Lightweight underground cable positioning method and system

Abstract

The invention provides a lightweight underground cable positioning method and system, and belongs to the technical field of underground pipeline detection. According to the invention, a multi-sensor fusion technical scheme is adopted, orthogonal magnetic field signals of different depths are synchronously acquired through symmetrically arranged double electromagnetic induction coil groups, the cable burial depth is inversed based on a signal amplitude difference, a three-axis fluxgate sensor is utilized to detect a space magnetic field vector component, and the horizontal direction of the cable is calculated through vector analysis; the system adopts a collaborative architecture of a microcontroller and a programmable logic device, realizes real-time analysis and fusion processing of multi-dimensional signals, and is equipped with an adjustable gain amplification circuit, a power frequency band-pass filter circuit and a multi-stage power supply management unit to form a complete lightweight hardware platform. The system effectively solves the problems that traditional positioning equipment is heavy, insufficient in precision, poor in anti-interference performance and the like, and has the outstanding advantages of being high in measurement precision, high in environmental adaptability, good in portability and the like.

Description

Technical Field

[0001] This invention relates to the field of underground pipeline detection technology, and more particularly to the field of underground cable positioning technology using multi-sensor fusion, specifically to a lightweight underground cable positioning method and system. Background Technology

[0002] Precise location of underground cables is a key technological aspect of safe operation and maintenance in urban underground spaces. The core challenge lies in achieving simultaneous and accurate measurement of cable burial depth and horizontal orientation. Current technologies generally rely on magnetic field detection principles, using the signal analysis of the magnetic field generated by the energized cable itself to invert its spatial location. However, the underground environment presents complex electromagnetic interference, and there is a nonlinear coupling relationship between cable burial depth, orientation, and magnetic field signal characteristics. This places extremely high demands on sensor sensitivity, signal processing algorithm robustness, and system real-time performance. Achieving stable and reliable depth and orientation calculations under high-noise conditions has always been a technical challenge in this field.

[0003] Existing positioning systems suffer from the following structural defects: In terms of hardware architecture, most traditional solutions employ multi-coil arrays to separately implement depth measurement and orientation functions. This design approach results in bulky and excessively heavy equipment, severely hindering the convenience of on-site operation. More significantly, the densely arranged coils generate substantial electromagnetic coupling interference, which directly reduces the accuracy of measurement data. While a simplified single-coil design reduces equipment weight, it cannot simultaneously meet the dual requirements of depth measurement accuracy and orientation positioning stability. At the signal processing level, the separate functional modules lack an effective data synchronization mechanism, leading to timing discrepancies between depth measurement and orientation data, affecting the final spatial positioning accuracy. Furthermore, traditional systems do not fully utilize the properties of ferromagnetic materials, making the signal acquisition stage susceptible to interference from stray magnetic fields. The single-processor architecture struggles to handle the real-time processing demands of multi-channel data, further limiting the full potential of the system.

[0004] The aforementioned technical defects result in traditional underground cable positioning systems having prominent problems such as insufficient reliability, poor environmental adaptability, and cumbersome operation, making it difficult to meet the precise positioning needs of modern urban complex underground environments. Summary of the Invention

[0005] Based on the above description, the present invention provides a lightweight underground cable positioning method and system to solve the technical problems of insufficient sensor coordination, low functional integration and bulky equipment in traditional positioning systems.

[0006] According to a first aspect of the present invention, a lightweight underground cable positioning method is provided, comprising: Two orthogonal magnetic field signals at different depths generated by the underground cable are simultaneously sensed, and the burial depth of the cable is calculated based on the amplitude difference between the two magnetic field sensing signals. The components of the spatial magnetic field vector generated by the underground cable on the X, Y, and Z axes are detected to calculate the horizontal direction of the cable. The burial depth and horizontal direction of the cable are analyzed and fused in real time to retrieve the burial depth and cable direction.

[0007] Based on the above technical solution, the present invention can be further improved as follows.

[0008] Further, the steps of simultaneously sensing two magnetic field induction signals include: By using two symmetrically arranged sensing points at different depths, magnetic field change signals in orthogonal directions are collected synchronously. Calculate the amplitude ratio or difference between the two magnetic field induction signals, and combine it with the geometric spacing between the sensing points to invert the burial depth of the cable using a magnetic field attenuation model.

[0009] Furthermore, the step of simultaneously sensing two magnetic field induction signals also includes: Impedance matching is performed on the induced orthogonal magnetic field signals to reduce signal distortion; Differential amplification is used to amplify the impedance-matched signal in the pre-stage and suppress common-mode noise interference.

[0010] Further steps for detecting the space magnetic field vector include: Simultaneously acquire magnetic field intensity component data along the X, Y, and Z axes at the spatial magnetic field vector detection point; The azimuth angle is calculated based on the ratio of the X-axis and Y-axis magnetic field components in the horizontal plane to determine the horizontal direction of the cable.

[0011] Furthermore, prior to the analysis and fusion steps, a signal conditioning step is included, specifically: Each magnetic field induction signal is bandpass filtered, and the center frequency of the filter is set to the cable power frequency. The programmable gain adjustment method is adopted to automatically optimize the amplification factor according to the signal strength, thereby limiting the signal amplitude to the optimal detection range.

