A blockchain-based method for drone swarm networked task execution decision-making

Abstract

This invention relates to a blockchain-based method for networked task execution decision-making in drone swarms. The method includes: establishing a drone swarm based on a proxy proof-of-stake blockchain network A, with a representative node being the swarm leader drone; establishing a drone cluster based on a practical Byzantine fault-tolerant blockchain network B, with the master node being the cluster leader drone and backup nodes being consensus node drones, each of which is the swarm leader drone within its respective drone swarm; verifying task requests on the blockchain; the cluster leader drone propagating the verification result on blockchain network B and updating the block; and the consensus node drones propagating the verification result on blockchain network A and updating the block. This invention ensures the reliability and accuracy of node information acquired by drones, while also guaranteeing the autonomy of the drone swarm or individual drones, enabling autonomous mechanisms for drones in different environments.

Description

Technical Field

[0001] This invention relates to the fields of drone swarm communication networking and blockchain, specifically to a blockchain-based method for decision-making in networked drone swarm task execution. Background Technology

[0002] The explosive growth of information has made "big data" a key characteristic of our time. Currently, many forms of social security are rapidly transforming towards informatization and intelligentization, and collaborative mission decision support supported by "big data" will become an important service model in the future. Receiving data within limited time, ensuring the authenticity and reliability of data, processing large amounts of data in a timely manner, and providing optimal decisions are critical issues that urgently need to be addressed in socialized drone services.

[0003] Existing UAV control methods typically employ a master control console to allocate resources and process tasks for all UAVs. The master control console possesses global environmental information and the status information of all UAVs, while each UAV only needs to handle data input / output and action execution. This structure is relatively simple and easy to manage. However, since all UAV actions require commands from the master control console, as the number of UAV network nodes increases, the resource consumption and computational complexity resulting from communication increase dramatically.

[0004] Moreover, this traditional centralized control method for drones is gradually revealing significant drawbacks in future information missions: it can only control a limited number of drones, has low network security (if the control node is maliciously attacked and damaged, the entire network will be paralyzed), and cannot handle the spread of malicious messages when encountering network node intrusions, resulting in the inability to transmit correct control commands. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a blockchain-based method for decision-making in networked drone swarm operations.

[0006] The method includes:

[0007] A number of drones establish a drone swarm based on a blockchain network A, wherein the blockchain network A is established using proxy proof-of-stake as the consensus mechanism, and the representative node is the master drone of the swarm;

[0008] The establishment of several drone swarms is based on a drone cluster of blockchain network B, which uses practical Byzantine fault tolerance as a consensus mechanism. The master node is the cluster master drone, the backup node is the consensus node drone, and each consensus node drone is the swarm master drone in its drone swarm.

[0009] Any drone within the drone cluster acts as the mission drone, which sends a mission request to the master drone of its swarm, which then forwards the mission request to the cluster master drone.

[0010] The task request submitted by the task drone is verified on the blockchain. The verification result is propagated among the consensus node drones by the cluster master drone, and the cluster master drone updates the block of blockchain network B. The consensus node drones propagate the verification result within their respective drone swarms and update the block of blockchain network A.

[0011] Furthermore, the verification of the task request submitted by the task drone on the blockchain refers to the verification of the task by all the consensus nodes, and the verification result is the result of more than 2 / 3 of the consensus nodes verifying the same result.

[0012] Preferably, the consensus node verifies the task by determining whether the task drone's distance from the task destination and the drone's energy and safety attribute values ​​meet the threshold.

[0013] Furthermore, the verification of the task request submitted by the task drone on the blockchain refers to the verification of the task request by a smart contract, which is stored on the blockchain network B between the consensus node drones.

[0014] Preferably, the smart contract is created by all the consensus node drones.

[0015] Preferably, the consensus node drone propagates the verification result within its drone swarm and updates the blocks of blockchain network A, including:

[0016] Create a smart contract within the drone swarm;

[0017] If the verification result passes, the task will be verified again through the smart contract within the drone swarm.

[0018] The task is considered successful if both the initial verification result and the re-verification result are passed; otherwise, the task is considered rejected.

[0019] Furthermore, the representative node is selected in a rotating manner.

[0020] Furthermore, after completing the task, the task drone will feed back the data obtained from the task to the drone cluster.

