Low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning

Abstract

The present invention provides a low-altitude industrial chain information intelligent collaboration and airspace management platform based on federated learning. The present invention is divided into three stages: data collection, data preprocessing and analysis application through cluster self-organizing networking and decentralized federated learning, to achieve low-altitude scene data communication with dynamic topology and static topology, and meet the high-efficiency communication needs in low-altitude operation scenarios. The system adopts differentiated data transmission strategies and optimizes the design for single machines, same-task clusters, cooperative clusters and non-cooperative clusters, thereby improving communication efficiency and resource utilization. The data preprocessing stage combines multi-source data fusion, data anonymization and decentralized federated learning aggregation to ensure data privacy protection and secure sharing among the three parties of industry, academia and research. The analysis and application stage realizes low-altitude aircraft task scheduling, airspace traffic control and environmental condition assessment through aircraft analysis, airspace analysis, environmental analysis and digital twin technology.

Description

Technical Field

[0001] The present invention belongs to the field of low-altitude airspace management and intelligent information collaboration technology, and in particular relates to a low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning. Background Art

[0002] Currently, low-altitude airspace management faces multiple challenges amidst the rapidly growing demand for low-altitude aircraft applications, including limited airspace resources, complex low-altitude aircraft swarm scheduling, and difficulties in protecting data privacy. However, existing centralized and traditional airspace management methods have significant shortcomings in addressing these challenges:

[0003] Communication bottlenecks and single-point failure risks associated with centralized management: Traditional centralized airspace management methods rely on a central server or control system, requiring all low-altitude aircraft data and mission instructions to be processed and issued through the central server. This model is prone to communication bottlenecks as the number of low-altitude aircraft increases, leading to data transmission delays and increased communication burden. Furthermore, if the central server fails, the entire airspace management system may fail, impacting the safety of the low-altitude aircraft swarm and the continuity of mission execution.

[0004] The conflict between multi-party data sharing and privacy protection: Low-altitude airspace management typically involves the collaboration of multiple entities (such as enterprises, research institutions, and government departments). Each party's data contains sensitive information, necessitating both data sharing to achieve joint airspace management and protecting their respective data privacy. The current centralized data sharing model poses significant risks in terms of data privacy protection. Traditional encryption technologies struggle to support multi-party collaborative modeling while ensuring data privacy, making the balance between multi-party data sharing and privacy protection difficult.

[0005] Heterogeneous, multi-source data integration is difficult: Low-altitude airspace management requires the real-time collection and integration of dynamic and static data from various sources (such as aircraft status, weather information, and environmental data). This data comes from a variety of sources and formats, increasing the complexity of data fusion and unified processing. Existing data integration methods lack effective technical means for processing multi-source, heterogeneous data, making it difficult to provide a consistent and real-time global data view. This limits the real-time and accuracy of the system for mission scheduling and airspace monitoring.

[0006] Poor adaptability to dynamic environments: Low-altitude aircraft operating in low-altitude airspace must cope with dynamic changes in meteorological conditions and airspace traffic. Traditional airspace management systems lack real-time awareness of the environment and airspace conditions. They lack timely prediction and application of environmental data such as wind speed, temperature, and humidity, making it difficult to dynamically optimize the flight paths of low-altitude aircraft. This reduces aircraft safety and mission completion rates in complex environments.

[0007] Lack of intelligent collaboration mechanisms suitable for the low-altitude economic industry chain: With the development of the low-altitude economy, low-altitude airspace management is no longer limited to a single task, but now involves multiple application scenarios such as logistics distribution, inspection, and monitoring. However, existing systems lack support for intelligent collaboration among industry, academia, research, and application, making it difficult to achieve data collaboration and task optimization among all parties in the low-altitude economy. There is a lack of an intelligent collaboration platform that meets the needs of the low-altitude economic industry chain. Summary of the Invention

[0008] In view of this, the present invention aims to overcome the shortcomings of the above-mentioned problems in the prior art and proposes a low-altitude industrial chain information intelligent collaboration and airspace management platform based on federated learning.

