A method and system for intelligent pile foundation low-strain detection and analysis

Abstract

The present invention discloses a method for intelligent low-strain detection and analysis of pile foundations. A cloud database for pile foundation projects is constructed. The cloud database stores designed pile foundation parameters to be detected corresponding to the project. By comparing data collected by different sensing devices, it is determined whether complex calculations are required to determine the quality analysis of the actual pile foundation. The closer the data obtained by different sensing devices are, the simpler the calculation and verification work required is. The size of the object in the image is determined by image recognition and processing technology. The second pile length data is calculated based on the mapping relationship between the pile body material in the designed pile foundation parameters and the propagation speed of the first vibration force. When the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is greater than a threshold, the trained pile foundation low-strain detection and analysis model is used to match and analyze the data, and a pile foundation quality determination result is automatically issued based on the matching result without performing additional analysis and verification.

Description

Technical Field

[0001] The present invention relates to the field of detection and analysis technology, and in particular to a method and system for intelligent pile foundation low-strain detection and analysis. Background Art

[0002] Currently, pile structures in construction projects are primarily constructed using poured concrete. Failure to properly control the pouring speed can easily lead to missing piles. Therefore, when inspecting pile quality, its integrity is a fundamental component of inspection. Pile foundation structures are located underground, making visual inspection difficult. Traditional inspection methods, such as acoustic testing and ultrasonic testing, are often used. When analyzing data, interfering factors must be eliminated to improve the accuracy of the results. Existing inspection technologies primarily rely on manual on-site measurement of data and graphs, which are then exported to a computer for manual counting and graphical analysis to produce the correct results. This approach is inefficient and is believed to carry a high probability of error. The development of an intelligent pile foundation quality analysis system can improve analysis efficiency and accuracy, reducing the potential for error caused by human intervention. However, training this system requires a significant amount of data and computing resources, resulting in significant cost and time investment. At the same time, for low-strain detection methods, measurement errors also need to be verified to ensure the accuracy of detection and analysis. However, existing technical solutions either do not perform corresponding verification or use complex data modeling for direct verification, resulting in large amounts of calculation and long calculation time. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention discloses an intelligent pile foundation low strain detection and analysis method, the method comprising:

[0004] Step 1: Build a cloud database for pile foundation projects. The cloud database stores the designed pile foundation parameters to be tested corresponding to the project. The designed pile foundation parameters include pile length, pile diameter, pile material, embedment depth, and soil type around the pile.

[0005] Step 2: Acquire image data of an actual pile foundation corresponding to the designed pile foundation to be inspected by a first sensing device, then apply a first vibration force to the top of the actual pile foundation by a second sensing device, and simultaneously acquire a dynamic strain waveform corresponding to the first vibration force propagating downward from the top of the pile foundation;

[0006] Step 3: The first sensing device is a camera device fixed at a preset position on the construction site and capable of capturing a complete image of the actual pile foundation. The actual pile foundation is analyzed based on the internal and external parameters determined by the camera device and the fixed position information, and the first pile length data and the first pile diameter data obtained by the first sensing device are determined. That is, the size of the object in the image is determined through image recognition and processing technology, and the second pile length data is calculated based on the mapping relationship between the pile material in the designed pile foundation parameters and the propagation speed of the first vibration force.

[0007] Step 4: When the similarity between the value obtained by subtracting the embedding depth from the second pile length data and the first pile length data is greater than or equal to a first threshold, the newly input waveform data is matched and analyzed using the trained pile foundation low-strain detection and analysis model, and a pile foundation quality determination result is automatically issued based on the matching and analysis results without performing additional analysis and verification;

[0008] Step 5: When the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is less than a first threshold, the newly input waveform graphic data is matched and analyzed according to the trained pile foundation low-strain detection analysis model. After obtaining the analysis results, a pile-soil solid finite element model is established according to the data collected by the second sensing device and the design pile foundation parameters in the cloud database. The response speed of the pile top during transient excitation is solved using the ANSYS / LS-DYNA program. The analysis results are verified and quantitatively analyzed through numerical simulation of the foundation pile.

