A blockchain-based unmanned aerial vehicle cluster task coordination method

Abstract

The application provides a kind of unmanned aerial vehicle cluster task cooperation method based on blockchain, which first starts first unmanned aerial vehicle and initializes airspace network, realizes dynamic ad hoc network;Wherein each unmanned aerial vehicle is granted digital certificate containing identity information and basic right before task execution, and the validity of certificate is verified in real time through smart contract;Then the unmanned aerial vehicle cluster adopts lightweight blockchain technology for situation synchronization;Finally, in the process of task execution, the unmanned aerial vehicle group automatically completes task allocation and decision based on smart contract, while the hash value of key state data is chained and stored;After completing the task, the data is analyzed and a visual report is generated by reading the data stored on the chain.The application solves the problems of single point failure, expansion bottleneck and security threat existing in traditional centralized control architecture, realizes the autonomous cooperation, safe communication and trusted storage of unmanned aerial vehicle cluster in complex environment, greatly improves the reliability, efficiency and security of task execution.

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 via wireless communication (such as Wi-Fi, 4G / 5G) or satellite communication. Wireless communication is suitable for short-range, low-latency scenarios but is susceptible to signal interference; satellite communication is suitable for long-distance transmission but is costly and has limited bandwidth. In complex environments (such as urban canyons or high-voltage power line corridors), drones may experience data transmission interruptions or loss due to unstable signals or node outages, and data may be intercepted or tampered with. Existing solutions lack adaptive transmission mechanisms for weak network environments, making it difficult to guarantee mission continuity.

[0006] Traditional drone mission collaboration systems lack effective identity authentication and access control mechanisms. Unauthorized users may impersonate legitimate drones or ground control stations to access the network, steal information, tamper with commands, or interfere with normal flight missions. Unencrypted authentication information may be intercepted and stolen, leading to the leakage of user identity information and even unauthorized control of drones. Furthermore, traditional authentication methods may require complex interaction processes, resulting in lengthy drone networking times and affecting the timely execution of flight missions.

[0007] Existing drone data storage and authentication mechanisms have significant shortcomings. If all the continuous perception data generated by drones were uploaded to the blockchain, it would require substantial storage space, increasing the storage costs of the blockchain system. For large-scale drone data, this level of storage cost is unsustainable, and the full dataset may contain redundant information, wasting storage resources. Furthermore, uploading all data to the blockchain slows down blockchain processing speed, affecting data upload efficiency and making data querying and retrieval complex and time-consuming. Existing solutions lack intelligent strategies for tiered data storage, making it difficult to balance storage costs and data availability. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the purpose of this invention is to provide an unmanned system based on lightweight blockchain.

[0009] The new drone task collaboration method solves the problems of single point of failure, scalability bottleneck and security threats in the traditional centralized control architecture. It enables drone swarms to autonomously collaborate, communicate securely and store evidence in complex environments, and greatly improves the reliability, efficiency and security of task execution.

[0010] The application is implemented by the following technical scheme: a UAV cluster task coordination method based on a blockchain, comprising multiple UAVs and multiple center servers, wherein the center servers are arranged at different positions; a center server cluster deployed at each position is displayed on a graph, the servers are connected through a network, and there is a chain Chain1 between the center servers, which is used to store information that needs to be stored by the consensus center server; the outside is a lightweight blockchain Chain2, which is deployed on a UAV cluster, and the UAVs can communicate with each other and complete situation synchronization and task coordination through a smart contract; wherein each UAV in the UAV cluster can be connected and communicated with the center server independently, and the UAV cluster can exist in multiple groups, and the chains in different UAV clusters have different numbers, and the UAV data is isolated through different chains; and the data flow in the UAV task coordination is as follows: the UAV caches the synchronized flight situation, task data, consensus information and contract information data that need to be cached to the local, and the UAVs synchronize the data through a lightweight blockchain; any UAV can transmit data to the server, and the UAV transmits video data, picture data, processing results and task result data to the server, and the server transmits task data to the UAV; the servers synchronize full data, and each server stores full data locally, and the servers synchronize the full data through a blockchain, and the method comprises the following steps:

[0011] (1) Start the first UAV and broadcast its own identity information to initialize the airspace network, and allow the subsequent UAVs to declare their existence by periodically sending beacon frames and establish a dynamic neighbor table;

[0012] (2) Grant a digital certificate to each UAV before task execution, and write it into a blockchain distributed ledger to achieve permanent storage; when the device accesses the network, the validity of the certificate is verified in real time through a smart contract;

[0013] (3) The UAV cluster uses a smart contract in lightweight blockchain technology to synchronize the situation, and selects RAFT or PBFT consensus mechanism according to the scene requirements to adapt to the data synchronization requirements in different scenes;

[0014] (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;

[0015] (5) After the task is completed, the on-chain stored data is read to comprehensively review the task execution process, and a visual report is generated for subsequent task optimization.

