Low-altitude aircraft airspace distributed distribution method based on NFT and game theory

Abstract

The invention discloses a low-altitude aircraft airspace distributed dynamic allocation method based on NFT and a game theory. The method comprises the following steps: S1, conflict perception and airspace digitization: abstracting a conflict airspace and a time window into a passing right NFT and carrying out uplink when an aircraft perceives a potential route conflict; s2, distributed bidding: triggering an airspace distribution event through an intelligent contract, and enabling all conflicting parties to participate in distributed bidding based on a private benefit function; s3, block chain consensus and passing right distribution: after the block chain network sorts bid consensus, the smart contract distributes NFT to the winner with the highest bid according to a VCG mechanism and determines a second high payment price; s4, the right of pass is executed, verification and settlement are carried out, all the aircrafts adjust the trajectory in a distributed mode based on bid sorting to form continuous traffic flows, and NFT automatic destruction is carried out after passing; according to the method, decentralized efficient distribution of airspace resources is realized, and the problems of calculation bottleneck, single-point fault and response delay existing in a traditional centralized system are solved.

Description

Technical Field

[0001] This invention relates to the field of air traffic management technology for low-altitude aircraft, and in particular to a distributed allocation method for airspace of low-altitude aircraft based on NFT and game theory. Background Technology

[0002] With the surge in the number of low-altitude aircraft, low-altitude airspace resources are becoming increasingly scarce, and airspace conflicts between aircraft are becoming more prominent. However, existing airspace resource allocation schemes mainly suffer from the following technical limitations: 1. Centralized control-based technologies typically rely on ground control centers or cloud servers for unified scheduling. These solutions achieve management by pre-dividing airspace and setting fixed routes. While this ensures basic order, the computational load on the central node increases dramatically with the number of aircraft, creating a performance bottleneck. Furthermore, its open-loop "plan-execute-adjust" control model struggles to effectively handle transient conflicts in free airspace without fixed routes, exhibiting significant response delays and a lack of real-time coordination capabilities.

[0003] 2. While distributed coordination schemes avoid the drawbacks of centralized systems through local communication, many rely on simple avoidance rules (such as priority or distance-based rules), lacking precise decision-making mechanisms and effective incentive-compatible designs. In real-world scenarios where aircraft belong to different stakeholders, these simplistic rules can easily lead to inefficient coordination and even strategic fraud, failing to achieve optimal allocation of airspace resources.

[0004] 3. Existing technologies generally lack response mechanisms to the individualized needs of aircraft. The dynamic priority changes of different aircraft due to private information such as mission urgency, remaining energy, and commercial value are difficult to accurately reflect and effectively coordinate within the existing fixed rule system. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned problems by proposing a distributed allocation method for airspace of low-altitude aircraft based on NFT and game theory.

[0006] To achieve the above objectives, the following technical solution was adopted: A distributed airspace allocation method for low-altitude aircraft based on NFT and game theory, the method comprising the following steps: S1: Conflict perception and airspace digitization; The aircraft uses its onboard sensors and inter-aircraft communication to obtain its own flight trajectory and that of other nearby aircraft, and predicts the target conflict point where their respective flight trajectories intersect.

[0007] Specifically, the prediction of the target conflict point includes the following sub-steps: S101: Continuously acquire real-time status data of itself and other surrounding aircraft using airborne sensors and inter-aircraft communication. The real-time status data includes at least position, velocity vector, heading angle, and acceleration. S102: Based on the acquired state data, infer the future predicted trajectory of itself and surrounding aircraft through a kinematic model; S103: Using a conflict prediction algorithm, calculate the minimum spatial distance between its own predicted trajectory and other aircraft; S104: A preset safety threshold is established, and the minimum spatial distance is judged. If the minimum spatial distance is less than the preset safety threshold, a potential conflict is determined to exist. The spatial location of the conflict point and the conflict time window are calculated using a conflict prediction algorithm. .

[0008] The three-dimensional space and conflict time window of the target conflict point are defined as follows: \left [ {{t}_{strat},{t}_{end}} \right ] They are collectively defined and packaged into a single airspace resource block, and a unique airspace access right NFT is constructed based on the airspace resource block. This NFT is then submitted to the blockchain network.

[0009] When the NFT is successfully minted and recorded on the blockchain, its associated smart contract will automatically trigger an airspace allocation event, which will be broadcast to the entire blockchain network.

[0010] S2: Distributed bidding; After other aircraft detect the airspace allocation event, verify that they are participating parties in the conflict, and calculate their benefit function based on their own private information. .