[0021] Furthermore, the step of feeding back the data obtained from the task to the drone swarm includes:

[0022] The smart contract stored on the blockchain verifies whether the task drone meets the security threshold for sharing information and whether the drone swarm to which the task drone belongs meets the security threshold for sharing to the cluster. If both are met, the data obtained from the task is fed back to the drone swarm.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] This invention proposes a practical Byzantine fault-tolerant, proxy proof-of-stake consensus algorithm for building consensus on blocks in swarms performing different tasks, and a task contract with intelligent decision-making capabilities established based on work requirements. This enables the formation of large-scale UAV swarms with task execution as the objective, using a liquid swarm as the unit. Furthermore, this invention proposes a swarm consensus algorithm for building autonomous UAV swarms of different sizes that can quickly achieve block consensus, and a small-swarm autonomous reconnaissance method. While conducting rapid reconnaissance, it can quickly achieve consistency in the work objectives of the UAV swarm, enabling timely completion of exploration, observation, and other tasks.

[0025] 1. Protecting Task Data Security. Future social oversight will be a digital social service, making data security a key focus in combating criminals. While modern service systems have established multi-factor authentication and multi-user simultaneous operation security measures, the inherent characteristics and built-in security features of blockchain significantly enhance the security and reliability of task data.

[0026] 2. Promote innovation in mission execution methods. By utilizing blockchain technology and combining it with flexible smart contracts built on the blockchain, an autonomous and secure mission command and control system can be constructed, transforming the centralized reconnaissance control mode into a decentralized distributed autonomous control mode. At the same time, it can also select drones of different swarm sizes for collaborative reconnaissance according to different environments.

[0027] 3. Enhance network security protection. With the proliferation of cyberattack methods, network security faces immense challenges. Military networks are at the core of military operations; the consequences of top-tier hackers illegally intruding into network information systems would be unimaginable. Blockchain technology can permanently record database dynamics; the configuration of each component in the system can be recorded and protected for continuous monitoring within the database. Attached Figure Description

[0028] Figure 1 The decision-making process for a mission drone requesting map exploration tasks is provided in this embodiment of the invention;

[0029] Figure 2 The decision-making process for a task drone request placeholder request provided in this embodiment of the invention;

[0030] Figure 3The decision-making process for drone swarm requesting assistance formation is provided in the embodiments of the present invention;

[0031] Figure 4 The decision-making process for a mission drone to request to join the swarm is provided in this embodiment of the invention;

[0032] Figure 5 The decision-making process for drone swarm re-formation requests provided in this embodiment of the invention; Detailed Implementation

[0033] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Before describing in detail the technical solutions of each embodiment of the present invention, the nouns and terms involved are explained. In this specification, components with the same name or the same number represent similar or identical structures and are for illustrative purposes only.

[0034] 1. A bee colony consensus algorithm that controls high-order-volume targets and can quickly achieve consensus on task objectives: Byzantine Fault Tolerance-Proxy Proof-of-Stake Consensus Algorithm.

[0035] 1.1 Implementing a large-scale UAV control algorithm: Proof-of-Stake

[0036] Proxy Proof-of-Stake (PoS) is a consensus algorithm based on voting. Unmanned aerial vehicle (UAV) nodes elect several representative nodes to operate the network, while professionally managed network servers ensure the security and performance of the blockchain network. In this mechanism, PoS does not require computational power to solve mathematical problems; instead, the UAV nodes elect a master node. If the master node is malicious or intentionally spreads false information, it can be voted out at any time, thus solving the performance problem of Proof-of-Stake (PoS).

[0037] This consensus algorithm is implemented in a small drone swarm, and its specific stages include: a) using rotation or other algorithms selected based on the scenario to select a node as the master node, and granting that node the right to produce blocks. b) The master node packages the drone activity information for that period into a block, signs the block with its private key, and broadcasts it to all nodes. c) When the master node receives the signed block from at least two-thirds of the different nodes, the block has been verified by all nodes and becomes an irreversible block linked into the blockchain. This master node is the swarm master drone, and the blockchain network based on delegated proof-of-stake is called blockchain network A.

[0038] 1.2 Implementing a Fast Consensus Algorithm: Byzantine Fault Tolerance Algorithm

[0039] Practical Byzantine Fault Tolerance (PBT) is an algorithm proposed in 1999 by Turing Award winner Barbara Liskov, designed to solve the consistency problem of state machine replicas in asynchronous distributed systems. In Proxy Proof-of-Stake (PPS), the nodes responsible for generating blocks are elected, and these elected nodes can be considered relatively trustworthy. These master nodes elected through PPS act as consensus nodes in PBT, thus forming a large drone swarm composed of multiple small drone clusters.

