Agricultural unmanned aerial vehicle wireless energy transmission and information relay method for low-altitude remote sensing data acquisition

Abstract

An agricultural unmanned aerial vehicle wireless energy transmission and information relay method oriented to low-altitude remote sensing data acquisition comprises the following steps: design of an agricultural unmanned aerial vehicle quick charging system, a relay strategy of an agricultural unmanned aerial vehicle, an agricultural unmanned aerial vehicle-infinite sensor network path planning algorithm based on the relay strategy, and a wireless energy transmission and information relay method based on the relay strategy. As an important supplement of aerial remote sensing, unmanned aerial vehicle remote sensing has high feasibility in a low-altitude remote sensing scheme in the agricultural field, (1) an unmanned aerial vehicle intelligent quick charging platform on a high-voltage transmission tower intelligently and quickly charges an unmanned aerial vehicle in the air by using a wireless energy transfer technology, and the problems of short endurance time and frequent battery replacement of the unmanned aerial vehicle are solved; (2) in the sparse wireless sensor network, the unmanned aerial vehicle can replace a wireless sensor network node to monitor some unperceived areas, and can also play a role of a relay node to transmit information, so that energy consumption generated by routing is reduced, the survival time of the whole network system is prolonged, and the system reliability is improved. The problem that the communication range of the unmanned aerial vehicle is limited in the agricultural data acquisition process is solved.

Description

Technical Field

[0001] The invention relates to the field of wireless energy transmission and information relay of agricultural unmanned aerial vehicles for low-altitude remote sensing data collection, and in particular to a wireless energy transmission and information relay method of agricultural unmanned aerial vehicles for low-altitude remote sensing data collection. Background Art

[0002] Spatial data is widely distributed, and 80% of the world's data is related to space. Agriculture is an important field of remote sensing application. Agricultural remote sensing data acquisition refers to the use of satellites, aircraft, etc. to monitor and remotely acquire data on ground agricultural targets over a large range. Remote sensing technology is a spatial information acquisition technology with the characteristics of large data acquisition range, fast information acquisition speed, short cycle, multiple means of acquiring information, and large amount of information. Agricultural remote sensing technology can objectively, accurately and timely provide various information on crop ecological environment and crop growth. It is mainly used in the monitoring and protection of agricultural land resources, large-area crop yield estimation and growth monitoring, agricultural meteorological disaster monitoring and crop simulation models. With the rapid development of remote sensing technology, especially the application of large-coverage multispectral sensors with high temporal and spatial resolution, high spatial-hyperspectral sensors, etc., the accuracy of agricultural remote sensing data has gradually improved, the amount of data has increased dramatically, the data format has become more and more complex, and the demand for multi-source data fusion is very urgent. Agricultural remote sensing data can reflect the agricultural production status over a large area and for a long time, and belongs to agricultural data at the macro and global level.

[0003] As an important supplement to aerial remote sensing, UAV remote sensing has received extensive attention and application in precision agriculture due to its many advantages, such as flexibility, strong operational selectivity, high accuracy, short operation cycle, good timeliness, low maintenance and use fees, economical applicability, and good safety. In recent years, with the development of sensor technology and advanced flight control and navigation technology, the stability, reliability, and operability of unmanned aerial vehicles have been greatly improved, making the low-altitude remote sensing solution in the agricultural field using agricultural UAVs with digital cameras highly feasible.

[0004] On the one hand, by configuring agricultural drones with professional imaging equipment such as high-definition digital cameras, high-definition cameras, multi-spectrometers, hyperspectrometers, thermal imagers, and various sensors, low-altitude and high-resolution remote sensing data can be obtained and analyzed and processed. It can be applied to crop growth and health assessment, crop yield prediction, natural disaster assessment, crop disease, insect and weed detection and early warning, crop growth environment monitoring, land resource measurement, livestock and poultry management, and livestock and poultry disease detection. In foreign countries, in 2002, NASA used agricultural drones equipped with high-resolution color multispectral imagers to photograph the planting areas of Kauai Coffee Company, and transmitted the captured images to computers in real time for enhancement and analysis to monitor weed outbreaks, expose irrigation and fertilization anomalies, etc.; Spanish researchers used unmanned aerial vehicles equipped with spectral equipment to detect the carotene content of leaves in the 400-800nm ​​spectral region; in 2013, José Manuel Penamail used agricultural drones carrying near-infrared multispectral cameras to analyze the weed coverage of corn fields, generated a weed network structure, and achieved satisfactory results in the image analysis of weed coverage estimation; French scholar Amélie Quiquerez used unmanned aerial vehicles to monitor the impact of farmland soil surface characteristics on environmental erosion, providing new insights into the description of soil patterns and soil erosion analysis of cultivated slopes. In my country, Li Bing and others used agricultural drones equipped with multispectral cameras, took winter wheat as the research object, and realized the monitoring of winter wheat coverage changes; Gao Hui and others used agricultural drones and imaging spectrometers to develop and demonstrate the application of low-altitude crop imaging spectral remote sensing platforms based on agricultural drones, established an application model based on low-altitude hyperspectral remote sensing information, and obtained imaging spectrometer remote sensing inversion models for crops in different growth periods and corresponding disaster remote sensing inversion models. Li Yumei from the Qinghai Provincial Institute of Geology, Mineral Surveying and Mapping carried out the work of "Application of Agricultural UAV Aerial Photography in Rural Land Contracting and Confirmation Work", with the aim of using agricultural drones' "monitoring and observation" applications to solve "problems such as inaccurate contracted land area, unclear boundaries, unclear spatial location, and incomplete registration books".

[0005] On the other hand, by equipping agricultural drones with high-precision locators, mechanical arms and other operating devices, agricultural drones become three-dimensional mobile robots that can complete various agricultural tasks. Current and predictable applications include: field sowing, plant protection spraying, field fertilization, field pollination, water surface feeding, facility inspection, etc. Domestic and foreign practices have shown that using agricultural drones for plant protection operations can greatly improve operation efficiency, and will not repeatedly crush the soil, will not damage crops, can improve the effective utilization rate of pesticides, and reduce labor to a certain extent. In terms of agricultural drone sowing, DroneSeed, an agricultural drone startup in Oregon, USA, has developed a high-speed sowing agricultural drone that can use compressed air to shoot seeds into the soil. This equipment has been used in forest reconstruction in the northwest of the United States, greatly improving the efficiency of afforestation.

[0006] But at the same time, there are some shortcomings and problems that need to be solved in the application of drones in agriculture:

[0007] Common agricultural drones are affected by the weight of the fuselage and the auxiliary devices they carry. The general flight time is 20 to 60 minutes. The communication distance varies from 0.5 to 15 km, depending on the communication module they carry. The DJI M200 agricultural drone has a maximum load of 2kg, a communication control range radius of 7km, and a maximum flight time of only 38 minutes, which seriously exposes the shortcomings of the low endurance of agricultural drones under load. Under low loads, for example, agricultural drones that are only equipped with image acquisition equipment have a maximum endurance of no more than 60 minutes. Since drones are used for large-scale operations during agricultural low-altitude data collection, low load-bearing capacity, short endurance, and narrow communication range will reduce their practicality.

[0008] In recent years, agricultural drones have developed rapidly. Lithium battery-driven agricultural drones are light in weight and small in size; they have low vibration and noise, and do not interfere with the work of the payload, such as taking pictures; they are easy to start and have high reliability. However, due to strict restrictions on size and weight, and the constraints of the current development of power battery technology, the flight time is short; the battery is frequently replaced; the battery is heavy, which increases the weight of the whole machine and reduces the carrying capacity of agricultural drones; the limited flight capacity and communication range have always seriously restricted the development of agricultural drones in the field of agricultural low-altitude remote sensing data collection. Therefore, many studies have carried out the following research on the above issues.

