Engineering structure multi-source sensing data collaborative monitoring method based on Internet of Things

Abstract

The invention discloses an engineering structure multi-source sensing data collaborative monitoring method based on the Internet of Things, relates to the technical field of engineering monitoring, and is used for solving the problem that the characterization accuracy of a monitoring result on the stress and deformation state of an actual structure is reduced. The construction cycle time is divided into different disturbance intensity levels, surrounding rock displacement data and support structure strain data are respectively obtained under each disturbance intensity level and are subjected to correlation marking, crack connectivity distribution and section convergence quantity of a tunnel section are obtained through three-dimensional laser scanning, section geometric features are formed through fusion, and the tunnel section is constructed. And judging whether the geometric state of the section is in a stable state, screening a historical prediction reference level based on a disturbance intensity level, and updating a historical prediction reference by using an association mark detection result of a current construction cycle, so that effective screening and reference adaptive correction of multi-source monitoring data under a unified construction condition are realized.

Description

Technical Field

[0001] This invention relates to the field of engineering monitoring technology, and more specifically, to a method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things. Background Technology

[0002] With the continuous expansion of the scale of underground projects such as urban rail transit, highway tunnels and underground integrated pipe corridors, structural safety monitoring during tunnel construction has become an important technical means to ensure project safety and construction quality. Especially under the construction conditions of drill-and-blast or micro-differential blasting, the surrounding rock and support structure exhibit significant nonlinear response characteristics under the periodic blasting disturbance, and their deformation, strain and crack evolution processes have obvious stages and randomness.

[0003] The existing technology has the following shortcomings: Currently, in practical engineering applications, the multi-source sensor data acquisition process does not incorporate the differences in blasting disturbance intensity within the construction cycle for hierarchical management. Furthermore, there is a lack of a mechanism for unified correlation and filtering of monitoring data such as surrounding rock displacement and support structure strain based on construction disturbance characteristics. Consequently, monitoring data from different construction stages and under different disturbance conditions are often mixed for analysis, leading to reduced accuracy in representing the actual structural stress and deformation state and increased uncertainty in identifying and predicting construction safety risks. Therefore, this paper proposes a collaborative monitoring method for multi-source sensor data of engineering structures based on the Internet of Things.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a collaborative monitoring method for multi-source sensor data of engineering structures based on the Internet of Things. This method addresses the problems mentioned in the background art by employing a grading mechanism for the intensity of blasting disturbance during the construction cycle and a multi-source sensor data association and update mechanism under the constraint of cross-sectional geometric stability.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a collaborative monitoring method for multi-source sensor data of engineering structures based on the Internet of Things, comprising the following steps: Step S1: Conduct construction monitoring on the tunnel section to be tested, retrieve construction status information and extract the current construction stage and construction cycle time, collect blasting vibration velocity data of the tunnel section to be tested within the construction cycle time, and then count the peak vibration velocity. Step S2: Integrate the peak vibration velocity with the current construction stage to initially divide the construction cycle time into disturbance intensity levels, detect the surrounding rock displacement data and support structure strain data of the tunnel section to be tested, and associate and mark the detection results based on the disturbance intensity levels; Step S3: Perform three-dimensional laser scanning on the cross-section of the tunnel to be tested to obtain the crack connectivity distribution and cross-sectional convergence of the cross-section. Evaluate the geometric characteristics of the cross-section by combining the crack connectivity distribution and cross-sectional convergence and determine whether the geometric state of the cross-section is in a stable state. Step S4: When the cross-sectional geometry is in a stable state, access the historical database to retrieve the historical prediction baseline level and filter the target disturbance intensity level. Update the historical prediction baseline level using the correlation marker detection results of the target disturbance intensity level.

[0007] In a preferred embodiment, in step S1, the construction status information, including the current construction stage and the construction cycle time corresponding to the current construction stage, is retrieved through the tunnel construction management unit. When the current construction phase is in the blasting operation phase, the blasting vibration velocity is collected by the blasting vibration monitoring device deployed on the cross section of the tunnel to be tested during the construction cycle time, and the blasting vibration velocity is used as the blasting vibration speed data.

