Low-altitude airspace resource dynamic pricing and transaction system

Abstract

This invention is applicable to the intersection of low-altitude economy and blockchain technology, providing a dynamic pricing and trading system for low-altitude airspace resources. It comprises four layers: a physical sensing and data mapping layer, a congestion quantification and pricing calculation layer, a blockchain trading and settlement layer, and a user interaction and rights layer, forming a closed-loop collaborative system. The physical sensing and data mapping layer collects airspace data and extracts state vectors; the congestion quantification and pricing calculation layer calculates congestion entropy based on a nonlinear impedance model and derives real-time dynamic prices by combining marginal social costs; the blockchain trading and settlement layer uses smart contracts to automate matching, settlement, and default penalties; and the user interaction and rights layer supports drone operators in submitting payments, obtaining digital tickets, and exercising airspace usage rights based on these tickets. This invention solves the problems of rigid airspace resource pricing, lagging congestion regulation, and high trust costs in transaction settlement in existing technologies, achieving efficient, transparent, and intelligent allocation of low-altitude airspace resources.

Description

Technical Field

[0001] This invention relates to the intersection of low-altitude economy and blockchain technology, specifically a dynamic pricing and trading system for low-altitude airspace resources. Background Technology

[0002] With the exponential growth of urban air mobility (UAM) and logistics drone applications, low-altitude airspace has transformed from an abundant natural resource into a scarce economic resource. To maintain flight safety and maximize airspace utilization, it is necessary to conduct refined grid division and dynamic capacity assessment of airspace, while simultaneously establishing efficient pricing, trading, and user rights protection mechanisms.

[0003] Currently, there are three main technical approaches for low-altitude airspace management and resource allocation both domestically and internationally: rule-based static airspace allocation schemes, centralized optimization-based traffic control schemes, and preliminary blockchain-integrated data storage schemes. Rule-based static airspace allocation schemes employ fixed flight path structures and charging models, lacking a feedback mechanism for real-time traffic conditions and failing to establish an effective system for the transfer of user rights, resulting in low airspace utilization. Centralized optimization-based traffic control schemes rely on a central server for global optimization, facing computational scalability bottlenecks and data privacy and security risks, leading to low user willingness to share core data and insufficient protection of rights. Blockchain-integrated data storage schemes mostly remain at the level of recording and simple payment, lacking in-depth computational and regulatory capabilities, and failing to establish a complete user interaction and rights management process. Transaction prices cannot truly reflect the real-time scarcity of resources, making it difficult to effectively protect user rights.

[0004] Existing technologies generally suffer from rigid airspace resource pricing mechanisms, lagging congestion mitigation capabilities, high transaction settlement trust costs, poor user experience, and ambiguous rights definitions, severely hindering the large-scale development of the low-altitude economy and leading to resource misallocation, safety hazards, and user rights disputes. Therefore, there is an urgent need to provide a dynamic pricing and trading system for low-altitude airspace resources to overcome the shortcomings in current practical applications. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic pricing and trading system for low-altitude airspace resources, effectively solving the problems mentioned in the background art.

[0006] This invention is implemented as follows: a dynamic pricing and trading system for low-altitude airspace resources, comprising: The system consists of a physical perception and data mapping layer, a congestion quantification and pricing calculation layer, a blockchain transaction and settlement layer, and a user interaction and rights layer, which interact sequentially and form a closed-loop control. The physical sensing and data mapping layer is used to collect multi-source data in the low-altitude three-dimensional airspace and extract state vectors. The congestion quantification and pricing calculation layer calculates congestion entropy based on the state vector through a nonlinear impedance model, and derives dynamic prices by combining marginal social costs. Based on the dynamic price, the blockchain transaction and clearing layer uses smart contracts to automate the matching of airspace resources, implement instant clearing, and impose penalties for default. The user interaction and rights layer is used by drone operators to submit payments, obtain airspace usage rights certificates in the form of digital road tickets, and verify airspace usage qualifications based on the digital road tickets.

