Engineering pile pore-forming quality detection method based on intelligent interconnection foundation platform

Abstract

The invention discloses an engineering pile hole-forming quality detection method based on an intelligent interconnection foundation platform, and relates to the technical field of engineering pile quality detection, and the method comprises the steps of S1, task issuing and data docking, S2, probe deployment and mud sound velocity calibration, S3, ultrasonic hole wall scanning, S4, sediment thickness measurement, S5, real-time data processing and twinborn feedback, and S6, quality evaluation and decision making. A plurality of groups of transducers are used for scanning a hole wall section at double angles through an orthogonal cross transducer array, millimeter-level distance measurement is realized through an ultrasonic total reflection principle, measurement errors caused by stretching and winding of a traditional measuring rope are avoided, a sediment interface is verified through pressure sudden change points and probe dip angle probe-dip angle coupling measurement, and the measurement accuracy is improved. The problem that a traditional measuring rope is touched by soft mud by mistake is solved, lowered design pile position information is automatically received, the method is suitable for complex stratums such as a pebble bed and a quicksand bed, a visual report and a hole forming model are generated, the recording efficiency is improved, and the hole collapse risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of engineering pile quality testing technology, and in particular to a method for testing the drilling quality of engineering piles based on an intelligent interconnected foundation platform. Background Technology

[0002] Engineering piles are structural components in building foundations used to transfer loads to deep soil layers. They are usually made of bored cast-in-place piles. During the drilling stage, testing technology is required to ensure that the integrity of the borehole wall, verticality, and sediment thickness meet the standards to avoid problems such as borehole collapse or diameter reduction.

[0003] In existing detection technologies, recording soil layer changes and physical properties relies on manual observation and handwritten records, which are easily affected by subjective experience. In complex geological conditions, key information is easily missed, and drilling parameters cannot be obtained in real time, making it difficult to dynamically assess borehole stability and increasing the risk of borehole collapse. The measuring rope is easily stretched and deformed when it encounters mud adsorption or borehole friction, and the measurement error in deep holes can reach more than 5%. When the sediment distribution at the bottom of the hole is uneven, the measuring rope may get stuck in the soft mud layer or touch the hard layer protrusion, resulting in distorted measurement values. Measuring only the diameter of a single profile cannot assess borehole ellipticity, local enlargement, and other issues, which is quite inconvenient. Summary of the Invention

[0004] The purpose of this invention is to provide a method for detecting the quality of drilling of engineering piles based on an intelligent interconnected foundation platform, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform, comprising the following steps:

[0006] S1: Task distribution and data docking. The host automatically pulls the foundation platform construction task data through 4G communication, generates a test task list according to the support pile segmentation and engineering pile zoning, and synchronizes it to the ultrasonic borehole wall host.

[0007] S2: Probe deployment and mud sound velocity calibration. Install the multi-functional probe onto the orifice positioning device, measure the sound wave propagation velocity in the mud, and correct the system sound velocity parameters.

[0008] S3: Ultrasonic borehole wall scanning, the probe is lowered at a constant speed and ultrasonic waves are emitted synchronously through multiple sets of positive transducers, and the collected data is used to construct a model;

[0009] S4: Sediment thickness measurement. After the probe reaches the bottom of the borehole, control the probe to monitor the point of sudden change in pressure and tilt angle, and record the sediment thickness.

[0010] S5: Real-time data processing and twin feedback, using AI algorithms to filter noise, generate purification waveforms, and automatically generate test reports;

[0011] S6: Quality assessment and decision-making. The platform automatically determines the quality of the borehole, pushes alarms and repair suggestions for non-conformities to the management personnel's terminal, updates the construction log and links it to the blockchain for evidence storage.

[0012] Preferably, step S1 includes the following steps:

[0013] S11: The host establishes a secure connection with the ground platform via the 5G wireless communication module and verifies device permissions;

[0014] S12: Automatically acquire construction task data, including pile location design coordinates, target hole diameter, hole depth, and allowable verticality deviation threshold;

[0015] S13: Divide the testing areas according to the construction progress, number the support piles by section and divide the engineering piles by zone, and generate a testing task list;

[0016] S14: Synchronize the task list to the local database of the ultrasonic borehole wall host and associate it with the pile location BIM model;

[0017] S15: The host automatically sorts the detection order according to the task priority and sends it to the probe control terminal.

