A low-altitude economy operation scheduling method based on big data analysis

Abstract

The application discloses a low-altitude economy operation scheduling method based on big data analysis, which comprises the following steps: collecting low-altitude economy operation big data and constructing a low-altitude economy operation topology graph; constructing an improved MixHop model and generating a multi-order resource coupling matrix; generating a multi-order pressure-receiving spectrum, constructing an adaptive airspace prism to form a current available low-altitude operation space; generating a double-state multi-resource commitment vector with a self-destruction time limit, and generating a revocable zero-knowledge proof; constructing a resource contention mirror sequence, performing reverse sequence pressure release verification to determine a formal commitment; constructing a residual energy-risk siny flow state space, and generating a minimum action kernel output scheduling scheme. Through the improved MixHop multi-order topology perception, the adaptive airspace prism and the double-state multi-resource commitment mechanism, the application realizes stable collaborative scheduling and controllable risk of multi-task and multi-resource in low-altitude economy operation.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a method for low-altitude economic operation scheduling based on big data analytics. Background Technology

[0002] With the rapid development of the low-altitude economy, services such as drone logistics, low-altitude inspection, emergency rescue, agricultural and forestry plant protection, low-altitude cultural tourism, and urban air traffic are continuously increasing, and flight missions from different operators are running concurrently in the same low-altitude airspace. Existing low-altitude operation scheduling methods typically allocate paths based on fixed corridors, static airspace grids, or single-hop adjacency relationships, and generate scheduling results by combining flight distance, corridor capacity, takeoff and landing windows, and mission priority.

[0003] However, low-altitude economic operation is not simply a matter of corridor passage. The availability of the current corridor is also continuously affected by remote take-off and landing points, refueling nodes, temporary hovering areas, emergency evacuation areas, and communication takeover areas. Existing methods cannot identify in advance the reverse occupancy pressure on the current corridor caused by remote resource gaps through multi-level topology relationships. This can easily lead to problems such as flights being unable to land, refuel, hover, evacuate, or have insufficient communication takeover after entering the corridor, which in turn can cause the spread of corridor congestion and instability in multi-mission scheduling.

[0004] Low-altitude economic operations involve multiple operating entities, and routes, payloads, mission objectives, and commercial scheduling strategies are highly sensitive. Existing centralized scheduling typically requires operating entities to submit complete mission information to complete resource conflict verification, making it difficult to balance privacy protection and reliable resource verification. Current scheduling results mainly focus on resolving local conflicts, lacking continuous constraints on the relationship between aircraft residual energy status and the evolution of operational risks, resulting in insufficient stability of scheduling schemes under complex airspace changes.

[0005] Therefore, how to provide a low-altitude economic operation scheduling method based on big data analysis is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose a low-altitude economic operation scheduling method based on big data analysis. This invention fully utilizes low-altitude operation big data, an improved MixHop model, multi-level topology sensing, adaptive airspace prism, revocable zero-knowledge proof, and surplus energy-risk symplectic flow analysis technology. It describes in detail the process of multi-level cascading pressure identification, privacy-preserving resource commitment verification, instability handover, and surplus energy risk-stabilized scheduling for resources such as corridors, takeoffs and landings, refueling, hovering, evacuation, and communication takeover in a multi-entity, multi-task concurrent low-altitude economic scenario. It has the advantages of accurate remote resource pressure identification, strong protection of operator privacy, timely resolution of scheduling conflicts, high airspace resource utilization, and strong operational safety and stability.

[0007] A low-altitude economic operation scheduling method based on big data analysis according to an embodiment of the present invention includes:

[0008] Collect big data on low-altitude economic operations and construct a topology map of low-altitude economic operations based on the big data.

[0009] The low-altitude economic operation topology is mapped into six types of resource-independent hypergraphs and a unified multi-layer heterogeneous hypergraph. An improved MixHop model is adopted, which introduces resource channel gating, adaptive hop weight allocation and cross-hop residual competition mechanism. Parallel multi-order neighborhood aggregation is performed on the one-hop, two-hop and three-hop neighborhoods to generate multi-order resource coupling matrices.

[0010] A multi-order support and anti-pressure spectrum is generated based on a multi-order resource coupling matrix. Multiple types of support and constraint surfaces are generated based on the multi-order support and anti-pressure spectrum and superimposed on an adaptive airspace prism to form the currently available low-altitude operating space.

[0011] For each flight mission, a dual-state multi-resource commitment vector with a self-destruction time limit is generated. A revocable zero-knowledge proof is generated for the main state vector and uploaded to the blockchain. If the flight mission does not enter the corresponding adaptive airspace prism within the preset time window, the main state commitment automatically expires, the backup state commitment takes effect immediately, and the locked resources are released simultaneously.

[0012] Based on revocable zero-knowledge proof, a resource contention mirror sequence is constructed. The reverse decompression verification is performed on the main state commitment. The main state commitment that meets the acceptance conditions is determined as the formal commitment. The main state commitment that causes acceptance instability is switched to the backup state commitment. The switching result is written back to the improved MixHop model and the adaptive spatial prism.

[0013] The formally committed mission is mapped as a surplus energy-risk state object, a surplus energy-risk symplectic flow state space is constructed, a surplus energy-risk symplectic flow minimum action kernel is generated, and a resource reconfiguration scheduling operation is triggered when the surplus energy-risk state of the flight mission deviates from the surplus energy-risk symplectic flow minimum action kernel, and a low-altitude economic operation scheduling scheme is output.

[0014] Optionally, the low-altitude economic operation big data includes aircraft status data, mission order data, low-altitude corridor data, take-off and landing station data, refueling node data, temporary hovering area data, emergency evacuation area data, communication coverage data, micro-meteorological data, ground population density data, temporary control data, and historical scheduling execution data.

[0015] Optionally, the construction of the low-altitude economic operation topology map based on low-altitude economic operation big data includes:

[0016] Low-altitude corridor data, take-off and landing station data, refueling node data, temporary hovering zone data, emergency evacuation zone data, and communication coverage data are converted into corridor nodes, take-off and landing nodes, refueling nodes, hovering nodes, evacuation nodes, and communication nodes, respectively. Corresponding connection edges are established between each node based on corridor continuity relationships, take-off and landing acceptance relationships, refueling continuity relationships, hovering transfer relationships, evacuation continuity relationships, communication coverage relationships, and temporary control constraints. The resource capacity, occupancy status, time window, spatial location, and availability status of each node are written into the node attributes. The continuity direction, passage capacity, time span, constraint type, and failure condition of each connection edge are written into the edge attributes to generate a low-altitude economic operation topology map.

[0017] Optionally, generating the multi-order resource coupling matrix includes:

[0018] Read the corridor nodes, take-off and landing nodes, energy replenishment nodes, hovering nodes, evacuation nodes and communication nodes in the low-altitude economic operation topology map, map them to the corresponding resource channels according to resource type, generate six types of independent hypergraphs, and superimpose them into a unified multi-layer heterogeneous hypergraph based on time window, spatial location, resource continuity relationship and control constraint relationship.

[0019] An improved MixHop model is constructed, which includes a resource channel gating layer, a skip-order parallel aggregation layer, an adaptive skip-order weight allocation layer, a cross-skip-order residual competition layer, and a resource coupling output layer.

[0020] The resource capacity, occupancy status, remaining resources, task time limit pressure, communication coverage quality, micro-weather risk, and control restriction status of each node in the unified multi-layer heterogeneous hypergraph are written into the node state vector. The node state vector is input into the resource channel gating layer, which generates the channel activation results corresponding to the corridor, take-off and landing, refueling, hovering, evacuation, and communication, respectively. Based on the channel activation results, the resource channel characteristics related to the current scheduling pressure are retained.

[0021] The resource channel features are input into the step-parallel aggregation layer, which simultaneously extracts the direct occupancy features of the one-hop neighborhood, the indirect acceptance features of the two-hop neighborhood, and the remote linkage features of the three-hop neighborhood, generating the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features corresponding to each resource channel.

[0022] The one-hop aggregation feature, two-hop aggregation feature, and three-hop aggregation feature are input into the adaptive hop order weight allocation layer. Based on the current node's corridor passage status, subsequent take-off and landing acceptance status, energy replenishment waiting status, hovering capacity status, evacuation reachability status, and communication takeover status, the participation ratio of the one-hop aggregation feature, two-hop aggregation feature, and three-hop aggregation feature in the current resource channel is determined respectively, and hop order fusion features are generated.

[0023] The jump-order fusion features corresponding to each resource channel are input into the cross-jump-order residual competition layer. The duplicate occupancy features among the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features are reduced. The gap features generated by remote take-off and landing nodes, power replenishment nodes, hovering nodes, evacuation nodes, and communication nodes are retained. The contradictory jump-order transmission features are competitively filtered. The resource coupling output layer combines the processed resource channel features into a multi-order resource coupling matrix.

[0024] Optionally, forming the currently available low-altitude operating space includes:

[0025] Read the multi-level resource coupling matrix, and extract the remote take-off and landing acceptance gap, remote replenishment waiting gap, remote hovering capacity gap, remote evacuation reachability gap and remote communication takeover gap of the current corridor node according to the connection relationship between corridor nodes, take-off and landing nodes, replenishment nodes, hovering nodes, evacuation nodes and communication nodes. Generate a multi-level acceptance back pressure spectrum according to resource type, time window, altitude layer and task priority.

