Adaptive Smart Contract Method and Application for UAV Network

Abstract

This application discloses an adaptive smart contract method and application for unmanned aerial vehicle (UAV) networks. The method includes continuously monitoring a global node table and determining whether the number of UAV nodes has decreased; if the number of UAV nodes has decreased, obtaining current node information; obtaining individual and group historical optimal data of the target node and the target learning node; the strong consensus node calculating the optimal position of the target node based on the current node information, the individual and group historical optimal data of the target node and the target learning node; and sending scheduling instructions to the target node. This application can quickly adjust and optimize the UAV network topology when the number of nodes decreases, ensuring the collaborative operation of the UAV network and achieving maximum coverage of the monitoring area.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to an adaptive smart contract method and application for UAV networks. Background Technology

[0002] During mission execution, drone swarms are prone to security issues, such as attackers physically damaging drone nodes, causing them to malfunction, the enemy gaining control of drone nodes by interfering with communications, or drone nodes themselves malfunctioning. These issues can alter the drone network topology, making it impossible for the current topology to support the entire drone swarm in continuing to collaboratively complete the mission.

[0003] In real battlefield environments, drone networks are vulnerable to malicious attacks. Attackers may physically damage drone nodes to cause them to malfunction or gain control of them by interfering with communications. This inevitably disrupts the collaborative operation of the drone network. However, the static topology used in traditional drone networks lacks flexibility and is therefore unable to cope with such threats or meet the demands of subsequent battlefield environments. Therefore, researching a strategy for dynamically optimizing the drone network topology is crucial for protecting the overall security of the drone network. Summary of the Invention

[0004] The purpose of this application is to provide an adaptive smart contract method and application for unmanned aerial vehicle (UAV) networks, in order to solve the technical problem that the static topology of existing UAV networks lacks flexibility and that the current topology cannot support the entire UAV cluster to continue to work together to complete tasks when UAV nodes fail.

[0005] To achieve the above objectives, the first aspect of this application provides an adaptive smart contract method for unmanned aerial vehicle (UAV) networks, comprising:

[0006] The drone node continuously monitors the global node table and determines whether the number of drone nodes has decreased. The global node table includes information on all drone nodes in the drone network.

[0007] If the number of drone nodes decreases, the strong consensus node obtains the current node information. The strong consensus node is the drone node with the highest credibility in the drone network. The current node information includes the current position and speed of all drone nodes in the drone network.

[0008] The strong consensus node acquires the individual historical best data and the group historical best data of the target node and the target learning node. The individual historical best data includes the individual optimal position and individual optimal coverage of the node when the node has the highest coverage of the sub-monitoring area. The group historical best data includes the group optimal position and group optimal coverage of the node when the UAV network has the highest coverage of the total monitoring area.

[0009] The strong consensus node calculates the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data.

[0010] The strong consensus node sends a scheduling instruction to the target node based on the target node's optimal position.

[0011] In one or more embodiments, the step of the strong consensus node calculating the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data includes:

[0012] The strong consensus node calculates the current fitness of all drone nodes based on the current node information and the fitness function;

[0013] The strong consensus node calculates the historical best fitness of the target node and the target learning node based on the individual optimal coverage of the target node and the target learning node and the fitness function.

[0014] The strong consensus node obtains the optimal position of the target node based on the current node information of the target node, the current fitness of all UAV nodes, the historical optimal fitness of the target node and the target learning node, and the individual optimal position and the group optimal position of the target node.

[0015] The fitness function is a function that is positively correlated with the fitness of a node and its individual coverage.

[0016] In one or more embodiments, the step of the strong consensus node calculating the current fitness of all drone nodes based on the current node information and the fitness function includes:

[0017] The strong consensus node calculates the current individual coverage rate of all drone nodes based on the current node information;

[0018] The strong consensus node substitutes the current individual coverage of all drone nodes into the fitness function to obtain the current fitness of all drone nodes.

[0019] In one or more embodiments, the method for calculating the historical best fitness includes:

[0020] The strong consensus node substitutes the node's individual optimal coverage rate into the fitness function to obtain the node's historical optimal fitness.

