Unmanned aerial vehicle-mounted high-frequency electromagnetic wave detection circuit unit

Abstract

The invention discloses an unmanned aerial vehicle-mounted high-frequency electromagnetic wave detection circuit unit, and relates to the field of unmanned aerial vehicle obstacle avoidance application. The signal acquisition and obstacle avoidance module is connected with the high-frequency signal sampling circuit, and performs electromagnetic field detection through a miniature antenna to obtain a high-frequency target signal; the abnormal discharge frequency spectrum detection circuit imports result data detected by the binocular distance measuring sensor module into the signal acquisition obstacle avoidance module; the method comprises the following steps: classifying electromagnetic field signals, analyzing the classified electromagnetic field signals, identifying three-dimensional information of an obstacle, judging whether the unmanned aerial vehicle is in a safe range or not based on visual perception, obtaining an obstacle avoidance flight instruction through operation of a PC, performing image processing, sensing the obstacle and the environment where the obstacle is located, and performing quick operation response by a flight control system. The abnormal discharge frequency spectrum detection circuit sends an instruction to perform autonomous obstacle avoidance, or realizes detection and obstacle avoidance of the power transmission line by detecting the electromagnetic field frequency, the electromagnetic field intensity and the electromagnetic field direction, and outputs an analysis result through a display module connected with the abnormal discharge frequency spectrum detection circuit.

Description

Technical Field

[0001] The present invention relates to the application field of unmanned aerial vehicle obstacle avoidance, and in particular to a high-frequency electromagnetic wave detection circuit unit carried by an unmanned aerial vehicle in the fields of high-frequency electromagnetic wave detection technology and electric power inspection technology. Background Art

[0002] As an emerging field, drones are developing rapidly and are applied in various industries. In the future, the use of drones will be more extensive. Therefore, the obstacle avoidance function of drones is particularly important. The obstacle avoidance function will greatly improve the safety of drones during flight and is a prerequisite for ensuring the navigation of drones. In recent years, due to the wide coverage area of ​​overhead transmission lines, the complex terrain and harsh natural environment of the areas where drones cross, the electric field and magnetic field generated by high-voltage power supplies are easy to interfere with the onboard electronic equipment of drones, and they cannot work normally, resulting in attitude imbalance, fixed-point hovering deviation and drift, flight out of control, and in serious cases, collision with transmission lines and crashes. The impact of transmission lines on electromagnetic fields Electromagnetic fields interfere with the normal operation of electronic equipment such as communications, navigation, radio and television Transmission lines generate electromagnetic fields, which affect the electromagnetic field strength of the surrounding environment. It is related to the voltage, current, frequency and other factors of the transmission lines. Electromagnetic fields may also have an impact. The electromagnetic interference of transmission lines will have a significant impact on the control and communication performance of drones, and even seriously threaten the application of drone inspections in the operation and maintenance of transmission lines.

[0003] The obstacle avoidance problem of unmanned aerial vehicles mainly refers to designing a method that enables the unmanned aerial vehicle to safely reach the target point from the starting point under a specified flight environment. This method not only needs to consider how to enable the unmanned aerial vehicle to safely avoid obstacles, but also needs to meet the requirements of the corresponding flight track and the physical constraints of the unmanned aerial vehicle itself. Having efficient obstacle avoidance capability has become one of the important guarantees for unmanned aerial vehicles to safely complete flight missions. Therefore, the obstacle avoidance problem of unmanned aerial vehicles during flight has gradually attracted the attention of many experts and scholars. In traditional unmanned aerial vehicle obstacle avoidance methods, most of the obstacle distance detection technologies use optical flow method and motion reconstruction object structure method. Due to the huge amount of calculation in the optical flow method and motion reconstruction object structure algorithm, it is difficult to extract enough feature points to achieve the depth measurement of the target obstacle, and it takes a long time to detect and calculate the obstacle position. Although the binocular recognition currently used by unmanned aerial vehicles has extremely powerful functions, it still has certain limitations. It is still difficult to achieve omnidirectional obstacle avoidance based on binocular vision. There are two reasons: one is that the two visual receiving systems after turning 90° will overlap in the processing of boundary points, resulting in deviations in the calculation results and even errors; the other is that the noise interference at different angles is different, and data processing cannot be realized synchronously.

