A method for airspace conflict detection and resolution based on convex optimization theory

Abstract

The application provides a method for airspace conflict detection and resolution based on convex optimization theory, and belongs to the technical field of airspace management and control. In terms of airspace conflict detection, the method establishes a convex set representation of the horizontal projection of the airspace from the perspective of convex optimization, and calculates the horizontal interval between different airspaces by solving the minimum interval of two convex sets, so as to solve the problems that the existing algorithm model is complex, the calculation steps are cumbersome, and in some cases, the accurate solution cannot be calculated. In the development of the airspace conflict resolution scheme, the method proposes linear weighting on multiple indexes describing the airspace adjustment amplitude extracted from different angles, and finally obtains a comprehensive measurement index. The weight can be flexibly adjusted according to actual requirements, so as to solve the problems that the existing method has poor flexibility and universality, and meet different requirements of users.

Description

Technical Field

[0001] This invention belongs to the field of airspace management technology, and in particular relates to a method for airspace conflict detection and resolution based on convex optimization theory. Background Technology

[0002] Currently, unmanned aerial vehicles (UAVs), as a high-tech product driving change across multiple industries, are increasingly attracting the attention of researchers. In the military field, UAVs can perform reconnaissance, surveillance, and strike missions; in civilian fields such as agriculture, geographic surveying, scientific research, and public administration, UAVs are also gaining increasing attention due to their advantages such as economy, convenience, and adaptability to harsh environments. At present, single aircraft are mainly used to perform missions in relatively simple environments. With the continuous advancement of UAV usage concepts, future applications of UAVs will no longer be limited to single UAVs performing missions in relatively large spaces. The use of multiple UAVs in the same mission area to perform complex missions will become an important pattern in the future military and civilian applications of UAVs. How to efficiently detect and resolve conflicts related to large-scale airspace demands based on mission plans and airspace situation, ensuring that airspace users can use limited airspace efficiently, safely, and flexibly, and avoiding conflicts and friendly fire, is an urgent problem that needs to be solved in the future.

[0003] The challenge in spatial conflict detection lies in determining the horizontal distance between horizontal projections of the spatial domain. Existing algorithms, such as the GJK algorithm and the EPA algorithm, are iterative methods with complex mathematical models and low computational efficiency. This is especially true for simple shapes like line segments, where distance calculations are cumbersome and, in some cases, cannot yield accurate solutions. Therefore, there is an urgent need to design a computationally fast algorithm with moderate model complexity to assist in spatial conflict detection.

[0004] Current methods for resolving airspace conflicts tend to consider the cost of conflict resolution from a single perspective, lacking a comprehensive metric to measure the costs of various aspects when changes to the solution are necessary. However, different users have different priorities regarding cost, which means that existing airspace conflict resolution methods cannot meet their diverse needs. Therefore, existing methods lack sufficient flexibility and versatility. Thus, there is an urgent need to design an airspace conflict resolution method that can flexibly adjust expectations to adapt to the requirements of different users.

[0005] For determining the horizontal distance between horizontal projections in a spatial domain, the existing technology is the GJK algorithm. The GJK (Gilbert-Johnson-Keerth) algorithm is a distance-tracking-based collision detection algorithm used to calculate the distance between two convex bodies in space. The basic process is as follows (e.g.) Figure 1 (as shown)

[0006] 1. Initialization: Select two convex polygons and determine an initial point as the boundary point of the Minkowski difference set;

[0007] 2. Calculate the Minkowski sum: Decompose the edges of the two convex polygons into vectors in a counter-clockwise (or clockwise) direction, then sort these vectors by their polar angles, and finally connect them end-to-end to construct the convex hull. This convex hull is the Minkowski sum of the two convex polygons.

[0008] 3. Expanding the Minkowski difference: Choose a direction to expand the Minkowski difference, calculate the new boundary points, and add them to the boundary point set;

[0009] 4. Judgment: Check if the new boundary point is the origin. If it is, it means the two convex polygons intersect, and the algorithm ends; otherwise, continue iterating.

[0010] 5. Update: Update the boundaries of the Minkowski difference set based on the new boundary points;

[0011] 6. Repeat: Repeat steps 3 to 5 until the maximum number of iterations is reached.

[0012] It's important to note that the GJK algorithm doesn't directly calculate the specific value of the minimum distance. Instead, it determines whether the two convex polygons intersect. If the two convex polygons intersect, their minimum distance is 0; if they don't intersect, their minimum distance is the minimum distance on the boundary of the Minkowski difference set.

[0013] The challenge of spatial conflict detection lies in determining the horizontal distance between horizontal projections in the spatial domain. Existing algorithms, such as the GJK algorithm and the EPA algorithm, are based on iterative methods with complex mathematical models and low computational efficiency. This is especially true for simple shapes like line segments, where distance calculations are cumbersome and, in some cases, cannot yield accurate solutions.

[0014] Current methods tend to consider the cost of conflict resolution from a single perspective, lacking a comprehensive metric to measure the costs of various aspects when changes in the solution are necessary due to airspace conflict resolution. However, different users have different concerns about costs, which means that the solutions provided by existing airspace conflict resolution methods cannot meet the diverse needs of users. Therefore, existing airspace conflict resolution methods lack sufficient flexibility and versatility. Summary of the Invention

[0015] To address the aforementioned technical problems, this invention proposes a spatial conflict detection and resolution scheme based on convex optimization theory, taking into account practical needs and applications.

[0016] The first aspect of this invention proposes a spatial conflict detection and resolution method based on convex optimization theory. The method includes:

[0017] Step S1: Collect basic information for airspace application;

[0018] Step S2: Detect conflicts in the space domain application based on convex optimization theory;

[0019] Step S3: Determine the priority of airspace conflict resolution principles;

[0020] Step S4: Determine the method for resolving airspace conflicts;

[0021] Step S5: Establish comprehensive indicators for evaluating airspace conflict resolution schemes;

[0022] Step S6: Determine the order of airspace conflict resolution;

[0023] Step S7: Solve for the optimal airspace conflict resolution scheme based on the greedy algorithm and resolution strategy.

[0024] According to the method of the first aspect of the present invention, in step S1, the basic information of the airspace application includes time domain information, altitude domain information, airspace horizontal projection information, airspace aircraft information, and airspace group information. The time domain information includes the lower limit and upper limit of the time interval, the altitude domain information includes the lower limit and upper limit of the altitude information, the airspace horizontal projection information includes the shape profile of the projection and the coordinates of the turning points on the projection profile, and the airspace aircraft information includes the trajectory of the aircraft and the speed of the aircraft.

[0025] According to the method of the first aspect of the present invention, in step S2, conflict detection of airspace applications specifically includes:

[0026] Detect airspace altitude range conflicts and time range conflicts; among which:

[0027] If the two do not conflict simultaneously, then the airspace does not constitute a conflict pair;

[0028] If both conflict simultaneously, solve the boundary equations of the spatial projection, establish the convex set representation of the spatial projection, solve for the minimum distance spacing of the convex set, and check whether the horizontal distance conflict is satisfied; where:

[0029] If the horizontal distance conflict is not satisfied, then the airspace does not constitute a conflict pair;

[0030] If a horizontal distance conflict is satisfied, then calculate the spatial coordinates at any time within the time intersection, and further check whether the time interval is greater than the safe distance; where:

[0031] If the distance is greater than the safe distance, then the airspace does not constitute a conflict pair;

[0032] If the distance is not greater than the safe distance, then the airspace constitutes a conflict pair.

