Tower burial depth detection system based on geomagnetic technology

Abstract

A tower burial depth detection system based on a geomagnetic technology belongs to the field of electric power facility detection and comprises a magnetic sensing unit, a signal processing unit and a magnetic sensing unit fixing device. The magnetic sensing unit comprises 8-16 TMR magnetoresistive sensors, the TMR magnetoresistive sensors are uniformly arranged to form an array plane, and the array plane is parallel to the ground, is perpendicular to the axis of the tower and is used for collecting three-component signals of a geomagnetic field around the tower; and the signal processing unit is connected with the magnetic sensing unit through a shielded twisted pair, is used for amplifying and denoising the original geomagnetic signal and calculating a burial depth value based on three-dimensional magnetic field distortion characteristics, is provided with a real-time database interface, and supports historical data calling. According to the method, the TMR magnetoresistive sensor array is combined with the wavelet signal processing technology, and the database and neural network technology is introduced, so that a data-driven intelligent detection model can be constructed, and the problem that a traditional linear model is insufficient in adaptability to a nonlinear magnetic field attenuation scene can be better solved.

Description

Technical Field

[0001] The invention belongs to the field of power facility detection, and in particular to a tower burial depth detection system based on geomagnetic technology. Background Art

[0002] In the safe operation of transmission lines, insufficient buried depth of towers is one of the main hidden dangers leading to pole-falling accidents. Taking the 10kV distribution network of Yunnan Power Grid as an example, most of its towers are located in complex terrains such as mountains and hills. They are affected by strong winds, ice and rain erosion all year round. Soil loosening, foundation settlement and other problems can easily lead to changes in the buried depth of towers. According to statistics, the risk of poles falling in strong winds increases by more than 40% when the buried depth is less than 20% of the design value. However, existing detection technologies have significant defects: Traditional measurement methods require the use of pile extraction equipment to pull out power poles or drill holes for detection. This not only damages the integrity of the roadbed and slope, but also takes up to 2-3 hours to inspect a single pole, making it unsuitable for batch surveys of in-service lines. Ground penetrating radar detection relies on high-frequency electromagnetic wave reflection. The signal attenuates severely in high-dielectric-constant strata such as red soil and rock. Detection accuracy drops sharply with increasing burial depth. Furthermore, the equipment costs as high as 100,000 to 200,000 yuan, making it difficult to popularize. Underground telescopes require the collaboration of multiple people to complete processes such as drilling, lowering the scope, and image recognition. A single inspection requires 4-5 people, and due to factors such as light and mud, the measurement error often exceeds 15%. Existing technologies are unable to operate under live conditions and require power outages for inspection, resulting in reduced power supply reliability. This is especially true for multi-segment lines in 10kV distribution networks, where a single power outage can affect thousands of users.

[0003] As a natural physical field, the Earth's magnetic field has an intensity of about 0.5 When passing through the steel bars of a tower, local distortion occurs due to differences in magnetic permeability. However, the sensitivity of traditional Hall sensors is only 10-100uGs, which is unable to capture such weak changes. Summary of the Invention

[0004] The present invention provides a tower buried depth detection system based on geomagnetic technology, which is used to solve the defects in the prior art.

[0005] The present invention is achieved through the following technical solutions: A tower burial depth detection system based on geomagnetic technology includes a magnetic sensing unit, a signal processing unit and a magnetic sensing unit fixing device; The magnetic sensing unit includes 8-16 TMR magnetoresistive sensors, which are evenly arranged to form an array plane. The array plane is parallel to the ground and perpendicular to the tower axis, and is used to collect the three-component (Bx, By, Bz) signals of the geomagnetic field around the tower. The signal processing unit is connected to the magnetic sensing unit via a shielded twisted pair cable, and is used to amplify and denoise the original geomagnetic signal, and calculate the burial depth value based on the three-dimensional magnetic field distortion characteristics. It also has a real-time database interface that supports historical data calls and dynamic model updates. The magnetic sensing unit fixing device can form an array plane of TMR magnetoresistive sensors and fix them on the periphery of the tower, thereby facilitating the TMR magnetoresistive sensors to stably acquire magnetic field signals.