[0009] Furthermore, the training of the pile foundation low strain detection and analysis model further includes: preprocessing and analyzing the dynamic strain waveform data collected by the second sensing device, wherein the preprocessing is to perform data cleaning on the collected waveform image to reduce the amount of calculated data, and at the same time convert the format of the cleaned data to obtain a data format corresponding to the data analysis, and divide the pile foundation quality into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile. According to the different pile foundation quality classifications as the evaluation and analysis results, the collected graphic data is labeled, and the labeled data is used to train the pile foundation low strain detection and analysis model.

[0010] Furthermore, the pile foundation low-strain detection and analysis model is a convolutional neural network model, in which, during the training process, convolution kernels are set in the convolution layer whose weights do not change with back propagation. The closer the value of the second pile length data minus the embedding depth is to the first pile length data, the more convolution kernels that do not change with back propagation and the more corresponding fixed-value weights, thereby reducing the computational complexity of data processing and improving the convergence speed.

[0011] Furthermore, the second sensing device includes sensors placed at the pile top and pile bottom for recording the propagation time and amplitude of the strain wave when the pile top is subjected to vibration force, and calculating the wave velocity in the pile foundation through the propagation time and amplitude.

[0012] The present invention also discloses an intelligent pile foundation low strain detection and analysis system, the system comprising:

[0013] A cloud-based database for pile foundation projects, wherein the cloud-based database stores the designed pile foundation parameters to be tested corresponding to the project, including pile length, pile diameter, pile material, embedment depth, and soil type surrounding the pile foundation;

[0014] first and second sensing devices, wherein the first sensing device acquires image data of an actual pile foundation corresponding to the designed pile foundation to be inspected, and the second sensing device applies a first vibration force to the top of the actual pile foundation, while acquiring a dynamic strain waveform corresponding to the first vibration force propagating downward from the top of the pile foundation;

[0015] a computing unit, wherein the first sensing device is a camera device fixed at a preset position on the construction site and capable of capturing a complete image of the actual pile foundation; the actual pile foundation is analyzed based on internal and external parameters determined by the camera device and fixed position information; first pile length data and first pile diameter data obtained by the first sensing device are determined, i.e., the size of the object in the image is determined through image recognition and processing technology; and second pile length data is calculated based on a mapping relationship between the pile material in the designed pile foundation parameters and the propagation speed of the first vibration force;

[0016] a first comparison and verification unit, when the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is greater than or equal to a first threshold, using the trained pile foundation low-strain detection and analysis model to match and analyze the newly input waveform graphic data, and automatically issuing a pile foundation quality determination result based on the matching and analysis results without performing additional analysis and verification;

[0017] The second comparison and verification unit is configured to match and analyze the newly input waveform graphic data according to the trained pile foundation low-strain detection and analysis model when the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is less than a first threshold value. After obtaining the analysis results, a pile-soil solid finite element model is established based on the data collected by the second sensing device and the design pile foundation parameters in the cloud database. The response speed of the pile top during transient excitation is solved using the ANSYS / LS-DYNA program. The analysis results are verified and quantitatively analyzed through numerical simulation of the foundation piles.

[0018] Furthermore, the training of the pile foundation low strain detection and analysis model further includes: preprocessing and analyzing the dynamic strain waveform data collected by the second sensing device, wherein the preprocessing is to perform data cleaning on the collected waveform image to reduce the amount of calculated data, and at the same time convert the format of the cleaned data to obtain a data format corresponding to the data analysis, and divide the pile foundation quality into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile. According to the different pile foundation quality classifications as the evaluation and analysis results, the collected graphic data is labeled, and the labeled data is used to train the pile foundation low strain detection and analysis model.

[0019] Furthermore, the pile foundation low-strain detection and analysis model is a convolutional neural network model, in which, during the training process, convolution kernels are set in the convolution layer whose weights do not change with back propagation. The closer the value of the second pile length data minus the embedding depth is to the first pile length data, the more convolution kernels that do not change with back propagation and the more corresponding fixed-value weights, thereby reducing the computational complexity of data processing and improving the convergence speed.