[0009] (1) In terms of aircraft design, we should design different drone models suitable for various agricultural applications, reduce unnecessary load, and maximize work efficiency; improve the ability of the aircraft to resist the impact of bad weather and improve its safety. At present, most agricultural drones are powered by batteries, which reduces the cost and body size compared to fuel-driven agricultural drones, while also improving portability and safety. In terms of flight navigation system, we should use the "GPS / Beidou+WiFi+base station" positioning method to improve navigation accuracy; optimize flight algorithms to improve the accuracy and intelligence of navigation planning. In terms of endurance, as agricultural drones are required to carry more and more equipment and require longer flight time, research on overhead lightning protection line energy extraction technology and wireless energy transmission technology for fast charging can greatly improve the endurance of agricultural drones.

[0010] (2) When collecting information, the information needs to be stored and forwarded through neighboring nodes, which will generate a lot of energy consumption. If a mobile node is introduced into the wireless sensor network to collect information, then agricultural drones are undoubtedly one of the best choices. With the strong mobility and sufficient energy supply of agricultural drones, they can act as a powerful node with unlimited energy and information collection in the sensor network, which can effectively reduce the energy consumed by information forwarding in the network and extend the survival time of the entire wireless sensor network. Wireless sensor network nodes are mainly randomly deployed in the monitoring area through manual setting or high-altitude sowing. Wireless sensor network nodes constitute wireless sensor networks mainly through self-organization. Wireless sensor network nodes have the ability to sense, collect, store and process data. They can monitor the information in the area for processing and analysis, and can also return data to agricultural drones in a multi-hop manner. Agricultural drones have strong transmission capabilities and high energy. At the same time, agricultural drones can also be equipped with corresponding processors and have data storage and processing capabilities. They can collect data in the entire area and transmit data information to observers for processing.

[0011] Wireless energy transmission technology is a new contactless power access mode that uses resonant magnetic coupling wireless power transmission to achieve power supply to airborne batteries. During the entire transmission process of power, there is no direct electrical connection between the power supply end and the airborne end, so there is no problem of mechanical wear, leakage and environmental aesthetics. For farmland information collection, sensors are the most important component. With the increasing application of agricultural drones, different types of drone sensors are gradually needed to be applied to agricultural drones. At the same time, more and more agricultural drone airborne experiments are designed and tested on drone platforms. However, many sensors are not suitable for airborne sensors at present. The sensors that can be installed on agricultural drones need to be lightweight, small in size, and can be remotely controlled and stored. At present, studies have used SLR cameras, Tetracamadc multispectral cameras and 6-channel multispectral and multispectral cameras, thermal infrared cameras, hyperspectral imaging instruments, lidar cameras, etc. At the same time, more and more sensors are gradually being used on agricultural drones to solve various types of data in farmland information collection. However, many agricultural drones have not yet been developed into portable ones, such as the lack of control, photography and storage components, or the portable sensors have not yet found a suitable carrier, such as the sensors are heavy and large in size, or the research content is in the exploratory stage. If placed directly in the natural environment, there are too many uncontrollable factors, resulting in the research content being too complicated.

[0012] On the one hand, the intelligent fast charging platform for drones on high-voltage transmission towers uses wireless energy transmission technology to intelligently and quickly charge agricultural drones in the air, solving the problem of short battery life and frequent battery replacement of agricultural drones. According to Maxwell's principle, when an alternating current flows through a conductor, an alternating magnetic field will be generated in space, and this alternating magnetic field will cut the closed plane of space composed of two lightning protection ground wires and the tower, which will generate an induced electromotive force on the plane. For a long time, the induced electricity on the ground wire has been regarded as a negative impact on the transmission line, and the technology used in the overhead lightning protection line energy extraction device has effectively utilized this ground wire induced electricity that has always been regarded as line loss, making it a reliable power supply for intelligent equipment on the transmission line. The intelligent fast charging platform for agricultural drones on high-voltage transmission towers breaks the constraint of low endurance of agricultural drone technology in the development of agricultural systems.

[0013] On the other hand, in a wireless sensor network system, if there is a lot of information in a certain monitoring area, the wireless sensor network nodes need to send and receive data frequently, which will cause the energy consumption of the nodes in this area to be huge, so that the nodes in this area die earlier than others, and the information in this area cannot be collected. The traditional static wireless sensor network system will cause the energy of the nodes in the local area with too much information to be exhausted in advance. However, due to the mobility of agricultural drones, they can fly near the nodes and communicate with the nodes directly, which not only reduces the energy consumed by data forwarding, but also effectively reduces the bit error rate caused by data forwarding, and prolongs the survival time of the entire network. In addition, the network wireless sensor network nodes are randomly distributed in the network. Agricultural drones can help wireless sensor network nodes locate and can replace wireless sensor network nodes to monitor certain unperceived areas. In sparse wireless sensor networks or extremes, agricultural drones can act as relay nodes for information transmission, reducing the energy consumption generated by routing, prolonging the survival time of the entire network system, and solving the problem of limited communication range of drones in the process of low-altitude remote sensing data collection in agriculture. Summary of the invention

[0014] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a wireless energy transmission and information relay method for agricultural drones for low-altitude remote sensing data collection, and to integrate the existing environmental energy-based and wireless energy transmission technologies to propose a new agricultural drone charging-relay strategy. For rechargeable agricultural drones, although existing work has studied scenarios based solely on environmental energy or wireless radio frequency, in some cases, some agricultural drones may not be able to obtain certain forms of energy. Therefore, in order to reduce dependence on traditional battery or cable power supply, it is necessary to integrate existing energy replenishment strategies, study the fast charging technology of agricultural drones, and study the relay strategies adopted by agricultural drones as mobile relay nodes. Therefore, the energy replenishment and information relay problems of agricultural drones are solved by studying the charging-relay strategy of agricultural drones. In order to achieve the above purpose, the technical solution of the present invention is:

[0015] The wireless energy transmission and information relay method of agricultural UAV for low-altitude remote sensing data collection consists of the following steps:

[0016] Step 1: Design of the fast charging system for agricultural drones

[0017] The electric energy of the transmitting circuit is taken from the overhead lightning arrester power supply or solar cell to charge the super capacitor and battery at the transmitting end. The transmitting circuit includes power frequency rectification and filtering, high frequency inversion and frequency adaptive circuit; the receiving circuit includes load matching network, high frequency rectification and filtering voltage stabilization circuit, and load charging management circuit.

[0018] In the circuit of the transmitting end, the overhead lightning arrester power supply or solar cell provides electric energy as the electric energy input of the high-frequency inverter circuit. The high-frequency inverter circuit is a full-bridge or half-bridge inverter controlled by a MOS tube, which has the characteristics of high frequency, high efficiency and low component loss. The high-frequency alternating current output by the inverter is supplied to the inductor L and the capacitor C, generating a high-frequency magnetic field in the coil, and the electric energy will be coupled to the receiving end by the magnetic field. The current transformer can obtain the amplitude and phase of the current in the coil. Among them, the phase of the current is the signal required for adaptive resonance. The signal is adjusted by the feedback circuit and fed to the drive circuit of the inverter, so that the inverter generates electric energy with the same frequency as the LC resonance frequency. The amplitude signal is a protection signal. When the current in the coil is greater than the rated value, the protection circuit acts to suspend the operation of the inverter until the coil current is reduced to the rated value or below.