[0026] The basic corridor prism is constructed based on the spatial boundary of the low-altitude corridor, the allowable altitude layer, the entry time window, and the exit time window. Within the basic corridor prism, a corridor passage layer, a take-off and landing receiving layer, a power replenishment layer, a hovering buffer layer, an evacuation retention layer, and a communication takeover layer are set up to generate the initial structure of the adaptive airspace prism.

[0027] The multi-level support and anti-pressure spectrum is back-mapped to the corresponding preceding corridor node, preceding altitude layer and preceding entry time window, and multiple types of support and constraint surfaces are generated according to the source of the remote resource gap;

[0028] The various types of acceptance constraint surfaces are written into the adaptive airspace prism. The entry boundary is contracted for the corridor passage layer, the landing window is rearranged for the take-off and landing acceptance layer, the energy replenishment access offset is performed for the energy replenishment continuation layer, the hovering capacity is released for the hovering buffer layer, the evacuation channel is locked for the evacuation retention layer, and the takeover boundary is adjusted for the communication takeover layer. The prism deformation amplitude is limited according to the aircraft's turning capability, climb capability, descent capability, communication command response time and control restriction status.

[0029] Record the deformation state, resource slot state, constraint-bearing facet state, and revocable boundary state of the adaptive airspace prism in each time window, and determine the adaptive airspace prism after deformation as the currently available low-altitude operating space.

[0030] Optionally, the step of generating a two-state multi-resource commitment vector with a self-destruction timeout for each flight mission, and generating a revocable zero-knowledge proof for the main state vector and uploading it to the blockchain, includes:

[0031] Read the currently available low-altitude operating space, the mission start point, mission end point, mission time limit, remaining endurance, aircraft type, operator identifier and execution priority, extract the available corridor segments, take-off and landing slots, refueling slots, hovering slots, evacuation slots and communication takeover slots for the flight mission, and generate the resource candidate chain corresponding to the flight mission.

[0032] Select resource combinations that meet the conditions of task time limit, lower limit of remaining energy, evacuation reachability and communication takeover from the resource candidate chain and write them into the main state vector. Select alternative resource combinations that do not occupy the same key resource slot as the main state vector and meet the conditions for continued execution and write them into the standby state vector to generate a dual-state resource commitment initial vector.

[0033] Based on the time required for the flight mission to reach the adaptive airspace prism entrance, the scheduling command response time, the resource slot retention time, and the remaining mission time limit, a self-destruction trigger piece is generated. The self-destruction trigger piece is then bound to the dual-state resource commitment initial vector, the flight mission identifier, and the operation entity identifier to form a dual-state multi-resource commitment vector with a self-destruction time limit.

[0034] Generate a primary and backup mutually exclusive commitment seal for the dual-state multi-resource commitment vector, so that the primary state vector enters the resource-locked state and the backup state vector enters the waiting-to-activate state. Generate a revocable zero-knowledge proof for the primary state vector and write the revocable zero-knowledge proof, commitment index, self-destruct trigger shard and primary and backup mutually exclusive commitment seal into the on-chain ledger.

[0035] During the execution of the flight mission, the actual entry time, actual entry corridor segment, actual entry altitude layer, and actual entry adaptive airspace prism status are read. When the flight mission enters the corresponding adaptive airspace prism within the self-destruction time limit, the main state commitment remains valid. When the flight mission does not enter the corresponding adaptive airspace prism within the self-destruction time limit, the main state commitment is automatically invalidated, the standby state commitment takes effect immediately, and the resource slot locked by the main state vector is released simultaneously.

[0036] Optionally, the step of constructing a resource contention mirror sequence based on revocable zero-knowledge proof and performing reverse decompression verification on the main state commitment includes:

[0037] Read the revocable zero-knowledge proof, main state vector, standby state vector, commitment index and self-destruct trigger piece corresponding to each flight mission in the on-chain ledger, and identify the resource contention objects of multiple main state commitments contending for the same resource slot according to the occupancy relationship of the corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot and communication takeover slot;

[0038] The resource contention objects are mirrored and sorted according to resource slot type, entry time window, exit time window, task time limit, remaining energy lower limit, evacuation reachability status and communication takeover status to generate a resource contention mirror sequence.

[0039] Following the reverse order of the resource contention mirror sequence, stress relief tests are performed on each primary commitment one by one. When each primary commitment is released, the mission time limit, lower limit of remaining energy, evacuation reachability, communication takeover status, and acceptance conditions within the currently available low-altitude operating space of the corresponding flight mission are re-verified.

[0040] If the active commitment still meets the task time limit, lower limit of remaining energy, evacuation reachability, communication takeover status and acceptance conditions after the stress relief test, the active commitment will be determined as a formal commitment and the corresponding resource slot of the active commitment will be kept locked.

[0041] When the dominant commitment triggers instability after the stress release test, the dominant commitment is switched to the corresponding standby commitment, the resource slot locked by the dominant commitment is released, the alternative resource slot recorded by the standby commitment is activated, and the formal commitment result, standby switching result and resource release result are written back to the improved MixHop model and the adaptive spatial prism.

[0042] Optionally, the output low-altitude economical operation scheduling scheme includes:

[0043] Read the flight corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot, communication takeover slot, mission time limit, remaining endurance, energy consumption per unit distance, refueling reachability, weather risk, communication risk, evacuation risk and ground takeover risk corresponding to the formally committed mission, and generate a remaining energy-risk status object.

[0044] The residual energy-risk status object is split into residual energy flow elements and risk flow elements. The dual binding relationship between residual energy flow elements and risk flow elements is established according to flight mission number, entry time window, corridor segment, altitude layer, refueling node, hovering node, evacuation node and communication takeover area, and the residual energy-risk dual flow elements are generated.

[0045] All surplus energy-risk dual flow elements are written into the same state space. Flow element connection edges are established according to the corridor connection relationship, energy replenishment connection relationship, hovering acceptance relationship, evacuation connection relationship and communication takeover relationship. The surplus energy decrease, risk increase, resource disturbance, mission time limit erosion and evacuation retention gap are written into the corresponding flow element connection edges to form the surplus energy-risk symmetric flow state space.

[0046] Within the energy-risk symmetric flow state space, candidate symmetric flow orbits are generated along the execution sequence of formally committed tasks, and candidate symmetric flow orbits that simultaneously meet the conditions of continuous corridor, take-off and landing capability, refueling capability, hovering capability, evacuation capability, communication capability, and task time limit capability are retained.

[0047] The action core of each candidate symmetric flow orbit is screened. The candidate symmetric flow orbits that maintain complete residual energy, continuous risk release, limited resource disturbance range, unexpired mission time limit and complete evacuation retention relationship are determined as core orbits. The residual energy flow elements, risk flow elements, flow element continuation edges and resource slots that are maintained in multiple core orbits are combined into the minimum action core of residual energy-risk symmetric flow.

[0048] When the formally committed task deviates from the minimum action kernel of the residual energy-risk symplectic flow, a resource reconfiguration scheduling operation is triggered. The result of the resource reconfiguration scheduling operation is written back to the adaptive airspace prism, the dual-state multi-resource commitment vector, and the resource contention mirror sequence. The low-altitude economic operation scheduling scheme is output within the currently available low-altitude operating space.

[0049] The beneficial effects of this invention are:

[0050] This invention constructs a unified low-altitude economic operation topology map by integrating low-altitude corridors, take-off and landing stations, refueling nodes, temporary hovering areas, emergency evacuation areas, and communication takeover areas. It employs an improved MixHop model with resource channel gating, adaptive step-by-step weight allocation, and cross-step-by-step residual competition mechanism for multi-level neighborhood aggregation. This model can simultaneously identify the chain reaction of adjacent and remote resources on the current corridor, and proactively detect the reverse pressure on the current flight mission caused by insufficient remote take-off and landing capacity, refueling waiting backlog, evacuation resource occupation, and insufficient communication takeover. This avoids problems such as inability to land, refuel, evacuate, or insufficient communication takeover after the flight mission has entered the corridor, thus improving the foresight and accuracy of low-altitude operation scheduling.

[0051] This invention generates multiple types of acceptance constraint surfaces through multi-order acceptance anti-pressure spectrum and superimposes them onto an adaptive airspace prism to form the currently available low-altitude operating space. This allows the low-altitude airspace to be dynamically adjusted according to the status of corridor passage, take-off and landing acceptance, refueling continuation, hovering buffer, evacuation retention, and communication takeover. Through a dual-state multi-resource commitment vector with self-destruction time limit and revocable zero-knowledge proof, resource occupancy verification is completed without exposing the complete route, payload information, and commercial mission content of the operating entity, thus balancing the resource reliability and mission privacy of multi-entity collaborative scheduling.