[0040] In practical Byzantine fault tolerance, the consensus nodes responsible for cross-validation are considered relatively trustworthy. An algorithm selects a master node from these consensus nodes to lead each consensus process. The specific consensus process consists of the following five phases: request phase, pre-preparation phase, preparation phase, confirmation phase, and response phase.

[0041] Request phase: The customer drone sends the request to the master drone node.

[0042] Pre-preparation phase: After receiving the request from the requesting drone node, the master drone node assigns a sequence number n to the request, broadcasts the sequence number allocation message and the request message from the requesting drone node, and constructs a preparation message to send to each drone node.

[0043] Preparation Phase: After receiving the pre-preparation message, the drone node verifies it. If the verification passes, it broadcasts the preparation message to other drone nodes. If the verification fails, the pre-preparation message is discarded. Within a certain time frame, if preparation messages are received from more than 2 / 3 of the different nodes in the entire network, the preparation phase is considered complete.

[0044] Submission Phase: After verifying the requests and order within the view, each drone node broadcasts a submission message. When more than 2 / 3 of the nodes in the network have submitted messages, it means that a majority of drone nodes have entered the submission phase. At this stage, consensus has been reached, and the drone nodes will execute the requests and write the data to the blockchain.

[0045] Response phase: The master drone node sends the consensus response to the client drone.

[0046] In this invention, the aforementioned blockchain network based on practical Byzantine fault tolerance is defined as blockchain network B, the elected master drone node is the cluster master drone, and each drone node is defined as a consensus node drone, which is the swarm master drone of each swarm.

[0047] By combining proxy proof-of-stake with practical Byzantine fault tolerance, blocks produced by the master node can be immediately verified by the other consensus nodes. Once a block passes cross-validation, it is considered immutable, thus shortening the block confirmation time and improving system throughput. This significantly enhances the efficiency of drones in latency-sensitive tasks in battlefield environments.

[0048] 2. Smart Contracts in Special Circumstances

[0049] A smart contract is a set of promises defined in digital form, including protocols that allow contract participants to execute these promises. It's essentially a contract where computer language replaces legal language to record terms and is automatically executed by a program. Due to the decentralized, immutable, transparent, and traceable characteristics of blockchain, once the protocol terms are triggered, there's no need to worry about the commands not being executed. It enables users to shift their trust from third-party institutions to the contract itself, and from the participants to the code itself.

[0050] The working principle of smart contracts consists of three steps: a) Contract construction: Multiple consensus nodes jointly participate in the creation of a smart contract, which clearly defines the rights and obligations of both parties in the transaction. Developers program these rights and obligations electronically, and the code includes conditions that trigger the automatic execution of the contract. b) Contract storage: The contract is distributed through a P2P network and stored in the blockchain. All nodes on the network can receive this contract. When the consensus conditions are met, the validating node rebroadcasts the contract to the entire network using a hash method. Other validating nodes receive the contract and compare it with their own stored contract. Through multiple rounds of verification and comparison, all validating nodes reach a consensus on the latest contract. c) Contract execution: The smart contract periodically checks for the existence of relevant events and triggering conditions. Events that meet the conditions are pushed to the queue for verification. Validating nodes on the blockchain first sign and authenticate the event to ensure its validity. Once a certain number of validating nodes reach a consensus on the event, the smart contract will be successfully executed and the user will be notified.

[0051] In this invention, the smart contract can be stored and run on blockchain network A and blockchain network B. When running on blockchain network A, the participants of the smart contract are drones within the swarm; when running on blockchain network B, the participants of the smart contract are consensus node drones and cluster master drones.

[0052] 3. Task execution contract based on practical Byzantine fault tolerance-proxy proof-of-stake and smart contracts

[0053] To ensure the credibility and safety of drone swarms of different sizes in different environments, each node in the drone swarm has its own security attribute. The higher the security attribute value, the higher the reliability.

