Electromagnetic and noise monitoring system based on unmanned aerial vehicle hoisting

Abstract

The invention discloses an electromagnetic and noise monitoring system based on unmanned aerial vehicle hoisting, which comprises an unmanned aerial vehicle main body, a self-adaptive hoisting mechanism, an integrated monitoring module, a data processing unit and a ground control terminal, and is characterized in that the unmanned aerial vehicle main body is detachably connected with the self-adaptive hoisting mechanism through a standardized mounting interface; the bottom of the self-adaptive hoisting mechanism is fixedly connected with the integrated monitoring module, the data processing unit is integrated in the integrated monitoring module, and the unmanned aerial vehicle body and the ground control terminal achieve data interaction through a dual-mode communication link. According to the method, the digital twin model is combined with the clustering algorithm, accurate positioning and diffusion prediction of the pollution source are realized, and the positioning error is less than 5 meters; the manual intervention cost is reduced through the functions of network disconnection continuous transmission, automatic calibration and the like, and the system can achieve 7 * 24-hour unattended monitoring.

Description

Technical Field

[0001] This invention relates to the intersection of environmental monitoring and drone applications, and in particular to an electromagnetic and noise monitoring system based on drone hoisting. Background Technology

[0002] With the acceleration of industrial modernization and urbanization, electromagnetic radiation and noise pollution have become significant factors affecting the ecological environment and human health. Precise and efficient monitoring technologies have become crucial for environmental governance. Currently, the mainstream electromagnetic and noise monitoring methods are mainly divided into two categories: fixed-site monitoring and mobile monitoring.

[0003] Fixed-site monitoring systems, such as the STT-OS-8 power frequency electromagnetic environment online monitoring system, employ an integrated design to achieve 24 / 7 real-time monitoring and possess high measurement accuracy. However, they have significant limitations: although their footprint is only 450mm x 450mm, the fixed installation method limits the monitoring range, making it impossible to cover remote areas or complex terrains; expanding to noise monitoring requires additional independent sites, making it difficult to achieve simultaneous spatiotemporal acquisition of electromagnetic and noise data, resulting in poor data correlation. Furthermore, the metal components of fixed sites are prone to causing electric field distortion, requiring integrated calibration to compensate for errors, increasing system deployment costs.

[0004] In mobile monitoring technology, UAV-mounted monitoring equipment has become a research hotspot. For example, the UAV-mounted radar jamming device disclosed in patent CN219758492U achieves the adjustment of the device's angle and position through a steering seat and lifting frame. However, this technology is only applicable to radar jamming scenarios and does not address the special needs of electromagnetic and noise monitoring. Its hoisting structure uses a simple screw connection and lacks shock absorption design. The vibration of the UAV during flight causes the monitoring data fluctuation error to exceed 25%. Furthermore, it does not consider the cross-interference of electromagnetic and noise signals and cannot achieve simultaneous monitoring of multiple parameters.

[0005] Existing UAV monitoring systems suffer from the following key shortcomings: First, insufficient anti-interference capability; electromagnetic radiation generated by the UAV's own motors can interfere with the monitoring module, leading to increased electromagnetic monitoring errors. Second, poor attitude adaptability; unable to quickly adjust the monitoring angle in complex terrain, making it difficult to capture peak pollution source data. Third, lagging data processing; relying on ground base stations for data analysis, making real-time early warning impossible. Fourth, weak environmental adaptability; monitoring stability significantly decreases under severe weather conditions such as high temperatures and strong winds. For example, while patent CN120370255A proposes a UAV monitoring method for complex electromagnetic environments, it only addresses the UAV's own identification and positioning, failing to solve the accuracy and synchronization issues of the mounted monitoring equipment.

[0006] Therefore, developing a UAV-mounted electromagnetic and noise synchronous monitoring system with high flexibility, high precision, and strong anti-interference capabilities has become an urgent technical challenge.