[0020] Furthermore, the second sensing device includes sensors placed at the pile top and pile bottom for recording the propagation time and amplitude of the strain wave when the pile top is subjected to vibration force, and calculating the wave velocity in the pile foundation through the propagation time and amplitude.

[0021] Compared with the prior art, the present invention has the following beneficial effects: the present invention combines the existing research methods, and on this basis proposes a method for hierarchically judging whether it is necessary to verify the results of the low-strain detection method, which greatly reduces the amount of calculation and the calculation time. When there may be large errors in the calculation results, finite element analysis is further performed, the pile-soil solid model is established using UG software, and the velocity response during transient excitation of the pile top is solved using the ANSYS / LS-DYNA program. By calculating the pile length and the defect position, the effectiveness of the numerical simulation of the pile-soil finite element model and the reliability of the calculation results are verified, and the foundation pile can be effectively numerically simulated. Based on the MATLAB platform, a quantitative analysis program for pile foundations was compiled using the least squares method according to the mathematical model between piles and soil. The pile foundation parameters were quantitatively analyzed by fitting the model curve with the measured curve. When the error of the calculation result may be small, no second verification was performed. At the same time, the present invention integrated data annotation by entering a large amount of existing detected graphic data and corresponding evaluation and analysis results into the system. The large amount of existing data annotations can be automatically matched and analyzed with the subsequent measured graphics to automatically issue the judgment results of the pile foundation quality. In order to accelerate the training convergence speed and accuracy of the model, the first detection parameter judgment method is adopted to optimize whether the parameters of the neural network need to be iteratively modified, thereby further reducing the amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but rather emphasis is placed on illustrating the principles of the embodiments. In the figures, the same reference numerals designate corresponding parts in different views.

[0023] Figure 1 This is a flow chart of a method for intelligent pile foundation low strain detection and analysis of the present invention. DETAILED DESCRIPTION

[0024] Example 1

[0025] like Figure 1 As shown, this embodiment provides a method for intelligent pile foundation low strain detection and analysis, the method comprising:

[0026] Step 1: Build a cloud database for pile foundation projects. The cloud database stores the designed pile foundation parameters to be tested corresponding to the project. The designed pile foundation parameters include pile length, pile diameter, pile material, embedment depth, and soil type around the pile.

[0027] Step 2: Acquire image data of an actual pile foundation corresponding to the designed pile foundation to be inspected by a first sensing device, then apply a first vibration force to the top of the actual pile foundation by a second sensing device, and simultaneously acquire a dynamic strain waveform corresponding to the first vibration force propagating downward from the top of the pile foundation;

[0028] Step 3: The first sensing device is a camera device fixed at a preset position on the construction site and capable of capturing a complete image of the actual pile foundation. The actual pile foundation is analyzed based on the internal and external parameters determined by the camera device and the fixed position information, and the first pile length data and the first pile diameter data obtained by the first sensing device are determined. That is, the size of the object in the image is determined through image recognition and processing technology, and the second pile length data is calculated based on the mapping relationship between the pile material in the designed pile foundation parameters and the propagation speed of the first vibration force.

[0029] Step 4: When the similarity between the value obtained by subtracting the embedding depth from the second pile length data and the first pile length data is greater than or equal to a first threshold, the newly input waveform data is matched and analyzed using the trained pile foundation low-strain detection and analysis model, and a pile foundation quality determination result is automatically issued based on the matching and analysis results without performing additional analysis and verification;

[0030] Step 5: When the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is less than a first threshold, the newly input waveform graphic data is matched and analyzed according to the trained pile foundation low-strain detection analysis model. After obtaining the analysis results, a pile-soil solid finite element model is established according to the data collected by the second sensing device and the design pile foundation parameters in the cloud database. The response speed of the pile top during transient excitation is solved using the ANSYS / LS-DYNA program. The analysis results are verified and quantitatively analyzed through numerical simulation of the foundation pile.

