Concrete filled steel tube void detection method and system based on Internet of Things and sensor

Abstract

The invention provides a concrete filled steel tube void detection method and system based on the Internet of Things and a sensor, and relates to the field of void detection.The system comprises a sample management module used for establishing a sample database; the data acquisition module is used for determining first void prediction data of the to-be-detected concrete filled steel tube based on the sample database and the structure data and the operation environment data of the to-be-detected concrete filled steel tube, performing impact resilience detection on the to-be-detected concrete filled steel tube to obtain impact resilience detection data, and determining the void prediction data of the to-be-detected concrete filled steel tube based on the impact resilience detection data; determining second void prediction data of the to-be-detected concrete-filled steel tube, and performing ultrasonic detection on the to-be-detected concrete-filled steel tube based on the second void prediction data of the to-be-detected concrete-filled steel tube to obtain ultrasonic detection data; and the void analysis module is used for generating void detection data of the sample concrete filled steel tube based on the ultrasonic detection data, and has the advantage of improving the void detection efficiency and accuracy of the concrete filled steel tube.

Description

Technical Field

[0001] The present invention relates to the field of void detection, and in particular to a method and system for detecting voids in steel tube concrete based on the Internet of Things and sensors. Background Art

[0002] Steel tube concrete combines the characteristics of concrete and steel pipe, and has the characteristics of good plasticity and toughness, and high bearing capacity, which enables it to play a good role in the stress process. Steel tube concrete is also widely used in industrial and civil construction, underground engineering, large-span bridge engineering, etc. However, due to the influence of factors such as concrete shrinkage, raw material properties and construction technology, the mechanical properties of steel tube concrete are seriously reduced due to the different degrees of debonding between the steel tube wall and concrete, which in turn affects the safety of the structure. The debonding of steel tube concrete, also known as debonding, refers to the separation between the steel tube and the core concrete at the contact point, or the phenomenon of looseness or even voids inside the core concrete.

[0003] In the prior art, optical fibers are embedded in the steel pipe before concrete is poured. The voids and cracks between the steel pipe and concrete cause micro-changes in the optical fibers. Through the corresponding conversion relationship between its mechanical and optical parameters, internal defects of steel pipe concrete can be effectively detected. At the same time, it has the ability of large-scale and continuous monitoring, and can qualitatively and quantitatively determine the width, cracking time and number of cracks inside the concrete, and conduct follow-up inspections. This method requires embedding optical fibers before concrete pouring, and is not suitable for the detection of steel pipe concrete that has already been built.

[0004] Therefore, it is necessary to provide a method and system for detecting voids in steel tube concrete based on the Internet of Things and sensors, so as to improve the efficiency and accuracy of detecting voids in steel tube concrete. Summary of the invention

[0005] The present invention provides a steel tube concrete void detection system based on the Internet of Things and sensors, comprising: a sample management module, used to establish a sample database, wherein the sample database is used to store multiple groups of sample steel tube concrete data, each group of the sample steel tube concrete data includes the structure data, operating environment data and void detection data of the sample steel tube concrete; a data acquisition module, including a void prediction unit and a void detection unit, wherein the void prediction unit is used to obtain the structure data and operating environment data of the steel tube concrete to be detected, the void prediction unit includes a plurality of sensors for obtaining the operating environment data, and based on the sample database and the structure data and operating environment data of the steel tube concrete to be detected, determines the structure data of the steel tube concrete to be detected. The first void prediction data of the soil, the void detection unit is used to perform an impact rebound test on the steel tube concrete to be detected based on the first void prediction data of the steel tube concrete to be detected, and obtain impact rebound detection data. The void prediction unit is also used to determine the second void prediction data of the steel tube concrete to be detected based on the impact rebound detection data. The void detection unit is used to perform an ultrasonic test on the steel tube concrete to be detected based on the second void prediction data of the steel tube concrete to be detected, and obtain ultrasonic test data; a void analysis module is used to receive the ultrasonic test data obtained by the void detection unit through a wireless network, and generate the void detection data of the sample steel tube concrete based on the ultrasonic test data.

[0006] Furthermore, the sample management module is also used for: for each of the sample steel tube concrete, based on the structural data of the sample steel tube concrete, establishing a three-dimensional model of the sample steel tube concrete, performing finite element analysis based on the three-dimensional model of the sample steel tube concrete, and generating a finite element analysis result of the sample steel tube concrete; for any two sample steel tube concretes, based on the finite element analysis results of the two sample steel tube concretes, calculating the structural similarity of the two sample steel tube concretes; based on the structural similarity of any two sample steel tube concretes, clustering multiple sample steel tube concretes to generate multiple sample clusters; for each sample cluster, based on the operating environment data of any two sample steel tube concretes included in the sample cluster, calculating the operating environment similarity of any two sample steel tube concretes included in the sample cluster, and based on the operating environment similarity of any two sample steel tube concretes included in the sample cluster, grouping multiple sample steel tube concretes included in the sample cluster to generate a sample group included in the sample cluster; based on the sample groups included in each sample cluster, establishing a sample relationship map; and storing the sample relationship map in the sample database.

[0007] Furthermore, the operating environment data at least includes the ambient temperature, ambient humidity and load at multiple time points; the sample management module calculates the operating environment similarity of any two sample steel tube concretes included in the sample cluster based on the operating environment data of any two sample steel tube concretes included in the sample cluster, including: for each sample steel tube concrete, performing variational modal decomposition on the ambient temperature of the sample steel tube concrete at multiple time points to generate multiple ambient temperature modal components, for each of the ambient temperature modal components, determining the characteristics of the ambient temperature modal component to generate an ambient temperature feature matrix, and performing variational modal decomposition on the ambient temperature of the sample steel tube concrete at multiple time points to generate a plurality of ambient temperature modal components. The ambient humidity is subjected to variational modal decomposition to generate a plurality of ambient humidity modal components. For each of the ambient humidity modal components, the characteristics of the ambient humidity modal components are determined to generate an ambient humidity characteristic matrix. The loads of the sample steel tube concrete at multiple time points are subjected to variational modal decomposition to generate a plurality of load modal components. For each of the load modal components, the characteristics of the load modal components are determined to generate a load characteristic matrix. For any two sample steel tube concretes, the operating environment similarity of any two sample steel tube concretes included in the sample cluster is calculated based on the ambient temperature characteristic matrix, the ambient humidity characteristic matrix and the load characteristic matrix of each sample steel tube concrete.

[0008] Furthermore, the void prediction unit determines first void prediction data of the steel tube concrete to be detected based on the sample database and the structural data and operating environment data of the steel tube concrete to be detected, including: determining at least one matching sample cluster from the multiple sample clusters based on the structural data of the steel tube concrete to be detected; determining at least one matching sample group from the at least one matching sample cluster based on the operating environment data of the steel tube concrete to be detected; determining multiple matching sample steel tube concretes from the at least one matching sample group based on the structural data and operating environment data of the steel tube concrete to be detected; determining the first void prediction data of the steel tube concrete to be detected based on multiple matching sample steel tube concretes through a first void prediction model, wherein the first void prediction data of the steel tube concrete to be detected includes at least at least one void prediction position of the steel tube concrete to be detected and the void type of each of the void prediction positions.

[0009] Furthermore, the void detection unit performs an impact rebound detection on the steel tube concrete to be detected based on the first void prediction data of the steel tube concrete to be detected, and obtains the impact rebound detection data, including: generating an impact rebound detection path and an impact rebound detection parameter sequence based on at least one void prediction position of the steel tube concrete to be detected and the void type of each void prediction position, wherein the impact rebound detection path includes at least one impact rebound detection position, and the impact rebound detection parameter sequence includes impact rebound detection parameters corresponding to each impact rebound detection position; based on the impact rebound detection path and the impact rebound detection parameter sequence, performing an impact rebound detection on the steel tube concrete to be detected, and obtaining the impact rebound detection data, wherein the impact rebound detection data includes the reflected wave of each impact rebound detection position.

