Conflict-free unmanned aerial vehicle time slot allocation method based on flight path operation

Abstract

The invention discloses a conflict-free unmanned aerial vehicle (UAV) time slot allocation method based on flight path operation, which is applied to a low-altitude flight management and service system, and comprises the following steps: generating a predicted arrival time window of a UAV at a specified point and a predicted arrival time sequence in the predicted arrival time window according to flight path prediction data; calculating to obtain a required arrival time group meeting the reliability requirement by considering the time and space constraint of entering the port, the flow control constraint and the flight conflict detection constraint; judging whether the unmanned aerial vehicle operator accepts the required arrival time group or not; and if the advised arrival time is accepted, generating a required arrival time instruction agreeing to execute according to the accepted advised arrival time so as to generate a port entering sorting queue based on the four-dimensional track. The conflict-free unmanned aerial vehicle time slot allocation method based on flight path operation meets the requirement of a flight path operation mode of a future low-altitude air traffic management system based on unmanned aerial vehicle prediction, is safer, more efficient and more environment-friendly compared with an existing unmanned aerial vehicle arrival sorting management method, and can greatly improve the operation efficiency of an unmanned aerial vehicle take-off and landing area.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of air traffic management, and particularly relates to a conflict-free unmanned aerial vehicle time slot allocation method based on track operation. BACKGROUND

[0002] With the rapid development of science and technology, unmanned aerial vehicles (UAVs) are increasingly widely used in military, civilian and commercial fields. In military reconnaissance, UAVs can penetrate into the enemy's area to obtain key intelligence. In civilian applications, they play an important role in express delivery, agricultural and forest plant protection, aerial photography for films and television, etc. In the commercial field, UAVs are also indispensable in power inspection, geological exploration, etc. However, the frequent operation of a large number of UAVs in limited airspace makes the airspace resources increasingly tense, and the risk of track conflict between UAVs increases dramatically.

[0003] To address this challenge, researchers have conducted a lot of research in the field of UAV time slot allocation and track planning. In the traditional UAV time slot allocation, time division multiple access (TDMA) technology is a commonly used means, which divides time into different time slots and allocates them to different UAVs for use, thereby avoiding signal conflict. For example, by establishing a mapping relationship of "type-priority-time slot", the time slot length is dynamically adapted to the characteristics of UAVs (low speed, light load) and eVTOLs (high speed, heavy load). For example, the UAV time slot is set to 500ms to meet the low-altitude and low-speed operation requirements, and the eVTOL time slot is set to 200ms to adapt to high-speed maneuvering scenarios, avoiding resource waste caused by long time slots. At the same time, a three-level priority system is constructed. High-priority tasks (such as emergency rescue helicopters) can trigger "time slot preemption", and low-priority tasks (such as agricultural plant protection UAVs) need to complete avoidance (including hovering, returning or route deviation) within 100ms, to ensure the time continuity of emergency tasks. However, this traditional TDMA method has obvious limitations when faced with large-scale and high-dynamic UAV application scenarios. When the number of UAVs is large and the flight tasks are complex and varied, the fixed time slot allocation method is difficult to adapt flexibly, and may lead to time slot resource waste or unreasonable allocation, thereby causing communication conflicts between UAVs, seriously affecting the efficiency and safety of flight tasks.

[0004] In the aspect of path planning, early UAV path planning relies on experience and simple mathematical models. With the development of technology, model-based methods predict the path by establishing a physical model of the UAV and solving the motion equation of the UAV using numerical methods. This method is suitable for simple flight environments, but the calculation cost is high. Optimization-based methods optimize the flight objectives of the UAV and achieve optimal or near-optimal paths under certain constraints. Common optimization algorithms include genetic algorithms, particle swarm optimization, etc., which can handle complex multi-objective optimization problems. In recent years, with the development of deep learning technology, machine learning-based methods have gradually emerged. Convolutional neural networks (CNN) can process image data taken by UAVs, and recurrent neural networks (RNN) can capture time series features in the flight process of UAVs. However, these methods require a large amount of labeled data in the training process, and the robustness to abnormal situations needs to be improved. In addition, existing path planning methods often have difficulty in comprehensively and accurately considering the impact of complex environmental factors (such as severe weather, terrain interference, electromagnetic interference, etc.) on the path, resulting in potential safety hazards in actual flight. Moreover, when multiple UAVs work together, how to achieve effective coordination between the paths of each UAV and avoid conflicts is also a major problem in current path planning research.

[0005] In summary, existing UAV time slot allocation and path planning technologies have difficulty meeting the needs of complex application scenarios, and there is an urgent need for an innovative method that can consider the path operation of UAVs and achieve conflict-free time slot allocation to improve the safety, efficiency, and airspace resource utilization of UAV operation. SUMMARY

[0006] To overcome the shortcomings of the prior art, the purpose of the present application is to provide a conflict-free UAV time slot allocation method based on path operation to solve the problem that traditional management and control methods cannot meet the demand for safe and efficient operation of air traffic in high-complexity airspace environments.

