Dynamic airspace gridding management method and system for low-altitude economy

Abstract

The invention discloses a dynamic airspace gridding management method and system for low-altitude economy, and belongs to the technical field of unmanned aerial vehicle traffic management, and the method comprises the steps: collecting airspace state data in real time through a multi-source sensing device, and constructing a four-dimensional space-time grid model; generating a four-dimensional space-time grid with a block chain hash code by fusing meteorological data, an airspace control rule and a real-time flight demand; receiving a space-time grid use request submitted by the aircraft through the smart contract, and calculating an optimal grid allocation scheme based on a deep reinforcement learning model; and the edge computing node executes local track prediction, issues a navigation instruction to the aircraft through the distributed account book, monitors a grid occupation state in real time, and triggers a dynamic grid recombination mechanism when sudden conflicts are detected. According to the method, the rigid constraint of static airspace division can be broken through, the cooperative conflict of multiple aircrafts is eliminated, and the marketization configuration of airspace resources is realized.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) traffic management technology, specifically a dynamic airspace grid management method and system for the low-altitude economy. Background Technology

[0002] The rapid development of the low-altitude economy and the explosive growth of aircraft such as drones and eVTOL have posed serious challenges to the traditional airspace management system. Existing airspace management technologies mainly suffer from the following key bottlenecks:

[0003] In terms of airspace resource allocation, the international mainstream still adopts a static allocation model. Taking the FAA's UTM system as an example, it allocates airspace resources by pre-setting fixed altitude layers and airway networks. This rigid allocation results in urban low-altitude utilization rates generally being less than 50%. Especially in areas with a high density of logistics drones, there is often a mismatch of resources, with "high-altitude layers idle and low-altitude layers congested."

[0004] In terms of conflict resolution mechanisms, existing systems generally rely on centralized control. NASA's UTM system uses a central server to process all flight plans. When the number of aircraft exceeds 1,000 per hour, the system response delay increases significantly to more than 5 seconds, making it difficult to meet the real-time requirements of urban air traffic (UAM).

[0005] The airspace resource trading system is lagging behind. Currently, the industry mainly uses a manual approval and fixed-fee model, which severely restricts time-sensitive applications such as emergency medical services.

[0006] These technological deficiencies have severely hampered the development of the low-altitude economy. Industry reports indicate that the idle rate of drone capacity due to inefficient airspace management is as high as 42%, resulting in direct economic losses exceeding 8 billion yuan annually. With the low-altitude economy being listed as a strategic emerging industry, the development of a new generation of intelligent air traffic control systems has become an urgent need for the industry. Summary of the Invention

[0007] The technical objective of this invention is to address the above-mentioned shortcomings by providing a dynamic airspace grid management method and system for low-altitude economy, which can break through the rigid constraints of static airspace division, eliminate multi-aircraft coordination conflicts, and realize the market-based allocation of airspace resources.

[0008] The technical solution adopted by this invention to solve its technical problem is:

[0009] A dynamic airspace grid management method for low-altitude economy, the implementation of which includes:

[0010] Four-dimensional dynamic grid coding: Real-time collection of airspace status data through multi-source sensing devices to construct a four-dimensional spatiotemporal grid model; By integrating meteorological data, airspace control rules and real-time flight requirements, a four-dimensional spatiotemporal grid with blockchain hash coding is generated.

[0011] Dynamic bidding for airspace resources: Receives spatiotemporal grid usage requests submitted by aircraft via smart contracts, and calculates the optimal grid allocation scheme based on a deep reinforcement learning model;

[0012] Distributed conflict pre-resolution: Edge computing nodes perform local trajectory prediction, issue navigation commands to the aircraft through a distributed ledger, and monitor grid occupancy status in real time. When a sudden conflict is detected, a dynamic grid reorganization mechanism is triggered.

[0013] This method enables dynamic and flexible allocation of airspace resources, improves the speed of conflict early warning response, and is particularly suitable for urban logistics drone swarms and eVTOL mixed operation scenarios.

[0014] Furthermore, the multi-source sensing device includes a millimeter-wave radar array, an ADS-B receiving station, and a 5G base station.

