Unmanned aerial vehicle cluster task collaboration method based on block chain

Abstract

The invention provides an unmanned aerial vehicle cluster task collaboration method based on a block chain, and the method comprises the steps: firstly starting a first unmanned aerial vehicle, initializing an airspace network, and achieving a dynamic ad hoc network; wherein each unmanned aerial vehicle is granted with a digital certificate containing identity information and basic authority before task execution, and the validity of the certificate is verified in real time through an intelligent contract; performing situation synchronization on the unmanned aerial vehicle cluster by adopting a lightweight block chain technology; finally, in the task execution process, the unmanned aerial vehicle group automatically completes task allocation and decision making based on the intelligent contract, and meanwhile, the hash value of the key state data is subjected to uplink evidence storage; and after the task is completed, reading data analysis of the fixed evidence on the chain and generating a visual report. According to the invention, the problems of single-point failure, expansibility bottleneck, security threat and the like existing in a traditional centralized control architecture are solved, autonomous coordination, secure communication and credible evidence storage of the unmanned aerial vehicle cluster in a complex environment are realized, and the reliability, efficiency and security of task execution are greatly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle network communication and cooperative control, and particularly relates to a method for task cooperation of an unmanned aerial vehicle cluster based on a block chain. BACKGROUND

[0002] A traditional unmanned aerial vehicle task cooperation system is based on a centralized architecture, and has a serious single-point failure risk. A ground control station (GCS) serves as a core node to manage task allocation, resource scheduling and situation synchronization of an unmanned aerial vehicle cluster. Once the control station fails due to network attacks, hardware failures or communication interruptions, the entire unmanned aerial vehicle system will be paralyzed. In critical scenarios such as emergency rescue, such defects may lead to disastrous consequences. In addition, the centralized architecture is difficult to meet the scalability requirements of a large-scale unmanned aerial vehicle cluster. With the increase in the number of nodes, the computing and communication load of the control station increases exponentially, which seriously affects its response speed.

[0003] The communication link of an unmanned aerial vehicle faces multiple security threats. The command and control link between the unmanned aerial vehicle and the ground station, as well as the cooperative communication link between the unmanned aerial vehicles, are exposed in an open space and are extremely vulnerable to eavesdropping, signal interference, data forgery and man-in-the-middle attacks. These security vulnerabilities directly threaten the confidentiality, integrity and availability of data transmission. Attackers may hijack unmanned aerial vehicles by forging control instructions, or make unmanned aerial vehicles lose control through denial-of-service attacks. Existing encryption schemes such as WPA2 and AES are difficult to implement real-time protection on resource-constrained unmanned aerial vehicle platforms, resulting in that traditional encryption schemes cannot meet the real-time requirements in a high-dynamic environment.

[0004] There are significant challenges in asymmetric encryption schemes in unmanned aerial vehicle networks. The use of asymmetric encryption algorithms in unmanned aerial vehicle networking can achieve lightweight two-way identity authentication between nodes, but key management faces serious problems: first, the initial distribution of keys requires a reliable trust anchor; second, frequent membership changes result in huge key update overhead; and finally, resource-constrained nodes are difficult to securely store a large amount of key materials. In addition, the computational complexity of traditional asymmetric encryption algorithms is high, which makes it difficult to efficiently run on low-power unmanned aerial vehicle platforms, limiting its application in large-scale clusters.

[0005] Current drone data transmission methods have significant limitations. Traditional methods rely on dedicated servers to process drone data and send commands, with data transmission primarily transmitted via wireless communications (such as Wi-Fi, 4G / 5G) or satellite communications. Wireless communications are suitable for short-distance, low-latency scenarios but are susceptible to signal obstruction and interference; satellite communications are suitable for long-distance transmission but are costly and have limited bandwidth. In complex environments (such as urban canyons and high-voltage power line corridors), drone data transmission may be interrupted or lost due to unstable signals or node disconnection, and data may be intercepted or tampered with. Existing solutions lack adaptive transmission mechanisms for weak network environments, making it difficult to ensure mission continuity.

[0006] Traditional drone mission coordination systems lack effective identity authentication and permission management mechanisms. Illegal users could disguise themselves as legitimate drones or ground control stations, access the network, steal information, tamper with commands, or disrupt normal flight missions. Unencrypted authentication information could be intercepted and stolen, leading to user identity leaks and even unauthorized control of drones. Furthermore, traditional authentication methods can require complex interaction processes, resulting in lengthy drone networking processes and hindering the timely execution of flight missions.

[0007] Existing drone data storage and authentication mechanisms have significant flaws. Uploading all the continuously generated drone sensor data to the blockchain would require significant storage space, increasing the storage costs of the blockchain system. For large-scale drone data, the storage cost is prohibitive, and the full data may contain redundant information, wasting storage resources. Furthermore, uploading all data to the blockchain slows down blockchain processing, impacting data uploading efficiency and making data queries and retrieval complex and time-consuming. Existing solutions lack intelligent strategies for tiered data storage, making it difficult to balance storage costs with data availability. Summary of the Invention

[0008] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an unmanned The new method of drone task collaboration solves the problems of single point failure, scalability bottlenecks and security threats existing in traditional centralized control architecture, realizes autonomous collaboration, secure communication and trusted evidence storage of drone clusters in complex environments, and greatly improves the reliability, efficiency and safety of mission execution.

[0009] The present invention is implemented through the following technical solution: a blockchain-based drone cluster task collaboration method, the method comprising the following steps: (1) Start the first UAV and broadcast its identity information to initialize the airspace network, allowing subsequent UAVs to declare their presence by periodically sending beacon frames and establish a dynamic neighbor table; (2) Before task execution, a digital certificate is granted to each UAV, and is written into a blockchain distributed ledger to achieve permanent evidence; when the device accesses the network, the validity of the certificate is verified in real time through a smart contract; (3) The UAV cluster uses a smart contract in lightweight blockchain technology for situation synchronization, and selects a RAFT or PBFT consensus mechanism according to the scene requirements to adapt to the data synchronization requirements in different scenes; (4) During task execution, the UAV group automatically completes task allocation and decision-making based on a smart contract, and uses a data hierarchical storage strategy to store the hash value of the key state data on the chain to ensure that the task execution process is transparent and traceable; (5) After the task is completed, the on-chain fixed data is read to comprehensively review the task execution process, and a visual report is generated for subsequent task optimization.

