Building construction site data acquisition system based on internet of things

Abstract

The application discloses a building construction site data acquisition system based on Internet of Things and relates to the technical field of building engineering. The system is provided with a sound wave acquisition module, three-dimensional space is laid out with acquisition points in underground pouring structures, and an excitation source position is set in combination with the position of a pre-buried anchoring support, so that sound wave reverberation signals generated by wideband vibration signals are used to realize high-density and high-time-efficiency data acquisition. The MEMS three-axis accelerometer arranged in the acquisition point can collect the sound wave decay time Tdec and the reverberation residual energy peak value Ares of each acquisition point in real time, form a standard vibration sound wave energy data set, and provide a data basis for subsequent structure integrity judgment. Compared with traditional manual detection, image recognition and external detection methods, the system has the advantages of not needing to be damaged and not needing to additionally lay out ultrasonic equipment, and can realize early warning of internal interlayers, cavities and cold joints and real-time visualization of structure states in the construction process.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of building engineering, in particular to a building construction site data acquisition system based on Internet of Things. BACKGROUND

[0002] The structural data intelligent acquisition system for building construction whole-process state sensing and abnormal early warning belongs to the application direction of Internet of Things technology in the field of engineering construction, and is further focused on the internal compactness and structural integrity monitoring problems in underground structure or mass concrete pouring engineering. In the current construction scenes of large-scale infrastructure engineering, underground pipe corridors, subway stations and deep foundation pits, the pouring quality of concrete directly affects the subsequent structural safety, and the system is proposed in this background, which is a dynamic sensing and decision system based on Internet of Things and fusing acoustic wave and vibration signal sensing, aiming to realize non-destructive, high-density and effective internal cavity detection and consistency judgment.

[0003] At present, for the detection of the structure filling quality in the above concrete pouring process, the mainstream method mainly depends on manual knocking method, laser scanning combined with image recognition or a small amount of ultrasonic probe point detection. These methods have significant limitations: the manual knocking method cannot penetrate the deep defects and has strong subjectivity; the image scanning is affected by light obstruction and shield interference; and the ultrasonic method needs to lay coupling medium, is inconvenient for construction, and is sparse in point, and it is difficult to form a continuous judgment model of the structure interior. Especially in the mass concrete structure or continuous bottom plate area, due to the complex internal vibration response path, the existing detection methods lack systematicness and spatial resolution, and it is easy to form a detection blind area, which leads to the inability to fully identify possible cavities, interlayers or cold joints.

[0004] The fundamental reason for the above-mentioned deficiencies is that the current detection means has single sensing dimension, discrete signal model, lack of spatial fusion algorithm and real-time inversion mechanism. When the concrete forms an unfilled area such as cavity, interlayer or cold joint after pouring, it will not be found in time, which will cause a series of abnormal consequences: early shrinkage stress concentration of structure, internal hydration heat stress concentration release, insufficient compactness leading to increased risk of carbonization of concrete and corrosion of steel bars in later period, finally causing the decline of structure bearing capacity and the weakening of anti-cracking performance, and even causing serious deviation in the overall durability and safety level evaluation of underground structure. Therefore, an intelligent acquisition system without damage, which can densely arrange, fuse acoustic wave and vibration dual-source information, and realize real-time response based on Internet of Things platform, is needed to realize scientific evaluation and early defect intervention of structure compactness. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides a building construction site data acquisition system based on Internet of Things, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: including a sound wave acquisition module, a central processing module, a sound wave reverberation analysis module, a vibration wavelength interference module, and a sound vibration consistency analysis module.

[0007] Acoustic wave acquisition module: By setting acquisition points on the underground cast-in-place structure and setting up vibration sensor groups within the acquisition points to form a three-dimensional acoustic wave acquisition matrix, the vibration acoustic wave energy data generated during the excitation process is acquired in real time.

[0008] Central processing module: Set up a data acquisition system, use the Internet of Things to transmit vibration and sound wave energy data to the data acquisition system, and preprocess the vibration and sound wave energy data in the data acquisition system to obtain a standard vibration and sound wave energy dataset.

[0009] Sound reverberation analysis module: Based on the standard vibration sound energy dataset, it calculates and outputs the vibration sound energy attenuation morphology dispersion factor Decho, and at the same time presets the vibration sound energy threshold Dth for preliminary comparative evaluation.

[0010] Vibration wavelength interference module: Based on the preliminary comparative evaluation results, an inversion correction mechanism is triggered. The inversion correction mechanism expands the acquisition points, resets the vibration sensor group, acquires vibration interference data, and calculates and outputs the structural vibration consistency factor Iinter based on the vibration interference data.

[0011] The acoustic-vibration consistency analysis module calculates the structural interference consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho, outputs the structural acoustic-vibration consistency summary index Scon, and performs a secondary comparative evaluation based on the output results of the structural acoustic-vibration consistency summary index Scon.

[0012] Preferably, the acoustic wave acquisition module includes an acoustic wave matrix construction unit and an excitation acquisition unit;

[0013] The acoustic matrix construction unit constructs a three-dimensional acoustic acquisition matrix by setting up acquisition points on the underground cast-in-place structure. These acquisition points are arranged in a three-dimensional space by horizontal and vertical arrangements. At the same time, a vibration sensor group is set up in each acquisition point.

[0014] The excitation acquisition unit generates excitation characteristics by setting an excitation source position in the underground cast-in-place structure and exciting the excitation source position. It then collects the vibration sound wave energy data by real-time monitoring of the broadband vibration signal generated during the excitation process through a three-dimensional acoustic wave acquisition matrix.

[0015] The vibration acoustic wave energy data includes the acoustic wave decay time Tdec at the i-th acquisition point. i And the peak residual energy Ares of the i-th acquisition point i .

[0016] Preferably, the central processing module includes a data transmission unit and a data processing unit.

[0017] The data transmission unit uses LoRa IoT communication to wirelessly connect the communication module of the vibration sensor group to the data acquisition system, and wirelessly transmits the real-time collected vibration sound wave energy data to the data acquisition system according to the MQTT transmission protocol and TLS encrypted connection.