[0009] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0010] A federated learning-based low-altitude industry chain information intelligent collaboration and airspace management platform, including a data acquisition module, a data pre-processing module, and an analysis and application module connected in sequence;

[0011] The data acquisition module is used to collect static and dynamic data in the low-altitude airspace and adopt differentiated transmission strategies according to the cluster type to optimize communication efficiency and resource utilization;

[0012] The data preprocessing module achieves data consistency integration, privacy protection and collaborative modeling through multi-source data fusion, data anonymization and sharding processing, and decentralized federated learning model aggregation;

[0013] The analysis application module is used to perform intelligent analysis of aircraft, airspace and environment, including aircraft safety assessment, path optimization, airspace traffic management, environmental condition prediction and digital twin simulation, to achieve intelligent support for low-altitude airspace management and low-altitude aircraft mission scheduling.

[0014] Furthermore, the data acquisition module obtains static topology data and dynamic topology data from the environment. The static topology data is basic information containing spatial topology distribution that does not change with time, and is used for long-term planning and airspace layout. The dynamic topology data is real-time data that changes with mission execution and airspace environment changes, and is used for real-time monitoring, path optimization and risk avoidance of low-altitude aircraft.

[0015] Furthermore, the data acquisition module adopts differentiated transmission strategies according to cluster types to optimize communication efficiency and resource utilization, specifically including:

[0016] Standalone mode: The low-altitude aircraft transmits its mission data and status information directly to the nearest regional base station via unicast;

[0017] Same-mission cluster mode: Low-altitude aircraft use multicast and ad hoc networking for communication. Several reporting nodes are selected in the cluster to collect data within the cluster, compress and aggregate the data, and finally generate a joint message and upload it to the regional base station;

[0018] Cooperative cluster mode: Low-altitude aircraft use multi-hop communication to transmit data, forwarding data through neighboring nodes to achieve efficient mission information sharing. At the same time, priority is set for uploading of critical mission data to ensure that time-sensitive data can be transmitted quickly.

[0019] Non-cooperative cluster mode: Low-altitude aircraft nodes communicate independently with regional base stations and are allocated independent communication frequency bands or channels to avoid interference between clusters and ensure the independence of each task and data security.

[0020] Furthermore, in the data preprocessing module, multi-source data fusion is used to integrate the topological and spatiotemporal information of heterogeneous data; data anonymization and sharding are used to protect data privacy; and decentralized federated learning model aggregation is used to achieve cross-party collaborative modeling without sharing original data, specifically including:

[0021] Multi-source data fusion: Multi-source data fusion integrates heterogeneous data from different sources and types, unifying static and dynamic data, topology, and spatiotemporal information into a standardized data format;

[0022] The heterogeneous data is defined as follows: assuming that the data sources in the low-altitude airspace include static data sets and dynamic data sets;

[0023] Topological integration targets spatially distributed multi-source data and uses a topological integration function to map data at different locations into a unified spatial coordinate system to form a spatial information matrix covering the entire domain.

[0024] Spatiotemporal information integration integrates dynamic data that changes over time into a topological structure in time series to form a spatiotemporal dataset. The spatiotemporal integrated data serves as the input of the global model, supporting the intelligent scheduling of low-altitude aircraft and airspace resource optimization in time-varying environments.

[0025] Data sharding divides the data of each participant into multiple shards based on content or spatial regions. Data anonymization anonymizes the data in each shard and protects the privacy of the data by adding differential privacy noise.

[0026] Decentralized federated learning model aggregation enables collaborative model updates among different participants, ensuring that the joint training of the global model is completed without leaving the local data. Specifically, it includes:

[0027] Local model training: Each participant trains its model parameters on the local dataset to obtain updated local model parameters;

[0028] Decentralized model aggregation: Decentralized federated learning achieves parameter aggregation through model parameter exchange between adjacent participants;

[0029] Global model output: After completing multiple rounds of decentralized aggregation, a global model is generated. The global model aggregates the local models of each participant to form a joint model that contains the data features of multiple parties, realizing cross-party collaborative modeling.

[0030] Furthermore, the analysis application module includes aircraft analysis, airspace analysis, environmental analysis and digital twin modules;

[0031] Aircraft analysis is used for flight safety assessment and path optimization, airspace analysis is used for airspace traffic management and segment speed control strategies, environmental analysis is used to predict wind fields and meteorological conditions, and the digital twin module is used to build a virtual airspace environment to support airspace simulation and visualization. Specifically, it includes:

[0032] The aircraft analysis module evaluates flight status and optimizes flight paths to ensure flight safety and minimize energy consumption during mission execution, including:

[0033] Flight safety assessment: Real-time monitoring of the flight status of low-altitude aircraft to ensure they fly within a safe range;