[0010] Furthermore, the void prediction unit is also used to determine the second void prediction data of the steel tube concrete to be detected based on the impact rebound detection data, including: for each of the impact rebound detection positions, generating a predicted reflection wave corresponding to the impact rebound detection position based on the structural data of the sample steel tube concrete and the impact rebound detection parameters corresponding to the impact rebound detection position through a reflection wave prediction model, performing variational modal decomposition on the predicted reflection wave corresponding to the impact rebound detection position to generate multiple predicted reflection wave modal components, extracting the characteristics of each of the predicted reflection wave modal components, performing variational modal decomposition on the reflection wave at the impact rebound detection position to generate multiple reflection wave modal components, extracting the characteristics of each of the reflection wave modal components, and predicting the void possibility and void type of the impact rebound detection position through a second void prediction model based on the characteristics of each of the predicted reflection wave modal components and the characteristics of each of the reflection wave modal components, wherein the second void prediction data of the steel tube concrete to be detected includes the void possibility and void type of each of the impact rebound detection positions.

[0011] Furthermore, the void detection unit performs ultrasonic detection on the steel tube concrete to be detected based on the second void prediction data of the steel tube concrete to be detected, and obtains ultrasonic detection data, including: generating an ultrasonic detection path and an ultrasonic detection parameter sequence based on the void possibility and void type of each of the impact rebound detection positions, wherein the ultrasonic detection path includes at least one ultrasonic detection position, and the ultrasonic detection parameter sequence includes ultrasonic detection parameters corresponding to each of the ultrasonic detection positions; performing ultrasonic detection on the steel tube concrete to be detected based on the ultrasonic detection path and the ultrasonic detection parameter sequence, and obtaining the ultrasonic detection data, wherein the ultrasonic detection data includes an ultrasonic detection signal of each of the ultrasonic detection positions.

[0012] Furthermore, the void analysis module generates void detection data of the sample steel tube concrete based on the ultrasonic detection data, including: for each of the ultrasonic detection positions, generating a predicted ultrasonic detection signal corresponding to the ultrasonic detection position based on the structural data of the sample steel tube concrete and the ultrasonic detection parameters corresponding to the ultrasonic detection position through an ultrasonic prediction model, performing variational modal decomposition on the predicted ultrasonic detection signal corresponding to the ultrasonic detection position to generate multiple predicted ultrasonic modal components, extracting the characteristics of each predicted ultrasonic modal component, performing variational modal decomposition on the ultrasonic detection signal at the ultrasonic detection position to generate multiple ultrasonic detection signal modal components, extracting the characteristics of each ultrasonic detection signal modal component, and determining the void characteristics of the ultrasonic detection position through a first void analysis model based on the characteristics of each predicted ultrasonic modal component and the characteristics of each ultrasonic detection signal modal component, wherein the void detection data of the sample steel tube concrete includes the void characteristics of each of the ultrasonic detection positions.

[0013] Furthermore, the air gap detection unit comprises a first annular track and a second annular track, and four sets of mounting mechanisms which are slidably matched with the first annular track and one set of mounting mechanisms which are slidably matched with the second annular track, wherein the four sets of mounting mechanisms are provided with cross-arranged wheat wheels and negative pressure fans at four positions facing the steel tube concrete to be detected, and the mounting mechanism connected to the second annular track is provided with a detection component facing the steel tube concrete to be detected, wherein the detection component comprises an impact rebound detection device and an ultrasonic detection device, wherein the impact rebound detection device is used to perform impact rebound detection on the steel tube concrete to be detected, and the ultrasonic detection device is used to perform impact rebound detection on the steel tube concrete to be detected. The sonic wave detection device is used to perform ultrasonic detection on the steel tube concrete to be detected; the McDonnell Douglas wheel is used to drive the device to climb on the steel tube concrete to be detected; the negative pressure fan assists the McDonnell Douglas wheel in climbing by adsorbing negative pressure on the steel tube concrete to be detected; the mounting mechanism is used to install or remove the McDonnell Douglas wheel, the negative pressure fan and the detection assembly; the mounting mechanism includes a first sliding plate and a second sliding plate that are slidably matched with a first annular track and a second annular track respectively, and a telescopic cylinder connected to the first sliding plate and the second sliding plate; the mounting mechanism also includes a mechanism for driving the first sliding plate and the second sliding plate to clamp or expand The screw rod is provided with a screw seat which is connected to the first sliding plate and the second sliding plate and is matched with the screw rod thread respectively, and the outer end of the screw rod is fixedly connected with a rotating handle; a moving component connected to the mounting mechanism is provided in the second annular track, and the moving component includes an annular rack embedded in the outer sliding groove of the second annular track, and a gear meshed with the annular rack and located in the reserved groove of the first sliding plate on the same side, the moving component also includes a connecting plate fixedly connected to the first sliding plate, and a rotating motor fixedly connected to the connecting plate and connected to the gear, and the moving component is used to drive the detection component to move around the steel tube concrete to be detected Movement; four groups of second sliding plates that slide with the second annular track are fixedly connected with a retractable telescopic rod and a mounting plate connected to the telescopic rod, and the telescopic rod is used to push the wheat wheel and the negative pressure fan to move axially; the mounting plates are respectively connected to the wheat wheel or the negative pressure fan on the same side, and a servo motor for driving the wheat wheel to rotate is arranged outside the wheat wheel, and the servo motor is connected to the mounting plate on the same side; the second sliding plate connected to the second annular track in the mounting mechanism is connected to the detection component; the first annular track is fixedly connected to the second annular track, and the first annular track and the second annular track are both spliced ​​and assembled.

[0014] The present invention provides a method for detecting the voids of steel tube concrete based on the Internet of Things and sensors, which is applied to the above-mentioned method for detecting the voids of steel tube concrete based on the Internet of Things and sensors, and comprises: establishing a sample database, wherein the sample database is used to store multiple groups of sample steel tube concrete data, each group of the sample steel tube concrete data comprises structural data, operating environment data and voids detection data of the sample steel tube concrete; acquiring the structural data of the steel tube concrete to be detected; determining first voids prediction data of the steel tube concrete to be detected based on the sample database and the structural data and operating environment data of the steel tube concrete to be detected; performing impact rebound detection on the steel tube concrete to be detected based on the first voids prediction data of the steel tube concrete to be detected, and acquiring impact rebound detection data; determining second voids prediction data of the steel tube concrete to be detected based on the impact rebound detection data; performing ultrasonic detection on the steel tube concrete to be detected based on the second voids prediction data of the steel tube concrete to be detected, and acquiring ultrasonic detection data; generating voids detection data of the sample steel tube concrete based on the ultrasonic detection data.

[0015] Compared with the prior art, the method and system for detecting voids in steel tube concrete based on the Internet of Things and sensors provided by the present invention have at least the following beneficial effects:

[0016] 1. Through the void prediction unit, the void situation can be preliminarily predicted based on the sample database and the structural data and operating environment data of the steel tube concrete to be tested, so as to select the detection means in a targeted manner. This avoids indiscriminate comprehensive detection and improves the detection efficiency. As a preliminary screening method, the impact rebound detection can quickly exclude steel tube concrete with no obvious signs of void, further narrow the scope of in-depth detection, and can more comprehensively evaluate the void situation of steel tube concrete. The impact rebound detection provides a preliminary judgment, while the ultrasonic detection can more accurately locate the void area and degree. The establishment of the sample database can be analyzed based on a large amount of historical data to improve the accuracy of prediction and detection. By optimizing the detection process and reducing unnecessary comprehensive detection, the detection cost can be significantly reduced. Accurate detection means can avoid unnecessary repair or replacement of steel tube concrete with no void or slight void, thereby saving material and labor costs.