[0018] Preferably, step S2 includes the following steps:

[0019] S21: Install the multi-functional probe onto the borehole positioning device and adjust the center of the probe to align with the axis of the pile hole;

[0020] S22: Start the mud sound velocity calibration mode. The probe emits ultrasonic waves at a fixed depth at the borehole opening and measures the round-trip propagation time of the sound waves in the mud.

[0021] S23: Calculate the current sound velocity value of the mud based on the known calibration distance and measured sound time;

[0022] S24: The system automatically verifies the rationality of the sound velocity value. If it exceeds the threshold, an alarm will be triggered to indicate abnormal mud.

[0023] S25: Synchronize the corrected sound velocity parameters to the host data processing module for subsequent aperture calculation compensation.

[0024] Preferably, the sound velocity value in step S23 is calculated using the following formula:

[0025]

[0026] Where c is the speed of sound, d is the calibration distance, and t is the sound time.

[0027] Preferably, step S3 includes the following steps:

[0028] S31: Start the probe uniform descent program, control the descent speed to be 0.5-1.0 m / s, and record the descent depth in real time;

[0029] S32: Synchronously activates multiple sets of orthogonally distributed ultrasonic transducers to alternately emit ultrasonic pulses at a fixed frequency;

[0030] S33: Receives reflected wave signals from each transducer in real time and records the round-trip propagation time of the sound wave;

[0031] S34: Based on the calibrated mud sound velocity, calculate the distance measurement values ​​in each direction and compensate for the sound velocity drift caused by temperature and pressure;

[0032] S35: Calculate the real-time borehole diameter based on the orthogonal direction distance measurement value and generate the borehole wall profile at the current depth;

[0033] S36: Integrate profile data at different depths to construct a three-dimensional point cloud model of the borehole wall and identify ellipticity, local diameter expansion, and diameter contraction defects;

[0034] S37: Real-time display of borehole wall morphology change curve, triggering an alarm when an anomaly is detected.

[0035] Preferably, the real-time aperture in step S35 is calculated using the following formula:

[0036] D = L1++L2++d

[0037] Where D is the aperture of the cross-section detection, L1 and L2 are the distances from the two transducers with opposite angles of the detection probe to the aperture wall, and d is the path of the receiving surface of the two transducers with opposite angles.

[0038] Preferably, step S4 includes the following steps:

[0039] S41: After the probe is lowered to the bottom of the hole, the probe attitude is detected by the tilt sensor. When the tilt angle exceeds the set threshold, the leveling program is automatically triggered.

[0040] S42: After leveling, control the probe to extend downwards at a constant speed, while monitoring the probe pressure value and probe tilt angle changes in real time;

[0041] S43: When the probe pressure value changes abruptly and the probe tilt angle changes abruptly at the same time, it is determined that the probe is in contact with the hard layer at the bottom of the sediment.

[0042] S44: Record the probe extension length at this time as the sediment thickness at the current measuring point, and repeat the measurement 3-5 times at different positions on the same cross section;

[0043] S45: Calculate the average value of multiple measurements as the representative value of the sediment thickness of the pile hole, and record the maximum and minimum values;

[0044] S46: When the thickness of sediment exceeds the design allowable value, an audible and visual alarm will be triggered immediately and the pile hole will be marked as unqualified;

[0045] S47: Link and store the sediment thickness measurement data with the location information, and generate a three-dimensional thermal map of sediment distribution.

[0046] Preferably, step S5 includes the following steps:

[0047] S51: Raw data preprocessing, time-domain and frequency-domain analysis of ultrasonic reflection signals, and wavelet transform algorithm to eliminate environmental noise interference;

[0048] S52: Extract key parameters, identify effective reflection peaks, calculate sound wave round-trip time, combine with calibrated sound velocity values, calculate the geometric parameters of aperture, depth, and verticality in real time, and extract the pressure-displacement curve feature points in the sediment thickness measurement.