[0015] Furthermore, the method for dividing the four-dimensional spatiotemporal grid satisfies:

[0016] grid_size=K1*(Vmax^2 / Rmin)+K2*ρ+K3*QoS;

[0017] Where Vmax is the maximum speed of flight in the area; Rmin is the minimum turning radius; ρ is the aircraft density; QoS is the quality of service level; and K1-K3 are adjustment coefficients.

[0018] Furthermore, four-dimensional dynamic mesh encoding includes the following steps:

[0019] Digital airspace modeling: A 3D base map of the city is constructed by scanning point clouds with LiDAR (accuracy ±5cm), and a dynamic layer including meteorological and building disturbances is superimposed to form a 4D airspace model.

[0020] Intelligent grid generation: The grid size is dynamically adjusted based on the aircraft's dynamic parameters to generate blockchain hash identifiers, which are then used to form intelligent grid data. It supports grid reorganization at the 50ms level (such as in response to sudden weather events).

[0021] Distributed evidence storage: Each grid cell writes to the consortium blockchain (Hyperledger Fabric) and stores large files such as real-time monitoring videos via IPFS.

[0022] Furthermore, the dynamic bidding method for airspace resources includes: performing multi-objective optimization based on a deep reinforcement learning-based airspace resource allocation model according to the four-dimensional spatiotemporal coordinates of the demand airspace, aircraft parameters, and bidding strategy parameters; calculating the optimal allocation scheme using an auction mechanism; and dynamically adjusting resource pricing.

[0023] Furthermore, the AI-driven airspace bidding model is implemented as follows:

[0024] Multi-objective optimization training to build a deep reinforcement learning environment;

[0025] Hybrid auction mechanism:

[0026] Bidding: Smart contracts submit encrypted bids, and zero-knowledge proofs protect privacy;

[0027] Liquidation: VCG mechanism calculates marginal cost and is designed to prevent market manipulation;

[0028] Settlement: Automatic payment in stablecoins, supporting cross-border multi-currency transactions;

[0029] Federated learning upgrade: Each operator trains an LSTM prediction model locally, and the central aggregator updates the global model using secure multi-party computation (SMPC).

[0030] Furthermore, conflict pre-defense includes:

[0031] Federated learning-based trajectory prediction;

[0032] Monte Carlo simulation in a digital twin environment;

[0033] Rapid generation and allocation of emergency grids;

[0034] The distributed conflict pre-resolution mechanism is specifically implemented by the following steps:

[0035] Edge node collaborative sensing: heterogeneous data acquisition and spatiotemporal alignment;

[0036] Federated learning prediction engine: Each edge node runs a lightweight LSTM and uses Paillier homomorphic encryption for gradient swapping;

[0037] Dynamic game decision-making: Conflict quantification and construction of non-cooperative game models;

[0038] Emergency command execution: Different measures are responded to in a tiered manner based on probability intervals, and conflict resolution records are stored across chains. This invention also claims protection for a dynamic airspace gridded management system for the low-altitude economy, comprising:

[0039] The airspace perception module is used to generate a four-dimensional spatiotemporal grid with blockchain hash encoding by integrating meteorological data, airspace control rules and real-time flight requirements.

[0040] The AI ​​decision engine is used to receive spatiotemporal grid usage requests submitted by aircraft through smart contracts and to calculate the optimal grid allocation scheme based on a deep reinforcement learning model.

[0041] The distributed ledger module performs local trajectory prediction and conflict pre-resolution through distributed edge nodes, and uses federated learning to achieve cross-carrier data collaboration.

[0042] The system achieves dynamic airspace grid management through the methods described above.

[0043] The present invention also claims a dynamic airspace grid management device for low-altitude economy, comprising: at least one memory and at least one processor;

[0044] The at least one memory is used to store a machine-readable program;

[0045] The at least one processor is used to call the machine-readable program to implement the above method.

[0046] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described method.

