Flexible variable-speed fixed-wing unmanned aerial vehicle energy-saving data acquisition method and system

Abstract

The invention discloses a flexible variable-speed fixed-wing unmanned aerial vehicle energy-saving data acquisition method and system, and the method comprises the following steps: obtaining position coordinates of a sensor node and a data center, and initializing an access sequence, a flight speed and a collection speed; according to a given access path, a convex optimization method is used for solving to obtain an optimal solution of the flight speed and the collection speed; obtaining a new access sequence according to all possible sequences of all node access sequences; judging whether a new path corresponding to the new access sequence meets the condition that the AOI of the node is smaller than the maximum AOI value or not; calculating the total energy consumed by the UAV under the new path; judging that the total energy of the UAV energy consumption meets a set condition, and outputting an optimal path and speed; different requirements of the fixed-wing UAV in different modes are fully considered: in a flight mode, in order to maintain the real-time performance of node data, the UAV needs to fly at a relatively high speed; and in the data receiving mode, the speed needs to be reduced to ensure complete receiving of the data.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an energy-saving data collection method and system for a fixed-wing unmanned aerial vehicle with flexible speed change. Background Art

[0002] The birth of the fifth generation mobile communication technology (5G) marks the arrival of a new era. It provides strong support for the Internet of Things (IoT) with high transmission speed, low latency and wide connectivity. From smart homes to smart cities, from industrial automation to smart agriculture, various devices and sensors can be interconnected in real time. According to statistics, the proportion of IoT devices in wireless connections will increase from 33% in 2018 to 50% in 2023, with a total of 14.7 billion connections. This undoubtedly brings unprecedented challenges to network operators. As a key link in the Internet of Things, sensors are responsible for sensing data in the environment and uploading these data to remote centers through data collectors for analysis and decision-making. Traditional data collectors are statically deployed. This design is simple and low-cost, but when sensors are located in remote areas with complex terrain, it is often difficult to ensure effective data collection. In addition, with the rapid development of real-time sensor monitoring applications, the timeliness requirements for monitoring data are also increasing. Once the data is not updated in time, it may cause a series of serious problems and even lead to catastrophic consequences.

[0003] To meet these challenges, unmanned aerial vehicles (UAVs) have become a solution with great potential due to their low cost, high mobility and easy deployment. UAVs are equipped with various sensors and cameras to monitor and collect data from ground targets in real time or regularly from the air. They are widely used in many fields such as industry, agriculture, and environmental monitoring, providing efficient and safe services to society. At the same time, as the application market of UAVs continues to expand, its classification standards are becoming more and more diverse. According to the different wing configurations, UAVs are mainly divided into two types: fixed-wing UAVs and rotary-wing UAVs. Rotary-wing UAVs can rise and fall freely and hover flexibly, but they have small load capacity and limited battery capacity. Compared with rotary-wing UAVs, fixed-wing UAVs are more energy-efficient, have strong load capacity, fast flight speed, and long endurance, but they cannot hover in a fixed position. These two types of UAVs each have unique advantages, so when choosing a UAV, it is necessary to determine the appropriate type according to the needs of different scenarios.

[0004] Compared with traditional IoT communication networks, IoT networks using UAVs have the following significant advantages: 1. Improving the life of IoT devices. UAVs can get close to IoT devices to reduce the attenuation of signals during propagation, thereby reducing the energy consumption of IoT devices during data upload. 2. Improving channel quality. By adjusting the flight position, UAVs can establish a stable and reliable line of sight (LoS) connection with ground devices and optimize the quality of the communication link. 3. Expanding coverage. Thanks to the higher flying altitude of UAVs, the probability of establishing a line of sight connection with ground devices is greatly increased, thereby effectively expanding the coverage area of ​​the network. These advantages make UAVs a highly promising technology in IoT networks. Although UAVs have many advantages, they still face a series of challenges in auxiliary IoT communication networks, including limited battery capacity and complex trajectory planning problems.