[0016] Further, the step (1) specifically comprises the following substeps:

[0017] (1.1) From the first drone start, broadcast identity signal containing its own ID, GPS coordinate information, to start the network and declare its existence; and generate cluster ID for distinguishing other adjacent clusters, define the format of beacon frame, cluster ID and time synchronization stamp; and periodically send beacon frame, while listening to the smart contract by task command center to encode the task rules, which contains task priority, resource allocation strategy and permission verification logic, when the drone node enters the network, automatically synchronize the smart contract copy obtained from the center server to the local trusted execution environment; on-chain store the hash value of the drone processing result and the situation information; the center server chain stores the original image data and secondary processing results that need to be fixed; the local cache of the drone stores the latest data, and the data exceeding the set storage time is overwritten according to the cycle coverage strategy; from the beacon frame of other nodes, constantly update the dynamic neighbor table according to the received information, this process will continue until the node discovery process ends;

[0018] (1.2) In the adaptive topology maintenance process, continuously update the beacon frame content, including the position,

[0019] The remaining power and received signal strength; if it is detected that the neighbor node has a response, then 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;

[0020] (1.3) In the data forwarding and load balancing process, when the node receives a data transmission request, the system will apply the time to live TTL and sequence number mechanism to prevent data flooding; then judge whether there is high-priority data to be transmitted, if yes, then prioritize its transmission, if no, then process the data in the first-in first-out FIFO manner.

[0021] Further, the step (2) specifically comprises the following sub-steps:

[0022] (2.1) Use the pre-set certificate distribution mechanism to burn the digital certificate containing the unique identification ID of the module mounted on the unmanned aerial vehicle device, public key, institution signature and permission information into the hardware security module HSM of the unmanned aerial vehicle, as the certificate of device identity, eliminating the dependence on online trusted institution CA;

[0023] (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 through session key C and attached with time stamp and random number; at the same time, generate message signature for integrity check and identity confirmation, this process is repeated continuously during the duration of communication and realizes the anti-replay attack ability through time stamp detection;

[0024] (2.3) In terms of permission control, when one party initiates a request and involves sensitive operation, the permission field in the certificate is actively extracted, and the smart contract on the blockchain is called 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 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.

[0025] Further, the process of two-way identity verification in step (2.2) is as follows:

[0026] (2.2.1) Device A sends a connection request to device B; the device A is a UAV or a ground base station; the device B is a UAV or a ground base station other than the device A;

[0027] (2.2.2) Device B sends its certificate to device A;

[0028] (2.2.3) Device A verifies the validity of the certificate of device B; including signature, validity period and revocation list;

[0029] (2.2.4) After verification, send the certificate of device A to device B;

[0030] (2.2.5) Device B verifies the validity of the certificate of device A; including signature, validity period and revocation list;

[0031] (2.2.6) Device A encrypts a symmetric key C with the public key of device B;

[0032] (2.2.7) Device B uses its own private key to decrypt and obtain the symmetric key C;

[0033] (2.2.8) Finally, device A and device B use the symmetric key for communication.

[0034] Further, the step (3) specifically includes the following sub-steps:

[0035] (3.1) Situation synchronization initialization stage, first load consensus algorithm configuration, and select appropriate consensus mechanism according to scene requirements; if the scene requires high security, select PBFT consensus mechanism; if the scene requires efficiency, select RAFT consensus mechanism; the consensus mechanism includes PBFT or RAFT for consensus, as well as non-Berlin fault-tolerant algorithms, Berlin fault-tolerant algorithms, PoW derivative algorithms, and simple consensus mechanisms using blockchain node layering;

[0036] (3.2) After entering the situation coordination stage, real-time state collection is continuously carried out, and the situation information of the UAV is fixed and stored through blockchain technology to ensure that the data cannot be tampered with and has traceability;

[0037] (3.3) Then the collected state information is checked by the smart contract to determine whether there is an abnormal situation; if an abnormality is detected, an alarm mechanism is triggered and the alarm information is recorded on the chain, and the cooperative strategy is automatically adjusted to cope with the abnormal situation; if no abnormality is found, the normal cooperative working state is continued.

[0038] Specifically, in step (4), the smart contract is obtained by encoding the task rules by the task command center, which contains task priority, resource allocation strategy and permission verification logic, and 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 hash value of the unmanned aerial vehicle processing result and the situation information are stored 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 recent data is cached locally, and the data exceeding the set storage time is overwritten according to the cycle coverage strategy.