[0004] In the power system, the failure rate of power cables is relatively high. Domestic and foreign operating experience and research results show that the early deterioration of power cable performance or service life depends largely on the dendritic aging of its insulating medium, especially the occurrence of corona discharge in overhead lines, which will lead to safety hazards in the line and cause more serious accidents. The causes of corona discharge in overhead lines are: in the early stage of cable commissioning and the end of cable operation, it is affected by the cable joint manufacturing process, cable body insulation dendritic aging, electro-thermal aging and accessory aging. Secondly, there are poor contact between the line and insulator hardware, dirty insulator surface, insulator aging, non-destructive electrical or line strand breakage of cable insulation (especially plastic cable), etc. These defects will form points where the electric field strength is locally enhanced, which may cause the electric field strength to exceed the breakdown strength of air, causing local discharge or corona discharge. The occurrence of corona discharge will cause overhead line power consumption and generate high-frequency pulse current, in which various high-order harmonics will cause radio interference. It will trigger a series of chemical reactions, produce ozone, nitric oxide, nitrogen dioxide and other highly corrosive substances, and cause serious line failures. Partial discharge in certain weak parts under the action of strong electric fields is a common problem in high-voltage insulation. Partial discharge is one of the potential threats faced by power grid operation and maintenance. Abnormal discharge electromagnetic waves are high-frequency electromagnetic waves, with a general frequency band of several hundred MHz. The bandwidth of partial discharge ultra-high frequency signals is as high as several GHz. Especially in the coastal area, the "three highs" climate will greatly increase the probability of flashover accidents. The cost of real-time sampling is very high; its envelope signal contains the defect characteristics of discharge. When partial discharge occurs, ultra-high frequency electromagnetic signals will be generated at the same time (the highest frequency can reach 3GHz). The ultra-high frequency electromagnetic signals will radiate to the surroundings. If they encounter metal shielding, they will be transmitted along the outer wall of the metal shell and radiate to the outside world. Therefore, the ultra-high frequency electromagnetic signals generated when partial discharge occurs can be received by capacitive or antenna ultra-high frequency sensors. Because the weak electromagnetic field interference signals are both highly concealed and changeable with the environment, they can only be monitored offline during detection, but the cost is high. There are great risks when operators carry equipment to the tower for online inspection. The actual operation is complicated and affected by factors such as weather (wind speed, etc.) and the charge of the huge hydrogen balloon itself. There are many measurement points and the workload on site is huge, making it difficult to implement. Through years of technical research and development and practice, based on the characteristics of drones such as accurate positioning, hovering in the air, and path cruising, it is possible to use drones equipped with electromagnetic field measuring instruments to measure the spatial electromagnetic fields of large-scale transmission facilities. Drone inspection technology has developed into an efficient, low-cost, and low-risk aerial inspection technology. It has greatly improved the inspection efficiency. However, the existing ranging technology has certain difficulties in application. Therefore, in the current drone inspection, most of the on-site operators still judge by naked eyes, which is extremely demanding for the staff and has certain safety hazards. In drone inspections, how to avoid collisions with transmission lines is the key to ensuring the safety of drones and power transmission.At present, the most widely used technologies are millimeter wave radar obstacle avoidance technology and patrol obstacle avoidance technology based on geographic information system navigation. However, millimeter wave radar obstacle avoidance technology has the problem of how to filter reflected waves; patrol obstacle avoidance technology based on geographic information system navigation has the problem of not being able to approach the transmission line at close range, and sometimes it is impossible to conduct clear patrol inspection and shooting of the transmission line, which is not conducive to the accuracy of fault diagnosis. When far away from the transmission line, the equal electric field value line tends to be semicircular, while when close to the transmission line, the equal electric field value line is irregular. It is impossible to simply obtain the distance between the observation point and the transmission line from the detected electric field value. At present, the single electric field, magnetic field and electric field and magnetic field gradients are not strictly monotonic on both sides of the positive and negative semi-axis, and cannot be used as an effective characteristic quantity of the distance. Due to the fact that the drone patrol process is completely in a strong electromagnetic field environment and the highly concentrated characteristics of the drone equipment, drones often encounter various electromagnetic interference problems. Any electronic device will generate electromagnetic interference (EMI) to varying degrees, and drones are no exception. Electromagnetic interference (EMI) of a certain frequency and power may interfere with other electronic devices around it. The interference signal will be transmitted from one electrical network to another through the conductive medium, causing malfunction or even accidents. Therefore, the requirements for discharge detection circuits are relatively high. Different detection technologies can make the obstacle avoidance ability of drones vary greatly, such as environment and weather. For image processing, light reflection will affect the detection results of the camera in the drone. Another example is the most widely used. Infrared Or laser ranging drone detection technology, because it is through Signal It is calculated by the computer, so it is easily disturbed by external factors, and it is difficult to complete a detailed description of the surrounding environment. There are certain limitations in obstacle avoidance. In actual operations, the performance of obstacle avoidance in complex environments is not satisfactory. For small objects such as wires, it cannot be completely accurately identified, and the function for automatic obstacle avoidance is not very suitable. Especially in drone obstacle avoidance technology, how to detect objects quickly and accurately is still a challenge.

[0005] The inspection technology of the high-frequency electromagnetic wave detection unit of the existing high-frequency electromagnetic wave detection circuit system has limitations: there are safety hazards, manual operation is required, autonomous obstacle avoidance electromagnetic field detection cannot be achieved, and the problem of drone obstacle avoidance needs to be solved. The detection band of the ultra-high frequency antenna used for partial discharge detection is required to be between 300-1000MHz, and the size must not be too large. Traditional electrostatic discharge ESD detection systems based on linearly polarized antennas are prone to polarization mismatch. The ultra-high frequency antennas currently used for partial discharge detection include loop antennas, planar spiral antennas, Hilbert fractal antennas, etc., which generally have large size, low sensitivity, single frequency band or small gain, narrow detection band, and cannot capture weak partial discharge signals. Summary of the invention

[0006] The purpose of the present invention is to address the problems and shortcomings of the prior art and to provide a high-frequency electromagnetic wave detection circuit unit for drones that can provide accurate information for drone obstacle avoidance, facilitate safe flight control of drones, quickly and accurately detect power transmission line faults, detect the electric field and magnetic field strengths at different distances between drones and high-voltage transmission lines, and improve inspection efficiency.

