Aircraft data evidence storage method and system based on block chain

Abstract

According to the block chain-based aircraft data evidence storage method and system provided by the invention, node types and edge types are defined according to a plurality of aircrafts which cooperatively execute tasks, task instructions, decision events, data abstracts, coordination actions and preset relationships among the aircrafts, the task instructions, the decision events, the data abstracts and the coordination actions, and a coordination task event atlas is constructed on a main block chain; the method comprises the following steps: constructing a local cooperative processing network according to cooperative information among multiple aircrafts, creating specific node association in an anchoring node association graph through the local cooperative processing network when a preset condition is met, and managing creation and updating of all nodes and edges in the graph through an intelligent contract. Compared with the prior art, the method has the advantages that the challenge of data evidence storage in a complex emergency collaboration scene is solved through the dynamic collaboration task event atlas and a hierarchical consensus anchoring mechanism, and structured, credible and efficient recording of task full-life-cycle information is realized, so that the credibility and auditing performance of data are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of blockchain and aircraft, and particularly to a method and system for storing and authenticating aircraft data based on blockchain. Background Art

[0002] In the scenario where multiple heterogeneous aircraft cooperate to execute dynamic reorganization tasks, such as switching from regular patrol to emergency response to emergencies, the existing blockchain-based aircraft data storage and authentication methods face the problems of how to efficiently and credibly record and manage the dynamic reorganization process itself on the basis of ensuring the basic credibility of the data independently collected by each aircraft, including early warning discovery, decision-making instructions, data handover, task parameter adjustment, and cooperation strategy update; how to clearly define and trace the contribution shares of each aircraft, which may belong to different rights and interests subjects, to the final data results in collaborative operations; how to ensure the accurate restoration of the collaborative operation timing and logic in the presence of communication asynchrony and data dependencies; how to economically and effectively store the high-frequency and minute cooperation status information between aircraft without sacrificing the integrity of key nodes; and how to construct a complete and auditable data pedigree and rights and interests distribution mechanism from the top-level task objectives to the off-chain original data of each participating aircraft. These problems make it difficult to obtain a comprehensive, coherent, and recognized credible evidence chain during the post-event audit of complex collaborative tasks, multi-party contribution evaluation, data rights and interests distribution, and emergency response process review.

[0003] Therefore, it is an urgent problem for those skilled in the art to provide a blockchain-based aircraft data storage and authentication method and system for solving the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a blockchain-based aircraft data storage and authentication method, which has clear logic, is safe, effective, reliable, and easy to operate, and improves data credibility and auditability by constructing a structured and traceable blockchain-based aircraft data storage system.

[0005] Based on the above purpose, the technical solution provided by the present invention is as follows: A blockchain-based aircraft data storage and authentication method, which is applied to the emergency scenario of aircraft cooperation in executing tasks and deployment, and includes the following steps: Define node types and edge types according to multiple aircraft cooperating in executing tasks, task instructions, decision-making events, data summaries, cooperation actions, and preset relationships therebetween, so as to construct a collaborative task event graph on the main blockchain; Construct a local collaborative processing network based on the collaborative information among multiple aircrafts executing tasks collaboratively. When the local collaborative processing network meets the preset conditions, create an anchor node in the collaborative task event graph according to the collaborative information, and associate the anchor node with a specific node in the collaborative task event graph; Manage the creation and update of all nodes and edges in the collaborative task event graph according to the smart contract deployed on the main blockchain.

[0006] Preferably, defining node types and edge types according to multiple aircrafts executing tasks collaboratively, task instructions, decision events, data summaries, collaborative actions, and preset relationships among them to construct a collaborative task event graph on the main blockchain includes the following steps: According to multiple aircrafts executing tasks collaboratively, task instructions, decision events, data summaries, and collaborative actions, and preset relationships among them, define node types including: aircraft node, task instruction node, decision event node, data summary node, and collaborative action node, and define edge types including: trigger edge, execution edge, generation edge, dependence edge, and belonging edge; On the main blockchain, create the aircraft node, the task instruction node, the decision event node, the data summary node, the collaborative action node, and the trigger edge, the execution edge, the generation edge, the dependence edge, and the belonging edge respectively according to the smart contract; Connect the trigger edge to connect the decision event node to the task instruction node, or connect the task instruction node to the collaborative action node, connect the execution edge to connect the aircraft node to the collaborative action node, connect the generation edge to connect the collaborative action node to the data summary node, connect the dependence edge between two data summary nodes or between two collaborative action nodes, and connect the belonging edge to connect the data summary node or the collaborative action node to the aircraft node to construct the collaborative task event graph.

[0007] Preferably, constructing a local collaborative processing network based on the collaborative information among multiple aircrafts executing tasks collaboratively, and when the local collaborative processing network meets the preset conditions, creating an anchor node in the collaborative task event graph according to the collaborative information and associating the anchor node with a specific node in the collaborative task event graph includes the following steps: Obtain the collaborative information through mutual communication among multiple aircrafts performing collaborative flight tasks, and construct the local collaborative processing network to record the collaborative information; When the local collaborative processing network meets the preset conditions, generate a collaborative event log and summary information according to the collaborative information, and encapsulate them into a collaborative anchor transaction; Verify the collaborative anchoring transaction according to the smart contract. After successful verification, create an anchoring node in the collaborative task event graph. Define and create an association edge, and connect the association edge to connect the anchoring node to the aircraft node or connect the association edge to connect the anchoring node to the parent collaborative action node.

