Prestressed hollow pile whole life cycle intelligent management method and system based on industrial internet

Abstract

The application provides a prestressed hollow pile full life cycle intelligent management method and system based on an industrial internet, and relates to the technical fields of intelligent building engineering and industrial internet. In the application, initial prestress data and concrete density data are first collected to construct an initial structure digital model; then, pile sinking prestress loss data are calculated, and the initial structure digital model is structurally corrected to generate an in-service structure digital model; then, measured sound wave reflection data are collected, and the in-service structure digital model is taken as a reference benchmark to generate benchmark sound wave reflection data in a nondestructive state; and through wave impedance difference data, a pile body defect position is matched in the digital model to generate an operation and maintenance work order. The technical scheme provided by the application realizes traceable management of the prestressed hollow pile full life cycle state and accurate positioning of the pile body defect position.

Description

Technical Field

[0001] This application relates to the fields of intelligent building engineering and industrial internet technology, and in particular to a method and system for intelligent management of the entire life cycle of prestressed hollow piles based on industrial internet. Background Technology

[0002] Prestressed hollow piles are foundation components prefabricated in a factory and driven into the ground by hammering or static pressure to bear the loads of the superstructure. Due to their high bearing capacity, economical cost, and convenient construction, they have been widely used in foundation engineering for industrial plants, municipal roads, docks and quays, and high-rise residential buildings. The entire life cycle of a prestressed hollow pile generally spans three stages: factory production, pile driving, and subsequent service. Each stage affects the actual structural state of the pile.

[0003] The factory production stage determines the density of the pile concrete and the initial stress level of the prestressed steel bars; the hammer impact stress and soil resistance around the pile during the construction and pile driving stage will cause local residual strain of the prestressed steel bars, thereby causing the loss of initial prestress; during the service stage, hidden defects such as cracks, spalling, and concrete spalling may occur in the pile body due to soil corrosion, groundwater erosion, load cycles, etc.

[0004] The existing technology uses a precast pile quality traceability method based on QR code scanning and database comparison. This method mainly involves attaching identification codes to the precast piles during the production stage and scanning the identification codes during the construction stage to retrieve the static load test data and factory parameters of the precast piles to complete the quality traceability.

[0005] This method has achieved a certain degree of correlation between data from the precast pile production stage and data from the construction stage, but it has the following shortcomings: First, it only traces data at the level of data archives and does not construct a digital model that can reflect the internal stress state and defect distribution of the pile body; Second, it does not consider the actual damage to the prestressed steel bars caused by the propagation attenuation of the hammer stress wave in the pile body medium during the pile driving stage, that is, it does not quantitatively calculate the prestress loss caused by construction; Third, the detection of pile defects during the service stage mostly adopts the low-strain reflected wave method or the high-strain dynamic measurement method, and the interpretation results rely on the empirically assumed uniform pile body model, which cannot be compared with the actual structural state of the pile body in service, resulting in insufficient defect location accuracy.

[0006] Furthermore, when conducting non-destructive testing on prestressed hollow piles that have been buried in the ground during the service phase, the surface of the pile body is covered by soil and the pile spacing is dense, making it difficult to implement external non-destructive testing methods. Although using acoustic testing of the pile cavity can avoid interference from external soil, due to the lack of reference reflection data that matches the actual medium state of the pile body, judging defects solely based on waveform changes is prone to missed detections and false detections. Summary of the Invention

[0007] The purpose of this application is to overcome the shortcomings of the above-mentioned background technology and provide a method and system for intelligent management of the entire life cycle of prestressed hollow piles based on the Industrial Internet.

[0008] Firstly, this application provides a method for intelligent management of the entire lifecycle of prestressed hollow piles based on the Industrial Internet, including:

[0009] To obtain initial prestress data and concrete density data of prestressed hollow piles during the factory production stage;

[0010] An initial structural digital model of the prestressed hollow pile is constructed based on the initial prestress data and the concrete density data.

[0011] During the pile driving stage, the impact stress data and soil resistance data of the prestressed hollow piles are obtained.

[0012] Based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the pile soil resistance data, calculate the pile prestress loss data of the initial prestress data.

[0013] The internal stress state of the initial structural digital model is structurally corrected using the prestress loss data of the driven piles, thereby generating the in-service structural digital model of the prestressed hollow piles.

[0014] During the service monitoring phase, measured acoustic reflection data collected by an acoustic wave detection sensor installed in the cavity of the prestressed hollow pile is received, and the in-service structure digital model is used as a reference benchmark to calculate and generate reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile.

[0015] Based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, the location of the prestressed hollow pile is matched in the digital model of the in-service structure to generate an operation and maintenance work order containing the location of the pile defect and send it to the operation and maintenance terminal.

[0016] Optionally, during the pile driving stage, the impact stress data and soil resistance data of the prestressed hollow piles are obtained, including:

[0017] During the pile driving stage, stress time history signals collected by stress sensors and acceleration time history signals collected by acceleration sensors are obtained from the pile driving hammer and the top of the prestressed hollow pile to obtain the pile impact stress data.

[0018] Obtain the pile penetration depth, penetration displacement, and strain change at different depths of the pile corresponding to each hammer blow;

[0019] The lateral frictional resistance at different depths of the prestressed hollow pile is determined based on the strain change value and the penetration displacement to obtain the soil resistance data of the pile driving.

[0020] Optionally, an initial structural digital model of the prestressed hollow pile is constructed based on the initial prestressing data and the concrete density data, including:

[0021] Based on the design dimensions of the prestressed hollow pile, a spatial discrete model of the pile body with spatial coordinates is established. The spatial discrete model of the pile body is defined to include concrete solid elements, prestressed steel reinforcement elements, and the boundary of the pile body cavity.

[0022] The concrete density data is filled into the concrete solid unit according to the spatial coordinates, and the material density parameter is set according to the concrete density value corresponding to each concrete solid unit.

[0023] In the discrete model of the pile body, the spatial position of each prestressed steel bar unit is determined according to the design layout of the prestressed steel bars. The corresponding initial prestress value is extracted from the initial prestress data and assigned to the corresponding prestressed steel bar unit based on the spatial position of each prestressed steel bar unit.

[0024] Establish the stress transfer relationship between each prestressed steel bar unit and the adjacent concrete solid unit, and combine the concrete solid unit with the material density parameter, the prestressed steel bar unit with the initial prestress value, the inner cavity boundary of the pile, and the stress transfer relationship to output the initial structural digital model.

[0025] Optionally, based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the pile soil resistance data, the pile prestress loss data of the initial prestress data is calculated, including:

[0026] The pile body is divided into multiple continuous depth sections along the axial direction of the prestressed hollow pile;

[0027] The concrete density data and the soil resistance data of the pile driving are mapped to the corresponding depth segments respectively, and the stress time history, peak amplitude, peak arrival time and impact duration in the impact stress data of the pile driving are extracted simultaneously.

