Airport resource scheduling analysis method based on big data platform

Abstract

The invention provides an airport resource scheduling analysis method based on a big data platform, and relates to the technical field of data processing, and the method comprises the steps: carrying out the conflict detection of an abnormal flight data set, recording the conflict type, occurrence time and related flight information, and obtaining a resource conflict data set; performing influence evaluation on the resource conflict data set, calculating an influence score of each conflict event on airport operation, sorting the conflict events according to the influence scores, determining a scheduling task which is adjusted preferentially, and obtaining priority scheduling task data; performing resource allocation optimization on the priority scheduling task data, calculating an optimal resource reallocation scheme based on airport available resource information, flight scheduling constraint conditions and historical scheduling adjustment records, adjusting gate position allocation, dynamically adjusting boarding gates and optimizing ground service equipment scheduling to obtain an optimized scheduling scheme; according to the invention, the autonomy and accuracy of airport resource scheduling analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and 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 existing technology, airport resource scheduling usually relies on manual experience and rule-based methods for management, mainly involving parking space allocation, gate scheduling, ground traffic control and other aspects. The traditional method mainly allocates resources based on fixed scheduling rules and first-come-first-served strategies, and combines historical data for simple statistical analysis to optimize resource utilization efficiency. Some technical solutions have introduced computer-aided decision-making, by building an airport operation database and combining basic optimization algorithms such as linear programming or heuristic algorithms to perform preliminary optimization allocation of resources. However, due to the complex airport operation environment and real-time changes in flight plans, the existing technology still has great limitations in dealing with dynamic adjustments and emergencies.

[0003] Existing technologies have great uncertainty when responding to emergencies. For example, when a flight is delayed or canceled due to weather reasons, the reallocation of parking stands, boarding gates, and ground service equipment requires quick decisions. In existing solutions, it is usually necessary to rely on on-duty dispatchers to manually adjust resource allocation based on their own experience. This method makes it difficult to quickly calculate the optimal solution, resulting in some resources being vacant or repeatedly scheduled. For example, during peak hours, some parking stands are vacant for a long time due to failure to adjust in time, while parking stands in other areas are overloaded, further exacerbating the pressure on airport operations. This scheduling method is difficult to accurately adapt to complex dynamic environments, which may result in waste of resources or extended waiting times for passengers, thus affecting the overall operating 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 technology.

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

[0006] Conflict detection is performed on the abnormal flight data set. The use of parking spaces, boarding gates and ground service equipment is calculated based on the time dimension and 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 data set.

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

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

[0009] Preferably, conflict detection is performed on the abnormal flight data set, and the use of parking spaces, boarding gates and ground service equipment is calculated based on the time dimension and the space dimension, and flights with resource competition are identified. The conflict type, occurrence time and flight information involved are recorded to obtain a resource conflict data set, including:

[0010] Obtain flight operation information from the abnormal flight data set, extract flight arrival time, departure time, parking space occupancy, boarding gate allocation, and ground service equipment usage to form flight resource occupancy data;

[0011] According to the flight resource occupancy data, the resource occupancy of each flight in adjacent time periods is analyzed according to the time dimension, the time intersection degree is calculated, and the flights that cross-use the same resources in the same time period are identified to form time conflict data;

[0012] Based on the flight resource occupancy data, the physical locations of the parking spaces, boarding gates and ground service equipment used by each flight are analyzed in spatial dimensions, spatial proximity is calculated, flights with spatial conflicts are identified, and spatial conflict data is generated;

[0013] A comprehensive analysis is performed on the time conflict data and space conflict data, duplicate records are removed, and conflict events that need to be adjusted are screened out in combination with flight scheduling constraints to obtain a resource conflict data set.

[0014] Preferably, according to the flight resource occupancy data, the resource occupancy of each flight in adjacent time periods is analyzed according to the time dimension, the time intersection degree is calculated, and the flights that cross-use the same resources in the same time period are identified to form time conflict data, including:

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

[0016] Based on the flight time occupancy data, the overlap of flights on the same resources in adjacent time periods is calculated, flights with time intersections are identified, and the overlapping time intervals are marked to form time intersection records;

[0017] Based on the time intersection records, time conflict events that affect the normal scheduling of flights are screened out, and the conflicting flights, conflicting time periods and resource types involved are recorded to form time conflict data.

[0018] Preferably, according to the flight resource occupancy data, the physical locations of the parking spaces, boarding gates and ground service equipment used by each flight are analyzed according to the spatial dimension, the spatial proximity is calculated, and flights with spatial conflicts are identified to form spatial conflict data, including:

[0019] According to the flight resource occupancy data, the geographical coordinates of the flight's parking positions, boarding gates and ground service equipment are determined to form flight space occupancy data;

[0020] Based on the flight space occupancy data, the physical distance between the resources used by adjacent flights in the same time period is calculated to form spatial proximity data;

[0021] The spatial proximity data that exceeds the preset spatial conflict threshold is screened to identify flights with spatial conflicts, and the conflicting resources, conflicting locations and affected flights are recorded to form spatial conflict data.

[0022] Preferably, an impact assessment is performed on the resource conflict dataset, and the impact score of each conflict event on airport operations is calculated based on the airport resource usage rules and flight priorities, including:

[0023] Extract the conflicting flight information from the resource conflict dataset, determine the number of flights involved in the conflict, the duration of the conflict, and the type of airport resources affected by the conflict, and form conflict influencing factor data;

[0024] According to the conflict influencing factor data and based on the flight priority information, the delay time of high-priority flights due to the conflict and the number of subsequent flights that may be affected are calculated to form flight impact data;

[0025] According to the conflict influencing factor data and based on the airport resource usage rules, the change in airport resource utilization rate caused by the conflict is calculated, and the resource idleness or overload that may be caused by the conflict is evaluated to form resource impact data;

[0026] Based on the flight impact data, resource impact data and resource conflict data set, a weighted sum is performed to calculate the conflict impact score of each conflict event.

[0027] The above solution of the present invention includes at least the following beneficial effects:

[0028] First, the comprehensive collection and time-series storage of airport operation data enables the dispatch system to grasp flight plans, parking space usage, gate scheduling status, distribution of ground service equipment and historical operation records in real time. This data-driven approach enables the dispatch system to quickly respond to changes in the airport's operating status and provide a solid data foundation for resource scheduling optimization. Compared with the existing technology that relies on simple analysis of historical statistical data, this method can dynamically update the airport's operating status, allowing the dispatch system to more accurately predict resource usage and reduce the problem of imbalanced resource allocation caused by data lag.

[0029] Secondly, by screening the time-series operation data for anomalies, we can automatically identify abnormal situations in flight scheduling, such as flight plan adjustments, equipment failures, weather impacts, etc., and filter out high-deviation flight data 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 overall operating 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, and record the conflict type, occurrence time and flight information involved to form a resource conflict data set. Compared with the existing scheduling method based on fixed rules, this method can analyze resource competition more intelligently, such as identifying the situation where multiple flights occupy the same parking space or boarding gate in the same time period, discover resource conflicts in advance and make optimization adjustments. This method overcomes the problem that traditional scheduling methods are difficult to reasonably allocate resources during peak hours, improves the balance of resource use, and reduces the situation where parking spaces are left vacant or overloaded for a long time.

[0031] In terms of impact assessment of conflict events, this method uses airport resource usage rules and flight priority calculation methods to quantify the impact of conflicts on airport operations, and determines the priority scheduling tasks 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 sequence based on data, thereby improving the scientificity and rationality of scheduling optimization. For example, when adjusting flights, it can give priority to the resource needs of high-priority flights such as international flights and connecting flights, avoid large-scale flight delays due to improper resource scheduling, and improve the stability of airport operations.

[0032] Finally, in 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 resource allocation more intelligent and dynamically adaptable. In contrast, existing solutions are mostly based on static scheduling rules, which are difficult to cope with scheduling changes caused by emergencies, resulting in delayed resource allocation. Through this method, the scheduling of parking spaces, boarding gates, and ground service equipment can more accurately match actual needs, avoid resource waste or overload, and improve overall resource utilization.