[0034] Path optimization: Optimize the path based on mission requirements and environmental conditions, with the goal of minimizing energy consumption and flight risks;

[0035] The airspace analysis module analyzes the traffic flow in the low-altitude airspace and achieves a reasonable allocation of airspace resources through traffic management and segment speed control strategies to ensure the orderly operation of different aircraft in the same airspace, including:

[0036] Traffic management: Optimize airspace traffic distribution, avoid route congestion, and ensure safe distances between aircraft;

[0037] Segment speed control strategy: Based on the real-time airspace environment and mission requirements, set the optimal speed of the aircraft in each segment to avoid congestion or collision risks

[0038] The environmental analysis module monitors and predicts wind and weather conditions in the airspace in real time, helping the aircraft make necessary flight adjustments to ensure flight safety and mission stability, including:

[0039] Wind field prediction: Using historical wind field data and real-time meteorological data, the wind speed and direction at low altitudes are predicted to ensure stable flight of the aircraft under different wind conditions.

[0040] Weather condition analysis: By analyzing environmental variables, forecasting weather trends, and providing early warning information for aircraft;

[0041] The digital twin module builds a virtual airspace environment to simulate and visualize low-altitude airspace, helping managers intuitively understand airspace conditions and provide decision support, including:

[0042] Virtual airspace environment construction: Based on multi-source data fusion and federated learning model output, a virtual airspace environment model is constructed to simulate the airspace conditions in which aircraft operate;

[0043] Simulation and visualization: Through simulation in a virtual environment, the real-time operating status of the aircraft and airspace traffic conditions are displayed, facilitating real-time monitoring and management of low-altitude airspace.

[0044] Compared with the existing technology, the low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning described in this invention has the following advantages:

[0045] This invention utilizes a federated learning framework to enable multi-party collaborative modeling through decentralized model aggregation, enabling participants to jointly build a global model without sharing original data. Compared to traditional centralized data sharing models, this platform supports cross-organizational data collaboration while ensuring data privacy, adapting to the multi-party participation needs of low-altitude airspace management.

[0046] This invention utilizes multi-source data fusion technology to achieve unified and standardized processing of static and dynamic data. It integrates multi-dimensional data such as airspace environment, aircraft status, and meteorological information, providing a global view for airspace management. Furthermore, the platform designs differentiated communication strategies based on the type of low-altitude aircraft cluster, effectively reducing the communication burden, improving data transmission efficiency, and ensuring the system's adaptability in various scenarios.

[0047] The platform uses real-time environmental analysis, combined with historical data and real-time weather information, to dynamically predict airspace weather conditions and provide early warning support for low-altitude aircraft. This capability enables low-altitude aircraft to adjust their flight strategies in complex and changing weather environments, ensuring stable mission execution and significantly improving the platform's environmental adaptability compared to existing technologies.

[0048] This invention leverages the airspace analysis module's traffic management and segment speed control strategies to dynamically allocate and prioritize low-altitude airspace resources, avoiding route congestion and improving airspace resource utilization. Compared to traditional airspace management systems, the platform ensures the orderly operation of low-altitude aircraft in high-density mission environments, reducing the waste of airspace resources.

[0049] Using digital twin technology, this invention creates a virtual airspace environment for simulating low-altitude aircraft missions and visualizing mission status. The digital twin module provides managers with an intuitive view of airspace status, enabling mission prediction and real-time adjustments, making the platform's intelligent collaboration and airspace management more intuitive and effective.

[0050] This invention is particularly well-suited for collaborative low-altitude economic industry chains involving industry, academia, research, and users, meeting the diverse needs of various participants in low-altitude airspace management for data sharing, secure collaboration, and intelligent analysis. The platform's decentralized design and intelligent analysis capabilities enable efficient and secure airspace management support across multiple application scenarios, laying a solid foundation for the intelligent development of the low-altitude economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0052] Figure 1 This is a schematic diagram of the structure of a low-altitude industrial chain information intelligent collaboration and airspace management platform based on federated learning in the present invention.

[0053] Figure 2 This is a schematic diagram of the differentiated communication strategy of the low-altitude industrial chain information intelligent collaboration and airspace management platform based on federated learning in the present invention.