[0021] In one or more embodiments, the step of the strong consensus node obtaining the optimal position of the target node based on the current node information of the target node, the current fitness of all UAV nodes, the historical best fitness of the target node and the target learning node, and the individual best position and the group best position of the target node includes:

[0022] Based on the current fitness of all UAV nodes and the historical best fitness of the target node, the self-learning factor of the target node is obtained.

[0023] Based on the self-learning factor, the individual optimal position, and the current position of the target node, the self-position weight of the target node is obtained;

[0024] Based on the current fitness of all drone nodes, the social learning factor of the target node is obtained;

[0025] Based on the social learning factor, optimal group position, and current position of the target node, the group position weight of the target node is obtained;

[0026] Based on the historical optimal fitness of the target node and the target learning node, the mutual learning factor of the target node is obtained;

[0027] Based on the mutual learning factor of the target node, its current position, and the individual optimal position of the target learning node, the mutual learning position weight of the target node is obtained.

[0028] Based on the target node's current position and velocity, its own position weight, group position weight, and mutually learned position weight, the optimal position of the target node is obtained.

[0029] In one or more embodiments, the self-learning factor of the target node is calculated using the following formula:

[0030] In the formula, The historical best fitness of the target node. This represents the current fitness of the node. As the initial self-learning factor;

[0031] The formula for calculating the social learning factor of the target node is as follows:

[0032] In the formula, This represents the current fitness of the node. Let q be the initial social learning factor, and q be the convergence factor.

[0033] The formula for calculating the mutual learning factor of the target nodes is as follows:

[0034] In the formula, pfit k The historical best fitness of the target learning node. The target node's historical best fitness.

[0035] In one or more embodiments, the formula for calculating the optimal position of the target node is as follows:

[0036]

[0037] In the formula, Let be the velocity vector of the j-th particle in the (k+1)-th iteration, and be the updated velocity representation. This represents the current velocity of the j-th particle in the k-th iteration; rand(0,1) is a random number between 0 and 1. This represents the position of the target node at the (k+1)th iteration, which is the optimal position of the target node. This represents the current position of the target node;

[0038] w k For inertia weighting factor, w k The update formula is as follows:

[0039]

[0040] In the formula, w max , w min These are the maximum and minimum values ​​of the inertia weight, respectively. T is the maximum number of iterations, and k is the current number of iterations.

[0041] To achieve the above objectives, a second aspect of this application provides an adaptive smart contract device for unmanned aerial vehicle (UAV) networks, comprising:

[0042] The quantity monitoring module is used to enable drone nodes to continuously monitor the global node table and determine whether the number of drone nodes has decreased. The global node table includes information on all drone nodes in the drone network.

[0043] The current information acquisition module is used to provide the strong consensus node with current node information when the number of drone nodes decreases. The strong consensus node is the drone node with the highest credibility in the drone network. The current node information includes the current position and speed of all drone nodes in the drone network.

[0044] The historical information acquisition module is used to enable the strong consensus node to acquire individual historical optimal data and group historical optimal data of the target node and the target learning node. The individual historical optimal data includes the individual optimal position and individual optimal coverage of the node when the node has the highest coverage of the sub-monitoring area. The group historical optimal data includes the group optimal position and group optimal coverage of the node when the UAV network has the highest coverage of the total monitoring area.

[0045] The calculation module is used to enable the strong consensus node to calculate the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data.

[0046] The instruction sending module is used to enable the strong consensus node to send scheduling instructions to the target node based on the optimal position of the target node.

[0047] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising:

[0048] At least one processor; and

[0049] A memory that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform the adaptive smart contract method as described in any of the above embodiments.

[0050] To achieve the above objectives, a fourth aspect of this application provides a machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the adaptive smart contract method as described in any of the above embodiments.