[0028] The propagation velocity of the stress wave and the medium wave impedance in each depth section are determined based on the concrete density value corresponding to each depth section and the preset equivalent elastic parameters.

[0029] The waveform amplitude and stress wave propagation energy when the stress wave propagates to each of the depth segments are determined based on the stress time history, the propagation velocity, and the medium wave impedance.

[0030] Calculate the waveform amplitude change value between adjacent depth segments, and determine the waveform amplitude change value as the waveform attenuation amount of the corresponding depth segment;

[0031] The energy dissipated by the pile-soil interaction in each depth segment is calculated based on the soil resistance value around the pile corresponding to each depth segment and the penetration displacement of the prestressed hollow pile in the corresponding depth segment.

[0032] The stress wave attenuation energy is determined based on the difference in stress wave propagation energy between adjacent depth sections, and the stress wave attenuation energy is added to the energy dissipated by the pile-soil interaction to obtain the local energy dissipation value of each depth section.

[0033] The local energy dissipation values ​​under each hammer blow are accumulated according to the order of hammer blows to obtain the cumulative energy dissipation values ​​for each depth segment.

[0034] Based on the pre-established correspondence between the cumulative energy dissipation value and the residual strain value of the prestressed steel bars, the residual strain value of the prestressed steel bars in each depth section is determined.

[0035] The initial strain value of the prestressed steel bar is determined using the initial prestress data. The difference between the initial strain value and the residual strain value, as well as the elastic modulus of the prestressed steel bar, is used to calculate the prestress loss value of the pile driving corresponding to each depth section, so as to obtain the prestress loss data of the pile driving.

[0036] Optionally, the internal stress state of the initial structural digital model is structurally corrected using the prestress loss data of the driven piles to generate an in-service structural digital model of the prestressed hollow piles, including:

[0037] Extract the spatial coordinates and initial prestress values ​​of each prestressed steel unit in the initial structural digital model;

[0038] Find the prestress loss value of the driven pile corresponding to the spatial coordinates of each prestressed steel reinforcement unit from the prestress loss data of the driven pile according to the depth position;

[0039] In the initial structural digital model, the initial prestress value is subtracted from the corresponding pile prestress loss value to obtain the remaining prestress data of each prestressed steel reinforcement unit;

[0040] The stress state of each prestressed steel bar unit is updated from the initial prestress value to the corresponding remaining prestress data, and the internal stress state of adjacent concrete solid units is updated synchronously according to the stress transfer relationship.

[0041] The initial prestress state in the initial structural digital model is retained, and the remaining prestress data and the updated internal stress state are written into the post-pile driving model state associated with the initial structural digital model to obtain the in-service structural digital model.

[0042] Optionally, during the service monitoring phase, measured acoustic reflection data collected by an acoustic wave detection sensor installed in the cavity of the prestressed hollow pile is received, and the in-service structural digital model is used as a reference benchmark to calculate and generate reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile, including:

[0043] The axial coordinates, circumferential angles, and sound wave emission direction within the cavity of the prestressed hollow pile are obtained by the acoustic wave detection sensor.

[0044] The acoustic wave detection sensor records the first time point when it emits a detection acoustic wave and the second time point when it receives a reflected acoustic wave, and collects the incident amplitude of the detection acoustic wave and the reflection amplitude, reflection phase and reflection frequency of the reflected acoustic wave.

[0045] The measured acoustic wave reflection data is formed by combining the time interval between the first time point and the second time point with the incident amplitude, the reflected amplitude, the reflected phase, and the reflected frequency;

[0046] Virtual detection nodes are set in the virtual cavity of the in-service structural digital model, corresponding to the axial coordinates, circumferential angles and sound wave emission directions of the acoustic wave detection sensor.

[0047] While retaining the remaining prestress data and concrete density data in the digital model of the in-service structure, the concrete solid elements in the digital model of the in-service structure are set to a defect-free reference state.

[0048] The virtual detection node is controlled to emit virtual detection sound waves toward the side wall of the in-service structural digital model, and the propagation path of the virtual detection sound waves in the in-service structural digital model is determined according to the spatial position of the virtual detection node and the direction of sound wave emission.

[0049] The acoustic wave propagation velocity and reference medium wave impedance at each position along the propagation path are determined based on the material density and remaining prestress data of the digital model of the in-service structure.

[0050] The reference reflection time span of the virtual probe sound wave in the defect-free reference state is calculated based on the sound wave propagation speed and the propagation path length.

[0051] The reference reflection amplitude, reference reflection phase, and reference reflection frequency under the defect-free reference state are determined using the reference medium wave impedance, the propagation path length, and the incident amplitude of the virtual probe acoustic wave.

[0052] The reference reflection time span, the reference reflection amplitude, the reference reflection phase, and the reference reflection frequency are associated and arranged according to the axial coordinates, circumferential angle, and sound wave emission direction of the acoustic wave detection sensor to generate the reference acoustic wave reflection data.

[0053] Optionally, based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, the location of the prestressed hollow pile is matched in the digital model of the in-service structure to generate a maintenance work order containing the location of the pile defect and send it to the maintenance terminal, including:

[0054] According to the axial coordinates, circumferential angle and sound wave emission direction of the sound wave detection sensor, the measured sound wave reflection data is correlated with the reference sound wave reflection data.

[0055] The measured reflection coefficient is determined based on the ratio between the incident amplitude and the reflected amplitude in the measured acoustic wave reflection data, and the reference reflection coefficient is determined based on the ratio between the virtual incident amplitude and the reference reflected amplitude in the reference acoustic wave reflection data.

[0056] The measured equivalent wave impedance is determined based on the measured reflection coefficient and the reference medium wave impedance of the corresponding propagation path in the digital model of the in-service structure.

[0057] Based on the reference reflection coefficient and the reference dielectric wave impedance, determine the reference equivalent wave impedance under the defect-free reference state;

[0058] The difference between the measured equivalent wave impedance and the reference equivalent wave impedance is calculated to obtain the wave impedance difference data;

[0059] Calculate the time difference between the measured reflection time span in the measured acoustic wave reflection data and the reference reflection time span in the reference acoustic wave reflection data;

[0060] Based on the axial coordinates, circumferential angle, sound wave emission direction, measured reflection time span, and sound wave propagation speed along the corresponding propagation path of the sound wave detection sensor, the candidate spatial coordinates corresponding to the reflection anomaly are determined.

