A method, device and storage medium for evaluating the collaborative capability of drone swarms based on an improved SIR model

Abstract

The present invention discloses a method, device, and storage medium for evaluating the collaborative capability of drone clusters based on an improved SIR model, relating to the technical field of drone collaborative capability evaluation. The method comprises analyzing drone state transition relationships using the improved SIR model; constructing a drone cluster static structure model using a hierarchical weighted complex network; determining drone state transition relationship parameters based on the drone cluster static structure model; substituting the drone state transition relationship parameters into the drone state transition relationship to obtain the number of drones in different states in the drone cluster within the next time period; and evaluating the collaborative capability of the drone cluster based on the number of drones in different states in the drone cluster within the next time period. The present invention can clearly and efficiently predict the number of drones in different states in the cluster at a future time point, thereby effectively evaluating the collaborative capability of the drone cluster.

Description

Technical Field

[0001] The present invention relates to a method, device and storage medium for evaluating the collaborative capability of unmanned aerial vehicle (UAV) clusters based on an improved SIR model, and belongs to the technical field of UAV collaborative capability evaluation. Background Art

[0002] Currently, quantitative analysis and evaluation of drone swarm capabilities is typically conducted through system modeling and simulation, using methods such as complex networks, Petri nets, and the UML language. During the operation of a drone swarm, the communication relationships between drones within the swarm are constantly changing due to external interference and disruption. Furthermore, the states of each drone constantly shift during actual operation. While traditional modeling methods can simulate drone behavior strategies, they struggle to capture how each drone's state changes over time, making them ineffective for reliably evaluating the collaborative capabilities of a drone swarm. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method, device and storage medium for evaluating the collaborative capability of drone clusters based on an improved SIR model, which can clearly and efficiently predict the number of drones in different states in the cluster at future time points, thereby realizing an effective evaluation of the collaborative operation capability of drone clusters.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a method for evaluating the collaborative capability of a UAV cluster based on an improved SIR model, comprising:

[0006] Use the improved SIR model to analyze the state transformation relationship of UAVs;

[0007] Using hierarchical weighted complex networks to construct a static structural model of drone swarms;

[0008] Determine the parameters of drone state transformation relationship based on the drone cluster static structure model;

[0009] Substitute the drone state transformation relationship parameters into the drone state transformation relationship to obtain the number of drones in different states in the drone cluster in the next time period;

[0010] The collaborative capability of the drone cluster is evaluated based on the number of drones in different states in the drone cluster within the next time period.

[0011] Furthermore, the use of the improved SIR model to analyze the drone state transformation relationship includes:

[0012] The states of a single UAV when performing a collaborative task are defined as standby state, working state, and fault state. The working state refers to the UAV performing the collaborative task, the fault state refers to the UAV malfunctioning, and the standby state refers to any other state except the working state and fault state.

[0013] The improved SIR model is used to analyze the UAV state transformation relationship and obtain the UAV state transformation relationship expression, which is:

[0014] ;

[0015] in, represents the probability of successful pairing of drones after accepting collaborative tasks, It represents the probability that a UAV in working state will switch to standby state after completing the collaborative task. Indicates the probability of a drone in standby mode malfunctioning due to external environmental influences. It indicates the probability of a UAV in working condition malfunctioning due to external environmental influences. represents the number of drones in standby state at time t, represents the number of UAVs in working state at time t, represents the number of UAVs in fault state at time t, Indicates the number of drones in standby state at time t+1, Indicates the number of drones in working state at time t+1, Indicates the number of drones in fault state at time t+1.

[0016] Furthermore, the use of a hierarchical weighted complex network to construct a static structure model of a UAV cluster includes:

[0017] Construct the correspondence between the static structure model of UAV swarm and the hierarchical weighted complex network;

[0018] Construct the initial state of the UAV swarm static structure model based on the corresponding relationship;

[0019] Based on the initial state of the UAV cluster static structure model, the probability of a new network node connecting to a node when joining the UAV cluster static structure model is calculated to obtain the UAV cluster static structure model.