[0006] As described above, a tower depth detection system based on geomagnetic technology is described. The TMR magnetoresistive sensor integrates a three-axis magnetoresistive element and can independently collect the X, Y, and Z axis components of the geomagnetic field. Its sensitivity threshold is ≤0.1μGs, the response time constant is <1μs, and the detection range is ±5×10 -4 T, the sampling frequency is 100-1000Hz, which meets the needs of collecting weak signals of the geomagnetic field.

[0007] In the above-mentioned tower burial depth detection system based on geomagnetic technology, the signal processing unit includes a preamplifier module, a digital filter module, a burial depth calculation module, a database module and a real-time display module; The preamplifier module uses a low-noise operational amplifier to differentially amplify the microvolt-level signal output by the sensor. The signal can gain 30-60dB and suppress common-mode interference. The digital filtering module adopts the wavelet decomposition technology based on the Mallat algorithm, which can perform 5-10 layers of adaptive decomposition on the original signal and perform time-frequency domain joint suppression on environmental interference such as 50Hz power frequency interference and radio frequency noise; The burial depth calculation module calculates the burial depth by establishing a functional relationship between the geomagnetic distortion characteristic parameters and the burial depth. The expression is: in, is the three-component magnetic field variation, is the rate of change of magnetic field gradient; The database module adopts a hybrid architecture of time series database and relational database to store magnetic field distortion data, true value of burial depth, environmental parameters and tower structure parameters, and supports multi-dimensional data retrieval and incremental learning; The real-time display module can dynamically display the three-component magnetic field waveform, distortion characteristic curve and burial depth calculation results, and supports historical data storage and USB export functions.

[0008] As described above, a tower depth detection system based on geomagnetic technology, the digital filtering module uses an STM32H7 series processor to implement db6 wavelet 5-8 layer decomposition, uses soft threshold denoising for each layer of high-frequency coefficients, and reconstructs a pure signal with a signal-to-noise ratio ≥ 20dB. The soft threshold calculation formula is: ,in, is the threshold, is the noise standard deviation, and N is the number of sampling points.

[0009] In the above-mentioned tower burial depth detection system based on geomagnetic technology, a field calibration database is built into the burial depth calculation module. The field calibration database uses a time series database to store high-frequency magnetic field waveforms, a relational database to manage structured parameters, and supports data standardization, feature extraction, and three-level labeling. The neural network algorithm inputs the characteristics of three-dimensional magnetic field variation, gradient, and environmental parameters through the design of a deep learning architecture including convolutional layers, pooling layers, and fully connected layers, outputs a burial depth prediction value, and uses the mixed loss function MSE+MAE for optimization training. Similar historical data is retrieved from the database to compare with the measured magnetic field distortion waveform. Transfer learning is combined to achieve real-time model fine-tuning, and the pre-stored model is substituted to calculate the burial depth. The calculation formula is: , where k and b are calibration coefficients that are adaptively adjusted according to geological conditions, including red soil, sandy soil, and rock.

[0010] As described above, in a tower buried depth detection system based on geomagnetic technology, the magnetic sensing unit fixing device includes an annular bracket and a supporting bracket. The MR magnetoresistive sensors are evenly arranged to form an array plane and fixedly installed on the annular bracket. The supporting bracket can fix the annular bracket to the outer periphery of the tower.

[0011] In the above-mentioned tower buried depth detection system based on geomagnetic technology, the material of the annular bracket is polytetrafluoroethylene.

[0012] In the above-mentioned tower buried depth detection system based on geomagnetic technology, the support bracket is made of corrosion-resistant aluminum alloy, the surface of which is anodized and adapted to harsh outdoor environments, and the support bracket has a load-bearing capacity of ≥5kg.

[0013] In the above-mentioned tower buried depth detection system based on geomagnetic technology, a bubble level is provided on the annular bracket.

[0014] In the above-mentioned tower buried depth detection system based on geomagnetic technology, the inner diameter of the annular support is 15-25 cm larger than the diameter of the tower.