[0033] In summary, compared with the existing technology, this method has the following beneficial effects: based on the real-time data collection and time-series storage of the big data platform, the dispatch system's perception of the airport's operating status is improved; through the screening of abnormal flight data, the impact of abnormal data on dispatch optimization is reduced, and the dispatch accuracy is improved; the resource conflict detection mechanism of the time dimension and the space dimension is adopted to accurately identify the resource competition situation and reduce the problem of resource vacancy or overload; the priority of conflict events is sorted based on the impact score to improve the scientific nature of dispatch optimization; through historical data analysis and resource optimization calculation, the airport resources are dynamically adjusted to improve the adaptive ability of the dispatch system. Finally, this method can effectively reduce flight delays, improve resource utilization, improve the overall efficiency of airport operations, and provide passengers with a smoother travel experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of an airport resource scheduling and analysis method based on a big data platform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying 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 to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

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

[0037] S100, obtaining airport operation data, including flight plan data, parking space usage data, boarding gate scheduling data, ground service equipment status data and historical operation records, storing the airport operation data in a big data platform, and annotating the airport operation data by time series to obtain time-series operation data;

[0038] S200, performing anomaly screening on the time-series operation data, eliminating missing data and erroneous data, and calculating the flight scheduling deviation value based on the deviation between the flight plan data and the actual operation data, screening out high-deviation flight data according to a preset deviation threshold, and obtaining an abnormal flight data set;

[0039] S300, performing conflict detection on the abnormal flight data set, calculating the usage overlap of parking spaces, 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 involved flight information to obtain a resource conflict data set;

[0040] S400, performing an impact assessment on the resource conflict data set, calculating the impact score of each conflict event on the airport operation based on the airport resource usage rules and flight priority, and sorting the conflict events according to the impact score, determining the scheduling tasks to be adjusted first, and obtaining the priority scheduling task data;

[0041] S500, optimizing resource allocation for priority dispatch task data, calculating the optimal resource reallocation plan based on airport available resource information, flight dispatch constraints and historical dispatch adjustment records, adjusting parking space allocation, dynamically adjusting boarding gates and optimizing ground service equipment dispatch, and obtaining an optimized dispatch 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 an embodiment of the present invention, during the operation of an airport, the rationality of resource scheduling is directly related to the punctuality of flights and the efficiency of resource utilization. By constructing an airport resource scheduling analysis method based on a big data platform, it is possible to effectively solve the problems of traditional resource allocation methods that rely on manual experience, slow response speed, and unreasonable scheduling, thereby improving resource utilization and reducing flight delay rates. First, the comprehensive collection and storage of airport operation data provides a basis for subsequent data analysis and optimization. By collecting flight plan data, parking space usage data, gate scheduling data, ground service equipment status data, and historical operation records, and storing them on a big data platform, the integrity and traceability of the data can be ensured. The introduction of time series annotation allows the original data to be managed in chronological order, which helps to capture dynamic changes in the airport operation process and provide a reliable data basis for subsequent analysis.

[0044] Abnormal screening of time-series operation data is the key to ensuring scheduling accuracy. Due to the complex operating environment of airports, abnormal situations such as missing data, sensor errors, and plan adjustments may occur. If these inaccurate data are not removed in time, the accuracy of scheduling decisions may be affected. By calculating the deviation between flight plan data and actual operation data, abnormal flights can be accurately identified, and high-deviation flight data can be screened according to the preset deviation threshold to form an abnormal flight data set. This process prevents scheduling analysis from being interfered 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] Conflict detection for airport resources can effectively identify possible resource competition problems between flights by analyzing resource usage in time and space dimensions. Traditional scheduling methods usually rely on static scheduling rules, which are difficult to adjust quickly in a complex operating environment. The data analysis-based method can calculate the intersection of parking spaces, boarding gates and ground service equipment in real time, accurately identify resource competition, and form a resource conflict data set. Recording the type of conflict, the time of occurrence and related flight information enables the scheduling system to take corresponding optimization measures for different types of conflicts, further improving the flexibility of scheduling.

[0046] In terms of impact assessment of conflict events, the introduction of airport resource usage rules and flight priority calculation methods can quantify the impact of different conflicts on airport operations, and determine the scheduling tasks that need to be adjusted first by means of impact scores. This method makes scheduling optimization more intelligent and data-driven, which 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 idle or overused resources.

[0047] For priority scheduling tasks, the optimal resource reallocation plan can be accurately determined by using an optimization calculation method based on airport available resource information, flight scheduling constraints and historical scheduling adjustment records. This method fully considers the flight schedule, resource scheduling constraints and historical scheduling adjustment experience to ensure the rationality of resource allocation. By adjusting parking 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 operating data is recorded in real time and updated to the big data platform for subsequent scheduling analysis, so that the scheduling system can be continuously optimized, the overall operation efficiency of the airport can be improved, and the risk of flight delays can be reduced.

[0048] The obtaining of airport operation data, storing the airport operation data in a big data platform, and performing time series annotation on the airport operation data to obtain time series operation data specifically includes:

[0049] In the process of airport operation management, resource scheduling involves a large amount of dynamic data, including flight plan data, parking space usage data, gate scheduling data, ground service equipment status data, and historical operation records. In order to ensure the accuracy of scheduling optimization, these data need to be systematically collected, stored, and processed so that they can serve as the basis for subsequent scheduling optimization.

[0050] The acquisition of airport operation data usually relies on the airport's internal operation management system, which can record the use of various airport resources in real time. For example, flight schedule data mainly comes from the operation coordination system between airlines and airports, including the estimated arrival time, estimated departure time, parking space allocation plan, etc.; parking space usage data reflects the current occupancy of airport parking spaces, including allocated flight information, remaining available parking spaces, special-purpose parking spaces, etc.; boarding gate scheduling data involves the use of each boarding gate, including flights that are boarding, flights expected to board, and possible adjustments; ground service equipment status data covers the real-time use of airport ground equipment, 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, which can provide data support for subsequent optimization.

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

[0052] In order to ensure the timeliness and availability of data, the airport operation data stored in the big data platform needs to be time-series labeled so that it can be managed in chronological order. The process of time series labeling includes adding a timestamp to each piece of data so that the data can be sorted and retrieved according to the time dimension. For example, for flight plan data, key time points such as the scheduled take-off time and the scheduled landing time of the 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 can be recorded according to the time dimension to ensure that the scheduling system can query the use 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 realize dynamic management of data, so that scheduling optimization can be calculated based on the latest operation data, improving the scientificity and accuracy of resource scheduling.

[0053] The abnormal screening of the time-series operation data, elimination of missing data and erroneous data, calculation of the flight scheduling deviation value based on the deviation between the flight plan data and the actual operation data, and screening out the high deviation flight data according to the preset deviation threshold to obtain the abnormal flight data set specifically include:

[0054] In the process of airport resource scheduling, the accuracy of data directly affects the effect of scheduling optimization. Due to the complex operating environment of the airport, missing data, erroneous data or abnormal data may appear during the data collection process. If these data are used directly for scheduling optimization, it may lead to unreasonable resource allocation and inaccurate scheduling adjustments. Therefore, before scheduling and analyzing the time-series operation data, it is necessary to screen for abnormalities to ensure the reliability of the data.

[0055] The first step of anomaly screening is to eliminate missing data. Missing data may come from a variety of situations. For example, due to network transmission problems, some sensor equipment fails to upload data on time, resulting in incomplete flight plan 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, they may affect the dispatch system's judgment of the airport's operating status. Therefore, in 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, historical data can be used to infer the possible values ​​of missing data, or data from adjacent time points can be used for interpolation and filling to improve data integrity.

[0056] While eliminating missing data, it is also necessary to identify and eliminate erroneous data. Erroneous data usually refers to abnormal data caused by incorrect data format, data entry errors or sensor failures. For example, the planned landing time of some flights is earlier than the take-off time, which is obviously illogical; the status data of some parking spaces show that they are occupied by multiple flights at the same time, which may be caused by data collection errors. In the data screening process, data validation rules can be used, such as checking whether the data conforms to logical constraints and whether it conforms to airport operating rules, to ensure the accuracy of the data. At the same time, the method of comparing historical data can be used to identify abnormal data that deviates too much from normal conditions and correct or eliminate them.

[0057] After eliminating missing data and erroneous 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 with actual operation data, such as the difference between the planned take-off time and the actual take-off time, the difference between the planned landing time and the actual landing time, etc. If the actual operation of a flight deviates greatly from the planned time, it means that the flight may be affected by emergencies, weather or other uncertain factors, and scheduling adjustments may be required.

