A large-span space structure defect identification method and system

Abstract

The application discloses a large-span space structure defect identification method and system, and belongs to the technical field of defect identification. The method comprises the following steps: drawing a structure distribution diagram of a large-span space, identifying a main steel structure, wherein the main steel structure is composed of a main load-bearing component and key nodes, determining a construction period of the large-span space, selecting a plurality of test points, using a detection device to sequentially test the strain of each test point, synchronously collecting strain data at all key nodes, collecting a strain data set, and creating parent nodes corresponding to the test points. By judging whether the target branch and the strain propagation path are the same, the application can identify the change state of the structure stress system, timely find hidden damage, improve the damage positioning ability and evolution evaluation ability, realize early warning and positioning of hidden damage, and overall improve the accuracy of health monitoring of the large-span space structure, thereby guaranteeing the persistence and stability of the large-span space structure.

Description

Technical Field

[0001] This invention relates to the field of defect identification technology, and in particular to a method and system for identifying defects in large-span spatial structures. Background Technology

[0002] Large-span spatial structures refer to load-bearing structural systems in steel buildings with a large span and typically no or only a few intermediate supporting members. Their spans are generally much larger than those achievable by conventional beam-column structures, and they are commonly used to cover large public or industrial spaces, such as stadiums, exhibition centers, airport terminals, convention centers, and large factories. Due to their large spans, numerous nodes, and complex stress paths, large-span spatial structures are susceptible to hidden defects caused by the coupling effects of various factors during long-term use. These defects include local buckling of components, fatigue cracks in welds, loosening of bolt connections, support settlement, and degradation of overall structural stiffness. These defects often do not manifest in obvious external forms in the early stages but gradually appear through stress redistribution or strain anomalies, making it difficult for traditional methods based on manual inspection or local monitoring points to capture the defect evolution process in a timely and comprehensive manner.

[0003] During the construction phase of a large-span spatial structure, a preset load is applied to the structure, and the stress propagation path and direction under the corresponding load are recorded to form initial stress propagation characteristics. After the structure is put into use and has been running for a period of time, the load is applied again under the same test conditions to obtain the stress propagation path and direction under the current state. The current stress propagation characteristics are compared and analyzed with the stress propagation characteristics during the construction phase to determine whether the two are consistent, thereby characterizing whether the force transmission of the structure has changed.

[0004] Therefore, "how to conduct stress propagation testing" is the technical problem that this invention aims to solve. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for identifying defects in large-span spatial structures, in order to solve the problem of "how to conduct stress propagation testing" mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying defects in large-span spatial structures, the method comprising: drawing a structural distribution map of the large-span space; identifying the main steel structure, wherein the main steel structure consists of main load-bearing components and key nodes; determining the construction period of the large-span space; selecting several test points; using testing equipment to sequentially perform strain tests on each test point; synchronously collecting strain data at all key nodes; summarizing the data to obtain a strain dataset; creating parent nodes corresponding one-to-one with the test points and child nodes corresponding one-to-one with the key nodes; collecting attribute data of the test points and writing it to the parent node; writing the strain data at each key node to the child node; attaching the child node to the parent node; and constructing a strain tree.

[0007] Edit a dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber and a redundant fiber. The dual-channel fiber optic deployment scheme is as follows: the primary fiber is segmented and fixed in the main load-bearing component by winding, and the redundant fiber is linearly deployed along the structural direction of the main steel structure.

[0008] The test points are selected, and a test scenario is constructed. The main optical fibers on other steel structures that are connected to the steel structure at the test points are defined as the influencing segments. Pulsed light signals are injected into the influencing segments and redundant optical fibers, and the return signals are collected. The phase offset position of the redundant optical fibers and the strain propagation path of the influencing segments are identified. Using strain sensors pre-installed at key nodes, the real-time values ​​of strain data are collected, and the phase offset position and strain propagation path are corrected.

[0009] Determine whether the test point coincides with the phase offset position. If they coincide, based on the test point, traverse the strain tree to find the parent-to-child branch corresponding to the test point, obtain the target branch, determine whether the target branch is the same as the strain propagation path, if they are not the same, compare to obtain the deviation part, generate a defect identification report, and send it to the preset terminal.