[0031] Furthermore, the training of the pile foundation low strain detection and analysis model further includes: preprocessing and analyzing the dynamic strain waveform data collected by the second sensing device, wherein the preprocessing is to perform data cleaning on the collected waveform image to reduce the amount of calculated data, and at the same time convert the format of the cleaned data to obtain a data format corresponding to the data analysis, and divide the pile foundation quality into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile. According to the different pile foundation quality classifications as the evaluation and analysis results, the collected graphic data is labeled, and the labeled data is used to train the pile foundation low strain detection and analysis model.

[0032] Furthermore, the pile foundation low-strain detection and analysis model is a convolutional neural network model, in which, during the training process, convolution kernels are set in the convolution layer whose weights do not change with back propagation. The closer the value of the second pile length data minus the embedding depth is to the first pile length data, the more convolution kernels that do not change with back propagation and the more corresponding fixed-value weights, thereby reducing the computational complexity of data processing and improving the convergence speed.

[0033] Furthermore, the second sensing device includes sensors placed at the pile top and pile bottom for recording the propagation time and amplitude of the strain wave when the pile top is subjected to vibration force, and calculating the wave velocity in the pile foundation through the propagation time and amplitude.

[0034] In this embodiment, the strain wave velocity is obtained based on the mapping relationship between the pile material in the designed pile foundation parameters and the propagation velocity of the first vibration force. Optionally, a second sensing device includes sensors placed at the top and bottom of the pile for recording the propagation time and amplitude of the strain wave when the pile top is subjected to the vibration force. The wave velocity in the pile foundation is calculated based on the propagation time and amplitude. By comparing the calculated wave velocity with the wave velocity in the mapping relationship, it is determined whether the construction material meets the expected quality requirements. When the difference between the calculated wave velocity and the wave velocity obtained according to the mapping relationship is less than a preset value, the construction material is determined to be preliminarily qualified.

[0035] Due to the problem of unqualified construction materials (for example, the materials are unqualified but the wave velocity of the corresponding specific wave is similar), the vibration force propagation velocity is inaccurate in the second pile length data calculated according to the mapping relationship between the pile body material in the designed pile foundation parameters and the propagation velocity of the first vibration force, and the calculated pile length data has a large error. Therefore, another beneficial effect of this embodiment compared with the prior art is: when the value of the second pile length data minus the embedded depth is greater than or equal to the similarity of the first pile length data, it is compared with the second threshold, wherein the second threshold is greater than the first threshold. When the similarity is greater than the second threshold, the second vibration force is applied to the pile top of the actual pile foundation by the second sensing device, and the dynamic strain waveform corresponding to the second vibration force propagating downward from the pile top is obtained. The second vibration force is a vibration force with an amplitude and frequency different from the first vibration force, and then jump back to step 1 for re-detection and analysis.

[0036] Compared with the existing technology, no additional calculation propagation speed is required to eliminate calculation errors that may be caused by unqualified construction materials.

[0037] Example 2

[0038] From a hardware description perspective, the intelligent low-strain detection and analysis system for pile foundations in this embodiment is a smart detection system designed specifically for pile foundation projects. It features real-time remote monitoring, eliminates the need for line measurement and layout, exports monitoring records as reports, and transmits construction data to the backend in real time. This system can upload data to the project management platform in real time via the mobile communication network built into the control terminal, enabling remote management of pile foundation construction. The present invention also discloses an intelligent low-strain detection and analysis system for pile foundations, comprising:

[0039] A cloud-based database for pile foundation projects, wherein the cloud-based database stores the designed pile foundation parameters to be tested corresponding to the project, including pile length, pile diameter, pile material, embedment depth, and soil type surrounding the pile foundation;

[0040] first and second sensing devices, wherein the first sensing device acquires image data of an actual pile foundation corresponding to the designed pile foundation to be inspected, and the second sensing device applies a first vibration force to the top of the actual pile foundation, while acquiring a dynamic strain waveform corresponding to the first vibration force propagating downward from the top of the pile foundation;