[0005] At present, the energy-saving dynamic deployment of UAV-assisted data collection has attracted the research of many scholars and experts, but the existing work is mainly based on the research of the characteristics and energy consumption model of rotary-wing UAVs, and there is a lack of relevant research on fixed-wing UAV data collection. At the same time, most studies assume that UAVs fly at a constant speed for data collection, ignoring the different requirements of different sensor nodes for information timeliness, which may lead to data failure problems. In view of the above analysis, the present invention will select fixed-wing UAVs as mobile data receivers to assist wireless sensor networks in real-time data collection, aiming to meet data timeliness while minimizing UAV energy consumption. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention provides a flexible variable speed fixed-wing UAV energy-saving data collection method, based on the strategy of combining the flight-collection mode of the fixed-wing UAV, while introducing the AoI to quantify the freshness of the node data, and providing the AoI calculation model under this strategy. At the same time, in order to support the differentiated requirements of the timeliness of different node data, a flexible variable speed fixed-wing UAV energy-saving data collection method and system are proposed.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is a method for collecting energy-saving data of a fixed-wing UAV with flexible speed change, comprising the following steps: Step 1, obtain the location coordinates of sensor nodes and data centers, and initialize the access order, flight speed, and collection speed; Step 2: Based on the given access path, use the convex optimization method to solve the optimal solution for the flight speed and collection speed; obtain a new access order based on all possible sorting of the access order of all nodes; determine whether the new path corresponding to the new access order satisfies the condition that the AOI of the node is less than the maximum AOI value. If not, adjust the order until it satisfies the condition; calculate the total energy consumed by the UAV under the new path; Step 3: Determine whether the total energy consumed by the UAV meets the set conditions. If so, output the optimal path and speed; otherwise, continue to execute step 2 until convergence.

[0008] Further, including flight mode: drones from data centers or sensor nodes Fly to sensor node and returns to the data center; Collection mode: After collecting data from the current sensor, the drone moves to the next sensor at a higher speed, and the drone enters the sensor node After entering the monitoring area, data collection begins.

[0009] Furthermore, in the acquisition mode, the drone slows down and adopts a symmetrical trajectory to extend the acquisition time, where Represents the acquisition speed, introduces the AOI performance index, and the kth node is the accessed node The AOI expression is:

[0010] in Indicates from The moment when data collection starts, and , For UAV Fly to time, .

[0011] Furthermore, the precise location coordinates of the sensor nodes and data centers are obtained through the positioning and navigation system; the access sequence is preliminarily planned according to task requirements and node distribution.

[0012] Furthermore, according to all possible sortings of all node access orders, obtaining a new access order includes: based on all possible sortings of all node access orders, obtaining the optimal access order of the nodes through dynamic programming, and using a genetic algorithm to approximate the solution to obtain a new access order.

[0013] Furthermore, the convex optimization method is used to solve the optimal solution of the flight speed and the collection speed, including: for speed optimization, the path of the UAV is first given, the expression of energy consumption is expanded, and the function is obtained to be a convex function in the positive domain, and the flight speed and the collection speed are solved by convex optimization theory.

[0014] Furthermore, the total energy consumption of the UAV satisfies the set condition that the absolute value of the previous energy consumption value minus the current energy consumption value divided by the absolute value of the previous energy consumption value is less than a, where a is the maximum tolerance.

[0015] The present invention also provides a fixed-wing UAV energy-saving data acquisition system with flexible speed change, which performs data acquisition based on the above-mentioned fixed-wing UAV energy-saving data acquisition method.

[0016] The present invention can also provide a computer device, including a processor and a memory, the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and when the processor executes the computer executable program, the energy-saving data collection method for fixed-wing unmanned aerial vehicles with flexible speed changes described in the present invention can be implemented.

[0017] At the same time, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the energy-saving data collection method for fixed-wing unmanned aerial vehicle with flexible speed change described in the present invention can be implemented.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: Since fixed-wing UAVs cannot hover when collecting data, the present invention proposes a fixed-wing UAV information collection strategy that combines flight mode and data receiving mode. This strategy fully takes into account the different needs of fixed-wing UAVs in different modes: in flight mode, in order to maintain the real-time nature of node data, the UAV needs to fly at a higher speed; while in data receiving mode, the speed needs to be reduced to ensure complete reception of data. Based on this, the present invention further proposes a flexible and variable-speed fixed-wing UAV energy-saving data collection method, which enables fixed-wing UAVs to be dynamically deployed with low energy consumption, effectively balancing the relationship between energy saving and data freshness. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart for realizing the present invention; Figure 2a This is the path and speed diagram when the AoI threshold is 100s under the strategy of the present invention. Figure 2b The path and speed diagram of the dynamic planning strategy when the AoI threshold is 100s. Figure 2c The path and speed graph of the greedy strategy when the AoI threshold is 100s; Figure 3a The UAV energy consumption comparison diagram of the strategy of the present invention, dynamic programming and greedy strategy under different AoI thresholds is shown in FIG. Figure 3b A comparison chart of the UAV task completion time of the strategy of the present invention, dynamic programming, and greedy strategy at different AoI thresholds; Figure 4The path and speed diagram of the strategy of the present invention when the AoI threshold of some nodes is changed to 40s; Figure 5a The AoI value of each sensor node before the partial AoI threshold is adjusted according to the strategy of the present invention.