[0020] Furthermore, the correspondence between the static structural model of the drone cluster and the hierarchical weighted complex network includes: the i-th drone corresponds to the i-th network node;

[0021] The capabilities of the i-th UAV correspond to the attributes of the i-th network node;

[0022] The number of relationships between the ith UAV and its surrounding UAVs that have communication link connections corresponds to the degree of the ith network node;

[0023] The communication relationship between the i-th UAV and the j-th UAV corresponds to the edge between the i-th network node and the j-th network node on the 0th network layer;

[0024] The communication capability between the i-th UAV and the j-th UAV corresponds to the communication capability between the i-th network node and the j-th network node on the 0th network layer. The weights of the edges between network nodes;

[0025] The Hth collaborative relationship between the i-th UAV and the j-th UAV corresponds to the edge between the i-th network node and the j-th network node on the H-th network layer;

[0026] The Nth type of collaboration capability between the i-th UAV and the j-th UAV corresponds to the weight of the edge between the i-th network node and the j-th network node on the N-th network layer;

[0027] The k-th drone cluster composed of multiple drones corresponds to the k-th community on the 0th network layer;

[0028] The key drone in the k-th drone cluster corresponds to the leader node of the k-th community on the 0th network layer.

[0029] Furthermore, the initial state of the static structure model of the UAV cluster is constructed according to the corresponding relationship, including:

[0030] According to the corresponding relationship, the number of communities in the static structure model of the drone cluster, the number of network nodes in each community, and the number of leader nodes in each community are determined;

[0031] The nodes within each community and the leading nodes of different communities are set to be fully connected to obtain the initial state of the static structure model of the drone cluster.

[0032] Furthermore, based on the initial state of the UAV cluster static structure model, the probability of a new network node connecting to a node when joining the UAV cluster static structure model is calculated to obtain the UAV cluster static structure model, including:

[0033] When the newly added network node is a non-leader node, the probability of the newly added network node connecting to a node in the 0th network layer is calculated as follows:

[0034] ;

[0035] in, Indicates that the newly added network node is in the 0th layer network layer and the network node connect, Indicates that a new network node has been added to the network node Community , Indicates that the newly added network node is in the 0th layer network layer and the community Network nodes in The probability of connection, Indicates that a new network node has joined the community The probability of Represents a network node Degree on the layer 0 network, represents the sum of the degrees of all network nodes at the 0th layer of the network. Indicates the number of nodes in the network;

[0036] When the newly added network node is a non-leader node, the probability that the newly added network node is connected to a network node in the remaining networks except the 0th network layer is calculated as follows:

[0037]

[0038] in, Indicates that the newly added node is in the rest of the network and network nodes except the 0th layer network layer connect, Represents a network node Degree on the layer 0 network;

[0039] When the newly added network node is a leader node, the probability of the newly added network node connecting to a leader node is calculated as follows:

[0040]

[0041] in, Indicates newly added network nodes and leader nodes connect, represents the probability that a new network node is a leader node, Indicates newly added network nodes and leader nodes Collaboration between The cost of collaborative tasks, Indicates newly added network nodes and leader nodes The distance between represents the impact coefficient of collaboration, represents the distance influence coefficient, represents the equilibrium function between the cooperative and distance situations, Indicates a newly added network node With different leadership nodes in completing The total cost consumed by collaborative tasks, Indicates a newly added network node The sum of the distances between different leading nodes.

[0042] Furthermore, the UAV state transition relationship parameters include the probability that the UAV successfully pairs after receiving a cooperation task , and the probability that the UAV in the working state transitions to the standby state after completing the cooperation task ;

[0043] The UAV state transition relationship parameters determined based on the static structure model of the UAV cluster are obtained through random experiments by the Monte Carlo method, specifically including: <000014Ⅰ>Based on the static structure model of the UAV cluster, different cooperation tasks are randomly selected, and the communication link between two network nodes in the model is randomly selected as the damaged state. The UAVs cooperate and pair to complete the cooperation task. After repeating multiple times, the success rate of the UAVs cooperating and pairing to complete the cooperation task is , and the probability that the cooperation task completion time is less than the preset time is [[ID=2O]].