[0049] S53: Three-dimensional model reconstruction. Based on multi-probe scanning data, the Delaunay triangulation algorithm is used to construct a three-dimensional mesh model of the borehole wall, and the sediment thickness data is mapped to the bottom elevation distribution to generate sediment thickness contour map.

[0050] S54: Quality index calculation, calculate the hole formation quality evaluation index;

[0051] S55: The report is automatically generated. It calls the preset template, automatically fills in the test data, and generates a PDF report containing a 3D model diagram, parameter curves, and quality rating. The report's key data is automatically synchronized to the blockchain evidence storage system.

[0052] S56: Digital twin update pushes the processed data to the BIM management platform in real time, overlays and displays the deviation cloud map between the measured hole shape and the design outline in the three-dimensional geological model, and intuitively displays the distribution of quality defects through color gradient.

[0053] Preferably, the hole formation quality evaluation indicators include hole diameter deviation rate, verticality, and sediment thickness qualification rate.

[0054] Preferably, step S6 includes the following steps:

[0055] S61: Determine the quality of hole formation. The platform automatically evaluates the quality of hole formation for engineering piles based on the collected data and preset quality standards.

[0056] S62: Non-conforming item alarm. The test results do not meet the quality standards. The platform generates a non-conforming item alarm and marks the specific problem points.

[0057] S63: Repair suggestion push. The platform will automatically generate repair suggestions based on the specific circumstances of the non-compliance items and push these suggestions to the terminal devices of relevant management personnel.

[0058] S64: Construction log update. The platform will automatically update the construction log with the test results, evaluation conclusions, alarm information and repair suggestions.

[0059] S65: Blockchain-based evidence storage. All testing data, evaluation results, and operation records will be linked to the blockchain for evidence storage.

[0060] The technical effects and advantages of this invention are as follows:

[0061] This invention utilizes multiple transducers in an orthogonal cross transducer array to scan the borehole wall profile at dual angles. It achieves millimeter-level distance measurement through the principle of total ultrasonic reflection, avoiding measurement errors caused by tension and entanglement in traditional measuring ropes. By measuring and verifying the sediment interface through pressure change points and probe tilt angle coupling, it solves the problem of "false contact with soft mud" in traditional measuring ropes. It automatically receives the design pile location information, analyzes historical detection data based on the collected data, automatically detects sand content and viscosity, and corrects the sound velocity coefficient. AI training is used to eliminate invalid clutter in the peak diagram. It is suitable for complex strata such as pebble layers and quicksand layers, generating a visual report and borehole model, improving recording efficiency, reducing the risk of borehole collapse, and providing high-precision data support for subsequent grouting construction. Attached Figure Description

[0062] Figure 1 This is a flowchart of the method for detecting the quality of drilling of engineering piles based on an intelligent interconnected foundation platform according to the present invention.

[0063] Figure 2 This is a flowchart illustrating the task assignment and data integration process of this invention.

[0064] Figure 3 This is a flowchart of the probe deployment and mud sound velocity calibration process of the present invention.

[0065] Figure 4 This is a flowchart of the ultrasonic borehole wall scanning process of the present invention.

[0066] Figure 5 This is a flowchart of the sediment thickness measurement process of the present invention.

[0067] Figure 6 This is a flowchart of the real-time data processing and twin feedback of the present invention.

[0068] Figure 7 This is a flowchart of the quality assessment and decision-making process for this invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] This invention provides, for example Figure 1-7 The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform, as shown, includes the following steps:

[0071] S1: Task distribution and data docking. The host automatically pulls the foundation platform construction task data through 4G communication, generates a test task list according to the support pile segmentation and engineering pile zoning, and synchronizes it to the ultrasonic borehole wall host.

[0072] S2: Probe deployment and mud sound velocity calibration. Install the multi-functional probe onto the orifice positioning device, measure the sound wave propagation velocity in the mud, and correct the system sound velocity parameters.