[0051] The advantages of this application, which differ from existing technologies, are:

[0052] When the number of UAV nodes decreases, this application calculates the self-learning factor, social learning factor, and mutual learning factor of the nodes by acquiring current node information and historical data of the nodes. Based on the above three factors, three position weights are obtained, the optimal position of the node is updated based on the three position weights, and scheduling instructions are sent based on the optimal position. This enables the rapid adjustment and optimization of the UAV network topology when the number of nodes decreases, ensuring the collaborative work of the UAV network and achieving maximum coverage of the monitoring area. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram of one implementation of the adaptive smart contract method for unmanned aerial vehicle networks of this application;

[0055] Figure 2 yes Figure 1 A flowchart illustrating one embodiment corresponding to S400;

[0056] Figure 3 yes Figure 2 A flowchart of one embodiment corresponding to S403;

[0057] Figure 4 This is a comparison chart of the average coverage of the four algorithms in the effect examples of this application;

[0058] Figure 5 This is a schematic diagram of one embodiment of the adaptive smart contract device for the drone network of this application;

[0059] Figure 6 This is a schematic diagram of one embodiment of the electronic device of this application. Detailed Implementation

[0060] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0061] In real battlefield environments, drone networks are vulnerable to malicious attacks. Attackers may physically damage drone nodes to cause them to malfunction or gain control of them by interfering with communications. This inevitably disrupts the collaborative operation of the drone network. However, the static topology used in traditional drone networks lacks flexibility and is therefore unable to cope with such threats or meet the demands of subsequent battlefield environments. Therefore, researching a strategy for dynamically optimizing the drone network topology is crucial for protecting the overall security of the drone network.

[0062] To address the issue of drone network nodes being unable to continue cooperating normally after being attacked or malfunctioning, this application provides an adaptive smart contract method for dynamically optimizing the drone network topology. This method can autonomously adjust and optimize the drone network topology when attacked and the number of normal nodes changes, thereby maximizing coverage and communication efficiency.

[0063] Specifically, please refer to Figure 1 , Figure 1 This is a schematic diagram of one implementation of the adaptive smart contract method for drone networks in this application.

[0064] like Figure 1 As shown, the method includes:

[0065] S100: The drone node continuously monitors the global node table and determines whether the number of drone nodes has decreased.

[0066] This includes information on all drone nodes in the drone network.

[0067] During the mission execution phase, drone nodes can continuously monitor the global node table. In the drone blockchain, the global node table can be published to all blocks as new blocks are generated, thereby achieving global updates in the drone network.

[0068] Since the trustworthiness of drone nodes is dynamically adjusted, when the trustworthiness of a drone falls below a threshold, the drone will be removed from the drone network. Therefore, the number of nodes in the drone network is also dynamically adjusted to remove untrustworthy drones from the task.

[0069] By continuously monitoring the global node table, it is possible to determine whether the number of drone nodes has decreased, so that the drone network topology can be quickly adjusted and optimized when the number of nodes decreases, ensuring the collaborative operation of the drone network.

[0070] If the number of drone nodes decreases, it also includes:

[0071] S200, strong consensus nodes obtain current node information.

[0072] Among them, the strong consensus node can be the drone node with the highest credibility in the drone network. It can be elected based on credibility ranking in each round of consensus, or it can be randomly assigned in the initial stage.

[0073] Current node information includes the current location and speed of all drone nodes in the drone network.

[0074] S300 and strong consensus nodes acquire individual historical best data and group historical best data of target nodes and target learning nodes.

[0075] Among them, the individual historical best data includes the individual optimal position and individual optimal coverage of the node when the node has the highest coverage of the sub-monitoring area; the group historical best data includes the group optimal position and group optimal coverage of the node when the UAV network has the highest coverage of the total monitoring area.

[0076] Coverage can be calculated by gridding the monitoring area and calculating the grid coverage of each drone node.