[0010] Further, the step (1) specifically includes the following sub-steps: (1.1) From the start of the first UAV, broadcast an identity signal containing its own ID and GPS coordinate information to start the network and announce its existence; and generate a cluster ID for distinguishing other adjacent clusters, define the format of the beacon frame, cluster ID and time synchronization timestamp; and periodically send beacon frames, while listening to the smart contract encoded by the task command center, which contains task priority, resource allocation strategy and permission verification logic, when the UAV node accesses the network, automatically synchronize the smart contract copy obtained from the center server to the local trusted execution environment; store the hash value of the UAV processing result and the situation information on the chain; the center server stores the original image data and the secondary processing result which needs to be fixed on the chain; the UAV locally caches the recent data, and the data exceeding the set storage time is overwritten according to the circular overwrite strategy; from the beacon frames of other nodes, constantly update the dynamic neighbor table according to the received information, this process will continue until the node discovery process ends; (1.2) In the adaptive topology maintenance process, continuously update the beacon frame content, including the position, remaining power and received signal strength of the node; if it is detected that the neighbor node has a response, update the routing table according to the preferred strategy; if the neighbor node has no response, trigger the routing reorganization mechanism to bypass the faulty or weakly connected node, this process continues throughout the network operation period; (1.3) In the data forwarding and load balancing process, when the node receives a data transmission request, the system applies the time to live TTL and sequence number mechanism to prevent data flooding; then determine whether there is high-priority data to be transmitted, if there is, prioritize its transmission, if not, process the data in a first-in, first-out FIFO manner.

[0011] Further, the step S2 specifically includes the following sub-steps: (2.1) Using a pre-set certificate distribution mechanism, a digital certificate containing the unique identification ID of the module mounted on the unmanned aerial vehicle device, the public key, the agency signature and the permission information is burned into the hardware security module HSM of the unmanned aerial vehicle as a device identity credential, eliminating the dependence on online trusted agency CA; (2.2) During communication establishment, both parties start a two-way identity verification process to ensure each other's identity; then enter the encrypted transmission link, data is encrypted by session key C and attached with time stamp and random number; At the same time, message signature is generated for integrity check and identity confirmation, which is repeated continuously during the duration of communication and realizes the anti-replay attack ability through time stamp detection; (2.3) In terms of permission control, when one party initiates a request and involves sensitive operation, the permission field in the certificate will be extracted and the smart contract on the block chain will be called for verification to determine whether the current operation is within its permission range, combined with time and space parameters for secondary confirmation; If the time and space parameters match, the corresponding operation is allowed to be executed; If not, the session is terminated immediately, and the abnormal behavior is written into the block chain alarm record; For non-sensitive operation request, normal communication continues.

[0012] Further, the process of two-way identity verification in step (2.2) is as follows: Step 1, device A sends a connection request to device B; the device A is an unmanned aerial vehicle or a ground base station; the device B is an unmanned aerial vehicle or a ground base station other than device A; Step 2, device B sends its certificate to device A; Step 3, device A verifies the validity of device B's certificate; including signature, validity period and revocation list; Step 4, after verification, send device A's certificate to device B; Step 5, device B verifies the validity of device A's certificate; including signature, validity period and revocation list; Step 6, device A encrypts a symmetric key C with device B's public key; Step 7, device B uses its own private key to decrypt and obtain the symmetric key C; Step 8, finally, device A and device B use the symmetric key for communication.

[0013] Further, the step (3) specifically includes the following sub-steps: (3.1) The situation synchronization initialization stage first loads the consensus algorithm configuration, and selects a suitable consensus mechanism according to the scene requirements. If the scene requires high security, the PBFT consensus mechanism is selected. If the scene requires efficiency, the RAFT consensus mechanism is selected. In addition to PBFT or RAFT consensus, there are non-Byzantine fault-tolerant algorithms, Byzantine fault-tolerant algorithms, PoW derivative algorithms, and simple consensus mechanisms using blockchain node layering. (3.2) After entering the situation coordination stage, real-time state acquisition is continuously performed. The situation information of the unmanned aerial vehicle is fixed and stored through blockchain technology to ensure that the data cannot be tampered with and has traceability. (3.3) Then, the collected state information is verified by the smart contract to determine whether there is an abnormal situation. If an abnormality is detected, the alarm mechanism is triggered and the alarm information is recorded on the chain, and the coordination strategy is automatically adjusted to cope with the abnormal situation. If no abnormalities are found, the normal coordination working state is continued.

[0014] Specifically, in step S4, the smart contract is obtained by encoding the task rules by the task command center, which includes task priority, resource allocation strategy and permission verification logic. When the unmanned aerial vehicle node enters the network, the smart contract copy obtained from the center server is automatically synchronized to the local trusted execution environment; the unmanned aerial vehicle processing result hash value and situation information are stored on the chain; the center server chain stores the original image data and secondary processing results that need to be fixed; the unmanned aerial vehicle locally caches recent data, and data exceeding the set storage time is overwritten according to the cycle coverage strategy.

[0015] Further, step S4 specifically includes the following sub-steps: (4.1) The task contract process starts with the deployment of a smart contract by the command center, and enters the task coordination stage; (4.2) The node continuously listens to the chain events and obtains the assigned sub-tasks; (4.3) Enter the task data processing link, realize high-precision time alignment through hardware-level timestamp synchronization technology PTP or GNSS, and perform alignment processing on multi-source data under the unified time reference; image preprocessing and GNSS data optimization are performed in parallel; the preprocessing includes noise reduction, enhancement and correction; after completing the data processing, the working mode is selected according to whether dynamic decision is required: if dynamic decision is enabled, global path planning and local obstacle avoidance are performed simultaneously; otherwise, enter the basic cruise mode; (4.4) The task execution result is stored on the chain after hash calculation to ensure traceability and tamper resistance; the task data is stored in stages; data synchronization is performed between nodes to ensure that all participants are consistent with the task state, and the operation is repeated continuously during the task duration; (4.5) When the task is completed, the settlement mechanism is automatically triggered, the corresponding processing is completed according to the contract terms, and the key data of the whole process is stored in the block chain, and a task execution and management closed loop system is constructed.