[0010] Furthermore, the steps of drawing a structural distribution diagram of the large-span space, identifying the main steel structure, wherein the main steel structure is composed of main load-bearing components and key nodes, and determining the construction period of the large-span space include: dividing the main steel structure into several blocks, setting a risk level for each block, wherein the risk level includes at least: high, medium, and low.

[0011] Count the number of key nodes in each block and establish a positive correlation between the number and the risk level.

[0012] Furthermore, the steps of collecting attribute data of test points, writing it to the parent node, writing strain data at each key node to the child node, and attaching the child node to the parent node to construct the strain tree include: configuring the influencing factors of strain data, wherein the influencing factors include at least: load and temperature.

[0013] A compensation model for strain data is constructed, a target compensation value is set, strain data, influencing factors and target compensation value are integrated to generate a training set, and the compensation model is trained.

[0014] Furthermore, the step of editing the dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber optic and a redundant fiber optic, includes: connecting one end of the dual-channel fiber optic to a pre-installed fiber optic demodulator, injecting a pulsed optical signal into the dual-channel fiber optic according to a preset frequency, and receiving a return signal.

[0015] When a phase shift is detected, the distance between the phase shift position and the fiber optic demodulator is calculated, written into a preset template, a risk report is generated, and sent to the preset terminal.

[0016] Furthermore, the steps of selecting the test points, constructing the test scenario, and defining the main optical fibers on other steel structures that are connected to the steel structure at the test points as affecting the segmentation include: establishing the correspondence between the test points and the phase offset positions.

[0017] The test point was reselected, and the phase offset position was corrected.

[0018] Furthermore, the step of determining whether the test point coincides with the phase offset position, and if they coincide, includes: when the test point does not coincide with the phase offset position, calibrating the return signal of the dual-channel optical fiber and the real-time value.

[0019] Obtain the calibration results and redetermine the phase offset position.

[0020] Furthermore, the system includes: a construction module for drawing a structural distribution map of a large-span space, identifying the main steel structure, wherein the main steel structure consists of main load-bearing components and key nodes, determining the construction period of the large-span space, selecting several test points, using testing equipment to sequentially perform strain tests on each test point, synchronously collecting strain data at all key nodes, summarizing to obtain a strain dataset, creating parent nodes corresponding one-to-one with the test points, and child nodes corresponding one-to-one with the key nodes, collecting attribute data of the test points and writing it to the parent node, writing the strain data at each key node to the child node, attaching the child nodes to the parent node, and constructing a strain tree.

[0021] The deployment module is used to edit the dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber and a redundant fiber. The dual-channel fiber optic deployment scheme is as follows: the primary fiber is segmented and fixed in the main load-bearing component by winding, and the redundant fiber is linearly deployed along the structural direction of the main steel structure.

[0022] The calibration module is used to select the test points, construct the test scenario, define the main optical fibers on other steel structures that are connected to the steel structure at the test points as the influencing segments, inject pulsed light signals into the influencing segments and redundant optical fibers, collect the return signals, identify the phase offset position of the redundant optical fibers and the strain propagation path of the influencing segments, and use strain sensors pre-installed at key nodes to collect real-time values ​​of strain data to correct the phase offset position and strain propagation path.

[0023] The judgment module is used to determine whether the test point and the phase offset position coincide. If they coincide, based on the test point, the strain tree is traversed to find the parent-to-child branch corresponding to the test point to obtain the target branch. It is then determined whether the target branch is the same as the strain propagation path. If they are not the same, the deviation part is compared to obtain the deviation part, a defect identification report is generated, and the report is sent to the preset terminal.

[0024] Furthermore, the construction module includes: a setting unit for dividing the main steel structure into several blocks and setting the risk level of each block, wherein the risk level includes at least: high, medium and low.

[0025] The statistical unit is used to count the number of key nodes in each block and establish a positive correlation between the number and the risk level.

[0026] A configuration unit for configuring factors influencing strain data, wherein the factors influencing strain data include at least load and temperature.

[0027] The training unit is used to construct a compensation model for strain data, set a target compensation value, integrate strain data, influencing factors and target compensation value, generate a training set, and train the compensation model.

[0028] Furthermore, the deployment module includes an access unit, used to connect one end of the dual-channel optical fiber to a pre-installed optical fiber demodulator, inject pulsed optical signals into the dual-channel optical fiber at a preset frequency, and receive return signals.

[0029] The calculation unit is used to calculate the distance between the phase shift position and the fiber optic demodulator when a phase shift is detected, write it into a preset template, generate a risk report, and send it to the preset terminal.