[0008] In a preferred embodiment, in step S1, if the blasting vibration velocity data is greater than a preset background vibration threshold, the blasting vibration velocity data is marked; otherwise, the blasting vibration velocity data is not marked. Arrange the marked blasting vibration velocity data in chronological order, and combine adjacent marked blasting vibration velocity data into blasting event segments; Within each blasting event segment, the maximum value of the marked blasting vibration velocity data is taken as the peak vibration velocity of the blasting event segment.

[0009] In a preferred embodiment, in step S2, the vibration velocity reference value of the current construction stage is retrieved from the historical database, and the ratio of the vibration velocity peak value to the vibration velocity reference value is used as the vibration velocity peak value coefficient. The peak vibration velocity is sorted in chronological order. The change in peak vibration velocity is obtained by subtracting the coefficients of adjacent peak vibration velocity. The change in peak vibration velocity is then sorted in descending order of numerical value. Based on the preset number of layers, the peak vibration velocity coefficients are selected sequentially to obtain the layered segmented peak vibration velocity. The construction cycle time is divided into disturbance intensity levels based on the acquisition time corresponding to the peak vibration velocity of each level.

[0010] In a preferred embodiment, in step S2, the surrounding rock displacement measurement value of the tunnel section to be tested is obtained by the surrounding rock displacement monitoring device in the time interval corresponding to each level of disturbance intensity. The mean value of the surrounding rock displacement measured before the time interval corresponding to the disturbance intensity level is used as the displacement reference value; the mean value of the surrounding rock displacement measured after the time interval corresponding to the disturbance intensity level is used as the displacement response value. The surrounding rock displacement data is calculated based on the displacement response value and the displacement reference value; The strain measurement value of the support structure of the tunnel section under test is obtained by using a support structure strain monitoring device. The strain data of the support structure are calculated based on the strain measurement values ​​of the support structure. The surrounding rock displacement data and support structure strain data corresponding to each disturbance intensity level are used as the detection results, and the detection results are associated with the disturbance intensity level.

[0011] In a preferred embodiment, in step S3, after the corresponding construction cycle is completed, the three-dimensional laser scanning device deployed in the tunnel is controlled to scan the tunnel section to be tested according to the preset scanning resolution and scanning angle to obtain the three-dimensional spatial point cloud data of the inner wall surface of the section. Three-dimensional spatial point cloud data consists of several discrete points with spatial coordinates, and each spatial point contains its three-dimensional coordinates in the tunnel cross-section coordinate system. Local plane fitting is performed on the three-dimensional spatial point cloud data to calculate the angle between the normal vectors of adjacent spatial points and the distance between points. When the angle between the normal vectors of adjacent spatial points is greater than the preset normal vector mutation threshold and the distance between points exceeds the preset gap judgment threshold, the crack feature points are judged to exist in the region. Spatial clustering is performed on the crack feature points to obtain several crack clusters. The interconnection of crack clusters is determined based on the minimum spatial distance and the consistency of the extension direction between crack clusters, thus forming a crack interconnection distribution.

[0012] In a preferred embodiment, in step S3, multiple characteristic azimuth angles uniformly distributed along the circumference are selected in the tunnel cross-section coordinate system. In each characteristic azimuth direction, the radial distance from the current scan section profile point to the geometric center of the section is determined, and the difference between this distance and the radial distance of the corresponding azimuth angle of the same section in the previous construction cycle or initial reference state is calculated to obtain the local convergence amount at each azimuth angle. Take the absolute value of the local convergence for all azimuth angles and calculate its average value as the cross-sectional convergence. The crack connectivity distribution and cross-sectional convergence are normalized. After normalization, the crack connectivity distribution and cross-sectional convergence are weighted and summed according to the preset fusion weights to obtain the cross-sectional geometric features. The change value of cross-sectional geometric features is obtained by calculating the magnitude of change in cross-sectional geometric features in adjacent construction cycles; When the change value of the cross-sectional geometric characteristics is less than the preset geometric stability judgment threshold, the geometric state of the tunnel cross-section under test is determined to be in a stable state. When the change value of the cross-sectional geometric characteristics exceeds the preset geometric stability judgment threshold, the cross-sectional geometric state is determined to be unstable.