[0077] Specifically, for any grid s in the monitoring area j node z i Probability of being able to cover:

[0078]

[0079] And d(z) i ,s j Let be the distance between the point and the grid, and therefore the probability that a grid point is covered by all sensor nodes is:

[0080] The coverage rate of the monitoring area is defined as the ratio of the coverage area of ​​the UAV node set to the area of ​​the monitoring area, as shown in the following formula:

[0081] It is understandable that the optimal position of each individual node and the optimal position of the group are not necessarily the same. When the coverage of the sub-monitoring area that a node is responsible for is the highest, the coverage of the entire drone network of the total monitoring area is not necessarily the highest; and vice versa.

[0082] S400 and strong consensus nodes calculate the optimal position of the target node based on current node information, individual historical best data of the target node and the target learning node, and group historical best data.

[0083] The target node updates its position by learning both the individual optimal position and the group optimal position, which includes both self-learning and social learning.

[0084] In order to avoid the problem of drone nodes getting stuck in local optima, a mutual learning mechanism was also introduced, that is, to learn from the target node to update the position.

[0085] Specifically, please refer to Figure 2 , Figure 2 yes Figure 1 A flowchart of one embodiment corresponding to S400.

[0086] like Figure 2 As shown, the method for calculating the optimal position of the target node includes:

[0087] S401: The strong consensus node calculates the current fitness of all drone nodes based on the current node information and the fitness function.

[0088] The fitness function is used to characterize the coverage of a node. It can be configured as any function that is proportional to the fitness and coverage, and can be obtained through presets.

[0089] For example, the fitness function can be Here, 'a' can be a positive correlation parameter, which means that the higher the coverage, the higher the fitness of the node.

[0090] By calculating the individual coverage rate of a node in the current node information (i.e., the coverage rate of the sub-monitoring area that the node is responsible for), and substituting the individual coverage rate of the node into the fitness function, the current fitness of the node can be obtained.

[0091] S402. Strong consensus nodes calculate the historical best fitness of the target node and the target learning node based on the individual optimal coverage and fitness function of the target node and the target learning node.

[0092] Furthermore, by substituting the individual optimal coverage rates of the target node and the target learning node into the fitness function, the historical optimal fitness of the target node can be calculated. and the historical best fitness pfit of the target learning node k .

[0093] S403. The strong consensus node obtains the optimal position of the target node based on the current node information of the target node, the current fitness of all drone nodes, the historical best fitness of the target node and the target learning node, and the individual best position and the group best position of the target node.

[0094] Specifically, based on the current fitness and historical best fitness of the target node, the self-learning factor of the target node can be obtained. When the historical best fitness of the target node is higher, it means that the node has achieved better results in the past search. Therefore, the node needs to pay more attention to its own experience and tends to maintain its historical best position. Its self-learning factor can also be higher.

[0095] Accordingly, based on the current fitness of all drone nodes, the social learning factor of the nodes can be obtained, which represents the degree to which the nodes tend to learn their group's optimal position.

[0096] In addition, based on the historical best fitness of the target node and the target learning node, the mutual learning factor of the target node can be obtained. The mutual learning factor represents the degree to which the target node learns the position of other nodes. When the best fitness of the target learning node is greater than the historical best fitness of the target node, the target node should move closer to the target learning node to improve the mutual learning ability of the target node. Conversely, it should move in the opposite direction to the target learning node to avoid subsequent resource consumption.

[0097] The specific calculation method is detailed below. Please refer to [link / reference]. Figure 3 , Figure 3 yes Figure 2 A flowchart of one embodiment corresponding to S403 is shown.

[0098] like Figure 3 As shown, the method for calculating the optimal position of the target node may include:

[0099] S4031. Based on the current fitness of all UAV nodes and the historical best fitness of the target node, the self-learning factor of the target node is obtained.

[0100] Among them, self-learning factor This represents the degree to which the target node tends to maintain its own optimal position. The calculation formula is as follows:

[0101] In the formula, The historical best fitness of the target node. This represents the current fitness of the node. This serves as the initial self-learning factor.

[0102] Understandable, when The larger the self-learning factor, the higher the self-learning factor. The larger the value, the more the target node tends to maintain its historical best position.

[0103] S4032. Based on the self-learning factor of the target node, the individual's optimal position, and the current position, obtain the self-position weight of the target node.