[0012] Further, the parsing and fusion steps include: A multi-sensor data fusion algorithm is used to synchronize and align the burial depth calculation data and horizontal orientation data of the cable in time. Optimal estimation of aligned multidimensional data is achieved through Kalman filtering or least squares algorithms.

[0013] Furthermore, it also includes signal quality assessment steps, specifically including: Real-time monitoring of the signal-to-noise ratio of each magnetic field induction signal; When the signal-to-noise ratio is lower than the preset threshold, the signal acquisition parameters will be automatically adjusted or a prompt will be made to remeasure.

[0014] According to a first aspect of the present invention, a lightweight underground cable locating system is provided, comprising: A symmetrically arranged dual electromagnetic induction coil group is used to synchronously sense two orthogonal magnetic field signals at different depths generated by the underground cable. A three-axis fluxgate sensor is used to detect the components of the spatial magnetic field vector generated by underground cables in the X, Y, and Z axes. The signal processing unit is used to calculate the burial depth of the cable based on the amplitude difference between the two induction signals, and to calculate the horizontal direction of the cable based on the components of the spatial magnetic field vector in the X, Y, and Z axes; it is also used to perform real-time analysis and fusion of the burial depth and horizontal direction of the cable, and to inversely determine the burial depth and cable direction of the cable.

[0015] Furthermore, the two electromagnetic induction coils in the dual electromagnetic induction coil group are arranged in parallel with a fixed spacing, and each electromagnetic induction coil includes a high permeability magnetic core and a multi-turn winding structure wound around the circumference of the magnetic core.

[0016] Furthermore, the signal processing unit comprises a collaborative architecture consisting of a microcontroller and a programmable logic device, wherein: The programmable logic device is used for synchronous acquisition and preprocessing of multi-channel signals; The microcontroller is used for parameter calculation of the burial depth and direction of the cable and for human-machine interaction control.

[0017] Furthermore, it also includes a power management unit, which comprises a cascaded first-stage isolated DC-DC converter circuit and a second-stage linear regulator circuit; wherein: The first-stage isolated DC-DC converter circuit generates positive and negative symmetrical voltages; The second-stage linear regulator circuit generates a low-voltage logic voltage based on the positive and negative symmetrical voltages; The output voltage of the second-stage linear regulator circuit is precisely set through a resistor divider network.

[0018] Compared with the prior art, the technical solution of this application has the following beneficial technical effects: This invention provides a lightweight underground cable positioning method and system based on a multi-sensor fusion technology approach: First, symmetrically arranged dual detection points synchronously sense orthogonal magnetic field signals. Utilizing the characteristic that magnetic field strength attenuates with distance, the cable burial depth is inverted based on the magnetic field attenuation model by calculating the amplitude difference between the two signals and combining it with the geometric spacing. Second, by detecting the components of the spatial magnetic field vector on three axes, the azimuth angle is calculated based on the vector relationship of the magnetic field components in the horizontal plane, thereby determining the horizontal direction of the cable. Finally, real-time data analysis and fusion algorithms are used to collaboratively process the depth and direction information, achieving precise spatial positioning of the cable. In terms of measurement accuracy, this invention is less affected by stray magnetic field interference from the environment. Differential measurement and vector analysis effectively improve the accuracy of depth and direction detection. In terms of system structure, a single triaxial sensor replaces the traditional multi-coil array to achieve a compass-like orientation function, realizing lightweight and integrated equipment and significantly optimizing human-machine interface. In terms of anti-interference performance, the differential characteristics of the dual signals and the mutual verification of vector data enhance reliability in complex environments. This invention solves the problems of traditional equipment being bulky, lacking precision, and having poor data synchronization, achieving a balance between high precision and practicality. Attached Figure Description

[0019] Figure 1 A flowchart of a lightweight underground cable positioning method provided in an embodiment of the present invention; Figure 2 A schematic diagram of the lightweight underground cable positioning system provided in an embodiment of the present invention; Figure 3 A schematic diagram of a three-axis fluxgate sensor provided in an embodiment of the present invention; Figure 4 A schematic diagram of a single-channel electromagnetic induction coil provided in an embodiment of the present invention; Figure 5 A schematic diagram of a filter circuit provided in an embodiment of the present invention; Figure 6 A schematic diagram of an amplifier circuit provided for an embodiment of the present invention; Figure 7 A schematic diagram of an analog-to-digital converter circuit provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of a first-stage isolated DC-DC converter circuit provided in an embodiment of the present invention. Figure 9 The schematic diagram of the second-stage linear voltage regulator circuit provided in the embodiment of the present invention. Detailed Implementation

[0020] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0022] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of other elements or features will be oriented "over" of other elements or features. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations (e.g., rotated 90 degrees or other orientations), and the spatial descriptive terms used herein will be interpreted accordingly.

[0023] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0024] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0025] Figure 1 The diagram shown is a flowchart of a lightweight underground cable positioning method provided by an embodiment of the present invention. Figure 2 For execution in one embodiment Figure 1 The system hardware structure block diagram of the method shown is illustrated. Combined with... Figure 1 as well as Figure 2 As shown, this embodiment provides a lightweight underground cable positioning method, including steps S1 to S3: S1, synchronously senses two orthogonal magnetic field signals at different depths generated by the underground cable, and calculates the burial depth of the cable based on the amplitude difference between the two magnetic field sensing signals; S2, detect the components of the spatial magnetic field vector generated by the underground cable on the X, Y, and Z axes to calculate the horizontal direction of the cable; S3 performs real-time analysis and fusion of the cable's burial depth and horizontal direction to retrieve the cable's burial depth and direction.

