An aircraft position conflict real-time detection method, device and equipment

Abstract

This invention relates to the field of airport operation management technology, specifically to a method, apparatus, and equipment for real-time detection of aircraft stand conflicts. It constructs a mutual exclusion bitmap matrix based on a pre-acquired list of mutually exclusive edges and bit mapping relationships between stand positions; encodes the current occupancy status of all stand positions into a binary integer occupancy bitmap; traverses the occupancy bitmap from low to high bits, and for each occupied stand i, performs a bitwise AND operation between the mutual exclusion bitmap and the occupancy bitmap to obtain a set of stands mutually exclusive with and occupied by stand i; extracts stand positions with stand numbers greater than i from this set to obtain conflict pairs of stand i, and integrates them into the stand conflict detection result. This solution achieves O(1) time complexity conflict detection by encoding stand occupancy status into a binary bitmap and combining it with a pre-constructed mutual exclusion bitmap matrix. It boasts high detection accuracy and significantly reduced detection time, meeting the requirements of high-concurrency scenarios.

Description

Technical Field

[0001] This invention relates to the field of airport operation management technology, specifically to a method, device, and equipment for real-time detection of aircraft parking position conflicts. Background Technology

[0002] Aircraft parking stand conflict detection is a core component of airport operational safety management. It mitigates collision risks during aircraft parking by identifying simultaneous occupancy of mutually exclusive parking stands in real time. With the rapid development of the civil aviation industry, the number of airport parking stands is constantly increasing. Airport operation systems need to process thousands of aircraft position update messages per second, placing stringent requirements on the real-time performance, high concurrency processing capabilities, and detection accuracy of parking stand conflict detection.

[0003] The mainstream aircraft stand conflict detection methods in the industry mainly employ set traversal or graph traversal algorithms. These methods determine whether conflict pairs exist by traversing the occupancy status of all aircraft stands and the mutual exclusion relationships between them, with a time complexity of O(n²) or even higher. In scenarios with a small number of aircraft stands, these methods can meet basic detection requirements. However, when the number of aircraft stands reaches hundreds, the computational load of the algorithm increases exponentially, making real-time performance difficult to guarantee. In high-concurrency aircraft position update scenarios, the performance bottleneck of traditional algorithms becomes even more prominent, easily leading to conflict detection delays and failing to provide timely warnings of aircraft stand conflict risks.

[0004] Therefore, current aircraft stand conflict detection methods suffer from insufficient real-time performance and inability to provide timely warnings of aircraft stand conflicts in scenarios with a high number of aircraft stands and high concurrency of aircraft position updates. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method, apparatus and equipment for real-time detection of aircraft stand conflicts, so as to solve the problem that the existing aircraft stand conflict detection methods have insufficient real-time performance and cannot provide timely warning of aircraft stand conflicts in scenarios with a high number of aircraft stands and high concurrency of aircraft position updates.

[0006] According to a first aspect of the present invention, a method for real-time detection of aircraft parking position conflicts is provided, comprising:

[0007] Based on the pre-acquired list of mutually exclusive edges between machine positions and the machine position bit mapping relationship, a mutual exclusion bitmap matrix is ​​constructed. The mutual exclusion bitmap matrix includes a mutual exclusion bitmap corresponding to each machine position. The mutual exclusion bitmap is a binary bitmap code used to reflect other machine positions that are mutually exclusive with the corresponding machine position. The machine position bit mapping relationship indicates that each machine position corresponds one-to-one with a bit in the bitmap code.

[0008] Based on the machine position bit mapping relationship, the current occupancy status of all machine positions is encoded into an occupancy bit map of binary integers; in the occupancy bit map, if the i-th bit of the binary integer is 1, it means that machine position i is occupied.

[0009] Traverse the occupied bitmap from low to high bits. For each occupied machine position i, perform a bitwise AND operation between the mutex bitmap of machine position i and the occupied bitmap to obtain a set of machine positions that are mutually exclusive with machine position i and are occupied.

[0010] From the set of positions that are mutually exclusive with position i and are already occupied, extract the positions with position numbers greater than i to obtain the conflict pairs of position i.

[0011] All conflict pairs from all camera positions are integrated into a single camera position conflict detection result.

[0012] Preferably, constructing a mutually exclusive bitmap matrix includes:

[0013] Based on the total number of airport stands p, create an integer list of length p+1 as the initial mutex bitmap matrix M, where all elements in the initial mutex bitmap matrix M are initially set to 0.

[0014] Iterate through each tuple in the list of mutually exclusive edges. For machine positions a and b that are mutually exclusive in the tuple, set the value of the (b-1)th bit of the mutual exclusion bitmap M[a] of machine position a to 1, and set the value of the (a-1)th bit of the mutual exclusion bitmap M[b] of machine position b to 1. Finally, the mutual exclusion bitmap of each machine position is obtained.