[0007] The above-mentioned purpose of the present invention can be achieved through the following technical scheme: a high-frequency electromagnetic wave detection circuit unit carried by an unmanned aerial vehicle, comprising: a signal acquisition and obstacle avoidance module based on stereoscopic vision, a binocular ranging sensor module and an abnormal discharge spectrum detection circuit equipped with an adapted unmanned aerial vehicle, and two touch-scene vision card modules supporting multi-directional movement, characterized in that: the signal acquisition and obstacle avoidance module is connected to the high-frequency signal sampling circuit, and electromagnetic field detection is performed through a micro-antenna designed based on the distribution characteristics of the corona discharge spectrum, the electric field, electromagnetic wave intensity, electromagnetic field direction and electromagnetic field frequency of the position of the unmanned aerial vehicle are detected, the high-frequency target signal is obtained, and the electromagnetic field signal collected in real time is preprocessed; the abnormal discharge spectrum detection circuit filters and reduces the noise of the electromagnetic field signal collected in real time, and then performs preprocessing operations and feature extraction on the data, and the extracted frequency and amplitude are extracted. The characteristics of the degree and phase electromagnetic field signals are used as sensor detection data, and the result data obtained by the binocular ranging sensor module is imported into the signal acquisition and obstacle avoidance module; the signal acquisition and obstacle avoidance module classifies the electromagnetic field signals according to the feature extraction results, analyzes the classified electromagnetic field signals, analyzes the actual three-dimensional electromagnetic field distribution of the electromagnetic field intensity, the change of the electromagnetic field intensity, and the electromagnetic field type, identifies the three-dimensional information of the obstacle, and judges whether the drone is in a safe range based on visual perception. The obstacle avoidance flight instruction is obtained through the calculation of the PC, and the obstacle and the environment are perceived through image processing. The flight control system responds quickly and issues instructions for autonomous obstacle avoidance, or detects the electromagnetic field frequency, electromagnetic field intensity and electromagnetic field direction to realize the detection and obstacle avoidance of the transmission line, and outputs the analysis results through the display module connected to the abnormal discharge spectrum detection circuit.

[0008] Compared with the prior art, the present invention has the following beneficial effects:

[0009] The present invention combines the radio frequency electromagnetic wave detection method with the drone. The drone is equipped with a high-frequency electromagnetic wave detection unit to detect the electric field and electromagnetic wave intensity at the location of the drone, and judge whether the drone is in a safe range. The detection data is wirelessly transmitted to the ground control center or directly provided to the drone flight control processing unit to achieve safe flight control of the drone and the obstacle avoidance function of the drone, thereby solving the safety and flight efficiency of the drone flight. At the same time, it can also overcome the harsh geographical environment problems often encountered during manual inspections, and plays a very important role in maintaining a safe distance for drones during line inspection operations. Compared with traditional manual inspections, drone power transmission line inspections are not restricted by terrain and environment, have a wide viewing angle, high accuracy, and good safety.

[0010] The present invention adopts a binocular distance measurement sensor module based on binocular stereo vision technology, uses two cameras to imitate the binocular effect of the human eye, obtains the distance information of the three-dimensional scene in space, and uses the intelligent "touch scene visual card" module to perceive more complex environmental parameters. The embedded characteristics of the visual card are not only small in size (square centimeters, equivalent to a 1 yuan coin), fast in speed (processing speed can reach 20FPS), and resistant to ambient light interference (ambient light 10-10000 lumens), but also multi-directional movement (3 directions of binocular 6 cameras) The obstacle avoidance range can reach 0.8-15 meters. At a distance of 1.4 meters from the visual card camera, it can identify obstacles as thick as electric wires, and can also avoid calling server resources. Equipped with two touch-scene vision card modules that support multi-directional movement, five-way obstacle avoidance can be basically achieved. There are no constraints on detection and recognition. The binoculars calculate the depth through parallax, and the depth calculation accuracy is very high with smaller errors. There is no need to consume additional resources to update the database, and it can effectively solve the problem of obstacle target detection in different viewing angles and different occlusion situations and the industry pain points of "front-end intelligence" of front-end video acquisition + intelligent analysis-back-end storage. Provide more accurate crossing coordinates for drones. In terms of cost, the binocular ranging sensor module is much cheaper than sensors such as lidar and structured light.

[0011] The present invention aims at the potential safety hazard of electromagnetic field radiation of high-voltage transmission lines to power line inspection drones. Based on electromagnetic field detection, the signal acquisition obstacle avoidance module and abnormal discharge spectrum detection circuit equipped with an adaptive drone are used to determine the situation of corona discharge electromagnetic radiation, timely grasp the operation of power system equipment, detect the electric field and magnetic field strength at different distances between the drone and the high-voltage transmission line, realize close-range detection of corona discharge of transmission equipment, and effectively support the safe and stable operation of the power system. In addition, weak electromagnetic field interference signals can be identified and amplified, solving the problem of large safety risks and various electromagnetic interference problems caused by workers carrying radio frequency electromagnetic wave detection devices to climb towers due to the high height of high-voltage overhead transmission lines.