[0017] 2. By building a three-dimensional model for each sample of steel tube concrete and performing finite element analysis, we can have a deeper understanding of the mechanical properties and behavior of the sample. This helps to more accurately simulate and predict the voiding of steel tube concrete in subsequent data analysis and voiding prediction. Calculating the structural similarity and operating environment similarity between sample steel tube concrete, and clustering and grouping, we can more finely distinguish different categories of steel tube concrete, so that more targeted strategies and methods can be adopted when performing voiding detection to improve the accuracy of detection. The establishment of the sample relationship map makes the data in the sample database no longer an isolated point, but an interrelated and hierarchical structure. This helps to better understand and utilize sample data and improve the efficiency of data analysis and mining. Through clustering and grouping, the samples in the sample database are organized into a form that is easier to manage and analyze, which is convenient for subsequent data query, update and maintenance. It can automatically calculate similarity and perform clustering and grouping based on the structural and operating environment data of the sample steel tube concrete. With the continuous increase and update of sample data, its internal structure and algorithm can be automatically adjusted and optimized to adapt to new detection needs and environmental changes. By performing variational mode decomposition and extracting features from the operating environment data, the changing patterns of environmental factors can be captured more accurately, and void prediction and detection can be performed based on these patterns, which improves adaptability and robustness. By clustering and grouping the sample steel tube concrete, samples with similar structures and similar operating environments can be tested first, which helps reduce repeated testing and improve detection efficiency. Based on historical data and current environmental data, possible void areas can be quickly predicted and located, thereby avoiding comprehensive testing of the entire steel tube concrete and further improving detection efficiency.

[0018] 3. The void prediction unit matches sample clusters and sample groups similar to the steel tube concrete structure to be tested and with similar operating environment from the sample database, and then determines multiple matching sample steel tube concrete, making the prediction process more accurate and targeted. This helps to reduce the prediction error caused by large sample differences. After the impact rebound detection, the void prediction unit further uses the reflection wave prediction model and variational mode decomposition technology to conduct in-depth analysis of the impact rebound detection data, so as to more accurately predict the void location and type. This multi-level prediction method significantly improves the prediction accuracy. The impact rebound detection path and detection parameter sequence generated based on the first void prediction data make the impact rebound detection process more orderly and efficient. It can give priority to the detection of locations with a higher predicted void probability, thereby reducing unnecessary detection time and cost. Through the combination of the first void prediction and the second void prediction, the detection range can be gradually narrowed and focused on the possible void area, which further improves the detection efficiency. The whole process of automatically matching relevant samples from the sample database, generating detection paths and parameters, performing impact rebound detection, and analyzing detection data does not require manual intervention or only requires a small amount of manual guidance, reflecting a high level of intelligence. The application of reflected wave prediction model and variational mode decomposition technology can automatically extract useful information from complex detection data and make predictions, which further improves the level of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0020] Figure 1 It is a module schematic diagram of a steel tube concrete void detection system based on the Internet of Things and sensors according to some embodiments of this specification;

[0021] Figure 2 is a schematic diagram of a sample relationship map according to some embodiments of this specification;

[0022] Figure 3 is a bottom view of the three-dimensional structure of the overall device shown in some embodiments of this specification;

[0023] Figure 4 is a three-dimensional structural exploded diagram of the overall device shown in some embodiments of this specification;

[0024] Figure 5 yes Figure 4 A partial enlarged view of the middle A;

[0025] Figure 6 yes Figure 4 A partial enlarged view of point B in the middle;

[0026] Figure 7 is a schematic diagram of a three-dimensional structure of a mobile assembly according to some embodiments of this specification;

[0027] Figure 8 It is a flow chart of a method for detecting voids in steel tube concrete based on the Internet of Things and sensors according to some embodiments of this specification.

[0028] In the figure, 1. steel tube concrete to be tested; 2. first circular track; 3. second circular track; 4. mounting mechanism; 401. first sliding plate; 402. second sliding plate; 403. telescopic cylinder; 404. thread holder; 405. screw rod; 406. turning handle; 407. annular rack; 408. connecting plate; 409. rotating motor; 4010. gear; 5. telescopic rod; 6. mounting plate; 7. wheat wheel; 8. negative pressure fan; 9. detection component; 10. servo motor. DETAILED DESCRIPTION

[0029] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0030] Figure 1 is a module schematic diagram of a steel tube concrete void detection system based on the Internet of Things and sensors according to some embodiments of this specification, such as Figure 1 As shown, the void detection system for steel tube concrete based on the Internet of Things and sensors may include a sample management module, a data acquisition module and a void analysis module.

[0031] The sample management module can be used to establish a sample database.

[0032] The sample database is used to store multiple groups of sample steel tube concrete data, each group of sample steel tube concrete data includes the structural data, operating environment data and void detection data of the sample steel tube concrete.

[0033] Specifically, the structural data of sample concrete-filled steel tubes may include:

[0034] 1. Steel pipe data

[0035] Steel pipe size: includes the outer diameter, wall thickness and length of the steel pipe.

[0036] Material properties: Mechanical properties parameters such as yield strength, tensile strength and elastic modulus of the steel pipe.

[0037] 2. Concrete data

[0038] Concrete strength: includes the axial compressive strength and axial tensile strength of concrete.

[0039] Concrete grade: such as C30, C40, etc., indicates the strength grade of concrete.

[0040] Casting method: whether centrifugal casting is used, and whether steam curing and other technologies are used.

[0041] 3. Component size and shape

[0042] Cross-sectional shape: The cross-sectional shape of concrete-filled steel tube can be circular, square or other shapes. Different cross-sectional shapes have an impact on the mechanical properties of the component.

[0043] Component size: cross-sectional dimensions, such as cross-sectional area, moment of inertia, etc.

[0044] 4. Design parameters

[0045] Steel content: the proportion of steel pipes in steel tube concrete components, used to calculate the stresses borne by the steel pipes and concrete respectively.

[0046] The hoop coefficient reflects the restraining effect of the steel pipe on the concrete.

[0047] Bearing capacity: The bearing capacity of concrete-filled steel tube components is an important parameter in the design, which determines the maximum weight that the component can bear.

[0048] The operating environment data includes at least the ambient temperature, ambient humidity and load at multiple time points.

[0049] In some embodiments, the sample management module is further configured to:

[0050] For each steel tube concrete sample, a three-dimensional model of the steel tube concrete sample is established based on the structural data of the steel tube concrete sample, and a finite element analysis is performed based on the three-dimensional model of the steel tube concrete sample to generate a finite element analysis result of the steel tube concrete sample;

[0051] For any two samples of steel tube concrete, the structural similarity of the two samples of steel tube concrete is calculated based on the finite element analysis results of the two samples of steel tube concrete;

[0052] Based on the structural similarity of any two samples of steel tube concrete, multiple samples of steel tube concrete are clustered to generate multiple sample clusters;

[0053] For each sample cluster, based on the operating environment data of any two sample steel tube concretes included in the sample cluster, the operating environment similarity of any two sample steel tube concretes included in the sample cluster is calculated; based on the operating environment similarity of any two sample steel tube concretes included in the sample cluster, a plurality of sample steel tube concretes included in the sample cluster are grouped to generate a sample group included in the sample cluster;

[0054] Based on the sample groups included in each sample cluster, a sample relationship map is established;

[0055] The sample relationship map is stored in the sample database.

[0056] Specifically, finite element analysis (FEA) is a technique that uses mathematical approximation to simulate real physical systems (geometry and load conditions). It discretizes complex force-bearing objects into simple standard units that can basically reflect their forces, and then solves and analyzes them. The process of finite element analysis usually includes the following main steps:

[0057] S21. Define the geometric area of ​​the problem: approximate the physical properties and geometric area of ​​the solution domain based on the actual problem; define the unit type: select the appropriate unit type, such as triangular or quadrilateral units in two-dimensional problems, tetrahedral or hexahedral units in three-dimensional problems, etc.; define the material properties of the unit: including physical parameters such as elastic modulus, Poisson's ratio, density, etc.; define the geometric properties of the unit: such as length, area, volume, etc.; define the connectivity of the unit: determine the connection relationship between units to construct an overall discrete model; define the basis function of the unit: provide interpolation functions for physical quantities within the unit.

