A UAV swarm collaborative perception method and system

Abstract

The present application relates to the field of drone communication technology, and specifically to a drone swarm collaborative perception method and system. The method includes: obtaining the distance between each AP and the routing table update period during the drone swarm collaborative perception process; obtaining the metric weight of each AP in the communication connection path to establish a connection with the next-hop AP, selecting the optimal routing path between drones, calculating the power adjustment characteristic value between each AP and each other AP with which it establishes a communication connection, obtaining the subcarrier interference characteristic value of each power vector, and using a particle swarm algorithm to obtain the optimal power vector of each AP as the subcarrier transmission power of each AP with which each AP establishes a communication connection, thereby completing the communication scheduling of the AP during the drone swarm collaborative perception process. The present application can improve the communication performance in drone swarm collaborative perception operations.

Description

Technical Field

[0001] The present application relates to the field of drone communication technology, and specifically to a drone swarm collaborative perception method and system. Background Art

[0002] In large indoor spaces like factories and airports, relying solely on a single drone for 3D modeling or periodic inspections limits coverage and takes a long time. Using collaborative sensing technology, multiple drones can simultaneously operate in different areas, rapidly expanding the overall operating area and achieving comprehensive coverage of the entire indoor space. This allows for multiple measurements or inspections of key areas in a short period of time. Furthermore, different drones can observe the same object or area from multiple angles, acquiring richer data. Collaborative sensing technology improves the efficiency and performance of drone operations, enhancing the measurement and inspection capabilities of indoor environments.

[0003] On the one hand, in indoor spaces like large factories and airports, complex internal structures easily block wireless signals, preventing them from achieving full wireless coverage. Furthermore, drone self-organizing networks have limited coverage in indoor environments, making them unable to effectively support collaborative operations within drone swarms or achieve stable communication between them. Furthermore, simultaneous communication between multiple drones in indoor environments can exacerbate adjacent frequency interference, leading to signal distortion, reduced data transmission reliability, and decreased collaborative perception performance. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a drone swarm collaborative perception method and system. The technical solutions adopted are as follows:

[0005] The present invention provides a method for cooperative sensing of a drone swarm, including the following steps:

[0006] Obtain the distance between each AP and the routing table update cycle during the collaborative sensing process of the drone group;

[0007] At the beginning of each cycle, for each communication connection path between drones, the number of communication connections established between each AP and the next-hop AP in the communication connection path of the previous cycle, as well as the difference in distance between each AP and the next-hop AP at the start of each cycle and the next cycle, are used to obtain the metric weight of the connection between each AP and the next-hop AP in the communication connection path. The weighted metric value of each communication connection path is obtained through weighted summation to select the optimal routing path between drones.

[0008] After selecting the optimal routing path between drones, dynamic communication scheduling is performed on the APs during the collaborative sensing process of the drone swarm, specifically including:

[0009] Extract the power vector of each AP as the initial population of the particle swarm algorithm. According to the transmission power of the corresponding subcarrier when each AP establishes a communication connection with other APs, and the center frequency difference between the subcarrier and other subcarriers when each AP establishes a communication connection with other APs, obtain the power adjustment characteristic value between each AP and the other APs with which it establishes a communication connection. Then, combined with the distance relationship and transmission power intensity between each AP and the AP with which it establishes a communication connection, as well as the average level of the subcarrier center frequency difference when each AP establishes a communication connection with other APs, obtain the subcarrier interference characteristic value of each power vector. Utilize the initial population of the particle swarm algorithm and the power adjustment characteristic value to obtain the optimal power vector as the subcarrier transmission power of each AP with which each AP establishes a communication connection, and complete the communication scheduling of APs in the collaborative perception process of the drone swarm.

[0010] The method for obtaining the power vector of each AP is as follows:

[0011] For each AP after route switching, extract the power range of each AP. Select N random numbers within the power range and arrange them in sequence to form the power vector of each AP. For the current AP, N is the number of APs that have established communication connections with the current AP after route switching, and each element in the power vector of the current AP is the transmit power of the subcarrier between the current AP and each other AP with which it has established communication connections.