[0007] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0008] The conflict-free UAV time slot allocation method based on path operation of the present application is applied in a low-altitude flight management and service system, and the steps are as follows:

[0009] Step S1: generating the predicted arrival time window of the UAV at a specified point and the predicted arrival time sequence within the predicted arrival time window according to the path prediction data;

[0010] Step S2: Based on the estimated arrival time sequence of the UAV at the specified point within the estimated arrival time window, considering the time and space constraints of the port entry, flow control constraints and flight conflict detection constraints, the required arrival time group meeting the reliability requirements is calculated;

[0011] Step S3: Determine whether the UAV operator accepts the required arrival time group; if accepted, generate the required arrival time instruction for execution according to the accepted recommended arrival time, to generate the four-dimensional track-based port entry sequencing queue; if not accepted, return to step S2 until the required arrival time instruction for execution is obtained.

[0012] Further, the step S1 specifically comprises:

[0013] Step S11: The low-altitude flight management and service system receives the track prediction data (EPP) sent by the UAV through the onboard equipment;

[0014] Step S12: The low-altitude flight management and service system calculates the estimated arrival time ETA (Estimate Time of Arrival, ETA) at the specified point according to the track prediction data, weather data and flight dynamic data of the UAV, and obtains the estimated arrival time window;

[0015] The estimated arrival time window is the time period predicted by the UAV for a certain waypoint, and the start time and end time respectively represent the fastest time and slowest time for the UAV to pass through a certain waypoint;

[0016] Step S13: The low-altitude flight management and service system considers the flight plan information, flight dynamic data of the UAV, current interval requirements and overall optimization target, and generates traffic sequencing, i.e. the estimated arrival time sequence.

[0017] Further, the step S2 specifically comprises:

[0018] Based on the time and space constraints of the port entry, a space-time hybrid causal model is used to reorder the estimated arrival time sequence obtained in step S1 to form an initial required arrival time sequence;

[0019] Based on the flow control constraints and flight conflict detection constraints, a correlation effect model is used to analyze the reliability of the initial required arrival time sequence, and a required arrival time group meeting the reliability requirements is calculated.

[0020] Further, in the process of forming the initial required arrival time sequence, based on the space constraints including the airspace capacity of the take-off and landing area and the meteorological conditions, a space-time hybrid causal model is established at the fusion point with the safety time interval as the time constraint condition, and the predicted arrival time sequence obtained in step S1 is reordered to form the initial required arrival time sequence with the minimum change cost and the minimum fuel consumption of flight as the target.

[0021] Further, in the process of calculating the required arrival time group meeting the reliability requirement, based on the constraint conditions of the space-time hybrid causal model, a correlation effect model is established by adding flow control constraints and flight conflict detection constraints, and the predicted arrival time sequence obtained in step S1 is reordered to form the required arrival time group meeting the reliability requirement with the minimum delay time as the target.

[0022] Further, the space-time hybrid causal model comprises a decision variable, an objective function and a constraint condition.

[0023] The decision variable comprises:

[0024] The required arrival time of the approaching unmanned aerial vehicle m calculated at the specified point ;

[0025] The required arrival time of the approaching unmanned aerial vehicle calculated at the specified point belongs to a time slot t ;

[0026] The ordering decision variable of the unmanned aerial vehicles m and n at the specified point ;

[0027] The expression of the objective function is:

[0028] ;

[0029] Wherein, is a cost coefficient caused by changing the overpass time in the flight plan, is the predicted overpass time of the unmanned aerial vehicle m in the flight plan; is the fuel consumption of the unmanned aerial vehicle during the approach; is a set of approaching unmanned aerial vehicles;

[0030] The constraint condition comprises an approach point safety interval constraint, an unmanned aerial vehicle overpass time and overpass order relationship constraint, a take-off and landing field acceptance rate constraint, an unmanned aerial vehicle required arrival time calculation time constraint and a time slot uniqueness constraint.

[0031] Further, in the constraint condition of the space-time hybrid causal model, the approach point safety interval constraint is a constraint of the regulatory safety time interval that must be maintained by two unmanned aerial vehicles at the same approach point, and the constraint relationship is:

[0032] ;

[0033] ;

[0034] ;

[0035] wherein, the unmanned aerial vehicle m is in front of the unmanned aerial vehicle n, or behind the unmanned aerial vehicle n, if the unmanned aerial vehicle m arrives in front of the unmanned aerial vehicle n, the sequencing decision variable ; if the unmanned aerial vehicle m arrives behind the unmanned aerial vehicle n, the sequencing decision variable ; represents the passing time of the unmanned aerial vehicle n at the approach point r, represents the passing time of the unmanned aerial vehicle m at the approach point r; , , are all sequencing decision variables; represents the safety interval required to be maintained at the critical point;

[0036] The time constraint of the unmanned aerial vehicle with the passing order relationship constraint being the following relationship is that is the passing time of the unmanned aerial vehicle n, is the passing time of the unmanned aerial vehicle m, and the constraint relationship is:

