Airport Resource Scheduling Analysis Method Based on Big Data Platform

Abstract

The present invention provides an airport resource scheduling analysis method based on a big data platform, which relates to the technical field of data processing. The method includes: detecting conflicts in the abnormal flight data set, and recording the conflict type, occurrence time, and flight information involved to obtain a resource conflict data set; evaluating the impact of the resource conflict data set, calculating the impact score of each conflict event on the airport operation, and sorting the conflict events according to the impact score to determine the scheduling tasks to be adjusted first, obtaining the priority scheduling task data; optimizing the resource allocation for the priority scheduling task data, calculating the optimal resource reallocation plan based on the airport available resource information, flight scheduling constraint conditions, and historical scheduling adjustment records, adjusting the apron allocation, dynamically adjusting the boarding gates, and optimizing the ground service equipment scheduling to obtain an optimized scheduling plan; the present invention improves the autonomy and accuracy of airport resource scheduling analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, in particular to an airport resource scheduling analysis method based on a big data platform. Background Art

[0002] In airport operation management, resource scheduling is an important link to ensure the normal operation of flights. In the prior art, airport resource scheduling usually relies on manual experience and rule-based methods for management, mainly involving multiple aspects such as apron allocation, boarding gate scheduling, and ground traffic control. Traditional methods mainly perform resource allocation based on fixed scheduling rules and a first-come, first-served strategy, and at the same time, simple statistical analysis is carried out in combination with historical data to optimize the resource utilization efficiency. Some technical solutions have introduced computer-aided decision-making. By constructing an airport operation database and combining basic optimization algorithms such as linear programming or heuristic algorithms, preliminary optimization allocation of resources is carried out. However, due to the complex airport operation environment and the real-time change of flight schedules, the prior art still has great limitations in dealing with dynamic adjustments and emergencies.

[0003] There is a great deal of uncertainty in the prior art when dealing with emergencies. For example, when a flight is delayed or cancelled due to weather reasons, rapid decisions need to be made for the reallocation of apron positions, boarding gates, and ground service equipment. In the existing solutions, it usually relies on the duty dispatcher to manually adjust the resource allocation according to their own experience. This method is difficult to quickly calculate the optimal solution, resulting in some resources being vacant or re-scheduled repeatedly. For example, during peak hours, some apron positions are left vacant for a long time due to failure to adjust in time, while apron positions in other areas are overloaded, further increasing the airport operation pressure. This scheduling method is difficult to accurately adapt to the complex dynamic environment, which may lead to resource waste or an extended waiting time for passengers, thus affecting the overall operation efficiency of the airport. Summary of the Invention

[0004] The purpose of the present invention is to provide an airport resource scheduling analysis method based on a big data platform, aiming to solve the problems mentioned in the background art.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] Perform conflict detection on the abnormal flight data set, calculate the usage intersection of apron positions, boarding gates, and ground service equipment based on the time dimension and the space dimension, identify the flights with resource competition, and record the conflict type, occurrence time, and flight information involved to obtain a resource conflict data set;

[0007] Conduct an impact assessment on the resource conflict dataset, calculate the impact scores of each conflict event on airport operations based on airport resource usage rules and flight priorities, sort the conflict events according to the impact scores, determine the scheduling tasks to be adjusted first, and obtain the priority scheduling task data;

[0008] Optimize the resource allocation for the priority scheduling task data. Based on the available airport resource information, flight scheduling constraint conditions, and historical scheduling adjustment records, calculate the optimal resource reallocation plan, adjust the apron allocation, dynamically adjust the boarding gates, and optimize the ground service equipment scheduling to obtain an optimized scheduling plan.

[0009] Preferably, conduct conflict detection on the abnormal flight dataset, calculate the cross-usage of apron, boarding gate, and ground service equipment based on the time dimension and space dimension, identify the flights with resource competition, and record the conflict type, occurrence time, and flight information involved to obtain the resource conflict dataset, including:

[0010] Obtain the flight operation information in the abnormal flight dataset, extract the arrival time, departure time, apron occupancy situation, boarding gate allocation situation, and ground service equipment usage situation of the flights to form flight resource occupancy data;

[0011] According to the flight resource occupancy data, analyze the resource occupancy situation of each flight in adjacent time periods according to the time dimension, calculate the time crossover degree, and identify the flights that cross-use the same resources in the same time period to form time conflict data;

[0012] According to the flight resource occupancy data, analyze the physical locations of the aprons, boarding gates, and ground service equipment used by each flight according to the space dimension, calculate the space proximity degree, and identify the flights with space conflicts to form space conflict data;

[0013] Conduct a comprehensive analysis of the time conflict data and space conflict data, remove duplicate records, and filter out the conflict events that need to be adjusted in combination with the flight scheduling constraint conditions to obtain the resource conflict dataset.

[0014] Preferably, according to the flight resource occupancy data, analyze the resource occupancy situation of each flight in adjacent time periods according to the time dimension, calculate the time crossover degree, and identify the flights that cross-use the same resources in the same time period to form time conflict data, including:

[0015] According to the flight resource occupancy data, determine the arrival time, departure time, apron occupancy period, boarding gate usage period, and ground service equipment usage period of each flight to form flight time occupancy data;

[0016] Calculate the overlap degree of flights using the same resources in adjacent time periods based on flight time occupancy data, identify flights with time intersections, and mark the overlapping time intervals to form time intersection records;

[0017] Filter out time conflict events that affect normal flight scheduling based on the time intersection records, and record the conflicting flights, conflict time periods, and resource types involved to form time conflict data.

[0018] Preferably, according to the flight resource occupancy data, analyze the physical locations of the parking positions, boarding gates, and ground service equipment used by each flight in the spatial dimension, calculate the spatial proximity, identify flights with spatial conflicts, and form spatial conflict data, including:

[0019] Determine the geographical location coordinates of the parking positions, boarding gates, and ground service equipment of the flights based on the flight resource occupancy data to form flight spatial occupancy data;

[0020] Calculate the physical distances between the resources used by adjacent flights in the same time period based on the flight spatial occupancy data to form spatial proximity data;

[0021] Filter the spatial proximity data that exceeds the preset spatial conflict threshold, identify flights with spatial conflicts, and record the conflicting resources, conflict locations, and affected flights to form spatial conflict data.

[0022] Preferably, conduct an impact assessment on the resource conflict data set, and calculate the impact scores of each conflict event on airport operations based on airport resource usage rules and flight priorities, including:

[0023] Extract the conflicting flight information in the resource conflict data set, determine the number of flights involved in the conflict, the conflict duration, and the types of airport resources affected by the conflict to form conflict impact factor data;

[0024] Based on the conflict impact factor data and flight priority information, calculate the delay time of high-priority flights due to conflicts and the number of subsequent flights that may be affected to form flight impact degree data;

[0025] Based on the conflict impact factor data and airport resource usage rules, calculate the change in airport resource utilization rate caused by the conflict, and evaluate the possible resource idleness or overload situation due to the conflict to form resource impact degree data;

[0026] Based on the flight impact degree data, resource impact degree data, and resource conflict data set, perform weighted summation to calculate the conflict impact scores of each conflict event.

[0027] The above solutions of the present invention at least include the following beneficial effects:

[0028] First, the comprehensive collection and time - series storage of airport operation data enable the scheduling system to have real - time access to flight plans, apron usage, boarding gate scheduling status, the distribution of ground service equipment, and historical operation records. This data - driven approach allows the scheduling system to quickly respond to changes in the airport operation status, providing a solid data foundation for resource scheduling optimization. Compared with the existing technology that relies on historical statistical data for simple analysis, this method can dynamically update the airport operation status, enabling the scheduling system to more accurately predict resource usage and reduce the problem of resource allocation imbalance caused by data lag.

[0029] Second, by screening abnormal situations in the time - series operation data, abnormal situations in flight scheduling can be automatically identified, such as flight plan adjustments, equipment failures, weather impacts, etc. Based on this, high - deviation flight data can be screened out to form an abnormal flight data set. This process can effectively reduce the impact of abnormal data on scheduling optimization and improve the accuracy of scheduling decisions. In the existing technology, the identification of abnormal flights usually relies on the subjective judgment of dispatchers, making it difficult to detect potential problems in a timely manner, resulting in inaccurate resource adjustments and affecting the overall operation efficiency.

[0030] In addition, this method introduces a resource conflict detection mechanism based on time and space dimensions, which can accurately identify flight resource competition situations, record conflict types, occurrence times, and flight information involved, forming a resource conflict data set. Compared with the existing fixed - rule - based scheduling methods, this method can analyze resource competition situations more intelligently. For example, it can identify situations where multiple flights occupy the same apron or boarding gate within the same time period, detect resource conflicts in advance, and make optimization adjustments. This method overcomes the problem of difficult resource allocation in peak periods in traditional scheduling methods, improves the balance of resource usage, and reduces the situation of long - term apron vacancy or over - load usage.

[0031] In terms of the impact assessment of conflict events, this method uses airport resource usage rules and flight priority calculation methods to quantify the impact degree of conflicts on airport operations and determine the scheduling tasks to be adjusted first based on the impact score. Compared with the existing technology that relies on dispatchers to manually adjust resources, this method can calculate the optimal adjustment order based on data, improving the scientificity and rationality of scheduling optimization. For example, when adjusting flights, it can give priority to ensuring the resource needs of high - priority flights such as international flights and transfer flights, avoiding large - scale flight delays caused by improper resource scheduling, and improving the stability of airport operations.

[0032] Finally, during the resource optimization process, this method can calculate the optimal resource reallocation plan based on the airport's available resource information, flight scheduling constraints, and historical scheduling adjustment records, and execute the optimized scheduling plan, making the resource allocation more intelligent and dynamically adaptable. In contrast, existing solutions are mostly based on static scheduling rules and are difficult to handle scheduling changes brought about by emergencies, resulting in a lag in resource allocation. Through this method, the scheduling of parking positions, boarding gates, and ground service equipment can more accurately match the actual needs, avoiding resource waste or overload and improving the overall resource utilization rate.