[0061] Determine whether the impedance difference data is greater than a preset anomaly detection condition;

[0062] When the wave impedance difference data is greater than the anomaly determination condition, the corresponding candidate spatial coordinates are determined as the candidate defect location;

[0063] Multiple candidate defect locations located within a preset spatial range are merged to obtain the pile body defect location, which includes axial depth, circumferential angle, and radial position.

[0064] In the digital model of the in-service structure, the location of the pile defect is set as a defect marker, and the range of the pile defect is determined according to the spatial distribution of adjacent defect markers;

[0065] Extract the location of the pile defect, the range of the pile defect, the wave impedance difference data, the time difference, the detection time, and the corresponding acoustic wave detection sensor number to form maintenance work order data;

[0066] The severity of the defect corresponding to the location of the pile defect is determined based on the wave impedance difference data. The severity of the defect is written into the maintenance work order data, the maintenance work order is generated, and it is transmitted to the maintenance terminal.

[0067] Secondly, this application provides an intelligent management system for the entire lifecycle of prestressed hollow piles based on the Industrial Internet, including:

[0068] The first acquisition module is used to acquire the initial prestress data and concrete density data of the prestressed hollow piles during the factory production stage.

[0069] A construction module is used to construct an initial structural digital model of the prestressed hollow pile based on the initial prestress data and the concrete density data.

[0070] The second acquisition module is used to acquire the pile impact stress data and pile soil resistance data of the prestressed hollow pile during the pile driving stage.

[0071] The first calculation module is used to calculate the prestress loss data of the initial prestress data based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the soil resistance data of the pile.

[0072] The correction module is used to perform structural correction on the internal stress state of the initial structural digital model using the prestress loss data of the driven piles, and generate the in-service structural digital model of the prestressed hollow piles.

[0073] The second calculation module is used to receive measured acoustic reflection data collected by an acoustic wave detection sensor installed in the cavity of the prestressed hollow pile during the service monitoring phase, and to calculate and generate reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile using the digital model of the in-service structure as a reference.

[0074] The matching generation module is used to match the location of the prestressed hollow pile in the digital model of the in-service structure based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, so as to generate an operation and maintenance work order containing the location of the pile defect and send it to the operation and maintenance terminal.

[0075] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to realize the intelligent management method for the entire life cycle of prestressed hollow piles based on the Industrial Internet as described in the first aspect above.

[0076] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a prestressed hollow pile full life cycle intelligent management method based on the Industrial Internet as described in the first aspect.

[0077] The beneficial effects of this application are as follows:

[0078] Firstly, this application constructs a digital model of the pile body that spans the three stages of factory production, construction and pile driving, and service monitoring. This enables the previously isolated production parameters, construction parameters, and service parameters to form a complete data chain through the digital model, avoiding the problem that traditional solutions only stay at the level of archival traceability.

[0079] Secondly, this application quantitatively calculates the prestress loss of pile driving based on the waveform attenuation of hammer stress wave in a medium structure with concrete density limitation and the soil resistance around the pile, which overcomes the problem that the existing construction stage can only judge the bearing capacity of the pile foundation by static load test and cannot reflect the true state of internal prestress.

[0080] Third, this application uses the digital model of the in-service structure as a non-destructive reference to generate reference acoustic wave reflection data, and compares the wave impedance difference with the measured acoustic wave reflection data. This avoids the false detection and missed detection caused by the traditional low strain reflection wave method relying on the empirical assumption of a uniform pile body model, and significantly improves the positioning accuracy of pile body defects.

[0081] Fourth, the maintenance work orders in this application are directly matched to the location and severity of defects in the pile body, which facilitates the maintenance terminal to arrange maintenance plans in a targeted manner, thereby improving the maintenance efficiency and safety level of prestressed hollow pile foundation engineering.

[0082] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0083] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0084] Figure 1 This is a schematic diagram of the overall process of the intelligent management method for the entire life cycle of prestressed hollow piles based on the Industrial Internet, as proposed in this application.

[0085] Figure 2 This is a block diagram of the prestressed hollow pile full life cycle intelligent management system of this application, which shows the calling relationship between the various functional modules.

[0086] Figure 3 This is a spatial discrete structural diagram of the initial structural digital model of this application. The diagram shows the spatial correspondence between concrete solid elements, prestressed steel reinforcement elements, and the boundaries of the pile cavity.

[0087] Figure 4 This is a schematic diagram illustrating the principle of dividing the pile shaft into depth segments and accumulating stress wave attenuation energy during the calculation of prestress loss data for pile driving in this application.

[0088] Figure 5 This is a schematic diagram of the path of the acoustic wave detection sensor emitting and receiving sound waves in the internal cavity of the pile during the service monitoring phase of this application. The diagram also shows the correspondence between the virtual detection node and the actual sensor in the digital model of the in-service structure.

[0089] Figure 6 This is a schematic diagram of the processing flow of this application, which matches the location of pile defects based on wave impedance difference data and generates maintenance work orders. Detailed Implementation

[0090] The specific embodiments of this application are described in detail below with reference to the accompanying drawings. The embodiments described below are preferred embodiments of this application, used to help those skilled in the art understand the technical solutions of this application, and are not intended to limit the scope of protection of this application.

[0091] like Figure 1 As shown, this application provides a prestressed hollow pile full life cycle intelligent management method based on the Industrial Internet, which includes steps S1 to S7 in sequence. Each step is executed sequentially, and the output of the previous step serves as the input of the next step. The specific implementation process of each step is described below.

[0092] Step S101: Obtain initial prestress data and concrete density data of prestressed hollow piles during the factory production stage.

[0093] During the factory production stage, prestressed hollow piles are prefabricated using standard processes such as centrifugal forming, prestressing tensioning, and autoclaving. Initial prestress data refers to the initial prestress value of each prestressed steel bar, along with its distribution parameters within the pile's spatial position, read by pressure sensors on the tensioning equipment after tensioning during factory production. This data is typically recorded as a vector set indexed by spatial coordinates. Concrete density data refers to the density index collected at preset intervals along the axial and circumferential directions of the pile concrete after centrifugal forming and curing using ultrasonic or rebound methods. This reflects the spatial distribution of porosity within the concrete. Here, density refers to a density index quantified based on medium wave velocity or wave impedance, with units consistent with those used in ultrasonic testing for medium wave impedance.

[0094] The reason for acquiring these two types of data simultaneously during the factory production stage is that the initial prestress data determines the initial stress level of the pile reinforcement, and the concrete density data determines the initial wave propagation characteristics of the pile medium. Together, they constitute the original input for the subsequent digital model.

[0095] Step S102: Construct an initial structural digital model of the prestressed hollow pile based on the initial prestress data and concrete density data.