[0039] Further, step (4) specifically includes the following sub-steps:

[0040] (4.1) The task contract process starts from deploying a smart contract by the command center, and enters the task cooperation stage;

[0041] (4.2) The node continuously listens to the chain events and obtains the assigned sub-tasks;

[0042] (4.3) Enter the task data processing link, realize high-precision time alignment through hardware-level time stamp 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, according to whether dynamic decision is needed, the working mode is selected: if dynamic decision is enabled, global path planning and local obstacle avoidance are performed simultaneously; otherwise, enter the basic cruise mode;

[0043] (4.4) The task execution result is stored on the chain after hash calculation to ensure traceability and non-tamperability; 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;

[0044] (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 on the blockchain to build a task execution and management closed loop system.

[0045] Further, the task contract in step (4.1) further includes a fault tolerance mechanism, and the fault tolerance mechanism in the task contract process includes the following sub-steps:

[0046] The fault-tolerant mechanism of the task contract flowchart enters a judgment link after the flow starts, and first detects whether there is a node failure;

[0047] (4.4.2) If a node failure is detected, which is manifested as an abnormal heartbeat detection, the smart contract triggers network reorganization; if no node failure is detected, the normal cooperative working state is maintained, and step (4.4.1) is continued to be executed;

[0048] (4.4.3) Automatically selecting a backup node to take over the tasks of the failed node;

[0049] (4.4.4) Updating the global network state through a consensus mechanism to ensure continuous operation and uninterrupted execution of tasks.

[0050] Further, the data hierarchical storage in the step (4.4) is specifically:

[0051] (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 that needs to be fixed; the unmanned aerial vehicle cache is responsible for storing the data collected in the recent time period; and the unmanned aerial vehicle on-chain consensus collects the hash value of the data;

[0052] (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;

[0053] (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;

[0054] (4.4.4) All images are stored in the local unmanned aerial vehicle through a cyclic coverage method;

[0055] (4.4.5) The data hash value and the image processing result are fixed on the unmanned aerial vehicle chain, and the unmanned aerial vehicle cluster performs fixed consensus on it.

[0056] Further, the step (5) specifically includes the following sub-steps:

[0057] (5.1) First, read the fixed data stored on the blockchain, including task execution logs, sensor collected data, smart contract interaction records, and communication data, and 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 records;

[0058] (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;

[0059] (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;

[0060] (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;

[0061] (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.

[0062] Compared with the prior art, the present application has the following beneficial effects:

[0063] 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

[0064] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings:

[0065] Figure 1 The network relationship diagram of the unmanned aerial vehicle task collaboration technology based on blockchain of the present application;

[0066] Figure 2 The working process diagram of the unmanned aerial vehicle task collaboration technology based on blockchain of the present application;

[0067] Figure 3 The automatic networking process diagram of the unmanned aerial vehicle task collaboration technology based on blockchain of the present application;

[0068] Figure 4 Identity verification flowchart of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application;

[0069] Figure 5 Two-way verification flowchart in the identity verification module of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application;

[0070] Figure 6 Situation synchronization flowchart of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application;

[0071] Figure 7 Task contract flowchart of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application;

[0072] Figure 8 Fault-tolerant mechanism flowchart in the task contract flow of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application;

[0073] Figure 9 Data hierarchical storage mechanism in the task contract flow of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application;

[0074] Figure 10 Task review flowchart of the unmanned aerial vehicle task coordination technology based on the blockchain of the present application. DETAILED DESCRIPTION

[0075] To make the objectives, technical solutions and advantages of the present application clearer, further detailed description will be given to the present application in combination with embodiments and drawings.

[0076] As shown in Figure 1 The present embodiment provides a kind of unmanned aerial vehicle task coordination method based on blockchain, including multiple unmanned aerial vehicles and multiple center servers, the center server of belonging is arranged in different positions;Center server cluster deployed in each position is shown in figure center, connect server by network, and there is a chain (Chain1) between center server for consensus center server needs to be stored in information;Outside is a lightweight blockchain (Chain2), deployment is on unmanned aerial vehicle cluster, unmanned aerial vehicle can communicate with each other, and situation synchronization, task coordination are completed by smart contract.The unmanned aerial vehicle cluster can exist multiple, and the chain in different unmanned aerial vehicle cluster has different number, and unmanned aerial vehicle data can be realized data isolation by different chain mode.

[0077] As shown in 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.

