Unmanned aerial vehicle search and rescue method and system based on Internet of Things

Abstract

The invention discloses an unmanned aerial vehicle search and rescue method and system based on the Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: obtaining the region information of a target search and rescue region, and marking a region with the average signal intensity smaller than or equal to a first target threshold value as a first target region based on the region information; dispatching the unmanned aerial vehicle to the first target area to execute a signal relay task based on the area information of the first target area, and starting to collect search and rescue information of the target search and rescue area; and adjusting a flight task and a flight path of the unmanned aerial vehicle based on the search and rescue information. The system is composed of an acquisition unit, a processing unit and an execution unit which are respectively used for acquiring area information, processing and dispatching the unmanned aerial vehicle and adjusting a flight task. The optimal ultimate position is determined based on various types of path loss calculation, and the success rate of unmanned aerial vehicle search and rescue is improved.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a drone search and rescue method and system based on the Internet of Things. Background Art

[0002] With the development of science and technology and the continuous advancement of Internet of Things (IoT) technology, drones are playing an increasingly important role in various application fields. With their high mobility, remote control capabilities and real-time data collection capabilities, drones are widely used in agricultural monitoring, environmental protection, logistics and transportation, disaster relief and other fields. In terms of disaster relief, drones can quickly reach the disaster area, obtain real-time environmental information and life sign data, and provide important decision-making support for search and rescue personnel.

[0003] For example, the Chinese patent with publication number CN115170374A discloses an air-based IoT rescue platform and emergency search and rescue system. The system uses patrol drones to form a temporary regional wireless network, actively obtains life information from the sensing terminals worn by ground personnel, and sends it to the ground control center. The system sends nuclear inspection drones to obtain data on abnormal targets, and the ground control center formulates a rescue plan, which improves the rescue efficiency and the survival rate of ground personnel.

[0004] However, the existing technology still has shortcomings and lacks specific considerations on the stability and coverage of the communication network, and faces problems of communication interruption or insufficient coverage in practical applications. Summary of the invention

[0005] In response to the shortcomings of the existing technology, the present application provides a drone search and rescue method and system based on the Internet of Things. The weak signal area is determined by collecting the average signal strength of the target area, and the optimal signal relay position is determined for the weak signal area based on the path loss during signal propagation, thereby improving the communication network stability and coverage of the overall target search and rescue area.

[0006] In a first aspect, the present application discloses a drone search and rescue method based on the Internet of Things, comprising: Acquire regional information of a target search and rescue area; wherein the regional information includes: geographic information, climate information, and signal information; based on the regional information, mark an area where the average signal strength is less than or equal to a first target threshold as a first target area; Based on the area information of the first target area, dispatching a drone to the first target area to perform a signal relay task and start collecting search and rescue information of the target search and rescue area; Based on the search and rescue information, the flight mission and flight path of the UAV are adjusted.

[0007] As an optional implementation, the drone includes: a first drone, a second drone and a third drone; The first UAV is used to perform signal strength detection in the target search and rescue area; The second UAV is used to perform a signal relay task in the first target area; The third drone is used to collect search and rescue information of the target search and rescue area.

[0008] As an optional implementation manner, dispatching a drone to the first target area to perform a signal relay task based on the area information of the first target area includes: Based on the signal strength and the communication link quality of the first target area, the relay position of the second UAV in the first target area is determined.

[0009] As an optional implementation manner, determining the relay position of the second UAV in the first target area based on the signal strength and the communication link quality of the first target area includes: Based on a GIS tool, the signal strength and the communication link quality of the first target area are mapped to a geographical location to form a visual chart; determining at least one alternative relay location based on the visual diagram; Calculate the path loss of the candidate relay position; wherein the calculation formula of the path loss is: ; in, is the path loss, A is the basic attenuation constant, B is the frequency attenuation coefficient, C is the height attenuation coefficient of the transmitting antenna, D is the distance attenuation coefficient, and f represents the signal transmission frequency used by the drone. is the height of the transmitting antenna deployed on the drone relative to the ground, is the height of the receiving antenna deployed on the drone relative to the ground, d is the straight-line distance from the transmitting antenna to the receiving antenna, is the receiving antenna height correction factor; The alternative relay locations are adjusted based on the path loss of the alternative relay locations.

[0010] As an optional implementation manner, the path loss further includes: terrain diffraction loss; and the path loss of calculating the candidate relay position further includes: Based on the regional information of the first target area, using GIS to extract the elevation information of the first target area; Determining obstacle information of the first target area based on the elevation information of the first target area; The functional expression of the terrain diffraction loss is: ; in, is the terrain diffraction loss, is the diffraction loss of the ith obstacle, n is the total number of obstacles on the path; the diffraction loss of the ith obstacle The function expression is: ,in, is the diffraction loss function calculated for each obstacle, and is the diffraction parameter of the ith obstacle; the function expression of the diffraction parameter of the ith obstacle is: ; is the height of the ith obstacle vertex, is the height of the straight path at the i-th obstacle point when there are no obstacles, is the wavelength of the signal, and are the distances from the ith obstacle to the transmitter and receiver, respectively. is the total distance from the sender to the receiver.

