Bridge pile foundation construction perpendicularity real-time detection system and method based on unmanned aerial vehicle group

Abstract

The invention discloses a bridge pile foundation construction perpendicularity real-time detection system and method based on an unmanned aerial vehicle group, and relates to the technical field of bridge pile foundation construction, and the system comprises a main detection unmanned aerial vehicle module which is used for vertically hovering above a pile hole and scanning along a hole wall to generate point cloud data; the slave positioning unmanned aerial vehicle group module is arranged on the periphery of the pile hole and provides positioning compensation for the master detection unmanned aerial vehicle module; the ground reflective target is arranged at the opening part of the pile hole and is used for aligning the initial positioning of the main detection unmanned aerial vehicle module with the point cloud coordinate system; the edge calculation terminal module is arranged on a construction site, receives and processes the point cloud data and calculates perpendicularity deviation; the result generation module visualizes the analysis result and outputs and feeds back the result to constructors, and the problems that a traditional method is low in efficiency and poor in precision and depends on manual work are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge pile foundation construction, and in particular to a real-time detection system and method for verticality of bridge pile foundation construction based on a swarm of unmanned aerial vehicles. Background Art

[0002] Bridge pile foundation construction is a critical step in bridge construction, directly impacting the stability and safety of the bridge structure. During construction, the verticality of the pile foundation is a crucial parameter that must be strictly controlled to ensure construction quality. However, traditional methods for detecting pile foundation verticality have many shortcomings. For example, while manual measurement can obtain verticality data to a certain extent, this method is extremely inefficient and, due to the limitations of manual operation, often fails to fully cover the entire length of the pile, limiting the accuracy of the measurement results. Single-drone scanning methods, while improving detection efficiency, often result in errors exceeding the allowable range (e.g., ±0.5% L) when measuring the bottom of deep-bored piles (deep exceeding 20 meters) due to limitations in drone hovering stability and lidar accuracy, compromising the accurate assessment of construction quality. Furthermore, construction sites are often plagued by dust and mechanical vibration, which can interfere with the normal operation of optical measurement equipment, leading to inaccurate measurement results. Therefore, developing an efficient and accurate real-time detection system for bridge pile foundation verticality is crucial. Summary of the Invention

[0003] The embodiments of the present invention provide a real-time detection system and method for the verticality of bridge pile foundation construction based on a drone swarm. The accuracy is improved through the collaborative positioning of master and slave drones, and a laser radar is used to scan the pile hole wall and fuse the differential positioning data. In combination with an anti-interference point cloud processing algorithm, rapid and high-precision detection of the verticality of the pile foundation during the construction phase is achieved, effectively preventing construction quality accidents caused by pile body deviation, and solving the problems of low efficiency and accuracy in traditional pile foundation verticality detection methods.

[0004] A real-time verticality detection system for bridge pile foundation construction based on drone swarms, including:

[0005] The main inspection drone module is used to hover vertically above the pile hole and scan along the hole wall to generate point cloud data;

[0006] A slave positioning drone swarm module is deployed outside the pile hole to provide positioning compensation for the master detection drone module;

[0007] The ground reflective target is set at the pile hole mouth and is used for the initial positioning of the main detection drone module and alignment with the point cloud coordinate system;

[0008] The edge computing terminal module is installed at the construction site to receive and process point cloud data and calculate vertical deviation;

[0009] The result generation module visualizes the analysis results and outputs feedback to construction personnel.

[0010] Furthermore, the main detection drone module includes a main drone, the bottom of which is integrated with a rotating laser radar, a millimeter wave radar, a visual sensor and a dual-frequency GPS+IMU, wherein:

[0011] The rotating laser radar has a scanning frequency of 100 Hz, an angular resolution of 0.1°, and a maximum range of 50 m, making it suitable for deep hole detection.

[0012] The visual sensor is used to aim at the ground reflective target;

[0013] The millimeter-wave radar has a detection radius of 30m and is used for real-time obstacle avoidance;

[0014] The dual-frequency GPS+IMU is used to provide initial positioning.

[0015] Furthermore, the slave positioning drone group module includes at least 3 slave drones, each of which is equipped with a differential GPS base station, a laser rangefinder and an ultra-wideband positioning module, wherein:

[0016] The differential GPS base station receives satellite signals in real time and generates real-time dynamic positioning correction data;

[0017] The laser rangefinder is used to emit a ranging laser beam to the master UAV to measure the relative distance to the master UAV;

[0018] The ultra-wideband positioning module is used to build a relative position network between drones and synchronize positioning data.