[0008] Preferably, the node type further includes: contribution marking node. On the main blockchain, after creating the aircraft node, the task instruction node, the decision event node, the data summary node, the collaborative action node, and the trigger edge, the execution edge, the generation edge, the depends on edge, and the belongs to edge according to the smart contract respectively, the following steps are further included: Create the contribution marking node according to the smart contract. Record contribution evaluation messages in the contribution marking node according to the trigger edge, the execution edge, the generation edge, and the contribution weight rule.

[0009] Preferably, the node type further includes: deviation event node. On the main blockchain, after creating the aircraft node, the task instruction node, the decision event node, the data summary node, the collaborative action node, and the trigger edge, the execution edge, the generation edge, the depends on edge, and the belongs to edge according to the smart contract respectively, the following steps are further included: Obtain the actual state parameters of the aircraft in the collaborative flight task. Judge whether the difference between the actual state parameters and the theoretical state parameters defined by the task instruction node or the collaborative action node exceeds a preset threshold. If so, create the deviation event node according to the smart contract.

[0010] Preferably, the edge type further includes: source from plan edge, response action edge, and impact data record edge. After creating the deviation event node according to the smart contract, the following steps are further included: Create the source from plan edge, the response action edge, and the impact data record edge according to the smart contract. Connect the source from plan edge to connect the original planned collaborative action node to the deviation event node. When the deviation event leads to the creation of a new collaborative action node, connect the response action edge to connect the deviation event node to the new collaborative action node. When the deviation event leads to the creation of a new data summary node, connect the impact data record edge to connect the deviation event node to the new data summary node.

[0011] Preferably, the node types further include: data quality nodes and evaluation subject nodes; After creating the aircraft node, the mission instruction node, the decision event node, the data summary node, the collaborative action node, as well as the trigger edge, the execution edge, the generation edge, the dependent on edge, and the belongs to edge respectively on the main blockchain according to the smart contract, the following steps are further included: When the collected data of any aircraft passes the quality evaluation of the evaluation subject, the data quality node and the evaluation subject node are created according to the smart contract.

[0012] Preferably, the edge types further include: declared quality edge and evaluation subject edge; After creating the data quality node and the evaluation subject node according to the smart contract when the collected data of any aircraft passes the quality evaluation of the evaluation subject, the following steps are further included: Create the declared quality edge and the evaluation subject edge according to the smart contract; Connect the declared quality edge to connect the data quality node to the data summary node, and connect the evaluation subject edge to connect the data quality node to the evaluation subject node.

[0013] Preferably, obtaining the contribution weight rule includes the following steps: Classify the collected data into different quality levels according to the data quality node; Assign different contribution weights to the collected data of different quality levels respectively according to the preset rules; Calculate and obtain the contribution scores of the collected data of different quality levels.

[0014] A blockchain-based aircraft data deposit and proof system includes: A graph construction module for defining node types and edge types according to multiple aircrafts, mission instructions, decision events, data summaries, collaborative actions for collaborative execution of tasks, and preset relationships therebetween, so as to construct a collaborative task event graph on the main blockchain; An anchoring and associating module for constructing a local collaborative processing network according to the collaborative information between multiple aircrafts for collaborative execution of tasks. When the local collaborative processing network meets the preset conditions, an anchoring node is created in the collaborative task event graph according to the collaborative information, and the collaborative information node is associated with a specific node in the collaborative task event graph; A smart contract management module for managing the creation and update of all nodes and edges in the collaborative task event graph according to the smart contract deployed on the main blockchain.

[0015] The method for storing aircraft data based on blockchain provided by the present invention defines node types and edge types according to multiple aircrafts that jointly execute tasks, task instructions, decision events, data digests, collaborative actions, and preset relationships among them, constructs a collaborative task event graph on the main blockchain, constructs a local collaborative processing network according to the collaborative information among multiple aircrafts, creates an association between an anchor node and a specific node in the graph when the preset conditions are met through the local collaborative processing network, and manages the creation and update of all nodes and edges in the graph through a smart contract.

[0016] Compared with the prior art, the present invention solves the challenges of data storage and evidence in complex emergency collaborative scenarios through a dynamic collaborative task event graph and a hierarchical consensus anchoring mechanism, and realizes the structured, trustworthy, and efficient recording of information throughout the task life cycle, so as to improve data credibility and auditability.

[0017] The present invention also provides a system for storing aircraft data based on blockchain. Since it belongs to the same technical concept as the method and solves the same technical problems, it should have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of a method for storing aircraft data based on blockchain provided by an embodiment of the present invention; Figure 2 It is a flowchart of step S1 provided by an embodiment of the present invention; Figure 3 It is a flowchart of step S2 provided by an embodiment of the present invention; Figure 4 It is a flowchart of the first implementation manner after step A2 provided by an embodiment of the present invention; Figure 5 It is a flowchart of the second implementation manner after step A2 provided by an embodiment of the present invention; Figure 6 It is a flowchart of the third implementation manner after step A2 provided by an embodiment of the present invention; Figure 7 It is a flowchart of obtaining the contribution weight rule in step C2 provided by an embodiment of the present invention; Figure 8Schematic diagram of a blockchain-based aircraft data deposit and certification system provided by an embodiment of the present invention. Detailed implementation manners

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] The embodiments of the present invention are written in a progressive manner.