[0015] By integrating the mutual exclusion bitmaps of all machine positions, a mutual exclusion bitmap matrix is ​​obtained.

[0016] Preferably, the process of encoding the current occupancy status of all machine positions into a binary integer includes:

[0017] Obtain the current occupancy status of all camera positions and integrate them into a camera position status list;

[0018] Given the total number of aircraft stands p at the airport, create an initial binary integer of number p bits, with an initial value of 0.

[0019] Iterate through the list of current machine positions. For each occupied machine position k, set the (k-1)th bit of the binary integer to 1 to obtain the encoded binary integer.

[0020] Preferably, the occupancy status of all camera positions at the current moment is obtained, including:

[0021] The plane projection of the airport apron is divided into several non-overlapping rectangular grids, and a unique ID is assigned to each grid.

[0022] Based on the physical attributes of each camera position, determine one or more grids to which the camera position belongs, and establish a mapping list between grid IDs and camera positions;

[0023] Based on the received real-time position coordinates of the aircraft, match the grid ID where the aircraft is located;

[0024] Based on the mapping list between the grid ID and the aircraft stand, the aircraft stand occupied by the aircraft is determined.

[0025] Preferably, the planar projection of the airport apron is divided into several non-overlapping rectangular grids, including:

[0026] Obtain the WGS-84 latitude and longitude coordinates of the airport apron, and convert the WGS-84 latitude and longitude coordinates of the airport apron into UTM projection coordinates; based on the UTM projection coordinates of the airport apron, divide the airport apron into several non-overlapping rectangular grids.

[0027] Preferably, after assigning a unique ID to each grid, the method further includes: establishing association relationships between adjacent grids;

[0028] Matching the grid ID where the aircraft is located includes: obtaining the coarse grid judgment ID of the aircraft based on the real-time position coordinates of the aircraft, and simultaneously retrieving all adjacent grid IDs of the coarse grid judgment ID; comparing the real-time position coordinates of the aircraft with the coarse grid judgment ID and all adjacent grid IDs to obtain the grid ID where the aircraft is located.

[0029] Preferably, after obtaining the station conflict detection results, the method further includes:

[0030] For conflict pairs with status changes detected in the station conflict detection results, corresponding logs are generated.

[0031] Preferably, the method further includes:

[0032] Output the alarm information corresponding to the conflict pairs where there are state changes;

[0033] Record the timestamp of the latest detection for each conflict;

[0034] For each conflict pair, if no conflict pair is detected again within a consecutive preset time period starting from the latest detection timestamp, the alarm information for the conflict pair is cleared.

[0035] According to a second aspect of the present invention, a real-time aircraft parking position conflict detection device is provided, comprising:

[0036] The mutual exclusion bitmap matrix construction module is used to construct a mutual exclusion bitmap matrix based on the pre-acquired list of mutual exclusion edges between machine positions and the machine position bit mapping relationship. The mutual exclusion bitmap matrix includes a mutual exclusion bitmap corresponding to each machine position. The mutual exclusion bitmap is a binary bitmap encoding. The machine position bit mapping relationship indicates that each machine position corresponds one-to-one with a bit in the bitmap encoding.

[0037] The real-time occupancy bitmap acquisition module is used to encode the occupancy status of all machine positions at the current moment into an occupancy bitmap of binary integers according to the machine position bit mapping relationship; in the occupancy bitmap, if the i-th bit of the binary integer is 1, it means that machine position i is occupied;

[0038] The real-time conflict detection module is used to traverse the occupancy bitmap from low to high bits. For each occupied machine position i, the mutual exclusion bitmap of machine position i is bitwise ANDed with the occupancy bitmap to obtain a set of machine positions that are mutually exclusive with and occupied by machine position i. From the set of machine positions that are mutually exclusive with and occupied by machine position i, machine positions with machine position numbers greater than i are extracted to obtain conflict pairs of machine position i. All conflict pairs of machine positions are integrated into a machine position conflict detection result.

[0039] According to a third aspect of the present invention, a real-time aircraft parking position conflict detection device is provided, comprising:

[0040] The main controller and the memory connected to the main controller;

[0041] The memory stores program instructions;

[0042] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0043] The technical solution provided by this invention may include the following beneficial effects:

[0044] It is understood that the technical solution presented in this invention can construct a mutual exclusion bitmap matrix based on a pre-acquired list of mutual exclusion edges and bit mapping relationships between machine positions; encode the current occupancy status of all machine positions into a binary integer occupancy bitmap; traverse the occupancy bitmap from low to high bits, and for each occupied machine position i, perform a bitwise AND operation between the mutual exclusion bitmap of machine position i and the occupancy bitmap to obtain a set of machine positions that are mutually exclusive with and occupied by machine position i; extract machine positions with machine position numbers greater than i from this set to obtain conflict pairs of machine position i, and integrate them into a machine position conflict detection result. This solution achieves conflict detection with O(1) time complexity by encoding the machine position occupancy status into a binary bitmap and combining it with a pre-constructed mutual exclusion bitmap matrix. It has high detection accuracy and significantly reduces detection time, meeting the requirements of high-concurrency scenarios.