[0047] Compared with existing technologies, the dynamic airspace grid management method and system of the present invention for low-altitude economy has the following advantages:

[0048] 1. Significantly improved airspace resource utilization: Through the AI ​​dynamic pricing mechanism, airspace resources are allocated on demand, and the utilization rate of urban low-altitude airspace has increased from ≤45% in the traditional static allocation to more than 82%, with airspace throughput increasing by 2.3 times during peak hours.

[0049] 2. Optimized computing performance: Supported by a dedicated AI acceleration architecture, achieving optimized computing performance.

[0050] 3. Breakthrough progress in conflict resolution efficiency: The multi-agent reinforcement learning system achieves a conflict early warning response of 200ms (25 times faster than the traditional 5-second response), and improves the success rate of obstacle avoidance under complex weather conditions.

[0051] 4. A new airspace market economy model has been established: an "airspace resource futures trading" mechanism has been created, which supports locking in airspace usage rights 6 months in advance, reducing price volatility by 40% and saving operators 25%-30% of route costs.

[0052] 5. Qualitative improvement in emergency response capabilities: The response time for emergency drone priority channel missions has been reduced from 30 minutes to 90 seconds, and the success rate of obtaining emergency air rights is 100%. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the four-dimensional dynamic mesh coding process provided in an embodiment of the present invention;

[0054] Figure 2 This is a flowchart illustrating the implementation process of the AI-driven airspace bidding model provided in this embodiment of the invention;

[0055] Figure 3 This is a flowchart illustrating the distributed conflict pre-resolution mechanism provided in an embodiment of the present invention. Detailed Implementation

[0056] The technical solution adopted by the present invention to solve its technical problem is as follows, provided in the embodiments of the present invention:

[0057] A dynamic airspace grid management method for low-altitude economy, the implementation of which includes:

[0058] Real-time airspace status data is collected through multi-source sensing devices (including millimeter-wave radar arrays, ADS-B receiving stations, and 5G base stations) to construct a four-dimensional spatiotemporal grid model; by integrating meteorological data, airspace control rules, and real-time flight requirements, a four-dimensional spatiotemporal grid with blockchain hash encoding is generated.

[0059] The system receives spatiotemporal grid usage requests submitted by aircraft via smart contracts and calculates the optimal grid allocation scheme based on a deep reinforcement learning model. Based on the reinforcement learning model, the system performs multi-objective optimization allocation of the grid usage requests submitted by aircraft, with optimization objectives including airspace utilization, conflict probability, and balance of operator interests.

[0060] Navigation commands are issued to the aircraft via a distributed ledger, and grid occupancy status is monitored in real time. When a sudden conflict is detected, a dynamic grid reorganization mechanism is triggered. Local trajectory prediction and conflict pre-resolution are performed through distributed edge nodes, and cross-operator data collaboration is achieved using federated learning.

[0061] The method for dividing the four-dimensional spatiotemporal grid satisfies:

[0062] grid_size=K1*(Vmax^2 / Rmin)+K2*ρ+K3*QoS;

[0063] Where Vmax is the maximum speed in the area; Rmin is the minimum turning radius; ρ is the aircraft density; QoS is the quality of service level; and K1-K3 are adjustment coefficients.

[0064] The dynamic bidding method for airspace resources includes the four-dimensional spatiotemporal coordinates of the demand airspace, aircraft parameters, and bidding strategy parameters. Based on deep reinforcement learning, the airspace resource allocation model performs multi-objective optimization, uses an auction mechanism to calculate the optimal allocation scheme, and dynamically adjusts resource pricing.

[0065] Conflict pre-resolution includes: federated learning-based trajectory prediction; Monte Carlo simulation in a digital twin environment; and rapid generation and allocation of emergency grids.

[0066] The implementation steps of this method include:

[0067] (1) Four-dimensional dynamic grid coding: The spatial domain is decomposed into spatiotemporal cubic units (x, y, z + time slice), and each unit is assigned a unique blockchain hash code. A non-uniform grid partitioning algorithm is used.

[0068] grid_size=max(Vmax*Δt,Dsafety)+α*QoS_level;

[0069] Where Vmax is the maximum flight speed in the area, and QoS_level is the service level weight.

