An integrated system for full-cycle flow capacity planning, regulation and closed-loop evaluation of a fusion operating airspace

Abstract

The application relates to the field of aviation technology, and particularly discloses an integrated system for whole-cycle flow capacity planning, regulation and closed-loop evaluation of a running airspace. The integrated system comprises a unified data center for gathering multi-source heterogeneous data and establishing unified data standards and space-time benchmarks; a strategic planning module for generating a medium and long-term capacity planning scheme; a pre-tactical allocation module for matching and optimizing capacity constraints and user demand to generate a time slot allocation scheme; a digital twin engine for constructing a virtual mirror corresponding to the physical airspace and providing a simulation deduction environment; a tactical regulation module for real-time conflict detection and regulation; and a closed-loop evaluation engine for collecting effect data, calculating evaluation indexes in real time during regulation execution, and automatically triggering a strategy correction process when the index deviation exceeds a threshold. The application realizes whole-cycle coverage and closed-loop optimization of airspace flow capacity management from strategic planning to tactical regulation, and can significantly improve the airspace utilization efficiency and the regulation response speed.

Description

Technical Field

[0001] This invention relates to the field of aviation technology, specifically to an integrated system for the full-cycle capacity flow planning, control, and closed-loop evaluation of operational airspace. Background Technology

[0002] With the gradual opening of low-altitude airspace, the rapid development of urban air traffic, and the continuous expansion of drone logistics delivery, the types, numbers, and operating modes of aircraft within integrated airspace are showing a dramatic increase, posing unprecedented and higher demands on airspace capacity and flow management. However, existing airspace capacity and flow management systems have the following three inherent defects in their architectural design, which seriously restrict their ability to cope with the above challenges.

[0003] First, the fragmented system architecture leads to difficulties in cross-level collaboration. In existing technologies, capacity planning, traffic allocation, and operational control belong to different functional modules, each developed and operated independently, lacking a unified data platform for effective connection. This fragmented architecture makes it difficult to unify predictive models, decision-making logic, and data standards across different levels. The capacity assessment results at the strategic level cannot be effectively transmitted to the time slot allocation at the pre-tactical level, and the allocation scheme at the pre-tactical level also lacks a smooth transition mechanism to the control instructions at the tactical level. This results in a disconnect between decisions at different levels, making it impossible to form a globally optimal capacity management strategy.

[0004] Secondly, the open-loop decision-making mechanism prevents the automatic verification and optimization of control measures. The decision-making process in existing systems ends with the issuance of control commands, lacking real-time feedback loops to verify the actual effects after command execution. Strategy adjustments rely entirely on controllers' manual experience and judgment, failing to automatically assess whether control measures have achieved the expected results, and thus unable to optimize subsequent decisions. Particularly in capacity assessment, current technology primarily relies on historical statistical data, unable to perceive the instantaneous impact of dynamic factors such as weather changes, airspace restriction adjustments, and special events on capacity, leading to significant deviations between capacity predictions and actual operational conditions. Traffic control measures based on such predictions often exhibit polarization: overly strict restrictions waste capacity resources, preventing aircraft from fully utilizing available airspace; overly lenient restrictions fail to effectively prevent congestion, increasing safety risks.

[0005] Third, the post-event evaluation model leads to a significant delay in response. Due to the combined effects of the two aforementioned shortcomings, the traffic allocation plans formulated at the pre-tactical level frequently fail during the tactical execution phase. However, the evaluation of control effectiveness generally adopts a post-event analysis model, requiring 15 to 30 minutes after the control measures are implemented to generate an evaluation report. This delay prevents timely detection and correction of deviations during the control process, and the resulting efficiency losses are already irreversible by the time the post-event evaluation report is generated.

[0006] In summary, there is an urgent need for an integrated solution for capacity planning, control, and evaluation that can span the entire strategic-pre-tactical-tactical lifecycle and has real-time closed-loop feedback capabilities. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide an integrated system for the full-cycle capacity flow planning, control and closed-loop evaluation of the operating airspace.

[0008] To achieve the above objectives, this invention provides an integrated system for the full-cycle capacity flow planning, control, and closed-loop evaluation of operational airspace, comprising: The unified data platform is used to aggregate heterogeneous data from multiple sources, including surveillance systems, flight planning systems, meteorological systems, and airspace management systems, to establish unified data standards and spatiotemporal benchmarks, and to provide data services for various functional modules. The strategic planning module, connected to the unified data platform, is used to generate medium- to long-term capacity planning schemes covering the next few hours based on historical operational data, seasonal patterns, and major event arrangements. These schemes include the maximum allowed number of flights, recommended flow rate, and a list of limiting conditions for each capacity zone. The pre-tactical allocation module, connected to the strategic planning module and the unified data platform, is used to receive operation requests submitted by users in each airspace, match and optimize the capacity constraints of the strategic layer with user needs, generate a time slot allocation scheme covering a time window of several hours in the future, and pre-detect and resolve potential conflicts. The digital twin engine, connected to the unified data platform, is used to build a high-fidelity virtual mirror of the physical airspace, including the construction of a three-dimensional airspace digital model, real-time trajectory synchronization mapping, capacity zone occupancy status update, and meteorological impact domain simulation, providing a unified simulation environment for planning, regulation, and evaluation. The tactical control module, connected to the digital twin engine, is used to receive the real-time situational image pushed by the digital twin engine, perform conflict detection based on the trajectory prediction results, generate control instructions for aircraft with potential conflicts, including trajectory adjustment, altitude layer change, and speed modulation, and send them to the relevant aircraft or ground station. The closed-loop evaluation engine, connected to the tactical control module and the digital twin engine, is used to collect effect data in real time during the control execution process, calculate the planning compliance, traffic matching, and control timeliness indicators. When any indicator deviates beyond the threshold, the strategy correction process is triggered, and the deviation diagnosis report and optimization suggestions are fed back to the corresponding level.

[0009] Furthermore, the strategic planning module includes a historical data mining engine, a capacity prediction model, and a multi-version planning generator; The historical data mining engine is connected to the unified data platform and is used to extract historical operational data from the unified data platform to establish a capacity-weather-traffic correlation model. The capacity prediction model is connected to the historical data mining engine and is used to combine current and forecast weather, special events, military activities and other inputs to predict the available capacity of each capacity zone in the future time window. The multi-version planning generator is connected to the capacity prediction model and is used to generate optimistic, baseline, and conservative capacity planning schemes based on different assumptions for decision-makers to choose from.