[0052] This invention uses a resource contention mirror sequence and reverse decompression verification to determine the dominant commitment that meets the acceptance conditions as a formal commitment, and switches the dominant commitment that triggers acceptance instability to a standby commitment, thereby reducing the chain of conflicts caused by resource contention. Furthermore, by using the surplus energy-risk symplectic flow state space and the surplus energy-risk symplectic flow minimum action kernel, it jointly constrains the remaining endurance, refueling reachability, communication takeover, evacuation reachability, and ground acceptance risks of formally committed missions. When a mission deviates from a stable operating state, it triggers resource reconfiguration scheduling operations, thereby improving the safety, stability, and airspace resource utilization efficiency of low-altitude economic operation scheduling. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a flowchart of a low-altitude economic operation scheduling method based on big data analysis proposed in this invention;

[0055] Figure 2 This is a schematic diagram of the residual energy-risk symplectic flow state space and minimum action kernel generation process of a low-altitude economic operation scheduling method based on big data analysis proposed in this invention. Detailed Implementation

[0056] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0057] refer to Figure 1 and Figure 2 A method for low-altitude economic operation scheduling based on big data analysis, comprising:

[0058] Collect big data on low-altitude economic operations and construct a topology map of low-altitude economic operations based on the big data.

[0059] The low-altitude economic operation topology is mapped into six types of resource-independent hypergraphs and a unified multi-layer heterogeneous hypergraph. An improved MixHop model is adopted, which introduces resource channel gating, adaptive hop weight allocation and cross-hop residual competition mechanism. Parallel multi-order neighborhood aggregation is performed on the one-hop, two-hop and three-hop neighborhoods to generate multi-order resource coupling matrices.

[0060] A multi-order support and anti-pressure spectrum is generated based on a multi-order resource coupling matrix. Multiple types of support and constraint surfaces are generated based on the multi-order support and anti-pressure spectrum and superimposed on an adaptive airspace prism to form the currently available low-altitude operating space.

[0061] For each flight mission, a dual-state multi-resource commitment vector with a self-destruction time limit is generated. A revocable zero-knowledge proof is generated for the main state vector and uploaded to the blockchain. If the flight mission does not enter the corresponding adaptive airspace prism within the preset time window, the main state commitment automatically expires, the backup state commitment takes effect immediately, and the locked resources are released simultaneously.

[0062] Based on revocable zero-knowledge proof, a resource contention mirror sequence is constructed. The reverse decompression verification is performed on the main state commitment. The main state commitment that meets the acceptance conditions is determined as the formal commitment. The main state commitment that causes acceptance instability is switched to the backup state commitment. The switching result is written back to the improved MixHop model and the adaptive spatial prism.

[0063] The formally committed mission is mapped as a surplus energy-risk state object, a surplus energy-risk symplectic flow state space is constructed, a surplus energy-risk symplectic flow minimum action kernel is generated, and a resource reconfiguration scheduling operation is triggered when the surplus energy-risk state of the flight mission deviates from the surplus energy-risk symplectic flow minimum action kernel, and a low-altitude economic operation scheduling scheme is output.

[0064] In this embodiment, the low-altitude economic operation big data includes aircraft status data, mission order data, low-altitude corridor data, take-off and landing station data, refueling node data, temporary hovering area data, emergency evacuation area data, communication coverage data, micro-meteorological data, ground population density data, temporary control data, and historical scheduling execution data.

[0065] In this embodiment, the construction of a low-altitude economic operation topology map based on low-altitude economic operation big data includes:

[0066] Low-altitude corridor data, take-off and landing station data, refueling node data, temporary hovering zone data, emergency evacuation zone data, and communication coverage data are converted into corridor nodes, take-off and landing nodes, refueling nodes, hovering nodes, evacuation nodes, and communication nodes, respectively. Corresponding connection edges are established between each node based on corridor continuity relationships, take-off and landing acceptance relationships, refueling continuity relationships, hovering transfer relationships, evacuation continuity relationships, communication coverage relationships, and temporary control constraints. The resource capacity, occupancy status, time window, spatial location, and availability status of each node are written into the node attributes. The continuity direction, passage capacity, time span, constraint type, and failure condition of each connection edge are written into the edge attributes to generate a low-altitude economic operation topology map.

[0067] In this embodiment, generating the multi-order resource coupling matrix includes:

[0068] The system reads corridor nodes, takeoff and landing nodes, refueling nodes, hovering nodes, evacuation nodes, and communication nodes from the low-altitude economic operation topology map. These nodes are then mapped to corresponding resource channels according to their resource types, generating six types of resource-independent hypergraphs. These are then superimposed into a unified multi-layered heterogeneous hypergraph based on time windows, spatial locations, resource continuity relationships, and regulatory constraints. The six types of resource-independent hypergraphs generated are as follows:

[0069] According to resource type, corridor nodes, take-off and landing nodes, energy replenishment nodes, hovering nodes, evacuation nodes and communication nodes are respectively classified into corridor resource channels, take-off and landing resource channels, energy replenishment resource channels, hovering resource channels, evacuation resource channels and communication resource channels, forming six independent node sets;

[0070] Within each resource channel, channel hyperedges are established based on the temporal succession, spatial adjacency, task occupancy, and control restriction relationships among resources of the same type, so that multiple nodes continuously occupied by the same flight mission under the same resource type are associated as a single resource occupancy hyperedge.

[0071] Each resource channel's set of nodes and channel hyperedges are combined into a corresponding resource-independent hypergraph. The node's resource capacity, occupancy status, remaining capacity, available time window, and spatial location are written into the node attributes. The channel hyperedge's continuation direction, occupancy period, constraint type, and failure condition are written into the hyperedge attributes, resulting in six types of resource-independent hypergraphs.

[0072] An improved MixHop model is constructed, which includes a resource channel gating layer, a skip-order parallel aggregation layer, an adaptive skip-order weight allocation layer, a cross-skip-order residual competition layer, and a resource coupling output layer.

[0073] The improved MixHop model is constructed as follows:

[0074] A resource channel gating layer is added to the input end of the traditional MixHop model, treating six independent hypergraphs of resources—corridors, takeoffs and landings, refueling, hovering, evacuation, and communication—as six input channels. Resource channels participating in the calculation are selected based on task succession relationships, resource occupancy status, gap acceptance status, and control restriction status. The traditional MixHop's one-hop, two-hop, and three-hop parallel aggregation structure is retained but modified into a jump-level parallel aggregation layer, extracting direct occupancy features, indirect acceptance features, and remote chain backpressure features. An adaptive jump-level weight allocation layer is added after the jump-level parallel aggregation layer, determining the participation ratio of each jump-level feature based on corridor passage, takeoff and landing acceptance, refueling waiting, hovering capacity, evacuation reachability, and communication takeover status. A cross-jump-level residual competition layer is added after this to reduce duplicate occupancy features, retain gap acceptance features, and filter conflict propagation features. The traditional MixHop output end is replaced with a resource coupling output layer, outputting a multi-order resource coupling matrix.

[0075] The resource capacity, occupancy status, remaining resources, task time limit pressure, communication coverage quality, micro-weather risk, and control restriction status of each node in the unified multi-layer heterogeneous hypergraph are written into the node state vector. The node state vector is input into the resource channel gating layer, which generates the channel activation results corresponding to the corridor, take-off and landing, refueling, hovering, evacuation, and communication, respectively. Based on the channel activation results, the resource channel characteristics related to the current scheduling pressure are retained.

[0076] The resource channel features are input into the step-parallel aggregation layer, which simultaneously extracts the direct occupancy features of the one-hop neighborhood, the indirect acceptance features of the two-hop neighborhood, and the remote linkage features of the three-hop neighborhood, generating the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features corresponding to each resource channel.

[0077] The one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features are input into the adaptive hop-order weight allocation layer. Based on the current node's corridor access status, subsequent takeoff and landing acceptance status, refueling waiting status, hovering capacity status, evacuation reachability status, and communication takeover status, the participation ratios of the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features in the current resource channel are determined respectively, generating hop-order fusion features. Specifically, the participation ratios of the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features in the current resource channel are determined as follows:

[0078] The basic participation ratio is set according to the current resource channel constraint source. The one-hop aggregation feature corresponds to the direct occupation status of the current node and adjacent nodes, the two-hop aggregation feature corresponds to the acceptance status of subsequent take-off, landing, energy replenishment and hovering resources, and the three-hop aggregation feature corresponds to the remote evacuation, communication takeover and cross-corridor interlocking status. The basic participation ratio of the three types of aggregation features is limited to a total of 100%.

[0079] Read the current node's corridor access status, subsequent takeoff and landing acceptance status, refueling waiting status, hovering capacity status, evacuation reachability status, and communication takeover status. When the current node has insufficient corridor capacity or communication takeover anomaly, increase the participation ratio of the first-hop aggregation feature; when the subsequent takeoff and landing window is insufficient, the refueling waiting exceeds the preset waiting number, or the hovering capacity is insufficient, increase the participation ratio of the second-hop aggregation feature; when remote evacuation is unreachable, the communication takeover area fails, or the cross-corridor mission chain is interrupted, increase the participation ratio of the third-hop aggregation feature.

[0080] After increasing the participation ratio of any jump-level aggregation feature, the participation ratio of jump-levels without triggered constraints is simultaneously reduced according to the constraint that the total sum equals 100%, and the resource type, trigger node, trigger time window and trigger reason corresponding to the triggered jump are retained to obtain the final participation ratio of one-hop aggregation feature, two-hop aggregation feature and three-hop aggregation feature in the current resource channel;

[0081] The jump-order fusion features corresponding to each resource channel are input into the cross-jump-order residual competition layer. The duplicate occupancy features among the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features are reduced. The gap features generated by remote take-off and landing nodes, power replenishment nodes, hovering nodes, evacuation nodes, and communication nodes are retained. The contradictory jump-order transmission features are competitively filtered. The resource coupling output layer combines the processed resource channel features into a multi-order resource coupling matrix.