[0019] After the electric energy is transmitted to the receiving end in the form of a high-frequency magnetic field, it needs to pass through a matching network. The matching network can match the load and the transmission system to achieve maximum transmission power when the load is large and high transmission efficiency when the load is small. The high-frequency rectifier filter voltage regulator circuit converts high-frequency AC power into DC power for use by the DC load. The charging control circuit can switch the power supply mode of the circuit. When the transmitted energy is sufficient to maintain the load operation, only the energy transmission is used for power supply instead of the battery, and the energy transmission system is allowed to charge the battery; otherwise, the two are allowed to power together. In addition to maintaining the load operation when the energy transmission power is insufficient, the battery must also ensure that the receiving end circuit is not disconnected. In addition, the feedback control circuit detects the current value of the coil through the current transformer, and adjusts the capacitance value of the controllable capacitor array at the receiving end accordingly to ensure resonant operation. Realizing fast, efficient, safe and reasonable power replenishment of the power battery is the basic principle of the intelligent fast charging design of agricultural drones. In addition, the applicability of the charger to various power batteries must also be considered. The control system of the intelligent fast charger adopts an intelligent variable pulse charging method, including charging pulse T1, intermittent pulse T2 and discharge pulse T3.

[0020] The experimental test is divided into field test and actual transmission line test. In the experimental field, a high-voltage test transformer is used to simulate high voltage conditions to test the impact of high-voltage electromagnetic interference on the system. In the actual transmission tower test, it is necessary to ensure that the system can work stably for up to half a year to ensure that the system can adapt to various abnormal conditions.

[0021] Step 2: Relay strategy for agricultural drones

[0022] Assume that in a complex environment, an agricultural drone starts from point A and flies to the destination B. The agricultural drone performs path planning based on the ant colony algorithm to find the optimal path from point A to point B. During the flight of the agricultural drone, it is necessary to collect information from the wireless sensor network nodes near the path.

[0023] Consider an agricultural drone-wireless sensor network, which includes a source node S, an agricultural drone relay node R, and a sink node D, where nodes S and R are rechargeable nodes. Assume that the system under consideration is a time slot system, and the network channel undergoes slow fading, which remains unchanged within a time slot t (length L). In order to reduce the dependence on traditional battery or cable power supply, the present invention will integrate the existing energy replenishment strategy and consider the channel conditions of the network to construct a relay strategy based on the following ideas.

[0024] The source node S adopts an energy replenishment strategy based on environmental energy, and the relay node R obtains node energy based on overhead lightning protection line energy extraction technology and wireless energy transmission technology to power itself. When node S cannot correctly transmit information to node D or the direct transmission energy consumption is relatively large, the agricultural drone node R uses wireless energy transmission technology to simultaneously obtain the wireless information and radio frequency energy of the S node, and uses the obtained energy to forward information for it. On the contrary, node R does not forward information for it in the current time slot, and reuses it in the subsequent time slot. The source node S needs the agricultural drone node R to assist in transmission when the channel state between S and D is poor or the direct transmission energy consumption is large. The diversity gain is obtained through the relay transmission of node R, which can improve the performance of the wireless sensor network, expand the coverage of the source node, and reduce the energy consumption of the source node. At the same time, when S does not need R to assist in transmission, the energy obtained by R from S can be used in subsequent time slots, thereby further improving the network performance. In order to evaluate the performance of the proposed charging-relay strategy, the throughput, interruption probability, etc. of the wireless sensor network are analyzed in the present invention. At this time, the correct representation of the end-to-end received signal-to-noise ratio of the sink node D is the key to solving the problem, because the throughput and the interruption probability are both functions of the signal-to-noise ratio.

[0025] Therefore, by considering whether node S needs node R to assist in transmission, we will use the knowledge of information theory to give a specific expression of the signal received by each node, and then use probability theory and other knowledge to express the signal-to-noise ratio. At this time, based on the distribution of channel states, the expression of network throughput and interruption probability can be obtained by using probability theory, calculus and other knowledge. In particular, due to the energy and information obtained by the agricultural drone node R, the signal-to-noise ratio, network throughput, interruption probability, etc. are used as parameters of the fast charging-relay strategy proposed by the project. The project will analyze the impact of these parameters on the performance of the proposed strategy. There are many nodes communicating with agricultural drones, but because the communication capacity of agricultural drones is certain, they can only communicate with a certain number of nodes within a certain range and time. If the number of wireless sensor network nodes is greater than the maximum number of nodes that the agricultural drone can communicate with, then the agricultural drone needs to selectively communicate with some nodes, and these nodes communicate with other remaining nodes as relay nodes.

[0026] Step 3: Agricultural UAV-Infinite Sensor Network Path Planning Algorithm Based on Relay Strategy

[0027] Step 1: Select nodes. When the agricultural drone reaches a path point, it will collect information from the wireless sensor network nodes within its communication range. Then every time the agricultural drone reaches a path point, the topology of the entire network will change, and then the distance between the agricultural drone and the wireless sensor network nodes needs to be recalculated. When the agricultural drone reaches path point i, the distance between the agricultural drone and all wireless sensor network nodes is calculated:

[0028]

[0029] Where (xi,yi,zi) is the coordinate of the agricultural drone when it reaches the path point, and (nodexj,nodeyj,nodezj) is the location information of the jth wireless sensor network node. Assuming that the communication radius of the agricultural drone is L = 280m, the nodes within the communication radius of the agricultural drone are: d ij ≤L. When the agricultural drone reaches the path point S, the distance D between the agricultural drone and all wireless sensor network nodes is calculated and compared with the communication radius L of the agricultural drone to obtain the number of wireless sensor network nodes N in the communication range;

[0030] Step 2: If N≤nmax, the agricultural UAV communicates directly with the wireless sensor network and calculates the total energy consumed, ending.

[0031] In the process of agricultural information collection, the infinite sensor network nodes are randomly distributed in space to collect environmental information. Each wireless sensor network node communicates through the wireless data transmission protocol 802.11. Before the agricultural drone sets off, it has learned the distribution location of the wireless sensor network nodes and the initial energy E0 of the wireless sensor network nodes. When the agricultural drone senses the wireless sensor network nodes within the communication range, the agricultural drone first communicates with the node with the shortest distance to ensure that the communication consumes the least energy. However, since the bandwidth of the agricultural drone communication is certain and the flight speed of the agricultural drone is very fast, the information received within a limited range and time is very limited. Therefore, in the communication process, the agricultural drone must selectively communicate with the wireless sensor network nodes. It is known from the previous communication relationship between the agricultural drone and the wireless sensor network that the energy consumed by the agricultural drone and the wireless sensor network node for communication mainly depends on the communication frequency f and the distance d. When the communication frequency f is certain, the energy consumed between the two is mainly determined by the communication distance. The larger the communication distance, the more energy is consumed, and the smaller the communication distance, the less energy is consumed. Therefore, when the agricultural drone flies according to a pre-planned route, it senses n wireless sensor network nodes within the communication range, which is set as S. If the maximum communication capability of the agricultural drone can communicate with nmax wireless sensor network nodes at the same time, if n<=n max , then the agricultural drone communicates directly with n nodes, and the energy consumed by the wireless sensor network nodes is Where E R，i The energy consumed by the agricultural drone to communicate with a certain node.

[0032] Step 3: If n>n max , agricultural drones must choose n max The best nodes carry out information, not only that, this n max The nodes that directly communicate with the agricultural drone must also act as relay nodes to transmit other node information to the agricultural drone, that is, randomly select n nodes from n wireless sensor network nodes. max When the agricultural drone collects information about wireless sensor network nodes, it can only communicate with limited nodes within the communication range. However, in order to obtain more environmental information, the nodes that communicate directly with the agricultural drone can be used as relay nodes to pass more information to the agricultural drone by collecting information from surrounding information nodes.