[0041] a computing unit, wherein the first sensing device is a camera device fixed at a preset position on the construction site and capable of capturing a complete image of the actual pile foundation; the actual pile foundation is analyzed based on internal and external parameters determined by the camera device and fixed position information; first pile length data and first pile diameter data obtained by the first sensing device are determined, i.e., the size of the object in the image is determined through image recognition and processing technology; and second pile length data is calculated based on a mapping relationship between the pile material in the designed pile foundation parameters and the propagation speed of the first vibration force;

[0042] a first comparison and verification unit, when the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is greater than or equal to a first threshold, using the trained pile foundation low-strain detection and analysis model to match and analyze the newly input waveform graphic data, and automatically issuing a pile foundation quality determination result based on the matching and analysis results without performing additional analysis and verification;

[0043] The second comparison and verification unit is configured to match and analyze the newly input waveform graphic data according to the trained pile foundation low-strain detection and analysis model when the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is less than a first threshold value. After obtaining the analysis results, a pile-soil solid finite element model is established based on the data collected by the second sensing device and the design pile foundation parameters in the cloud database. The response speed of the pile top during transient excitation is solved using the ANSYS / LS-DYNA program. The analysis results are verified and quantitatively analyzed through numerical simulation of the foundation piles.

[0044] Furthermore, the training of the pile foundation low strain detection and analysis model further includes: preprocessing and analyzing the dynamic strain waveform data collected by the second sensing device, wherein the preprocessing is to perform data cleaning on the collected waveform image to reduce the amount of calculated data, and at the same time convert the format of the cleaned data to obtain a data format corresponding to the data analysis, and divide the pile foundation quality into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile. According to the different pile foundation quality classifications as the evaluation and analysis results, the collected graphic data is labeled, and the labeled data is used to train the pile foundation low strain detection and analysis model.

[0045] Furthermore, the pile foundation low-strain detection and analysis model is a convolutional neural network model, in which, during the training process, convolution kernels are set in the convolution layer whose weights do not change with back propagation. The closer the value of the second pile length data minus the embedding depth is to the first pile length data, the more convolution kernels that do not change with back propagation and the more corresponding fixed-value weights, thereby reducing the computational complexity of data processing and improving the convergence speed.

[0046] Furthermore, the second sensing device includes sensors placed at the pile top and pile bottom for recording the propagation time and amplitude of the strain wave when the pile top is subjected to vibration force, and calculating the wave velocity in the pile foundation through the propagation time and amplitude.

[0047] The system's hardware, including a Beidou high-precision positioning and orientation terminal, depth sensors, inclination sensors, and hammer count sensors, are installed on the pile foundation drilling rig. The data collected by these sensors is processed and analyzed to provide information on the pile foundation's construction quality, depth, verticality, and other aspects. This data can also be transmitted in real time via mobile communication networks to a backend management system, enabling managers to monitor and guide the construction process.

[0048] The system's software application processes and analyzes collected data, generating reports on construction progress, quality, and pile foundation conditions, helping managers better understand the construction status and identify and resolve problems promptly. The software system can also generate various reports and charts as needed, facilitating data analysis and decision-making.

[0049] Data acquisition layer: This layer is responsible for collecting data from various sensors and equipment, including various parameters in the construction process, such as construction progress, construction quality, pile foundation conditions, etc.

[0050] Data processing layer: This layer is responsible for processing and analyzing the collected data, including data cleaning, data conversion, data mining, etc.

[0051] Data annotation: Based on the existing assessment and analysis results, the collected graphic data is annotated. For example, the pile foundation quality can be divided into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile.

[0052] Model training: Use labeled data to train the model, and improve the accuracy and generalization ability of the model by adjusting model parameters and optimizing algorithms.

[0053] Automatic matching and analysis: Use the trained model to match and analyze the newly input graphic data, and automatically issue the judgment result of the pile foundation quality based on the matching and analysis results.

[0054] Further realize real-time remote monitoring: The intelligent pile foundation low-strain detection and analysis system can transmit data to the background management system in real time through the mobile communication network. Managers can check the construction status anytime and anywhere through computers or mobile phones to realize remote monitoring and management.

[0055] Furthermore, monitoring records can be exported as reports: the intelligent pile foundation low-strain detection and analysis system can automatically generate various reports and charts from the collected data, making it easier for managers to conduct data analysis and decision-making.