[0037] ;

[0038] In the formula, the passing time of the following unmanned aerial vehicle is greater than the limit of the followed unmanned aerial vehicle; if the unmanned aerial vehicle n follows the unmanned aerial vehicle m, the passing time of the unmanned aerial vehicle n will be greater than the passing time of the unmanned aerial vehicle m; is a maximum number;

[0039] The landing field acceptance rate constraint is that the quantity of the unmanned aerial vehicles in the t period cannot exceed the acceptance rate of the landing field in the period, and the constraint relationship is:

[0040] ;

[0041] In the formula, the sum of the of all the unmanned aerial vehicles in the approach unmanned aerial vehicle set A in the t period is less than the acceptance rate in the period; represents the acceptance rate of the landing field in the t period; is a required arrival time time slot set;

[0042] The required arrival time calculation time constraint of the unmanned aerial vehicle is that the passing time of the unmanned aerial vehicle for calculating the required arrival time is limited by the time slot, and the constraint relationship is:

[0043] ;

[0044] In the formula, the passing time calculated by the UAV for the required arrival time is between the start time and the end time of the time slot; represents the start time of the time slot allocated for the required arrival time;

[0045] The time slot uniqueness constraint is to constrain one approaching UAV into one time slot, and the constraint relationship is:

[0046] ;

[0047] In the formula, one approaching UAV cannot be allocated to multiple time slots, and one time slot can have multiple UAVs.

[0048] Further, the association effect model comprises: decision variables, an objective function and constraint conditions; wherein,

[0049] The decision variables comprise:

[0050] Optimized required arrival time of the approaching UAV ;

[0051] represents whether the optimized required arrival time of the approaching UAV at the key point belongs to the of a time slot t: , otherwise ; only one required arrival time can be allocated to the same UAV at the same key point;

[0052] represents the ordering decision variable of the UAV m and the UAV n at the key point ; if the UAV m is in front of the UAV n, then , otherwise ;

[0053] represents whether there is a flight conflict between the UAV m and the UAV n ;

[0054] Set of flight trajectory sampling points of the UAV m ;

[0055] Set of times at which the UAV m passes the flight trajectory sampling points ;

[0056] Set of flight position coordinates of the UAV m at each flight trajectory sampling point , and the expression is as follows:

[0057] ;

[0058] In the formula, represents the flight position coordinate of the UAV m at the flight trajectory sampling point k at the time t, Let k represent the flight position coordinates of UAV n at time t at flight trajectory sampling point l, where k and l are flight trajectory sampling points respectively; t represents time, and T represents the total set of times t.

[0059] The magnitude of the flight speed adjustment of drone m when resolving a conflict ;

[0060] The speed adjustment amount of the drone (m) is a multiple of the base speed adjustment amount. ;

[0061] The expression for the objective function is:

[0062] ;

[0063] The objective function represents the minimum delay of the approaching UAV within the take-off and landing area after control optimization; the expression of the objective function is the difference between the estimated landing time after optimization and the expected landing time in the flight plan; the delay value includes the delay of the UAV within the sector and the delay on the take-off and landing runway; Key point The optimized arrival time of the incoming drone at the location; Key point The optimized arrival time of the approaching drone at the location. This refers to a key point in the set of key points R during the drone's approach process. This is another key point in the set of key points R during the drone's approach process. The runway is for the take-off and landing of incoming drones. The estimated time of arrival for drone m in the flight plan;

[0064] The constraints include:

[0065] Flow control constraints, collision detection constraints, flight speed constraints, approach point safety interval constraints, UAV transit time and transit sequence constraints, take-off and landing acceptance rate constraints, UAV required arrival time optimization constraints, and time slot uniqueness constraints.

[0066] Furthermore, in the constraints of the aforementioned correlation effect model,

[0067] The flow control constraint requires that the flow control time interval constraint be met between drones affected by flow control at the flow control point. The optimized arrival time of the approaching drone n at key point r. The optimized arrival time of the approaching UAV n at key point r is determined by the following constraint:

[0068] ;

[0069] ;

[0070] In the formula, This indicates that the drone swarm is affected by flow control restrictions; Indicates the flow control time interval limit value;

[0071] The collision detection constraint is that the distance between the locations of two UAVs m and n within the same study time period should be less than the safety interval. The constraint relationship is as follows:

[0072] ;

[0073] In the formula, This indicates whether there is a flight conflict between drone m and drone n. If drone m has a flight conflict with drone n, then... ,otherwise The set of sampling points for the flight trajectory of the UAV m is represented as follows: The time set of sampling points for the flight trajectory of UAV m is represented as: ; The safety interval is represented as the set of sampling points for the flight trajectory of the UAV n. The time set of sampling points of the flight trajectory of UAV n is represented as ;

[0074] The flight speed constraint is the speed constraint that a drone must overcome to resolve a flight conflict. The constraint relationship is as follows:

[0075] ;

[0076] ;

[0077] In the formula, This represents the flight speed of the drone, m. This indicates the drone's flight speed before the speed adjustment. This indicates the magnitude of the flight speed adjustment of the drone m when resolving a conflict; This indicates the drone's flight speed after adjustment; the speed adjustment amount (m) of the drone is the base speed adjustment. multiples .