[0013] In a preferred embodiment, in step S4, when it is determined that the geometric state of the tunnel section to be tested is in a stable state, the historical database is accessed to retrieve the historical prediction baseline level. Historical prediction benchmark hierarchy refers to the structural response benchmark formed by multi-source sensor data during tunnel construction and stored according to construction stage and disturbance intensity hierarchy. Each historical prediction benchmark level corresponds to a set of historical detection benchmark records formed under the same construction stage and the same disturbance intensity level. Historical monitoring benchmark records include the upper and lower boundaries of the benchmark interval for surrounding rock displacement characteristics and support structure strain characteristics; Further screening was conducted in historical testing benchmark records to identify target disturbance intensity levels that are consistent with the current construction cycle disturbance intensity level.

[0014] In a preferred embodiment, in step S4, the associated marker detection results obtained in the current construction cycle are compared with the historical detection benchmark interval defined by the corresponding historical detection benchmark record: When the detection result of the associated marker falls within the historical detection benchmark interval corresponding to the target perturbation intensity level, the detection result of the associated marker is determined to be a valid update sample, and the historical prediction benchmark level corresponding to the target perturbation intensity level is updated based on the valid update sample. The boundary of the benchmark interval in the historical test benchmark record is corrected during the update process; If the associated marker detection result does not fall within the historical detection benchmark range, no update processing will be performed on the historical prediction benchmark level.

[0015] The technical effects and advantages of this invention are as follows: This invention collects blasting vibration velocity data and extracts peak values ​​during the construction cycle, dividing the construction cycle into different disturbance intensity levels. Under each disturbance intensity level, surrounding rock displacement data and support structure strain data are acquired and associated. The crack connectivity distribution and cross-sectional convergence of the tunnel cross-section are obtained through three-dimensional laser scanning, and fused to form cross-sectional geometric features. Based on these features, it is determined whether the cross-sectional geometric state is stable. When the cross-sectional geometric state meets the stability condition, the historical prediction benchmark level is selected based on the disturbance intensity level, and the historical prediction benchmark is updated using the associated marker detection results of the current construction cycle. By introducing a disturbance intensity grading and geometric stability determination mechanism, effective screening of multi-source monitoring data and adaptive benchmark correction under unified construction conditions are achieved. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of the IoT-based multi-source sensor data collaborative monitoring method for engineering structures according to the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the steps of the IoT-based collaborative monitoring method for engineering structures using multi-source sensor data. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention collects blasting vibration velocity data and extracts peak values ​​during the construction cycle, dividing the construction cycle into different disturbance intensity levels. Under each disturbance intensity level, surrounding rock displacement data and support structure strain data are acquired and associated. The crack connectivity distribution and cross-sectional convergence of the tunnel cross-section are obtained through three-dimensional laser scanning and fused to form cross-sectional geometric features. Based on these features, it is determined whether the cross-sectional geometric state is stable. When the cross-sectional geometric state meets the stability condition, the historical prediction benchmark level is selected based on the disturbance intensity level, and the historical prediction benchmark is updated using the associated marker detection results of the current construction cycle.

[0020] Example 1, such as Figures 1 to 2 As shown, the IoT-based multi-source sensor data collaborative monitoring method for engineering structures includes the following steps: Step S1: Conduct construction monitoring on the tunnel section to be tested, retrieve construction status information and extract the current construction stage and construction cycle time, collect blasting vibration velocity data of the tunnel section to be tested within the construction cycle time, and then count the peak vibration velocity. Step S2: Integrate the peak vibration velocity with the current construction stage to initially divide the construction cycle time into disturbance intensity levels, detect the surrounding rock displacement data and support structure strain data of the tunnel section to be tested, and associate and mark the detection results based on the disturbance intensity levels; Step S3: Perform three-dimensional laser scanning on the cross-section of the tunnel to be tested to obtain the crack connectivity distribution and cross-sectional convergence of the cross-section. Evaluate the geometric characteristics of the cross-section by combining the crack connectivity distribution and cross-sectional convergence and determine whether the geometric state of the cross-section is in a stable state. Step S4: When the cross-sectional geometry is in a stable state, access the historical database to retrieve the historical prediction baseline level and filter the target disturbance intensity level. Update the historical prediction baseline level using the correlation marker detection results of the target disturbance intensity level.