[0016] Further, the task contract in step (4.1) further comprises a fault-tolerant mechanism, and the fault-tolerant mechanism in the task contract process comprises the following sub-steps: Step 1, the fault-tolerant mechanism of the task contract flowchart enters the judgment link after the flow starts, and first detects whether there is a node failure; Step 2, if a node failure is detected, which is manifested as an abnormal heartbeat detection, the intelligent contract is triggered to reorganize the network; if no node failure is detected, the normal cooperative working state is maintained, and step 1 is continued; Step 3, automatically selecting a standby node to take over the task of the failed node; Step 4, updating the network state through a consensus mechanism to ensure continuous operation and uninterrupted execution of the task.

[0017] Further, the data hierarchical storage in step (4.4) is specifically: (4.4.1) The data storage is divided into three parts: server cluster storage, unmanned aerial vehicle cache and unmanned aerial vehicle on-chain storage; the server cluster storage is responsible for storing and consensus of the data collected by the unmanned aerial vehicle which needs to be fixed; the unmanned aerial vehicle cache is responsible for storing the data collected in the recent time period; the unmanned aerial vehicle on-chain consensus collects the hash value of the data; (4.4.2) The data is collected and preliminarily processed by the unmanned aerial vehicle, and the collected data is divided into data that needs to be fixed and the rest of the collected data, and an image processing result is generated; (4.4.3) The image data that needs to be fixed is transmitted to the central server, and the central server cluster performs fixed consensus on the data; (4.4.4) Store all images in the unmanned aerial vehicle local by the method of circular coverage; (4.4.5) Store the data hash value and image processing result on the unmanned aerial vehicle chain, and the unmanned aerial vehicle cluster performs fixed consensus on it.

[0018] Further, the step (5) specifically comprises the following sub-steps: (5.1) First, read the fixed data stored on the block chain, including task execution log, sensor data, intelligent contract interaction record and communication data, then further extract specific task information from the chain, including task ID, execution time, unmanned aerial vehicle ID, flight trajectory, task completion status and abnormal event record; (5.2) Analyze and verify the read data, first analyze the task information and check the field integrity, if the field is missing, trigger the alarm, otherwise continue processing, then analyze the flight trajectory data, check the validity of the coordinates, and standardize the task state to a unified format, 0 represents success, 1 represents failure, and 2 represents partial completion; (5.3) Continue to analyze the abnormal event record, extract the detailed data embedded therein, and then enter the data cleaning stage, remove abnormal values and drift points, smooth the height data, complete the time series alignment, and perform the missing value completion or deletion operation; (5.4) After the data cleaning is completed, a structured data table is generated for subsequent analysis, and then the actual completion of the task is confirmed according to the cleaned data, and the evaluation dimensions include whether the trajectory is compliant, the degree of task target achievement, and the influence degree of abnormal events; (5.5) Finally, based on the analysis results, optimization suggestions are generated to guide the improvement and execution of subsequent tasks, forming a closed-loop task management.

[0019] Compared with the prior art, the present application has the following beneficial effects: The present application innovatively realizes a blockchain-based unmanned aerial vehicle cluster intelligent collaboration system, constructs a double-layer blockchain architecture of "central chain + light edge chain", combines a switchable consensus mechanism and intelligent contract technology, realizes autonomous collaboration, safe communication and trusted evidence of unmanned aerial vehicle cluster in complex environment, and creates a distributed unmanned collaborative system with invulnerability and traceability. In addition, the present application also solves the single-point failure risk existing in the traditional centralized control architecture, improves the expansibility and fault tolerance of the system, and greatly improves the task execution efficiency and safety of the unmanned aerial vehicle cluster through the optimized consensus mechanism and intelligent contract design. BRIEF DESCRIPTION OF DRAWINGS

[0020] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments, with reference to the accompanying drawings: Figure 1 The network relationship diagram of the unmanned aerial vehicle task collaboration technology based on the blockchain of the present application; Figure 2 The working process diagram of the unmanned aerial vehicle task collaboration technology based on the blockchain of the present application; Figure 3 The automatic networking process diagram of the unmanned aerial vehicle task collaboration technology based on the blockchain of the present application; Figure 4 The identity verification process diagram of the unmanned aerial vehicle task collaboration technology based on the blockchain of the present application; Figure 5This is a schematic diagram of the two-way verification process in the identity authentication module of the blockchain-based drone mission collaboration technology of the present invention; Figure 6 This is a schematic diagram of the situation synchronization process of the blockchain-based UAV mission collaboration technology of the present invention; Figure 7 This is a schematic diagram of the task contract process of the blockchain-based UAV task collaboration technology of the present invention; Figure 8 This is a flow chart of the fault-tolerant mechanism in the task contract process of the blockchain-based UAV task collaboration technology of the present invention; Figure 9 This is a schematic diagram of the data hierarchical storage mechanism in the task contract process of the blockchain-based UAV task collaboration technology of the present invention; Figure 10 This is a schematic diagram of the task review process of the blockchain-based drone task collaboration technology of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the embodiments and accompanying drawings.

[0022] like Figure 1 As shown, this embodiment provides a blockchain-based drone mission collaboration method, including multiple drones and multiple central servers, each of which is located in different locations. The center of the figure shows a cluster of central servers deployed in various locations, connected via a network. A chain (Chain1) exists between the central servers to maintain consensus on information that the central servers need to store and authenticate. On the outside is a lightweight blockchain (Chain2), deployed on the drone cluster. The drones can communicate with each other, achieving situational synchronization and mission collaboration through smart contracts. Each drone in the drone cluster can independently connect to the central server for communication and data transmission. Multiple drone clusters can exist, and chains in different drone clusters have different numbers. Drone data can be isolated using different chains.

