Shallow surface drilling tool positioning system based on unmanned aerial vehicle array and control method thereof

Abstract

The invention discloses a shallow earth surface drilling tool positioning system based on an unmanned aerial vehicle array and a control method of the shallow earth surface drilling tool positioning system. The system comprises the unmanned aerial vehicle array carrying a magnetic field generator, a magnetic sensor arranged on a drilling tool and an information processing module. The unmanned aerial vehicle array comprises a plurality of node unmanned aerial vehicles located in an airspace corresponding to the real-time position of a drilling tool and a central unmanned aerial vehicle, the plurality of node unmanned aerial vehicles are distributed in a preset formation, and the central unmanned aerial vehicle is located at a corresponding position of the preset formation; the central unmanned aerial vehicle acquires position information of the plurality of node unmanned aerial vehicles in real time and controls the plurality of node unmanned aerial vehicles to fly according to a preset formation; the magnetic sensor receives real-time magnetic field signals of preset frequencies of magnetic field generators carried by each node unmanned aerial vehicle and the central unmanned aerial vehicle, and sends all the magnetic field signals to the information processing module; and the information processing module calculates real-time position information of the drilling tool based on the multi-source magnetic field signals and the position information of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of electronic positioning and ranging technology, and in particular to a shallow surface drilling tool positioning system and its control method based on an unmanned aerial vehicle (UAV) array. Background Technology

[0002] In the rapid development of modern society, the development and utilization of underground space is becoming increasingly crucial. Whether it's the construction of infrastructure such as urban subways and underground utility tunnels, or the exploration and development of underground resources such as shallow mineral deposits, precise control over the spatial location of underground drilling tools is indispensable. In such projects, achieving real-time and accurate positioning of drilling tools is a core prerequisite for ensuring construction efficiency, project quality, and operational safety.

[0003] Currently, various technologies have been developed for shallow surface drilling tool positioning. Among them, ground-guided positioning systems (GSIs) use a transmitter placed near the drill bit to send electromagnetic signals to a ground receiver for positioning. However, these signals are easily attenuated by obstructions in complex terrain or confined spaces, leading to positioning interruptions, and long-term use can result in accumulated errors. Ultrasonic positioning technology uses sound wave propagation between a transmitter on the drill bit and surrounding receivers for distance measurement. However, sound waves in real media are prone to multipath effects due to reflection and refraction, and changes in medium conditions (such as temperature, pressure, and water content) significantly affect sound velocity stability, leading to positioning deviations and reduced real-time performance. While total stations offer high accuracy, they rely on line-of-sight and cannot operate in obstructed environments. Furthermore, each measurement requires cumbersome steps such as centering and leveling, resulting in poor operational convenience and making it difficult to meet the demands of rapid and efficient construction. In addition, magnetic positioning technology calculates position by detecting changes in the magnetic field caused by a magnetic source on the drill bit. Although it has a certain penetration capability, it is extremely sensitive to the subtraction of the background magnetic field and sensor calibration. It is easily interfered with in complex electromagnetic environments, and the system redundancy is insufficient. A single point of failure may cause the entire positioning link to fail.

[0004] In summary, existing technologies generally face multiple challenges such as complex deployment processes, poor mobility, weak anti-interference capabilities, and insufficient real-time performance, making it difficult to provide stable and reliable drill bit positioning services in complex and ever-changing shallow surface engineering environments. Summary of the Invention

[0005] The purpose of this invention is to provide a shallow surface drilling tool positioning system and its control method based on an unmanned aerial vehicle (UAV) array. By employing an aerial dynamic positioning platform composed of an UAV array and utilizing magnetic field signals for detection, the system effectively overcomes the shortcomings of traditional positioning technologies, such as cumbersome deployment, poor mobility, and susceptibility to signal interference in complex environments. This enables flexible, accurate, and stable real-time positioning of shallow surface drilling tools.

[0006] To address the aforementioned technical problems, a first aspect of this invention provides a shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array, comprising: an UAV array equipped with a magnetic field generator, a magnetic sensor mounted on the drilling tool, and an information processing module. The UAV array includes several node UAVs located in the airspace corresponding to the real-time position of the drill bit and a central UAV. The several node UAVs are distributed in a preset formation, and the central UAV is located at the corresponding position of the preset formation. The central UAV acquires the location information of the plurality of node UAVs in real time and controls the plurality of node UAVs to fly in the preset formation; The magnetic sensor receives real-time magnetic field signals of a preset frequency from the magnetic field generators carried by each node UAV and the central UAV, and sends all the magnetic field signals to the information processing module. The information processing module calculates the real-time position information of the drilling tool based on the multi-source magnetic field signals and the position information of the several node UAVs.

[0007] Furthermore, the preset array is a symmetrical arrangement with spatial geometric constraints.

[0008] Furthermore, the preset array is an isosceles triangle, an equilateral triangle, a solid triangle, or a regular tetrahedron.

[0009] Furthermore, the information processing module includes a first processing unit and a second processing unit; The first processing unit calculates the first position data of the drill bit based on the magnetic field signals of the plurality of node UAVs using an analytical geometry algorithm. The second processing unit calculates the second position data of the drill bit based on the magnetic field signal of the central UAV and using Hilbert transform and orthogonal demodulation technology. The information processing module performs weighted fusion processing on the first position data and the second position data of the drill bit to obtain the fused position data of the drill bit.

[0010] Furthermore, based on the magnetic field signals emitted by the plurality of node UAVs, the first processing unit constructs a near-field magnetic field observation model using the Biot-Savart law, calculates the distance between the drill bit and each of the node UAVs, and, in conjunction with the preset array geometric relationship of the plurality of UAVs, calculates the first position data of the drill bit based on an analytical geometric algorithm.

[0011] Furthermore, the second processing unit performs a Hilbert transform on the magnetic field signal of the central UAV to construct the in-phase reference component and the quadrature reference component required for quadrature demodulation. The magnetic field signal is then processed using quadrature demodulation technology to obtain the in-phase component and the quadrature component. The in-phase component and the quadrature component are multiplied to extract the characteristic signal of the magnetic field. Based on the characteristic signal, the distance, relative tilt angle, and relative azimuth angle of the drill bit relative to the central UAV are calculated using a magnetic dipole model to obtain the second position data of the drill bit.