[0033] According to the method of the first aspect of the present invention, in step S3, the priority of the airspace conflict resolution principles is as follows: ;in: Indicates: The number of rejected airspace domains is small; This means: prioritize adjusting the parameters of low-priority airspace while keeping high-priority airspace unchanged; This indicates that the airspace adjustment range shall not exceed the maximum allowable range, and the time range for airspace adjustment shall be avoided; It indicates that when resolving airspace conflicts, priority should be given to adjusting altitude and horizontal distance, and adjustments to the airspace time range should be avoided.

[0034] According to the method of the first aspect of the present invention, in step S4, determining the method for resolving airspace conflicts specifically includes:

[0035] Select a low-priority airspace for airspace parameter adjustment, and determine whether the conflict has been resolved; where:

[0036] If the conflict is resolved, the process ends.

[0037] If the conflict is not resolved, a higher priority airspace is selected for airspace parameter adjustment, and then the conflict is re-evaluated; where:

[0038] If the conflict is resolved, the process ends.

[0039] If the conflict is not resolved, the low-priority airspace application will be rejected.

[0040] According to the method of the first aspect of the present invention, in step S5, establishing a comprehensive index for evaluating airspace conflict resolution schemes specifically includes:

[0041] Adjusting the total amount using time , Adjust the total amount of height Total adjustment of horizontal parameters Total number of airspace applications rejected A total of four indicators were linearly weighted to measure the overall adjustment range of airspace. , Right now:

[0042]

[0043] in, For indicator weight vectors; The smaller the size, the smaller the overall adjustment range of the airspace;

[0044] When calculating each sub-index, a weight vector is constructed based on the differences in spatial importance at different priorities. , No. The weight of the priority spatial domain is Then the priority weight vector for:

[0045]

[0046] in, The number of priority zones in the airspace. If it is any one of the above four indicators, then The calculation method is as follows:

[0047]

[0048] All four indicators mentioned above are calculated in this manner;

[0049] According to the method of the first aspect of the present invention, in step S6, determining the order of airspace conflict resolution specifically includes:

[0050] Calculate the airspace altitude adjustment range and airspace horizontal adjustment range, and determine whether the conflict has been resolved and the adjustment constraints are met; where:

[0051] If so, then choose the method that minimizes the amount of money involved;

[0052] If not, calculate the time adjustment range to further determine whether the inflow has been resolved and meets the adjustment limits; where:

[0053] If so, then choose the method of adjusting the time;

[0054] If not, the conflict cannot be resolved.

[0055] According to the method of the first aspect of the present invention, in step S7, solving for the optimal airspace conflict resolution scheme specifically includes:

[0056] The dataset is preprocessed by grouping spatial domains with the same priority into a set of spatial domains, and then arranging them in descending order of spatial domain priority.

[0057] Select an unoptimized set of airspaces based on airspace priority, and calculate all possible sequences for resolving airspace conflicts within the set.

[0058] Multiple conflict resolution schemes are generated based on the conflict detection algorithm and the airspace conflict resolution method;

[0059] Calculate the evaluation indicators for different schemes and select the scheme with the smallest overall adjustment range;

[0060] Determine whether each spatial domain set has been optimized; where:

[0061] If not, then the data should be preprocessed again;

[0062] If so, then obtain the optimal solution.

[0063] A second aspect of this invention proposes a spatial conflict detection and resolution system based on convex optimization theory. The system includes a processing unit configured to perform the following steps:

[0064] Step S1: Collect basic information for airspace application;

[0065] Step S2: Detect conflicts in the space domain application based on convex optimization theory;

[0066] Step S3: Determine the priority of airspace conflict resolution principles;

[0067] Step S4: Determine the method for resolving airspace conflicts;

[0068] Step S5: Establish comprehensive indicators for evaluating airspace conflict resolution schemes;

[0069] Step S6: Determine the order of airspace conflict resolution;

[0070] Step S7: Solve for the optimal airspace conflict resolution scheme based on the greedy algorithm and resolution strategy.

[0071] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the spatial conflict detection and resolution method based on convex optimization theory disclosed herein.

[0072] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a spatial conflict detection and resolution method based on convex optimization theory disclosed herein.

[0073] In summary, regarding airspace conflict detection, the technical solution of this invention establishes a convex set representation of the horizontal projection of the airspace from the perspective of convex optimization. It calculates the horizontal interval between different airspaces by solving for the minimum distance between two convex sets, thus addressing the problems of complex existing algorithm models, cumbersome calculation steps, and the inability to calculate accurate solutions in some cases. In formulating airspace conflict resolution schemes, the technical solution of this invention proposes linearly weighting multiple indicators describing the airspace adjustment magnitude extracted from different perspectives to obtain a comprehensive metric. The weights can be flexibly adjusted according to actual needs, thus solving the problems of poor flexibility and versatility of existing methods and meeting different user requirements. Using this metric as the target, and combining the heuristic strategies formulated in the above modeling process, a step-by-step optimization strategy can be adopted, assisted by a greedy algorithm, to ultimately formulate the optimal scheme for airspace conflict resolution. Attached Figure Description

[0074] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0075] Figure 1 This is a flowchart illustrating the calculation of the spatial distance between two convex bodies based on the GJK algorithm in the prior art.

[0076] Figure 2 This is a flowchart of a spatial conflict detection and resolution method based on convex optimization theory according to an embodiment of the present invention;

[0077] Figure 3 A diagram showing basic information about airspace applications according to an embodiment of the present invention;

[0078] Figure 4 This is a flowchart of the airspace conflict detection process according to an embodiment of the present invention;

[0079] Figure 5 This is a flowchart of an airspace conflict resolution method according to an embodiment of the present invention;

[0080] Figure 6 This is a flowchart illustrating the spatial parameter adjustment process according to an embodiment of the present invention.

[0081] Figure 7 A flowchart of a greedy algorithm for finding the optimal solution according to an embodiment of the present invention;

[0082] Figure 8 This is an example of basic information for airspace application in the first embodiment of the present invention;

[0083] Figure 9 This is a schematic diagram of the spatial time interval positional relationship in the first embodiment of the present invention;

[0084] Figure 10 This is a schematic diagram of the horizontal projection of the airspace in the first embodiment of the present invention;

[0085] Figure 11 This is a schematic diagram of the circumscribed quadrilateral in the first embodiment of the present invention;

[0086] Figure 12 This is a schematic diagram of a pentagonal projection in the first embodiment of the present invention;

[0087] Figure 13 This is a schematic diagram of the normal vectors inside and outside the pentagonal boundary line in the first embodiment of the present invention;

[0088] Figure 14This is a diagram showing the numerical variation of a straight line along the normal direction in the first embodiment of the present invention;

[0089] Figure 15 This is a schematic diagram of the minimum spacing of the convex set in the first embodiment of the present invention;

[0090] Figure 16 This is a schematic diagram of the runway-shaped projection decomposition in the first embodiment of the present invention;

[0091] Figure 17 This is a diagram showing the horizontal projection relative positions of airspace conflict pairs in the first embodiment of the present invention.