[0054] Figure 3 This is a schematic diagram of the low-altitude airspace zoning and functional distribution of a low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning in the present invention. DETAILED DESCRIPTION

[0055] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0056] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0057] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0058] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0059] like Figure 1 The present invention provides a low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning, the main modules of which are as follows:

[0060] Data acquisition module: used to obtain data on the spatial static topological distribution and spatial dynamic topological distribution in the low-altitude airspace environment, and design differentiated communication strategies based on cluster types to optimize communication efficiency and resource utilization. Specifically including:

[0061] Static and dynamic data acquisition: The data acquisition module obtains static topological data (such as airspace control information, base station information) and dynamic topological data (such as low-altitude aircraft status, real-time sensor information) from the environment. Let the static data set be D static , the dynamic data set is D dynamic , then the total data D is expressed as:

[0062] D=D static ∪D dynamic

[0063] Static topological data refers to basic information about spatial topological distribution that does not change over time and is primarily used for long-term planning and airspace layout. This type of data includes but is not limited to the following:

[0064] Airspace control information: covers airspace divisions, flight restricted areas, flight altitude restrictions, etc. This information provides boundary constraints for low-altitude aircraft path planning and mission execution, ensuring compliance with airspace management regulations and safety requirements.

[0065] Base station information: The geographic location, signal coverage, and channel allocation of communication base stations within the low-altitude airspace. Base station information determines the optimal selection of data transmission paths and provides stable communication support for low-altitude aircraft during flight.

[0066] Land-based sensor data: mainly includes basic environmental data collected by ground-based meteorological sensors (such as temperature and humidity sensors, air pressure sensors, etc.). These data provide basic parameters of the ground environment for low-altitude aircraft flights.

[0067] Meteorological remote sensing data: Wide-area meteorological information obtained by remote sensing equipment, including wind speed, wind direction, temperature, humidity, cloud cover, and rainfall conditions. This meteorological information provides the basic meteorological background of low-altitude airspace in the form of static data, especially providing a wide range of meteorological conditions reference before the start of a mission. The spatial distribution information of meteorological remote sensing data is set as G weather , which can be used to predict the meteorological conditions for low-altitude aircraft routes, is expressed as:

[0068] G weather ={W wind ,W temperature ,W humidity ,W precipitation}

[0069] Among them, W wind 、W temperature 、W humidity and W precipitation Represent the distribution of wind speed, temperature, humidity and rainfall respectively.

[0070] Dynamic topology data is data acquired in real time that changes with mission execution and airspace environment, and is primarily used for real-time monitoring, path optimization, and risk avoidance of low-altitude aircraft. This type of data includes but is not limited to the following:

[0071] Aircraft status: includes low-altitude aircraft location information (GPS coordinates), speed, acceleration, attitude (yaw, pitch, roll angle), remaining battery power and other flight status information.

[0072] Onboard sensor data: Various sensors carried by low-altitude aircraft collect detailed information and environmental conditions of the mission execution area in real time. The various sensors include but are not limited to cameras, lidar, infrared sensors, meteorological sensors, etc. Onboard sensor data D onboard It can be expressed as:

[0073] D onboard ={image, lidar, infrared sensor, wind speed}

[0074] Among them, image data is real-time images taken by onboard cameras, which are used for target recognition, environmental perception, terrain detection, etc.; lidar data is three-dimensional terrain maps or obstacle information generated by lidar, which provides spatial structure information for low-altitude aircraft, supports path adjustment and safe flight; infrared sensors obtain temperature data, which is used to determine heat sources or monitor ambient temperature changes; wind speed data is the local wind speed measured by onboard meteorological sensors.

[0075] Airspace operation status: Real-time acquisition of low-altitude airspace operation traffic conditions, including the location, speed, mission status, etc. of other nearby aircraft, to ensure the safe flight and dynamic avoidance of low-altitude aircraft in the airspace. airspace Contains status information of other aircraft, used for airspace flow control and conflict avoidance, expressed as:

[0076] R airspace ={Position other ,Velocity other ,Mission other}

[0077] Among them, Position other Refers to the current position of other aircraft in the low-altitude airspace; Velocity other is the current velocity component of other aircraft; Mission other Refers to the mission type currently being performed by surrounding aircraft, used by the system to determine the flight intention of the aircraft.

[0078] Real-time environmental information: Real-time environmental data obtained through ground-based and airborne sensors is updated as the environment changes dynamically. Including wind speed, temperature, humidity, air pressure, etc., to ensure that low-altitude aircraft can adapt to the environment while being able to respond quickly to sudden weather changes. Real-time environmental information real-time The expression is:

[0079] E real-time ={E wind ,E temperature ,E humidity ,E pressure}

[0080] Among them, E wind The real-time ambient wind speed is used to monitor the wind impact in the current area; E temperature is the temperature information of the flight area; E humidity is the humidity information of the flight area; E pressure It is the atmospheric pressure information of the environment.