[0073] S3: Ultrasonic borehole wall scanning, the probe is lowered at a constant speed and ultrasonic waves are emitted synchronously through multiple sets of positive transducers, and the collected data is used to construct a model;

[0074] S4: Sediment thickness measurement. After the probe reaches the bottom of the borehole, control the probe to monitor the point of sudden change in pressure and tilt angle, and record the sediment thickness.

[0075] S5: Real-time data processing and twin feedback, using AI algorithms to filter noise, generate purification waveforms, and automatically generate test reports;

[0076] S6: Quality assessment and decision-making. The platform automatically determines the quality of the borehole, pushes alarms and repair suggestions for non-conformities to the management personnel's terminal, updates the construction log and links it to the blockchain for evidence storage.

[0077] Step S1 includes the following steps:

[0078] S11: The host establishes a secure connection with the ground platform through the 5G wireless communication module to verify device permissions. It uses 5G communication technology to achieve high-speed, low-latency data transmission, ensuring real-time synchronization of detection task data. Device permission verification is encrypted with digital certificates to prevent unauthorized device access and ensure data security. Compared with traditional 4G networks, 5G can improve data transmission efficiency by more than 50%, making it particularly suitable for the real-time transmission of large-capacity 3D point cloud data. At the same time, the permission management mechanism can trace operation records, meeting the security audit requirements of major projects.

[0079] S12: Automatically acquires construction task data, including pile location design coordinates, target borehole diameter, borehole depth, and allowable deviation threshold for verticality. The system automatically pulls design parameters to avoid manual input errors and ensure that the testing standards are consistent with the design requirements. By intelligently parsing BIM model data, it can automatically identify the differentiated quality requirements of special geological areas. Compared with the traditional paper drawing comparison method, the efficiency is improved and the risk of human misinterpretation is completely eliminated, laying the foundation for subsequent accurate testing.

[0080] S13: The system divides the testing area according to the construction progress, divides the support piles into sections and engineering piles into zones, generates a testing task list, dynamically links to the construction progress plan, intelligently optimizes the testing route, reduces equipment transfer time by more than 30%, and the zone management function supports parallel testing of multiple work surfaces. It is particularly suitable for linear projects such as subway tunnels. The system automatically marks high-risk areas such as quicksand layers for priority testing, achieving optimal allocation of quality control resources.

[0081] S14: Synchronize the task list to the local database of the ultrasonic borehole wall host and associate it with the pile location BIM model. The offline working mode ensures that the operation can still be carried out normally in areas with poor network signal. The detection data is automatically synchronized later. The BIM model association realizes the "what you see is what you get" navigation detection. The historical data comparison function can quickly identify repeated problem areas and help analyze the causes of quality defects.

[0082] S15: The host automatically sorts the detection order according to the task priority and sends it to the probe control terminal. The intelligent scheduling algorithm takes into account factors such as urgency, equipment location, and geological risks to optimize the detection route and improve the overall efficiency by 40%. It supports the insertion of sudden tasks and progress adjustment. The system automatically replans the path, and priority management ensures that key pile positions such as load-bearing piles are detected first, reducing the overall risk of the project.

[0083] Step S2 includes the following steps:

[0084] S21: Install the multi-functional probe onto the borehole positioning device, adjust the probe center to align with the pile hole axis, and the laser-assisted centering system can control the installation deviation within ±2mm, which is 5 times more accurate than the traditional mechanical centering. The adaptive clamp is compatible with different hole diameters of 300-1500mm, and the electronic level displays the tilt status in real time to ensure the accuracy of the detection benchmark.

[0085] S22: Start the mud sound velocity calibration mode. The probe emits ultrasonic waves at a fixed depth at the borehole opening and measures the round-trip propagation time of the sound waves in the mud. The calibration process is fully automated, avoiding human measurement errors. It adopts a multiple measurement and averaging algorithm to control the sound velocity calibration error within ±0.5%. It intelligently identifies abnormal values ​​and automatically remeasures to ensure the accuracy of subsequent borehole diameter calculations.

[0086] S23: Calculate the current sound velocity value of the mud based on the known calibration distance and measured sound time. The dynamic sound velocity compensation technology can adapt to muds with different specific gravities and solve the problem of aperture calculation error caused by the traditional fixed sound velocity value.

