WO2019186594A1

WO2019186594A1 - Method and system for auto task allocation & recommending ground equipment, manpower to improve turnaround time of aircrafts in airport

Info

Publication number: WO2019186594A1
Application number: PCT/IN2019/050257
Authority: WO
Inventors: Amit SHUKHIJA
Original assignee: Zestiot Technologies Pvt. Ltd.
Priority date: 2018-03-29
Filing date: 2019-03-28
Publication date: 2019-10-03
Also published as: WO2019186592A1; WO2019186591A1

Abstract

A computing system for task automation & recommending ground equipment to improve turnaround operations of aircraft. The computing system includes at least one processor. The computing system further includes a memory coupled to the at least one processor. The computing system also includes an analytics module to obtain at least one operational parameter from at least one ground equipment during turnaround operations of an aircraft from at least one sensor. The computing system also analyze the at least one operational parameter related to the at least one ground equipment to determine the availability of the at least one equipment using artificial intelligence and machine learning. The computing system further generate at least one task allocation or recommendation based on the analysis of the at least one obtained operational parameter for allotment of the at least one ground equipment.

Description

METHOD AND SYSTEM FOR AUTO TASK ALLOCATION & RECOMMENDING GROUND EQUIPMENT, MANPOWER TO IMPROVE TURNAROUND TIME OF

AIRCRAFTS IN AIRPORT

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD

[0001] The present disclosure relates to a field of aviation industry. More particular, the present disclosure relates to a system for an aviation platform to integrate the journeys of passenger, baggage, Airport and ground operations, automating flow of operations, seamless flow of passenger during boarding & de-plane, and improvement of on time performance of airlines and airports by recommending ground equipment for optimum utilization based on artificial intelligence and machine learning algorithms.

BACKGROUND

[0002] Aviation industry is one of the most rapid growing industries owing to increase in number of people travelling from one place to another. There has been a growth in the number of airports and number of aircrafts all over the world in the last few years. The increase in number of airports is owed to the fact that air traffic has been increasing both nationally and internationally. The air traffic is expected to grow continuously in the future. The airport authorities have a huge task of controlling and ensure smooth flow of day to day operations. The airport authorities face many issues such as handling a large number of travelers, airport capacity, minimizing delays, improving passenger experience, surface safety and the like. The airport authorities have a complex task of checking credentials of each traveler at multiple checkpoints. This leads to an increase in processing time and proves to be a pain point for the travelers. There is a continuous need to reduce the processing time and simplify the task of handling each traveler and efficiently managing the operations. I n addition, the airport authorities need provide the right infrastructure and enable large number of operations for every flight undergoing turnaround as well as timely departure of flights. These operations are performed using multiple equipment and systems. Number of operations is time bound with parallel operations and any single operation delay can decreases the on time performance of the airlines and airport operations. There is a continuous need to increase the on time performance of the ground and airports operations so that the turnaround time is decreased for each flight.

OBJECT OF THE DISCLOSURE

[0003] A primary object of the present disclosure is to provide an integrated platform for passenger, baggages, airport and ground operations to improve on time performance of airlines and airports, improve passenger experience and deliver predictability in operations.

[0004] Another object of the present disclosure is to reduce turnaround time of flights.

[0005] Yet another object of the present disclosure is to reduce time taken in performing multiple ground operations between the time when a flight lands and departures after a specified period of time by digitalization the turnaround operations for easy monitoring and action , these actions could in turn increase the on time performance of airlines and airports.

[0006] Yet another object of the present disclosure is to reduce time taken for identity verification on multiple checkpoints on an airport.

[0007] Yet another object of present disclosure is to use loT, computer vision, artificial intelligence, situational awareness, airport operational load & incoming/outgoing fl ight operations for detection of various operations around the aircraft.

[0008] Yet another object of the present disclosure is to determine root cause of flight delays.

[0009] Yet another object of the disclosure is to provide detection & alert if any turn around operation is delayed in realtime.

[0010] Yet another object of the present disclosure is to recommend ground equipment for automatic task allocation to improve the aircraft turnaround time in airport. [0011] Yet another object of the present disclosure is to recommend manpower (ground support, ramp staff) for automatic task allocation to improve the aircraft turnaround time in airport..

[0012] Yet another object of the present disclosure is to improve ground equipment utilization, reducing risks and increasing compliance.

[0013] Yet another object of the present disclosure is to provide anti-collision devices / systems for airport air sight operations.