[0023] like Figure 1As shown, the data flow in the present blockchain-based unmanned aerial vehicle task coordination is as follows: the unmanned aerial vehicles synchronize flight situation, task data, consensus information, contract information and other data contents, the unmanned aerial vehicles cache the data that needs to be cached to the local, and the unmanned aerial vehicles synchronize the data through a lightweight blockchain; any unmanned aerial vehicle can transmit data to the server, and the unmanned aerial vehicles transmit video data, picture data, processing results, task result data and the like to the server, and the server transmits task data to the unmanned aerial vehicles; the servers synchronize full data, each server stores the full data locally, and the servers synchronize the full data through a blockchain.

[0024] The present blockchain-based unmanned aerial vehicle task coordination workflow will be described below in combination with Figure 2 Step 1: Start the first unmanned aerial vehicle and broadcast its own identity information to initialize the airspace network, and allow subsequent unmanned aerial vehicles to declare their existence by periodically sending beacon frames and establish a dynamic neighbor table; the unmanned aerial vehicle completes the ad hoc network in the air, provides the dynamic and flexible networking capability of the unmanned aerial vehicle cluster in a complex environment, and ensures that the task can be executed even in poor network conditions; Step 2: Grant a digital certificate to each unmanned aerial vehicle before task execution, and write it into the blockchain distributed ledger to achieve permanent evidence; when the device accesses the network, the validity of the certificate is verified in real time through a smart contract; the unmanned aerial vehicles complete identity verification through a distributed digital certificate verification scheme to ensure the authenticity of the identity of the unmanned aerial vehicles and the ground control station and the confidentiality of the communication, prevent illegal access and data leakage; Step 3: The unmanned aerial vehicle cluster uses a smart contract in lightweight blockchain technology to synchronize the situation, selects RAFT or PBFT consensus mechanism according to the scene requirements to adapt to the data synchronization requirements in different scenes; the unmanned aerial vehicles synchronize the situation and perform data chaining, use blockchain technology to realize real-time synchronization and sharing of key information among the unmanned aerial vehicle cluster, and ensure the consistency and reliability of the data, especially in a weak network environment; Step 4: During task execution, the unmanned aerial vehicle group automatically completes task allocation and decision-making based on a smart contract, and uses a data hierarchical storage strategy to store the hash value of the key state data on the chain to ensure the transparency and traceability of the task execution process; the unmanned aerial vehicles perform the task contract, realize the autonomous allocation, decision-making and dynamic adjustment of the unmanned aerial vehicle cluster based on the smart contract, enhance the task execution efficiency and adaptability, and ensure the consistency of the decision-making information of each node; ​Step 5, after the task is completed, read the on-chain fixed data to conduct a comprehensive review of the task execution process, and generate a visual report for subsequent task optimization; After the completion of the UAV task, the task review is carried out, the data fixed on the chain is analyzed, the task completion is evaluated and the subsequent strategy is optimized to ensure the accuracy and traceability of the task results, and to support flight trajectory analysis and effect evaluation.

[0025] The following will be described in detail Figure 3 , the first unmanned aerial vehicle is started and the identity information is broadcasted to initialize the airspace network, allowing subsequent unmanned aerial vehicles to declare their existence by periodically sending beacon frames and establishing a dynamic neighbor table; This process mainly includes the following contents: The air self-organizing network process starts from the first unmanned aerial vehicle starting, at this time the node will broadcast the identity signal containing its ID, GPS coordinate and other information to start the network and declare its existence, and is divided into three parallel processes. Process 1, in the node discovery stage, a dynamic neighbor table is established (after each unmanned aerial vehicle is started, the network module in the unmanned aerial vehicle on-board program will initialize an empty dynamic neighbor table. The content of the subsequent dynamic neighbor table is updated according to the interaction information between unmanned aerial vehicles; For example, the dynamic neighbor table of unmanned aerial vehicle 1 has unmanned aerial vehicle 2, the dynamic neighbor table of unmanned aerial vehicle 2 has unmanned aerial vehicle 3 and unmanned aerial vehicle 4, through the interaction between unmanned aerial vehicle 1 and unmanned aerial vehicle 2, unmanned aerial vehicle 1 adds the content in the dynamic neighbor table of unmanned aerial vehicle 2 to its own dynamic neighbor table), and periodically sends beacon frames, while listening to beacon frames from other nodes, and constantly updating the dynamic neighbor table according to the received information, this process will continue until the node discovery process is completed. Process 2, in the adaptive topology maintenance process, the content of the beacon frame is continuously updated, including the position, remaining power and received signal strength of the node. If a neighbor node is detected to have a response, the routing table is updated according to the preferred strategy (such as preferentially selecting high-power, strong-signal nodes); If the neighbor node has no response, the routing reorganization mechanism is triggered to bypass the nodes that may be faulty or weakly connected, ensuring the stability and efficiency of the network topology structure, which continues throughout the network operation. Process 3, in the data forwarding and load balancing process, when the node receives a data transmission request, TTL (Time to Live) and sequence number mechanism are applied to prevent data flooding. Then it is judged whether there is high-priority data to be transmitted, if there is, it is preferentially scheduled to be sent, if there is not, it is processed in the first-in-first-out (FIFO) manner.

[0026] The whole process embodies the cooperative working mechanism of air self-organizing network in node discovery, topology maintenance and data forwarding, with good dynamic adaptability and communication efficiency.

[0027] The following will be described in detail Figure 4, detailed description task execution for each unmanned aircraft is granted digital certificate, while writing in the blockchain distributed ledger to achieve permanent evidence; when the device access network, through the smart contract real-time certificate validity process chart, the process mainly includes the following content: Identity verification process from the certificate issuance phase, by the trusted authority (CA) to generate digital certificate, the certificate contains device ID, public key, permission information and validity period, etc., and be pre-installed in the unmanned aircraft hardware security module (HSM), as the device identity of the authoritative credentials.

[0028] In the communication establishment, both sides start the two-way identity verification process, to ensure each other identity real and reliable.

[0029] Subsequently into the encryption transmission link, data through the session key C encryption, and attached timestamp and random number to enhance security, while generating message signature for integrity check and identity confirmation, this process in the communication continues to repeat, guarantee the security of the whole communication process, and through the timestamp detection to prevent replay attack ability.