[0033] In summary, compared with the prior art, this method has the following beneficial effects: improving the scheduling system's perception ability of the airport's operating status based on real-time data collection and time-series storage on the big data platform; reducing the impact of abnormal data on scheduling optimization through screening of abnormal flight data and improving scheduling accuracy; adopting a resource conflict detection mechanism in both the time dimension and the space dimension to accurately identify resource competition situations and reduce resource vacancy or overload problems; prioritizing conflict events based on impact scores to improve the scientific nature of scheduling optimization; dynamically adjusting airport resources through historical data analysis and resource optimization calculations to improve the self-adaptive ability of the scheduling system. Finally, this method can effectively reduce flight delays, improve resource utilization rates, enhance the overall efficiency of airport operations, and provide passengers with a smoother travel experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flowchart of the airport resource scheduling analysis method based on the big data platform provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0036] As Figure 1 shown, the embodiments of the present invention propose an airport resource scheduling analysis method based on the big data platform, and the method includes:

[0037] S100. Obtain airport operation data, including flight plan data, parking position usage data, boarding gate scheduling data, ground service equipment status data, and historical operation records, store the airport operation data in the big data platform, and perform time series annotation on the airport operation data to obtain time-series operation data;

[0038] S200. Perform anomaly screening on the time - sequenced operation data, eliminate missing data and error data, calculate the flight scheduling deviation value based on the deviation between the flight plan data and the actual operation data, and screen out the high - deviation flight data according to the preset deviation threshold to obtain the abnormal flight data set;

[0039] S300. Perform conflict detection on the abnormal flight data set, calculate the cross - utilization of apron positions, boarding gates, and ground service equipment based on the time dimension and space dimension, identify the flights with resource competition, and record the conflict type, occurrence time, and flight information involved to obtain the resource conflict data set;

[0040] S400. Perform impact assessment on the resource conflict data set, calculate the impact score of each conflict event on the airport operation based on the airport resource usage rules and flight priorities, and sort the conflict events according to the impact score to determine the scheduling tasks to be adjusted first to obtain the priority scheduling task data;

[0041] S500. Optimize the resource allocation for the priority scheduling task data, calculate the optimal resource re - allocation plan based on the airport available resource information, flight scheduling constraints, and historical scheduling adjustment records, adjust the apron position allocation, dynamically adjust the boarding gates, and optimize the ground service equipment scheduling to obtain the optimized scheduling plan;

[0042] S600. Execute the optimized scheduling plan, dynamically adjust the airport resources, and record the adjusted operation data in real - time and update it to the big data platform.

[0043] In the embodiment of the present invention, during the airport operation, the rationality of resource scheduling is directly related to the flight punctuality rate and the resource utilization efficiency. By constructing an airport resource scheduling analysis method based on the big data platform, problems such as relying on manual experience, slow response speed, and unreasonable scheduling in the traditional resource allocation method can be effectively solved, the resource utilization rate can be improved, and the flight delay rate can be reduced. First, the comprehensive collection and storage of airport operation data provide a basis for subsequent data analysis and optimization. By collecting flight plan data, apron position usage data, boarding gate scheduling data, ground service equipment status data, and historical operation records and storing them in the big data platform, the integrity and traceability of the data can be ensured. The introduction of time - series annotation enables the original data to be managed in chronological order, which helps to capture the dynamic changes during the airport operation process and provides a reliable data basis for subsequent analysis.

[0044] The abnormal screening of time-series operation data is the key to ensuring scheduling accuracy. Due to the complex airport operation environment, abnormal situations such as data loss, sensor errors, and schedule adjustments may occur. If these inaccurate data are not removed in time, it may affect the accuracy of scheduling decisions. By calculating the deviation between flight schedule data and actual operation data, abnormal flights can be accurately identified, and high-deviation flight data can be screened based on a preset deviation threshold to form an abnormal flight data set. This process enables scheduling analysis to be unaffected by abnormal data, improves data quality, and enables subsequent conflict detection and scheduling optimization to be based on more accurate data, ensuring the reliability of scheduling optimization.

[0045] Regarding the conflict detection of airport resources, by analyzing the resource usage situation in the time dimension and space dimension, it is possible to effectively identify potential resource competition problems among flights. Traditional scheduling methods usually rely on static scheduling rules and are difficult to adjust quickly in a complex operation environment. The data-based method can calculate the usage intersection of apron positions, boarding gates, and ground service equipment in real time, accurately identify resource competition, and form a resource conflict data set. Recording the conflict type, occurrence time, and relevant flight information enables the scheduling system to take corresponding optimization measures for different types of conflicts, further enhancing the flexibility of scheduling.

[0046] In terms of the impact assessment of conflict events, by introducing the calculation methods of airport resource usage rules and flight priorities, the impact degree of different conflicts on airport operation can be quantified, and the scheduling tasks that need to be adjusted first can be determined through impact scoring. This method makes scheduling optimization more intelligent and data-driven, can avoid unreasonable scheduling caused by inaccurate human judgment, and improve the accuracy of resource scheduling. At the same time, the optimized scheduling task data ensures the rationality of resource reallocation and avoids resource idleness or overuse.

[0047] For the priority scheduling tasks, by adopting an optimization calculation method based on airport available resource information, flight scheduling constraint conditions, and historical scheduling adjustment records, the optimal resource reallocation plan can be accurately determined. This method fully considers the flight time arrangement, resource scheduling constraint conditions, and historical scheduling adjustment experience to ensure the rationality of resource allocation. By adjusting apron positions, dynamically adjusting boarding gates, and optimizing ground service equipment scheduling, the overall utilization efficiency of airport resources can be improved. Finally, the optimized scheduling plan is executed, and the adjusted operation data is recorded in real time and updated to the big data platform for subsequent scheduling analysis, enabling the scheduling system to continuously optimize, improve the overall operation efficiency of the airport, and reduce the risk of flight delays.

[0048] Among them, the obtaining of airport operation data, storing the airport operation data in the big data platform, and performing time series annotation on the airport operation data to obtain time-series operation data specifically include:

[0049] During the process of airport operation management, resource scheduling involves a large amount of dynamic data, including flight schedule data, apron usage data, boarding gate scheduling data, ground service equipment status data, and historical operation records, etc. To ensure the accuracy of scheduling optimization, it is necessary to systematically collect, store, and process this data so that it can serve as the basis for subsequent scheduling optimization.

[0050] The acquisition of airport operation data usually relies on the internal operation management system of the airport, which can record the usage of various airport resources in real time. For example, flight schedule data mainly comes from the operation coordination system between airlines and the airport, including the estimated arrival time, estimated departure time, apron allocation plan of flights, etc.; apron usage data reflects the occupancy of the current airport aprons, including the assigned flight information, remaining available aprons, special-purpose aprons, etc.; boarding gate scheduling data involves the usage of each boarding gate, including the flights currently boarding, the flights scheduled to board, and possible adjustments; ground service equipment status data covers the real-time usage of ground handling equipment at the airport, such as baggage transfer systems, refueling trucks, boarding bridges, etc. These data play an important role in optimizing the scheduling of airport resources. In addition, historical operation records include past flight scheduling information, resource usage, and emergency response situations, which can provide data support for subsequent optimization.

[0051] After completing data collection, the data needs to be stored in a big data platform. The introduction of the big data platform can solve problems such as large amounts of airport operation data, complex data formats, and diverse data sources. Through the big data platform, the collected airport operation data can be standardized, enabling data from different sources to be stored in a unified data format and ensuring data consistency, integrity, and traceability. At the same time, the big data platform can support real-time data storage, allowing the data to be continuously updated as the airport operation changes, providing the latest operation status information for the scheduling system.

[0052] To ensure the timeliness and availability of data, it is necessary to perform time series annotation on the airport operation data stored in the big data platform so that it can be managed in chronological order. The process of time series annotation includes adding timestamps to each piece of data, enabling the data to be sorted and retrieved according to the time dimension. For example, for flight schedule data, key time points such as the scheduled departure time and scheduled arrival time of a flight can be recorded, so that the operation status of the flight can be clearly tracked in subsequent scheduling analysis. For resource data such as parking positions, boarding gates, and ground equipment, their occupancy status can be recorded according to the time dimension to ensure that the scheduling system can query the usage status of resources at any time and analyze the scheduling trend of resources in combination with historical data. Through time series storage, the airport scheduling system can achieve dynamic management of data, enabling scheduling optimization to be calculated based on the latest operation data, and improving the scientificity and accuracy of resource scheduling.

[0053] Among them, the abnormal screening of the time-series operation data, eliminating missing data and error data, calculating the flight scheduling deviation value based on the deviation between the flight schedule data and the actual operation data, and screening out high-deviation flight data according to the preset deviation threshold to obtain the abnormal flight data set, specifically includes:

[0054] In the process of airport resource scheduling, the accuracy of data directly affects the effect of scheduling optimization. Due to the complex airport operation environment, missing data, error data, or abnormal data may occur during the data collection process. If these data are directly used for scheduling optimization, it may lead to unreasonable resource allocation and inaccurate scheduling adjustment. Therefore, before performing scheduling analysis on the time-series operation data, it is necessary to first perform abnormal screening to ensure the reliability of the data.

[0055] The first step in abnormal screening is to eliminate missing data. Missing data may come from various situations. For example, due to network transmission problems, some sensing devices fail to upload data on time, resulting in incomplete flight schedule data; or due to airport system maintenance, the real-time status data of some resources fails to be updated in time. If these missing data are not processed, it may affect the scheduling system's judgment of the airport operation status. Therefore, during the data screening process, it is first necessary to check the integrity of the data and use data repair methods to supplement the missing values. For example, the possible values of the missing data can be inferred using historical data, or the data at adjacent time points can be used for interpolation filling to improve the integrity of the data.