[0045] Furthermore, the evaluation of the cooperation ability of the UAV cluster according to the number of UAVs in different states in the UAV cluster in the next time period includes:[[ID=²4]]

[0046] Let the number of cooperation tasks arranged for the UAV cluster at time T be R,

[0047] When W(T + 1) < W(T) < F(T + 1), it is determined that the cooperation ability of the UAV cluster is level one;

[0048] When F(T + 1) < W(T + 1) < W(T), it is determined that the cooperation ability of the UAV cluster is level two;

[0049] When W(T) < W(T + 1) < F(T + 1), it is determined that the cooperation ability of the UAV cluster is level three;

[0050] When W(T) < W(T + 1), and W(T + 1) - W(T) < R / 2, it is determined that the cooperation ability of the UAV cluster is level four;

[0051] When W(T) < W(T + 1), and W(T + 1) - W(T) < R, it is determined that the cooperation ability of the UAV cluster is level five;

[0052] Among them, the cooperation ability of the UAV cluster increases gradually from level one to level five.

[0053] In the second aspect, the present invention also provides an evaluation device for the cooperation ability of a UAV cluster based on an improved SIR model, including:

[0054] A UAV state conversion analysis module is configured to analyze the UAV state conversion relationship using an improved SIR model;

[0055] A model building module is configured to build a static structure model of a UAV cluster using a hierarchical weighted complex network;

[0056] a parameter determination module configured to determine the parameters of the UAV state transformation relationship based on the UAV cluster static structure model;

[0057] In the next stage, the drone state acquisition module is configured to substitute the drone state transformation relationship parameters into the drone state transformation relationship to obtain the number of drones in different states in the drone cluster in the next time period;

[0058] The drone collaboration capability evaluation module is configured to evaluate the drone cluster collaboration capability based on the number of drones in different states in the drone cluster within the next time period.

[0059] In a third aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating the collaborative capability of drone clusters based on the improved SIR model as described in any one of the above items is implemented.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention improves the traditional SIR model to make it conform to the characteristics of UAV collaborative operation, and adopts a complex network to model the UAV cluster, abstracting each UAV as a node in the network and the communication relationship between UAVs as edges in the network. It can clearly and efficiently predict the number of UAVs in different states in the cluster at future time points, thereby realizing an effective evaluation of the UAV cluster's collaborative operation capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Schematic diagram of a flow chart of a method for evaluating the collaborative capability of a UAV cluster based on an improved SIR model in one embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the relationship between the three states of the drone in one embodiment of this embodiment;

[0064] Figure 3 Schematic diagram of the initial state structure of a static structure model of a UAV cluster in a method for evaluating the cooperative capability of UAV clusters based on an improved SIR model in one embodiment of the present invention;

[0065] Figure 4This is a structural diagram of a static structure model of a drone cluster in a method for evaluating the collaborative capability of a drone cluster based on an improved SIR model in one embodiment of the present invention, when a newly added node is a non-leader node;

[0066] Figure 5 This is a structural diagram of a static structure model of a drone cluster in an embodiment of the drone cluster collaboration capability evaluation method based on an improved SIR model after a newly added node is added as a leader node;

[0067] Figure 6 This is a schematic diagram of the state change trend of the drone in Example 1 of the present invention;

[0068] Figure 7 Schematic diagram of the state change trend of the drone in Example 2 of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0070] Example 1:

[0071] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the collaborative capability of a UAV cluster based on an improved SIR model, comprising the following steps:

[0072] The improved SIR model is used to analyze the state transformation relationship of the UAV. This embodiment improves the SIR model and defines the three states of a single UAV when performing a mission as the standby state S (Standby), the working state W (Working) and the fault state F (Fault). The relationship between the three states is as follows: Figure 2 shown.

[0073] Initially, all drones are in standby mode. As the system begins accepting tasks, some enter active mode. When a drone cannot complete its assigned task independently and requires collaboration with other drones, these active drones will seek out other standby drones capable of collaborating. In real-world environments, this process is often constrained by the system's inherent collaboration rules and the collaborative environment. Overly strict collaboration process restrictions and inappropriate network connectivity can reduce the success rate of collaboration. As the collaborative task progresses smoothly, other standby nodes participating in the collaborative process will transition to active mode. After the collaborative process concludes, all participating nodes return to standby mode. Furthermore, due to external environmental influences, nodes in both standby and active modes have a certain probability of becoming incapable of operating and becoming faulty. Faulty nodes cannot accept collaborative tasks and return to standby mode after the fault is corrected.