[0096] After acquiring the two types of data mentioned above, an initial structural digital model is constructed to link the two types of data to a unified spatial coordinate system. The construction process is as follows: Figure 3 As shown: Based on the design dimensions of the prestressed hollow pile (e.g., pile length 12 meters, outer diameter 400 mm, inner diameter 250 mm, etc., specific design parameters can be set according to the actual production model), a spatial discrete model of the pile body with spatial coordinates is established. The established spatial discrete model of the pile body includes concrete solid elements, prestressed steel reinforcement elements, and the boundary of the pile cavity. The concrete solid elements are uniformly divided along the axial and circumferential directions of the pile body using finite element discretization. For example, the axial length of each concrete solid element can be set to 100 mm, and the circumferential angle can be set to 15 degrees. The specific discretization accuracy can be set according to the actual computing capabilities. The reason for establishing the spatial discrete model in the above manner is that the subsequent calculation of prestress loss during pile driving and the generation of acoustic reflection references during service stage both need to be calculated based on the stress state and medium parameters of each element. Without spatial discretization, fine-grained defect location cannot be achieved.

[0097] The concrete density data is filled into concrete solid elements according to spatial coordinates, and the material density parameter is set according to the concrete density value corresponding to each concrete solid element. The conversion between the material density parameter and the concrete density value can be expressed by the following formula:

[0098]

[0099] In the formula, The material density parameter of the concrete solid element. For the theoretical density of concrete design, This refers to the concrete porosity index at the corresponding location (which can be obtained by normalizing the density value). This is an empirical coefficient, and its value can be obtained from the mix design test of the same batch of concrete. For example, it can be a value between 0.85 and 0.95.

[0100] In the discrete spatial model of the pile body, the spatial position of each prestressed steel bar unit is determined according to the designed layout of the prestressed steel bars. Taking a conventional PHC pipe pile as an example, the prestressed steel bars are arranged at equal angular intervals along the circumference of the pile body within the concrete cover, and each steel bar is continuously arranged along the axial direction of the pile body. Based on the spatial position of each prestressed steel bar unit, the corresponding initial prestress value is extracted from the initial prestress data and used as the initial stress state value of the prestressed steel bar unit.

[0101] Establish the stress transfer relationship between each prestressed steel reinforcement unit and the adjacent concrete solid unit. This stress transfer relationship can adopt the complete bond assumption, that is, it is assumed that there is no relative slippage between the prestressed steel reinforcement and the adjacent concrete after tensioning and locking, and the stress change of any prestressed steel reinforcement unit is transferred to the adjacent concrete solid unit according to the contact area weight between it and the adjacent concrete solid unit.

[0102] The initial structural digital model is obtained by combining concrete solid elements with material density parameters, prestressed steel reinforcement elements with initial prestress values, the internal cavity boundaries of the pile, and stress transfer relationships. This initial structural digital model not only records the geometry of the pile body but also fully reflects the prestress distribution and medium density distribution within the pile, providing a reference benchmark for stress updates during subsequent pile driving stages.

[0103] Step S103: During the pile driving stage, obtain the pile impact stress data and pile soil resistance data of the prestressed hollow pile.

[0104] During the pile driving stage, after the prestressed hollow piles are hoisted to the pile location by a crane, a hydraulic pile hammer or diesel hammer is used to drive the pile into the foundation by successive hammer blows from the top of the pile downwards. To collect data during the pile driving stage, stress sensors and acceleration sensors need to be pre-installed between the pile hammer and the top of the prestressed hollow pile. The stress sensor collects stress time history signals, and the acceleration sensor collects acceleration time history signals. These two types of signals are sampled, filtered, and time-domain aligned before being combined to obtain the pile driving impact stress data. The pile driving impact stress data includes the stress peak value caused by each hammer blow, the stress time history curve, and the corresponding acceleration time history curve.

[0105] The system synchronously acquires the pile penetration depth, penetration displacement, and strain change values ​​at different depths of the pile corresponding to each hammer blow. The pile penetration depth can be measured by a displacement sensor mounted on the top of the pile, or accumulated by a pile driving recorder based on the number of hammer blows; the strain change values ​​at different depths of the pile can be acquired in real time by fiber optic strain sensors arranged at predetermined intervals along the inner wall of the pile.

[0106] The lateral frictional resistance at different depths of the prestressed hollow pile is determined based on the strain change and penetration displacement, thus obtaining the soil resistance data for pile driving. The lateral frictional resistance can be determined using the following algorithm: For any depth, the difference in strain change between two adjacent strain measurement points above and below that depth is read. Combining the pile length between the two measuring points and pile cross-sectional area Through formula The average lateral frictional resistance along the perimeter of this pile section was calculated. ,

[0107] in, The equivalent elastic modulus of the pile body. The circumference of the pile body The cross-sectional area of ​​the pile body The difference between the strain changes at two adjacent strain measurement points. This represents the length of the pile between two adjacent strain measurement points. The lateral frictional resistance at each depth of the pile is calculated in the same manner to obtain the soil resistance data reflecting the pile-soil interaction state at each depth.

[0108] Step S104: Calculate the prestress loss data of the initial prestress data based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the soil resistance data of the pile.

[0109] This step is necessary because during pile driving, the hammer stress wave attenuates in the concrete medium as it propagates downwards along the pile, and the soil resistance around the pile dissipates some of the stress wave energy. When the energy dissipation is significant enough, the prestressed steel bars will develop a certain amount of residual strain due to repeated impacts, leading to a decrease in the prestress value. If this loss is not calculated, the initial structural digital model will not be able to reflect the true internal stress state of the pile after pile driving, and the defect judgment during the service monitoring phase will be distorted due to benchmark deviation.

[0110] like Figure 4 As shown, the calculation process for prestress loss data in pile driving is as follows:

[0111] First, the pile body is divided into multiple continuous depth sections along the axial direction of the prestressed hollow pile. The length of each depth section can be set according to the pile body size and calculation accuracy. For example, a 12-meter-long pile body can be divided into 24 continuous depth sections in 0.5-meter segments.

[0112] Secondly, the concrete density data and the soil resistance data of the pile driving are mapped to the corresponding depth sections, and the stress time history, peak amplitude, peak arrival time and impact duration in the pile driving impact stress data are extracted simultaneously.

[0113] Then, based on the concrete density value corresponding to each depth section and the preset equivalent elastic parameters, the propagation velocity of the stress wave and the medium wave impedance within each depth section are determined. The equivalent elastic parameters refer to the equivalent elastic modulus of concrete under dynamic impact loads. and equivalent Poisson's ratio Its value can be pre-calibrated based on dynamic test blocks of the same batch of concrete, for example... Static elastic modulus can be taken 1.1 times that. Speed ​​of transmission. The calculation formula is ,in, These are the material density parameters for the concrete solid element in the corresponding depth section. Medium wave impedance. The calculation formula is .