[0010] Furthermore, the pre-tactical allocation module includes a traffic demand aggregator, a time slot optimization allocator, and a conflict pre-detection engine; The traffic demand aggregator is used to aggregate the runtime slot requests of each airspace user according to priority and runtime constraints; The time slot optimization allocator is connected to the traffic demand aggregator and is used to establish a mathematical programming model with the goal of maximizing the total system throughput and with capacity constraints and user fairness constraints, to solve for the optimal time slot allocation scheme. The conflict pre-detection engine is connected to the time slot optimization allocator and is used to perform conflict simulation on the allocation scheme based on the trajectory prediction results, identify potential conflicts, and feed them back to the time slot optimization allocator for iterative resolution.

[0011] Furthermore, the tactical control module includes a trajectory planner, a conflict resolution decision-maker, and a command issuance interface; The trajectory planner is connected to the digital twin engine and is used to generate avoidance trajectory schemes for aircraft with conflict risks in the digital twin image. The conflict resolution decision-maker is connected to the trajectory planner and is used to select the control command with the lowest overall cost from multiple candidate solutions. The command issuance interface is connected to the conflict resolution decision-maker and is used to send control commands to the aircraft FMS system or ground station and receive execution confirmation feedback.

[0012] Furthermore, the digital twin engine includes a three-dimensional airspace digital model, a real-time trajectory synchronization mapping module, and a trajectory prediction and inference module; The three-dimensional airspace digital model is used to establish a three-dimensional geometric representation of the spatial boundaries, flight restrictions, and altitude layer structure of the capacity area. The real-time track synchronization mapping module is connected to the monitoring system and is used to receive monitoring data and synchronize the aircraft's actual position, speed, and heading to the virtual mirror. The trajectory prediction and simulation module is connected to the real-time trajectory synchronization and mapping module, and is used to predict future trajectories based on the current situation and flight plan, supporting short-term trajectory forecasts of 5 to 30 minutes.

[0013] Furthermore, the closed-loop evaluation engine includes an effect monitoring module, a deviation analysis module, and a strategy optimizer; The effect monitoring module is used to calculate the planning compliance index, traffic matching index, and control timeliness index in real time. The planning compliance index is used to characterize the deviation rate between the planned number of flights and the actual number of flights executed. The traffic matching index is used to characterize the matching rate between the allocated time slots and the actual occupied time slots. The control timeliness index is used to characterize the time interval from conflict identification to control completion. The deviation analysis module is connected to the effect monitoring module and is used to locate the source of deviation when the indicator exceeds the threshold, and to distinguish between capacity prediction deviation, allocation algorithm deviation, and control execution deviation. The strategy optimizer is connected to the deviation analysis module and is used to generate strategy correction suggestions based on deviation diagnosis results. For deviations that can be corrected by the tactical layer itself, it generates adjustment correction instructions. For deviations involving upper-level strategies, it sends adjustment requests to the strategic planning module or the pre-tactical allocation module.

[0014] Furthermore, the integrated operational airspace includes integrated military and civil aviation airspace operation scenarios and UAM urban air traffic and traditional civil aviation collaborative operation scenarios.

[0015] In one optional implementation, when the strategic planning module generates a medium- to long-term capacity planning scheme, the capacity prediction model performs capacity prediction using the following steps: obtaining historical operational data samples and current weather forecast data from the unified data platform; establishing a capacity-weather-flow correlation model using the historical data mining engine; inputting current weather forecast data, special activity plans, and military activity restrictions into the capacity prediction model; predicting the available capacity of each capacity zone within a future time window based on the correlation model; and outputting a multi-version capacity planning scheme including optimistic, baseline, and conservative versions.

[0016] In one optional implementation, when the pre-tactical allocation module performs time slot optimization allocation, the time slot optimization allocator employs the following steps to solve the problem: receiving runtime slot requests from each airspace user; aggregating and sorting the requests according to user priority and operational constraints; establishing a mathematical programming model with the goal of maximizing the total system throughput; setting capacity constraints to ensure that the number of inbound flights in each time slot does not exceed the available capacity of that time slot; setting user fairness constraints to ensure that the runtime slot allocation for each user meets protocol requirements; solving for the optimal time slot allocation scheme; and sending the scheme to the conflict pre-detection engine for verification.

[0017] In one optional implementation, the tactical control module performs the following steps during real-time control: receiving real-time situational image data pushed by the digital twin engine; predicting aircraft trajectories within a future time window using the trajectory prediction and simulation module; performing conflict detection on the predicted trajectories to identify potential conflicts; triggering the trajectory planner to generate multiple candidate avoidance schemes when a potential conflict is identified; evaluating the comprehensive cost of each candidate scheme using the conflict resolution decision-maker and selecting the scheme with the lowest cost as the control command; sending the control command to the relevant aircraft or ground station through the command issuance interface; receiving execution confirmation feedback and updating the trajectory status in the digital twin image.

[0018] In one optional implementation, the closed-loop evaluation engine performs the following steps when evaluating the effect: continuously collecting execution data from the tactical control module and real-time situational data from the digital twin engine; calculating the planning compliance index, traffic matching index, and control timeliness index for the current period; comparing each index with preset thresholds; triggering the deviation analysis process when any index exceeds the threshold; using the deviation analysis module to locate the source of the deviation and determine whether it is a capacity prediction deviation, allocation algorithm deviation, or control execution deviation; generating corresponding strategy correction suggestions based on the source of the deviation; for tactical layer deviations, directly generating control correction instructions and issuing them for execution; for strategic layer or pre-tactical layer deviations, sending adjustment requests to the corresponding modules; and feeding the deviation data back to the historical data mining engine for model update and optimization.

[0019] In one optional implementation, the unified data platform adopts the following data processing flow: receiving aircraft position, speed, and heading data from the surveillance system; receiving flight plans and operation application data from the flight planning system; receiving real-time meteorological observation and forecast data from the meteorological system; receiving airspace restrictions and capacity zone configuration data from the airspace management system; standardizing the above multi-source heterogeneous data to establish a unified data format and spatiotemporal reference; pushing the required data in real time according to the data requests of each functional module; and maintaining a historical data warehouse for use by the historical data mining engine.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves full-scale coverage from long-term capacity planning to real-time operational control through a three-layer architecture consisting of a strategic planning module, a pre-tactical allocation module, and a tactical control module (with second-level response). The three modules are interconnected via a unified data platform and receive policy feedback through a closed-loop evaluation engine, forming a complete management loop of "planning → allocation → control → evaluation → correction." This fundamentally solves the problem in existing technologies where system fragmentation leads to disconnected decision-making at different levels and difficulties in collaborative optimization.