[0015] The advantages of the present invention are: The present invention utilizes the combination of TMR magnetoresistive sensor array and wavelet signal processing technology, and by introducing database and neural network technology, it can build a data-driven intelligent detection model, which can better solve the problem of insufficient adaptability of traditional linear models to nonlinear magnetic field attenuation scenarios. The present invention does not require contact with towers or ground excavation and can operate on live lines of 10kV and below, thus avoiding power outage losses. The non-contact layout of the ring array eliminates the physical contact interference of traditional detection and is suitable for real-time monitoring of running transmission lines. The TMR magnetoresistive sensor in this invention has a sensitivity of 0.1μGs, which is 100 times more accurate than traditional Hall sensors and can capture micro-distortions in the magnetic field caused by a 1cm burial depth change. The circular array layout forms a spatial magnetic field gradient measurement, and through multi-sensor data fusion, the signal-to-noise ratio is further improved by 15-20dB. The wavelet decomposition algorithm in this invention automatically adjusts the number of decomposition layers based on the characteristics of the ambient noise, achieving precise suppression of 50Hz power frequency interference, radio frequency noise, etc. in the time and frequency domains. Through three-dimensional magnetic field vector synthesis calculation, the influence of single-direction magnetic field fluctuations is eliminated, and measurement accuracy can still be maintained around substations with severe electromagnetic interference. The present invention uses an on-site calibration model (calibrated with three sets of towers at known burial depths) to adapt to the magnetic field attenuation characteristics of different geological conditions, such as red soil (magnetic permeability μ≈1.2μ0), sandy soil (μ≈1.05μ0), and rock (μ≈1.1μ0). The measurement error is ≤5%, significantly better than ground penetrating radar (error ≥15%). The overall weight of the present invention is ≤3.5kg, and it can be carried and operated by one person. The single detection process (installation-collection-solution) takes less than 3 minutes. The average daily detection volume can reach 50 towers, which is more than 10 times more efficient than traditional methods and supports batch operation and maintenance surveys. After the present invention introduces the neural network, the solution error is greatly reduced, and through the accumulation of a large amount of database and incremental learning, the device can automatically adapt to new geological areas, and when sufficient data is accumulated, there is no need for manual recalibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 It is a schematic diagram of the detection operation of the present invention; Figure 2 Schematic diagram of the planar layout of the TMR magnetoresistive sensor array of the present invention; Figure 3 It is a signal acquisition and processing flow chart of the present invention; Figure 4 is a system architecture diagram of the database and neural network module of the present invention; Figure 5It is a schematic diagram of the neural network model structure of the present invention; Figure 6 It is a schematic diagram of the dynamic learning process of the present invention. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0019] like Figure 1-3 As shown, a tower burial depth detection system based on geomagnetic technology includes a magnetic sensing unit, a signal processing unit and a magnetic sensing unit fixing device; The magnetic sensing unit includes 8-16 TMR magnetoresistive sensors, which are evenly arranged to form an array plane. The array plane is parallel to the ground and perpendicular to the tower axis, and is used to collect the three-component (Bx, By, Bz) signals of the geomagnetic field around the tower. The signal processing unit is connected to the magnetic sensing unit via a shielded twisted pair cable, and is used to amplify and denoise the original geomagnetic signal, and calculate the burial depth value based on the three-dimensional magnetic field distortion characteristics. It also has a real-time database interface that supports historical data calls and dynamic model updates. The magnetic sensing unit fixing device can form an array plane of TMR magnetoresistive sensors and fix them on the periphery of the tower, thereby facilitating the TMR magnetoresistive sensors to stably acquire magnetic field signals.

[0020] Preferably, the TMR magnetoresistive sensor described in this embodiment integrates a three-axis magnetoresistive element, which can independently collect the X, Y, and Z axis components of the geomagnetic field, with a sensitivity threshold of ≤0.1μGs, a response time constant of <1μs, and a detection range of ±5×10 -4 T, the sampling frequency is 100-1000Hz, which meets the needs of collecting weak signals of the geomagnetic field.