[0082] The resource channel gating layer includes:

[0083] Node-Resource Index Table: Records the resource type, node number, time window, and spatial location of corridor nodes, take-off and landing nodes, refueling nodes, hovering nodes, evacuation nodes, and communication nodes;

[0084] Channel state tensor: Stores the resource capacity, occupied amount, remaining capacity, available time window, control status and failure conditions of each node in each resource channel;

[0085] Mission continuity mask matrix: Marks whether there is a route continuity, refueling continuity, takeoff and landing continuity, evacuation continuity or communication takeover continuity between the current flight mission and each resource channel;

[0086] Back pressure flag: Records the activation status of resource channels corresponding to insufficient remote take-off and landing, power replenishment waiting, insufficient hovering, unreachable evacuation, and insufficient communication takeover;

[0087] Channel output buffer: Stores gated characteristics of flight corridor channels, take-off and landing channels, power replenishment channels, hovering channels, evacuation channels, and communication channels;

[0088] After receiving the six types of independent hypergraphs of resources, the resource channel gating layer first writes different resource nodes into the corresponding channels according to the node-resource index table, and then writes the node's capacity, occupancy, remaining window, control status, and failure conditions into the channel state tensor. The task continuation mask matrix masks resource channels that are not related to the current flight task based on the flight mission's corridor entry order, landing requirements, refueling requirements, hovering requirements, evacuation requirements, and communication takeover requirements. When there is a gap in the acceptance of a certain remote resource, the acceptance backpressure flag sets the corresponding channel to the participation state. The channel output buffer only outputs the characteristics of the six types of resource channels related to the current scheduling pressure for use by the skip-level parallel aggregation layer.

[0089] The skip-step parallel aggregation layer includes:

[0090] One-hop neighborhood index area: records the corridor nodes, take-off and landing nodes, power replenishment nodes, hovering nodes, evacuation nodes and communication nodes that are directly connected to the current node;

[0091] Two-hop neighborhood index area: records the resource nodes that can be reached after crossing an intermediate node;

[0092] Three-hop neighborhood index area: records resource nodes that form a remote chain effect after crossing two intermediate nodes;

[0093] Parallel skip-order computation branch: processes the resource states in the one-hop neighborhood, two-hop neighborhood, and three-hop neighborhood respectively;

[0094] Skip Feature Temporary Storage Area: Stores features that are directly occupied by one hop, indirectly inherited by two hops, and remotely linked by three hops respectively;

[0095] After receiving the channel features output by the resource channel gating layer, the hop-parallel aggregation layer first generates a one-hop neighborhood index area, a two-hop neighborhood index area, and a three-hop neighborhood index area based on the connection edges in the unified multi-layer heterogeneous hypergraph. The one-hop neighborhood index area is used to extract the current occupancy of adjacent corridors, the available windows of adjacent take-off and landing points, and the takeover status of adjacent communication areas. The two-hop neighborhood index area is used to extract the acceptance status of subsequent power replenishment nodes, subsequent hovering areas, and subsequent take-off and landing points. The three-hop neighborhood index area is used to extract the chain effects of remote power replenishment queuing, insufficient remote landing windows, remote evacuation channel occupancy, and remote communication blind spots on the current corridor. The parallel hop-parallel calculation branch processes the node features of the three hops simultaneously and writes the processing results into the hop-parallel feature temporary storage area to form the one-hop direct occupancy feature, the two-hop indirect acceptance feature, and the three-hop remote chain feature.

[0096] The adaptive step-by-step weight allocation layer includes:

[0097] Jump Status Scoring Table: Records the resource consumption intensity, capacity gap intensity, task time limit impact, and remaining energy impact corresponding to the characteristics of the first jump, second jump, and third jump;

[0098] Task Dependency Table: Records the actual order of the current flight mission's dependencies on corridor, takeoff and landing, refueling, hovering, evacuation, and communication resources;

[0099] Time decay marker area: records the interval between the occurrence time of remote resources and the current corridor entry time;

[0100] Safety Priority Calibration Table: The retention priority of step-hopping features is determined in the order of evacuation reachability, communication takeover, takeoff and landing acceptance, refueling continuation, hovering buffer and corridor passage;

[0101] Skip-step fusion feature region: stores the one-hop, two-hop, and three-hop fusion results after weight allocation;

[0102] After receiving the one-hop direct occupancy feature, two-hop indirect acceptance feature, and three-hop remote chaining feature from the step-hop feature temporary storage area, the step-hop status scoring table first records the occupancy intensity and acceptance gap intensity corresponding to the three steps respectively; the task dependency table determines whether the current task will actually use the corresponding remote resource. If the flight task does not need to replenish power, the participation ratio of the remote step-hop in the replenishment channel is reduced. If the flight task must pass through the subsequent landing window, the participation ratio of the two-hop or three-hop in the take-off and landing channel is increased; the time-series attenuation marking area retains or weakens the remote step-hop feature according to the interval between the remote resource occurrence time and the current entry time; the safety priority calibration table increases the participation ratio of the corresponding step-hop feature when evacuation is unreachable, communication takeover is insufficient, or take-off and landing cannot be accepted; finally, the step-hop fusion feature area outputs a step-hop fusion feature that can simultaneously reflect the current occupancy, subsequent acceptance, and remote chaining effects.

[0103] The cross-step residual competition layer includes:

[0104] Duplicate Occupancy Marker Table: Records the repeated occupancy of the same corridor, the same landing slot, the same refueling slot, and the same communication control slot in the first, second, and third hop characteristics;

[0105] Contradictory Feature Marker Table: Records feature items that lead to opposite judgments about the same resource state across different jump levels;

[0106] Gap Reservation Zone: Specifically designed to store gap characteristics caused by insufficient remote takeoffs and landings, waiting for refueling, insufficient hovering, inaccessibility during evacuation, and insufficient communication takeover;

[0107] Residual competition queue: The characteristics to be competed for are arranged in the order of resource security, namely evacuation, communication, take-off and landing, refueling, hovering and corridor;

[0108] Residual output area: Stores the cross-step residual results after reducing duplicate information, retaining gaps, and completing conflict screening;

[0109] After receiving the hop fusion features, the cross-hop residual competition layer first identifies the repeated resource occupancy information in different hops and reduces the repeated occupancy features to prevent the same corridor or resource slot from being repeatedly counted as pressure. The conflict feature labeling table identifies situations where one hop shows passage but three hops show that subsequent landings, refueling, or communication takeovers are impossible. The gap reservation area retains the gap features caused by insufficient remote resources as restrictive features to avoid the risk of the current corridor being idle and covering subsequent takeovers. The residual competition queue processes conflict features in the order of evacuation reachability, communication takeover, takeoff and landing takeover, refueling continuation, hovering buffer, and corridor passage. The residual output area outputs the cross-hop residual results after competition filtering.

[0110] The resource-coupled output layer includes:

[0111] Six-channel feature aggregation area: Receives residual output results from six resource channels: corridor, take-off and landing, power replenishment, hovering, evacuation and communication;

[0112] Mission resource chain: records the sequence of resource usage for each flight mission from entering the corridor, changing altitude, refueling and reconnection, hovering buffer, evacuation and retention to communication takeover;

[0113] Multi-order relation writing area: Write the first-hop direct occupancy relation, the second-hop indirect inheritance relation, the third-hop remote linkage relation, and the cross-hop residual results into a unified output structure;

[0114] Back pressure source label area: Marks the source resource, source time window, source height layer, source node, and triggering reason corresponding to each pressure received;

[0115] Multi-order resource coupling matrix buffer: Stores the final generated multi-order resource coupling matrix and receives the write-back result after the master state commitment is switched to the standby state commitment;

[0116] After receiving the output from the cross-step residual competition layer, the resource coupling output layer collects the features of the six resource channels in a unified manner in the six-channel feature aggregation area. The task resource list establishes a correspondence between the current corridor node and the subsequent take-off and landing nodes, refueling nodes, hovering nodes, evacuation nodes, and communication nodes according to the actual execution order of the flight mission. The multi-level relationship writing area writes the one-hop occupation, two-hop acceptance, three-hop chaining, and residual competition results of the current node into a unified matrix structure. The backpressure source labeling area marks the source of each pressure, such as remote refueling waiting, insufficient remote landing window, evacuation channel occupation, or communication takeover delay. The multi-level resource coupling matrix buffering area outputs the multi-level resource coupling matrix to the multi-level acceptance backpressure spectrum generation step. After the resource contention mirror sequence completes the standby switch, it receives the switchover result and updates the corresponding resource relationship.