[0007] To address this, we propose an electromagnetic and noise monitoring system based on drone-mounted installation. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies by proposing an electromagnetic and noise monitoring system based on drone hoisting.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] An electromagnetic and noise monitoring system based on UAV hoisting includes a UAV body, an adaptive hoisting mechanism, an integrated monitoring module, a data processing unit, and a ground control terminal. The UAV body and the adaptive hoisting mechanism are detachably connected via a standardized mounting interface. The bottom of the adaptive hoisting mechanism is fixedly connected to the integrated monitoring module. The data processing unit is integrated inside the integrated monitoring module. The UAV body and the ground control terminal achieve data interaction through a dual-mode communication link.

[0011] Preferably, the brushless motor of the main body of the UAV uses silicon carbide power devices with a rated power of not less than 500W and an efficiency of more than 95%. The six sets of motors adopt a distributed control architecture. When a single set of motors fails, the remaining motors automatically compensate through the power adjustment module to ensure flight stability.

[0012] The main body of the drone adopts a six-rotor redundant design and is equipped with a power adjustment module and an attitude sensing unit. The power adjustment module includes a brushless motor and a variable pitch propeller, and adjusts the flight attitude through a vector control algorithm. The attitude sensing unit integrates an IMU inertial measurement module, a GPS positioning module and a barometric altimeter, with a sampling frequency of not less than 100Hz and a positioning accuracy of centimeter level.

[0013] Preferably, both the X-axis rotation mechanism and the Y-axis swing mechanism of the three-axis steering seat are equipped with angle encoders with a resolution of 0.01°, which provide real-time feedback of rotation angle data to the attitude locking module to form a closed-loop control; the electric push rod of the Z-axis lifting mechanism adopts ball screw drive and undergoes hard anodizing treatment, which improves wear resistance by 30%;

[0014] The adaptive hoisting mechanism includes a three-axis steering seat, a damping unit, and an attitude locking module. The three-axis steering seat consists of an X-axis rotation mechanism, a Y-axis swing mechanism, and a Z-axis lifting mechanism. The X-axis rotation mechanism uses a servo motor to drive a harmonic reducer, achieving continuous rotation from 0 to 360°. The Y-axis swing mechanism is controlled by dual servo motors, with a swing angle range of -90° to 90°. The Z-axis lifting mechanism is driven by an electric push rod, with a lifting stroke of 0-500mm and a positioning accuracy of ±0.5mm. The damping unit includes a parallel structure of metal springs and dampers, with damping components installed in the X, Y, and Z axes, and a natural frequency below 5Hz. The attitude locking module integrates a laser positioning sensor and a gyroscope to detect and monitor the module's attitude in real time and trigger rapid response adjustments.

[0015] Preferably, the metal spring of the buffer and shock absorption unit is made of titanium alloy with a wire diameter of 3-5mm and an effective number of 4-6 turns. The damper adopts a hydraulic damping structure, and the damping coefficient can be adaptively adjusted according to the vibration frequency. When the vibration frequency exceeds 10Hz, the damping coefficient automatically increases by 50%.

[0016] The integrated monitoring module adopts a shielded and isolated design, including an electromagnetic detection unit, a noise acquisition unit, an environmental calibration unit, and a data acquisition card. The electromagnetic detection unit integrates a triaxial omnidirectional electric field sensor and a magnetic field sensor, with an electric field measurement range of 0.01V / m-100kV / m and a frequency range of 20Hz-1GHz, and a magnetic field measurement range of 0.01μT-10mT. The sensor is wrapped with a double-layer shield, with the inner layer made of permalloy and the outer layer made of aluminum alloy, achieving a shielding effectiveness of over 60dB. The noise acquisition unit uses a preamplifier microphone with a frequency response range of 20Hz-20kHz and a measurement accuracy of ±0.5dB. The microphone head is equipped with a windproof noise reduction cover. The environmental calibration unit integrates a temperature sensor, a humidity sensor, and a barometric pressure sensor. The data acquisition card uses a 16-bit AD converter with a sampling rate of 1MHz, supporting synchronous sampling of electromagnetic and noise signals with a time synchronization error of less than 1μs.