[0018] The data processing unit preprocesses the vibration and acoustic wave energy data in the data acquisition system to obtain a standard vibration and acoustic wave energy dataset.

[0019] The preprocessing includes excitation time synchronization calibration, denoising and filtering, and normalization.

[0020] The excitation time synchronization calibration is achieved by setting the same excitation event as the starting point for all acquisition points, recording the excitation start timestamp of this excitation, and aligning the time axis of all vibration and acoustic energy data acquired during the excitation process with the excitation start timestamp as 0 seconds.

[0021] The denoising and filtering process uses a bandpass filter to filter the vibration acoustic energy data, retaining only the vibration acoustic energy data obtained from the structural acoustic frequency band, and uses wavelet denoising to process the instantaneous peak values ​​of the vibration acoustic energy data.

[0022] The normalization process uses the Max-Min normalization method to normalize the vibration and acoustic energy data after denoising and filtering, thereby eliminating the influence of the dimensions of all parameters in the vibration and acoustic energy data.

[0023] Preferably, the acoustic reverberation analysis module includes a vibration acoustic wave energy attenuation morphology analysis unit and a vibration acoustic wave energy morphology evaluation unit.

[0024] The vibration acoustic energy attenuation pattern analysis unit calculates and outputs the vibration acoustic energy attenuation pattern dispersion factor Decho based on the standard vibration acoustic energy data obtained from all sampling points. This factor measures the difference between the attenuation rate and reverberation intensity of each sampling point and the global mean.

[0025] The vibration sound wave energy attenuation morphology dispersion factor Decho is calculated and output using the following algorithm formula.

[0026] ;

[0027] In the formula, n represents the total number of data collection points. This represents the average sound wave decay time across all sampling points. This represents the peak value of the residual energy of the echo at all sampling points. The standard deviation of sound wave decay time. The standard deviation of the peak residual energy of the echo is represented by the standard deviation of the peak residual energy. Reverberation intensity sensitive adjustment coefficient.

[0028] Preferably, the vibration sound wave energy pattern assessment unit obtains the vibration sound wave energy attenuation pattern dispersion factor Decho in the defect-free region, takes the upper limit of the 95% confidence interval as the vibration sound wave energy threshold Dth, and then performs a preliminary comparison assessment between the vibration sound wave energy attenuation pattern dispersion factor Decho and the vibration sound wave energy threshold Dth to determine the vibration sound wave energy propagation situation. Based on the preliminary comparison assessment results, a trigger inversion correction mechanism is performed. The specific assessment content is as follows.

[0029] When the dispersion factor of vibration sound wave energy attenuation morphology Decho is less than or equal to the vibration sound wave energy threshold Dth, it indicates that the sound wave propagation state of the underground cast-in-place structure is stable, which indicates that the casting is successful.

[0030] When the dispersion factor of vibration sound wave energy attenuation morphology Decho is greater than the vibration sound wave energy threshold Dth, it indicates that the sound wave propagation state of the underground cast-in-place structure is abnormal, and the inversion correction mechanism is triggered at this time.

[0031] Preferably, the vibration wavelength interference module includes a sampling point reconstruction unit and a structural interference unit;

[0032] The acquisition point reconstruction unit, after triggering the inversion correction mechanism through preliminary comparison and evaluation, expands the acquisition points, sets up a vibration sensor group within the expanded acquisition points, increases the sampling frequency of the vibration sensor group by 50%, and increases the excitation frequency range by 20% to acquire vibration interference data. The vibration interference data is then transmitted to the data acquisition system through the data transmission unit. In the data acquisition system, the vibration interference data is normalized to eliminate the dimensional influence of all parameters in the vibration interference data.

[0033] The vibration interferometric data includes the vibration interferometric symmetry offset vector Vsymmetric of the j-th reconstructed acquisition point. j The phase deviation vector Vx of the j-th reconstructed acquisition point j .

[0034] Preferably, the structural interference unit extracts vibration interference data for calculation and outputs a structural vibration consistency factor Iinter to measure the severity of local acoustic disturbances in the underground cast-in-place structure.

[0035] The structural vibration consistency factor Iinter is calculated and output using the following algorithm.

[0036] .

[0037] In the formula, m represents the total number of reconstructed data collection points. This represents the mean of the vibration interference symmetric offset vectors at all reconstructed acquisition points. denoted as the mean of the phase deviation vector of all reconstructed acquisition points, where u and v represent the weight values ​​of symmetry offset and phase anomaly, respectively.

[0038] Preferably, the acoustic vibration consistency analysis module includes an acoustic vibration analysis unit, a consistency evaluation unit, and a strategy execution unit.

[0039] The acoustic vibration analysis unit calculates the structural vibration consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho by combining the obtained structural vibration consistency factor Iinter and the output structural acoustic vibration consistency summary index Scon to measure the risk of continuous damage to the current structural region.

[0040] The structural acoustic-vibration consistency index Scon is calculated and output using the following algorithm formula.

[0041] ;

[0042] In the formula, The standard deviation represents the dispersion factor of the energy attenuation morphology of vibrating sound waves.

[0043] Preferably, the consistency assessment unit performs a secondary comparative assessment based on the output results of the structural acoustic-vibration consistency summary index Scon, and classifies the results into different response levels to determine the overall consistency of the underground cast-in-place structure. The specific assessment content is as follows.

[0044] When the structural acoustic-vibration consistency index Scon < 0.9, it is classified as a Level 1 response.

[0045] When 0.9 ≤ Scon, the structural acoustic-vibration consistency index, is less than 1.3, it is classified as a level two response.

[0046] When the structural acoustic-vibration consistency index Scon ≥ 1.3, it is classified as a level three response.

[0047] Preferably, the strategy execution unit executes different control strategies based on different response levels, and the specific control strategies are as follows.

[0048] When classified as a Level 1 response, no intervention is required.

[0049] When the response level is classified as Level II, samples are collected and re-examined the following day. If the response level remains Level II, vibration treatment is initiated.