[0011] As an optional implementation, the calculating the path loss of the candidate relay position further includes: rain attenuation loss; The functional expression of the rain attenuation loss is: ; in, is the rain attenuation loss, k and is the signal transmission frequency coefficient used with the drone, R is the rainfall intensity, is the path length through the rain cell.

[0012] As an optional implementation manner, adjusting the flight mission and flight path of the drone based on the search and rescue information includes: The second drone is also used to collect first search and rescue information of the target search and rescue area; In response to detecting first target information in the first search and rescue information collected in the first target area, adjusting the number of the second drones performing the signal relay task in the first target area based on the first target information; wherein the first target information includes: at least one of image, sound, thermal imaging, and life sign detection data exceeding a preset data volume; In response to detecting the first target information in the first search and rescue information collected in the target search and rescue area, the third drone is dispatched to the collection geographical location of the first target information in the target search and rescue area to collect second search and rescue information.

[0013] As an optional implementation manner, the resolution and frame rate of the image data in the second search and rescue information are greater than the resolution and frame rate of the image data in the first search and rescue information; The sampling rate and bit depth of the sound data in the second search and rescue information are greater than the sampling rate and bit depth of the sound data in the first search and rescue information; The temperature resolution per pixel of the thermal imaging data in the second search and rescue information is greater than the temperature resolution per pixel of the thermal imaging data in the first search and rescue information.

[0014] In a second aspect, the present application also provides a drone search and rescue system based on the Internet of Things, comprising: A collection unit, used to obtain regional information of a target search and rescue area; wherein the regional information includes: geographic information, climate information, and signal information; based on the regional information, marking an area where the average signal strength is less than or equal to a first target threshold as a first target area; a processing unit, configured to dispatch a drone to the first target area to perform a signal relay task based on the area information of the first target area, and to start collecting search and rescue information of the target search and rescue area; The execution unit adjusts the flight mission and flight path of the UAV based on the search and rescue information.

[0015] Compared with the prior art, the beneficial effects of the present invention are: accurately calculating the path loss, including terrain diffraction loss and rain attenuation loss, and dynamically deploying the relay position of the drone based on these data, ensuring the quality and stability of signal transmission, and adapting to search and rescue missions under different terrain and climatic conditions.

[0016] The mission types of different types of drones are dynamically adjusted based on real-time search and rescue information to achieve collaborative search and rescue of multiple types of drones, thereby improving the overall search and rescue efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a drone search and rescue method based on the Internet of Things provided in an embodiment of the present application; Figure 2 A distribution map of a first target area in a target search and rescue area provided in an embodiment of the present application; Figure 3 A diagram of a path loss function provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an Internet of Things-based drone search and rescue system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0019] See also Figure 1 As shown, it is a flow chart of a drone search and rescue method based on the Internet of Things provided by an embodiment of the present application, the method includes steps S101 to S103, wherein: S101: Obtaining regional information of a target search and rescue area; wherein the regional information includes: geographic information, climate information, and signal information; based on the regional information, marking an area with an average signal strength less than or equal to a first target threshold as a first target area.

[0020] S102: Based on the area information of the first target area, dispatch a drone to the first target area to perform a signal relay task, and start collecting search and rescue information of the target search and rescue area.

[0021] S103: Adjusting the flight mission and flight path of the UAV based on the search and rescue information.

[0022] In specific implementation, the embodiment of the present application obtains the geographic information of the target search and rescue area through a geographic information system (GIS). The system is a technical system that collects, stores, manages, calculates, analyzes, displays and describes the relevant geographic distribution data in the entire or part of the earth's surface (including the atmosphere) space with the support of computer hardware and software systems. Most of the known mountainous and forested areas have stored detailed regional information in the GIS system. Based on the application scenario and application area of ​​the present application, the GIS system corresponding to the area can be selected to obtain regional information, and the GIS software can be used to import geographic data sets related to the search and rescue area, such as satellite images, topographic maps, population distribution maps, etc. Commonly used GIS software are ArcGIS, QGIS, Google Earth Pro and GRASS GIS.

[0023] In specific implementation, the geographic information in the regional information of the target search and rescue area can be obtained through the GIS system, while the climate information can be obtained through the local weather station and related data interfaces. In addition, the regional information also includes signal information, and there are many ways to obtain signal information, such as ground signal measurement, drone-assisted signal detection, satellite signal data, existing network service provider data, etc.