[0078] The present blockchain-based unmanned aerial vehicle task coordination workflow will be described below in combination with Figure 2

[0079] Step 1, start the first unmanned aerial vehicle and broadcast the identity information of the unmanned aerial vehicle 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 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;

[0080] 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 vehicle and the ground control station and the confidentiality of the communication, and prevent illegal access and data leakage;

[0081] Step 3, the unmanned aerial vehicle cluster uses a smart contract in lightweight blockchain technology to synchronize the situation, selects a 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, and use blockchain technology to realize real-time synchronization and sharing of key information among the unmanned aerial vehicle cluster, ensure the consistency and reliability of the data, especially in a weak network environment;

[0082] 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 chain and evidence the hash value of the key state data, ensuring 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 efficiency and adaptability of task execution, and ensure the consistency of the decision-making information of each node;

[0083] ​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.

[0084] The following will be described in detail Figure 3 , start the first unmanned aerial vehicle and broadcast its own identity information 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 an identity signal containing its own ID, GPS coordinates and other information to start the network and declare its existence, and is divided into three parallel processing 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 contents of the subsequent dynamic neighbor table are 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 contents of 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 updates 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 contents of the beacon frame are 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, high-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, the 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 for transmission, if there is not, it is processed in the first-in-first-out (FIFO) manner.

[0085] 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.

[0086] The following will be described in detail Figure 4, the detailed description task execution for each unmanned aircraft is granted a digital certificate, and at the same time, write 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:

[0087] Identity verification process from the certificate issuing 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 aerial vehicle hardware security module (HSM), as the device identity of the authoritative credentials.

[0088] When the communication is established, both sides start the mutual identity authentication process, to ensure that each other identity real and reliable.

[0089] Then 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 realize the anti replay attack ability.

[0090] 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, to improve 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.

[0091] In the above-mentioned mutual identity authentication process, the steps are shown in Figure 5 :

[0092] 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);

[0093] Step 2, device B sends its certificate to device A;

[0094] Step 3, device A verifies the validity of device B's certificate (including signature, validity period and revocation list);

[0095] Step 4, after verification, send device A's certificate to device B;

[0096] Step 5, device B verifies the validity of device A's certificate (including signature, validity period and revocation list);

[0097] Step 6, Device A encrypts a symmetric key C with the public key of Device B;

[0098] Step 7, Device B decrypts with its own private key to obtain symmetric key C;

[0099] Step 8, Finally, Device A and Device B communicate using the symmetric key.

[0100] In the identity verification process, in addition to the method of using a central server as a trusted authority to issue a digital certificate containing device serial number, public key, validity period, etc. for each drone, and preloading it into the device HSM module through physical USB, distributed public key infrastructure (DPKI) combined with blockchain or distributed ledger technology can also be used to register drone identity information on the chain and manage certificate life cycle through smart contracts, achieving decentralized and tamper-proof identity authentication; or identity-based encryption (IBE) can be used, which directly uses the unique identifier of the drone (such as serial number) as the public key, and the private key is distributed by the key generation center (PKG), thus avoiding the complexity of preloading certificates. Another solution is self-sovereign identity public key (SSI), which allows the drone to generate a public-private key pair autonomously and register it to a distributed network through a decentralized identifier (DID), and combines 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 physical unclonable function (PUF) can be combined with 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 the 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.

[0101] In terms of secure communication, in addition to using simple public key encryption to exchange session keys to achieve secure communication, a middleware-based secure communication architecture can also be used, such as implementing trusted message relay through a message broker and cooperating with TLS two-way authentication to ensure end-to-end security; or using a lightweight key agreement protocol to achieve forward-secure key exchange, which is suitable for resource-constrained Internet of Things devices; an identity-based encryption scheme can also be introduced to allow drones to directly use a unique identifier (such as a serial number) as a public key for encrypted communication without the need for pre-installed certificates; for high-dynamic network environments, a group key management protocol can be used to achieve dynamic key updates for group communication; if privacy protection needs to be enhanced, zero-knowledge proof (ZKP) can be used to implement 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 achieve lightweight key derivation in wireless communication, and trusted execution environment (TEE)-based schemes can ensure the security of key generation and storage. These methods can be selected based on the specific application scenario, balancing between computational overhead, communication efficiency, security strength, and deployment complexity, to achieve efficient message routing while ensuring forward security.

[0102] 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, and the RAFT or PBFT consensus mechanism is selected according to the scene requirements to adapt to the data synchronization requirements in different scenarios; (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 UAV node enters the network, the smart contract copy obtained from the center server is automatically synchronized to the local trusted execution environment; the hash value of the UAV processing result and the situation information are stored on the chain; the center server chain stores the original image data and the 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.)