[0104] Specifically, the distance between the individual optimal position and the current position of the target node can be calculated, and a quantified self-position weight can be obtained based on the self-learning factor.

[0105] For example, the formula for calculating the self-position weight of the target node can be as follows:

[0106]

[0107] In the formula, The optimal position for the target node. The current position of the target node is given by , and rand(0,1) is a random number between 0 and 1.

[0108] S4033. Based on the current fitness of all drone nodes, obtain the social learning factor of the target node.

[0109] Among them, social learning factor This represents the degree to which the target node tends to maintain its own optimal position within the group, and can be calculated using the following formula:

[0110] In the formula, This represents the current fitness of the node. The current fitness of the target node. q is the initial social learning factor, and q is the convergence factor used to accelerate the global convergence speed. It can be a fixed value such as 2, 3, 4, etc.

[0111] S4034. Based on the social learning factor of the target node, the optimal position of the group, and the current position, obtain the group position weight of the target node.

[0112] Specifically, the distance between the target node's optimal group position and its current position can be calculated, and a quantified group position weight can be obtained based on the social learning factor.

[0113] For example, the formula for calculating the group position weight of the target node can be as follows:

[0114]

[0115] In the formula, The optimal position for the target node in the population. The current position of the target node is given by , and rand(0,1) is a random number between 0 and 1.

[0116] S4035. Based on the historical best fitness of the target node and the target learning node, obtain the mutual learning factor of the target node.

[0117] The mutual learning factor represents the degree to which the target node tends to learn the optimal position of the individual target learning node, and the calculation formula is as follows:

[0118] In the formula, pfit k The historical best fitness of the target learning node. The target node's historical best fitness.

[0119] S4036. Based on the mutual learning factor of the target node, the current position, and the individual optimal position of the target learning node, obtain the mutual learning position weight of the target node.

[0120] Specifically, the distance between the optimal position of the target learning node and the current position of the target node can be calculated, and a quantified mutual learning position weight can be obtained based on the mutual learning factor.

[0121] For example, the formula for calculating the mutual learning position weights can be as follows:

[0122]

[0123] In the formula, T k The optimal position of the target learning node for an individual. The current position of the target node is given by , and rand(0,1) is a random number between 0 and 1.

[0124] S4037. Based on the target node's current position and velocity, its own position weight, group position weight, and mutually learned position weight, the optimal position of the target node is obtained.

[0125] By using the self-position weight, group position weight, and mutual learning position weight obtained through the above steps to correct the current position and velocity of the target node, the optimal position of the target node can be obtained.

[0126] Specifically, the formula for calculating the optimal position of the target node can be as follows:

[0127]

[0128] In the formula, Let be the velocity vector of the j-th particle in the (k+1)-th iteration, and be the updated velocity representation. This represents the current velocity of the j-th particle in the k-th iteration; rand(0,1) is a random number between 0 and 1. This represents the position of the target node at the (k+1)th iteration, which is the optimal position of the target node. This represents the current position of the target node.

[0129] w k For inertia weighting factor, w k The update formula is as follows:

[0130]

[0131] In the formula, w max , w min These are the maximum and minimum values ​​of the inertia weight, respectively. T is the maximum number of iterations, and k is the current number of iterations.

[0132] Understandably, the optimal position of the target node is constantly changing iteratively. In each iteration, the individual historical best data and the group historical best data of each node may change. When the individual coverage rate of a node is greater than the historical maximum individual coverage rate in each iteration, that individual coverage rate will be used as the new individual optimal coverage rate. Similarly, when the group coverage rate of the UAV network is greater than the historical maximum group coverage rate in each iteration, that group coverage rate will be used as the new group coverage rate.

[0133] Correspondingly, the inertia weighting factor w k It will continue to update until the maximum number of iterations T is reached. When the maximum number of iterations T is reached, the optimal position of the final target node will be output.

[0134] The S500 and strong consensus nodes send scheduling instructions to the target node based on the target node's optimal location.

[0135] Based on the optimal location of the target node, the strong consensus node can send scheduling instructions to the target node.