[0030] Furthermore, the correction module includes a correspondence unit for establishing a correspondence between the test point and the phase offset position.

[0031] The selection unit is used to reselect the test point and correct the phase offset position.

[0032] Compared with existing technologies, the beneficial effects of this invention are as follows: By collecting stress data of large-span spaces during construction, initial stress distribution data and overall stress response characteristics can be obtained, and the stress propagation path and coupling relationship of the structure can be identified, providing a data foundation for subsequent structural monitoring of large-span spaces. By constructing a strain tree, the test results of each test point can be displayed intuitively, clearly showing the correlation between the load input location and the structural response area, facilitating the tracking of the influence relationship of stress from local to overall, and improving the ability to analyze strain propagation paths. By installing distributed optical fibers, continuous monitoring of the overall strain of large-span spaces can be achieved, risk points can be identified in a timely manner, and the reliability and accuracy of structural damage diagnosis can be improved. By judging whether the target branch is the same as the strain propagation path, the changing state of the structural stress system can be identified, latent damage can be detected in a timely manner, and the damage location and evolution assessment capabilities can be improved, achieving early warning and location of latent damage. Overall, the accuracy of health monitoring of large-span space structures is improved, ensuring the long-term stability of large-span space structures. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the method for identifying defects in large-span spatial structures provided in an embodiment of the present invention.

[0034] Figure 2 This is a first sub-flowchart of the method for identifying defects in large-span spatial structures provided in an embodiment of the present invention.

[0035] Figure 3 This is a second sub-flowchart of the method for identifying defects in large-span spatial structures provided in an embodiment of the present invention.

[0036] Figure 4 The third sub-flowchart of the method for identifying defects in large-span spatial structures provided in this embodiment of the invention.

[0037] Figure 5 The fourth sub-flowchart of the method for identifying defects in large-span spatial structures provided in this embodiment of the invention.

[0038] Figure 6 This is a block diagram of the large-span spatial structure defect identification system provided in an embodiment of the present invention.

[0039] Figure 7 This is a block diagram of the components of the construction module in the large-span spatial structure defect identification system provided in the embodiments of the present invention.

[0040] Figure 8 This is a block diagram of the layout modules in the large-span spatial structure defect identification system provided in an embodiment of the present invention.

[0041] Figure 9 This is a block diagram of the correction module in the large-span spatial structure defect identification system provided in an embodiment of the present invention.

[0042] Figure 10 This is a block diagram of the judgment module in the large-span spatial structure defect identification system provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0044] In Example 1, Figure 1 The implementation flow of the defect identification method for large-span spatial structures provided by the embodiments of the present invention is shown below in detail: S100: Draw the structural distribution diagram of the large-span space, identify the main steel structure, wherein the main steel structure is composed of main load-bearing components and key nodes, determine the construction period of the large-span space, select several test points, use detection equipment to perform strain tests on each test point in sequence, synchronously collect strain data at all key nodes, summarize to obtain strain dataset, create parent nodes corresponding one-to-one with test points, and child nodes corresponding one-to-one with key nodes, collect attribute data of test points and write them to the parent node, write the strain data at each key node to the child node, attach the child node to the parent node, and construct a strain tree.

[0045] Based on the structural design drawings and on-site measurement data of the large-span space, draw a structural distribution diagram of the large-span space, which can be a stadium or exhibition center, etc. The structural distribution diagram should indicate the hardware facilities and building layout, and identify the main steel structure in the structural distribution diagram. The main steel structure consists of main load-bearing components and key nodes. The main load-bearing components are the structural members that bear the main load transmission, and the key nodes are the nodes that play a key connection and control role in the force transmission process. Determine the time period corresponding to the large-span space, including the design period, construction period, and service period. The construction period is the construction period of the large-span space.