[0103] The process mainly includes the following steps:

[0104] Step 1, the situation synchronization process starts from the initialization phase, first loads the consensus algorithm configuration, and selects the appropriate consensus mechanism according to the scene requirements, if the scene requires high security, selects the PBFT consensus mechanism (the operator will select actively according to the scene situation in advance, the default is the PBFT consensus mechanism), if more attention is paid to efficiency, selects the RAFT consensus mechanism. (If the network condition is stable, the RAFT protocol is used; if node failure occurs, the lightweight PBFT protocol is used to ensure fault tolerance.)

[0105] Step 2, after entering the situation coordination phase, real-time state acquisition is continuously carried out, and the situation information of the unmanned aerial vehicle, including the longitude, latitude, height and speed of the unmanned aerial vehicle, is stored through blockchain technology to ensure that the data is not tamperable and has traceability;

[0106] Step 3, then the collected state information is verified by using a smart contract, and it is judged whether there is an abnormal situation. If an abnormality is detected, an 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 working state is continued. This process is repeated continuously during the task duration, ensuring efficient synchronization and consistency of situation information between nodes, and building a safe, reliable and self-adaptive coordination working mechanism.

[0107] Optionally, in the above scheme, PBFT or RAFT is used for consensus according to different application scenarios. In addition to this method, there are other consensus algorithms available in blockchains, such as Paxos, Raft, Viewstamped Replication (VR) and Zab for classic non-Berlin fault-tolerant algorithms; PBFT, SBFT, DBFT and HoneyBadgerBFT for Byzantine fault-tolerant algorithms; PoW derivatives including Bitcoin PoW, Ethash, RandomX, Equihash and CuckooCycle; PoS and its variants such as pure PoS (Peercoin, Blackcoin), hybrid mechanisms (DPoS, LPoS, PoSV, MPoS) and new PoS (Ouroboros, Casper FFG, Tendermint); other innovative consensus mechanisms such as PoET, PoA, PoSpace, PoET, PoH, Algorand's Pure PoS, Avalanche consensus, Hashgraph's Gossip about Gossip, and specific scene optimization Firefly, Snowball and Conflux's GHOST variant, etc.

[0108] Or use the simple consensus mechanism of blockchain node layering. First, according to the performance and credibility, the nodes are divided into three layers of core layer, intermediate layer and edge layer. The core layer is composed of a few high-performance full nodes (such as central servers) running strong consistency algorithms (such as optimized PBFT or Tendermint), which are 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 undertake transaction pre-execution and error detection. The edge layer broadcasts transactions through a lightweight protocol (such as Gossip network) and uses Merkle proof to verify block header information. The key data synchronization uses the "checkpoint + incremental update" mode. This layered structure controls the consensus complexity through the core layer, disperses the verification pressure through the intermediate layer, and optimizes the access efficiency through the edge layer, while maintaining the Byzantine fault tolerance capability, which can increase the transaction throughput by 3-5 times.

[0109] Optionally, the above method uses a smart contract in lightweight blockchain technology for unmanned aerial vehicle situation synchronization, and lightweight AI collaboration can also be used. Through a distributed situation awareness architecture and intelligent optimization algorithm, efficient situation synchronization of the unmanned aerial vehicle group is achieved. A lightweight CNN-LSTM hybrid model is deployed on the edge computing node, and a federated learning framework is used to make each unmanned aerial vehicle upload only the model gradient parameters after local training, which reduces a large amount of communication load compared to the traditional smart contract solution. Combined with the Gossip protocol, near real-time state propagation is achieved, 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 network conditions and 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 computational overhead. At the same time, a layered anomaly detection mechanism is designed, which first identifies abnormal behavior by the local LSTM model, then cooperatively verifies through the federated averaging algorithm, and finally arbitrates the disputed state by the edge node.

[0110] The following will be described in detail Figure 7 , the task contract flow chart in the task coordination of unmanned aerial vehicle based on blockchain,

[0111] During task execution, the unmanned aerial vehicle 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 the transparency and traceability of the task execution process. The process mainly includes the following steps:

[0112] Step 1, the task contract flow starts with the deployment of a smart contract from the command center, entering the task coordination stage.

[0113] Step 2, the node continuously listens to the chain events and obtains the assigned subtasks;

[0114] 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 data processing, select the working mode according to whether dynamic decision is needed: if dynamic decision is enabled, perform global path planning and local obstacle avoidance simultaneously; otherwise, enter the basic cruise mode;

[0115] Step 4, the task execution result is stored in the chain after hash calculation, ensuring that the process is traceable and tamper-proof. Task data is stored in a hierarchical manner. Data synchronization is performed between nodes to ensure consistent understanding of task status by all participants. This series of operations is repeated continuously during the task duration;

[0116] 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 in the blockchain, building a safe, transparent, and trusted task execution and management closed-loop system.