[0011] Specifically, each aircraft obtains the conflict time window \left [ {{t}_{strat},{t}_{end}} \right ] Subsequently, its onboard computing unit will continuously conflict time windows \left [ {{t}_{strat},{t}_{end}} \right ] Discretize to generate a set over time t. ,in, , For each time point in set T (j∈1~n), calculate its corresponding benefit function The benefit function The calculation formula is: Where 'i' represents the aircraft itself; Value of the basic task; The cost per unit of time delay is calculated using the following formula: , The value of the basic task is k, which is an urgency coefficient set according to the task type. The absolute delay time is the difference between the actual transit time t and the expected transit time. The absolute difference, expected to pass through time It is the originally planned time for the aircraft to pass through the target conflict point, as determined by its pre-loaded flight mission plan; The energy cost function represents the additional energy consumed when accelerating, decelerating, or circling to match the travel time t.

[0012] Specifically, the value of the basic task With delay costs It is determined based on a predefined task urgency level mapping table, which defines corresponding urgency levels for different task types. Each urgency level is associated with a basic task value parameter and an urgency coefficient parameter k.

[0013] Specifically, the unit time delay cost The calculation formula is:

[0014] in, The value of the basic task is k, which is an urgency coefficient set according to the task type.

[0015] Based on the benefit function, the optimal bid is calculated using a game theory model. and optimal transit time Optimal transit time To make the benefit function The time t corresponding to the attainment of the maximum value.

[0016] Specifically, in all calculated benefit functions Find the function that maximizes the benefit. and its optimal transit time , maximizing the benefit function As the best offer Best bid The expression is: The aircraft will encrypt the best bid. And its own digital signature is submitted to the blockchain network.

[0017] S3: Blockchain consensus and access rights allocation; Includes the following sub-steps: S301: Consensus Confirmation; The encrypted optimal bid for the aircraft in step S2 of the blockchain network receiving process It also uses digital signatures and a consensus algorithm to verify and sort all bids, forming a bid list. .

[0018] S302: Winner Decision; Based on the smart contract pre-built on the blockchain, traverse the bid list. And select the best bid from them. The tallest aircraft is the winning aircraft.

[0019] S303: Payment and Settlement; According to the payment rules preset in the game model, calculate the actual payment price p of the winning aircraft and complete the payment.

[0020] Specifically, the game model uses a VCG mechanism or a second-highest price sealed auction rule to pre-set the payment rules, so that the actual payment price p of the winning aircraft is the second-highest optimal bid among all bids.

[0021] S304: Right-of-way allocation; The passage right NFT is assigned to the blockchain address of the winning aircraft.

[0022] S4: Right-of-way enforcement, verification, and settlement; All participating aircraft have submitted bids based on the list obtained in step S301. The aircraft adjusts its trajectory in a distributed manner to form a safe and continuous traffic flow. The winning aircraft obtains priority passage by virtue of the right-of-way NFT. After passage is completed, the NFT is automatically destroyed.

[0023] Specifically, step S4 includes the following sub-steps: S401: Generate a cooperative passage sequence; Based on the bid sorting list obtained in step S3 Map it to a physical passage sequence and physical access sequence Send to every aircraft involved in the conflict, including the winning aircraft; S402: Distributed computing; Each aircraft receives a physical pass sequence. Subsequently, based on the physical access sequence Calculate its own target travel time .

[0024] Among them, the optimal passage time of the winning aircraft As a standard passage time, the aircraft follows a preset safety time interval. and its own physical access sequence The ranking order m in the calculation determines the target travel time. The calculation formula is: Where m is the ranking order of the aircraft, m is an integer starting from 0, and the winning aircraft corresponds to m=0.

[0025] S403: Cooperative trajectory planning; The aircraft calculates the target travel time based on step S402. Based on its current state, the system solves the time-energy optimal control problem through the local flight control system and generates path trajectory adjustment commands.

[0026] Specifically, before solving the optimal control problem, the aircraft utilizes its current state and the target travel time. The required theoretical speed adjustment is calculated and compared with the efficient cruise threshold. If the theoretical speed adjustment is not higher than the efficient cruise threshold, a speed fine-tuning strategy is adopted; if the theoretical speed adjustment exceeds the efficient cruise threshold, a path trajectory adjustment strategy is adopted. The efficient cruise threshold is determined by the aircraft's own performance.

[0027] Specifically, step S4 also includes an economic compensation mechanism: The actual payment price p paid by the winning aircraft is stored in a pre-set compensation pool on the blockchain, and the compensation for unwinning aircraft is determined according to their bids in the bidding list. The ranking in the system determines the economic compensation obtained from the compensation pool.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention transforms the complex airspace scheduling problem into an efficient local market decision-making problem by dynamically casting conflicting airspace resources into right-of-way NFTs and conducting distributed bidding based on a non-cooperative game model. It overcomes the computational bottlenecks and response latency issues of traditional centralized systems, and can meet the real-time coordination needs for instantaneous conflicts in high-density, highly dynamic low-altitude environments.

[0029] This invention employs a distributed architecture, where the decision-making process does not rely on any single central node. Each aircraft can independently perceive, decide, and execute, thereby fundamentally avoiding the risk of single points of failure. Even in the event of a communication interruption with the cloud server, the aircraft cluster can still operate autonomously and safely through local negotiation.

