load of the steel structure; S4. Combine the tilt value and buckling load of the steel structure to perform stability analysis and obtain the bearing capacity of the steel structure; S5. Assess the load-bearing capacity of the steel structure and issue an early warning when the load-bearing capacity of the steel structure exceeds the load-bearing capacity threshold.

2. The method for monitoring the tilt of a steel structure according to claim 1, characterized in that, The method for obtaining the distribution map of the sensing device based on the improved average modal kinetic energy model is as follows: S11. Select the pre-installation node of the sensing device and calculate the modal kinetic energy of the pre-installation node; S12. Construct a unit average modal kinetic energy model, and input the modal kinetic energy into the unit average modal kinetic energy model to perform kinetic energy contribution analysis, and obtain the arrangement scheme of the sensing device. S13. Construct a stability model for the sensing device, input the arrangement scheme into the stability model for stability analysis, sort the arrangement schemes according to the stability analysis results, and draw a distribution diagram of the sensing device based on the arrangement scheme ranked first according to the stability analysis results.

3. The method for monitoring the tilt of a steel structure according to claim 2, characterized in that, After obtaining the sensor arrangement plan, the following is also included: Signal-to-noise ratio analysis of the sensor arrangement scheme: in, For the first The signal-to-noise ratio of each layout scheme The average modal kinetic energy of the unit. For the first Modal guarantee criterion values ​​for each layout scheme; Normalize the signal-to-noise ratio: in, For the first The degree of visualization of trend changes in each layout scheme; To achieve the minimum signal-to-noise ratio, This represents the maximum signal-to-noise ratio.

4. The method for monitoring the tilt of a steel structure according to claim 3, characterized in that, The optimized model of the sensing device is as follows: in, The objective function of the optimization model for the sensing device is... Let be the element-average modal kinetic energy function. For comprehensive information functions, For comprehensive information, The modal matrix function, It is a robustness function.

5. The method for monitoring the tilt of a steel structure according to claim 1, characterized in that, The method for structural damage analysis of the first steel structure data based on the theory of internal force redistribution in steel structures is as follows: S21. Perform buckling analysis on the steel structure based on the first steel structure data to obtain the distribution of defects in the steel structure; S22. Construct a sensitivity index model, and analyze the steel structure defect distribution by inputting the sensitivity index model to obtain the sensitive steel structure members; S23. A damage model of the steel structure is constructed by simulating the damage of the sensitive steel structure members in the form of stiffness reduction. S24. Based on the theory of redistribution of internal forces in steel structures, the damage model of the steel structure is simulated under load conditions to obtain a set of several sets of multi-overlapping members. S25. Select the group of multiple overlapping members with a repetition count greater than the repetition threshold as the damage placement location of the sensing device.

6. The method for monitoring the tilt of a steel structure according to claim 1, characterized in that, The process of collecting the second steel structure data also includes filtering the second steel structure data.

7. The method for monitoring the tilt of a steel structure according to claim 6, characterized in that, The method for filtering the second steel structure data is as follows: Collect wind load, wind speed, and noise from the sensing device; When the wind load wind speed is greater than or equal to 15 m / s or the noise is greater than or equal to 1%, the second steel structure data collected by the sensing device is discarded.

8. A steel structure monitoring system, characterized in that, include: A sensor arrangement module is used to obtain a distribution map of the sensor devices based on an improved average modal kinetic energy model, install the sensor devices according to the distribution map, and collect first steel structure data through the sensor devices. The structural damage analysis module is used to perform structural damage analysis on the first steel structure data based on the theory of redistribution of internal forces in steel structures, adjust the arrangement of the sensing device according to the results of the structural damage analysis, and collect the second steel structure data. The steel structure model analysis module is used to construct a steel structure model, input the second steel structure data into the steel structure model to perform tilt analysis and eigenvalue buckling analysis, and obtain the tilt value and buckling load of the steel structure. A stability analysis module is used to perform stability analysis by combining the tilt value and buckling load of the steel structure to obtain the load-bearing capacity of the steel structure. The early warning module is used to determine the load-bearing capacity of the steel structure and issue an early warning when the load-bearing capacity of the steel structure exceeds the load-bearing capacity threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a steel structure tilt monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a steel structure tilt monitoring method as described in any one of claims 1 to 7.

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