[0058] S22. Discretize the continuous physical system into a finite number of small units. The grid division should ensure accuracy and uniformity, while considering computational efficiency and accuracy. Determine the boundary conditions and loads of the system, such as fixed constraints, displacement constraints, force loads, temperature loads, etc., and apply them to the discretized grid.

[0059] S23. Assemble the units into the total matrix equation (joint equation group) of the entire discrete domain. This step is performed at the nodes of adjacent units to ensure the continuity of the state variables and their derivatives. Use numerical calculation methods (such as direct method, iterative method, etc.) to solve the total matrix equation and obtain the approximate values ​​of physical quantities such as displacement, stress, strain, etc. of each unit.

[0060] The finite element analysis results of the sample steel tube concrete may include multiple finite element analysis features, wherein the multiple finite element analysis features may at least include a maximum stress position, a stress distribution pattern, a displacement change trend, and the like.

[0061] For example, the structural similarity of two sample concrete-filled steel tubes can be calculated based on the following formula:

[0062]

[0063] Among them, S ((i,j),structure) is the structural similarity between the i-th sample CFST and the j-th sample CFST, V (i,k) is the kth finite element analysis characteristic of the ith sample of CFST, V (j,k) is the kth finite element analysis feature of the jth sample CFST, ψ1 is the preset parameter, ψ1>0, and K is the total number of finite element analysis features.

[0064] A plurality of sample steel tube concretes can be clustered based on the structural similarity of any two sample steel tube concretes by a hierarchical clustering algorithm to generate a plurality of sample clusters. A plurality of sample steel tube concretes included in a sample cluster can be grouped based on the operating environment similarity of any two sample steel tube concretes included in the sample cluster by a hierarchical clustering algorithm to generate a sample group included in the sample cluster.

[0065] Figure 2 is a schematic diagram of a sample relationship map according to some embodiments of this specification, such as Figure 2 As shown, the sample relationship graph may include three types of nodes. The first type of node is used to represent sample clusters, the second type of node is used to represent sample groups, and the third type of node is used to represent sample steel tube concrete. The edge between any two nodes represents the belonging relationship between the nodes.

[0066] In some embodiments, the sample management module calculates the similarity of the operating environments of any two sample steel tube concrete blocks included in the sample cluster based on the operating environment data of any two sample steel tube concrete blocks included in the sample cluster, including:

[0067] For each steel tube concrete sample, variational modal decomposition is performed on the ambient temperature of the steel tube concrete sample at multiple time points to generate multiple ambient temperature modal components. For each ambient temperature modal component, the characteristics of the ambient temperature modal component are determined to generate an ambient temperature characteristic matrix. Variational modal decomposition is performed on the ambient humidity of the steel tube concrete sample at multiple time points to generate multiple ambient humidity modal components. For each ambient humidity modal component, the characteristics of the ambient humidity modal component are determined to generate an ambient humidity characteristic matrix. Variational modal decomposition is performed on the load of the steel tube concrete sample at multiple time points to generate multiple load modal components. For each load modal component, the characteristics of the load modal component are determined to generate a load characteristic matrix.

[0068] For any two samples of steel tube concrete, based on the environmental temperature characteristic matrix, environmental humidity characteristic matrix and load characteristic matrix of each sample of steel tube concrete, the operating environment similarity of any two samples of steel tube concrete included in the sample cluster is calculated.

[0069] Specifically, variational mode decomposition can be performed based on the following process:

[0070] S31, determining the number of modes to be decomposed, that is, the number of components, and other parameters in the algorithm, such as penalty coefficient, convergence tolerance, etc.;

[0071] S32. Assume that the original signal can be decomposed into K modal components, each modal component has a center frequency, perform Hilbert transform on each modal component to obtain its analytical signal, mix the analytical signal with the estimated center frequency, modulate the spectrum of each modal component to the corresponding baseband, and construct a variational problem under the constraint that the sum of each modal component is equal to the original signal;

[0072] S33. In order to solve the above constrained variational problem, Lagrange multipliers and quadratic penalty factors are introduced to transform the constrained variational problem into an unconstrained variational problem. The center frequency and bandwidth of each modal component and the corresponding modal components are solved through multiple iterative updates using optimization algorithms such as the alternating direction multiplier method. Through iterative solutions, K modal components are finally obtained, each of which has an independent center frequency and exhibits sparse characteristics in the frequency domain.

[0073] The characteristics of modal components may include center frequency, mean, standard deviation, and component-related parameters.

[0074] The component correlation parameters can characterize the correlation between two modal components of the same original signal and can be calculated based on the following formula:

[0075]

[0076] Among them, C (e,f) are the component-related parameters of the e-th modal component and the f-th modal component of the same original signal, M is the total number of sampled data points, V (e,m) is the value of the mth sampled data point of the eth modal component, V (f,m) is the value of the mth sampled data point of the fth modal component.

[0077] A row vector of the characteristic matrix can correspond to a modal component, and an element of the row vector can represent a feature of the modal component.

[0078] For example, the similarity of the operating environments of two sample CFSTs can be calculated based on the following formula:

[0079] S ((i,j),environment) =a 11 ×S ((i,j),temperature) +a 12 ×S ((i,j),humidity) +a 13 ×S ((i,j),load)

[0080]

[0081] Among them, S ((i,j),environment) is the similarity of the operating environment between the i-th sample CFST and the j-th sample CFST, a 11 、a 12 and a 13 is the preset weight, a 11 、a 12 and a 13 Both are greater than 0, a 11 +a 12 +a 13 =1, S ((i,j),temperature) is the similarity of the ambient temperature characteristic matrix between the i-th sample CFST and the j-th sample CFST, S ((i,j),humidity) is the similarity of the environmental humidity feature matrix between the i-th sample CFST and the j-th sample CFST, S ((i,j),load) is the similarity of the load characteristic matrix between the i-th sample CFST and the j-th sample CFST, ψ2 is a preset parameter, ψ2 is greater than 0, V (i,(p,q)) is the value of the element in the pth row and qth column of the ambient temperature characteristic matrix of the i-th sample concrete-filled steel tube, V (j,(p,q)) is the value of the element in the pth row and qth column of the ambient temperature characteristic matrix of the jth sample steel tube concrete, P is the total number of rows of the ambient temperature characteristic matrix, Q is the total number of columns of the ambient temperature characteristic matrix, and the calculation method of the similarity of the ambient humidity characteristic matrix and the load characteristic matrix is ​​similar to the calculation method of the ambient temperature characteristic matrix similarity, which will not be repeated here.

[0082] The data acquisition module may include a void prediction unit and a void detection unit, wherein the void prediction unit is used to obtain the structural data and operating environment data of the steel tube concrete 1 to be detected, and determine the first void prediction data of the steel tube concrete 1 to be detected based on the sample database and the structural data and operating environment data of the steel tube concrete 1 to be detected. The void prediction unit may include a variety of sensors (for example, temperature sensors, humidity sensors, etc.), and the various sensors may transmit the collected data to the data analysis device of the void prediction unit through a wireless network.