[0050] When classified as a Level 3 response, the current area is marked in red to indicate a need for meticulous construction intervention, and localized directional grouting and vibration treatment should be initiated immediately.

[0051] This invention provides an Internet of Things (IoT)-based data acquisition system for building construction sites. It offers the following advantages.

[0052] (1) This system, by setting up an acoustic wave acquisition module, deploys acquisition points in three-dimensional space within the underground cast-in-place structure and sets the excitation source position in conjunction with the location of the pre-embedded anchor support, realizing high-density and high-time-efficiency data acquisition using acoustic reverberation signals generated by broadband vibration signals. The MEMS triaxial accelerometers installed at the acquisition points can collect the acoustic wave decay time Tdec and the peak value of the reverberation residual energy Ares at each acquisition point in real time, forming a standard vibration acoustic wave energy dataset, providing a data basis for subsequent structural integrity assessment. Compared with traditional manual inspection, image recognition, and external detection methods, this system has the advantages of being non-destructive and requiring no additional ultrasonic equipment, enabling early warning and real-time visualization of the structural status of internal interlayers, cavities, and cold joints during construction.

[0053] (2) The system transmits vibration acoustic energy data to the data acquisition system in real time through the central processing module. After excitation synchronization calibration, noise reduction filtering, and normalization, the vibration acoustic energy attenuation morphology dispersion factor Decho is constructed and preliminarily evaluated in the acoustic reverberation analysis module. When the vibration acoustic energy attenuation morphology dispersion factor Decho is higher than the vibration acoustic energy threshold Dth constructed based on the defect-free region, the system automatically triggers the inversion correction mechanism of the vibration wavelength interference module, expands the acquisition points in the abnormal region and increases the sensor sampling frequency, acquires the vibration interference symmetry offset vector Vsym and phase deviation vector Vx, and calculates the structural vibration consistency factor Iinter, realizing the fine inversion and difference enhancement identification of abnormal parts inside the structure. This mechanism has high response sensitivity and local structural continuity judgment ability, significantly improving the positioning accuracy and analysis dimension of internal defects in heterogeneous concrete structures.

[0054] (3) In the acoustic-vibration consistency analysis module, the system outputs a structural acoustic-vibration consistency index Scon by integrating the acoustic anomaly factor Decho and the structural vibration consistency factor Iinter. Based on its numerical range, the system performs a secondary assessment of structural consistency and classifies the response level: when the structural acoustic-vibration consistency index Scon < 0.9, it is automatically archived; when 0.9 ≤ Scon < 1.3, it is marked for review the next day; and when Scon ≥ 1.3, it performs local grouting and high-frequency vibration. Through this mechanism, a refined handling strategy can be automatically matched to the structural anomaly level, constructing a closed-loop control system covering the entire process from anomaly detection and assessment to construction intervention. This effectively achieves early identification, early response, and early reinforcement of structural quality risks, ensuring stable project quality and improved construction efficiency. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the Internet of Things-based construction site data acquisition system of the present invention.

[0056] Figure 2 This is a schematic diagram of the layout of the three-dimensional acoustic wave acquisition matrix of the present invention. Detailed Implementation

[0057] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1, please refer to Figure 1 and Figure 2 This invention provides a data acquisition system for construction sites based on the Internet of Things. To achieve the above objectives, this invention is implemented through the following technical solutions: including a sound wave acquisition module, a central processing module, a sound wave reverberation analysis module, a vibration wavelength interference module, and a sound vibration consistency analysis module.

[0059] Acoustic wave acquisition module: By setting acquisition points on the underground cast-in-place structure and setting up vibration sensor groups within the acquisition points to form a three-dimensional acoustic wave acquisition matrix, the vibration acoustic wave energy data generated during the excitation process is acquired in real time.

[0060] Central processing module: Set up a data acquisition system, use the Internet of Things to transmit vibration and sound wave energy data to the data acquisition system, and preprocess the vibration and sound wave energy data in the data acquisition system to obtain a standard vibration and sound wave energy dataset.

[0061] Sound reverberation analysis module: Based on the standard vibration sound energy dataset, it calculates and outputs the vibration sound energy attenuation morphology dispersion factor Decho, and at the same time presets the vibration sound energy threshold Dth for preliminary comparative evaluation.

[0062] Vibration wavelength interference module: Based on the preliminary comparative evaluation results, the inversion correction mechanism is triggered. The inversion correction mechanism expands the acquisition points, resets the vibration sensor group, acquires vibration interference data, and calculates and outputs the structural vibration consistency factor Iinter based on the vibration interference data.

[0063] The acoustic-vibration consistency analysis module calculates the structural interference consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho, outputs the structural acoustic-vibration consistency summary index Scon, and performs a secondary comparative evaluation based on the output results of the structural acoustic-vibration consistency summary index Scon.

[0064] In this embodiment, the system constructs an integrated structural condition assessment system consisting of a sound wave acquisition module, a central processing module, a sound wave reverberation analysis module, a vibration wavelength interference module, and a sound-vibration consistency analysis module. This system enables real-time, high-resolution, non-destructive detection and dynamic intervention control of issues such as concrete compaction, residual cavities, and interlayer defects in underground cast-in-place structures. Specifically, the sound wave acquisition module acquires broad-coverage vibration sound wave energy data through a three-dimensional acquisition matrix; the central processing module efficiently transmits the raw signals via IoT communication and performs unified timing and preprocessing; the sound wave reverberation analysis module constructs and initially evaluates the vibration sound wave energy attenuation morphology dispersion factor Decho; the vibration wavelength interference module activates a local resampling mechanism and structural inversion analysis based on the evaluation results to further calculate the structural vibration consistency factor Iinter; and finally, the sound-vibration consistency analysis module comprehensively outputs the structural sound-vibration consistency summary index Scon, achieving secondary judgment and anomaly level classification. Compared to existing methods such as manual tapping, radar imaging, or point-based ultrasonic testing, this invention's system achieves full-process, multi-modal structural condition identification and predictive repair decisions without requiring additional ultrasonic equipment or drilling. This significantly improves the accuracy and reliability of detecting hidden defects in deeply buried, large-volume concrete structures. Furthermore, through collaborative triggering and tiered handling strategies between modules, a complete closed loop of "perception to analysis to response to feedback" is formed during the construction process. This allows quality problems to be identified and repaired before the concrete initially sets, effectively reducing rework rates and increasing the first-time acceptance rate. While ensuring structural quality stability and construction progress, this system also provides a valuable technological paradigm for intelligent monitoring and automated decision-making in complex engineering scenarios.