[0081] like Figure 2 As shown in the figure, the differentiated communication strategy based on cluster type is designed, and the data transmission mode is flexibly adjusted according to the cluster type of low-altitude aircraft (stand-alone mode, same-mission cluster mode, cooperative cluster mode, and non-cooperative cluster mode), thereby optimizing the utilization of communication resources and reducing data redundancy. Specifically, it is expressed as:

[0082] 1. Standalone mode: Low-altitude aircraft perform tasks independently without the need for coordination with other low-altitude aircraft, so the data transmission path is relatively simple. Each low-altitude aircraft transmits its mission data and status information directly to the nearest regional base station in the form of unicast. This is suitable for scenarios with small data volumes and simple communication paths. Suppose the mission data of a single low-altitude aircraft is D single , the unicast transmission path is P single :

[0083] P single :D single →Nearest ground station

[0084] Among them, the communication overhead C in stand-alone mode is single Mainly depends on the distance d single , which can be expressed as:

[0085] C single =f(d single )

[0086] 2. Same-mission cluster mode: Applicable to scenarios where multiple low-altitude aircraft collaborate to perform the same mission. In order to reduce communication load and data redundancy, the low-altitude aircraft in the cluster use multicast and ad hoc networking for communication. Several reporting nodes are selected in the cluster. It is used to collect data within the cluster and perform data compression and aggregation to finally generate a joint message U task And upload to the regional base station.

[0087] Let the data of each node in the cluster be D i , the reporting node will report the data of all nodes Summarize and compress to generate a joint message U task , expressed as:

[0088]

[0089] Where f(g) is the data compression and aggregation function. The communication overhead of the reporting node C report It can be expressed as:

[0090] C report =g(|S task |,d report )

[0091] Where \S task | is the number of nodes in the cluster, d report The distance between the reporting node and the regional base station.

[0092] 3. Collaborative Cluster Mode: Suitable for scenarios where multiple low-altitude aircraft collaborate to complete complex tasks, such as those requiring frequent data sharing, coordinated path planning, and task allocation. In this mode, low-altitude aircraft use multi-hop communication to transmit data, forwarding data through neighboring nodes to achieve efficient mission information sharing. Furthermore, mission-critical data is prioritized for upload to ensure the rapid transmission of time-sensitive data.

[0093] Assume that the key task data in the cooperative cluster is D key , the node set in the cooperative cluster is S coop , the neighbor node is N(i),

[0094]

[0095] Where g(g) represents the priority transmission strategy. Multi-hop transmission path P coop It can be expressed as:

[0096] P coop :D key →N(i)→regional base station

[0097] Communication overhead of cooperative cluster C coop Depends on the number of nodes in the cluster |S coop | and the number of nodes n in the multi-hop communication path hops :

[0098] C coop =h(|S coop |,n hops )

[0099] 4. Non-cooperative cluster mode: Suitable for low-altitude aircraft clusters performing different missions in the same airspace. These low-altitude aircraft only share essential motion data. In this mode, each low-altitude aircraft node independently communicates with the regional base station and is allocated a separate communication frequency band or channel to avoid interference between clusters, ensuring the independence of each mission and data security.

[0100] Let each node of the non-cooperative cluster be S non-coop, whose task data is The transmission path is

[0101] →Nearest ground station

[0102] The total communication cost C of a non-cooperative cluster non-coop Expressed as the sum of the independent communication costs of each node:

[0103]

[0104] where f(d i ) represents the transmission distance d from a single node to the base station i The communication overhead generated.

[0105] The data preprocessing module includes multi-source data fusion, data anonymization, and decentralized federated learning model aggregation. Multi-source data fusion is used to integrate topological and spatiotemporal information of heterogeneous data; data anonymization and sharding are used to protect data privacy; decentralized federated learning model aggregation is used to achieve cross-party collaborative modeling without sharing original data. Specifically, it includes:

[0106] Multi-source data fusion: Multi-source data fusion aims to integrate heterogeneous data from different sources and types, unify static and dynamic data, topology and spatiotemporal information into a standardized data format, ensure data consistency and improve the accuracy of system analysis.