[0117] In the task contract process execution of the unmanned aerial vehicle task collaboration based on the blockchain, there is a fault-tolerant mechanism step as shown in Figure 8 .

[0118] Step 1, the fault-tolerant mechanism of the task contract process flow enters the judgment link after the flow starts, first detects whether there is a node failure;

[0119] Step 2, if a node failure is detected, which manifests as an abnormal heartbeat detection, trigger the smart contract to perform network reorganization; if no node failure is detected, maintain normal collaborative working state and continue to Step 1;

[0120] Step 3, automatically select a backup node to take over the task of the failed node;

[0121] Step 4, update the network state through the consensus mechanism to ensure continuous operation and uninterrupted execution of the task.

[0122] In the fault recovery process, it has anti-destroying characteristics, including seamless task connection, automatic range expansion, and power self-adaptation, etc., to ensure that the entire task network can still operate stably and adjust flexibly under dynamic changes or partial node failure.

[0123] In the task contract process execution, the data hierarchical storage method is as follows Figure 9As shown, data storage is divided into three parts: server cluster storage, drone cache, and drone-on-chain storage. The server cluster storage is responsible for storing and reaching consensus on the data collected by the drones that requires authentication; the drone cache stores data collected within the last seven days; and the drone-on-chain consensuses the hash values ​​of the collected data. Data is collected and initially processed by the drones, dividing it into data requiring authentication and the remaining collected data, and generating image processing results. The image data requiring authentication is transmitted to the central server, where the central server cluster reaches consensus on authentication. All images are stored locally on the drones using a cyclic overwrite method. The data hash values ​​and image processing results are then authenticated on the drone-on-chain, and the drone cluster reaches consensus on authentication.

[0124] The following will combine Figure 10 The task is described in detail after completion, including reading on-chain proof data and the task execution process.

[0125] Conduct a comprehensive review and generate a visual report for subsequent task optimization. The specific process mainly includes the following steps:

[0126] Step 1: First, read the 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.

[0127] Step 2 involves parsing and verifying the read data. First, the task information is parsed and the completeness of the fields is checked. If any field is missing, an alarm is triggered; otherwise, processing continues. Then, the flight trajectory data is parsed, the validity of the coordinates is verified, and the task status is standardized to a uniform format (0 for success, 1 for failure, and 2 for partial completion).

[0128] Step 3: Continue to parse the abnormal event records, extract the detailed data nested within them, and then enter the data cleaning stage to remove outliers and drift points, smooth the high-order data, complete the time series alignment, and complete or delete missing values.

[0129] Step 4: After the data cleaning is completed, a structured data table is generated for subsequent analysis. Then, the actual completion of the task is confirmed based on the cleaned data. The evaluation dimensions include whether the trajectory is compliant, the degree to which the task objectives are achieved, and the impact of abnormal events.

[0130] Step 5: Finally, based on the above analysis results, optimization suggestions are generated to guide the improvement and execution of subsequent tasks, forming a closed-loop task management process, which improves task traceability, problem location capabilities, and overall execution efficiency.

[0131] The application also provides a blockchain-based unmanned aerial vehicle task coordination system, comprising the following modules:

[0132] An air self-organizing network module is used to establish an initial network by broadcasting identity information by the unmanned aerial vehicle and to manage a dynamic neighbor table established by subsequent unmanned aerial vehicles through a beacon frame;

[0133] An identity authentication module is responsible for preset certificate distribution, blockchain storage and real-time verification of smart contracts, and comprises a certificate burning function of a hardware security module;

[0134] A situation synchronization module selects a RAFT or PBFT consensus protocol according to a network expected state before the unmanned aerial vehicle starts, and realizes flight situation synchronization according to a smart contract after the unmanned aerial vehicle starts;

[0135] A task contract module integrates a local trusted smart contract execution engine, realizes data hierarchical storage, and realizes automatic task allocation and on-chain data storage;

[0136] A task review module provides a blockchain data analysis tool and a visualization tool, and supports task process tracing and performance evaluation report generation.

[0137] The above specific embodiments further specifically describe the purposes, technical solutions and beneficial effects of the application. It should be understood that the above description is only for specific embodiments of the application and is not used to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application should be included in the protection scope of the application.