[0056] At the same time, construction data is transmitted to the background in real time: the system can transmit the collected construction data to the background management system in real time, and managers can view the construction data at any time to discover and solve problems in a timely manner.

[0057] High degree of automation: The intelligent pile foundation low strain detection and analysis system has a high degree of automation and can automatically complete data collection, processing and analysis, reducing the possibility of manual intervention and errors.

[0058] Multifunctional application: The system not only has pile foundation detection and analysis functions, but can also be expanded as needed, such as adding personnel management, material management, construction progress management and other functions.

[0059] Strong data processing capability: The intelligent pile foundation low-strain detection and analysis system integrates a large amount of existing detection graphics and data into data control, and can quickly match, identify, process and analyze the detected graphic data, thereby improving data processing efficiency and accuracy.

[0060] High security: The system adopts a variety of security measures, including data encryption transmission, data backup and recovery functions, to ensure the security and integrity of data.

[0061] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0062] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] Although the present invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the present invention. Therefore, it is intended that the above detailed description is considered to be illustrative and not restrictive, and it should be understood that the following claims (including all equivalents) are intended to limit the spirit and scope of the present invention. These embodiments are understood to be merely illustrative of the present invention and not intended to limit the scope of protection of the present invention. After reading the content of the record of the present invention, the technical staff may make various changes or modifications to the present invention, and these equivalent variations and modifications fall within the scope defined by the claims of the present invention.

Claims

1. A method for intelligent pile foundation low strain detection and analysis, characterized in that: The method comprises: Step 1: Build a cloud database for pile foundation projects. The cloud database stores the designed pile foundation parameters to be tested corresponding to the project. The designed pile foundation parameters include pile length, pile diameter, pile material, embedment depth, and soil type around the pile. Step 2: Acquire image data of an actual pile foundation corresponding to the designed pile foundation to be inspected by a first sensing device, then apply a first vibration force to the top of the actual pile foundation by a second sensing device, and simultaneously acquire a dynamic strain waveform corresponding to the first vibration force propagating downward from the top of the pile foundation; Step 3: The first sensing device is a camera device fixed at a preset position on the construction site and capable of capturing a complete image of the actual pile foundation. The actual pile foundation is analyzed based on the internal and external parameters determined by the camera device and the fixed position information, and the first pile length data and the first pile diameter data obtained by the first sensing device are determined. That is, the size of the object in the image is determined through image recognition and processing technology, and the second pile length data is calculated based on the mapping relationship between the pile material in the designed pile foundation parameters and the propagation speed of the first vibration force. Step 4: When the similarity between the value obtained by subtracting the embedding depth from the second pile length data and the first pile length data is greater than or equal to a first threshold, the newly input waveform data is matched and analyzed using the trained pile foundation low-strain detection and analysis model, and a pile foundation quality determination result is automatically issued based on the matching and analysis results without performing additional analysis and verification; Step 5: When the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is less than a first threshold, the newly input waveform graphic data is matched and analyzed according to the trained pile foundation low-strain detection analysis model. After obtaining the analysis results, a pile-soil solid finite element model is established according to the data collected by the second sensing device and the design pile foundation parameters in the cloud database. The response speed of the pile top during transient excitation is solved using the ANSYS / LS-DYNA program. The analysis results are verified and quantitatively analyzed through numerical simulation of the foundation pile.

2. The method for intelligent pile foundation low strain detection and analysis according to claim 1, characterized in that: The training of the pile foundation low strain detection and analysis model further includes: preprocessing and analyzing the dynamic strain waveform data collected by the second sensing device, wherein the preprocessing is to perform data cleaning on the collected waveform image to reduce the amount of calculated data, and at the same time convert the format of the cleaned data to obtain a data format corresponding to the data analysis, and classify the pile foundation quality into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile. According to the different pile foundation quality classifications as the evaluation and analysis results, the collected graphic data is labeled, and the labeled data is used to train the pile foundation low strain detection and analysis model.