[0046] During the construction of the large-span space, several test points are selected based on the structural distribution characteristics and the arrangement of the main load-bearing components. Strain tests are conducted at each test point using testing equipment. These tests can include static load testing, gradual loading testing, and vibration excitation testing. Static load testing involves applying a constant or gradually increasing static load and collecting the structural strain response. Gradual loading testing involves applying a load in a slow, continuously varying manner and obtaining the quasi-static strain response characteristics of the structure. Vibration excitation testing involves applying a controllable low-amplitude vibration load to the structure using equipment such as a vibrator. It is important to note that only one test method is selected at a time. Strain tests are performed sequentially at each test point. Strain sensors are used to collect strain data at each key node during the test. The strain data from each key node are then compiled to obtain a strain dataset, with one dataset corresponding to each test point. Testing equipment can include jacks, counterweight systems, vibrators, hydraulic vibration tables, and impact hammers. Parent nodes are created to correspond one-to-one with test points, and child nodes are created to correspond one-to-one with key nodes. Attribute data for each test point is collected, including spatial location, test type, and structural properties. This attribute data is written to the corresponding parent node. Strain data at each key node during the test is written to the child nodes. Simply put, each child node represents the strain data of a key node. Child nodes are attached to parent nodes to generate a strain tree. Both parent and child nodes are logical nodes, not physical processing devices. The advantage of this approach is that by constructing a tree structure, the dispersed strain data can be organized in an orderly manner according to spatial location and structural relationships. This allows strain data from different key nodes to form a unified data analysis unit based on the parent node, facilitating subsequent analysis of strain propagation paths, inter-node coupling relationships, and changes in structural stress states, thereby improving the structured representation and analysis efficiency of strain data.

[0047] S200: Edit the dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber and a redundant fiber. The dual-channel fiber optic deployment scheme is as follows: the primary fiber is segmented and fixed in the main load-bearing component by winding, and the redundant fiber is linearly deployed along the structural direction of the main steel structure.

[0048] Based on the layout characteristics of the main steel structure and the distribution of the main load-bearing components of the large-span spatial structure, a dual-channel optical fiber deployment path is established within the main steel structure. The dual-channel optical fiber refers to an optical fiber group composed of a primary fiber and redundant fibers. Following this dual-channel fiber deployment scheme, the optical fibers are laid throughout the large-span space. Specifically, the main load-bearing components are divided into several segments. The primary fiber is evenly wound around the outside of the main load-bearing components within each segment, and a signal transmitter and receiver (fiber optic demodulator) are installed within each segment, thus forming independent monitoring units. Each monitoring unit establishes a data interaction relationship with the data processing center through a communication link. It is important to note that the primary fiber is only laid within the main load-bearing components. Along the overall structural orientation of the main steel structure, the redundant optical fibers are continuously and linearly deployed, allowing them to sequentially traverse all main steel structures and key nodes, ensuring full-range, seamless deployment. The advantage of this method is that by using two different spatial deployment methods, it achieves collaborative sensing of strain characteristics at different scales within the large-span space, thereby providing a multi-dimensional data foundation for subsequent strain propagation path analysis and structural condition assessment.

[0049] S300: Select the test point, construct the test scenario, define the main optical fiber on other steel structures that are connected to the steel structure at the test point as the influencing segment, inject pulse light signal into the influencing segment and redundant optical fiber, collect the return signal, identify the phase offset position of the redundant optical fiber and the strain propagation path of the influencing segment, and use strain sensors pre-installed at key nodes to collect real-time values ​​of strain data to correct the phase offset position and strain propagation path.

[0050] After a period of service in the large-span space, test points are selected, and strain tests are conducted at these points using detection equipment to identify potentially affected main steel structures and determine the primary optical fibers laid within them. These primary optical fibers are defined as the influencing segments. Using the fiber optic demodulator corresponding to each influencing segment, pulsed light signals are injected into the segment, while simultaneously, pulsed light signals are injected from one end of a redundant fiber. The returned signals are collected, demodulated, and analyzed. Based on the phase change characteristics, the phase shift position in the redundant fiber is located. Combined with the strain response variation patterns in the influencing segments at different spatial locations, the strain propagation path is determined. The influencing segments characterize the direction and diffusion of strain in the main structure, while the redundant fiber is used for global scanning and rapid location of abnormal strain events. Strain sensors installed at key nodes collect real-time strain data to correct the phase shift position and strain propagation path, ensuring that the phase shift position coincides with the test point.

[0051] S400: Determine whether the test point coincides with the phase offset position. If they coincide, based on the test point, traverse the strain tree, find the parent-to-child branch corresponding to the test point, obtain the target branch, determine whether the target branch is the same as the strain propagation path, if they are not the same, compare to obtain the deviation part, generate a defect identification report, and send it to the preset terminal.