[0014] Yet another object of the present disclosure is to detect situational awareness of airport environment - Weather conditions like temperature, cloud conditions, sunlight, phase of the day, total flights at the airport at that time, upcoming flights, total flights being served by the ground handler under turnaround, available drivers for the ground equipment vs the total drivers, ground equipment performance history, age of the ground equipment vehicle, nearby available flight bays, upcoming flights & outgoing flights

[0015] Yet another object of the present disclosure is to detect & understand the availability of equipment & human manpower (ground staff, ram staff, duty manager, super visor)

[0016] Yet another object of the present disclosure is to detect & understand the change in schedule of upcoming flights & recommend changes in task allocated for equipment & man power.

[0017] Yet another object of the present disclosure is to use machine learning & artificial intelligence to understand the situational awareness, recommend task for equipment & manpower to improve utilization, faster decision making & improve the aircraft turnaround time in airport. SUMMARY

[0018] In an aspect, the present disclosure provides a computer system or edge devices, sensors and nodes, cloud computing. The computer system includes one or more processors and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of instructions causes the one or more processors to perform a method for enabling real-time quick verification of the passengers across the way from Airport entry to aircraft on board. The method includes a first step of receiving a set of data through the mobile application during web check-in or alternate sources through booking system or online travel agent. The set of data includes Aadhar number, Flight/pnr (passenger name record) details and image of each passenger taken from front and back camera of the portable communication device associated with the passenger or similar methods. The identity of the passenger is validated using Aadhar number, the image of a passenger with the one stored in country specific security identification system. In addition, when the data entered by the passenger matches with the stored database, the application displays successful identity validation at airport entry through appropriate display terminal. At this time, a virtual token is created at the airport entry / security identification step which is used for later along the journey of passenger at the airport. Based on the successful validation, passengers are allowed to walk inside the airport. After airport entry validation, the plurality of passengers is allowed to move at the check-in counter. Using cameras and Al (Artificial intelligence) solution passenger identity is validated based on web check-in and airport entry confirmation. Airline staff does not need to see id of the passenger again. Integrated system with airline check-in can enable auto printing of boarding pass immediately after passenger identity is validated against virtual token created at the airport entry. In addition, the airline staff available at the airport facility gets the baggage tag printed (based on the identity of the passenger) and tags it on the bag for quick check-in at the airport. In another step, the plurality of passengers walks to security clearance (security hold area). Camera and Al solution at the security hold area detects the identity of passenger and reconfirms with web check/airport entry validation. Security staff available at the airport facility does not ask to see any boarding pass (paper or electronic) as the identity of the passenger is displayed on a display screen available next to security personnel. The display screen displays image data and airport entry images and Aadhar (or similar identity) number (as recognized by respective national social security / identification database. In next step, the passengers are allowed to move to the boarding gate. In addition, the camera/Artificial Intelligence solution at the gate again re-verifies the identity of the passenger to the one validated during web check-in, airport entry, security clearance. Further, the passengers who pass the previous security stages (security clearance for boarding gate) are allowed to pass the gate for boarding. Each passenger has to get his/her identity validated only once at the airport and cameras/AI solution will enable faster movement while ensuring the security of passengers.

[0019] In yet another aspect, the present disclosure provides a computer system for enabling real-time monitoring of multiple ground, airport, passenger and baggages operations for airlines. The real-time monitoring of multiple ground operations are performed between a time when an incoming flight lands and arrives at an assigned gate near airport runway and when the flight departs for another trip. The airport or airline authorities employ the plurality of ground equipment as soon as the aircraft reaches the specified gate. Further, computer system includes a plurality of loT sensors 108 installed at different places. The different places where the sensors can be installed are ground equipment, identity cards associated with personnel deployed for turnaround of aircraft, inside airport and the like, baggage tags reader and passenger biometric/cameras/AI based edge devices throughout the journey of passengers. The plurality of loT sensors include RFID sensor, touch sensor, GPS sensor, IR/Proximity sensor, beacon, gauge, camera, temperature sensor and Wi-Fi or similar sensors for coverage of ground, airport, baggage and passenger journeys at the airport. The plurality of loT sensors are configured to collect data associated with each activity performed by each of the plurality of one or multiple journeys at the airport (aircraft, ground operations, baggages, passenger and airport terminal operations). In addition, the plurality of loT sensors is configured to allow real time visualization of the different activities performed during the time the aircraft is getting ready for the next flight. The plurality of loT sensors are linked with a smart monitoring, prediction and analytics system. The smart monitoring, prediction and analytics system receives a set of data associated with a time taken by each of the plurality of ground equipment to complete specified task. In addition, the smart monitoring, prediction and analytics system enables reduction in turnaround time for the aircraft and further avoidance of delays in the entire network operations of the associated aircraft. The smart monitoring, prediction and analytics system allows real time visibility of the various ground operations performed in and around the aircraft. The real time visibility is facilitated with the help of the data received from the plurality of loT sensors. The real time visibility provided by the real time data is utilized for determining the critical path, artificial intelligence based model to predict delays and simulation of future load factors of sectors/airport terminal and ground operations. The critical path corresponds to one or more airport/ground/baggage and passenger journey related operations undertaken at the same time. The smart monitoring, prediction and analytics system determines the weakest link in the multiple operations because of which the flight may get delayed. The smart monitoring, prediction and analytics system sets a threshold or equivalent precision time schedules on time percentage for different activities above which the activity or ground equipment will be termed as a weakest link. The percentage is calculated based on a number of times the activity is taking more time and the number of times the ground equipment is utilized in a day. The smart monitoring, prediction and analytics system helps the airlines improve the on time performance. A precision time schedule can be a set of KPIs for various operations and sub operations for optimal performance of each flight turn around operations. The smart monitoring, prediction and analytics system takes into account data associated with the plurality of parameters in order to determine the critical path for each different aircraft. The smart monitoring, prediction and analytics system helps in optimization of use of ground equipment based on the real time data associated with the ground handling activities. BRIEF DESCRIPTION OF THE FIGURES