[0092] Figures 18a-18b This is a three-dimensional spatial diagram of the conflict pair in the first embodiment of the present invention;

[0093] Figure 19 This is a real-time trajectory projection diagram of the collision pair in the first embodiment of the present invention;

[0094] Figure 20 This is a schematic diagram of horizontal distance conflict resolution in the first embodiment of the present invention;

[0095] Figure 21 This is a schematic diagram of airspace conflict pairs in the first embodiment of the present invention;

[0096] Figure 22 As described in the first embodiment of the present invention A graph showing how the value changes with the number of optimization iterations;

[0097] Figures 23a-23b This is a comparison before and after conflict resolution in the first embodiment of the present invention;

[0098] Figure 24 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0099] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0100] Definitions of abbreviations and key terms (the full English names and Chinese translations of the English abbreviations mentioned below, or detailed explanations of the Chinese technical terms, are provided here):

[0101] GJK (Gilbert-Johnson-Keerthi): An iterative algorithm for collision detection, developed by Gilbert, Johnson, and Keerthi in 1988. It is primarily used to detect whether a collision occurs between two convex objects and to calculate the shortest distance between them.

[0102] EPA (Epanding Polytop Algorithm) is an algorithm for calculating the penetration depth and direction of a collision between two polygons, which can separate the two colliding polygons. Its basic principle is to calculate the distance from the origin to the edge of the nearest Minkowski difference polygon; this distance is the penetration depth, and the perpendicular vector from the origin to that edge is the direction of the penetration vector. This algorithm is very useful in collision detection, helping to determine the severity of the collision and how to handle it.

[0103] As mentioned earlier, airspace conflict detection refers to detecting and identifying potential airspace conflicts within a set of airspace request applications. For two airspace requests, determining whether they conflict generally requires the following three conditions to be met simultaneously:

[0104] (1) Altitude: The airspace altitude ranges overlap or the distance between altitude ranges is less than the altitude safety interval;

[0105] (2) Horizontal distance: The horizontal projections of the airspace coincide or the distance between the horizontal projections is less than the horizontal safety interval;

[0106] (3) Time: The time intervals of the airspace overlap or the distance between the time intervals is less than the safe time interval.

[0107] Airspace conflict resolution involves resolving conflicts by altering parameters such as altitude, horizontal separation, and time between two conflicting airspaces (i.e., the "conflicting pair"). Unresolved airspace conflicts can result in the rejection of one party's airspace request. Airspace conflict resolution generally considers the following four criteria:

[0108] Principle 1: Minimize the number of airspace rejections;

[0109] Principle 2: Prioritize adjusting parameters for low-priority airspaces, and try to keep high-priority airspaces unchanged;

[0110] Principle 3: There are limitations on the range of airspace adjustments, which cannot exceed the maximum allowable range. The number and range of airspace adjustments should be minimized as much as possible.

[0111] Principle 4: When resolving airspace conflicts, prioritize adjusting altitude and horizontal distance, and strive to avoid adjusting the time range of airspace.

[0112] Airspace conflict resolution should also consider the strong temporal correlation between different airspaces ("airspace groups"), and the relative temporal relationships between the airspaces in the airspace group should not be changed during resolution. At the same time, if the speed and trajectory of the aircraft are taken into account, the conflict pair may no longer exist, thereby increasing the scope and time of airspace use, i.e., space reuse.

[0113] In response, this invention proposes a spatial conflict detection and resolution scheme based on convex optimization theory, taking into account practical needs and applications.

[0114] The first aspect of this invention proposes a spatial conflict detection and resolution method based on convex optimization theory. The method includes (e.g.) Figure 2 (as shown)

[0115] Step S1: Collect basic information for airspace application;

[0116] Step S2: Detect conflicts in the space domain application based on convex optimization theory;

[0117] Step S3: Determine the priority of airspace conflict resolution principles;

[0118] Step S4: Determine the method for resolving airspace conflicts;

[0119] Step S5: Establish comprehensive indicators for evaluating airspace conflict resolution schemes;

[0120] Step S6: Determine the order of airspace conflict resolution;

[0121] Step S7: Solve for the optimal airspace conflict resolution scheme based on the greedy algorithm and resolution strategy.

[0122] According to the method of the first aspect of the present invention, in step S1, the basic information of the airspace application includes (e.g.) Figure 3 As shown): time domain information, altitude domain information, airspace horizontal projection information, airspace aircraft information, and airspace group information. The time domain information includes the lower and upper limits of the time interval. The altitude domain information includes the lower and upper limits of the altitude information. The airspace horizontal projection information includes the shape profile of the projection and the coordinates of the turning points on the projection profile. The airspace aircraft information includes the trajectory and speed of the aircraft.

[0123] According to the method of the first aspect of the present invention, in step S2, conflict detection of airspace requests specifically includes (e.g.) Figure 4 (as shown)

[0124] Detect airspace altitude range conflicts and time range conflicts; among which:

[0125] If the two do not conflict simultaneously, then the airspace does not constitute a conflict pair;

[0126] If both conflict simultaneously, solve the boundary equations of the spatial projection, establish the convex set representation of the spatial projection, solve for the minimum distance spacing of the convex set, and check whether the horizontal distance conflict is satisfied; where:

[0127] If the horizontal distance conflict is not satisfied, then the airspace does not constitute a conflict pair;

[0128] If a horizontal distance conflict is satisfied, then calculate the spatial coordinates at any time within the time intersection, and further check whether the time interval is greater than the safe distance; where:

[0129] If the distance is greater than the safe distance, then the airspace does not constitute a conflict pair;

[0130] If the distance is not greater than the safe distance, then the airspace constitutes a conflict pair.

[0131] In some embodiments, firstly, without considering the aircraft's trajectory and speed, based on the upper and lower bounds of the airspace application altitude and time parameter value intervals, it is detected whether two airspaces simultaneously satisfy the altitude and time conditions for a conflict to occur. If so, the horizontal distance between the airspaces is detected. For the detection of the horizontal distance, the boundary equation of the projection is solved, and then a convex inequality description of the projection is established, transforming the projection into a convex set of points. The horizontal interval between different airspaces is calculated by solving for the minimum distance between points within the two convex sets. If the constraint condition of the horizontal distance is satisfied, it can be determined that the two airspaces constitute a conflict pair. Secondly, the aircraft's trajectory and speed are considered. In the already formed conflict pair, the horizontal coordinates within the time intersection of the two trajectories are calculated based on the aircraft's trajectory and speed, and the horizontal distance is calculated to further determine whether the conflict is established.

[0132] According to the method of the first aspect of the present invention, in step S3, the priority of the airspace conflict resolution principles is as follows: ;in: Indicates: The number of rejected airspace domains is small; This means: prioritize adjusting the parameters of low-priority airspace while keeping high-priority airspace unchanged; This indicates that the airspace adjustment range shall not exceed the maximum allowable range, and the time range for airspace adjustment shall be avoided; It indicates that when resolving airspace conflicts, priority should be given to adjusting altitude and horizontal distance, and adjustments to the airspace time range should be avoided.