[0012] Furthermore, the information processing module dynamically calculates the first weight corresponding to the first location data and the second weight corresponding to the second location data based on the measurement noise variance of the first location data and the measurement noise variance of the second location data. The first location data is weighted using the first weight, and the second location data is weighted using the second weight. The weighted data are then summed to obtain the fused location data.

[0013] Furthermore, the information processing module inputs the fused position data into a discrete Kalman filter for state recursion and optimization, and outputs the final optimized real-time position of the drill bit.

[0014] Furthermore, the central UAV also includes a UAV array control unit, used to control the magnetic field generator of the UAV array to work in a time-division multi-frequency mode, which includes: an environmental magnetic field background acquisition period, a single UAV working period, and a multi-UAV cooperative positioning period; The environmental magnetic field background acquisition period is when the UAV array control unit controls all magnetic field generators to shut down during the corresponding period, so that the magnetic sensor can acquire the background magnetic field of the drill bit. The single UAV working period is defined as the UAV array control unit controlling the magnetic field generator of the central UAV to turn on during the corresponding period, while the magnetic field generators of the other node UAVs are turned off. The multi-UAV collaborative positioning period is when the UAV array control unit controls the magnetic field generator of the node UAV to turn on during the corresponding period.

[0015] Accordingly, a second aspect of the present invention provides a control method for a shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array, which controls the aforementioned shallow surface drilling tool positioning system based on an UAV array, including: The central UAV acquires the location information of several node UAVs in real time and controls the several node UAVs to fly in a preset formation, with the central UAV located at the corresponding position in the preset formation. Based on the magnetic sensor installed on the drilling tool, the real-time magnetic field signal of the preset frequency of the magnetic field generator carried by each node UAV and the central UAV is received respectively. The information processing module receives all the magnetic field signals sent by the magnetic sensor and calculates the real-time position information of the drilling tool based on the magnetic field signals and the position information of the several node UAVs.

[0016] The above-described technical solutions of the embodiments of the present invention have the following beneficial technical effects: 1. By using drones as a mobile platform for the magnetic field generator, an aerial dynamic positioning benchmark was established, completely eliminating the dependence of traditional ground positioning systems on fixed base stations. It can quickly adjust and maintain the optimal observation array in the air according to the real-time position of the drilling tool and the construction progress, effectively avoiding the obstruction of ground obstacles and the limitations of complex terrain. Thus, it can achieve rapid and flexible deployment and high-precision stable positioning in various complex construction scenarios, greatly improving the mobility and environmental adaptability of the entire positioning system. 2. A dual-path parallel solution and data fusion architecture is adopted. One path is based on the geometric configuration of the node UAV array and uses analytical methods for positioning, while the other path is based on the magnetic signal of the central UAV and uses feature demodulation for positioning. Finally, the two are fused through an optimal weighting algorithm based on noise variance. This architecture makes full use of the complementary advantages between different positioning principles, effectively suppresses the systematic errors and random noise of single methods, and can still output reliable position information through the other path and fusion algorithm even when the signal quality of a single path deteriorates. This significantly improves the accuracy, stability and overall robustness of the positioning results in complex electromagnetic environments. 3. Through a time-division multi-frequency operating mode coordinated by the central UAV, not only can the node UAVs be assisted in positioning correction, but the magnetic field signal transmission of the entire system is also intelligently managed. This mode achieves accurate background interference subtraction by setting a dedicated background magnetic field acquisition period, and avoids mutual interference between multiple magnetic field signals through time-division and frequency-division strategies. Furthermore, it can adaptively switch to anti-interference modes such as frequency hopping when strong environmental interference is detected. This dynamic and reconfigurable signal transmission strategy fundamentally improves the signal-to-noise ratio and purity of the signal, ensures the quality of the data on which the positioning calculation depends, and optimizes the utilization efficiency of limited spectrum resources, enabling the system to maintain excellent positioning performance even in harsh industrial electromagnetic environments.

[0017] 4. By conducting risk assessments on various aspects such as positioning accuracy, UAV malfunctions, and environmental interference, and monitoring indicators such as the measurement noise variance of the positioning algorithm, UAV flight status parameters, and environmental interference intensity, timely measures such as activating backup plans, dispatching backup UAVs, and adjusting signal processing strategies are taken based on the assessment results to ensure stable system operation. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the principle of the shallow surface drilling tool positioning system based on UAV array provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a central unmanned aerial vehicle provided in an embodiment of the present invention; Figure 3 This is a flowchart of the shallow surface drilling tool positioning process based on an unmanned aerial vehicle array provided in an embodiment of the present invention; Figure 4 This is a flowchart of the control method for a shallow surface drilling tool positioning system based on an unmanned aerial vehicle array provided in an embodiment of the present invention.

[0019] Figure label: 110. Node UAV; 120. Central UAV; 121. Central UAV magnetic field generator; 122. UAV array control unit; 2. Magnetic sensor; 3. Information processing module. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0021] Please refer to Figure 1 , Figure 2 and Figure 3 The first aspect of this invention provides a shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array, comprising: an UAV array equipped with a magnetic field generator, a magnetic sensor 200 mounted on the drilling tool, and an information processing module 300; the UAV array includes a plurality of node UAVs 110 located in the airspace corresponding to the real-time position of the drilling tool and a central UAV 120, the plurality of node UAVs 110 being distributed in a preset formation, and the central UAV 120 being located at a corresponding position in the preset formation; the central UAV 120 acquiring the position information of the plurality of node UAVs 110 in real time and controlling the plurality of node UAVs 110 to fly in the preset formation; the magnetic sensor 200 receiving real-time magnetic field signals of a preset frequency from the magnetic field generators mounted on each node UAV 110 and the central UAV 120, and sending all magnetic field signals to the information processing module 300; the information processing module 300 calculating the real-time position information of the drilling tool based on the multi-source magnetic field signals and the position information of the plurality of node UAVs 110.