[0030] In terms of permission control, when one party initiates a request and involves sensitive operation, extract the permission field in the certificate, and call the smart contract on the blockchain for verification, to determine whether the current operation is within its permission range, combined with the time and space parameters for secondary confirmation. If the time and space parameters match, the corresponding operation is allowed to be executed; if not, the session is terminated immediately, and the abnormal behavior is written into the blockchain alarm record, improving the overall audit and traceability ability. For non-sensitive operation request, continue to carry on the normal communication. The whole process builds a security system covering identity authentication, encrypted communication and fine-grained permission control.

[0031] In the above two-way identity verification process, the steps are shown in Figure 5 : Step 1, device A (unmanned aerial vehicle or ground base station) sends a connection request to device B (unmanned aerial vehicle or ground base station except device A); Step 2, device B sends its certificate to device A; Step 3, device A verifies the validity of device B's certificate (including signature, validity period and revocation list); Step 4, after verification, send device A's certificate to device B; Step 5, device B verifies the validity of device A's certificate (including signature, validity period and revocation list); Step 6, device A encrypts a symmetric key C with device B's public key; Step 7, device B uses its own private key to decrypt and obtain the symmetric key C; Step 8, finally, device A and device B use the symmetric key for communication.

[0032] In the identity verification process, in addition to using the method of issuing a digital certificate containing device serial number, public key, validity period, etc. for each drone by the central server as a trusted authority and preloading it into the device HSM module through physical USB, a distributed public key infrastructure (DPKI) combined with blockchain or distributed ledger technology can be used to register drone identity information on the chain and manage the certificate life cycle through smart contracts, realizing decentralized and tamper-proof identity authentication; or identity-based encryption (IBE) can be used, which directly uses the unique identifier (such as serial number) of the drone as the public key, and the private key is distributed by the key generation center (PKG), thereby avoiding the complexity of preloading certificates. Another solution is self-sovereign identity (SSI), which allows the drone to generate a public-private key pair autonomously and register it with the distributed network through a decentralized identifier (DID), combined with verifiable credentials (VC) to achieve flexible identity management. In addition, consensus-based group authentication technology (such as group signature or threshold signature) can support anonymous authentication or distributed key management for drone groups, while a physical unclonable function (PUF) can be combined with a lightweight CA to dynamically issue short-term certificates based on hardware uniqueness to enhance security. For dynamic access control scenarios, attribute-based access control (ABAC) can dynamically authorize based on drone attributes without relying on preloaded certificates. Finally, hybrid solutions (such as hierarchical CA or cross-chain interoperability) can balance the advantages of centralization and distribution, adapting to mutual recognition needs of different manufacturers or networks. These technologies can be flexibly selected or combined according to the security, privacy, and resource limitations of the drone.

[0033] In terms of secure communication, in addition to using simple public key encryption to exchange session keys for secure communication, a middleware-based secure communication architecture can be used, such as implementing trusted message relay through a message broker and ensuring end-to-end security with TLS two-way authentication; or a lightweight key agreement protocol can be used to achieve forward-secure key exchange, suitable for resource-constrained IoT devices; identity-based encryption schemes can also be introduced, allowing drones to use unique identifiers (such as serial numbers) as public keys for encrypted communication without preloading certificates; for high-dynamic network environments, group key management protocols can be used to achieve dynamic key updates for group communication; if privacy protection is needed, zero-knowledge proof (ZKP) can be combined to achieve secure session establishment after anonymous authentication; in the quantum-resistant security scenario, lattice-based key encapsulation mechanisms can be used instead of traditional public key encryption; in addition, physical layer security-based methods (such as channel feature key generation) can be used to derive lightweight keys in wireless communication, while trusted execution environment (TEE) solutions can ensure the security of key generation and storage. These methods can be selected based on specific application scenarios, balancing computation overhead, communication efficiency, security strength, and deployment complexity, achieving efficient message routing while ensuring forward security.

[0034] The following will be described in detail Figure 6 , the situation synchronization of the UAV cluster using the smart contract in the lightweight blockchain technology, according to the scene requirement, the RAFT or PBFT consensus mechanism is selected to adapt to the data synchronization requirement in different scenes; (The smart contract is obtained by encoding the task rules by the task command center, which contains the task priority, resource allocation strategy and permission verification logic, when the UAV node enters the network, the smart contract copy obtained from the center server is automatically synchronized to the local trusted execution environment; the UAV processing result hash value and situation information are stored on the chain; the center server chain stores the original image data and secondary processing result which needs to be fixed; the local cache of the UAV stores the recent data, and the data exceeding the set storage time is overwritten according to the cycle coverage strategy.) The process mainly includes the following steps: Step 1, the situation synchronization process starts from the initialization stage, first loads the consensus algorithm configuration, and selects the appropriate consensus mechanism according to the scene requirement, if the scene requires high security, select PBFT consensus mechanism (the operator will select actively according to the scene situation in advance, the default is PBFT consensus mechanism), if more attention is paid to efficiency, select RAFT consensus mechanism. (If the network condition is stable, RAFT protocol is used; if node failure occurs, lightweight PBFT protocol is used to ensure fault tolerance capability); Step 2, after entering the situation coordination stage, real-time state acquisition is carried out continuously, the situation information of the UAV is collected, including the longitude, latitude, height and speed of the UAV. Through the blockchain technology, the data is fixed and stored, ensuring that the data cannot be tampered with and has traceability; Step 3, then the collected state information is verified by the smart contract, and it is judged whether there is an abnormal situation. If an abnormality is detected, the alarm mechanism is triggered and the alarm information is recorded on the chain, and the coordination strategy is automatically adjusted to cope with the abnormal situation; if no abnormality is found, the normal coordination state is maintained.

[0035] This process is repeated during the task duration, ensuring efficient synchronization and consistency of situation information between nodes, and building a safe, reliable and adaptive coordination mechanism.

[0036] Alternatively, in addition to the PBFT or RAFT used for consensus according to different application scenarios in the above scheme, there are other consensus algorithms available in the remaining blockchains. Classical non-BFT algorithms include Paxos, Raft, Viewstamped Replication (VR), and Zab; BFT algorithms include PBFT, SBFT, DBFT, and HoneyBadgerBFT; PoW derivatives include Bitcoin PoW, Ethash, RandomX, Equihash, and CuckooCycle; PoS and its variants include pure PoS (Peercoin, Blackcoin), hybrid mechanisms (DPoS, LPoS, PoSV, MPoS), and new PoS (Ouroboros, Casper FFG, Tendermint); other innovative consensus mechanisms include PoET, PoA, PoSpace, PoET, PoH, Algorand's Pure PoS, Avalanche consensus, Hashgraph's Gossip about Gossip, and specific scenario-optimized Firefly, Snowball, and Conflux's GHOST variants.