[0052] The test point is checked to see if it coincides with the phase offset position. If they do, it indicates that the test results of the redundant fiber, the influence segment, and the strain sensor are consistent and accurate. In the strain tree, the parent and child nodes corresponding to the test point are located, and the corresponding branch in the strain tree is defined as the target branch. The stress change trend and propagation characteristics in the target branch are compared with the strain propagation path. If they are the same, it indicates that the force flow transmission path in the main steel structure has not changed; if they are different, it indicates that the strain propagation path differs from the stress change trend and propagation characteristics recorded in the target branch. The target branch and the strain propagation path are compared segment by segment to identify the deviation. This deviation is mapped to the actual main steel structure to determine the actual location information, which is then written into a preset template to generate a defect identification report, which is then sent to a preset terminal (the management terminal within the large-span space).

[0053] In Example 2, Figure 2 The first sub-flowchart of the method for identifying defects in large-span spatial structures provided by an embodiment of the present invention is shown. The following details the steps of drawing a structural distribution map of a large-span space, identifying the main steel structure, wherein the main steel structure is composed of main load-bearing components and key nodes, and determining the construction period of the large-span space, as follows: S101: Divide the main steel structure into several blocks and set a risk level for each block, wherein the risk level includes at least: high, medium and low.

[0054] Based on the layout, orientation, and building distribution of the main steel structure, the main steel structure is divided into several blocks, each of which is a part of the main steel structure. Each block is assigned a corresponding risk level, which includes high, medium, and low. The higher the risk level, the greater the safety risk within the block.

[0055] S102: Count the number of key nodes in each block and establish a positive correlation between the number and the risk level.

[0056] Based on the risk level of each segment, a set number of key nodes is assigned to each segment, with the number positively correlated with the risk level—that is, the higher the risk level, the higher the number of key nodes. A larger number of key nodes allows for more stress tests within the corresponding segment. The advantages of this approach are: it enables the acquisition of more comprehensive stress data, improves the probability and accuracy of detecting and locating abnormal stress events, and reduces monitoring blind spots caused by insufficient test points.

[0057] In Example 3, Figure 2 The first sub-flowchart of the method for identifying defects in large-span spatial structures provided by an embodiment of the present invention is shown. The following details the steps of collecting attribute data of test points, writing them to the parent node, writing the strain data of each key node to the child node, attaching the child node to the parent node, and constructing a strain tree, as follows: S103: Configure the influencing factors of strain data, wherein the influencing factors include at least: load and temperature.

[0058] Identify the factors that may affect the strain data, i.e., the influencing factors, which include the load and temperature during strain testing.

[0059] S104: Construct a compensation model for strain data, set a target compensation value, integrate strain data, influencing factors and target compensation value, generate a training set, and train the compensation model.

[0060] A compensation model is constructed using deep learning algorithms. This model can directly employ existing drift compensation or nonlinear error compensation models. Target compensation values ​​under corresponding operating conditions are obtained through calibration experiments, high-precision sensor measurements, or manual settings. A training set is then built to train the aforementioned drift compensation or nonlinear error compensation model. The compensation model learns the correlation between strain data and various influencing factors, identifies the impact of environmental changes, equipment status changes, and signal transmission errors on strain measurement results, and outputs corresponding compensation values ​​based on the input real-time strain data and influencing factor data. Here, the compensation value refers to the strain data compensation value. During the model construction phase, a target compensation value is set. This target compensation value is obtained through calibration experiments, high-precision sensor measurements, or manually defined. Strain data, influencing factors, and the target compensation value are integrated to generate a training set, which is then used to train the compensation model. In practical use, when real-time strain data is collected, the corresponding influencing factors should be collected simultaneously. The real-time value and influencing factors are input into the compensation model, and the output results are used to offset the real-time value.

[0061] In Example 4, Figure 3The second sub-flowchart of the method for identifying defects in large-span spatial structures provided by an embodiment of the present invention is shown. The following details the steps of editing the dual-channel optical fiber deployment scheme, wherein the dual-channel optical fiber consists of a primary optical fiber and a redundant optical fiber, as follows: S201: Connect one end of the dual-channel optical fiber to a pre-installed optical fiber demodulator, inject pulsed light signals into the dual-channel optical fiber at a preset frequency, and receive the return signal.

[0062] One end of the dual-channel optical fiber is connected to a pre-installed fiber optic demodulator, which has all the functions of a signal transmitter and receiver in the S300. It can generate pulsed optical signals, inject them into the main fiber and redundant fiber, and receive the scattered return signals.