[0033] From the above formula, we can conclude that the energy of path loss is proportional to the communication distance d. Therefore, the closer the communication distance, the less energy is consumed. Therefore, the selection of relay points is a very important issue. When the number of nodes within the communication range of the agricultural drone is greater than n maxWhen , it is assumed that the node that directly communicates with the agricultural drone is used as the relay node, set to L, then the other nodes within the communication range are set U = SL, then the method for selecting the relay node is Selection Method

[0034]

[0035] Among them, F1 is the distance between the relay node and the agricultural drone, and F2 is the distance between the relay node and the remaining nodes. The selection methods are substituted into the equation in turn. When a specific combination makes F obtain the minimum value, the nodes of this combination are taken as relay nodes, and the remaining infinite sensor network nodes need to transmit information to the agricultural drone through the relay nodes.

[0036] Step 4: Calculate the distance Di between the relay point and the agricultural drone, and the distance Dj between the interruption point and the wireless sensor network node, and let D = Di + Dj; in order to minimize the energy consumed by the wireless sensor network node, it is necessary to select a node that is relatively close to the agricultural drone as the relay node, and also consider the distance between other wireless sensor network nodes and the relay node, so that the total energy consumed by the entire system communication can be minimized. So we will select the equation for the relay node as:

[0037] F=F1+F2(3)

[0038] Step 5: Repeat steps 3 and 4 to find the minimum value of D.

[0039] Step 6: Calculate the total energy consumed E = Σ ni =1E i +Σ ni =-1

[0040] The convergence analysis of the proposed algorithm is studied, and the stability judgment of the agricultural drone-wireless network system is derived. In addition, the influence of various network parameters on the proposed charging relay strategy and algorithm performance is verified by theoretical and experimental means.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] (1) Using overhead lightning protection line energy harvesting technology and magnetic coupling resonance wireless energy transmission to quickly charge drones can greatly improve the endurance of agricultural drones, and is unique in promoting the application of drones in the field of agricultural low-altitude remote sensing data collection.

[0043] (2) In sparse wireless sensor networks, agricultural drones are used as relay nodes to transmit information, which reduces the energy consumption generated by routing and prolongs the life of the entire network system. This is innovative in expanding the communication range of drones for low-altitude remote sensing data collection in agriculture.

[0044] (3) Integrate the existing energy replenishment strategies and construct a relay strategy for rechargeable agricultural drone-wireless sensor networks. Through theoretical and experimental means, study the influence of various system parameters such as charging time, power split ratio, node channel fading parameters, etc. on the performance of the proposed fast charging-relay strategy, and prove that the proposed strategy and the corresponding algorithm have unique convergence and stability issues. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of power supply principle (viewed from above);

[0046] Figure 2 Schematic diagram of the power system connected to the ground network (viewed from above);

[0047] Figure 3 Standard wireless power transfer system block diagram;

[0048] Figure 4 Magnetic coupling resonance wireless power transmission main circuit;

[0049] Figure 5 The relationship between load power and frequency (when the distance is close);

[0050] Figure 6 The relationship between load power and frequency (when the distance is far);

[0051] Figure 7 The relationship between the receiving end load efficiency and the distance (mutual inductance coefficient) and the load resistance (when the internal resistance of the inductor is small);

[0052] Figure 8 The relationship between the receiving end load efficiency and the distance (mutual inductance coefficient) and the load resistance (when the internal resistance of the inductor is large);

[0053] Fig. 9 The overall installation plan of the system;

[0054] Fig.10 Circuit block diagram and energy and signal flow diagram of the transmitter;

[0055] Fig.11 Circuit block diagram and energy and signal flow diagram of the receiving end;

[0056] Fig.12 Variable pulse intelligent fast charging system;

[0057] Fig.13 Charging current pulse; DETAILED DESCRIPTION

[0058] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0059] like Figure 1-13 As shown,

[0060] Experimental example:

[0061] The wireless energy transmission and information relay method of agricultural UAV for low-altitude remote sensing data collection consists of the following steps:

[0062] Overhead lightning arrester power supply

[0063] Although the concept of current in the ground wire has long been known to people, how to extract this electrical energy has long been a problem. We connect the power extraction device in series to the ground wire network, such as Figure 1 As shown:

[0064] Since the primary end of the power supply has a large impedance, a voltage U is generated at the power input port when current flows through it. Therefore, according to Ohm's law:

[0065] P=U*IU:power port voltage

[0066] I: Current flowing through the primary of the power supply

[0067] However, in actual engineering applications, it is impossible to use the primary end of the power supply like Figure 1 As shown in the figure, it is directly connected to the ground wire network. Therefore, in practical applications, we consider utilizing the characteristics of the overhead ground wire network with segmented grounding to realize the connection operation of the power-taking equipment without changing the original ground wire structure. Figure 2 A typical power supply access method is given

[0068] If our electrical equipment is installed in Figure 2 If the ground wire power supply is installed on the "straight tower 2" shown in the figure, the ground wire power supply also needs to be installed on the "straight tower 2". In the segmented grounding section, the grounding point of the ground wire is on the "tension tower 1", and the overhead lightning protection ground wire and the iron tower on other towers are connected through an insulator string with a discharge gap. Therefore, we can connect one end of the input end of the power supply system to the "ground wire 1" and short-circuit the other end with the "ground wire 2". Therefore, a closed path is formed by "ground wire 1", "straight tower 2", "ground wire 2" and "tension tower 1". The induced current flows in this closed path, and we can connect the power supply device in series to the ground wire network without changing the original ground wire.

[0069] Wireless power transmission

[0070] The standard wireless power transmission system block diagram is as follows Figure 3 As shown:

[0071] (1) Circuit model of magnetically coupled resonant wireless power transmission

[0072] The magnetic coupling resonant wireless energy transmission system realizes resonant energy transmission through a combination circuit of two inductors L and capacitors C with the same resonant frequency, such as Figure 4 As shown. The coil is divided into a transmitting end and a receiving end. The LC at the transmitting end is connected to a high-frequency power supply, and the LC at the receiving end is connected to a load. Since the two coils are in a resonant state, even if the two coils are far apart and have a small mutual inductance coefficient, efficient wireless energy transmission can be achieved, which is much farther than the traditional inductive wireless transmission distance.

[0073] Through the simulation of the previous test, we can get the relationship between the transmission efficiency of the system and the physical quantities such as power frequency, distance, coil internal resistance, load resistance, etc. The simulation results are as follows: Figure 5 shown.

[0074] Depend on Figure 5 and 6 As shown in the figure, when the distance changes (the mutual inductance is negatively correlated with the distance and positively correlated with the coil diameter), the load power vs. frequency curve changes greatly. When the distance is close, the system's resonant frequency splits, resulting in two frequencies that maximize the power, and these two frequencies are located on both sides of the resonant frequency of a single LC. When the distance is far, the system does not have a frequency splitting phenomenon, and the maximum power of the load is obtained at the resonant frequency.

[0075] The frequency splitting phenomenon makes adaptive frequency and parameter control more difficult, and the actual high-voltage transmission line insulation distance is relatively long, so try to use smaller coils so that there is no frequency splitting phenomenon within the specified distance. At the same time, smaller coils are lighter and easier to install. Figure 7 , 8 The following phenomena can be seen:

[0076] (1) Load efficiency decreases as the distance increases. Within a certain range of relatively close distance, the efficiency decrease is not obvious, and the system is in the strong coupling or critical coupling region. When the distance increases further, the efficiency decreases seriously, and the system is in the weak coupling region. Therefore, it is best to let the system work in the critical coupling region.

[0077] (2) The efficiency of the load is also related to the load resistance value. When the efficiency is maximum, the load resistance matches the system resistance. This matching point will change with the distance and frequency. Therefore, it is necessary to design a matching network at the receiving end to ensure the highest load efficiency.

[0078] (3) Comparison Figure 7 and 8It can be seen that, when other conditions are the same, an increase in the internal resistance of the inductor (a decrease in the quality factor) will seriously reduce the transmission efficiency. Therefore, in practice, an inductor coil with a smaller internal resistance and a capacitor with a smaller equivalent series loss should be selected.