[0019] Furthermore, the ground reflective target includes an annular reflective belt, which is fixedly arranged on the upper edge of the concrete casing at the mouth of the pile hole and is concentric with the center of the pile hole.

[0020] Furthermore, the edge computing terminal module is deployed at the construction site, receives point cloud data via 5G, and runs a verticality analysis algorithm, which includes a data receiving and synchronization unit, a point cloud filtering unit, and a verticality calculation unit.

[0021] Furthermore, the data receiving and synchronization unit is used to receive multi-source data from the main detection drone module and the slave positioning drone swarm module and complete time and space alignment, including the original point cloud data transmitted by the main detection drone module, the RTK differential positioning correction parameters sent by the slave positioning drone swarm module and the preset design coordinates of the ground reflective target, match the point cloud data and positioning data through the 5G communication timestamp, and convert the scanning data of the main drone into the designed pile foundation coordinate system.

[0022] Furthermore, the point cloud filtering unit is used to remove environmental noise points and retain valid hole wall point clouds. Its processing flow includes removing noise points with reflectivity lower than a dynamic threshold, and removing points with excessive deviation from the hole center based on the theoretical radius of the pile hole.

[0023] Furthermore, the verticality calculation unit is used to fit the actual hole wall center line and calculate the depth deviation value.

[0024] Furthermore, the result generation module is used to generate a visual report and make correction suggestions for the result generated by the verticality calculation unit, and transmit it to a lower terminal device for display to construction personnel.

[0025] In a second aspect, an embodiment of the present invention provides a method for real-time detection of verticality of bridge pile foundation construction based on a drone swarm, comprising the following steps:

[0026] S1, system initialization and benchmark calibration;

[0027] S1-1, ground reflective target deployment, install the circular reflective tape on the pile hole casing, ensuring that its center coincides with the designed pile foundation axis;

[0028] S1-2: Use three slave drones to be deployed in an equilateral triangle within 50m of the pile hole, activate the differential GPS base station, and form a dynamic positioning network;

[0029] S1-3, each slave UAV synchronizes its position through the ultra-wideband positioning unit and broadcasts dynamic positioning correction signals in real time;

[0030] In step S1-4, the master UAV hovers directly above the pile hole, identifies the target center through vision, and integrates the positioning data with that of the slave UAV to initialize the precise coordinates;

[0031] S2, synchronous acquisition of multi-source data;

[0032] S2-1, the main UAV descends vertically at a constant speed, and the rotating lidar scans to generate a point cloud of the hole wall and simultaneously records the reflection intensity;

[0033] S2-2, the millimeter-wave radar onboard the main UAV monitors the location of the construction machinery in real time and triggers the obstacle avoidance strategy;

[0034] S2-3, the slave UAV continuously transmits real-time dynamic positioning correction signals and uses a laser rangefinder to measure the relative distance to the master UAV, correcting the master UAV's position in real time;

[0035] S3, edge computing real-time processing;

[0036] S3-1, point cloud preprocessing, reflection intensity filtering and geometric filtering;

[0037] S3-2, hole wall centerline fitting, divided into 1m depth segments, using the least squares method to fit the actual hole wall centerline, and calculate the deviation value of each segment;

[0038] S3-3, dynamic threshold adjustment: if the environmental noise rate is greater than 10%, the reflection intensity threshold is automatically increased to ensure that the effective point cloud retention rate is greater than or equal to 80%;

[0039] S4, real-time feedback and construction correction;

[0040] S4-1, the edge computing terminal module generates a deviation curve diagram and a three-dimensional thermal map, marking the out-of-limit area;

[0041] S4-2, displayed to construction workers via mobile terminals.

[0042] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0043] The present invention achieves real-time detection of verticality during bridge pile foundation construction through the collaborative operation of master and slave drones, combined with lidar scanning, dynamic positioning compensation, and edge computing technology. The master drone descends vertically along the pile hole and uses high-precision lidar to collect point cloud data on the hole wall. The slave drone group builds a dynamic RTK differential positioning network to correct the master drone's position in real time. Ground reflective targets provide an initial positioning reference. The edge computing terminal filters, fits, and analyzes the point cloud data to generate a verticality deviation report. Finally, AR devices or mobile terminals are used to guide construction personnel to correct the pile hole, forming a "collection-processing-feedback" closed loop. Its single pile detection time is short and the accuracy is high. The point cloud filtering unit effectively suppresses dust and vibration interference, and has a high noise rejection rate. This solves the pain points of traditional methods, which are low efficiency, poor accuracy, and reliance on manual labor. It provides an efficient, high-precision, fully automated verticality detection solution for bridge pile foundation construction, significantly improving construction quality and safety.

