Unmanned aerial vehicle cluster cooperation capability evaluation method and device based on improved SIR model, and storage medium

Abstract

The invention discloses an unmanned aerial vehicle cluster cooperation capability assessment method and device based on an improved SIR model, and a storage medium, and relates to the technical field of unmanned aerial vehicle cooperation capability assessment, and the method comprises the steps: analyzing an unmanned aerial vehicle state conversion relation through employing the improved SIR model; constructing an unmanned aerial vehicle cluster static structure model by using a layered weighted complex network; determining unmanned aerial vehicle state conversion relation parameters based on the unmanned aerial vehicle cluster static structure model; substituting the unmanned aerial vehicle state conversion relation parameters into the unmanned aerial vehicle state conversion relation to obtain the number of unmanned aerial vehicles in different states in the unmanned aerial vehicle cluster in the next time period; and evaluating the cooperation capability of the unmanned aerial vehicle cluster according to the number of unmanned aerial vehicles in different states in the unmanned aerial vehicle cluster in the next time period. According to the invention, the number of unmanned aerial vehicles in different states in the cluster at the future time point can be clearly and efficiently predicted, so that the collaborative operation capability of the unmanned aerial vehicle cluster can be effectively evaluated.

Description

Technical Field

[0001] The present invention relates to a method, a device and a storage medium for evaluating the cooperative capability of a drone cluster based on an improved SIR model, and belongs to the technical field of evaluating the cooperative capability of drones. Background Art

[0002] At present, the quantitative analysis and evaluation of the capabilities of drone clusters is usually carried out through the modeling and simulation of the system. The modeling methods used include complex networks, petri nets, UML language, etc. In the working process of drone clusters, due to external interference and damage, the communication relationship between drones in the cluster is constantly changing. At the same time, the status of each drone is constantly switching during the actual operation. Although traditional modeling methods can simulate the behavior strategy of drones, it is difficult to reflect the change law of each drone's status over time, and it is not good to reliably evaluate the collaborative ability of drone clusters. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art 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 achieving 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 cooperative capability of a drone cluster based on an improved SIR model, comprising:

[0006] The improved SIR model is used 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 the 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 drone cluster collaboration capability 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 state transformation relationship of the drone 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 process of the UAV performing a collaborative task, the fault state refers to the process of the UAV failing, and the standby state refers to the other states of the UAV except the working state and fault state.

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

[0014] ;

[0015] in, It represents the probability of successful pairing of drones after accepting the collaborative task. It indicates the probability that a UAV in working state will switch to standby state after completing the collaborative task. It indicates the probability of a drone in standby state 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 drones in fault state at time t, Indicates the number of drones in standby state at time t+1, represents the number of drones in working state at time t+1, Indicates the number of drones in a faulty state at time t+1.

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

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

[0018] Construct the initial state of the UAV cluster static structure model according to 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 ith UAV correspond to the attributes of the ith 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 ith UAV and the jth UAV corresponds to the edge between the ith network node and the jth 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 type of collaboration relationship between the ith UAV and the jth UAV corresponds to the edge between the ith network node and the jth network node on the Hth network layer;

[0026] The Nth type of cooperation capability between the ith UAV and the jth UAV corresponds to the weight of the edge between the ith network node and the jth network node on the Nth network layer;

[0027] The kth drone cluster consisting of multiple drones corresponds to the kth community on the 0th network layer;

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

[0029] Furthermore, the initial state of the static structure model of the drone 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 static structure model of the drone cluster, the probability of a newly added network node connecting to a node when joining the static structure model of the drone cluster is calculated to obtain the static structure model of the drone cluster, including:

[0033] 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 node in the 0th network layer is:

[0034] ;

[0035] in, Indicates that the newly added network node is in the 0th layer of the 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 of the 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 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:

[0037]

[0038] in, Indicates that the newly added node is in the network and network nodes other than 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 calculation expression for the probability of the newly added network node connecting to a leader node is:

[0040]

[0041] in, Indicates newly added network nodes and leader nodes connect, represents the probability that the newly added network node is a leader node, Indicates newly added network nodes and leader nodes Collaboration between The cost of a collaborative task, 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 leaders in completing The total cost consumed by collaborative tasks, Indicates the newly added network node The sum of the distances from different leading nodes

[0042] Furthermore, the UAV state transition relationship parameters include the probability that the UAVs successfully pair after receiving a collaborative task and the probability that the UAVs in the working state transition to the standby state after completing the collaborative 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:

[0044] Based on the static structure model of the UAV cluster, different collaborative tasks are randomly selected, and the communication links between two network nodes in the model are randomly selected to be in a damaged state. The UAVs complete the collaborative task through collaborative pairing. After repeating multiple times, the success rate of the UAVs completing the collaborative task through collaborative pairing is and the probability that the collaborative task completion time is less than the preset time is .

