Patent Application
20200168108
Embodiments of the subject matter described herein relate generally to using machine learning techniques for analyzing flight data. More particularly, embodiments of the subject matter relate to employing machine learning to create a contextual model for use in-flight to make predictions and provide advisories, and for use post-flight to analyze data from completed flights.
Machine learning is a method of data analysis that automates analytical model building, by using algorithms to parse data, learn from the data, and then make a determination or prediction based on learning from the data. By employing machine learning, a computer system can progressively improve its performance on a specific task using algorithms to build a mathematical model of sample data (i.e., training data) in order to make predictions or decisions without the computer system being explicitly programmed to perform the task. Thus, the computer system can learn from the sample data, identify patterns, and make decisions with minimal human intervention. Machine learning techniques may be applied to any situation for which decisions are made based on complex data analysis. For example, machine learning may be applied to flight data for improved analysis of flight events and increased situational awareness.
Accordingly, it is desirable to provide machine learning applications for flight data analysis and decision making. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.
A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures.
The following detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description.
The present disclosure describes a method of flight data analytics utilizing artificial intelligence to train and process contextual data to improve aircraft operator performance. Contextual data may include aircraft operator background (e.g., familiar airports), aircraft operator training records, environmental factors (e.g., temperature, visibility, weather, etc), and airport data (e.g., runway information, traffic information, taxi information, etc). The contextual data may be processed by the artificial intelligence model to understand operator performance and achievements and may provide proactive assistance to operators when approaching flight segments that may reach or exceed a predetermined likelihood of negative safety event. For example, a proactive warning may be presented to the operator to assist in stabilized approach, and avoiding runway overruns.
the contextual data may be received from multiple sources and correlated to provide predictions of safety related events during aircraft flight. The correlation may be performed by machine learning approaches, such as logistic regression, and inputted into the trained artificial intelligence model to determine probabilities and predictions of safety related events occurring, and proactive warnings and assistance may then be provided to aircraft operators.
Turning now to the figures,
The computing device 102 may be implemented by any computing device that includes at least one processor, some form of memory hardware, a user interface, and communication hardware. For example, the computing device 102 may be implemented using a personal computing device, such as a tablet computer, a laptop computer, a personal digital assistant (PDA), a smartphone, or the like. In this scenario, the computing device 102 is capable of storing, maintaining, and executing an Electronic Flight Bag (EFB) application configured to determine and present flight data analysis, including approach procedure stability prediction data and/or landing performance analysis data. In other embodiments, the computing device 102 may be implemented using a computer system onboard the aircraft 106, which is configured to determine and present flight data analysis.
The ground-based central computer system 104 may be implemented by any computer system that includes at least one processor, some form of memory hardware, a user interface, and communication hardware. For example, the ground-based central computer system 104 may be implemented using a desktop computer, a laptop computer, a mainframe computer system, a server system comprising one or more servers, or the like. In other embodiments, the ground-based central computer system 104 may be implemented using a personal computing device, such as a tablet computer, a laptop computer, a personal digital assistant (PDA), a smartphone, or the like. The ground-based central computer system 104 is configured to host, and permit access to, a “web application” or “web app”, which is an internet-based or intranet-based software application for presenting flight data analysis. The ground-based central computer system 104 permits secondary computer systems (e.g., the computing device 102) to access the web app and associated functionality and features via the data communication network 112.
The aircraft 106 may be any aviation vehicle for which approach procedure stability predictions are relevant and applicable during completion of a flight route, and/or past landing performance analysis is relevant and applicable following completion of a flight route. The aircraft 106 may be implemented as an airplane, helicopter, spacecraft, hovercraft, or the like. The one or more avionics systems 108 may include a Flight Management System (FMS), Automatic Dependent Surveillance-Broadcast (ADS-B) devices, Terrain Awareness and Warning System (TAWS) devices, navigation devices, weather radar, brake systems, or the like. Data obtained from the one or more avionics systems 108 may include, without limitation: flight plan data, current and updated flight parameter data, weather data, airport data, runway analysis data, aircraft performance data, or the like.