[0045] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0047] Figure 1 This is a schematic diagram illustrating the steps of a real-time detection method for aircraft parking position conflicts according to an exemplary embodiment;

[0048] Figure 2 This is a schematic block diagram of a linear pipeline structure according to an exemplary embodiment;

[0049] Figure 3 This is a schematic block diagram of an aircraft parking position conflict real-time detection device according to an exemplary embodiment. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0051] In one embodiment, Figure 1 This is a schematic diagram illustrating the steps of a real-time aircraft parking space conflict detection method according to an exemplary embodiment. The core idea of ​​this embodiment is to encode the parking space occupancy status and mutual exclusion relationships into a binary bitmap, and utilize the parallel characteristics of bit operations to achieve an efficient algorithm that can detect all conflict pairs in a single traversal.

[0052] See Figure 1 A method for real-time detection of aircraft parking position conflicts is provided, including:

[0053] Step S11: Construct a mutual exclusion bitmap matrix based on the pre-obtained list of mutually exclusive edges between machine positions and the bit mapping relationship between machine positions. The bit mapping relationship between machine positions indicates that each machine position corresponds one-to-one with a bit in the bitmap.

[0054] In this embodiment, the mutual exclusion bitmap matrix includes a mutual exclusion bitmap corresponding to each aircraft position. The mutual exclusion bitmap is a binary bitmap code used to reflect other aircraft positions that are mutually exclusive with the corresponding aircraft position. According to the aircraft position bit mapping relationship, the lowest bit (bit 0) of the bitmap code corresponds to aircraft position 1, the highest bit corresponds to aircraft position p (p is the total number of aircraft positions), and aircraft position i corresponds to the (i-1)th bit of the bitmap, so that each aircraft position in the airport forms a unique one-to-one correspondence with a bit in the binary bitmap.

[0055] This step constructs a mutual exclusion bitmap matrix, which only needs to be executed once when the system starts. It assigns a bitmap encoding to each machine position that stores all its mutual exclusion machine positions, and subsequent conflict detection can be directly called by looking up the table.

[0056] In a preferred embodiment, constructing a mutually exclusive bitmap matrix includes:

[0057] S111. Based on the total number of airport stands p, create an integer list of length p+1 as the initial mutex bitmap matrix M. All elements in the initial mutex bitmap matrix M are initially set to 0.

[0058] A 1-based index is used, with list index 0 left unused. Indices 1 through p correspond to airport gate numbers 1 through p, directly mapped to the gate numbers to avoid index conversion errors in subsequent bitwise operations. The initial value of 0 indicates that the mutual exclusion bitmaps for all gates are blank, and no mutually exclusive gates have been marked.

[0059] S112. Traverse each tuple in the list of mutually exclusive edges. For machine positions a and b that are mutually exclusive in the tuple, set the value of the (b-1)th bit of the mutual exclusion bitmap M[a] of machine position a to 1, and set the value of the (a-1)th bit of the mutual exclusion bitmap M[b] of machine position b to 1. Finally, obtain the mutual exclusion bitmap of each machine position.

[0060] This step first iterates through each tuple (a, b) in the predefined list of mutually exclusive edges (edges) (representing that position a and position b are mutually exclusive), and then performs bidirectional bit marking on each pair of mutually exclusive positions.

[0061] In terms of algorithm implementation, the core mapping rule is that the (k-1)th bit of the bitmap corresponds to the kth gate of the airport (the least significant bit bit 0 corresponds to gate 1, bit 1 corresponds to gate 2, and so on). Therefore, when marking gate k, the binary bit mask corresponding to the gate needs to be obtained through the bit operation 1<<(k-1).

[0062] Therefore, the processing of machine position a is to perform a bitwise OR (|=) operation between the bit mask 1<<(b-1) corresponding to machine position b and M[a], and set the bit corresponding to machine position b in M[a] to 1, thus completing the mutual exclusion mark of machine position a to machine position b.

[0063] Similarly, the processing of machine position b is to perform a bitwise OR (|=) operation between the bitmask 1<<(a-1) corresponding to machine position a and M[b], and set the bit corresponding to machine position a in M[b] to 1, thus completing the mutual exclusion mark of machine position b to machine position a.