[0020] Figure 5b The AoI value of each sensor node after adjusting part of the AoI threshold according to the strategy of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The energy-saving data collection method for fixed-wing UAV with flexible speed change proposed in the present invention is divided into two modes according to the state of UAV data collection, namely, flight mode and collection mode. The flight mode is when the UAV collects data from the data center. or sensor node Fly to sensor node And return to the data center The sensor nodes are used to collect various types of data (such as environmental parameters, images, and videos); the sensor nodes can be pre-deployed or deployed by UAV. The UAV takes off from the starting point and goes to each sensor node for data collection according to the preset flight plan and navigation system. During the flight, the UAV needs to monitor its own status in real time; when the UAV arrives near the sensor node, it collects data from the sensor node through wireless communication; after completing the data collection task, the UAV returns to the starting point.

[0023] After collecting data from the current sensor, in order to reduce the task completion time and improve the timeliness of the data, the drone should move to the next sensor at a higher speed, denoted as The acquisition mode is when the drone enters the sensor node After the monitoring area is set, the data collection process begins. In this mode, the drone should slow down and adopt a symmetrical trajectory to extend the collection time. Represents the acquisition speed. Since the information generated by the sensor has a high requirement for timeliness, the AOI performance indicator is introduced. The kth node is the access node The AOI expression is:

[0024] in Indicates from The moment when data collection starts, and , For UAV Fly to time, ,The total energy consumption of the UAV during data collection can be divided into the ,energy consumption generated by flight and data collection, and the calculation method is as follows:

[0025] UAVs have limited onboard energy, so this application uses the total energy consumption of UAVs as a performance indicator. The total energy consumption of UAVs is minimized by optimizing the access sequence, flight speed, and collection speed. To solve this problem, this application divides it into two sub-problems: speed optimization and access sequence optimization. Speed ​​optimization is to find a set of optimal speed accesses. and For speed optimization, the path of the UAV is first given, and the expression of energy consumption is expanded to obtain a convex function in the positive domain. The flight speed and the collection speed are solved using convex optimization theory. After the flight speed and the collection speed are obtained, a path is found according to the flight speed and the collection speed to minimize the total energy consumption of the UAV. The feasible solution set of this problem is defined by all possible arrangements of the access order of the nodes, which is a typical business hotel problem. Therefore, the present invention first uses dynamic programming to obtain the optimal access order of the nodes, and then uses a genetic algorithm to approximately solve the problem.

[0026] See attached Figure 1 The specific implementation steps of the present invention are as follows: Step 1: Input the location coordinates of sensor nodes and data centers, and initialize the access sequence, flight speed, and collection speed.

[0027] Step 2: Under a given access path, we use convex optimization technology to find the optimal flight speed and collection speed. After obtaining the optimal speed solution, we use dynamic programming to obtain the optimal access order of nodes in all possible sorting of node access orders, and then use genetic algorithms to approximate the solution to obtain a new access order. We then determine whether the generated new path satisfies the node's AOI less than the maximum AOI value, that is, If it is not satisfied, the order is readjusted until it meets the condition; then the total energy consumed by the UAV on this path is calculated.

[0028] Step 3: Determine whether the UAV energy consumption value satisfies the absolute value of the previous energy consumption value minus the current energy consumption value divided by the absolute value of the previous energy consumption value, that is, whether it converges, where a is the maximum tolerance, that is, ; If satisfied, output the optimal path and speed; otherwise, continue with step 2 until convergence.

[0029] The present invention can provide a fixed-wing UAV energy-saving data acquisition system with flexible speed change, and perform data acquisition based on the fixed-wing UAV energy-saving data acquisition method described above.