[0133] In some embodiments, airspace conflict resolution generally considers the following four criteria:

[0134] Principle 1: Minimize the number of airspace rejections;

[0135] Principle 2: Prioritize adjusting parameters for low-priority airspaces, and try to keep high-priority airspaces unchanged;

[0136] Principle 3: There are limitations on the range of airspace adjustments, which cannot exceed the maximum allowable range. The number and range of airspace adjustments should be minimized as much as possible.

[0137] Principle 4: When resolving airspace conflicts, prioritize adjusting altitude and horizontal distance, and strive to avoid adjusting the time range of airspace.

[0138] The four principles can conflict under certain circumstances. To resolve these conflicts, different priorities for airspace conflict resolution principles should be established based on the different needs of users. Here, based on general usage requirements, let the first principle be... The priority of each principle is Therefore, the priority of the airspace conflict resolution principles is established as follows:

[0139]

[0140] According to the method of the first aspect of the present invention, in step S4, determining the method for resolving airspace conflicts specifically includes (e.g.) Figure 5 (as shown)

[0141] Select a low-priority airspace for airspace parameter adjustment, and determine whether the conflict has been resolved; where:

[0142] If the conflict is resolved, the process ends.

[0143] If the conflict is not resolved, a higher priority airspace is selected for airspace parameter adjustment, and then the conflict is re-evaluated; where:

[0144] If the conflict is resolved, the process ends.

[0145] If the conflict is not resolved, the low-priority airspace application will be rejected.

[0146] In some embodiments, airspace conflict resolution can be achieved through two methods: adjustment of airspace parameters (time, altitude, horizontal projection) and rejection of airspace applications. Based on conflict resolution principles and actual circumstances, the method with the least airspace adjustment is selected for airspace resolution, and these methods are integrated into a comprehensive judgment strategy, which constitutes the airspace conflict resolution approach.

[0147] First, formulate a strategy for selecting conflict resolution methods. According to the formula... Principle 1 has the highest priority and should minimize the number of airspace rejections; therefore, airspace parameter adjustments should be prioritized.

[0148] Secondly, an airspace parameter adjustment strategy should be formulated. Adjustable airspace parameters include time, altitude, and horizontal distance. According to principle four, altitude and horizontal distance should be adjusted first to resolve conflicts. When both are adjustable, the parameter with the smallest adjustment should be selected. Only when conflicts between altitude and horizontal distance cannot be eliminated should time parameters be considered. Since changing time parameters within an airspace group is too costly, time parameters within non-airspace groups should be changed first when time conflicts exist. When adjusting only non-airspace group airspaces cannot resolve time conflicts, adjusting air transport group time parameters should be considered, but the new conflict pairs generated after adjustment should be used as a benchmark. If the new conflict pairs are below a threshold, adjustments are possible; if they are above the threshold, due to the excessive generation of new conflict pairs, airspace applications that conflict with airspace groups should be rejected to achieve conflict resolution.

[0149] Finally, according to principle two, airspace parameter adjustments and airspace application rejections are prioritized for low-priority airspaces. Since airspace groups cannot reject applications, when any non-airspace group airspace has a conflict with the airspace group that cannot be resolved by parameter adjustments, the non-airspace group airspace is rejected.

[0150] According to the method of the first aspect of the present invention, in step S5, establishing a comprehensive index for evaluating airspace conflict resolution schemes specifically includes:

[0151] Adjusting the total amount using time , Adjust the total amount of height Total adjustment of horizontal parameters Total number of airspace applications rejected A total of four indicators were linearly weighted to measure the overall adjustment range of airspace. , Right now:

[0152]

[0153] in, For indicator weight vectors; The smaller the size, the smaller the overall adjustment range of the airspace;

[0154] When calculating each sub-index, a weight vector is constructed based on the differences in spatial importance at different priorities. , No. The weight of the priority spatial domain is Then the priority weight vector for:

[0155]

[0156] in, The number of priority zones in the airspace. If it is any one of the above four indicators, then The calculation method is as follows:

[0157]

[0158] All four indicators mentioned above are calculated in this manner;

[0159] In some embodiments, airspace conflict resolution is mainly achieved by adjusting airspace parameters and rejecting some airspace applications; therefore, a time-based adjustment is used to adjust the total amount. Adjust the total amount at a high level Total adjustment of horizontal parameters Total number of airspace applications rejected A total of four indicators were linearly weighted to measure the overall adjustment range of airspace. , Right now:

[0160]

[0161] in This is the indicator weight vector. The smaller the value, the smaller the overall adjustment range of the airspace. Since the various indicators have different dimensions, the linear weighted summation needs to be normalized.

[0162] When calculating each sub-index, the differences in spatial importance at different priorities are taken into account, therefore a weight vector is constructed simultaneously. Let the first The weight of the priority spatial domain is Then the priority weight vector as follows:

[0163]

[0164] in, The number of priority zones in the airspace. If it is any one of the above four indicators, then The calculation method is as follows:

[0165]

[0166] All four indicators mentioned above are calculated in this manner;

[0167] The specific values ​​of the weights can be flexibly adjusted according to the user's needs.

[0168] According to the method of the first aspect of the present invention, in step S6, determining the order of airspace conflict resolution specifically includes (e.g.) Figure 6 (as shown)

[0169] Calculate the airspace altitude adjustment range and airspace horizontal adjustment range, and determine whether the conflict has been resolved and the adjustment constraints are met; where:

[0170] If so, then choose the method that minimizes the amount of money involved;

[0171] If not, calculate the time adjustment range to further determine whether the inflow has been resolved and meets the adjustment limits; where:

[0172] If so, then choose the method of adjusting the time;

[0173] If not, the conflict cannot be resolved.

[0174] In some embodiments, for multiple airspaces of the same priority, there is a question of the order in which airspace conflict resolution should be carried out. Based on the principles of airspace conflict resolution and the values ​​of comprehensive evaluation indicators under different resolution orders, the resolution order with the smallest adjustment range is selected.

[0175] According to the method of the first aspect of the present invention, in step S7, solving the optimal spatial conflict resolution scheme specifically includes (e.g.) Figure 7 (as shown)

[0176] The dataset is preprocessed by grouping spatial domains with the same priority into a set of spatial domains, and then arranging them in descending order of spatial domain priority.

[0177] Select an unoptimized set of airspaces based on airspace priority, and calculate all possible sequences for resolving airspace conflicts within the set.

[0178] Multiple conflict resolution schemes are generated based on the conflict detection algorithm and the airspace conflict resolution method;

[0179] Calculate the evaluation indicators for different schemes and select the scheme with the smallest overall adjustment range;

[0180] Determine whether each spatial domain set has been optimized; where:

[0181] If not, then the data should be preprocessed again;

[0182] If so, then obtain the optimal solution.

[0183] In some embodiments, finding the optimal airspace conflict resolution scheme can be divided into two steps: first, generating an airspace conflict resolution scheme based on the airspace conflict detection algorithm and the resolution algorithm; and second, finding the optimal resolution scheme based on the greedy algorithm.