[0022] The shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array provided in this invention constructs an aerial dynamic magnetic field transmission and acquisition network using UAVs as mobile platforms. In this system, a UAV array is composed of several node UAVs 110 distributed in a specific geometric formation and a central UAV 120 located at a key position in the formation. This array is monitored in real time by the central UAV 120, which actively maintains the relative positions between the nodes, thereby forming a stable and known spatial reference benchmark above the drilling tool's operating airspace. Each UAV is equipped with a magnetic field generator, capable of transmitting magnetic field signals with preset characteristics to the drilling tool's location. Simultaneously, a magnetic sensor 200 installed at the front end of the drilling tool synchronously receives magnetic field signals from all UAVs and the background magnetic field of its environment. All this raw magnetic field data is transmitted in real time to the information processing module 300, which, through comprehensive analysis and calculation of the multi-source magnetic field signals, ultimately accurately retrieves the real-time three-dimensional coordinates of the drilling tool underground.

[0023] Specifically, the preset array is a symmetrical arrangement with spatial geometric constraints. This means that the arrangement of the node UAVs 110 in space follows specific geometric rules and symmetry principles, such as isosceles triangles, equilateral triangles, or more complex tetrahedral configurations. This arrangement, through strict geometric constraints, constructs a stable reference frame with definite mathematical relationships in space for the positioning system. Its symmetry ensures that the magnetic field signals observed from the drill bit's location have predictable spatial distribution characteristics, thus providing a reliable physical basis and mathematical model for subsequent positioning algorithms based on analytical geometry, effectively improving the accuracy of position calculation and the overall stability of the system.

[0024] Furthermore, the preset formations are isosceles triangles, equilateral triangles, solid triangles, or regular tetrahedrons and their three-dimensional extensions.

[0025] Specifically, the preset formations include various implementation methods such as isosceles triangles, equilateral triangles, solid triangular arrangements, and regular tetrahedral arrangements. When using planar arrangements such as isosceles or equilateral triangles, the central UAV 120 is located at the centroid of the triangular geometric plane, and this symmetrical configuration is maintained by monitoring and adjusting the positions of the node UAVs 110. In the solid triangular arrangement, the node UAVs 110 are distributed at different heights to form a spatial configuration, and the central UAV 120 is located at the key reference point of this spatial structure. In the regular tetrahedral arrangement, the four UAVs are located at the four vertices of the regular tetrahedron. At this time, the system replaces the supervision function of a single central UAV 120 with the cooperative positioning between nodes, forming a completely symmetrical spatial detection network. The three-dimensional expansion of the regular tetrahedral arrangement is achieved by adding UAV nodes, placing them at the vertices of more complex convex polyhedra (such as regular octahedrons and cubes), or constructing multi-level nested regular tetrahedral structures to form a three-dimensional detection network with wider coverage and better geometric configuration, thereby significantly improving the system's vertical resolution, spatial positioning accuracy, and overall fault tolerance.

[0026] In one specific embodiment of the present invention, the UAV array consists of two parts: one part is an array of UAVs ABC arranged in an isosceles triangle, and the other part is a central UAV 120D located at the centroid of the triangle, with all four UAVs on the same horizontal plane. Each UAV is equipped with a magnetic field generator, which can adjust the magnetic field strength and excitation frequency independently and in real time. Each UAV has independent positioning calculation capabilities, but to improve positioning accuracy and operational efficiency, the present invention adopts a strategy of data fusion between the collaborative calculation results of the triangular array of UAVs and the independent calculation results of the central UAV 120D to output the final positioning result.

[0027] In a triangular drone array ABC, three drones are numbered A, B, and C, while the drone at the center is numbered D. The array is arranged in isosceles triangles to ensure geometric symmetry. Let the plane of the array be... bottom edge Length is ,vertex The distance from the perpendicular bisector to the base line is Establish a coordinate system with the midpoint of AC as the origin, and the x-axis pointing along AB. The y-axis points to C along the perpendicular bisector of the base, and the z-axis points vertically upward. The theoretical coordinates of UAV A, B, and C are expressed as follows: , , To further achieve precise signal differentiation and processing, each magnetic field generator in the triangular UAV array ABC is assigned a non-overlapping operating frequency. The sensor can use this information to decode the frequency, distinguish the signal source, and calculate the field strength of each source.

[0028] The central UAV 120 not only serves as a direct positioning reference but also performs online calibration of the triangular UAV array. Its theoretical coordinates are as follows: and allocate independent frequencies During the actual positioning process, the central UAV 120 uses its onboard magnetic field generator 121 to emit a unique frequency signal for independent position calculation. On the other hand, it monitors the deviation between the actual and theoretical positions of each UAV in the triangular array in real time to correct the positioning error of the triangular array online, thereby maintaining the geometric symmetry of the array and the stability of the coordinate system.

[0029] In theory, increasing the number of drones carrying magnetic field generators can provide more measurement information and enhance fault tolerance; however, this invention prefers a "three-drone isosceles triangle (at the same height)" scheme, which satisfies the need for sufficient triangulation information while avoiding the computing power and deployment costs caused by redundancy, thus balancing accuracy and economy.

[0030] In addition, alternative solutions may include the following three: ① Equilateral triangle deployment: Deploying the three drones in an equilateral triangle at the same height (with equal distances between each pair) can achieve more uniform planar geometric constraints in a nearly isotropic geological environment, potentially simplifying the calculation and improving accuracy. ② Solid triangular deployment: While keeping the number of drones constant, adjusting the flight altitude of one or two drones creates spatial configurations at different altitudes, enhancing vertical observability and improving three-dimensional geometric conditions. ③ Tetrahedral solid deployment: When high three-dimensional positioning accuracy is required, deploying the four drones at the four vertices of a regular tetrahedron provides more comprehensive and uniform three-dimensional magnetic field coverage, suitable for high-precision three-dimensional positioning scenarios.

[0031] The UAV array control unit 122, mounted on the central UAV 120, undertakes the coordination and control functions in the system. The central UAV 120 obtains its own and the measured positions of each UAV in the triangular UAV array ABC in real time through this unit, and processes the above information to make control decisions.