[0024] See also Figure 2 , Figure 2A distribution map of the first target area in a target search and rescue area provided in an embodiment of the present application. In a specific implementation, through the signal information of the target search and rescue area, the area where the average signal strength is lower than the first target threshold can be marked as the first target area, that is, the weak signal area. In the weak signal area, the signal transmission quality is poor, which will affect the search and rescue mission. For example, the first target area A, the first target area B, and the first target area C in the figure.

[0025] In addition, the minimum area and maximum area of ​​the first target area can be set in combination with the specific rescue mission objectives to optimize subsequent steps. For example, if the rescue mission goal is to send real-time communication broadcasts, a signal blind area with an average signal strength within a certain area below the first target threshold can be allowed in the target rescue area, because when the area of ​​the signal blind area is less than a certain value, the broadcast information can be received in real time in the non-signal blind area surrounding the signal blind area and transmitted to the signal blind area through sound waves. For another example, if the target search and rescue mission is to transmit video data in real time, the allowed signal blind area will be set smaller.

[0026] The first target threshold can be determined in combination with the specific search and rescue mission type. For example, when the current search and rescue mission requires the transmission of a large amount of high-definition video, a higher first target threshold can be set to meet the data transmission requirements. For another example, when the current search and rescue mission only requires the drone to go to the target area to broadcast preset information, a lower first target threshold can be set.

[0027] For the first target area, the present application improves the signal quality of the area by dispatching a drone to perform a signal relay task to meet the signal quality requirements of the search and rescue mission. After the signal strength of the entire target search and rescue area has been improved to above the first target threshold, the search and rescue mission is started.

[0028] It can be understood that the search and rescue mission here refers to the search and rescue mission that requires drones to perform, such as target search, broadcasting, and material delivery.

[0029] In specific implementation, the drone will collect search and rescue information in the entire target search and rescue area. The search and rescue information may include various types, such as video, sound, smoke information, flame information, living body information, etc. Based on the collected search and rescue information, the specific mission objectives and flight paths of the drone are adjusted. For example, if there is a target individual in the search and rescue information of a certain place in the target search and rescue area, more drones can be dispatched to perform subsequent search and rescue missions.

[0030] In this way, by detecting the signal information of the target search and rescue area and dispatching drones to relay signals in areas with poor signal quality, the signal strength of the entire target search and rescue area can be effectively improved, providing a guarantee for subsequent search and rescue missions.

[0031] Regarding S101 above:

[0032] In the specific implementation, the drone is equipped with a variety of sensors, such as high-resolution cameras, infrared sensors, GPS and wireless signal detectors, to obtain real-time regional information of the search and rescue area. Such regional information may include, but is not limited to: topography, current weather conditions, vegetation coverage, and possible man-made obstacles. In addition, if the above information of the target search and rescue area is recorded in the GIS system, it can also be directly obtained through the GIS system.

[0033] Drones collect data in real time through IoT technology and transmit it back to the control center, which can use this data to update regional information and data analysis in real time.

[0034] Among them, the control center can be set up in a location with good signals at the regional boundary of the target search and rescue area, or it can be set up in the target search and rescue area according to specific needs. If the signal quality permits, it can also be set up in a farther location. For example, the target search and rescue area is in a mountainous area, and the command center is set up in an urban area.

[0035] In a specific implementation, after determining the location of the first target area in the target search and rescue area, a drone is deployed in the first target area to perform a signal relay task to enhance the communication capability in the area and ensure smooth search and rescue communications.

[0036] The drone will carry mobile communication relay equipment and be deployed in the first target area.

[0037] While performing the relay mission, the drone is also used to collect search and rescue information, including images, sounds, thermal imaging, and possible signs of life, and sends the collected data to the control center in real time for analysis to identify needs and priorities within the search and rescue area, such as identifying the location of trapped people and estimating emergency conditions.

[0038] After receiving the search and rescue information and real-time environmental data collected from the first target area, the control center dynamically adjusts the flight mission and path of the drone, taking into account factors such as the shortest path, flight safety, and energy consumption optimization.

[0039] In specific implementations, the flight mission and path of the drone can be dynamically adjusted through the ant colony optimization algorithm. The ant colony optimization algorithm (ACO) is a swarm intelligence algorithm that simulates the foraging behavior of ants. By simulating the process of ants secreting and tracking pheromones between path nodes, the ant colony optimization can find the global optimal or approximately optimal path.