[0056] While removing missing data, it is also necessary to identify and remove incorrect data. Incorrect data usually refers to abnormal data caused by incorrect data formats, data entry errors, or sensor failures. For example, the planned landing time of some flights is earlier than the takeoff time, which is obviously illogical; the status data of some parking positions shows that they are occupied by multiple flights at the same time, and this situation may be caused by data collection errors. During the data screening process, data validation rules can be used, such as checking whether the data conforms to logical constraints and airport operation rules, etc., to ensure the accuracy of the data. At the same time, the method of comparing with historical data can also be used to identify abnormal data with too large deviations from the normal situation and make corrections or deletions.

[0057] After removing missing data and incorrect data, it is necessary to further calculate the flight scheduling deviation value to identify flights that are greatly affected by abnormal situations. The calculation of the scheduling deviation value is based on the comparison of flight plan data and actual operation data, such as the difference between the planned takeoff time and the actual takeoff time of a flight, the difference between the planned landing time and the actual landing time, etc. If there is a large deviation between the actual operation of a flight and the planned time, it indicates that the flight may be disturbed by emergencies, weather conditions or other uncertain factors and may require scheduling adjustments.

[0058] In order to screen out flight data that is greatly affected, it is necessary to set a preset deviation threshold. For example, if the difference between the planned takeoff time and the actual takeoff time of a flight exceeds a certain time range, it is considered that the flight has a large scheduling deviation and should be treated as an abnormal flight. Similarly, if there is a large deviation between the parking position usage time of a flight and the planned time, it may mean that the ground service time of the flight is affected and corresponding adjustments are needed. By setting a reasonable deviation threshold, high-deviation flight data can be effectively screened out to form an abnormal flight data set, so that in the subsequent scheduling optimization process, these abnormal flights can be adjusted preferentially to improve the flexibility and adaptability of airport resource scheduling.

[0059] Through the above abnormal screening method, it can be ensured that the data used by the scheduling system is accurate and reliable, thereby improving the accuracy of scheduling optimization. Compared with the existing method of screening abnormal data relying on manual judgment, this method can automatically detect abnormal situations in the data, reduce human intervention, and improve the intelligent level of scheduling adjustments. At the same time, by establishing an abnormal flight data set, the scheduling system can respond more quickly to emergencies in airport operations, optimize resource allocation, reduce the risk of flight delays, and improve the overall operation efficiency of the airport.

[0060] In a preferred embodiment of the present invention, conflict detection is performed on the abnormal flight dataset, the cross - usage of apron positions, boarding gates, and ground service equipment is calculated based on the time dimension and the space dimension, flights with resource competition are identified, and the conflict type, occurrence time, and flight information involved are recorded to obtain a resource conflict dataset, including:

[0061] Obtain the flight operation information in the abnormal flight dataset, extract the arrival time, departure time, apron position occupancy, boarding gate allocation, and ground service equipment usage of the flight to form flight resource occupancy data;

[0062] According to the flight resource occupancy data, analyze the resource occupancy of each flight in adjacent time periods according to the time dimension, calculate the time intersection degree, identify the flights that cross - use the same resources in the same time period, and form time conflict data;

[0063] According to the flight resource occupancy data, analyze the physical locations of the apron positions, boarding gates, and ground service equipment used by each flight according to the space dimension, calculate the space proximity degree, identify the flights with space conflicts, and form space conflict data;

[0064] Perform comprehensive analysis on the time conflict data and the space conflict data, remove duplicate records, and combine flight scheduling constraint conditions to screen out conflict events that need to be adjusted to obtain a resource conflict dataset.

[0065] In the embodiment of the present invention, for the abnormal flight dataset existing in the airport operation process, the identification of resource conflicts is the core of scheduling optimization. The existing manual scheduling method usually relies on empirical judgment and is difficult to quickly identify resource competition problems in complex environments. However, the data - analysis - based method can accurately identify flights with resource competition through calculations in the time dimension and the space dimension on the basis of the abnormal flight dataset, improving the scheduling efficiency and accuracy.

[0066] Analyzing the resource usage of flights based on the time dimension can calculate the resource cross - situation in adjacent time periods and form time conflict data. In practical applications, there may be cross - situations in the arrival time, departure time, apron position occupancy period, boarding gate usage period, and ground service equipment usage period of flights. If not optimized, it may lead to multiple flights occupying the same resource simultaneously, affecting the overall operation efficiency of the airport. By calculating the flight time intersection degree, flights that cross - use the same resources in the same time period can be accurately identified, and the overlapping time intervals can be marked to form time intersection records. This method enables the scheduling system to automatically analyze the time conflict situation of flight scheduling and avoid flight delays or scheduling chaos problems caused by resource usage cross - overs.

[0067] Analyzing the resource usage of flights in the spatial dimension can calculate the spatial proximity of flights and form spatial conflict data. During the use of apron positions, boarding gates, and ground service equipment by flights, unreasonable resource allocation may occur due to excessive proximity. For example, if flights at adjacent apron positions simultaneously require the use of the same ground equipment and the scheduling fails to make reasonable arrangements, it may cause conflicts in equipment use and affect the efficiency of flight operations. By calculating the spatial proximity, flights with spatial conflicts can be accurately identified, ensuring reasonable resource allocation and improving the scientific nature of airport scheduling.

[0068] Through the comprehensive analysis of time conflict data and spatial conflict data, duplicate records can be eliminated, and conflict events that need to be adjusted can be screened out in combination with flight scheduling constraints to form a resource conflict dataset. This method enables the scheduling system to intelligently identify and optimize the use of flight resources, improve the automation level of flight scheduling, reduce manual intervention, and enhance the overall scheduling efficiency.

[0069] Among them, the comprehensive analysis of time conflict data and spatial conflict data, removing duplicate records, and screening out conflict events that need to be adjusted in combination with flight scheduling constraints to obtain a resource conflict dataset specifically includes:

[0070] During the process of airport resource scheduling, the resource competition between flights may manifest as time conflicts or spatial conflicts. Time conflicts usually refer to multiple flights attempting to use the same resource within the same time period. For example, a certain apron position is occupied by two flights within the same time period, or multiple flights are scheduled to use adjacent boarding gates within the same time period, resulting in overcrowding in the boarding area. Spatial conflicts mainly involve the physical position relationship between flights. For example, the distance between some apron positions is relatively close, and flights assigned adjacent apron positions may need to share certain ground service equipment, leading to tight equipment scheduling. Or due to site restrictions, there may be cross-interference during the taxiing of some flights. Therefore, when optimizing airport resource scheduling, it is necessary to comprehensively analyze time conflict data and spatial conflict data to ensure that the final scheduling adjustment can effectively alleviate resource competition and improve airport operation efficiency.

[0071] When analyzing time conflict data and space conflict data, it is first necessary to remove duplicate records. Since flight resource conflicts may have manifestations in both the time and space dimensions, some conflict events may be marked in both datasets. For example, two flights scheduled at adjacent boarding gates within a similar time period may be identified as either a time conflict or a space conflict. Therefore, when conducting data analysis, it is necessary to check for duplicate-marked conflict events and merge relevant records to avoid distorting the scheduling optimization results due to duplicate calculations. At the same time, the dynamic changes in flight operations need to be considered. For example, some conflicts may resolve themselves in a short period (such as a time conflict disappearing due to a flight delay). Therefore, records that have no impact need to be excluded when finally screening conflict events.

[0072] After removing duplicate records, it is necessary to screen the resource conflict data in combination with flight scheduling constraints to determine which conflict events need to be adjusted first. Flight scheduling constraints include various factors, such as flight priorities, flight operation rules, ground service time requirements, etc. For example, international flights usually have a higher scheduling priority. Therefore, if there is a conflict in the apron allocation between an international flight and a domestic flight, it may be necessary to adjust the scheduling plan of the domestic flight first and try to maintain the established arrangements of the international flight. In addition, some flights may be restricted by specific requirements of the airline. For example, some flights may need to be arranged at adjacent boarding gates to the other flights of the same airline to facilitate passenger transfers. Therefore, these operational requirements need to be met as much as possible during the adjustment process.

[0073] After the above analysis and screening, the conflict events that need to be adjusted can be finally determined, and a resource conflict dataset can be formed. This dataset contains information such as the flights that need to be scheduled for optimization, the types of resources involved, and the severity of the conflicts, providing data support for subsequent resource optimization. Compared with the traditional method that relies on manual judgment, this data analysis method can improve the accuracy of conflict identification, make the scheduling adjustment more scientific and reasonable, and improve the overall utilization efficiency of airport resources.

[0074] In a preferred embodiment of the present invention, the analyzing the resource occupancy of each flight in adjacent time periods according to the flight resource occupancy data, calculating the time intersection degree, and identifying the flights that cross-use the same resources within the same time period to form time conflict data includes:

[0075] According to the flight resource occupancy data, determine the arrival time, departure time, apron occupancy period, boarding gate usage period, and ground service equipment usage period of each flight to form flight time occupancy data;

[0076] According to the flight time occupancy data, calculate the overlap degree of flights for the same resources in adjacent time periods, identify the flights with time intersections, and mark the overlapping time intervals to form time intersection records.

[0077] According to the time intersection records, filter out the time conflict events that affect the normal scheduling of flights, and record the conflicting flights, conflict time periods, and resource types involved to form time conflict data.

[0078] In the embodiments of the present invention, in the process of airport resource scheduling, the identification and optimization of time conflicts are important links to improve the flight punctuality rate. Since the arrival time, departure time, apron occupancy period, boarding gate usage period, and ground service equipment usage period of flights all have a certain degree of uncertainty, it is possible that multiple flights compete for the same resources in the same time period. If the scheduling system fails to accurately identify these time conflicts, it may lead to resource usage conflicts, thereby affecting the normal operation of flights. Therefore, by analyzing the resource occupancy situation of flights from the time dimension and calculating the time intersection degree, time conflicts can be effectively identified, and the scientificity and accuracy of airport scheduling can be improved.