[0021] The specific implementation is as follows: In step S1, construction monitoring is carried out on the tunnel section to be tested. Construction status information is retrieved through the tunnel construction management unit. The construction status information reflects the current construction operation status of the tunnel section, including the current construction stage and construction cycle time. The current construction phase is the tunnel section operation phase identifier obtained according to the construction procedures; the construction cycle time is the time interval determined within the current construction phase, with one complete construction operation cycle as the unit. The blasting operation stage is the construction operation that applies the most concentrated instantaneous dynamic disturbance to the surrounding rock and support structure during tunnel construction. When the current construction phase is in the blasting operation phase, the blasting operation phase includes multiple blasting events. During the construction cycle time, the blasting vibration velocity is collected by the blasting vibration monitoring device deployed on the cross section of the tunnel to be tested, and the blasting vibration velocity is used as the blasting vibration velocity data. The preset background vibration threshold is compared with the blasting vibration velocity data to filter blasting event segments. If the blasting vibration velocity data is greater than the preset background vibration threshold, the blasting vibration velocity data is marked; otherwise, the blasting vibration velocity data is not marked. Arrange the marked blasting vibration velocity data in chronological order, and combine adjacent marked blasting vibration velocity data into blasting event segments; Within each blasting event segment, the maximum value of the marked blasting vibration velocity data is taken as the peak vibration velocity of the blasting event segment.

[0022] It should be noted that the tunnel construction management unit is a construction control platform used to record and manage tunnel construction procedures and operation stages; the preset background vibration threshold can be set according to the statistical distribution of vibration data from historical non-blasting operation stages.

[0023] In step S2, different disturbance intensity levels exert different degrees of instantaneous dynamic disturbance on the surrounding rock and support structure. During the current construction phase, the peak vibration velocity within the construction cycle time is sorted in chronological order, and the blasting event section is divided into different disturbance intensity levels based on the distribution of the peak vibration velocity. Retrieve the vibration velocity reference value for the current construction stage from the historical database. The vibration velocity reference value reflects the historical level of the peak vibration velocity of the blasting event under the current construction stage. The ratio of the peak vibration velocity to the reference vibration velocity value is used as the peak vibration velocity coefficient. The change in peak velocity obtained by subtracting adjacent peak velocity coefficients is arranged in descending order of numerical value. Then, the peak velocity coefficients are selected sequentially based on the preset number of layers to obtain the layer-segmented peak velocity. The construction cycle time is divided into disturbance intensity layers according to the acquisition time corresponding to the layer-segmented peak velocity. Within the time intervals corresponding to each level of disturbance intensity, the surrounding rock displacement measurement values ​​of the tunnel section under test are obtained through the surrounding rock displacement monitoring device. Among them, the average value of the surrounding rock displacement measurement within the preset baseline time window before the start time of the time segment corresponding to the disturbance intensity level is used as the displacement reference value; the average value of the surrounding rock displacement measurement within the preset evaluation time window after the end of the time segment corresponding to the disturbance intensity level is used as the displacement response value. The difference between the displacement response value and the displacement reference value is used as the surrounding rock displacement data; It should be explained that the preset baseline time window is used to eliminate the influence of monitoring noise and short-term disturbances on the initial state judgment of displacement and strain, and can be set according to the sampling frequency of the monitoring equipment and historical stable data; the preset evaluation time window can be set according to the length of the disturbance intensity level or the difference in construction stages. The strain measurement value of the support structure of the tunnel section under test is obtained by using a support structure strain monitoring device. The average strain measurement value of the support structure within the preset baseline time window before the start of the time interval corresponding to the disturbance intensity level is used as the strain reference value, and the average strain measurement value of the support structure within the preset evaluation time window after the end of the time interval corresponding to the disturbance intensity level is used as the strain response value. The difference between the strain response value and the strain reference value is used as the strain data of the support structure, reflecting the degree of change in the stress state of the support structure under the action of the disturbance intensity hierarchy. The surrounding rock displacement data and support structure strain data corresponding to each disturbance intensity level are used as the detection results, and the detection results are associated with the disturbance intensity level.

[0024] It should be noted that the preset number of levels is used to limit the number of disturbance intensity levels, and can be set according to the construction management requirements for the accuracy of disturbance classification and the number of historical blasting events; the surrounding rock displacement monitoring device is a structural deformation monitoring device that detects the displacement changes of the surrounding rock of the tunnel section relative to the initial reference position; the support structure strain monitoring device is a structural stress monitoring device that detects the strain changes of the stressed components of the tunnel support structure under external loads.