[0040] In this problem, the flight area can be abstracted as a graph structure, where nodes represent flight points and edges represent paths. Initially, the pheromone concentration on each path is set to the same value. Heuristic information can be defined based on three factors: path length, flight safety, and energy consumption, so that pheromones and heuristic information can jointly guide ants to choose paths. Specifically, each ant starts from the starting point and chooses the next flight point by comprehensively considering the pheromone concentration and heuristic information until the entire path is completed. In this process, paths with shorter path lengths, higher safety factors, and lower energy consumption will accumulate more pheromones, thereby increasing the probability of being selected by subsequent ants.

[0041] In addition, the fitness function is used to comprehensively evaluate the pros and cons of each path, including path length, flight safety, and energy consumption. Paths with higher fitness values ​​will further enhance their pheromone concentrations, while paths with lower fitness values ​​will gradually reduce their pheromone concentrations. By continuously iterating and updating pheromone concentrations, the ant colony optimization algorithm can gradually approach the optimal solution.

[0042] Among them, the structure of the fitness function can be based on the weighted summation of the distance, safety factor and energy consumption between each node on the path, so as to evaluate the comprehensive advantages and disadvantages of the path. Specifically, the total length of the path can be calculated by accumulating the distance between all nodes, flight safety can be evaluated by accumulating the safety factor between all nodes, and energy consumption is the sum of the energy consumption between all nodes. In order to reflect the importance of different goals, different weight coefficients are set for these factors, such as the weight coefficient of path length, the weight coefficient of flight safety and the weight coefficient of energy consumption. By adjusting these weight coefficients, different optimization goals can be prioritized according to actual needs. For example, if flight safety is more important than path length and energy consumption, the weight of flight safety can be increased. The lower the fitness value, the better the path, because a shorter distance, a higher safety factor and a lower energy consumption are set as the optimization goals. In this way, the fitness function can flexibly reflect the needs of different application scenarios, thereby helping the ant colony optimization algorithm to find the optimal solution when dynamically adjusting the flight mission and path of the drone.

[0043] As an optional implementation, the drones in the present application may be multiple types, including: a first drone, a second drone, and a third drone; The first drone is used to perform signal strength detection in the target rescue area, find an area with a signal strength less than or equal to a first target threshold, and mark it as the first target area; The second UAV is used to perform a signal relay task at the first target area;

[0044] The third drone is used to collect search and rescue information of the target search and rescue area.

[0045] In this way, different types of drones have different flight performance and load capacity, and this difference can make search and rescue missions more flexible and efficient. High-performance drones can cover a larger search and rescue area and carry more rescue supplies, while light drones can fly flexibly in narrower and more complex terrains.

[0046] In the specific implementation, drones with different performance will be assigned to different tasks. For example, drones with long flight time and large payload capacity are suitable for search missions covering a large area and carrying a large amount of rescue supplies; while small drones can perform fine search missions in complex terrain, or play a role in tasks such as communication relay. Performance differences make task allocation more efficient and resource utilization more reasonable. In addition, since the cost of equipping drones for some specific needs is relatively high, the above method achieves the search and rescue goals by reasonably deploying different types of drones, eliminating the cost of equipping each drone with expensive equipment.

[0047] In a specific implementation, the first drone is mainly responsible for performing detailed signal strength detection. This includes using signal detection equipment to scan the designated search and rescue area to identify areas with low signal strength, which generally indicate possible communication barriers or equipment failure areas.

[0048] Once an area is marked as the first target area, the first drone can continuously monitor the signal strength in the area and provide real-time updates to the control center.

[0049] In a specific implementation, when facing an area with no signal at all, a first drone equipped with satellite communication capability can be used to maximize its signal strength detection capability. The location where the first drone that is performing a signal detection task loses signal transmission can also be directly determined as the first target area.

[0050] In a specific implementation, the signal relay task is performed by the second UAV, which is equipped with a communication relay device. The relay device is deployed in the first target area marked by the first UAV to enhance the signal strength in the area.

[0051] While performing the signal relay mission, the second drone can also collect additional information about the area, such as environmental images, sounds and other possible emergency signals, to assist in the positioning and planning of further rescue operations.

[0052] As an optional implementation, the third drone is responsible for collecting search and rescue information, especially those that require high resolution and high data density. This includes using high-precision cameras and other sensors to collect image, sound, and thermal imaging data to more accurately identify and locate trapped people or important search and rescue targets.

[0053] The information collected by the third drone is of higher quality than the search and rescue data collected by the second drone, and is suitable for complex or dangerous rescue operations, providing a more detailed on-site assessment.

[0054] For example, in a search and rescue operation after an urban earthquake, the first drone was deployed first and identified several areas with weak signals. Subsequently, the second drone was sent to these areas to deploy relay equipment, restore communications and assist subsequent rescue operations. At the same time, the third drone carried a high-resolution sensor into the collapsed building area, and the high-definition images and data it collected were crucial to guide rescuers to enter safely and perform rescue operations.