[0074] By analyzing the quantitative analysis expressions of each UAV state transformation, the UAV state transformation relationship expression is obtained as follows:

[0075] ;

[0076] in, represents the probability of successful pairing of drones after accepting collaborative tasks, It represents the probability that a UAV in working state will switch to standby state after completing the collaborative task. Indicates the probability of a drone in standby mode malfunctioning due to external environmental influences. It indicates the probability of a UAV in working condition malfunctioning due to external environmental influences. represents the number of drones in standby state at time t, represents the number of UAVs in working state at time t, represents the number of UAVs in fault state at time t, Indicates the number of drones in standby state at time t+1, Indicates the number of drones in working state at time t+1, Indicates the number of drones in fault state at time t+1.

[0077] In the expression, 、 It is related to factors such as the quality of the drone itself and does not need to be determined later. Therefore, the actual relationship parameters required are and In this embodiment, =5%, =5%.

[0078] A hierarchical weighted complex network is used to construct a static structure model of drone clusters. Specifically:

[0079] First, a one-to-one correspondence is established between the static structure of the drone cluster and the state of the complex network model at a certain moment:

[0080] The i-th drone corresponds to the i-th network node;

[0081] The capabilities of the i-th UAV correspond to the attributes of the i-th network node;

[0082] The number of relationships between the ith UAV and its surrounding UAVs that have communication link connections corresponds to the degree of the ith network node;

[0083] The communication relationship between the i-th UAV and the j-th UAV corresponds to the edge between the i-th network node and the j-th network node on the 0th network layer;

[0084] The communication capability between the i-th UAV and the j-th UAV corresponds to the communication capability between the i-th network node and the j-th network node on the 0th network layer. The weights of the edges between network nodes;

[0085] The Hth collaborative relationship between the i-th UAV and the j-th UAV corresponds to the edge between the i-th network node and the j-th network node on the H-th network layer;

[0086] The Nth type of collaboration capability between the i-th UAV and the j-th UAV corresponds to the weight of the edge between the i-th network node and the j-th network node on the N-th network layer;

[0087] The k-th drone cluster composed of multiple drones corresponds to the k-th community on the 0th network layer;

[0088] The key drone in the k-th drone cluster corresponds to the leader node of the k-th community on the 0th network layer.

[0089] After determining the corresponding relationship, the initial state of the UAV cluster static structure model is constructed according to the corresponding relationship and the actual situation of the UAV cluster, including:

[0090] According to the corresponding relationship, the number of communities, the number of network nodes in each community, and the number of leadership nodes in each community in the static structure model of the drone cluster are determined. The nodes in each community and the leadership nodes of different communities are set to be fully connected to obtain the initial state of the static structure model of the drone cluster.

[0091] In this embodiment, if Figure 3 As shown in Figure 1, a three-layer initialization network is constructed, with a total of 9 network nodes divided into three communities, and each community has a leader node.

[0092] Based on the initial state of the UAV cluster static structure model, the probability of a new network node connecting to a node when joining the UAV cluster static structure model is calculated to obtain the UAV cluster static structure model. The calculation of the probability of a new network node connecting to a node when joining the UAV cluster static structure model is divided into the following cases:

[0093] When the newly added network node is a non-leader node, the probability of the newly added network node connecting to a node in the 0th network layer is calculated as follows:

[0094] ;

[0095] in, Indicates that the newly added network node is in the 0th layer network layer and the network node connect, Indicates that a new network node has been added to the network node Community , Indicates that the newly added network node is in the 0th layer network layer and the community Network nodes in The probability of connection, Indicates that a new network node has joined the community The probability of Represents a network node Degree on the layer 0 network, represents the sum of the degrees of all network nodes at the 0th layer of the network. Indicates the number of nodes in the network.

[0096] like Figure 4 As shown in , when the newly added network node is a non-leader node, the calculation expression for the probability that the newly added network node is connected to a network node in the remaining networks except the 0th network layer is:

[0097]

[0098] in, Indicates that the newly added node is in the rest of the network and network nodes except the 0th layer network layer connect, Represents a network node Degree on the tier 0 network.

[0099] This is because when a new network node is connected to other network nodes at a network layer other than the 0th network layer, it must be connected to the network node at the 0th network layer.