Claims

1. A method for coordinating tasks of a cluster of unmanned aerial vehicles (UAVs) based on a blockchain, the method comprising: It includes multiple unmanned aerial vehicles and multiple central servers, the central servers are arranged at different positions, and there is a chain Chain1 between the central servers for consensus of information that needs to be stored and fixed by the central servers; An external lightweight blockchain Chain2 is arranged on the unmanned aerial vehicle cluster, the unmanned aerial vehicles can communicate with each other, and the situation synchronization and task cooperation are completed through a smart contract; each unmanned aerial vehicle in the unmanned aerial vehicle cluster can be connected and communicated with the central server to transmit data, there can be multiple unmanned aerial vehicle clusters, and the chains in different unmanned aerial vehicle clusters have different numbers, and the unmanned aerial vehicle data is isolated through different chains; and the data flow in the unmanned aerial vehicle task cooperation is that the unmanned aerial vehicles cache the synchronized flight situation, task data, consensus information and contract information data that need 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 vehicle transmits video data, picture data, processing results and task result data to the server, and the server transmits task data to the unmanned aerial vehicle; The servers synchronize full data, each server stores full data locally, and the servers synchronize the full data through a blockchain, and the method comprises the following steps: (1) starting the first unmanned aerial vehicle and broadcasting the identity information of the unmanned aerial vehicle 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; (2) before task execution, a digital certificate is granted to each unmanned aerial vehicle, and the certificate is written into a blockchain distributed ledger to achieve permanent storage; when the device accesses the network, the validity of the certificate is verified in real time through a smart contract; (3) the unmanned aerial vehicle cluster uses a smart contract in lightweight blockchain technology to synchronize the situation, and selects 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 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 key state data on a chain to ensure that the task execution process is transparent and traceable; (5) after the task is completed, the fixed data on the chain is read, the task execution process is fully reviewed, and a visual report is generated for subsequent task optimization. 2.The UAV swarm task coordination method of claim 1, wherein, The step (1) specifically comprises the following sub-steps: (1.1) starting from the first unmanned aerial vehicle, broadcast the identity signal containing the ID, GPS coordinate information of the unmanned aerial vehicle to start the network and declare the existence of the unmanned aerial vehicle; and generate a cluster ID for distinguishing other adjacent clusters, define the format of the beacon frame, the cluster ID and the time synchronization timestamp; And periodically send beacon frame, while listening to the smart contract by the task command center to encode the task rules, including task priority, resource allocation strategy and permission check logic, when the unmanned aerial vehicle node into the network automatically synchronize the smart contract copy from the center server to the local trusted execution environment; On-chain storage of unmanned aerial vehicle processing result hash value and situation information; Center server chain storage of raw image data and secondary processing results that need to be fixed; Unmanned aerial vehicle local cache recent data, data exceeding the set storage time is overwritten according to the cycle coverage strategy; From the beacon frame of other nodes, according to the received information, constantly update the dynamic neighbor table, 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 of the node, the remaining power and the received signal strength; If the neighbor node has response, the routing table is updated according to the preferred strategy; If the neighbor node has no response, the routing reorganization mechanism is triggered to bypass the fault or weak connection node, and this process continues during the network operation; (1.3) in the data forwarding and load balancing process, when the node receives the data transmission request, the system applies the time to live TTL and sequence number mechanism to prevent data flooding; Then judge whether there is high priority data to be transmitted, if yes, schedule its transmission first, if not, process the data in first in first out FIFO mode. 3.The UAV swarm task coordination method of claim 1, wherein, The step (2) specifically includes the following substeps: (2.1) using the pre-set certificate distribution mechanism, the digital certificate containing the unique identification ID of the unmanned aerial vehicle device mounted module, public key, agency signature and permission information is burned into the hardware security module HSM of the unmanned aerial vehicle, as the certificate of device identity, eliminating the dependence on online trusted agency CA; (2.2) during communication establishment, both sides start the mutual authentication process to ensure each other's identity; Then enter the encryption transmission link, the data is encrypted by session key C and attached with timestamp and random number; At the same time, message signature is generated for integrity check and identity confirmation, which is repeated constantly during the communication and realizes the anti-replay attack ability through timestamp 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 judge whether the current operation is within its permission range, and the time and space parameters are combined 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, continue normal communication. 4.The method of claim 3, wherein, The process of mutual authentication in step (2.2) is as follows: (2.2.1) 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 except the device A; (2.2.2) device B sends its certificate to device A; (2.2.3) device A verifies the validity of device B's certificate; Including signature, validity period and revocation list; (2.2.4) After the verification, send the certificate of device A to device B; (2.2.5) Device B verifies the validity of the certificate of device A; Including signature, validity period and revocation list; (2.2.6) Device A encrypts a symmetric key C with the public key of device B; (2.2.7) Device B decrypts it using its own private key to obtain the symmetric key C; (2.2.8) Finally, device A and device B use the symmetric key for communication.