[0087] S24: The system automatically verifies the rationality of the sound velocity value. If it exceeds the threshold, an alarm is triggered to indicate abnormal mud. The preset reasonable range is 1400-1800 m / s. The system automatically identifies mud contamination or abnormal gas content. Timely warnings can avoid subsequent erroneous detections, and historical data statistics function assists in analyzing mud quality trends.

[0088] S25: The corrected sound velocity parameters are synchronized to the host data processing module for subsequent aperture calculation compensation. The sound velocity parameters are shared in real time to all calculation modules to ensure data consistency. The temperature and pressure compensation algorithm can eliminate the influence of sound velocity drift in deep hole environments and maintain stable accuracy in 50m deep holes.

[0089] The sound velocity value in step S23 is calculated using the following formula:

[0090]

[0091] Where c is the speed of sound, d is the calibration distance, and t is the sound time.

[0092] Step S3 includes the following steps:

[0093] S31: Start the probe uniform descent program, control the descent speed to 0.5-1.0 m / s, and record the descent depth in real time. The adaptive speed adjustment can achieve automatic speed reduction in complex strata to ensure data quality.

[0094] S32: Simultaneously activates multiple orthogonally distributed ultrasonic transducers to alternately emit ultrasonic pulses at a fixed frequency. The probe uses four groups arranged in a cross shape to achieve full-section scanning, reducing the detection blind zone by 95%. The time-division emission strategy avoids signal interference, and the data acquisition integrity rate reaches 100%.

[0095] S33: Receives reflected wave signals from each transducer in real time and records the round-trip propagation time of the sound wave;

[0096] S34: Based on the calibrated mud sound velocity, calculate the distance measurement value in each direction and compensate for the sound velocity drift caused by temperature and pressure. The multi-parameter compensation algorithm controls the deep hole measurement error within ±0.1%.

[0097] S35: Calculates the real-time borehole diameter based on the orthogonal direction distance measurement value, generates a borehole wall profile at the current depth, dual profile detection can identify ellipticity deformation of more than 0.5%, real-time display function allows operators to immediately detect anomalies, data refresh rate reaches 10Hz, meeting the needs of dynamic monitoring;

[0098] S36: Integrates profile data at different depths to construct a three-dimensional point cloud model of the borehole wall, identifies ellipticity, local diameter expansion and diameter contraction defects, and uses a triangular mesh reconstruction algorithm to achieve a three-dimensional model with a resolution of 1mm. The intelligent recognition algorithm can automatically label the location and size of defects, and historical data comparison shows the evolution trend of borehole shape.

[0099] S37: Real-time display of borehole wall morphology change curve; alarm triggered when an anomaly is detected; multi-level early warning mechanism distinguishes between minor deformation and serious defects; audible and visual alarms ensure immediate response from on-site personnel; and original data of abnormal sections are automatically saved for future reference.

[0100] The real-time aperture in step S35 is calculated using the following formula:

[0101] D = L1++L2++d

[0102] Where D is the aperture of the cross-section detection, L1 and L2 are the distances from the two transducers with opposite angles of the detection probe to the aperture wall, and d is the path of the receiving surface of the two transducers with opposite angles.

[0103] Step S4 includes the following steps:

[0104] S41: After the probe is lowered to the bottom of the hole, the probe attitude is detected by the tilt sensor. When the tilt angle exceeds the set threshold, the leveling program is automatically triggered.

[0105] S42: After leveling, the control probe extends downwards at a constant speed, while simultaneously monitoring the probe pressure and tilt angle changes in real time. The servo motor controls the probe extension speed fluctuation to be less than ±1%. Multi-parameter synchronous acquisition frequency reaches 100Hz to ensure interface recognition accuracy, and the intelligent speed adjustment function adapts to sediment layers of different hardness.

[0106] S43: When the probe pressure value changes abruptly and the probe tilt angle changes abruptly at the same time, it is determined that the probe is in contact with the hard layer at the bottom of the sediment. The dynamic threshold adjustment algorithm adapts to different geological conditions and maintains high reliability in both soft soil and rock strata. Suspicious data points are dynamically marked for verification.