[0032] Let the actual coordinates of the triangular UAV array ABC measured by the central UAV 120 be: The central UAV 120 transmits the actual measurement values ​​it receives through the UAV array control unit 122. Compared with theoretical value The difference was used to calculate the translation correction for the array: Subsequently, the UAV array control unit 122 will adjust the amount. The command was issued to each drone in the triangular array, instructing them to perform translational adjustments so that the center of gravity of the three drones was aligned with... Alignment. To further maintain the isosceles triangle distribution, the control unit synchronously monitors and constrains geometric parameters such as base length, vertex symmetry, and equal length of the two sides, and applies micro-rotations and scale corrections when necessary, thereby maintaining the geometric stability and positioning accuracy of the array in complex environments.

[0033] Each drone in the drone array carries a magnetic field generator. As a key component, the frequency adjustment and time-division transmission mechanism of the magnetic field generator play a crucial role in achieving precise positioning. The central drone 120 acts as the command unit in the entire system, coordinating power configuration, frequency planning, and time-division through its onboard drone array control unit 122.

[0034] First, four drones equipped with magnetic field generators were configured with multi-level power adjustments to adapt to different distances and environmental attenuation conditions. Each magnetic field generator was assigned an independent and non-overlapping operating frequency, labeled as follows: Frequency selection is based on the analysis of electromagnetic spectrum occupancy and interference source distribution, and is planned according to preset frequency intervals and avoidance rules to reduce overlap with common interference frequency bands.

[0035] When the drone in the central UAV 120 detects that the intensity of the ambient magnetic field interference exceeds a preset threshold, the system switches to a time-sharing transmission mode. Within a complete work cycle, multiple specific time periods are finely divided to meet different functional requirements.

[0036] During the environmental magnetic field background acquisition period: The central UAV 120 sends instructions to all UAVs via the control circuit, while simultaneously shutting down the magnetic field generators carried by each UAV. The high-precision triaxial magnetic sensor 200 on the drilling tool collects the background magnetic field for subsequent background magnetic field subtraction.

[0037] Single UAV working period: In order to achieve high-precision positioning of the drill bit by the central UAV 120, a series of specific time periods will be arranged to obtain the magnetic field signal generated only by the magnetic field generator 121 of the central UAV 120. The information processing module 300 obtains the drill bit positioning information through the positioning algorithm.

[0038] Multi-UAV collaborative positioning period: The central UAV 120 sends instructions to all UAVs in the UAV array ABC, and at the same time turns on all of their magnetic field generators. The information is transmitted to the information processing module 300 through the magnetic sensor 200 carried by the subsequent drilling tool. The module performs demodulation and background magnetic field response removal, and then accurately calculates the position information of the drilling tool through positioning algorithm and data fusion.

[0039] To further improve anti-interference capability and spectrum utilization in complex electromagnetic environments, in addition to the above-mentioned "fixed non-overlapping frequencies," the following two candidate schemes can also be adopted: ① Frequency hopping scheme: The excitation frequency of each magnetic field generator changes within the allowable bandwidth according to a preset frequency hopping sequence. The receiver performs frequency domain demodulation and amplitude-phase estimation according to the subcarrier orthogonality condition to divide the magnetic field signals transmitted by different UAV magnetic field generators. ② Orthogonal frequency division multiplexing scheme: The signals from multiple UAVs are modulated onto mutually orthogonal subcarriers and transmitted simultaneously. The receiver performs frequency domain demodulation and amplitude-phase estimation according to the subcarrier orthogonality condition to divide the magnetic field signals transmitted by different UAV magnetic field generators.

[0040] Furthermore, the information processing module 300 includes a first processing unit and a second processing unit; the first processing unit calculates the first position data of the drill bit based on the magnetic field signals of several node UAVs 110 using an analytical geometric algorithm; the second processing unit calculates the second position data of the drill bit based on the magnetic field signals of the central UAV 120 using Hilbert transform and orthogonal demodulation technology; the information processing module 300 performs weighted fusion processing on the first position data and the second position data of the drill bit to obtain the fused position data of the drill bit.

[0041] The information processing module 300 adopts a dual-path parallel processing architecture to improve the accuracy and reliability of positioning. The first processing unit specifically processes the magnetic field signals from the node UAVs 110, which are distributed in a geometric array. It converts multiple magnetic field strength measurements into the first position data of the drill string relative to the fixed spatial configuration through analytical geometric algorithms. At the same time, the second processing unit focuses on processing the magnetic field signals from the central UAV 120. It uses Hilbert transform and orthogonal demodulation technology to accurately extract features containing distance and direction information from the signals, and then calculates the second position data of the drill string relative to the central reference point. Finally, the core function of the information processing module 300 is to perform optimal weighted fusion of the two position estimates obtained based on different physical principles and algorithms. By dynamically allocating weights and combining the advantages of both, it generates a final drill string fused position data that is superior to either single result in terms of accuracy and robustness.

[0042] The above processing mechanism utilizes the spatial stability of the geometric positioning of the 110-array node UAV and the directional sensitivity of the single-point positioning of the 120-array central UAV through independent operation and collaborative fusion of dual heterogeneous algorithms. It effectively overcomes the inherent limitations of a single measurement principle in complex underground environments and significantly improves the overall positioning accuracy and fault tolerance of the system under adverse conditions such as magnetic field interference and signal attenuation.

[0043] Furthermore, the first processing unit constructs a near-field magnetic field observation model based on the magnetic field signals emitted by several node UAVs 110 through the Biot-Savart law, calculates the distance between the drill string and each node UAV 110, and calculates the first position data of the drill string based on the preset array geometric relationship of several UAVs using an analytical geometric algorithm.

[0044] The first processing unit transforms the geometric advantages of the array of node UAVs 110 into precise spatial coordinates. First, based on the near-field magnetic field observation model of the Biot-Savart law, it inversely calculates the precise distance information between the drill string and each node UAV 110 from the magnetic field strength measurements received by the magnetic sensor 200 from each node UAV 110. After obtaining multiple distance observations, this unit fully utilizes the known and stable geometric constraints formed by the preset array of node UAVs 110, constructing and solving a system of spatial equations through analytical geometric algorithms to determine the three-dimensional spatial coordinates of the drill string in the current coordinate system, i.e., the first position data. By closely integrating physical field measurements with the spatial geometric model, the intangible magnetic field signal is transformed into a tangible geometric constraint problem, enabling the system to fully utilize the spatial reference framework formed by the UAV array, effectively improving the objectivity and accuracy of position calculation in complex media environments.