[0022] The embodiments of the present invention provide a blockchain-based aircraft data deposit and certification method and system. The technical problem mainly solved in the prior art is that it is difficult to obtain a comprehensive, coherent, and mutually recognized credible evidence chain during the post-audit of complex collaborative tasks, multi-party contribution evaluation, data rights and interests distribution, and the review of the emergency response process.

[0023] As Figure 1 shown, a blockchain-based aircraft data deposit and certification method, which is applied to the emergency scenario of aircraft collaborative task execution and deployment, includes the following steps: S1. Define node types and edge types according to multiple aircrafts executing tasks collaboratively, task instructions, decision events, data summaries, collaborative actions, and the preset relationships among them, so as to construct a collaborative task event graph on the main blockchain; S2. Construct a local collaborative processing network according to the collaborative information among multiple aircrafts executing tasks collaboratively. When the local collaborative processing network meets the preset conditions, create an anchor node in the collaborative task event graph according to the collaborative information, and associate the anchor node with specific nodes in the collaborative task event graph; S3. Manage the creation and update of all nodes and edges in the collaborative task event graph according to the smart contract deployed on the main blockchain.

[0024] In step S1, by defining node types (such as aircraft nodes, task instruction nodes, decision event nodes, data summary nodes, collaborative action nodes) and edge types according to multiple aircrafts executing tasks collaboratively, task instructions, decision events, data summaries, collaborative actions, and the preset relationships among them, capture the key entities, events, and their complex causal, temporal, and attribution relationships in the task; In step S2, for the high-frequency, low-value but important small collaborative information within the aircraft cluster (such as sensor parameter fine-tuning communication and avoidance path negotiation), this information is quickly interacted and recorded between aircraft through a local collaborative processing network that builds a dedicated communication link. When the preset conditions are met, the local collaborative processing network creates an anchor node in the collaborative task event graph and associates the anchor node with a specific node in the graph; In step S3, the smart contract deployed on the main chain is the hub of this solution. The graph management contract is responsible for defining the structure of the graph (Schema), providing interfaces for creating and updating graph elements, and managing access rights. The anchor processing contract is responsible for receiving and verifying anchor transactions from the local network and integrating them into the main graph.

[0025] By constructing a dynamic collaborative task event graph and combining it with a hierarchical consensus anchoring mechanism, this technical solution can bring the following significant technical effects in emergency response task scenarios where multiple heterogeneous aircraft perform dynamic reorganization: Achieve complete traceability of the decision chain for dynamic task reorganization: Through special decision event nodes, task instruction nodes (including task reorganization types) and the triggering relationships between them, the complete decision chain from early warning discovery to the issuance and adjustment of new tasks is clearly and tamper-proof recorded, providing a solid decision-making basis for post-audit; Accurately express the complex collaboration and strong data correlation between heterogeneous multiple aircraft: The nodes and edges of the event graph can flexibly and accurately depict the actions performed by different aircraft in different time and space, the data generated, and the complex time sequence and logical dependencies between them (such as analytical dependencies between data, and sequential or conditional relationships between behaviors), thereby accurately restoring the full picture of collaborative operations; Provide a data basis for contribution definition and trusted incentives under the participation of multiple stakeholders: Aircraft nodes can record information about the affiliated institutions, and data and actions are clearly attributed to specific aircraft through the graph. Combined with optional contribution marking nodes and smart contract rules, it can provide a transparent and verifiable quantitative basis for the contribution evaluation of different stakeholders based on the discovery, execution, data generation and other behaviors recorded in the graph; efficiently realize the hierarchical evidence of high-frequency micro-collaboration and key events: process high-frequency micro-collaboration information through a local collaborative processing network, and anchor its summary to the main chain event graph, which not only avoids the problems of main chain congestion and excessive costs, but also retains the traceability and integrity proof of these micro-collaboration processes, and realizes the hierarchical, differentiated and efficient recording of key and auxiliary information; build a comprehensive, coherent and auditable end-to-end trusted data genealogy: based on the event graph, it can trace back from the final results to all relevant original data collection records, task instructions and decision-making processes step by step, and combine the secure and verifiable association between the on-chain summary and the off-chain original data to form a complete trusted data genealogy covering the entire life cycle of the task, which greatly improves the efficiency, accuracy and credibility of emergency response task audits, multi-party contribution assessments, data rights and interests division and process review.

[0026] As Figure 2 shown, preferably, step S1 includes the following steps: A1. According to the preset relationships among multiple aircrafts that cooperate to execute tasks, task instructions, decision events, data digests, and cooperative actions and among them, define the node types including: aircraft node, task instruction node, decision event node, data digest node, and cooperative action node, and define the edge types including: trigger edge, execution edge, generation edge, dependent on edge, and belongs to edge; A2. On the main blockchain, create an aircraft node, a task instruction node, a decision event node, a data digest node, a cooperative action node, as well as a trigger edge, an execution edge, a generation edge, a dependent on edge, and a belongs to edge respectively according to the smart contract; A3. Connect the trigger edge to connect the decision event node to the task instruction node, or connect the task instruction node to the cooperative action node, connect the execution edge to connect the aircraft node to the cooperative action node, connect the generation edge to connect the cooperative action node to the data digest node, connect the dependent on edge between two data digest nodes, or between two cooperative action nodes, and connect the belongs to edge to connect the data digest node or the cooperative action node to the aircraft node to construct a cooperative task event graph.