[0021] This invention constructs a high-fidelity virtual mirror image corresponding to the physical airspace using a digital twin engine. This allows strategic planning schemes, pre-tactical time slot allocation schemes, and tactical control commands to be simulated and verified in digital space before being issued for execution. This "verify first, then execute" model significantly reduces the operational risks associated with trial and error directly in the actual airspace and effectively solves the problem of the lack of state feedback and effect verification mechanisms between planning and execution in existing technologies.

[0022] This invention utilizes a closed-loop evaluation engine to continuously collect performance data during the control and regulation process, calculating three quantitative indicators in real time: planning compliance, traffic matching, and control timeliness. When any indicator deviates beyond a preset threshold, the system automatically triggers a strategy correction process. The deviation analysis module precisely locates the source of the deviation (capacity prediction deviation, allocation algorithm deviation, or control and regulation execution deviation), and the strategy optimizer generates correction suggestions based on this and feeds them back to the appropriate level. Thus, strategy correction shifts from traditional manual experience-based judgment to an automated system decision-making model, and the evaluation and optimization of control and regulation effects transitions from post-event analysis to real-time online monitoring, reducing response latency from minutes to seconds.

[0023] This invention, through the tight coupling of a digital twin engine and a closed-loop evaluation engine, enables capacity constraints to be precisely applied down to each individual flight, achieving refined management of airspace capacity. Simultaneously, the automation of strategy correction reduces control response latency from minutes in traditional methods to seconds, significantly improving the system's ability to respond quickly to unforeseen situations. Attached Figure Description

[0024] Figure 1 This is a structural framework diagram of the integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of the integrated operating airspace proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the digital twin engine in this invention; Figure 3 This is a three-layer collaborative workflow framework diagram of the strategic planning module, the pre-tactical allocation module, and the tactical control module in this invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer and more complete, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention in any way.

[0026] Example 1

[0027] Reference Figure 1 This system includes: a unified data platform, a strategic planning module, a pre-tactical allocation module, a digital twin engine, a tactical control module, and a closed-loop evaluation engine.

[0028] In this embodiment, the integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of the integrated operation airspace aggregates multi-source heterogeneous data from the monitoring system, flight planning system, meteorological system, and airspace management system through a unified data platform. This establishes unified data standards and spatiotemporal benchmarks, providing data services to various functional modules. The strategic planning module, connected to the unified data platform, generates medium- to long-term capacity planning schemes covering a 6- to 24-hour time window based on historical operational data, seasonal patterns, and major event arrangements. This includes the maximum allowed number of flights in each capacity zone, recommended flow rates, and a list of limiting conditions. The pre-tactical allocation module, connected to the strategic planning module and the unified data platform, receives operational requests from airspace users, matches and optimizes the capacity constraints at the strategic level with user needs, generates time slot allocation schemes covering a 1- to 6-hour time window, and pre-detects and resolves potential conflicts. The digital twin engine connects to the unified data platform to build a high-fidelity virtual mirror of the physical airspace. This includes constructing a 3D airspace digital model, real-time track synchronization mapping, capacity zone occupancy status updates, and meteorological impact domain simulations, providing a unified simulation environment for planning, control, and evaluation. The tactical control module connects to the digital twin engine to receive real-time situational images pushed by the engine. Based on track prediction results, it performs conflict detection and generates control commands for aircraft with potential conflicts, including track adjustments, altitude layer changes, and speed modulations, which are then sent to relevant aircraft or ground stations. The closed-loop evaluation engine connects to the tactical control module and the digital twin engine to collect real-time effect data during control execution, calculate planning compliance, flow matching, and control timeliness indicators. When any indicator deviates beyond a threshold, a strategy correction process is triggered, feeding back deviation diagnosis reports and optimization suggestions to the corresponding level.

[0029] In one embodiment, the present invention also provides an integrated system for the full lifecycle capacity flow planning, control and closed-loop evaluation of the operational airspace. In this system, the strategic planning module includes a historical data mining engine, a capacity prediction model and a multi-version planning generator.

[0030] The historical data mining engine connects to the unified data platform to extract historical operational data and establish a capacity-weather-flow correlation model. The capacity forecasting model connects to the historical data mining engine to combine current and forecasted weather, special events, military activities, and other inputs to predict the available capacity of each capacity zone within a future time window. The multi-version planning generator connects to the capacity forecasting model to generate optimistic, baseline, and conservative capacity planning schemes based on different assumptions, allowing decision-makers to choose the appropriate option.

[0031] In this embodiment, the strategic planning module is the strategic decision-making center of the entire integrated system, responsible for generating medium- and long-term capacity planning schemes. This module specifically includes three core components: a historical data mining engine, a capacity prediction model, and a multi-version planning generator. Figure 3 As shown, the strategic planning module receives multi-dimensional inputs from the unified data platform, including historical operational data, current weather forecast data, special activity plans, and military activity restrictions. It establishes a capacity-weather-flow correlation model through a historical data mining engine and uses a capacity prediction model to predict capacity within future time windows. Finally, the multi-version planning generator outputs three versions of capacity planning schemes: optimistic, baseline, and conservative.

[0032] In this embodiment, the historical data mining engine extracts historical operational data from the unified data platform to establish a capacity-weather-flow correlation model. The core of this correlation model lies in revealing the quantitative relationship between weather conditions (rainfall, visibility, wind speed, turbulence level, etc.) and airspace capacity. Specifically, the historical data mining engine first retrieves historical operational data samples from the unified data platform for at least three years. Each sample includes date and time, weather condition parameters, capacity zone configuration parameters, and actual flight counts. Then, machine learning methods (such as gradient boosting trees or neural networks) are used to establish a multi-input multi-output mapping model. The input vector of this model is represented as:

[0033] Here, represents the time feature vector, which includes the weekday type, holiday identifier, and hourly time period information of the runtime segment. Each component is numerically encoded and used as the model input. Represents the i-th type of weather characteristic parameter, Let represent the configuration parameters of the j-th capacity zone, m represent the total number of categories of weather feature parameters, and n represent the total number of capacity zones. The model's output vector is represented as:

[0034] in, This represents the available capacity of the k-th capacity zone (the maximum number of flights allowed to enter per unit time).