[0079] After obtaining the flight resource occupancy data, it is necessary to calculate the overlap degree of flights for the same resources in adjacent time periods to form time intersection records. The calculation of time intersection records needs to consider multiple factors, including the arrival and departure times of flights, the occupancy time of aprons, the usage of boarding gates, etc. For example, if there is partial overlap in the parking times of two flights and only one apron is available, it may cause one of the flights to be unable to taxi into the apron on time. By calculating the time intersection degree, such resource conflict situations can be accurately identified and adjusted in advance to avoid flight delays or ground resource scheduling chaos.

[0080] After identifying the time conflict events, it is necessary to filter out the time conflict events that affect the normal scheduling of flights, and record the conflicting flights, conflict time periods, and resource types involved to form time conflict data. The establishment of time conflict data enables the scheduling system to optimize resource allocation in a targeted manner and reduce flight scheduling problems caused by time conflicts. For example, during peak hours, some flights may be unable to use the apron as planned due to time conflicts, and if not adjusted in time, it may lead to delays of multiple subsequent flights. Therefore, by establishing time conflict data, the prediction ability of the scheduling system can be improved, enabling it to identify potential problems in advance and prioritize the resolution of these conflicts in scheduling optimization.

[0081] The establishment of time conflict data not only helps optimize the current flight schedule but also can be used to improve subsequent scheduling strategies. For example, by analyzing historical time conflict data, regular problems in airport operations can be identified, such as excessive flight density during certain time periods and high usage frequencies of certain parking positions. These data can provide decision-making support for future scheduling optimization and improve the long-term utilization efficiency of airport resources. Ultimately, through accurate time conflict identification and optimization, flight delays can be effectively reduced, the accuracy and stability of airport scheduling can be improved, the overall operating efficiency of the airport can be optimized, and the travel experience of passengers can be enhanced.

[0082] In a preferred embodiment of the present invention, according to the flight resource occupancy data, analyzing the physical locations of the parking positions, boarding gates, and ground service equipment used by each flight in the spatial dimension, calculating the spatial proximity, and identifying flights with spatial conflicts to form spatial conflict data, including:

[0083] Based on the flight resource occupancy data, determining the geographical location coordinates of the flight's parking position, boarding gate, and ground service equipment to form flight spatial occupancy data;

[0084] Based on the flight spatial occupancy data, calculating the physical distances between the resources used by adjacent flights within the same time period to form spatial proximity data;

[0085] Screening the spatial proximity data that exceeds the preset spatial conflict threshold, identifying flights with spatial conflicts, and recording the conflict resources, conflict locations, and affected flights to form spatial conflict data.

[0086] In the embodiment of the present invention, during the operation of the airport, the detection of spatial conflicts is crucial for ensuring the reasonable allocation of flight resources. Different from time conflicts, spatial conflicts mainly involve the physical location distribution problems of flights at parking positions, boarding gates, and ground service equipment. Due to limited airport resources, multiple flights may operate simultaneously in adjacent areas, resulting in uneven resource allocation or scheduling interference, thus affecting the normal operation of flights. Analyzing the resource occupancy of flights in the spatial dimension can accurately identify resource competition problems that may arise due to too close physical distances and form spatial conflict data, thereby optimizing the overall distribution of airport resources and improving the scientificity and rationality of flight scheduling.

[0087] After obtaining the space occupancy data of flights, the physical distance between the resources used by adjacent flights within the same time period can be calculated to form space proximity data. This calculation method takes into account the geographical location coordinates of the aircraft stands, boarding gates, and ground service equipment of the flights, enabling the scheduling system to accurately identify the resource tightness based on the spatial distribution. For example, some aircraft stands are relatively close, and the ground service equipment requirements of two flights overlap, which may cause resource contention and affect the service quality of the flights. By calculating the space proximity, such problems can be identified and the resources can be optimized and adjusted.

[0088] To ensure the accuracy of space conflict identification, a preset space conflict threshold is further introduced. By screening the space proximity data exceeding this threshold, flights with space conflicts can be accurately identified, avoiding airport operation problems caused by unbalanced resource scheduling. For example, if the physical distance between the aircraft stands of two flights is too close and their ground equipment requirements are both high, it may affect the coordination arrangement of ground crew and equipment, thus affecting the overall operation efficiency of the airport. By calculating the space conflict data and recording the conflict resources, conflict locations, and affected flights, the scheduling system can actively adjust the resource allocation plan, reduce the impact of resource tightness on flight operations, and improve the overall operation efficiency of the airport.

[0089] In a preferred embodiment of the present invention, the impact assessment of the resource conflict dataset is based on the airport resource usage rules and flight priorities to calculate the impact scores of each conflict event on airport operations, including:

[0090] Extract the conflict flight information from the resource conflict dataset, determine the number of flights involved in the conflict, the conflict duration, and the types of airport resources affected by the conflict to form conflict impact factor data;

[0091] According to the conflict impact factor data and based on the flight priority information, calculate the delay time suffered by high-priority flights due to the conflict and the number of subsequent flights that may be affected to form flight impact degree data;

[0092] According to the conflict impact factor data and based on the airport resource usage rules, calculate the change in the airport resource utilization rate caused by the conflict and evaluate the possible resource idleness or overload situation due to the conflict to form resource impact degree data;

[0093] According to the flight impact degree data, resource impact degree data, and resource conflict dataset, perform weighted summation to calculate the conflict impact scores of each conflict event;

[0094] ,

[0095] To measure the impact degree of airport resource conflicts on the overall operation, 、 , , is the weight coefficient, is the flight impact degree, is the priority of the flight, is the delay time affected by the conflict of the flight, is the weight coefficient of the indirectly affected flight , is the possible delay time that the flight may be affected by, is the number of directly affected flights, is the number of indirectly affected flights, is the resource impact degree, is the weight coefficient of the resource, is the overload rate affected by the resource, expressed as the change in the adjusted usage amount, is the number of affected resources, is the time dimension impact term, used to calculate the average delay time caused by the conflict, representing the time impact on airport scheduling, is the space dimension impact term, is the proximity of the flight to adjacent flights in space.

[0096] In the embodiment of the present invention, during the airport scheduling process, the impact degrees of resource conflicts on the overall operation of the airport are different. Different flight priorities, different resource types, and different time periods will all result in different impact degrees of conflict events. Therefore, it is necessary to calculate the impact scores of each conflict event based on impact assessment in order to determine the conflict problems to be solved preferentially and improve the pertinence and effectiveness of scheduling optimization. By quantifying the impact of each conflict event on airport operation, under the conditions of limited time and resources, the conflicts with greater impact can be preferentially optimized, and the scientificity and accuracy of airport scheduling can be improved.

[0097] The calculation of the impact score first needs to extract the conflict flight information involved in the resource conflict dataset, including the number of flights, the conflict duration, and the types of affected airport resources. The more the number of flights, the greater the possible impact of the conflict, so a higher score needs to be given. At the same time, events with a long conflict duration will have a greater impact on subsequent flights, so they also need to occupy a higher weight in the score calculation. The types of affected airport resources further determine the severity of the conflict. For example, boarding gate resources are usually more important than ground equipment, so the weights of different resources need to be considered in the impact score calculation.

[0098] To further improve the accuracy of scoring, flight priority information is introduced. Flights with different priorities are affected differently in conflicts. For example, international flights, transfer flights, long-haul flights, etc. usually have higher scheduling priorities. Therefore, the delays of these flights have a greater impact on the overall operation of the airport. In the scoring calculation, by calculating the delay time of high-priority flights and the number of subsequent flights that may be affected, the specific impact of conflict events on flight operations can be quantified. For example, if the delay of an international flight affects the connection of multiple transfer flights, the impact score of this conflict event should be higher to ensure that this problem is prioritized in scheduling optimization.

[0099] The calculation of resource impact degree further enhances the rationality of impact scoring. Resource conflicts may lead to the overload or idleness of certain resources, thus affecting the overall resource utilization efficiency. For example, if a certain type of resource is overloaded for a long time due to conflicts, it may affect the scheduling arrangements of subsequent flights. Therefore, by calculating the change in the utilization rate of airport resources caused by conflicts and evaluating the possible resource idleness or overload situations, more comprehensive data support can be provided for scoring calculation. By performing a weighted sum of the flight impact degree and the resource impact degree, the conflict impact score is finally calculated, so as to ensure that scheduling optimization can prioritize solving the most serious conflicts and improve the overall optimization effect of airport scheduling.

[0100] Among them, the conflict impact score The calculation formula is as follows:

[0101] ,

[0102] Among them, , , , Are weight coefficients, which can be adjusted according to airport operation strategies to adapt to different optimization goals.

[0103] The core function of this formula is to comprehensively evaluate the impact degree of resource conflicts occurring during airport operations on the overall operation, so as to determine the conflict events to be prioritized and optimize the scheduling strategy.

[0104] Flight impact degree Reflects the direct and indirect impacts of conflicts on flight operations. Indicates the flight Priority. High-priority flights, such as international flights and transfer flights, are more critically affected in conflicts, so higher weights are assigned. Represents the flight The delay time affected by the conflict. The longer the delay time, the greater the impact. Represents the indirectly affected flight The connecting flight weight, for example, if a flight is delayed and the subsequent flights are affected, the weight of the subsequent flight can be used to reflect its impact on the overall airport operation. Represents a flight The possible delay time it may suffer. The technical effect of this part is to comprehensively evaluate the direct and indirect delay situations of flights and quantify the interference degree of conflict events on airport flight scheduling.

[0105] Resource impact degree Reflects the impact of conflict events on the use of airport resources. Represents a resource The usage weight, for example, the usage weight of a boarding gate may be higher than that of ground equipment because boarding gate resources are scarce and have a greater impact on flight operations. Represents a resource The overload rate of the resource, that is, the change in the usage rate of the resource after a conflict occurs. For example, a certain parking bay is overloaded due to a conflict, or some ground service equipment is idle or overloaded due to adjustment. The technical effect of this part is to identify the most severely affected resources in the airport operation process by measuring the change in the resource usage rate, so as to optimize the scheduling strategy and make the resource allocation more reasonable.