[0064] The bitwise OR operation ensures that multiple mutually exclusive relationships at the same position will not be overwritten, but will only be incremented bit by bit.

[0065] S113. Integrate the mutual exclusion bitmaps of all machine positions to obtain the mutual exclusion bitmap matrix.

[0066] After all mutually exclusive edges have been traversed, a complete mutual exclusion bitmap matrix is ​​formed. M[i] corresponding to each index i in this matrix is ​​a set of all machine positions that are mutually exclusive with machine position i, encoded in bitmap form. This matrix is ​​only constructed once during the system initialization phase, and can be directly looked up and called during subsequent conflict detection without repeated calculation.

[0067] Understandably, the time complexity of this technical solution is O(E), where E is the number of mutually exclusive edges. It can be constructed with only one traversal and without nested loops. The space complexity is O(p), requiring only a list of integers of length p+1. The total number of random bits in the storage space grows linearly, resulting in extremely low space consumption. By marking the bidirectional bits of the (a,b) tuple, the symmetry of the mutually exclusive relationship is ensured, which conforms to the actual business logic of mutual exclusion at airport gates.

[0068] In practical applications, for example, if the total number of workstations is p=5, the list of mutually exclusive edges is: edges=[(1,4),(1,5),(4,5),(2,4)].

[0069] First, initialize: M = [0,0,0,0,0,0] (indexes 0-5, 0 is unused);

[0070] Iterate through and process each edge:

[0071] Processing (1,4): M[1]|=1<<3 results in M[1]=8(0b1000), M[4]|=1<<0 results in M[4]=1(0b0001);

[0072] Processing (1,5): M[1]|=1<<4 results in M[1]=24(0b11000), M[5]|=1<<0 results in M[5]=1(0b0001);

[0073] Processing (4,5): M[4]|=1<<4 results in M[4]=17(0b10001), M[5]|=1<<3 results in M[5]=9(0b1001);

[0074] Processing (2,4): M[2]|=1<<3 results in M[2]=8(0b1000), M[4]|=1<<1 results in M[4]=19(0b10011);

[0075] The final matrix is ​​obtained as: M=[0,24,8,0,19,9] (in decimal).

[0076] Step S12: According to the machine position bit mapping relationship, the current occupancy status of all machine positions is encoded into an occupancy bit map of binary integers. If the i-th bit of the binary integer is 1, it means that machine position i is occupied.

[0077] The occupancy bitmap is a binary bitmap that is dynamically generated in real time. Its function is to compress the currently occupied bit into an integer, which is used for subsequent fast bit operations with the mutex bitmap to determine conflicts.

[0078] It also uses a machine bit mapping relationship to ensure that the bit mapping rules are completely consistent with those of the mutual exclusion bitmap.

[0079] In a preferred embodiment, the process of encoding the occupancy status of all machine positions at the current moment into a binary integer includes:

[0080] Obtain the current occupancy status of all camera positions and integrate them into a camera position status list;

[0081] Based on the total number of aircraft stands p at the airport, create an initial binary integer of number p bits, with an initial value of 0, indicating that all aircraft stands are initially idle.

[0082] Iterate through the list of current machine positions. For each occupied machine position k, set the (k-1)th bit of the binary integer to 1 to obtain the encoded binary integer.

[0083] In the algorithm implementation, for each occupied machine slot number k, calculate the bit mask corresponding to that machine slot, mask=1. (k 1) Perform a bitwise OR (|=) operation between the mask and the binary integer to set the binary bit corresponding to position k to 1 without overwriting other marked positions.

[0084] After all occupied positions have been processed, the resulting binary integer is the bitmap encoding of the current global occupied state.

[0085] Understandably, the time complexity of this technical solution is O(m), where m is the number of currently occupied aircraft stands. Compared to existing technical solutions, this solution is faster. The space complexity is O(1), using only one integer to store the occupancy status of all aircraft stands at the airport. In terms of compatibility, it is completely consistent with the bit order of the mutual exclusion bitmap matrix, and can directly perform & operations to determine conflicts.

[0086] In a real-world scenario, for example, given a total of 5 camera positions (p=5), and currently occupied positions 1 and 4.

[0087] Initialize the binary integer occ = 0.

[0088] Processor 1: mask=1<<0=1, occ=0|1=1 (binary 00001).

[0089] Processor 4: mask=1<<3=8, occ=1|8=9 (binary 01001).

[0090] The final bitmap is 9 (binary 01001), where bit 0 = 1 indicates that slot 1 is occupied, bit 3 = 1 indicates that slot 4 is occupied, and the remaining bits = 0 indicate that they are free.

[0091] After completing steps S11 and S12, bitmap conflict detection is performed. Essentially, this involves using the bitwise AND (&) operation natively supported by the CPU to complete the conflict determination with "zero loops and nanosecond-level efficiency".