[0027] In step A1, the aircraft node (AircraftNode): records the unique identifier of the aircraft (such as DID), type, affiliated institution, etc.

[0028] The task instruction node (InstructionNode): records the instruction ID, sender, receiver, instruction content (such as a new route, sensor parameters, cooperation strategy), and timestamp. In particular, it includes the subtype of "task reorganization instruction".

[0029] The decision event node (DecisionNode): records the key decision points, such as "emergency task start", "cooperation goal confirmation", includes the decision basis (such as the warning data digest pointing to aircraft A), the parties participating in the decision, and the decision result.

[0030] The data digest node (DataDigestNode): records the data hash, metadata (type, collection time, sensor parameters, aircraft status), off-chain storage pointer, and the ID of the affiliated aircraft node.

[0031] The cooperative action node (ActionNode): records the key behaviors of the aircraft, such as "instruction reception confirmation", "arrival at the specified area", "initiation / reception of data sharing", "completion of sensor adjustment", includes the ID of the executing aircraft, the associated instruction ID, timestamp, and status parameters.

[0032] In step A3, the Triggers edge: connects a decision event node to a task instruction node, or a task instruction node to a collaborative action node.

[0033] The Executes edge: connects an aircraft node to a collaborative action node. The Generates edge: connects a collaborative action node (such as a data collection action) to a data summary node.

[0034] The DependsOn edge: connects one data summary node to another data summary node (indicating data analysis dependency), or one collaborative action to another collaborative action (indicating the sequence or conditional relationship of behaviors).

[0035] The BelongsTo edge: connects a data summary node or a collaborative action node to an aircraft node, indicating ownership.

[0036] In this embodiment, it is assumed that in a geological disaster emergency response task, aircraft A (the discoverer), aircraft B (video monitoring), aircraft C (3D modeling), and aircraft D (LiDAR scanning) participate in collaboration.

[0037] Vehicle A generates preliminary data on potential hazard points, and its summary is encapsulated as a `Data Summary Node` (DA_A_Alert), which is associated with the `Vehicle Node` (FN_A) through a `BelongsTo` edge. After receiving the alert, the Ground Command Center makes an emergency response decision and generates a `Decision Event Node` (DE_Activate Emergency), which points to a `Task Instruction Node` (IN_Reorganization_BCD) of the "Task Reorganization" type through a `Triggers` edge. This instruction node details the new task assignments for B, C, and D (e.g., B is responsible for video, C is responsible for 3D modeling, and D is responsible for lidar), and is respectively associated with FN_B, FN_C, and FN_D through `RelatesTo` edges. Vehicle B receives and confirms the new instruction, generating a `Collaborative Action Node` (AN_Confirm_B), which is associated with FN_B through an `Executes` edge and is reversely associated with IN_Reorganization_BCD through a `Triggers` edge (indicating response to the instruction). Vehicles C and D perform similar operations. Vehicle B conducts video surveillance in the target area and periodically uploads the summary of its video stream and key frame metadata as new `Data Summary Nodes` (DA_B_VideoFrame1, DA_B_VideoFrame2, ...) to the chain. Each `Data Summary Node` is associated with a `Collaborative Action Node` (AN_Collect Video_B) through a `Generates` edge. This action node `BelongsTo` FN_B and may `DependOn` IN_Reorganization_BCD. If the lidar scan of Vehicle D is a focused scan based on a specific crack area observed in the video of Vehicle B, the `Data Summary Node` (DA_D_Point Cloud) generated by D will clearly point to the relevant DA_B_VideoFrameX through a `DependOn` edge.

[0038] In this way, the event graph clearly records the entire process from alert discovery to task reorganization decision-making, then to specific instruction issuance, confirmation, execution, and data collection, and the logical dependencies between the data (such as D's dependence on B's data) are also clearly expressed. During post-event auditing, one can start from any node and trace its cause and effect.

[0039] As Figure 3 shown, preferably, step S2 includes the following steps: B1. Communicate with each other among multiple vehicles for the collaborative flight task, obtain collaborative information, and construct a local collaborative processing network to record the collaborative information; B2. When the local collaborative processing network meets the preset conditions, generate a collaborative event log and summary information according to the collaborative information, and encapsulate them as a collaborative anchoring transaction; B3. Verify the collaborative anchored transaction according to the smart contract. After successful verification, create an anchored node in the collaborative task event graph; B4. Define and create associated edges, and connect the associated edges to the aircraft node or connect the associated edges to the parent collaborative action node.

[0040] In step B1, for the high-frequency and minute collaborative information within the aircraft cluster (for example, aircraft A sends to aircraft B "Approaching your left side, please maintain the current altitude", and aircraft B replies "Received, maintaining altitude"), this information is interacted and recorded in a temporary network composed of dedicated communication links among the aircraft. This network can adopt lightweight consensus, such as simple confirmation based on a leader or multi-signature. The recorded content includes the two parties of the interaction, the timestamp, and the summary of the information content; In step B2, after the local collaborative processing network reaches preset conditions such as: a certain time window, a certain number of micro-collaborative events occur, or after a certain key collaborative stage is completed (such as a group of aircraft completing the collaborative scanning of a specific sub-region), it will encapsulate the Merkle root of all micro-collaborative event logs during this time period, together with the summary information of this batch of collaborations (such as the participating aircraft, start and end times, and main collaborative types), into a "collaborative anchored transaction"; In step B3, this anchored transaction is submitted to the graph management smart contract of the main chain. After the contract verifies the signature and format of the anchored transaction, it will create a special anchored node (MicroCoordinationAnchorNode) in the event graph, recording the Merkle root and summary information; In step B4, define and create associated edges in the time graph, and connect the associated edges to the aircraft node or connect the associated edges to the parent collaborative action node. In this way, the main chain graph does not store all the minute details, but retains the verifiable index of these details.