[0107] S44: Record the probe extension length at this time as the sediment thickness at the current measurement point, and repeat the measurement 3-5 times at different positions on the same cross section. The multi-point measurement strategy overcomes the unevenness of sediment, improves representativeness by 80%, automatically records the maximum and minimum values ​​and standard deviation, comprehensively reflects the sediment distribution, and automatically triggers retesting for abnormal points.

[0108] S45: Calculate the average value of multiple measurement results as the representative value of the sediment thickness of the pile hole, and record the maximum and minimum values. The weighted average algorithm prioritizes reliable measuring points to reduce the impact of outliers. Complete data records meet the requirements of different acceptance standards and automatically generate statistical process control charts.

[0109] S46: When the sediment thickness exceeds the design allowable value, an audible and visual alarm is immediately triggered and the pile hole is marked as unqualified. Multi-level alarms distinguish between minor exceedances and serious defects, automatically associate with the design allowable value, adapt to different pile type requirements, and print unqualified labels on site to simplify the process.

[0110] S47: The sediment thickness measurement data is linked and stored with location information, and a 3D thermal map of sediment distribution is generated. This 3D visualization intuitively displays the sediment accumulation status, assisting in borehole cleaning decisions. Historical data is used to compare and evaluate the cleaning effect. The report can be directly imported into the acceptance document.

[0111] Step S5 includes the following steps:

[0112] S51: Raw data preprocessing, time-domain and frequency-domain analysis of ultrasonic reflection signals, and wavelet transform algorithm to eliminate environmental noise interference;

[0113] S52: Key parameter extraction, identification of effective reflection peaks, calculation of sound wave round-trip time, combined with calibrated sound velocity values, real-time calculation of geometric parameters such as aperture, depth, and verticality, and extraction of pressure-displacement curve feature points in sediment thickness measurement. The feature extraction algorithm shortens the processing time to 50ms / point, improving efficiency. It automatically marks suspicious data and suggests review, reducing the risk of human error.

[0114] S53: 3D model reconstruction. Based on multi-probe scanning data, a 3D mesh model of the borehole wall is constructed using the Delaunay triangulation algorithm. The sediment thickness data is mapped to the bottom elevation distribution, generating a sediment thickness contour map. The 1mm precision 3D model allows for magnified observation of local defects. The contour interval is adjustable to meet different precision requirements. The model is lightweight and supports mobile viewing.

[0115] S54: Quality index calculation, calculates hole formation quality evaluation index, custom formula editor supports different standard requirements, automatically generates compliance rate statistical charts, key index trend analysis predicts potential risks.

[0116] S55: The report is automatically generated. It calls the preset template, automatically fills in the test data, and generates a PDF report containing a 3D model diagram, parameter curves, and quality rating. The report's key data is automatically synchronized to the blockchain evidence storage system.

[0117] S56: Digital twin update pushes processed data to the BIM management platform in real time, overlays and displays the deviation cloud map between the measured hole shape and the design outline in the 3D geological model, intuitively displays the distribution of quality defects through color gradient, and makes quality deviations visible at a glance through virtual and real fusion technology. It supports access from multiple terminals, allows remote experts to provide real-time guidance, and has a historical version traceability function to analyze the development process of defects.

[0118] The evaluation indicators for hole formation quality include hole diameter deviation rate, verticality, and sediment thickness qualification rate.

[0119] Step S6 includes the following steps:

[0120] S61: Determining Hole Formation Quality. Based on collected data and preset quality standards, the platform automatically assesses the quality of the engineering pile hole formation. Through AI algorithm analysis, the platform can quickly identify key indicators such as the integrity of the hole wall and the uniformity of sediment distribution, ensuring the accuracy and reliability of the assessment results. Simultaneously, the platform supports multi-dimensional comparative analysis, providing a scientific basis for subsequent decision-making.

[0121] S62: Non-conforming item alarm. When the test results do not meet the quality standards, the platform generates a non-conforming item alarm and marks the specific problem points. The alarm information is displayed through a visual interface and is accompanied by an audio reminder to ensure that managers can discover the problem as soon as possible. In addition, the platform also supports querying and statistics of historical alarm records, which facilitates tracing and analyzing the root cause of the problem.