[0055] Regarding S102 above: As an optional implementation, based on the area information of the first target area, dispatching the drone to the first target area to perform the signal relay task includes: Based on the signal strength and communication link quality of the first target area, the relay position of the second UAV in the first target area is determined.

[0056] In a specific implementation, the specific location of the second UAV as a signal relay point in the first target area has a greater impact on the signal relay effect. During deployment, a signal quality distribution map can be drawn based on the signal information obtained by the first UAV, and the location with the best signal quality around the first target area can be determined as the signal relay point.

[0057] As an optional implementation manner, based on the signal strength and the communication link quality of the first target area, determining the relay position of the second UAV in the first target area includes: Based on a GIS tool, the signal strength and the communication link quality of the first target area are mapped to a geographical location to form a visual chart; determining at least one alternative relay location based on the visual diagram; Calculate the path loss of each candidate relay position; wherein the calculation formula of the path loss is: ; in, is the path loss, A is the basic attenuation constant, B is the frequency attenuation coefficient, C is the height attenuation coefficient of the transmitting antenna, D is the distance attenuation coefficient, and f represents the signal transmission frequency used by the drone. is the height of the transmitting antenna deployed on the drone relative to the ground, is the height of the receiving antenna deployed on the drone relative to the ground, d is the straight-line distance from the transmitting antenna to the receiving antenna, is the receive antenna height correction factor to compensate for the additional signal attenuation due to the relatively low receive antenna.

[0058] In the specific implementation, the GIS tool is first used to map the signal strength and communication link quality of the first target area to the geographic location to form a visual chart. The specific steps are as follows: The first drone flies in the target search and rescue area to collect signal strength and communication link quality data including Wi-Fi signal strength (RSSI), LTE / 5G signal strength, data transmission rate and packet loss rate. These data are uploaded to the IoT platform, i.e., the control center, for centralized storage and processing. The processed data is mapped through the GIS tool to convert the signal strength and communication link quality data into a visual chart on the geographic location. In the visual chart, areas with high signal strength and good communication link quality are marked as green areas, while areas with low signal strength and poor communication link quality are marked as red areas. Based on the generated visual chart, several alternative relay locations are determined, which should be as close to the red area as possible and located in the green area to ensure effective coverage of the relay signal.

[0059] The path loss calculation formula above is used to calculate the path loss of each alternative relay location. The specific values ​​of A, B, C, and D can be set according to the target usage scenario. In a mountainous environment, A=69.55, B=26.16, C=13.82, and D= ;

[0060] By calculating the path loss of each alternative relay position, the relay position with the minimum path loss is selected. The second UAV is dispatched to the determined optimal relay position to establish a relay link and achieve high-quality signal transmission in the first target area, thereby improving the communication efficiency and reliability of the search and rescue mission.

[0061] For example, the frequency is set to 1500 MHz, the transmitting antenna height is 50m, the receiving antenna height is 10m, the transmission distance is 4km, and the height correction factor is The function expression is: ; ; Calculating Path Loss : ;

[0062] The calculated path loss is approximately 135.8252 dB, based on which the transmit power, antenna height or number of relay points required can be adjusted.

[0063] In a specific implementation, the path loss calculation for each candidate relay location is based on multiple factors: this includes the height of the transmitting and receiving antennas, the actual distance the signal needs to cover, the signal frequency, etc.

[0064] The specific number of relay points can be determined based on the calculation of path loss, which includes evaluating the signal coverage, considering the signal quality requirements, and estimating the signal gain that the relay station can provide.

[0065] First, determine the target device's receive sensitivity, which is the minimum signal strength at which the device can reliably receive a signal. For example, a common Wi-Fi receiver may require a signal strength of at least -75 dBm to maintain a stable connection.

[0066] Use the above path loss formula to calculate the given maximum distance at which the signal strength just meets the receiver sensitivity requirement. For example, if the transmit power is 20 dBm and the receiver sensitivity is -75 dBm, the required path loss is: ; If, based on the previous example, the path loss formula is When the transmission distance is 4 km, the distance that satisfies the 95 dB path loss can be found by adjusting this equation. This includes adjusting the signal transmission frequency used by the drone, the height of the transmitting antenna deployed on the drone relative to the ground, the height of the receiving antenna deployed on the drone relative to the ground, the straight-line distance from the transmitting antenna to the receiving antenna, etc. The specific adjustment method can use genetic mutation algorithm, particle swarm optimization algorithm and simulated annealing algorithm.

[0067] It is also possible to estimate the number of relay points required to cover the target area based on the relay range of a single second drone. This can be done by dividing the total distance to be covered by the coverage radius of a single relay station. If there are geographic or environmental obstacles, additional relay stations will be required to ensure continuous coverage.