[0012] The method for obtaining the power adjustment characteristic value between each AP and other APs with which the AP has established communication connections is as follows:

[0013] ;in, Indicates the power adjustment characteristic value between the current AP and the nth AP, Indicates the distance between the current AP and the nth AP; Indicates the maximum distance between the current AP and all APs; Indicates the subcarrier spacing of OFDMA; Indicates the minimum absolute value of the difference between the center frequency of the subcarrier of the current AP when establishing a communication connection with the nth AP and the center frequency of all other subcarriers; represents an exponential function with a natural constant as its base;

[0014] The method for obtaining the subcarrier interference characteristic value of each power vector is:

[0015] ;in, The subcarrier interference characteristic value of the power vector of the current AP is represented, and N represents the number of APs that establish next-hop communication connections with the current AP; represents an exponential function with a natural constant as its base; Indicates the transmit power of the current AP when establishing a communication connection with the nth AP; Indicates the average of the center frequency differences of the subcarriers closest to the current AP when establishing a communication connection with the nth AP.

[0016] The method of using a particle swarm algorithm to obtain an optimal power vector further includes:

[0017] The initial population is used as the input of the particle swarm algorithm, the subcarrier interference eigenvalue is used as the fitness of the power vector, and the power adjustment eigenvalue between the current AP and other APs with which it establishes communication connections is used as the inertia factor. The particle swarm algorithm is used to obtain the optimal power vector corresponding to the current AP.

[0018] Preferably, the method for obtaining the metric weight of each AP in the communication connection path establishing a connection with the next-hop AP is:

[0019] Where, Indicates the metric weight of the communication connection established between the hth AP and the next-hop AP in the current communication connection path t. Indicates the number of times the h-th AP in the communication connection path t establishes a communication connection with the next-hop AP in the previous cycle; Indicates the maximum number of communication connections established by all APs in the communication connection path t in the previous cycle; The absolute value of the distance difference between the hth AP and the next-hop AP in the communication connection path t at the start time of the current cycle and the start time of the next cycle; Indicates the distance between the hth AP and the next-hop AP in the communication connection path t at the start of the current cycle.

[0020] Preferably, the method for obtaining the weighted metric value of each communication connection path is:

[0021] ;in, Indicates the weighted metric value of the communication connection path t in the current cycle, Indicates the number of AP hops in the communication connection path t in the current cycle; Indicates the metric weight of the communication connection established between the hth AP and the next-hop AP in the current communication connection path t.

[0022] Preferably, the method for selecting the optimal routing path between drones is: counting the minimum value of the weighted metric values ​​of all communication connection paths between drones in the current cycle, and taking the communication connection path corresponding to the minimum value as the optimal routing path between drones.

[0023] Preferably, the minimum and maximum transmission powers between the current AP and other APs with which communication connections are established before route switching are counted to form the power range of the current AP.

[0024] An embodiment of the present application also provides a drone swarm collaborative perception system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned drone swarm collaborative perception methods are implemented.

[0025] As can be seen from the above, the UAV swarm collaborative perception method and system provided by this application has at least the following beneficial effects:

[0026] This application builds a communication network for the collaborative perception of drone swarms based on existing fixed wireless routers in the room, combined with mobile APs carried by drones. It also achieves high-precision indoor positioning through a built-in UWB micro base station card, avoiding communication instability and low positioning accuracy caused by insufficient signal coverage in complex indoor environments, thereby improving the collaborative perception performance of drone swarms.

[0027] This application also improves the adaptability of the traditional RIP protocol in drone swarm collaborative sensing scenarios by using a weighted metric calculation method based on the frequency and distance changes of communication connections established by each AP. By introducing a dynamic weighting mechanism, this not only improves the stability of the communication path but also enhances the communication network's adaptability to dynamic changes in indoor drone swarm collaborative sensing operations.

[0028] Furthermore, this application dynamically adjusts the OFDMA subcarrier transmission power through the particle swarm optimization algorithm, combines the path selection strategy with the location information of different APs, and minimizes adjacent frequency interference while ensuring the communication quality between drones, thereby improving the communication performance in the collaborative perception operation of the drone group. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 A flowchart of the steps of a drone swarm collaborative perception method provided in this application;

[0031] Figure 2 This application provides a flow chart of the dynamic communication scheduling of APs during the collaborative perception of drone swarms. DETAILED DESCRIPTION

[0032] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a drone swarm collaborative perception method and system proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0033] Unless otherwise specified and limited, terms such as "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs.

[0034] The following describes in detail a method and system for collaborative perception of drone swarms provided by this application with reference to the accompanying drawings.