[0136] Specifically, for each node in the drone network, a strong consensus node can publish the updated target location of each node to the drone network through newly generated blocks, causing each node to move towards the updated target location, thereby forming a new topology and ensuring the collaborative effect of each drone node in the drone network to achieve maximum coverage of the monitoring area.

[0137] The effectiveness of the technical solution in this application will be verified below with specific examples.

[0138] Example of effect:

[0139] Different algorithms were incorporated into the blockchain of the drone network in the form of smart contracts, and simulation experiments were conducted using the following data as the benchmark parameters of the algorithms.

[0140]

[0141] This example compares the smart contract DPSO algorithm provided in this application with conventional PSO, artificial fish swarm, and MOEA / P algorithms to simulate topology optimization after a drone node is attacked. The higher the optimized coverage, the better it can meet the needs of tasks in the actual environment, thus demonstrating that the drone network is more robust.

[0142] The experiment set different initial numbers of UAV nodes (range: 10, 15, 20, 25). Two faulty nodes were randomly selected from the entire UAV network. Figure 4 , Figure 4 This is a comparison chart of the average coverage of the four algorithms in the effect examples of this application.

[0143] like Figure 4 As shown, the basic PSO algorithm and the artificial fish swarm algorithm have lower average coverage rates than the other two algorithms when the number of nodes is small, at 26.33% and 27.10% respectively; the average coverage rate of the artificial fish swarm algorithm increases with the number of nodes and exceeds that of the MOEA / P algorithm; the average coverage rate of the DPSO algorithm in this application is always the highest.

[0144] Experiments demonstrate that the algorithm proposed in this application not only achieves higher coverage than the basic PSO algorithm, but also outperforms other optimized algorithms in maintaining the overall integrity of the drone network. This proves that the algorithm can provide strong robustness for blockchain-based drone networks and improve their reliability.

[0145] This application also provides an adaptive smart contract device for drone networks; please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of one embodiment of the adaptive smart contract device for the drone network of this application.

[0146] like Figure 5 As shown, the adaptive smart contract device includes a quantity monitoring module 21, a current information acquisition module 22, a historical information acquisition module 23, a calculation module 24, and an instruction sending module 25.

[0147] The quantity monitoring module 21 is used to enable drone nodes to continuously monitor the global node table and determine whether the number of drone nodes has decreased. The global node table includes information on all drone nodes in the drone network.

[0148] The current information acquisition module 22 is used to provide the strong consensus node with the current node information when the number of drone nodes decreases. The strong consensus node is the drone node with the highest credibility in the drone network. The current node information includes the current position and speed of all drone nodes in the drone network.

[0149] The historical information acquisition module 23 is used to enable strong consensus nodes to acquire individual historical optimal data and group historical optimal data of target nodes and target learning nodes. Individual historical optimal data includes the individual optimal position and individual optimal coverage of the node when the node has the highest coverage of the sub-monitoring area. Group historical optimal data includes the group optimal position and group optimal coverage of the node when the UAV network has the highest coverage of the total monitoring area.

[0150] The calculation module 24 is used to enable strong consensus nodes to calculate the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data.

[0151] The instruction sending module 25 is used to enable the strong consensus node to send scheduling instructions to the target node based on the optimal position of the target node.

[0152] As referred above Figures 1 to 4 This specification describes an adaptive smart contract method for a drone network according to embodiments thereof. The details mentioned in the above description of the method embodiments also apply to the adaptive smart contract device for a drone network according to embodiments thereof. The above-described adaptive smart contract device for a drone network can be implemented in hardware, software, or a combination of hardware and software.

[0153] This application also provides an electronic device, please refer to... Figure 6 , Figure 6 This is a schematic diagram of one embodiment of the electronic device of this application.

[0154] like Figure 6 As shown, the electronic device 30 may include at least one processor 31, a memory 32 (e.g., non-volatile memory), a RAM 33, and a communication interface 34, and the at least one processor 31, memory 32, RAM 33, and communication interface 34 are connected together via a bus 35. The at least one processor 31 executes at least one computer-readable instruction stored or encoded in the memory 32.