[0070] (2) AI-driven airspace resource bidding model: The aircraft submits a flight plan containing spatiotemporal grid requirements through a smart contract. Based on a multi-objective optimization algorithm of reinforcement learning, the objective function is: maximize airspace utilization + minimize conflict probability + balance operator rights.

[0071] (3) Distributed conflict pre-resolution mechanism: Edge computing nodes perform local trajectory prediction, and federated learning is introduced to improve the prediction accuracy under cross-carrier data privacy.

[0072] The specific implementation plan is as follows:

[0073] Example 1:

[0074] like Figure 1 The diagram shown illustrates the four-dimensional dynamic mesh encoding provided in Embodiment 1 of this method. The result is used as input for the AI ​​spatial bidding model. Figure 1 The four-dimensional dynamic mesh encoding implementation shown includes the following steps:

[0075] (1) Digital modeling of airspace: A three-dimensional base map of the city (accuracy ±5cm) is constructed by scanning the point cloud of LiDAR, and dynamic layers such as meteorological and building disturbances are superimposed to form a 4D airspace model.

[0076] (2) Intelligent grid generation: The grid size is dynamically adjusted based on the aircraft dynamic parameters to generate blockchain hash identifiers and form intelligent grid data, supporting 50ms-level grid reorganization (such as response to sudden weather events).

[0077] (4) Distributed evidence storage: Each grid cell is written to the consortium blockchain (Hyperledger Fabric) and stored in IPFS for large files such as real-time monitoring videos.

[0078] Example 2:

[0079] like Figure 2 The diagram illustrates the AI-driven airspace bidding model provided in Embodiment 2 of this method. It is used to achieve more rational allocation of airspace resources, seek the optimal dynamic supply and demand balance, and build a market-based trading ecosystem. Figure 2 As shown, the AI-driven airspace bidding model implementation includes the following steps:

[0080] (1) Multi-objective optimization training to build a deep reinforcement learning environment.

[0081] (2) Hybrid auction mechanism:

[0082] Bidding: Smart contracts submit encrypted bids, and zero-knowledge proofs protect privacy.

[0083] Liquidation: VCG mechanism calculates marginal cost and is designed to prevent market manipulation.

[0084] Settlement: Stablecoin automatic payment, supports cross-border multi-currency transactions.

[0085] (3) Federated learning upgrade: Each operator trains the LSTM prediction model locally, and the central aggregator uses secure multi-party computation (SMPC) to update the global model.

[0086] Example 3:

[0087] like Figure 3 The diagram shown is a schematic of the distributed conflict pre-resolution mechanism provided in Embodiment 3 of this method. It is used for collaborative conflict resolution among multiple aircraft. Figure 3 As shown, the implementation of the distributed conflict pre-resolution mechanism includes the following steps:

[0088] (1) Edge node collaborative perception: heterogeneous data acquisition and spatiotemporal alignment.

[0089] (2) Federated learning prediction engine: Each edge node runs a lightweight LSTM and uses Paillier homomorphic encryption for gradient exchange.

[0090] (3) Dynamic game decision-making: Conflict quantification and construction of non-cooperative game model.

[0091] (4) Emergency command execution: Different measures are responded to in a probability interval and conflict handling records are stored across chains.

[0092] This method is applicable to dynamic airspace resource allocation and real-time conflict resolution in low-altitude economic scenarios such as urban air traffic (UAM) and logistics drone swarms. It solves the technical bottlenecks of existing air traffic control systems in dynamic demand response, multi-aircraft conflict early warning, and efficient airspace resource allocation, and provides core infrastructure support for the development of the low-altitude economy.

[0093] This invention also provides a dynamic airspace grid management system for the low-altitude economy, which includes an airspace perception module, an AI decision engine, and a distributed ledger module. It can realize dynamic and elastic allocation of airspace resources, improve the speed of conflict early warning response, and is particularly suitable for urban logistics drone swarms and eVTOL mixed operation scenarios.