[0045] Furthermore, the second processing unit performs a Hilbert transform on the magnetic field signal of the central UAV 120 to construct the in-phase reference component and the quadrature reference component required for quadrature demodulation. The magnetic field signal is then processed using quadrature demodulation technology to obtain the in-phase component and the quadrature component. The in-phase component and the quadrature component are multiplied to extract the characteristic signal of the magnetic field. Based on the characteristic signal, the distance, relative tilt angle, and relative azimuth angle of the drill bit relative to the central UAV 120 are calculated using a magnetic dipole model to obtain the second position data of the drill bit.

[0046] The second processing unit achieves precise relative positioning of the drill string relative to the central UAV 120 using advanced signal processing technology. This unit first performs a Hilbert transform on the specific frequency magnetic field signal emitted by the central UAV 120, generating two mutually orthogonal reference signal components. Then, using orthogonal demodulation technology, it separates the in-phase and quadrature components containing amplitude and phase information from the original magnetic field signal, multiplies these two components to synthesize a characteristic signal that fully characterizes the spatial distribution of the magnetic field. Based on this characteristic signal, the second processing unit further uses a field distribution model of magnetic dipoles in space to analytically calculate and simultaneously obtain three key spatial parameters: the straight-line distance, relative tilt angle, and relative azimuth angle between the drill string and the central UAV 120, thus uniquely determining the drill string's second position data in space. Applying signal modulation and demodulation technology from the communication field to magnetic positioning significantly enhances the ability to perceive the drill string's spatial attitude by extracting deep features of the magnetic field signal rather than simply relying on field strength amplitude. This allows the system to maintain stable positioning performance even under conditions of weak signal strength or interference, providing an independent and reliable single-point positioning information source for the entire system.

[0047] Furthermore, the information processing module 300 dynamically calculates the first weight corresponding to the first location data and the second weight corresponding to the second location data based on the measurement noise variance of the first location data and the measurement noise variance of the second location data. The first location data is weighted using the first weight, and the second location data is weighted using the second weight. The weighted data are then summed to obtain the fused location data.

[0048] The weighted fusion process of the information processing module 300 continuously evaluates and acquires the measurement noise variances corresponding to the first position data based on array geometry calculation output by the first processing unit and the second position data based on single-point feature demodulation output by the second processing unit. Based on these two variance values, the information processing module 300 dynamically calculates and assigns a real-time updated fusion weight to the first and second position data respectively. The basic principle is to assign a larger weight to the position result with lower measurement uncertainty, i.e., higher data quality. Subsequently, the information processing module 300 uses these two weight coefficients to weight the two position data respectively and performs vector summation on the weighted results to obtain a comprehensive fused position data. The system self-adjusts according to the performance fluctuations of the two positioning subsystems under actual working conditions. When the accuracy of one subsystem decreases due to environmental interference, the system automatically reduces its contribution, thereby ensuring that the final output fusion result always tends to the more reliable data source. This significantly improves the adaptability, robustness, and accuracy stability of the entire positioning system in complex and changing environments.

[0049] Furthermore, the information processing module 300 inputs the fused position data into a discrete Kalman filter for state recursion and optimization, and outputs the final optimized real-time position of the drill bit.

[0050] After obtaining the weighted fused position data, the information processing module 300 further inputs it into a discrete Kalman filter for state recursion and optimization processing. This filter, based on the system dynamics model of the drill string and combined with the observed fused position data, uses a "prediction-correction" recursive mechanism to optimally estimate the motion state of the drill string: first, it predicts the current state and error covariance based on the previous state estimate and the system model; then, it corrects the predicted value based on the current actual observation, thus obtaining the optimal state estimate for the current moment. This state recursive optimization process effectively suppresses the influence of measurement noise and random interference by fully utilizing system model knowledge and real-time observation information, significantly improving the smoothness, continuity, and tracking accuracy of the drill string position output during dynamic processes. This ensures that the final real-time drill string position data output by the system possesses both good instantaneous accuracy and stable long-term reliability.

[0051] Furthermore, the UAV array control unit 122 can be used to control the magnetic field generators of the UAV array to operate in a time-division multi-frequency mode. The time-division multi-frequency mode includes: an environmental magnetic field background acquisition period, a single UAV working period, and a multi-UAV collaborative positioning period. During the environmental magnetic field background acquisition period, the control unit controls all magnetic field generators to be turned off during the corresponding period, so that the magnetic sensor 200 can collect the background magnetic field of the drilling tool. During the single UAV working period, the control unit controls the magnetic field generator 121 of the central UAV 120 to be turned on during the corresponding period, while the magnetic field generators of the other node UAVs 110 are turned off. During the multi-UAV collaborative positioning period, the control unit controls the magnetic field generators of the node UAVs 110 to be turned on during the corresponding period.

[0052] The control unit of the central UAV 120 serves as the core of the entire system's collaborative control. Its operating principle is based on preset timing logic and real-time environmental perception, enabling unified scheduling and precise management of the magnetic field emission behavior of the entire UAV array. By generating and issuing precise timing control commands, this unit coordinates all UAVs equipped with magnetic field generators to enter a time-division multi-frequency working mode, ensuring that only specific magnetic field generator groups are in the emission state at different times. This separates magnetic field signals of different functions in the time dimension, creating a clear and orderly data acquisition environment for subsequent signal processing and positioning calculations.

[0053] The time-division multi-frequency mode is specifically divided into three time periods with different functions: During the environmental magnetic field background acquisition period, the control unit instructs all magnetic field generators to turn off. At this time, the magnetic sensor 200 on the drill bit can measure the pure background environmental magnetic field, providing a reference for subsequent signal differential processing; During the single UAV working period, the control unit only turns on the magnetic field generator of the central UAV 120 itself, aiming to obtain a set of pure data for independent relative positioning that is not interfered with by signals from other UAVs; During the multi-UAV collaborative positioning period, the control unit turns on the magnetic field generators of all node UAVs 110, using the spatial geometry they form to provide synchronous magnetic field signal input for the array-based positioning method.