[0025] In step S3, a three-dimensional laser scanning process is performed on the tunnel cross-section to obtain geometric parameter data for quantitatively characterizing the geometric state of the cross-section. Specifically, after the corresponding construction cycle is completed, the three-dimensional laser scanning device deployed in the tunnel is controlled to scan the tunnel cross-section to be tested according to a preset scanning resolution and scanning angle to obtain three-dimensional spatial point cloud data of the inner wall surface of the cross-section.

[0026] It should be noted that the three-dimensional laser scanning device is a non-contact spatial measurement device deployed inside the tunnel. It is used to acquire the geometric shape of the inner wall of the tunnel cross section during construction. Based on the laser flight time, the propagation time of the laser beam from emission to reception is calculated, and the spatial distance from the laser scanning point to the scanning device is obtained accordingly. Combined with the laser emission direction information, the spatial distance is converted into three-dimensional spatial coordinates in the tunnel coordinate system, thereby forming three-dimensional spatial point cloud data composed of multiple spatial coordinate points.

[0027] Three-dimensional spatial point cloud data consists of several discrete points with spatial coordinates. Each spatial point contains its three-dimensional coordinates in the tunnel cross-section coordinate system, which are used as the raw data for cross-section geometric state analysis.

[0028] Based on the acquired 3D spatial point cloud data, the crack connectivity distribution is extracted. Specifically, local plane fitting is performed on the 3D spatial point cloud data to calculate the surface normal vector corresponding to each spatial point, and geometric abrupt change discrimination conditions are constructed based on the angle between the normal vectors of adjacent spatial points and the distance between points. When the angle between the normal vectors of adjacent spatial points is greater than a preset normal vector abrupt change threshold, and the distance between points exceeds a preset gap judgment threshold, crack feature points are judged to exist in the region.

[0029] It should be noted that the normal vector mutation threshold is an angle threshold used to determine whether the surface orientation of adjacent spatial points has changed significantly. It is defined as the upper limit of the allowable angle between the surface normal vectors of adjacent spatial points. By statistically analyzing the distribution of the angle between the normal vectors of adjacent spatial points in the point cloud data of crack-free areas, the upper quantile value of its statistical distribution is selected as the normal vector mutation threshold. The gap judgment threshold is a distance threshold used to determine whether there is an actual geometric discontinuity between adjacent spatial points. It is defined as the minimum abnormal judgment value of the distance between adjacent spatial points. By statistically analyzing the distribution of the distance between points on a normal continuous surface, a distance value higher than the normal fluctuation range of the distance between points is selected as the gap judgment threshold.

[0030] Spatial clustering of crack feature points is performed to obtain several crack clusters. Based on the minimum spatial distance and consistency of extension direction between crack clusters, it is determined whether the crack clusters are interconnected, thus forming a crack connectivity distribution. The crack connectivity distribution is used to quantitatively describe whether cracks in the cross section of the tunnel under test form a continuous through structure. Its numerical results reflect the degree of local geometric discontinuity of the cross section.

[0031] While extracting the crack connectivity distribution, the cross-sectional convergence is calculated based on three-dimensional spatial point cloud data. Specifically, multiple characteristic azimuth angles uniformly distributed along the circumference are selected in the tunnel cross-sectional coordinate system. The radial distance from the current scanned cross-sectional contour point to the geometric center of the cross-section is determined in the direction of each characteristic azimuth angle. The difference between this distance and the radial distance of the corresponding azimuth angle of the same cross-section in the previous construction cycle or initial reference state is calculated to obtain the local convergence at each azimuth angle.

[0032] Furthermore, the absolute value of the local convergence corresponding to all azimuth angles is taken and the average value is calculated as the cross-sectional convergence, which is used to quantitatively characterize the shrinkage level of the overall geometric dimensions of the tunnel cross-section under test.

[0033] The crack connectivity distribution and cross-sectional convergence are normalized to map them to a uniform numerical range, thereby eliminating the influence of dimensional differences on the fusion calculation. After normalization, the crack connectivity distribution and cross-sectional convergence are weighted and summed according to the preset fusion weights to obtain the cross-sectional geometric features.