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

[0046] Let the number of collaborative tasks assigned to the UAV cluster at time T be R

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

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

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

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

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

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

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

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

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

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

[0057] The next stage drone state acquisition module is configured to substitute the drone state conversion relationship parameters into the drone state conversion 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 according to 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 a drone cluster 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 abstracting 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 effective evaluation of the collaborative operation capability of the UAV cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of a flow chart of a method for evaluating the cooperative capability of a drone cluster based on an improved SIR model in an embodiment of the present invention;

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

[0064] Figure 3 It is a schematic diagram of the initial state structure of a static structure model of a drone cluster of a drone cluster collaboration capability evaluation method based on an improved SIR model in one embodiment of the present invention;

[0065] Figure 4It is a structural schematic diagram of a static structure model of a drone cluster in a method for evaluating the collaboration 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 It is a structural schematic diagram of a static structure model of a drone cluster in a method for evaluating the collaboration 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 leader node;

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

[0068] Figure 7 Schematic diagram of the state change trend of the drone according to 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 solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0070] Embodiment 1:

[0071] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the cooperative capability of a drone 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 three states of a single UAV when performing a task: standby state S (Standby), working state W (Working) and fault state F (Fault). The relationship between the three states is as follows: Figure 2 shown.

[0073] The drones are all in standby state at the initial state. As the system begins to accept work tasks, some drones enter the working state after receiving the tasks. When the drone cannot complete the work tasks it undertakes alone and needs other drones to cooperate with it, these drones in the working state will look for other drones in the standby state that have the ability to work with it. In the actual environment, this process is often restricted by the system's own collaborative rules and collaborative working environment. Overly strict collaborative process restrictions and unreasonable network connection structure settings may reduce the success rate of collaboration. As the collaborative tasks proceed smoothly, other standby nodes that join the collaborative process will be transferred to the working state. After the collaborative process ends, all participating nodes will return to the standby state. In addition, due to the influence of the external environment, there is a certain probability that the nodes in the standby state and the working state will lose their working ability and become faulty. The nodes in the faulty state cannot accept collaborative tasks. After the fault is repaired, the nodes will be transferred to the standby state.

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

[0075] ;

[0076] in, It represents the probability of successful pairing of drones after accepting the collaborative task. It indicates the probability that a UAV in working state will switch to standby state after completing the collaborative task. It indicates the probability of a drone in standby state 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 drones in fault state at time t, Indicates the number of drones in standby state at time t+1, represents the number of drones in working state at time t+1, Indicates the number of drones in a faulty 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 ith drone corresponds to the ith network node;

[0081] The capabilities of the ith UAV correspond to the attributes of the ith 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 ith UAV and the jth UAV corresponds to the edge between the ith network node and the jth 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 type of collaboration relationship between the ith UAV and the jth UAV corresponds to the edge between the ith network node and the jth network node on the Hth network layer;

[0086] The Nth type of cooperation capability between the ith UAV and the jth UAV corresponds to the weight of the edge between the ith network node and the jth network node on the Nth network layer;

[0087] The kth drone cluster consisting of multiple drones corresponds to the kth community on the 0th network layer;

[0088] The key drone in the kth drone cluster corresponds to the leader node of the kth 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, and 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, a three-layer initialization network is constructed, with a total of 9 network nodes divided into three communities, each of which 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 calculation expression for the probability that the newly added network node is connected to a node in the 0th network layer is:

[0094] ;

[0095] in, Indicates that the newly added network node is in the 0th layer of the 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 of the 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 FIG. 1 , 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 network and network nodes other than the 0th layer network layer connect, Represents a network node Degree on the tier 0 network.

[0099] This is because when a newly added network node is connected to other network nodes at a network layer other than the 0th network layer, it is necessary to have a connection relationship with 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, follow the network layer corresponding to the task and use the shortest path priority principle to find the leader node of other communities with the lowest cost to complete the task, and establish a 0th layer network layer connection with it, and simultaneously establish a network layer connection corresponding to the collaboration.

[0101] like Figure 5 As shown in FIG. 1 , when the newly added network node is a leader node, it is assumed that the newly added network node is only connected to the leader node and not to the non-leader node. The calculation expression for the probability of the newly added network node being connected to a leader node is:

[0102] ;

[0103] in, Indicates newly added network nodes and leader nodes connect, represents the probability that the newly added 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 to 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 leaders 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 Monte Carlo method is used to test the success rate of UAV collaborative pairing. After a large number of experiments, the success rate of UAV collaborative pairing to complete collaborative tasks 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] Suppose the number of cooperation tasks assigned to the UAV cluster at time T is 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 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 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 level - one cooperation ability, but Figure 7Or collaboration ability Figure 6 This shows that when the failure rate and repair rate are not very different, simply improving the success rate of drone collaborative pairing can only significantly increase the amount of work completed in the short term, but as time goes by, the number of drones working, the number of failures and the number of standby will eventually stabilize. At this time, if you still want to improve the system's ability to complete operations, you can only do so by formulating an anti-destruction strategy and increasing the repair rate of destroyed nodes.