[0021] Preferably, the signal processing unit described in this embodiment includes a preamplifier module, a digital filter module, a burial depth calculation module, a database module and a real-time display module; The preamplifier module uses a low-noise operational amplifier to differentially amplify the microvolt-level signal output by the sensor. The signal can gain 30-60dB and suppress common-mode interference. The digital filtering module adopts the wavelet decomposition technology based on the Mallat algorithm, which can perform 5-10 layers of adaptive decomposition on the original signal and perform time-frequency domain joint suppression on environmental interference such as 50Hz power frequency interference and radio frequency noise; The burial depth calculation module calculates the burial depth by establishing a functional relationship between the geomagnetic distortion characteristic parameters and the burial depth. The expression is: in, is the three-component magnetic field variation, is the rate of change of magnetic field gradient; The database module adopts a hybrid architecture of time series database and relational database to store magnetic field distortion data, true value of burial depth, environmental parameters and tower structure parameters, and supports multi-dimensional data retrieval and incremental learning; The real-time display module can dynamically display the three-component magnetic field waveform, distortion characteristic curve and burial depth calculation results, and supports historical data storage and USB export functions.

[0022] Preferably, the digital filtering module described in this embodiment adopts the STM32H7 series processor to realize the db6 wavelet 5-8 layer decomposition, and adopts soft threshold denoising for the high frequency coefficients of each layer to reconstruct the pure signal with a signal-to-noise ratio ≥ 20dB. The soft threshold calculation formula is ,in, is the threshold, is the noise standard deviation, and N is the number of sampling points.

[0023] Preferably, the depth calculation module described in this embodiment has a built-in field calibration database. The field calibration database uses a time series database to store high-frequency magnetic field waveforms, a relational database to manage structured parameters, and supports data standardization, feature extraction, and three-level annotation. The neural network algorithm designs a deep learning architecture including a convolutional layer, a pooling layer, and a fully connected layer. It inputs the features of the three-dimensional magnetic field variation, gradient, and environmental parameters, outputs the depth prediction value, and uses the mixed loss function MSE+MAE for optimization training. It retrieves similar historical data from the database to compare the measured magnetic field distortion waveform, combines transfer learning to achieve real-time model fine-tuning, and substitutes it into the pre-stored model to calculate the depth. The calculation formula is: , where k and b are calibration coefficients that are adaptively adjusted according to geological conditions, including red soil, sandy soil, and rock.

[0024] Preferably, the magnetic sensing unit fixing device described in this embodiment includes an annular bracket and a supporting bracket. The MR magnetoresistive sensors are evenly arranged to form an array plane and fixedly mounted on the annular bracket. The supporting bracket can fix the annular bracket to the periphery of the tower.

[0025] Preferably, the material of the annular bracket described in this embodiment is polytetrafluoroethylene.

[0026] Preferably, the support bracket described in this embodiment is made of corrosion-resistant aluminum alloy, the surface of which is anodized and adapted to harsh outdoor environments. The support bracket has a load-bearing capacity of ≥5kg.

[0027] Preferably, a bubble level is provided on the annular bracket described in this embodiment.

[0028] Preferably, the inner diameter of the annular support described in this embodiment is 15-25 cm larger than the diameter of the tower.

[0029] How it works First, magnetic field distortion is collected. When the Earth's magnetic field passes through the tower's steel bars, the magnetic lines of force are deflected due to differences in magnetic permeability. The sensor array synchronously collects three-component magnetic field signals, forming an original waveform containing distortion characteristics. Environmental parameters and tower information are also recorded simultaneously and stored in the database as training samples. After amplification and wavelet denoising, the collected original signal is input into the neural network to extract spatiotemporal features (three-dimensional magnetic field distortion gradient, sensor array spatial distribution characteristics); By comparing the measured waveform with the standard geomagnetic model, the three-component magnetic field changes ΔBx, ΔBy, ΔBz and the gradient change rate ▽B are calculated to determine the distortion characteristic parameters; The characteristic parameters are substituted into the calibration model and corrected based on parameters such as tower diameter and steel bar distribution. The neural network calculates the buried depth based on the trained model. At the same time, incremental learning is performed based on the current detection data and similar cases in the database, and the model parameters are dynamically updated. Finally, the buried depth value is output and displayed.

[0030] Example like Figure 1-6 As shown, the ring bracket is installed 1.8-2.0m above the tower ground. Use a bubble level to adjust the bracket plane to ensure it is parallel to the horizontal component of the geomagnetic field (deviation ≤ 1°). Use a tape measure to measure the tower diameter. Adjustable brackets are installed at the bottom of the support bracket. The adjustable brackets are clamped to the tower using U-bolts. The bolt torque is controlled at 8-10N*m to prevent the support bracket from shaking.