[0083] Specifically include:

[0084] Based on the structural data of the steel tube concrete 1 to be detected, at least one matching sample cluster is determined from multiple sample clusters. For example, the structural similarity between the structural data of the steel tube concrete 1 to be detected and the structural data of any sample steel tube concrete in the sample cluster can be calculated, and the sample cluster to which the sample steel tube concrete having a structural similarity greater than a first structural similarity threshold belongs is taken as the matching sample cluster;

[0085] Based on the operating environment data of the steel tube concrete 1 to be detected, at least one matching sample group is determined from at least one matching sample cluster. For example, the structural similarity between the structural data of the steel tube concrete 1 to be detected and the structural data of any sample steel tube concrete in the sample group included in the matching sample cluster can be calculated, and the sample group to which the sample steel tube concrete having a structural similarity greater than a second structural similarity threshold belongs is taken as the matching sample group;

[0086] Based on the structural data and operating environment data of the steel tube concrete to be detected 1, a plurality of matching sample steel tube concretes are determined from at least one matching sample group. For example, the structural similarity between the structural data of the steel tube concrete to be detected 1 and the structural data of the sample steel tube concretes included in the matching sample group can be calculated, and the sample steel tube concretes whose structural similarity is greater than a third structural similarity threshold are taken as matching sample steel tube concretes;

[0087] The first void prediction data of the steel tube concrete 1 to be tested is determined based on a plurality of matching sample steel tube concretes through the first void prediction model, wherein the first void prediction data of the steel tube concrete 1 to be tested includes at least one void prediction position of the steel tube concrete 1 to be tested and the void type of each void prediction position, wherein the first void prediction model may be a convolutional neural network (CNN) model, and the void type may be surface void, shallow void, deep void, etc. The position of the void of the surface void is within 0.02m from the test surface. This void usually occurs in the surface layer of the steel tube concrete structure, such as a tiny separation at the interface between the steel tube and the concrete or a cavity in the surface layer of the concrete. The position of the void of the shallow void is within 0.02 to 0.1m from the test surface. This type of void may involve tiny cracks or peeling inside the concrete, as well as a shallow separation layer between the steel tube and the concrete. The position of the void of the deep void is above 0.1m from the test surface. Deep voids may involve larger cavities or cracks inside the concrete, as well as deeper separation areas between the steel tube and the concrete. Such voids have a significant impact on the safety and durability of the structure. The predicted void position may be a position in the steel tube concrete 1 to be tested where a certain probability of voids occurs.

[0088] The void detection unit is used to perform an impact rebound detection on the steel tube concrete 1 to be detected based on the first void prediction data of the steel tube concrete 1 to be detected, and obtain impact rebound detection data.

[0089] Specifically include:

[0090] Based on at least one predicted void position of the steel tube concrete 1 to be detected and the void type of each predicted void position, an impact rebound detection path and an impact rebound detection parameter sequence are generated, wherein the impact rebound detection path includes at least one impact rebound detection position, and the impact rebound detection parameter sequence includes an impact rebound detection parameter corresponding to each impact rebound detection position (for example, impact energy, impact frequency, number of measurements, etc.);

[0091] Based on the impact rebound detection path and the impact rebound detection parameter sequence, the impact rebound detection is performed on the steel tube concrete 1 to be detected to obtain impact rebound detection data, wherein the impact rebound detection data includes the reflected wave of each impact rebound detection position.

[0092] The void prediction unit is further used to determine second void prediction data of the steel tube concrete 1 to be tested based on the impact rebound test data.

[0093] Specifically include:

[0094] For each impact rebound detection position, a predicted reflection wave corresponding to the impact rebound detection position is generated through a reflection wave prediction model based on the structural data of the sample steel tube concrete and the impact rebound detection parameters corresponding to the impact rebound detection position, and a variational modal decomposition is performed on the predicted reflection wave corresponding to the impact rebound detection position to generate multiple predicted reflection wave modal components, and the characteristics of each predicted reflection wave modal component are extracted. The reflected wave at the impact rebound detection position is subjected to variational modal decomposition to generate multiple reflection wave modal components, and the characteristics of each reflection wave modal component are extracted. The second void prediction model is used to predict the void possibility and void type of the impact rebound detection position based on the characteristics of each predicted reflection wave modal component and the characteristics of each reflection wave modal component, wherein the second void prediction data of the steel tube concrete 1 to be detected includes the void possibility and void type of each impact rebound detection position, wherein the reflection wave prediction model can be a generative adversarial network (GAN) model, and the second void prediction model can be a long short-term memory network (LSTM) model.

[0095] The void detection unit is used to receive the ultrasonic detection data acquired by the void detection unit through a wireless network, and based on the second void prediction data of the steel tube concrete 1 to be detected, perform ultrasonic detection on the steel tube concrete 1 to be detected to acquire ultrasonic detection data.

[0096] Specifically include:

[0097] Based on the possibility and type of air gap at each impact rebound detection position, an ultrasonic detection path and an ultrasonic detection parameter sequence are generated, wherein the ultrasonic detection path includes at least one ultrasonic detection position, and the ultrasonic detection parameter sequence includes ultrasonic detection parameters corresponding to each ultrasonic detection position (for example, transmitting voltage, transmitting pulse width, ultrasonic frequency, etc.);

[0098] Based on the ultrasonic detection path and the ultrasonic detection parameter sequence, ultrasonic detection is performed on the steel tube concrete 1 to be detected to obtain ultrasonic detection data, wherein the ultrasonic detection data includes an ultrasonic detection signal at each ultrasonic detection position.

[0099] The void analysis module can be used to generate void detection data of sample steel tube concrete based on ultrasonic detection data.

[0100] Specifically include:

[0101] For each ultrasonic detection position, a predicted ultrasonic detection signal corresponding to the ultrasonic detection position is generated through an ultrasonic prediction model based on the structural data of the sample steel tube concrete and the ultrasonic detection parameters corresponding to the ultrasonic detection position, and the predicted ultrasonic detection signal corresponding to the ultrasonic detection position is subjected to variational modal decomposition to generate multiple predicted ultrasonic modal components, and the characteristics of each predicted ultrasonic modal component are extracted. The ultrasonic detection signal at the ultrasonic detection position is subjected to variational modal decomposition to generate multiple ultrasonic detection signal modal components, and the characteristics of each ultrasonic detection signal modal component are extracted. The void characteristics of the ultrasonic detection position are determined based on the characteristics of each predicted ultrasonic modal component and the characteristics of each ultrasonic detection signal modal component through the first void analysis model, wherein the void detection data of the sample steel tube concrete includes the void characteristics of each ultrasonic detection position, the ultrasonic prediction model can be a generative adversarial network model, and the first void analysis model can be a long short-term memory network model.

[0102] The void detection data of the sample steel tube concrete can be determined based on the void characteristics of each ultrasonic detection position through the second void analysis model, wherein the second void analysis model can be a convolutional neural network model, and the void detection data can include the void positions of the steel tube concrete 1 to be detected and the void type and void size information of each void position.

[0103] See also Figure 3 - Figure 7In some embodiments, the void detection unit includes a first circular track 2 and a second circular track 3, and four groups of mounting mechanisms 4 that are slidably matched with the first circular track 2 and one group of mounting mechanisms 4 that are slidably matched with the second circular track 3. The four groups of mounting mechanisms 4 are provided with cross-arranged Mech wheels 7 and negative pressure fans 8 at four positions facing the steel tube concrete 1 to be detected, and the mounting mechanisms 4 connected to the second circular track 3 are provided with a detection component 9 facing the steel tube concrete 1 to be detected, wherein the detection component 9 includes an impact rebound detection device and an ultrasonic detection device, the impact rebound detection device is used to perform impact rebound detection on the steel tube concrete 1 to be detected, and the ultrasonic detection device is used to perform ultrasonic detection on the steel tube concrete 1 to be detected; the Mech wheel 7 is used to drive the device to climb on the steel tube concrete 1 to be detected; the negative pressure fan 8 is adsorbed on the steel tube concrete 1 to be detected by negative pressure to assist the climbing of the Mech wheel 7; the mounting mechanism 4 is used to install or disassemble the Mech wheel 7, the negative pressure fan 8 and the detection component 9.

[0104] The installation mechanism 4 can be installed on the first annular track 2 and the second annular track 3 of different diameters according to the shape requirements of the steel tube concrete 1 to be tested, which is convenient for performing hollow detection on the steel tube concrete 1 to be tested and greatly improves the application range of the device.