[0030] The game theory models preferably employed in this invention, such as VCG or second-highest-bid auctions, can effectively incentivize aircraft to bid based on their actual mission requirements (i.e., private benefit functions). This ensures that airspace passage rights are always allocated to the aircraft with the highest valuation, thereby spontaneously achieving Pareto optimal allocation of airspace resources in a distributed environment and greatly improving the overall social benefits of airspace utilization.

[0031] By deeply dualizing the bidding results (bid ranking list) on the blockchain with the flight control commands of the aircraft, this invention achieves a closed loop from "information space decision-making" to "physical space action." All aircraft perform distributed trajectory planning and adjustment based on the same consensus result, forming a safe, continuous, and orderly traffic flow, effectively avoiding chaos and collision risks.

[0032] The on-chain compensation pool and economic compensation mechanism introduced in this invention enable aircraft that fail in resource allocation to receive certain economic compensation. This reflects the fairness of the system and also introduces reasonable price signals and economic levers for the use of airspace resources, helping to guide aircraft to form more efficient flight plans and promoting the healthy development of the low-altitude economy. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the structure of the distributed airspace allocation method for low-altitude aircraft based on NFT and game theory in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the distributed airspace allocation method for low-altitude aircraft based on NFT and game theory in Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the distributed airspace allocation method for low-altitude aircraft based on NFT and game theory in Embodiment 1 of the present invention. Detailed Implementation

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0035] The distributed airspace allocation method described in this invention is primarily applicable to resolving potential conflicts within a short-to-medium-term prediction range, i.e., a time window of 30 seconds to several minutes. This method is based on the reasonable assumption that the total time required for conflict prediction and negotiation allocation is much shorter than the time from the current moment to the occurrence of the conflict. During system design, by optimizing communication protocols, consensus algorithms, and computational efficiency, the total time is ensured to be controlled within the second range, thereby reserving sufficient safety margin for physical execution.

[0036] like Figure 1 As shown, a distributed airspace allocation method for low-altitude aircraft based on NFT and game theory specifically includes the following steps: S1: Conflict perception and airspace digitization; Each aircraft is equipped with onboard sensors (including GPS / BeiDou positioning modules and IMU inertial measurement units), an inter-aircraft communication module (supporting protocols such as ADS-B and C-V2X), a blockchain light node client, and an onboard computing unit that carries core algorithms. This computing unit is responsible for running core algorithms such as conflict prediction, benefit function calculation, and trajectory planning.

[0037] Blockchain networks integrate smart contracts, which encapsulate the rules and logic agreed upon by the participants and can automatically execute predefined rules on the blockchain. Blockchain networks also employ low-latency consensus algorithms such as Proof-of-Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT).

[0038] Furthermore, in this application, a smart contract is a computer program deployed on a blockchain that can be automatically executed when predetermined conditions are met. The innovation of this invention lies not in the smart contract itself, but in a series of dedicated smart contract rules, state logic, and functional module combinations designed specifically to solve the problem of distributed allocation of airspace for low-altitude aircraft.

[0039] First, the aircraft A, which is performing medical emergency missions, continuously acquires its own position, velocity vector, and heading angle data, as well as those of the surrounding aircraft B, which is used for fresh food logistics, and aircraft C, which is used for daily inspections, through its onboard sensors and inter-aircraft communication module, and predicts the target conflict point where their respective flight trajectories intersect.

[0040] Specifically, the target conflict point is predicted through the following sub-steps: S101: First, aircraft A continuously acquires real-time status data of itself, aircraft B, and aircraft C using onboard sensors and inter-aircraft communication, including position, velocity vector, heading angle, and acceleration.

[0041] S102: Next, the onboard computing unit of aircraft A, based on the acquired real-time status data, uses a kinematic model to predict the trajectories of itself, as well as aircraft B and aircraft C, within the next 30 seconds.

[0042] S103: Then, aircraft A uses a conflict prediction algorithm to calculate the minimum spatial distance between its predicted trajectory and other aircraft.

[0043] S104: Finally, the preset safety threshold is set at 150 meters, and the minimum spatial distance calculated in S103 is judged. It is found that the minimum spatial distance is less than the preset safety threshold of 150 meters. Therefore, aircraft A determines that there is a potential conflict between the three aircraft, and calculates the spatial location of the conflict point and the conflict time window using a conflict prediction algorithm. .

[0044] Next, aircraft A uses its onboard computing unit to process the three-dimensional space of the target conflict point and the conflict time window. They jointly define and package them into a single airspace resource block, and based on the airspace resource block, they construct a unique, one-time airspace access right NFT and submit the NFT to the blockchain network.

[0045] When the NFT is successfully minted and recorded on the blockchain, its associated smart contract will automatically trigger an airspace allocation event, which will be broadcast to the entire blockchain network.