[0035] See also Figure 1 , which shows a flowchart of a method for cooperative perception of a drone swarm provided by an embodiment of the present application, including the following steps:

[0036] Step 1: Obtain the distance between each AP and the routing table update cycle during the drone swarm collaborative sensing process.

[0037] Indoor spaces like large factories and airports, complex internal structures can easily block wireless signals, preventing them from achieving full wireless coverage. Furthermore, drone self-organizing networks have limited coverage in indoor environments, making them unable to effectively support collaborative operations within drone swarms or achieve stable communication between them.

[0038] In this embodiment, a wireless router installed indoors is preferably used as a fixed access point (AP) and a wireless router installed at the drone as a mobile AP. This jointly establishes a WiFi network for wireless communication during the drone swarm's collaborative sensing process. The wireless router includes a built-in UWB micro base station card to enable precise indoor positioning of the drone. Specifically, this embodiment employs a TDOA algorithm to measure the time difference between the drone's UWB transmission signal reaching each AP to determine the distance between the drone and each AP, and to calculate the distance between the APs. The specific process is well known to those skilled in the art and will not be detailed in this embodiment.

[0039] The physical layer and data link layer of the WiFi network adopt the 802.11ax protocol, use the 5 GHz frequency band and support OFDMA; the network layer of the WiFi network adopts the RIP routing protocol, and the default routing table update period, which is subsequently referred to as the period in this embodiment, is 30 seconds.

[0040] At this point, the construction of the communication network in the collaborative perception process of the drone swarm has been completed, avoiding the problems of unstable communication and low positioning accuracy caused by insufficient signal coverage in complex indoor environments.

[0041] Step 2: At the beginning of each cycle, for each communication connection path between drones, according to the number of times each AP establishes a communication connection with the next-hop AP in the communication connection path of the previous cycle, and the difference in distance between each AP and the next-hop AP at the start of each cycle and the next cycle, obtain the metric weight of each AP in the communication connection path to establish a connection with the next-hop AP. The weighted metric value of each communication connection path is obtained by weighted summation to select the optimal routing path between drones.

[0042] In a drone swarm's wireless communication network, simultaneous communication between the same AP and multiple APs can cause adjacent-channel interference, degrading communication quality. When an AP is frequently accessed by multiple APs within the same period, it indicates that the AP may be under heavy load or has a large signal coverage area, making it prone to adjacent-channel interference with other APs.

[0043] RIP is a distance-vector routing protocol. The communication path between APs, specifically the path between each AP and its next-hop AP, is determined and acquired through the RIP routing protocol. RIP uses the number of hops as a metric to determine the optimal path, without considering the impact of adjacent channel interference. Furthermore, drones move dynamically in indoor environments. RIP does not utilize known information about the dynamic location of drone swarms, making it insufficiently adaptable to communication routing for collaborative sensing within drone swarms.

[0044] This embodiment updates the routing table at the beginning of each cycle and calculates the metric value of each path. The specific process is as follows:

[0045] In this embodiment, taking the current cycle as an example, for the path of establishing a communication connection between any two drones, the metric weight of the connection between each AP and the next-hop AP in each communication connection path is calculated:

[0046] Where, Indicates the metric weight of the communication connection established between the hth AP and the next-hop AP in the current communication connection path t. Indicates the number of times the h-th AP in the communication connection path t establishes a communication connection with the next-hop AP in the previous cycle; Indicates the maximum number of communication connections established by all APs in the communication connection path t in the previous cycle; The absolute value of the distance difference between the hth AP and the next-hop AP in the communication connection path t at the start time of the current cycle and the start time of the next cycle; Indicates the distance between the hth AP and the next AP in the communication connection path t at the start of the current cycle, reflecting the reliability and stability of the hop in the current cycle. The larger it is, the less reliable and stable the communication connection path t is in the current cycle.

[0047] It is understandable that when an AP is frequently accessed by multiple other APs in the previous cycle, it indicates that the AP may be in a high-load state, or its signal coverage is large, which is prone to cause adjacent frequency interference with other APs. As part of the weight, it avoids connecting to APs that have established a high number of connections in the previous cycle, thereby reducing potential adjacent channel interference. At the same time, it balances the network load and improves the overall communication efficiency of the network.