[0044] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0047] In the attached figure:

[0048] Figure 1 A communication block diagram disclosed in an embodiment of the present invention;

[0049] Figure 2 A schematic structural diagram of an embodiment disclosed in the embodiment of the present invention;

[0050] Figure 3 A flow chart of the method disclosed in an embodiment of the present invention;

[0051] Figure 4 The embodiment disclosed in the present invention Figure 3 Flowchart of step 1 in [1];

[0052] Figure 5 The embodiment disclosed in the present invention Figure 3 Flowchart for step 2 in [1];

[0053] Figure 6 The embodiment disclosed in the present invention Figure 3 Flowchart for step 3 in [1];

[0054] Figure 7 The embodiment disclosed in the present invention Figure 3 Flowchart for step 4 in .

[0055] Reference numerals:

[0056] 10. Main detection drone module; 11. Main drone; 12. Rotating lidar; 13. Millimeter-wave radar; 14. Visual sensor; 15. Dual-frequency GPS+IMU; 20. Slave positioning drone swarm module; 21. Slave drone; 22. Differential GPS base station; 23. Laser rangefinder; 24. Ultra-wideband positioning unit; 30. Ground reflective target; 31. Annular reflective tape; 40. Edge computing terminal module; 41. Data receiving and synchronization unit; 42. Point cloud filtering unit; 43. Verticality calculation unit; 50. Result generation module. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] Example 1

[0059] like Figure 1-2As shown, an embodiment of the present invention provides a real-time detection system for the verticality of bridge pile foundation construction based on a drone swarm. Through the cooperation of a master drone 11 and three slave drones 21, positioning compensation is achieved for the master drone 11 to improve the accuracy. The master drone 11 is used to collect data in the pile hole to form a hole wall point cloud. Finally, the point cloud data is edge-computed in real time to generate a deviation curve diagram and a three-dimensional thermal map of the pile hole, which are then transmitted to mobile terminal devices such as AR glasses, mobile phones, tablets, etc., to facilitate construction personnel to correct the pile hole based on feedback information.

[0060] It should be noted that Figure 2 This is a schematic diagram of an embodiment. The position ratios of the master drone 11, the slave drone 21, and the pile hole in the figure do not represent the actual situation. The specific details are subject to the description in the following embodiments.

[0061] The specific arrangement of the equipment is as follows, including a master detection drone module 10, a slave positioning drone group module 20 and a ground reflective target 30. Specifically,

[0062] The main detection drone module 10 includes a main drone 11 , the bottom of which is integrated with a rotating laser radar 12 , a millimeter-wave radar 13 , a visual sensor 14 and a dual-frequency GPS+IMU 15 .

[0063] The master UAV 11 is the main flying device for collecting pile hole data and is used to carry the above-mentioned sensors.

[0064] The rotating laser radar 12 is used to scan a plane parallel to the pile hole axis, with the scanning head tilted downward by 0° (i.e., straight down). The rotating laser radar 12 selected in this embodiment has a scanning frequency of 100 Hz, an angular resolution of 0.1°, and a maximum ranging of 50 m (suitable for deep hole detection). As it descends along the pile hole, it scans the hole wall and generates point cloud data.

[0065] The millimeter-wave radar 13 has a detection radius of 30m and monitors the position of construction hoisting equipment in real time. If it intrudes within the scanning radius of 10m, the obstacle avoidance strategy is triggered (if the hoisting equipment intrudes within the 10m range, the scanning is suspended and the equipment is hovered).

[0066] The visual sensor 14 is used for auxiliary positioning with the ground reflective target 30. When the main UAV 11 hovers 2m above the pile hole, center alignment is performed through visual recognition.

[0067] Dual-frequency GPS+IMU15 provides initial positioning, and after fusing the differential signal with the slave drone 21, the positioning accuracy reaches ±3mm. It should be noted that among the accuracy indicators of the master drone 11IMU, the gyroscope zero bias stability is ≤0.01° / h.