[0107] The heterogeneous data is defined as follows: the data sources in the low-altitude airspace include the static data set D dynamic ={D UAV ,D sensor ,D airspace}, and dynamic data set D dynamic ={D UAV ,D sensor ,D airspace};

[0108] The topological integration is to use the topological integration function T for spatially distributed multi-source data. topo (·) Mapping data at different locations to a unified spatial coordinate system to form a spatial information matrix M covering the entire area space :

[0109] M space =T topo (D static ,D dynamic )

[0110] The spatiotemporal information integration: the dynamic data that changes with time is integrated into the topological structure in time series to form a spatiotemporal dataset M spatio-temp. Let the time dimension be t, then the space-time integration function T spatio-temp Expressed as:

[0111] M spatio-temp =T spatio-temp (M space ,t)

[0112] Among them, T spatio-temp (·) is used to ensure the consistency of spatial and temporal information. The spatiotemporally integrated data can be used as input for global models, supporting intelligent scheduling of low-altitude aircraft and airspace resource optimization in time-varying environments.

[0113] Data anonymization and sharding: Data anonymization and sharding are used to protect data privacy and ensure that the original data is not leaked when data is exchanged between different participants. The data sharding divides the data of each participant into multiple shards according to content or spatial area. Let the data set of each participant be D i , whose fragment is D i ={D i1 ,D i2 ,...,D in}; The data anonymization is performed on each shard data D ij Anonymization is performed by adding differential privacy noise ∈ ij Achieve data privacy protection. Anonymize data D i ' j The expression is:

[0114] D i ' j =D ij +∈ ij ,∈ ij ~Laplace(0,δ)

[0115] Among them, δ is the differential privacy parameter, which controls the intensity of the noise.

[0116] Decentralized federated learning model aggregation: Decentralized federated learning model aggregation is used to achieve collaborative model updates between different participants, ensuring that the joint training of the global model is completed without leaving the local data. Specifically, it includes:

[0117] Local model training: Each participant i uses the local dataset D i ′ to train its model parameters θ i , get the updated local model parameters Assume that the loss function is L i (θ), then the local model update can be expressed as:

[0118]

[0119] Where η is the learning rate, is the gradient of the loss on the local data.

[0120] Decentralized model aggregation: Decentralized federated learning achieves parameter aggregation by exchanging model parameters between adjacent participants. Suppose the neighbor node set of participant i is The model parameters after decentralized aggregation are for:

[0121]

[0122] Among them, w ij is the weight, satisfying Through multiple rounds of aggregation, the model parameters of all nodes gradually converge, and finally a globally consistent model θ is obtained. global .

[0123] Global model output: After completing multiple rounds of decentralized aggregation, the generated global model θ global It can be expressed as:

[0124]

[0125] Among them, α i is the data volume weight of the participants, satisfying The global model aggregates the local models of each participant to form a joint model that contains data features from multiple parties, thereby achieving cross-party collaborative modeling.

[0126] The analysis application modules include aircraft analysis, airspace analysis, environmental analysis, and digital twin modules; aircraft analysis is used for flight safety assessment and path optimization, airspace analysis is used for airspace traffic management and segment speed control strategies, environmental analysis is used for wind field and meteorological condition prediction, and the digital twin module is used to build a virtual airspace environment and support airspace simulation and visualization. Specifically, it includes:

[0127] The aircraft analysis module evaluates flight status and optimizes flight paths to ensure flight safety and minimize energy consumption during mission execution. The main functions of this module are as follows:

[0128] Flight safety assessment: Real-time monitoring of the flight status (position, speed, attitude, etc.) of low-altitude aircraft to ensure that it flies within a safe range. Suppose the state set of low-altitude aircraft is S UAV ={Position, Velocity, Orientation}, flight safety assessment model F safe Determine whether the flight meets safety conditions:

[0129]

[0130] Path optimization: Optimize the path based on mission requirements and environmental conditions, with the goal of minimizing energy consumption and flight risk. Let the path planning function be p(x), and the path optimization objective function be:

[0131]

[0132] Among them, c risk (x) and c energy (x) represents the risk and energy cost in the path respectively. The optimal configuration of the flight path is achieved by solving this optimization problem.