[0114] Next, based on the stress time history, propagation velocity, and medium impedance, the waveform amplitude and propagation energy of the stress wave at each depth segment are determined. Stress wave propagation energy. It can be derived from the formula The calculation yields the following result: For the stress time history at the corresponding depth section, Let be the cross-sectional area of ​​the pile, and let the integral interval be the duration of a single hammer impact.

[0115] Then calculate the waveform amplitude change between adjacent depth segments, and determine the waveform amplitude change as the waveform attenuation amount of the corresponding depth segment.

[0116] Then, based on the soil resistance values ​​around the piles and the penetration displacement of the prestressed hollow piles in the corresponding depth sections, the energy dissipation due to pile-soil interaction in each depth section is calculated. The formula for calculating the energy dissipated by pile-soil interaction is: In the formula, The soil resistance around the pile in this depth section, The circumference of the pile body The length of the depth segment. This represents the penetration displacement corresponding to the current hammer blow.

[0117] Then, the stress wave attenuation energy is determined based on the energy difference of stress wave propagation between adjacent depth sections. and attenuate the energy of the stress wave. Energy dissipated by interaction between pile and soil By summing the values, the local energy dissipation values ​​for each depth segment are obtained. .

[0118] Then, the local energy dissipation values ​​under each hammer blow are accumulated according to the order of the hammer blows to obtain the cumulative energy dissipation value for each depth segment. .

[0119] Then, based on the pre-established correspondence between the cumulative energy dissipation value and the residual strain value of the prestressed steel reinforcement, the residual strain value of the prestressed steel reinforcement in each depth section is determined. This correspondence can be pre-calibrated through full-scale indoor tests: prestressed hollow pile samples are placed on a reaction frame and loaded with impact loads of different amplitudes, while simultaneously recording the cumulative energy dissipation value and the residual strain value of the prestressed steel reinforcement. The test data are then linearly or polynomially fitted to obtain a form such as... The relation, where, This represents the residual strain value of the prestressed steel reinforcement. , These are the fitting coefficients. In practical applications, the above relationships or the corresponding tables obtained from calibration are stored in the computing device for real-time retrieval during the pile driving stage.

[0120] Finally, the initial strain value of the prestressed steel bars is determined using the initial prestress data. ,in, This is the initial prestress value. The elastic modulus of prestressed steel bars; combined with the difference between the initial strain value and the residual strain value. and the elastic modulus of prestressed steel bars Calculate the prestress loss value of the pile driving in each depth section. The prestress loss values ​​of piles at different depths are combined to obtain the prestress loss data of piles.

[0121] Step S106: During the service monitoring phase, the measured acoustic reflection data collected by the acoustic wave detection sensor installed in the cavity of the prestressed hollow pile is received, and the in-service structure digital model is used as a reference benchmark to calculate and generate reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile.

[0122] During the service monitoring phase, the prestressed hollow piles have fully penetrated the foundation, and the pile tops bear the loads of the superstructure. At this stage, the outer surface of the pile is completely surrounded by soil, making it impossible to directly inspect the pile's condition using external non-destructive testing methods. Therefore, this application employs an acoustic wave detection sensor installed within the cavity of the prestressed hollow pile for internal cavity non-destructive testing. The acoustic wave detection sensor can be an integrated multi-directional probe capable of moving up and down and rotating within the pile's cavity, or an array of multiple fixed probes arranged at equal intervals along the axial direction.

[0123] like Figure 5 As shown, the data acquisition process for measured acoustic wave reflection during the service monitoring phase includes: acquiring the axial coordinates, circumferential angles, and acoustic wave emission direction of the acoustic wave detection sensor within the cavity of the prestressed hollow pile; and recording the first time point at which the acoustic wave detection sensor emits the detection acoustic wave. and the second time point at which the reflected sound wave is received. And collect the incident amplitude of the sound wave. and the amplitude of the reflected sound wave Reflection phase and reflection frequency The above parameters are combined according to the axial coordinates, circumferential angles, and sound wave emission direction of the acoustic wave detection sensor to form the measured sound wave reflection data.

[0124] Since the actual medium state of each prestressed hollow pile changes during its service life due to prestress loss during pile driving, using a uniform empirical model as a benchmark will inevitably introduce errors. Therefore, this application uses the aforementioned in-service structure digital model as a benchmark to simulate and generate benchmark acoustic wave reflection data. The generation process is as follows:

[0125] First, virtual detection nodes are set in the virtual cavity of the in-service structural digital model, corresponding to the axial coordinates, circumferential angles, and sound wave emission directions of the acoustic wave detection sensor.

[0126] Secondly, while retaining the remaining prestress data and concrete density data in the digital model of the in-service structure, the concrete solid elements in the digital model of the in-service structure are set to a defect-free reference state, that is, assuming that the pile body does not have any defects such as cracks, spalling, or voids except for the stress state. The purpose of this step is to construct a reference state that should be presented if the pile body is intact, as the basis for subsequent difference comparisons.

[0127] Next, the virtual detection nodes are controlled to emit virtual detection sound waves towards the sidewall of the in-service structural digital model. The propagation path of the virtual detection sound waves within the in-service structural digital model is determined based on the spatial location of the virtual detection nodes and the direction of sound wave emission. The propagation path can be determined using a ray tracing algorithm, i.e., starting from the virtual detection node, the sound waves are progressively advanced along the spatial coordinate grid of the in-service structural digital model according to the direction of sound wave emission until they reach the outer surface of the pile body or reach the maximum effective distance. The length of the propagation path is denoted as... .

[0128] Then, based on the material density of the digital model of the in-service structure With remaining prestress data Determine the speed of sound propagation at various locations along the propagation path. and reference dielectric impedance It should be noted here that the presence of prestress alters the equivalent modulus of elasticity of concrete: when concrete is under triaxial compression, its equivalent modulus of elasticity is higher than that under free stress, and conversely, it decreases under tension. Therefore, the dynamic equivalent modulus of elasticity of concrete used in the pile driving stage... Starting from this point, the corrected dynamic elastic modulus under service conditions is obtained using the following correction relationship. :

[0129]

[0130] In the formula, This represents the remaining prestress at that location (positive under compression). For concrete compressive strength, The influence coefficient of compressive stress on the dynamic elastic modulus can be determined based on indoor calibration tests, for example, a value between 0.1 and 0.3. The corrected dynamic elastic modulus... Substituting these values ​​into the following equation yields the acoustic wave propagation velocity and the reference medium impedance at that location:

[0131]

[0132]

[0133] Next, the reference reflection time span of the virtual probe sound wave in a defect-free reference state is calculated based on the sound wave propagation speed and the propagation path length:

[0134]

[0135] In the formula, The length of the propagation path, The average speed of sound wave propagation along the propagation path.