[0046] S2: Distributed bidding; Aircraft B and C, after monitoring and capturing the airspace allocation event via the blockchain network and verifying their own status as participants in the conflict, join the airspace allocation event. Each aircraft calculates its own benefit function based on its private information, including its three-dimensional spatial position, velocity vector, heading, and energy consumption. The benefit function Used to quantify the subjective gains that an aircraft can obtain at a specific passage time t.

[0047] Specifically, each aircraft acquires a conflict time window. Subsequently, its onboard computing unit will continuously conflict time windows \left [ {{t}_{strat},{t}_{end}} \right ] Discretize multiple transit times t and generate a set of transit times t. ,in, , .

[0048] Furthermore, before calculating the benefit function, the onboard computing unit of each aircraft queries a predefined mission urgency level mapping table (as shown in Table 1) based on its flight mission. This table defines corresponding urgency levels for different mission types, and each urgency level is associated with a basic mission value parameter. And a urgency coefficient parameter k:

[0049] As shown in Table 1, the basic values ​​in the table are... The urgency coefficient k is a preset range value. The specific value is uniformly set by the airspace management department, or selected by the aircraft according to the specific sub-category of its mission when reporting the mission.

[0050] For a certain transit time in set T (j∈1~n), calculate its corresponding benefit function Each time t corresponds to a benefit function. The specific calculation formula is as follows: Where i represents the aircraft itself, and in this example, i represents three aircraft, A, B, and C respectively; Value of the basic task; The cost per unit of time delay is calculated using the following formula: in, The value of the basic task is k, which is an urgency coefficient set according to the task type.

[0051] The absolute delay time is the difference between the actual transit time t and the expected transit time. The absolute difference, the expectation over time It is the original planned time for the aircraft to pass through the target conflict point, as determined by its pre-loaded flight mission plan.

[0052] This is the energy cost function, representing the additional energy consumed during acceleration, deceleration, or ziplining to match the travel time t. The calculation formula is: Where c is the energy consumption coefficient. To satisfy the required velocity change through practice t.

[0053] Furthermore, in addition to using a static mission urgency classification mapping table, this invention can also use a dynamic scoring model to classify spacecraft missions, with the specific calculation formula as follows: in, The task type factor can be read from the aircraft's flight mission management system. In this embodiment, it is medical = 100, logistics = 60, and inspection = 40. As an environmentally urgent factor, it can obtain real-time weather, airspace congestion, and other data by accessing the air traffic control API; As a time urgency factor, it can calculate 1 / (task deadline - current time) based on the internal task clock. The more urgent the time, the higher the score. , , In this embodiment, the weighting coefficient is used. =0.6, =0.25, =0.15. The dynamic calculation of the underlying value can more accurately reflect the real-time situation, and the delay cost coefficient k can also be adjusted according to real-time factors.

[0054] Next, their respective benefit functions were calculated. Then, based on the benefit function The optimal bids for each party are calculated using a game theory model. and optimal transit time Optimal transit time To make the benefit function The time t corresponding to the attainment of the maximum value.

[0055] Specifically, in all calculated benefit functions Find the function that maximizes the benefit. and its optimal transit time , maximizing the benefit function As the best offer Best bid The expression is: The aircraft will encrypt the best bid. And its own digital signature is submitted to the blockchain network.

[0056] S3: Blockchain consensus and access rights allocation; Includes the following sub-steps: S301: Consensus Confirmation; The encrypted optimal bid for the aircraft in step S2 of the blockchain network receiving process The system uses its digital signature and a consensus algorithm to verify and sort the bids of all aircraft from highest to lowest, forming a bid list. .

[0057] S302: Winner Decision; Smart contracts on the blockchain automatically execute and iterate through the bid list. And select the best bid from them. The highest-ranking aircraft, A, is the winning aircraft.

[0058] S303: Payment and Settlement; According to the payment rules preset in the game model, calculate the actual payment price p of the winning aircraft and complete the payment.

[0059] Specifically, the pre-set payment rules in the game model adopt the Vickrey-Clark-Groves (VCG) mechanism or the second-highest price sealed auction rule, so that the actual payment price p of the winning aircraft is the second-highest optimal bid among all bids. In this embodiment, according to the order in the bid list, the bids are ranked from highest to lowest as follows: > > That is, the actual price paid for the winning aircraft. .

[0060] To facilitate understanding, the following intuitive example illustrates how this mechanism works and its effects: Assume that the true valuations (i.e., their maximum benefit function values) of aircraft A, B, and C for a certain airspace right-of-way NFT are 100, 80, and 60 units, respectively. Under the VCG mechanism, their optimal strategy is to bid truthfully, i.e., to bid... , , .

[0061] When the highest bidder, A, wins and acquires the NFT communication right, the actual price paid by A is not its own bid of 100, but rather the total value loss caused to other participants B and C by its winning action. That is, in this auction, if aircraft A had not participated, aircraft B would have won with a bid of 80. However, due to aircraft A's participation, B lost the NFT communication right, and aircraft C could not have won even if aircraft A had not participated. Therefore, the loss caused to aircraft B by aircraft A's participation is 80, so the actual price paid by aircraft A is 80.