[0007] As a further aspect of the present invention: the physical sensing and data mapping layer includes: Multi-source sensors are used to collect data on the position, speed, and density of drones in the airspace; A three-dimensional mesh cell divides the low-altitude airspace into several airspace cells and extracts the state vector of each airspace cell at a specific time. The state vector includes the number of aircraft per unit volume and the average flow velocity.

[0008] As a further aspect of the present invention: the nonlinear impedance model in the congestion quantification and pricing calculation layer is a three-dimensional spatial comprehensive impedance function. The expression is: ; in, For free flow time, The impedance coefficient, It is a non-linear growth factor. For spatial units At any moment The actual density, For spatial units Effective capacity, This is a weighted coefficient for converting risk into time cost. For risk sensitivity factors, For a safe threshold density, This represents the weighting coefficient for network spillover effects. For spatial units The adjacency set, To the adjacent unit Inflow airspace unit The transition probability weights, Adjacent units At any moment density, Adjacent units At any moment The average flow velocity.

[0009] As a further aspect of the present invention: the congestion quantification and pricing calculation layer derives dynamic prices based on marginal social costs. The dynamic price The expression is: ; in, Basic resource fee, This is the time value conversion factor. Based on the free circulation time, For spatial units At any moment Traffic, This is a risk correction factor. Market sensitivity coefficient For real-time demand intensity, To ensure real-time supply capacity, , To prevent tiny quantities with a denominator of zero.

[0010] As a further aspect of the present invention: the marginal social cost includes average private cost and marginal external congestion cost, wherein the average private cost is... The marginal external congestion cost is The passage time function is expressed as: .

[0011] As a further aspect of the present invention: the blockchain transaction and clearing layer includes an oracle, smart contracts, and an order book; The oracle is used to reliably upload the state vectors collected by the physical sensing and data mapping layer to the blockchain and update the spatial state in the smart contract. The order book is used to store purchase orders from drone operators and sales orders from airspace agents; The smart contract incorporates continuous bidirectional auction logic and an integer linear programming algorithm to achieve transaction matching and settlement.

[0012] As a further aspect of the present invention: the smart contract solves for the transaction set using integer linear programming. The objective function is to maximize social welfare. The expression is: ; The constraints include individual rationality constraints, supply and demand balance constraints, and variable integrity constraints.

[0013] As a further aspect of the present invention: the smart contract uses the k-DA mechanism to calculate the market liquidation price. The expression is: ; in This is the market preference coefficient. .

[0014] As a further aspect of this invention: the blockchain transaction and clearing layer implements atomic settlement, including fund transfer, equity minting, and space status update. Fund transfer satisfies the conservation of total account funds, and equity minting generates digital road tickets in the form of non-fungible tokens, expressed as: ; The airspace status update feeds the transaction results back to the physical sensing and data mapping layer, updating the density of airspace units.

[0015] As a further aspect of the present invention: the digital pass in the user interaction and rights layer includes a spatial unit identifier and time window information.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: Firstly, regarding the scientific nature and regulatory capacity of pricing mechanisms, existing technologies mostly employ fixed rates or static pricing based on simple rules (such as peak / off-peak), failing to accurately reflect the real-time scarcity of airspace resources, leading to an inefficient state of "congestion without price, idleness without compensation." In contrast, this invention, based on a rigorously derived Marginal Social Cost (MSC) model, precisely quantifies and internalizes the external delay cost caused to the system by each additional drone as a price. This gives the price signal a clear physical meaning, enabling it to sensitively change non-linearly with traffic density (increasing by a factor of β). In particular, the introduced supply and demand index adjustment term allows the system to automatically circuit off overloaded demand through price barriers under extreme congestion conditions, solving the physical congestion problem from an economic perspective, achieving Pareto improvement, and is expected to reduce average delay time by more than 20%.