[0065] Example 2, please refer to Figure 1 and Figure 2Specifically, the acoustic wave acquisition module includes an acoustic wave matrix construction unit and an excitation acquisition unit.

[0066] The acoustic wave matrix construction unit constructs a three-dimensional acoustic wave acquisition matrix by setting up acquisition points on the underground cast-in-place structure. The acquisition points are arranged in a three-dimensional space by horizontal and vertical arrangement. At the same time, a vibration sensor group is set in each acquisition point.

[0067] The horizontal deployment involves setting up 8 collection points on each layer of the structure to form a monitoring surface.

[0068] Vertical deployment involves placing a set of sampling points every 2 meters in height to create a three-dimensional spatial sampling system.

[0069] The excitation acquisition unit generates excitation characteristics by setting excitation source positions in the underground cast-in-place structure and exciting the excitation source positions. It then collects the vibration sound wave energy data by real-time monitoring of the broadband vibration signals generated during the excitation process through a three-dimensional acoustic wave acquisition matrix.

[0070] The vibration sensor group includes a MEMS triaxial accelerometer.

[0071] The excitation source is located at the anchorage bracket that has been pre-embedded during construction.

[0072] The excitation feature is to generate a broadband vibration signal by striking the anchor support with an electric impact hammer, thereby exciting the anchor support. The vibration sound wave energy is collected by multiple reflections formed by the broadband vibration signal propagating inside the underground cast-in-place structure.

[0073] Vibrational acoustic energy data includes the acoustic decay time Tdec at the i-th acquisition point. i And the peak residual energy Ares of the i-th acquisition point i .

[0074] The acoustic attenuation time Tdec at the i-th sampling point i The vibration sound wave energy is continuously collected by a MEMS triaxial accelerometer, and the time interval from the start of excitation to the energy decay to 30% is determined.

[0075] Peak residual energy of the i-th sampling point (Ares) i The peak value of residual vibration sound wave energy during the continuous reverberation stage after the excitation of vibration sound wave energy is collected by a MEMS triaxial accelerometer.

[0076] In this embodiment, the system constructs a three-dimensional acoustic sensing system suitable for quality assessment of underground cast-in-place structures by setting up an acoustic matrix construction unit and an excitation acquisition unit. It constructs a three-dimensional acoustic acquisition point array covering the entire structure through a combination of horizontal and vertical layout. Each acquisition point integrates a MEMS triaxial accelerometer, forming a vibration acoustic energy sensing network with spatial distribution characteristics. Combined with the pre-embedded anchoring supports as excitation source locations, a broadband vibration signal is periodically excited by an electric impact hammer, achieving acoustic wave injection and multiple reflection excitation within the underground structure. This allows for the acquisition of two key response indicators—acoustic wave decay time Tdec and reverberation residual energy peak Ares—without damaging the structural surface. Compared to traditional methods of assessing concrete compactness relying on surface tapping, point detection, or manual sampling, this module achieves full-structure, three-dimensional, high-density, and high-time-frequency resolution acquisition of acoustic data, significantly improving the probability of detecting potential internal defects such as interlayers, cavities, and cold joints. Meanwhile, the standardized setting of the excitation source location and the consistent excitation of the vibration signal ensure the comparability and temporal stability of the collected data, effectively supporting the accuracy of subsequent acoustic reverberation analysis. This module provides quantifiable, visualized, and comparable raw basic data for the acoustic response characteristics of underground structures, and has significant engineering application value and promotion prospects in improving the accuracy of building construction quality monitoring and realizing intelligent early warning.

[0077] Example 3, please refer to Figure 1 Specifically, the central processing module includes a data transmission unit and a data processing unit.

[0078] The data transmission unit uses LoRa IoT communication to wirelessly connect the communication module of the vibration sensor group to the data acquisition system. Based on the MQTT transmission protocol and TLS encrypted connection, it wirelessly transmits the real-time collected vibration sound wave energy data to the data acquisition system.

[0079] The data processing unit preprocesses the vibration and acoustic energy data in the data acquisition system to obtain a standard vibration and acoustic energy dataset.

[0080] Preprocessing includes excitation time synchronization calibration, denoising and filtering, and normalization.

[0081] Excitation time synchronization calibration is achieved by setting the same excitation event as the starting point for all acquisition points, recording the excitation start timestamp of this excitation, and aligning the time axis of all vibration and acoustic energy data acquired during the excitation process with the excitation start timestamp as 0 seconds.

[0082] The denoising and filtering process uses a bandpass filter to filter the vibration acoustic energy data, retaining only the vibration acoustic energy data obtained from the structural acoustic frequency band. At the same time, wavelet denoising is used to process the instantaneous peak values ​​of the vibration acoustic energy data.

[0083] Normalization is performed on the denoised and filtered vibrational sound energy data by using the Max-Min normalization method, thereby eliminating the influence of the dimensions of all parameters in the vibrational sound energy data.