[0054] 3.1 Single UAV missions - target reconnaissance and site exploration:

[0055] Environmental reconnaissance is one of the fundamental actions in networked swarm missions, even serving as the basis and prerequisite for various operations. Through "full-dimensional, real-time, and accurate" environmental reconnaissance and surveillance, the current environmental situation is continuously understood, and information such as the location, movement, situation, and relevant geographical conditions of targets is obtained in a timely manner. Based on our overall objectives and considering the characteristics of obstacles and flying objects in the current environment, taking effective measures and actively creating and promptly taking action are essential behavioral decisions for UAVs in mission execution.

[0056] like Figure 1 As shown, when a task drone requests to perform a small-scale map exploration task, it first sends a request (proxy proof-of-stake) to the swarm leader drone within its own swarm. The swarm leader drone then forwards this request to other consensus node drones according to the Practical Byzantine Fault Tolerance consensus algorithm. When determining whether to approve the request, the consensus node drones need to invoke the relevant exploration task contract. This contract contains drone reconnaissance rules, such as whether the distance between the task drone and the exploration location is close, and whether the task drone's energy and security attribute values ​​meet thresholds. Based on these rules, the consensus node drones can determine whether the task drone can execute the task and thus provide a consensus result.

[0057] If a mission drone needs to share its exploration results with the entire drone swarm, it is necessary to check whether the mission drone meets the security threshold for sharing information and whether the drone swarm to which the mission drone belongs meets the security threshold for sharing to the swarm. The smart contract for this mission can be used to directly determine whether to approve the request to share the survey results. This ensures the security of information dissemination by the mission drone while also accelerating the speed at which the drone swarm shares terrain survey information, thereby improving mission efficiency.

[0058] 3.2 Small-scale drone swarm mission execution - precise maneuvering, positioning and control:

[0059] Networked mission execution, based on overall intent, employs methods such as blitzkrieg and split-and-penetrate maneuvers to focus on targets, achieving advantageous positions locally or overall through precise positioning. Depending on the real-time environmental situation, various UAV mission units dynamically transition between different roles such as reconnaissance, exploration, and early warning to grasp the overall mission execution process within the current environment.

[0060] like Figure 2As shown, mission drones often encounter latency-sensitive tasks when operating in different environments, such as quickly securing advantageous aerial positions and deploying patrol formations. To avoid missing opportunities and having other aircraft seize advantageous positions or losing deployment space, rapid verification of drone swarm mission requests is crucial. Therefore, when executing latency-sensitive tasks within a drone swarm, the swarm does not need to wait for responses and confirmations from consensus node drones (i.e., more than 2 / 3 of the consensus nodes responding in practical Byzantine fault tolerance). Instead, it can directly determine whether to approve the request through a smart contract based on its own security attributes and those of the drones executing the task. This maximizes the speed at which the swarm drones execute tasks, efficiently completing latency-sensitive missions and seizing opportunities.

[0061] 3.3 Collaborative Mission Completion by Drones of Different Sizes - Target Selection and Integrated Transmission:

[0062] The capability to select specific targets based on mission requirements and transmit large-scale, critical data information via multiple UAVs. It involves tailoring data offloading to the target based on its characteristics and fully leveraging the advantageous transmission conditions of each working unit. Selecting the correct data transmission target and the appropriate amount of data to transmit for that target is crucial to the success of the mission. During mission execution, it is essential to fully utilize the data transmission efficiency of the UAV swarm, emphasizing overall synergy.

[0063] like Figure 3 As shown, when a mission drone discovers that it needs to select and confirm a mission data transmission target and requires assistance from other drones in the swarm, its drone swarm needs to know the positions of other drone swarms to determine which drones can join the assistance mission and which data transmission target is the optimal mission target.

[0064] At this point, the drone swarm has high requirements for the latest data from other drone swarms within the cluster. Therefore, the operation is as follows: When the swarm leader drone requests assistance from the consensus node drone, it waits for the latest block update to obtain the latest location information of other nodes in the drone swarm recorded in the latest block. This allows it to use the smart contract for this task to add drones that meet the conditions to the formation and determine the transmission target. The smart contract is confirmed after the block is updated, maximizing the accuracy of the drone swarm obtaining information from other nodes, improving the accuracy of the formation assistance task execution, and increasing the task execution precision. The smart contract rules for requesting assistance can be: whether the distance is less than the maximum distance from the requesting drone swarm to assist, whether the distance is less than the flight delay limit for completing the data transmission target, and whether the energy threshold and security attribute threshold for executing the task are met.