[0079] A full-bridge inverter, i.e., a class D power amplifier, is used as the high-frequency inverter of this system. The class D power amplifier has the characteristics of high frequency (up to 2MHz), high efficiency (ideal efficiency up to 100%, actual efficiency 80%-90%), small control loss, and easy driving, and is suitable for driving LC resonant loads. In practice, soft switching technology for current zero-crossing shutdown, dead zone technology to prevent bridge arm direct flow, and isolation drive technology for convenient control of switches can be added to improve the performance of the inverter.

[0080] The matching network at the receiving end can help improve the power and efficiency of the system. When the actual load is running, the equivalent load resistance value is the smallest when the load is heavy, and the equivalent load resistance value is the largest when the load is light. Therefore, the load at heavy load should be matched to the load with maximum power transmission through the matching network, that is, the load resistance is equal to the equivalent internal resistance of the system to ensure that the system can work under any load conditions. When the load is light, the load resistance increases. Therefore, if the matching network at heavy load is still used, the load resistance will be greater than the equivalent internal resistance of the system, and the efficiency will increase. Therefore, this solution can take into account both the power and efficiency of the system.

[0081] Wireless power transmission control

[0082] Wireless power transmission adaptation determines whether the transmission system can work efficiently for a long time, and protection and anti-interference circuits determine whether the system can work safely and reliably. The resonance adaptation of the present invention is divided into transmitting end frequency adaptation and receiving end capacitance adaptation.

[0083] The transmitter frequency adaptation adopts the LC self-resonance method, that is, by detecting the oscillating current in the coil, when the current passes through zero, the polarity of the inverter power tube is reversed. Under this control method, the polarity of the inverter voltage is always consistent with the current direction. Therefore, in the early stage of oscillation establishment, the current in the line gradually increases until the power provided by the power supply in each cycle is balanced with the line loss, and the system enters a stable working period. Using this self-resonance solution, the output frequency of the inverter can follow the inherent resonant frequency of LC at any time.

[0084] The receiving end capacitor adaptation adopts the method of current amplitude detection and capacitor array switching. According to the design in the previous theoretical analysis, this energy transmission system does not have the frequency splitting phenomenon, so as long as the receiving end current reaches the maximum by switching the capacitor array, tuning can be achieved. This method can be implemented by the single-chip microcomputer measurement and control system.

[0085] In actual transmission lines, short circuits and lightning strikes may damage the energy transmission system, so it is necessary to add a protection circuit. At the energy transformer, a varistor and a voltage detection circuit can be added. Since the varistor has a very fast action speed, as long as the line current increases suddenly, the varistor value will quickly decrease to achieve a shunting effect; at the same time, if the voltage detection circuit detects that the line is overcurrent, it will short-circuit the energy coil through the control circuit to protect the energy transformer.

[0086] In the energy transmission circuit, a current limiting circuit should also be added. When the oscillating current in the coil is too large, the protection circuit will be activated and the inverter will stop working. At this time, the high-frequency oscillation will slowly decay until the coil current is less than the protection action value. There is high-frequency corona interference near the actual transmission line, and the high-frequency magnetic field in the energy transmission coil will also interfere with the work of the circuit board. Therefore, it is necessary to improve the shielding design of the system. For example, shielding materials are added to the shell, the shell is designed to be streamlined to prevent discharge, and metal shielding is added to the control part of the circuit board. In addition, improving the component layout of the circuit board, reasonably designing the grounding, and isolating the signal control part from the power part can also effectively improve the system's anti-interference performance.

[0087] Relay strategy for agricultural drone-wireless sensor networks

[0088] When collecting information, the information needs to be stored and forwarded through neighboring nodes, which will generate a lot of energy consumption. If a mobile node is introduced into this network to collect information, then agricultural drones are undoubtedly one of the best choices. With the powerful mobility and sufficient energy supply of agricultural drones, it can act as a powerful node with unlimited energy and information collection that can be moved at will in the network, which can effectively reduce the energy consumed by information forwarding in the network and extend the survival time of the entire network.

[0089] Wireless sensor network nodes are mainly deployed randomly in the monitoring area through manual setting or high-altitude sowing. Wireless sensor network nodes constitute wireless sensor networks mainly through self-organization. Wireless sensor network nodes have the ability to sense, collect, store and process data. They can process and analyze the information in the monitoring area, and can also return data to agricultural drones in a multi-hop manner. Agricultural drones have strong transmission capabilities and high energy. At the same time, agricultural drones can also be equipped with corresponding processors and have data storage and processing capabilities. They can collect data in the entire area and transmit data information to observers for processing.

[0090] Agricultural wireless sensor nodes adopt an energy replenishment strategy based on environmental energy, while agricultural drones as relay nodes obtain energy through overhead lightning protection line energy extraction technology and wireless energy transmission technology, and use the obtained energy to forward data for sensor nodes, thereby solving the energy acquisition and information relay problems of agricultural drone-wireless sensor networks. Taking into account the energy replenishment process of agricultural drones as relay nodes, channel fading states, relay protocols of relay nodes, etc., this paper studies how to use information theory, probability theory, stability theory, etc. to analyze the performance of the proposed fast charging-relay strategy, such as interruption probability, network throughput, network stability, etc. The influence of various system parameters, such as charging time, power split ratio, node channel fading parameters, etc., on the performance of the proposed fast charging-relay strategy is studied through theoretical and experimental means.

[0091] Routing selection in agricultural drone-wireless sensor networks

[0092] The energy consumption of wireless sensor network nodes is a very important issue. The energy of wireless sensor network nodes is related to the survival time of the entire network. This section mainly focuses on how to reduce the energy consumption of wireless sensor network nodes when agricultural drones collect information from wireless sensor network nodes.

[0093] The main problem that limits the nodes of wireless sensor networks is the problem of energy consumption. If a large amount of energy is consumed when sending and receiving information, aggregating information, and forwarding information, it is necessary to reduce the energy consumption of the wireless sensor network and extend the life cycle of the entire network. In order to reduce the energy consumption of wireless sensor networks, the main purpose of the present invention is to reduce the energy consumed by agricultural drones-wireless sensor networks and wireless sensor networks when forwarding information by designing an agricultural drone-wireless sensor network information collection method.

[0094] The design of the present invention mainly studies the energy consumed by the communication between nodes and agricultural drones in three-dimensional space. The main energy consumption of wireless sensor network nodes can be divided into the consumption of their communication with wireless sensor networks and the consumption of the wireless sensor network nodes themselves. Since the energy consumed by the wireless sensor network nodes themselves is very small, it can be ignored. The energy consumed by the wireless sensor network nodes and agricultural drones accounts for the majority. It has been learned that the energy consumption of wireless sensor network communication is mainly determined by frequency and distance. If the frequency is certain, then the distance mainly affects the energy consumption of infinite sensor network communication. In order to maximize the survival time of the entire network, agricultural drones need to select nodes with a closer distance to communicate while collecting information, so that the entire network is in the maximum information collection coverage. However, since the communication bandwidth of agricultural drones is certain, it is assumed that the maximum communication capacity of agricultural drones can be and limit the infinite sensor network nodes at the same time. The main problem is the problem of energy consumption. If a large amount of energy is consumed when sending and receiving information, aggregating information, and forwarding information routing, it is necessary to reduce the energy consumption of the wireless sensor network and extend the life cycle of the entire network. In order to reduce the energy consumption of wireless sensor networks, an agricultural UAV-wireless sensor network information collection method is designed to reduce the energy consumed by the agricultural UAV-wireless sensor network and the wireless sensor network when forwarding information.