[0068] Using GIS tools can help plan the exact location of the relay stations to ensure that the entire target area is covered as designed. These tools can simulate signal coverage under different relay station configurations to help determine the optimal relay station location.

[0069] For example, in a 5 square kilometer search and rescue area with complex terrain and multiple first target areas, the purpose is to ensure stable wireless communication coverage in the entire area so that the search and rescue drone can transmit video feedback and positioning data in real time.

[0070] A relay station is set up in the center and four corners of the first target area, for a total of five relay points.

[0071] Among them, the central relay station is located at the geometric center of the area, and the four-corner relay stations are located at the four corners of the area.

[0072] The path loss from each relay point to the farthest end of its coverage area is calculated to confirm whether all areas can be effectively covered. The path loss formula used is as above, assuming that the relay point height is 50 meters, the receiving point (drone) height is 10 meters, the frequency used is 2400 MHz, and the maximum distance is 1.5 kilometers (half of the area diagonal).

[0073] If it is found that the path loss of some relay stations is too high, resulting in the inability to effectively cover the specified area, it is necessary to adjust the location of the relay points or increase the number of relay points.

[0074] For example, calculations show that the coverage of the four-corner repeater stations is not sufficient to reach the central part of the area.

[0075] Add an extra relay point at the center of the area to ensure that the path loss from the center point to any edge is within an acceptable range.

[0076] Move some edge relay points inward to reduce the distance from these relay points to the farthest point in their respective coverage areas. In the genetic mutation algorithm, the distance limit between the relay point and the center of the first target area can be used as a constraint condition. By introducing a penalty function, the fitness value of the solution that violates the constraint is penalized, thereby guiding the algorithm to generate a solution that meets the range limit. In addition, individuals that do not meet the target range limit can be directly discarded in the selection stage to ensure that the individuals participating in the next generation meet the target range limit.

[0077] After adjustment, recalculate the path loss of all relay points to verify whether the new relay point layout meets the coverage requirements.

[0078] In this way, the relay station layout of the UAV search and rescue system can be systematically evaluated and optimized to ensure efficient communication coverage in complex environments. This not only improves the search and rescue efficiency, but also ensures the safety and real-time nature of the search and rescue operation.

[0079] As an optional implementation manner, the path loss further includes: terrain diffraction loss; and calculating the path loss of each candidate relay position further includes: Based on the regional information of the first target area, using GIS to extract the elevation information of the first target area; Determining obstacle information of the first target area based on the area information of the first target area; Determining the terrain diffraction loss based on the elevation information of the first target area and the obstacle information of the first target area; The functional expression of the terrain diffraction loss is: ; in, is the terrain diffraction loss, is the diffraction loss of the ith obstacle, n is the total number of obstacles on the path; the diffraction loss of the ith obstacle The function expression is: ,in, is the diffraction loss function calculated for each obstacle, and is the diffraction parameter of the ith obstacle; the function expression of the diffraction parameter of the ith obstacle is: ; is the height of the ith obstacle vertex, is the height of the straight path at the i-th obstacle point when there are no obstacles, is the wavelength of the signal, and are the distances from the ith obstacle to the transmitter and receiver, respectively. is the total distance from the sender to the receiver.

[0080] In a specific implementation, elevation information provides information about the height of points on the surface of the area relative to a specific vertical reference (usually sea level), which can be obtained through a GIS system. Further, the precise geographic location of obstacles can be determined, including latitude and longitude coordinates and the height of the vertex of each obstacle, as well as the height of the intersection with the path. In a multi-obstacle environment, radio waves usually cannot propagate in a straight line to the target, but need to bypass these obstacles. This bypass usually causes additional losses, called diffraction losses. The calculation of diffraction losses depends on the relative position between the wave propagation path and the obstacles.

[0081] In a specific implementation, if there are multiple terrain obstacles on the path, the terrain diffraction loss can be added to the calculation of the path loss, where the diffraction loss function is It can be described using the following approximate formula: ; In a specific implementation, in wireless communications, rain can have a significant impact on signal propagation, especially for high-frequency signals. In order to accurately consider the impact of these environmental factors on signal attenuation, an additional attenuation model can be introduced to specifically estimate the attenuation effect under different weather conditions.

[0082] As an optional implementation manner, the calculating the path loss of each candidate relay position further includes: rain attenuation loss; The functional expression of the rain attenuation loss is: ; in, is the rain attenuation loss, k and is a coefficient related to the signal transmission frequency used by the drone, R is the rainfall intensity, is the path length through the rain cell.