[0100] In addition, there is a certain probability that the newly added network node can become a leader node. At this time, in addition to establishing a connection with the leader node within the community, the newly added network node should also, based on the needs of the collaborative task, find the leader node of other communities with the lowest cost to complete the task according to the network layer corresponding to the task and the principle of shortest path priority, and establish a layer 0 network layer connection with it, and simultaneously establish a network layer connection corresponding to the collaboration.

[0101] like Figure 5 As shown in , when the newly added network node is a leader node, it is defaulted that the newly added network node is only connected to the leader node and not to non-leader nodes. The calculation expression for the probability of the newly added network node connecting to a leader node is:

[0102] ;

[0103] in, Indicates newly added network nodes and leader nodes connect, represents the probability that a new network node is a leader node, Indicates newly added network nodes and leader nodes Collaboration between The cost consumed by a collaborative task is calculated by adding the weights of all edges between two network nodes on the network layer representing the collaborative relationship and the attribute values ​​of the two network nodes. Indicates newly added network nodes and leader nodes The distance between the two network nodes is calculated by summing the weights of all edges connecting the two network nodes on the 0th network layer.

[0104] represents the impact coefficient of collaboration, represents the distance influence coefficient, represents the equilibrium function between the cooperative and distance situations, Indicates a newly added network node With different leadership nodes in completing The total cost consumed by collaborative tasks, Indicates a newly added network node The sum of distances to different leader nodes.

[0105] Based on the static structure model of the UAV cluster, different collaborative tasks are randomly selected from the task library, and the communication link between any two network nodes in the network model is randomly selected to be in a damaged state. The success rate of UAV collaborative pairing is tested by the Monte Carlo method. After a large number of experiments, the success rate of UAV collaborative pairing to complete the collaborative task is obtained, which is , the probability that the collaborative task completion time is less than the preset time is .

[0106] Substitute the determined UAV state conversion relationship parameters into the UAV state conversion relationship to obtain the number of UAVs in different states in the UAV cluster in the next time period.

[0107] Evaluate the cooperation ability of the UAV cluster according to the number of UAVs in different states in the UAV cluster in the next time period. Specifically:

[0108] Let the number of cooperation tasks arranged for the UAV cluster at time T be R.

[0109] When W(T + 1) < W(T) < F(T + 1), it is determined that the cooperation ability of the UAV cluster is level one;

[0110] When F(T + 1) < W(T + 1) < W(T), it is determined that the cooperation ability of the UAV cluster is level two;

[0111] When W(T) < W(T + 1) < F(T + 1), it is determined that the cooperation ability of the UAV cluster is level three;

[0112] When W(T) < W(T + 1), and W(T + 1) - W(T) < R / 2, it is determined that the cooperation ability of the UAV cluster is level four;

[0113] When W(T) < W(T + 1), and W(T + 1) - W(T) < R, it is determined that the cooperation ability of the UAV cluster is level five.

[0114] It should be noted that the cooperation ability of the human - machine cluster increases gradually from level one to level five.

[0115] In this embodiment, = 40%, = 10%, and the UAV state change trend is obtained as Figure 6 shown. From Figure 6 it can be judged that as time goes by, after the three - state transformation trends tend to be gentle, the cooperation ability of the UAV cluster is level one.

[0116] Embodiment 2:

[0117] The difference between this embodiment and Embodiment 1 is only that = 60%, and the UAV state change trend is obtained as Figure 7 shown. From Figure 7 it can be judged that as time goes by, after the three - state transformation trends tend to be gentle, the cooperation ability of the UAV cluster is level one.

[0118] Comparing Figure 6 and Figure 7 it can be seen that although both are at the first - level cooperation ability, but Figure 7Or collaboration ability Figure 6 This shows that when the failure rate and repair rate are not much different, simply improving the success rate of drone collaborative pairing can only significantly increase the amount of work completed in the short term. However, as time goes by, the number of working drones, the number of failures, and the number of standby drones will eventually stabilize. At this time, if we still want to improve the system's work completion capability, we can only do so by formulating anti-destruction strategies and increasing the repair rate of destroyed nodes.

[0119] Example 3:

[0120] This embodiment also provides a device for evaluating the collaborative capability of a drone swarm based on an improved SIR model, comprising:

[0121] The UAV state conversion analysis module is configured to analyze the UAV state conversion relationship using the improved SIR model.