[0117] In this embodiment, forming a currently available low-altitude operating space includes:

[0118] Read the multi-order resource coupling matrix, and extract the remote take-off and landing acceptance gap, remote replenishment waiting gap, remote hovering capacity gap, remote evacuation reachability gap, and remote communication takeover gap for the current corridor node according to the continuity relationship between corridor nodes, take-off and landing nodes, replenishment nodes, hovering nodes, evacuation nodes, and communication takeover gap. Generate a multi-order acceptance backpressure spectrum according to resource type, time window, altitude layer, and task priority. Specifically, the generation of the multi-order acceptance backpressure spectrum is as follows:

[0119] Based on the connection relationship between the current corridor node and subsequent take-off and landing nodes, energy replenishment nodes, hovering nodes, evacuation nodes and communication nodes in the multi-order resource coupling matrix, the remaining capacity, occupied time period, available time window, connection distance, connection altitude layer and task priority of each remote resource node are read to form a set of remote resource acceptance status corresponding to the current corridor node.

[0120] The remote resource acceptance status set is matched with the landing, refueling, hovering, evacuation and communication takeover requirements of the flight missions to be entered in the current corridor node. Resource nodes with demand exceeding capacity, demand time window and available time window mismatch, connecting altitude layer mismatch, evacuation path unreachable or communication takeover delay exceeding limit are identified and marked as remote acceptance gap nodes.

[0121] Backpressure records are generated based on the resource type, occurrence time window, corresponding altitude layer, affected corridor nodes, affected task priority, and gap source of the remote receiving gap node. All backpressure records corresponding to the same current corridor node are arranged in chronological order and resource succession order to form a multi-level receiving backpressure spectrum.

[0122] A basic corridor prism is constructed based on the spatial boundary of the low-altitude corridor, the permissible altitude layer, the entry time window, and the exit time window. Within this basic corridor prism, a corridor passage layer, a takeoff and landing receiving layer, a power replenishment layer, a hovering buffer layer, an evacuation retention layer, and a communication takeover layer are set up to generate the initial structure of the adaptive airspace prism. Specifically, the construction of the basic corridor prism involves:

[0123] Read the corridor number, starting point coordinates, ending point coordinates, corridor width, permissible altitude layer, direction of passage, entry time window and exit time window of the low-altitude corridor, determine the flightable boundary of the corridor in horizontal space as the prism bottom surface, determine the permissible altitude layer as the prism height range, and determine the entry time window and exit time window as the prism time boundary.

[0124] The bottom surface of the prism is divided into the front entrance, the rear exit and the lateral boundary according to the direction of air corridor passage. Different altitude layers are written into the corresponding altitude slices so that each altitude slice records the range that the aircraft can enter, the range that can be maintained, the range that can be exited and the boundary that is prohibited from crossing.

[0125] The bottom boundary, height slice, and time boundary of the air corridor are bound together to form a basic air corridor prism containing spatial, height, and time boundaries. The air corridor capacity, current occupancy status, control restriction status, and communication coverage status are written into the attributes of the basic air corridor prism.

[0126] The multi-order support and resistance spectrum is back-mapped to the corresponding preceding corridor node, preceding altitude layer, and preceding entry time window. Multiple types of support constraint surfaces are generated according to the source of the remote resource gap. Specifically, the generation of multiple types of support constraint surfaces according to the source of the remote resource gap is as follows:

[0127] Read the sources of gaps in each backpressure record in the multi-level backpressure spectrum, and map the gaps in remote take-off and landing acceptance, remote energy replenishment waiting, remote hovering capacity, remote evacuation reachability, and remote communication takeover to take-off and landing acceptance constraints, energy replenishment continuation constraints, hovering buffer constraints, evacuation retention constraints, and communication takeover constraints, respectively. Extract the affected corridor nodes, affected altitude layers, and affected entry time windows corresponding to each gap.

[0128] Based on the connection relationship between the affected corridor nodes and the distant gap nodes, various bearing constraints are back-projected to the entrance boundary, height slice and time boundary of the preceding corridor node to determine the corresponding facet's position of action, height layer of action, start and end time of action, type of restricted resource and priority of restricted task;

[0129] Merge the same source, adjacent time and continuous height layer of the same preceding corridor node to generate multiple types of bearing constraint facets, and write the facet number, gap source, effective corridor, effective height layer, effective time window, restricted entry condition and release condition into each bearing constraint facet;

[0130] The various types of takeover constraint surfaces include a no-entry surface for restricting early entry, a return flow surface for guiding power replenishment, a buffer surface for releasing hover capacity, a retention surface for preserving evacuation paths, and a takeover surface for avoiding communication blind spots.

[0131] The various types of acceptance constraint surfaces are written into the adaptive airspace prism. The entry boundary is contracted for the corridor passage layer, the landing window is rearranged for the take-off and landing acceptance layer, the energy replenishment access offset is performed for the energy replenishment continuation layer, the hovering capacity is released for the hovering buffer layer, the evacuation channel is locked for the evacuation retention layer, and the takeover boundary is adjusted for the communication takeover layer. The prism deformation amplitude is limited according to the aircraft's turning capability, climb capability, descent capability, communication command response time and control restriction status.

[0132] Record the deformation state, resource slot state, constraint-bearing facet state, and revocable boundary state of the adaptive airspace prism in each time window, and determine the adaptive airspace prism after deformation as the currently available low-altitude operating space.

[0133] In this embodiment, the step of generating a two-state multi-resource commitment vector with a self-destruction time limit for each flight mission, and generating a revocable zero-knowledge proof for the main state vector and uploading it to the blockchain includes:

[0134] Read the currently available low-altitude operating space, the mission start point, mission end point, mission time limit, remaining endurance, aircraft type, operator identifier and execution priority, extract the available corridor segments, take-off and landing slots, refueling slots, hovering slots, evacuation slots and communication takeover slots for the flight mission, and generate the resource candidate chain corresponding to the flight mission.

[0135] Select resource combinations that meet the conditions of task time limit, lower limit of remaining energy, evacuation reachability and communication takeover from the resource candidate chain and write them into the main state vector. Select alternative resource combinations that do not occupy the same key resource slot as the main state vector and meet the conditions for continued execution and write them into the standby state vector to generate a dual-state resource commitment initial vector.

[0136] Based on the time required for the flight mission to reach the adaptive airspace prism entrance, the scheduling command response time, the resource slot retention time, and the remaining mission time limit, a self-destruct trigger piece is generated. This self-destruct trigger piece is then bound to the dual-state resource commitment initial vector, the flight mission identifier, and the operator entity identifier to form a dual-state multi-resource commitment vector with a self-destruct time limit. Specifically, the generation of the self-destruct trigger piece involves:

[0137] Read the current flight mission position, target adaptive airspace prism entrance, planned entry corridor segment, planned entry altitude layer, current flight speed, remaining endurance, and communication command response time to determine the entry preparation time required for the flight mission to reach the adaptive airspace prism entrance.

[0138] Read the resource slot retention time corresponding to the corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot and communication takeover slot locked by the main state vector, and determine the effective time range of the main state commitment allowed to be maintained in combination with the remaining amount of mission time limit;

[0139] The preparation time, communication command response time, resource slot retention time, and remaining task time limit are aligned. The earliest deadline that restricts the main state commitment to continue is taken as the self-destruct time limit. A self-destruct trigger piece is generated that records the flight mission identifier, main state vector index, standby state vector index, self-destruct time limit, trigger condition, and resource release object.

[0140] Generate a primary-backup mutual exclusion commitment signature for the dual-state multi-resource commitment vector, putting the primary vector into a resource-locked state and the backup vector into a pending activation state. Generate a revocable zero-knowledge proof for the primary vector, and write the revocable zero-knowledge proof, commitment index, self-destruct trigger shard, and primary-backup mutual exclusion commitment signature into the on-chain ledger. Specifically, the generation of the primary-backup mutual exclusion commitment signature for the dual-state multi-resource commitment vector is as follows:

[0141] Read the primary and backup vectors from the dual-state multi-resource commitment vector, extract the corridor segments, take-off and landing slots, energy replenishment slots, hovering slots, evacuation slots and communication takeover slots locked by the primary vector, and the alternative corridor segments, alternative take-off and landing slots, alternative energy replenishment slots, alternative hovering slots, alternative evacuation slots and alternative communication takeover slots recorded by the backup vector, and generate the primary and backup resource indexes;

[0142] Perform mutual exclusion checks on the primary and backup resource indexes to confirm that the primary and backup vectors do not occupy the same critical resource slot at the same time, and confirm that the backup vector only enters the active state when the primary commitment fails, is revoked, or is switched, and generate a primary-backup mutual exclusion relationship record.

[0143] The primary and backup mutual exclusion relationship record, flight mission identifier, operation entity identifier, primary vector index, backup vector index, self-destruct trigger piece and resource release object are bound together to form a primary and backup mutual exclusion commitment seal. The primary and backup mutual exclusion commitment seal is used to mark that the primary vector is in a resource locked state and the backup vector is in a pending activation state, and restricts the primary vector and backup vector to be effective at the same time window.

[0144] During the execution of the flight mission, the actual entry time, actual entry corridor segment, actual entry altitude layer, and actual entry adaptive airspace prism status are read. When the flight mission enters the corresponding adaptive airspace prism within the self-destruction time limit, the main state commitment remains valid. When the flight mission does not enter the corresponding adaptive airspace prism within the self-destruction time limit, the main state commitment is automatically invalidated, the standby state commitment takes effect immediately, and the resource slot locked by the main state vector is released simultaneously.

