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 sense, detect, manage & integrate the journeys of passenger, baggage, Airport and ground operations, automating flow of turnaround operations, seamless flow of passenger during boarding and de-boarding of plane, and improvement of on time performance of airlines and airports. 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 multi ple 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. In 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 predictability in operations. Also to provide a computing system and method for managing of baggage loading and unloading operations first bag & last bag, passenger boarding & de-plane, chocks on & chocks off, fueling, catering, clearing of aircraft including managing and monitoring of all aircraft operations. [0004] Another object of the present disclosure is to sense, detect & track turn around operations of the aircraft using loT sensors

[0005] Yet another object of the present disclosure is to sense, track & detect chocks on & off operations

[0006] Yet another objective of the present invention is to sense, track & detect baggage loading/unloading, first bag & last bag, first trolley dispatch & last trolley dispatch, containers, movement of baggage trolleys from/to aircraft to/from baggage handling area of airport terminals.

[0007] Yet another object of present disclosure is to use loT where at least one sensor is selected from 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, computer vision, artificial intelligence for detection of various operations around the aircraft

[0008] Yet another object of the present disclosure is to sense, track & detect root cause of flight delays. [0009] Yet another object of the present disclosure is to sense, detect & track fueling, catering, cleaning, auxiliary & ground power operations of aircraft turnaround . [0010] Yet another object of the disclosure is to provide detection & alert if any turn around operation is delayed in real time.

[0011] Yet another object of the present disclosure is to allow tracking of baggage's through BBA, BMA, aircraft/baggage trolley loading/unloading. [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, track & monitor passenger boarding & deplane.

[0015] Yet another object of the present disclosure is to detect, track & monitor step ladder, push back, tractor & trolley, aerobridge operations

[0016] Yet another object of the present disclosure is to detect, track & monitor cabin crew reporting, pre-flight security, load sheet, crew & pilot onboarding & de-plane, catering operations

SUMMARY

[0017] In an aspect, the present disclosure provides a computer system or edge devices, sensors, cameras 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 turnaround operations.

[0018] 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, cameras 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, motion, rotation, gyrometer, accelerometer 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 getti ng 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

[0019] FIG. 1 illustrates a block diagram of an interactive computing environment for enabling real-time monitoring of multiple ground operations for airlines. It also illustrates a computing system for detecting, sensing and managing turnaround operations of an aircraft, in accordance with various embodiments of the present disclosure.

[0020] FIG. 2 also illustrates a flow diagram of a method for detecting, sensing and managing turnaround operations of an aircraft, in accordance with various embodiments of the present disclosure.

[0021] FIG. 3 illustrates a block diagram of a computing device, in accordance with various embodiments of the present disclosure.

DETAILED DESCRIPTION

[0022] 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.

[0023] 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 (can be interchangeably referred to as computing 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.

[0024] 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.

[0025] 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. [0026] 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. [0027]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. [0028] 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.

[0029] 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.

[0030] 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 plurality 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. I n 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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. I n an embodiment of the present disclosure, the live data can be utilized to simulate different scenarios and peak load factor at the airports.

[0035] 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 analytics 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.

[0036] I n 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. I n 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.

[0037] 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.

[0038] 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. [0039] The present disclosure describes a computing system (also referred as smart monitoring, prediction and analytics system) 112 for detecting, sensing and managing of turnaround operations of aircraft. The computing system 112 includes atleast one processor and a memory coupled to the at least one processor. The memory includes an analytics module to obtain at least one aircraft operational parameter during turnaround operations of an aircraft from a plurality of sensors 104A & 108. The memory further analyze the atleast one aircraft operational parameter related to the turnaround operations of the aircraft to determine and optimize the turnaround time of the aircraft using artificial intelligence and machine learning and generate an alert based on the analysis of the at least one obtained aircraft operational parameter. The computing system 112 in an embodiment includes at least one user interface to present the at least one aircraft operational parameter and the generated alert for monitoring of the turnaround operations of the aircraft in real time. In addition the computing system also include a configuration module to configure aircraft turn around activities to be performed to monitor the at least one aircraft operational parameter based on at least one of airline operations and airport conditions. The computing system 112 in another embodiment further includes at least one aircraft operational parameter is selected from a group consisting of position, distance, proximity, global positioning system (GPS) position, flight details, navigation database expiry and cycle, doors open and close, and ground service panels open and close. Further, the plurality of sensors 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 and a baggage counter. The computing system 112 wherein the turnaround operations include baggage loading and unloading operations of the aircraft and passenger coach boarding and deplane operations. The computing system 112 wherein the turnaround operations include baggage loading and unloading operations of the aircraft and passenger coach boarding and deplane operations.

[0040] FIG 2 illustrates a method 200 that includes steps of obtaining 202 atleast one aircraft operational parameter during turnaround operations of an aircraft from a plurality of sensors. The method further includes analyzing 204, the atleast one aircraft operational parameter related to the turnaround operations of the aircraft to determine and optimize 206 the turnaround time of the aircraft using artificial intelligence and machine learning. The method also includes generating 208 an alert based on the analysis of the atleast one obtained aircraft operational parameter. In further step the method also includes presenting 210 the at least one aircraft operational parameter and the generated alert on at least one user interface for monitoring the turnaround operations of the aircraft in real time. In one embodiment the at least one aircraft operational parameter is selected from a group consisting of position, distance, proximity, global positioning system (GPS) position, flight details, navigation database expiry and cycle, doors open and close, and ground service panels open and close. In another embodiment the plurality of sensors 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 and a baggage counter. Also computer vision can use Artificial intelligence for sensing & detection of various operations like chocks on, off, hold open & close, ACL on & off, catering truck arrival, position & loading, pallet/baggage loading/unloading, fueling using computer vision, machine learning & Al. Using cameras, video/images & machine learning model either on edge device or on cloud, it will be able to detect various operations & improve turn around operations. The method 200 wherein the turnaround operations include baggage loading and unloading operations of the aircraft and passenger coach boarding and deplane operations.