[0084] In this embodiment, the system constructs a core central system with a highly reliable and efficient data flow and intelligent processing mechanism by setting up a data transmission unit and a data processing unit. The data transmission unit employs LoRa low-power wide-area IoT communication, combined with the MQTT protocol and TLS encryption technology, to achieve stable and low-latency wireless transmission of raw vibration acoustic energy data collected by each vibration sensor group to the data acquisition system in complex construction site environments. This effectively solves the problems of complex wiring, easy packet loss during transmission, and weak data encryption capabilities in traditional construction monitoring. Based on this, the data processing unit achieves high-quality standardized conversion of structural acoustic response data by setting three key preprocessing steps: excitation time synchronization calibration, denoising and filtering, and maximum / minimum normalization. Synchronization calibration ensures that the time axes of all acquisition points are uniformly aligned, enhancing the consistency of subsequent analysis; filtering and wavelet denoising effectively suppress high-frequency interference signals such as mechanical and electromagnetic signals frequently occurring in the construction environment; and normalization eliminates dimensional differences caused by different excitation intensities or sensing sensitivities, ensuring consistent input of subsequent modeling data. Compared to existing methods based on manual export and offline analysis, this module significantly improves the automation and real-time level of data processing, enabling the entire acoustic acquisition chain to achieve closed-loop optimization from "on-site acquisition" to "remote standard dataset output". This provides a stable and accurate raw data foundation for subsequent acoustic reverberation anomaly detection and comprehensive structural consistency assessment, further enhancing the overall intelligent response capability and construction quality assurance level of the system.

[0085] Example 4, please refer to Figure 1 Specifically, the acoustic reverberation analysis module includes a vibration acoustic energy attenuation morphology analysis unit and a vibration acoustic energy morphology evaluation unit.

[0086] The vibration acoustic energy attenuation morphology analysis unit calculates and outputs the vibration acoustic energy attenuation morphology dispersion factor Decho based on the standard vibration acoustic energy data obtained from all acquisition points. This factor measures the difference between the attenuation rate and reverberation intensity of each acquisition point and the global mean.

[0087] The vibration sound wave energy attenuation morphological dispersion factor Decho is calculated and output using the following algorithm formula.

[0088] .

[0089] In the formula, n represents the total number of data collection points. This represents the average sound wave decay time across all sampling points. This represents the peak value of the residual energy of the echo at all sampling points. The standard deviation of sound wave decay time. The standard deviation of the peak residual energy of the echo is represented by the standard deviation of the peak residual energy. The reverberation intensity sensitivity adjustment coefficient, the specific value of which is set by the user, is used to control the weighting effect on reverberation.

[0090] The calculation logic and physical meaning of the formula This indicates an abnormality in the sound wave attenuation rate at a certain acquisition point, as measured by standardization.

[0091] The weighted summation of the vibration sound wave energy attenuation morphology dispersion factor Decho, which measures the abnormal energy concentration and reflection phenomenon at a certain sampling point, indicates how much the attenuation rate and reverberation intensity of each sampling point differ from the global mean. A larger difference indicates that the internal reflection characteristics of the structure at that point are inconsistent, and there may be cavities, interlayers, and multiple reflections of sound waves within the cavity, forming an echo tail phenomenon; cold seams or separation interfaces may cause enhanced reflection, but the reverberation will not attenuate.

[0092] The vibration acoustic wave energy pattern assessment unit obtains the vibration acoustic wave energy attenuation pattern dispersion factor Decho in the defect-free area, takes the upper limit of the 95% confidence interval as the vibration acoustic wave energy threshold Dth, and then conducts a preliminary comparison assessment between the vibration acoustic wave energy attenuation pattern dispersion factor Decho and the vibration acoustic wave energy threshold Dth to determine the vibration acoustic wave energy propagation. Based on the preliminary comparison assessment results, a trigger inversion correction mechanism is implemented. The specific assessment content is as follows.

[0093] When the dispersion factor of vibration sound wave energy attenuation morphology Decho is less than or equal to the vibration sound wave energy threshold Dth, it indicates that the sound wave propagation state of the underground cast-in-place structure is stable, which indicates that the casting is successful.

[0094] When the dispersion factor of vibration sound wave energy attenuation morphology Decho is greater than the vibration sound wave energy threshold Dth, it indicates that the sound wave propagation state of the underground cast-in-place structure is abnormal, that is, there is a significant abnormal sound wave response. There may be cavities or interlayers inside the structure, which triggers the inversion correction mechanism.

[0095] In this embodiment, the system constructs a vibration and acoustic energy attenuation morphology analysis unit and a vibration and acoustic energy morphology evaluation unit to achieve accurate identification and intelligent prediction of abnormal internal structural characteristics during the early stages of structural casting. First, the vibration and acoustic energy attenuation morphology analysis unit calculates and outputs the vibration and acoustic energy attenuation morphology dispersion factor Decho based on multi-point distributed standard vibration and acoustic energy data. This effectively quantifies the dispersion of attenuation rate and reverberation intensity at each sampling point relative to the global distribution, providing a quantitative basis for identifying differences in acoustic reflection characteristics in local areas. Its algorithm integrates standardization processing and reverberation sensitivity adjustment mechanisms, significantly enhancing the sensitivity and discriminative power for structural anomalies such as echo tails and concentrated reflections. Furthermore, the vibration and acoustic energy morphology evaluation unit extracts the vibration and acoustic energy attenuation morphology dispersion factor Decho from defect-free areas to construct a 95% confidence interval, and sets a vibration and acoustic energy threshold Dth accordingly, forming a data-driven dynamic threshold judgment mechanism to ensure the adaptability and rationality of the judgment results. When the vibration and acoustic energy attenuation morphology dispersion factor Decho exceeds this threshold, the subsequent vibration inversion module is automatically triggered, achieving seamless integration of anomaly detection and response mechanisms. Compared to existing technologies that rely on manual drilling or fuzzy threshold judgment, this module significantly improves the efficiency and intelligence level of identifying hidden defects in underground structures, avoiding the time and cost waste caused by numerous trial inspections. It is particularly suitable for early, wide-area, non-invasive defect early warning needs in large-volume concrete structures. Ultimately, it achieves real-time construction quality perception, rapid local anomaly location, and effective initiation of subsequent optimization operations, thereby improving the overall structural reliability, detection accuracy, and construction response efficiency during the construction process.

[0096] Example 5, please refer to Figure 1 Specifically, the vibration wavelength interference module includes a data acquisition point reconstruction unit and a structural interference unit.