[0035] In this embodiment, the capacity prediction model combines current weather forecast data, special event plans, and military activity restrictions as inputs to predict the available capacity of each capacity zone within a future time window. The prediction process of the capacity prediction model includes: First, obtaining weather forecast data for the future time window (6 to 24 hours) from a unified data platform, including hourly parameters such as temperature, precipitation, visibility, wind direction, and wind speed. Second, receiving the spatiotemporal range and activity level information of special event plans (such as air shows, marathons, major political events, etc.). Third, obtaining the airspace range, altitude range, and time window of military activity restrictions. Then, mapping and calculating the above inputs according to the correlation model established by the historical data mining engine to obtain the predicted available capacity of each capacity zone in each future time period. Finally, evaluating the confidence level of the prediction results and outputting the capacity prediction results containing the predicted values ​​and confidence intervals.

[0036] In this embodiment, the multi-version planning generator generates optimistic, baseline, and conservative capacity planning schemes based on different assumptions. The optimistic version assumes weather conditions are better than forecast, the event is canceled or scaled back, and the capacity is the upper bound of the predicted value (e.g., the predicted value multiplied by 1.15). The baseline version assumes weather conditions are consistent with the forecast and the event proceeds normally, and the capacity is the predicted value. The conservative version assumes weather conditions are worse than forecast, the event takes place as scheduled and is large in scale, and the capacity is the lower bound of the predicted value (e.g., the predicted value multiplied by 0.85). Each version of the planning scheme includes the capacity value for each capacity zone at each time period, recommended flow rate, a list of constraints, and contingency plan suggestions, allowing decision-makers to choose the appropriate scheme based on the actual situation.

[0037] In one embodiment, the present invention also provides an integrated system for the full-cycle capacity planning, control and closed-loop evaluation of the integrated operation airspace. In this system, the pre-tactical allocation module includes a flow demand aggregator, a time slot optimization allocator and a conflict pre-detection engine.

[0038] The traffic demand aggregator aggregates runtime slot requests from various airspace users based on priority and operational constraints. The time slot optimization allocator, connected to the traffic demand aggregator, establishes a mathematical programming model with the goal of maximizing total system throughput, constrained by capacity and user fairness, to solve for the optimal time slot allocation scheme. The conflict pre-detection engine, also connected to the time slot optimization allocator, performs conflict simulations on the allocation scheme based on flight path prediction results, identifies potential conflicts, and feeds them back to the time slot optimization allocator for iterative resolution.

[0039] In this embodiment, the pre-tactical allocation module is a key intermediate layer connecting strategic planning and tactical control. It is responsible for matching and optimizing the capacity constraints of the strategic layer with user needs, generating a time slot allocation scheme covering a time window of 1 to 6 hours in the future. Figure 3As shown, the pre-tactical allocation module specifically includes three core components: a traffic demand aggregator, a time slot optimization allocator, and a conflict pre-detection engine.

[0040] In this embodiment, the traffic demand aggregator aggregates runtime slot requests from various airspace users according to priority and operational constraints. Specifically, the traffic demand aggregator receives runtime slot requests submitted by airspace users such as airlines, general aviation operators, and drone operators. Each request includes: applicant identifier, departure and arrival airport or flight path origin, destination, expected departure time window, flight type (scheduled flight, business flight, training flight, cargo plane, etc.), and priority level (e.g., emergency flights have the highest priority). The traffic demand aggregator first sorts the requests in descending order according to priority level, then sorts requests of the same priority according to the first-come, first-served principle, and finally deduplicates and merges the requests, outputting an aggregated demand list. The aggregation process follows these constraints: multiple requests for the same aircraft cannot overlap; the number of takeoffs and landings at the same airport cannot exceed the airport's capacity; special aircraft (such as VIP flights) must be allocated to designated time slots.

[0041] In this embodiment, the time slot optimization allocator establishes a mathematical programming model with the goal of maximizing the total system throughput and constraints of capacity and user fairness, to solve for the optimal time slot allocation scheme. The mathematical programming model of the time slot optimization allocator is expressed as follows: Objective function:

[0042] Constraints: (Capacity constraints) (Decision variables) (Fairness constraints) in, This represents the set of all runtime slot requests. This represents the set of applications within time period t. A binary decision variable representing whether to accept application i. This indicates the weight of application i (considering factors such as priority, flight type, and operating costs). Let represent the available capacity during time period t, where T represents the set of time periods. This represents the actual time slot allocated to application i. and These represent the lower and upper limits of the expected time slot for application i, respectively.

[0043] In this embodiment, the time slot optimization allocator uses a mixed integer programming solver (such as Gurobi, CPLEX) or a heuristic algorithm (such as genetic algorithm, particle swarm optimization) to solve the above mathematical programming model, obtaining the optimal time slot allocation scheme that maximizes the total system throughput. During the solution process, to balance system efficiency and user fairness, a fairness adjustment coefficient α is introduced, modifying the objective function to:

[0044] in, This represents the average weight of all applications. The value of α ranges from 0 to 1. By adjusting α, a balance can be achieved between system efficiency and user fairness.

[0045] In this embodiment, the conflict pre-detection engine performs conflict simulation on the allocation scheme based on the trajectory prediction results, identifies potential conflicts, and feeds them back to the time slot optimization allocator for iterative resolution. The conflict pre-detection engine calls the trajectory prediction simulation module of the digital twin engine to predict the trajectory of each aircraft in the time slot allocation scheme for the next 1 to 6 hours. The trajectory prediction adopts an intent-based trajectory prediction method, comprehensively considering factors such as the aircraft's flight plan, air traffic control instructions, and weather detour intentions, generating multiple possible candidate trajectories. For each pair of aircraft trajectory combinations, its closest approach distance (CPA) and closest approach time (TCPA) are calculated. Time (of which) To predict the time window, The minimum safe interval is used to determine the existence of potential conflicts. The conflict pre-detection engine feeds back the identified conflicts to the time slot optimization allocator, triggering an iterative resolution process: the time slot optimization allocator re-optimizes and allocates the requests involved in the conflict, attempting to eliminate the conflict by adjusting time slots, changing tracks, etc., until all conflicts are resolved or the maximum number of iterations is reached.

[0046] In one embodiment, the present invention also provides an integrated system for the full-cycle capacity flow planning, control and closed-loop evaluation of the integrated operating airspace. In this system, the tactical control module includes a trajectory planner, a conflict resolution decision-maker and a command issuance interface.

[0047] The trajectory planner connects to the digital twin engine to generate avoidance trajectory plans for aircraft at risk of conflict within the digital twin mirror image. The conflict resolution decision-maker connects to the trajectory planner to select the control command with the lowest overall cost from multiple candidate options. The command issuance interface connects to the conflict resolution decision-maker to send control commands to the aircraft's FMS system or ground station and receive execution confirmation feedback.