[0122] The model building module is configured to build a static structure model of a UAV cluster using a hierarchical weighted complex network.

[0123] The parameter determination module is configured to determine the drone state transformation relationship parameters based on the drone cluster static structure model.

[0124] The next stage drone state acquisition module is configured to substitute the drone state transformation relationship parameters into the drone state transformation relationship to obtain the number of drones in different states in the drone cluster within the next time period.

[0125] The UAV collaboration capability evaluation module is configured to evaluate the collaboration capability of the UAV cluster based on the number of UAVs in different states in the UAV cluster within the next time period.

[0126] Example 4:

[0127] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating the collaborative capability of drone clusters based on the improved SIR model as described in Example 1 is implemented.

[0128] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating the collaborative capability of UAV swarms based on an improved SIR model, characterized in that: include: The improved SIR model is used to analyze the state transformation relationship of drones, including: The states of a single UAV when performing a collaborative task are defined as standby state, working state, and fault state. The working state refers to the process of the UAV performing the collaborative task, the fault state refers to the process of the UAV experiencing a fault, and the standby state refers to all other states of the UAV except the working state and the fault state. The improved SIR model is used to analyze the UAV state transformation relationship and obtain the UAV state transformation relationship expression, which is: ； in, represents the probability of successful pairing of drones after accepting collaborative tasks, It represents the probability that a UAV in working state will switch to standby state after completing the collaborative task. Indicates the probability of a drone in standby mode malfunctioning due to external environmental influences. It indicates the probability of a UAV in working condition malfunctioning due to external environmental influences. represents the number of drones in standby state at time t, represents the number of UAVs in working state at time t, represents the number of UAVs in fault state at time t, Indicates the number of drones in standby state at time t+1, Indicates the number of drones in working state at time t+1, represents the number of drones in fault state at time t+1; A hierarchical weighted complex network is used to construct a static structure model of a UAV swarm, including: Construct the correspondence between the static structure model of UAV swarm and the hierarchical weighted complex network; Construct the initial state of the UAV swarm static structure model based on the corresponding relationship; Based on the initial state of the UAV cluster static structure model, the probability of a new network node connecting to a node when joining the UAV cluster static structure model is calculated to obtain the UAV cluster static structure model; Determine the parameters of drone state transformation relationship based on the drone cluster static structure model; Substitute the drone state transformation relationship parameters into the drone state transformation relationship to obtain the number of drones in different states in the drone cluster in the next time period; The collaborative capability of the drone cluster is evaluated based on the number of drones in different states in the drone cluster within the next time period.

2. The method for evaluating the collaborative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The correspondence between the static structural model of the UAV cluster and the hierarchical weighted complex network includes: the i-th UAV corresponds to the i-th network node; The capabilities of the i-th UAV correspond to the attributes of the i-th network node; The number of relationships between the ith UAV and its surrounding UAVs that have communication link connections corresponds to the degree of the ith network node; The communication relationship between the i-th UAV and the j-th UAV corresponds to the edge between the i-th network node and the j-th network node on the 0th network layer; The communication capability between the i-th UAV and the j-th UAV corresponds to the communication capability between the i-th network node and the j-th network node on the 0th network layer. The weights of the edges between network nodes; No. The Hth cooperative relationship between the first UAV and the jth UAV corresponds to the edge between the i-th network node and the j-th network node on the H-th network layer; The Nth type of collaboration capability between the i-th UAV and the j-th UAV corresponds to the weight of the edge between the i-th network node and the j-th network node on the N-th network layer; The k-th drone cluster composed of multiple drones corresponds to the k-th community on the 0th network layer; The key drone in the k-th drone cluster corresponds to the leader node of the k-th community on the 0th network layer.

3. The method for evaluating the collaborative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The initial state of the static structure model of the UAV cluster is constructed according to the corresponding relationship, including: According to the corresponding relationship, the number of communities in the static structure model of the drone cluster, the number of network nodes in each community, and the number of leader nodes in each community are determined; The nodes within each community and the leading nodes of different communities are set to be fully connected to obtain the initial state of the static structure model of the drone cluster.