[0094] The airspace perception module collects airspace status data in real time through multi-source sensing devices (including millimeter-wave radar arrays, ADS-B receiving stations, and 5G base stations) to construct a four-dimensional spatiotemporal grid model. By integrating meteorological data, airspace control rules, and real-time flight requirements, it generates a four-dimensional spatiotemporal grid with blockchain hash encoding. The method for dividing the four-dimensional spatiotemporal grid satisfies:

[0095] grid_size=K1*(Vmax^2 / Rmin)+K2*ρ+K3*QoS;

[0096] Where Vmax is the maximum speed in the area; Rmin is the minimum turning radius; ρ is the aircraft density; QoS is the quality of service level; and K1-K3 are adjustment coefficients.

[0097] The AI ​​decision engine receives spatiotemporal grid usage requests submitted by aircraft through smart contracts, calculates the optimal grid allocation scheme based on a deep reinforcement learning model, and performs multi-objective optimization allocation of the grid usage requests submitted by aircraft based on the reinforcement learning model. The optimization objectives include airspace utilization, conflict probability, and balance of operator interests.

[0098] The distributed ledger module issues navigation commands to the aircraft via the distributed ledger and monitors grid occupancy status in real time. When a sudden conflict is detected, a dynamic grid reorganization mechanism is triggered. Local trajectory prediction and conflict pre-resolution are performed through distributed edge nodes, and cross-operator data collaboration is achieved using federated learning. Conflict pre-resolution includes: federated learning-based trajectory prediction; Monte Carlo simulation in a digital twin environment; and rapid generation and allocation of emergency grids.

[0099] This system implements dynamic airspace grid management through the dynamic airspace grid management method for low-altitude economy described in the above embodiments. The implementation steps include:

[0100] (1) Four-dimensional dynamic grid coding: The spatial domain is decomposed into spatiotemporal cubic units (x, y, z + time slice), and each unit is assigned a unique blockchain hash code. A non-uniform grid partitioning algorithm is used.

[0101] grid_size=max(Vmax*Δt,Dsafety)+α*QoS_level;

[0102] Where Vmax is the maximum flight speed in the area, and QoS_level is the service level weight.

[0103] (2) AI-driven airspace resource bidding model: The aircraft submits a flight plan containing spatiotemporal grid requirements through a smart contract. Based on a multi-objective optimization algorithm of reinforcement learning, the objective function is: maximize airspace utilization + minimize conflict probability + balance operator rights.

[0104] (3) Distributed conflict pre-resolution mechanism: Edge computing nodes perform local trajectory prediction, and federated learning is introduced to improve the prediction accuracy under cross-carrier data privacy.

[0105] This invention also provides a dynamic airspace grid management device for low-altitude economy, comprising: at least one memory and at least one processor;

[0106] The at least one memory is used to store a machine-readable program;

[0107] The at least one processor is used to call the machine-readable program to implement the dynamic airspace grid management method for low-altitude economy described in the above embodiments.

[0108] This invention also provides a computer-readable medium storing computer instructions. When executed by a processor, these instructions implement the dynamic airspace grid management method for low-altitude economy described in the above embodiments. Specifically, a system or apparatus equipped with a storage medium storing software program code that implements the functions of any of the embodiments described above can be provided, enabling the computer (or CPU or MPU) of the system or apparatus to read and execute the program code stored in the storage medium.

[0109] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0110] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0111] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0112] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion unit connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion unit execute some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0113] The present invention has been shown and described in detail above with reference to the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above embodiments, those skilled in the art will know that more embodiments of the present invention can be obtained by combining the code review methods in the different embodiments. These embodiments are also within the protection scope of the present invention.

Claims

1. A dynamic airspace grid management method for low-altitude economy, characterized in that, The implementation of this method includes: Four-dimensional dynamic grid coding: Real-time collection of airspace status data through multi-source sensing devices to construct a four-dimensional spatiotemporal grid model; By integrating meteorological data, airspace control rules and real-time flight requirements, a four-dimensional spatiotemporal grid with blockchain hash coding is generated. Dynamic bidding for airspace resources: Receives spatiotemporal grid usage requests submitted by aircraft via smart contracts, and calculates the optimal grid allocation scheme based on a deep reinforcement learning model; Distributed conflict pre-resolution: Edge computing nodes perform local trajectory prediction, issue navigation commands to the aircraft through a distributed ledger, and monitor grid occupancy status in real time. When a sudden conflict is detected, a dynamic grid reorganization mechanism is triggered.