[0095] Design of a fast charging system for agricultural drones

[0096] The overall installation plan of the system is as follows: Fig. 9 , where the power of the transmitting circuit is charged by the overhead lightning arrester power supply or solar cell to the super capacitor and battery at the transmitting end. The transmitting circuit includes power frequency rectification and filtering, high frequency inversion and frequency adaptive circuit. The receiving circuit includes a load matching network, a high frequency rectification and filtering voltage stabilization circuit, and a load charging management circuit.

[0097] The circuit block diagram and energy and signal flow diagram of the transmitter are as follows: Fig.10As shown. In the figure, the overhead lightning protection line energy source or solar cell provides electrical energy as the electrical energy input of the high-frequency inverter circuit. The high-frequency inverter circuit is a full-bridge or half-bridge inverter controlled by a MOS tube, which has the characteristics of high frequency, high efficiency and low component loss. The high-frequency alternating current output by the inverter is supplied to the inductor L and the capacitor C, generating a high-frequency magnetic field in the coil, and the electrical energy will be coupled to the receiving end by the magnetic field. The current transformer can obtain the amplitude and phase of the current in the coil. Among them, the phase of the current is the signal required for adaptive resonance. The signal is adjusted by the feedback circuit and fed to the drive circuit of the inverter, so that the inverter generates electrical energy with the same frequency as the LC resonance frequency. The amplitude signal is a protection signal. When the current in the coil is greater than the rated value, the protection circuit will act to suspend the operation of the inverter until the coil current is reduced to the rated value or below. The circuit block diagram of the receiving end and the energy and signal flow diagram are shown as follows. Fig.11 shown.

[0098] Fig.11 In the process, after the electric energy is transmitted to the receiving end in the form of a high-frequency magnetic field, it needs to pass through a matching network. The matching network can match the load and the transmission system to achieve maximum transmission power when the load is large, and high transmission efficiency when the load is small. The high-frequency rectifier filter voltage regulator circuit converts the high-frequency AC power into DC power for the DC load. The charging control circuit can switch the power supply mode of the circuit. When the transmitted energy is sufficient to maintain the load operation, only the energy transmission is used for power supply instead of the battery, and the energy transmission system is allowed to charge the battery; otherwise, the two are allowed to power together. In addition to maintaining the load operation when the energy transmission power is insufficient, the battery must also ensure that the receiving end circuit is not disconnected. In addition, the feedback control circuit detects the current value of the coil through the current transformer, and adjusts the capacitance value of the controllable capacitor array at the receiving end accordingly to ensure resonant operation.

[0099] Realizing fast, efficient, safe and reasonable power replenishment of power batteries is the basic principle of intelligent fast charging design for agricultural UAVs. In addition, the applicability of the charger to various power batteries must also be considered. Fig.12 The figure shows the control system composition of the intelligent fast charger. This system is different from the continuous current charging and pulse current charging methods used by traditional chargers. It adopts an intelligent variable pulse charging method, that is, Fig.13 The charging current pulse shown includes charging pulse T1, intermittent pulse T2 and discharge pulse T3. The experimental test of the present invention is divided into field test and actual transmission line test. In the experimental field, a high-voltage test transformer is used to simulate high voltage conditions to test the impact of high-voltage electromagnetic interference on the system. In the actual transmission tower test, it is necessary to ensure that the system can work stably for up to half a year to ensure that the system can adapt to various abnormal conditions.

[0100] Relay strategy for agricultural drones

[0101] Assume that in a complex environment, an agricultural drone starts from point A and flies to the destination B. The agricultural drone performs path planning based on the ant colony algorithm to find the optimal path from point A to point B. During the flight of the agricultural drone, it is necessary to collect information from the wireless sensor network nodes near the path.

[0102] Consider an agricultural drone-wireless sensor network, which includes a source node S, an agricultural drone relay node R,

[0103] and a sink node D, where nodes S and R are rechargeable nodes. Assume that the system under consideration is a time slot system, and the network channel undergoes slow fading, which remains unchanged within a time slot t (length L). In order to reduce the dependence on traditional battery or cable power supply, the present invention will integrate the existing energy replenishment strategy and consider the channel conditions of the network to construct a relay strategy based on the following ideas.

[0104] The source node S adopts an energy replenishment strategy based on environmental energy, and the relay node R obtains node energy based on overhead lightning protection line energy extraction technology and wireless energy transmission technology to power itself. When node S cannot correctly transmit information to node D or the direct transmission energy consumption is relatively large, the agricultural drone node R uses wireless energy transmission technology to simultaneously obtain the wireless information and radio frequency energy of the S node, and uses the obtained energy to forward information for it. On the contrary, node R does not forward information for it in the current time slot, and reuses it in the subsequent time slot. The source node S needs the agricultural drone node R to assist in transmission when the channel state between S and D is poor or the direct transmission energy consumption is large. The diversity gain is obtained through the relay transmission of node R, which can improve the performance of the wireless sensor network, expand the coverage of the source node, and reduce the energy consumption of the source node. At the same time, when S does not need R to assist in transmission, the energy obtained by R from S can be used in subsequent time slots, thereby further improving the network performance. In order to evaluate the performance of the proposed charging-relay strategy, the throughput, interruption probability, etc. of the wireless sensor network are analyzed in the present invention. At this time, the correct representation of the end-to-end received signal-to-noise ratio of the sink node D is the key to solving the problem, because the throughput and the interruption probability are both functions of the signal-to-noise ratio.

[0105] Therefore, by considering whether node S needs node R to assist in transmission, we will use the knowledge of information theory to give a specific expression of the signal received by each node, and then use probability theory and other knowledge to express the signal-to-noise ratio. At this time, based on the distribution of channel states, the expression of network throughput and interruption probability can be obtained by using probability theory, calculus and other knowledge. In particular, due to the energy and information obtained by the agricultural drone node R, the signal-to-noise ratio, network throughput, interruption probability, etc. are used as parameters of the fast charging-relay strategy proposed by the project. The project will analyze the impact of these parameters on the performance of the proposed strategy. There are many nodes communicating with agricultural drones, but because the communication capacity of agricultural drones is certain, they can only communicate with a certain number of nodes within a certain range and time. If the number of wireless sensor network nodes is greater than the maximum number of nodes that the agricultural drone can communicate with, then the agricultural drone needs to selectively communicate with some nodes, and these nodes communicate with other remaining nodes as relay nodes.

[0106] Path planning algorithm for agricultural UAV-infinite sensor networks based on relay strategy

[0107] The steps of the agricultural drone-infinite sensor network path planning algorithm designed by the present invention are as follows:

[0108] Step 1: Select nodes. When the agricultural drone reaches a path point, it will collect information from the wireless sensor network nodes within its communication range. Then, every time the agricultural drone reaches a path point, the topology of the entire network will change, and the distance between the agricultural drone and the wireless sensor network node needs to be recalculated. When the agricultural drone reaches path point i, the distance between the agricultural drone and the wireless sensor network node is calculated.

[0109] The distance between the human machine and all wireless sensor network nodes:

[0110]

[0111] Where (xi,yi,zi) is the coordinate of the agricultural drone when it reaches the path point, and (nodexj,nodeyj,nodezj) is the location information of the jth wireless sensor network node. Assuming that the communication radius of the agricultural drone is L = 280m, the nodes within the communication radius of the agricultural drone are: d ij ≤L. When the agricultural drone reaches the path point S, the distance D between the agricultural drone and all wireless sensor network nodes is calculated and compared with the communication radius L of the agricultural drone to obtain the number of wireless sensor network nodes N in the communication range;

[0112] Step 2: If N≤n max ,The agricultural UAV communicates directly with the wireless sensor network and ,calculates the total energy consumed ,end.