[0017] Secondly, regarding the security and trust costs of the transaction system, existing centralized platforms suffer from data monopoly and single point of failure risks. Furthermore, transaction clearing relies on traditional financial institutions, resulting in long settlement cycles (typically T+1), which cannot meet the second-level response requirements of drone logistics. This invention introduces blockchain technology to construct a decentralized transaction environment. Automated matching and atomic swaps implemented through smart contracts ensure "money and rights exchanged simultaneously," eliminating default risks and reducing settlement time to the second level. Distributed ledger technology makes all transaction records immutable and traceable across the entire network, resolving data trust issues among multiple operating entities and significantly reducing auditing and friction costs.

[0018] Finally, regarding the system's adaptability and scalability, existing technologies often rely on globally optimized path planning algorithms, whose computational complexity increases exponentially with the number of nodes (NP-Hard), making it difficult to adapt to the dynamic changes of large-scale drone swarms. This invention employs a distributed market mechanism, decomposing the complex global optimization problem into local optimal decisions based on price signals by each operator (Decentralized Decision Making). Smart contracts automatically update prices according to local supply and demand conditions, eliminating the need for heavy global calculations by a central node, thus giving the system extremely strong horizontal scalability. As the number of drones increases, the system only needs to add blockchain nodes to support high-frequency concurrent transactions, avoiding the computational bottleneck of centralized servers. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a hierarchical architecture diagram of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The core of this invention lies in constructing a real-time dynamic pricing mathematical model that integrates macro-level traffic flow theory to accurately quantify the scarcity of airspace resources and congestion externalities. Simultaneously, it combines blockchain smart contract technology to build a decentralized resource trading and automatic settlement mechanism. By "hard-coding" the pricing model into smart contracts, the system can automatically adjust prices based on real-time monitored airspace conditions and complete equity transfer and fund settlement instantly upon transaction completion. This completely solves the problems of pricing lag, lack of trust, and inefficient contract fulfillment, achieving efficient, transparent, and intelligent allocation of low-altitude airspace resources.

[0023] The present invention will be further explained below with reference to specific embodiments.

[0024] Please see Figure 1 The low-altitude airspace resource dynamic pricing and trading system provided in this embodiment of the invention includes: The system consists of a physical perception and data mapping layer, a congestion quantification and pricing calculation layer, a blockchain transaction and settlement layer, and a user interaction and rights layer, which interact sequentially and form a closed-loop control. The physical sensing and data mapping layer is used to collect multi-source data in the low-altitude three-dimensional airspace and extract state vectors. The congestion quantification and pricing calculation layer calculates congestion entropy based on the state vector through a nonlinear impedance model, and derives dynamic prices by combining marginal social costs. Based on the dynamic price, the blockchain transaction and clearing layer uses smart contracts to automate the matching of airspace resources, implement instant clearing, and impose penalties for default. The user interaction and rights layer is used by drone operators to submit payments, obtain airspace usage rights certificates in the form of digital road tickets, and verify airspace usage qualifications based on the digital road tickets.

[0025] In this embodiment, traffic flow in the physical world is regulated by economic levers, and distributed ledger technology is used to ensure the reliability and automation of transactions.

[0026] For a more specific example, please refer to Figure 1 The physical sensing and data mapping layer includes: Multi-source sensors are used to collect data on the position, speed, and density of drones in the airspace; A three-dimensional mesh cell divides the low-altitude airspace into several airspace cells and extracts the state vector of each airspace cell at a specific time. The state vector includes the number of aircraft per unit volume and the average flow velocity.