[0041] The edge types in the graph also include: RelatesTo edges: General association relationships, such as connecting a task reorganization instruction to the original task.

[0042] In this embodiment, the above-mentioned minute collaborative information between aircraft B and aircraft D (such as B sending "crack propagation direction vector {X, Y, Z}, confidence 0.85", and D replying "Received, scan path adjusted to this direction") is recorded in the local collaborative processing network constructed between them through a dedicated communication link. This network adopts lightweight consensus (such as signature confirmation by both parties), and the recorded content includes interaction timestamps, senders, receivers, information content summaries, etc. For example, the records are as follows: Record 1: {Time: T1, From: FN_B, To: FN_D, Type: ScanAssist, ContentDigest: Hash("direction vector {X, Y, Z}, confidence 0.85")}; Record 2: {Time: T2, From: FN_D, To: FN_B, Type: AckScanAdjust, ContentDigest: Hash("confirmed adjustment")}; After aircraft B and D complete the collaborative scan of this specific crack area, or reach a preset time window (such as every 5 minutes), the local collaborative processing network calculates the Merkle root (MTR_BD_Collaboration1) of this batch of micro-collaboration event logs (Record 1, Record 2,...). A "collaborative anchoring transaction" is submitted to the graph management smart contract on the main chain. This transaction contains MTR_BD_Collaboration1 and a summary of this batch of collaboration (such as participants: FN_B, FN_D; collaboration time period: T1 - Tn; collaboration purpose: crack detail scan). After the smart contract verifies this anchoring transaction, an `anchoring node` (MCAN_BD_Collaboration1) is created in the event graph on the main chain. This node records MTR_BD_Collaboration1 and the summary information, and is connected to the `aircraft nodes` FN_B and FN_D, as well as their current relevant parent `collaborative action nodes` (such as AN_CollaborativeScanCrack) through the `RelatesTo` edge.

[0043] Through this hierarchical anchoring mechanism, the main chain event graph does not store all the details of minute collaborations, avoiding the burden on the main chain. However, through the Merkle root in the anchoring node, the overall verifiability and non-deniability of these minute collaborative behaviors recorded in the local network are retained. When needed, verification can be carried out by combining the detailed micro-collaboration logs stored off-chain and the Merkle root anchored on-chain, ensuring the complete traceability of the collaboration process.

[0044] As Figure 4 shown, preferably, the node type also includes: contribution marker nodes; After step A2, the following steps are further included: C1. Create contribution marker nodes according to the smart contract; C2. Record the contribution evaluation message in the contribution marking node according to the trigger edge, execution edge, generation edge, and contribution weight rule.

[0045] In steps C1 to C2, create a contribution marking node in the graph according to the contribution evaluation auxiliary contract in the smart contract. The contribution marking node initially marks the contribution type of specific data or behavior (such as "primary discovery", "key evidence provision"), which can be generated by the smart contract according to preset rules or multi-party confirmation; according to the "trigger", "execution", "generation" relationships recorded in the graph (i.e., trigger edge, execution edge, and generation edge), as well as the contribution weight rule or the result of multi-party voting, automatically or semi-automatically record the preliminary contribution evaluation information in the contribution marking node.

[0046] As Figure 5 shown, preferably, the node type also includes: deviation event node; After step A2, the following steps are further included: D1. Obtain the actual state parameters of the aircraft in the cooperative flight mission; D2. Determine whether the difference between the actual state parameters and the theoretical state parameters defined by the mission instruction node or cooperative action node exceeds a preset threshold; D3. If so, create a deviation event node according to the smart contract.

[0047] In steps D1 to D3, obtain the actual state parameters of the aircraft through the aircraft-borne system monitoring. When the aircraft-borne system monitors that the difference between its actual state parameters (such as position, attitude, sensor readings) and the theoretical state parameters defined by the currently effective "mission instruction node" or "cooperative action node" exceeds the preset threshold, create a deviation event node in the graph according to the smart contract.

[0048] In this embodiment, the deviation event node records the following information: Associated planned task identifier: a reference pointing to the originally planned "mission instruction node" or "cooperative action node"; Deviation trigger reason: record the reason code (e.g., meteorological conditions, obstacle avoidance operation, equipment failure, autonomous path optimization) and supplementary text description that cause the deviation. This information can be derived from the fusion judgment result of airborne sensor data or ground control instructions; Aircraft immediate response measures: record the type of response measures taken by the aircraft for the deviation situation (e.g., route adjustment, sensor scan mode change, operation suspension, backup system activation) and related key parameters (e.g., new waypoint coordinates, new scan area definition); Deviation event spatio-temporal characteristics: record the start timestamp, end timestamp or duration of the deviation event, as well as the geographical scope of the deviation or a quantified deviation degree index; Responsible aircraft identifier: record the identity identifier of the aircraft that performs this deviation behavior.