[0136] Furthermore, the reference reflection amplitude under a defect-free reference state is determined using the reference medium wave impedance, the propagation path length, and the incident amplitude of the virtual probe acoustic wave:

[0137]

[0138] In the formula, To virtually detect the incident amplitude of the sound wave, The reference reflection coefficient is derived from the reference medium wave impedance at the outer wall of the pile. With the outer soil medium wave impedance The mutation between them is determined by the following formula:

[0139]

[0140] in, (Values ​​can be taken from a pre-calibrated table according to the type of soil layers around the pile.) The sound wave attenuation coefficient is the sound wave propagation attenuation coefficient. The propagation path length is given. The reference reflection phase is determined by the sign of the abrupt change in the reference medium wave impedance (the reflection phase reverses by 180 degrees when the outer medium wave impedance is less than the inner one), and the reference reflection frequency is derived from the frequency response characteristics of the reference medium wave impedance at each position along the propagation path.

[0141] Finally, the reference reflection time span Reference reflection amplitude The reference reflection phase and reference reflection frequency are associated and arranged according to the axial coordinates, circumferential angle and sound wave emission direction of the acoustic wave detection sensor to generate reference acoustic wave reflection data.

[0142] Step S107: Based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, match the location of the prestressed hollow pile in the digital model of the in-service structure to generate an operation and maintenance work order containing the location of the pile defect and send it to the operation and maintenance terminal.

[0143] like Figure 6 As shown, the process of matching the location of pile defects and generating maintenance work orders is as follows:

[0144] First, the measured sound wave reflection data is correlated with the reference sound wave reflection data according to the axial coordinates, circumferential angle, and sound wave emission direction of the sound wave detection sensor. The reason for correlating according to these spatial parameters is that the comparison between the measured value and the reference value only has physical meaning when the sensor's spatial position and emission direction are completely consistent.

[0145] Secondly, based on the incident amplitude in the measured sound wave reflection data With reflection amplitude The ratio between them determines the measured reflection coefficient:

[0146]

[0147] Correspondingly, the ratio between the virtual incident amplitude and the reference reflection amplitude is read from the generated reference acoustic wave reflection data to obtain the reference reflection coefficient. .

[0148] Then, based on the measured reflection coefficient Reference dielectric wave impedance corresponding to the propagation path in the digital model of the in-service structure The measured equivalent wave impedance is determined by the following formula. :

[0149]

[0150] Similarly, based on the reference reflection coefficient and reference dielectric impedance Determine the reference equivalent wave impedance under a defect-free reference state :

[0151]

[0152] The reason for using equivalent wave impedance as the comparison physical quantity is that equivalent wave impedance comprehensively reflects the overall change of the medium state at the reflection interface, and is more sensitive to medium defects than a single reflection amplitude or reflection time.

[0153] Next, the difference between the measured equivalent wave impedance and the reference equivalent wave impedance is calculated to obtain the wave impedance difference data:

[0154]

[0155] Then calculate the measured reflection time span in the measured sound wave reflection data. Compared with the reference reflection time span in the reference acoustic wave reflection data Time difference between:

[0156]

[0157] Time difference It reflects the depth offset of the reflective interface, that is, the distance by which the defect location deviates from the outer wall of the pile along the propagation direction.

[0158] Then, based on the axial coordinates, circumferential angles, sound wave emission direction, and measured reflection time span of the acoustic wave detection sensor... and the average speed of sound along the corresponding propagation path The candidate spatial coordinates corresponding to the reflection anomaly are determined. Specifically, the distance from the reflection anomaly location to the sensor is equal to the average sound wave propagation speed along the corresponding propagation path. Compared with the measured reflection time span Half of the product, that is:

[0159]

[0160] By combining the axial coordinates, circumferential angles, and sound wave emission direction of the acoustic wave detection sensor, the candidate spatial coordinates (x, y, z) of the reflection anomaly in the digital model of the in-service structure can be obtained through the conversion from polar coordinates to rectangular coordinates.

[0161] Next, determine the impedance difference data. Whether it exceeds a preset anomaly detection threshold. The anomaly detection threshold can be set in a relative proportion form, i.e., when... Greater than the preset threshold At that time, it was considered that there was a defect, and the threshold was set. It can be obtained based on the test pile calibration of the same batch of projects, for example, 0.05 (i.e. 5%); the specific threshold can be set according to the accuracy requirements of the project.

[0162] exist When the anomaly detection criteria are met, the corresponding candidate spatial coordinates are determined as candidate defect locations. Multiple candidate defect locations within a preset spatial range are merged to obtain the pile defect location, which includes axial depth, circumferential angle, and radial position. The merging algorithm can employ density clustering: if the spatial distance between two candidate defect locations is less than a preset merging radius... If they are the same defect, their spatial coordinates are taken as a weighted average, and the weights can be calculated using... Preset merge radius It can be set according to the sensor's spatial resolution, for example, 50 mm.

[0163] Then, in the digital model of the in-service structure, the location of pile defects is set as defect markers, and the range of pile defects is determined based on the spatial distribution of adjacent defect markers. The range of pile defects can be represented by the convex hull boundary of adjacent defect markers, reflecting the degree of contiguousness of defects in the pile space.

[0164] Extract data on the location, extent, and impedance difference of pile defects. Time difference The detection time and corresponding acoustic wave detection sensor number are used to generate maintenance work order data. This is based on the wave impedance difference data. To determine the severity of defects corresponding to their locations in the pile body, a grading mapping rule can be used. The relative proportion of defects is divided into three levels: minor, moderate, and severe. For example, a relative proportion between 5% and 10% is considered minor, 10% to 20% is considered moderate, and greater than 20% is considered severe. The specific grading thresholds can be set according to the actual engineering quality control standards. The severity of the defects is written into the maintenance work order data, a maintenance work order is generated, and it is transmitted to the maintenance terminal via an industrial internet protocol (such as MQTT or HTTP).

[0165] like Figure 2 As shown, this application also provides an intelligent management system for the entire lifecycle of prestressed hollow piles based on the Industrial Internet, used to implement the above method. The system includes seven modules, each corresponding to steps S1 to S7:

[0166] The first acquisition module 100 corresponds to step S101, and is used to acquire the initial prestress data and concrete density data of the prestressed hollow pile during the factory production stage.

[0167] The construction module 200 corresponds to step S102, which is used to construct the initial structural digital model of the prestressed hollow pile based on the initial prestress data and concrete density data.

[0168] The second acquisition module 300 corresponds to step S103, which is used to acquire the pile impact stress data and pile soil resistance data of the prestressed hollow pile during the pile driving stage.