[0058] In order to filter out flight data that is more affected, it is necessary to set a preset deviation threshold. For example, if the difference between the scheduled departure time and the actual departure 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 space usage time of a flight and the planned time, it may mean that the ground service time of the flight is affected and needs to be adjusted accordingly. By setting a reasonable deviation threshold, high-deviation flight data can be effectively filtered out to form an abnormal flight data set, so that in the subsequent scheduling optimization process, these abnormal flights can be adjusted first to improve the flexibility and adaptability of airport resource scheduling.

[0059] The above-mentioned abnormal screening method can ensure that the data used by the dispatch system is accurate and reliable, thereby improving the accuracy of dispatch optimization. Compared with the existing abnormal data screening method that relies on manual judgment, this method can automatically detect abnormalities in the data, reduce human intervention, and improve the intelligence level of dispatch adjustment. At the same time, by establishing an abnormal flight data set, the dispatch 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, the conflict detection is performed on the abnormal flight data set, the use of parking spaces, boarding gates and ground service equipment is calculated based on the time dimension and the space dimension, the flights with resource competition are identified, and the conflict type, occurrence time and involved flight information are recorded to obtain the resource conflict data set, including:

[0061] Obtain flight operation information from the abnormal flight data set, extract flight arrival time, departure time, parking space occupancy, boarding gate allocation, and ground service equipment usage to form flight resource occupancy data;

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

[0063] Based on the flight resource occupancy data, the physical locations of the parking spaces, boarding gates and ground service equipment used by each flight are analyzed in spatial dimensions, spatial proximity is calculated, flights with spatial conflicts are identified, and spatial conflict data is generated;

[0064] A comprehensive analysis is performed on the time conflict data and space conflict data, duplicate records are removed, and conflict events that need to be adjusted are screened out in combination with flight scheduling constraints to obtain a resource conflict data set.

[0065] In the embodiment of the present invention, for the abnormal flight data set 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 experience judgment, which is difficult to quickly identify resource competition problems in complex environments. The method based on data analysis can accurately identify resource-competing flights based on the abnormal flight data set through calculations in the time dimension and the space dimension, thereby improving scheduling efficiency and accuracy.

[0066] By analyzing the resource usage of flights based on the time dimension, the resource intersection of adjacent time periods can be calculated to form time conflict data. In actual applications, the arrival time, departure time, parking space occupancy period, boarding gate usage period and ground service equipment usage period of flights may all overlap. If not optimized, multiple flights may occupy the same resource at the same time, affecting the overall operation efficiency of the airport. By calculating the flight time intersection degree, it is possible to accurately identify flights that overlap the same resources in the same time period, mark the overlapping time intervals, and form a time intersection record. This method enables the scheduling system to automatically analyze the time conflicts of flight scheduling and avoid flight delays or scheduling confusion caused by overlapping resource usage.

[0067] By analyzing the use of flight resources in the spatial dimension, the spatial proximity of flights can be calculated to form spatial conflict data. When flights are using parking stands, boarding gates, and ground service equipment, the resource allocation may be unreasonable due to the close distance. For example, if flights at adjacent parking stands need to use the same ground equipment at the same time, and the scheduling is not arranged reasonably, it may cause conflicts in the use of equipment and affect the efficiency of flight operations. By calculating spatial proximity, flights with spatial conflicts can be accurately identified, ensuring the reasonable allocation of resources and improving the scientific nature of airport scheduling.

[0068] Through the comprehensive analysis of time conflict data and space conflict data, duplicate records can be eliminated, and the conflict events that need to be adjusted can be screened out in combination with flight scheduling constraints to form a resource conflict data set. 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 improve overall scheduling efficiency.

[0069] The time conflict data and space conflict data are comprehensively analyzed to remove duplicate records, and the conflict events that need to be adjusted are screened out in combination with flight scheduling constraints to obtain a resource conflict data set, which specifically includes:

[0070] In the process of airport resource scheduling, resource competition between flights may manifest as time conflict or space conflict. Time conflict usually refers to multiple flights trying to use the same resources in the same time period, such as a parking space being occupied by two flights in the same time period, or multiple flights being scheduled to use adjacent boarding gates in the same time period, resulting in overcrowding in the boarding area. Spatial conflict mainly involves the physical location relationship between flights. For example, some parking spaces are close to each other, and flights that are assigned adjacent parking spaces at the same time may need to share some ground service equipment, resulting in tight equipment scheduling, or due to site restrictions, some flights may have cross-interference during taxiing. Therefore, when optimizing airport resource scheduling, it is necessary to comprehensively analyze time conflict data and space 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 necessary to remove duplicate records first. Since flight resource conflicts may have both time and space dimensions, some conflict events may be marked in both data sets. For example, two flights are scheduled at adjacent boarding gates in a similar time period, which may be identified as both time conflicts and space conflicts. Therefore, when performing data analysis, it is necessary to check whether there are duplicate marked conflict events and merge related records to avoid distortion of scheduling optimization results due to repeated calculations. At the same time, it is also necessary to consider the dynamic changes of flight operations. For example, some conflicts may resolve themselves in a short period of time (such as flight delays causing time conflicts to disappear), so it is necessary to eliminate records that have no impact 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 conflicting events need to be adjusted first. Flight scheduling constraints include a variety of factors, such as flight priority, flight operating rules, ground service time requirements, etc. For example, international flights usually have a higher scheduling priority, so if an international flight conflicts with a domestic flight in the parking space arrangement, it may be necessary to adjust the scheduling plan of the domestic flight first, while trying to maintain the established arrangement of the international flight. In addition, some flights may be subject to specific requirements of the airline. For example, some flights may need to be arranged at adjacent boarding gates with other flights of the same airline to facilitate passenger transfers, so these operational needs 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 data set can be formed. This data set contains information such as the flights that need to be optimized for scheduling, the types of resources involved, and the severity of the conflict, 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 scheduling adjustments more scientific and reasonable, and improve the overall utilization efficiency of airport resources.

[0074] In a preferred embodiment of the present invention, the resource occupancy of each flight in adjacent time periods is analyzed according to the time dimension based on the flight resource occupancy data, the time intersection degree is calculated, and the flights that cross-use the same resources in the same time period are identified to form time conflict data, including:

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

[0076] Based on the flight time occupancy data, the overlap of flights on the same resources in adjacent time periods is calculated, flights with time intersections are identified, and the overlapping time intervals are marked to form time intersection records;

[0077] Based on the time intersection records, time conflict events that affect the normal scheduling of flights are screened out, and the conflicting flights, conflicting time periods and resource types involved are recorded to form time conflict data.

[0078] In an embodiment of the present invention, in the process of airport resource scheduling, the identification and optimization of time conflicts are important links in improving flight punctuality. Since the arrival time, departure time, parking space occupancy period, boarding gate usage period and ground service equipment usage period of a flight all have certain uncertainties, 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 of flights in the time dimension and calculating the time intersection, time conflicts can be effectively identified, improving the scientificity and accuracy of airport scheduling.

[0079] After obtaining the flight resource occupancy data, it is necessary to calculate the degree of overlap of flights for the same resources in adjacent time periods to form a time intersection record. The calculation of time intersection records needs to consider multiple factors, including flight arrival and departure times, parking space occupancy time, boarding gate usage, etc. For example, if the parking times of two flights partially overlap and only one parking space is available, one of the flights may not be able to slide into the parking space on time. By calculating the degree of time intersection, this resource conflict can be accurately identified and adjusted in advance to avoid flight delays or confusion in ground resource scheduling.

[0080] After identifying time conflict events, it is necessary to screen out time conflict events that affect the normal scheduling of flights, and record the conflicting flights, conflicting time periods, and the types of resources involved, so as 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 not be able to use parking spaces as planned due to time conflicts, and if they are not adjusted in time, multiple subsequent flights may be delayed. Therefore, by establishing time conflict data, the predictive ability of the scheduling system can be improved, enabling it to identify potential problems in advance and prioritize resolving these conflicts in scheduling optimization.

[0081] The establishment of time conflict data not only helps to optimize the current flight scheduling, but can also 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 in certain time periods and high frequency of use of certain parking spaces. These data can provide decision 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 operation efficiency of the airport can be optimized, and the travel experience of passengers can be improved.