[0027] In this embodiment, in order to overcome the limitations of traditional two-dimensional planar traffic flow theory, this invention derives a nonlinear congestion impedance function applicable to low-altitude heterogeneous aircraft based on the fluid dynamics continuity equation. First, based on the Macroscopic Fundamental Diagram (MFD), the low-altitude airspace is considered as a fluid field; assuming the first... i Each three-dimensional mesh unit (Airspace Grid Unit, AGU) at time... t The state is determined by density (Number of aircraft per unit volume) and average flow velocity describe; According to Greenshields' linear velocity-density assumption, the average velocity of an aircraft in the airspace decreases linearly with increasing density: in, This refers to the free-flow velocity (the ideal velocity when there is no congestion). The blocking density (the limiting density when the airspace is completely saturated); according to the law of conservation of flow, the flow rate... q Defined as the product of density and velocity Substituting into the velocity formula above, we can obtain the parabolic relationship between flow rate and density: To quantify the "impedance" or "cost" of congestion, we define the unit travel time. The characteristic length of the cell. The required time, i.e. ; Substituting the modified Greenshields model into the model, we obtain the basic travel time model: in For free-flow time; In practical engineering, when near When the denominator approaches 0, the numerical value becomes unstable. Therefore, this invention introduces an engineering-modified BPR (Bureau of Public Roads) function form and extends it with higher-order nonlinearity to adapt to the three-dimensional characteristics of low-altitude flight; Thus, the three-dimensional spatial domain synthetic impedance function is derived. It comprises three parts: basic fluid resistance, security risk potential energy, and network spillover effects. The formula derivation and variable details are as follows: Item 1 (Basic Fluid Impedance): This item is derived from the Taylor series approximation derived by Greenshields above. The effective capacity of this spatial cell is denoted as , and the blocking density is denoted as . The converted value of the project. This is the impedance coefficient (usually taken as 0.15). This is a non-linear growth factor (typically taken as 4.0). This term represents the growth rate when the actual density... Exceeding capacity At that time, flight delays were presented The exponential growth reflects the nonlinear characteristics of physical congestion.

[0028] Item 2 (Safety Risk Potential): The core constraint of low-altitude flight is not only its slow speed, but also the risk of collision. This invention introduces a risk model based on gas molecule collision theory. Let the minimum safe distance between two drones be... The current average interval is Collision probability It is inversely proportional to the exponential of the interval. Therefore, this invention constructs an exponential risk penalty term. ; in, This is a weighted coefficient for converting risk into time cost. For a safe threshold density, This is a risk sensitivity factor.

[0029] Derivation meaning: When density Approaching the safety threshold At that time, this term rapidly increases the impedance value through an exponential function. This forces the pricing system to output extremely high prices, thereby achieving a "soft circuit breaker" at the economic level and preventing physical collisions.

[0030] Item 3 (Network Spillover Effect): Airspace is not isolated; congestion is propagating. This invention defines... For unit The set of adjacencies (including the six directions: up, down, front, back, left, and right).

[0031] From adjacent units Inflow unit The transition probability weights.

[0032] : The "viscosity" of adjacent units.

[0033] Derivation Significance: This term uses weighted summation to simulate the propagation of traffic waves (Shockwave). If neighboring cell j has high density and slow speed (i.e., congestion), even if this cell... Very empty, enter Drones also face the risk of "getting in but not getting out." This item extends the local state mapping to the global topology state.

[0034] Through the above derivation, this invention obtains a nonlinear scalar field that comprehensively describes the physical state of the low atmosphere. This provides a solid physical basis for subsequent economic pricing.

[0035] For a more specific example, please refer to Figure 1 The nonlinear impedance model in the congestion quantification and pricing calculation layer is a three-dimensional spatial comprehensive impedance function. ; The congestion quantification and pricing calculation layer derives dynamic prices based on marginal social cost. ; The marginal social cost includes average private cost and marginal external congestion cost.

[0036] In this embodiment, the Pareto optimal requirement price for resource allocation is equal to the marginal social cost, which is the sum of private costs and external congestion costs. Set a specific operator k Planned airspace units i The private utility it obtains is (For example, the revenue from completing a delivery task), the generalized cost paid (including time and monetary costs) is ; The total social welfare W of the system is defined as the total utility of all users minus the total social cost: in For demand, For spatial units i The total operating cost function. The total operating cost is determined by the flow rate. With average travel time The product determines that, i.e. .