[0097] After the acquisition point reconstruction unit triggers the inversion correction mechanism through preliminary comparison and evaluation, it expands the acquisition points. The expansion of acquisition points is achieved by traversing the vibration sound wave energy attenuation morphology dispersion factor Decho of each acquisition point i. i The system identifies areas where the sound wave propagation is abnormal in the underground concrete structure. Within these areas, a 1m×1m grid is used to expand the data collection points. Vibration sensor groups are then installed at these expanded collection points. The sampling frequency of the vibration sensor groups is increased by 50%, and the excitation frequency range is increased by 20%. Vibration interference data is collected and then transmitted to the data acquisition system via a data transmission unit.

[0098] In the data acquisition system, the vibration interference data is normalized to eliminate the influence of the dimensions of all parameters in the vibration interference data.

[0099] Vibration interferometric data includes the vibration interferometric symmetry offset vector Vsym for the j-th reconstructed acquisition point. j The phase deviation vector Vx of the j-th reconstructed acquisition point j .

[0100] The vibration interference symmetric offset vector Vsym of the j-th reconstructed acquisition point. j The vibration response amplitude of all reconstructed acquisition points is collected by MEMS triaxial accelerometers. The vibration response amplitude of the symmetrical reconstructed acquisition points is obtained by calculating the square difference. The significance of the parameter collection is that if there are material defects or interlayers, reflection, absorption or deflection will occur, causing the response of symmetrical points to be inconsistent.

[0101] The phase deviation vector Vx of the reconstructed acquisition point j j Vibration acceleration signals were acquired using a MEMS triaxial accelerometer, and a short-time Fourier transform (STFT) was performed on the vibration acceleration signals to extract the complex spectrum value corresponding to the main frequency component and obtain the vibration phase. Based on the absolute value of the vibration phase difference between two adjacent reconstruction acquisition points, the phase deviation vector Vx was obtained. The significance of the parameter acquisition is that in continuous dense structures, the vibration phase propagation has strong regularity. If there are cavities or separation surfaces, the phase will be misaligned, that is, abrupt changes will occur.

[0102] The structural interference unit extracts vibration interference data for calculation and outputs a structural vibration consistency factor Iinter to measure the severity of local acoustic disturbances in underground cast-in-place structures.

[0103] The structural vibration consistency factor Iinter is calculated and output using the following algorithm.

[0104] ;

[0105] In the formula, m represents the total number of reconstructed data collection points. This represents the mean of the vibration interference symmetric offset vectors at all reconstructed acquisition points. This represents the mean of the phase deviation vector of all reconstructed acquisition points. u and v represent the weight values ​​of symmetry offset and phase anomaly, respectively. Their specific values ​​are set by the user, and u+v=1.

[0106] This is used to amplify the impact of aggregate symmetry imbalance in underground cast-in-place structures and determine which reconstruction sampling point is abnormally severe.

[0107] The location used to determine whether there are phase abrupt change points in underground cast-in-place structures is usually a material boundary, cold joint, or cavity.

[0108] The overall average normalization process is used to construct a unified index representing the severity of local fluctuation field disturbances in underground cast-in-place structures.

[0109] In this embodiment, the system dynamically identifies potential problem areas within the structure based on outliers of the vibration acoustic energy attenuation dispersion factor Decho from previous acoustic reverberation analysis results within the acquisition point reconstruction unit. Reconstruction acquisition points are then deployed within these areas using a 1m×1m high-density grid. By increasing the sampling and excitation frequencies, and simultaneously acquiring the vibration interference symmetry offset vector Vsym and phase deviation vector Vx, a dual-feature perspective of the vibration response is constructed, providing accurate high-frequency data support for subsequent interferometric analysis. The structural interferometric unit further calculates the structural vibration consistency factor Iinter based on the aforementioned bidirectional interference characteristics, achieving a synergistic quantitative analysis of vibration symmetry imbalance and phase misalignment. This index effectively integrates the energy deflection and phase jump behavior caused by factors such as material defects and interlayer cavities in the vibration propagation path, forming a unified and normalized structural disturbance assessment factor. Compared to traditional manual detection or low-density acoustic acquisition methods, this module, while ensuring non-destructive monitoring, can dynamically adjust the reconstruction range and acquisition accuracy, achieving directional amplification and precise identification of potential cavities, cold seams, and poorly filled areas. In summary, this module completes the entire response path from "anomaly detection to reconstruction deployment to interference acquisition and causal quantification." It not only breaks through the limitations of conventional sparse point deployment and coarse response, but also achieves early identification, intelligent positioning and quantitative diagnosis of local disturbances inside the structure by integrating multi-dimensional analysis methods of symmetry and phase. This significantly improves the defect response efficiency, processing accuracy and quality controllability of underground concrete pouring structures during the construction stage.

[0110] Example 6, please refer to Figure 1 Specifically, the acoustic vibration consistency analysis module includes an acoustic vibration analysis unit, a consistency evaluation unit, and a strategy execution unit.

[0111] The acoustic vibration analysis unit calculates the structural vibration consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho by combining the obtained structural vibration consistency factor Iinter and the output structural acoustic vibration consistency summary index Scon, which measures the risk of continuous damage to the current structural region.

[0112] The structural acoustic-vibration consistency index Scon is calculated and output using the following algorithm formula.

[0113] ;

[0114] In the formula, This represents the acoustic vibration signal adjustment coefficient, measuring the degree of local inconsistency in acoustic anomalies. Its value ranges from 0.5 to 0.7, with a default setting of 0.5. In assessing structural density, a value of 0.7 is preferred. The standard deviation represents the dispersion factor of the energy attenuation morphology of vibrating sound waves.

[0115] The molecular section summarizes the degree of interference between acoustic anomalies and structural vibrations, and uses acoustic vibration signal adjustment coefficients. This is used to balance the differences between the two dimensions, so that the two results have equivalent evaluation weights.

[0116] The denominator is used to penalize highly volatile acoustic anomalies. If the acoustic response in a certain area is highly discrete, it indicates that the structure is inconsistent and the overall stability score should be appropriately reduced.

[0117] The overall physical meaning of the formula is that if the summative index Scon of structural acoustic and vibration consistency is larger, it means that the structure is less known and more likely to have cavities, interlayers or cold joints; if the summative index Scon of structural acoustic and vibration consistency is smaller, it means that the structure is more consistent and the underground cast-in-place structure has good compactness and does not require treatment.