[0078] Furthermore, the reliability analysis using the correlation effect model in step S2 includes: lateral analysis and longitudinal analysis; wherein, the lateral analysis is to perform reliability analysis on the required arrival time windows of all waypoints of a single wayline, and the longitudinal analysis is to perform reliability analysis on the required arrival time windows of all wayslines on a single waypoint.

[0079] Furthermore, step S3 specifically includes:

[0080] Step S31: Send a required arrival time group, including at least one suggested required arrival time, to the drone operator;

[0081] Step S32: Determine whether the drone operator accepts the required arrival time group. If accepted, proceed to step S33; otherwise, proceed to step S35.

[0082] Step S33: Select a desired arrival time from the required arrival time group as the accepted arrival time, calculate and adjust the flight speed to meet the desired arrival time;

[0083] Step S34: Generate the required arrival time instruction to be executed and send it to the low-altitude flight management and service system; The low-altitude flight management and service system generates an arrival sorting queue based on the four-dimensional flight path according to the required arrival time instruction to complete the allocation of the current UAV time slot;

[0084] Step S35: The Low Altitude Flight Management and Service System sends a new required arrival time group to the UAV operator and returns to step S32 until the UAV operator receives the accepted suggested arrival time and sends an agreed-upon required arrival time instruction to the Low Altitude Flight Management and Service System. The Low Altitude Flight Management and Service System generates an arrival sorting queue based on four-dimensional flight paths according to the agreed-upon required arrival time instruction, and completes the allocation of UAV time slots.

[0085] The beneficial effects of this invention are:

[0086] The method of this invention meets the requirements of future low-altitude air traffic management systems based on predicted flight path operation modes, and is safer, more efficient, and more environmentally friendly than existing UAV arrival sequencing management methods; the main technical effects are as follows:

[0087] 1. By combining flight path prediction data, the expected arrival time window and corresponding time series of UAVs at designated points are generated, which provides a basic time reference for subsequent accurate planning of UAV arrival process, clarifies the time range and specific time series of UAVs arriving at designated nodes, and lays the initial data foundation for arrival sorting.

[0088] 2. Taking into account the time and space constraints of arrival at the port, flow control constraints, and flight conflict detection constraints, the required arrival time group that meets the reliability requirements was calculated, and reasonable time schemes that meet multiple key constraints were effectively screened, ensuring the safety and feasibility of the UAV arrival time planning.

[0089] 3. By judging whether the drone operator accepts the required arrival time group, if accepted, the required arrival time instruction to be executed is generated and an arrival sorting queue based on four-dimensional flight path is constructed. If not accepted, the calculation is recalculated. This respects the actual operational needs of the operator and can ultimately determine the executable time instruction through iterative optimization, ensuring the effectiveness and feasibility of the drone arrival sorting queue generated based on four-dimensional flight path. Attached Figure Description

[0090] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0091] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0092] Reference Figure 1 As shown, the present invention provides a conflict-free UAV time slot allocation method based on flight path operation, which is applied to a low-altitude flight management and service system. The steps are as follows:

[0093] Step S1: Generate the expected arrival time window of the UAV at the specified point and the expected arrival time series within the expected arrival time window based on the trajectory prediction data; specifically including:

[0094] Step S11: The Low Altitude Flight Management and Service System receives EPP (Early Prediction Plan) data transmitted by the UAV through its onboard equipment.

[0095] Step S12: The low-altitude flight management and service system calculates the estimated time of arrival (ETA) at the designated point based on the flight path prediction data, weather data, and UAV flight dynamic data, and obtains the estimated arrival time window.

[0096] The estimated arrival time window is the time period during which the UAV is predicted to pass through a certain waypoint. The start time and the end time represent the fastest and slowest times when the UAV passes through a certain waypoint, respectively.

[0097] Step S13: The low-altitude flight management and service system takes into account flight plan information, UAV flight dynamic data, current interval requirements and overall optimization objectives to generate traffic sequencing, which is the expected arrival time series.

[0098] Step S2: Based on the expected arrival time series of the UAV at the designated point within its predicted arrival time window, considering time and space constraints for arrival, flow control constraints, and flight conflict detection constraints, calculate the required arrival time set that meets reliability requirements; specifically including:

[0099] Based on the time and space constraints of arrival, a spatiotemporal hybrid causal model is used to reorder the expected arrival time series obtained in step S1 to form the initial required arrival time series.

[0100] Based on flow control constraints and flight conflict detection constraints, a correlation effect model is used to conduct reliability analysis on the initial required arrival time series, and the required arrival time group that meets the reliability requirements is calculated.