[0113] In the process of agricultural information collection, the infinite sensor network nodes are randomly distributed in space to collect environmental information. Each wireless sensor network node communicates through the wireless data transmission protocol 802.11. Before the agricultural drone sets off, it has learned the distribution location of the wireless sensor network nodes and the initial energy E0 of the wireless sensor network nodes. When the agricultural drone senses the wireless sensor network nodes within the communication range, the agricultural drone first communicates with the node with the shortest distance to ensure that the communication consumes the least energy. However, since the bandwidth of the agricultural drone communication is certain and the flight speed of the agricultural drone is very fast, the information received within a limited range and time is very limited. Therefore, in the communication process, the agricultural drone must selectively communicate with the wireless sensor network nodes. It is known from the previous communication relationship between the agricultural drone and the wireless sensor network that the energy consumed by the agricultural drone and the wireless sensor network node for communication mainly depends on the communication frequency f and the distance d. When the communication frequency f is certain, the energy consumed between the two is mainly determined by the communication distance. The larger the communication distance, the more energy is consumed, and the smaller the communication distance, the less energy is consumed. Therefore, when the agricultural drone flies according to a pre-planned route, it senses n wireless sensor network nodes within the communication range, which is set as S. If the maximum communication capability of the agricultural drone can communicate with nmax wireless sensor network nodes at the same time, if n<=n max , then the agricultural drone communicates directly with n nodes. At this time, the energy consumed by the wireless sensor network nodes is Where E R,i The energy consumed by the agricultural drone to communicate with a certain node.

[0114] Step 3: If n>n max , agricultural drones must choose n max The best nodes carry out information, not only that, this n max The nodes that directly communicate with agricultural drones must also act as relay nodes to pass other node information to agricultural drones, that is, nmax selected relay points L are randomly selected from n wireless sensor network nodes; when agricultural drones collect wireless sensor network node information, agricultural drones can only communicate with limited nodes within the communication range. However, in order to obtain more environmental information, the nodes that directly communicate with agricultural drones can act as relay nodes, and pass more information to agricultural drones by collecting information from surrounding information nodes.

[0115] From the above formula, we can conclude that the energy of path loss is proportional to the communication distance d. Therefore, the closer the communication distance, the less energy is consumed. Therefore, the selection of relay points is a very important issue. When the number of nodes within the communication range of the agricultural drone is greater than nmax When , it is assumed that the node that directly communicates with the agricultural drone is used as the relay node, set to L, then the other nodes within the communication range are set U = SL, then the method for selecting the relay node is Selection Method

[0116]

[0117] From the above formula, we can conclude that the energy of path loss is proportional to the communication distance d. Therefore, the closer the communication distance, the less energy is consumed. Therefore, the selection of relay points is a very important issue. When the number of nodes within the communication range of the agricultural drone is greater than n max When , it is assumed that the node that directly communicates with the agricultural drone is used as the relay node, set to L, then the other nodes within the communication range are set U = SL, then the method for selecting the relay node is Selection Method

[0118]

[0119] Among them, F1 is the distance between the relay node and the agricultural drone, and F2 is the distance between the relay node and the remaining nodes. Substitute the selection methods into the equation in turn,

[0120] When a specific combination makes F reach the minimum value, the nodes of this combination are taken as relay nodes, and the remaining infinite sensor network nodes need to transmit information to the agricultural drone through the relay nodes.

[0121] Step 4: Calculate the distance Di between the relay point and the agricultural drone, and the distance Dj between the interruption point and the wireless sensor network node, and let D = Di + Dj; in order to minimize the energy consumed by the wireless sensor network nodes, it is necessary to select nodes that are relatively close to the agricultural drone as relay nodes, and also consider the distances between other wireless sensor network nodes and the relay nodes, so that the entire system can be connected.

[0122] The total energy consumed by the signal is minimized. Therefore, we will select the equation in the relay node as:

[0123] F=F1+F2(6)

[0124] Among them, F1 is the distance between the relay node and the agricultural drone, and F2 is the distance between the relay node and the remaining nodes. Substitute the selection methods into the equation in turn,

[0125] When a specific combination makes F reach the minimum value, the nodes of this combination are taken as relay nodes, and the remaining infinite sensor network nodes need to transmit information to the agricultural drone through the relay nodes.

[0126] Step 5: Repeat steps 3 and 4 to find the maximum value of D.

[0127] Step 6: Calculate the total energy consumed

[0128] E=Σ ni =1 E i +Σ ni =-1

[0129] The convergence analysis of the proposed algorithm is studied, and the stability judgment of the agricultural drone-wireless network system is derived. In addition, the influence of various network parameters on the proposed charging-relay strategy and algorithm performance is verified by theoretical and experimental means.

[0130] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that are not conceived through creative work should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope defined in the claims.