[0017] Preferably, the triaxial omnidirectional electric field sensor of the electromagnetic detection unit adopts the capacitive coupling principle, and the sensor probe is made of polytetrafluoroethylene insulating material with a temperature resistance range of -40℃ to 120℃; the magnetic field sensor adopts the fluxgate principle, and the sampling rate can be adjusted according to the monitoring scenario, and the sampling rate is automatically increased to 200Hz in areas with strong interference.

[0018] The data processing unit adopts an "edge computing + cloud collaboration" architecture, integrating an FPGA and an ARM processor. The FPGA is responsible for real-time data preprocessing, including signal filtering, noise reduction, and synchronization alignment. The ARM processor runs calibration and feature extraction algorithms, and performs temperature compensation and linear calibration on electromagnetic and noise data based on environmental parameters. The data processing unit also has a storage module with a capacity of at least 16GB, supporting the function of resuming data transmission after network outage.

[0019] Preferably, the preamplifier microphone of the noise acquisition unit adopts a differential amplifier circuit with a common-mode rejection ratio greater than 80dB, and the windproof and noise-reducing cover adopts a honeycomb structure, made of polyurethane foam with a thickness of 5-8mm, which can reduce airflow noise by more than 30dB; the ground control terminal includes a display and control module and a data analysis module. The display and control module displays the UAV attitude, monitoring data and positioning information in real time, and supports three-dimensional scene visualization.

[0020] The data analysis module constructs a monitoring area model based on digital twin technology, associates electromagnetic and noise data with spatial location, identifies pollution source locations through clustering algorithms, and uses a trend prediction model to achieve pollution spread early warning; the dual-mode communication link adopts a 5G+WiFi6 combination, with a transmission rate of up to 1Gbps and a maximum communication distance of 10km.

[0021] Preferably, the FPGA of the data processing unit adopts a Xilinx Zynq series chip, which integrates an ARM Cortex-A9 processor and supports hardware acceleration computing; the calibration algorithm adopts a piecewise linear interpolation algorithm, and sets 50 calibration nodes in the frequency range of 20Hz-1GHz to ensure measurement accuracy at different frequencies.

[0022] Preferably, the data analysis module of the ground control terminal is developed using Python language, integrates the TensorFlow deep learning framework, and the trend prediction model is built based on LSTM neural network. The input parameters include historical monitoring data, environmental parameters and terrain data, and the prediction accuracy is over 90%.

[0023] Preferably, the standardized mounting interface adopts a quick-release design, including a positioning pin and a locking wrench, with a positioning accuracy of ±0.1mm, a locking torque of 20-30N·m, an installation time of no more than 2 minutes, and a waterproof sealing ring at the interface, with a protection level of IP67.

[0024] Preferably, the system further includes a battery management module, which integrates a battery balancing circuit and a remaining power estimation algorithm, supports hot-swappable battery replacement, with a replacement time of no more than 1 minute, uses lithium iron phosphate batteries, has a cycle life of more than 2000 cycles, and a capacity retention rate of more than 80% in low-temperature environments.

[0025] The present invention has the following beneficial effects:

[0026] Enhanced scene adaptability: The three-axis steering mount enables full attitude adjustment of the monitoring module. Combined with the attitude locking module, it can work stably in complex environments with wind speed ≤15m / s and slope ≤30°. The six-rotor redundant design and power adjustment module ensure that the system can operate normally in high temperature (-40℃ to 60℃) and high humidity (relative humidity ≤95%) environments, and its application range covers a variety of scenarios such as cities, mountains, and industrial parks.

[0027] Monitoring efficiency is greatly improved: the maneuverability of drones expands the monitoring range to more than 10 times that of traditional fixed stations, and the quick-release mounting design reduces the equipment deployment time to less than 5 minutes; the edge computing architecture enables real-time processing and analysis of monitoring data, with a data response latency of less than 100ms, which is 80% more efficient than the traditional ground processing mode.