[0030] The effect of the present invention can be further illustrated by the following simulation example.

[0031] In the simulation of the present invention, the computer system is Windows 10 and the simulation environment is MATLAB 2019b; Figure 2a , Figure 2b as well as Figure 2c , the path and speed comparison diagram of the present invention, dynamic programming and greedy strategy when the AoI threshold is 100s. It can be seen that the length of the path obtained by the present invention and dynamic programming is the smallest. The total distance of the path generated by the greedy strategy is obviously longer, which leads to the fact that the flight speed of the comparison scheme must be increased accordingly to ensure that the information freshness of the node is lower than the set value. The fundamental reason for this difference is the different strategy selection. Specifically, dynamic programming can find the optimal path by decomposing the problem and storing the optimal solution of the sub-problem. However, as the number of nodes increases, the complexity will also rise sharply, which makes the solution of large-scale TSP quite time-consuming. The solution proposed by the present invention is based on the improvement of genetic algorithm. The information age limit and energy consumption requirements are used as part of the screening mechanism to ensure that the generated population meets the conditions, and the chromosomes with high fitness are retained to reduce the search time. When the greedy strategy is adopted, the UAV only selects the nearest sensor for data collection until all sensor nodes are visited, which obviously ignores the limit of node freshness.

[0032] refer to Figure 3a and Figure 3b , the UAV energy consumption and task completion time comparison diagram of the present invention, dynamic programming and greedy strategy under different AoI thresholds. It is not difficult to find that as the threshold of the information age limit increases, the energy consumption of the UAV decreases and then stabilizes. This is because as the information age limit is relaxed, the UAV can reduce the flight speed to reduce energy consumption, thereby increasing the task completion time. This phenomenon shows that the pursuit of higher information freshness will inevitably be accompanied by higher energy consumption. In addition, the UAV energy consumption generated by the method of the present invention is slightly significantly lower than that based on the greedy strategy. At the same time, the task completion time also presents similar results.

[0033] Figure 4 To make the sensor and After the information age threshold is changed to 40s, the path and speed graphs generated by the method proposed in the present invention are Figure 2b In the case of UAV, the access order is: 0→9→8→1→7→3→4→5→2→6→0. and When the AoI threshold is , the access order becomes: 0→6→2→8→1→5→4→7→3→9→0. We can find that the node and The order of nodes is adjusted to the end. It can be concluded that lowering the AoI threshold of the nodes will make the UAV visit them last. This order adjustment ensures that the information storage time of the nodes is compressed to the shortest before the UAV returns to the data center, so that their actual AoI values ​​are lower than the threshold.

[0034] refer to Figure 5a and Figure 5b , intuitively describes the impact of node AoI constraints on the UAV path planning generated by the present invention, where, Figure 5a To adjust the AoI value of each sensor node before some AoI thresholds, Figure 5b is the AoI value of each sensor node after adjustment. By comparison, it can be clearly seen that when the AoI threshold is lowered, the sensor and The actual AoI values ​​are all lower than 40s, indicating that the method proposed in the present invention can flexibly respond to the personalized requirements of different nodes for AoI, adjust the node access order of UAV in real time, and ensure that the requirements of information freshness are met.

[0035] Based on the concept of the method, the present invention can also provide a flexible variable speed fixed-wing UAV energy-saving data acquisition system, including an initialization module, a planning energy consumption calculation module and a result acquisition module: The initialization module is used to obtain the location coordinates of sensor nodes and data centers, and initialize the access sequence, flight speed, and collection speed; The energy consumption calculation module is used to obtain the optimal solution of flight speed and collection speed based on the given access path by using the convex optimization method; obtain a new access order based on all possible sorting of the access order of all nodes; determine whether the new path corresponding to the new access order satisfies the condition that the AOI of the node is less than the maximum AOI value, and if not, adjust the order until it satisfies the condition; calculate the total energy consumed by the UAV under the new path; The result acquisition module is used to determine whether the total energy consumption of the UAV meets the set conditions. If so, the optimal path and speed are output; otherwise, it jumps back to the planning energy consumption calculation module for planning and calculation until convergence.

[0036] On the other hand, the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the energy-saving data collection method for fixed-wing unmanned aerial vehicle with flexible speed change described in the present invention can be implemented.