[0063] S202: When a phase shift is detected, the distance between the phase shift position and the fiber optic demodulator is calculated, written into a preset template, a risk report is generated, and sent to the preset terminal.

[0064] Extract the characteristic parameters of the returned signal corresponding to the phase shift, including: phase change, signal propagation delay and sampling time, etc., and calculate the distance between the phase shift position and the fiber demodulator by combining the light propagation speed in the optical fiber. Write the distance into a preset template, generate a risk report, and send it to the preset terminal.

[0065] In Example 5, Figure 4 The third sub-flow diagram of the defect identification method for large-span spatial structures provided by the embodiment of the present invention is shown. The following details the steps of selecting the test point, constructing the test scenario, and defining the main optical fiber on other steel structures that are connected to the steel structure at the test point as the influencing segment, as follows: S301: Establish the correspondence between the test point and the phase offset position.

[0066] Strain tests were performed sequentially at each test point. If a phase shift was detected, a correspondence between the two was established.

[0067] S302: Reselect the test point and correct the phase offset position.

[0068] Select new test points and perform strain tests again to determine whether the phase shift position has changed. If it has not changed, it means that the phase shift is not caused by single test error, environmental noise interference, or difference in test point selection, but that there is indeed a stable abnormal strain characteristic at the corresponding position.

[0069] In Example 6, Figure 5The fourth sub-flowchart of the method for identifying defects in large-span spatial structures provided by an embodiment of the present invention is shown. The following details the step of determining whether the test point and the phase offset position coincide, and if they coincide, based on the test point: S401: When the test point and the phase offset position do not coincide, calibrate the return signal of the dual-channel optical fiber and the real-time value.

[0070] When the test point does not coincide with the phase offset position, it indicates that there is a deviation between the actual test point and the phase offset position obtained by monitoring through dual-channel optical fiber. The calibration process is then initiated to calibrate the optical fiber and strain sensor.

[0071] S402: Obtain the calibration results and redetermine the phase offset position.

[0072] Based on the calibration results, the phase offset position is re-determined, and it is then determined whether the phase offset position coincides with the test point.

[0073] Figure 6 The diagram illustrates the composition of a large-span spatial structure defect identification system provided in an embodiment of the present invention. The large-span spatial structure defect identification system 1 includes: a construction module 11, used to draw a structural distribution map of the large-span space, identify the main steel structure, wherein the main steel structure is composed of main load-bearing components and key nodes, determine the construction period of the large-span space, select several test points, use detection equipment to sequentially perform strain tests on each test point, synchronously collect strain data at all key nodes, summarize to obtain a strain dataset, create parent nodes corresponding one-to-one with the test points, and child nodes corresponding one-to-one with the key nodes, collect attribute data of the test points and write it to the parent node, write the strain data at each key node to the child node, attach the child node to the parent node, and construct a strain tree.

[0074] The deployment module 12 is used to edit the dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber optic and a redundant fiber optic. The dual-channel fiber optic deployment scheme is as follows: the primary fiber optic is segmented and fixed in the main load-bearing component by winding, and the redundant fiber optic is linearly deployed along the structural direction of the main steel structure.

[0075] The calibration module 13 is used to select the test point, construct the test scenario, define the main optical fiber on other steel structures that are connected to the steel structure at the test point as the influencing segment, inject pulse light signals into the influencing segment and redundant optical fiber, collect the return signal, identify the phase offset position of the redundant optical fiber and the strain propagation path of the influencing segment, and use strain sensors pre-installed at key nodes to collect real-time values ​​of strain data to correct the phase offset position and strain propagation path.

[0076] The judgment module 14 is used to determine whether the test point and the phase offset position coincide. If they coincide, based on the test point, the strain tree is traversed to find the parent-to-child branch corresponding to the test point, the target branch is obtained, and it is determined whether the target branch and the strain propagation path are the same. If they are not the same, the deviation part is compared, a defect identification report is generated, and it is sent to the preset terminal.

[0077] Figure 7 The diagram shows the composition of the construction module 11 in the large-span spatial structure defect identification system provided in the embodiment of the present invention. The construction module 11 includes: a setting unit 111, used to divide the main steel structure into several blocks and set the risk level of each block, wherein the risk level includes at least: high, medium and low.