[0169] The first calculation module 400 corresponds to step S104, which is used to calculate the prestress loss data of the initial prestress data based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the soil resistance data of the pile.

[0170] The correction module 500 corresponds to step S105, which is used to perform structural correction on the internal stress state of the initial structural digital model using the prestress loss data of the pile driving to generate an in-service structural digital model of the prestressed hollow pile.

[0171] The second calculation module 600 corresponds to step S106, which is used to receive the measured acoustic reflection data collected by the acoustic wave detection sensor set in the cavity of the prestressed hollow pile during the service monitoring stage, and to calculate and generate the reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile using the in-service structure digital model as a reference.

[0172] The matching generation module 700 corresponds to step S107, which is used to match the location of the prestressed hollow pile in the digital model of the in-service structure based on the wave impedance difference data between the measured sound wave reflection data and the reference sound wave reflection data, so as to generate an operation and maintenance work order containing the location of the pile defect and send it to the operation and maintenance terminal.

[0173] The computing device of this application includes a processing component and a storage component. The storage component stores one or more computer instructions, which, when executed by the processing component, implement the aforementioned intelligent management method for the entire lifecycle of prestressed hollow piles based on the Industrial Internet. The processing component may be a general-purpose processor, a dedicated processor, a digital signal processor, or a programmable logic device; the storage component may be a random access memory, a read-only memory, flash memory, or a solid-state drive, etc.; the computer storage medium of this application stores a computer program, which, when executed by a computer, implements the aforementioned method.

[0174] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any obvious modifications and equivalent substitutions made by those skilled in the art within the scope of the technical solutions of this application should be included within the protection scope of this application.

Claims

1. A method for intelligent management of the entire lifecycle of prestressed hollow piles based on the Industrial Internet, characterized in that, include: To obtain initial prestress data and concrete density data of prestressed hollow piles during the factory production stage; An initial structural digital model of the prestressed hollow pile is constructed based on the initial prestress data and the concrete density data. During the pile driving stage, the impact stress data and soil resistance data of the prestressed hollow piles are obtained. Based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the pile soil resistance data, the pile prestress loss data of the initial prestress data is calculated, including: dividing the pile body into multiple continuous depth segments along the axial direction of the prestressed hollow pile; mapping the concrete density data and the pile soil resistance data to the corresponding depth segments respectively, and simultaneously extracting the stress time history, peak amplitude, peak arrival time, and impact duration from the pile impact stress data; determining the propagation velocity and medium wave impedance of the stress wave in each depth segment based on the concrete density value and preset equivalent elastic parameters corresponding to each depth segment; determining the waveform amplitude and stress wave propagation energy when the stress wave propagates to each depth segment based on the stress time history, the propagation velocity, and the medium wave impedance; calculating the waveform amplitude change value between adjacent depth segments, and determining the waveform amplitude change value as the waveform attenuation of the corresponding depth segment; based on The pile-soil resistance value corresponding to each depth segment and the penetration displacement of the prestressed hollow pile within the corresponding depth segment are used to calculate the pile-soil interaction dissipation energy in each depth segment. The stress wave attenuation energy is determined based on the stress wave propagation energy difference between adjacent depth segments, and the stress wave attenuation energy is accumulated with the pile-soil interaction dissipation energy to obtain the local energy dissipation value for each depth segment. The local energy dissipation value under each hammer blow is accumulated according to the hammering sequence to obtain the cumulative energy dissipation value for each depth segment. Based on the pre-established correspondence between the cumulative energy dissipation value and the residual strain value of the prestressed steel reinforcement, the residual strain value of the prestressed steel reinforcement within each depth segment is determined. The initial strain value of the prestressed steel reinforcement is determined using the initial prestress data, and the pile prestress loss value corresponding to each depth segment is calculated by combining the difference between the initial strain value and the residual strain value and the elastic modulus of the prestressed steel reinforcement to obtain the pile prestress loss data. The internal stress state of the initial structural digital model is structurally corrected using the prestress loss data of the driven piles, thereby generating the in-service structural digital model of the prestressed hollow piles. During the service monitoring phase, measured acoustic reflection data collected by an acoustic wave detection sensor installed in the cavity of the prestressed hollow pile is received, and the in-service structure digital model is used as a reference benchmark to calculate and generate reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile. Based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, the location of the prestressed hollow pile is matched in the digital model of the in-service structure to generate an operation and maintenance work order containing the location of the pile defect and send it to the operation and maintenance terminal.

2. The method according to claim 1, characterized in that, During the pile driving stage, the impact stress data and soil resistance data of the prestressed hollow piles are obtained, including: During the pile driving stage, stress time history signals collected by stress sensors and acceleration time history signals collected by acceleration sensors are obtained from the pile driving hammer and the top of the prestressed hollow pile to obtain the pile driving impact stress data. Obtain the pile penetration depth, penetration displacement, and strain change at different depths of the pile corresponding to each hammer blow; The lateral frictional resistance at different depths of the prestressed hollow pile is determined based on the strain change value and the penetration displacement to obtain the soil resistance data of the pile driving.

3. The method according to claim 2, characterized in that, Based on the initial prestressing data and the concrete density data, an initial structural digital model of the prestressed hollow pile is constructed, including: Based on the design dimensions of the prestressed hollow pile, a spatial discrete model of the pile body with spatial coordinates is established. The spatial discrete model of the pile body is defined to include concrete solid elements, prestressed steel reinforcement elements, and the boundary of the pile body cavity. The concrete density data is filled into the concrete solid unit according to the spatial coordinates, and the material density parameter is set according to the concrete density value corresponding to each concrete solid unit. In the discrete model of the pile body, the spatial position of each prestressed steel bar unit is determined according to the design layout of the prestressed steel bars. The corresponding initial prestress value is extracted from the initial prestress data and assigned to the corresponding prestressed steel bar unit based on the spatial position of each prestressed steel bar unit. Establish the stress transfer relationship between each prestressed steel bar unit and the adjacent concrete solid unit, and combine the concrete solid unit with the material density parameter, the prestressed steel bar unit with the initial prestress value, the inner cavity boundary of the pile and the stress transfer relationship to output the initial structural digital model.