5. The method of claim 1, wherein, The step (3) specifically includes the following sub-steps: (3.1) The situation synchronization initialization stage first loads the consensus algorithm configuration, and selects the appropriate consensus mechanism according to the scene requirements. If the scene requires high security, select PBFT consensus mechanism, if the scene requires efficiency, select RAFT consensus mechanism; In addition to PBFT or RAFT for consensus, there are non-Bayesian fault-tolerant algorithms; Byzantine fault-tolerant algorithm; PoW derivative algorithm and simple consensus mechanism using block chain node layering; (3.2) After entering the situation coordination stage, real-time state acquisition is continuously carried out, and the situation information of the unmanned aerial vehicle is fixed and stored through block chain technology, ensuring that the data cannot be tampered with and has traceability; (3.3) Then use the smart contract to check the collected state information to determine if there is an abnormal situation; If an anomaly is detected, trigger the alarm mechanism and record the alarm information on the chain, and automatically adjust the coordination strategy to deal with the abnormal situation; If no abnormalities are found, continue to maintain normal coordination state. 6.The UAV swarm task coordination method of claim 1, wherein, In step (4), the smart contract is obtained by encoding the task rules from 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 hash value of the unmanned aerial vehicle processing result and the situation information are stored on the chain; The center server stores the original image data and the secondary processing result which need to be fixed on the chain; The local cache of the unmanned aerial vehicle stores the latest data, and the data exceeding the set storage time is overwritten according to the cycle coverage strategy.

7. The method of claim 1, wherein, In step (4), the following sub-steps are included: (4.1) The task contract process starts with the deployment of smart contract from the command center, entering the task coordination stage; (4.2) The node continuously listens to the on-chain events and obtains the assigned sub-tasks; (4.3) Enter the task data processing link, realize high-precision time alignment through hardware-level time stamp synchronization technology PTP or GNSS, and align the processing of multi-source data under the unified time reference; Respectively and in parallel, image preprocessing and GNSS data optimization are performed; The preprocessing includes noise reduction, enhancement and correction; After completing the data processing, select the working mode according to whether dynamic decision is needed: 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, ensuring that the process is traceable and tamper-proof; Task data is stored in a hierarchical manner; Data synchronization is performed between nodes to ensure that all participants have consistent task status, and this 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, so that a task execution and management closed loop system is constructed. 8.The method of claim 7, wherein, The task contract in the step (4.1) further comprises a fault tolerance mechanism, and the fault tolerance mechanism in the task contract process comprises the following sub-steps: (4.1.1) The fault tolerance mechanism of the task contract flowchart enters the judgment link after the flowchart starts, and first detects whether there is a node failure; (4.1.2) If a node failure is detected, which is manifested as an abnormal heartbeat detection, the intelligent contract is triggered to recombine the network; if no node failure is detected, the normal cooperative working state is maintained, and step (4.1.1) is continued; (4.1.3) Automatically select a standby node to take over the task of the failed node; (4.1.4) Update the network state through the consensus mechanism to ensure continuous operation and uninterrupted execution of the task. 9.The UAV swarm task coordination method of claim 7, wherein, The data hierarchical storage in the 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 hash value of the data collected by the unmanned aerial vehicle on-chain consensus; (4.4.2) The data collected by the unmanned aerial vehicle are preliminarily processed, and the collected data are divided into data needing to be fixed and the rest of the collected data, and an image processing result is generated; (4.4.3) The image data needing to be fixed are transmitted to the central server, and the central server cluster performs fixed consensus on the data; (4.4.4) All images are stored in the local unmanned aerial vehicle through the method of cyclic coverage; (4.4.5) The data hash value and the image processing result are fixed on the unmanned aerial vehicle chain, and the unmanned aerial vehicle cluster performs fixed consensus thereon. 10.The UAV swarm task coordination method of claim 1, wherein, 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 collected data, intelligent contract interaction record and communication data, then further extract the 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 an alarm, otherwise continue processing; then analyze the flight trajectory data, check the validity of the coordinates, and standardize the task status 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, then enter the data cleaning stage, remove abnormal values and drift points, smooth the height data, align the time sequence, and complete 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 track is compliant, the degree of task target achievement, and the influence degree of abnormal events; (5.5) Finally, based on the analysis results above, optimization suggestions are generated to guide the improvement and execution of subsequent tasks, forming a closed-loop task management.

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