[0048] On the other hand, drones move dynamically in indoor environments, and their positions change over time. Untimely route switching can lead to communication link interruption or data transmission delay. Considering that the routes of drone swarms operating indoors are known, we introduce As a weighting factor, it measures the rate of change of the distance between two APs in a communication path. If the distance changes significantly, meaning the drone is moving quickly, the path is less stable and should be avoided. This allows the RIP protocol to predict path stability in advance, improving the adaptability of routing switches for drone swarm collaborative sensing operations.

[0049] Furthermore, for each communication connection path between any two drones in the current cycle, the weighted metric value is calculated by weighted summation based on the metric weight of each AP in the communication connection path establishing a connection with the next-hop AP:

[0050] ;in, Indicates the number of AP hops in the communication connection path t in the current cycle; Indicates the metric weight of the communication connection established between the hth AP and the next-hop AP in the current communication connection path t; It represents the weighted metric value of the communication connection path t in the current cycle to more comprehensively evaluate the overall performance of the path. The smaller it is, the better the overall performance of the communication connection path.

[0051] Understandably, the traditional RIP protocol relies solely on hop count as a metric, ignoring dynamic factors in real-world communication environments, such as adjacent channel interference, distance variations between APs, and AP load. By weighting the RIP protocol metric, the stability and reliability of the path under the mobile state of the drone swarm are reflected, improving the accuracy of evaluating the overall path performance.

[0052] Finally, for drones that need to perform collaborative sensing communication in a WiFi network, the above-mentioned method and process of this embodiment are used to obtain the weighted metric values ​​of each communication connection path between any two drones, and select the communication connection path with the smallest metric value as the optimal routing path between the drones.

[0053] At this point, according to the above process of this embodiment, dynamic route selection between drones can be completed.

[0054] Step 3: After selecting the optimal routing path between drones, dynamic communication scheduling is performed on the APs during the collaborative perception process of the drone swarm.

[0055] Furthermore, after selecting the optimal routing path between drones, this embodiment extracts the optimal transmit power of subcarriers between APs during the drone swarm collaborative sensing process. OFDMA technology divides the 5 GHz frequency band into multiple subcarriers, each of which can be independently assigned to a different AP. Multiple APs within the same channel can simultaneously communicate using different subcarriers. However, if adjacent subcarriers have similar frequencies, adjacent-channel interference is likely to occur. Excessive transmit power can further exacerbate adjacent-channel interference.

[0056] During indoor drone swarm operations, the relative positions of APs constantly change, leading to fluctuations in communication distance and variations in subcarrier center frequency differences. This requires dynamic adjustment of transmit power. Therefore, in this embodiment, an adaptive power allocation strategy is employed to ensure communication quality between drones while reducing adjacent channel interference and improving the overall performance of the communication network.

[0057] First, after each route switching in each cycle, for each AP, in this embodiment, the current AP is used as an example to illustrate, and the number N of APs that establish communication connections with the current AP after the route switching, as well as the maximum transmission power MAX and the minimum transmission power MIN before the switching are counted, and the power range of the current AP is constructed. .

[0058] Furthermore, in this embodiment, for the current AP, N random numbers are selected within its power range and arranged in sequence to construct the power vector of the current AP, where each element in the power vector is the transmission power of the subcarrier between the current AP and each AP with which it establishes a communication connection.

[0059] Correspondingly, this method is used to randomly generate 30 power vectors, which are recorded as the initial population and used as the initial input of the particle swarm algorithm in the dynamic communication scheduling process of APs.

[0060] Next, for each communication connection between an AP and its next-hop AP, this embodiment obtains its power adjustment characteristic value. Taking the current AP as an example, the power adjustment characteristic value between the current AP and each other AP with which the current AP has established a communication connection is calculated as follows:

[0061] ;in, Indicates the distance between the current AP and the nth AP; Indicates the maximum distance between the current AP and all APs; represents the OFDMA subcarrier spacing, which is 312.5 kHz in this embodiment; Indicates the minimum absolute value of the difference between the center frequency of the subcarrier of the current AP when establishing a communication connection with the nth AP and the center frequency of all other subcarriers; It represents an exponential function with a natural constant as the base, and its purpose is to reflect and The relationship between the numerical values ​​​​of Indicates the power adjustment characteristic value between the current AP and the nth AP with which the AP establishes a communication connection, reflecting the subsequent transmit power adjustment range. The larger the value, the larger the adjustable range of the AP is when establishing a communication connection with the nth AP.