[0082] In a preferred embodiment of the present invention, the physical locations of parking spaces, boarding gates and ground service equipment used by each flight are analyzed according to the spatial dimension based on the flight resource occupancy data, the spatial proximity is calculated, and flights with spatial conflicts are identified to form spatial conflict data, including:

[0083] According to the flight resource occupancy data, the geographical coordinates of the flight's parking positions, boarding gates and ground service equipment are determined to form flight space occupancy data;

[0084] Based on the flight space occupancy data, the physical distance between the resources used by adjacent flights in the same time period is calculated to form spatial proximity data;

[0085] The spatial proximity data that exceeds the preset spatial conflict threshold is screened to identify flights with spatial conflicts, and the conflicting resources, conflicting locations and affected flights are recorded to form spatial conflict data.

[0086] In an embodiment of the present invention, during the operation of an airport, the detection of spatial conflicts is crucial to ensure the reasonable allocation of flight resources. Unlike time conflicts, spatial conflicts mainly involve the physical location distribution of flights at parking stands, 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, thereby affecting the normal operation of flights. By analyzing the resource occupancy of flights based on spatial dimensions, it is possible to accurately identify resource competition problems that may arise due to 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 flight space occupancy data, the physical distance between the resources used by adjacent flights in the same time period can be calculated to form spatial proximity data. This calculation method takes into account the geographical coordinates of the flight's parking positions, boarding gates, and ground service equipment, allowing the scheduling system to accurately identify the degree of resource tension based on the spatial distribution. For example, some parking positions are close to each other, and the ground service equipment requirements of two flights overlap, which may cause resource competition and affect the service quality of the flight. By calculating spatial proximity, such problems can be identified and resources can be optimized and adjusted.

[0088] In order to ensure the accuracy of spatial conflict identification, a preset spatial conflict threshold is further introduced. By screening the spatial proximity data that exceeds this threshold, flights with spatial conflicts can be accurately identified to avoid airport operation problems caused by imbalanced resource scheduling. For example, if the parking spaces of two flights are too close in physical distance and their ground equipment requirements are high at the same time, it may affect the coordination of ground staff and equipment, thereby affecting the overall operation efficiency of the airport. By calculating spatial conflict data and recording conflicting resources, conflicting locations and affected flights, the scheduling system can actively adjust the resource allocation plan, reduce the impact of resource shortages 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 data set is performed, and the impact score of each conflict event on the airport operation is calculated based on the airport resource usage rules and flight priorities, including:

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

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

[0092] According to the conflict influencing factor data and based on the airport resource usage rules, the change in airport resource utilization rate caused by the conflict is calculated, and the resource idleness or overload that may be caused by the conflict is evaluated to form resource impact data;

[0093] According to the flight impact data, resource impact data and resource conflict data set, a weighted sum is performed to calculate the conflict impact score of each conflict event; ,

[0094] To measure the impact of airport resource conflicts on overall operations, , , , is the weight coefficient, is the flight impact, For flights The priority of For flights Delays due to conflicts, Indirectly affected flights The weight coefficient of For flights Possible delays, is the number of flights directly affected, is the number of flights indirectly affected, is the resource impact, For resources The weight coefficient of For resources The affected overload ratio, expressed as the adjusted usage change, is the number of affected resources, It is the time dimension impact item, which is used to calculate the average delay time caused by the conflict and represents the time impact on airport scheduling. is the spatial dimension influence term, For flights Proximity in space to neighboring flights.

[0095] In an embodiment of the present invention, during the airport scheduling process, there are differences in the degree of impact of resource conflicts on the overall operation of the airport. Different flight priorities, different resource types, and different time periods will lead to different degrees of impact of conflict events. Therefore, it is necessary to calculate the impact score of each conflict event based on the impact assessment in order to determine the conflict problems that need to be solved first and improve the pertinence and effectiveness of scheduling optimization. By quantifying the impact of each conflict event on the operation of the airport, it is possible to prioritize and optimize conflicts with greater impact under limited time and resource conditions, thereby improving the scientificity and accuracy of airport scheduling.

[0096] The calculation of the impact score first requires extracting the conflicting flight information involved in the resource conflict dataset, including the number of flights, the duration of the conflict, and the type of airport resources affected. The more flights there are, the greater the impact of the conflict may be, so a higher score is required. At the same time, events with a long duration of conflict will have a greater impact on subsequent flights, so they also need to have a higher weight in the score. The type of airport resources affected further determines 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 calculation of the impact score.

[0097] In order to further improve the accuracy of the scoring, flight priority information is introduced. Flights of different priorities are affected differently in conflicts. For example, international flights, connecting flights, long-distance flights, etc. usually have higher scheduling priorities, so 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 the conflict event on flight operations can be quantified. For example, if the delay of an international flight affects the connection of multiple connecting flights, the impact score of the conflict event should be higher to ensure that this problem is given priority during scheduling optimization.

[0098] The calculation of resource impact further enhances the rationality of the impact score. Resource conflicts may cause 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 a conflict, it may affect the scheduling of subsequent flights. Therefore, by calculating the changes in airport resource utilization caused by the conflict and evaluating the possible idleness or overload of resources, more comprehensive data support can be provided for the score calculation. By taking the weighted sum of the flight impact and resource impact, the conflict impact score is finally calculated, thereby ensuring that the scheduling optimization can give priority to resolving the conflicts with the most serious impact and improve the overall optimization effect of airport scheduling.

[0099] The conflict impact score The calculation formula is as follows: ,

[0100] in, , , , is the weight coefficient, which can be adjusted according to the airport operation strategy to adapt to different optimization objectives.

[0101] The core function of this formula is to comprehensively evaluate the impact of resource conflicts occurring during airport operations on overall operations, thereby determining the conflict events that need to be resolved first and optimizing scheduling strategies.

[0102] Flight impact reflects the direct and indirect impact of the conflict on flight operations, Indicate flight High-priority flights, such as international flights and connecting flights, are more critically affected by the conflict and are therefore given higher weights. Representative flights The delay time affected by the conflict, the longer the delay time, the greater the impact. Indirectly affected flights The weight of connecting flights can be used to reflect the impact of a flight delay on the overall airport operation. Representative flights The technical effect of this part is to quantify the degree of interference of conflict events on airport flight scheduling by comprehensively evaluating the direct and indirect delays of flights.

[0103] Resource Impact Reflects the impact of conflict events on airport resource utilization, Representative resources For example, the utilization 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. Representative resources The overload rate is the change in the utilization rate of the resource after the conflict occurs, such as a parking space being overloaded due to a conflict, or some ground service equipment being idle or overloaded due to adjustments. The technical effect of this part is to identify the most affected resources in the airport operation process by measuring the changes in resource utilization rate, so as to optimize the scheduling strategy and make resource allocation more reasonable.

[0104] Time dimension impact factor Quantify the impact of conflict events over time, and Respectively indicate flights and flights The technical effect of this part is to calculate the average delay time caused by conflicts to reflect the time lag of the overall operation of the airport. Conflict events with longer average delays will be given higher priority so that quick adjustments can be made to reduce the impact on subsequent flight plans.

[0105] Spatial dimension impact factor Assess the spatial impact of conflict events, Indicate flight The calculation method of spatial proximity to adjacent flights can measure the degree of spatial conflict between flights based on the location coordinates of the flight's parking positions, boarding gates and ground service equipment. The technical effect of this part is to identify which conflict events may cause excessive resource density in a certain area of ​​the airport by calculating the physical proximity of adjacent flights. For example, multiple flights are concentrated at adjacent boarding gates or parking positions, making scheduling more complicated, which requires priority optimization.

[0106] In summary, conflict impact score The flight impact, resource impact, time dimension impact factor and space dimension impact factor are combined to form a comprehensive indicator to evaluate the impact of conflict events on the overall operation of the airport. The technical advantage of this formula is that it not only takes into account the direct impact on flights, but also comprehensively analyzes the chain reaction between flights, changes in resource load, delay propagation in time and conflicts in space. This enables the airport dispatch system to prioritize the conflict events that have the greatest impact on airport operations based on the size of the conflict impact score, thereby improving the airport's dispatch efficiency, reducing flight delays, optimizing resource utilization, and improving passengers' travel experience.

[0107] In a preferred embodiment of the present invention, the delay time of the high-priority flight due to the conflict and the number of subsequent flights that may be affected are calculated based on the flight priority information to form the flight impact degree, including:

[0108] Based on the flight priority information, the priority level of each flight is determined, and the conflicting flights involved in the time conflict data and the space conflict data are extracted to form the conflicting flight data;

[0109] Based on the number of conflicting flights, calculate the expected delay time of high-priority flights when conflicts occur, and analyze the impact of the delay on the connection of subsequent segments of the flight to form flight delay impact data;

[0110] According to the flight delay impact data, the subsequent flight plans of the affected flights are extracted, and the subsequent flight delay propagation caused by the conflict is calculated to form flight delay propagation data;

[0111] The number of indirectly affected flights in the flight delay propagation data is counted, and the impact of the conflict on the entire airport flight plan is evaluated in combination with the flight priority information to form flight impact data.