[0068] Furthermore, the slave positioning drone group module 20 includes at least three slave drones 21, each of which is equipped with a differential GPS base station 22, a laser rangefinder 23 and an ultra-wideband positioning module, wherein:

[0069] The differential GPS base station 22 receives satellite signals in real time and generates real-time dynamic positioning correction data. The base station uses the RTCM3.2 protocol and broadcasts the correction data through the 5G network;

[0070] The laser rangefinder 23 is used to emit a ranging laser beam to the master UAV 11 to measure the relative distance to the master UAV 11;

[0071] The ultra-wideband positioning module is used to build a relative position network between drones and synchronize positioning data.

[0072] Furthermore, the ground reflective target 30 includes an annular reflective belt 31, which is fixedly arranged on the upper edge of the concrete casing at the mouth of the pile hole and is concentric with the center of the pile hole.

[0073] In this embodiment, the edge computing terminal module 40 is deployed at the construction site, receives point cloud data via 5G, and runs a verticality analysis algorithm, which includes a data receiving and synchronization unit 41, a point cloud filtering unit 42, and a verticality calculation unit 43.

[0074] The data receiving and synchronization unit 41 is used to receive multi-source data from the master detection drone module 10 and the slave positioning drone swarm module 20 and complete spatiotemporal alignment, including the original point cloud data (including three-dimensional coordinates and reflection intensity) transmitted by the master detection drone module 10, the RTK differential positioning correction parameters sent by the slave positioning drone swarm module 20, and the preset design coordinates of the ground reflective target 30 (theoretical position of the pile center).

[0075] The processing flow is as follows: point cloud data and positioning data are matched through 5G communication timestamps, and it follows the PTP protocol to achieve microsecond-level synchronization.

[0076] Convert the scanning data of the master drone 11 to the designed pile foundation coordinate system (with the target center as the origin): Among them, R is the rotation matrix and T is the translation vector, which are calculated from the positioning data of the slave drone 21 and the target position.

[0077] Furthermore, the point cloud filtering unit 42 is used to remove environmental noise points and retain valid hole wall point clouds.

[0078] The processing flow includes: removing noise points (such as dust and water mist) with reflectivity below the dynamic threshold:

[0079] Retention conditions: I 点 ≥I 阈值 (Default I阈值 =0.3I max , dynamically adjusted according to the environment).

[0080] Based on the theoretical radius r of the pile hole 设计 , remove points with large deviation from the hole center:

[0081] Elimination conditions:

[0082] Furthermore, the verticality calculation unit 43 is used to fit the actual hole wall centerline and calculate the depth deviation value.

[0083] The processing flow is as follows:

[0084] Segment fitting: The pile hole is divided into segments of 1m depth, and the least squares method is used to fit the center coordinates of each segment (X i , Y i ):

[0085]

[0086] Deviation calculation: Compare the design axis (0, 0, Z i ), calculate the deviation of each segment:

[0087]

[0088] In this embodiment, the result L forming module is used to generate a visual report and correction suggestions for the result generated by the verticality calculation unit 43, and transmit it to the lower terminal device for display to the construction personnel.

[0089] Specifically, outputting a view based on the structure calculated by the verticality calculation unit 43 includes:

[0090] Deflection Plot: Plotting Δ i The curve changes with depth, marking the over-limit area (Δ i >1.0% L);

[0091] 3D heat map: The displacement is mapped to the pile hole model surface, with red indicating an overrun;

[0092] Trimming Guide: Calculate the azimuth angle θ of the hole to be expanded i =arctan(Y i / X i ) and thickness d i =Δ i -0.8%L.

[0093] The above data and charts are transmitted to the corresponding terminal devices via 5G, such as AR glasses, mobile phones, tablets, etc., based on which construction workers can quickly correct the pile holes.

[0094] Specifically, based on the edge computing terminal module 40, when the above drones are performing system initialization and dynamic benchmark construction, they first launch three slave drones 21 to 50m outside the pile hole, arrange them in an equilateral triangle (side length 50m), start the onboard differential GPS base station 22, and form a dynamic RTK differential positioning network. Each slave drone 21 synchronizes its position through the ultra-wideband positioning unit 24 and broadcasts the RTK differential positioning correction signal in real time.