[0037] To maximize social welfare, this invention requires control over traffic flow. Find the derivative and locate the first-order optimal condition, let According to the chain rule of calculus, the derivation process is as follows: First, the marginal cost (MC) of total cost with respect to flow is: Expanding the above expression, applying the multiplication differentiation rule: In this expression: Part One This represents the average private cost (APC), which is the value of the time a user spends in congested airspace. This cost is borne by the user (represented by the time lost) and is not reflected in any charges.

[0038] Part Two This represents the Marginal External Congestion Cost (MECC). The Marginal External Congestion Cost is the core of congestion pricing; it is the sum of the increase in travel time for all other existing drones in the airspace caused by simply adding one drone. This is also the part that this invention needs to internalize through pricing.

[0039] To make the pricing model concrete and computable, this invention incorporates the travel time function. Related to the congestion impedance defined above Assume the baseline free-flow time is... Combining the modified BPR-type function structure, the travel time model is as follows: (This section simplifies the derivation and demonstrates the core logic, focusing on the density term. In the actual system...) (Including risk corrections and overflow terms). Next, this invention will... Find the partial derivatives: Substituting this derivative into the definition of MECC, we obtain the dynamic congestion pricing formula: The optimal congestion fee does not increase linearly, but rather increases with saturation. of The power is directly proportional to the power.

[0040] However, considering the unique characteristics of the low-altitude economy, cost-based pricing alone may not be sufficient to quickly mitigate sudden, extreme demand. Therefore, this invention introduces a supply-demand imbalance elastic adjustment mechanism. The real-time demand intensity is defined. Real-time supply capacity is the number of requests currently attempting to enter this airspace (including pending orders in the order book). (Remaining capacity). The final real-time dynamic pricing model. From basic resource fees The derived congestion fee And the components of supply and demand adjustment items: in, To cover the fixed costs of infrastructure operation and maintenance, The Value of Time (VOT) factor converts the congestion cost per unit of time into a monetary unit (e.g., yuan / minute). This factor can be dynamically adjusted based on the commercial value of airspace. Market sensitivity coefficient To prevent tiny quantities with a denominator of zero.

[0041] Index Term The introduction of [this element] is the key design of this invention: when demand [is needed] D When the price approaches or exceeds the remaining supply S, the denominator approaches 0, and the exponential function causes the price to grow explosively, creating a "soft wall" at the economic level, forcibly suspending overloaded demand, and ensuring that airspace safety is not breached.

[0042] For a more specific example, please refer to Figure 1 The blockchain transaction and clearing layer includes oracles, smart contracts, and order books; The oracle is used to reliably upload the state vectors collected by the physical sensing and data mapping layer to the blockchain and update the spatial state in the smart contract. The order book is used to store purchase orders from drone operators and sales orders from airspace agents; The smart contract incorporates continuous bidirectional auction logic and an integer linear programming algorithm to achieve transaction matching and settlement. The smart contract solves for the transaction set using integer linear programming. The objective function is to maximize social welfare. W ; The constraints include individual rationality constraints, supply and demand balance constraints, and variable integrity constraints; The smart contract uses the k-DA mechanism to calculate the market liquidation price. ; The blockchain transaction and clearing layer achieves atomic settlement, including fund transfer, equity minting, and airspace status update. Fund transfer satisfies the conservation of total account funds, and equity minting generates digital road tickets in the form of non-fungible tokens. The airspace status update feeds the transaction results back to the physical sensing and data mapping layer, updating the density of airspace units.