[0133] like Figure 3 As shown, the airspace analysis module analyzes the traffic conditions in the low-altitude airspace and realizes the rational allocation of airspace resources through traffic management and segment speed control strategies, ensuring the orderly operation of different aircraft in the same airspace. The main functions of this module are as follows:

[0134] Airspace flow management: Airspace flow management aims to optimize the flow distribution in the airspace, avoid route congestion, and ensure a safe distance between aircraft. Assume that the set of aircraft in the airspace is The traffic management objectives are:

[0135]

[0136] Among them, u(R i ) represents the efficiency of resource utilization, v(R j ) represents the resource conflict cost, and this objective function is used to achieve the rational allocation of resources in the airspace.

[0137] Segment speed control strategy: Based on the real-time airspace environment and mission requirements, set the optimal speed of the aircraft in each segment to avoid congestion or collision risks. Speed ​​control strategy function V control Expressed as:

[0138]

[0139] Among them, Distance to_other Indicates the distance between the current aircraft and other aircraft, Mission priority Indicates the task priority.

[0140] The environmental analysis module monitors and predicts wind and weather conditions in the airspace in real time, helping the aircraft make necessary flight adjustments to ensure flight safety and mission stability. The main functions of this module are as follows:

[0141] Wind field prediction: Based on historical wind field data and real-time meteorological data, the wind speed and direction in low-altitude airspace are predicted to ensure the stable flight of the aircraft under different wind conditions. predBased on time t, it is expressed as:

[0142] W pred (t+1)=f(W(t),Δt)

[0143] Where W(t) represents the current wind field state and Δt is the time step.

[0144] Weather Condition Analysis: By analyzing environmental variables such as temperature, humidity, and air pressure, weather change trends are predicted and early warning information is provided to aircraft. Weather Condition Prediction Model E pred Expressed as:

[0145] E pred (t+1)=g(E(t),Δt)

[0146] Where E(t) represents the current weather conditions.

[0147] The digital twin module builds a virtual airspace environment to simulate and visualize low-altitude airspace, helping managers intuitively understand airspace conditions and provide decision support. The main functions of this module are as follows:

[0148] Virtual airspace environment construction: Based on multi-source data fusion and federated learning model output, a virtual airspace environment model M is constructed. twin , used to simulate the airspace conditions for aircraft operations:

[0149] M twin =h(D fused ,θ global )

[0150] Among them, h(·) is the virtual environment generation function, D fused is the fused multi-source data, θ global This is the global model output by federated learning.

[0151] Simulation and visualization: Through the virtual environment simulation, the real-time operation status of the aircraft and the airspace traffic situation are displayed, so as to facilitate the real-time monitoring and management of the low-altitude airspace. The output of simulation visualization is V sim Expressed as:

[0152] V sim =Visualize(M twin ,S UAV )

[0153] This simulation module helps managers predict and optimize task execution in a virtual environment.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning, characterized by: It includes a data acquisition module, a data pre-processing module and an analysis application module connected in sequence; The data acquisition module is used to collect static and dynamic data in the low-altitude airspace and adopt differentiated transmission strategies according to the cluster type to optimize communication efficiency and resource utilization; The data preprocessing module achieves data consistency integration, privacy protection and collaborative modeling through multi-source data fusion, data anonymization and sharding processing, and decentralized federated learning model aggregation; The analysis application module is used to perform intelligent analysis of aircraft, airspace and environment, including aircraft safety assessment, path optimization, airspace traffic management, environmental condition prediction and digital twin simulation, to achieve intelligent support for low-altitude airspace management and low-altitude aircraft mission scheduling.

2. The low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning according to claim 1 is characterized by: The data acquisition module obtains static topology data and dynamic topology data from the environment. Static topology data is basic information containing spatial topology distribution that does not change with time, and is used for long-term planning and airspace layout. Dynamic topology data is real-time data that changes with mission execution and airspace environment changes, and is used for real-time monitoring, path optimization and risk avoidance of low-altitude aircraft.

3. The low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning according to claim 1 is characterized by: The data acquisition module adopts differentiated transmission strategies according to cluster types to optimize communication efficiency and resource utilization, specifically including: Standalone mode: The low-altitude aircraft transmits its mission data and status information directly to the nearest regional base station via unicast; Same-mission cluster mode: Low-altitude aircraft use multicast and ad hoc networking for communication. Several reporting nodes are selected in the cluster to collect data within the cluster, compress and aggregate the data, and finally generate a joint message and upload it to the regional base station; Cooperative cluster mode: Low-altitude aircraft use multi-hop communication to transmit data, forwarding data through neighboring nodes to achieve efficient mission information sharing. At the same time, priority is set for uploading of critical mission data to ensure that time-sensitive data can be transmitted quickly. Non-cooperative cluster mode: Low-altitude aircraft nodes communicate independently with regional base stations and are allocated independent communication frequency bands or channels to avoid interference between clusters and ensure the independence of each task and data security.