[0101] Specifically, in the process of forming the initial required arrival time series, based on spatial constraints including take-off and landing area airspace capacity and meteorological conditions, at the fusion point, a spatiotemporal hybrid causal model is established with a safe time interval as the time constraint condition. With the goal of minimizing modification costs and minimizing flight fuel consumption, the expected arrival time series obtained in step S1 is reordered to form the initial required arrival time series.

[0102] Specifically, in the process of calculating the required arrival time group that meets the reliability requirements, based on the constraints of the spatiotemporal hybrid causal model, by adding flow control constraints and flight conflict detection constraints, a correlation effect model is established. With the goal of minimizing delay time, the expected arrival time series obtained in step S1 is reordered to form the required arrival time group that meets the reliability requirements.

[0103] The spatiotemporal hybrid causal model includes: decision variables, objective function, and constraints; wherein,

[0104] Decision variables include:

[0105] The required arrival time of the approaching drone m at the designated point is calculated. ;

[0106] This indicates whether the calculated required arrival time of the approaching drone at the specified point belongs to a certain time slot t. ;

[0107] This represents the decision variable for ranking the preceding and following drones m and n at a specified point. ;

[0108] The expression for the objective function is:

[0109] ;

[0110] in, The cost factor is the cost incurred due to changes in the flight schedule's departure time. The estimated time of arrival for drone m in the flight plan; Fuel consumption during the drone's arrival at the port; For the assembly of incoming drones;

[0111] The constraints include: safety interval constraints at approach points, constraints on the relationship between the time and order of UAV passage points, constraints on the acceptance rate of take-off and landing sites, constraints on the calculation time of the required arrival time of UAVs, and constraints on the uniqueness of time slots.

[0112] Among the constraints of the spatiotemporal hybrid causal model, the approach point safety interval constraint is a constraint on the controlled safety time interval that two UAVs must maintain at the same approach point. The constraint relationship is as follows:

[0113] ;

[0114] ;

[0115] ;

[0116] Where drone m arrives either before or after drone n, and if drone m arrives before drone n, then the ranking decision variable... If drone m arrives after drone n, then the ranking decision variable... ; This represents the transit time of drone n at its approach point r. This represents the transit time of drone m at its approach point r; , , All are ranking decision variables; This indicates the safe distance that needs to be maintained at critical points;

[0117] The time constraint for drones that are tail-like in terms of the relationship between their arrival time and arrival order. Let n be the time elapsed for the drone. Let m be the transit time of the drone. The constraint relationship is as follows:

[0118] ;

[0119] In the formula, the time of the trailing drone to pass the point is greater than the limit of the drone being followed; if drone n follows drone m, then the time of drone n to pass the point will be greater than the time of drone m to pass the point. It is a very large number;

[0120] The takeoff and landing field acceptance rate constraint is that the number of drones in time period t cannot exceed the acceptance rate of the takeoff and landing field in that time period. The constraint relationship is as follows:

[0121] ;

[0122] In the formula, the total number of drones in the drone set A that arrives during time period t. The sum is less than the acceptance rate during that period; This indicates the acceptance rate of the takeoff and landing field during time period t; The set of time slots for the required arrival time;

[0123] The constraint for calculating the required arrival time of the UAV is the constraint that the time of arrival of the UAV is exceeded by the time slot to which it belongs. The constraint relationship is as follows:

[0124] ;

[0125] In the formula, the time elapsed for the arrival time required by the UAV to be calculated is between the start and end times of the corresponding time slot; Indicates the start time of the allocated arrival time slot;

[0126] The time slot uniqueness constraint is used to confine an approaching UAV to a specific time slot. The constraint relationship is as follows:

[0127] ;

[0128] In this formula, an incoming drone cannot be assigned to multiple time slots, but a time slot can have multiple drones.

[0129] The correlation effect model includes: decision variables, objective function, and constraints; wherein,

[0130] Decision variables include:

[0131] Optimized arrival time for approaching drones ;

[0132] This indicates whether the required arrival time of the approaching drone at the key point belongs to a certain time slot t. If it belongs to then ,otherwise The same drone can only be assigned one required arrival time at the same key point.

[0133] This represents the ranking decision variable for the preceding and following drones m and n at key points. If drone m is in front of n, then ,otherwise ;

[0134] This indicates whether there is a flight conflict between drone m and drone n. ;

[0135] UAV flight trajectory sampling point set ;

[0136] Time set of sampling points of the flight trajectory of UAV m ;

[0137] Set of flight position coordinates of UAV m at each flight trajectory sampling point The expression is as follows:

[0138] ;

[0139] In the formula, This represents the flight position coordinates of the UAV m at time t, at the flight trajectory sampling point k. Let k represent the flight position coordinates of UAV n at time t at flight trajectory sampling point l, where k and l are flight trajectory sampling points respectively; t represents time, and T represents the total set of times t.