[0048] In this embodiment, the tactical control module is the real-time operation control center of the integrated system, responsible for conflict detection and resolution on a second-level timescale. Figure 3As shown, the tactical control module specifically includes three core components: a trajectory planner, a conflict resolution decision-maker, and a command issuance interface.

[0049] In this embodiment, the trajectory planner generates avoidance trajectory schemes for aircraft with conflict risks in a digital twin mirror image. The trajectory planner receives a real-time situational image pushed by the digital twin engine, which includes the position, speed, heading, altitude, and flight plan information of all currently airborne aircraft. The trajectory planner first uses the trajectory prediction and simulation module to predict the aircraft trajectories for the next 5 to 30 minutes, and then performs conflict detection on the predicted trajectories. When a potential conflict is detected, the trajectory planner generates multiple candidate avoidance schemes in the virtual environment of the digital twin mirror image. The candidate scheme generation strategy includes: Option 1: Horizontal Yaw Avoidance. Maintain the current altitude and change course to pass through the conflict zone from the side. Yaw angle. The calculation formula is:

[0050] in, For the aircraft's ground speed, This is the difference between the expected time of the conflict and the current time.

[0051] Option 2: Vertical Layer Change. Maintain the current flight path and avoid collisions by ascending or descending to an adjacent altitude layer. The magnitude of the altitude layer change... Must meet (Minimum vertical safety interval).

[0052] Option 3: Speed ​​Modulation. By decreasing or increasing the aircraft's ground speed, the aircraft are staggered in time. Speed ​​adjustment amount. The calculation formula is:

[0053] Option 4: Combined Strategy. This involves using two or three of the following methods: horizontal yaw, vertical layer change, and speed modulation.

[0054] In this embodiment, the conflict resolution decision-maker selects the control instruction with the lowest overall cost from multiple candidate solutions. The overall cost function is defined as:

[0055] in, Indicates the increase in fuel consumption. Indicates the flight delay time. This indicates the impact on passenger comfort (usually characterized by vertical acceleration or horizontal overload). This represents the residual risk (the probability that the conflict is not completely eliminated) resulting from implementing the plan. All four components have been dimensionless. These are the weighting coefficients for each component. The conflict resolution decision-maker calculates the comprehensive cost of each candidate solution and selects the solution with the lowest cost as the optimal control command. When multiple solutions have equal costs, they are selected according to priority rules: priority is given to the solution with the least impact on other aircraft, priority is given to the solution with the lowest execution complexity, and priority is given to the solution with the smallest deviation from the original flight plan.

[0056] In this embodiment, the command issuance interface sends control commands to the Aircraft Flight Management System (FMS) or ground station and receives execution confirmation feedback. The command issuance interface supports multiple issuance methods: for aircraft with data link communication capabilities, control commands are sent to the aircraft's FMS system via ACARS or Link 2000+ data link, and the FMS system automatically executes the track or speed commands; for aircraft without data link capabilities or in emergency situations, control commands are issued to the pilot via voice channel, and the pilot executes them after confirmation. The command issuance interface also receives execution confirmation feedback from the aircraft, including the actual executed track parameters, the estimated time to reach the new altitude layer, etc., and updates the execution results in the digital twin image.

[0057] In one embodiment, the present invention also provides an integrated system for the full-cycle capacity flow planning, control and closed-loop evaluation of the integrated operation airspace. In this system, the digital twin engine includes a three-dimensional airspace digital model, a real-time trajectory synchronization mapping module and a trajectory prediction and inference module.

[0058] A 3D airspace digital model is used to establish a 3D geometric representation of the spatial boundaries, flight restrictions, and altitude layer structure of the capacity area. A real-time track synchronization mapping module connects to the monitoring system to receive monitoring data and synchronize the aircraft's actual position, speed, and heading to the virtual mirror. A track prediction and simulation module connects to the real-time track synchronization mapping module to predict future tracks based on the current situation and flight plan, supporting short-term track forecasts of 5 to 30 minutes.

[0059] In this embodiment, the digital twin engine serves as the virtual-real mapping foundation for the integrated system, providing a unified simulation environment for planning, control, and evaluation. For example... Figure 2 As shown, the digital twin engine specifically includes three core components: a three-dimensional airspace digital model, a real-time trajectory synchronization mapping module, and a trajectory prediction and inference module.

[0060] In this embodiment, the 3D airspace digital model establishes a 3D geometric representation of the spatial boundaries, flight restrictions, and altitude layer structure of the capacity zone. The 3D airspace digital model employs 3D geometric modeling technology to accurately reconstruct the geometric structure of the airspace, capacity zone boundaries, flight restriction zones, danger zones, and restricted areas in digital space. The model's spatial reference coordinate system adopts the WGS-84 geodetic coordinate system, with a horizontal coordinate accuracy better than 1 meter and a vertical coordinate accuracy better than 0.5 meters. The geometric representation of the 3D airspace digital model uses a 3D voxel model or a 3D mesh model, supporting spatial queries, collision detection, and visualization rendering. The model also includes a temporal dimension attribute of the airspace, representing the effective status of each restriction zone at different time periods. For example, if the effective time of a military aviation activity restricted area is from 14:00 to 16:00 daily, the 3D airspace digital model automatically marks this area as unavailable during that time period.

[0061] In this embodiment, the real-time track synchronization mapping module is connected to the surveillance system, receives surveillance data, and synchronizes the aircraft's actual position, speed, and heading to the virtual mirror. The real-time track synchronization mapping module supports access from multiple surveillance data sources, including primary radar (PSR), secondary radar (SSR), ADS-B, and multipoint positioning system (MLAT). The module fuses multi-source surveillance data and uses Kalman filtering or multiple hypothesis tracking (MHT) algorithms to generate a unified track report. The track report includes the aircraft's 24-bit address code, call sign, position (longitude, latitude, altitude), speed (ground speed, vertical speed), heading, and timestamp. The real-time track synchronization mapping module updates the track report to the digital twin mirror at a frequency of at least once per second, maintaining real-time synchronization between the virtual mirror and the physical airspace. The synchronization process uses a low-latency data transmission protocol, with an end-to-end latency of no more than 500 milliseconds.