[0026] The order of steps S1 and S2 can be interchanged, or steps S1 and S2 can be executed simultaneously.

[0027] Understandably, given the shortcomings in the background technology, this invention proposes a lightweight underground cable positioning method. This method achieves precise positioning of underground cables based on a multi-sensor data fusion approach. First, two orthogonal magnetic field signals from different depths are simultaneously acquired using symmetrically arranged sensor nodes. Utilizing the attenuation characteristic of magnetic field strength with distance, a magnetic field attenuation model is established by calculating the amplitude difference between the two signals and combining it with geometric parameters, thereby retrieving the cable's burial depth. Simultaneously, a three-dimensional magnetic field detection mechanism detects the spatial magnetic field vector components. Based on the vector relationship of the magnetic field components in the horizontal plane, the azimuth angle is calculated to determine the cable's horizontal direction. Finally, a collaborative processing architecture is used to analyze and fuse the depth and direction data in real time, and a multi-sensor data fusion algorithm is employed to achieve precise spatial positioning of the cable.

[0028] In terms of measurement performance, the present invention significantly improves the detection accuracy of depth and direction by combining differential measurement mechanism with vector analysis, effectively overcoming the limitations of single sensor solutions. In terms of system structure, by optimizing sensor layout and processing flow, it achieves a balance between lightweight equipment and functional integration, greatly improving the convenience of on-site operation. In terms of engineering applicability, adaptive signal processing and anti-interference algorithms enhance reliability in complex environments. The integrated solution effectively solves the pain points of poor data synchronization and weak environmental adaptability in traditional technologies, providing a high-precision and high-efficiency technical means for underground cable positioning.

[0029] Based on the above technical solution, this embodiment can be further improved as follows.

[0030] In one possible implementation, step S1, the step of synchronously sensing two magnetic field induction signals, includes: By using two symmetrically arranged sensing points at different depths, magnetic field change signals in orthogonal directions are collected synchronously. Calculate the amplitude ratio or difference between the two magnetic field induction signals, and combine it with the geometric spacing between the sensing points to invert the burial depth of the cable using a magnetic field attenuation model.

[0031] It is understood that this embodiment is based on the differential magnetic field detection principle for cable burial depth detection. Specifically, two sensor nodes symmetrically arranged at different depths synchronously collect magnetic field signals in orthogonal directions. Utilizing the inverse square law of magnetic field strength attenuation with distance, the amplitude ratio or difference of the two signals is calculated. Combined with the known geometric spacing parameters of the sensor nodes, a physical model of the magnetic field gradient and cable burial depth is established. The cable burial depth is then accurately calculated through model inversion.

[0032] In this embodiment, firstly, the differential measurement mechanism effectively suppresses environmental common-mode interference, significantly improving the signal-to-noise ratio and ensuring high reliability of depth measurement results even in complex electromagnetic environments; secondly, the geometrically constrained model-based calculation avoids the errors of traditional empirical formulas, and the combination of theoretical models and measured data improves the consistency of measurement accuracy; and thirdly, the non-contact measurement method reduces dependence on the underground environment, adapts to different soil types and burial conditions, and enhances the engineering applicability and robustness of the method.

[0033] For example, two magnetic field change signals can be acquired by using a dual-channel electromagnetic induction coil group arranged at the two sensing points. Figure 4 The diagram shown is a schematic of one possible electromagnetic induction coil. Now, let's take... Figure 4 The working principle of an electromagnetic induction coil will be illustrated using an example.

[0034] The electromagnetic induction coil module includes two circuits with identical schematics, each containing two parallel electromagnetic induction coils that output signals. Both circuits integrate an electromagnetic induction coil, a voltage follower, and a preamplifier. The individual electromagnetic induction coil circuitry is shown below. Figure 4 As shown.

[0035] Figure 4In this circuit, L1 represents the electromagnetic induction coil, which is made of enameled copper wire wound around a silicon steel sheet. The silicon steel sheet has a relative permeability of 7000. A square coil is preferred, with a side length of 1.5 cm and a cross-sectional area of ​​approximately 2.25 square centimeters. It has 375 turns, an inductance of 6.2 mH, and an equivalent series resistance of 0.94 Ω. U7 in this circuit is an OPA2227 high-precision, low-noise operational amplifier used for voltage follower design. Its function is impedance matching and reducing distortion of the induced electromotive force signal on the electromagnetic induction coil. U8 is a low-power precision instrumentation amplifier (model INA128), and its peripheral circuitry constitute the preamplifier. The INA128's internal structure is equivalent to a differential amplifier circuit, effectively suppressing common-mode noise during preamplification. The gain calculation formula for the preamplifier circuit is as follows: ,in The corresponding resistor R1 in the circuit.