4. The low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning according to claim 1 is characterized by: In the data preprocessing module, multi-source data fusion is used to integrate the topological and spatiotemporal information of heterogeneous data; data anonymization and sharding are used to protect data privacy; Decentralized federated learning model aggregation is used to achieve cross-party collaborative modeling without sharing original data, including: Multi-source data fusion: Multi-source data fusion integrates heterogeneous data from different sources and types, unifying static and dynamic data, topology, and spatiotemporal information into a standardized data format; The heterogeneous data is defined as follows: assuming that the data sources in the low-altitude airspace include static data sets and dynamic data sets; Topological integration targets spatially distributed multi-source data and uses a topological integration function to map data at different locations into a unified spatial coordinate system to form a spatial information matrix covering the entire domain. Spatiotemporal information integration integrates dynamic data that changes over time into a topological structure in time series to form a spatiotemporal dataset. The spatiotemporal integrated data serves as the input of the global model, supporting the intelligent scheduling of low-altitude aircraft and airspace resource optimization in time-varying environments. Data sharding divides the data of each participant into multiple shards based on content or spatial regions. Data anonymization anonymizes the data in each shard and protects the privacy of the data by adding differential privacy noise. Decentralized federated learning model aggregation enables collaborative model updates among different participants, ensuring that the joint training of the global model is completed without leaving the local data. Specifically, it includes: Local model training: Each participant trains its model parameters on the local dataset to obtain updated local model parameters; Decentralized model aggregation: Decentralized federated learning achieves parameter aggregation through model parameter exchange between adjacent participants; Global model output: After completing multiple rounds of decentralized aggregation, a global model is generated. The global model aggregates the local models of each participant to form a joint model that contains the data features of multiple parties, realizing cross-party collaborative modeling.

5. The low-altitude industry chain information intelligent collaboration and airspace management platform based on federated learning according to claim 1 is characterized by: The analysis application modules include aircraft analysis, airspace analysis, environmental analysis and digital twin modules; Aircraft analysis is used for flight safety assessment and path optimization, airspace analysis is used for airspace traffic management and segment speed control strategies, environmental analysis is used to predict wind fields and meteorological conditions, and the digital twin module is used to build a virtual airspace environment to support airspace simulation and visualization. Specifically, it includes: The aircraft analysis module evaluates flight status and optimizes flight paths to ensure flight safety and minimize energy consumption during mission execution, including: Flight safety assessment: Real-time monitoring of the flight status of low-altitude aircraft to ensure they fly within a safe range; Path optimization: Optimize the path based on mission requirements and environmental conditions, with the goal of minimizing energy consumption and flight risks; The airspace analysis module analyzes the traffic flow in the low-altitude airspace and achieves a reasonable allocation of airspace resources through traffic management and segment speed control strategies to ensure the orderly operation of different aircraft in the same airspace, including: Traffic management: Optimize airspace traffic distribution, avoid route congestion, and ensure safe distances between aircraft; Segment speed control strategy: Based on the real-time airspace environment and mission requirements, set the optimal speed of the aircraft in each segment to avoid congestion or collision risks The environmental analysis module monitors and predicts wind and weather conditions in the airspace in real time, helping the aircraft make necessary flight adjustments to ensure flight safety and mission stability, including: Wind field prediction: Using historical wind field data and real-time meteorological data, the wind speed and direction at low altitudes are predicted to ensure stable flight of the aircraft under different wind conditions. Weather condition analysis: By analyzing environmental variables, forecasting weather trends, and providing early warning information for aircraft; The digital twin module builds a virtual airspace environment to simulate and visualize low-altitude airspace, helping managers intuitively understand airspace conditions and provide decision support, including: Virtual airspace environment construction: Based on multi-source data fusion and federated learning model output, a virtual airspace environment model is constructed to simulate the airspace conditions in which aircraft operate; Simulation and visualization: Through simulation in a virtual environment, the real-time operating status of the aircraft and airspace traffic conditions are displayed, facilitating real-time monitoring and management of low-altitude airspace.

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