[0012] The present invention adopts a stereoscopic vision signal acquisition obstacle avoidance module, which can assist the drone to recognize the surrounding scene faster and better, and realize automatic obstacle avoidance through stereoscopic vision recognition. Its stereoscopic vision is based on the principle of parallax. Machine Vision It is an important form of detection. It uses imaging equipment to obtain images of the object to be measured from different positions, and obtains the distance information of the three-dimensional scene in space and the three-dimensional information of the target object by calculating the position deviation between the corresponding points of the image. The information obtained has a decisive influence on the obstacle avoidance effect. In the obstacle avoidance technology of drones, the stereoscopic vision technology used in the signal acquisition obstacle avoidance module has the advantages of simple system structure, low cost, high efficiency, and appropriate accuracy. Compared with traditional ultrasonic, infrared, and laser detection, stereoscopic vision has high accuracy and is not easily affected by external interference. The basic principle of the electromagnetic field detection technology used is that the electromagnetic field frequency detection is to detect and avoid obstacles on the transmission line by detecting the electromagnetic field frequency. The signal acquisition obstacle avoidance module collects abnormal discharge micro-antenna signals designed according to the distribution characteristics of the corona discharge spectrum, performs electromagnetic field strength detection, electromagnetic field direction detection, and electromagnetic field frequency detection, collects electromagnetic field signals in real time, and performs data preprocessing, which can realize close-range discharge detection on drones, and provides a new and convenient detection method for overhead line corona discharge detection, which allows drones to make more accurate and smooth action choices.

[0013] The micro-antenna finite element model design and simulation method of the present invention can be used as an effective characteristic quantity of distance, and the electric field or magnetic field values ​​at different heights can be used as a characteristic quantity for judging the distance of the transmission line. The abnormal discharge micro-antenna signal acquisition designed according to the corona discharge spectrum distribution characteristics is used to perform electromagnetic field intensity detection, electromagnetic field direction detection and electromagnetic field frequency detection. The abnormal discharge micro-antenna with a Hilbert fractal structure equipped with an adaptive drone has the characteristics of miniaturization and lightness, and has a space filling characteristic. Its strong space filling ability is conducive to the miniaturization design of the antenna. By utilizing the self-similarity and space filling property of the fractal structure, while increasing the bandwidth, the gain is increased and the size is reduced, so that the antenna can have a shorter size while keeping the basic radiation characteristics unchanged. Problems such as the size and bandwidth of the fractal spiral antenna can be effectively improved. While having the size reduction characteristic, the efficiency of the antenna is effectively kept from dropping sharply. The space filling property of the fractal structure can reduce the size of the antenna, and the self-similarity of the fractal structure can increase the resonance points and widen the bandwidth; when the antenna starts working, the different current paths generated by these similar fractal structures interact with each other to produce different resonance points, and the number and position of the resonance points are changed, which can not only reduce the size of the device, but also improve the impedance matching effect. The optimal impedance matching characteristics of the antenna can effectively avoid polarization mismatch and improve the shortcomings of the antenna such as narrow frequency band and large volume. The interaction of the fractal radiation unit during operation changes the electromagnetic radiation distribution of the entire space. The rectangular spiral fractal phase center has a convergence effect on the reflected electromagnetic waves, which can enhance the radiation intensity of the axial area of ​​the antenna. After multiple simulations and optimizations, it can be found that the rectangular fractal spiral dual-section antenna has a wider bandwidth. The performance of the optimized antenna has been significantly improved, with stronger signal receiving capability and wider frequency band. Since the rectangular fractal spiral bipartite antenna is a miniaturized antenna structure with good radiation directivity, it can monitor the electromagnetic field strength of the transmission line in real time. Compared with the two-dimensional Hilbert tag antenna and ordinary antenna, the rectangular fractal spiral bipartite antenna has more advantages: it has multi-band and wide-band characteristics, especially it has similar radiation characteristics when working in multiple frequency bands. This miniaturized fractal structure antenna can achieve high-efficiency resonance at 915MHz, improving the inspection efficiency. Therefore, the fractal antenna designed according to the curve has the characteristics of multi-band and size reduction, which can meet the requirements of small size and wide bandwidth for partial discharge detection antenna of electrical equipment.

[0014] In order to verify the performance of the system, the present invention conducted field measurements on a 500kV overhead transmission line and verified the discharge detection function of the antenna, so that the antenna can meet the detection of abnormal discharge electromagnetic wave frequency bands under the condition of a smaller size structure and meet the requirements of discharge detection sensitivity. By operating the drone to detect the vicinity of the insulator hardware on the left side, right side, and jump string of the overhead line tower, the discharge signals of different parts were successfully measured, and the relative strength of the discharge could be distinguished. The experimental results show that the electromagnetic field detection technology used can be applied to the maintenance and inspection of transmission lines, and can also be applied to fault diagnosis and early warning of transmission lines, improving the operational reliability and stability of the lines as well as work efficiency and safety.