[0184] First, the data is preprocessed, and spatial domains with the same priority are grouped into a spatial domain set, which is then arranged in descending order of priority, resulting in the following spatial domain set:

[0185]

[0186] in, Indicates priority as The There are several airspaces. Regarding the resolution order between airspace sets, according to principle two, the order can be from left to right. First, optimize the high-priority airspace set. After the high-priority airspace set is optimized, adjust it to the optimal resolution order within the group. Then optimize the lower-priority airspace set, and so on, until the lowest-priority airspace set is optimized. For the resolution problem within an airspace set, the evaluation index of all possible orders can be calculated through permutations and combinations, and the order with the smallest adjustment range can be selected as the optimal order within the airspace set.

[0187] The following describes the first embodiment (as shown in the accompanying drawings) with reference to the accompanying drawings. Figure 2 (As shown in the image) will be explained in detail.

[0188] To verify the superiority of the designed algorithm in airspace conflict detection and resolution, this paper uses an airspace request set based on a drone aerial photography scenario, with the data stored in Excel format.

[0189] 1. Collect basic information for airspace applications (partial data such as...) Figure 8 (As shown)

[0190] This dataset has 5 categories of spatial shapes, namely:

[0191] ① indicates a circular airspace; ② indicates a rectangular airspace; ③ indicates a pentagonal airspace; ④ indicates a hexagonal airspace; ⑤ indicates a runway-shaped airspace.

[0192] The constraints for airspace conflict determination and resolution are shown in the table below:

[0193] Table 1: Constraints for Airspace Conflict Judgment and Resolution

[0194]

[0195] 2. Conflict detection for airspace requests based on convex optimization theory

[0196] Without considering the aircraft's trajectory and speed, for any two different airspaces, we first check whether the two airspaces simultaneously satisfy the altitude and time constraints required for a conflict to occur, based on the upper and lower bounds of the altitude and time parameter value ranges. If they do, we then further check the horizontal distance between the airspaces.

[0197] The time and altitude parameters of the airspace are both numerical ranges. The interval between the intervals can be obtained by using the upper and lower bounds of the interval. Based on this, it can be determined whether two airspaces meet the conflicting time and altitude constraints. The detection process is described in detail below using the airspace time parameter as an example.

[0198] Suppose two different airspaces , The time intervals are respectively , The safe time interval is Then there are three cases: (1) the intervals do not overlap; (2) the intervals partially overlap; (3) the intervals completely overlap; the three cases are as follows Figure 9 As shown.

[0199] For case (1), the time constraint for conflict determination is as follows:

[0200]

[0201] Cases (2) and (3) have overlapping intervals, satisfying the time constraint for conflict determination. For case 2, the upper and lower bounds of the interval satisfy the following relationship:

[0202]

[0203] For case (3), the upper and lower bounds of the interval satisfy:

[0204]

[0205] Therefore, for any two spatial domains, if the upper and lower bounds of their time intervals satisfy any one of the three constraint conditions mentioned above, it can be determined that there is a time conflict between the two spatial domains.

[0206] The detection of altitude conflicts between airspaces is similar and will not be elaborated upon here.

[0207] For detecting horizontal distance conflicts in the spatial domain, the horizontal distance in the spatial domain can be obtained by establishing a convex set representation of the projection based on the boundary equation of the projection and solving for the minimum distance between two convex sets.

[0208] Data analysis reveals that the horizontal projection of the airspace exhibits five possible forms: circular, rectangular, pentagonal, hexagonal, and runway-shaped. Figure 10 As shown.

[0209] The horizontal projection of the spatial domain is obviously A convex set on a plane.

[0210] Circular boundaries cannot be described by straight lines, so they are classified as first-class projections. Rectangles, pentagons, and hexagons are all polygons, and their boundaries can be described by straight line equations, so they can be classified as second-class projections. As for the racetrack shape, calculating its boundary constraints is more complicated, but when calculating the minimum distance of the convex set, it can be regarded as a figure spliced ​​by a rectangle and a circle. Therefore, a convex set description of the racetrack shape projection is no longer established.

[0211] First, consider the simplest type of projection, namely circular projection. Given the coordinates of the four vertices of its circumscribed quadrilateral, such as... Figure 11 As shown.

[0212] The equation for the circular boundary can then be calculated using the following formula:

[0213]

[0214] The convex set representation of the corresponding circular projection is:

[0215]

[0216] Next, we consider the more complex second type of projection, namely polygonal projection. The boundaries of polygonal projections are all straight lines, and the equations of these lines can be solved to establish a system of convex inequalities to describe the convex set. Below, we will use a pentagon as an example to establish a convex set description for the pentagon.

[0217] The general form of a straight line can be expressed as:

[0218]

[0219] in Let be the normal vector of the line. To determine the constants of the line, we can first find the normal vector of the boundary line, then solve the system of known points to obtain the equation of the boundary line. Given the coordinates of the pentagon's vertices, such as... Figure 12 As shown.

[0220] straight line normal vector for:

[0221]

[0222] The straight line can be obtained The equation is:

[0223]

[0224] Considering that the projection is a closed pentagon, therefore It has both internal and external orientations, such as Figure 13 As shown.

[0225] Orientation affects the form of the description of a pentagonal convex set, which is crucial for determining... Orientation: Consider the straight line as a contour line in a two-dimensional plane. In this case, the normal vector of the line is the gradient vector of the contour line, that is, the direction of the steepest increase in the value of the contour line. For example... Figure 14 As shown.

[0226] To maintain consistency with the description of the first type of convex set and to facilitate the solution of the minimum distance between convex sets later, the outward normal of the straight line is uniformly chosen here. To solve for the equations of the boundary lines, the convex set description of the pentagonal projection is as follows:

[0227]

[0228] Promote it to polygon, that is:

[0229]

[0230] After establishing the projected convex set, the minimum spacing between the convex sets can be further solved.

[0231] First, consider the minimum spacing between any two shape projections other than the runway projection. The calculation. Let the convex set of the unknown projection be described as... , indicating that it could be either a polygon or a circle, take , Its convex inequality is described as follows:

[0232]

[0233] Minimum spacing between two convex sets It is the distance between the closest pair of points among the points it contains. One of them is In the middle, another one is In, such as Figure 15 As shown.

[0234] The minimum spacing of a convex set can be found by solving the following convex programming problem. .

[0235]

[0236] Next, consider the calculation of the runway-shaped projection spacing. The runway-shaped projection can be decomposed into a rectangular projection, and the convex set is described as... And two semicircular projections, but when calculating the spacing, the two semicircular projections can be regarded as two circular projections, let their convex set descriptions be respectively The supplementary projection is included Therefore, it will not affect the distance calculation. For example... Figure 16 As shown.

[0237] Find them separately and The spacing between them are respectively , , Then the distance between the runway-shaped projection and other shape projections is:

[0238]

[0239] Let the horizontal safety interval be Based on the horizontal distance constraint caused by the conflict, we can obtain:

[0240]

[0241] When the conditions are met, it can be determined that there is a horizontal distance conflict between airspaces.

[0242] Furthermore, in the existing conflict pair, the horizontal coordinates within the time intersection of the two trajectories are calculated based on the aircraft's trajectory and speed, and the horizontal distance is calculated to further determine whether the conflict is valid.