[0140] The magnitude of the flight speed adjustment of drone m when resolving a conflict ;

[0141] The speed adjustment amount of the drone (m) is a multiple of the base speed adjustment amount. ;

[0142] The expression for the objective function is:

[0143] ;

[0144] The objective function represents the minimum delay of the approaching UAV within the take-off and landing area after control optimization; the expression of the objective function is the difference between the estimated landing time after optimization and the expected landing time in the flight plan; the delay value includes the delay of the UAV within the sector and the delay on the take-off and landing runway; Key point The optimized arrival time of the incoming drone at the location; Key point The optimized arrival time of the approaching drone at the location. This refers to a key point in the set of key points R during the drone's approach process. This is another key point in the set of key points R during the drone's approach process. The runway is for the take-off and landing of incoming drones. The estimated time of arrival for drone m in the flight plan;

[0145] The constraints include:

[0146] Flow control constraints, collision detection constraints, flight speed constraints, approach point safety interval constraints, UAV transit time and transit sequence constraints, take-off and landing acceptance rate constraints, UAV required arrival time optimization constraints, and time slot uniqueness constraints.

[0147] Among the constraints of the correlation effect model,

[0148] The flow control constraint requires that the flow control time interval constraint be met between drones affected by flow control at the flow control point. The optimized arrival time of the approaching drone n at key point r. The optimized arrival time of the approaching UAV n at key point r is determined by the following constraint:

[0149] ;

[0150] ;

[0151] In the formula, This indicates that the drone swarm is affected by flow control restrictions; Indicates the flow control time interval limit value;

[0152] The collision detection constraint is that the distance between the locations of two UAVs m and n within the same study time period should be less than the safety interval. The constraint relationship is as follows:

[0153] ;

[0154] In the formula, This indicates whether there is a flight conflict between drone m and drone n. If drone m has a flight conflict with drone n, then... ,otherwise The set of sampling points for the flight trajectory of the UAV m is represented as follows: The time set of sampling points for the flight trajectory of UAV m is represented as: ; The safety interval is represented as the set of sampling points for the flight trajectory of the UAV n. The time set of sampling points of the flight trajectory of UAV n is represented as ;

[0155] The flight speed constraint is the speed constraint that a drone must overcome to resolve a flight conflict. The constraint relationship is as follows:

[0156] ;

[0157] ;

[0158] In the formula, This represents the flight speed of the drone, m. This indicates the drone's flight speed before the speed adjustment. This indicates the magnitude of the flight speed adjustment of the drone m when resolving a conflict; This indicates the drone's flight speed after adjustment; the speed adjustment amount (m) of the drone is the base speed adjustment. multiples .

[0159] Specifically, the reliability analysis using the correlation effect model in step S2 includes: lateral analysis and longitudinal analysis; wherein, the lateral analysis is to perform reliability analysis on the required arrival time windows of all waypoints of a single wayline, and the longitudinal analysis is to perform reliability analysis on the required arrival time windows of all wayslines on a single waypoint.

[0160] Step S3: Determine whether the drone operator accepts the required arrival time group; if accepted, generate an agreed-upon required arrival time instruction based on the accepted suggested arrival time to generate an arrival sorting queue based on the four-dimensional flight path; if not accepted, return to step S2 until an agreed-upon required arrival time instruction is obtained; specifically including:

[0161] Step S31: Send a required arrival time group, including at least one suggested required arrival time, to the drone operator;

[0162] Step S32: Determine whether the drone operator accepts the required arrival time group. If accepted, proceed to step S33; otherwise, proceed to step S35.

[0163] Step S33: Select a desired arrival time from the required arrival time group as the accepted arrival time, calculate and adjust the flight speed to meet the desired arrival time;

[0164] Step S34: Generate the required arrival time instruction to be executed and send it to the low-altitude flight management and service system; The low-altitude flight management and service system generates an arrival sorting queue based on the four-dimensional flight path according to the required arrival time instruction to complete the allocation of the current UAV time slot;

[0165] Step S35: The Low Altitude Flight Management and Service System sends a new required arrival time group to the UAV operator and returns to step S32 until the UAV operator receives the accepted suggested arrival time and sends an agreed-upon required arrival time instruction to the Low Altitude Flight Management and Service System. The Low Altitude Flight Management and Service System generates an arrival sorting queue based on four-dimensional flight paths according to the agreed-upon required arrival time instruction, and completes the allocation of UAV time slots.

[0166] This invention has many specific applications. The above description is only a preferred embodiment of this invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of this invention, and these improvements should also be considered within the scope of protection of this invention.

Claims

1. A conflict-free UAV time slot allocation method based on flight path operation, applied to a low-altitude flight management and service system, characterized in that, The steps are as follows: Step S1: Generate the expected arrival time window of the UAV at the specified point and the expected arrival time series within the expected arrival time window based on the trajectory prediction data; Step S2: Based on the expected arrival time series of the UAV at the designated point, considering the time and space constraints of arrival, flow control constraints and flight conflict detection constraints, calculate the required arrival time group that meets the reliability requirements. Step S3: Determine whether the drone operator accepts the required arrival time group; if accepted, generate the required arrival time instruction to be executed according to the accepted suggested arrival time, so as to generate an arrival sorting queue based on the four-dimensional track; if not accepted, return to step S2 until the required arrival time instruction to be executed is obtained.