[0092] Step S13: Traverse the occupied bitmap from low bit to high bit. For each occupied machine position i, perform a bitwise AND operation between the mutex bitmap of machine position i and the occupied bitmap to obtain a set of machine positions that are mutually exclusive with machine position i and are occupied.

[0093] This step identifies each currently occupied camera position i during the traversal process, based on the binary bit of the corresponding camera position i in the occupancy bitmap being 1. For each camera position i determined to be occupied, the system retrieves the pre-constructed mutex bitmap specific to camera position i and performs a bitwise AND operation between this mutex bitmap and the overall camera position occupancy bitmap. Leveraging the parallel processing characteristics of bitwise operations, the bitmap result obtained from this operation is a set of camera positions presented in binary encoding. All camera positions in this set simultaneously satisfy two conditions: they have a predefined mutual exclusion relationship with camera position i and they are currently occupied. In other words, all camera positions that are mutually exclusive with camera position i and are currently occupied are accurately selected.

[0094] Step S14: From the set of machine positions that are mutually exclusive with machine position i and are already occupied, extract machine positions with machine position numbers greater than i to obtain conflict pairs of machine position i.

[0095] After filtering the set of mutually exclusive and occupied positions with position i, to avoid duplicate detection of conflict pairs (e.g., (i,j) and (j,i) being judged as conflicting multiple times), it is necessary to further extract positions with position numbers greater than i from this set to generate conflict pairs for position i. Specifically, a dedicated mask `mask_gt_i` is constructed. This mask is generated through bitwise operations, clearing all the lower i bits of the bitmap of the corresponding position set, retaining only the higher bits of the position number greater than i. A bitwise AND operation is performed between this mask and the bitmap of the mutually exclusive and occupied position set, accurately filtering out positions with position numbers less than or equal to i, retaining only a subset of positions with position numbers greater than i. Then, using bitwise manipulation techniques, all positions `j` in this subset that are set are enumerated. Each position `j` is combined with the currently occupied position `i`, and the resulting (i,j) pair is the conflict pair corresponding to position i, thus ensuring the uniqueness and accuracy of the conflict pair records.

[0096] Step S15: Integrate all conflict pairs of camera positions into camera position conflict detection results.

[0097] In a specific application scenario, suppose there are five camera positions with mutual exclusion relationships: [(1,4), (1,5), (4,5), (2,4)].

[0098] Among them, M[1]=0b11000 (mutually exclusive with positions 4 and 5), M[2]=0b01000 (mutually exclusive with position 4), M[4]=0b10011 (mutually exclusive with positions 1, 2 and 5), and M[5]=0b10001 (mutually exclusive with positions 1 and 4).

[0099] The bitmap occupancy is 11010, indicating that positions 2, 4, and 5 are occupied.

[0100] The final output is [(2,4),(4,5)].

[0101] Understandably, the time complexity of this technical solution is O(p+k), where p is the total number of machine positions and k is the number of conflict pairs. Compared to the traditional O(n²) algorithm, the performance improvement is significant, where n is the total number of data points. Compared to the traditional set traversal algorithm, the bitmap algorithm improves performance by approximately 10 times when the number of machine positions is 100, and by approximately 50 times when the number of machine positions is 500. The algorithm's time complexity is reduced from O(n²) to O(p+k), where k is the actual number of conflict pairs, and in most scenarios, k < 0. <p²。

[0102] In a preferred embodiment, to enable rapid querying of the status of occupied aircraft positions, this embodiment provides the following technical solution to obtain the occupancy status of all aircraft positions at the current moment, including:

[0103] The plane projection of the airport apron is divided into several non-overlapping rectangular grids, and a unique ID is assigned to each grid. Based on the physical attributes of each aircraft stand, one or more grids to which the stand belongs are determined, and a mapping list between grid IDs and aircraft stands is established. Based on the received real-time position coordinates of the aircraft, the grid ID of the aircraft is matched. Based on the mapping list between grid IDs and aircraft stands, the aircraft stand occupied by the aircraft is determined.

[0104] This embodiment relies on gridded spatial indexing technology. Through the process of spatial division, mapping establishment, location matching and stand retrieval, it accurately obtains the occupancy status of all stands at the current moment. The specific implementation logic is as follows: First, the plane projection of the airport apron is regularly divided into several non-overlapping rectangular grids (such as 20m×20m), and a unique identifier ID is assigned to each grid to build the gridded indexing foundation of the airport space.

[0105] Subsequently, based on the actual physical spatial attributes of each aircraft station, one or more grids covered by its geographical range are determined (e.g., long-haul aircraft stations may span two adjacent grids). Based on this correspondence, a mapping list between grid IDs and aircraft stations is established, forming a direct lookup relationship, which provides a basis for subsequent rapid retrieval.