[0062] In this process, for Flight A, it acquired the NFT access rights worth 100 but only paid 80, gaining a "consumer surplus" of 20. Therefore, Flight A is more willing to bid truthfully. If Flight A bids maliciously, for example, raising its bid to 120 to win, and another hidden participant with a true bid of 110 or Flight B also raises its bid to 110, Flight A will be forced to buy the NFT access rights worth only 100 for 110, resulting in a direct loss. Alternatively, deliberately lowering the price may make it difficult to win the auction. Therefore, truthfully reporting its true valuation is the safest and most cost-effective strategy for each Flight A.

[0063] Through a game theory model, the actual cost to winning aircraft A is not controlled by itself, but rather depends on the optimal bid of second-place aircraft B. This avoids participants being motivated to conceal or misreport information for speculative gain, thus preventing overall system inefficiency. By eliminating this incentive for fraud at the algorithmic level, the system spontaneously achieves globally optimal allocation as participants pursue their own interests and truthfully report their actual valuations.

[0064] S304: Right-of-way allocation; The passage rights NFT will be allocated to the blockchain address of the winning aircraft.

[0065] S4: Right-of-way enforcement, verification, and settlement; The bid lists of aircraft A, B, and C obtained in step S301 The aircraft adjusts its trajectory in a distributed manner to form a safe and continuous traffic flow. The winning aircraft gains priority passage by using the right-of-way NFT. After passage is completed, the NFT is automatically destroyed.

[0066] Specifically, step S4 includes the following sub-steps: S401: Generate a cooperative passage sequence; The blockchain network sorts the bids according to the bid ranking list obtained in step S3. Based on the consensus algorithm, it is mapped to a physical passage sequence. and the generated physical pass sequence Send to aircraft A, B, and C. Among them, the physical passage sequence... Based on blockchain consensus, it has the characteristics of being immutable and universally recognized. Furthermore, this sequence specifies the physical priority order of aircraft passing through conflict airspace, solving the problem of consistency of actions in distributed systems.

[0067] S402: Distributed computing; Each aircraft receives a physical pass sequence. Subsequently, its onboard computing unit is based on the physical access sequence. Calculate its own target travel time .

[0068] First, a safe time interval is preset. The time is 4 seconds, and the optimal passage time is for winning aircraft A. As a standard transit time, aircraft A, B, and C operate according to a preset safety time interval. and their respective physical access sequences Given the ranking order m, calculate the target travel time for each. The calculation formula is: Where m is the rank order of the aircraft, due to the physical access sequence It is a list, so m is an integer starting from 0. The winning aircraft A corresponds to m=0, and the other aircraft are arranged in order.

[0069] S403: Cooperative trajectory planning; Each aircraft calculates the target travel time based on step S402. Based on its current state, such as speed, position, and heading, the aircraft's local flight control system solves a time-energy optimal control problem, generating a path trajectory adjustment command that satisfies both time constraints and energy efficiency.

[0070] Furthermore, before solving the optimal control problem, the aircraft needs to first determine which basic strategy to adopt to meet the target travel time. First, based on the current real-time location and the coordinates of the target conflict point obtained in step S1, the remaining distance S between them is calculated, combined with the target passage time. Calculate the time from the current time to the target travel time. The remaining time, and based on its current speed. Calculate the time to reach the target passage. Required speed adjustment The calculation formula is: in, The time from the current time to the target travel time The remaining time, i.e. By using the laws of physical motion, the theoretical average velocity can be derived. ,Right now Finally, the theoretical speed adjustment amount is obtained by solving the problem. The aircraft will meet the target passage time. Under the premise of [the above], the strategy with the lowest cost can be autonomously selected between speed fine-tuning and path fine-tuning, thereby achieving the unity of local optimization and global optimization.

[0071] Next, adjust the theoretical speed. High-efficiency cruise threshold in the aircraft's own performance database Comparison: if This indicates that the time requirement can be met through gentle and efficient acceleration or deceleration. Therefore, the system selects the "speed fine-tuning" strategy. if This indicates that the required speed adjustment is too drastic; simply adjusting the speed would exceed the efficient cruise range, leading to a significant decrease in energy efficiency or exceeding the flight envelope. Therefore, the system selects a "path optimization" strategy, using a slight, energy-efficient detour to "trade" for more travel time.