[0020] FIG. 1 illustrates a block diagram of an interactive computing environment for enabling real-time monitoring of multiple ground operations for airlines, in accordance with various embodiments of the present disclosure; and [0021] FIG. 2 illustrates another block diagram of interactive computing system to recommend ground equipment for improving turnaround operations of aircraft in an airport, in accordance with various embodiments of the present disclosure;

[0022] FIG. 3 illustrates a flow diagram showing a method to recommend ground equipment for improving turnaround operations of aircraft in an airport, in accordance with various embodiments of the present disclosure; and

[0023] FIG. 4 illustrates a block diagram of a computing device, in accordance with various embodiments of the present disclosure.

DETAILED DESCRIPTION

[0024] FIG. 1 illustrates a block diagram 100 of an interactive computing environment for enabling real-time monitoring of multiple ground, airport operations, baggage and passenger journey for airlines and airports, in accordance with various embodiments of the present disclosure. The real-time monitoring of multiple ground and airport operations are performed between a time when an incoming flight lands and arrives at an assigned gate near airport runway and when the flight departs for another trip. The multiple ground operations are the operations which are essential operations to be performed in order for the flight or aircraft to get ready for another trip on time.

[0025] The interactive computing environment includes an airport facility 102, an aircraft 104, a plurality of ground equipment 106 and a plurality of loT sensors 108. In addition, the interactive computing environment includes a communication network 110, a smart monitoring, prediction and analytics system 112 and a plurality of stakeholders 114. The airport facility 102 corresponds to an airport for providing facility of travelling by air to passengers. In addition, multiple flights arrive and depart from the airport facility 102 at fixed intervals of time as per the schedule. The airport facility 102 includes multiple airport authorities who are responsible for carrying out various ground operations. The airport authorities include multiple personnel inside control towers, terminal buildings, security personnel, air sight operations, baggage management and the like.

[0026] The ground operations correspond to multiple ground handling operations to be performed when an aircraft remains on the ground. In general, the airport authorities need to perform various ground handling services whenever an aircraft reaches an airport. Typically, the ground handling services include ramp services, baggage loading and unloading, passenger services, cargo and mail services, load control, communication and flight operations services, representation and supervision services.

[0027] The airport facility 102 includes multiple areas such as terminal buildings, air traffic control tower 102a, airport operations control center 102b, runway, multiple gates for the aircrafts to halt near, check in counters, immigration, security screening and hold area, domestic and international customs and the like. The aircraft 104 may be any airplane belonging to any airline company. The aircraft 104 includes aircraft sensors 104a installed at different locations outside the aircraft 104. The aircraft sensors 104a include positioning, GPS, ADS Mode B, Surface mounting radar, pressure sensors, temperature sensors, force sensors, torque sensors, speed sensors, position and displacement sensors, level sensors, proximity sensors, flow sensors, accelerometers, gyroscopes, pitot probes, radar sensors, Angle-of-Attack (AoA) sensors, altimeter sensors, smoke detection sensors, cameras / Al edge compute sensors and the like. The aircraft 104 reaches the airport facility 102. The aircraft 104 lands on the runway of the airport facility 102. Accordingly, the aircraft 104 reaches and halts near an assigned gate. In general, there are multiple gates at the airport facility 102 and multiple aircrafts halt at each gate after landing, before taking off and the like. The aircraft 104 includes passengers 104b and baggages 104c.