[0062] It is understandable that the distance between two APs The larger the value, the greater the transmission power required to maintain communication between the two. Therefore, the calculated value is The larger the subcarrier center frequency difference, the higher the subsequent transmission power. The smaller the value, the more likely the connection is to cause adjacent channel interference to other channels, and the power needs to be appropriately reduced. The smaller the value, the smaller the range of subsequent transmit power increases. By comprehensively considering the distance and the subcarrier center frequency difference, power adjustment can meet the communication needs of the drone while avoiding aggravating adjacent frequency interference.

[0063] Furthermore, for the power vector corresponding to each AP, in this embodiment, taking the current AP as an example, based on the power adjustment characteristic values ​​between the current AP and each other AP with which it establishes a communication connection, combined with the distance relationship and transmit power intensity between the current AP and the AP with which it establishes a communication connection, and the average level of the subcarrier center frequency difference when each AP establishes a communication connection with the other APs, the subcarrier interference characteristic value of the power vector of the current AP is calculated:

[0064] ; Where N represents the number of APs that establish next-hop communication connections with the current AP; Indicates the power adjustment characteristic value of the current AP when establishing a communication connection with the nth AP; Indicates the distance between the current AP and the nth AP; It represents an exponential function with a natural constant as the base, and its purpose is to reflect and The relationship between the numerical values ​​and the restrictions The numerical value of Indicates the transmit power of the current AP when establishing a communication connection with the nth AP, that is, the size of the nth element of the power vector; Indicates the average of the center frequency differences of the subcarriers closest to the current AP when establishing a communication connection with the nth AP. Indicates the subcarrier interference characteristic value of the current AP's power vector. This value reflects the relative average level of adjacent channel interference between different subcarriers of the AP under the control of this power vector. A larger S value indicates a greater relative average level of adjacent channel interference between different subcarriers of the AP.

[0065] It is understandable that The larger the value, the greater the power required to establish a communication connection with the next-hop AP. However, in order to avoid excessive power adjustment causing increased interference to adjacent channels, the power adjustment for the AP is appropriately limited based on the value. At the same time, to ensure reliable communication between APs that are far away, the degree of adjustment of their transmit power is increased to ensure that the AP has a stable transmission distance. The fitness is calculated using the weights. Furthermore, the closer the subcarrier center frequencies are and the stronger the subcarrier transmit power is, the greater the adjacent-channel interference generated between them. The larger the calculated subcarrier interference eigenvalue is, the greater the relative average degree of adjacent-channel interference between different subcarriers of the AP.

[0066] Furthermore, a particle swarm optimization algorithm is used to dynamically adjust AP communications. The initial population is used as the input to the particle swarm algorithm, the subcarrier interference eigenvalue is used as the fitness of the power vector, and the power adjustment eigenvalue between the current AP and the other APs with which it establishes a communication connection is used as the inertia factor. In this embodiment, the maximum number of iterations is set to 30. The optimal power vector for the current AP is output, and each element in the optimal power vector is used as the subcarrier transmit power between the current AP and the APs with which it establishes a communication connection. This completes the dynamic communication scheduling of the AP, ensuring the quality of communication between drones while minimizing adjacent frequency interference and improving the communication performance of the drone swarm's collaborative sensing operations.

[0067] Specifically, in this embodiment, the flow diagram of the AP dynamic communication scheduling process in the drone group collaborative perception process is as follows: Figure 2 shown.

[0068] Based on the same inventive concept as the above method, an embodiment of the present application also provides a drone swarm collaborative perception system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned drone swarm collaborative perception methods are implemented.

[0069] It should be understood that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0071] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