[0106] Time dimension impact factor Quantifies the impact of conflict events in terms of time. And Respectively represent the flight And the flight The direct and indirect delay times suffered. The technical effect of this part is to calculate the average delay time caused by the conflict to reflect the time lag of the overall airport operation. Conflict events with a longer average delay time will be given a higher priority for quick adjustment to reduce the impact on subsequent flight schedules.

[0107] Space dimension impact factor Evaluates the impact of conflict events in terms of space. Represents the flight The proximity to adjacent flights in space. The calculation method can be based on the position coordinates of the flight's parking bay, boarding gate, and ground service equipment to measure the degree of spatial conflict between flights. The technical effect of this part is to identify which conflict events may lead to a high resource density in a certain area of the airport, such as multiple flights concentrated at adjacent boarding gates or parking bays, making the scheduling more complex and thus requiring priority optimization, by calculating the physical proximity of adjacent flights.

[0108] In summary, the conflict impact score Combined with the flight impact degree, resource impact degree, time dimension impact factor, and space dimension impact factor, a comprehensive index is formed to evaluate the impact degree of conflict events on the overall operation of the airport. The technical advantage of this formula is that it not only considers the direct impact on flights but also comprehensively analyzes the chain reaction between flights, changes in resource load, delay propagation in time, and conflict situations in space. This enables the airport scheduling system to prioritize adjusting the conflict events with the greatest impact on airport operations according to the size of the conflict impact score, thereby improving the scheduling efficiency of the airport, reducing flight delays, optimizing resource utilization, and enhancing the travel experience of passengers.

[0109] In a preferred embodiment of the present invention, calculating the delay time suffered by high-priority flights due to conflicts and the number of subsequent flights that may be affected based on flight priority information to form a flight impact degree includes:

[0110] Based on flight priority information, determine the priority levels of each flight, and extract the conflict flights involved in the time conflict data and space conflict data to form conflict flight data;

[0111] According to the number of conflict flights, calculate the expected delay time of high-priority flights at the time of conflict, and analyze the impact of the delay on the connection of subsequent flight segments of this flight to form flight delay impact data;

[0112] According to the flight delay impact data, extract the subsequent flight plans of the affected flights, and calculate the delay propagation of subsequent flights caused by conflicts to form flight delay propagation data;

[0113] Count the number of indirectly affected flights in the flight delay propagation data, and combine flight priority information to evaluate the impact degree of conflicts on the overall airport flight plan to form flight impact degree data.

[0114] In the embodiment of the present invention, flight priority information plays a key role in the process of optimizing airport scheduling. Different flights have different importance in airport scheduling. For example, international flights are usually more important than domestic short-haul flights, and the delay of transfer flights may affect the itineraries of more passengers. Therefore, it is necessary to prioritize ensuring the normal operation of these flights. By calculating the delay time suffered by high-priority flights due to conflicts and further analyzing its impact on subsequent flights, the optimization effect of scheduling adjustments on the overall flight operation can be quantified, and the intelligence level of the scheduling system can be improved.

[0115] To calculate the flight impact degree, it is necessary to first determine the priority levels of each flight and extract the flight information involved in the time conflict data and space conflict data. The setting of flight priority levels can be adjusted according to the airport operation strategy. For example, long-haul flights and connecting flights usually have higher priorities, while short-haul flights may have lower priorities. By extracting the conflict flight data, conflict events with greater impacts can be screened out, and the impact degree of these conflicts on flight operations can be further calculated.

[0116] For high-priority flights, calculating their expected delay time at the time of conflict is the key to optimizing the schedule. Since flight delays not only affect the operation of the current flight but may also cause a chain reaction to subsequent flights, it is necessary to further analyze the impact of delays on flight connections. For example, if a flight delay causes multiple subsequent flights to be unable to take off as scheduled, the impact degree of this flight is greater, and its schedule needs to be optimized first.

[0117] The calculation of flight delay impact data can be further extended to indirectly affected flights, that is, calculating the spread of flight delays caused by conflicts. For example, the delay of an international flight may cause multiple connecting flights to be unable to take off on time, thus affecting the itineraries of a large number of passengers. By counting the flight delay spread data, the number of indirectly affected flights can be calculated, and combined with flight priority information, the impact degree of conflicts on the overall airport flight plan can be evaluated. For example, if the delay of a certain flight causes multiple high-priority flights to be affected, the schedule of this flight needs to be optimized first to reduce the impact of delays on the overall airport operation.

[0118] By calculating the flight impact degree, the impact of conflict events on the overall airport operation can be quantified, and more scientific data support can be provided for schedule optimization. The advantage of this method is that it not only considers the direct impact of flights but also comprehensively analyzes flight connection relationships, ensuring that the schedule optimization plan can minimize delays, improve airport operation efficiency, and enhance the travel experience of passengers.

[0119] Among them, based on the flight priority information, determining the priority levels of each flight and extracting the conflict flights involved in the time conflict data and space conflict data to form conflict flight data specifically includes:

[0120] During the airport resource scheduling process, the importance of different flights varies. Therefore, when dealing with resource conflicts, it is necessary to determine the priority levels of each flight based on flight priority information to ensure that high-priority flights can be given priority during the resource scheduling process. Flight priorities are usually jointly determined by airport management rules and airline operation requirements. Flights with higher priorities generally include international flights, long-haul flights, transfer flights, and flights with fixed scheduled times and high requirements for punctuality. For example, the boarding gate allocation for international flights is usually affected by customs and immigration areas, so its operational stability needs to be prioritized during the scheduling process. Delays in transfer flights may affect passengers' subsequent itineraries, so it is necessary to minimize their impact from conflicts during the resource scheduling process.

[0121] After determining the flight priority levels, it is necessary to extract the conflicting flights involved in the time conflict data and space conflict data to identify the set of affected flights. Time conflict data mainly includes flight pairs that conflict due to overlapping resource allocations, such as flights scheduled to use the same parking bay during the same time period, or flights for which the ground service equipment cannot provide services on time due to overly tight time arrangements. Space conflict data mainly involves conflicts in physical locations, such as the possible cross-interference of adjacent parking bay flights on the taxiing path, or situations where the boarding gate arrangement is unreasonable, resulting in overcrowding in the boarding area.

[0122] By combining the time conflict data and space conflict data, conflict flight data can be formed to clarify the set of flights affected by the conflict, and these flights can be sorted according to their priorities, so as to preferentially adjust the flights that have a greater impact on the overall airport operation during the resource scheduling optimization process. For example, if a flight has a lower priority and is not greatly affected by the conflict, its resource adjustment can be arranged to ensure that higher-priority flights can operate according to the plan. Compared with traditional scheduling methods, this method can more accurately identify the flights that need to be preferentially adjusted, improve the rationality of resource scheduling, reduce flight delays caused by conflicts, and optimize the overall operation efficiency of the airport.

[0123] In a preferred embodiment of the present invention, based on the airport resource usage rules, calculating the change in airport resource utilization rate caused by the conflict and evaluating the possible resource idling or overloading situations due to the conflict to form a resource impact degree includes:

[0124] Extracting the conflicting flights and conflicting resource information in the resource conflict data, and determining the current usage of the involved parking bays, boarding gates, and ground service equipment to form conflict resource occupancy data;

[0125] Calculate the changes in the utilization rates of various airport resources before and after the conflict occurs based on the conflict resource occupancy data, including the increase in resource idle time caused by flight schedule adjustments or the excessive usage frequency of equipment, and form the data on the changes in resource utilization rates.

[0126] Based on the data on the changes in resource utilization rates and the airport resource usage rules, evaluate the impact degree of the conflict on different types of resources, and form the resource load assessment data.

[0127] Based on the resource load assessment data, determine whether the conflict will lead to an imbalance in resource allocation, and mark the resources with severe overload or idle conditions to form the resource impact degree data.

[0128] In the embodiments of the present invention, during the airport resource scheduling process, the occurrence of conflict events may lead to an imbalance in resource usage, thereby affecting the overall operation efficiency. Some resources may be overused due to conflict events, while some other resources may be idle, affecting the smoothness of airport operations. Therefore, during the process of calculating the conflict impact score, it is necessary to further consider the impact of the conflict on the utilization rate of airport resources to ensure that the scheduling optimization can effectively alleviate the problem of uneven resource allocation.

[0129] The calculation of the resource impact degree first requires extracting the conflict flight and conflict resource information from the resource conflict data to determine the current usage status of the involved parking positions, boarding gates, and ground service equipment. Since different resources have different importance levels, the changes in their usage status may have different degrees of impact on airport operations. For example, if the utilization rate of a parking position is too high, it may lead to nowhere for flights to park, and if the boarding gate resource is overused, it may lead to a decrease in the boarding efficiency of passengers. Therefore, it is necessary to calculate the changes in the utilization rates of various airport resources before and after the conflict occurs according to different types of resource situations, including the increase in resource idle time caused by flight schedule adjustments or the overload situation caused by the excessive usage frequency of resources.

[0130] When calculating the changes in resource utilization rates, the impact degree of conflict events on different types of resources can be evaluated based on the airport resource usage rules. For example, if a certain type of resource is in an overloaded state for a long time due to scheduling adjustments, it may affect the scheduling stability of subsequent flights, thereby affecting the overall flight operation plan. On the other hand, if a certain type of resource is not reasonably used due to adjustments, resulting in a large amount of idle time, it means that the scheduling optimization fails to fully utilize the resource efficiency. Therefore, during the process of calculating the resource impact degree, it is necessary to evaluate the load conditions of different resources and, in combination with the resource usage rules, determine whether the conflict will lead to an imbalance in resource allocation.

[0131] Finally, based on the resource load assessment data, resources with severe overload or idle conditions can be marked, and this data can be used to optimize the scheduling plan. By quantifying the resource impact degree, the scheduling system can intelligently identify problems in resource scheduling, optimize and adjust for resource imbalance situations, improve the overall utilization efficiency of airport resources, reduce the risk of flight delays, and enhance the travel experience of passengers.