[0065] 3.4 Execution Mechanism for Spatiotemporally Sensitive UAV Swarm Autonomous Tasks Based on Practical Byzantine Fault Tolerance, Proxy Proof-of-Stake, and Smart Contracts – Recombination and Continuous Operation:

[0066] Upon completion of a task, new tasks can be identified based on evolving circumstances, and task execution resources can be reorganized and mobilized accordingly. By fully leveraging the advantages of networked task execution, efficiency is improved in both time and space. The results of the current task are immediately incorporated into the next round of tasks for comparison and reference, enabling the continuous and time-saving completion of multiple tasks and achieving rapid and efficient task execution.

[0067] In modern work environments, large numbers of drones are deployed in swarms to perform large-scale, complex tasks. For swarms of drones, the accuracy of command execution, the reliability of information transmission between drones, the agility of response to emergencies, and their resilience to sudden software and hardware attacks all require careful consideration and handling.

[0068] The decision-making mechanism for time- and space-sensitive UAV swarm autonomous tasks can be mainly divided into swarm consensus algorithms based on block consistency and task contract designs with intelligent behavioral decision-making capabilities. Nodes form an interconnected network through existing communication methods, and the nodes in the network jointly maintain a distributed blockchain database. The decentralization, security, and smart contracts inherent in blockchain technology provide the premise and guarantee for the autonomous operation of military UAVs. Each node in the swarm maintains a blockchain that records the interaction information of all UAVs over a period of time, and this node has the ability to autonomously compute consensus and make decisions. Through this distributed task execution strategy, the dependence of UAVs on a central control console is greatly reduced, significantly improving the efficiency of both the UAV swarm and individual UAVs in completing time-sensitive tasks. This allows UAVs to execute tasks efficiently and adjust their strategies in a timely manner according to different environmental conditions.

[0069] like Figure 4As shown, when drone 100 requests to join swarm 2, the P2P network between drones causes drone 100 to request joining information from the swarm leader drone of swarm 2. Since the drone joining operation has low time sensitivity but high security requirements, the drone joining operation first involves the swarm leader drone sending a request to the consensus node drones. Through the relevant smart contract within the swarm, the swarm leader drone broadcasts the request to all consensus node drones for a joint decision. When the swarm leader drone receives the block update and the response to the agreement request (or rejection request) from the swarm leader drone, it then, based on the security attributes of drone 100 and the security attributes of swarm 2 itself (requirements for the number of drones, energy, and trustworthiness), conducts an internal voting election through the smart contract to decide whether drone 100 can join swarm 2. After confirmation, the swarm leader drone sends blockchain information signed with its own private key and grants relevant rights to the requesting drone 100. Drone 100 receives this information, updates its own block to achieve block consensus, and joins the swarm (or receives a rejection message). After the contract operation is completed, the swarm leader drone sends relevant information to all nodes in the swarm to directly update the block information and replies with a confirmation message from the swarm leader drone. The swarm leader drone then broadcasts this update to all drones in the swarm. This method can greatly improve the security of drones and prevent the impact of malicious nodes joining the drone fleet.

[0070] like Figure 5 As shown, when a drone swarm needs to change its flight formation due to sudden environmental changes, the swarm leader drone sends a formation change policy request to the cluster leader drone. This swarm leader drone (the requesting node) does not need to wait for a response from the consensus node; instead, it can directly determine whether to change the formation based on its own security attributes via a smart contract. When the smart contract requirements are met, it can update its own block and disseminate it to all nodes in the swarm and all consensus nodes within the cluster. This achieves cluster consensus while simultaneously initiating the formation change, promptly addressing various unforeseen circumstances that may arise in a rapidly changing environment and saving task completion time.

[0071] The present invention will now be explained with reference to a specific embodiment.

[0072] Taking the data transmission behavior of a drone swarm targeting a large area as an example, assume that the leader drone in the swarm is L, and the leader drone L commands Q_n small drone swarms, each of which is Q_k (1≤k≤n). The target area for the data transmission of the swarm U is A.

[0073] Assuming that during the data transmission process of a drone swarm, the drone swarm can fly to the high-altitude territory (HT) or low-altitude territory (LT) to occupy the data transmission position, and different altitudes can transmit mission data to different receiving points in the target area; the leader drone does not directly perform the transmission action, but only needs to adjust the flight position of the swarm's slave drones, and the slave drones in each swarm transmit the same target.