Claims

1. A drone swarm collaborative perception method, characterized in that: The following steps are involved: Using drones as APs, the distance between APs and the routing table update cycle during the collaborative sensing process of the drone swarm are obtained; At the beginning of each cycle, for each communication connection path between drones, the number of communication connections established between each AP and the next-hop AP in the communication connection path of the previous cycle, as well as the difference in distance between each AP and the next-hop AP at the start of each cycle and the next cycle, are used to obtain the metric weight of the connection between each AP and the next-hop AP in the communication connection path. The weighted metric value of each communication connection path is obtained through weighted summation to select the optimal routing path between drones. After selecting the optimal routing path between drones, dynamic communication scheduling is performed on the APs during the collaborative sensing process of the drone swarm, specifically including: Extract the power vector of each AP as the initial population of the particle swarm algorithm. According to the distance between different APs when each AP establishes a communication connection with other APs, and the center frequency difference between the subcarrier and other subcarriers when each AP establishes a communication connection with other APs, obtain the power adjustment characteristic value between each AP and the other APs with which it establishes a communication connection. Then, combined with the distance relationship and the intensity of the transmission power between each AP and the AP with which it establishes a communication connection, as well as the average level of the subcarrier center frequency difference when each AP establishes a communication connection with other APs, obtain the subcarrier interference characteristic value of each power vector. Using the initial population of the particle swarm algorithm and the power adjustment characteristic value, obtain the optimal power vector of each AP, which is used as the subcarrier transmission power of each AP with which each AP establishes a communication connection, and complete the communication scheduling of APs in the collaborative perception process of the drone swarm. The method for obtaining the power vector of each AP is as follows: For each AP after route switching, extract the power range of each AP. Select N random numbers within the power range and arrange them in sequence to form the power vector of each AP. For the current AP, N is the number of APs that have established communication connections with the current AP after route switching, and each element in the power vector of the current AP is the transmit power of the subcarrier between the current AP and each other AP with which it has established communication connections. The method for obtaining the power adjustment characteristic value between each AP and other APs with which the AP has established communication connections is as follows: ;in, Indicates the power adjustment characteristic value between the current AP and the nth AP with which it establishes a communication connection. Indicates the distance between the current AP and the nth AP; Indicates the maximum distance between the current AP and all APs; Indicates the subcarrier spacing of OFDMA; Indicates the minimum absolute value of the difference between the center frequency of the subcarrier of the current AP when establishing a communication connection with the nth AP and the center frequency of all other subcarriers; represents an exponential function with a natural constant as its base; The method for obtaining the subcarrier interference characteristic value of each power vector is: ;in, The subcarrier interference characteristic value of the power vector of the current AP is represented, and N represents the number of APs that establish next-hop communication connections with the current AP; represents an exponential function with a natural constant as its base; Indicates the transmit power of the current AP when establishing a communication connection with the nth AP; Indicates the average of the center frequency differences of the subcarriers closest to the current AP when establishing a communication connection with the nth AP. The method of using a particle swarm algorithm to obtain an optimal power vector further includes: The initial population is used as the input of the particle swarm algorithm, the subcarrier interference eigenvalue is used as the fitness of the power vector, and the power adjustment eigenvalue between the current AP and other APs with which it establishes communication connections is used as the inertia factor. The particle swarm algorithm is used to obtain the optimal power vector corresponding to the current AP.

2. The UAV swarm collaborative perception method according to claim 1, characterized in that: The method for obtaining the metric weight of each AP in the communication connection path establishing a connection with the next-hop AP is as follows: Where, Indicates the metric weight of the communication connection established between the hth AP and the next-hop AP in the current communication connection path t. Indicates the number of times the h-th AP in the communication connection path t establishes a communication connection with the next-hop AP in the previous cycle; Indicates the maximum number of communication connections established by all APs in the communication connection path t in the previous cycle; The absolute value of the distance difference between the hth AP and the next-hop AP in the communication connection path t at the start time of the current cycle and the start time of the next cycle; Indicates the distance between the hth AP and the next-hop AP in the communication connection path t at the start of the current cycle.

3. The UAV swarm collaborative perception method according to claim 1, characterized in that: The method for obtaining the weighted metric value of each communication connection path is: ;in, Indicates the weighted metric value of the communication connection path t in the current cycle, Indicates the number of AP hops in the communication connection path t in the current cycle; Indicates the metric weight of the communication connection established between the hth AP and the next-hop AP in the current communication connection path t.

4. The UAV swarm collaborative perception method according to claim 1, characterized in that: The method for selecting the optimal routing path between drones is: counting the minimum value of the weighted metric values ​​of all communication connection paths between drones in the current cycle, and taking the communication connection path corresponding to the minimum value as the optimal routing path between drones.

5. The UAV swarm collaborative perception method according to claim 1, characterized in that: The minimum and maximum transmit powers between the current AP and other APs with which it has established communication connections before route switching are counted to form the power range of the current AP.

6. A drone swarm collaborative perception system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the drone swarm collaborative perception method as described in any one of claims 1 to 5 are implemented.

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