[0036] Figure 4 In the process, when the change in the magnetic field of the cable causes the inductor L1 (6.2mH) to generate an induced electromotive force, the signal first enters the voltage follower composed of operational amplifier U7 (OPA2227) for impedance matching to suppress signal attenuation; then it is differentially amplified by precision instrumentation amplifier U8 (INA128PAG4), whose gain is precisely set by resistor R3 (5.8kΩ) (gain G=1+50kΩ / R3), effectively suppressing common-mode interference and amplifying differential-mode signals; finally, the amplified signal is transmitted to the back-end processing module through resistor R0 (0Ω) to complete the high-fidelity conversion from magnetic field to electrical signal.

[0037] In one possible implementation, the method further includes a signal conditioning step, specifically comprising: Each magnetic field induction signal is bandpass filtered, and the center frequency of the filter is set to the cable power frequency. The programmable gain adjustment method is adopted to automatically optimize the amplification factor according to the signal strength, thereby limiting the signal amplitude to the optimal detection range.

[0038] like Figures 5-7 The signal conditioning circuit located at the output of the electromagnetic induction coil specifically includes... Figure 5 The filter module circuit shown Figure 6 The amplifier module circuit shown and Figure 7 The analog-to-digital converter circuit shown.

[0039] Understandably, the current in the cable is at the power frequency of 50Hz. In order to reduce noise interference and thus more accurately calculate the cable burial depth, the signal collected by the electromagnetic induction coil needs to be filtered. Figure 5The filter module circuit shown is designed to use a sixth-order Chebyshev high-pass filter and a sixth-order Chebyshev low-pass filter to form a bandpass filter with a center frequency of 50Hz. The specific circuit is as follows... Figure 5 As shown, six dual-channel high-precision low-noise operational amplifiers (U3.1, U3.2, U5.1, U5.2, U6.1, U6.2, model OPA2227) are selected to reduce the circuit space occupation.

[0040] like Figure 5 As shown, the signal filtering principle of the electromagnetic induction coil is as follows: The signal first enters the first-stage filter network consisting of operational amplifier U3.1 (including resistors R6=3kΩ, R7=6.2kΩ and capacitor C7=3.6μF), forming a high-pass filter with a cutoff frequency of approximately 7.3Hz, eliminating DC bias and low-frequency noise. It then undergoes a second-stage filter via U3.2 (resistors R9=910Ω, R10=680Ω and capacitor C11=1μF forming a low-pass filter with a cutoff frequency of approximately 234Hz), suppressing high-frequency interference. Subsequent signals pass through U5.1, U5.2, U6.1, and... The U6.2 multi-stage operational amplifier cascaded filter structure (such as R19=3.3kΩ and C21=13μF combined to form a high-pass characteristic with a cutoff frequency of about 3.7Hz, and R20=3.3kΩ and C17=3.6μF to achieve low-pass filtering) ultimately forms a band-pass filter characteristic centered on the 50Hz power frequency. The signal in the passband is coupled to the output through capacitor C19=510nF, while common-mode interference is filtered out by decoupling capacitors such as C10=1μF and C12=1μF, thereby outputting a pure power frequency magnetic field signal at the OUT terminal.

[0041] go through Figure 5 The signal processed by the filter circuit is sent into Figure 6 The amplifier module circuit shown.

[0042] Because the signal gain requirements differ when measuring cables at different burial depths, Figure 6 The amplifier module circuit shown uses the AD603 programmable gain amplifier to achieve adjustable gain. As a low-noise, wide-bandwidth programmable gain amplifier, the AD603 has linear gain control characteristics and can automatically adjust the gain according to the input signal strength, thereby optimizing the signal output quality.

[0043] Specifically, Figure 6In the amplifier module circuit shown, the input signal IN enters the first-stage operational amplifier U9 (model: AD8011ARZ) via resistor R22. Its primary gain is pre-amplified through its negative feedback network (such as resistor R23). After amplification, the signal is sent to the second-stage operational amplifier U10 (model: AD8011ARZ), where a secondary amplification is achieved through an adjustable gain network composed of components such as resistors R25 and R26. The gain value is precisely controlled by the resistor ratio. Finally, operational amplifier U11 (AD8561ARZ) serves as the output buffer stage. An internal comparator structure eliminates offset voltage, and an output current limiting protection network composed of resistors R27 and R28 (1kΩ) ensures stable signal transmission to the OUT terminal. During this process, capacitors C24 and C3-C6 (1MHz decoupling capacitors) filter out high-frequency power supply noise, and resistor R29 and connector HDR-F_254_1x2P provide gain control mode switching functionality, enabling configurable amplification with manual / automatic gain.

[0044] Next, we need to Figure 6 The signal processed by the amplifier circuit shown is sent to Figure 7 The analog-to-digital converter circuit performs analog-to-digital conversion processing.

[0045] like Figure 7 As shown, the analog-to-digital converter U11 uses the AD7606 chip for data acquisition, converting the amplified and filtered 50Hz analog signal into a digital signal and transmitting it to the FPGA for processing. The AD7606 can ensure the timing consistency of signal conversion among the coils through multi-channel synchronous sampling. The AD7606 circuit schematic is shown below. Figure 7 As shown.