[0145] In this embodiment, the step of constructing a resource contention mirror sequence based on revocable zero-knowledge proof and performing reverse decompression verification on the main state commitment includes:

[0146] Read the revocable zero-knowledge proof, main state vector, standby state vector, commitment index and self-destruct trigger piece corresponding to each flight mission in the on-chain ledger, and identify the resource contention objects of multiple main state commitments contending for the same resource slot according to the occupancy relationship of the corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot and communication takeover slot;

[0147] The resource contention objects are mirrored and sorted according to resource slot type, entry time window, exit time window, task time limit, remaining energy lower limit, evacuation reachability status and communication takeover status to generate a resource contention mirror sequence.

[0148] Following the reverse order of the resource contention mirror sequence, stress relief tests are performed on each primary commitment one by one. When each primary commitment is released, the mission time limit, lower limit of remaining energy, evacuation reachability, communication takeover status, and acceptance conditions within the currently available low-altitude operating space of the corresponding flight mission are re-verified.

[0149] If the active commitment still meets the task time limit, lower limit of remaining energy, evacuation reachability, communication takeover status and acceptance conditions after the stress relief test, the active commitment will be determined as a formal commitment and the corresponding resource slot of the active commitment will be kept locked.

[0150] When the dominant commitment triggers instability after the stress release test, the dominant commitment is switched to the corresponding standby commitment, the resource slot locked by the dominant commitment is released, the alternative resource slot recorded by the standby commitment is activated, and the formal commitment result, standby switching result and resource release result are written back to the improved MixHop model and the adaptive spatial prism.

[0151] In this embodiment, the output low-altitude economical operation scheduling scheme includes:

[0152] Read the flight corridor segment, takeoff and landing slot, refueling slot, hovering slot, evacuation slot, communication takeover slot, mission time limit, remaining endurance, energy consumption per unit distance, refueling reachability, weather risk, communication risk, evacuation risk, and ground takeover risk corresponding to the officially committed mission, and generate a surplus energy-risk status object. Specifically, generating the surplus energy-risk status object involves:

[0153] Read the corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot and communication takeover slot corresponding to the officially committed mission, establish mission resource occupation record according to flight mission number, entry time window, altitude layer and resource slot, and write mission time limit, remaining endurance, energy consumption per unit distance and refueling reachability status into the remaining energy side status.

[0154] Read the meteorological risk, communication risk, evacuation risk and ground acceptance risk of the officially committed mission in the corresponding corridor segment, and combine them with the take-off and landing slot acceptance status, the refueling slot waiting status, the hovering slot remaining capacity, the evacuation slot reachability status and the communication takeover slot availability status to generate the risk side status.

[0155] The surplus energy status and the risk status are bound together according to the same flight mission number, the same entry time window and the same resource slot to form a surplus energy-risk status object;

[0156] The residual energy-risk status object is split into residual energy flow elements and risk flow elements. A dual binding relationship is established between residual energy flow elements and risk flow elements according to flight mission number, entry time window, corridor segment, altitude layer, refueling node, hovering node, evacuation node, and communication takeover area, generating residual energy-risk dual flow elements. Specifically, the establishment of the dual binding relationship between residual energy flow elements and risk flow elements is as follows:

[0157] According to the same flight mission number and the same entry time window, the remaining endurance, energy consumption per unit distance, refueling reachability and mission time limit remaining in the energy reserve side status are written into the energy reserve flow element, and the weather risk, communication risk, evacuation risk, ground acceptance risk and resource slot occupation risk in the risk side status are written into the risk flow element.

[0158] Based on the flight corridor segment, altitude layer, refueling node, hovering node, evacuation node and communication takeover area corresponding to the flight mission, the surplus energy flow element is matched one by one with the risk flow element on the same resource continuation chain, so that each surplus energy flow element is bound to the risk flow element related to the same mission, the same time window and the same resource slot.

[0159] Write a dual binding marker between the surplus energy flow element and the risk flow element. The dual binding marker records the flight mission number, entry time window, resource slot, surplus energy change status, risk change status and continuation conditions, and generates a surplus energy-risk dual flow element that can simultaneously characterize the changes in remaining range and the changes in operational risk.

[0160] All surplus energy-risk dual flow elements are written into the same state space. Flow element connection edges are established according to the corridor connection relationship, energy replenishment connection relationship, hovering acceptance relationship, evacuation connection relationship and communication takeover relationship. The surplus energy decrease, risk increase, resource disturbance, mission time limit erosion and evacuation retention gap are written into the corresponding flow element connection edges to form the surplus energy-risk symmetric flow state space.

[0161] Within the energy-risk symmetric flow state space, candidate symmetric flow orbits are generated along the execution sequence of formally committed tasks, and candidate symmetric flow orbits that simultaneously meet the conditions of continuous corridor, take-off and landing capability, refueling capability, hovering capability, evacuation capability, communication capability, and task time limit capability are retained.

[0162] The action core of each candidate symmetric flow orbit is screened. The candidate symmetric flow orbits that maintain complete residual energy, continuous risk release, limited resource disturbance range, unexpired mission time limit and complete evacuation retention relationship are determined as core orbits. The residual energy flow elements, risk flow elements, flow element continuation edges and resource slots that are maintained in multiple core orbits are combined into the minimum action core of residual energy-risk symmetric flow.

[0163] When the formally committed task deviates from the minimum action kernel of the residual energy-risk symplectic flow, a resource reconfiguration scheduling operation is triggered. The result of the resource reconfiguration scheduling operation is written back to the adaptive airspace prism, the dual-state multi-resource commitment vector, and the resource contention mirror sequence. The low-altitude economic operation scheduling scheme is output within the currently available low-altitude operating space.

[0164] Example 1: Within a comprehensive low-altitude economic operation test area, the system receives a continuous 120-minute scheduling window for mission requests, comprising 286 low-altitude flight missions. These include 168 logistics delivery missions, 46 inspection missions, 12 emergency transport missions, 32 agricultural and forestry operations missions, and 28 low-altitude cultural tourism missions. The test area is equipped with 42 low-altitude corridors, 18 takeoff and landing slots, 9 refueling nodes, 6 temporary hovering areas, 5 emergency evacuation routes, and 11 communication takeover areas. Initial data collection includes 286 aircraft status records, 42 corridor occupancy records, 18 sets of takeoff and landing window records, 9 sets of refueling interface records, 6 sets of hovering capacity records, 5 sets of evacuation reachability records, and 11 sets of communication coverage records. During the data collection, the average occupancy rate of the core corridor was 67.8%, the average number of aircraft waiting at the refueling nodes was 3.9, the average delay for communication takeover was 1.7 seconds, there were refueling queues at the far end of some corridors at 2-hop positions, and there were tight landing windows at the 3-hop positions.

[0165] The system constructs a low-altitude economic operation topology map based on collected data. 42 flight corridors are converted into 42 corridor nodes, 18 takeoff and landing slots into 18 takeoff and landing nodes, 9 refueling nodes into 9 refueling nodes, 6 temporary hovering areas into 6 hovering nodes, 5 emergency evacuation channels into 5 evacuation nodes, and 11 communication takeover areas into 11 communication nodes. The system establishes 214 connection edges based on corridor continuity, takeoff and landing acceptance, refueling continuity, hovering transfer, evacuation continuity, and communication coverage relationships, including 96 corridor continuity edges, 38 takeoff and landing acceptance edges, 31 refueling continuity edges, 18 hovering transfer edges, 11 evacuation continuity edges, and 20 communication coverage edges. Each node is recorded with resource capacity, occupied capacity, remaining capacity, time window, and availability status, generating a unified low-altitude economic operation topology map.

[0166] During the model training phase, the system used 73,200 historical operation samples, including 51,240 training samples, 10,980 validation samples, and 10,980 test samples. The training samples included 21,400 corridor passage samples, 12,800 takeoff and landing acceptance samples, 11,600 refueling waiting samples, 8,400 hovering buffer samples, 4,300 evacuation reachable samples, and 4,700 communication takeover samples. A single training sample recorded the following: corridor occupancy rate 0.72, 2 remaining windows for subsequent takeoffs and landings, 5 aircraft waiting for refueling, 1 aircraft remaining hovering capacity, evacuation passage status available, communication takeover delay 2.1 seconds, and output label "following aircraft acceptance pressure". After training, the traditional single-hop graph model achieved a 78.6% accuracy rate in identifying acceptance instability in the test samples, while the improved MixHop model of this invention achieved 91.8%.

[0167] After scheduling begins, the system maps the low-altitude economic operation topology into six independent hypergraphs representing six resource categories: corridors, takeoffs and landings, refueling, hovering, evacuation, and communication. These are then overlaid into a unified multi-layered heterogeneous hypergraph. An improved MixHop model extracts 1-hop, 2-hop, and 3-hop neighborhood features for each node. Taking corridor node A17 as an example, its current 1-hop corridor occupancy rate is 0.58, not meeting the no-entry condition; however, 2-hop refueling node B04 has 7 aircraft waiting, 3-hop takeoff and landing node L09 has 0 remaining landing windows, and the communication takeover zone C06 has a delay increased to 2.8 seconds. The system retains refueling, takeoff and landing, and communication channel features through resource channel gating, increases the participation rate of 3-hop remote takeover features to a major participation state through adaptive hop-level weight allocation, and reduces duplicate corridor occupancy features through cross-hop-level residual competition, ultimately generating a multi-order resource coupling matrix. In this matrix, the remote refueling backpressure level of node A17 changes from low to tense, and the remote takeoff and landing backpressure level changes from normal to saturated.