[0034] It should be noted that the fusion weights are used to reflect the relative importance of crack connectivity distribution and cross-sectional convergence in the assessment of cross-sectional geometric stability. Their setting is based on historical statistical results of their influence on cross-sectional geometric stability. Specifically, historical samples with completed construction and clearly determined cross-sectional geometric states as stable or unstable are selected from the historical database. The variation range and discriminative power of crack connectivity distribution and cross-sectional convergence under different geometric states are statistically analyzed. By calculating the sensitivity of each parameter to the cross-sectional geometric state determination results, the normalized sensitivity is used as the fusion weight.

[0035] The cross-sectional geometric features characterize the local crack connectivity evolution and the overall cross-sectional convergence evolution. The change range of cross-sectional geometric features in adjacent construction cycles is calculated to obtain the cross-sectional geometric feature change value. When the change value of the cross-sectional geometric characteristics is less than the preset geometric stability judgment threshold, the geometric state of the tunnel cross-section under test is determined to be in a stable state. When the change value of the cross-sectional geometric characteristics exceeds the preset geometric stability judgment threshold, the cross-sectional geometric state is determined to be unstable.

[0036] It should be noted that the geometric stability threshold is a numerical limit used to determine whether the cross-sectional geometric state is stable. It is defined as the maximum allowable variation of cross-sectional geometric features between consecutive construction cycles. The specific setting method is based on the statistical analysis of the variation of cross-sectional geometric features in the determined stable state in historical construction cycle data. The distribution range, mean and standard deviation of the variation values ​​of cross-sectional geometric features in multiple construction cycles are calculated, and the sum of the mean and standard deviation is used as the geometric stability threshold.

[0037] By quantifying and integrating the distribution of crack connectivity and the convergence of cross sections, an objective expression of cross section geometric characteristics is achieved, avoiding the one-sidedness caused by a single geometric parameter. This provides a unified and quantifiable geometric criterion for subsequent monitoring data screening and prediction benchmark updates.

[0038] In step S4, when the geometric state of the tunnel section to be tested is determined to be stable, the historical prediction benchmark update process is initiated. Specifically, when the geometric characteristic values ​​of the section meet the corresponding geometric stability judgment conditions, the structural response detection results collected in the current construction cycle are considered to be representative of the project and can be used as valid samples for prediction benchmark updates. At this time, the historical database is accessed to retrieve the historical prediction benchmark level.

[0039] It should be noted that the historical database is a structured data storage unit used to store and manage multi-source monitoring historical data and corresponding prediction benchmark information generated during tunnel construction. The historical database uses the tunnel cross-section as the basic index object and stores historical prediction benchmarks hierarchically according to construction stage and disturbance intensity level to distinguish the structural response characteristics under different construction conditions.

[0040] Historical prediction baseline refers to the structural response baseline formed by multi-source sensor data during tunnel construction and stored according to construction stage and disturbance intensity hierarchy.

[0041] The historical database pre-classifies and stores historical prediction benchmark levels according to construction stage and disturbance intensity level. Each historical prediction benchmark level corresponds to a set of historical detection benchmark records formed under the same construction stage and the same disturbance intensity level.

[0042] Historical monitoring benchmark records are derived from previous construction cycles in which the cross-sectional geometry was determined to be in a stable state. They are used to describe the normal variation range of surrounding rock displacement data and support structure strain data under the corresponding construction conditions.

[0043] Historical testing benchmark records include the upper and lower boundaries of the benchmark intervals for surrounding rock displacement characteristics and support structure strain characteristics, serving as the basis for subsequent testing result screening and updating.

[0044] Subsequently, based on the disturbance intensity hierarchy, a target disturbance intensity hierarchy that is consistent with the current construction cycle disturbance intensity hierarchy was further screened from the historical detection benchmark records.

[0045] The target disturbance intensity hierarchy is used to limit the screening of subsequent test results and the updating of prediction benchmarks to be carried out only under the same construction action conditions, thereby avoiding the mixing of structural responses between different disturbance conditions.

[0046] After determining the target disturbance intensity level, retrieve the historical detection benchmark interval corresponding to the target disturbance intensity level, and compare the associated marker detection results obtained in the current construction cycle with the historical detection benchmark interval.