[0105] In addition, by setting up the Mech wheel 7 and the negative pressure fan 8, the negative pressure fan 8 can be adsorbed on the surface of the steel tube concrete 1 to be tested by negative pressure adsorption through subsequent components, which is used to stabilize the overall device, and then the Mech wheel 7 is driven by the driving component to climb on the surface of the steel tube concrete 1 to be tested, so that the Mech wheel 7 and the negative pressure fan 8 can drive the entire device to stably climb on the surface of the steel tube concrete 1 to be tested.

[0106] The mounting mechanism 4 includes a first sliding plate 401 and a second sliding plate 402 that are slidably matched with the first annular track 2 and the second annular track 3, respectively, and a telescopic cylinder 403 connected to the first sliding plate 401 and the second sliding plate 402. By providing the telescopic cylinder 403, the first sliding plate 401 and the second sliding plate 402 can be guided when they are close to or away from each other, so that the first sliding plate 401 and the second sliding plate 402 can be removed or installed at the first annular track 2. The telescopic cylinder 403 can be telescopic, which can not only facilitate the guidance of the first sliding plate 401 and the second sliding plate 402, but also facilitate the stability of the first sliding plate 401 and the second sliding plate 402 when they are close to or away from each other.

[0107] The mounting mechanism 4 further includes a screw rod 405 for driving the first sliding plate 401 and the second sliding plate 402 to clamp or unfold, and a threaded seat 404 connected to the first sliding plate 401 and the second sliding plate 402 respectively and threadedly matched with the screw rod 405, and a rotating handle 406 is fixedly connected to the outer end of the screw rod 405. By setting the screw rod 405, the rotation of the screw rod 405 can drive the single threaded seat 404 to move axially at the screw rod 405, so as to drive the first sliding plate 401 and the second sliding plate 402 to move closer to or away from each other, which is convenient for installation or disassembly at the first annular track 2 or the second annular track 3, and the screw rod 405 has a threaded self-locking property, which can avoid the risk of the first sliding plate 401 and the second sliding plate 402 shaking and falling off at the first annular track 2 or the second annular track 3. The handle 406 is convenient for driving the screw rod 405 to rotate. By setting the handle 406, it is convenient to rotate the handle 406, so that the handle 406 drives the screw rod 405 to rotate, thereby facilitating the installation or removal of the wheat wheel 7, the negative pressure fan 8 and the detection component 9. In addition, the driving device can be installed by itself to drive the rotation of the screw rod 405, so that the first sliding plate 401 and the second sliding plate 402 can be clamped and fixed at the first annular track 2 or the second annular track 3.

[0108] A moving assembly connected to the mounting mechanism 4 is provided in the second annular track 3, and the moving assembly includes an annular rack 407 embedded in the outer sliding groove of the second annular track 3, and a gear 4010 meshing with the annular rack 407 and located in the reserved groove of the first sliding plate 401 on the same side. The moving assembly also includes a connecting plate 408 fixedly connected to the first sliding plate 401, and a rotating motor 409 fixedly connected to the connecting plate 408 and connected to the gear 4010. The moving assembly is used to drive the detection assembly 9 to move around the steel tube concrete 1 to be detected.

[0109] By setting the annular rack 407, the annular rack 407 and the second annular track 3 are both spliced ​​and assembled. By starting the rotating motor 409, the rotating motor 409 drives the gear 4010 to rotate, and the gear 4010 is meshed with the annular rack 407, thereby driving the detection component 9 to slide on the second annular track 3, so that the detection component 9 can perform impact rebound detection and ultrasonic detection on the steel tube concrete 1 to be detected during the movement.

[0110] The wheat wheel 7 is used to drive the device to climb at the steel tube concrete 1 to be tested; the negative pressure fan 8 assists the climbing of the wheat wheel 7 by adsorbing on the steel tube concrete 1 to be tested through negative pressure; the detection component 9 is used to detect the hollowness of the steel tube concrete 1 to be tested; the installation mechanism 4 is used to install or disassemble the wheat wheel 7, the negative pressure fan 8 and the detection component 9.

[0111] refer to Figures 1 to 5The detection component 9 is provided with a knocking device and a transverse strain gauge. The exciting source knocks the steel tube or building materials at a fixed frequency after moving to a certain position of the steel tube concrete or other building materials, so that the elastic waves generated by the knocking are received by the array detector.

[0112] The excitation source, such as a hammer, is used to strike the steel tube concrete to be tested 1 and generate elastic waves. The array detector is used to collect elastic wave data generated by the excitation source striking the steel tube concrete to be tested 1. The data processing unit performs denoising, impact energy calculation and other processing on the collected elastic wave data.

[0113] The four sets of second sliding plates 402 that are slidably matched with the second annular rails 3 are fixedly connected with retractable telescopic rods 5 and mounting plates 6 connected to the telescopic rods 5. The telescopic rods 5 are used to push the wheat wheel 7 and the negative pressure fan 8 to move axially.

[0114] By setting up a telescopic rod 5, a driving component is arranged inside the telescopic rod 5 to drive the telescopic rod 5 to extend and retract, so as to facilitate pushing the mounting plate 6 to move axially, thereby pushing the wheat wheel 7, the negative pressure fan 8 and the detection component 9 to contact the surface of the steel tube concrete 1 to be detected.

[0115] Then, the whole device is driven by the wheat wheel 7 to climb on the surface of the steel tube concrete 1 to be tested, and is stabilized by negative pressure adsorption on the surface of the steel tube concrete 1 to be tested by the negative pressure fan 8, so that the whole device can be easily moved up and down on the surface of the steel tube concrete 1 to be tested to the position to be tested for air gap detection.

[0116] The mounting plate 6 is respectively connected to the mechanized wheel 7 or the negative pressure fan 8 on the same side. A servo motor 10 for driving the mechanized wheel 7 to rotate is arranged outside the mechanized wheel 7, and the servo motor 10 is connected to the mounting plate 6 on the same side.

[0117] By providing a servo motor 10, the wheat wheel 7 can be driven to rotate, so that the wheat wheel 7 can drive the entire device to climb on the surface of the steel tube concrete 1 to be inspected, and the negative pressure fan 8 can assist the stability of the entire device during climbing.

[0118] The second sliding plate 402 connected to the second annular track 3 in the mounting mechanism 4 is connected to the detection component 9. By setting the detection component 9, the detection component 9 has an excitation source and an array detector. The excitation source strikes the steel tube concrete 1 to be detected to generate elastic waves, and the array-arranged detectors are used to collect these elastic wave data.

[0119] The second sliding plate 402 connected to the second annular track 3 in the mounting mechanism 4 is connected to the detection component 9 .

[0120] The first annular track 2 is fixedly connected to the second annular track 3, and both the first annular track 2 and the second annular track 3 are spliced ​​and assembled. By setting the first annular track 2 and the second annular track 3, both the first annular track 2 and the second annular track 3 are truncated and spliced, so that they can be easily assembled on the surface of the steel tube concrete 1 to be tested, thereby facilitating the subsequent void detection of the steel tube concrete 1 to be tested.

[0121] The working principle of the air gap detection unit is as follows:

[0122] When testing the steel tube concrete 1 for voids, first use a combination of a first annular track 2 and a second annular track 3 of different diameters according to the shape of the steel tube concrete 1 to be tested, and install the first annular track 2 and the second annular track 3 on the steel tube concrete 1 to be tested by means of stable clip-on assembly.