[0155] It should be understood that the computer-executable instructions stored in memory 32, when executed, cause at least one processor 31 to perform the above-described combinations in the various embodiments of this specification. Figure 1-Figure 4 The description includes various operations and functions.

[0156] In the embodiments of this specification, electronic device 30 may include, but is not limited to: personal computer, server computer, workstation, desktop computer, laptop computer, notebook computer, mobile electronic device, smartphone, tablet computer, cellular phone, personal digital assistant (PDA), handheld device, messaging device, wearable electronic device, consumer electronic device, etc.

[0157] According to one embodiment, a program product, such as a machine-readable medium, is provided. The machine-readable medium may have instructions (i.e., the elements implemented in software as described above), which, when executed by a machine, cause the machine to perform the above-described combinations of the various embodiments of this specification. Figure 1-Figure 4 The various operations and functions described. Specifically, a system or apparatus equipped with a readable storage medium storing software program code that implements the functions of any of the embodiments described above, and enabling the computer or processor of the system or apparatus to read and execute the instructions stored in the readable storage medium.

[0158] In this case, the program code read from the readable medium itself can perform the functions of any of the above embodiments, and therefore the machine-readable code and the readable storage medium storing the machine-readable code constitute a part of this specification.

[0159] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer or the cloud via a communication network.

[0160] Those skilled in the art will understand that the various embodiments disclosed above can be modified and varied without departing from the spirit of the invention. Therefore, the scope of protection of this specification should be defined by the appended claims.

[0161] It should be noted that not all steps and units in the above process and system structure diagrams are mandatory; some steps or units can be omitted according to actual needs. The execution order of each step is not fixed and can be determined as needed. The device structure described in the above embodiments can be a physical structure or a logical structure. That is, some units may be implemented by the same physical client, or some units may be implemented by multiple physical clients, or they may be jointly implemented by certain components in multiple independent devices.

[0162] In the above embodiments, the hardware units or modules can be implemented mechanically or electrically. For example, a hardware unit, module, or processor may include permanent dedicated circuitry or logic (such as a dedicated processor, FPGA, or ASIC) to perform the corresponding operation. The hardware unit or processor may also include programmable logic or circuitry (such as a general-purpose processor or other programmable processor), which can be temporarily configured by software to perform the corresponding operation. The specific implementation method (mechanical, dedicated permanent circuitry, or temporarily configured circuitry) can be determined based on cost and time considerations.

[0163] The specific embodiments described above with reference to the accompanying drawings are exemplary embodiments, but do not represent all embodiments that can be implemented or fall within the scope of the claims. The term "exemplary" as used throughout this specification means "serving as an example, instance, or illustration" and does not imply that it is "preferred" or "advantageous" compared to other embodiments. Specific details are included to provide an understanding of the described techniques. However, these techniques can be practiced without these specific details. In some instances, well-known structures and apparatuses are shown in block diagram form to avoid obscuring the concepts of the described embodiments.

[0164] The foregoing description of this disclosure is provided to enable any person skilled in the art to implement or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles applicable herein can be applied to other variations without departing from the scope of this disclosure. Therefore, this disclosure is not limited to the examples and designs described herein, but is consistent with the widest scope of the principles and novel features disclosed herein.

Claims

1. An adaptive smart contract method for unmanned aerial vehicle (UAV) networks, characterized in that, include: The drone node continuously monitors the global node table and determines whether the number of drone nodes has decreased. The global node table includes information on all drone nodes in the drone network. If the number of drone nodes decreases, the strong consensus node obtains the current node information. The strong consensus node is the drone node with the highest credibility in the drone network. The current node information includes the current position and speed of all drone nodes in the drone network. The strong consensus node acquires the individual historical optimal data and the group historical optimal data of the target node and the target learning node. The individual historical optimal data includes the individual optimal position and individual optimal coverage of the node when the node has the highest coverage of the sub-monitoring area. The group historical optimal data includes the group optimal position and group optimal coverage of the node when the UAV network has the highest coverage of the total monitoring area. The target node is the UAV node at the location to be scheduled, and the target learning node is another randomly selected UAV node. The strong consensus node calculates the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data. The strong consensus node sends a scheduling instruction to the target node based on the target node's optimal position.