[0041] 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 aircraft operational parameter during turnaround operations of an aircraft from atleast one sensor. The processor further analyze the atleast one aircraft operational parameter related to the turnaround operations of the aircraft to determine and optimize the turnaround time of the aircraft using artificial intelligence and machine learning and generate an alert based on the analysis of the at least one obtained aircraft operational parameter. [0042] The present invention discloses a system for enabling real time visibility of loading and unloading of passenger luggage in an aircraft in an airport environment. The loading and unloading operations are performed by a ground handling team at the airport in between flights. The ground handling team utilizes equipment for loading passenger luggage to the aircraft about to take off. The system receives real time data from aircraft sensors for determining current location of the aircraft on the airport and where the aircraft is exactly positioned. In addition, the ground handling team utilizes the equipment for unloading passenger luggage from the aircraft which has just landed on the airport and scheduled for another take off soon. The equipment corresponds to one or more carts or containers in which the luggage is loaded in a loading dock at the airport terminal. Accordingly, the one or more carts or containers are transferred to the corresponding flight from the loading dock at the airport terminal using a suitable transferring means to the flight positioned at an assigned gate. The passenger luggage is then unloaded and transferred to a cargo hold area in the flight by unloading the luggage manually by a baggage handler at the unloading area and using a conveyer aligned with the cargo hold area of the aircraft. Similarly, the ground handling team unloads the passenger luggage from the cargo hold area of the aircraft using the same ground equipment used for loading purpose. The cargo hold area may be at a rear end of the aircraft or front end of the aircraft or any other location on the aircraft. The system takes into account the exact position of the cargo hold area. The system utilizes one or more sensors for determining a time taken during loading of passenger luggage to the aircraft about to take off. In addition, the system utilizes one or more sensors for determining a time taken during unloading of passenger luggage from the aircraft which has just landed and scheduled for another take off soon. The one or more sensors are installed on the ground equipment used for loading, transfer and unloading of passenger luggage to and from the aircraft. The one or more sensors collect real time data associated with the loading and unloading operations. The data is collected during the loading of luggage into the carts or containers at the loading dock on the airport terminal, during the transfer of the carts or containers from the loading dock at the airport terminal to a point near the cargo hold area of the aircraft, during unloading of the luggage from the carts or containers to the aircraft about to take off and during unloading of the luggage from the aircraft into the carts or containers. The data collected from the one or more sensors provides information associated with the time taken during the loading and unloading operations. For example, time taken in travelling from airport terminal to a point near the aircraft may be 1 minute or time taken to unload the luggage from the carts or containers may be 7 minutes. The system performs real time tracking of the luggage carts to detect the movement of the luggage carts and determine response time of the ground handling team responsible for loading and unloading operations. In addition, the system keeps a check on a direction of movement of the luggage carts in real time using the data from the one or more sensors in order to determine which operation has been performed and which operations has to be performed. For example, if the luggage cart is moving away from the aircraft, that means that the loading or unloading from the aircraft is complete and if the luggage cart is moving towards the aircraft that means that the loading or unloading from the aircraft is yet to be performed. The system provides real time visibility of the luggage carts to and from the aircraft and helps determines time taken during each stage for calculating estimated turnaround time. The system takes into accou nt a current direction in which the aircraft is positioned at the airport. The one or more sensors transmit the data to a cloud based system in real time on a continuous basis. The sensor data is displayed on a monitor screen of a device hosting the cloud based system for real time view or visibility of the loading and unloading operations. The system utilizes the combinations of the data collected from the one or more sensors in order to determine an estimated turnaround time for the aircraft which has just landed and scheduled to take off. The system may take into account the distance travelled by the luggage carts from the airport terminal to the aircraft and vice versa. The system may take into account a distance of the aircraft from the airport terminal. The system takes into account a standard time taken for performing loading and unloading operations in order to determine the actual deviation from a standard turnaround time. The system performs analytics and derives insights from the data to take certain business decisions.

[0043] FIG. 3 illustrates a block diagram of a computing device 300, in accordance with various embodiments of the present disclosure. The computing device 300 includes a bus 302 that directly or indirectly couples the following devices: memory 304, one or more processors 306, one or more presentation components 308, one or more input/output (I/O) ports 310, one or more input/output components 312, and an illustrative power supply 314. The bus 302 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 3 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. 3 is merely illustrative of an exemplary computing device 300 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. 3 and reference to "computing device." [0044] The computing device 300 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 300 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 300. 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.

[0045] Memory 304 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 304 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 300 includes one or more processors that read data from various entities such as memory 304 or I/O components 312. The one or more presentation components 308 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 310 allow the computing device 300 to be logically coupled to other devices including the one or more I/O components 312, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.