[0123] At this time, the wheat wheel 7 and the negative pressure fan 8 are cross-arranged and connected to the first circular track 2 through the first sliding plate 401 and the second sliding plate 402, and the first sliding plate 401 or the second sliding plate 402 is slidably matched with the slide groove in the first circular track 2, so that by rotating the handle 406, the handle 406 drives the screw rod 405 to rotate, so that the screw rod 405 drives the first sliding plate 401 and the second sliding plate 402 to approach each other through the wire seat 404, so that the first sliding plate 401 and the second sliding plate 402 are stably installed on the first circular track 2, and a relative number of wheat wheels 7 and negative pressure fans 8 can be installed according to the number of cross-sections of the steel tube concrete 1 to be detected, and at the same time, the detection component 9 is installed on the second circular track 3 through the installation mechanism 4 in the same way as the above steps.

[0124] Thus, through the telescopic rod 5, the driving component inside the telescopic rod 5 pushes the telescopic rod 5 to extend and retract, so that the telescopic rod 5 pushes the mounting plate 6, so that the wheat wheel 7 and the negative pressure fan 8 are in close contact with the steel tube concrete 1 to be tested, and the negative pressure fan 8 is provided with a suction cup, which can stably adsorb with the cross section of the steel tube concrete 1 to be tested, and by starting the servo motor 10, the servo motor 10 drives the wheat wheel 7 to rotate, so that the wheat wheel 7 can drive the entire device to climb on the surface of the steel tube concrete 1 to be tested, and the climbing stability is improved by the negative pressure fan 8 to climb to the predetermined position.

[0125] And by starting the rotating motor 409, the output shaft of the rotating motor 409 drives the gear 4010 to rotate, so that the gear 4010 is meshed with the annular rack 407 for transmission, and the detection component 9 can be driven to move axially at the second annular track 3, so as to facilitate the empty detection of different positions of the steel tube concrete 1 to be detected.

[0126] As a supplement, in this embodiment, the telescopic rod 5 is pushed by an internal driving component, and the wheat wheel 7 is driven by the driving component to achieve climbing. The driving component in the telescopic rod 5 is mainly a built-in linear driver and a controller for precise control. The linear driver is connected to the telescopic part of the telescopic rod 5 and is precisely controlled by the controller to accurately adjust the telescopic speed, position, and stop position. The wheat wheel 7 mainly relies on the servo motor 10 to provide power, and the driving component is a controller that controls the servo motor speed, position, acceleration and deceleration parameters to ensure the stability and accuracy of the climbing process.

[0127] In addition, the control systems of the servo motor and the linear drive can be integrated to achieve unified control and coordinated action. PLC can be used as the main control unit to achieve the coordinated work of each drive component through programming, which will not be described in detail here.

[0128] Figure 8 is a flow chart of a method for detecting a void in a steel tube concrete based on the Internet of Things and sensors according to some embodiments of this specification, such as Figure 8 As shown, the method for detecting voids in steel tube concrete based on the Internet of Things and sensors may include the following steps.

[0129] Step 810, establishing a sample database, wherein the sample database is used to store multiple groups of sample steel tube concrete data, each group of sample steel tube concrete data includes structural data, operating environment data and void detection data of the sample steel tube concrete;

[0130] Step 820, obtaining structural data of the steel tube concrete to be tested;

[0131] Step 830, determining first void prediction data of the steel tube concrete to be tested based on the sample database and the structural data and operating environment data of the steel tube concrete to be tested;

[0132] Step 840, based on the first void prediction data of the steel tube concrete to be tested, an impact rebound test is performed on the steel tube concrete to be tested to obtain impact rebound test data;

[0133] Step 850, determining second void prediction data of the steel tube concrete to be tested based on the impact rebound test data;

[0134] Step 860, based on the second void prediction data of the steel tube concrete to be tested, ultrasonic testing is performed on the steel tube concrete to be tested to obtain ultrasonic testing data;

[0135] Step 870: Generate void detection data of the sample steel tube concrete based on the ultrasonic detection data.

[0136] The steel tube concrete void detection system based on the Internet of Things and sensors can be used to perform the steel tube concrete void detection method based on the Internet of Things and sensors. For more descriptions of the steel tube concrete void detection method based on the Internet of Things and sensors, please refer to Figure 1-Figure 7 The relevant description will not be repeated here.

[0137] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A steel tube concrete void detection system based on the Internet of Things and sensors, characterized in that: include: A sample management module is used to establish a sample database, wherein the sample database is used to store multiple groups of sample steel tube concrete data, each group of sample steel tube concrete data includes structural data, operating environment data and void detection data of the sample steel tube concrete; A data acquisition module, comprising a void prediction unit and a void detection unit, wherein the void prediction unit is used to obtain structural data and operating environment data of a steel tube concrete to be detected, the void prediction unit includes a plurality of sensors for obtaining the operating environment data, and determines first void prediction data of the steel tube concrete to be detected based on the sample database and the structural data and operating environment data of the steel tube concrete to be detected, the void detection unit is used to perform an impact rebound test on the steel tube concrete to be detected based on the first void prediction data of the steel tube concrete to be detected, and obtain impact rebound test data, the void prediction unit is also used to determine second void prediction data of the steel tube concrete to be detected based on the impact rebound test data, and the void detection unit is used to perform an ultrasonic test on the steel tube concrete to be detected based on the second void prediction data of the steel tube concrete to be detected, and obtain ultrasonic test data; The void analysis module is used to receive the ultrasonic detection data acquired by the void detection unit through a wireless network, and generate void detection data of the sample steel tube concrete based on the ultrasonic detection data.

2. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 1 is characterized in that: The sample management module is also used for: For each of the sample steel tube concrete, based on the structural data of the sample steel tube concrete, a three-dimensional model of the sample steel tube concrete is established, and based on the three-dimensional model of the sample steel tube concrete, a finite element analysis is performed to generate a finite element analysis result of the sample steel tube concrete; For any two samples of steel tube concrete, based on the finite element analysis results of the two samples of steel tube concrete, calculating the structural similarity of the two samples of steel tube concrete; Based on the structural similarity of any two samples of steel tube concrete, multiple samples of steel tube concrete are clustered to generate multiple sample clusters; For each of the sample clusters, based on the operating environment data of any two sample steel tube concretes included in the sample cluster, the operating environment similarity of any two sample steel tube concretes included in the sample cluster is calculated; based on the operating environment similarity of any two sample steel tube concretes included in the sample cluster, a plurality of sample steel tube concretes included in the sample cluster are grouped to generate a sample group included in the sample cluster; Establishing a sample relationship map based on the sample groups included in each of the sample clusters; The sample relationship map is stored in the sample database.

3. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 2 is characterized in that: The operating environment data at least includes the ambient temperature, ambient humidity and load at multiple time points; The sample management module calculates the similarity of the operating environments of any two sample steel tube concrete blocks included in the sample cluster based on the operating environment data of any two sample steel tube concrete blocks included in the sample cluster, including: For each sample steel tube concrete, variational modal decomposition is performed on the ambient temperature of the sample steel tube concrete at multiple time points to generate multiple ambient temperature modal components. For each of the ambient temperature modal components, the characteristics of the ambient temperature modal components are determined to generate an ambient temperature characteristic matrix. Variational modal decomposition is performed on the ambient humidity of the sample steel tube concrete at multiple time points to generate multiple ambient humidity modal components. For each of the ambient humidity modal components, the characteristics of the ambient humidity modal components are determined to generate an ambient humidity characteristic matrix. Variational modal decomposition is performed on the load of the sample steel tube concrete at multiple time points to generate multiple load modal components. For each of the load modal components, the characteristics of the load modal components are determined to generate a load characteristic matrix. For any two samples of steel tube concrete, based on the environmental temperature characteristic matrix, environmental humidity characteristic matrix and load characteristic matrix of each sample of steel tube concrete, the similarity of the operating environment of any two samples of steel tube concrete included in the sample cluster is calculated.

4. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 2 or 3, characterized in that: The void prediction unit determines first void prediction data of the steel tube concrete to be detected based on the sample database and the structural data and operating environment data of the steel tube concrete to be detected, including: Based on the structural data of the steel tube concrete to be detected, determining at least one matching sample cluster from the multiple sample clusters; Based on the operating environment data of the steel tube concrete to be tested, determining at least one matching sample group from the at least one matching sample cluster; Based on the structural data and the operating environment data of the steel tube concrete to be tested, determining a plurality of matching sample steel tube concrete from the at least one matching sample group; The first void prediction data of the steel tube concrete to be detected is determined based on a plurality of matching sample steel tube concretes through a first void prediction model, wherein the first void prediction data of the steel tube concrete to be detected includes at least one void prediction position of the steel tube concrete to be detected and a void type of each void prediction position.

5. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 4 is characterized in that: The void detection unit performs an impact rebound test on the steel tube concrete to be detected based on the first void prediction data of the steel tube concrete to be detected, and obtains the impact rebound test data, including: Based on at least one predicted void position of the steel tube concrete to be detected and the void type of each predicted void position, an impact rebound detection path and an impact rebound detection parameter sequence are generated, wherein the impact rebound detection path includes at least one impact rebound detection position, and the impact rebound detection parameter sequence includes an impact rebound detection parameter corresponding to each impact rebound detection position; Based on the impact rebound detection path and the impact rebound detection parameter sequence, the impact rebound detection is performed on the steel tube concrete to be detected to obtain the impact rebound detection data, wherein the impact rebound detection data includes the reflected wave of each impact rebound detection position.

6. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 5 is characterized in that: The void prediction unit is further used to determine second void prediction data of the steel tube concrete to be tested based on the impact rebound detection data, including: For each of the impact rebound detection positions, a predicted reflected wave corresponding to the impact rebound detection position is generated by a reflection wave prediction model based on the structural data of the sample steel tube concrete and the impact rebound detection parameters corresponding to the impact rebound detection position, variational modal decomposition is performed on the predicted reflected wave corresponding to the impact rebound detection position to generate multiple predicted reflected wave modal components, the features of each predicted reflected wave modal component are extracted, variational modal decomposition is performed on the reflected wave at the impact rebound detection position to generate multiple reflected wave modal components, the features of each reflected wave modal component are extracted, and the void possibility and void type of the impact rebound detection position are predicted based on the features of each predicted reflected wave modal component and the features of each reflected wave modal component by a second void prediction model, wherein the second void prediction data of the steel tube concrete to be detected includes the void possibility and void type of each of the impact rebound detection positions.

7. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 6 is characterized in that: The void detection unit performs ultrasonic detection on the steel tube concrete to be detected based on the second void prediction data of the steel tube concrete to be detected to obtain ultrasonic detection data, including: Based on the possibility and type of air gap at each of the impact rebound detection positions, an ultrasonic detection path and an ultrasonic detection parameter sequence are generated, wherein the ultrasonic detection path includes at least one ultrasonic detection position, and the ultrasonic detection parameter sequence includes ultrasonic detection parameters corresponding to each of the ultrasonic detection positions; Based on the ultrasonic detection path and the ultrasonic detection parameter sequence, ultrasonic detection is performed on the steel tube concrete to be detected to obtain the ultrasonic detection data, wherein the ultrasonic detection data includes an ultrasonic detection signal at each of the ultrasonic detection positions.

8. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 7 is characterized in that: The void analysis module generates void detection data of the sample steel tube concrete based on the ultrasonic detection data, including: For each of the ultrasonic detection positions, a predicted ultrasonic detection signal corresponding to the ultrasonic detection position is generated through an ultrasonic prediction model based on the structural data of the sample steel tube concrete and the ultrasonic detection parameters corresponding to the ultrasonic detection position, and variational modal decomposition is performed on the predicted ultrasonic detection signal corresponding to the ultrasonic detection position to generate multiple predicted ultrasonic modal components, and the characteristics of each predicted ultrasonic modal component are extracted. Variational modal decomposition is performed on the ultrasonic detection signal at the ultrasonic detection position to generate multiple ultrasonic detection signal modal components, and the characteristics of each ultrasonic detection signal modal component are extracted. The void characteristics of the ultrasonic detection position are determined based on the characteristics of each predicted ultrasonic modal component and the characteristics of each ultrasonic detection signal modal component through a first void analysis model, wherein the void detection data of the sample steel tube concrete includes the void characteristics of each of the ultrasonic detection positions.

9. The steel tube concrete void detection system based on the Internet of Things and sensors according to claim 2 or 3, characterized in that: The air gap detection unit comprises a first annular track and a second annular track, and four groups of mounting mechanisms which are slidably matched with the first annular track and one group of mounting mechanisms which are slidably matched with the second annular track. The four groups of mounting mechanisms are provided with cross-arranged McIlroy wheels and negative pressure fans at four positions facing the steel tube concrete to be detected, and the mounting mechanism connected to the second annular track is provided with a detection component facing the steel tube concrete to be detected, wherein the detection component comprises an impact rebound detection device and an ultrasonic detection device, the impact rebound detection device is used to perform impact rebound detection on the steel tube concrete to be detected, and the ultrasonic detection device is used to perform ultrasonic detection on the steel tube concrete to be detected; the McIlroy wheel is used to drive the device to climb on the steel tube concrete to be detected; the negative pressure fan assists the climbing of the McIlroy wheel by adsorbing negative pressure on the steel tube concrete to be detected; the mounting mechanism is used to install or disassemble the McIlroy wheel, the negative pressure fan and the detection component; The mounting mechanism comprises a first sliding plate and a second sliding plate respectively slidably matched with the first annular track and the second annular track, and a telescopic cylinder connected with the first sliding plate and the second sliding plate; The mounting mechanism further includes a screw rod for driving the first sliding plate and the second sliding plate to clamp or unfold, and a screw seat respectively connected to the first sliding plate and the second sliding plate and mating with the screw rod thread, and the outer end of the screw rod is fixedly connected to a rotating handle; A moving assembly connected to the mounting mechanism is provided in the second annular track, the moving assembly includes an annular rack embedded in the outer sliding groove of the second annular track, and a gear meshed with the annular rack and located in the reserved groove of the first sliding plate on the same side, the moving assembly also includes a connecting plate fixedly connected to the first sliding plate, and a rotating motor fixedly connected to the connecting plate and connected to the gear, the moving assembly is used to drive the detection assembly to move around the steel tube concrete to be detected; Four sets of second sliding plates that are slidably matched with the second annular track are fixedly connected with retractable telescopic rods and mounting plates connected with the telescopic rods, and the telescopic rods are used to push the wheat wheel and the negative pressure fan to move axially; The mounting plates are respectively connected to the wheat wheel or the negative pressure fan on the same side, and a servo motor for driving the wheat wheel to rotate is arranged outside the wheat wheel, and the servo motor is connected to the mounting plate on the same side; The second sliding plate connected to the second annular track in the mounting mechanism is connected to the detection assembly; The first annular track is fixedly connected to the second annular track, and the first annular track and the second annular track are both spliced ​​and assembled.

10. A method for detecting voids in steel tube concrete based on the Internet of Things and sensors, characterized in that: The steel tube concrete void detection system based on the Internet of Things and sensors applied to any one of claims 1 to 9 comprises: Establishing a sample database, wherein the sample database is used to store multiple groups of sample steel tube concrete data, each group of the sample steel tube concrete data includes the structure data, operation environment data and void detection data of the sample steel tube concrete; Acquire structural data of the steel tube concrete to be tested; Determine first void prediction data of the steel tube concrete to be detected based on the sample database and the structural data and operating environment data of the steel tube concrete to be detected; Based on the first void prediction data of the steel tube concrete to be tested, an impact rebound test is performed on the steel tube concrete to be tested to obtain impact rebound test data; Determining second void prediction data of the steel tube concrete to be tested based on the impact rebound test data; Based on the second void prediction data of the steel tube concrete to be detected, ultrasonic testing is performed on the steel tube concrete to be detected to obtain ultrasonic testing data; Based on the ultrasonic detection data, void detection data of the sample steel tube concrete is generated.

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