[0046] Specifically, such as Figure 7 As shown, the analog-to-digital converter U11 uses the AD7606TSTZ-EP chip to achieve multi-channel synchronous sampling: 8 analog input signals (V1-V8) are filtered by an RC filter network to suppress high-frequency noise before being connected to the V1-V8 pins of the U11 chip; the chip is powered by a ±5V dual power supply (AVCC / AVSS) and the logic interface is driven by a 3V3 digital power supply (VDRIVE). The internal reference voltage (VREF) is filtered and stabilized to 2.5V by capacitors C32 / C33; when the CONVST A / B pin receives the conversion start pulse from the controller, the 8 channels perform analog-to-digital conversion synchronously, quantizing the analog signal into a 16-bit digital quantity through a successive approximation (SAR) architecture; after conversion, the data is transmitted to the processor in parallel output through control pins such as CS, RD, and BUSY, with the conversion results of channels V1-V8 being output sequentially through the DB0-DB15 data bus, ultimately achieving high-precision synchronous digitization of multiple magnetic field signals.

[0047] In one possible implementation, the step of detecting the space magnetic field vector includes: Simultaneously acquire magnetic field intensity component data along the X, Y, and Z axes at the spatial magnetic field vector detection point; for example, through... Figure 3 The single triaxial fluxgate sensor shown is used for spatial magnetic field vector detection. The azimuth angle is calculated based on the ratio of the X-axis and Y-axis magnetic field components in the horizontal plane to determine the horizontal direction of the cable.

[0048] Now Figure 3 The three-axis fluxgate sensor shown illustrates the detection of spatial magnetic field vectors.

[0049] In this embodiment, the three-axis fluxgate sensor uses the RM3100 sensor, and its schematic diagram is shown below. Figure 3 As shown, it consists of two SEN-XY-F geomagnetic sensors, one SEN-ZF geomagnetic sensor, and a MAGI2C control chip. The two SEN-XY-F geomagnetic sensors and one SEN-ZF geomagnetic sensor are located in three orthogonal directions. The SEN-XY-F sensors are used to detect the magnetic field strength in two vertical directions (X-axis and Y-axis) on the horizontal plane, while the SEN-ZF sensor is used to detect the magnetic field strength in the vertical direction (Z-axis). This type of sensor has a range of -800μT to +800μT and a sensitivity of up to 13nT, which can effectively distinguish the vector component of the magnetic field formed by the current in the cable, ensuring the accuracy of cable alignment inversion. The RM3100 communicates via the standard two-wire I2C protocol. Its I2C address is determined by the AD0 and AD1 pin levels (default 0x20, expandable to 0x21-0x23). Interaction is achieved through register access, including writing to the CCX / CCY / CCZ registers to set the Cycle Count to balance sensitivity and noise. Continuous or single-shot measurement modes are configured via the CMM and TMRC registers (continuous mode requires setting the update rate, single-shot mode is triggered by the POLL register). Data readiness is determined by the STATUS register or the DRDY pin. Subsequently, 24-bit two's complement data (3 bytes per axis, high-order bits first) is read from the MX, MY, and MZ registers, merged, and converted to magnetic field values ​​at a sensitivity of 75 LSB / μT, completing data acquisition.

[0050] When a spatial magnetic field is present, each axis coil operates based on the fluxgate principle—the magnetic core is periodically saturated through high-frequency alternating excitation. The external magnetic field alters the symmetry of the core saturation, inducing a second harmonic signal in the coil that is proportional to the axial magnetic field strength. This signal is then internally pre-amplified and synchronously demodulated before being converted into a digital signal by a 24-bit Σ-Δ ADC. Finally, the three-axis magnetic field component data is output through the I2C interface (SCL / SDA pins). In the peripheral circuit, decoupling capacitors filter out power supply noise, and R12 / R13 are I2C bus matching resistors to ensure the accuracy and stability of vector data acquisition, ultimately achieving high-sensitivity digital detection of the spatial magnetic field vector.

[0051] In one possible implementation, step S3, the parsing and fusion step, includes: A multi-sensor data fusion algorithm is used to synchronize and align the burial depth calculation data and horizontal orientation data of the cable in time. Optimal estimation of aligned multidimensional data is achieved through Kalman filtering or least squares algorithms.

[0052] It is understood that this embodiment is based on a multi-sensor data collaborative processing mechanism. The time synchronization algorithm is used to align the cable burial depth signal detected by the dual electromagnetic induction coils with the direction signal of the three-axis fluxgate, eliminating data deviation caused by sensor response delay. Subsequently, Kalman filtering and least squares fusion algorithm are used to perform weighted optimization estimation of depth data (based on magnetic field attenuation model inversion) and direction data (based on vector azimuth calculation). Environmental interference is suppressed by dynamic noise covariance matrix. Finally, the three-dimensional spatial parameters of the cable are output through coordinate transformation model.

[0053] In terms of accuracy, this embodiment effectively reduces the positioning error of a single sensor through the complementary fusion of multi-dimensional data. In terms of real-time performance, the collaborative architecture of FPGA parallel preprocessing and MCU serial computation effectively reduces data processing latency, meeting dynamic measurement requirements. In terms of reliability, the algorithm has adaptive fault tolerance for signal loss or transient interference. Even if a single sensor malfunctions, it can still maintain basic positioning function through historical data prediction, thus improving overall anti-interference performance.

[0054] In one possible implementation, after completing step S3, the method further includes a signal quality assessment step, specifically including: Real-time monitoring of the signal-to-noise ratio of each magnetic field induction signal; When the signal-to-noise ratio is lower than the preset threshold, the signal acquisition parameters will be automatically adjusted or a prompt will be made to remeasure.