[0028] Enhanced intelligence: The combination of digital twin models and clustering algorithms enables precise location and spread prediction of pollution sources, with a location error of less than 5 meters; functions such as network interruption resume transmission and automatic calibration reduce the cost of manual intervention, and the system can achieve 24 / 7 unattended monitoring. Attached Figure Description

[0029] Figure 1 This is a diagram of an electromagnetic and noise monitoring system based on drone hoisting proposed in this invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0031] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0032] See Figure 1 The present invention proposes an electromagnetic and noise monitoring system based on UAV hoisting, which includes a UAV body, an adaptive hoisting mechanism, an integrated monitoring module, a data processing unit and a ground control terminal. The components interact with each other through redundant communication links.

[0033] The drone's main body adopts a redundant six-rotor design, equipped with a power adjustment module and an attitude sensing unit. The power adjustment module includes a brushless motor and a variable-pitch propeller, adjusting flight attitude through vector control algorithms. The attitude sensing unit integrates an IMU inertial measurement module, a GPS positioning module, and a barometric altimeter, with a sampling frequency of no less than 100Hz and positioning accuracy down to the centimeter level. The drone's main body has a standardized mounting interface at its bottom, connecting to an adaptive hoisting mechanism via a quick-release structure.

[0034] The adaptive lifting mechanism is a core innovative component, comprising a three-axis steering mount, a damping and shock absorption unit, and an attitude locking module. The three-axis steering mount consists of an X-axis rotation mechanism, a Y-axis swing mechanism, and a Z-axis lifting mechanism. The X-axis rotation mechanism uses a servo motor to drive a harmonic reducer, achieving continuous rotation from 0 to 360°. The Y-axis swing mechanism is controlled by dual servo motors, with a swing angle range of -90° to 90°. The Z-axis lifting mechanism is driven by an electric push rod, with a lifting stroke of 0-500mm and a positioning accuracy of ±0.5mm. The damping and shock absorption unit includes a parallel structure of metal springs and dampers, with damping components in the X, Y, and Z axes. Its natural frequency is below 5Hz, attenuating over 80% of UAV vibration. The attitude locking module integrates a laser positioning sensor and a gyroscope to monitor the module's attitude in real time. When the UAV's attitude change exceeds a threshold, a rapid response mechanism is triggered to ensure stability in the monitored direction.

[0035] The integrated monitoring module employs a shielded and isolated design, comprising an electromagnetic detection unit, a noise acquisition unit, an environmental calibration unit, and a data acquisition card. The electromagnetic detection unit integrates a triaxial omnidirectional electric field sensor and a magnetic field sensor, with an electric field measurement range of 0.01V / m-100kV / m and a frequency range of 20Hz-1GHz, and a magnetic field measurement range of 0.01μT-10mT. The sensor is encased in a double-layer shield, with an inner layer of permalloy and an outer layer of aluminum alloy, achieving a shielding effectiveness of over 60dB. The noise acquisition unit uses a preamplified microphone with a frequency response range of 20Hz-20kHz and a measurement accuracy of ±0.5dB, meeting the requirements of GB3096-2020 standards. The microphone head is equipped with a windproof noise reduction cover to reduce airflow noise interference. The environmental calibration unit integrates a temperature sensor, a humidity sensor, and a barometric pressure sensor, acquiring environmental parameters in real time for monitoring data compensation. The data acquisition card uses a 16-bit AD converter with a sampling rate of 1MHz, supporting synchronous sampling of electromagnetic and noise signals with a time synchronization error of less than 1μs.

[0036] The data processing unit adopts an "edge computing + cloud collaboration" architecture. The edge computing module integrates an FPGA and an ARM processor. The FPGA is responsible for real-time data preprocessing, including signal filtering, noise reduction, and synchronization alignment. The ARM processor runs calibration and feature extraction algorithms, performing temperature compensation and linear calibration on electromagnetic and noise data based on environmental parameters, and extracting characteristic parameters such as peak value, mean value, and spectral distribution. The data processing unit also has a storage module that supports local caching of 16GB of data and automatically initiates a resume function when communication is interrupted.