[0083] The absorption and scattering of radio waves by raindrops will cause signal attenuation, and this effect increases significantly with increasing frequency. It is a coefficient related to the signal transmission frequency used by the drone, and its specific value can be found in the table provided by the International Telecommunication Union.

[0084] For example, a UAV uses a frequency of 10 GHz to operate under moderate rainfall (5 mm / h) conditions, and the signal passes through the rain area for a distance of 1 km. According to the above formula, the rain attenuation loss can be calculated: ; This model can be combined with the terrain diffraction model above to calculate the total path loss.

[0085] In this way, the signal propagation conditions in a specific search and rescue mission can be estimated more accurately, thereby improving the reliability and efficiency of the UAV communication system. By dynamically adjusting the communication strategy to adapt to environmental conditions, the success rate of the search and rescue mission can be significantly improved.

[0086] For the overall composition of the above path loss function, see Figure 3 , Figure 3 A path loss function composition diagram provided in an embodiment of the present application, wherein the total loss function can be composed of the superposition sum of the path loss PL, the terrain diffraction loss, and the rain attenuation loss.

[0087] In this way, the path loss of each alternative relay point for the drone to perform the relay task can be calculated more accurately, so that the alternative relay point with lower path loss can be selected to perform the relay task.

[0088] Regarding S103 above: As an optional implementation manner, adjusting the flight mission and flight path of the drone based on the search and rescue information includes: The second drone is also used to collect first search and rescue information of the target search and rescue area; In response to detecting first target information in the first search and rescue information collected in the first target area, wherein the first target information includes: at least one of image, sound, thermal imaging, and life sign detection data exceeding a preset data volume; Based on the first target information, adjusting the number of the second drones that perform the signal relay task in the first target area; In response to detecting the first target information in the first search and rescue information collected in the target search and rescue area, the third drone is dispatched to the collection geographical location of the first target information in the target search and rescue area to collect second search and rescue information.

[0089] As an optional implementation manner, the resolution and frame rate of the image data in the second search and rescue information are greater than the resolution and frame rate of the image data in the first search and rescue information; The sampling rate and bit depth of the sound data in the second search and rescue information are greater than the sampling rate and bit depth of the sound data in the first search and rescue information; The temperature resolution per pixel of the thermal imaging data in the second search and rescue information is greater than the temperature resolution per pixel of the thermal imaging data in the first search and rescue information.

[0090] In the specific implementation, the second drone is responsible for collecting preliminary search and rescue information in the first target area, such as images, sounds, thermal imaging, and signs of life.

[0091] The data collected by the first UAV is analyzed using an onboard or control center, and target information exceeding a preset data volume is automatically identified, which may indicate the presence of a search and rescue target.

[0092] According to the first search and rescue information collected from the first target area, the number and flight path of the second drones are dynamically adjusted. For example, if the detected target information indicates that a higher signal strength is required for data transmission, more second drones can be dispatched to the area to perform signal relay tasks. The relay positions of these newly deployed second drones can be set based on the method above, and the difference lies only in the different expected values ​​of the target path loss, for example, higher signal strength requires lower path loss.

[0093] A third drone is dispatched to the geographical location of the determined first target information to collect more detailed second search and rescue information. The second search and rescue information collected by the third drone will have a higher quality standard than the first search and rescue information. This includes image data with higher resolution and frame rate, sound data with higher sampling rate and bit depth, and thermal imaging data with higher resolution. These high-quality data can provide clearer visual and sound information, helping the rescue team to more accurately locate and assess the condition of the victim.

[0094] In this way, drones can not only effectively perform search and rescue missions, but also significantly improve rescue efficiency and success rates, especially in complex or inaccessible environments.

[0095] Based on the same inventive concept, an embodiment of the present application also provides an Internet of Things-based drone search and rescue system corresponding to an Internet of Things-based drone search and rescue method. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned Internet of Things-based drone search and rescue method, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated.

[0096] Reference Figure 4 FIG. 1 is a schematic diagram of a structure of an unmanned aerial vehicle search and rescue system based on the Internet of Things provided in an embodiment of the present application, wherein the system comprises: The acquisition unit 10 acquires regional information of a target search and rescue area, wherein the regional information includes: geographic information, climate information, and signal information; based on the regional information, an area whose signal strength is less than or equal to a first target threshold is marked as a first target area; The processing unit 20 dispatches a drone to the first target area to perform a signal relay task based on the area information of the first target area, and starts collecting search and rescue information of the target search and rescue area; The execution unit 30 adjusts the flight mission and flight path of the UAV based on the search and rescue information.