[0168] The system generates a multi-order acceptance counter-pressure spectrum based on a multi-order resource coupling matrix. For the A17 corridor, the counter-pressure spectrum records a gap of 4 aircraft waiting for remote refueling, 3 gaps for remote landing windows, and 5 missions with communication takeover delays exceeding limits. The system reverse-maps these gaps to the preceding entry time windows of A17 and generates multiple types of acceptance constraint surfaces. Subsequently, the system shrinks the entry boundary of the second altitude layer of the A17 corridor in the adaptive airspace prism, shifting the entry windows of low-priority missions 4 minutes later, while guiding 12 logistics missions to the A19 corridor, reassigning 5 missions with low endurance to refueling node B02, and transferring 3 cultural and tourism missions to the communication takeover area C03. After the adjustment, the expected occupancy rate of the A17 corridor decreased from 82.4% to 69.1%, the number of aircraft waiting for refueling at B04 decreased from 7 to 4, and the number of landing conflict missions at L09 decreased from 6 to 2.

[0169] During the resource commitment phase, the system generates bi-state multi-resource commitment vectors with self-destruction timeouts for each of the 286 missions. Each primary vector records the corridor segment, landing slot, refueling slot, hovering slot, evacuation slot, and communication takeover slot, while each backup vector records the corresponding alternative resources. Taking mission T083 as an example, the primary vector locks the A17 corridor, L09 landing slot, B04 refueling slot, H02 hovering slot, E03 evacuation slot, and C06 communication takeover slot, while the backup vector locks the A19 corridor, L11 landing slot, B02 refueling slot, H04 hovering slot, E02 evacuation slot, and C03 communication takeover slot. The system generates revocable zero-knowledge proofs from the primary vectors, with an average generation time of 0.46 seconds and an average on-chain write time of 0.31 seconds. If mission T083 fails to enter the corresponding adaptive airspace prism within the 5-minute self-destruction timeout, the primary state commitment will automatically expire, while the backup state commitment will take effect immediately, releasing resource slots A17, L09, and B04.

[0170] During the contention verification phase, the system reads 286 commitment indices from the on-chain ledger, identifying 39 groups of resource contention objects, including 13 groups of corridor contention, 9 groups of takeoff and landing slot contention, 11 groups of refueling slot contention, and 6 groups of communication takeover slot contention. The system generates a resource contention mirror sequence and performs reverse-order pressure relief verification on the primary commitments. Taking the B04 refueling slot contention as an example, with 6 missions simultaneously locking adjacent refueling windows, the system first releases the refueling slot to take over the mission with the highest subsequent pressure, T083, for testing. It was found that after the release, the number of waiting B04 missions decreased from 7 to 5, the pressure on the A17's preceding no-entry zone decreased, and the T083 standby refueling node B02 could still reach within the remaining flight range. Therefore, T083 was switched to a standby commitment. After 39 groups of contention verification, the system determined 247 formally committed missions and 39 standby switchover missions.

[0171] During the surplus energy-risk scheduling phase, the system mapped 247 officially committed missions to surplus energy-risk state objects. Each object recorded remaining range, energy consumption per unit distance, refueling reachability, weather risk, communication risk, evacuation risk, and ground takeover risk. The system constructed a surplus energy-risk symbiotic flow state space, generating 126 candidate symbiotic flow orbits, of which 84 orbits met the conditions of continuous corridor, takeoff and landing capacity, refueling reachability, hovering capacity, evacuation reachability, and communication takeover capability. The system further screened core orbits that maintained complete surplus energy, continuous risk release, limited resource disturbance, and complete evacuation relationships, forming the minimum impact core of the surplus energy-risk symbiotic flow. During operation, 16 missions deviated from the minimum impact core, including 9 performing early refueling, 3 performing corridor avoidance, 2 performing communication takeover transfer, and 2 performing hovering waiting.

[0172] On the same batch of 286 missions, the method of this invention was compared with the traditional fixed corridor single-hop scheduling method.

[0173] The traditional method resulted in 19 sorties failing to land as planned after entering the flight corridor, while the invention resulted in 3 sorties.

[0174] The traditional method resulted in 24 sorties where refueling could not be completed as planned after entering the flight corridor, while this invention resulted in 4 sorties.

[0175] Traditional methods require 15 sorties for communication takeover missions, while this invention requires only 2 sorties.

[0176] The conventional method results in 7 instances of temporary hovering zone overcapacity, while this invention results in only 1 instance.

[0177] Traditional methods result in the evacuation channel being occupied 5 times by ordinary tasks, while this invention results in 0 times.

[0178] The average takeoff waiting time using traditional methods is 8.4 minutes, while this invention reduces it to 4.9 minutes.

[0179] The average waiting time for energy replenishment using traditional methods is 10.8 minutes, while that of this invention is 5.7 minutes.

[0180] The on-time completion rate of traditional methods is 84.6%, while that of this invention is 95.3%.

[0181] The average utilization rate of the corridor using traditional methods is 70.5%, while that of this invention is 83.1%.

[0182] Traditional methods require 11 sorties with remaining energy below the safety limit, while this invention requires only 2 sorties.

[0183] Traditional methods result in 5 instances of scheduling instability cascading propagation, while this invention results in only 1 instance.

[0184] Traditional methods require 100% submission of complete task information in plaintext, while this invention requires 0%.

[0185] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A low-altitude economic operation scheduling method based on big data analysis, characterized in that, include: Collect big data on low-altitude economic operations and construct a topology map of low-altitude economic operations based on the big data. The low-altitude economic operation topology is mapped into six types of resource-independent hypergraphs and a unified multi-layer heterogeneous hypergraph. An improved MixHop model is adopted, which introduces resource channel gating, adaptive hop weight allocation and cross-hop residual competition mechanism. Parallel multi-order neighborhood aggregation is performed on the one-hop, two-hop and three-hop neighborhoods to generate multi-order resource coupling matrices. A multi-order support and anti-pressure spectrum is generated based on a multi-order resource coupling matrix. Multiple types of support and constraint surfaces are generated based on the multi-order support and anti-pressure spectrum and superimposed on an adaptive airspace prism to form the currently available low-altitude operating space. For each flight mission, a dual-state multi-resource commitment vector with a self-destruction time limit is generated. A revocable zero-knowledge proof is generated for the main state vector and uploaded to the blockchain. If the flight mission does not enter the corresponding adaptive airspace prism within the preset time window, the main state commitment automatically expires, the backup state commitment takes effect immediately, and the locked resources are released simultaneously. Based on revocable zero-knowledge proof, a resource contention mirror sequence is constructed. The reverse decompression verification is performed on the main state commitment. The main state commitment that meets the acceptance conditions is determined as the formal commitment. The main state commitment that causes acceptance instability is switched to the backup state commitment. The switching result is written back to the improved MixHop model and the adaptive spatial prism. The formally committed mission is mapped as a surplus energy-risk state object, a surplus energy-risk symplectic flow state space is constructed, a surplus energy-risk symplectic flow minimum action kernel is generated, and a resource reconfiguration scheduling operation is triggered when the surplus energy-risk state of the flight mission deviates from the surplus energy-risk symplectic flow minimum action kernel, and a low-altitude economic operation scheduling scheme is output.

2. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The low-altitude economic operation big data includes aircraft status data, mission order data, low-altitude corridor data, take-off and landing station data, refueling node data, temporary hovering area data, emergency evacuation area data, communication coverage data, micro-meteorological data, ground population density data, temporary control data, and historical dispatch execution data.

3. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The construction of the low-altitude economic operation topology map based on low-altitude economic operation big data includes: Low-altitude corridor data, take-off and landing station data, refueling node data, temporary hovering zone data, emergency evacuation zone data, and communication coverage data are converted into corridor nodes, take-off and landing nodes, refueling nodes, hovering nodes, evacuation nodes, and communication nodes, respectively. Corresponding connection edges are established between each node based on corridor continuity relationships, take-off and landing acceptance relationships, refueling continuity relationships, hovering transfer relationships, evacuation continuity relationships, communication coverage relationships, and temporary control constraints. The resource capacity, occupancy status, time window, spatial location, and availability status of each node are written into the node attributes. The continuity direction, passage capacity, time span, constraint type, and failure condition of each connection edge are written into the edge attributes to generate a low-altitude economic operation topology map.

4. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The generation of the multi-order resource coupling matrix includes: Read the corridor nodes, take-off and landing nodes, energy replenishment nodes, hovering nodes, evacuation nodes and communication nodes in the low-altitude economic operation topology map, map them to the corresponding resource channels according to resource type, generate six types of independent hypergraphs, and superimpose them into a unified multi-layer heterogeneous hypergraph based on time window, spatial location, resource continuity relationship and control constraint relationship. An improved MixHop model is constructed, which includes a resource channel gating layer, a skip-order parallel aggregation layer, an adaptive skip-order weight allocation layer, a cross-skip-order residual competition layer, and a resource coupling output layer. The resource capacity, occupancy status, remaining resources, task time limit pressure, communication coverage quality, micro-weather risk, and control restriction status of each node in the unified multi-layer heterogeneous hypergraph are written into the node state vector. The node state vector is input into the resource channel gating layer, which generates the channel activation results corresponding to the corridor, take-off and landing, refueling, hovering, evacuation, and communication, respectively. Based on the channel activation results, the resource channel characteristics related to the current scheduling pressure are retained. The resource channel features are input into the step-parallel aggregation layer, which simultaneously extracts the direct occupancy features of the one-hop neighborhood, the indirect acceptance features of the two-hop neighborhood, and the remote linkage features of the three-hop neighborhood, generating the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features corresponding to each resource channel. The one-hop aggregation feature, two-hop aggregation feature, and three-hop aggregation feature are input into the adaptive hop order weight allocation layer. Based on the current node's corridor passage status, subsequent take-off and landing acceptance status, energy replenishment waiting status, hovering capacity status, evacuation reachability status, and communication takeover status, the participation ratio of the one-hop aggregation feature, two-hop aggregation feature, and three-hop aggregation feature in the current resource channel is determined respectively, and hop order fusion features are generated. The jump-order fusion features corresponding to each resource channel are input into the cross-jump-order residual competition layer. The duplicate occupancy features among the one-hop aggregation features, two-hop aggregation features, and three-hop aggregation features are reduced. The gap features generated by remote take-off and landing nodes, power replenishment nodes, hovering nodes, evacuation nodes, and communication nodes are retained. The contradictory jump-order transmission features are competitively filtered. The resource coupling output layer combines the processed resource channel features into a multi-order resource coupling matrix.

5. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The formation of the currently available low-altitude operating space includes: Read the multi-level resource coupling matrix, and extract the remote take-off and landing acceptance gap, remote replenishment waiting gap, remote hovering capacity gap, remote evacuation reachability gap and remote communication takeover gap of the current corridor node according to the connection relationship between corridor nodes, take-off and landing nodes, replenishment nodes, hovering nodes, evacuation nodes and communication nodes. Generate a multi-level acceptance back pressure spectrum according to resource type, time window, altitude layer and task priority. The basic corridor prism is constructed based on the spatial boundary of the low-altitude corridor, the allowable altitude layer, the entry time window, and the exit time window. Within the basic corridor prism, a corridor passage layer, a take-off and landing receiving layer, a power replenishment layer, a hovering buffer layer, an evacuation retention layer, and a communication takeover layer are set up to generate the initial structure of the adaptive airspace prism. The multi-level support and anti-pressure spectrum is back-mapped to the corresponding preceding corridor node, preceding altitude layer and preceding entry time window, and multiple types of support and constraint surfaces are generated according to the source of the remote resource gap; The various types of acceptance constraint surfaces are written into the adaptive airspace prism. The entry boundary is contracted for the corridor passage layer, the landing window is rearranged for the take-off and landing acceptance layer, the energy replenishment access offset is performed for the energy replenishment continuation layer, the hovering capacity is released for the hovering buffer layer, the evacuation channel is locked for the evacuation retention layer, and the takeover boundary is adjusted for the communication takeover layer. The prism deformation amplitude is limited according to the aircraft's turning capability, climb capability, descent capability, communication command response time and control restriction status. Record the deformation state, resource slot state, constraint-bearing facet state, and revocable boundary state of the adaptive airspace prism in each time window, and determine the adaptive airspace prism after deformation as the currently available low-altitude operating space.

6. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The process of generating a two-state, multi-resource commitment vector with a self-destruction timeout for each flight mission, and generating a revocable zero-knowledge proof for the main state vector and uploading it to the blockchain, includes: Read the currently available low-altitude operating space, the mission start point, mission end point, mission time limit, remaining endurance, aircraft type, operator identifier and execution priority, extract the available corridor segments, take-off and landing slots, refueling slots, hovering slots, evacuation slots and communication takeover slots for the flight mission, and generate the resource candidate chain corresponding to the flight mission. Select resource combinations that meet the conditions of task time limit, lower limit of remaining energy, evacuation reachability and communication takeover from the resource candidate chain and write them into the main state vector. Select alternative resource combinations that do not occupy the same key resource slot as the main state vector and meet the conditions for continued execution and write them into the standby state vector to generate a dual-state resource commitment initial vector. Based on the time required for the flight mission to reach the adaptive airspace prism entrance, the scheduling command response time, the resource slot retention time, and the remaining mission time limit, a self-destruction trigger piece is generated. The self-destruction trigger piece is then bound to the dual-state resource commitment initial vector, the flight mission identifier, and the operation entity identifier to form a dual-state multi-resource commitment vector with a self-destruction time limit. Generate a primary and backup mutually exclusive commitment seal for the dual-state multi-resource commitment vector, so that the primary state vector enters the resource-locked state and the backup state vector enters the waiting-to-activate state. Generate a revocable zero-knowledge proof for the primary state vector and write the revocable zero-knowledge proof, commitment index, self-destruct trigger shard and primary and backup mutually exclusive commitment seal into the on-chain ledger. During the execution of the flight mission, the actual entry time, actual entry corridor segment, actual entry altitude layer, and actual entry adaptive airspace prism status are read. When the flight mission enters the corresponding adaptive airspace prism within the self-destruction time limit, the main state commitment remains valid. When the flight mission does not enter the corresponding adaptive airspace prism within the self-destruction time limit, the main state commitment is automatically invalidated, the standby state commitment takes effect immediately, and the resource slot locked by the main state vector is released simultaneously.

7. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The construction of a resource contention mirror sequence based on revocable zero-knowledge proof, and the execution of reverse decompression verification on the main state commitment, includes: Read the revocable zero-knowledge proof, main state vector, standby state vector, commitment index and self-destruct trigger piece corresponding to each flight mission in the on-chain ledger, and identify the resource contention objects of multiple main state commitments contending for the same resource slot according to the occupancy relationship of the corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot and communication takeover slot; The resource contention objects are mirrored and sorted according to resource slot type, entry time window, exit time window, task time limit, remaining energy lower limit, evacuation reachability status and communication takeover status to generate a resource contention mirror sequence. Following the reverse order of the resource contention mirror sequence, stress relief tests are performed on each primary commitment one by one. When each primary commitment is released, the mission time limit, lower limit of remaining energy, evacuation reachability, communication takeover status, and acceptance conditions within the currently available low-altitude operating space of the corresponding flight mission are re-verified. If the active commitment still meets the task time limit, lower limit of remaining energy, evacuation reachability, communication takeover status and acceptance conditions after the stress relief test, the active commitment will be determined as a formal commitment and the corresponding resource slot of the active commitment will be kept locked. When the dominant commitment triggers instability after the stress release test, the dominant commitment is switched to the corresponding standby commitment, the resource slot locked by the dominant commitment is released, the alternative resource slot recorded by the standby commitment is activated, and the formal commitment result, standby switching result and resource release result are written back to the improved MixHop model and the adaptive spatial prism.

8. The low-altitude economic operation scheduling method based on big data analysis according to claim 1, characterized in that, The output low-altitude economical operation scheduling scheme includes: Read the flight corridor segment, take-off and landing slot, refueling slot, hovering slot, evacuation slot, communication takeover slot, mission time limit, remaining endurance, energy consumption per unit distance, refueling reachability, weather risk, communication risk, evacuation risk and ground takeover risk corresponding to the formally committed mission, and generate a remaining energy-risk status object. The residual energy-risk status object is split into residual energy flow elements and risk flow elements. The dual binding relationship between residual energy flow elements and risk flow elements is established according to flight mission number, entry time window, corridor segment, altitude layer, refueling node, hovering node, evacuation node and communication takeover area, and the residual energy-risk dual flow elements are generated. All surplus energy-risk dual flow elements are written into the same state space. Flow element connection edges are established according to the corridor connection relationship, energy replenishment connection relationship, hovering acceptance relationship, evacuation connection relationship and communication takeover relationship. The surplus energy decrease, risk increase, resource disturbance, mission time limit erosion and evacuation retention gap are written into the corresponding flow element connection edges to form the surplus energy-risk symmetric flow state space. Within the energy-risk symmetric flow state space, candidate symmetric flow orbits are generated along the execution sequence of formally committed tasks, and candidate symmetric flow orbits that simultaneously meet the conditions of continuous corridor, take-off and landing capability, refueling capability, hovering capability, evacuation capability, communication capability, and task time limit capability are retained. The action core of each candidate symmetric flow orbit is screened. The candidate symmetric flow orbits that maintain complete residual energy, continuous risk release, limited resource disturbance range, unexpired mission time limit and complete evacuation retention relationship are determined as core orbits. The residual energy flow elements, risk flow elements, flow element continuation edges and resource slots that are maintained in multiple core orbits are combined into the minimum action core of residual energy-risk symmetric flow. When the formally committed task deviates from the minimum action kernel of the residual energy-risk symplectic flow, a resource reconfiguration scheduling operation is triggered. The result of the resource reconfiguration scheduling operation is written back to the adaptive airspace prism, the dual-state multi-resource commitment vector, and the resource contention mirror sequence. The low-altitude economic operation scheduling scheme is output within the currently available low-altitude operating space.

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