[0043] In this embodiment, the decentralized optimization solution process based on the Continuous Double Auction (CDA) theory is as follows: (1) Smart Contract State Transition First, this invention models the entire transaction system as a finite state automaton (FSM), assuming the global state of the smart contract at block height h is... The state transition function is The global state vector is defined as: in: Order book status, including Buyer bids and Seller asks. Buyer ID To make a bid, For timestamps; User account balance status For token balance; The physical spatial state refers to the occupancy density of grid g at time t. The state transition equation describes the transaction event. How to drive system status updates: (2) The mathematical formulation of the two-way auction matching algorithm is within the smart contract. Transaction matching is a discrete optimization problem of maximizing social welfare. Let a time window be defined... Inside, the system collected a set of payment receipts. and sell orders (Supply generated by the pricing model).

[0044] Buyer i The bid was The demand is .

[0045] seller j The price quoted for (airspace agency) is The supply is The smart contract solves the following integer linear programming (ILP) problem to determine the transaction set. ( Indicates buyer i With the seller j make a deal): Constraints: Individual Rationality: A transaction will only be completed if the bid price is higher than the ask price.

[0046] Supply-Demand Balance Constraints: Variable integrity constraints: (3) Derivation of the Market Clearing Price: To ensure incentive compatibility of transactions, smart contracts use the k-DA (k-Double Auction) mechanism to calculate the final transaction price. When the match is successfully made (i.e. When the liquidation price is calculated, the formula is as follows: in This represents the market preference coefficient. If... The price is the buyer's offer, and at this point, the buyer's surplus (ConsumerSurplus) is 0, while the seller receives the entire surplus, which incentivizes airspace supply. If The price is the seller's asking price, which helps reduce logistics costs. This system is set up... This means taking the middle value of the bid-ask spread to balance the interests of both parties.

[0047] (4) Automatic Settlement and Atomic Settlement: Once the optimal solution is found... and liquidation price The smart contract immediately triggers an atomic state update. The mathematical logic for this part is as follows: For each trade pair Execute the following state update equation: Token Transfer: This operation is atomic, that is... The system's total funds are conserved. Tokenization: Generating non-fungible tokens (NFTs) as travel passes. .

[0048] Physics Feedback: Feeds back transaction results to the physical state, influencing pricing in the next round. Mapping economic transaction results directly back to the three-dimensional spatial domain comprehensive impedance function density in This will boost the next moment. This forms a closed-loop negative feedback control.

[0049] For a more specific example, please refer to Figure 1 The digital pass in the user interaction and rights layer includes airspace unit identifiers and time window information.

[0050] In summary, the core of this invention lies in mapping the nonlinear congestion effect of the physical layer to the dynamic price of the economic layer, and enforcing it through smart contracts at the code layer, thereby forming an adaptive and highly reliable low-altitude resource management technology solution.

[0051] The innovation of this invention lies in: In the physical sensing and data mapping layer, a nonlinear impedance function structure is introduced. Specifically, it utilizes the power function term of the density ratio ( ) and the reciprocal of speed ( The congestion level of airspace units is characterized by a weighted combination of factors. A safety risk correction factor (based on the exponential relationship between average spacing and safety threshold) and an adjacent airspace spillover effect term (based on a weighted summation of spatial coupling weights) are introduced to correct the traditional road resistance function. This model breaks through the limitations of traditional two-dimensional planar traffic flow theory and is the physical basis for achieving accurate pricing.

[0052] In the congestion quantification and pricing calculation layer, the marginal external congestion cost (MECC), obtained by differentiating the total social welfare function, is used in the formula. In this process, congestion time delays are converted into monetized price signals using a time value of money (VOT) factor, and then compared with the real-time supply-demand ratio. The exponential function of ) Combining these methods with composite pricing, and utilizing the explosive growth characteristics of exponential functions, airspace resource overload circuit breakers can be implemented.

[0053] In the blockchain transaction and settlement layer, continuous two-way auction (CDA) logic and k-DA settlement price calculation method are built into the blockchain smart contract. The smart contract not only serves as an accounting tool, but also directly executes the matching algorithm (integer programming solution) to maximize social welfare. At the same time, it protects the generation and circulation mechanism of Digital Road Tickets (DoRW), that is, after a successful transaction, an NFT certificate containing spatiotemporal permissions is automatically minted, and this certificate is used to verify physical access qualifications.