[0037] Alternatively, a simple consensus mechanism using a hierarchical blockchain node structure can be used. First, the nodes are divided into three layers according to performance and credibility: core layer, intermediate layer, and edge layer. The core layer consists of a few high-performance full nodes (such as central servers) running strong consistency algorithms (such as optimized PBFT or Tendermint), and is responsible for transaction ordering and block finalization. The intermediate layer uses a dynamic election mechanism (such as DPoS rotation or Algorand's random draw) to organize ordinary verification nodes, which perform secondary verification and signature aggregation on the blocks output by the core layer, and also handle transaction pre-execution and error detection. The edge layer broadcasts transactions through a lightweight protocol (such as a Gossip network) and verifies block header information using Merkle proofs, with key data synchronization using a "checkpoint + incremental update" mode. This hierarchical structure controls consensus complexity through the core layer, disperses verification pressure through the intermediate layer, and optimizes access efficiency through the edge layer, while maintaining Byzantine fault tolerance, and can increase transaction throughput by 3-5 times.

[0038] Optionally, the above method uses a smart contract in lightweight blockchain technology for the situation synchronization of the UAV, and in addition, lightweight AI cooperation can also be used. Through the distributed situation awareness architecture and the intelligent optimization algorithm, efficient situation synchronization of the UAV group is realized. A lightweight CNN-LSTM hybrid model is deployed on the edge computing node, and the federal learning framework is used to make each UAV upload only the model gradient parameters after local training, which reduces a large amount of communication load compared with the traditional smart contract scheme. Combined with the Gossip protocol, near real-time state propagation is realized, and through adaptive compression technology, key state data such as position and power are compressed. Reinforcement learning is introduced to dynamically optimize the synchronization strategy, and based on the network condition and the task urgency, the data synchronization frequency is automatically adjusted. After local consensus verification at the edge node, only the hash digest is stored on the chain, which not only retains the tamper-proofing feature of the blockchain, but also reduces the computing overhead. At the same time, a hierarchical anomaly detection mechanism is designed. First, the local LSTM model identifies abnormal behavior, and then the federated average algorithm is used for collaborative verification. Finally, the edge node arbitrates the disputed state. In the following Figure 7 , the task contract flow in the task coordination of the UAV based on the blockchain will be described in detail. During task execution, the UAV group automatically completes task allocation and decision-making based on the smart contract, and uses a data hierarchical storage strategy to store the hash value of the key state data on the chain to ensure that the task execution process is transparent and traceable. The flow mainly includes the following steps: Step 1: The task contract flow starts with the deployment of a smart contract from the command center, entering the task coordination stage. Step 2: The node continuously listens to the on-chain events and obtains the assigned sub-tasks. Step 3: Enter the task data processing link. High-precision time alignment is achieved through hardware-level timestamp synchronization technology (such as PTP or GNSS), and multi-source data is aligned under the unified time reference. Next, image preprocessing (including noise reduction, enhancement, and correction) and GNSS data optimization are performed in parallel. After completing the data processing, the working mode is selected according to whether dynamic decision-making is needed: if dynamic decision-making is enabled, global path planning and local obstacle avoidance are performed simultaneously; otherwise, enter the basic cruise mode; Step 4: The task execution result is stored on the chain after hash calculation to ensure that the process is traceable and tamper-proof. Task data is stored hierarchically. Data synchronization is performed between nodes to ensure consistent awareness of task status among all participants. This series of operations is repeated continuously during the task duration; Step 5: When the task is completed, the settlement mechanism is automatically triggered, the corresponding processing is completed according to the contract terms, and the key data of the whole process is stored on the blockchain, building a safe, transparent, and trustworthy task execution and management closed-loop system.

[0039] In the task contract flow execution in the task coordination of the UAV based on the blockchain, there is a fault-tolerant mechanism stepFigure 8 As shown: Step 1: The fault-tolerant mechanism of the task contract flowchart enters the judgment phase from the beginning of the process, first detecting whether there is a node failure.

[0040] Step 2: If a node failure is detected, manifested as an abnormal heartbeat detection, the smart contract is triggered to reorganize the network; if no node failure is detected, the normal collaborative working state is maintained and step 1 is continued; Step 3: Automatically select a backup node to take over the tasks of the failed node; Step 4: Update the entire network status through the consensus mechanism to ensure continuous operation and uninterrupted execution of tasks;

[0041] It has anti-destruction characteristics during the fault recovery process, including seamless task connection, automatic range expansion, and power adaptation capabilities, ensuring that the entire mission network can still operate stably and adjust flexibly in the event of dynamic changes or failure of some nodes.

[0042] In the execution of the task contract process, the data hierarchical storage method is as follows: Figure 9 As shown, data storage is divided into three parts: server cluster storage, drone cache, and drone on-chain storage. The server cluster is responsible for storing and agreeing on drone-collected data requiring verification; the drone cache is responsible for storing data collected recently (approximately the last seven days); and the drone on-chain consensus hashes the collected data. Data is collected by drones and initially processed. The collected data is divided into data requiring verification and other collected data, and image processing results are generated. The image data requiring verification is transmitted to the central server, where the central server cluster performs verification consensus on the data. All images are stored locally on the drone using a circular overwrite method. The data hash and image processing results are verified on the drone chain, where the drone cluster performs verification consensus.