The server system 110 may include any number of application servers, and each server may be implemented using any suitable computer. In some embodiments, the server system 110 includes one or more dedicated computers. In some embodiments, the server system 110 includes one or more computers carrying out other functionality in addition to server operations. Each server of the server system 110 may be maintained by any applicable business or organization (e.g., government and regulatory agencies, universities and other research-based organizations, airlines, aviation safety monitoring organizations, safety data aggregators, weather data aggregators, traffic data aggregators), and each server of the server system 110 may store and provide any type of data used to perform flight data analysis. Such data may include, without limitation: historical weather data (e.g., wind speed, cloud cover, visibility, precipitation, temperature); airport and air traffic control (ATC) data (e.g., airport, runway length, ATIS, airport arrival rate, arrival/departure taxi time); Automatic Terminal Information Service (ATIS) data (e.g., current weather data, active runways, available approaches, Notices to Airmen (NOTAMs)); aircraft and flight-specific data (e.g., flight state data (Quick Access Recorder data, Aircraft Standard Communication Bus data), flight plan data, aircraft condition data, condition-based maintenance (CBM) data, aircraft specifications, NOTAMs); and human factor data associated with flight crew members (e.g., pilot certifications, hours of training data, duty cycle data). Exemplary embodiments of the server system 110 may include weather services, operations and performance data services (e.g., the Federal Aviation Administration System Wide Information Management, archived data, and/or current data), or the like.
The computing device 102 is usually located onboard the aircraft 106, and the computing device 102 communicates with the one or more avionics systems 108 via wired and/or wireless communication connection. The computing device 102 and the server system 110 are generally disparately located, and the computing device 102 communicates with the server system 110 via the data communication network 112 and/or via communication mechanisms onboard the aircraft 106. Similarly, the ground-based central computer system 104 is usually located external to the aircraft 106, in a ground-based location, and is configured to communicate with one or more secondary computer systems (e.g., the computing device 102), the one or more avionics systems 108, and the server system 110 via wired and/or wireless communication connection established via the data communication network 112.
The data communication network 112 may be any digital or other communications network capable of transmitting messages or data between devices, systems, or components. In certain embodiments, the data communication network 112 includes a packet switched network that facilitates packet-based data communication, addressing, and data routing. The packet switched network could be, for example, a wide area network, the Internet, or the like. In various embodiments, the data communication network 112 includes any number of public or private data connections, links or network connections supporting any number of communications protocols. The data communication network 112 may include the Internet, for example, or any other network based upon TCP/IP or other conventional protocols. In various embodiments, the data communication network 112 could also incorporate a wireless and/or wired telephone network, such as a cellular communications network for communicating with mobile phones, personal digital assistants, and/or the like. The data communication network 112 may also incorporate any sort of wireless or wired local and/or personal area networks, such as one or more IEEE 802.3, IEEE 802.16, and/or IEEE 802.11 networks, and/or networks that implement a short range (e.g., Bluetooth) protocol. For the sake of brevity, conventional techniques related to data transmission, signaling, network control, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein.
During typical operation, the ground-based central computer system 104 obtains relevant data associated with flight data modeling and analysis from the one or more remote servers of the server system 110. The ground-based computer system 104 then uses the relevant data to create and train a contextual artificial intelligence (AI) model for use in (1) performing landing performance analysis and determining probable cause of events occurring during past flights that are already completed; and (2) predicting approach stability in-flight, based on current flight data parameters and conditions and a probable stable or unstable condition of the aircraft 106. The ground-based central computer system 104 is further configured to perform landing performance analysis, generate reports and other landing performance results information, and to host a web application for presenting the landing performance results and reporting, such that other secondary, external computer systems (e.g., the computing device 102) can access the landing performance data provided by the ground-based central computer system 104. Further, during typical operation, the computing device 102 receives a trained contextual AI model for purposes of predicting stability of approach during flight of the aircraft 106. The computing device 102 receives current updated aircraft parameter data during flight and updates the contextual AI model continuously during flight, when the aircraft 106 is located far enough away from a designated landing runway that the flight crew onboard the aircraft 106 can make flight adjustments to alter the approach procedure and ensure a stable approach, without requiring the aircraft to “go-around” and begin the approach procedure again.
The at least one processor 202 may be implemented or performed with one or more general purpose processors, a content addressable memory, a digital signal processor, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination designed to perform the functions described here. In particular, the at least one processor 202 may be realized as one or more microprocessors, controllers, microcontrollers, or state machines. Moreover, the at least one processor 202 may be implemented as a combination of computing devices, e.g., a combination of digital signal processors and microprocessors, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other such configuration.