[0055] Understandably, this embodiment is based on the principle of multi-dimensional signal characteristic dynamic monitoring and intelligent adjustment. It establishes a comprehensive quality evaluation system by analyzing key indicators such as the signal-to-noise ratio, waveform integrity, and stability of each magnetic field induction signal in real time. When signal quality degradation is detected, the system automatically triggers an adaptive parameter adjustment mechanism to dynamically optimize gain, filter parameters, or reference calibration values. If automatic adjustment still fails to meet the expected standards, it proactively prompts intervention. This embodiment effectively prevents invalid data transmission through pre-signal quality control, ensuring the reliability of system output results; it forms a closed-loop control of "monitoring-evaluation-adjustment," reducing reliance on operator experience; and by eliminating abnormal data segments and optimizing acquisition parameters in real time, it ensures that measurement results are always maintained at a high precision.

[0056] Based on the foregoing method embodiments, this embodiment also provides a lightweight underground cable positioning system for performing the foregoing methods, such as... Figure 2 As shown, the system includes: A symmetrically arranged dual electromagnetic induction coil group is used to synchronously sense two orthogonal magnetic field signals at different depths generated by the underground cable. A three-axis fluxgate sensor is used to detect the components of the spatial magnetic field vector generated by underground cables in the X, Y, and Z axes. The signal processing unit is used to calculate the burial depth of the cable based on the amplitude difference between the two induction signals, and to calculate the horizontal direction of the cable based on the components of the spatial magnetic field vector in the X, Y, and Z axes; it is also used to perform real-time analysis and fusion of the burial depth and horizontal direction of the cable, and to inversely determine the burial depth and cable direction of the cable.

[0057] It is understood that the lightweight underground cable positioning system provided by the present invention corresponds to the lightweight underground cable positioning method provided in the foregoing embodiments. The relevant technical features of the lightweight underground cable positioning system can be referred to the relevant technical features of the lightweight underground cable positioning method, and will not be repeated here.

[0058] In one possible implementation, the two electromagnetic induction coils in the dual electromagnetic induction coil group are arranged in parallel with a fixed spacing, and each electromagnetic induction coil includes a high permeability magnetic core and a multi-turn winding structure wound around the core.

[0059] In this embodiment, the dual electromagnetic induction coil group adopts a parallel symmetrical arrangement with a fixed spacing. Each coil core is built based on a high-permeability magnetic core, with multiple turns tightly wound around the core. The high-permeability magnetic core significantly concentrates and enhances the penetration of alternating magnetic fields, enabling the coils to effectively capture changes in the spatial magnetic field. The multi-turn winding structure further amplifies the induced electromotive force. The parallel fixed-spacing layout ensures that the two coils are in a known geometric relationship. When the cable magnetic field passes through, a predictable amplitude difference occurs due to the different perpendicular distances from the cable. In terms of detection performance, the combination of the high-permeability magnetic core and the multi-turn winding significantly improves the magnetic field detection sensitivity, ensuring reliable capture of weak signals. In terms of measurement accuracy, the parallel fixed-spacing symmetrical structure provides a stable baseline for differential measurements. By comparing the amplitude difference between the two signals, environmental common-mode interference is effectively eliminated, significantly improving the accuracy of depth inversion. In terms of engineering practicality, this structure achieves miniaturization and weight reduction of the sensor while ensuring performance, laying the foundation for the portability of the entire system.

[0060] In one possible implementation, the signal processing unit comprises a collaborative architecture consisting of a microcontroller and a programmable logic device, wherein: The programmable logic device is used for synchronous acquisition and preprocessing of multi-channel signals; The microcontroller is used for parameter calculation of the cable's burial depth and direction, as well as for human-machine interaction control. It is understood that the signal processing unit in this embodiment adopts a collaborative architecture of microcontroller and programmable logic device (FPGA) to achieve high-efficiency signal processing through hardware-level task division. Specifically, the FPGA, with its parallel processing capabilities, is specifically responsible for the synchronous acquisition, digital filtering, and real-time preprocessing of multi-channel magnetic field signals, ensuring accurate synchronization of high-timeliness underlying signal processing; while the MCU, relying on its complex algorithm execution capabilities, focuses on parameter calculation of cable burial depth and direction, multi-sensor data fusion, and human-machine interaction control, realizing high-level decision-making and system management.

[0061] The collaborative architecture of this embodiment, in terms of performance, ensures both the synchronization and real-time performance of multi-channel data acquisition and the accuracy of complex algorithm calculations through the organic combination of parallel and serial processing. In terms of system optimization, the hardware division of labor effectively avoids resource conflicts in a single processor architecture, significantly improving the overall operating efficiency and stability of the system. In terms of engineering applications, this architecture combines processing speed and algorithm flexibility, enabling the system to simultaneously meet the requirements of high-precision positioning and real-time response in complex field environments.

[0062] In one possible implementation, such as Figure 2As shown, the system also includes a power management unit, which comprises a cascaded first-stage isolated DC-DC converter and a second-stage linear regulator; wherein: The first-stage isolated DC-DC converter circuit generates positive and negative symmetrical voltages; The second-stage linear regulator circuit generates a low-voltage logic voltage based on the positive and negative symmetrical voltages; The output voltage of the second-stage linear regulator circuit is precisely set through a resistor divider network.