[0054] By combining time-division multiplexing with frequency planning, crosstalk between multi-source magnetic field signals is fundamentally avoided, and effective monitoring and subtraction of the environmental background magnetic field are achieved. This significantly improves the purity and signal-to-noise ratio of the useful signal, ultimately ensuring the quality and reliability of the data source on which the positioning calculation depends even in complex on-site electromagnetic environments, thus enhancing the overall system's environmental adaptability and positioning accuracy.

[0055] During the ambient magnetic field background acquisition period, a highly sensitive triaxial magnetic sensor 200 is equipped on the drilling tool. When the UAV array magnetic field generator is turned off, the triaxial magnetic sensor 200 begins to acquire background ambient magnetic field information and accurately records the background spectrum. ,in Represents frequency. The triaxial magnetic sensor 200 can record the background magnetic field strength at different frequencies in detail, generating complete background magnetic field spectrum characteristic data.

[0056] When the magnetic field generator of the UAV array is activated according to the time-sharing transmission strategy, during the single UAV's working period, the central single UAV first transmits the electric field. During the multi-UAV collaborative positioning period, the triaxial magnetic sensor 200 pairs of magnetic field generators emit specific frequency signals. Demodulation processing is performed to acquire and record the electric field strength of each signal source. .

[0057] Background differential technology is used to remove interference from the background magnetic field on the measurement signal, based on the background differential formula: Complete background difference pair Wavelet denoising and adaptive filtering are performed sequentially. Wavelet denoising reduces random high-frequency and impulse components at multiple scales, using a db4 / sym8 wavelet configuration, employing 1–2 level decomposition, and calculating noise variance using MAD estimation at the detail level, with symmetric extension. Adaptive filtering performs online parameter tuning based on the statistical characteristics of the input signal, continuously tracking the slow drift of the underground magnetic field and suppressing narrowband interference to optimize signal quality.

[0058] During the multi-UAV collaborative positioning period, the information processing module 300 at the drilling tool processes the net magnetic field strength from the triangular UAV array ABC. , , Formula for the near-field magnetic field observation model based on the Biot-Savart law: The distance between the drilling tool and each drone can be calculated. , , .

[0059] Let the drill string coordinates obtained by the triangular UAV array geometry method be... Since the plane containing the triangular UAV array ABC is of equal height is On this plane, the position of the drill bit can be obtained by subtracting the squared terms from pairwise measurement equations using analytical geometry. Approximate analytical solution on the plane: Take any point and substitute it back into the distance equation to find... Combining the prior assumption that "the target is underground", taking the negative root yields: From equations (3)-(6), we can obtain This provides initial values ​​and constraints for subsequent numerical optimization and data fusion.

[0060] During the single-drone operation period, the information processing module 300 at the drilling tool receives the net magnetic signal from the central drone 120. The signal is subjected to Hilbert transform to construct the in-phase reference component required for quadrature demodulation. Orthogonal reference components The in-phase component is obtained through quadrature demodulation. Orthogonal components Multiplying the two yields the characteristic signal of the magnetic field. The positioning information between the magnetic transmitter of the central UAV 120 and the drilling tool, including distance, relative tilt angle, and relative azimuth angle, is determined using the characteristic signals through the following formula: The distance r between the 120-terminal magnetic transmitter of the central UAV and the drill bit is: in The magnetic moment is known.

[0061] The relative tilt angle r between the magnetic launcher carried by the central UAV 120 and the drill bit for: The relative azimuth angle of the distance r between the magnetic launcher carried by the central UAV 120 and the drill bit. We obtain it from the following formula: In summary, the distance r and tilt angle are obtained. and azimuth This information, calculated by the central UAV 120, provides the drill string positioning information. .

[0062] To further improve the accuracy of drill bit position estimation and fully utilize the complementary advantages of different positioning methods, linear weighted fusion of multi-source data is adopted to improve noise resistance and optimize estimation variance.

[0063] Let the drill string position obtained by the triangular UAV array geometry method be... The drill string position was calculated by the central UAV 120. Establish a measurement model: in, For the system's actual location, Find the drill bit position for a triangular UAV array. And the center's drone 120 to find the location of the drilling tool Measurement noise. To obtain the fused position estimate. Linear fusion estimation is used: The estimation error after fusion is: The variance of the estimation error is: right Taking the derivative and setting it to zero, we obtain the optimal fusion weights as follows: Therefore, the estimated value of the least mean square error fusion can be obtained as follows: Fusion results As the observation output, a discrete Kalman filter is further used for state recursion, and the final drill bit positioning is output and pushed to the construction control system for trajectory correction.

[0064] When dealing with large amounts of data, machine learning algorithms are introduced to predict and optimize drill string positions. A large amount of magnetic field signal data and corresponding actual drill string position data are used as the training set to train a neural network model. During actual positioning, the model can quickly and accurately predict the drill string position based on the real-time acquired magnetic field signals.

[0065] Furthermore, the final drill string positioning is compared with the preset trajectory. When the deviation exceeds the preset accuracy threshold (horizontal deviation > ±5 cm, vertical deviation > ±3 cm), the system immediately activates the correction parameter calculation module to generate correction parameters that allow the drill string to return to the ideal trajectory. These parameters are then transmitted in real-time via wireless communication to the UAV array control unit 122, which is centered on the central UAV 120. The central UAV 120 analyzes the parameters and issues adjustment commands to each UAV in the triangular UAV array ABC according to a preset collaborative control strategy. For example, when the drill string deviates in the x-axis direction, the UAV array adjusts its deployment and excitation configuration in that direction to change the magnetic field guidance and achieve trajectory correction.

[0066] If a drone malfunctions, the information processing module 300 installed on the drilling tool performs rapid fault diagnosis to determine if a single drone has failed. Once confirmed, the construction control system issues a scheduling command, switching the formation to multi-source positioning mode. Each drone can independently calculate its position estimate based on its own observed physical quantities, ensuring the continuity and reliability of the positioning link.

[0067] Meanwhile, due to the variable environment during drilling, if the intensity of environmental magnetic field interference exceeds the set safety threshold, the central UAV 120 control energy responds quickly, adjusting the magnetic field generator strategy based on preset response strategies and real-time environmental data. When the interference intensifies, the control magnetic field generator collects environmental magnetic field data at encrypted time intervals (e.g., the original interval is 10 seconds, shortened to 5 seconds after encryption) to assess the environmental conditions and effectively assist in correcting the drill string trajectory.