[0049] Preferably, the edge types further include: DerivedFromPlan edges, RespondedWithAction edges, and AffectedDataRecord edges; As Figure 5 shown, after step D3, the following steps are further included: D4. Create DerivedFromPlan edges, RespondedWithAction edges, and AffectedDataRecord edges according to the smart contract; D51. Connect the collaborative action node of the original plan of the DerivedFromPlan edge to the deviation event node; D52. When the deviation event causes the creation of a new collaborative action node, connect the RespondedWithAction edge from the deviation event node to the new collaborative action node; D53. When the deviation event causes the creation of a new data summary node, connect the AffectedDataRecord edge from the deviation event node to the new data summary node.

[0050] In step D4, define the edge types related to the deviation event, and create DerivedFromPlan edges, RespondedWithAction edges, and AffectedDataRecord edges in the graph according to the smart contract; Steps D51, D52, and D53 are steps executed in parallel. The "DerivedFromPlan" edge: connects the "collaborative action node" of the original plan to the corresponding "plan deviation event node"; The "RespondedWithAction" edge: If the deviation event causes the aircraft to execute a new, specific "collaborative action node" not in the original plan (such as an emergency avoidance maneuver), then this edge points from the "plan deviation event node" to the newly generated action node; The "AffectedDataRecord" edge: points from the "plan deviation event node" to the newly created "data summary node" during the deviation. Alternatively, add a specific marker to the metadata of the new "data summary node" to indicate that its data was collected under the planned deviation state.

[0051] Through the above steps, the planned deviation behavior of the aircraft and its related context information can be structurally recorded in the event graph, coexisting with the regular task execution records and being clearly distinguishable. This mechanism enables users not only to trace the execution of the original plan during the post - analysis phase, but also to understand the details of various plan deviation events that occurred during the task execution, including their causes, the aircraft's response measures, and the specific impact on the data collection process, thus providing data support for task auditing, data quality grading evaluation, aircraft behavior pattern analysis, and iterative improvement of emergency collaboration strategies.

[0052] Preferably, the node types further include: data quality nodes and evaluation subject nodes; AsFigure 6 As shown, after step A2, the following steps are further included: E1. When the collected data of any aircraft is evaluated for the quality of the main body, create a data quality node and an evaluation main body node according to the smart contract.

[0053] In the actual application process, the quality of the data collected by the aircraft may vary. For example, the image may be blurred due to smoke occlusion, the sensor readings may be abnormal due to strong electromagnetic interference, low-quality data may be misused, or the contribution evaluation of the aircraft that generates low-quality data may be biased, affecting the accuracy and fairness of the entire emergency response task evaluation. Based on this, define a data quality node and an evaluation main body node in the graph. When the collected data completes the quality evaluation according to the evaluation main body, create a data quality node and an evaluation main body node in the graph according to the smart contract.

[0054] In this embodiment, the data quality node records the following information: Associated data summary identifier: Clearly point to the existing "data summary node" it evaluates; Evaluation main body identifier: Record the unit or system that performs this data quality evaluation (for example: the ID of the on-board quality evaluation module of the aircraft itself, the ID of a specific evaluation service of the ground data processing center, or the digital identity of a specific authorized expert); Evaluation timestamp: Record the time when the quality evaluation is completed; Quality evaluation parameter set: Include one or more groups of parameters describing data quality, such as: quantitative quality score (such as 0-100 points), quality level (such as: excellent, good, qualified, poor, unavailable), quality feature label (such as: clear, complete, noisy, partially missing, out of valid range), specific quality problem description text or code, evaluation method and basis: Record the identifier of the algorithm, standard or rule used in this quality evaluation.

[0055] The quality evaluation includes two types. If it is a preliminary on-board evaluation, the evaluation main body corresponds to this aircraft; if it is a detailed ground station evaluation, the evaluation main body corresponds to the ground evaluation system.

[0056] As Figure 6 shown, preferably, the edge types also include: declared quality edge and evaluation main body edge; After step E1, the following steps are further included: E2. Create a declared quality edge and an evaluation main body edge according to the smart contract; E3. Connect the declared quality edge to connect the data quality node to the data summary node, and connect the evaluation main body edge to connect the data quality node to the evaluation main body node.

[0057] In step E2, define the edge types related to quality assessment, and create a claim quality edge and an assessment subject edge in the graph through a smart contract. In step E3, the "StatesQualityOf" edge: points from the "data quality claim node" to the "data summary node" it assesses, indicating that this claim is a description of the quality of the target data summary; the "AssessedBy" subject edge: points from the "data quality claim node" to the "aircraft node" that performs the assessment (if it is an on-board assessment) or an "assessment subject node" representing a ground assessment system.

[0058] In this embodiment, when a "data summary node" is created, the relevant quality assessment process can be triggered. After the quality assessment is completed, the authorized assessment subject submits a request to create a "data quality node" by interacting with the graph management smart contract, and establishes the associated edges with the corresponding "data summary node" and the assessment subject. The graph management smart contract is responsible for verifying the legality of the request and recording the new quality node and associated edges in the event graph on the blockchain.