[0063] Understandably, the power management unit adopts a cascaded architecture design. It achieves electrical isolation between input and output through a first-stage isolated DC-DC converter circuit, and uses high-frequency transformer technology to convert the initial voltage into a stable DC voltage with positive and negative symmetry, effectively suppressing common-mode interference and ground loop noise. The second-stage linear regulator circuit is based on the symmetrical voltage output of the previous stage. It accurately sets the low-voltage logic voltage value through a feedback control mechanism and a resistor divider network. The voltage divider network determines the output voltage reference through a high-precision resistor ratio, and the linear regulator dynamically adjusts the output to maintain voltage accuracy.

[0064] In this embodiment, the power isolation design cuts off the noise conduction path, protecting the sensor and signal processing circuit from power fluctuations and improving anti-interference capabilities. Linear voltage regulation combined with precision voltage division provides a low-ripple, high-stability power supply environment, ensuring the accuracy of magnetic field measurement data and improving voltage quality. The cascaded architecture balances isolation safety and voltage regulation accuracy, supports lightweight and miniaturized devices through optimized circuit structure, and provides an efficient and reliable energy foundation for the entire positioning system by improving system integration.

[0065] More specifically, with Figure 8 as well as Figure 9 The power module circuit diagram shown is used as an example for illustration. Among them, Figure 8 This is the schematic diagram of the first-stage isolated DC-DC converter circuit. Figure 9 This is the schematic diagram of the second-stage linear voltage regulator circuit.

[0066] like Figure 8 As shown, a 12V / 5V DC-DC converter circuit based on the UWE1205S chip is presented as the first-stage isolated DC-DC converter circuit. Figure 8 In the voltage conversion circuit shown, LED1 is an indicator light showing whether the 12V / ±5V conversion circuit is working. When the light is on, it indicates that the switch is open and the circuit is working. SW1 is the switch. The ±5V voltage in this circuit is achieved through the UWE1205S chip. This series of chips has a high degree of integration and only requires additional parallel connection of capacitors C4, C5, and C6 as shown in the figure at the input and output terminals to filter out ripple and improve energy storage and buffering capabilities.

[0067] During operation, efficient voltage conversion is achieved through the isolated DC-DC converter module U1 (UWE1205S-1WR3): When an external 12V power supply is input via the connector, it is first filtered by an electrolytic capacitor C4 (100μF) to suppress voltage fluctuations. Then, the power supply is fed into the U1 (UWE1205S-1WR3) module after being controlled by switch SW1. This module integrates a high-frequency transformer and switch control circuit, converting the 12V input to an isolated 5V output through PWM modulation. At the output, a filter network composed of parallel electrolytic capacitors C5 and C6 (both 22μF) effectively smooths the output voltage ripple, ensuring the stability of the 5V power supply. Simultaneously, the red LED1 is directly powered by the 12V input through a current-limiting resistor R13 (1kΩ), forming a power input status indication circuit. Figure 8 The circuit shown avoids ground noise interference between the preceding and following stages through modular isolation conversion, and the dual-capacitor filtering strategy ensures the purity of the output voltage, providing a high-quality power supply foundation for the subsequent precision measurement circuit.

[0068] like Figure 9 As shown, a 5V / 3.3V DC-DC converter circuit based on RT8096CHGJ5 is demonstrated as a second-stage linear regulator circuit. Figure 9 In the voltage conversion circuit shown, the 3.3V voltage conversion is achieved through resistors R1 and R2, and its output voltage expression is: As a preferred option, resistor R1 is a 90.9K resistor (1% accuracy class), and resistor R2 is a 20K resistor (1% accuracy class). LED2 is an LED indicator to show whether the 5V / 3.3V conversion circuit is working properly; if the light is on, the conversion circuit is working properly; otherwise, it is not working properly.

[0069] During operation, the +5V input voltage is filtered by capacitor C1 (10μF) and then fed into the VIN pin (pin 4) of chip U2 (model RT8096CHGJ5). The internal MOS switch controls the inductor L2 (2.2μH) through PWM to achieve energy storage-to-energy release conversion. The output voltage is precisely sampled by voltage divider resistors R1 (90.9kΩ) and R2 (20kΩ) (feedback voltage VFB=0.6V), and locked to 3.3V output by formula VOUT=0.6×(1+R1 / R2). The capacitor C2 (68pF) connected to the LX pin (pin 2) forms a resonance suppression circuit with the inductor L2. The output is filtered by capacitor C3 (1μF) and then generates a pure 3V3 voltage through resistor R4 (0Ω). At the same time, resistor R5 (1kΩ) limits the current and drives indicator LED2 as a power status indicator. Figure 9 The circuit dynamically adjusts the duty cycle through a feedback loop to achieve efficient step-down conversion. The LC filter composed of inductor L2 and capacitor C3 effectively suppresses switching ripple and provides a stable low-voltage power supply for subsequent precision digital circuits.