[0112] In an embodiment of the present invention, flight priority information plays a key role in the airport scheduling optimization process. Different flights have different importance in airport scheduling. For example, international flights are usually more important than domestic short-haul flights, and delays in connecting flights may affect the itineraries of more passengers, so it is necessary to give priority to the normal operation of these flights. By calculating the delay time of high-priority flights due to conflicts and further analyzing their 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.

[0113] To calculate the flight impact, it is necessary to first determine the priority level 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-distance flights and connecting flights usually have higher priorities, while short-distance flights may have lower priorities. By extracting conflicting flight data, conflict events with greater impact can be screened out, and the degree of impact of these conflicts on flight operations can be further calculated.

[0114] For high-priority flights, calculating their expected delay time when conflicts occur is the key to optimizing scheduling. Since flight delays not only affect the operation of the current flight, but may also cause a chain reaction on 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 planned, the impact on this flight is greater and its scheduling needs to be optimized first.

[0115] The calculation of flight delay impact data can be further extended to indirectly affected flights, that is, to calculate the propagation of flight delays caused by conflicts. For example, a delay in an international flight may cause multiple connecting flights to fail to take off on time, thus affecting the itineraries of a large number of passengers. By counting flight delay propagation data, the number of indirectly affected flights can be calculated, and combined with flight priority information, the impact of the conflict on the airport's overall flight plan can be evaluated. For example, if the delay of a flight will cause multiple high-priority flights to be affected, it is necessary to prioritize the optimization of the flight's scheduling to reduce the impact of the delay on the overall airport operation.

[0116] By calculating the flight impact, we can quantify the impact of conflict events on the overall operation of the airport and provide more scientific data support for scheduling optimization. The advantage of this method is that it not only takes into account the direct impact of flights, but also comprehensively analyzes the relationship between flight connections to ensure that the scheduling optimization plan can minimize delays, improve airport operation efficiency, and enhance passengers' travel experience.

[0117] The method of determining the priority level of each flight based on the flight priority information and extracting the conflicting flights involved in the time conflict data and the space conflict data to form the conflicting flight data specifically includes:

[0118] In the process of airport resource scheduling, different flights have different importances. Therefore, when dealing with resource conflicts, it is necessary to determine the priority level of each flight based on flight priority information to ensure that high-priority flights can be given priority in the resource scheduling process. Flight priority is usually determined by airport management rules and airline operating needs. Flights with higher priority usually include international flights, long-distance flights, connecting flights, and flights with fixed schedules and high punctuality requirements. For example, the boarding gate allocation of international flights is usually affected by customs and border inspection areas, so its operational stability needs to be given priority in the scheduling process. Delays in connecting flights may affect passengers' subsequent itineraries, so it is necessary to try to avoid conflicts during resource scheduling.

[0119] After determining the flight priority level, it is necessary to extract the conflicting flights involved in the time conflict data and space conflict data to identify the affected flight set. The time conflict data mainly contains flight pairs that conflict due to overlapping resource allocation, such as flights scheduled to use the same parking space in the same time period, or flights that are too tightly scheduled to allow ground service equipment to provide services on time. The space conflict data mainly involves conflicts in physical locations, such as flights at adjacent parking spaces may have cross-interference on the taxi path, or unreasonable boarding gate arrangements may lead to overcrowding in the boarding area.

[0120] By combining time conflict data and space conflict data, conflict flight data can be formed, the set of flights affected by the conflict can be identified, and these flights can be sorted according to their priorities, so that flights that have a greater impact on the overall operation of the airport can be adjusted first during resource scheduling optimization. For example, if a flight has a lower priority and is not greatly affected by the conflict, it can be scheduled for resource adjustment to ensure that higher priority flights can operate as planned. Compared with traditional scheduling methods, this method can more accurately identify flights that need priority adjustment, improve the rationality of resource scheduling, reduce flight delays caused by conflicts, and optimize the overall operational efficiency of the airport.

[0121] In a preferred embodiment of the present invention, the change in airport resource utilization rate caused by the conflict is calculated based on the airport resource utilization rule, and the resource idleness or overload situation that may be caused by the conflict is evaluated to form the resource impact degree, including:

[0122] Extract conflicting flights and conflicting resources information from resource conflict data, determine the current usage of the parking spaces, boarding gates and ground service equipment involved, and form conflicting resource occupancy data;

[0123] Based on the conflict resource occupancy data, calculate the changes in the utilization rate of various airport resources before and after the conflict, including the increase in resource idle time or excessive frequency of equipment use caused by flight adjustments, and form resource utilization rate change data;

[0124] According to the resource utilization rate change data and based on the airport resource utilization rules, the impact of conflicts on different types of resources is evaluated to form resource load assessment data;

[0125] Based on the resource load assessment data, determine whether the conflict will lead to an imbalance in resource allocation, and mark resources that are severely overloaded or idle to form resource impact data.

[0126] In the embodiment 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 operating efficiency. Some resources may be overused due to conflict events, while other resources may be idle, affecting the stability of airport operations. Therefore, in the process of calculating the conflict impact score, it is necessary to further consider the impact of the conflict on the airport resource utilization rate to ensure that scheduling optimization can effectively alleviate the imbalance problem of resource allocation.

[0127] The calculation of resource impact first requires extracting the conflicting flights and conflicting resources information from the resource conflict data, and determining the current usage of the parking spaces, boarding gates, and ground service equipment involved. Due to the different importance of different resources, changes in their usage status may have different degrees of impact on airport operations. For example, excessive utilization of parking spaces may result in flights having nowhere to park, while excessive use of boarding gate resources may reduce passenger boarding efficiency. Therefore, it is necessary to calculate the changes in the utilization of various types of airport resources before and after the conflict occurs based on different types of resources, including increased resource idle time due to flight adjustments, or overload due to excessive resource usage.

[0128] When calculating resource utilization changes, the impact 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 overloaded 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 used reasonably due to adjustments, resulting in a large amount of idle time, it means that the scheduling optimization has failed to give full play to the efficiency of resource utilization. Therefore, in the process of calculating the resource impact, it is necessary to evaluate the load conditions of different resources and combine the resource usage rules to determine whether the conflict will lead to an imbalance in resource allocation.

[0129] Finally, based on the resource load assessment data, resources that are severely overloaded or idle can be marked, and these data can be used to optimize the scheduling plan. By quantifying the impact of resources, the scheduling system can intelligently identify problems in resource scheduling and optimize and adjust resource imbalances, thereby improving the overall efficiency of airport resource utilization, reducing the risk of flight delays, and improving passenger travel experience.

[0130] The resource load assessment data is formed by evaluating the impact of conflicts on different types of resources based on the resource utilization rate change data and the airport resource utilization rules, specifically including:

[0131] During airport operations, the balance of resource use directly affects the stability of flight scheduling. If some resources are overloaded for a long time, it may affect the overall operating efficiency of the airport, while if some resources are idle for a long time, it may mean that resource allocation is unreasonable. Therefore, when optimizing airport resource scheduling, it is necessary to evaluate the impact of conflicts on different types of resources based on resource utilization rate change data to ensure the rationality of resource allocation and improve the stability of airport operations.

[0132] The acquisition of resource utilization change data is usually based on airport operation data and historical scheduling records. For example, by comparing the changes in the utilization rate of a certain parking stand in different time periods, it can be determined whether the parking stand is resource-constrained or idle. If a parking stand is overloaded for a long time during peak hours, while other parking stands are less used, it may mean that the scheduling plan needs to be adjusted to optimize the overall utilization of the parking stand. Similarly, for boarding gates and ground service equipment, the rationality of their scheduling can also be evaluated based on utilization data, and it can be determined whether adjustments are needed.

[0133] 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 spaces, and certain boarding gates may need to be given priority to specific airlines. Therefore, when evaluating resource load, it is necessary to combine the airport's operating rules to ensure that the scheduling optimization plan can meet actual operational needs. Through in-depth analysis of resource utilization rate change data, resource load evaluation data can be formed to provide data support for subsequent scheduling optimization, improve the overall utilization efficiency of airport resources, reduce operational problems caused by unreasonable resource scheduling, and optimize the stability of flight operations.