[0028] The aircraft 104 may be about to take off or just checked in to the airport facility 102. In an example, let's say the aircraft 104 has just completed a trip and landed on the runway of the airport facility 102 at 8 am and scheduled to take off at 8.45 am from the airport facility 102. The airline, ground handling, airport operations and airport authorities need to perform multiple ground operations on or near the aircraft 104 in a time interval of say 8 am to 8.45 am in order for the aircraft 104 to depart on time. The multiple ground operations include transporting passengers through coaches, re-fueling of the aircraft, cleaning of the aircraft, de boarding and boarding of passengers, unloading and loading of baggages, security and frisking of passengers, below and above the wing ground operations and other necessary ground operations known in the art.

[0029]The aircraft 104 may stop at any specified gate of the airport terminal. The airline and airport authorities employ the plurality of ground equipment 106 as soon as the aircraft 104 reaches the specified gate. The plurality of ground equipment 106 includes passenger coaches, ambulance, passenger ladder, sky gourmet, food catering services and the like. Further, the plurality of loT sensors 108 are installed at different places. The different places where the sensors can be installed are ground equipment, identity cards associated with airline and airport authorized personnel deployed for turnaround of aircraft, inside airport, baggage tags reader and passenger biometric/cameras/AI based edge devices throughout the journey of passengers and the like. The plurality of loT sensors 108 include RFID sensor, touch sensor, GPS sensor, IR/Proximity sensor, beacon, gauge, camera and Al powered edge computing device, temperature sensor and Wi-Fi or similar sensors for coverage of ground, airport, baggage and passenger journeys at the airport. In an embodiment of the present disclosure, there may be more sensors installed on different places.

[0030] The plurality of loT sensors 108 is configured to collect data associated with each activity performed by each of the plurality of airlines or airport ground equipment 106. The activities are performed during one or multiple journeys at the airport (aircraft, ground operations, baggages, passengers and airport terminal operations). In addition, the plurality of loT sensors 108 is configured to allow real time visualization of the different activities performed during the time the aircraft is getting ready for the next flight. The plurality of loT sensors 108 are linked with the smart monitoring, prediction and analytics system 112. In addition, the plurality of loT sensors 108 is linked with the smart monitoring, prediction and analytics system 112 through the communication network 110. The communication network 110 enables the plurality of loT sensors 108 to wirelessly transmit data to the smart monitoring, prediction and analytics system 112. [0031] The communication network 110 provides a medium to transfer the data between the smart monitoring, prediction and analytics system 112 and the plurality of loT sensors 108 in a secured and encrypted manner. Further, the medium for communication may be infrared, microwave, radio frequency (RF) and the like. The smart monitoring, prediction and analytics system 112 receives a set of data associated with a time taken by each of the plurality of ground equipment 106 to complete specified task. In addition, the smart monitoring, prediction and analytics system 112 enables reduction in turnaround time for the aircraft 104 and further avoidance of delays in the entire network operations of the associated aircraft. The turnaround time corresponds to a time taken for the aircraft 104 to depart for the next flight after completing a previous flight. [0032] The smart monitoring, prediction and analytics system 112 allows real time visibility of the various ground operations performed in and around the aircraft 104. The real time visibility is facilitated with the help of the data received from the plu rality of loT sensors 108. The real time visibility of the ground operations helps the airlines to discover blind spots due to which the turnaround time for the aircraft is getting increased. In addition, the real time visibility provided by the real time data is utilized for determining the critical path, artificial intelligence based model to predict delays and simulation of future load factors of sectors/airport terminal and ground operations. The critical path corresponds to one or more airport/ground/baggage and passenger journey related operations undertaken at the same time. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 determines the weakest link in the multiple operations because of which the flight may get delayed. In addition, there may be different critical paths for different aircrafts. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 sets a threshold or equivalent precision time schedules on time percentage for different activities above which the activity or ground equipment will be termed as a weakest link. The percentage is calculated based on a number of times the activity is taking more time and the number of times the ground equipment is utilized in a day. The smart monitoring, prediction and analytics system 112 includes capability to predict flight delays, utilize artificial intelligence to understand the past performance combined with real time visibility of turn around operations to recover and avoid delays. The smart monitoring, prediction and analytics system 112 can help in recalibration of KPIs for below and above the wing operations of the aircraft. Further, it can help with network planning of the aircraft and change management of further turn around planned for an aircraft. A precision time schedule can be a set of KPIs for various operations and sub operations for optimal performance of each flight turn around operations.