[0070] This invention provides a lightweight underground cable positioning method and system. Based on the core principle of multi-sensor fusion and collaborative processing, it captures orthogonal magnetic field signals at different depths through symmetrically arranged dual electromagnetic induction coil groups. Utilizing the attenuation characteristic of magnetic field strength with distance, and combining the amplitude difference of the two signals, it accurately inverts the cable burial depth. Simultaneously, a three-axis fluxgate sensor detects the spatial magnetic field vector components, and the cable route is determined by analyzing the magnetic field direction angle in the horizontal plane. The signal processing unit adopts a collaborative architecture of microcontroller and programmable logic device to achieve synchronous acquisition, real-time filtering, and fusion calculation of multi-dimensional data. The power management unit provides a high-precision, low-noise power supply environment for the system through cascaded isolation conversion and linear voltage regulation design.

[0071] In terms of performance, this invention improves the accuracy of depth and direction measurement to the centimeter level by combining differential measurement and vector analysis; in terms of structure, it replaces the traditional multi-coil array with a single triaxial sensor, achieving reduced equipment weight and optimized portability; in terms of reliability, a dual anti-interference mechanism of hardware filtering and software algorithm ensures stable operation in complex environments; ultimately, it forms a lightweight, high-precision, and highly adaptable underground cable positioning solution, effectively solving the technical pain points of traditional equipment being bulky, lacking accuracy, and having poor environmental adaptability.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A lightweight underground cable positioning method, characterized in that, Includes the following steps: Two orthogonal magnetic field signals at different depths generated by the underground cable are simultaneously sensed, and the burial depth of the cable is calculated based on the amplitude difference between the two magnetic field sensing signals. The components of the spatial magnetic field vector generated by the underground cable on the X, Y, and Z axes are detected to calculate the horizontal direction of the cable. The burial depth and horizontal direction of the cable are analyzed and fused in real time to retrieve the burial depth and cable direction.

2. The lightweight underground cable positioning method according to claim 1, characterized in that, The steps for synchronously sensing two magnetic field induction signals include: By using two symmetrically arranged sensing points at different depths, magnetic field change signals in orthogonal directions are simultaneously acquired and preprocessed. Calculate the amplitude ratio or difference between the two magnetic field induction signals, and combine it with the geometric spacing between the sensing points to invert the burial depth of the cable using a magnetic field attenuation model.

3. The lightweight underground cable positioning method according to claim 1, characterized in that, The steps for detecting the space magnetic field vector include: Simultaneously acquire magnetic field intensity component data along the X, Y, and Z axes at the spatial magnetic field vector detection point; The azimuth angle is calculated based on the ratio of the X-axis and Y-axis magnetic field components in the horizontal plane to determine the horizontal direction of the cable.

4. The lightweight underground cable positioning method according to claim 3, characterized in that, Before the analysis and fusion steps, there is also a signal conditioning step, which specifically includes: Each magnetic field induction signal is bandpass filtered, and the center frequency of the filter is set to the cable power frequency. The programmable gain adjustment method is adopted to automatically optimize the amplification factor according to the signal strength, thereby limiting the signal amplitude to the optimal detection range.

5. The lightweight underground cable positioning method according to claim 4, characterized in that, The parsing and fusion steps include: A multi-sensor data fusion algorithm is used to synchronize and align the burial depth calculation data and horizontal orientation data of the cable in time. Optimal estimation of aligned multidimensional data is achieved through Kalman filtering or least squares algorithms.

6. The lightweight underground cable positioning method according to claim 1, characterized in that, It also includes a signal quality assessment step, specifically including: Real-time monitoring of the signal-to-noise ratio of each magnetic field induction signal; When the signal-to-noise ratio is lower than the preset threshold, the signal acquisition parameters will be automatically adjusted or a prompt will be made to remeasure.

7. A lightweight underground cable positioning system, characterized in that, include: A symmetrically arranged dual electromagnetic induction coil group is used to synchronously sense two orthogonal magnetic field signals at different depths generated by the underground cable. A three-axis fluxgate sensor is used to detect the components of the spatial magnetic field vector generated by underground cables in the X, Y, and Z axes. The signal processing unit is used to calculate the burial depth of the cable based on the amplitude difference between the two induction signals, and to calculate the horizontal direction of the cable based on the components of the spatial magnetic field vector in the X, Y, and Z axes. It is also used to perform real-time analysis and fusion of the burial depth and horizontal direction of cables, and to deduce the burial depth and direction of cables.

8. The lightweight underground cable positioning system according to claim 7, characterized in that, The two electromagnetic induction coils in the dual electromagnetic induction coil group are arranged in parallel with a fixed spacing, and each electromagnetic induction coil includes a high permeability magnetic core and a multi-turn winding structure wound around the circumference of the magnetic core.

9. The lightweight underground cable positioning system according to claim 7, characterized in that, The signal processing unit comprises a collaborative architecture consisting of a microcontroller and a programmable logic device, wherein: The programmable logic device is used for synchronous acquisition and preprocessing of multi-channel signals; The microcontroller is used for parameter calculation of the burial depth and direction of the cable and for human-machine interaction control.

10. The lightweight underground cable positioning system according to claim 7, characterized in that, It also includes a power management unit, which comprises a cascaded first-stage isolated DC-DC converter and a second-stage linear regulator; wherein: The first-stage isolated DC-DC converter circuit generates positive and negative symmetrical voltages; The second-stage linear regulator circuit generates a low-voltage logic voltage based on the positive and negative symmetrical voltages; The output voltage of the second-stage linear regulator circuit is precisely set through a resistor divider network.

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