2. The adaptive smart contract method according to claim 1, characterized in that, The steps by which the strong consensus node calculates the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data include: The strong consensus node calculates the current fitness of all drone nodes based on the current node information and the fitness function; The strong consensus node calculates the historical best fitness of the target node and the target learning node based on the individual optimal coverage of the target node and the target learning node and the fitness function. The strong consensus node obtains the optimal position of the target node based on the current position and speed of the target node, the current fitness of all UAV nodes, the historical best fitness of the target node and the target learning node, and the individual best position and the group best position of the target node. The fitness function is a function that is positively correlated with the fitness of a node and its individual coverage.

3. The adaptive smart contract method according to claim 2, characterized in that, The steps by which the strong consensus node calculates the current fitness of all drone nodes based on the current node information and the fitness function include: The strong consensus node calculates the current individual coverage rate of all drone nodes based on the current node information; The strong consensus node substitutes the current individual coverage of all drone nodes into the fitness function to obtain the current fitness of all drone nodes.

4. The adaptive smart contract method according to claim 2, characterized in that, The method for calculating the historical best fitness includes: The strong consensus node substitutes the node's individual optimal coverage rate into the fitness function to obtain the node's historical optimal fitness.

5. The adaptive smart contract method according to claim 2, characterized in that, The steps by which the strong consensus node obtains the optimal position of the target node based on the current position and speed of the target node, the current fitness of all UAV nodes, the historical best fitness of the target node and the target learning node, and the individual best position and the group best position of the target node include: Based on the current fitness of all UAV nodes and the historical best fitness of the target node, the self-learning factor of the target node is obtained. Based on the self-learning factor, the individual optimal position, and the current position of the target node, the self-position weight of the target node is obtained; Based on the current fitness of all drone nodes, the social learning factor of the target node is obtained; Based on the social learning factor, optimal group position, and current position of the target node, the group position weight of the target node is obtained; Based on the historical optimal fitness of the target node and the target learning node, the mutual learning factor of the target node is obtained; Based on the mutual learning factor of the target node, its current position, and the individual optimal position of the target learning node, the mutual learning position weight of the target node is obtained. Based on the target node's current position and velocity, its own position weight, group position weight, and mutually learned position weight, the optimal position of the target node is obtained.

6. An adaptive smart contract device for unmanned aerial vehicle (UAV) networks, characterized in that, include: The quantity monitoring module is used to enable drone nodes to continuously monitor the global node table and determine whether the number of drone nodes has decreased. The global node table includes information on all drone nodes in the drone network. The current information acquisition module is used to provide the strong consensus node with current node information when the number of drone nodes decreases. The strong consensus node is the drone node with the highest credibility in the drone network. The current node information includes the current position and speed of all drone nodes in the drone network. The historical information acquisition module is used to enable the strong consensus node to acquire individual historical optimal data and group historical optimal data of the target node and the target learning node. The individual historical optimal data includes the individual optimal position and individual optimal coverage of the node when the node has the highest coverage of the sub-monitoring area. The group historical optimal data includes the group optimal position and group optimal coverage of the node when the UAV network has the highest coverage of the total monitoring area. The target node is the UAV node at the location to be scheduled, and the target learning node is another randomly selected UAV node. The calculation module is used to enable the strong consensus node to calculate the optimal position of the target node based on the current node information, the individual historical best data of the target node and the target learning node, and the group historical best data. The instruction sending module is used to enable the strong consensus node to send scheduling instructions to the target node based on the optimal position of the target node.

7. An electronic device, comprising: At least one processor; as well as A memory that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform the adaptive smart contract method for a drone network as described in any one of claims 1 to 5.

8. A machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform an adaptive smart contract method for a drone network as described in any one of claims 1 to 5.

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