Claims

1. The wireless energy transmission and information relay method of agricultural UAV for low-altitude remote sensing data collection is composed of the following steps: Step 1: Design of the fast charging system for agricultural drones The electric energy of the transmitting circuit is taken from the overhead lightning protection line power supply or solar cell to charge the super capacitor and battery at the transmitting end; the transmitting circuit includes power frequency rectification and filtering, high frequency inversion and frequency adaptive circuit; the receiving circuit includes load matching network, high frequency rectification and filtering voltage stabilization circuit, and load charging management circuit; In the circuit of the transmitting end, the overhead lightning arrester power supply or solar cell provides electric energy as the electric energy input of the high-frequency inverter circuit; the high-frequency inverter circuit is a full-bridge or half-bridge inverter controlled by a MOS tube, which has the characteristics of high frequency, high efficiency and low component loss; the high-frequency alternating current output by the inverter is supplied to the inductor coil L and the capacitor C, generating a high-frequency magnetic field in the coil, and the electric energy will be coupled to the receiving end by the magnetic field; the current transformer can obtain the amplitude and phase of the current in the coil; among them, The phase of the current is the signal required for adaptive resonance. This signal is adjusted by the feedback circuit and fed into the drive circuit of the inverter, so that the inverter generates electric energy with the same frequency as the LC resonance frequency. The amplitude signal is a protection signal. When the current in the coil is greater than the rated value, the protection circuit is activated to suspend the operation of the inverter until the coil current is reduced to the rated value or below. After the electric energy is transmitted to the receiving end in the form of a high-frequency magnetic field, it needs to pass through a matching network; the matching network can match the load and the transmission system to achieve maximum transmission power when the load is large, and higher transmission efficiency when the load is small; the high-frequency rectifier filter voltage regulator circuit converts high-frequency AC power into DC power for use by the DC load; the charging control circuit can switch the power supply mode of the circuit. When the transmitted energy is sufficient to maintain the load, only the energy transmission is used instead of the battery, and the energy transmission system is allowed to charge the battery; otherwise, the two are allowed to power together; in addition to maintaining the load when the energy transmission power is insufficient, the battery must also ensure that the receiving end circuit is not disconnected; in addition, the feedback control circuit detects the current value of the coil through a current transformer, and adjusts the capacitance value of the controllable capacitor array at the receiving end accordingly to ensure resonant operation; achieving fast, efficient, safe and reasonable power replenishment of the power battery is the basic principle of the intelligent fast charging design of agricultural drones. In addition, the applicability of the charger to various power batteries must also be considered; the control system of the intelligent fast charger adopts an intelligent variable pulse charging method, including charging pulse T1, intermittent pulse T2 and discharge pulse T3; The experimental test is divided into site test and actual transmission line test. In the experimental site, a high-voltage test transformer is used to simulate high voltage conditions to test the impact of high-voltage electromagnetic interference on the system. In the actual transmission tower test, it is necessary to ensure that the system can work stably for up to half a year to ensure that the system can adapt to various abnormal conditions. Step 2: Relay strategy for agricultural drones Assume that in a complex environment, an agricultural drone starts from point A and flies to the destination B. The agricultural drone performs path planning based on the ant colony algorithm to find the optimal path from point A to point B. During the flight of the agricultural drone, it is necessary to collect information from the wireless sensor network nodes near the path. Consider an agricultural drone-wireless sensor network, which includes a source node S, an agricultural drone relay node R, and a sink node D, where nodes S and R are rechargeable nodes; assuming that the system under consideration is a time slot system, the network channel undergoes slow fading, which remains unchanged within a time slot t (length L); in order to reduce the dependence on traditional battery or cable power supply, the present invention will integrate the existing energy replenishment strategy and consider the channel conditions of the network to construct a relay strategy based on the following ideas; The source node S adopts an energy replenishment strategy based on environmental energy, and the relay node R obtains node energy based on overhead lightning protection line energy extraction technology and wireless energy transmission technology to power itself; when node S cannot correctly transmit information to node D or the direct transmission energy consumption is relatively large, the agricultural drone node R uses wireless energy transmission technology to simultaneously obtain the wireless information and radio frequency energy of the S node, and uses the obtained energy to forward the information for it; conversely, node R does not forward the information in the current time slot and reuses it in the subsequent time slot; the source node S needs the agricultural drone node R to assist in transmission when the channel state between S and D is poor or the direct transmission energy consumption is large, etc. In this case, diversity gain is obtained through the relay transmission of node R, which can improve the performance of the wireless sensor network, expand the coverage of the source node, reduce the energy consumption of the source node, etc.; at the same time, when S does not need R's auxiliary transmission, the energy obtained by R from S can be used in subsequent time slots, thereby further improving the network performance; in order to evaluate the performance of the proposed charging-relay strategy, the present invention will analyze the throughput and interruption probability of the wireless sensor network; at this time, the correct representation of the end-to-end received signal-to-noise ratio of the destination node D is the key to solving the problem, because the throughput and interruption probability are both functions of the signal-to-noise ratio; Therefore, by considering whether node S needs node R to assist in transmission, we will use the knowledge of information theory to give a specific expression for each node to receive the signal, and then use probability theory and other knowledge to express the signal-to-noise ratio; at this time, based on the distribution of channel states, the network throughput and interruption probability expressions can be obtained using probability theory, calculus and other knowledge; in particular, due to the energy and information obtained by the agricultural drone node R, the signal-to-noise ratio, network throughput, interruption probability, etc. are used as parameters of the fast charging-relay strategy proposed by the project, and the project will analyze the impact of these parameters on the performance of the proposed strategy; there are many nodes communicating with agricultural drones, but because the communication capacity of agricultural drones is certain, they can only communicate with a certain number of nodes within a certain range and time; if the number of wireless sensor network nodes is greater than the maximum number of nodes that the agricultural drone can communicate with, then the agricultural drone needs to selectively communicate with some nodes, and these nodes act as relay nodes to communicate with other remaining nodes; Step 3: Agricultural UAV-Infinite Sensor Network Path Planning Algorithm Based on Relay Strategy Step 1: Select nodes; when the agricultural drone reaches a path point, it will collect information from the wireless sensor network nodes within its communication range; then, whenever the agricultural drone reaches a path point, When the agricultural drone reaches path point i, the topology of the entire network will change, and the distance between the agricultural drone and the wireless sensor network node needs to be recalculated. The distance between the human machine and all wireless sensor network nodes: Where (xi, yi, zi) is the coordinate of the agricultural drone when it arrives at the path point, (nodexj, nodeyj, nodezj) is the location information of the jth wireless sensor network node; Assuming that the communication radius of the agricultural drone is L = 280m, the nodes within the communication radius of the agricultural drone are: dij≤L; When the agricultural drone arrives at the path point S, the distance D between the agricultural drone and all wireless sensor network nodes is calculated and compared with the communication radius L of the agricultural drone to obtain the number of wireless sensor network nodes N in the communication range; Step 2: If N≤nmax, the agricultural UAV communicates directly with the wireless sensor network and calculates the total energy consumed, ending; In the process of agricultural information collection, the infinite sensor network nodes are randomly distributed in space to collect environmental information; each wireless sensor network node communicates through the wireless data transmission protocol 802.11; before the agricultural drone sets off, it has learned the distribution location of the wireless sensor network nodes and the initial energy E0 of the wireless sensor network nodes; when the agricultural drone senses the wireless sensor network nodes within the communication range, the agricultural drone first communicates with the node with the shortest distance to ensure that the communication consumes the least energy; however, since the bandwidth of agricultural drone communication is certain and the flight speed of agricultural drone is very fast, the information received is very limited within a limited range and time; therefore, in the communication process, the agricultural drone must selectively communicate with the wireless sensor network nodes; It is known from the above communication relationship between agricultural drones and wireless sensor networks that the energy consumed by agricultural drones and wireless sensor network nodes for communication mainly depends on the communication frequency f and the distance d; when the communication frequency f is constant, the energy consumed between the two is mainly determined by the communication distance. The greater the communication distance, the more energy is consumed, and the smaller the communication distance, the less energy is consumed; so when the agricultural drone flies according to a pre-planned route, it senses n infinite sensor network nodes within the communication range, which are set as set S; if the maximum communication capability of the agricultural drone can communicate with nmax wireless sensor network nodes at the same time, if n<=nmax, then the agricultural drone communicates directly with n nodes, then the energy consumed by the wireless sensor network nodes at this time is Where ER,i,is the energy consumed by the agricultural UAV to communicate with a certain node; Step 3: If n>nmax, the agricultural drone must select nmax optimal nodes for information transmission. Not only that, the nmax nodes that directly communicate with the agricultural drone must also serve as relay nodes to transmit other node information to the agricultural drone, that is, nmax selected relay points L are randomly selected from n wireless sensor network nodes; when the agricultural drone collects wireless sensor network node information, the agricultural drone can only communicate with limited nodes within the communication range; however, in order to obtain more environmental information, the nodes that directly communicate with the agricultural drone can serve as relay nodes, and transmit more information to the agricultural drone by collecting information from surrounding information nodes; From the above formula, it can be concluded that the energy of path loss is proportional to the communication distance d; therefore, the closer the communication distance, the less energy is consumed; therefore, the selection of relay points is a very important issue; when the number of nodes within the communication range of the agricultural drone is greater than nmax, it is assumed that the nodes that communicate directly with the agricultural drone are used as relay nodes, set as set L, then the other nodes within the communication range are set U = SL, then the selection method of relay nodes is Selection Method F1 is the distance between the relay node and the agricultural drone, and F2 is the distance between the relay node and the remaining The distance between nodes; so we will relay nodes Substitute the selection methods into the equation in turn, When a certain combination makes F reach the minimum value, the node of this combination is taken as the relay node, and the rest are The limited sensor network nodes need to pass the information to the agricultural drone through the relay nodes; Step 4: Calculate the distance D between the relay point and the agricultural drone, and the distance between the interruption point and the wireless sensor network node as Dj, and let D = Di + Dj; In order to minimize the energy consumed by the wireless sensor network nodes, it is necessary to select nodes that are relatively close to the agricultural drone as relay nodes, and the distances between other wireless sensor network nodes and the relay nodes must also be considered, so that the total energy consumed by the entire system communication can be minimized. The amount is the smallest; so we will select the equation in the relay node as: F=F1+F2(3) Step 5: Repeat steps 3 and 4 to find the minimum value of D; Step 6: Calculate the total energy consumed E = Σni = 1Ei + Σni = -1. The convergence analysis of the proposed algorithm is studied, and the stability judgment of the agricultural drone-wireless network system is derived; in addition, the influence of various network parameters on the proposed charging relay strategy and algorithm performance is verified through theoretical and experimental means.

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