[0134] In a preferred embodiment of the present invention, the resource allocation optimization of the priority scheduling task data is performed, based on the airport available resource information, flight scheduling constraints and historical scheduling adjustment records, the optimal resource reallocation plan is calculated, the parking space allocation is adjusted, the boarding gate is dynamically adjusted and the ground service equipment scheduling is optimized to obtain the optimized scheduling plan, including:

[0135] According to the priority dispatch task data, extract the current resource allocation of the flight to be adjusted, and query the airport available resource information to form available resource data;

[0136] Based on flight scheduling constraints, candidate resource adjustment plans are calculated to eliminate resource allocations that do not conform to scheduling rules and form a set of feasible scheduling plans;

[0137] Based on historical scheduling adjustment records, analyze the optimization effects of different feasible scheduling schemes in similar conflict scenarios, calculate the execution cost and resource utilization of each feasible scheduling scheme, and form scheduling optimization evaluation indicators;

[0138] According to the scheduling optimization evaluation indicators, the optimal resource allocation plan is selected according to the preset optimization goals, and the flight parking positions, boarding gates and ground service equipment scheduling are adjusted to form an optimized scheduling plan.

[0139] In the embodiment of the present invention, based on the priority scheduling task data, resource allocation optimization is required to ensure the rational use of airport resources and improve the overall efficiency of flight scheduling. In the traditional scheduling method, resource allocation usually relies on fixed rules and is difficult to adapt to the dynamically changing airport operating environment. However, through the method based on data analysis, the optimal resource reallocation plan can be calculated based on the comprehensive consideration of the airport available resource information, flight scheduling constraints and historical scheduling adjustment records, thereby improving the intelligent level of scheduling optimization.

[0140] The first step in optimizing the scheduling task is to extract the current resource allocation of the flight to be adjusted and query the airport's available resource information to form available resource data. The acquisition of available resource data needs to take into account the real-time status of airport operations, such as the number of currently available parking spaces, idle boarding gates, and available ground service equipment. The availability of different resources will affect the feasibility of the scheduling plan, so it is necessary to ensure the real-time and accuracy of the data.

[0141] After obtaining the available resource data, it is necessary to calculate the candidate resource adjustment plan based on the flight scheduling constraints, and eliminate the resource allocation that does not meet the scheduling rules to form a set of feasible scheduling plans. Scheduling constraints may include flight priority, parking space type, ground service requirements, etc. For example, some large international flights require specific types of parking spaces, and some flights need to have boarding gates close to customs inspection areas. Therefore, when calculating candidate scheduling plans, it is necessary to ensure that the plans meet these constraints.

[0142] In order to improve the scientific nature of scheduling optimization, historical scheduling adjustment records are further introduced to analyze the optimization effects of different feasible scheduling schemes in similar conflict scenarios. By calculating the execution cost and resource utilization of different scheduling schemes, data support can be provided for scheme selection. For example, if a scheduling scheme can effectively reduce flight delays and improve resource utilization during the historical adjustment process, this scheme can be given priority. On this basis, according to the preset optimization goals, the optimal resource allocation scheme is selected, and the parking positions, boarding gates and ground service equipment of the flight are adjusted to form an optimized scheduling scheme.

[0143] Ultimately, the execution of the optimized scheduling plan will dynamically adjust airport resources and record the adjusted operating data 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 operations can be improved.

[0144] The calculation of candidate resource adjustment schemes based on flight scheduling constraints, excluding resource allocation situations that do not conform to scheduling rules, and forming a set of feasible scheduling schemes specifically includes:

[0145] In the process of airport resource scheduling, flight resource allocation is affected by a variety of operational constraints, including but not limited to flight priority, resource type matching, ground service requirements, flight schedule restrictions, airline operating rules, etc. If resources are directly allocated without considering these constraints, 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 plan that meets the rules based on the flight scheduling constraints to form a set of feasible scheduling plans.

[0146] The calculation of candidate resource adjustment plans is first analyzed based on the airport's available resource information. When adjusting flight resources, it is necessary to clarify the currently available parking spaces, boarding gates, ground service equipment and other resources, and determine possible resource reallocation plans in combination with flight scheduling tasks. For example, if the original parking space of a flight can no longer be used due to adjustments to other flights, it is necessary to find other parking spaces that meet the requirements as candidate solutions. Similarly, if the boarding gate resources need to be readjusted, it is necessary to screen out candidate solutions that meet the scheduling requirements 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 needs during the subsequent optimization process.

[0147] After generating candidate resource adjustment plans, it is necessary to combine flight scheduling constraints to screen out scheduling plans that meet the rules and eliminate unreasonable resource allocation. Flight scheduling constraints include multiple aspects, such as:

[0148] 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 with high-priority flights due to adjustments.

[0149] Resource type matching: Some parking slots may only be suitable for certain types of flights. For example, large aircraft may require a specific size of parking slot, while small aircraft can use general parking slots. Therefore, during the adjustment process, it is necessary to ensure that the allocated resources match the flight demand.

[0150] Time constraints: The take-off and landing times of a flight determine the time range of its resource requirements. For example, some flights require longer ground service time, while some flights have shorter gate usage time. Therefore, when adjusting resources, it is necessary to ensure that the time arrangement does not lead to resource usage conflicts.

[0151] Airline operating rules: Some airlines may wish to arrange boarding gates for flights of the same airline in adjacent areas to facilitate passengers' transfers. Such operational needs need to be met as much as possible during the adjustment process.

[0152] Ground service requirements: Some flights may require special ground services, such as baggage transfer, cargo handling, catering supplies, etc. Therefore, when adjusting resources, it is necessary to ensure that the allocated resources can meet the corresponding service requirements.

[0153] By screening candidate resource adjustment plans based on the above constraints, resource allocation situations that do not conform to the scheduling rules can be eliminated to form 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.

[0154] In a preferred embodiment of the present invention, the optimization effects of different feasible scheduling schemes in similar conflict scenarios are analyzed based on historical scheduling adjustment records, the execution cost and resource utilization of each feasible scheduling scheme are calculated, and the scheduling optimization evaluation index is formed, including:

[0155] Based on the historical scheduling adjustment records, historical cases similar to the current conflict event are screened out, and the corresponding scheduling adjustment plans are extracted to form historical scheduling case data;

[0156] Based on historical dispatch 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;

[0157] Based on historical dispatch case data, analyze the resource utilization efficiency of various resource adjustment plans in conflict mitigation, including the change in resource utilization rate after the adjustment plan is implemented, the resource load balance of each area of ​​the airport and the dispatch stability, and form resource utilization data;

[0158] The execution cost data and resource utilization data are combined to form scheduling optimization evaluation indicators.

[0159] In the embodiment of the present invention, during the airport resource scheduling process, the selection of the scheduling optimization scheme directly affects the flight punctuality rate, the utilization efficiency of airport resources and the stability of the overall operation. Due to the complex airport operating environment, different scheduling schemes may perform differently in terms of execution cost, flight delays, resource allocation balance, etc. Therefore, based on the historical scheduling adjustment records, analyzing the optimization effects of different feasible scheduling schemes in similar conflict scenarios, and calculating the execution cost and resource utilization of each scheduling scheme, data support can be provided for the final scheduling optimization decision, thereby ensuring that the selected scheme achieves the optimal configuration of resource scheduling while complying with the airport operating rules.

[0160] This method first screens historical scheduling adjustment records, extracts historical cases similar to the current conflict event, and extracts the corresponding scheduling adjustment plan to form historical scheduling case data. Compared with the traditional manual decision-making method that relies on the dispatcher's experience and judgment, this method analyzes past successful adjustment cases to provide reference for the current optimization plan and improve the scientific nature of scheduling decisions. For example, if historical data shows that in similar parking space conflicts, the use of a specific resource adjustment plan can effectively reduce flight delays, then this plan can be used as a reference plan for priority selection.

[0161] After obtaining historical scheduling case data, it is necessary to further calculate the execution costs of various resource adjustment plans in past conflict scenarios to quantify the economic impact of different plans. Execution costs include flight delay costs, ground service adjustment costs, and equipment reallocation costs. For example, some plans may involve adjustments to flight taxi paths, resulting in additional fuel consumption, while other plans may involve the reallocation of ground equipment, increasing equipment usage costs. By quantifying these execution costs, the economic feasibility of different plans can be evaluated, avoiding the selection of plans with excessively high costs but no obvious optimization effects, and improving the economy of airport operations.