[0132] Among them, according to the resource usage rate change data, based on the airport resource usage rules, evaluating the impact degree of conflicts on different types of resources to form resource load assessment data specifically includes:

[0133] During the operation of the airport, the balance of resource usage directly affects the stability of flight scheduling. If some resources are in an overloaded state for a long time, it may affect the overall operation efficiency of the airport, while if some resources are idle for a long time, it may mean unreasonable resource allocation. Therefore, when optimizing airport resource scheduling, it is necessary to evaluate the impact degree of conflicts on different types of resources based on the resource usage rate change data to ensure the rationality of resource allocation and improve the stability of airport operation.

[0134] The acquisition of resource usage rate change data is usually based on airport operation data and historical scheduling records. For example, by comparing the usage rate changes of a certain parking bay in different time periods, it can be judged whether there is a situation of resource shortage or idleness for this parking bay. If a certain parking bay is in an overloaded state for a long time during peak hours while other parking bays are less used, it may mean that the scheduling plan needs to be adjusted to optimize the overall utilization rate of the parking bays. Similarly, for boarding gates and ground service equipment, their scheduling rationality can also be evaluated based on the usage rate data and it can be judged whether adjustment is needed.

[0135] Based on the airport resource usage rules, the rationality of resource load can be further evaluated. For example, different types of flights may require different parking bays, and certain boarding gates may need to be given priority to specific airlines, etc. Therefore, when conducting resource load assessment, it is necessary to combine the operation rules of the airport to ensure that the scheduling optimization plan can meet the actual operation requirements. Through in-depth analysis of the resource usage rate change data, resource load assessment data can be formed, providing data support for subsequent scheduling optimization, improving the overall utilization efficiency of airport resources, reducing operation problems caused by unreasonable resource scheduling, and optimizing the stability of flight operation.

[0136] In a preferred embodiment of the present invention, for optimizing resource allocation of the priority scheduling task data, based on the airport available resource information, flight scheduling constraint conditions, and historical scheduling adjustment records, calculating the optimal resource reallocation plan, adjusting the parking bay allocation, dynamically adjusting the boarding gates, and optimizing the ground service equipment scheduling to obtain the optimized scheduling plan, including:

[0137] Extract the current resource allocation of the flight to be adjusted based on the priority scheduling task data, and query the available airport resources information to form available resource data;

[0138] Calculate candidate resource adjustment plans based on flight scheduling constraints, exclude resource allocation situations that do not conform to the scheduling rules, and form a set of feasible scheduling plans;

[0139] Analyze the optimization effects of different feasible scheduling plans in similar conflict scenarios based on historical scheduling adjustment records, calculate the execution costs and resource utilization rates of each feasible scheduling plan, and form scheduling optimization evaluation indicators;

[0140] Select the optimal resource allocation plan according to the scheduling optimization evaluation indicators, and adjust the apron position, boarding gate and ground service equipment scheduling of the flight to form an optimized scheduling plan.

[0141] In the embodiment of the present invention, based on the priority scheduling task data, it is necessary to optimize resource allocation to ensure the reasonable use of airport resources and improve the overall efficiency of flight scheduling. In traditional scheduling methods, resource allocation usually relies on fixed rules and is difficult to adapt to the dynamically changing airport operating environment. By using a data analysis-based method, an optimal resource reallocation plan can be calculated based on comprehensive consideration of available airport resources information, flight scheduling constraints and historical scheduling adjustment records, improving the intelligent level of scheduling optimization.

[0142] The first step in optimizing the scheduling task is to extract the current resource allocation of the flight to be adjusted and query the available airport resources information to form available resource data. Obtaining the available resource data needs to consider the real-time status of airport operations, such as the current number of available apron positions, idle boarding gates, and deployable ground service equipment. The available status of different resources will affect the feasibility of the scheduling plan, so it is necessary to ensure the timeliness and accuracy of the data.

[0143] After obtaining the available resource data, it is necessary to calculate candidate resource adjustment plans based on flight scheduling constraints and eliminate resource allocation situations that do not conform to the scheduling rules to form a set of feasible scheduling plans. Scheduling constraints may include flight priorities, apron position types, ground service requirements, etc. For example, some large international flights require specific types of apron positions, and the boarding gates of some flights need to be close to the customs inspection area. Therefore, when calculating candidate scheduling plans, it is necessary to ensure that the plans meet these constraints.

[0144] To improve the scientific nature of scheduling optimization, historical scheduling adjustment records are further introduced to analyze the optimization effects of different feasible scheduling plans in similar conflict scenarios. By calculating the execution costs and resource utilization rates of different scheduling plans, data support can be provided for plan selection. For example, if a certain scheduling plan can effectively reduce flight delays and improve resource utilization rates during historical adjustments, then this plan can be preferentially selected. On this basis, according to the preset optimization goals, the optimal resource allocation plan is selected, and the aircraft parking positions, boarding gates, and ground service equipment of flights are adjusted to form an optimized scheduling plan.

[0145] Finally, the implementation of the optimized scheduling plan will dynamically adjust the airport resources and record the operation data after adjustment in real time for subsequent scheduling analysis. By continuously optimizing the scheduling plan, the overall utilization efficiency of airport resources can be improved, flight delays caused by unreasonable resource scheduling can be reduced, and the overall stability of airport operation can be enhanced.

[0146] Among them, calculating the candidate resource adjustment plan based on flight scheduling constraint conditions, excluding resource allocation situations that do not conform to the scheduling rules, and forming a set of feasible scheduling plans specifically includes:

[0147] During the airport resource scheduling process, flight resource allocation is affected by various operation constraints, and these constraint conditions include but are not limited to flight priorities, resource type matching, ground service requirements, flight schedule restrictions, airline operation rules, etc. If resource allocation is directly carried out without considering these constraint conditions, it may lead to flight scheduling conflicts, unreasonable resource allocation, and even affect the overall airport operation efficiency. Therefore, when optimizing resource scheduling, it is necessary to first calculate the candidate resource adjustment plan and screen the plans that conform to the rules based on flight scheduling constraint conditions to form a set of feasible scheduling plans.

[0148] The calculation of the candidate resource adjustment plan is first based on the analysis of airport available resource information. When adjusting flight resources, it is necessary to clarify the currently available resources such as aircraft parking positions, boarding gates, and ground service equipment, and combine with flight scheduling tasks to determine possible resource reallocation plans. For example, if the originally scheduled parking position of a certain flight cannot be used continuously due to the adjustment of other flights, then it is necessary to find other eligible parking positions as candidate plans. Similarly, if the boarding gate resources need to be readjusted, then the candidate plans that meet the scheduling requirements need to be screened from the available boarding gates. In this process, it is necessary to ensure that the resource adjustment plan has a certain degree of flexibility so that it can be dynamically adjusted according to different requirements during subsequent optimization.

[0149] After generating the candidate resource adjustment plan, it is necessary to combine flight scheduling constraint conditions to screen out the scheduling plans that conform to the rules and exclude unreasonable resource allocation situations. Flight scheduling constraint conditions include multiple aspects, such as:

[0150] Flight priority restrictions: High-priority flights (such as international flights, long-haul flights, and connecting flights) need to be given priority in resource allocation to avoid unnecessary interference to high-priority flights due to adjustments.

[0151] Resource type matching: Some parking positions may only be suitable for specific types of flights. For example, large airliners may require specific-sized parking positions, while small airliners can use general parking positions. Therefore, during the adjustment process, it is necessary to ensure that the allocated resources match the flight requirements.

[0152] Time constraints: The takeoff and landing times of flights determine the time range of their resource requirements. For example, some flights require a long ground service time, while some flights have a short boarding gate usage time. Therefore, when adjusting resources, it is necessary to ensure that the time arrangement does not cause resource usage conflicts.

[0153] Airline operation rules: Some airlines may hope to arrange the boarding gates of flights of the same airline in adjacent areas for the convenience of passengers transferring. Such operation requirements need to be met as much as possible during the adjustment process.

[0154] Ground service requirements: Some flights may require special ground services, such as baggage transfer, freight handling, catering replenishment, etc. Therefore, when adjusting resources, it is necessary to ensure that the allocated resources can meet the corresponding service requirements.

[0155] By screening the candidate resource adjustment plans based on the above constraints, resource allocation situations that do not conform to the scheduling rules can be excluded, forming a set of feasible scheduling plans. Compared with the traditional manual adjustment method, this method can automatically screen reasonable scheduling plans, improve the scientific nature of resource allocation, reduce the occurrence of scheduling conflicts, and improve the overall stability of airport operations.

[0156] In a preferred embodiment of the present invention, based on the historical scheduling adjustment records, analyze the optimization effects of different feasible scheduling plans in similar conflict scenarios, calculate the execution costs and resource utilization rates of each feasible scheduling plan, and form scheduling optimization evaluation indicators, including:

[0157] Based on the historical scheduling adjustment records, screen out historical cases similar to the current conflict event, and extract the corresponding scheduling adjustment plans to form historical scheduling case data;

[0158] According to the historical scheduling case data, calculate the execution costs of various resource adjustment plans in past conflict scenarios, including flight delay costs, ground service adjustment costs, and equipment reallocation costs, to form execution cost data;

[0159] According to the historical scheduling case data, analyze the resource utilization efficiency of various resource adjustment plans in conflict mitigation, including the change in resource utilization rate after the implementation of the adjustment plan, the resource load balance in each area of the airport, and the scheduling stability, to form resource utilization rate data;

[0160] Merge the execution cost data with the resource utilization rate data to form scheduling optimization evaluation indicators.