[0033] In an example, a passenger coach may be taking more time than usual in reaching near the aircraft and then leaving from the aircraft. Similarly, there may be multiple ground operations which are taking more time. The smart monitoring, prediction and analytics system 112 helps the airlines improve the on time performance. In general, a precision time schedule for each aircraft is pre-defined according to a plurality of parameters. The plurality of parameters includes origin, destination, type of aircraft, load factor and the like. The smart monitoring, prediction and analytics system 112 takes into account data associated with the plurality of parameters in order to determine the critical path for each different aircraft and precision time schedule of the aircraft turn around operations. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 sets a threshold for each activity performed by corresponding ground equipment for each corresponding aircraft. In an example, the precision time schedule for an aircraft Y says that a fueling truck F should be near an aircraft A1 at arrival minus 2 minutes. In addition, the fueling truck F should be positioned to the aircraft A1 at departure minus 40 minutes. Moreover, the fueling truck F should start fueling at departure minus 38 minutes. Further, the fueling truck F should finish fueling at departure minus 5 minutes. Similarly, the precision time schedule is pre-defined for all ground handling and airport operations activities including passenger, baggage, airport terminal and ground operations. [0034] In an example, the smart monitoring, prediction and analytics system 112 collects data from the loT sensors about the different activities such as a passenger coach a, a passenger coach B and a passenger ladder C for 10 aircrafts coming to and departing from an airport X. The smart monitoring, prediction and analytics system 112 takes into account a precision time schedule for each of the 10 aircrafts. The smart monitoring, prediction and analytics system 112 collects the data from the loT sensors in real time about the different ground activities performed for each of the 10 aircrafts. The smart monitoring, prediction and analytics system 112 determines that the passenger coach B is taking more time than usual for multiple aircrafts. The smart monitoring, prediction and analytics system 112 determines the passenger coach B as a critical path. Similarly, the smart monitoring, prediction and analytics system 112 can determine the performance of multiple flights over a time to recommend actionable insights for faster turn-around of aircraft at one/multiple airports.

[0035] The smart monitoring, prediction and analytics system 112 supplies the data associated with the multiple ground operations to the plurality of stakeholders 114 in real time. The plurality of stakeholders 114 may include airport authorities, airline personnel corresponding to the aircrafts, entities performing ground handling activities or any other entities for which the data is crucial for managing airport operations. The smart monitoring, prediction and analytics system 112 alerts the plurality of stakeholders 114 in real time about the weakest link due to which the flights might be getting delayed, any cascading impact on further ground operations, network operations of the aircrafts or cascading impacts on further planning and operations at one/multiple airports. Accordingly, the plurality of stakeholders 114 may take necessary action for rectifying the errors in ground operations. The improvement in the on time performance can be seen again and again through the real time visibility provided by the smart monitoring, prediction and analytics system 112 with the aid of the plurality of loT sensors 108. [0036] The smart monitoring, prediction and analytics system 112 helps in optimization of use of ground equipment based on the real time data associated with the ground handling activities. The smart monitoring, prediction and analytics system 112 provides a recommendation associated with a best way to optimize the use of the ground equipment in order to avoid flight delay. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 utilizes machine learning and artificial intelligence techniques to predict delay in flights. The delay in flights can be predicted based on the past set of data and real time data associated with the ground operations. In an embodiment of the present disclosure, the live data can be utilized to simulate different scenarios and peak load factor at the airports.

[0037] In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 enables automated time stamping of different ground operations. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 performs SLA monitoring and provides rewards to entities/stakeholders and charges penalty against the same causing delay. The smart monitoring, prediction and analytics system 112 enables increase in efficiency in ground operations and enables cost savings. In an embodiment of the present disclosure, the smart monitoring, prediction and ana lytics system 112 generates reports and performs analytics in real time. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 allows the passengers a real time visibility in delay of flights, wait time in queues, baggage tracking and the like.

[0038] In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 in combination with the loT sensors can be employed for creating smart work places. In an embodiment of the present disclosure, the loT sensors can be installed at various places in an office, employee ID cards, office cabs, entry gates, cafeteria and the like. In an embodiment of the present disclosure, the data can be used to track employee movements inside an office, information about long queues in cafeteria, automate the entry process, attendance of employees, improve employee experience, reduce risks, automate billing of transport and cafeteria usage, improve desk utilization and the like. [0039] In an example, the smart monitoring, prediction and analytics system 112 sends location information and arrival message to an employee who then boards an office cab. The office cab reaches the office. The employee enters the office building and goes through security check where the employee ID and asset information is displayed. Further, the smart monitoring, prediction and analytics system 112 sends regular alerts to the employee sitting at the desk in case of long sitting hours. The smart monitoring, prediction and analytics system 112 collects data about the desk occupancy in real time. The smart monitoring, prediction and analytics system 112 tracks movement of the employee in floor, cafeteria, meeting rooms and records utilization of the meeting rooms, time spent and informs employee about lunch hours and waiting time.