[0243] Given a known trajectory such as a broken line, circle, polygon, rectangle, or racetrack, we can calculate the coordinates of any point in time within the upper and lower bounds of any trajectory by considering the order of the trajectories. However, in practice, we only need to consider the coordinates within the intersection of the time intervals of two trajectories. The method for calculating the intersection of the time intervals of two trajectories is as follows: Taking a pentagon as an example, find the coordinates of its trajectory at any given time.

[0244] Given the five coordinates of the pentagon Let its x and y coordinates be vectors. , vector For ease of calculation, the coordinates of the first point are repeated once at the end of the vector.

[0245] Then the total length of the locus of the pentagon for:

[0246]

[0247] in This represents the backward difference of the coordinate sequence. Since the aircraft is flying at a constant speed, its flight speed... It can be represented as:

[0248]

[0249] The vector after coordinate difference Normalization, that is, the direction vector of the line containing each edge. .

[0250] Therefore, the aircraft is Coordinates It can be represented in segments as follows:

[0251]

[0252] in Indicates reaching the th The time at each coordinate point, This represents the unit direction vector between two points. The coordinates of polylines, polygonal trajectories, circles, and runway trajectories can also be represented using parametric equations.

[0253] Within the time intersection of the two spacecraft, the time interval is discretized with equal step sizes to obtain the two coordinate vectors and the distance between the two spacecraft at the same moment within the time intersection. .

[0254] The conditions for space reuse are:

[0255]

[0256] If this condition is not met, it can be determined that the two airspaces are in conflict.

[0257] The solution is obtained using MATLAB. The dataset provides 98 spatial domain request parameters, with potential collision pairs of [number missing]. The initial pool of 4753 potential conflict pairs was reduced to 190 after initial screening, significantly decreasing the search space. Further conflict detection was conducted on these 190 potential conflict pairs based on the aircraft's flight trajectory and speed, resulting in 11 accurate conflict pairs. Detailed airspace application number data is shown in Table 2.

[0258] Table 2: Conflict Pair Numbers

[0259]

[0260] The relative positional distribution of some conflicts to the horizontal projection is as follows: Figure 17 As shown. Selecting the conflict pairs (11,13) and (51,72), the 3D plot is as follows. Figures 18a-18b As shown. Selecting the collision pair (45, 78), the real-time trajectory projection of the aircraft is shown below. Figure 19 As shown, within the time overlap, the real-time distance between the two spacecraft may be less than the safe interval, so a conflict will still be detected even after spatial reuse is introduced.

[0261] 3. Determine the priority of airspace conflict resolution principles.

[0262] Let the first The priority of each principle is In light of practical considerations and the relevance of the tasks, rejecting the airspace application would directly impact the execution of other tasks. Therefore, Set it to the highest priority; when it conflicts with other criteria, principle one takes precedence. Secondly, since high-priority tasks are those of greater importance, they should be kept as unchanged as possible; therefore, let... In summary, the principles for resolving airspace conflicts should be prioritized as follows:

[0263]

[0264] This allows for further determination of the order and method of resolving airspace conflicts.

[0265] 4. Determine the methods for resolving airspace conflicts

[0266] There are two approaches to resolving airspace conflicts: adjusting airspace parameters and rejecting airspace applications. First, after developing detailed strategies for each approach, we consider the principles of airspace conflict resolution and establish a comprehensive airspace conflict resolution method.

[0267] Consider the adjustment strategy for airspace parameters. The adjustable airspace parameters are time, altitude, and horizontal distance. First, consider the strategy for resolving time conflicts, as detailed above. There are three possible time intervals for conflict pairs. Let the minimum adjustment time required to resolve time conflicts be... , The calculation is as follows:

[0268]

[0269] Considering that parameters can be adjusted in both airspaces that caused the collision, let the maximum time adjustment range be... ,therefore Should meet:

[0270]

[0271] The height adjustment method is similar; let the minimum adjustment height required to resolve height conflicts be... The calculation is as follows:

[0272]

[0273] Let the maximum height adjustment range be ,but Should meet:

[0274]

[0275] To adjust the horizontal distance parameter, it is necessary to first determine the direction of the fastest increase in horizontal distance. Translation along this direction can resolve horizontal conflicts with minimal adjustment. Analysis of the spatial projection data shows that the range of horizontal distance adjustment is much smaller than the length of the spatial boundary. Therefore, it can be approximated that the points with the closest projection distance will not deviate from their original boundaries. Thus, consider two arbitrary spatial domains... , The horizontal spacing is ,in Along any increase direction Translation, the distance after translation is , like Figure 20 As shown.

[0276] It can be seen that, respectively along , After translation, the horizontal distance of the projection is: Therefore, the direction vector formed by the two points with the closest projection distance is... This is the direction of the fastest increase in horizontal distance. Proof:

[0277] Given two convex polygons and Then there exists a pair of points. and ,in , making that little bit and points It can only be a vertex of the corresponding convex polygon or on its edge.

[0278] Due to the maximum horizontal distance that a polygon can move Since the side lengths of the polygons are constant, the points where the shortest distance between the two polygons remains unchanged after translation. Therefore, the shortest distance between convex polygons is transformed into the distance between two points or from a point to a line. Thus, the direction vector formed by the two points with the closest projected distance is... This is the direction in which the horizontal distance increases the fastest.

[0279] Let the minimum adjustment distance required to resolve horizontal distance conflicts be... ,but The calculation is as follows:

[0280]

[0281] Let the maximum horizontal distance adjustment range be ,but Should meet:

[0282]

[0283] At the same time, due to From this, we can draw the following conclusion:

[0284]

[0285] That is, if the projected areas overlap, the horizontal distance conflict cannot be resolved.

[0286] Secondly, a rejection strategy for airspace applications should be formulated. Principle 1 has the highest priority; therefore, airspace application rejection should only be used when adjustments to airspace parameters cannot resolve the conflict. Analysis shows that airspace application rejection can be used to resolve conflicts in the following situations.

[0287] (1) Airspace Conflicts with one or more airspaces, and completely resolving these conflicts exceeds The parameter can be adjusted within a certain range;

[0288] (2) Eliminate by adjusting spatial parameters and After the airspace conflict, and New conflicts arise, but are being resolved. and After the conflict, Reconnect with Conflict arises;

[0289] The above situations are referred to as the two conditions for the rejection of airspace applications. If either condition is met, according to Principle 2, the low-priority airspace application is rejected and the high-priority airspace is reserved.

[0290] Finally, based on the principles of conflict resolution and the actual situation, the approach that minimizes the adjustment of airspace is selected for airspace resolution. The above are combined into a complete set of judgment strategies, which is the way to establish airspace conflict resolution.

[0291] First, a strategy for selecting conflict resolution methods should be formulated. Principle one has the highest priority: minimizing the number of airspace rejections. Therefore, adjusting airspace parameters should be prioritized.