[0072] After selecting the basic strategy, the aircraft will solve a time-energy optimal control problem, which is essentially a control problem achieved through a single control input. This minimizes the performance index J. If a speed fine-tuning strategy is chosen, the optimal control problem is constructed as finding the optimal speed profile while keeping the approximate path unchanged. To minimize performance metric J: in, For the target arrival time, The goal is achieved within a certain timeframe; The energy consumption function represents the speed-related parameters, and solving it yields specific acceleration or deceleration commands. α and β are weighting coefficients used to balance time accuracy and energy consumption. By solving this problem, the algorithm obtains a speed control from the current speed... The optimal speed-time curve smoothly transitions to the target speed, and finally outputs a sequence of speed control commands until the flight control system performs gentle acceleration or deceleration.

[0073] If a path optimization strategy is chosen, the optimal control problem is constructed as finding a new spatial trajectory while allowing for path deviation. Its performance indicators are: in, This is a weighting coefficient used to weigh the impact of path length; This is a path length penalty term used to avoid generating overly circuitous paths; The energy consumption related to the flight path is calculated, resulting in a specific trajectory optimized in both space and time. By solving this problem, the algorithm obtains a trajectory optimized in both space and time. This trajectory ensures timely arrival at the target area while avoiding drastic speed changes through slight detours, ultimately outputting a path command for the flight control system to execute.

[0074] Upon receiving the adjustment instructions, each aircraft simultaneously begins executing its own. Throughout the process, the aircraft continuously exchange status information via inter-aircraft communication and perform distributed verification of the validity of the right-of-way NFTs to ensure that only the legitimate winner can exercise the priority right of way.

[0075] Furthermore, during this process, the onboard sensors of each aircraft will continuously monitor the surrounding environment. Once a collision risk is detected to exceed the final safety threshold, the local emergency obstacle avoidance algorithm will be triggered immediately to prioritize the safety of the aircraft itself.

[0076] Once aircraft A, holding the right-of-way NFT, successfully passes the collision point, the blockchain network's smart contract will automatically trigger a destruction function, permanently removing the NFT from the blockchain and thus immediately releasing that specific airspace-time resource.

[0077] Specifically, to ensure that aircraft that do not win in resource allocation receive a certain economic return for their time investment, thus encouraging them to continue trusting and participating in future market collaborations and preventing the system from collapsing due to a "winner-takes-all" mentality, this invention also includes an economic compensation mechanism: In step S3, the actual payment price p paid by the winning aircraft A is deposited into a pre-set compensation pool on the blockchain. Once the winning aircraft A passes the collision point, the smart contract will, according to a pre-set fixed compensation ratio, return the funds deposited in the compensation pool in step S3 to the winning aircraft based on the bid list. The ranking order is assigned to the unwinnable aircraft B and C. The expression for the fixed compensation ratio is: h ranges from 1 to N-1, where N is the number of aircraft and m is the ranking number. The winner is m=1, the second place is m=2, and so on, ensuring that the sum of all weights is 1, and the higher the ranking, the higher the weight.

[0078] Furthermore, in addition to the aforementioned fixed compensation ratio, dynamic compensation can also be selected. Unsuccessful aircraft can submit proof of their actual loss of benefits due to the avoidance maneuver to the smart contract. The specific calculation formula is as follows: in, For the sake of loss of benefits; The benefit function is the same as the optimal benefit function calculated in step S1. The original planned passage time refers to the time it would take for an aircraft to pass through the conflict point according to its original flight plan under ideal conditions where there is no conflict and no need to avoid it. Actual transit time refers to the event in which an aircraft actually passes through a conflict point after implementing a collaborative avoidance strategy based on bidding results. This can be obtained through the aircraft's positioning system.

[0079] After calculating the loss and benefit Finally, the compensation amount received by each winning aircraft is determined by the following formula: in, The loss benefit calculated for the m-th winning aircraft; The sum of losses and benefits for all winning aircraft, where N is the total number of aircraft. The total amount in the compensation pool is the actual price p paid by the winning aircraft in step S303.

[0080] Furthermore, to ensure absolute safety and reliability in real-world environments, this invention also includes a real-time protection mechanism and a security redundancy layer independent of market bidding mechanisms.

[0081] Real-time performance guarantee mechanism: By pre-analyzing the worst-case processing time of each step, and designing time budget allocation, timeout trigger degradation strategy and dynamic optimization strategy based on the analysis results.

[0082] In this embodiment, the worst-case processing time is defined as: S1 collision detection and NFT casting includes sensor data acquisition, trajectory prediction, collision detection, and NFT on-chaining. The worst-case processing time is defined as T. 感知 ≤500 milliseconds; S2 distributed auction, including benefit function calculation, bid encryption, and submission, has a worst-case processing time defined as T. 竞价 ≤300 milliseconds; S3 blockchain consensus and access allocation, including bid verification, sorting, payment settlement, and NFT distribution, have a worst-case processing time defined as T. 共识 ≤400 milliseconds; S4 right-of-way enforcement and trajectory adjustment include passage sequence generation, target time calculation, and trajectory planning. The worst-case processing time is defined as T. 执行 ≤200 milliseconds.