[0040] In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 allows virtual reality and augmented reality inside the aircrafts. The aircrafts include multiple cameras installed around the aircrafts. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 provides live augmented reality. The smart monitoring, prediction and analytics system 112 utilizes virtual reality or augmented reality or both to show a live feed of an aircraft moving or taking off as if the passenger is a pilot. The live feed is provided with the help of multiple cameras around the aircraft. [0041] FIG. 2 illustrates a block diagram of a computing system 200 to recommend ground equipment for improving turnaround operations of aircraft in an airport including atleast one processor. The computing system 200 further includes a memory 204 coupled to the at least one processor. The memory comprises an analytics module 206 to obtain at least one operational parameter from atleast one ground equipment during turnaround operations of an aircraft from atleast one sensor. The computing system 200 is capable to analyze the atleast one operational parameter related to the atleast one ground equipment to determine the availability of the atleast one quipment using artificial intelligence and machine learning. The computing system 200 also generate atleast one recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment.

[0042] The computing system 200 further includes atleast one user interface 208 to present the at least one operational parameter and the generated recommendation for allotment of the atleast one ground equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time. In the computing system 200 the turnaround operations include aircraft operations and ground operations. In the computing system 200 the at least one operational parameter is selected from a group consisting of but not limited to flight schedule, availability of equipment, geographical position, distance, proximity, global positioning system (GPS) position, movement details, navigation database expiry and cycle, doors open and closes, ground service panels open and close, power unit panel details and engine start and stop details. In the computing system 200 the atleast one sensor is selected from a group of internet of things (IOT) sensors and aircraft sensors consisting of and not limited to a video camera, an audio sensor, a temperature sensor, a global positioning system (GPS) sensor, a distance sensor, a proximity sensor, a position sensor, a dimension sensor, RFID sensor, touch sensor beacon, gauge.

[0043] FIG. 3 illustrates a flow diagram of a method 300 to recommend ground equipment for improving turnaround operations of aircraft in an airport. The method 300 includes steps for obtaining 302 at least one operational parameter during turnaround operations of an aircraft from atleast one sensor. The method 300 further includes step for analyzing 304, the atleast one operational parameter related to the atleast one ground equipment to determine the availability 304 of the atleast one equipment using artificial intelligence and machine learning to improve the efficiency of the turnaround operations of the aircraft. The method 300 also include step to generate 308 atleast one recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment. The method further includes step for presenting 310 the at least one operational parameter and the generated recommendation for allotment of the atleast one ground equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time. [0044] In the method 300 the turnaround operations include aircraft operations and ground operations. Further, in method 300 the at least one operational parameter is selected from a group consisting of but not limited to flight schedule, availability of equipment, geographical position, distance, proximity, global positioning system (GPS) position, movement details, navigation database expiry and cycle, doors open and closes, ground service panels open and close, power unit panel details and engine start and stop details. In method 300 wherein the atleast one sensor is selected from but not limited to a group of internet of things (IOT) sensors and aircraft sensors consisting of a video camera, an audio sensor, a temperature sensor, a global positioning system (GPS) sensor, a distance sensor, a proximity sensor, a position sensor, a dimension sensor, RFID sensor, touch sensor beacon, gauge.