[0031] The output cables of the TMR magnetoresistive sensors are connected to the differential inputs of the signal processing unit via shielded twisted-pair cables, with cable lengths of 8 meters or less to minimize signal attenuation. After powering on the system, the system enters calibration mode and moves the sensor array to an open area free of metal interference. A one-minute background geomagnetic signal is collected to establish a standard reference model. Environmental parameters such as soil permeability and humidity are also measured and entered into a database, serving as a baseline for subsequent distortion calculations.

[0032] A neural network was trained on a server using historical data (≥1000 sets). Signals were preprocessed using a 5-layer DB6 wavelet decomposition. The input layer consisted of a feature matrix of 8 sensors × 3 components × 128 time steps. Model parameters were optimized through 200 iterations. After completing 20 valid field inspections, magnetic field distortion characteristics and true depth values ​​were extracted from the new data. Historical data with similar magnetic permeability and tower types were retrieved from the database. Transfer learning was used to fine-tune the model, focusing on optimizing prediction accuracy for the current scenario.

[0033] The sampling rate was set to 500 Hz, and geomagnetic signals were collected continuously for 60 seconds, resulting in ≥12,000 sampling points. After denoising using a five-layer wavelet decomposition, the three-component data (Bx, By, and Bz) were calculated using a neural network. Combined with parameters such as soil magnetic permeability μ and tower diameter D, the predicted depth h and confidence level were output.

[0034] If the confidence level is less than 0.7, the system automatically triggers re-detection; if the confidence level is insufficient for three consecutive detections, the database marks the scene as a "complex environment" and prioritizes allocating more training resources in the future.

[0035] Test the same tower three times in a row, take the average, and calculate the standard deviation. If the error is greater than 5%, retest. Typically, for a 12m tower with a designed burial depth of 1.8m, the test results should be within the range of 1.71-1.89m.

[0036] It is forbidden to place metal objects (such as construction machinery, steel bars) within 5m of the detection area to avoid interference from the distortion of the geomagnetic field. -3 T) when working, it is necessary to stay more than 10m away from the work; Calibrate the sensor sensitivity quarterly using a standard magnetic field source (such as a Helmholtz coil generating a 10μGs magnetic field). Replace the sensor if the deviation exceeds 5%. For long-term storage, place it in a magnetic shielding box to prevent the long-term effect of the geomagnetic field from causing the sensor zero point to drift. The database storage capacity is checked every quarter. When the amount of historical data exceeds 5,000 groups, the automatic bucketing mechanism is activated (bucketing by magnetic permeability interval of 0.1μ0) to improve retrieval efficiency.

[0037] Stop operation when wind speed is greater than 10m / s (level 5 wind) to prevent the bracket from shaking and affecting signal stability; the device must be dismantled before thunderstorms to prevent induced lightning strikes from damaging the circuit. The grounding resistance of the equipment should be ≤4Ω.

[0038] From the above description, it can be seen that the technical solution described in the embodiment of the present invention uses geomagnetic technology to measure the buried depth of the transmission line tower. The waveform information of the complete geomagnetic signal obtained can be compared with the actual magnetic induction intensity of the earth's magnetic field, and the buried depth of the transmission line tower can be calculated using the difference. Combined with neural network training and database retrieval comparison, the equipment detection accuracy is greatly improved and the work efficiency is high.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A tower depth detection system based on geomagnetic technology, characterized by: It includes a magnetic sensing unit, a signal processing unit and a magnetic sensing unit fixing device; The magnetic sensing unit includes 8-16 TMR magnetoresistive sensors, which are evenly arranged to form an array plane. The array plane is parallel to the ground and perpendicular to the tower axis, and is used to collect the three-component (Bx, By, Bz) signals of the geomagnetic field around the tower. The signal processing unit is connected to the magnetic sensing unit via a shielded twisted pair cable, and is used to amplify and denoise the original geomagnetic signal, and calculate the burial depth value based on the three-dimensional magnetic field distortion characteristics. It also has a real-time database interface that supports historical data calls and dynamic model updates. The magnetic sensing unit fixing device can form an array plane of TMR magnetoresistive sensors and fix them on the periphery of the tower, thereby facilitating the TMR magnetoresistive sensors to stably acquire magnetic field signals.