2. The collision-free UAV time slot allocation method based on flight path operation according to claim 1, characterized in that, Step S1 specifically includes: Step S11: The low-altitude flight management and service system receives trajectory prediction data sent by the UAV through its onboard equipment; Step S12: The low-altitude flight management and service system calculates the estimated time of arrival (ETA) at the designated point based on the flight path prediction data, weather data, and UAV flight dynamic data, and obtains the estimated time of arrival window; Step S13: The low-altitude flight management and service system takes into account flight plan information, UAV flight dynamic data, current interval requirements and overall optimization objectives to generate traffic sequencing, which is the expected arrival time series.

3. The collision-free UAV time slot allocation method based on flight path operation according to claim 1, characterized in that, Step S2 specifically includes: Based on the time and space constraints of arrival, a spatiotemporal hybrid causal model is used to reorder the expected arrival time series obtained in step S1 to form the initial required arrival time series. Based on flow control constraints and flight conflict detection constraints, a correlation effect model is used to conduct reliability analysis on the initial required arrival time series, and the required arrival time group that meets the reliability requirements is calculated.

4. The collision-free UAV time slot allocation method based on flight path operation according to claim 3, characterized in that, In the process of forming the initial required arrival time series, based on spatial constraints including take-off and landing area airspace capacity and meteorological conditions, at the fusion point, a spatiotemporal hybrid causal model is established with a safe time interval as the time constraint condition. With the goal of minimizing modification costs and minimizing flight fuel consumption, the expected arrival time series obtained in step S1 is reordered to form the initial required arrival time series.

5. The collision-free UAV time slot allocation method based on flight path operation according to claim 3, characterized in that, In the process of calculating the required arrival time group that meets the reliability requirements, based on the constraints of the spatiotemporal hybrid causal model, by adding flow control constraints and flight conflict detection constraints, a correlation effect model is established. With the goal of minimizing delay time, the expected arrival time series obtained in step S1 is reordered to form the required arrival time group that meets the reliability requirements.

6. The collision-free UAV time slot allocation method based on flight path operation according to claim 3, characterized in that, The spatiotemporal hybrid causal model includes: decision variables, objective function, and constraints; wherein... Decision variables include: The required arrival time of the approaching drone m at the designated point is calculated. ; This indicates whether the calculated required arrival time of the approaching drone at the specified point belongs to a certain time slot t. ; This represents the decision variable for ranking the preceding and following drones m and n at a specified point. ; The expression for the objective function is: ； in, The cost factor is the cost incurred due to changes in the flight schedule's departure time. The estimated time of arrival for drone m in the flight plan; Fuel consumption during the drone's arrival at the port; For the assembly of incoming drones; The constraints include: safety interval constraints at approach points, constraints on the relationship between the time and order of UAV passage points, constraints on the acceptance rate of take-off and landing sites, constraints on the calculation time of the required arrival time of UAVs, and constraints on the uniqueness of time slots.

7. The collision-free UAV time slot allocation method based on flight path operation according to claim 6, characterized in that, In the constraints of the spatiotemporal hybrid causal model, the approach point safety interval constraint is the control safety time interval that two UAVs must maintain at the same approach point. The constraint relationship is as follows: ； ； ； Where drone m arrives either before or after drone n, and if drone m arrives before drone n, then the ranking decision variable... If drone m arrives after drone n, then the ranking decision variable... ; This represents the transit time of drone n at its approach point r. This represents the transit time of drone m at its approach point r; , , All are ranking decision variables; This indicates the safe distance that needs to be maintained at critical points; The time constraint for drones that are tail-like in terms of the relationship between their arrival time and arrival order. Let n be the time elapsed for the drone. Let m be the transit time of the drone. The constraint relationship is as follows: ； In the formula, the time of the trailing drone to pass the point is greater than the limit of the drone being followed; if drone n follows drone m, then the time of drone n to pass the point will be greater than the time of drone m to pass the point. It is a very large number; The takeoff and landing field acceptance rate constraint is that the number of drones in time period t cannot exceed the acceptance rate of the takeoff and landing field in that time period. The constraint relationship is as follows: ； In the formula, the total number of drones in the drone set A that arrives during time period t. The sum is less than the acceptance rate during that period; This indicates the acceptance rate of the takeoff and landing field during time period t; The set of time slots for the required arrival time; The constraint for calculating the required arrival time of the UAV is the constraint that the time of arrival of the UAV is exceeded by the time slot to which it belongs. The constraint relationship is as follows: ； In the formula, the time elapsed for the arrival time required by the UAV to be calculated is between the start and end times of the corresponding time slot; Indicates the start time of the allocated arrival time slot; The time slot uniqueness constraint is used to confine an approaching UAV to a specific time slot. The constraint relationship is as follows: ； In this formula, an incoming drone cannot be assigned to multiple time slots, but a time slot can have multiple drones.