4. The method for evaluating the collaborative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The method of calculating the probability of a new network node connecting to a node when joining the static structure model of the drone cluster based on the initial state of the drone cluster static structure model, and obtaining the drone cluster static structure model, includes: When the newly added network node is a non-leader node, the probability of the newly added network node connecting to a node in the 0th network layer is calculated as follows: ； in, Indicates that the newly added network node is in the 0th layer network layer and the network node connect, Indicates that a new network node has been added to the network node Community , Indicates that the newly added network node is in the 0th layer network layer and the community Network nodes in The probability of connection, Indicates that a new network node has joined the community The probability of Represents a network node Degree on the layer 0 network, represents the sum of the degrees of all network nodes at the 0th layer of the network. Indicates the number of nodes in the network; When the newly added network node is a non-leader node, the calculation expression for the probability that the newly added network node connects to a network node in the remaining networks except the 0th network layer is as follows: ； in, Indicates that the newly added node is in the rest of the network and network nodes except the 0th layer network layer connect, Represents a network node Degree on the layer 0 network; When the newly added network node is a leader node, the calculation expression for the probability that the newly added network node connects to a leader node is as follows: ； in, Indicates newly added network nodes and leader nodes connect, represents the probability that a new network node is a leader node, Indicates newly added network nodes and leader nodes Collaboration between The cost of collaborative tasks, Indicates newly added network nodes and leader nodes The distance between represents the impact coefficient of collaboration, represents the distance influence coefficient, represents the equilibrium function between the cooperative and distance situations, Indicates a newly added network node With different leadership nodes in completing The total cost consumed by collaborative tasks, Indicates a newly added network node The sum of distances to different leader nodes.

5. The method for evaluating the collaborative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The UAV state conversion relationship parameters include the probability of the UAV successfully achieving pairing after accepting the collaborative task. , the probability that a UAV in working state will switch to standby state after completing the collaborative task ; The parameter for determining the UAV state transition relationship based on the UAV cluster static structure model is obtained through a random experiment using the Monte Carlo method, specifically including: Based on the static structure model of the UAV cluster, different collaborative tasks are randomly selected, and the communication link between two network nodes in the model is randomly selected to be damaged. The UAVs are paired to complete the collaborative tasks. After repeated multiple times, the success rate of the UAV collaborative pairing to complete the collaborative tasks is obtained. , the probability that the collaborative task completion time is less than the preset time is .

6. The method for evaluating the collaborative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The evaluation of the cooperation ability of the UAV cluster according to the number of UAVs in different states in the UAV cluster in the next time period includes: Let the number of cooperation tasks arranged for the UAV cluster at time T be R. When W(T + 1) < W(T) < F(T + 1), it is determined that the cooperation ability of the UAV cluster is level one; When F(T + 1) < W(T + 1) < W(T), it is determined that the cooperation ability of the UAV cluster is level two; When W(T) < W(T + 1) < F(T + 1), it is determined that the cooperation ability of the UAV cluster is level three; When W(T) < W(T + 1) and W(T + )) - W(T) < R / 2, it is determined that the cooperation ability of the UAV cluster is level four; When W(T) < W(T + 1) and W(T + 1) - W(T) < R, it is determined that the cooperation ability of the UAV cluster is level five; Among them, the cooperation ability of the UAV cluster increases gradually from level one to level five.

7. A device for evaluating the collaborative capability of drone swarms based on an improved SIR model, the device being configured to execute the method for evaluating the collaborative capability of drone swarms based on an improved SIR model as claimed in any one of claims 1 to 6, characterized in that: Including: A UAV state transition analysis module configured to analyze the UAV state transition relationship using an improved SIR model; A model construction module configured to construct a UAV cluster static structure model using a hierarchical weighted complex network; A parameter determination module configured to determine the UAV state transition relationship parameter based on the UAV cluster static structure model; A next-stage UAV state acquisition module configured to substitute the UAV state transition relationship parameter into the UAV state transition relationship to obtain the number of UAVs in different states in the UAV cluster in the next time period; A UAV cooperation ability evaluation module configured to evaluate the UAV cluster cooperation ability according to the number of UAVs in different states in the UAV cluster in the next time period.

8. A computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the UAV cluster cooperation ability evaluation method based on the improved SIR model as described in any one of claims 1 to 6.

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