[0037] The present invention can also provide a computer device, including a processor and a memory, the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and when the processor executes the computer executable program, the energy-saving data collection method for fixed-wing unmanned aerial vehicle with flexible speed change described in the present invention can be implemented.

[0038] The computer device may be a laptop computer, a desktop computer or a workstation.

[0039] The processor can be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), or an off-the-shelf field programmable gate array (FPGA).

[0040] The memory described in the present invention may be an internal storage unit of a notebook computer, a desktop computer or a workstation, such as a memory or a hard disk; or an external storage unit, such as a mobile hard disk or a flash memory card.

[0041] Computer-readable storage media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media may include: read-only memory (ROM), random access memory (RAM), solid-state drive (SSD) or optical disk, etc. Among them, random access memory may include resistance random access memory (ReRAM) and dynamic random access memory (DRAM).

[0042] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for collecting energy-saving data of a fixed-wing UAV with flexible speed change, characterized in that: The following steps are involved: Step 1, obtain the location coordinates of sensor nodes and data centers, and initialize the access order, flight speed, and collection speed; Step 2: Based on the given access path, use the convex optimization method to solve the optimal solution for the flight speed and collection speed; obtain a new access order based on all possible sorting of the access order of all nodes; determine whether the new path corresponding to the new access order satisfies the condition that the AOI of the node is less than the maximum AOI value. If not, adjust the order until it satisfies the condition; calculate the total energy consumed by the UAV under the new path; Step 3: Determine whether the total energy consumed by the UAV meets the set conditions. If so, output the optimal path and speed; otherwise, continue to execute step 2 until convergence.

2. The energy-saving data collection method for fixed-wing UAV with flexible speed change according to claim 1 is characterized in that: Includes flight modes: drones from data centers or sensor nodes Fly to sensor node and returns to the data center; Collection mode: After collecting data from the current sensor, the drone moves to the next sensor at a higher speed, and the drone enters the sensor node After entering the monitoring area, data collection begins.

3. The energy-saving data collection method for fixed-wing UAV with flexible speed change according to claim 2 is characterized in that: In acquisition mode, the drone slows down and adopts a symmetrical trajectory to extend the acquisition time. Represents the acquisition speed, introduces the AOI performance index, and the kth node is the accessed node The AOI expression is: in Indicates from The moment when data collection starts, and , For UAV Fly to time, .

4. The energy-saving data collection method for fixed-wing UAV with flexible speed change according to claim 1 is characterized in that: The precise location coordinates of sensor nodes and data centers are obtained through the positioning and navigation system; the access sequence is preliminarily planned based on task requirements and node distribution.

5. The energy-saving data collection method for fixed-wing UAV with flexible speed change according to claim 1 is characterized in that: According to all possible sorting of all node access sequences, obtaining a new access sequence includes: based on all possible sorting of all node access sequences, obtaining the optimal access sequence of nodes through dynamic programming, using genetic algorithms to approximate the solution, and obtaining a new access sequence.

6. The energy-saving data collection method for fixed-wing UAV with flexible speed change according to claim 1 is characterized in that: The convex optimization method is used to solve the optimal solution of the flight speed and the collection speed, including: for speed optimization, the path of the UAV is first given, the expression of energy consumption is expanded, and the function is obtained to be a convex function in the positive domain, and the flight speed and the collection speed are solved by convex optimization theory.

7. The energy-saving data collection method for fixed-wing UAV with flexible speed change according to claim 1 is characterized in that: The total energy consumption of the UAV satisfies the set condition that the absolute value of the previous energy consumption value minus the current energy consumption value divided by the absolute value of the previous energy consumption value is less than a, where a is the maximum tolerance.

8. A flexible speed-changing fixed-wing UAV energy-saving data acquisition system, characterized in that: Data collection is performed based on the fixed-wing UAV energy-saving data collection method described in any one of claims 1 to 7.

9. A computer device, characterized in that: It includes a processor and a memory, the memory is used to store computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes them, and when the processor executes part or all of the computer executable programs, it can implement the energy-saving data collection method for fixed-wing unmanned aerial vehicles with flexible speed changes as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the energy-saving data collection method for fixed-wing unmanned aerial vehicle with flexible speed change as described in any one of claims 1 to 7 can be implemented.

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