[0045] The present invention focuses on a system for providing a set of recommendations for allotment of ground equipment for increasing efficiency of ground operations for improvement of turnaround time for an aircraft in an airport environment. The set of recommendations is done for enabling efficient utilization of the ground equipment for on time turnaround of the aircrafts. The system receives real time data from aircraft sensors, camera based sensors near the bay of the airport, engine shut down information of the aircraft using the camera sensors, sound signatures from the aircraft engine, information from airport systems for determining current location of the aircraft on the airport and where the aircraft is exactly positioned. The plurality of ground handling equipment includes passenger coaches, ambulance, passenger ladder, catering, fuelling, water, clearing, APU/GPU, chocks on & chocks off, de-icing, push back, baggage & cargo loading, unloading, crew & pilot pickup/drop cars, loading sheet, security check, maintenance check, and the like. Each of the plurality of ground handling equipment performs a specific task for enabling the aircraft to get ready for another take off on time. The ground handling team utilizes the plurality of ground equipment for performing a plurality of ground handling operations inside and outside the aircraft about to take off. In addition, the ground handling team utilizes the plurality of ground equipment for performing a plurality of ground handling operations inside and outside the aircraft which has just landed on the airport and scheduled for another take off soon. The system utilizes one or more sensors for determining a time taken during the plurality of ground operations performed by corresponding plurality of ground equipment for the aircraft about to take off. In addition, the system utilizes one or more sensors for determining a time taken during the plurality of ground operations performed by corresponding plurality of ground equipment for the aircraft which has just landed and scheduled for another take off soon. The one or more sensors are installed on each of the plurality of ground equipment, identity cards associated with airline and airport authorized personnel deployed for turnaround of aircraft. The one or more sensors enable the real time visibility of the plurality of ground operations. The sensors are configured to collect data associated with each activity performed by each of the plurality of ground equipment. The sensor data includes capture data when Baggage Freight Loader for moving bags using conveyer belt into belly of aircraft, data is collected when BFL is near the aircraft, collect data when BFL is positioned to the aircraft, collect data when hold gate of the aircraft is open or closed, collect data when belt of the BFL is moving in one direction loading of baggagee from trolley to aircraft and collect data when baggage are being unloaded from the aircraft to the trolley, location, speed of equipment, position, distance from aircraft, accelerometer data for motion and direction of equipment, how fast equipment is running and how quickly brakes are applied, position and direction of equipment with respect to aircraft, when pushback tractor starts pushing aircraft back and whether it is attached to aircraft or not, collect baggage data, passenger coach coming near to the aircraft, direction PC has come from and where it has come, how long passenger coach door was open, when ladder arrives near bay of airport or gate of airport, when each and every equipment is near the aircraft, when each equipment was positioned near the aircraft, when PC picks passenger from the gate of airport and when PC drops the passengers to the drop location, how much time for taking passengers from airport gate to drop location, when PC picks passenger from the aircraft to drop location of airport and when PC drops the passengers to the drop location of airport, how much time for taking passengers from aircraft to drop location near aircraft. The activities are performed during one or multiple journeys at the airport (aircraft and ground operations). The system analyses the data associated with the equipment currently in operation and equipment available for allotment, data related to incoming flights and other outgoing flights scheduled for take-off at a later time, current status of all the ground equipment and the like. The system makes a set of predictions based on the analysis of the data related to availability status of the equipment for future flights, predicted time at which the operation by each of the equipment will be complete, predicted time in which each operation will be complete and the like. The system provides a set of recommendations for the allotment of the plurality of ground equipment for different flights based on the predictive analysis. The system may utilize machine learning algorithms for enabling efficient utilization and allotment of ground equipment in real time for on time turnaround of the aircrafts.

[0046] During journey of an aircraft, turnaround activities (e.g., scheduled turnaround activities and aircraft operational parameters) may be monitored from touchdown to takeoff of the aircraft. The scheduled turnaround activities, for example, may include ground handling activities and aircraft activities. Further, the ground handling activities, for example, may include refueling, cargo door open, cargo door close, toilet drain cycle, water filling, and the like. Similarly, the aircraft activities, for example, may include touchdown, braking start, brake fans start, brake fans stop, breaking release, parking brake on, engine stop, aircraft arrival, aircraft docking, aircraft pull away, takeoff braking start, reaching taxi speed, engine stops, and the like. The aircraft operational parameters, for example, may include cabin temperature, cargo temperature, flight deck temperature, wheel temperature, wheel pressu re, fuel temperature, auxiliary power unit (APU) start and stop, APU bleed valve open and close, ground power unit (GPU) connection and disconnection, air conditioning unit connection and disconnection, cabin ready, evacuation slides status, landing runway, global positioning system (GPS) position, flight details, navigation database expiry and cycle, water quantity requested, water quantity filled, refueling quantity requested and refueling quantity filled, doors open and close, and ground service panels open and close. Example aircraft operational parameter may include cabin temperature, cargo temperature, flight deck temperature, wheel temperature, wheel pressure, fuel temperature, auxiliary power unit (APU) start and stop, APU bleed valve open and close, ground power unit (GPU) connection and disconnection, air conditioning unit connection and disconnection, cabin ready, evacuation slides status, landing runway, global positioning system (GPS) position, flight details, navigation database expiry and cycle, water quantity requested, water quantity filled, refueling quantity requested and refueling quantity filled, doors open and close, and/or ground service panels open and close.

[0047] A non-transitory computer-readable medium having computer executable instructions stored thereon, which when executed by a processor causes the processor to obtain at least one operational parameter during turnaround operations of an aircraft from atleast one sensor, analyze the atleast one operational parameter related to the atleast one ground equipment to determine the availability of the atleast one equipment using artificial intelligence and machine learning to improve the efficiency of the turnaround operations of the aircraft and generate atleast one recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment.