[0054] Physical sensor data acquisition Oracle Trusted On-Chain Smart contract state ( )renew Triggering pricing formula recalculation Generate a new order book. This closed-loop feedback mechanism, which influences the next round of transactions, ensures that the on-chain transaction price is always anchored to the real scarcity in the off-chain physical world, which is the key difference between this system and traditional static transaction systems.

[0055] This invention aims to solve core problems such as measuring resource scarcity, internalizing congestion externalities, and building trust in multi-party transactions in low-altitude open environments by deeply integrating macro traffic flow theory, microeconomic pricing models, and blockchain smart contract technology.

[0056] First, this invention relates to low-altitude airspace traffic flow management and congestion control technology. With the exponential growth of urban air traffic (UAM) and logistics drone applications, low-altitude airspace has transformed from an abundant natural resource into a scarce economic resource. To maintain flight safety and maximize airspace utilization, refined grid partitioning and dynamic capacity assessment of the airspace are essential. This involves real-time monitoring of aircraft density in three-dimensional space and traffic flow modeling based on fluid dynamics analogies. This invention utilizes an improved Macroscopic Fundamental Diagram (MFD) model and a variant of the BPR (Bureau of Public Roads) road resistance function to establish a mathematical model describing the nonlinear relationship between low-altitude flight flow, density, and speed. By introducing a three-dimensional collision risk probability field, the system can quantify the congestion level of a specific airspace unit within a specific time window, i.e., "congestion entropy," thereby providing a physical-level quantitative basis for dynamic pricing.

[0057] Secondly, this invention relates to dynamic pricing and game theory techniques based on market mechanisms. Traditional airspace management often employs a "first-come, first-served" quota system or a static weight-based charging model. This rigid mechanism fails to reflect the real-time scarcity of resources, leading to severe congestion on popular routes (such as those over CBDs or around logistics hubs) while resources on less popular routes remain idle. This invention introduces dynamic pricing theory based on Marginal Social Cost (MSC), internalizing the externalities of congestion, such as time delays and safety risks, into price signals. This requires constructing a Stackelberg game model that includes drone operators (demand side) and airspace managers (supply side). Utilizing the principle of utility maximization in microeconomics, a real-time price adjustment mechanism is derived to guide automatic equilibrium of traffic flow. This mechanism not only considers the immediate supply-demand ratio but also incorporates historical data and future traffic forecasts, adjusting the spatiotemporal distribution of airspace demand through price levers to achieve "peak shaving and valley filling."

[0058] Finally, this invention relates to the application of blockchain technology and smart contracts in resource trading. Transactions generated by dynamic pricing are characterized by high frequency, small amounts, and multiple parties (P2P). Traditional centralized clearing systems face problems such as high trust costs, large settlement delays, and data silos. This invention utilizes the decentralized, immutable, and traceable characteristics of blockchain to construct a distributed ledger for airspace resource trading. Through smart contracts deployed on the chain, automated matching of transactions, instant clearing, and default penalties based on a continuous double auction (CDA) mechanism are achieved. Smart contracts solidify the mathematically derived pricing model into code logic, ensuring the transparency and fairness of transaction execution. Simultaneously, cryptographic techniques are used to protect the security and privacy of sensitive commercial data such as flight plans, constructing a trusted low-altitude resource trading market without third-party intermediaries.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic pricing and trading system for low-altitude airspace resources, characterized in that, include: The system consists of a physical perception and data mapping layer, a congestion quantification and pricing calculation layer, a blockchain transaction and settlement layer, and a user interaction and rights layer, which interact sequentially and form a closed-loop control. The physical sensing and data mapping layer is used to collect multi-source data in the low-altitude three-dimensional airspace and extract state vectors. The congestion quantification and pricing calculation layer calculates congestion entropy based on the state vector through a nonlinear impedance model, and derives dynamic prices by combining marginal social costs. Based on the dynamic price, the blockchain transaction and clearing layer uses smart contracts to automate the matching of airspace resources, implement instant clearing, and impose penalties for default. The user interaction and rights layer is used by drone operators to submit payments, obtain airspace usage rights certificates in the form of digital road tickets, and verify airspace usage qualifications based on the digital road tickets.