[0162] 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 adjustment plan is implemented, the resource load balance in various areas of the airport, and the scheduling stability. For example, if a scheduling scheme can reduce the overload of parking space resources and improve the utilization of boarding gate resources after optimization, then the scheme performs better in resource optimization and is worth applying in the current scenario. In contrast, if a scheme can alleviate the current conflict but will cause other resources to be overloaded or idle, then the scheme may not be suitable for the current scheduling optimization.

[0163] Finally, the execution cost data and resource utilization data are combined 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 subsequent selection of optimization schemes. Through this method, the airport scheduling system can select the best scheme from multiple alternatives, 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 operational risks caused by temporary adjustments, make the airport scheduling system more intelligent and adaptive, and improve the overall stability of the airport operation and the punctuality of flights.

[0164] In a preferred embodiment of the present invention, the optimal resource allocation scheme is selected according to the scheduling optimization evaluation index and the preset optimization target, and the parking positions, boarding gates and ground service equipment scheduling of the flight are adjusted to form an optimized scheduling scheme, including:

[0165] According to the scheduling optimization evaluation index, feasible scheduling schemes are extracted, and the comprehensive scores of different scheduling schemes are calculated according to the preset optimization objectives to form a candidate set of optimization schemes; ,

[0166] In order to measure the pros and cons of the scheduling optimization scheme, , , is the weight coefficient, Improve flight punctuality. For flights Delay time before scheduling optimization, For flights Delay time after scheduling optimization, Optimize values ​​for resource utilization, For resources The utilization rate before adjustment, For resources Adjusted utilization, Adjust the cost for scheduling, Cost of flight scheduling adjustments, Adjust costs for ground staff, reallocate costs for ground equipment;

[0167] According to the candidate set of optimization solutions, the solution with the highest score is selected as the optimal resource allocation solution, and the parking spaces, boarding gates and ground service equipment involved in the solution are adjusted to form an optimized scheduling solution;

[0168] Execute optimized scheduling plans, monitor the adjusted airport operations in real time, feed back the adjusted resource usage data to the big data platform, and update the airport resource scheduling information.

[0169] In the embodiment of the present invention, during the airport resource scheduling optimization process, different resource adjustment schemes may have different performances in terms of flight punctuality 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 index and the preset optimization target, and form a candidate set of optimization schemes to ensure that the final selected scheme can achieve the optimal resource scheduling configuration while meeting the airport operation needs.

[0170] Compared with traditional scheduling optimization methods, this method makes the scheduling optimization process more intelligent, quantifiable, and adaptable to the operating needs of different airports through a multi-dimensional scoring mechanism. For example, during peak hours, airports may give priority to improving flight punctuality, while during non-peak hours, they may be more inclined to optimize resource utilization or reduce operating costs. Therefore, by presetting the optimization goals, this method enables the scheduling system to dynamically adjust the scoring criteria according to different operating scenarios, thereby improving the flexibility of scheduling optimization.

[0171] The scoring calculation of the optimization plan is based on multiple key indicators, including the flight on-time rate improvement value, resource utilization optimization value and scheduling adjustment cost. During the scoring calculation process, different weight parameters can be set according to the airport's operating objectives. For example, when the airport's main goal is to reduce flight delays, the weight of the flight on-time rate improvement value can be increased so that the system gives priority to scheduling plans that can effectively reduce flight delays; when the airport is more concerned about resource utilization, the weight of the resource optimization value can be increased to ensure that the scheduling plan can evenly allocate parking spaces, boarding gates and ground service equipment; when the airport hopes to reduce the operating cost of scheduling adjustments, the weight of the scheduling adjustment cost can be increased so that the final plan can minimize additional costs while optimizing resource allocation.

[0172] Through the above-mentioned 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 takes into account the experience of historical data, but also combines the current operation status of the airport, the available resources and the optimization goals, so that the scheduling solution is more in line with the actual operation needs of the airport. Compared with the traditional method of relying on the experience of the dispatcher for manual decision-making, this method can greatly improve the intelligence of scheduling decisions, reduce manual intervention, and improve the accuracy of decisions.

[0173] Finally, after executing the optimized scheduling plan, the method will dynamically adjust the airport resources, monitor the execution effect of the optimization plan 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 resource allocation more accurate, and improve overall operational efficiency.

[0174] In summary, this method can select optimization solutions according to different airport operation requirements, making scheduling optimization more intelligent and precise. Compared with traditional scheduling methods, this method can more flexibly adapt to the dynamic needs of airports, improve flight punctuality, optimize resource utilization, and reduce scheduling adjustment costs, thereby improving the overall efficiency of airport operations and providing passengers with a smoother travel experience.

[0175] Among them, the comprehensive score of the scheduling plan Used to evaluate the pros and cons of different scheduling plans in order to select the best plan to optimize airport resource scheduling. , , is a weight coefficient that can be adjusted according to the airport operation strategy. The core goal of this formula is to evaluate the overall effect of each scheduling plan by comprehensively considering the improvement of flight punctuality, optimization of resource utilization and scheduling adjustment costs, so as to ensure that the final plan can maximize the airport operation efficiency and reduce the scheduling cost.

[0176] Improved flight punctuality This reflects the effectiveness of the optimization scheme in reducing flight delays. and Respectively indicate flights Delay time before and after optimization. The technical effect of this part is to measure the improvement of flight punctuality after scheduling optimization, reflecting whether the airport resource scheduling has effectively reduced the overall flight delays. If the optimization plan successfully reduces the delay time of most flights, the value of this indicator is large, which means that the scheduling effect of the plan is good.

[0177] Resource utilization optimization value Measure the performance of the optimization plan in improving the efficiency of airport resource utilization, and Respectively represent resources The change in utilization before and after scheduling optimization, The technical effect of this part is to reflect whether the scheduling optimization has improved the balanced utilization of airport resources and avoided the situation where some resources are over-occupied or idle for a long time. For example, if a parking space 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 scheme performs better in resource optimization.

[0178] Scheduling adjustment costs The additional costs incurred in the implementation of the scheduling optimization plan were quantified. Represents the cost of flight scheduling adjustments, such as additional fuel consumption due to scheduling adjustments, increased flight taxiing costs, or delay compensation costs caused by changes in take-off and landing sequences. Represents the cost of adjusting ground staff, including security checks and redeployment of boarding arrangements due to gate adjustments, and increased ground staff working hours due to scheduling adjustments. Represents the cost of reallocation of ground equipment, such as the resource loss caused by the extra movement or waiting time of equipment such as baggage transfer systems, flight refueling equipment, and boarding bridges due to scheduling adjustments. The technical effect of this part is to measure the economic cost brought by the actual implementation of the scheduling optimization plan. If a plan performs well in terms of punctuality and resource utilization, but the implementation cost is too high, its final score may be reduced to avoid the situation where the optimization benefits are offset by high costs.

[0179] In summary, the overall score of the scheduling plan It combines multiple key factors such as flight punctuality improvement, resource utilization optimization, and scheduling adjustment costs to form a comprehensive evaluation index to ensure that the final scheduling plan can not only improve the efficiency of airport operations, but also maximize the optimization effect under controllable costs. The technical advantage of this formula is that it quantifies the degree of improvement in flight delays, the balance of resource allocation, and the scheduling adjustment costs, making different scheduling plans comparable and avoiding scheduling decisions based solely on experience or simple rules. By adjusting the weight coefficient , , , airport managers can flexibly adjust the optimization direction according to different operational goals, such as minimizing delays, optimizing resource utilization or reducing operating costs, making scheduling decisions more scientific, accurate and efficient.

[0180] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The airport resource scheduling analysis method based on the big data platform is characterized by: The method comprises: Conflict detection is performed on the abnormal flight data set. The use of parking spaces, boarding gates and ground service equipment is calculated based on the time dimension and 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 data set. Conduct an impact assessment on the resource conflict dataset, calculate the impact score of each conflict event on airport operations based on airport resource usage rules and flight priorities, sort the conflict events according to the impact score, determine the priority scheduling tasks, and obtain priority scheduling task data; Optimize resource allocation for priority scheduling task data, calculate the optimal resource reallocation plan based on airport available resource information, flight scheduling constraints and historical scheduling adjustment records, adjust parking space allocation, dynamically adjust boarding gates and optimize ground service equipment scheduling to obtain an optimized scheduling plan.