[0097] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0099] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0100] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0101] The preferred embodiments of the present invention disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details in detail, nor do they limit the present application to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can understand and use the present application well. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A drone search and rescue method based on the Internet of Things, characterized in that: include: Acquire regional information of a target search and rescue area; wherein the regional information includes: geographic information, climate information, and signal information; based on the regional information, mark an area where the average signal strength is less than or equal to a first target threshold as a first target area; Based on the area information of the first target area, dispatching a drone to the first target area to perform a signal relay task and start collecting search and rescue information of the target search and rescue area; Based on the search and rescue information, the flight mission and flight path of the UAV are adjusted.

2. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 1, characterized in that: The drones include: a first drone, a second drone and a third drone; The first UAV is used to perform signal strength detection in the target search and rescue area; The second UAV is used to perform a signal relay task in the first target area; The third drone is used to collect search and rescue information of the target search and rescue area.

3. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 2, characterized in that: The dispatching the UAV to the first target area to perform the signal relay task based on the area information of the first target area includes: Based on the signal strength and the communication link quality of the first target area, the relay position of the second UAV in the first target area is determined.

4. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 3, characterized in that: The determining the relay position of the second UAV in the first target area based on the signal strength and the communication link quality of the first target area includes: Based on a GIS tool, the signal strength and the communication link quality of the first target area are mapped to a geographical location to form a visual chart; determining at least one alternative relay location based on the visual diagram; Calculate the path loss of the candidate relay position; wherein the calculation formula of the path loss is: ； in, is the path loss, A is the basic attenuation constant, B is the frequency attenuation coefficient, C is the height attenuation coefficient of the transmitting antenna, D is the distance attenuation coefficient, and f represents the signal transmission frequency used by the drone. is the height of the transmitting antenna deployed on the drone relative to the ground, is the height of the receiving antenna deployed on the drone relative to the ground, d is the straight-line distance from the transmitting antenna to the receiving antenna, is the receiving antenna height correction factor; The alternative relay locations are adjusted based on the path loss of the alternative relay locations.

5. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 4, characterized in that: The path loss also includes: terrain diffraction loss; the path loss of the candidate relay position calculation also includes: Based on the regional information of the first target area, using GIS to extract the elevation information of the first target area; Determining obstacle information of the first target area based on the elevation information of the first target area; The functional expression of the terrain diffraction loss is: ； in, is the terrain diffraction loss, is the diffraction loss of the ith obstacle, n is the total number of obstacles on the path; the diffraction loss of the ith obstacle The function expression is: ,in, is the diffraction loss function calculated for each obstacle, and is the diffraction parameter of the ith obstacle; the function expression of the diffraction parameter of the ith obstacle is: ； is the height of the ith obstacle vertex, is the height of the straight path at the i-th obstacle point when there are no obstacles, is the wavelength of the signal, and are the distances from the ith obstacle to the transmitter and receiver, respectively. is the total distance from the sender to the receiver.

6. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 5, characterized in that: The calculating of the path loss of the candidate relay position also includes: rain attenuation loss; The functional expression of the rain attenuation loss is: ； in, is the rain attenuation loss, k and is the signal transmission frequency coefficient used with the drone, R is the rainfall intensity, is the path length through the rain cell.

7. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 6, characterized in that: The adjusting the flight mission and flight path of the UAV based on the search and rescue information includes: The second drone is also used to collect first search and rescue information of the target search and rescue area; In response to detecting first target information in the first search and rescue information collected in the first target area, adjusting the number of the second drones performing the signal relay task in the first target area based on the first target information; wherein the first target information includes: at least one of image, sound, thermal imaging, and life sign detection data exceeding a preset data volume; In response to detecting the first target information in the first search and rescue information collected in the target search and rescue area, the third drone is dispatched to the collection geographical location of the first target information in the target search and rescue area to collect second search and rescue information.

8. The method for searching and rescuing by unmanned aerial vehicles based on the Internet of Things according to claim 7, characterized in that: The resolution and frame rate of the image data in the second search and rescue information are greater than the resolution and frame rate of the image data in the first search and rescue information; The sampling rate and bit depth of the sound data in the second search and rescue information are greater than the sampling rate and bit depth of the sound data in the first search and rescue information; The temperature resolution per pixel of the thermal imaging data in the second search and rescue information is greater than the temperature resolution per pixel of the thermal imaging data in the first search and rescue information.

9. An unmanned aerial vehicle search and rescue system based on the Internet of Things, characterized in that: include: A collection unit is configured to obtain regional information of a target search and rescue area, wherein the regional information includes: geographic information, climate information, and signal information; based on the regional information, an area whose signal strength is less than or equal to a first target threshold is marked as a first target area; The processing unit dispatches a drone to the first target area to perform a signal relay task based on the area information of the first target area, and starts collecting search and rescue information of the target search and rescue area; The execution unit adjusts the flight mission and flight path of the UAV based on the search and rescue information.

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