[0106] After receiving the real-time position coordinates of the aircraft, if the received coordinates are WGS-84 latitude and longitude coordinates, they are first converted from WGS-84 latitude and longitude coordinates to UTM projected coordinates to eliminate distance calculation errors under different latitudes, and then the grid ID where the aircraft is located in real time is matched according to the grid division rules.

[0107] Finally, based on the pre-established mapping list of grid IDs and aircraft stands, the corresponding aircraft stand is quickly retrieved through the matched grid ID, so as to accurately determine the aircraft stand currently occupied by the aircraft. The whole process uses a gridded spatial index design to simplify the query operation of aircraft stand occupancy determination into a table lookup process with an O(1) time complexity, which greatly improves the query efficiency of aircraft stand occupancy status.

[0108] Preferably, the planar projection of the airport apron is divided into several non-overlapping rectangular grids, including:

[0109] Obtain the WGS-84 latitude and longitude coordinates of the airport apron, and convert the WGS-84 latitude and longitude coordinates of the airport apron into UTM projection coordinates; based on the UTM projection coordinates of the airport apron, divide the airport apron into several non-overlapping rectangular grids.

[0110] This technical solution can eliminate distance calculation errors at different latitudes and ensure the uniformity of grid division.

[0111] Preferably, after assigning a unique ID to each grid, the method further includes: establishing association relationships between adjacent grids;

[0112] Matching the grid ID where the aircraft is located includes: obtaining the coarse grid judgment ID of the aircraft based on the real-time position coordinates of the aircraft, and simultaneously retrieving all adjacent grid IDs of the coarse grid judgment ID; comparing the real-time position coordinates of the aircraft with the coarse grid judgment ID and all adjacent grid IDs to obtain the grid ID where the aircraft is located.

[0113] The advantage of this preferred embodiment is that it can narrow down the scope of precise comparison. For example, based on the aircraft's planar coordinates, it can quickly and roughly match the grid in which the aircraft is located (such as G5), and simultaneously retrieve all adjacent grids of the target grid (such as G4, G6, G9) to avoid missing judgments across grids. Then, precise coordinate comparison is performed on these grids, rather than the entire airport's aircraft positions, thereby further improving the comparison efficiency and accuracy.

[0114] Since determining whether an item is occupied involves a direct table lookup, the time complexity is O(1). Preferably, Manhattan distance (|Δx|+|Δy|) can be used instead of Euclidean distance to avoid square root operations and simplify distance calculations using grid units.

[0115] The gridded index reduces the time complexity of the decision from O(n) to O(1). When the number of machines is 500, the time for a single decision is reduced from about 50μs to about 1μs, which is about 50 times better than the performance.

[0116] In a preferred embodiment, after obtaining the machine position conflict detection result, incremental conflict lifecycle management can be performed. By maintaining the active state set of conflict pairs and the most recent detection timestamp, the complete lifecycle tracking of conflict generation, persistence, and resolution can be achieved, avoiding repeated alarms and providing an automatic timeout resolution mechanism.

[0117] After obtaining the gate conflict detection results, this embodiment further includes: generating corresponding logs for conflict pairs with status changes in the gate conflict detection results; outputting alarm information corresponding to the conflict pairs with status changes; recording the timestamp of the latest detection for each conflict pair; and for each conflict pair, if no conflict pair is detected again within a consecutive preset time period starting from the latest detection timestamp, then the alarm information for the conflict pair is cleared.

[0118] In practical implementation, set difference operations can be used to efficiently identify newly added and resolved conflicts, with only state changes being logged. Simultaneously, a recent detection timestamp is maintained for each conflict pair; if no conflict is detected for a consecutive preset period, it is automatically considered resolved, handling false alarms caused by data latency or network jitter. Furthermore, it can implement set freezing deduplication optimization, automatically handling the equivalence of duplicate conflict pairs (i,j) and (j,i), avoiding duplicate storage.

[0119] Understandably, incremental updates avoid duplicate alerts, reducing log output by approximately 80%. The timeout mechanism effectively handles false alarms caused by data latency, reducing the false alarm rate by approximately 90%.

[0120] In terms of real-time data stream processing architecture, an asynchronous message queue architecture can be adopted, which consumes real-time aircraft location data through Kafka and uses a batch processing window mechanism to balance real-time performance and system load, thereby achieving a high-throughput conflict detection pipeline.