[0037] The ground control terminal includes a display and control module and a data analysis module. The display and control module shows the UAV's attitude, monitoring data, and positioning information in real time, and supports 3D scene visualization. The data analysis module constructs a monitoring area model based on digital twin technology, correlates electromagnetic and noise data with spatial location, identifies pollution source locations through clustering algorithms, and uses a trend prediction model to achieve pollution diffusion early warning. The ground control terminal and the UAV communicate via 5G+WiFi6 dual-mode, with a transmission rate of up to 1Gbps and a maximum communication distance of 10km.

[0038] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An electromagnetic and noise monitoring system based on UAV hoisting, comprising a UAV body, an adaptive hoisting mechanism, an integrated monitoring module, a data processing unit, and a ground control terminal, wherein the UAV body and the adaptive hoisting mechanism are detachably connected via a standardized mounting interface, the bottom of the adaptive hoisting mechanism is fixedly connected to the integrated monitoring module, the data processing unit is integrated inside the integrated monitoring module, and the UAV body and the ground control terminal achieve data interaction via a dual-mode communication link.

2. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 1, characterized in that: The brushless motor of the main body of the drone uses silicon carbide power devices with a rated power of not less than 500W and an efficiency of more than 95%. The six sets of motors adopt a distributed control architecture. When a single set of motors fails, the remaining motors automatically compensate through the power adjustment module to ensure flight stability. The main body of the drone adopts a six-rotor redundant design and is equipped with a power adjustment module and an attitude sensing unit. The power adjustment module includes a brushless motor and a variable pitch propeller, and adjusts the flight attitude through a vector control algorithm. The attitude sensing unit integrates an IMU inertial measurement module, a GPS positioning module and a barometric altimeter, with a sampling frequency of not less than 100Hz and a positioning accuracy of centimeter level.

3. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 1, characterized in that: The X-axis rotation mechanism and Y-axis swing mechanism of the three-axis steering mount are both equipped with angle encoders with a resolution of 0.01°, which provide real-time feedback of rotation angle data to the attitude locking module to form a closed-loop control; the electric push rod of the Z-axis lifting mechanism adopts ball screw drive and has a hard anodized surface treatment, which improves wear resistance by 30%; The adaptive hoisting mechanism includes a three-axis steering seat, a damping unit, and an attitude locking module. The three-axis steering seat consists of an X-axis rotation mechanism, a Y-axis swing mechanism, and a Z-axis lifting mechanism. The X-axis rotation mechanism uses a servo motor to drive a harmonic reducer, achieving continuous rotation from 0 to 360°. The Y-axis swing mechanism is controlled by dual servo motors, with a swing angle range of -90° to 90°. The Z-axis lifting mechanism is driven by an electric push rod, with a lifting stroke of 0-500mm and a positioning accuracy of ±0.5mm. The damping unit includes a parallel structure of metal springs and dampers, with damping components installed in the X, Y, and Z axes, and a natural frequency below 5Hz. The attitude locking module integrates a laser positioning sensor and a gyroscope to detect and monitor the module's attitude in real time and trigger rapid response adjustments.

4. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 1, characterized in that: The metal spring of the buffer and shock absorption unit is made of titanium alloy with a wire diameter of 3-5mm and an effective number of 4-6 turns. The damper adopts a hydraulic damping structure, and the damping coefficient can be adaptively adjusted according to the vibration frequency. When the vibration frequency exceeds 10Hz, the damping coefficient automatically increases by 50%. The integrated monitoring module adopts a shielded and isolated design, including an electromagnetic detection unit, a noise acquisition unit, an environmental calibration unit, and a data acquisition card. The electromagnetic detection unit integrates a triaxial omnidirectional electric field sensor and a magnetic field sensor, with an electric field measurement range of 0.01V / m-100kV / m and a frequency range of 20Hz-1GHz, and a magnetic field measurement range of 0.01μT-10mT. The sensor is wrapped with a double-layer shield, with the inner layer made of permalloy and the outer layer made of aluminum alloy, achieving a shielding effectiveness of over 60dB. The noise acquisition unit uses a preamplifier microphone with a frequency response range of 20Hz-20kHz and a measurement accuracy of ±0.5dB. The microphone head is equipped with a windproof noise reduction cover. The environmental calibration unit integrates a temperature sensor, a humidity sensor, and a barometric pressure sensor. The data acquisition card uses a 16-bit AD converter with a sampling rate of 1MHz, supporting synchronous sampling of electromagnetic and noise signals with a time synchronization error of less than 1μs.

5. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 1, characterized in that: The electromagnetic detection unit's triaxial omnidirectional electric field sensor adopts the capacitive coupling principle, and the sensor probe is made of polytetrafluoroethylene insulating material with a temperature resistance range of -40℃ to 120℃; the magnetic field sensor adopts the fluxgate principle, and the sampling rate can be adjusted according to the monitoring scenario, automatically increasing to 200Hz in areas with strong interference. The data processing unit adopts an "edge computing + cloud collaboration" architecture, integrating an FPGA and an ARM processor. The FPGA is responsible for real-time data preprocessing, including signal filtering, noise reduction, and synchronization alignment. The ARM processor runs calibration and feature extraction algorithms, and performs temperature compensation and linear calibration on electromagnetic and noise data based on environmental parameters. The data processing unit also has a storage module with a capacity of at least 16GB, supporting the function of resuming data transmission after network outage.

6. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 5, characterized in that: The preamplifier microphone of the noise acquisition unit adopts a differential amplifier circuit with a common-mode rejection ratio greater than 80dB. The windproof and noise-reducing cover adopts a honeycomb structure and is made of polyurethane foam with a thickness of 5-8mm, which can reduce airflow noise by more than 30dB. The ground control terminal includes a display and control module and a data analysis module. The display and control module displays the UAV attitude, monitoring data and positioning information in real time and supports three-dimensional scene visualization. The data analysis module constructs a monitoring area model based on digital twin technology, associates electromagnetic and noise data with spatial location, identifies pollution source locations through clustering algorithms, and uses a trend prediction model to achieve pollution spread early warning; the dual-mode communication link adopts a 5G+WiFi6 combination, with a transmission rate of up to 1Gbps and a maximum communication distance of 10km.

7. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 6, characterized in that: The FPGA of the data processing unit uses Xilinx Zynq series chips, which integrates an ARM Cortex-A9 processor and supports hardware acceleration computing. The calibration algorithm adopts a piecewise linear interpolation algorithm, and sets 50 calibration nodes in the frequency range of 20Hz-1GHz to ensure measurement accuracy at different frequencies.

8. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 7, characterized in that: The data analysis module of the ground control terminal is developed using Python and integrates the TensorFlow deep learning framework. The trend prediction model is built based on the LSTM neural network. The input parameters include historical monitoring data, environmental parameters and terrain data, and the prediction accuracy is over 90%.

9. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 8, characterized in that: The standardized mounting interface adopts a quick-release design, including a positioning pin and a locking wrench, with a positioning accuracy of ±0.1mm, a locking torque of 20-30N·m, and an installation time of no more than 2 minutes. The interface is equipped with a waterproof sealing ring, and the protection level reaches IP67.

10. The electromagnetic and noise monitoring system based on UAV hoisting according to claim 9, characterized in that: The system also includes a battery management module, which integrates a battery balancing circuit and a remaining power estimation algorithm. It supports hot-swappable battery replacement with a replacement time of no more than 1 minute. The battery is a lithium iron phosphate battery with a cycle life of more than 2,000 cycles and a capacity retention rate of more than 80% in low-temperature environments.

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