[0062] In this embodiment, the trajectory prediction and extrapolation module predicts future trajectories based on the current situation and flight plan, supporting short-term trajectory forecasts of 5 to 30 minutes. The module employs a hybrid prediction method combining physics-based trajectory prediction and data-driven intent recognition. The physics model, based on the aircraft's dynamic characteristics, considers environmental factors such as current speed, heading, altitude, and wind speed and direction to calculate the future position. The intent recognition module analyzes information such as the aircraft's flight plan, air traffic control history, and the operating procedures of its affiliated airline to infer the aircraft's flight intent (e.g., whether it is preparing for an approach, whether it needs to detour due to weather conditions, etc.). The output of the trajectory prediction and extrapolation module is multiple candidate trajectories with probability weights. Each trajectory includes predicted positions, speeds, and altitudes for future times, as well as the probability weights for that trajectory. The mathematical representation of the prediction results is as follows:

[0063] in, This represents the i-th prediction time. These represent the predicted values ​​of position, velocity, and altitude at that moment, respectively. This represents the weight of the predicted trajectory.

[0064] In one embodiment, the present invention also provides an integrated system for the full-cycle capacity flow planning, control and closed-loop evaluation of the integrated operation airspace. In this system, the closed-loop evaluation engine includes an effect monitoring module, a deviation analysis module and a strategy optimizer.

[0065] The performance monitoring module calculates planning compliance, traffic matching, and control timeliness in real time. Planning compliance represents the deviation rate between planned and actual flights; traffic matching represents the match rate between allocated and actually occupied time slots; and control timeliness represents the time interval from conflict identification to control completion. The deviation analysis module, connected to the performance monitoring module, identifies the source of deviation when indicators exceed thresholds, distinguishing between capacity prediction deviation, allocation algorithm deviation, and control execution deviation. The strategy optimizer, connected to the deviation analysis module, generates strategy correction suggestions based on deviation diagnosis results. For deviations that can be corrected at the tactical level, it generates control correction instructions; for deviations involving upper-level strategies, it sends adjustment requests to the strategic planning module or pre-tactical allocation module.

[0066] In this embodiment, the closed-loop evaluation engine serves as the central hub for the integrated system's effect evaluation and strategy correction, achieving a complete closed loop of execution-monitoring-evaluation-correction. The closed-loop evaluation engine calculates various evaluation indicators by collecting real-time execution data from the tactical control module and real-time situational data from the digital twin engine, determining whether a strategy correction process needs to be triggered.

[0067] In this embodiment, the effect monitoring module calculates the planning compliance rate, traffic matching rate, and control timeliness rate in real time. The formula for calculating the planning compliance rate (CR) is as follows:

[0068] when When = 0, if If CR = 0, then CR = 100%; otherwise, CR = 0%.

[0069] in, The planning flights generated for the strategic planning module This refers to the actual number of flights executed. The formula for calculating the Matching Rate (MR) is:

[0070] when When = 0, if If MR = 0, then MR = 100%; otherwise, MR = 0%.

[0071] in, The number of time slots allocated to the pre-tactical allocation module. This refers to the number of times the actual occupied time slot matches the allocated time slot. The Timeliness Index (TI) is calculated using the following formula:

[0072] in, For conflict identification time, To control the completion time, The preset time threshold is used. The effect monitoring module continuously calculates the above indicators and compares the calculation results with the preset threshold. When any indicator exceeds the threshold, the deviation analysis process is triggered.

[0073] In this embodiment, the deviation analysis module locates the source of deviation when the indicator exceeds the threshold, distinguishing between capacity prediction deviation, allocation algorithm deviation, and control execution deviation. The deviation analysis module employs a multi-source data comparison analysis method: first, it compares the capacity prediction values ​​from the strategic planning module with the actual operating data to calculate the capacity prediction deviation. Secondly, by comparing the time slot allocation scheme of the pre-tactical allocation module with the actual operational trajectory, the deviation of the allocation algorithm is calculated. Next, the control commands issued by the tactical control module are compared with the actual execution results to calculate the control execution deviation. The bias analysis module uses Bayesian inference or decision tree classification methods to identify the main sources of bias. When When the primary source of deviation is the data source, it indicates a systematic error in the capacity prediction model, requiring feedback to the historical data mining engine for model updates; when... When the primary source of deviation is the time slot optimization allocation algorithm, it indicates that the constraints or objective function need to be adjusted; when When the main source of deviation is the aircraft, it indicates a problem with the execution of control commands, and it is necessary to analyze whether the problem is a communication link issue or an aircraft response issue.

[0074] In this embodiment, the strategy optimizer generates strategy correction suggestions based on the deviation diagnosis results. For deviations that can be corrected by the tactical layer itself (such as communication delay issues in control execution deviations), the strategy optimizer directly generates control correction instructions and issues them for execution, compensating for the deviation by adjusting control parameters (such as increasing lead time or changing the instruction format). For deviations involving upper-level strategies (such as capacity prediction deviations or allocation algorithm deviations), the strategy optimizer sends adjustment requests to the corresponding modules: a capacity adjustment request to the strategic planning module, requesting a re-prediction and release of a new capacity planning scheme; and an allocation adjustment request to the pre-tactical allocation module, requesting a re-optimization of the time slot allocation scheme. The strategy optimizer also feeds back deviation data to the historical data mining engine for model update optimization, forming a closed-loop mechanism for continuous improvement.

[0075] In one embodiment, the present invention also provides an integrated system for the full lifecycle capacity flow planning, control and closed-loop evaluation of the integrated operation airspace, which also includes a data processing flow of a unified data platform.

[0076] The unified data platform adopts the following data processing flow: receiving aircraft position, speed, and heading data from the surveillance system; receiving flight plans and operation application data from the flight planning system; receiving real-time meteorological observation and forecast data from the meteorological system; receiving airspace restrictions and capacity zone configuration data from the airspace management system; standardizing the above multi-source heterogeneous data to establish a unified data format and spatiotemporal benchmark; pushing the required data in real time according to the data requests of each functional module; and maintaining a historical data warehouse for use by the historical data mining engine.

[0077] In this embodiment, the unified data platform serves as the data infrastructure for the entire integrated system, responsible for aggregating, cleaning, storing, and distributing multi-source heterogeneous data from various source systems. The data processing flow of the unified data platform includes the following steps: Data Access: The unified data platform receives aircraft position, speed, and heading data from the surveillance system via standardized interfaces (such as ADSB interface, ASTERIX protocol, and XML / JSON message queue), with a receiving frequency of no less than once per second; it receives flight plans and operation application data from the flight planning system via the Fixm / SWIM protocol; it receives real-time meteorological observation and forecast data from the meteorological system via the WXXM / METAR / TAF protocol; and it receives airspace restrictions and capacity zone configuration data from the airspace management system via the AIXM protocol.