2. The dynamic airspace grid management method for low-altitude economy according to claim 1, characterized in that, The multi-source sensing equipment includes a millimeter-wave radar array, an ADS-B receiving station, and a 5G base station.

3. The dynamic airspace grid management method for low-altitude economy according to claim 1, characterized in that, The method for dividing the four-dimensional spatiotemporal grid satisfies: grid_size=K1*(Vmax^2 / Rmin)+K2*ρ+K3*QoS; Where Vmax is the maximum speed of flight in the area; Rmin is the minimum turning radius; ρ is the aircraft density; QoS is the quality of service level; and K1-K3 are adjustment coefficients.

4. A dynamic airspace grid management method for low-altitude economy according to claim 1 or 3, characterized in that, Four-dimensional dynamic mesh encoding includes the following steps: Digital airspace modeling: A 3D base map of the city is constructed by scanning point clouds with LiDAR, and a dynamic layer including meteorological and building disturbances is superimposed to form a 4D airspace model. Intelligent mesh generation: The mesh size is dynamically adjusted based on the aircraft's dynamic parameters to generate blockchain hash identifiers, which are then used to form intelligent mesh data, supporting mesh reorganization at the 50ms level. Distributed evidence storage: Each grid cell is written to the consortium blockchain and real-time monitoring videos are stored via IPFS.

5. The dynamic airspace grid management method for low-altitude economy according to claim 1, characterized in that, The dynamic bidding method for airspace resources includes: performing multi-objective optimization based on the four-dimensional spatiotemporal coordinates of the demand airspace, aircraft parameters, and bidding strategy parameters; calculating the optimal allocation scheme using an auction mechanism; and dynamically adjusting resource pricing.

6. A dynamic airspace grid management method for low-altitude economy according to claim 1 or 5, characterized in that, The AI-driven airspace bidding model is implemented as follows: Multi-objective optimization training to build a deep reinforcement learning environment; Hybrid auction mechanism: Bidding: Smart contracts submit encrypted bids, and zero-knowledge proofs protect privacy; Liquidation: VCG mechanism calculates marginal cost and is designed to prevent market manipulation; Settlement: Automatic payment in stablecoins, supporting cross-border multi-currency transactions; Federated learning upgrade: Each operator trains the LSTM prediction model locally, and the central aggregator uses secure multi-party computation to update the global model.

7. The dynamic airspace grid management method for low-altitude economy according to claim 1, characterized in that, Conflict pre-defense includes: Federated learning-based trajectory prediction; Monte Carlo simulation in a digital twin environment; Rapid generation and allocation of emergency grids; The distributed conflict pre-resolution mechanism is specifically implemented by the following steps: Edge node collaborative sensing: heterogeneous data acquisition and spatiotemporal alignment; Federated learning prediction engine: Each edge node runs a lightweight LSTM and uses Paillier homomorphic encryption for gradient swapping; Dynamic game decision-making: Conflict quantification and construction of non-cooperative game models; Emergency command execution: Different measures are responded to in a probability-range-based manner, and conflict handling records are stored across chains.

8. A dynamic airspace grid management system for low-altitude economy, characterized in that, include: The airspace perception module is used to generate a four-dimensional spatiotemporal grid with blockchain hash encoding by integrating meteorological data, airspace control rules and real-time flight requirements. The AI ​​decision engine is used to receive spatiotemporal grid usage requests submitted by aircraft through smart contracts and to calculate the optimal grid allocation scheme based on a deep reinforcement learning model. The distributed ledger module performs local trajectory prediction and conflict pre-resolution through distributed edge nodes, and uses federated learning to achieve cross-carrier data collaboration. The system achieves dynamic airspace grid management through the method described in any one of claims 1-7.

9. A dynamic airspace grid management device for low-altitude economy, characterized in that, include: At least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to invoke the machine-readable program to implement the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, The computer-readable medium stores computer instructions that, when executed by a processor, implement the method described in any one of claims 1 to 7.

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