3. The method for intelligent pile foundation low strain detection and analysis according to claim 2, characterized in that: The pile foundation low-strain detection and analysis model is a convolutional neural network model, in which, during the training process, convolution kernels are set in the convolution layer whose weights do not change with back propagation. The closer the value of the second pile length data minus the embedding depth is to the first pile length data, the more convolution kernels that do not change with back propagation and the more corresponding fixed value weights, thereby reducing the computational complexity of data processing and improving the convergence speed.

4. The method for intelligent pile foundation low strain detection and analysis according to claim 1, characterized in that: The second sensing device includes sensors placed at the top and bottom of the pile for recording the propagation time and amplitude of the strain wave when the pile top is subjected to vibration force, and calculating the wave velocity in the pile foundation through the propagation time and amplitude.

5. An intelligent pile foundation low strain detection and analysis system, characterized in that: The system comprises: A cloud-based database for pile foundation projects, wherein the cloud-based database stores the designed pile foundation parameters to be tested corresponding to the project, including pile length, pile diameter, pile material, embedment depth, and soil type surrounding the pile foundation; first and second sensing devices, wherein the first sensing device acquires image data of an actual pile foundation corresponding to the designed pile foundation to be inspected, and the second sensing device applies a first vibration force to the top of the actual pile foundation, while acquiring a dynamic strain waveform corresponding to the first vibration force propagating downward from the top of the pile foundation; a computing unit, wherein the first sensing device is a camera device fixed at a preset position on the construction site and capable of capturing a complete image of the actual pile foundation; the actual pile foundation is analyzed based on internal and external parameters determined by the camera device and fixed position information; first pile length data and first pile diameter data obtained by the first sensing device are determined, i.e., the size of the object in the image is determined through image recognition and processing technology; and second pile length data is calculated based on a mapping relationship between the pile material in the designed pile foundation parameters and the propagation speed of the first vibration force; a first comparison and verification unit, when the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is greater than or equal to a first threshold, using the trained pile foundation low-strain detection and analysis model to match and analyze the newly input waveform graphic data, and automatically issuing a pile foundation quality determination result based on the matching and analysis results without performing additional analysis and verification; The second comparison and verification unit is configured to match and analyze the newly input waveform graphic data according to the trained pile foundation low-strain detection and analysis model when the similarity between the value of the second pile length data minus the embedding depth and the first pile length data is less than a first threshold value. After obtaining the analysis results, a pile-soil solid finite element model is established based on the data collected by the second sensing device and the design pile foundation parameters in the cloud database. The response speed of the pile top during transient excitation is solved using the ANSYS / LS-DYNA program. The analysis results are verified and quantitatively analyzed through numerical simulation of the foundation piles.

6. The intelligent pile foundation low strain detection and analysis system according to claim 5, characterized in that: The training of the pile foundation low strain detection and analysis model further includes: preprocessing and analyzing the dynamic strain waveform data collected by the second sensing device, wherein the preprocessing is to perform data cleaning on the collected waveform image to reduce the amount of calculated data, and at the same time convert the format of the cleaned data to obtain a data format corresponding to the data analysis, and classify the pile foundation quality into insufficient pile foundation bearing capacity, excessive pile foundation inclination, cracked pile foundation or broken pile. According to the different pile foundation quality classifications as the evaluation and analysis results, the collected graphic data is labeled, and the labeled data is used to train the pile foundation low strain detection and analysis model.

7. The intelligent pile foundation low strain detection and analysis system according to claim 6, characterized in that: The pile foundation low-strain detection and analysis model is a convolutional neural network model, in which, during the training process, convolution kernels are set in the convolution layer whose weights do not change with back propagation. The closer the value of the second pile length data minus the embedding depth is to the first pile length data, the more convolution kernels that do not change with back propagation and the more corresponding fixed value weights, thereby reducing the computational complexity of data processing and improving the convergence speed.

8. The intelligent pile foundation low strain detection and analysis system according to claim 5, characterized in that: The second sensing device includes sensors placed at the top and bottom of the pile for recording the propagation time and amplitude of the strain wave when the pile top is subjected to vibration force, and calculating the wave velocity in the pile foundation through the propagation time and amplitude.

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