[0119] Embodiment 3:

[0120] This embodiment also provides a UAV cluster collaboration capability evaluation device based on an improved SIR model, including:

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

[0122] The model building module is configured to build a static structure model of a drone 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 drone collaboration capability evaluation module is configured to evaluate the collaboration capability of the drone cluster according to the number of drones in different states in the drone cluster within the next time period.

[0126] Embodiment 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 a drone cluster 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 cooperative capability of UAV clusters based on an improved SIR model, characterized in that: include: The improved SIR model is used to analyze the state transformation relationship of UAVs; Using hierarchical weighted complex networks to construct a static structural model of drone swarms; Determine the parameters of the 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 drone cluster collaboration capability 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 cooperative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The improved SIR model is used to analyze the state transformation relationship of the UAV, 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 a collaborative task, the fault state refers to the process of the UAV failing, and the standby state refers to the other states of the UAV except the working state and fault state. The improved SIR model is used to analyze the state transformation relationship of the UAV, and the UAV state transformation relationship expression is obtained. The UAV state transformation relationship expression is: ； in, It represents the probability of successful pairing of drones after accepting the collaborative task. It indicates the probability that a UAV in working state will switch to standby state after completing the collaborative task. It indicates the probability of a drone in standby state 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 drones in fault state at time t, Indicates the number of drones in standby state at time t+1, represents the number of drones in working state at time t+1, Indicates the number of drones in a faulty state at time t+1.

3. The method for evaluating the cooperative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: The method of using a hierarchical weighted complex network to construct a static structure model of a drone cluster includes: Construct the correspondence between the static structure model of drone swarm and the hierarchical weighted complex network; Construct the initial state of the UAV cluster static structure model according to 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.

4. The method for evaluating the cooperative capability of UAV clusters based on the improved SIR model according to claim 3 is characterized in that: The correspondence between the UAV cluster static structure model and the hierarchical weighted complex network includes: the ith UAV corresponds to the ith network node; The capabilities of the ith UAV correspond to the attributes of the ith 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 ith UAV and the jth UAV corresponds to the edge between the ith network node and the jth 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; The Hth type of collaboration relationship between the ith UAV and the jth UAV corresponds to the edge between the ith network node and the jth network node on the Hth network layer; The Nth type of cooperation capability between the ith UAV and the jth UAV corresponds to the weight of the edge between the ith network node and the jth network node on the Nth network layer; The kth drone cluster consisting of multiple drones corresponds to the kth community on the 0th network layer; The key drone in the kth drone cluster corresponds to the leader node of the kth community on the 0th network layer.

5. The method for evaluating the cooperative capability of UAV clusters based on the improved SIR model according to claim 3 is characterized in that: The initial state of constructing the static structure model of the drone cluster according to the corresponding relationship includes: 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.

6. The method for evaluating the cooperative capability of UAV clusters based on the improved SIR model according to claim 3 is characterized in that: Calculating the probability of a new network node connecting to a node when it joins the static structure model of the UAV cluster based on the initial state of the static structure model of the UAV cluster, and obtaining the static structure model of the UAV cluster, including: When the new network node is a non-leader node, the calculation expression for the probability of the new network node connecting to a node in the 0th network layer is: ； in, Indicates that the newly added network node is in the 0th layer of the 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 of the 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 new network node is a non-leader node, the calculation expression for the probability of the new network node connecting to a network node in the remaining networks other than the 0th network layer is: ； in, Indicates that the newly added node is in the network and network nodes other than the 0th layer network layer connect, Represents a network node Degree on the layer 0 network; When the new network node is a leader node, the calculation expression for the probability of the new network node connecting to a leader node is: ； in, Indicates newly added network nodes and leader nodes connect, represents the probability that the newly added network node is a leader node, Indicates newly added network nodes and leader nodes Collaboration between The cost of a collaborative task, 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 leaders in completing The total cost consumed by collaborative tasks, Indicates a newly added network node The sum of distances to different leader nodes.

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

8. The method for evaluating the cooperative capability of UAV clusters based on the improved SIR model according to claim 1 is characterized in that: Evaluating 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, including: Let the number of cooperation tasks assigned to 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 + 1) - 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.

9. A drone swarm collaboration capability evaluation device based on an improved SIR model, 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 static structure model of the UAV cluster using a hierarchical weighted complex network; A parameter determination module, configured to determine the UAV state transition relationship parameters based on the static structure model of the UAV cluster; A next-stage UAV state acquisition module, configured to substitute the UAV state transition relationship parameters 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 cooperation ability of the UAV cluster according to the number of UAVs in different states in the UAV cluster in the next time period.

10. 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 8.

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