[0015] The present invention can use the electric field or magnetic field values ​​at different heights as characteristic quantities for judging the distance of the transmission line, judging whether the drone is in a safe range, detecting the distance between the drone and the high-voltage power line and the magnetic field strength, and then realizing the obstacle avoidance function of the drone. By detecting the electromagnetic field frequency, electromagnetic field strength and electromagnetic field direction, the detection and obstacle avoidance of the transmission line can be realized, and the abnormal discharge high-frequency spectrum detection circuit outputs the analysis result through the display module. This method combines detection with drones, and through the drone, a high-mobility, long-endurance flying platform, it can replace manual tower climbing, and use the drone to carry wireless remote control equipment, high-definition image acquisition and transmission equipment, and auxiliary sensor modules. The wireless transmission module sends the image back to the ground operating platform for analysis. The present invention can also be applied to the electromagnetic field detection of transmission lines and high-voltage towers, real-time monitoring of the electromagnetic field strength of transmission lines, and can also be applied to industrial inspections, planning inspection routes, real-time tracking and positioning, automatic obstacle avoidance, etc., to improve inspection efficiency and safety. And there is no need for operators to go to the bottom of the tower in person, with high efficiency and no inspection "blind spots". BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the circuit principle of the high-frequency electromagnetic wave detection circuit unit carried by an unmanned aerial vehicle of the present invention;

[0017] Figure 2 is a planar schematic diagram of a fractal spiral antenna structure;

[0018] Figure 3 yes Figure 2 Schematic diagram of the cup-shaped fractal spiral radiation patch as the basic unit using the finite element method.

[0019] The technical solutions in the embodiments of the present application are clearly and completely described below in conjunction with the drawings in the embodiments of the present application, but the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. DETAILED DESCRIPTION

[0020] See also Figure 1. In the preferred exemplary embodiment described below, a high-frequency electromagnetic wave detection circuit unit carried by an unmanned aerial vehicle includes: an adaptive unmanned aerial vehicle, a signal acquisition and obstacle avoidance module based on stereoscopic vision, a binocular ranging sensor module and an abnormal discharge spectrum detection circuit, and two touch-scene vision card modules supporting multi-directional movement, wherein: the signal acquisition and obstacle avoidance module is connected to the high-frequency signal sampling circuit, and electromagnetic field detection is performed through a micro-antenna designed based on the distribution characteristics of the corona discharge spectrum to detect the electric field, electromagnetic wave intensity, electromagnetic field direction and electromagnetic field frequency at the location of the unmanned aerial vehicle, obtain high-frequency target signals, and perform data preprocessing on the electromagnetic field signals collected in real time; the abnormal discharge spectrum detection circuit filters and reduces the noise of the electromagnetic field signals collected in real time, and then performs preprocessing operations and feature extraction on the data, and extracts the extracted frequency, amplitude, phase The characteristics of the electromagnetic field signal are used as sensor detection data, and the result data obtained by the binocular ranging sensor module is imported into the signal acquisition and obstacle avoidance module; the signal acquisition and obstacle avoidance module classifies the electromagnetic field signal according to the feature extraction result, analyzes the classified electromagnetic field signal, analyzes the actual three-dimensional electromagnetic field distribution of the electromagnetic field intensity, the change of the electromagnetic field intensity, and the electromagnetic field type, identifies the three-dimensional information of the obstacle, and judges whether the drone is in a safe range based on visual perception. The obstacle avoidance flight instruction is obtained through the calculation of the PC, and the obstacle and the environment are perceived through image processing. The flight control system responds quickly and issues instructions for autonomous obstacle avoidance, or detects the electromagnetic field frequency, electromagnetic field intensity and electromagnetic field direction to realize the detection and obstacle avoidance of the transmission line, and outputs the analysis results through the display module connected to the abnormal discharge spectrum detection circuit.

[0021] The signal acquisition and obstacle avoidance module detects the electric field and magnetic field strength of the circuit by designing the electromagnetic field sensing unit, and uses the electric field or magnetic field values ​​at different heights as the characteristic quantity for judging the distance of the transmission line. It detects the magnetic field strength between the drone and the high-voltage power line, and transmits the processed measurement data results wirelessly to the ground flight control center or the drone flight control system to guide the drone to perform safe line patrol operations. At the same time, the processing results are also transmitted to the ground end through the wireless transceiver module. After being processed by the ground management software, the electric field and magnetic field strength, direction information and distance from the high-voltage transmission line of the drone are provided to the drone operator or the drone flight control unit. At the same time, these data will also be displayed on the PC screen.

[0022] In the image acquisition and preprocessing stage, a binocular camera is used as a visual sensor. Through its software development kit SDK, the software source is configured to acquire images of the surrounding environment, convert the video data into an image sequence, and remove the discharge signal, background noise and image noise through Gaussian filtering and median filtering. The Gaussian filter smoothes the image in a low-pass manner. These two filters are used in the system to make the image smoother and eliminate the problems caused by noise.

[0023] The binocular distance measurement sensor module uses the touch scene vision card module to simulate the human eye vision to intelligently perceive the surrounding environment parameters and obtain the distance information of the three-dimensional scene in space. By continuously sending light pulses to the target and then using the binocular distance measurement sensor module to receive the light returned from the object, the distance to the measured target and the three-dimensional information of the target are obtained by detecting the round-trip flight time of the light pulse, thus completing the three-dimensional detection of the target object and calculating the accurate coordinate position.

[0024] The touch scene vision card module uses a binocular stereo camera to capture two left and right image pairs at the same time, extracts image feature points and performs stereo matching on the left and right views, calculates corresponding matching points in the image pairs, uses trigonometric relationships to calculate the disparity of the target point in the left and right images, measures and calculates the stereo information of environmental objects, and thereby calculates a stereo depth map of the environment, reconstructing a three-dimensional image and point cloud map of the surrounding space.