[0292] Secondly, an airspace parameter adjustment strategy should be formulated. Adjustable airspace parameters include time, altitude, and horizontal distance. According to principle four, altitude and horizontal distance should be adjusted first to resolve conflicts. When both are adjustable, the parameter with the smallest adjustment should be selected. Only when conflicts between altitude and horizontal distance cannot be eliminated should time parameters be considered. Since changing time parameters within an airspace group is too costly, time parameters within non-airspace groups should be changed first when time conflicts exist. When adjusting only non-airspace group airspaces cannot resolve time conflicts, adjusting air transport group time parameters should be considered, but the new conflict pairs generated after adjustment should be used as a benchmark. If the new conflict pairs are below a threshold, adjustments are possible; if they are above the threshold, due to the excessive generation of new conflict pairs, airspace applications that conflict with airspace groups should be rejected to achieve conflict resolution.

[0293] Finally, according to principle two, airspace parameter adjustments and airspace application rejections are prioritized for low-priority airspaces. Since airspace groups cannot reject applications, when any non-airspace group airspace has a conflict with the airspace group that cannot be resolved by parameter adjustments, the non-airspace group airspace is rejected.

[0294] 5. Determine the order of airspace conflict resolution.

[0295] Let the airspace be numbered as Priority level is The airspace is , The smaller the value, the higher the priority. Consider the following airspace conflict scenarios, where the airspace is represented by its horizontal plane projection, such as... Figure 21 As shown.

[0296] In the scenario shown in the diagram above, the airspace , , They do not conflict with each other, but are all related to the airspace set. These constitute conflict pairs, and resolving airspace conflicts in the aforementioned situations involves addressing the airspace set. Airspace conflict resolution is highly interconnected, as parameters are adjusted or airspace applications are rejected in each airspace. Therefore, appropriate criteria need to be established to achieve a reasonable order of airspace conflict resolution.

[0297] First, consider the order of resolving airspace conflicts of different priorities. According to principle two, priority should be given to ensuring the normal application and use of high-priority airspace, i.e. The smaller the value, Airspace conflict resolution has a higher priority. (Ignoring this) In the above-mentioned situation, airspace conflict mediation shall be based on Airspace conflict resolution is carried out sequentially.

[0298] Next, consider the airspace conflict resolution order under the same priority. There is no heuristic strategy for determining the resolution order of airspaces with the same priority; therefore, it is necessary to calculate the overall adjustment magnitude of the airspace under different resolution orders and select the order scheme with the smallest adjustment magnitude. There are two resolution order schemes:

[0299]

[0300] Calculate the total adjustment range for each scheme and compare them to select the scheme with the smallest adjustment range.

[0301] Finally, the above is a discussion of the overall order of adjustment of the airspace set; specifically, for each... Even with the resolution of airspace conflicts, the order of parameter adjustments or rejections remains an issue. For example, the set of airspace that conflicts with it is The order of parameter adjustment or rejection is as follows: There are several species, but analysis shows that... various airspaces in China and The conflicts are independent of each other, and adjusting or rejecting their parameters does not affect other airspaces. Therefore, the order in which parameters are adjusted or rejected in other airspaces during the airspace resolution process can be ignored.

[0302] 6. Establish a comprehensive index for evaluating airspace conflict resolution schemes.

[0303] Airspace conflict resolution is mainly achieved by adjusting airspace parameters and rejecting some airspace applications; therefore, a time-based adjustment is used to adjust the total amount. Adjust the total amount at a high level Total adjustment of horizontal parameters Total number of airspace applications rejected A total of four indicators were linearly weighted to measure the overall adjustment range of airspace. , Right now:

[0304]

[0305] in This is the indicator weight vector. The smaller the value, the smaller the overall adjustment range of the airspace. Since the various indicators have different dimensions, the linear weighted summation needs to be normalized.

[0306] When calculating each sub-index, the differences in spatial importance at different priorities are taken into account, therefore a weight vector is constructed simultaneously. Let the first The weight of the priority spatial domain is Then the priority weight vector as follows:

[0307]

[0308] in, The number of priority zones in the airspace. If it is any one of the above four indicators, then The calculation method is as follows:

[0309]

[0310] All four indicators mentioned above are calculated in this manner;

[0311] The specific values ​​of the weights can be flexibly adjusted according to the user's needs. Airspace priority itself has relative importance, and based on the principles of airspace conflict resolution, the relative importance of the four indicators is determined as follows:

[0312]

[0313] Weight vector , The determination of the weight vector can be based on the actual situation. Here, we select the weight vectors whose adjacent terms have equal differences, that is, adjacent terms have the same difference in importance:

[0314]

[0315] 7. Based on a greedy algorithm and a defined resolution strategy, find the optimal airspace conflict resolution scheme.

[0316] Finding the optimal airspace conflict resolution solution can be divided into two steps: first, generating an airspace conflict resolution solution based on the airspace conflict detection algorithm and the resolution algorithm; second, finding the optimal resolution solution based on the greedy algorithm.

[0317] First, the data is preprocessed, and spatial domains with the same priority are grouped into a spatial domain set, which is then arranged in descending order of priority, resulting in the following spatial domain set:

[0318]

[0319] in Indicates priority as The There are several airspaces. Regarding the resolution order between airspace sets, according to principle two, the order can be from left to right. First, optimize the high-priority airspace set. After the high-priority airspace set is optimized, adjust it to the optimal resolution order within the group. Then optimize the lower-priority airspace set, and so on, until the lowest-priority airspace set is optimized. For the resolution problem within an airspace set, the evaluation index of all possible orders can be calculated through permutations and combinations, and the order with the smallest adjustment range can be selected as the optimal order within the airspace set.

[0320] Based on the above strategy, a greedy algorithm is established to find the optimal solution.

[0321] The airspace has four priorities, and there are four airspace sets to be optimized. Four optimizations are required, and the optimal solution is obtained from each optimization. Value changes with the number of optimizations, such as Figure 22 As shown.

[0322] It can be seen that the final solution shows a significant decrease compared to the unoptimized solution, indirectly proving the effectiveness of the algorithm. However, as the number of optimizations increases, the optimization effect gradually decreases, indicating that the optimization of the low-priority airspace set has a relatively small impact on the overall adjustment range of the final solution. This may be related to the lower weights of the low-priority airspace. This also shows that when only considering the airspace adjustment range, optimizing only the high-priority airspace can still yield a better solution, thus saving computing power and time and facilitating rapid airspace planning by the military. The handling of the conflict pairs found in Problem 1 is as follows:

[0323] Table 3: Conflict Resolution Schemes

[0324]

[0325] Taking the conflict pair (51, 72) as an example, the changes before and after conflict resolution are as follows: Figures 23a-23b As shown in the figure, the above resolution strategy effectively resolves the conflict pairs.

[0326] As can be seen, the above scheme transforms the determination of horizontal spacing in airspace conflicts into a quadratic convex optimization problem, mathematically guaranteeing that the obtained distance is the minimum distance and the globally optimal solution, resulting in faster convergence compared to the GJK algorithm. It also proves that horizontal adjustment of the polygon along the line connecting the two points with the minimum distance is the optimal adjustment. For airspace applications with airspace groups, a heuristic strategy and greedy algorithm are used for step-by-step optimization to ensure the best possible solution within a finite time. Strict adherence to space reuse rules is used to screen conflicting airspace applications, ensuring that aircraft maintain a safe distance at all times, preventing misjudgments and rejections, and maximizing the guarantee of airspace applications. A relatively scientific conflict scheme evaluation index has been established, demonstrating good scalability.