[0048] FIG. 4 illustrates a block diagram of a computing device 400, in accordance with various embodiments of the present disclosure. The computing device 400 includes a bus 402 that directly or indirectly couples the following devices: memory 404, one or more processors 406, one or more presentation components 408, one or more input/output (I/O) ports 410, one or more input/output components 412, and an illustrative power supply 414. The bus 402 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 4 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 4 is merely illustrative of an exemplary computing device 400 that can be used in connection with one or more embodiments of the present invention. The distinction is not made between such categories as "node/ "loT sensors," "Edge computing device," "workstation," "server," "laptop," "hand-held device," etc., as all are contemplated within the scope of FIG. 4 and reference to "computing device."

[0049] The computing device 400 typically includes a variety of computer readable media. The computer-readable media can be any available media that can be accessed by the computing device 400 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer storage media and communication media. The computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 400. The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer- readable media.

[0050] Memory 404 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 404 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical-disc drives, etc. The computing device 400 includes one or more processors that read data from various entities such as memory 404 or I/O components 412. The one or more presentation components 408 present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. The one or more I/O ports 410 allow the computing device 400 to be logically coupled to other devices including the one or more I/O components 412, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.

Claims

CLAIMS What is claimed is:

1. A computing system to automate task allocation & recommend ground equipment for improving turnaround operations of aircraft comprising: atleast one processor; memory coupled to the at least one processor, the memory comprises an analytics module to: obtain at least one operational parameter from atleast one ground equipment during turnaround operations of an aircraft from atleast one sensor; analyze the atleast one operational parameter related to the atleast one ground equipment to determine the availability of the atleast one equipment using artificial intelligence and machine learning; and generate atleast one task allocation or recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment.

2. The computing system of claim 1, further comprising: atleast one user interface to present the at least one operational parameter, task allocation and the generated recommendation for allotment of the atleast one ground equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time.

3. The computing system of claim 1, further comprising: atleast one user interface to present the at least one operational parameter, task allocation and the generated recommendation for allotment of the atleast one manpower to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time.

4. The computing system of claim 1, further comprising: atleast one machine learning & artificial intelligence model to understand the situational awareness of airport environment and the generated recommendation for allotment of the atleast one manpower or equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time.

5. The computing system of claim 1, further comprising: atleast one system to understand the daily roster of allocation equipment & manpower, at least one system to understand the current flights & operational load at airportport, at least one system to understand the weather information, at least one system to understand the incoming & outgoing flight traffic for allotment of the atleast one manpower or equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time.

6. The computing system of claim 1, wherein the turnaround operations include aircraft operations and ground operations.

7. The computing system of claim 1, wherein the at least one operational parameter is selected from a group consisting of flight schedule, availability of equipment, geographical position, distance, proximity, global positioning system (GPS) position, movement details, navigation database expiry and cycle, doors open and closes, ground service panels open and close, power unit panel details and engine start and stop details.

8. The computing system of claim 1, wherein the atleast one sensor is selected from a group of internet of things (IOT) sensors and aircraft sensors consisting of a video camera, an audio sensor, a temperature sensor, a global positioning system (GPS) sensor, a distance sensor, a proximity sensor, a position sensor, a dimension sensor, RFID sensor, touch sensor beacon, gauge, motion, accelerometer, gyrometer.

9. A method comprising:

obtaining, at least one operational parameter during turnaround operations of an aircraft from atleast one sensor; analyzing, the atleast one operational parameter related to the atleast one ground equipment or man power to determine the availability of the atleast one equipment or manpower using artificial intelligence and machine learning to improve the efficiency of the turnaround operations of the aircraft; and

generating, atleast one task allocation or recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment.

10. The method of claim 9, further comprises: presenting the at least one operational parameter and the generated recommendation for allotment of the atleast one ground equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time.

11. The method of claim 9, further comprises: presenting the at least one operational parameter and the generated recommendation for allotment of the atleast one man power to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time

12. The method of claim 9, wherein the turnaround operations include aircraft operations and ground operations.

13. The method of claim 9, wherein the at least one operational parameter is selected from a group consisting of flight schedule, availability of equipment, geographical position, distance, proximity, global positioning system (GPS) position, movement details, navigation database expiry and cycle, doors open and closes, ground service panels open and close, power unit panel details and engine start and stop details.

14. The method of claim 9, wherein the atleast one sensor is selected from a group of internet of things (IOT) sensors and aircraft sensors consisting of a video camera, an audio sensor, a temperature sensor, a global positioning system (GPS) sensor, a distance sensor, a proximity sensor, a position sensor, a dimension sensor, RFID sensor, touch sensor beacon, gauge motion, accelerometer, gyrometer.