[0095] At this time, the master drone 11 hovers 2 meters above the pile hole, identifies the center of the circular reflective tape 31 through the visual sensor 14, and integrates the positioning data with the slave drone 21 to initialize the precise coordinates:

[0096]

[0097] Furthermore, the main drone 11 starts scanning, descending vertically at a constant speed of 0.5 m / s, and the rotating laser radar 12 scans to generate the hole wall point cloud {x i ,y i ,z i ,I i}, synchronously record the reflection intensity I i ;

[0098] The slave drone 21 performs dynamic compensation on the master drone 11. That is, the slave drone 21 continuously transmits RTK correction signals and uses the laser rangefinder 23 to measure the relative distance to the host drone, thereby correcting the master drone 11's position in real time:

[0099]

[0100] Example 2

[0101] The embodiment of the present invention also discloses a real-time detection method for verticality of bridge pile foundation construction based on drone swarm, such as Figure 2 , including the following steps:

[0102] S1, system initialization and benchmark calibration;

[0103] S1-1, deploying the ground reflective target 30, installing the annular reflective tape 31 on the pile hole casing, and using a total station to calibrate its center to coincide with the designed pile foundation axis;

[0104] S1-2: Use three slave drones 21 to be arranged in an equilateral triangle within 50 meters of the pile hole, start the differential GPS base station 22, and form a dynamic RTK positioning network;

[0105] S1-3, each slave UAV 21 synchronizes its position through the ultra-wideband positioning unit 24 and broadcasts a dynamic positioning correction signal in real time;

[0106] S1-4, the master UAV 11 hovers directly above the pile hole, identifies the target center through the visual sensor 14, fuses the RTK data of the slave UAV 21 with the laser ranging offset, and calculates the initial coordinates;

[0107] S2, synchronous acquisition of multi-source data;

[0108] S2-1, the main UAV 11 descends vertically at a constant speed, and the rotating laser radar 12 scans to generate a point cloud of the hole wall and simultaneously records the reflection intensity;

[0109] S2-2, the millimeter-wave radar 13 on the main UAV 11 monitors the position of the construction machinery in real time and triggers the obstacle avoidance strategy;

[0110] S2-3, the slave UAV 21 continuously transmits real-time dynamic positioning correction signals, and measures the relative distance to the master UAV 11 through the laser rangefinder 23, and corrects the posture of the master UAV 11 in real time;

[0111] S3, edge computing real-time processing;

[0112] S3-1, point cloud preprocessing, reflection intensity filtering and geometric filtering;

[0113] S3-2, hole wall centerline fitting, divided into 1m depth segments, using the least squares method to fit the actual hole wall centerline, and calculate the deviation value of each segment;

[0114] S3-3, dynamic threshold adjustment: if the environmental noise rate is greater than 10%, the reflection intensity threshold is automatically increased to ensure that the effective point cloud retention rate is greater than or equal to 80%;

[0115] S4, real-time feedback and construction correction;

[0116] S4-1, the edge computing terminal module 40 generates a deviation curve diagram and a three-dimensional thermal map, marking the out-of-limit area;

[0117] S4-2, displayed to construction workers via mobile terminals.

[0118] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0119] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0120] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0121] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0122] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0123] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A real-time verticality detection system for bridge pile foundation construction based on drone swarms, characterized by: include: A main detection drone module (10) is used to hover vertically above the pile hole and scan along the hole wall to generate point cloud data; A slave positioning drone group module (20) is arranged outside the pile hole to provide positioning compensation for the master detection drone module (10); A ground reflective target (30) is provided at the pile hole mouth and is used for aligning the initial positioning of the main detection drone module (10) with the point cloud coordinate system; An edge computing terminal module (40) is provided at the construction site to receive and process point cloud data and calculate verticality deviation; The result generation module (50) visualizes the analysis results and outputs them as feedback to the construction personnel.

2. A real-time verticality detection system for bridge pile foundation construction based on drone swarms as claimed in claim 1, characterized in that: The main detection drone module (10) includes a main drone (11), the bottom of which is integrated with a rotating laser radar (12), a millimeter wave radar (13), a visual sensor (14) and a dual-frequency GPS+IMU (15), wherein: The rotating laser radar (12) has a scanning frequency of 100 Hz, an angular resolution of 0.1°, and a maximum range of 50 m, and is suitable for deep hole detection; The visual sensor (14) is used to align with the ground reflective target (30); The millimeter wave radar (13) has a detection radius of 30m and is used for real-time obstacle avoidance; The dual-frequency GPS+IMU (15) is used to provide initial positioning.

3. The real-time verticality detection system for bridge pile foundation construction based on drone swarms according to claim 1 is characterized in that: The slave positioning drone group module (20) includes at least three slave drones (21), each of which is equipped with a differential GPS base station (22), a laser rangefinder (23) and an ultra-wideband positioning module, wherein: The differential GPS base station (22) receives satellite signals in real time and generates real-time dynamic positioning correction data; The laser rangefinder (23) is used to emit a ranging laser beam toward the master UAV (11) to measure the relative distance to the master UAV (11); The ultra-wideband positioning module is used to build a relative position network between drones and synchronize positioning data.