[0083] The system's total worst-case processing time is: In this embodiment, T 总It was kept within 1.5 seconds, far shorter than the start time of the conflict event window. To ensure sufficient safety margin, the aircraft's onboard computing unit starts a real-time timer for monitoring when initiating the allocation process.

[0084] The timeout-triggered degradation strategy is implemented if any step exceeds its time budget, or if the total processing time is not less than the total worst-case processing time T. 总 If 90% of the value is reached, the following degradation process will be triggered: (A) Forcefully terminate the currently ongoing complex optimization calculations, including but not limited to: trajectory prediction iteration, benefit function discretization calculation, and solving time-energy optimal control problems; (B) Ignore the dynamic bidding of the aircraft and directly generate the passage sequence based on a predefined static priority rule that is agreed upon by all aircraft. This rule prioritizes the urgency level in the mission urgency level mapping table (as shown in Table 1), and in the case of the same level, it is further sorted by fixed information such as the aircraft identification number.

[0085] (C) The aircraft that first triggers the timeout condition, or the blockchain smart contract, broadcasts the passage sequence generated based on the above static priority rules to all conflicting parties.

[0086] (D) Upon receiving a degraded passage sequence, all aircraft will perform avoidance using a pre-set conservative trajectory adjustment strategy with lower computational load, based on their position in the sequence. The conservative trajectory adjustment strategy includes, but is not limited to, waiting at a fixed deceleration rate or a standard detour path.

[0087] The security redundancy layer operates continuously as a parallel, higher-priority monitoring system. The operating mechanism of the security redundancy layer is as follows: First, during the negotiation and bidding process in steps S1 to S4, the onboard sensors of all aircraft continuously monitor the actual distance to surrounding aircraft.

[0088] Next, multiple security thresholds are preset within the security redundancy layer, including a preset security threshold and a final security threshold. The preset security threshold is the threshold used to trigger NFT casting in step S104; the final security threshold is a higher security threshold, which is 50 meters in this embodiment. This final security threshold is independent of the spatial allocation process.

[0089] Finally, at any time, once any aircraft detects that the distance to other aircraft is less than the final safety threshold, a degrading process will be immediately and unconditionally triggered: Immediately halt any ongoing bidding, consensus, or NFT transfer processes. The aircraft's flight control system will immediately take over and execute the highest priority emergency maneuvers, such as emergency braking and evasive turns, based on a mature local obstacle avoidance algorithm. Broadcast emergency avoidance signals to surrounding aircraft or air traffic control systems to coordinate avoidance.

[0090] This mechanism ensures that the physical flight safety of the system is ultimately guaranteed in any extreme circumstances such as communication delays, computation timeouts, or prediction errors, enabling economically driven distributed markets to operate within an absolutely secure framework.

[0091] In this embodiment, the present invention abstracts conflicting airspace resources into tradable right-of-way NFTs, establishes a distributed bidding market, and employs a VCG mechanism to ensure incentive compatibility, enabling aircraft to spontaneously achieve optimal system configuration through autonomous decision-making. Trusted automatic execution is achieved through blockchain smart contracts, and a compensation mechanism is innovatively introduced to maintain system fairness. At the physical execution level, aircraft autonomously select optimal strategies such as speed fine-tuning or route detours based on bidding results, forming a safe and efficient traffic flow. This technical solution transforms airspace management from a "centralized command" to a "distributed market" paradigm, improving airspace utilization efficiency and reducing energy consumption without a central node. It avoids fundamental problems of traditional systems in low-altitude, high-density environments, such as response delays, single points of failure, and low coordination efficiency, providing an innovative technological foundation for future urban air traffic.

[0092] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of this application. Those skilled in the art may find other optimizations and additional functions in this application. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A distributed airspace allocation method for low-altitude aircraft based on NFT and game theory, characterized in that: Includes the following steps: S1: Conflict perception and airspace digitization; The aircraft uses its onboard sensors and inter-aircraft communication to obtain its own flight trajectory and that of other aircraft in the vicinity, and predicts the target conflict point where their respective flight trajectories intersect. The aircraft will determine the three-dimensional space and time window of the target conflict point. They are collectively defined and packaged into a single airspace resource block, and a unique airspace access right NFT is constructed based on the aforementioned airspace resource block. This NFT is then submitted to the blockchain network. When the NFT is successfully minted and recorded on the blockchain, its associated smart contract will automatically trigger an airspace allocation event, which will be broadcast to the entire blockchain network. S2: Distributed bidding; After other aircraft detect the airspace allocation event, and verify that they are involved in the conflict, they calculate their benefit function based on their own private information. ; Based on the benefit function, the optimal bid is calculated using a game theory model. and optimal transit time ; The optimal passage time To make the benefit function The time t corresponding to achieving the maximum value; The aircraft will encrypt the best bid. And submit its own digital signature to the blockchain network; S3: Blockchain consensus and access rights allocation; Includes the following sub-steps: S301: Consensus Confirmation; The encrypted optimal bid for the aircraft in step S2 of the blockchain network receiving process It also uses digital signatures and a consensus algorithm to verify and sort all bids, forming a bid list. ; S302: Winner Decision; Based on the smart contract pre-installed on the blockchain, traverse the bid list. And select the best bid from them. The tallest aircraft is the winning aircraft. S303: Payment and Settlement; According to the pre-set payment rules of the game model, the actual payment price p of the winning aircraft is calculated and the payment is completed; wherein, the pre-set payment rules in the game model adopt the VCG mechanism or the second-highest price sealed auction rule, so that the actual payment price p of the winning aircraft is the second-highest optimal bid among all bids; S304: Right-of-way allocation; The blockchain network will allocate the passage right NFT to the blockchain address of the winning aircraft; S4: Right-of-way enforcement, verification, and settlement; All participating aircraft have submitted bids based on the list obtained in step S301. The aircraft adjusts its trajectory in a distributed manner to form a safe and continuous traffic flow. The winning aircraft obtains priority passage by virtue of the right-of-way NFT. After passage is completed, the NFT is automatically destroyed.

2. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 1, characterized in that: In step S1, the prediction of the target conflict point includes the following sub-steps: S101: Continuously acquire real-time status data of itself and other surrounding aircraft using airborne sensors and inter-aircraft communication. The real-time status data includes at least position, velocity vector, heading angle, and acceleration. S102: Based on the acquired state data, infer the future predicted trajectory of itself and surrounding aircraft through a kinematic model; S103: Using a conflict prediction algorithm, calculate the minimum spatial distance between its own predicted trajectory and other aircraft; S104: A preset safety threshold is established, and the minimum spatial distance is judged. If the minimum spatial distance is less than the preset safety threshold, it is determined that a potential conflict exists, and the spatial location of the conflict point and the conflict time window are calculated using a conflict prediction algorithm. .

3. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 1, characterized in that: In step S2, each aircraft obtains a conflict time window. Subsequently, its onboard computing unit will handle consecutive conflict time windows. Discretize to generate a set over time t. ,in, , For each time point in set T (j∈1~n), calculate its corresponding benefit function The benefit function The calculation formula is: Where 'i' represents the aircraft itself; Value of the basic task; The cost per unit of time delay is calculated using the following formula: , The value of the basic task is k, which is an urgency coefficient set according to the task type. The absolute delay time is the difference between the actual transit time t and the expected transit time. The absolute difference, the expected value over time It is the originally planned time for the aircraft to pass through the target conflict point, as determined by its pre-loaded flight mission plan; The energy cost function represents the additional energy consumed when accelerating, decelerating, or circling around a point in time t.

4. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 3, characterized in that: The value of the basic task With delay costs It is determined based on a predefined task urgency level mapping table, which defines corresponding urgency levels for different task types. Each urgency level is associated with a basic task value parameter and an urgency coefficient parameter k.

5. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 1, characterized in that: In step S2, all calculated benefit functions Find the function that maximizes the benefit. and its optimal transit time , maximizing the benefit function As the best offer Best bid The expression is .

6. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 1, characterized in that: Step S4 includes the following sub-steps: S401: Generate a cooperative passage sequence; Based on the bid sorting list obtained in step S3 Map it to a physical passage sequence and physical access sequence Send to every aircraft involved in the conflict, including the winning aircraft; S402: Distributed computing; Each aircraft receives a physical pass sequence. Subsequently, based on the physical access sequence Calculate its own target travel time ; S403: Cooperative trajectory planning; The aircraft calculates the target travel time based on step S402. Based on its current state, the system solves the time-energy optimal control problem through the local flight control system and generates path trajectory adjustment commands.

7. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 6, characterized in that: In step S402, the optimal passage time of the winning aircraft is determined. As a standard passage time, the aircraft follows a preset safety time interval. and its own physical access sequence The ranking order m in the calculation determines the target travel time. The calculation formula is: Where m is the ranking order of the aircraft, m is an integer starting from 0, and the winning aircraft corresponds to m=0.

8. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 6, characterized in that: In step S403, before solving the optimal control problem, the aircraft utilizes its current state and the target travel time. The required theoretical speed adjustment is calculated and compared with the efficient cruise threshold. If the theoretical speed adjustment is not higher than the efficient cruise threshold of the aircraft, a speed fine-tuning strategy is adopted. If the theoretical speed adjustment exceeds the efficient cruise threshold, a path trajectory adjustment strategy is adopted. The efficient cruise threshold is obtained from the aircraft's own performance database.

9. The distributed airspace allocation method for low-altitude aircraft based on NFT and game theory as described in claim 1, characterized in that: Step S4 also includes an economic compensation mechanism: The actual payment price p paid by the winning aircraft is stored in a compensation pool on the blockchain, and the non-winning aircraft are compensated according to their bids in the bidding list. The ranking order determines the economic compensation allocated from the compensation pool according to a preset fixed ratio.

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