[0068] Accordingly, please refer to Figure 4 A second aspect of the present invention provides a control method for a shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array, which controls the aforementioned shallow surface drilling tool positioning system based on an UAV array, including: In step S100, the central UAV 120 acquires the location information of several node UAVs 110 in real time and controls the several node UAVs 110 to fly in a preset formation, with the central UAV 120 located at the corresponding position in the preset formation.

[0069] The entire positioning reference network is constructed and maintained through the UAV array control unit 122 mounted on the central UAV 120. The central UAV 120 continuously acquires real-time high-precision positioning data from each node UAV 110 via inter-UAV communication links. This data comes from the fusion calculation results of the satellite positioning modules and inertial measurement units mounted on each UAV. The control unit compares the acquired actual position with the preset ideal array position. When a node UAV 110 deviates from its theoretical position due to airflow disturbance or flight error, the control unit sends adjustment commands containing three-dimensional spatial coordinates or heading speed to the corresponding node UAV 110 through the flight control system, based on preset array geometric constraints. Especially in an isosceles triangle layout, the central UAV 120 not only maintains its reference position at the triangle's centroid but also coordinates the synchronous movement of all node UAVs 110 by calculating array translation and rotation, thus maintaining a spatial reference frame with precise geometric relationships throughout the drill bit's movement, providing a stable and reliable measurement benchmark for subsequent magnetic field positioning.

[0070] In step S200, based on the magnetic sensor 200 installed on the drill bit, the real-time magnetic field signal of the preset frequency of the magnetic field generator carried by each node UAV 110 and the central UAV 120 is received respectively.

[0071] The control unit of the central UAV 120 precisely controls the transmission status of the entire system's magnetic field generators according to a preset time-division multi-frequency operating mode. During the environmental magnetic field background acquisition period, all magnetic field generators are turned off, and the broadband triaxial magnetic sensor 200 at the drill bit's front end acquires and records pure background magnetic field spectrum data, which includes inherent magnetic interference characteristics from the Earth's magnetic field and surrounding industrial equipment. During the single-UAV operation period, only the magnetic field generator 121 of the central UAV 120 transmits continuous or pulse-modulated magnetic field signals at its dedicated frequency. At this time, the signals received by the magnetic sensor 200 mainly reflect the relative spatial relationship between the central UAV 120 and the drill bit. During the multi-UAV collaborative positioning period, each node UAV 110 in the triangular array simultaneously activates its magnetic field generator. Each generator transmits magnetic field signals at a pre-assigned and non-overlapping specific frequency, and the magnetic sensor 200 at the drill bit synchronously receives these composite magnetic field signals from different spatial orientations. All acquired raw magnetic field data is transmitted to the information processing module 300 in real time via wired or wireless transmission.

[0072] In step S300, the information processing module 300 receives all magnetic field signals sent by the magnetic sensor 200, and calculates the real-time position information of the drilling tool based on the magnetic field signals and the position information of several node UAVs.

[0073] The information processing module 300 first preprocesses the received magnetic field signal, including background subtraction of the signal during the working period using data from the background acquisition period, and improving the signal-to-noise ratio using wavelet denoising techniques. Subsequently, the processing is divided into two parallel solution paths: In the first path, the first processing unit, for the signal during the multi-UAV collaborative positioning period, separates the magnetic field components from each node UAV 110 through frequency domain analysis. Based on the Biot-Savart law near-field magnetic field observation model, the magnetic field strength is converted into distance observations from the drill string to each node. Then, combined with the known precise geometric configuration of the UAV array, the spherical intersection algorithm in analytical geometry is used to solve for the first position coordinates of the drill string. In the second path, the second processing unit, for the signal during the single UAV working period, uses Hilbert transform to construct an orthogonal reference signal, extracts the in-phase and orthogonal components of the signal through orthogonal demodulation technology, and calculates the characteristic parameters representing the magnetic field vector relationship. Finally, based on the magnetic dipole field model, the distance, tilt angle, and azimuth angle of the drill string relative to the central UAV 120 are inverted, thereby solving for the second position coordinates of the drill string. Finally, the information processing module 300 uses an adaptive weighted fusion algorithm to generate the optimal fused position estimate based on the real-time noise characteristics of the two solution results. It can also use a discrete Kalman filter to smooth and optimize the position sequence, and output the final high-precision real-time position information of the drill bit.

[0074] Through the coordinated execution of the above three steps, the system realizes a complete positioning process from spatial benchmark construction and signal acquisition to position calculation. It combines the flexibility of dynamic UAV arrays with the penetrating advantage of magnetic positioning technology, effectively solving the problems of poor environmental adaptability, complex deployment and insufficient accuracy faced by traditional shallow surface drilling tool positioning technology, and providing stable and reliable all-round positioning services for underground drilling operations.

[0075] The embodiments of the present invention aim to protect a shallow surface drill positioning system and its control method based on an unmanned aerial vehicle (UAV) array, which has the following effects: 1. By using drones as a mobile platform for the magnetic field generator, an aerial dynamic positioning benchmark was established, completely eliminating the dependence of traditional ground positioning systems on fixed base stations. It can quickly adjust and maintain the optimal observation array in the air according to the real-time position of the drilling tool and the construction progress, effectively avoiding the obstruction of ground obstacles and the limitations of complex terrain. Thus, it can achieve rapid and flexible deployment and high-precision stable positioning in various complex construction scenarios, greatly improving the mobility and environmental adaptability of the entire positioning system. 2. A dual-path parallel solution and data fusion architecture is adopted. One path is based on the geometric configuration of the node UAV array and uses analytical methods for positioning, while the other path is based on the magnetic signal of the central UAV and uses feature demodulation for positioning. Finally, the two are fused through an optimal weighting algorithm based on noise variance. This architecture makes full use of the complementary advantages between different positioning principles, effectively suppresses the systematic errors and random noise of single methods, and can still output reliable position information through the other path and fusion algorithm even when the signal quality of a single path deteriorates. This significantly improves the accuracy, stability and overall robustness of the positioning results in complex electromagnetic environments. 3. Through a time-division multi-frequency operating mode coordinated by the central UAV, not only can node UAVs be assisted in positioning correction, but the magnetic field signal transmission of the entire system is also intelligently managed. This mode achieves accurate background interference subtraction by setting a dedicated background magnetic field acquisition period, and avoids mutual interference between multiple magnetic field signals through time-division and frequency-division strategies. Furthermore, it can adaptively switch to anti-interference modes such as frequency hopping when strong environmental interference is detected. This dynamic and reconfigurable signal transmission strategy fundamentally improves the signal-to-noise ratio and purity of the signal, ensures the quality of the data on which the positioning calculation depends, and optimizes the utilization efficiency of limited spectrum resources, enabling the system to maintain excellent positioning performance even in harsh industrial electromagnetic environments.