2. The low-altitude airspace resource dynamic pricing and trading system according to claim 1, characterized in that, The physical sensing and data mapping layer includes: Multi-source sensors are used to collect data on the position, speed, and density of drones in the airspace; A three-dimensional mesh cell divides the low-altitude airspace into several airspace cells and extracts the state vector of each airspace cell at a specific time. The state vector includes the number of aircraft per unit volume and the average flow velocity.

3. The low-altitude airspace resource dynamic pricing and trading system according to claim 2, characterized in that, The nonlinear impedance model in the congestion quantification and pricing calculation layer is a three-dimensional spatial comprehensive impedance function. The expression is: ； in, For free flow time, The impedance coefficient, It is a non-linear growth factor. For spatial units At any moment The actual density, For spatial units Effective capacity, This is a weighted coefficient for converting risk into time cost. For risk sensitivity factors, For a safe threshold density, This represents the weighting coefficient for network spillover effects. For spatial units The adjacency set, To the adjacent unit Inflow airspace unit The transition probability weights, Adjacent units At any moment density, Adjacent units At any moment The average flow velocity.

4. The low-altitude airspace resource dynamic pricing and trading system according to claim 3, characterized in that, The congestion quantification and pricing calculation layer derives dynamic prices based on marginal social cost. The dynamic price The expression is: ； in, Basic resource fee, This is the time value conversion factor. Based on the free circulation time, For spatial units At any moment Traffic, This is a risk correction factor. Market sensitivity coefficient For real-time demand intensity, To ensure real-time supply capacity, , To prevent tiny quantities with a denominator of zero.

5. The low-altitude airspace resource dynamic pricing and trading system according to claim 4, characterized in that, The marginal social cost includes average private cost and marginal external congestion cost, where the average private cost is: The marginal external congestion cost is The passage time function is expressed as: 。 6. The low-altitude airspace resource dynamic pricing and trading system according to claim 1, characterized in that, The blockchain transaction and clearing layer includes oracles, smart contracts, and order books; The oracle is used to reliably upload the state vectors collected by the physical sensing and data mapping layer to the blockchain and update the spatial state in the smart contract. The order book is used to store purchase orders from drone operators and sales orders from airspace agents; The smart contract incorporates continuous bidirectional auction logic and an integer linear programming algorithm to achieve transaction matching and settlement.

7. The low-altitude airspace resource dynamic pricing and trading system according to claim 6, characterized in that, The smart contract solves for the transaction set using integer linear programming. The objective function is to maximize social welfare. The expression is: ； The constraints include individual rationality constraints, supply and demand balance constraints, and variable integrity constraints.

8. The low-altitude airspace resource dynamic pricing and trading system according to claim 7, characterized in that, The smart contract uses the k-DA mechanism to calculate the market liquidation price. The expression is: ； in This is the market preference coefficient. .

9. The low-altitude airspace resource dynamic pricing and trading system according to claim 8, characterized in that, The blockchain transaction and clearing layer achieves atomic settlement, including fund transfer, equity minting, and airspace status update. Fund transfer satisfies the conservation of total account funds, and equity minting generates digital road tickets in the form of non-fungible tokens, expressed as: ； The airspace status update feeds the transaction results back to the physical sensing and data mapping layer, updating the density of airspace units.

10. The low-altitude airspace resource dynamic pricing and trading system according to claim 1, characterized in that, The digital pass in the user interaction and rights layer includes airspace unit identifiers and time window information.

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