The at least one processor 202 is communicatively coupled to the system memory 204. The system memory 204 is configured to store any obtained or generated data associated with flight data analysis and/or contextual artificial intelligence (AI) modeling, machine learning, landing performance analysis and reporting, approach procedure stability predictions, and graphical elements associated with the system. The system memory 204 may be realized using any number of devices, components, or modules, as appropriate to the embodiment. Moreover, the computing device 200 could include system memory 204 integrated therein and/or a system memory 204 operatively coupled thereto, as appropriate to the particular embodiment. In practice, the system memory 204 could be realized as RAM memory, flash memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or any other form of storage medium known in the art. In certain embodiments, the system memory 204 includes a hard disk, which may also be used to support functions of the computing device 200. The system memory 204 can be coupled to the at least one processor 202 such that the at least one processor 202 can read information from, and write information to, the system memory 204. In the alternative, the system memory 204 may be integral to the at least one processor 202. As an example, the at least one processor 202 and the system memory 204 may reside in a suitably designed application-specific integrated circuit (ASIC).
The communication device 206 is suitably configured to communicate data between the central computer system 200 and one or more secondary external computer systems, one or more remote servers, and one or more avionics systems onboard an aircraft. The communication device 206 may transmit and receive communications over a wireless local area network (WLAN), the Internet, a satellite uplink/downlink, a cellular network, a broadband network, a wide area network, or the like. As described in more detail below, data received by the communication device 206 may include, without limitation: historical weather data (e.g., wind speed, cloud cover, visibility, precipitation, temperature); airport and air traffic control (ATC) data (e.g., airport, runway length, ATIS, airport arrival rate, arrival/departure taxi time); Automatic Terminal Information Service (ATIS) data (e.g., current weather data, active runways, available approaches, Notices to Airmen (NOTAMs)); aircraft and flight-specific data (e.g., flight state data (Quick Access Recorder data, Aircraft Standard Communication Bus data), flight plan data, aircraft condition data, condition-based maintenance (CBM) data, aircraft specifications, NOTAMs); and human factor data associated with flight crew members (e.g., pilot certifications, hours of training data, duty cycle data). Data provided by the communication device 206 may include, without limitation: a data upload file comprising a contextual AI model, an updated contextual AI model, a trained contextual AI model, landing performance reporting and results data, and the like.
The historical data aggregation module 208 is configured to create an aggregate set of historical data for use in creating and training the contextual AI model, wherein the aggregate set of historical data may include, without limitation: historical weather data (e.g., wind speed, cloud cover, visibility, precipitation, temperature); airport and air traffic control (ATC) data (e.g., airport, runway length, ATIS, airport arrival rate, arrival/departure taxi time); Automatic Terminal Information Service (ATIS) data (e.g., current weather data, active runways, available approaches, Notices to Airmen (NOTAMs)); aircraft and flight-specific data (e.g., flight state data (Quick Access Recorder data, Aircraft Standard Communication Bus data), flight plan data, aircraft condition data, condition-based maintenance (CBM) data, aircraft specifications, NOTAMs); and human factor data associated with flight crew members (e.g., pilot certifications, hours of training data, duty cycle data). Exemplary embodiments of the server system 110 may include weather services, operations and performance data services (e.g., the Federal Aviation Administration System Wide Information Management, archived data, and/or current data), or the like.
The current data acquisition module 210 is configured to obtain real-time flight data, including a current updated set of flight parameters from one or more avionics systems onboard an aircraft during flight, for use in making approach stability predictions in-flight. The current data acquisition module 210 is further configured to obtain updated data uploads including current landing performance data to include in the set of aggregated historical landing data.
The contextual model module 212 is configured to create a contextual artificial intelligence (AI) model for use in performing landing analysis post-flight, and making approach procedure stability predictions in-flight. The contextual model module 212 obtains a set of aggregate contextual data comprising at least aircraft and flight-specific data, airport and air traffic control (ATC) data, weather data, and human factor data associated with flight crew members of the one or more aircraft. The contextual model module 212 creates the contextual AI model using the set of aggregate contextual data and applies the contextual AI model to a set of flight data, to perform a statistical analysis. The contextual model module 212 generates a set of results based on the statistical analysis, wherein the set of results comprises at least one of probable causes of aircraft performance events and probable aircraft performance events resulting from current conditions. The contextual model module 212 presents the set of results, via the display device 222. The contextual model module 212 obtains a machine learning framework, wherein the machine learning framework comprises at least one of an artificial neural network (ANN) and machine learning algorithms. The contextual model module 212 trains the machine learning framework using the set of aggregate contextual data, to generate the contextual AI model. After the contextual AI model is created, applying the contextual AI model to the set of flight data further comprises using the trained machine learning framework including the set of aggregate contextual data, to perform the statistical analysis.