[0121] This architecture is a linear pipeline structure; see [link / reference] Figure 2 The system is composed of a Kafka message queue, a data consumption module, a batch processing window, a location update module, a conflict detection module, and an incremental conflict lifecycle management module. Each module performs its own function, flows unidirectionally, and is not nested or coupled. In its implementation, on the one hand, it uses asynchronous Kafka consumption, subscribing to a specified aircraft location topic based on AIOKafkaConsumer, and pushes the parsed real-time messages to an asynchronous queue, thus decoupling data consumption and processing. On the other hand, it designs a batch processing window processor that non-blockingly retrieves queue messages at 50ms intervals and aggregates them into batches. When the 1-second batch processing window threshold is reached, the processing flow is triggered. First, it traverses the batch to parse aircraft latitude and longitude, gate ID, and other information, calls the gate occupancy determination method to update the gate status, then performs bitmap conflict detection, and passes the detection results to the conflict registry to complete conflict status updates and timeout checks. After the window processing is completed, the gate status is cleared to avoid cross-window accumulation. Simultaneously, a 5-second interval is set for periodic log output, and the maximum capacity of the asynchronous queue is set to 5000 records to prevent data overflow. The overall architecture adopts an asynchronous non-blocking design, with data consumption and batch processing being independent asynchronous tasks. Even if a single module is temporarily blocked, it will not affect the overall data flow, providing excellent high availability. This architecture can support processing over 5000 aircraft position update messages per second, with end-to-end latency for conflict detection less than 100ms, fully meeting the business requirements for real-time monitoring of airport parking space conflicts.

[0122] The proposed real-time aircraft stand conflict detection method based on bitmap optimization achieves significant performance improvements while maintaining detection accuracy: Detection performance: With 500 stands, the single detection time is <1ms, approximately 50 times faster than traditional algorithms; System throughput: Supports processing over 5000 position update messages per second, meeting the needs of high-concurrency scenarios; Real-time performance: Conflict detection latency is <100ms, meeting the requirements of real-time airport operation monitoring; Reliability: Incremental lifecycle management and timeout mechanisms effectively reduce the false alarm rate by approximately 90%. This method provides efficient and reliable technical support for airport operation safety and has significant practical value and promotional implications.

[0123] In another embodiment, see Figure 3 A real-time detection device for aircraft parking position conflicts is provided, comprising:

[0124] The mutual exclusion bitmap matrix construction module 101 is used to construct a mutual exclusion bitmap matrix based on the pre-acquired list of mutual exclusion relationship edges between machine positions and the machine position bit mapping relationship. The mutual exclusion bitmap matrix includes a mutual exclusion bitmap corresponding to each machine position. The mutual exclusion bitmap is a binary bitmap encoding. The machine position bit mapping relationship indicates that each machine position corresponds one-to-one with the bits in the bitmap encoding.

[0125] The real-time occupancy bitmap acquisition module 102 is used to encode the occupancy status of all machine positions at the current time into an occupancy bitmap of binary integers according to the machine position bit mapping relationship; in the occupancy bitmap, if the i-th bit of the binary integer is 1, it means that machine position i is occupied;

[0126] The real-time conflict detection module 103 is used to traverse the occupancy bitmap from low to high bits. For each occupied machine position i, the mutual exclusion bitmap of machine position i is bitwise ANDed with the occupancy bitmap to obtain a set of machine positions that are mutually exclusive with and occupied by machine position i. From the set of machine positions that are mutually exclusive with and occupied by machine position i, machine positions with machine position numbers greater than i are extracted to obtain conflict pairs of machine position i. All conflict pairs of machine positions are integrated into a machine position conflict detection result.

[0127] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0128] In another embodiment, a real-time aircraft parking position conflict detection device is provided, comprising:

[0129] A master controller, and a memory connected to the master controller; the memory storing program instructions; the master controller executing the program instructions stored in the memory to perform the method described in any of the preceding embodiments.

[0130] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0131] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0132] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0133] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0134] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0135] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0136] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0137] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0138] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for real-time detection of aircraft parking position conflicts, characterized in that, include: Based on the pre-acquired list of mutually exclusive edges between machine positions and the machine position bit mapping relationship, a mutual exclusion bitmap matrix is ​​constructed. The mutual exclusion bitmap matrix includes a mutual exclusion bitmap corresponding to each machine position. The mutual exclusion bitmap is a binary bitmap code used to reflect other machine positions that are mutually exclusive with the corresponding machine position. The machine position bit mapping relationship indicates that each machine position corresponds one-to-one with a bit in the bitmap code. Based on the machine position bit mapping relationship, the current occupancy status of all machine positions is encoded into an occupancy bit map of binary integers; in the occupancy bit map, if the i-th bit of the binary integer is 1, it means that machine position i is occupied. Traverse the occupied bitmap from low to high bits. For each occupied machine position i, perform a bitwise AND operation between the mutex bitmap of machine position i and the occupied bitmap to obtain a set of machine positions that are mutually exclusive with machine position i and are occupied. From the set of positions that are mutually exclusive with position i and are already occupied, extract the positions with position numbers greater than i to obtain the conflict pairs of position i. All conflict pairs from all camera positions are integrated into a single camera position conflict detection result.