[0161] In the embodiment of the present invention, during the airport resource scheduling process, the selection of the scheduling optimization plan directly affects the on-time rate of flights, the utilization efficiency of airport resources, and the overall operation stability. Due to the complex airport operation environment, different scheduling plans may perform differently in terms of execution cost, flight delay situation, resource allocation balance, etc. Therefore, based on the historical scheduling adjustment records, analyzing the optimization effects of different feasible scheduling plans in similar conflict scenarios, and calculating the execution cost and resource utilization rate of each scheduling plan, can provide data support for the final scheduling optimization decision, so as to ensure that the selected plan achieves the optimal allocation of resource scheduling while conforming to the airport operation rules.

[0162] This method first forms historical scheduling case data by screening historical scheduling adjustment records, extracting historical cases similar to the current conflict event, and extracting the corresponding scheduling adjustment plans. Compared with the traditional manual decision-making method that relies on the dispatcher's experience judgment, this method provides a reference for the current optimization plan by analyzing past successful adjustment cases, improving the scientific nature of scheduling decisions. For example, if historical data shows that in a similar apron conflict situation, adopting a certain specific resource adjustment plan can effectively reduce flight delays, then this plan can be used as a reference plan for priority selection.

[0163] After obtaining the historical scheduling case data, it is necessary to further calculate the execution cost of various resource adjustment plans in past conflict scenarios to quantify the economic impact of different plans. The execution cost includes flight delay cost, ground service adjustment cost, and equipment reallocation cost. For example, some plans may involve the adjustment of the flight taxiing path, resulting in additional fuel consumption, while other plans may involve the reallocation of ground equipment, increasing the equipment usage cost. By quantifying these execution costs, the economic feasibility of different plans can be evaluated, avoiding the selection of plans with too high costs but insignificant optimization effects, and improving the economy of airport operations.

[0164] In addition to the execution cost, it is also necessary to analyze the resource utilization efficiency of different scheduling schemes in conflict mitigation. This includes the change in resource utilization rate after the implementation of the adjustment scheme, the resource load balance in each area of the airport, and the scheduling stability. For example, if a certain scheduling scheme can reduce the overuse of apron resources after optimization and improve the utilization rate of boarding gate resources, then this scheme performs better in terms of resource optimization and is worthy of application in the current scenario. In contrast, if a certain scheme can relieve the current conflict but causes overload or idleness of other resources, then this scheme may not be suitable for the current scheduling optimization.

[0165] Finally, the execution cost data and the resource utilization rate data are merged to form a scheduling optimization evaluation index. This index can comprehensively reflect the applicability of different scheduling schemes and provide a quantitative basis for the selection of subsequent optimization schemes. Through this method, the airport scheduling system can select the optimal scheme from multiple alternative schemes, improve the accuracy of scheduling optimization, and ensure reasonable resource allocation and high economic feasibility. In addition, the introduction of historical data can reduce the uncertainty of decision-making, reduce the operation risks brought by ad-hoc adjustments, make the airport scheduling system more intelligent and adaptive, and improve the overall operation stability of the airport and the on-time rate of flights.

[0166] In a preferred embodiment of the present invention, the step of selecting the optimal resource allocation scheme according to the scheduling optimization evaluation index and adjusting the scheduling of apron, boarding gate and ground service equipment for flights to form an optimized scheduling scheme includes:

[0167] Extracting feasible scheduling schemes according to the scheduling optimization evaluation index, and calculating the comprehensive scores of different scheduling schemes according to the preset optimization goals to form a candidate set of optimization schemes; wherein,

[0168] ,

[0169] To measure the quality of the scheduling optimization scheme, , , are weight coefficients, is the improvement value of flight on-time rate, is the flight delay time before scheduling optimization, is the flight delay time after scheduling optimization, is the optimization value of resource utilization rate, is the resource usage rate before adjustment, is the resource usage rate after adjustment, is the scheduling adjustment cost, is the cost of flight schedule adjustment, is the cost of ground crew adjustment, is the cost of reallocating ground equipment;

[0170] According to the candidate set of optimization schemes, select the scheme with the highest score as the optimal resource allocation scheme, and adjust the parking positions, boarding gates and ground service equipment involved in this scheme to form an optimized scheduling scheme;

[0171] Execute the optimized scheduling scheme, and monitor the operation of the adjusted airport in real time. Feed the adjusted resource usage data back to the big data platform to update the airport resource scheduling information.

[0172] In the embodiments of the present invention, during the optimization process of airport resource scheduling, different resource adjustment schemes may perform differently in aspects such as flight punctuality rate improvement, resource utilization optimization, and scheduling adjustment cost control. Therefore, it is necessary to calculate the comprehensive scores of different scheduling schemes based on the scheduling optimization evaluation indicators according to the preset optimization objectives, and form a candidate set of optimization schemes to ensure that the finally selected scheme can achieve the optimal resource scheduling configuration while meeting the airport operation requirements.

[0173] Compared with the traditional scheduling optimization method, this method makes the scheduling optimization process more intelligent and quantifiable through a multi-dimensional scoring mechanism, and can adapt to different airport operation requirements. For example, during peak hours, the airport may give priority to improving the flight punctuality rate, while during off-peak hours, it may be more inclined to optimize resource utilization or reduce operating costs. Therefore, through the preset optimization objectives, this method enables the scheduling system to dynamically adjust the scoring criteria according to different operation scenarios, improving the flexibility of scheduling optimization.

[0174] The scoring calculation of the optimization scheme is based on multiple key indicators, including the improvement value of flight punctuality rate, the optimization value of resource utilization, and the scheduling adjustment cost. During the scoring calculation process, different weight parameters can be set according to the operation objectives of the airport. For example, when the main objective of the airport is to reduce flight delays, the weight of the improvement value of flight punctuality rate can be increased, so that the system preferentially selects scheduling schemes that can effectively reduce flight delays; when the airport pays more attention to resource utilization, the weight of the resource optimization value can be increased to ensure that the scheduling scheme can evenly allocate parking positions, boarding gates and ground service equipment; when the airport hopes to reduce the operating costs of scheduling adjustments, the weight of the scheduling adjustment cost can be increased, so that the final scheme can minimize additional costs while optimizing resource allocation.

[0175] Through the above scoring calculation, this method can form a candidate set of optimization solutions, and select the solution with the highest score from the candidate solutions as the final execution solution. The selection of the optimization solution not only considers the experience of historical data, but also combines the current operating status of the airport, the availability of resources, and the optimization objectives, making the scheduling solution more in line with the actual operating needs of the airport. Compared with the traditional method of relying on the experience of dispatchers for manual decision-making, this method can greatly improve the intelligence level of scheduling decisions, reduce manual intervention, and improve the accuracy of decisions.

[0176] Finally, after implementing the optimized scheduling solution, this method will dynamically adjust the airport resources, and monitor the execution effect of the optimization solution in real time, and feed back the adjusted resource usage data to the big data platform for subsequent scheduling analysis and optimization. Through this closed-loop optimization mechanism, the airport scheduling system can continuously optimize itself, make the resource allocation more accurate, and improve the overall operation efficiency.

[0177] In summary, this method can select the optimization solution according to different airport operating needs, making the scheduling optimization more intelligent and precise. Compared with the traditional scheduling method, this method can more flexibly adapt to the dynamic needs of the airport, improve the flight punctuality rate, optimize the resource utilization rate, and reduce the scheduling adjustment cost, thereby improving the overall efficiency of airport operations and providing a smoother travel experience for passengers.

[0178] Among them, the comprehensive score of the scheduling solution is used to evaluate the advantages and disadvantages of different scheduling solutions, so as to select the optimal solution to optimize the airport resource scheduling. 、 、 are weight coefficients, which can be adjusted according to the airport operation strategy. The core objective of this formula is to evaluate the overall effect of each scheduling solution by comprehensively considering the improvement of flight punctuality rate, the optimization of resource utilization rate, and the scheduling adjustment cost, so as to ensure that the final solution can maximize the airport operation efficiency and reduce the scheduling cost.

[0179] The improvement value of flight punctuality rate reflects the effect of the optimization solution in reducing flight delays. and respectively represent the delay time of the flight before and after optimization. The technical effect of this part is to measure the improvement of flight punctuality rate after scheduling optimization, and reflect whether the airport resource scheduling effectively reduces the overall flight delay. If the optimization solution successfully reduces the delay time of most flights, the value of this indicator is relatively large, indicating that the scheduling effect of this solution is better.

[0180] The optimization value of resource utilization rate Measure the performance of the optimization plan in improving the utilization efficiency of airport resources, and respectively represent the change in the utilization rate of resources before and after the scheduling optimization, and \(n\) is the number of affected resources. The technical effect of this part is to reflect whether the scheduling optimization has improved the balanced utilization rate of airport resources and avoid the situation of over-occupation or long-term idleness of some resources. For example, if a certain parking position is used more evenly after optimization and there is no overload or vacancy caused by scheduling imbalance, the value of this indicator is larger, indicating that the scheduling plan performs better in resource optimization.

[0181] Scheduling adjustment cost quantifies the additional costs generated during the implementation of the scheduling optimization plan, where \(C_{1}\) represents the flight scheduling adjustment cost, such as additional fuel consumption due to scheduling adjustment, increased flight taxiing cost, or delay compensation cost caused by the change of takeoff and landing sequence. \(C_{2}\) represents the ground crew adjustment cost, including the redeployment of security inspection and boarding arrangements caused by the boarding gate adjustment, and the increase in ground crew working hours caused by the scheduling adjustment. \(C_{3}\) represents the cost of reallocating ground equipment, such as the resource loss caused by the additional movement or waiting time of equipment such as the baggage transfer system, flight refueling equipment, and boarding bridge due to scheduling adjustment. The technical effect of this part is to measure the economic cost brought by the scheduling optimization plan during the actual implementation process. If a certain plan performs excellently in terms of on-time rate and resource utilization rate, but the implementation cost is too high, its final score may be reduced to avoid the situation where the optimization benefit is offset by the high cost.