[0047] The associated marker detection results are the surrounding rock displacement data and support structure strain data that have been bound with disturbance intensity level markers in step S2. By judging whether the associated marker detection results fall within the historical detection benchmark interval defined by the corresponding historical detection benchmark record, it is used to determine whether the associated marker detection results conform to the normal structural response characteristics under the same construction stage and the same disturbance intensity level conditions.

[0048] When the correlation marker detection result falls within the historical detection benchmark interval corresponding to the target perturbation intensity level, the correlation marker detection result is determined to be a valid update sample, and the historical prediction benchmark level corresponding to the target perturbation intensity level is updated based on the valid update sample.

[0049] The update process is achieved by correcting the boundary of the benchmark interval in the historical detection benchmark record. Specifically, the surrounding rock displacement data and support structure strain data obtained in the current construction cycle are used as new statistical samples and introduced into the historical detection benchmark record of the corresponding disturbance intensity level. By updating the statistical distribution of the structural response characteristics under the disturbance intensity level, the upper and lower boundaries of the original benchmark interval are numerically recalculated, so that the corrected benchmark interval covers the representative structural response value range in the updated sample set. This allows the historical prediction benchmark level to gradually reflect the range of structural response changes under real engineering conditions as the construction process progresses and data accumulates.

[0050] When the associated marker detection result does not fall within the historical detection benchmark range, no update processing is performed on the historical prediction benchmark level to prevent abnormal or non-representative data from interfering with the prediction benchmark.

[0051] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0052] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0053] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0054] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0055] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A collaborative monitoring method for multi-source sensor data of engineering structures based on the Internet of Things, characterized in that: Includes the following steps: Step S1: Conduct construction monitoring on the tunnel section to be tested, retrieve construction status information and extract the current construction stage and construction cycle time, collect blasting vibration velocity data of the tunnel section to be tested within the construction cycle time, and then count the peak vibration velocity. Step S2: Integrate the peak vibration velocity with the current construction stage to initially divide the construction cycle time into disturbance intensity levels, detect the surrounding rock displacement data and support structure strain data of the tunnel section to be tested, and associate and mark the detection results based on the disturbance intensity levels; Step S3: Perform three-dimensional laser scanning on the cross-section of the tunnel to be tested to obtain the crack connectivity distribution and cross-sectional convergence of the cross-section. Evaluate the geometric characteristics of the cross-section by combining the crack connectivity distribution and cross-sectional convergence and determine whether the geometric state of the cross-section is in a stable state. Step S4: When the cross-sectional geometry is in a stable state, access the historical database to retrieve the historical prediction baseline level and filter the target disturbance intensity level. Update the historical prediction baseline level using the correlation marker detection results of the target disturbance intensity level.

2. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 1, characterized in that: In step S1, the construction status information, including the current construction stage and the corresponding construction cycle time, is retrieved through the tunnel construction management unit. When the current construction phase is in the blasting operation phase, the blasting vibration velocity is collected by the blasting vibration monitoring device deployed on the cross section of the tunnel to be tested during the construction cycle time, and the blasting vibration velocity is used as the blasting vibration speed data.

3. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 2, characterized in that: In step S1, if the blasting vibration velocity data is greater than the preset background vibration threshold, the blasting vibration velocity data is marked; otherwise, the blasting vibration velocity data is not marked. Arrange the marked blasting vibration velocity data in chronological order, and combine adjacent marked blasting vibration velocity data into blasting event segments; Within each blasting event segment, the maximum value of the marked blasting vibration velocity data is taken as the peak vibration velocity of the blasting event segment.

4. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 1, characterized in that: In step S2, the vibration velocity reference value of the current construction stage is retrieved from the historical database, and the ratio of the peak vibration velocity to the reference value is used as the peak vibration velocity coefficient. The peak vibration velocity is sorted in chronological order. The change in peak vibration velocity is obtained by subtracting the coefficients of adjacent peak vibration velocity. The change in peak vibration velocity is then sorted in descending order of numerical value. Based on the preset number of layers, the peak vibration velocity coefficients are selected sequentially to obtain the layered segmented peak vibration velocity. The construction cycle time is divided into disturbance intensity levels based on the acquisition time corresponding to the peak vibration velocity of each level.

5. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 4, characterized in that: In step S2, the surrounding rock displacement measurement value of the tunnel section to be tested is obtained through the surrounding rock displacement monitoring device in the time interval corresponding to each level of disturbance intensity. The mean value of the surrounding rock displacement measured before the time interval corresponding to the disturbance intensity level is used as the displacement reference value; the mean value of the surrounding rock displacement measured after the time interval corresponding to the disturbance intensity level is used as the displacement response value. The surrounding rock displacement data is calculated based on the displacement response value and the displacement reference value; The strain measurement value of the support structure of the tunnel section under test is obtained by using a support structure strain monitoring device. The strain data of the support structure are calculated based on the strain measurement values ​​of the support structure. The surrounding rock displacement data and support structure strain data corresponding to each disturbance intensity level are used as the detection results, and the detection results are associated with the disturbance intensity level.

6. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 1, characterized in that: In step S3, after the corresponding construction cycle is completed, the three-dimensional laser scanning device deployed in the tunnel is controlled to scan the tunnel section to be tested according to the preset scanning resolution and scanning angle to obtain the three-dimensional spatial point cloud data of the inner wall surface of the section. Three-dimensional spatial point cloud data consists of several discrete points with spatial coordinates, and each spatial point contains its three-dimensional coordinates in the tunnel cross-section coordinate system. Local plane fitting is performed on the three-dimensional spatial point cloud data to calculate the angle between the normal vectors of adjacent spatial points and the distance between points. When the angle between the normal vectors of adjacent spatial points is greater than the preset normal vector mutation threshold and the distance between points exceeds the preset gap judgment threshold, the crack feature points are judged to exist in the region. Spatial clustering is performed on the crack feature points to obtain several crack clusters. The interconnection of crack clusters is determined based on the minimum spatial distance and the consistency of the extension direction between crack clusters, thus forming a crack interconnection distribution.

7. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 1, characterized in that: In step S3, multiple characteristic azimuth angles uniformly distributed along the circumference are selected in the tunnel cross-section coordinate system; In each characteristic azimuth direction, the radial distance from the current scan section profile point to the geometric center of the section is determined, and the difference between this distance and the radial distance of the corresponding azimuth angle of the same section in the previous construction cycle or initial reference state is calculated to obtain the local convergence amount at each azimuth angle. Take the absolute value of the local convergence for all azimuth angles and calculate its average value as the cross-sectional convergence. The crack connectivity distribution and cross-sectional convergence are normalized. After normalization, the crack connectivity distribution and cross-sectional convergence are weighted and summed according to the preset fusion weights to obtain the cross-sectional geometric features. The change value of cross-sectional geometric features is obtained by calculating the magnitude of change in cross-sectional geometric features in adjacent construction cycles; When the change value of the cross-sectional geometric characteristics is less than the preset geometric stability judgment threshold, the geometric state of the tunnel cross-section under test is determined to be in a stable state. When the change value of the cross-sectional geometric characteristics exceeds the preset geometric stability judgment threshold, the cross-sectional geometric state is determined to be unstable.

8. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 7, characterized in that: In step S4, when it is determined that the geometric state of the tunnel section to be tested is in a stable state, the historical database is accessed to retrieve the historical prediction baseline level. Historical prediction benchmark hierarchy refers to the structural response benchmark formed by multi-source sensor data during tunnel construction and stored according to construction stage and disturbance intensity hierarchy. Each historical prediction benchmark level corresponds to a set of historical detection benchmark records formed under the same construction stage and the same disturbance intensity level. Historical monitoring benchmark records include the upper and lower boundaries of the benchmark interval for surrounding rock displacement characteristics and support structure strain characteristics; Further screening was conducted in historical testing benchmark records to identify target disturbance intensity levels that are consistent with the current construction cycle disturbance intensity level.

9. The method for collaborative monitoring of multi-source sensor data of engineering structures based on the Internet of Things according to claim 8, characterized in that: In step S4, the associated marker detection results obtained in the current construction cycle are compared with the historical detection benchmark interval defined by the corresponding historical detection benchmark record: When the detection result of the associated marker falls within the historical detection benchmark interval corresponding to the target perturbation intensity level, the detection result of the associated marker is determined to be a valid update sample, and the historical prediction benchmark level corresponding to the target perturbation intensity level is updated based on the valid update sample. The boundary of the benchmark interval in the historical test benchmark record is corrected during the update process; If the associated marker detection result does not fall within the historical detection benchmark range, no update processing will be performed on the historical prediction benchmark level.

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