[0025] The airborne module of the signal acquisition and obstacle avoidance module is located on the UAV, and includes an electromagnetic field sensing unit, a signal filtering and conditioning module, an obstacle avoidance control processing unit and a wireless transmission unit. The electromagnetic field sensing unit senses the magnetic field strength of the electric field. The signal filtering and conditioning module uses a high-speed operational amplifier to build a filtering circuit. The distance information and depth information sensed by the electromagnetic field sensing unit are filtered and integrated through the filtering circuit to obtain the distance between the UAV and the obstacle in front. At the same time, a weak signal preamplifier circuit is built by using a high-sensitivity triode preamplifier for amplification. The amplified electromagnetic field signal is sent to the A / D converter for digital processing of A / D conversion to obtain the magnetic field strength in three orthogonal directions, and the direction of the magnetic field is calculated based on this.

[0026] The obstacle avoidance control processing unit measures the electric field strength in three orthogonal directions through three mutually orthogonal capacitors, calculates the threat factor of the obstacle based on the distance, finds the ideal safe area by comparing the distance and the threshold, and then provides flight instructions for the drone based on the (x, y) coordinates of the detection area of ​​the binocular ranging sensor module. At the same time, the built-in autonomous obstacle avoidance algorithm software detects whether the A / D sampling is completed, analyzes the data and determines whether the field strength exceeds the limit, determines whether it exceeds the limit according to the set alarm limit and issues a warning signal, and continuously loops through the obstacle avoidance algorithm software program to achieve uninterrupted measurement. The changing trend of the measurement data reflects the distance between the drone and the high-voltage transmission line. When the detection data exceeds the set warning threshold, an alarm is given.

[0027] The autonomous obstacle avoidance algorithm software analyzes the threat points of spatial obstacles, calculates the minimum collision time under collision conditions, determines the expected speed and direction based on the obstacle position, and at the same time, divides the optional speed in the simulation iteration calculation into two equal parts, determines the obstacle avoidance speed based on the principle of minimum penalty factor, and achieves its autonomous obstacle avoidance purpose. It ensures that it can safely reach the target in a complex environment and improves flight stability and safety.

[0028] The obstacle avoidance control processing unit receives the control command of the ground PC and receives the measurement data of the binocular ranging sensor module, processes and calculates the data, communicates with the flight control main chip through the serial port, sends the data after data calculation to the airborne wireless transceiver unit, and sends the collected and processed data to the ground wireless receiving module through the wireless sending unit. In this embodiment, the airborne wireless transceiver unit and the ground wireless transceiver module both use industrial-grade wireless transmission modules. The module uses a low-intermediate frequency transceiver SX1278 main chip for half-duplex transmission compatible with 3.3V and 5V I / O port voltages, and transmits through long-distance radio LORA remote modem spread spectrum transmission, transistor logic circuit TTL level output, the module uses its high coding efficiency, strong error correction capability, and efficient cyclic interleaving error correction coding algorithm. In the case of sudden interference, it actively corrects the interfered data packet, with a maximum continuous error correction of 64 bits, which greatly improves the module's anti-interference and transmission distance. In the absence of an error correction algorithm, this erroneous data packet can only be discarded. LORA spread spectrum enables the module to have a longer communication distance.

[0029] The signal acquisition obstacle avoidance module uses binocular stereo camera signal acquisition Asin(w o t+φ) and the trigonometric function model signal are sent to the abnormal discharge high-frequency spectrum detection circuit. First, the correctness of the circuit is verified in the ADS RF circuit simulation software, and then the actual circuit is made and debugged. Then, the signal detected by the spectrum is debugged in the signal spectrum detection circuit by using the phase-sensitive detection principle. The sinusoidal signal sinw generated by the DDS signal source on the abnormal discharge high-frequency spectrum detection circuit board is detected. o t performs phase-locked correlation calculation with the abnormal discharge electromagnetic wave induced voltage signal output by the antenna to realize envelope detection of the discharge electromagnetic wave, where A is the amplitude, t is the period, and φ is the phase.

[0030] Sinw generated by DDS signal source oThe t signal is phase-shifted by 90° and is sent to two parallel multipliers to calculate the distance between the two points using the conversion of the geographic coordinate system and the GPS information. The automatic adjustment of the UAV gimbal angle is achieved based on the GPS information, so that the binocular ranging sensor module can track and shoot the towers in the inspection line during the inspection process, and determine the magnitude of the input signal. Then, the Asinφ0 and Acosφ0 of the 2-fold frequency signal are filtered out through the two-way filter circuit debugging module with the initial phase φ0. The analog quantities after comparison with the standard quantity or reference quantity by the two-way analog-to-digital converter ADC are converted into binary values. A unique digital code is assigned to each magnitude, and the code corresponding to the input signal is determined. The digital signals X and Y are processed and converted into digital signals, and sent to the display module.

[0031] See also Figure 2 Design and simulation of micro-antenna finite element model. The abnormal discharge micro-antenna adopts the finite element method to design the cup fractal spiral radiation patch, and uses the Hilbert fractal curve as the basic unit of the rectangular spiral to establish two rectangular fractal spiral lines that interlock with each other in a clockwise direction, forming a double fractal spiral structure in the rectangular area. Finally, the finite element model of the micro-antenna is established by the three-dimensional electromagnetic simulation software (HFSS).

[0032] The two clockwise interlocking fractal spiral antenna curves are continuous and have no intersections. They have strict self-similarity and space filling properties. The micro-antenna finite element model design and simulation method are used as effective characteristic quantities of distance. Therefore, the electric field or magnetic field values ​​at different heights can be used as characteristic quantities to judge the distance of the transmission line.