[0122] S63: Repair suggestion push. The platform will automatically generate repair suggestions based on the specific circumstances of the non-compliance items and push these suggestions to the terminal devices of relevant managers. The suggestions are detailed and actionable, including repair steps, required materials and estimated time. At the same time, the platform supports real-time tracking of repair progress to ensure that the problem is resolved in a timely manner.

[0123] S64: Construction Log Update. The platform will automatically update the construction log with the test results, assessment conclusions, alarm information, and repair suggestions. The log format is standardized and supports querying by time, project, or problem type. In addition, log data can be exported to multiple formats for easy archiving or sharing with other systems.

[0124] S65: Blockchain-based evidence storage. All testing data, evaluation results, and operation records are linked to the blockchain for evidence storage. Blockchain technology provides decentralized security for data and supports multi-party verification and auditing. The evidence storage information includes timestamps, operator and equipment information, etc., providing legal protection for project quality.

[0125] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting the quality of borehole formation in engineering piles based on an intelligent interconnected foundation platform, characterized in that, Includes the following steps: S1: Task distribution and data docking. The host automatically pulls the foundation platform construction task data through 4G communication, generates a test task list according to the support pile segmentation and engineering pile zoning, and synchronizes it to the ultrasonic borehole wall host. S2: Probe deployment and mud sound velocity calibration. Install the multi-functional probe onto the orifice positioning device, measure the sound wave propagation velocity in the mud, and correct the system sound velocity parameters. S3: Ultrasonic borehole wall scanning, the probe is lowered at a constant speed and ultrasonic waves are emitted synchronously through multiple sets of positive transducers, and the collected data is used to construct a model; S4: Sediment thickness measurement. After the probe reaches the bottom of the borehole, control the probe to monitor the point of sudden change in pressure and tilt angle, and record the sediment thickness. S5: Real-time data processing and twin feedback, using AI algorithms to filter noise, generate purification waveforms, and automatically generate test reports; S6: Quality assessment and decision-making. The platform automatically determines the quality of the borehole, pushes alarms and repair suggestions for non-conformities to the management personnel's terminal, updates the construction log and links it to the blockchain for evidence storage.

2. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, Step S1 includes the following steps: S11: The host establishes a secure connection with the ground platform via the 5G wireless communication module and verifies device permissions; S12: Automatically acquire construction task data, including pile location design coordinates, target hole diameter, hole depth, and allowable verticality deviation threshold; S13: Divide the testing areas according to the construction progress, number the support piles by section and divide the engineering piles by zone, and generate a testing task list; S14: Synchronize the task list to the local database of the ultrasonic borehole wall host and associate it with the pile location BIM model; S15: The host automatically sorts the detection order according to the task priority and sends it to the probe control terminal.

3. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, Step S2 includes the following steps: S21: Install the multi-functional probe onto the borehole positioning device and adjust the center of the probe to align with the axis of the pile hole; S22: Start the mud sound velocity calibration mode. The probe emits ultrasonic waves at a fixed depth at the borehole opening and measures the round-trip propagation time of the sound waves in the mud. S23: Calculate the current sound velocity value of the mud based on the known calibration distance and measured sound time; S24: The system automatically verifies the rationality of the sound velocity value. If it exceeds the threshold, an alarm will be triggered to indicate abnormal mud. S25: Synchronize the corrected sound velocity parameters to the host data processing module for subsequent aperture calculation compensation.

4. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 3, characterized in that, The sound velocity value in step S23 is calculated using the following formula: Where c is the speed of sound, d is the calibration distance, and t is the sound time.

5. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, Step S3 includes the following steps: S31: Start the probe uniform descent program, control the descent speed to be 0.5-1.0 m / s, and record the descent depth in real time; S32: Synchronously activates multiple sets of orthogonally distributed ultrasonic transducers to alternately emit ultrasonic pulses at a fixed frequency; S33: Receives reflected wave signals from each transducer in real time and records the round-trip propagation time of the sound wave; S34: Based on the calibrated mud sound velocity, calculate the distance measurement values ​​in each direction and compensate for the sound velocity drift caused by temperature and pressure; S35: Calculate the real-time borehole diameter based on the orthogonal direction distance measurement value and generate the borehole wall profile at the current depth; S36: Integrate profile data at different depths to construct a three-dimensional point cloud model of the borehole wall and identify ellipticity, local diameter expansion, and diameter contraction defects; S37: Real-time display of borehole wall morphology change curve, triggering an alarm when an anomaly is detected.

6. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 5, characterized in that, The real-time aperture in step S35 is calculated using the following formula: D = L1++L2++d Where D is the aperture of the cross-section detection, L1 and L2 are the distances from the two transducers with opposite angles of the detection probe to the aperture wall, and d is the path of the receiving surface of the two transducers with opposite angles.

7. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, Step S4 includes the following steps: S41: After the probe is lowered to the bottom of the hole, the probe attitude is detected by the tilt sensor. When the tilt angle exceeds the set threshold, the leveling program is automatically triggered. S42: After leveling, control the probe to extend downwards at a constant speed, while monitoring the probe pressure value and probe tilt angle changes in real time; S43: When the probe pressure value changes abruptly and the probe tilt angle changes abruptly at the same time, it is determined that the probe is in contact with the hard layer at the bottom of the sediment. S44: Record the probe extension length at this time as the sediment thickness at the current measuring point, and repeat the measurement 3-5 times at different positions on the same cross section; S45: Calculate the average value of multiple measurements as the representative value of the sediment thickness of the pile hole, and record the maximum and minimum values; S46: When the thickness of sediment exceeds the design allowable value, an audible and visual alarm will be triggered immediately and the pile hole will be marked as unqualified; S47: Link and store the sediment thickness measurement data with the location information, and generate a three-dimensional thermal map of sediment distribution.

8. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, Step S5 includes the following steps: S51: Raw data preprocessing, time-domain and frequency-domain analysis of ultrasonic reflection signals, and wavelet transform algorithm to eliminate environmental noise interference; S52: Extract key parameters, identify effective reflection peaks, calculate sound wave round-trip time, combine with calibrated sound velocity values, calculate the geometric parameters of aperture, depth, and verticality in real time, and extract the pressure-displacement curve feature points in the sediment thickness measurement. S53: Three-dimensional model reconstruction. Based on multi-probe scanning data, the Delaunay triangulation algorithm is used to construct a three-dimensional mesh model of the borehole wall, and the sediment thickness data is mapped to the bottom elevation distribution to generate sediment thickness contour map. S54: Quality index calculation, calculate the hole formation quality evaluation index; S55: The report is automatically generated. It calls the preset template, automatically fills in the test data, and generates a PDF report containing a 3D model diagram, parameter curves, and quality rating. The report's key data is automatically synchronized to the blockchain evidence storage system. S56: Digital twin update pushes the processed data to the BIM management platform in real time, overlays and displays the deviation cloud map between the measured hole shape and the design outline in the three-dimensional geological model, and intuitively displays the distribution of quality defects through color gradient.

9. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, The quality evaluation indicators for hole formation include hole diameter deviation rate, verticality, and sediment thickness qualification rate.

10. The method for detecting the quality of borehole formation of engineering piles based on an intelligent interconnected foundation platform according to claim 1, characterized in that, Step S6 includes the following steps: S61: Determine the quality of hole formation. The platform automatically evaluates the quality of hole formation for engineering piles based on the collected data and preset quality standards. S62: Non-conformance alarm. The test results do not meet the quality standards. The platform generates a non-conformance alarm and marks the specific problem points. S63: Repair suggestion push. The platform will automatically generate repair suggestions based on the specific circumstances of the non-compliance items and push these suggestions to the terminal devices of relevant management personnel. S64: Construction log update. The platform will automatically update the construction log with the test results, evaluation conclusions, alarm information and repair suggestions. S65: Blockchain-based evidence storage. All testing data, evaluation results, and operation records will be linked to the blockchain for evidence storage.

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