2. The method according to claim 1, characterized in that, Constructing a mutual exclusion bitmap matrix includes: Based on the total number of airport stands p, create an integer list of length p+1 as the initial mutex bitmap matrix M, where all elements in the initial mutex bitmap matrix M are initially set to 0. Iterate through each tuple in the list of mutually exclusive edges. For machine positions a and b that are mutually exclusive in the tuple, set the value of the (b-1)th bit of the mutual exclusion bitmap M[a] of machine position a to 1, and set the value of the (a-1)th bit of the mutual exclusion bitmap M[b] of machine position b to 1. Finally, the mutual exclusion bitmap of each machine position is obtained. By integrating the mutual exclusion bitmaps of all machine positions, a mutual exclusion bitmap matrix is ​​obtained.

3. The method according to claim 1, characterized in that, The process of encoding the current occupancy status of all machine positions into binary integers includes: Obtain the current occupancy status of all camera positions and integrate them into a camera position status list; Given the total number of aircraft stands p at the airport, create an initial binary integer of number p bits, with an initial value of 0. Iterate through the list of current machine positions. For each occupied machine position k, set the (k-1)th bit of the binary integer to 1 to obtain the encoded binary integer.

4. The method according to claim 1, characterized in that, Get the current occupancy status of all camera positions, including: The plane projection of the airport apron is divided into several non-overlapping rectangular grids, and a unique ID is assigned to each grid. Based on the physical attributes of each camera position, determine one or more grids to which the camera position belongs, and establish a mapping list between grid IDs and camera positions; Based on the received real-time position coordinates of the aircraft, match the grid ID where the aircraft is located; Based on the mapping list between the grid ID and the aircraft stand, the aircraft stand occupied by the aircraft is determined.

5. The method according to claim 4, characterized in that, The plane projection of the airport apron is divided into several non-overlapping rectangular grids, including: Obtain the WGS-84 latitude and longitude coordinates of the airport apron, and convert the WGS-84 latitude and longitude coordinates of the airport apron into UTM projection coordinates; based on the UTM projection coordinates of the airport apron, divide the airport apron into several non-overlapping rectangular grids.

6. The method according to claim 4, characterized in that, After assigning a unique ID to each grid, the process also includes: establishing associations between adjacent grids; Matching the grid ID where the aircraft is located includes: obtaining the coarse grid judgment ID of the aircraft based on the real-time position coordinates of the aircraft, and simultaneously retrieving all adjacent grid IDs of the coarse grid judgment ID; comparing the real-time position coordinates of the aircraft with the coarse grid judgment ID and all adjacent grid IDs to obtain the grid ID where the aircraft is located.

7. The method according to claim 1, characterized in that, After obtaining the gate conflict detection results, the following is also included: For conflict pairs with status changes detected in the station conflict detection results, corresponding logs are generated.

8. The method according to claim 7, characterized in that, Also includes: Output the alarm information corresponding to the conflict pairs where there are state changes; Record the timestamp of the latest detection for each conflict; For each conflict pair, if no conflict pair is detected again within a consecutive preset time period starting from the latest detection timestamp, the alarm information for the conflict pair is cleared.

9. A real-time detection device for aircraft parking position conflicts, characterized in that, include: The mutual exclusion bitmap matrix construction module is used to construct a mutual exclusion bitmap matrix based on the pre-acquired list of mutual exclusion edges between machine positions and the machine position bit mapping relationship. The mutual exclusion bitmap matrix includes a mutual exclusion bitmap corresponding to each machine position. The mutual exclusion bitmap is a binary bitmap encoding. The machine position bit mapping relationship indicates that each machine position corresponds one-to-one with a bit in the bitmap encoding. The real-time occupancy bitmap acquisition module is used to encode the occupancy status of all machine positions at the current moment into an occupancy bitmap of binary integers according to the machine position bit mapping relationship; in the occupancy bitmap, if the i-th bit of the binary integer is 1, it means that machine position i is occupied; The real-time conflict detection module is used to traverse the occupancy bitmap from low to high bits. For each occupied machine position i, the mutual exclusion bitmap of machine position i is bitwise ANDed with the occupancy bitmap to obtain a set of machine positions that are mutually exclusive with and occupied by machine position i. From the set of machine positions that are mutually exclusive with and occupied by machine position i, machine positions with machine position numbers greater than i are extracted to obtain conflict pairs of machine position i. All conflict pairs of machine positions are integrated into a machine position conflict detection result.

10. A real-time aircraft parking position conflict detection device, characterized in that, include: The main controller and the memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory to perform the method as described in any one of claims 1 to 8.

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