[0074] Assume that the slave device is capable of transmitting both small and large data packets to the target. Meanwhile, considering the rational use of resources, the slave device uses small data packet transmission as the primary data transmission mode of the UAV swarm, and large data packets as the secondary data transmission mode. Small data packet transmission is used first to transmit encrypted information to the target. Only when the task execution time is insufficient or certain task execution rules are met can large data packet transmission be performed to the target.

[0075] For transmission target allocation decisions (i.e., selecting different flight potentials), each swarm and the leader drone need to reach consensus based on blockchain technology, coordinate the drone swarm to reach a unique and mutually acceptable result, and ensure the authenticity and reliability of this process to ensure the stable operation of the drone swarm decision-making system. For the transmission methods adopted by the slave swarms, real-time decisions are generated using smart contracts based on the specific environment, enabling the small swarm to make autonomous decisions in time- and space-sensitive environments.

[0076] Based on the above analysis, the core of the smart contract for target data transmission tasks in a drone swarm is the abstraction of task execution rules and the construction and solution of mathematical models. The final result of the smart contract can provide the task working mode for each drone swarm and the allocation of transmission targets at different flight altitudes, making resource utilization most rational and minimizing time consumption. The final result of the smart contract will be written into the blockchain through network consensus and distributed to the consensus nodes of the drones executing the task. Each drone consensus node will then distribute the result to all drone nodes in the swarm to execute the task. The smart contract within the swarm will allocate the specific latitude and longitude of the transmission point, transmission distance, and other necessary task execution conditions. The main steps of the collaborative target allocation decision model are as follows:

[0077] When a drone swarm or target situation meets one of the following rules—time redistribution, operational status redistribution, target situation redistribution, or forced command redistribution—the drone swarm autonomously initiates target area data transmission and allocation. Then, based on the effect rules and allocation constraints, a target allocation mathematical model is constructed. The drone swarm solves this model by invoking a built-in intelligent optimization algorithm. Finally, according to the multi-drone swarm collaboration rules, the target area allocation result that meets the data transmission requirements of the current number of drones in the swarm is selected; the unselected target allocation results are kept as backups for later use.

[0078] The autonomous driving force for the practical application of the collaborative target assignment decision model is the collaborative assignment rule, the realization bridge is the mathematical model, and the performance influencing factor is the solution algorithm. The collaborative assignment rule determines whether the model can be close to the actual work requirements and whether it can abstract and condense the actual work experience; the rationality and completeness of the mathematical model determine whether the model is useful, applicable, and effective in actual work; the speed and accuracy of model solution directly affect the timeliness and effectiveness of data transmission actions in actual work.

[0079] The collaborative target assignment rules are used to constrain the process and results of collaborative target assignment from the perspective of task execution. According to functions, they can be divided into three categories: target re-assignment rules, inter-swarm collaborative rules, and effect evaluation rules, which are used to constrain the process, results, and evaluation of collaborative target area assignment respectively. The effect evaluation rules mainly include information rules, power rules, efficiency rules, time rules, etc.; the manifestation form of the assignment constraint rules is the constraint conditions of the target assignment mathematical model, which is closely related to the specific mathematical model. In this project, the "IF--THEN" form will be adopted first to construct the target re-assignment rules and inter-swarm collaborative rules; subsequently, the calculation methods of each element of the effect evaluation rules will be given to establish a collaborative target assignment mathematical model, and the assignment constraint rules will provide the constraint conditions of the model.

[0080] In the actual work environment and system simulation process, considering the constraints of computing resources, the collaborative target assignment of the UAV swarm is not carried out continuously, but needs to be carried out autonomously according to certain rules. These rules include work time re-assignment rules, work status re-assignment rules, target situation re-assignment rules, and forced command re-assignment rules, etc.

[0081] Due to the continuous maneuver of the target and the continuous change of the possible complex environment, the time supported by the result of a single collaborative target assignment of the UAV swarm is limited. When the interval between the current task transmission execution time t_act and the time of the most recent collaborative target assignment t_est is greater than or equal to the re-assignment time interval threshold, the UAV swarm autonomously starts the target re-assignment process.