[0043] The following will be combined Figure 10 , describe in detail the process of reading the on-chain certificate data after the task is completed, and Conduct a comprehensive review and generate a visual report for subsequent task optimization. The specific process mainly includes the following steps: Step 1: First, read the solid evidence data stored on the blockchain. This data includes task execution logs, sensor data, smart contract interaction records, and communication data. Then, further extract specific task information from the chain, such as task ID, execution time, drone ID, flight trajectory, task completion status, and abnormal event records. Step 2, the parsed data is analyzed and verified, first analyze the task information and check the field integrity, if the field is missing, trigger the alarm, otherwise continue processing. Then parse the flight trajectory data, check the validity of the coordinates, and standardize the task status to a unified format (0 for success, 1 for failure, 2 for partial completion); Step 3, continue to parse the abnormal event record, extract the detailed data embedded therein, and then enter the data cleaning stage, remove outliers and drift points, smooth the height data, align the time series, and complete the missing value completion or deletion operation; Step 4, after data cleaning, generate structured data table for subsequent analysis, then confirm the actual completion of the task according to the cleaned data, evaluate the dimensions including whether the trajectory is compliant, the degree of task target achievement and the influence degree of abnormal events, etc. Step 5, finally, based on the above analysis results, generate optimization suggestions for guiding the improvement and execution of subsequent tasks, form a closed-loop task management process, improve the task traceability, problem positioning ability and overall execution efficiency.

[0044] The application also provides a blockchain-based unmanned aerial vehicle task coordination system, comprising the following modules: Air self-organizing network module: used for unmanned aerial vehicle to broadcast identity information to establish initial network, and manage dynamic neighbor table established by subsequent unmanned aerial vehicles through beacon frame; Identity verification module: responsible for pre-installed certificate distribution, blockchain storage and real-time verification of smart contract, including certificate burning function of hardware security module; Situation synchronization module: selects RAFT or PBFT consensus protocol according to network expected state before starting the unmanned aerial vehicle, and realizes flight situation synchronization according to smart contract after starting the unmanned aerial vehicle; Task contract module: integrates local trusted smart contract execution engine, realizes data hierarchical storage, realizes automatic task allocation and on-chain data storage; Task review module: provides blockchain data analysis tool and visualization tool, supports task process tracing and performance evaluation report generation.

[0045] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the application. It should be understood that the above description is only a specific embodiment of the application and is not intended to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A blockchain-based UAV cluster task collaboration method, characterized by: The method comprises the following steps: (1) Start the first UAV and broadcast its identity information to initialize the airspace network, allowing subsequent UAVs to declare their presence by periodically sending beacon frames and establish a dynamic neighbor table; (2) Before the mission is executed, a digital certificate is granted to each drone and written into the blockchain distributed ledger for permanent storage; when the device is connected to the network, the validity of the certificate is verified in real time through the smart contract; (3) The drone cluster uses smart contracts in lightweight blockchain technology for situation synchronization, and selects RAFT or PBFT consensus mechanisms according to scenario requirements to adapt to data synchronization requirements in different scenarios; (4) During mission execution, the drone group automatically completes task allocation and decision-making based on smart contracts, and uses a data hierarchical storage strategy to store the hash value of key status data on the chain to ensure that the mission execution process is transparent and traceable; (5) After the task is completed, the on-chain solid evidence data is read, the task execution process is comprehensively reviewed, and a visual report is generated for subsequent task optimization.

2. The UAV cluster task coordination method according to claim 1, characterized in that: The step (1) specifically includes the following sub-steps: (1.1) When the first drone is powered on, it broadcasts an identity signal containing its own ID and GPS coordinates to start the network and announce its presence. It also generates a cluster ID to distinguish other neighboring clusters and defines the format of the beacon frame, cluster ID, and time synchronization stamp. And periodically send beacon frames, while listening to the smart contract. The task command center encodes the task rules, which include task priority, resource allocation strategy and permission verification logic. When the drone node joins the network, it automatically synchronizes the smart contract copy obtained from the central server to the local trusted execution environment; the chain stores the hash value of the drone processing result and situation information; the central server chain stores the original image data and secondary processing results that need to be authenticated; the drone locally caches the latest data, and the data that exceeds the set storage time is overwritten according to the cyclic overwriting strategy; the beacon frame from other nodes continuously updates the dynamic neighbor table based on the received information. This process will continue until the node discovery process ends; (1.2) During the adaptive topology maintenance process, beacon frame content is continuously updated, including the node's location, remaining battery power, and received signal strength. If a response from a neighboring node is detected, the routing table is updated according to the optimal strategy. If a neighboring node does not respond, a routing reorganization mechanism is triggered to bypass faulty or weakly connected nodes. This process continues throughout the network operation. (1.3) In data forwarding and load balancing, when a node receives a data transmission request, the system applies the TTL (Time to Live) and sequence number mechanism to prevent data flooding. It then determines whether there is high-priority data to be transmitted. If so, it is scheduled for transmission first. If not, the data is processed sequentially in a first-in-first-out (FIFO) manner.

3. The UAV swarm task coordination method according to claim 1, characterized in that: The step S2 specifically includes the following sub-steps: (2.1) Using a pre-installed certificate distribution mechanism, a digital certificate containing the unique identification ID, public key, organization signature, and permission information of the module mounted on the drone device is burned into the drone's hardware security module (HSM) as a credential for the device's identity, eliminating reliance on online trusted organizations (CAs). (2.2) When communication is established, both parties initiate a two-way authentication process to ensure each other's identity; The data then enters the encrypted transmission phase, where it is encrypted using the session key C and appended with a timestamp and random number. A message signature is also generated for integrity verification and identity confirmation. This process is repeated continuously throughout the communication, and timestamp detection is used to prevent replay attacks. (2.3) In terms of permission control, when a party initiates a request involving sensitive operations, the permission field in the certificate will be actively extracted and the smart contract on the blockchain will be called for verification to determine whether the current operation is within its permission range. At the same time, a secondary confirmation is performed based on the time and space parameters. If the time and space parameters match, the corresponding operation is allowed to be executed; if they do not match, the session is terminated immediately and the abnormal behavior is written into the blockchain alarm record; for non-sensitive operation requests, normal communication continues.

4. The UAV swarm task coordination method according to claim 3, characterized in that: The process of two-way authentication in step (2.2) is as follows: Step 1: Device A sends a connection request to device B; device A is a drone or a ground base station; device B is a drone or a ground base station other than device A. Step 2: Device B sends its certificate to Device A. Step 3: Device A verifies the validity of device B's certificate. including signatures, validity periods, and revocation lists; Step 4: After verification, send the certificate of device A to device B; Step 5: Device B verifies the validity of Device A's certificate. including signatures, validity periods, and revocation lists; Step 6: Device A encrypts a symmetric key C using the public key of device B. Step 7: Device B uses its own private key to decrypt and obtain the symmetric key C; Step 8: Finally, device A and device B use the symmetric key to communicate.