[0078] The statistics unit 112 is used to count the number of key nodes in each block and establish a positive correlation between the number and the risk level.

[0079] Configuration unit 113 is used to configure the influencing factors of strain data, wherein the influencing factors include at least: load and temperature.

[0080] Training unit 114 is used to construct a compensation model for strain data, set a target compensation value, integrate strain data, influencing factors and target compensation value, generate a training set, and train the compensation model.

[0081] Figure 8 The diagram shows the composition of the deployment module 12 in the large-span spatial structure defect identification system provided in the embodiment of the present invention. The deployment module 12 includes: an access unit 121, which is used to connect one end of the dual-channel optical fiber to a pre-installed optical fiber demodulator, inject pulse optical signals into the dual-channel optical fiber at a preset frequency, and receive return signals.

[0082] The calculation unit 122 is used to calculate the distance between the phase shift position and the fiber optic demodulator when a phase shift is detected, write it into a preset template, generate a risk report, and send it to the preset terminal.

[0083] Figure 9 The diagram shows the composition of the correction module 13 in the large-span spatial structure defect identification system provided in the embodiment of the present invention. The correction module 13 includes: a correspondence unit 131, used to establish the correspondence between the test point and the phase offset position.

[0084] The selection unit 132 is used to reselect the test point and correct the phase offset position.

[0085] Figure 10The diagram shows the composition of the judgment module 14 in the large-span spatial structure defect identification system provided in the embodiment of the present invention. The judgment module 14 includes: a calibration unit 141, used to calibrate the return signal of the dual-channel optical fiber and the real-time value when the test point does not coincide with the phase offset position.

[0086] The acquisition unit 142 is used to acquire the calibration result and redetermine the phase offset position.

[0087] The construction module 11 is mainly used to complete step S100, the deployment module 12 is mainly used to complete step S200, the correction module 13 is mainly used to complete step S300, and the judgment module 14 is mainly used to complete step S400.

[0088] The setting unit 111 is mainly used to complete step S101, the statistics unit 112 is mainly used to complete step S102, the configuration unit 113 is mainly used to complete step S103, and the training unit 114 is mainly used to complete step S104.

[0089] The access unit 121 is mainly used to complete step S201, and the calculation unit 122 is mainly used to complete step S202.

[0090] The corresponding unit 131 is mainly used to complete step S301, and the selection unit 132 is mainly used to complete step S302.

[0091] The calibration unit 141 is mainly used to complete step S401, and the acquisition unit 142 is mainly used to complete step S402.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying defects in large-span spatial structures, characterized in that, The method includes: Draw a structural distribution diagram of the large-span space, identify the main steel structure, which consists of main load-bearing components and key nodes, determine the construction period of the large-span space, select several test points, and use testing equipment to conduct strain tests on each test point in sequence. Edit a dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber and a redundant fiber. The dual-channel fiber optic deployment scheme is as follows: the primary fiber is segmented and fixed in the main load-bearing component by winding, and the redundant fiber is linearly deployed along the structural direction of the main steel structure. Select the test points, construct a test scenario, define the main optical fibers on other steel structures that are connected to the steel structure at the test points as the influencing segments, inject pulsed light signals into the influencing segments and redundant optical fibers, collect the return signals, and identify the phase offset position of the redundant optical fibers and the strain propagation path of the influencing segments. Determine whether the test point coincides with the phase offset position. If they coincide, based on the test point, traverse the pre-constructed strain tree, find the parent-to-child branch corresponding to the test point, obtain the target branch, determine whether the target branch is the same as the strain propagation path, if they are different, compare to obtain the deviation part, generate a defect identification report, and send it to the preset terminal.

2. The method for identifying defects in large-span spatial structures according to claim 1, characterized in that, The method further includes: Synchronously collect strain data at all key nodes, summarize to obtain strain dataset, create parent nodes corresponding one-to-one with test points, and child nodes corresponding one-to-one with key nodes, collect attribute data of test points and write to parent nodes, write strain data at each key node to child nodes, attach child nodes to parent nodes, and construct strain tree.

3. The method for identifying defects in large-span spatial structures according to claim 2, characterized in that, The method further includes: By using strain sensors pre-installed at key nodes, real-time values ​​of strain data are collected to correct the phase offset position and strain propagation path.