2. The airport resource scheduling analysis method based on the big data platform according to claim 1 is characterized in that: Conflict detection is performed on the abnormal flight data set. The use of parking spaces, boarding gates and ground service equipment is calculated based on the time dimension and 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 data set, including: Obtain flight operation information from the abnormal flight data set, extract flight arrival time, departure time, parking space occupancy, boarding gate allocation, and ground service equipment usage to form flight resource occupancy data; According to the flight resource occupancy data, the resource occupancy of each flight in adjacent time periods is analyzed according to the time dimension, the time intersection degree is calculated, and the flights that cross-use the same resources in the same time period are identified to form time conflict data; Based on the flight resource occupancy data, the physical locations of the parking spaces, boarding gates and ground service equipment used by each flight are analyzed in spatial dimensions, spatial proximity is calculated, flights with spatial conflicts are identified, and spatial conflict data is generated; A comprehensive analysis is performed on the time conflict data and space conflict data, duplicate records are removed, and conflict events that need to be adjusted are screened out in combination with flight scheduling constraints to obtain a resource conflict data set.

3. The airport resource scheduling analysis method based on the big data platform according to claim 2 is characterized in that: According to the flight resource occupancy data, the resource occupancy of each flight in adjacent time periods is analyzed according to the time dimension, the time intersection degree is calculated, and the flights that cross-use the same resources in the same time period are identified to form time conflict data, including: According to the flight resource occupancy data, the arrival time, departure time, parking space occupancy period, boarding gate usage period and ground service equipment usage period of each flight are determined to form flight time occupancy data; Based on the flight time occupancy data, the overlap of flights on the same resources in adjacent time periods is calculated, flights with time intersections are identified, and the overlapping time intervals are marked to form time intersection records; Based on the time intersection records, time conflict events that affect the normal scheduling of flights are screened out, and the conflicting flights, conflicting time periods and resource types involved are recorded to form time conflict data.

4. The airport resource scheduling analysis method based on a big data platform according to claim 2 or 3 is characterized in that: Based on the flight resource occupancy data, the physical locations of the parking spaces, boarding gates and ground service equipment used by each flight are analyzed according to the spatial dimension, the spatial proximity is calculated, and flights with spatial conflicts are identified to form spatial conflict data, including: According to the flight resource occupancy data, the geographical coordinates of the flight's parking positions, boarding gates and ground service equipment are determined to form flight space occupancy data; Based on the flight space occupancy data, the physical distance between the resources used by adjacent flights in the same time period is calculated to form spatial proximity data; The spatial proximity data that exceeds the preset spatial conflict threshold is screened to identify flights with spatial conflicts, and the conflicting resources, conflicting locations and affected flights are recorded to form spatial conflict data.

5. The airport resource scheduling analysis method based on the big data platform according to claim 1 is characterized in that: The impact assessment of resource conflict datasets is performed to calculate the impact score of each conflict event on airport operations based on airport resource usage rules and flight priorities, including: Extract the conflicting flight information from the resource conflict dataset, determine the number of flights involved in the conflict, the duration of the conflict, and the type of airport resources affected by the conflict, and form conflict influencing factor data; According to the conflict influencing factor data and based on the flight priority information, the delay time of high-priority flights due to the conflict and the number of subsequent flights that may be affected are calculated to form flight impact data; According to the conflict influencing factor data and based on the airport resource usage rules, the change in airport resource utilization rate caused by the conflict is calculated, and the resource idleness or overload that may be caused by the conflict is evaluated to form resource impact data; Based on the flight impact data, resource impact data and resource conflict data set, a weighted sum is performed to calculate the conflict impact score of each conflict event.

6. The airport resource scheduling analysis method based on the big data platform according to claim 5 is characterized in that: Based on flight priority information, the delay time of high-priority flights due to conflicts and the number of subsequent flights that may be affected are calculated to form the flight impact, including: Based on the flight priority information, the priority level of each flight is determined, and the conflicting flights involved in the time conflict data and the space conflict data are extracted to form the conflicting flight data; Based on the number of conflicting flights, calculate the expected delay time of high-priority flights when conflicts occur, and analyze the impact of the delay on the connection of subsequent segments of the flight to form flight delay impact data; According to the flight delay impact data, the subsequent flight plans of the affected flights are extracted, and the subsequent flight delay propagation caused by the conflict is calculated to form flight delay propagation data; The number of indirectly affected flights in the flight delay propagation data is counted, and the impact of the conflict on the entire airport flight plan is evaluated in combination with the flight priority information to form flight impact data.

7. The airport resource scheduling analysis method based on a big data platform according to claim 5 is characterized in that: Based on the airport resource usage rules, calculate the change in airport resource utilization caused by the conflict, and evaluate the resource idleness or overload that may be caused by the conflict to form the resource impact, including: Extract conflicting flights and conflicting resources information from resource conflict data, determine the current usage of the parking spaces, boarding gates and ground service equipment involved, and form conflicting resource occupancy data; Based on the conflict resource occupancy data, calculate the changes in the utilization rate of various airport resources before and after the conflict, including the increase in resource idle time or excessive frequency of equipment use caused by flight adjustments, and form resource utilization rate change data; According to the resource utilization rate change data and based on the airport resource utilization rules, the impact of conflicts on different types of resources is evaluated to form resource load assessment data; Based on the resource load assessment data, determine whether the conflict will lead to an imbalance in resource allocation, and mark resources that are severely overloaded or idle to form resource impact data.

8. The airport resource scheduling analysis method based on a big data platform according to claim 1 is characterized in that: Optimize resource allocation for priority dispatch task data, calculate the optimal resource reallocation plan based on airport available resource information, flight dispatch constraints and historical dispatch adjustment records, adjust parking space allocation, dynamically adjust boarding gates and optimize ground service equipment dispatch, and obtain the optimal dispatch plan, including: According to the priority dispatch task data, extract the current resource allocation of the flight to be adjusted, and query the airport available resource information to form available resource data; Based on flight scheduling constraints, candidate resource adjustment plans are calculated to eliminate resource allocations that do not conform to scheduling rules and form a set of feasible scheduling plans; Based on historical scheduling adjustment records, analyze the optimization effects of different feasible scheduling schemes in similar conflict scenarios, calculate the execution cost and resource utilization of each feasible scheduling scheme, and form scheduling optimization evaluation indicators; According to the scheduling optimization evaluation indicators, the optimal resource allocation plan is selected according to the preset optimization goals, and the flight parking positions, boarding gates and ground service equipment scheduling are adjusted to form an optimized scheduling plan.

9. The airport resource scheduling analysis method based on a big data platform according to claim 8 is characterized in that: Based on historical scheduling adjustment records, analyze the optimization effects of different feasible scheduling schemes in similar conflict scenarios, calculate the execution cost and resource utilization of each feasible scheduling scheme, and form scheduling optimization evaluation indicators, including: Based on the historical scheduling adjustment records, historical cases similar to the current conflict event are screened out, and the corresponding scheduling adjustment plans are extracted to form historical scheduling case data; Based on historical dispatch 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; Based on historical dispatch case data, analyze the resource utilization efficiency of various resource adjustment plans in conflict mitigation, including the change in resource utilization rate after the adjustment plan is implemented, the resource load balance of each area of ​​the airport and the dispatch stability, and form resource utilization data; Based on the execution cost data and resource utilization data, the feasibility of different scheduling schemes is evaluated, and the comprehensive score of each scheduling scheme is calculated to form a scheduling optimization evaluation index.

10. The airport resource scheduling analysis method based on a big data platform according to claim 9 is characterized in that: According to the scheduling optimization evaluation indicators, the optimal resource allocation plan is selected according to the preset optimization goals, and the parking positions, boarding gates and ground service equipment scheduling of flights are adjusted to form an optimized scheduling plan, including: According to the scheduling optimization evaluation indicators, feasible scheduling schemes are extracted, and the comprehensive scores of different scheduling schemes are calculated according to the preset optimization goals to form a candidate set of optimization schemes; According to the candidate set of optimization solutions, the solution with the highest score is selected as the optimal resource allocation solution, and the parking spaces, boarding gates and ground service equipment involved in the solution are adjusted to form an optimized scheduling solution; Execute optimized scheduling plans, monitor the adjusted airport operations 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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