[0327] A second aspect of this invention proposes a spatial conflict detection and resolution system based on convex optimization theory. The system includes a processing unit configured to perform the following steps:

[0328] Step S1: Collect basic information for airspace application;

[0329] Step S2: Detect conflicts in the space domain application based on convex optimization theory;

[0330] Step S3: Determine the priority of airspace conflict resolution principles;

[0331] Step S4: Determine the method for resolving airspace conflicts;

[0332] Step S5: Establish comprehensive indicators for evaluating airspace conflict resolution schemes;

[0333] Step S6: Determine the order of airspace conflict resolution;

[0334] Step S7: Solve for the optimal airspace conflict resolution scheme based on the greedy algorithm and resolution strategy.

[0335] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the spatial conflict detection and resolution method based on convex optimization theory disclosed herein.

[0336] Figure 24 This is a structural diagram of an electronic device according to an embodiment of the present invention, such as... Figure 24As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0337] Those skilled in the art will understand that Figure 24 The structure shown is merely a structural diagram of the part related to the technical solution of this disclosure and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0338] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a spatial conflict detection and resolution method based on convex optimization theory disclosed herein.

[0339] In summary, regarding airspace conflict detection, the technical solution of this invention establishes a convex set representation of the horizontal projection of the airspace from the perspective of convex optimization. It calculates the horizontal interval between different airspaces by solving for the minimum distance between two convex sets, thus addressing the problems of complex existing algorithm models, cumbersome calculation steps, and the inability to calculate accurate solutions in some cases. In formulating airspace conflict resolution schemes, the technical solution of this invention proposes linearly weighting multiple indicators describing the airspace adjustment magnitude extracted from different perspectives to obtain a comprehensive metric. The weights can be flexibly adjusted according to actual needs, thus solving the problems of poor flexibility and versatility of existing methods and meeting different user requirements. Using this metric as the target, and combining the heuristic strategies formulated in the above modeling process, we can adopt a step-by-step optimization strategy, assisted by a greedy algorithm, to ultimately formulate the optimal scheme for airspace conflict resolution.

[0340] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for airspace conflict detection and resolution based on convex optimization theory, characterized in that, The method comprises: Step S1, collecting basic information of airspace applications; Step S2, detecting conflicts of airspace applications based on convex optimization theory; Step S3, determining priority of airspace conflict resolution principles; Wherein, the priority of the space conflict resolution principle is: ; Wherein: represents: the number of rejected airspace is small; represents: the parameters of low-priority airspace are adjusted preferentially, and the high-priority airspace remains unchanged; represents: the adjustment range of the airspace does not exceed the maximum allowed range, avoiding adjustment of the airspace time range; represents: the height and horizontal distance are adjusted preferentially when the airspace conflict is resolved, avoiding adjustment of the airspace time range; Step S4, determining ways of airspace conflict resolution; Step S5, establishing comprehensive indexes for evaluating airspace conflict resolution schemes; The establishment of the comprehensive indexes for evaluating airspace conflict resolution schemes specifically comprises: Total amount of time adjustment Total amount of height adjustment Total amount of horizontal parameter adjustment Total amount of airspace application rejection The four indicators are linearly weighted to measure the overall adjustment range of airspace , that is: wherein, is the index weight vector; The smaller the total adjustment range of the spatial domain is smaller. In calculating each sub-index, a weight vector is constructed according to the difference in the importance of airspace of different priorities , the weight of airspace of the first priority is , and the weight of airspace of the second priority is , then the priority weight vector is Wherein, The priority number of the space domain, If any one of the above four indexes is The calculation method is as follows: The above four indexes are calculated in this way; Step S6, determining the sequence of airspace conflict resolution; Step S7, solving the optimal airspace conflict resolution scheme based on the greedy algorithm and resolution strategies.

2. The method of airspace conflict detection and resolution based on convex optimization theory according to claim 1, wherein, In the step S1, the basic information of the airspace application includes time domain information, height domain information, airspace horizontal projection information, airspace vehicle information, and airspace group information. The time domain information includes the lower and upper limits of the time interval. The height domain information includes the lower and upper limits of the height information. The airspace horizontal projection information includes the shape profile of the projection and the coordinates of the turning points on the projection profile. The airspace vehicle information includes the trajectory of the aircraft and the speed of the aircraft.

3. The method of airspace conflict detection and resolution based on convex optimization theory according to claim 2, characterized in that, In the step S2, the conflict detection of the airspace application specifically comprises: Detecting the height interval conflict and the time interval conflict of the airspace; wherein: If both are not conflicted at the same time, the airspace does not constitute a conflict pair; If both are conflicted at the same time, the boundary equation of the airspace projection is solved, the convex set representation of the airspace projection is established, and the minimum distance interval of the convex set is solved to detect whether the horizontal distance conflict is met; wherein: If the horizontal distance conflict is not met, the airspace does not constitute a conflict pair; If the horizontal distance conflict is met, the spatial coordinates at any time within the time intersection are calculated to further detect whether the time interval is greater than the safety distance; wherein: If it is greater than the safety distance, the airspace does not constitute a conflict pair; If it is not greater than the safety distance, the airspace constitutes a conflict pair.

4. The method of airspace conflict detection and resolution based on convex optimization theory according to claim 3, characterized in that, In the step S4, the way of airspace conflict resolution specifically comprises: Selecting low-priority airspace for airspace parameter adjustment to determine whether the conflict is resolved; wherein: If the conflict is resolved, it is ended; If the conflict is not resolved, high-priority airspace is selected for airspace parameter adjustment to determine whether the conflict is resolved; wherein: If the conflict is resolved, it is ended; If the conflict is not resolved, the low-priority airspace application is rejected.

5. The method of airspace conflict detection and resolution based on convex optimization theory according to claim 4, characterized in that, In the step S6, the sequence of airspace conflict resolution specifically comprises: Calculating the airspace height adjustment range and the airspace horizontal adjustment range, and determining whether the conflict is resolved and meets the adjustment limit; wherein: If yes, the smallest way of the total adjustment range is selected; If no, the time adjustment range is calculated to further determine whether the conflict is resolved and meets the adjustment limit; wherein: If yes, the way of adjusting the time is selected; If no, the conflict cannot be resolved.

6. The method of airspace conflict detection and resolution based on convex optimization theory according to claim 5, wherein, In the step S7, solving the optimal airspace conflict resolution scheme specifically comprises: Pretreating the data set, grouping the airspace with the same priority into an airspace set, and arranging them in order from high to low according to the airspace priority; Selecting the unoptimized airspace set according to the airspace priority, and calculating all possible sequences of airspace conflict resolution in the airspace set; Generating multiple resolution schemes according to the conflict detection algorithm and the airspace conflict resolution way; Calculate evaluation indexes of different schemes, and select a scheme with minimum overall adjustment amplitude; Determine whether each airspace set is optimized; wherein: If not, re-process the data; If yes, obtain the optimal resolution scheme.

7. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the airspace conflict detection and resolution method based on the convex optimization theory in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the airspace conflict detection and resolution method based on the convex optimization theory in any one of claims 1-6.

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