[0078] Data Standardization: The unified data platform standardizes the multi-source heterogeneous data received, unifying the time format, coordinate system, and unit of measurement of different data sources into a standard format. Time uses UTC time and is accurate to milliseconds; the coordinate system is uniformly set to the WGS-84 geodetic coordinate system; altitude uses standard atmospheric pressure altitude (QNE) or corrected sea level pressure altitude (QNH), and the altitude layer type is labeled; speed is expressed in knots (KT) or meters per second (m / s).

[0079] Data quality control: The unified data platform performs quality checks on standardized data, identifying and handling abnormal data. Quality checks include: completeness checks (checking for missing required fields), consistency checks (checking for contradictions in related fields), timeliness checks (checking if data timestamps are within their validity period), and reasonableness checks (checking if values ​​are within a reasonable range). For detected abnormal data, strategies such as interpolation, prediction, or removal are used for processing.

[0080] Data storage and distribution: The unified data platform pushes processed data to each functional module in real time. Data distribution adopts a publish-subscribe model, with each module subscribing to the data types it needs based on its own requirements. The unified data platform also maintains a historical data warehouse, using a distributed storage system (such as HDFS or object storage) to store historical operational data for use by the historical data mining engine. The retention period for historical data in the warehouse is no less than three years.

[0081] In one embodiment, the present invention also provides a three-layer collaborative workflow for an integrated system that combines full-cycle capacity flow planning, control, and closed-loop evaluation of the operational airspace.

[0082] The three-tiered collaborative workflow of the strategic planning module, the pre-tactical allocation module, and the tactical control module includes: the strategic planning module first generates a medium- to long-term capacity planning scheme covering the next 6 to 24 hours and publishes the scheme to the unified data platform; the pre-tactical allocation module obtains the strategic planning scheme from the unified data platform, generates a time slot allocation scheme covering the next 1 to 6 hours based on user needs, and distributes the scheme to relevant aircraft and ground stations; the tactical control module obtains real-time situational awareness from the digital twin engine and performs second-level conflict detection and control; when the closed-loop evaluation engine detects a deviation exceeding the threshold, it sends an adjustment request to the corresponding level according to the source of the deviation, realizing cross-level closed-loop feedback.

[0083] In this embodiment, the three-layer collaborative workflow of the integrated system for full-cycle airspace capacity planning, control, and closed-loop evaluation embodies a progressive refinement from strategy to tactics and a feedback loop from tactics to strategy. For example... Figure 3 As shown, the workflow is divided into the following stages: Phase 1: Strategic Planning. The strategic planning module obtains historical operational data, current weather forecasts, and special event plans from the unified data platform. It uses a historical data mining engine to build a capacity-weather-flow correlation model, and a capacity forecasting model to predict the available capacity of each capacity zone for the next 6 to 24 hours. A multi-version planning generator produces three versions of capacity planning schemes: optimistic, baseline, and conservative. The strategic planning module publishes the selected capacity planning scheme to the unified data platform for use by the pre-tactical allocation module and the closed-loop evaluation engine.

[0084] Phase Two: Pre-Tactical Allocation. The pre-tactical allocation module obtains strategic planning schemes and user operation requests from the unified data platform, aggregates and sorts the requests using a traffic demand aggregator, solves for the optimal time slot allocation scheme using a time slot optimization allocator, and performs conflict simulation and iterative resolution of the scheme using a conflict pre-detection engine. The pre-tactical allocation module distributes the time slot allocation scheme to relevant aircraft and ground stations, and synchronizes the scheme to the digital twin engine for subsequent simulation and deduction.

[0085] Phase Three: Tactical Control. The tactical control module obtains a real-time situational image from the digital twin engine, performs short-term trajectory prediction using the trajectory prediction and simulation module, and identifies potential conflicts using a conflict detection algorithm. When a conflict is detected, the trajectory planner generates candidate avoidance schemes, the conflict resolution decision-maker selects the scheme with the lowest overall cost as the control command, and the command issuance interface sends the command to the aircraft or ground station and receives execution confirmation feedback.

[0086] Phase Four: Closed-Loop Evaluation. The closed-loop evaluation engine continuously collects execution data from the tactical control module and real-time situational data from the digital twin engine, calculating indicators such as planning compliance, traffic matching, and control timeliness. When any indicator exceeds a threshold, the deviation analysis module locates the source of the deviation, and the strategy optimizer generates strategy correction suggestions. For tactical-level deviations, control correction instructions are directly generated; for strategic-level or pre-tactical-level deviations, adjustment requests are sent to the corresponding modules. The collaborative work between the three layers ensures the continuity and consistency of airspace capacity management across the entire time scale.

[0087] In one embodiment, the present invention also provides an application scenario for an integrated system that combines full-cycle capacity flow planning, control, and closed-loop evaluation of the operational airspace.

[0088] The integrated airspace includes scenarios of integrated operation of military and civil aviation airspace and scenarios of collaborative operation between UAM urban air traffic and traditional civil aviation.

[0089] In this embodiment, the integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated airspace operations can be applied to the scenario of integrated military and civil aviation operations. In this scenario, the system needs to manage the operations of both military aircraft (fighter jets, transport aircraft, helicopters, etc.) and civil aircraft (passenger aircraft, cargo aircraft, general aviation aircraft, etc.). The system accesses data from both the military and civil aviation air traffic control systems through a unified data platform, enabling real-time sharing of military and civil aviation operational information. When generating capacity planning schemes, the strategic planning module needs to consider the time period and airspace scope of military aviation activities and reserve necessary dedicated military airspace; the pre-tactical allocation module needs to ensure the execution of priority military aviation tasks when allocating time slots; and the tactical control module needs to follow the different operational rules and interval standards of military and civil aviation when performing conflict resolution. The virtual image built by the digital twin engine needs to simultaneously include the operational status of both military and civil aviation aircraft, providing a unified decision support environment for collaborative military and civil aviation operations.

[0090] In this embodiment, the integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated airspace operations can be applied to the scenario of collaborative operation between UAM urban air traffic and traditional civil aviation. In this scenario, the system needs to manage the collaborative operation of UAM aircraft (such as electric vertical takeoff and landing aircraft, eVTOL) and traditional civil aircraft operating within urban areas. UAM aircraft are characterized by frequent takeoffs and landings, flexible routes, and low flight altitudes, posing new challenges to airspace management. When generating capacity planning schemes, the strategic planning module needs to consider factors such as the capacity of UAM dedicated takeoff and landing fields (Vertiports) and the density restrictions of UAM routes; the pre-tactical allocation module needs to handle a large number of UAM runtime slot requests and perform pre-detection of conflicts between UAM and civil aircraft; the tactical control module needs to cope with the rapidly changing operational status of UAM aircraft and perform high-frequency conflict detection and control. The digital twin engine needs to construct a three-dimensional digital model including urban buildings, UAM takeoff and landing fields, and low-altitude airspace to provide a high-precision simulation environment for UAM operations.