4. The method for identifying defects in large-span spatial structures according to claim 1, characterized in that, The steps of drawing a structural distribution diagram of the large-span space, identifying the main steel structure, wherein the main steel structure consists of main load-bearing components and key nodes, and determining the construction period of the large-span space include: The main steel structure is divided into several blocks, and a risk level is set for each block, wherein the risk level includes at least: high, medium and low; Count the number of key nodes in each block and establish a positive correlation between the number and the risk level.

5. The method for identifying defects in large-span spatial structures according to claim 2, characterized in that, The steps for constructing a strain tree include: collecting attribute data from test points, writing it to the parent node, writing strain data from each key node to the child nodes, and attaching the child nodes to the parent node. Factors influencing the configuration of strain data, wherein the influencing factors include at least: load and temperature; A compensation model for strain data is constructed, a target compensation value is set, strain data, influencing factors and target compensation value are integrated to generate a training set, and the compensation model is trained.

6. The method for identifying defects in large-span spatial structures according to claim 1, characterized in that, The steps for editing the dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber and a redundant fiber optic, include: One end of the dual-channel optical fiber is connected to a pre-installed optical fiber demodulator. Pulse light signals are injected into the dual-channel optical fiber at a preset frequency, and the return signal is received. When a phase shift is detected, the distance between the phase shift position and the fiber optic demodulator is calculated, written into a preset template, a risk report is generated, and sent to the preset terminal.

7. The method for identifying defects in large-span spatial structures according to claim 6, characterized in that, The steps of selecting the test points, constructing the test scenario, and defining the main optical fibers on other steel structures that are connected to the steel structure at the test points as affecting the segmentation include: Establish the correspondence between test points and phase offset positions; The test point was reselected, and the phase offset position was corrected.

8. The method for identifying defects in large-span spatial structures according to claim 3, characterized in that, The step of determining whether the test point coincides with the phase offset position, and if they coincide, based on the test point, includes: When the test point does not coincide with the phase offset position, calibrate the return signal and the real-time value of the dual-channel optical fiber; Obtain the calibration results and redetermine the phase offset position.

9. A defect identification system for large-span spatial structures, characterized in that, The system includes: The module is used to draw the structural distribution map of the large-span space, identify the main steel structure, which consists of main load-bearing components and key nodes, determine the construction period of the large-span space, select several test points, use testing equipment to perform strain tests on each test point in sequence, synchronously collect strain data at all key nodes, summarize to obtain strain dataset, create parent nodes corresponding one-to-one with test points and child nodes corresponding one-to-one with key nodes, collect attribute data of test points and write it to the parent node, write the strain data at each key node to the child node, attach the child node to the parent node, and construct a strain tree. The deployment module is used to edit the dual-channel fiber optic deployment scheme, wherein the dual-channel fiber optic consists of a primary fiber and a redundant fiber. The dual-channel fiber optic deployment scheme is as follows: the primary fiber is segmented and fixed in the main load-bearing component by winding, and the redundant fiber is linearly deployed along the structural direction of the main steel structure. The calibration module is used to select the test point, construct the test scenario, define the main optical fiber on other steel structures that are connected to the steel structure at the test point as the influencing segment, inject pulse light signals into the influencing segment and redundant optical fiber, collect the return signal, identify the phase offset position of the redundant optical fiber and the strain propagation path of the influencing segment, and use strain sensors pre-installed at key nodes to collect real-time values ​​of strain data to correct the phase offset position and strain propagation path. The judgment module is used to determine whether the test point and the phase offset position coincide. If they coincide, based on the test point, the strain tree is traversed to find the parent-to-child branch corresponding to the test point to obtain the target branch. It is then determined whether the target branch is the same as the strain propagation path. If they are not the same, the deviation part is compared to obtain the deviation part, a defect identification report is generated, and the report is sent to the preset terminal.

10. The large-span spatial structure defect identification system according to claim 9, characterized in that, The building module includes: The setting unit is used to divide the main steel structure into several blocks and set the risk level of each block, wherein the risk level includes at least: high, medium and low. The statistical unit is used to count the number of key nodes in each block and establish a positive correlation between the number and the risk level. A configuration unit for configuring the influencing factors of strain data, wherein the influencing factors include at least: load and temperature; The training unit is used to construct a compensation model for strain data, set a target compensation value, integrate strain data, influencing factors and target compensation value, generate a training set, and train the compensation model.

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