4. The real-time verticality detection system for bridge pile foundation construction based on drone swarms according to claim 1 is characterized in that: The ground reflective target (30) comprises an annular reflective belt (31), which is fixedly arranged on the upper edge of the concrete casing at the mouth of the pile hole and is concentric with the center of the pile hole.

5. The real-time verticality detection system for bridge pile foundation construction based on drone swarms according to claim 1 is characterized in that: The edge computing terminal module (40) is deployed at the construction site, receives point cloud data via 5G, and runs a verticality analysis algorithm, which includes a data receiving and synchronization unit (41), a point cloud filtering unit (42) and a verticality calculation unit (43).

6. A real-time verticality detection system for bridge pile foundation construction based on drone swarms as claimed in claim 5, characterized in that: The data receiving and synchronization unit (41) is used to receive multi-source data of the master detection drone module (10) and the slave positioning drone group module (20) and complete spatiotemporal alignment, including the original point cloud data transmitted by the master detection drone module (10), the RTK differential positioning correction parameters sent by the slave positioning drone group module (20) and the preset design coordinates of the ground reflective target (30), match the point cloud data with the positioning data through the 5G communication timestamp, and convert the scanning data of the master drone (11) into the design pile foundation coordinate system.

7. The real-time verticality detection system for bridge pile foundation construction based on drone swarms according to claim 6 is characterized in that: The point cloud filtering unit (42) is used to remove environmental noise points and retain effective hole wall point clouds. Its processing flow includes removing noise points with reflectivity lower than a dynamic threshold and removing points with excessive deviation from the hole center based on the pile hole theoretical radius.

8. The real-time verticality detection system for bridge pile foundation construction based on drone swarms according to claim 7 is characterized in that: The verticality calculation unit (43) is used to fit the actual hole wall center line and calculate each depth deviation value.

9. A real-time verticality detection system for bridge pile foundation construction based on drone swarms as claimed in claim 8, characterized in that: The result generation module (50) is used to generate a visual report and make correction suggestions on the result generated by the verticality calculation unit (43), and transmit the result to a lower terminal device for display to construction personnel.

10. A method for real-time detection of verticality of bridge pile foundation construction based on drone swarms, using a real-time detection system for verticality of bridge pile foundation construction based on drone swarms as described in any one of claims 1 to 9, characterized in that: The following steps are involved: S1, system initialization and benchmark calibration; S1-1, deploying the ground reflective target (30), installing the annular reflective tape (31) on the pile hole casing, ensuring that its center coincides with the designed pile foundation axis; S1-2, using three slave drones (21) arranged in an equilateral triangle within 50 m of the pile hole, starting the differential GPS base station (22) to form a dynamic positioning network; S1-3, each slave UAV (21) synchronizes its position through an ultra-wideband positioning unit (24) and broadcasts a dynamic positioning correction signal in real time; S1-4, the master UAV (11) hovers just above the pile hole, identifies the target center through vision, and fuses the positioning data with the slave UAV (21) to initialize the precise coordinates; S2, synchronous acquisition of multi-source data; S2-1, the main UAV (11) descends vertically at a constant speed, and the rotating laser radar (12) scans to generate a point cloud of the hole wall and simultaneously records the reflection intensity; S2-2, the millimeter-wave radar (13) on the main UAV (11) monitors the position of the construction machinery in real time and triggers the obstacle avoidance strategy; S2-3, the slave UAV (21) continuously transmits real-time dynamic positioning correction signals, and measures the relative distance with the master UAV (11) through the laser rangefinder (23), and corrects the posture of the master UAV (11) in real time; S3, edge computing real-time processing; S3-1, point cloud preprocessing, reflection intensity filtering and geometric filtering; S3-2, hole wall centerline fitting, divided into 1m depth segments, using the least squares method to fit the actual hole wall centerline, and calculate the deviation value of each segment; S3-3, dynamic threshold adjustment: if the environmental noise rate is greater than 10%, the reflection intensity threshold is automatically increased to ensure that the effective point cloud retention rate is greater than or equal to 80%; S4, real-time feedback and construction correction; S4-1, the edge computing terminal module (40) generates a deviation curve diagram and a three-dimensional thermal map, and marks the over-limit area; S4-2, connecting the mobile terminal and the edge computing terminal module (40) via Bluetooth / WiFi, and transmitting the trimming parameters to the visual device of the construction personnel in real time.

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