4. The method according to claim 1, characterized in that, Using the prestress loss data of the driven piles, the internal stress state of the initial structural digital model is structurally corrected to generate an in-service structural digital model of the prestressed hollow piles, including: Extract the spatial coordinates and initial prestress values ​​of each prestressed steel unit in the initial structural digital model; Find the prestress loss value of the driven pile corresponding to the spatial coordinates of each prestressed steel reinforcement unit from the prestress loss data of the driven pile according to the depth position; In the initial structural digital model, the initial prestress value is subtracted from the corresponding pile prestress loss value to obtain the remaining prestress data of each prestressed steel reinforcement unit; The stress state of each prestressed steel bar unit is updated from the initial prestress value to the corresponding remaining prestress data, and the internal stress state of adjacent concrete solid units is updated synchronously according to the stress transfer relationship. The initial prestress state in the initial structural digital model is retained, and the remaining prestress data and the updated internal stress state are written into the post-pile driving model state associated with the initial structural digital model to obtain the in-service structural digital model.

5. The method according to claim 4, characterized in that, During the service monitoring phase, measured acoustic reflection data collected by an acoustic wave detection sensor installed inside the prestressed hollow pile are received. Using the in-service structural digital model as a reference, reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile is calculated and generated, including: The axial coordinates, circumferential angles, and sound wave emission direction within the cavity of the prestressed hollow pile are obtained by the acoustic wave detection sensor. The acoustic wave detection sensor records the first time point when it emits a detection acoustic wave and the second time point when it receives a reflected acoustic wave, and collects the incident amplitude of the detection acoustic wave and the reflection amplitude, reflection phase and reflection frequency of the reflected acoustic wave. The measured acoustic wave reflection data is formed by combining the time interval between the first time point and the second time point with the incident amplitude, the reflected amplitude, the reflected phase, and the reflected frequency; Virtual detection nodes are set in the virtual cavity of the in-service structural digital model, corresponding to the axial coordinates, circumferential angles and sound wave emission directions of the acoustic wave detection sensor. While retaining the remaining prestress data and concrete density data in the digital model of the in-service structure, the concrete solid elements in the digital model of the in-service structure are set to a defect-free reference state. The virtual detection node is controlled to emit virtual detection sound waves toward the side wall of the in-service structural digital model, and the propagation path of the virtual detection sound waves in the in-service structural digital model is determined according to the spatial position of the virtual detection node and the direction of sound wave emission. The acoustic wave propagation velocity and reference medium wave impedance at each position along the propagation path are determined based on the material density and remaining prestress data of the digital model of the in-service structure. The reference reflection time span of the virtual probe sound wave in the defect-free reference state is calculated based on the sound wave propagation speed and the propagation path length. The reference reflection amplitude, reference reflection phase, and reference reflection frequency under the defect-free reference state are determined using the reference medium wave impedance, the propagation path length, and the incident amplitude of the virtual probe acoustic wave. The reference reflection time span, the reference reflection amplitude, the reference reflection phase, and the reference reflection frequency are associated and arranged according to the axial coordinates, circumferential angle, and sound wave emission direction of the acoustic wave detection sensor to generate the reference acoustic wave reflection data.

6. The method according to claim 5, characterized in that, Based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, the location of the prestressed hollow pile is matched in the digital model of the in-service structure to generate a maintenance work order containing the location of the pile defect and send it to the maintenance terminal, including: According to the axial coordinates, circumferential angle and sound wave emission direction of the sound wave detection sensor, the measured sound wave reflection data is correlated with the reference sound wave reflection data. The measured reflection coefficient is determined based on the ratio between the incident amplitude and the reflected amplitude in the measured acoustic wave reflection data, and the reference reflection coefficient is determined based on the ratio between the virtual incident amplitude and the reference reflected amplitude in the reference acoustic wave reflection data. The measured equivalent wave impedance is determined based on the measured reflection coefficient and the reference medium wave impedance of the corresponding propagation path in the digital model of the in-service structure. Based on the reference reflection coefficient and the reference dielectric wave impedance, determine the reference equivalent wave impedance under the defect-free reference state; The difference between the measured equivalent wave impedance and the reference equivalent wave impedance is calculated to obtain the wave impedance difference data; Calculate the time difference between the measured reflection time span in the measured acoustic wave reflection data and the reference reflection time span in the reference acoustic wave reflection data; Based on the axial coordinates, circumferential angle, sound wave emission direction, measured reflection time span, and sound wave propagation speed along the corresponding propagation path of the sound wave detection sensor, the candidate spatial coordinates corresponding to the reflection anomaly are determined. Determine whether the impedance difference data is greater than a preset anomaly detection condition; When the wave impedance difference data is greater than the anomaly determination condition, the corresponding candidate spatial coordinates are determined as the candidate defect location; Multiple candidate defect locations located within a preset spatial range are merged to obtain the pile body defect location, which includes axial depth, circumferential angle, and radial position. In the digital model of the in-service structure, the location of the pile defect is set as a defect marker, and the range of the pile defect is determined according to the spatial distribution of adjacent defect markers; Extract the location of the pile defect, the range of the pile defect, the wave impedance difference data, the time difference, the detection time, and the corresponding acoustic wave detection sensor number to form maintenance work order data; The severity of the defect corresponding to the location of the pile defect is determined based on the wave impedance difference data. The severity of the defect is written into the maintenance work order data, the maintenance work order is generated, and it is transmitted to the maintenance terminal.

7. A prestressed hollow pile full life cycle intelligent management system based on the Industrial Internet, characterized in that, The system is used to implement the intelligent management method for the entire life cycle of prestressed hollow piles based on the Industrial Internet as described in any one of claims 1-6; the system includes: The first acquisition module is used to acquire the initial prestress data and concrete density data of the prestressed hollow piles during the factory production stage. A construction module is used to construct an initial structural digital model of the prestressed hollow pile based on the initial prestress data and the concrete density data. The second acquisition module is used to acquire the pile impact stress data and pile soil resistance data of the prestressed hollow pile during the pile driving stage. The first calculation module is used to calculate the prestress loss data of the initial prestress data based on the waveform attenuation of the pile impact stress data in the medium structure defined by the concrete density data and the soil resistance data of the pile. The correction module is used to perform structural correction on the internal stress state of the initial structural digital model using the prestress loss data of the driven piles, and generate the in-service structural digital model of the prestressed hollow piles. The second calculation module is used to receive measured acoustic reflection data collected by an acoustic wave detection sensor installed in the cavity of the prestressed hollow pile during the service monitoring phase, and to calculate and generate reference acoustic reflection data corresponding to the undamaged state of the prestressed hollow pile using the digital model of the in-service structure as a reference. The matching generation module is used to match the location of the prestressed hollow pile in the digital model of the in-service structure based on the wave impedance difference data between the measured acoustic wave reflection data and the reference acoustic wave reflection data, so as to generate an operation and maintenance work order containing the location of the pile defect and send it to the operation and maintenance terminal.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to realize the intelligent management method for the entire life cycle of prestressed hollow piles based on the Industrial Internet as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a prestressed hollow pile full lifecycle intelligent management method based on the Industrial Internet as described in any one of claims 1-6.

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