[0033] The antenna finite element model consists of a dielectric substrate, a microstrip antenna patch, a base cup feed probe and a ground plane. A metal ground plane is printed on the back of the dielectric substrate. The rectangular slotted microstrip patch controls the distribution and flow of current by etching a self-similar fractal slot shape that can match the impedance between the feeder and the radiator on the copper-clad dielectric board, generating electromagnetic waves radiating outward. The rectangular slots on the metal radiating microstrip patch improve the radiation characteristics and matching degree, and increase the antenna impedance bandwidth.

[0034] See also Figure 3 . Hilbert foot cup fractal spiral radiation patch bottom base foot cup top patch left and right side metal microstrip width d = L / 2 n -1, the fractal radiation patch on the left realizes the low frequency band, and the fractal radiation patch on the right realizes the high frequency band. Referring to the bending line, the total length of the fractal spiral antenna guiding curve S = (2 2n -1)d=(2 n+1)L, where L is the width of the top of the fractal microstrip and n is the fractal order. Moreover, a wide-band fourth-order fractal antenna with good directivity and a lower frequency limit of 300MHz and an upper frequency limit of more than 1GHz can be designed.

[0035] Abnormal discharge micro-antenna structure optimization design. The abnormal discharge micro-antenna uses ANSYS electromagnetic simulation software to simulate and calculate the antenna radiation directivity, return loss and other characteristics, and uses genetic algorithm and particle swarm algorithm to automatically simulate intelligent algorithms, conduct discharge detection experimental analysis on the antenna, test return loss, directional pattern, gain and other related performance parameters, compare the relationship between its resonant frequency, antenna efficiency and directional pattern and the fractal order, verify the discharge detection function of the antenna, use HFSS for simulation optimization, simulate and calculate the configuration of the optimal antenna model parameters, calculate the fractal antenna parameters and optimization selection, and optimize the fractal spiral antenna structure parameters, broaden the microstrip patch detection band, and use the binary code decoding of each fractal radiation patch as the ultra-high frequency structural parameters to calculate the dimensions of each aspect of the antenna model, select a set of optimal parameters to make the antenna, and then process the fractal spiral antenna into a physical antenna, so that the antenna can meet the detection of abnormal discharge electromagnetic wave frequency bands under the condition of a smaller size structure, and meet the requirements of discharge detection sensitivity. Moreover, after multiple simulations and optimizations, it can be found that the fractal antenna has a wider bandwidth and a longer current path.

[0036] The embodiments in this application and all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

Claims

1. A high-frequency electromagnetic wave detection circuit unit for an unmanned aerial vehicle, comprising: The system is equipped with an adaptive drone, a signal acquisition and obstacle avoidance module based on stereo vision, a binocular ranging sensor module and an abnormal discharge spectrum detection circuit, as well as two touch-scene vision card modules that support multi-directional movement. The system is characterized in that: the signal acquisition and obstacle avoidance module is connected to the high-frequency signal sampling circuit, and electromagnetic field detection is performed through a micro-antenna designed based on the distribution characteristics of the corona discharge spectrum to detect the electric field, electromagnetic wave intensity, electromagnetic field direction and electromagnetic field frequency at the location of the drone, obtain high-frequency target signals, and perform data preprocessing on the electromagnetic field signals collected in real time; the abnormal discharge spectrum detection circuit filters and reduces noise on the electromagnetic field signals collected in real time, and then performs preprocessing operations and feature extraction on the data, and uses the extracted frequency, amplitude and phase electromagnetic field signal features as sensor detection data. The result data obtained by the binocular ranging sensor module is imported into the signal acquisition and obstacle avoidance module; the signal acquisition and obstacle avoidance module classifies the electromagnetic field signal according to the feature extraction result, analyzes the classified electromagnetic field signal, analyzes the actual three-dimensional electromagnetic field distribution of the electromagnetic field intensity, the change of the electromagnetic field intensity, and the electromagnetic field type, identifies the three-dimensional information of the obstacle, and judges whether the UAV is in a safe range based on visual perception. The obstacle avoidance flight instruction is obtained through the calculation of the PC, and the obstacle and the environment are perceived through image processing. The flight control system responds quickly and issues instructions for autonomous obstacle avoidance, or detects the electromagnetic field frequency, electromagnetic field intensity and electromagnetic field direction to realize the detection and obstacle avoidance of the transmission line, and outputs the analysis results through the display module connected to the abnormal discharge spectrum detection circuit.

2. The unmanned aerial vehicle high-frequency electromagnetic wave detection circuit unit according to claim 1, characterized in that: The signal acquisition and obstacle avoidance module detects the electric field and magnetic field strength of the circuit by designing the electromagnetic field sensing unit, and uses the electric field or magnetic field values ​​at different heights as the characteristic quantity for judging the distance of the transmission line. It detects the magnetic field strength between the drone and the high-voltage power line, and transmits the processed measurement data results wirelessly to the ground flight control center or the drone flight control system to guide the drone to perform safe line patrol operations. At the same time, the processing results are also transmitted to the ground end through the wireless transceiver module. After being processed by the ground management software, the electric field and magnetic field strength, direction information and distance from the high-voltage transmission line of the drone are provided to the drone operator or the drone flight control unit. At the same time, these data will also be displayed on the PC screen.