[0118] The consistency assessment unit performs a secondary comparative assessment based on the output of the structural acoustic-vibration consistency summary index Scon, and classifies the underground cast-in-place structure into different response levels based on the secondary comparative assessment results. The specific assessment content is as follows.

[0119] When the structural acoustic-vibration consistency index Scon < 0.9, it indicates the presence of minor local disturbances, which are classified as Level 1 response.

[0120] When 0.9 ≤ Scon < 1.3, it indicates that there is moderate inconsistency, which is classified as a level two response.

[0121] When the structural acoustic-vibration consistency index Scon ≥ 1.3, it indicates that there is a serious structural discontinuity, and it is classified as a level three response.

[0122] The strategy execution unit executes different control strategies based on different response levels. The specific control strategies are as follows.

[0123] When classified as a Level 1 response, it indicates that the sound waves and vibrations are consistent, the structural condition is good, and problems such as cavities, interlayers, and cold joints are minor, so no intervention is required.

[0124] When classified as a Level II response, it indicates that there is a moderate fluctuation in structural consistency, which may be caused by occasional construction disturbances, uneven pouring, abnormal boundaries, etc. It is not enough to be immediately judged as a quality problem, but it needs to be closely observed. In this case, samples are collected and re-examined the next day. If it continues to be a Level II response, then vibration treatment should be initiated.

[0125] When classified as a Level 3 response, it indicates that the area is clearly identified as a high-risk area of ​​structural discontinuity, with obvious problems such as interlayers, cavities, cold joints, or significant inadequate filling. At this time, the current area will be marked as red, prompting refined construction intervention and immediate initiation of local directional grouting and vibration treatment.

[0126] In this embodiment, the system outputs a structural acoustic-vibration consistency index, Scon, by performing a unified-dimensional fusion calculation of the structural vibration consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho in the acoustic-vibration analysis unit. This systematically quantifies the potential risk level of local damage in underground structures. Furthermore, the evaluation results are multidimensionally constrained by acoustic-vibration signal adjustment coefficients and fluctuation tolerance adjustment factors to ensure the scientific validity and robustness of the assessment results. Compared to traditional structural consistency analysis methods that rely primarily on human experience, this index constructs a multi-source fusion diagnostic model that considers both the acoustic and vibration fields, significantly improving the accuracy of the judgment. Further, the consistency assessment unit establishes a hierarchical assessment mechanism based on the structural acoustic-vibration consistency index Scon, dividing the structural state into three response levels. This allows the system to qualitatively identify and manage discontinuity risks at different levels. Within the strategy execution unit, each response level corresponds to a clear and executable on-site intervention plan, ranging from a Level 1 response requiring no intervention, to a Level 2 response with a delayed observation-review mechanism, and finally to a Level 3 response involving targeted reinforcement and vibration. This enables dynamic tracking and precise handling of structural anomalies. In summary, this module completes a closed-loop process from anomaly identification and level classification to response control, significantly improving the intelligence, precision, and real-time performance of underground cast-in-place structure quality monitoring. Compared to traditional solutions relying solely on one-time testing and manual analysis, this system constructs a quantifiable indicator system for continuous evaluation and an immediate response mechanism, greatly reducing the risk of missed or incorrect quality assessments and providing a solid guarantee for the construction safety and subsequent quality stability of complex cast-in-place structures.

[0127] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A construction site data acquisition system based on the Internet of Things, characterized in that: include: Acoustic wave acquisition module: By setting acquisition points on the underground cast-in-place structure and setting up vibration sensor groups within the acquisition points to form a three-dimensional acoustic wave acquisition matrix, the vibration acoustic wave energy data generated during the excitation process is acquired in real time. Central processing module: Set up a data acquisition system, use the Internet of Things to transmit vibration and sound wave energy data to the data acquisition system, and preprocess the vibration and sound wave energy data in the data acquisition system to obtain a standard vibration and sound wave energy dataset; Sound reverberation analysis module: Based on the standard vibration sound energy dataset, it calculates and outputs the vibration sound energy attenuation morphology dispersion factor Decho, and at the same time presets the vibration sound energy threshold Dth for preliminary comparative evaluation; The acoustic reverberation analysis module includes a vibration acoustic energy attenuation morphology analysis unit and a vibration acoustic energy morphology evaluation unit. The vibration acoustic energy attenuation pattern analysis unit calculates and outputs the vibration acoustic energy attenuation pattern dispersion factor Decho based on the standard vibration acoustic energy data obtained from all sampling points, which measures the difference between the attenuation rate and reverberation intensity of each sampling point and the global mean. The vibration acoustic wave energy attenuation morphology dispersion factor Decho is calculated and output using the following algorithm formula; ； In the formula, n represents the total number of data collection points. This represents the average sound wave decay time across all sampling points. This represents the peak value of the residual energy of the echo at all sampling points. The standard deviation of sound wave decay time. The standard deviation of the peak residual energy of the echo is represented by the standard deviation of the peak residual energy. Resonance intensity sensitive adjustment coefficient; Vibration wavelength interference module: Based on the preliminary comparative evaluation results, the inversion correction mechanism is triggered to expand the acquisition points, reset the vibration sensor group, acquire vibration interference data, and calculate and output the structural vibration consistency factor Iinter based on the vibration interference data; The vibration wavelength interference module includes a data acquisition point reconstruction unit and a structural interference unit; The acquisition point reconstruction unit, after triggering the inversion correction mechanism through preliminary comparison and evaluation, expands the acquisition points, sets up a vibration sensor group within the expanded acquisition points, increases the sampling frequency of the vibration sensor group by 50%, and increases the excitation frequency range by 20% to acquire vibration interference data. The vibration interference data is then transmitted to the data acquisition system through the data transmission unit. In the data acquisition system, the vibration interference data is normalized to eliminate the dimensional influence of all parameters in the vibration interference data. The vibration interferometric data includes the vibration interferometric symmetry offset vector Vsymmetric of the j-th reconstructed acquisition point. j The phase deviation vector Vx of the j-th reconstructed acquisition point j ; The structural interference unit extracts vibration interference data for calculation and outputs a structural vibration consistency factor Iinter to measure the severity of local acoustic disturbances in underground cast-in-place structures. The structural vibration consistency factor Iinter is calculated and output using the following algorithm; ； In the formula, m represents the total number of reconstructed data collection points. This represents the mean of the vibration interference symmetric offset vectors at all reconstructed acquisition points. The mean of the phase deviation vector of all reconstructed acquisition points is represented by u, and the weight values ​​of symmetry offset and phase anomaly are represented by v, respectively. The acoustic-vibration consistency analysis module calculates the structural interference consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho, outputs the structural acoustic-vibration consistency summary index Scon, and performs a secondary comparative evaluation based on the output results of the structural acoustic-vibration consistency summary index Scon.