[0059] As Figure 7 shown, preferably, the contribution weight rules in step C2 are obtained through the following steps: F1. Classify the collected data into different quality levels according to the data quality node. F2. Assign different contribution weights to the collected data of different quality levels according to preset rules. F3. Calculate and obtain the contribution scores of the collected data of different quality levels.

[0060] In steps F1 to F3, by querying the data quality node, various collected data of the aircraft are classified into different quality levels. According to preset rules, different contribution weights are assigned to the collected data of different quality levels, and the contribution scores of the collected data of different quality levels are calculated. In the embodiment, the contribution score of a piece of collected data is obtained corresponding to its quality. Higher-quality data obtains a higher contribution score, while data marked as "unavailable" is not included in the effective contribution; the correspondence between quality and contribution can be set according to the actual scenario. When the data fusion and analysis application calls the graph data, it can screen the data that meets specific quality requirements for processing according to the information of the "data quality node", improving the reliability of the analysis results. Through the above steps, the quality information of the data collected by the aircraft can be independently, credibly, and structurally recorded in the event graph and associated with specific data summaries. This enables full consideration of data quality factors during mission audits, multi-source data fusion, contribution assessment, and decision support, thereby improving the accuracy and practicality of the entire emergency response collaborative task evidence preservation system.

[0061] As Figure 8 shown, a blockchain-based aircraft data evidence preservation system includes: A graph construction module for defining node types and edge types based on multiple aircraft, mission instructions, decision events, data summaries, collaborative actions, and preset relationships among them during collaborative mission execution, so as to construct a collaborative task event graph on the main blockchain; An anchoring association module for constructing a local collaborative processing network based on the collaborative information among multiple aircraft during collaborative mission execution. When the local collaborative processing network meets the preset conditions, it creates an anchoring node in the collaborative task event graph according to the collaborative information and associates the collaborative information node with specific nodes in the collaborative task event graph; A smart contract management module for managing the creation and update of all nodes and edges in the collaborative task event graph according to the smart contracts deployed on the main blockchain.

[0062] The present invention also provides a blockchain-based aircraft data evidence preservation system. In this system, a graph construction module, an anchoring association module, and a smart contract management module are provided. Through the graph construction module, a "dynamic collaborative task event graph" on the main chain is constructed to structurally record the key events and their interrelationships during the whole process from the triggering, reorganization, execution to completion of the emergency response task. At the same time, through the anchoring association module, one or more local processing networks serving high-frequency and minor collaborations within the aircraft cluster are introduced (for example, temporary side chains or state channels constructed based on the communication links between aircraft). These local networks use lightweight consensus mechanisms to quickly process and record minor collaborative information. The processing results of the local networks (such as state summaries or hashes of key micro-collaborative events) are periodically or on-demand anchored to the event graph on the main chain to form a verifiable association. The smart contract management module deploys smart contracts on the main chain, is responsible for managing the construction rules of the event graph, verifying the validity of the anchored information, and can initially mark the ownership of rights and interests according to the contribution information recorded in the graph.

[0063] In the embodiments provided in the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, and can be electrical, mechanical, or other forms.

[0064] In addition, in each embodiment of the present invention, each functional module can be entirely integrated in a processor, or each module can be separately used as a device, or two or more modules can be integrated in a device; each functional module in each embodiment of the present invention can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0065] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed through program instructions and related hardware. The foregoing program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, they perform the steps including the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), magnetic disks, or optical discs that can store program codes.

[0066] It should be understood that in the present application, if terms such as "system", "device", "unit" and / or "module" are used, they are only a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the term can be replaced by other expressions.

[0067] As shown in the present application and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. A method or device may also include other steps or elements. An element defined by the statement "comprising one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

[0068] Hereinafter, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0069] If a flowchart is used in this application, the flowchart is used to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0070] The above has introduced in detail a method and system for aircraft data deposit and certification based on blockchain provided by the present invention. The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A blockchain-based method for storing flight vehicle data, which is applied to the emergency scenario of flight vehicles collaborating to execute tasks and deployments, and is characterized in that It includes the following steps: Define node types and edge types based on multiple aircrafts that cooperate to execute tasks, task instructions, decision events, data summaries, cooperative actions, and preset relationships among them, so as to construct a cooperative task event graph on the main blockchain; Construct a local cooperative processing network based on the cooperation information among multiple aircrafts that cooperate to execute tasks. When the local cooperative processing network meets the preset conditions, create an anchor node in the cooperative task event graph according to the cooperation information, and associate the anchor node with a specific node in the cooperative task event graph; Manage the creation and update of all nodes and edges in the cooperative task event graph according to the smart contract deployed on the main blockchain.

2. The method for storing flight vehicle data based on blockchain according to claim 1, wherein, The step of defining node types and edge types based on multiple aircrafts that cooperate to execute tasks, task instructions, decision events, data summaries, cooperative actions, and preset relationships among them, so as to construct a cooperative task event graph on the main blockchain includes the following steps: Define node types including: aircraft node, task instruction node, decision event node, data summary node, and cooperative action node, and define edge types including: trigger edge, execution edge, generation edge, dependent on edge, and belongs to edge according to multiple aircrafts that cooperate to execute tasks, task instructions, decision events, data summaries, cooperative actions, and preset relationships among them; On the main blockchain, create the aircraft node, the task instruction node, the decision event node, the data summary node, the cooperative action node, as well as the trigger edge, the execution edge, the generation edge, the dependent on edge, and the belongs to edge respectively according to the smart contract; Connect the trigger edge to connect the decision event node to the task instruction node, or connect the task instruction node to the cooperative action node, connect the execution edge to connect the aircraft node to the cooperative action node, connect the generation edge to connect the cooperative action node to the data summary node, connect the dependent on edge between two data summary nodes or between two cooperative action nodes, and connect the belongs to edge to connect the data summary node or the cooperative action node to the aircraft node to construct the cooperative task event graph.