[0076] 4. By conducting risk assessments on various aspects such as positioning accuracy, UAV malfunctions, and environmental interference, and monitoring indicators such as the measurement noise variance of the positioning algorithm, UAV flight status parameters, and environmental interference intensity, timely measures such as activating backup plans, dispatching backup UAVs, and adjusting signal processing strategies are taken based on the assessment results to ensure stable system operation.

[0077] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array, characterized in that, include: The drone array equipped with a magnetic field generator, a magnetic sensor (200) mounted on the drilling tool, and an information processing module (300) are also included. The UAV array includes several node UAVs (110) located in the airspace corresponding to the real-time location of the drill bit and a central UAV (120). The several node UAVs (110) are distributed in a preset formation, and the central UAV (120) is located at the corresponding position of the preset formation. The central UAV (120) acquires the location information of the plurality of node UAVs (110) in real time and controls the plurality of node UAVs (110) to fly in the preset formation; The magnetic sensor (200) receives real-time magnetic field signals of a preset frequency from the magnetic field generators carried by each node UAV (110) and the central UAV (120), and sends all the magnetic field signals to the information processing module (300). The information processing module (300) calculates the real-time position information of the drilling tool based on the multi-source magnetic field signal and the position information of the several node UAVs.

2. The shallow surface drilling tool positioning system based on UAV array according to claim 1, characterized in that, The preset array is a symmetrical arrangement with spatial geometric constraints.

3. The shallow surface drilling tool positioning system based on UAV array according to claim 2, characterized in that, The preset formation is an isosceles triangle, an equilateral triangle, a solid triangle, or a regular tetrahedron.

4. The shallow surface drilling tool positioning system based on UAV array according to any one of claims 1-3, characterized in that, The information processing module (300) includes a first processing unit and a second processing unit; The first processing unit calculates the first position data of the drill bit based on the magnetic field signals of the plurality of node UAVs (110) using an analytical geometry algorithm; The second processing unit calculates the second position data of the drill bit based on the magnetic field signal of the central UAV (120) and Hilbert transform and orthogonal demodulation technology; The information processing module (300) performs weighted fusion processing on the first position data and the second position data of the drill bit to obtain the fused position data of the drill bit.

5. The shallow surface drilling tool positioning system based on UAV array according to claim 4, characterized in that, The first processing unit constructs a near-field magnetic field observation model based on the magnetic field signals emitted by the plurality of node UAVs (110) through the Biot-Savart law, calculates the distance between the drill and each of the node UAVs (110), and calculates the first position data of the drill based on the preset array geometric relationship of the plurality of UAVs using an analytical geometric algorithm.

6. The shallow surface drilling tool positioning system based on UAV array according to claim 4, characterized in that, The second processing unit performs Hilbert transform on the magnetic field signal of the central UAV (120), constructs the in-phase reference component and the quadrature reference component required for quadrature demodulation, and processes the magnetic field signal through quadrature demodulation technology to obtain the in-phase component and the quadrature component. The in-phase component and the quadrature component are multiplied to extract the characteristic signal of the magnetic field. Based on the characteristic signal, the distance, relative tilt angle and relative azimuth angle of the drill relative to the central UAV (120) are calculated through the magnetic dipole model, and the second position data of the drill is obtained.

7. The shallow surface drilling tool positioning system based on UAV array according to claim 4, characterized in that, The information processing module (300) dynamically calculates the first weight corresponding to the first location data and the second weight corresponding to the second location data based on the measurement noise variance of the first location data and the measurement noise variance of the second location data. It uses the first weight to weight the first location data and the second weight to weight the second location data, and sums the weighted data to obtain the fused location data.

8. The shallow surface drilling tool positioning system based on UAV array according to claim 7, characterized in that, The information processing module (300) inputs the fused position data into a discrete Kalman filter for state recursion and optimization, and outputs the final optimized real-time position of the drill bit.

9. The shallow surface drilling tool positioning system based on UAV array according to claim 1, characterized in that, The central UAV (120) also includes a UAV array control unit (122) for controlling the magnetic field generator of the UAV array to work in a time-division multi-frequency mode, which includes: an environmental magnetic field background acquisition period, a single UAV working period, and a multi-UAV cooperative positioning period; The environmental magnetic field background acquisition period is when the UAV array control unit (122) controls all magnetic field generators to shut down during the corresponding period, so that the magnetic sensor (200) can acquire the background magnetic field of the drill bit; The single UAV working period is when the UAV array control unit (122) controls the magnetic field generator of the central UAV (120) to turn on during the corresponding period, while the magnetic field generators of the other node UAVs (110) are turned off. The multi-UAV collaborative positioning period is when the UAV array control unit (122) controls the magnetic field generator of the node UAV (110) to turn on during the corresponding period.

10. A control method for a shallow surface drilling tool positioning system based on an unmanned aerial vehicle (UAV) array, characterized in that, Controlling the shallow surface drill positioning system based on an unmanned aerial vehicle array as described in any one of claims 1-9 includes: The central UAV (120) acquires the location information of several node UAVs (110) in real time and controls the several node UAVs (110) to fly in a preset formation, with the central UAV (120) located at the corresponding position of the preset formation. Based on the magnetic sensor (200) installed on the drill bit, the real-time magnetic field signal of the preset frequency of the magnetic field generator carried by each node UAV (110) and the central UAV (120) is received respectively. The information processing module (300) receives all the magnetic field signals sent by the magnetic sensor (200) and calculates the real-time position information of the drill bit based on the magnetic field signals and the position information of the several node UAVs.

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