[0091] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of operational airspace, characterized in that, include: A unified data platform is used to aggregate heterogeneous data from multiple sources, including surveillance systems, flight planning systems, meteorological systems, and airspace management systems, and to establish unified data standards and spatiotemporal benchmarks. The strategic planning module, connected to the unified data platform, is used to generate medium- to long-term capacity planning schemes covering a time window of several hours in the future based on historical operational data. The pre-tactical allocation module, connected to the strategic planning module and the unified data platform, is used to receive operation requests submitted by users in each airspace, match and optimize the capacity constraints of the strategic layer with user needs, and generate a time slot allocation scheme covering a time window of several hours in the future. The digital twin engine, connected to the unified data platform, is used to build a high-fidelity virtual mirror of the physical airspace, including the construction of a three-dimensional airspace digital model, real-time track synchronization mapping, capacity zone occupancy status update, and meteorological impact domain simulation. The tactical control module, connected to the digital twin engine, is used to receive the real-time situational image pushed by the digital twin engine, perform conflict detection based on the trajectory prediction results, generate control instructions for aircraft with potential conflicts, and send them to the relevant aircraft or ground stations. The closed-loop evaluation engine, connected to the tactical control module and the digital twin engine, is used to collect effect data in real time during the control execution process, calculate the planning compliance, traffic matching, and control timeliness indicators. When any indicator deviates beyond the threshold, the strategy correction process is triggered, and the deviation diagnosis report and optimization suggestions are fed back to the corresponding level.

2. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 1, characterized in that, The strategic planning module includes a historical data mining engine, a capacity prediction model, and a multi-version planning generator. The historical data mining engine is connected to the unified data platform and is used to extract historical operational data from the unified data platform to establish a capacity-weather-flow correlation model. The capacity prediction model is connected to the historical data mining engine and is used to predict the available capacity of each capacity zone within a future time window by combining current and forecast weather, special events, and military activities. The multi-version planning generator is connected to the capacity prediction model and is used to generate optimistic, baseline, and conservative capacity planning schemes based on different assumptions.

3. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 1, characterized in that, The pre-tactical allocation module includes a traffic demand aggregator, a time slot optimization allocator, and a conflict pre-detection engine. The traffic demand aggregator is used to aggregate the runtime slot requests of each airspace user according to priority and operational constraints. The time slot optimization allocator is connected to the traffic demand aggregator and is used to establish a mathematical programming model with the goal of maximizing the total system throughput and with capacity constraints and user fairness constraints, to solve for the optimal time slot allocation scheme. The conflict pre-detection engine is connected to the time slot optimization allocator and is used to perform conflict simulation on the allocation scheme based on the trajectory prediction results, identify potential conflicts, and feed them back to the time slot optimization allocator for iterative resolution.

4. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 1, characterized in that, The tactical control module includes a trajectory planner, a conflict resolution decision-maker, and a command issuance interface; the trajectory planner is connected to the digital twin engine and is used to generate avoidance trajectory schemes for aircraft with conflict risks in the digital twin image; the conflict resolution decision-maker is connected to the trajectory planner and is used to select the control command with the lowest overall cost from multiple candidate schemes. The command issuance interface is connected to the conflict resolution decision-maker and is used to send control commands to the aircraft or ground station and receive execution confirmation feedback.

5. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 1, characterized in that, The digital twin engine includes a three-dimensional airspace digital model, a real-time trajectory synchronization mapping module, and a trajectory prediction and deduction module; the three-dimensional airspace digital model is used to establish a three-dimensional geometric representation of the spatial boundaries, flight restrictions, and altitude layer structure of the capacity area. The real-time trajectory synchronization mapping module is connected to the monitoring system and is used to receive monitoring data and synchronize the aircraft's actual position, speed, and heading to the virtual mirror. The trajectory prediction and simulation module is connected to the real-time trajectory synchronization mapping module and is used to predict future trajectories based on the current situation and flight plan, supporting short-term trajectory forecasts of 5 to 30 minutes.

6. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 1, characterized in that, The closed-loop evaluation engine includes an effect monitoring module, a deviation analysis module, and a strategy optimizer. The effect monitoring module is used to calculate planning compliance indicators, traffic matching indicators, and control timeliness indicators in real time. The deviation analysis module is connected to the effect monitoring module and is used to locate the source of deviation when the indicators exceed the threshold, and distinguish between capacity prediction deviation, allocation algorithm deviation, and control execution deviation. The strategy optimizer is connected to the deviation analysis module and is used to generate strategy correction suggestions based on the deviation diagnosis results. For deviations that can be corrected by the tactical layer, it generates control correction instructions. For deviations involving upper-level strategies, it sends adjustment requests to the strategic planning module or the pre-tactical allocation module.

7. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 1, characterized in that, The integrated airspace includes scenarios for integrated military and civil aviation operations and scenarios for collaborative operations between UAM urban air traffic and traditional civil aviation.

8. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 2, characterized in that, The capacity prediction model employs the following steps to predict capacity: obtaining historical operational data samples and current weather forecast data from the unified data platform; establishing a capacity-weather-flow correlation model using the historical data mining engine; and inputting current weather forecast data, special activity plans, and military activity restrictions into the capacity prediction model. Based on the aforementioned correlation model, the available capacity of each capacity zone within a future time window is predicted; the output includes a multi-version capacity planning scheme containing optimistic, baseline, and conservative versions.

9. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of integrated operational airspace as described in claim 3, characterized in that, The time slot optimization allocator employs the following steps to solve the problem: receiving runtime slot requests from users in each airspace; aggregating and sorting the requests according to user priority and runtime constraints; establishing a mathematical programming model with the goal of maximizing the total system throughput; and setting capacity constraints to ensure that the number of incoming flights in each time slot does not exceed the available capacity of that time slot. Set user fairness constraints; solve for the optimal time slot allocation scheme; The proposed solution is sent to the conflict pre-detection engine for verification.

10. The integrated system for full-cycle capacity flow planning, control, and closed-loop evaluation of fused operational airspace according to claim 6, characterized in that, In the effect monitoring module, the planning compliance index is used to characterize the deviation rate between the planned number of flights and the actual number of flights executed, the flow matching index is used to characterize the matching rate between the allocated time slots and the actual occupied time slots, and the control timeliness index is used to characterize the time interval from conflict identification to control completion.

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