The landing performance analysis module 214 is configured to perform landing performance analysis based on an aggregate set of historical landing data.
The landing performance web app module 216 hosts a web application to present landing performance analysis results and reporting, and to provide access to the landing performance analysis data and associated web app functionality. In practice, the landing performance web app module 216 enables a user to interact with landing performance data, to make selections, to view aggregate sets of data, to request conclusions, such as a list of the probable causes of particular aircraft performance events that occurred during one or more aircraft landings for which landing performance data is available.
The approach prediction analysis module 218 is configured to use the contextual AI model and continuously updated flight data parameters (obtained from one or more aircraft onboard avionics devices) to predict the stability or instability of a current approach procedure, during flight.
The approach prediction trained model upload module 220 is configured to provide a data transmission for use in-flight, wherein the data transmission includes a trained contextual AI model that has been trained using a historical set of aggregate approach procedure data.
The display device 222 is configured to display various icons, text, and/or graphical elements associated with flight data analysis using a contextual AI model, and the like. In an exemplary embodiment, the display device 222 is communicatively coupled to the at least one processor 202. The at least one processor 202 and the display device 222 are cooperatively configured to display, render, or otherwise convey one or more graphical representations or images associated with flight data analysis and predictions using a contextual AI model on the display device 222, as described in greater detail below. In an exemplary embodiment, the display device 222 is realized as an electronic display configured to graphically display flight data analysis and prediction data, as described herein. It will be appreciated that although the display device 222 may be implemented using a single display, certain embodiments may use additional displays (i.e., a plurality of displays) to accomplish the functionality of the display device 222 described herein.
The at least one processor 302 may be implemented or performed with one or more general purpose processors, a content addressable memory, a digital signal processor, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination designed to perform the functions described here. In particular, the at least one processor 302 may be realized as one or more microprocessors, controllers, microcontrollers, or state machines. Moreover, the at least one processor 302 may be implemented as a combination of computing devices, e.g., a combination of digital signal processors and microprocessors, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other such configuration.
The at least one processor 302 may be communicatively coupled to the system memory 304. The system memory 304 may be configured to store any obtained or generated data associated with flight data analysis and/or contextual artificial intelligence (AI) modeling, machine learning, landing performance analysis and reporting, approach procedure stability predictions, and graphical elements associated with the system. The system memory 304 may be realized using any number of devices, components, or modules, as appropriate to the embodiment. Moreover, the computing device 300 could include system memory 304 integrated therein and/or a system memory 304 operatively coupled thereto, as appropriate to the particular embodiment. In practice, the system memory 304 could be realized as RAM memory, flash memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or any other form of storage medium known in the art. In certain embodiments, the system memory 304 may include a hard disk, which may also be used to support functions of the computing device 300. The system memory 304 can be coupled to the at least one processor 302 such that the at least one processor 302 can read information from, and write information to, the system memory 304. In the alternative, the system memory 304 may be integral to the at least one processor 302. As an example, the at least one processor 302 and the system memory 304 may reside in a suitably designed application-specific integrated circuit (ASIC).
The user interface 306 may be suitably configured to present appropriate information and/or data to an user operating the computing device 300. The user interface 306 may be displayed via the display device 314 and may contain icons, graphs, and user interactive elements.
The communication device 308 may be suitably configured to communicate data between the central computer system 300 and one or more secondary external computer systems, one or more remote servers, and one or more avionics systems onboard an aircraft. The communication device 308 may transmit and receive communications over a wireless local area network (WLAN), the Internet, a satellite uplink/downlink, a cellular network, a broadband network, a wide area network, or the like. As described in more detail below, data received by the communication device 308 may include, without limitation: historical weather data (e.g., wind speed, cloud cover, visibility, precipitation, temperature); airport and air traffic control (ATC) data (e.g., airport, runway length, ATIS, airport arrival rate, arrival/departure taxi time); Automatic Terminal Information Service (ATIS) data (e.g., current weather data, active runways, available approaches, Notices to Airmen (NOTAMs)); aircraft and flight-specific data (e.g., flight state data (Quick Access Recorder data, Aircraft Standard Communication Bus data), flight plan data, aircraft condition data, condition-based maintenance (CBM) data, aircraft specifications, NOTAMs); and human factor data associated with flight crew members (e.g., pilot certifications, hours of training data, duty cycle data). Data provided by the communication device 308 may include, without limitation: a data upload file comprising a contextual AI model, an updated contextual AI model, a trained contextual AI model, landing performance reporting and results data, and the like.