2. The IoT-based construction site data acquisition system according to claim 1, characterized in that: The acoustic wave acquisition module includes an acoustic wave matrix construction unit and an excitation acquisition unit; The acoustic matrix construction unit constructs a three-dimensional acoustic acquisition matrix by setting up acquisition points on the underground cast-in-place structure. These acquisition points are arranged in a three-dimensional space by horizontal and vertical arrangements. At the same time, a vibration sensor group is set up in each acquisition point. The excitation acquisition unit generates excitation characteristics by setting an excitation source position in the underground cast-in-place structure and exciting the excitation source position. It then collects the vibration sound wave energy data by real-time monitoring of the broadband vibration signal generated during the excitation process through a three-dimensional acoustic wave acquisition matrix. The vibration acoustic wave energy data includes the acoustic wave decay time Tdec at the i-th acquisition point. i And the peak residual energy Ares of the i-th acquisition point i .

3. The IoT-based construction site data acquisition system according to claim 2, characterized in that: The central processing module includes a data transmission unit and a data processing unit; The data transmission unit wirelessly connects the communication module of the vibration sensor group to the data acquisition system using LoRa IoT communication, and wirelessly transmits the real-time collected vibration sound wave energy data to the data acquisition system according to the MQTT transmission protocol and TLS encrypted connection. The data processing unit obtains a standard vibration and sound wave energy dataset by preprocessing the vibration and sound wave energy data in the data acquisition system. The preprocessing includes excitation time synchronization calibration, noise reduction and filtering, and normalization. The excitation time synchronization calibration is achieved by setting the same excitation event as the starting point for all acquisition points, recording the excitation start timestamp of this excitation, and aligning the time axis of all vibration and acoustic energy data acquired during the excitation process with the excitation start timestamp as 0 seconds. The denoising and filtering process uses a bandpass filter to filter the vibration acoustic energy data, retaining only the vibration acoustic energy data obtained from the structural acoustic frequency band, and uses wavelet denoising to process the instantaneous peak values ​​of the vibration acoustic energy data. The normalization process uses the Max-Min normalization method to normalize the vibration and acoustic energy data after denoising and filtering, thereby eliminating the influence of the dimensions of all parameters in the vibration and acoustic energy data.

4. The IoT-based construction site data acquisition system according to claim 3, characterized in that: The vibration acoustic wave energy pattern assessment unit obtains the vibration acoustic wave energy attenuation pattern dispersion factor Decho from the defect-free region, takes the upper limit of the 95% confidence interval as the vibration acoustic wave energy threshold Dth, and then performs a preliminary comparison assessment between the vibration acoustic wave energy attenuation pattern dispersion factor Decho and the vibration acoustic wave energy threshold Dth to determine the vibration acoustic wave energy propagation. Based on the preliminary comparison assessment results, a trigger inversion correction mechanism is implemented. The specific assessment content is as follows: When the dispersion factor of vibration sound wave energy attenuation morphology Decho is less than or equal to the vibration sound wave energy threshold Dth, it indicates that the sound wave propagation state of the underground cast-in-place structure is stable, which indicates that the casting is successful. When the dispersion factor of vibration sound wave energy attenuation morphology Decho is greater than the vibration sound wave energy threshold Dth, it indicates that the sound wave propagation state of the underground cast-in-place structure is abnormal, and the inversion correction mechanism is triggered at this time.

5. The IoT-based construction site data acquisition system according to claim 4, characterized in that: The acoustic vibration consistency analysis module includes an acoustic vibration analysis unit, a consistency evaluation unit, and a strategy execution unit; The acoustic vibration analysis unit calculates the structural vibration consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho by combining the obtained structural vibration consistency factor Iinter and the vibration sound wave energy attenuation morphology dispersion factor Decho, and outputs the structural acoustic vibration consistency summary index Scon to measure the risk of continuous damage to the current structural region. The structural acoustic-vibration consistency index Scon is calculated and output using the following algorithm formula; ； In the formula, The standard deviation represents the dispersion factor of the energy attenuation morphology of vibrating sound waves.

6. The IoT-based construction site data acquisition system according to claim 5, characterized in that: The consistency assessment unit performs a secondary comparative assessment based on the output of the structural acoustic-vibration consistency summary index Scon, and classifies the results into different response levels to determine the overall consistency of the underground cast-in-place structure. The specific assessment content is as follows: When the structural acoustic-vibration consistency index Scon < 0.9, it is classified as a Level 1 response. When 0.9 ≤ Scon, the structural acoustic-vibration consistency index, is less than 1.3, it is classified as a level two response. When the structural acoustic-vibration consistency index Scon ≥ 1.3, it is classified as a level three response.

7. The IoT-based construction site data acquisition system according to claim 6, characterized in that: The strategy execution unit executes different control strategies based on different response levels. The specific control strategies are as follows: When classified as a Level 1 response, no intervention is required. When the response level is classified as Level II, samples are collected and re-examined the following day. If the response level remains Level II, vibration treatment is initiated. When classified as a Level 3 response, the current area is marked in red to indicate a need for meticulous construction intervention, and localized directional grouting and vibration treatment should be initiated immediately.

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