3. The method for storing aircraft data based on blockchain according to claim 2, wherein, The step of constructing a local cooperative processing network based on the cooperation information among multiple aircrafts that cooperate to execute tasks. When the local cooperative processing network meets the preset conditions, create an anchor node in the cooperative task event graph according to the cooperation information, and associate the anchor node with a specific node in the cooperative task event graph includes the following steps: Obtain the cooperation information through communication among multiple aircrafts performing cooperative flight tasks, and construct the local cooperative processing network to record the cooperation information; When the local cooperative processing network meets the preset conditions, generate a cooperative event log and summary information according to the cooperation information, and encapsulate them into a cooperative anchor transaction; Verify the cooperative anchor transaction according to the smart contract. After the verification passes, create an anchor node in the cooperative task event graph. Define and create associated edges to connect the associated edges to the aircraft node from the anchor node or to connect the associated edges to the collaborative action node at the parent level from the anchor node.

4. The method for storing aircraft data based on blockchain according to claim 3, wherein, The node type further includes: contribution marker nodes; On the main blockchain, after creating the aircraft node, the task instruction node, the decision event node, the data summary node, the collaborative action node, and the trigger edge, the execution edge, the generation edge, the depends-on edge, and the belongs-to edge according to the smart contract, the following steps are further included: Create the contribution marker nodes according to the smart contract; Record contribution evaluation messages in the contribution marker nodes according to the trigger edge, the execution edge, the generation edge, and the contribution weight rule.

5. The method for storing flight vehicle data based on blockchain according to claim 3, wherein, The node type further includes: deviation event nodes; On the main blockchain, after creating the aircraft node, the task instruction node, the decision event node, the data summary node, the collaborative action node, and the trigger edge, the execution edge, the generation edge, the depends-on edge, and the belongs-to edge according to the smart contract, the following steps are further included: Obtain the actual state parameters of the aircraft in the collaborative flight task; Judge whether the difference between the actual state parameters and the theoretical state parameters defined by the task instruction node or the collaborative action node exceeds a preset threshold; If so, create the deviation event nodes according to the smart contract.

6. The method for storing aircraft data based on blockchain according to claim 5, wherein The edge type further includes: source-from-plan edges, response-action edges, and impact-data-record edges; After creating the deviation event nodes according to the smart contract, the following steps are further included: Create the source-from-plan edges, the response-action edges, and the impact-data-record edges according to the smart contract; Connect the source-from-plan edges of the original plan from the collaborative action node to the deviation event nodes; When the deviation event leads to the creation of a new collaborative action node, connect the response-action edges from the deviation event node to the new collaborative action node; When the deviation event leads to the creation of a new data summary node, connect the impact-data-record edges from the deviation event node to the new data summary node.

7. The method for storing aircraft data based on blockchain according to claim 4, characterized in that The node type further includes: data quality nodes and evaluation subject nodes; On the main blockchain, after creating the aircraft node, the task instruction node, the decision event node, the data summary node, the collaborative action node, and the trigger edge, the execution edge, the generation edge, the depends-on edge, and the belongs-to edge according to the smart contract, the following steps are further included: When the collected data of any aircraft passes the quality evaluation of the evaluation subject, create the data quality nodes and the evaluation subject nodes according to the smart contract.

8. The method for storing flight vehicle data based on blockchain according to claim 7, wherein, The edge type further includes: declare-quality edges and evaluation-subject edges; After creating the data quality nodes and the evaluation subject nodes according to the smart contract when the collected data of any aircraft passes the quality evaluation of the evaluation subject, the following steps are further included: Create the declare-quality edges and the evaluation-subject edges according to the smart contract; Connect the declared quality edge to connect the data quality node to the data summary node, and connect the evaluation subject edge to connect the data quality node to the evaluation subject node.

9. The method for storing aircraft data based on blockchain as claimed in claim 8, wherein Obtain the contribution weight rule, including the following steps: Classify the collected data into different quality levels according to the data quality node; Assign different contribution weights to the collected data of different quality levels respectively according to preset rules; Calculate and obtain the contribution scores of the collected data of different quality levels.

10. An aircraft data deposit and certification system based on blockchain, characterized in that Include: A graph construction module, configured to define node types and edge types according to multiple aircrafts that cooperate to execute tasks, task instructions, decision events, data summaries, cooperative actions, and preset relationships therebetween, so as to construct a cooperative task event graph on the main blockchain; An anchoring association module, configured to construct a local cooperative processing network according to the cooperative information between multiple aircrafts that cooperate to execute tasks. When the local cooperative processing network meets preset conditions, create an anchoring node in the cooperative task event graph according to the cooperative information, and associate the cooperative information node with a specific node in the cooperative task event graph; An intelligent contract management module, configured to manage the creation and update of all nodes and edges in the cooperative task event graph according to the intelligent contract deployed on the main blockchain.

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