5. The UAV swarm task coordination method according to claim 1, characterized in that: The step (3) specifically includes the following sub-steps: (3.1) In the initialization phase of situation synchronization, first load the consensus algorithm configuration and select Choose an appropriate consensus mechanism. If the scenario requires high security, choose the PBFT consensus mechanism; if the scenario requires efficiency, choose the RAFT consensus mechanism. In addition to PBFT or RAFT, the consensus mechanisms mentioned above include non-Byzantine fault-tolerant algorithms; Byzantine fault-tolerant algorithms; PoW derivative algorithms; and simple consensus mechanisms using blockchain node layering. (3.2) After entering the situation coordination stage, real-time status collection is continuously carried out, and the situation information of the drone is stored in a secure manner through blockchain technology to ensure that the data cannot be tampered with and is traceable; (3.3) The collected status information is then verified using smart contracts to determine whether there are any anomalies. If an anomaly is detected, an alarm mechanism is triggered and the alarm information is recorded on the chain. At the same time, the collaborative strategy is automatically adjusted to deal with the anomaly. If no abnormality is found, continue to maintain normal collaborative working status.

6. The UAV swarm task coordination method according to claim 1, characterized in that: In step S4, the smart contract is obtained by encoding the task rules by the mission command center, which includes task priority, resource allocation strategy and permission verification logic. When the drone node joins the network, the smart contract copy obtained from the central server is automatically synchronized to the local trusted execution environment; the hash value of the drone processing result and situation information are stored on the chain; the central server chain stores the original image data and secondary processing results that need to be authenticated; the drone locally caches the latest data, and data that exceeds the set storage time is overwritten according to the cyclic overwriting strategy.

7. The UAV swarm task coordination method according to claim 1, characterized in that: Step S4 specifically includes the following sub-steps: (4.1) The mission contract process begins with the deployment of smart contracts in the command center and enters the mission coordination stage; (4.2) The node continuously monitors on-chain events and obtains assigned subtasks; (4.3) Entering the mission data processing phase, high-precision time alignment is achieved through hardware-level timestamp synchronization technology PTP or GNSS, and multi-source data is aligned under a unified time reference; Image preprocessing and GNSS data optimization are performed in parallel; the preprocessing includes noise reduction, enhancement, and correction. After data processing, the operating mode is selected based on whether dynamic decision-making is required: if dynamic decision-making is enabled, global path planning and local obstacle avoidance are performed simultaneously; otherwise, basic cruise mode is entered. (4.4) The task execution results are hashed and stored on the blockchain to ensure that the process is traceable and cannot be tampered with; Store task data in a hierarchical manner; synchronize data between nodes to ensure that all participants have a consistent understanding of the task status and that the operation is repeated continuously during the task duration; (4.5) When the task is completed, the settlement mechanism is automatically triggered, the corresponding processing is completed according to the terms of the contract, and the key data of the entire process is stored on the blockchain, thus building a closed-loop system for task execution and management.

8. The UAV cluster task coordination method according to claim 1, characterized in that: The task contract in step (4.1) also includes a fault tolerance mechanism. The fault tolerance mechanism judgment during the execution of the task contract process specifically includes the following sub-steps: Step 1: The fault tolerance mechanism of the task contract flowchart enters the judgment phase from the beginning of the process, first detecting whether there is a node failure; Step 2: If a node failure is detected, manifested as an abnormal heartbeat detection, the smart contract is triggered to reorganize the network; if no node failure is detected, the normal collaborative working state is maintained and step 1 is continued; Step 3: Automatically select a backup node to take over the tasks of the failed node; Step 4: Update the entire network status through the consensus mechanism to ensure continuous operation and uninterrupted execution of tasks.

9. The UAV swarm task coordination method according to claim 1, characterized in that: The data hierarchical storage in step (4.4) is specifically as follows: (4.4.1) Data storage is divided into three parts: server cluster storage, drone cache, and drone chain storage; The server cluster storage is responsible for storing and consensus-generating the data collected by the drone that needs to be authenticated; The drone cache is responsible for storing the data collected in the most recent time period; the hash value of the data collected by the drone chain consensus; (4.4.2) Data is collected and preliminarily processed by drones, the collected data is divided into data requiring verification and other collected data, and image processing results are generated; (4.4.3) The image data that needs to be authenticated is transmitted to the central server, and the central server cluster reaches a consensus on the data authentication; (4.4.4) All images are stored locally on the drone using a loop overwriting method; (4.4.5) The data hash value and image processing results are recorded on the drone chain, and the drone cluster reaches a consensus on the record.

10. The UAV cluster task coordination method according to claim 1, characterized in that: The step (5) specifically includes the following sub-steps: (5.1) First, read the solid evidence data stored on the blockchain, including task execution logs, sensor data, smart contract interaction records, and communication data. Then, further extract specific task information from the chain, including task ID, execution time, drone ID, flight trajectory, task completion status, and abnormal event records; (5.2) Parse and verify the read data. First, parse the mission information and check the field integrity. If a field is missing, trigger an alarm; otherwise, continue processing. Then parse the flight trajectory data, verify the validity of the coordinates, and standardize the mission status to a unified format, with 0 indicating success, 1 indicating failure, and 2 indicating partial completion. (5.3) Continue parsing the abnormal event records and extracting the detailed data embedded therein. Then enter the data cleaning phase to remove outliers and drift points, smooth the height data, complete time series alignment, and fill in or delete missing values. (5.4) After data cleaning is complete, a structured data table is generated for subsequent analysis. The actual completion of the task is then confirmed based on the cleaned data. The evaluation dimensions include whether the trajectory is compliant, the degree of achievement of the task objectives, and the impact of abnormal events. (5.5) Finally, optimization suggestions are generated based on the above analysis results to guide the improvement and execution of subsequent tasks, forming a closed-loop task management.

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