[0082] Since the UAV swarm task data transmission action can be carried out in different complex environments, it will also face relatively large energy consumption during the task execution process, which affects the completion efficiency and effect of the action. In a certain system simulation environment, at each simulation moment, each simulation entity has a definite working state value (0 - 1) to represent the available working state of the entity at present, and each UAV swarm uses a set

[0083] {x_1, x_2, x_3,...} (0 < x_n < 1, n ∈ N) to represent the working state of the UAVs in the swarm (the number of sets is the number of UAV swarms, and the number of elements in the set is the number of UAVs in the swarm).

[0084] During the dynamic transmission connection between the target and the drone swarm within the target area, the drone's operating status, deployment location, and performance parameters may continuously and in real time change, causing changes in its own potential. As a drone swarm with autonomous adjustment capabilities, it can make target reallocation decisions based on the potential changes of the target's maneuvering position, operating status, and signal characteristics. By utilizing a block-consensus algorithm (Practical Byzantine Fault Tolerance-Proxy Proof-of-Stake), the drones can achieve consensus on the readjusted mission transmission target.

[0085] This invention proposes an effective swarm consensus algorithm and smart contracts for different drone tasks. It improves task execution efficiency and security in various environments, ensuring the reliability and accuracy of node information acquired by drones, while also guaranteeing the autonomy of the drone swarm or individual drones, thus realizing an autonomous mechanism for drones in different environments. The block-consistent swarm consensus algorithm leverages several advantages of blockchain technology, effectively handling data management, information communication between drones, and task execution while ensuring the security of task data. Smart contract-based task decision-making effectively enhances the autonomy of the drone swarm, enabling rapid action without waiting for confirmation from all consensus nodes in the cluster, improving precise maneuverability and quick positioning and control capabilities. For tasks with high security requirements, multiple intelligent voting decisions can be made, greatly reducing the generation and spread of malicious nodes or malicious messages. This maintains autonomy while improving the overall integrity of the drone swarm and ensuring the security and reliability of nodes within the swarm.

[0086] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for networked task execution decision-making in drone swarms based on blockchain, characterized in that, Includes the following steps: A number of drones establish a drone swarm based on a blockchain network A, wherein the blockchain network A is established using proxy proof-of-stake as the consensus mechanism, and the representative node is the master drone of the swarm; Several drone swarms establish a drone cluster based on a blockchain network B. The blockchain network B uses Practical Byzantine Fault Tolerance as the consensus mechanism. The master node is the cluster master drone, and the backup nodes are the consensus node drones. Each consensus node drone is the swarm master drone in its respective drone swarm. Any drone within the drone cluster acts as the mission drone, which sends a mission request to the master drone of its swarm, which then forwards the mission request to the cluster master drone. The task request submitted by the task drone is verified on blockchain network B. The verification result is propagated among the consensus node drones by the cluster master drone, and the blockchain network B is updated by the cluster master drone. The consensus node drones propagate the verification result within their respective drone swarms and update the blockchain network A.

2. The method according to claim 1, characterized in that, The process of verifying the task request submitted by the task drone on the blockchain network B means that all the consensus node drones verify the task, and the result of verification by more than 2 / 3 of the consensus node drones is the verification result.

3. The method according to claim 2, characterized in that, The consensus node drone verifies the task by determining whether the task drone meets the threshold based on the distance between the task drone and the task destination, the drone's energy value, and security attribute values.

4. The method according to claim 1, characterized in that, The verification of the task request submitted by the task drone on the blockchain network B refers to the verification of the task request by a smart contract, which is stored on the blockchain network B among the consensus node drones.

5. The method according to claim 4, characterized in that, The smart contract is created by all the consensus node drones.

6. The method according to claim 4, characterized in that, The consensus node drone propagates the verification result within its drone swarm and updates the blocks of blockchain network A, including: Create a smart contract within the drone swarm; If the verification result passes, the task will be verified again through the smart contract within the drone swarm. The task is considered successful if both the initial verification result and the re-verification result are passed; otherwise, the task is considered rejected.

7. The method according to claim 1, characterized in that, The representative node is selected in a rotating manner.

8. The method according to claim 1, characterized in that, After completing the task, the task drone will feed back the data obtained from the task to the drone cluster.

9. The method according to claim 8, characterized in that, The step of feeding back the data obtained from the task to the drone cluster includes: The smart contract stored on blockchain network B verifies whether the task drone meets the security threshold for sharing information and whether the drone swarm to which the task drone belongs meets the security threshold for sharing to the cluster. If both are met, the data obtained from the task is fed back to the drone swarm.

Images