[0182] In summary, the comprehensive score of the scheduling plan combines multiple key factors such as the improvement of flight on-time rate, the optimization of resource utilization rate, and the scheduling adjustment cost to form a comprehensive evaluation index, ensuring that the finally selected scheduling plan can not only improve the airport operation efficiency, but also maximize the optimization effect under the condition of controllable cost. The technical advantage of this formula is that it makes different scheduling plans comparable by quantifying the improvement degree of flight delays, the balance of resource allocation, and the scheduling adjustment cost, and avoids making scheduling decisions only relying on experience or simple rules. By adjusting the weight coefficients 、[[ID=?]] 、[[ID=?]] ,airport managers can flexibly adjust the optimization direction according to different operation objectives, such as minimizing delays, optimizing resource utilization, or reducing operation costs, making the scheduling decision more scientific, accurate, and efficient.

[0183] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An airport resource scheduling analysis method based on a big data platform, characterized in that, The method includes: Performing conflict detection on the abnormal flight dataset, calculating the usage cross - situation of parking positions, boarding gates, and ground service equipment based on the time dimension and space dimension, identifying flights with resource competition, and recording the conflict type, occurrence time, and flight information involved to obtain a resource conflict dataset; Conducting impact assessment on the resource conflict dataset, calculating the impact score of each conflict event on airport operations based on airport resource usage rules and flight priorities, and sorting the conflict events according to the impact score to determine the scheduling tasks to be adjusted first, obtaining the priority scheduling task data; Optimizing resource allocation for the priority scheduling task data, calculating the optimal resource re - allocation plan based on airport available resource information, flight scheduling constraint conditions, and historical scheduling adjustment records, adjusting parking position allocation, dynamically adjusting boarding gates, and optimizing ground service equipment scheduling to obtain an optimized scheduling plan; Conducting impact assessment on the resource conflict dataset, calculating the impact score of each conflict event on airport operations based on airport resource usage rules and flight priorities, including: Extracting the conflict flight information in the resource conflict dataset, determining the number of flights involved in the conflict, the conflict duration, and the type of airport resources affected by the conflict to form conflict impact factor data; Based on the conflict impact factor data and flight priority information, calculating the delay time suffered by high - priority flights due to the conflict and the number of subsequent flights that may be affected to form flight impact degree data; Based on the conflict impact factor data and airport resource usage rules, calculating the change in airport resource utilization rate caused by the conflict, and evaluating the possible resource idling or overloading situation due to the conflict to form resource impact degree data; According to the flight impact degree data, resource impact degree data, and resource conflict dataset, performing weighted summation to calculate the conflict impact score of each conflict event; Based on flight priority information, calculating the delay time suffered by high - priority flights due to the conflict and the number of subsequent flights that may be affected to form the flight impact degree, including: Based on flight priority information, determining the priority levels of each flight, and extracting the conflict flights involved in the time - conflict data and space - conflict data to form conflict flight data; According to the number of conflict flights, calculating the expected delay time of high - priority flights at the time of conflict, and analyzing the impact of the delay on the connection of subsequent flight segments of this flight to form flight delay impact data; According to the flight delay impact data, extracting the subsequent flight plans of the affected flights, and calculating the propagation of subsequent flight delays caused by the conflict to form flight delay propagation data; Counting the number of indirectly affected flights in the flight delay propagation data, and combining flight priority information to evaluate the impact degree of the conflict on the entire airport flight plan to form flight impact degree data.

2. The airport resource scheduling analysis method based on a big data platform according to claim 1, characterized in that Performing conflict detection on the abnormal flight dataset, calculating the usage cross - situation of parking positions, boarding gates, and ground service equipment based on the time dimension and space dimension, identifying flights with resource competition, and recording the conflict type, occurrence time, and flight information involved to obtain a resource conflict dataset, including: Obtain the flight operation information in the abnormal flight dataset, extract the arrival time, departure time, apron occupancy, boarding gate allocation, and ground service equipment usage of the flight, and form flight resource occupancy data; According to the flight resource occupancy data, analyze the resource occupancy of each flight in adjacent time periods in terms of the time dimension, calculate the time intersection degree, identify the flights that cross - use the same resources in the same time period, and form time conflict data; According to the flight resource occupancy data, analyze the physical locations of the aprons, boarding gates, and ground service equipment used by each flight in terms of the space dimension, calculate the space proximity degree, identify the flights with space conflicts, and form space conflict data; Conduct a comprehensive analysis of the time conflict data and space conflict data, remove duplicate records, and combine flight scheduling constraints to screen out the conflict events that need to be adjusted to obtain the resource conflict dataset.

3. The airport resource scheduling analysis method based on a big data platform according to claim 2, characterized in that According to the flight resource occupancy data, analyze the resource occupancy of each flight in adjacent time periods in terms of the time dimension, calculate the time intersection degree, identify the flights that cross - use the same resources in the same time period, and form time conflict data, including: According to the flight resource occupancy data, determine the arrival time, departure time, apron occupancy period, boarding gate usage period, and ground service equipment usage period of each flight, and form flight time occupancy data; According to the flight time occupancy data, calculate the overlap degree of the same resources used by flights in adjacent time periods, identify the flights with time intersections, and mark the overlapping time intervals to form time intersection records; According to the time intersection records, screen out the time conflict events that affect the normal flight scheduling, and record the conflict flights, conflict time periods, and resource types involved to form time conflict data.

4. The method for analyzing airport resource scheduling based on a big data platform according to claim 2 or 3, wherein According to the flight resource occupancy data, analyze the physical locations of the aprons, boarding gates, and ground service equipment used by each flight in terms of the space dimension, calculate the space proximity degree, identify the flights with space conflicts, and form space conflict data, including: According to the flight resource occupancy data, determine the geographical location coordinates of the aprons, boarding gates, and ground service equipment of the flight, and form flight space occupancy data; According to the flight space occupancy data, calculate the physical distance between the resources used by adjacent flights in the same time period to form space proximity degree data; Screen the space proximity degree data that exceeds the preset space conflict threshold, identify the flights with space conflicts, and record the conflict resources, conflict locations, and affected flights to form space conflict data.

5. The method for analyzing airport resource scheduling based on a big data platform according to claim 1, wherein Based on the airport resource usage rules, calculate the change in the airport resource utilization rate caused by the conflict, and evaluate the possible resource idling or overloading situations caused by the conflict to form the resource impact degree, including: Extract the conflict flight and conflict resource information in the resource conflict data, determine the current usage of the involved aprons, boarding gates, and ground service equipment, and form conflict resource occupancy data; According to the conflict resource occupancy data, calculate the change in the utilization rate of various airport resources before and after the conflict, including the increase in resource idling time or the excessive usage frequency of equipment caused by flight adjustments, to form resource utilization rate change data; Based on the data of resource utilization rate changes and the airport resource usage rules, evaluate the impact degree of conflicts on different types of resources to form resource load assessment data; Based on the resource load assessment data, determine whether the conflict will lead to unbalanced resource allocation, and mark the resources with serious overload or idle conditions to form resource impact degree data.

6. The airport resource scheduling analysis method based on a big data platform according to claim 1, wherein, Optimize the resource allocation for the priority scheduling task data. Based on the airport available resource information, flight scheduling constraint conditions, and historical scheduling adjustment records, calculate the optimal resource reallocation plan, adjust the apron allocation, dynamically adjust the boarding gates, and optimize the ground service equipment scheduling to obtain the optimized scheduling plan, including: According to the priority scheduling task data, extract the current resource allocation situation of the flights to be adjusted, and query the airport available resource information to form available resource data; Based on the flight scheduling constraint conditions, calculate the candidate resource adjustment plans, and exclude the resource allocation situations that do not conform to the scheduling rules to form a set of feasible scheduling plans; Based on the historical scheduling adjustment records, analyze the optimization effects of different feasible scheduling plans in similar conflict scenarios, calculate the execution costs and resource utilization rates of each feasible scheduling plan to form scheduling optimization evaluation indicators; According to the scheduling optimization evaluation indicators, select the optimal resource allocation plan according to the preset optimization goals, and adjust the apron, boarding gates, and ground service equipment scheduling of the flights to form the optimized scheduling plan.

7. The method for analyzing airport resource scheduling based on a big data platform according to claim 6, wherein Based on the historical scheduling adjustment records, analyze the optimization effects of different feasible scheduling plans in similar conflict scenarios, calculate the execution costs and resource utilization rates of each feasible scheduling plan to form scheduling optimization evaluation indicators, including: Based on the historical scheduling adjustment records, screen out the historical cases similar to the current conflict event, and extract the corresponding scheduling adjustment plans to form historical scheduling case data; According to the historical scheduling case data, calculate the execution costs of various resource adjustment plans in past conflict scenarios, including flight delay costs, ground service adjustment costs, and equipment reallocation costs, to form execution cost data; According to the historical scheduling case data, analyze the resource utilization efficiency of various resource adjustment plans in conflict mitigation, including the changes in resource utilization rates after the implementation of the adjustment plans, the resource load balance of each area of the airport, and the scheduling stability, to form resource utilization rate data; According to the execution cost data and resource utilization rate data, conduct a feasibility assessment of different scheduling plans, calculate the comprehensive scores of each scheduling plan to form scheduling optimization evaluation indicators.

8. The method for analyzing airport resource scheduling based on a big data platform according to claim 7, wherein According to the scheduling optimization evaluation indicators, select the optimal resource allocation plan according to the preset optimization goals, and adjust the apron, boarding gates, and ground service equipment scheduling of the flights to form the optimized scheduling plan, including: According to the scheduling optimization evaluation indicators, extract the feasible scheduling plans, and calculate the comprehensive scores of different scheduling plans according to the preset optimization goals to form a candidate set of optimization plans; According to the candidate set of optimization plans, select the plan with the highest score as the optimal resource allocation plan, and adjust the apron, boarding gates, and ground service equipment involved in this plan to form the optimized scheduling plan; Execute the optimized scheduling plan, monitor the adjusted airport operation situation in real time, feed back the adjusted resource usage data to the big data platform, and update the airport resource scheduling information.

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