3. The unmanned aerial vehicle high-frequency electromagnetic wave detection circuit unit according to claim 1, characterized in that: In the image acquisition and preprocessing stage, a binocular camera is used as a visual sensor. Through its software development kit SDK, the surrounding environment image is acquired by configuring the software source, and the video data is converted into an image sequence. The discharge signal, background noise and image noise are removed through Gaussian filtering and median filtering. The Gaussian filter smoothes the image in a low-pass manner.

4. The high-frequency electromagnetic wave detection circuit unit for unmanned aerial vehicles according to claim 1, characterized in that: The binocular distance measurement sensor module uses the touch scene vision card module to simulate the human eye vision to intelligently perceive the surrounding environment parameters and obtain the distance information of the three-dimensional scene in space. By continuously sending light pulses to the target and then using the binocular distance measurement sensor module to receive the light returned from the object, the distance to the measured target and the three-dimensional information of the target are obtained by detecting the round-trip flight time of the light pulse, thus completing the three-dimensional detection of the target object and calculating the accurate coordinate position.

5. The unmanned aerial vehicle-mounted high-frequency electromagnetic wave detection circuit unit according to claim 4, characterized in that: The touch scene vision card module uses a binocular stereo camera to capture two left and right image pairs at the same time, extracts image feature points and performs stereo matching on the left and right views, calculates corresponding matching points in the image pairs, uses trigonometric relationships to calculate the disparity of the target point in the left and right images, measures and calculates the stereo information of environmental objects, and thereby calculates a stereo depth map of the environment, reconstructing a three-dimensional image and point cloud map of the surrounding space.

6. The unmanned aerial vehicle-mounted high-frequency electromagnetic wave detection circuit unit according to claim 1, characterized in that: The airborne module of the signal acquisition and obstacle avoidance module is located on the UAV, and includes an electromagnetic field sensing unit, a signal filtering and conditioning module, an obstacle avoidance control processing unit and a wireless transmission unit. The electromagnetic field sensing unit senses the magnetic field strength of the electric field. The signal filtering and conditioning module uses a high-speed operational amplifier to build a filtering circuit. The distance information and depth information sensed by the electromagnetic field sensing unit are filtered and integrated through the filtering circuit to obtain the distance between the UAV and the obstacle in front. At the same time, a weak signal preamplifier circuit is built by using a high-sensitivity triode preamplifier for amplification. The amplified electromagnetic field signal is sent to the A / D converter for digital processing of A / D conversion to obtain the magnetic field strength in three orthogonal directions, and the direction of the magnetic field is calculated based on this.

7. The unmanned aerial vehicle-mounted high-frequency electromagnetic wave detection circuit unit according to claim 6, characterized in that: The obstacle avoidance control processing unit measures the electric field strength in three orthogonal directions through three mutually orthogonal capacitors, calculates the threat factor of the obstacle based on the distance, finds the ideal safe area by comparing the distance and the threshold, and then provides flight instructions for the drone based on the (x, y) coordinates of the detection area of ​​the binocular ranging sensor module. At the same time, the built-in autonomous obstacle avoidance algorithm software detects whether the A / D sampling is completed, analyzes the data and determines whether the field strength exceeds the limit, determines whether it exceeds the limit according to the set alarm limit and issues a warning signal, and continuously loops through the obstacle avoidance algorithm software program to achieve uninterrupted measurement. The changing trend of the measurement data reflects the distance between the drone and the high-voltage transmission line. When the detection data exceeds the set warning threshold, an alarm is given.

8. The high-frequency electromagnetic wave detection circuit unit for unmanned aerial vehicles according to claim 1, characterized in that: The autonomous obstacle avoidance algorithm software analyzes the threat points of spatial obstacles, calculates the minimum collision time under collision conditions, determines the expected speed and direction based on the position of the obstacle, and at the same time, divides the optional speed in the simulation iterative calculation into two equal parts, and determines the obstacle avoidance speed based on the principle of minimum penalty factor to achieve its autonomous obstacle avoidance purpose.

9. The high-frequency electromagnetic wave detection circuit unit for unmanned aerial vehicles according to claim 6, characterized in that: The obstacle avoidance control processing unit receives control instructions from the ground PC and the measurement data from the binocular ranging sensor module, processes and calculates the data, communicates with the flight control main chip through the serial port, sends the processed data to the airborne wireless transceiver unit, and sends the collected and processed data to the ground wireless receiving module through the wireless sending unit.

10. The unmanned aerial vehicle-mounted high-frequency electromagnetic wave detection circuit unit according to claim 1, characterized in that: The signal acquisition obstacle avoidance module uses binocular stereo camera signal acquisition Asin(w o t+φ) and the trigonometric function model signal are sent to the abnormal discharge high-frequency spectrum detection circuit. First, the correctness of the circuit is verified in the ADS RF circuit simulation software, and then the actual circuit is made and debugged. Then, the signal detected by the spectrum is debugged in the signal spectrum detection circuit by using the phase-sensitive detection principle. The sinusoidal signal sinw generated by the DDS signal source on the abnormal discharge high-frequency spectrum detection circuit board is detected. o t performs phase-locked correlation calculation with the abnormal discharge electromagnetic wave induced voltage signal output by the antenna to realize envelope detection of the discharge electromagnetic wave, where A is the amplitude, t is the period, and φ is the phase.

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