2. The tower depth detection system based on geomagnetic technology according to claim 1, characterized in that: The TMR magnetoresistive sensor integrates a three-axis magnetoresistive element and can independently collect the X, Y, and Z axis components of the geomagnetic field. Its sensitivity threshold is ≤0.1μGs, the response time constant is <1μs, and the detection range is ±5×10 -4 T, the sampling frequency is 100-1000Hz, which meets the needs of collecting weak signals of the geomagnetic field.

3. The tower depth detection system based on geomagnetic technology according to claim 1 is characterized in that: The signal processing unit includes a preamplifier module, a digital filter module, a burial depth calculation module, a database module and a real-time display module; The preamplifier module uses a low-noise operational amplifier to differentially amplify the microvolt-level signal output by the sensor. The signal can gain 30-60dB and suppress common-mode interference. The digital filtering module adopts the wavelet decomposition technology based on the Mallat algorithm, which can perform 5-10 layers of adaptive decomposition on the original signal and perform time-frequency domain joint suppression on environmental interference such as 50Hz power frequency interference and radio frequency noise; The burial depth calculation module calculates the burial depth by establishing a functional relationship between the geomagnetic distortion characteristic parameters and the burial depth. The expression is: in, is the three-component magnetic field variation, is the rate of change of magnetic field gradient; The database module adopts a hybrid architecture of time series database and relational database to store magnetic field distortion data, true value of burial depth, environmental parameters and tower structure parameters, and supports multi-dimensional data retrieval and incremental learning; The real-time display module can dynamically display the three-component magnetic field waveform, distortion characteristic curve and burial depth calculation results, and supports historical data storage and USB export functions.

4. The tower depth detection system based on geomagnetic technology according to claim 3 is characterized in that: The digital filtering module adopts STM32H7 series processor to realize 5-8 layer decomposition of db6 wavelet, uses soft threshold denoising for high frequency coefficients of each layer, and reconstructs a pure signal with a signal-to-noise ratio ≥ 20dB. The soft threshold calculation formula is ,in, is the threshold, is the noise standard deviation, and N is the number of sampling points.

5. The tower depth detection system based on geomagnetic technology according to claim 3 is characterized in that: The burial depth calculation module has a built-in field calibration database. The field calibration database uses a time series database to store high-frequency magnetic field waveforms and a relational database to manage structured parameters. It supports data standardization, feature extraction, and three-level annotation. The neural network algorithm uses a deep learning architecture including convolutional layers, pooling layers, and fully connected layers to input the characteristics of three-dimensional magnetic field changes, gradients, and environmental parameters, and outputs a burial depth prediction value. The mixed loss function MSE+MAE is used for optimization training. Similar historical data is retrieved from the database to compare with the measured magnetic field distortion waveform. Transfer learning is combined to achieve real-time model fine-tuning and substitute it into the pre-stored model to calculate the burial depth. The calculation formula is: , where k and b are calibration coefficients that are adaptively adjusted according to geological conditions, including red soil, sandy soil, and rock.

6. The tower depth detection system based on geomagnetic technology according to claim 1, characterized in that: The magnetic sensing unit fixing device includes an annular bracket and a supporting bracket. The MR magnetoresistive sensors are evenly arranged to form an array plane and fixedly mounted on the annular bracket. The supporting bracket can fix the annular bracket to the periphery of the tower.

7. The tower depth detection system based on geomagnetic technology according to claim 6, characterized in that: The material of the annular bracket is polytetrafluoroethylene.

8. The tower depth detection system based on geomagnetic technology according to claim 6, characterized in that: The support bracket is made of corrosion-resistant aluminum alloy, the surface of which is anodized and adapted to harsh outdoor environments. The support bracket has a load-bearing capacity of ≥5kg.

9. The tower depth detection system based on geomagnetic technology according to claim 6, characterized in that: A bubble level is provided on the annular support.

10. The tower depth detection system based on geomagnetic technology according to claim 6, characterized in that: The inner diameter of the annular support is 15-25 cm larger than the diameter of the tower.

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