8. The collision-free UAV time slot allocation method based on flight path operation according to claim 7, characterized in that, The correlation effect model includes: decision variables, objective function, and constraints; wherein... Decision variables include: Optimized arrival time for approaching drones ; This indicates whether the required arrival time of the approaching drone at the key point belongs to a certain time slot t. If it belongs to then ,otherwise The same drone can only be assigned one required arrival time at the same key point. This represents the ranking decision variable for the preceding and following drones m and n at key points. If drone m is in front of n, then ,otherwise ; This indicates whether there is a flight conflict between drone m and drone n. ; UAV flight trajectory sampling point set ; Time set of sampling points of the flight trajectory of UAV m ; Set of flight position coordinates of UAV m at each flight trajectory sampling point The expression is as follows: ； In the formula, This represents the flight position coordinates of the UAV m at time t, at the flight trajectory sampling point k. Let k represent the flight position coordinates of UAV n at time t at flight trajectory sampling point l, where k and l are flight trajectory sampling points respectively; t represents time, and T represents the total set of times t. The magnitude of the flight speed adjustment of drone m when resolving a conflict ; The speed adjustment amount of the drone (m) is a multiple of the base speed adjustment amount. ; The expression for the objective function is: ； The objective function represents the minimum delay of the approaching UAV within the take-off and landing area after control optimization; the expression of the objective function is the difference between the estimated landing time after optimization and the expected landing time in the flight plan; the delay value includes the delay of the UAV within the sector and the delay on the take-off and landing runway; Key point The optimized arrival time of the incoming drone at the location; Key point The optimized arrival time of the approaching drone at the location. This refers to a key point in the set of key points R during the drone's approach process. This is another key point in the set of key points R during the drone's approach process. The runway is for the take-off and landing of incoming drones. The estimated time of arrival for drone m in the flight plan; The constraints include: Flow control constraints, collision detection constraints, flight speed constraints, approach point safety interval constraints, UAV transit time and transit sequence constraints, take-off and landing acceptance rate constraints, UAV required arrival time optimization constraints, and time slot uniqueness constraints.

9. The collision-free UAV time slot allocation method based on flight path operation according to claim 8, characterized in that, Among the constraints of the correlation effect model, The flow control constraint requires that the flow control time interval constraint be met between drones affected by flow control at the flow control point. The optimized arrival time of the approaching drone n at key point r. The optimized arrival time of the approaching UAV n at key point r is determined by the following constraint: ； ； In the formula, This indicates that the drone swarm is affected by flow control restrictions; Indicates the flow control time interval limit value; The collision detection constraint is that the distance between the locations of two UAVs m and n within the same study time period should be less than the safety interval. The constraint relationship is as follows: ； In the formula, This indicates whether there is a flight conflict between drone m and drone n. If drone m has a flight conflict with drone n, then... ,otherwise ; The set of sampling points for the flight trajectory of UAV m is represented as ; The time set of sampling points of the flight trajectory of the UAV m is represented as: ; The safety interval is represented as the set of sampling points for the flight trajectory of the UAV n. The time set of sampling points of the flight trajectory of UAV n is represented as ; The flight speed constraint is the speed constraint that a drone must overcome to resolve a flight conflict. The constraint relationship is as follows: ； ； In the formula, This represents the flight speed of the drone, m. This indicates the drone's flight speed before the speed adjustment. This indicates the magnitude of the flight speed adjustment of the drone m when resolving a conflict; This indicates the drone's flight speed after adjustment; the speed adjustment amount (m) of the drone is the base speed adjustment. multiples .

10. The collision-free UAV time slot allocation method based on flight path operation according to claim 1, characterized in that, Step S3 specifically includes: Step S31: Send a required arrival time group, including at least one suggested required arrival time, to the drone operator; Step S32: Determine whether the drone operator accepts the required arrival time group. If accepted, proceed to step S33; otherwise, proceed to step S35. Step S33: Select a desired arrival time from the required arrival time group as the accepted arrival time, calculate and adjust the flight speed to meet the desired arrival time; Step S34: Generate the required arrival time instruction to be executed and send it to the low-altitude flight management and service system; The low-altitude flight management and service system generates an arrival sorting queue based on the four-dimensional flight path according to the required arrival time instruction to complete the allocation of the current UAV time slot; Step S35: The Low Altitude Flight Management and Service System sends a new required arrival time group to the UAV operator and returns to step S32 until the UAV operator receives the accepted suggested arrival time and sends an agreed-upon required arrival time instruction to the Low Altitude Flight Management and Service System. The Low Altitude Flight Management and Service System generates an arrival sorting queue based on four-dimensional flight paths according to the agreed-upon required arrival time instruction, and completes the allocation of UAV time slots.

Images