The landing performance web app module 310 may host a web application to present landing performance analysis results and reporting, and to provide access to the landing performance analysis data and associated web app functionality. The landing performance analysis results and reporting may be conducted via the landing performance reporting and results module 312. In practice, the landing performance web app module 310 and the landing performance reporting and results module 312 may enable a user to interact with landing performance data, to make selections, to view aggregate sets of data, to request conclusions, such as a list of the probable causes of particular aircraft performance events that occurred during one or more aircraft landings for which landing performance data is available.
The display device 314 may be configured to display various icons, text, and/or graphical elements associated with flight data analysis using a contextual AI model, and the like. In an exemplary embodiment, the display device 314 is communicatively coupled to the at least one processor 302. The at least one processor 302 and the display device 314 are cooperatively configured to display, render, or otherwise convey one or more graphical representations or images associated with aircraft landing performance using a contextual AI model on the display device 314. It will be appreciated that although the display device 314 may be implemented using a single display, certain embodiments may use additional displays (i.e., a plurality of displays) to accomplish the functionality of the display device 314 described herein.
The at least one processor 402 may be implemented or performed with one or more general purpose processors, a content addressable memory, a digital signal processor, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination designed to perform the functions described here. In particular, the at least one processor 402 may be realized as one or more microprocessors, controllers, microcontrollers, or state machines. Moreover, the at least one processor 402 may be implemented as a combination of computing devices, e.g., a combination of digital signal processors and microprocessors, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other such configuration.
The at least one processor 402 may be communicatively coupled to the system memory 404. The system memory 404 may be configured to store any obtained or generated data associated with flight data analysis and/or contextual artificial intelligence (AI) modeling, machine learning, landing performance analysis and reporting, approach procedure stability predictions, and graphical elements associated with the system. The system memory 404 may be realized using any number of devices, components, or modules, as appropriate to the embodiment. Moreover, the computing device 400 could include system memory 404 integrated therein and/or a system memory 404 operatively coupled thereto, as appropriate to the particular embodiment. In practice, the system memory 404 could be realized as RAM memory, flash memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or any other form of storage medium known in the art. In certain embodiments, the system memory 404 may include a hard disk, which may also be used to support functions of the computing device 400. The system memory 404 can be coupled to the at least one processor 402 such that the at least one processor 402 can read information from, and write information to, the system memory 404. In the alternative, the system memory 404 may be integral to the at least one processor 402. As an example, the at least one processor 402 and the system memory 404 may reside in a suitably designed application-specific integrated circuit (ASIC).
The user interface 406 may be suitably configured to present appropriate information and/or data to an user operating the computing device 400. The user interface 406 may be displayed via the display device 416 and may contain icons, graphs, and user interactive elements.
The communication device 408 may be suitably configured to communicate data between the central computer system 400 and one or more secondary external computer systems, one or more remote servers, and one or more avionics systems onboard an aircraft. The communication device 408 may transmit and receive communications over a wireless local area network (WLAN), the Internet, a satellite uplink/downlink, a cellular network, a broadband network, a wide area network, or the like. As described in more detail below, data received by the communication device 408 may include, without limitation: historical weather data (e.g., wind speed, cloud cover, visibility, precipitation, temperature); airport and air traffic control (ATC) data (e.g., airport, runway length, ATIS, airport arrival rate, arrival/departure taxi time); Automatic Terminal Information Service (ATIS) data (e.g., current weather data, active runways, available approaches, Notices to Airmen (NOTAMs)); aircraft and flight-specific data (e.g., flight state data (Quick Access Recorder data, Aircraft Standard Communication Bus data), flight plan data, aircraft condition data, condition-based maintenance (CBM) data, aircraft specifications, NOTAMs); and human factor data associated with flight crew members (e.g., pilot certifications, hours of training data, duty cycle data). Data provided by the communication device 408 may include, without limitation: a data upload file comprising a contextual AI model, an updated contextual AI model, a trained contextual AI model, landing performance reporting and results data, and the like.
The real-time flight data module 410 may be configured to obtain real-time flight data, including a current updated set of flight parameters from one or more avionics systems onboard an aircraft during flight, for use in making approach stability predictions in-flight. The real-time flight data acquisition module 410 is further configured to obtain updated data uploads including current landing performance data to include in the set of aggregated historical landing data.
The approach prediction analysis module 412 may be configured to use the contextual AI model and continuously updated flight data parameters (obtained from one or more aircraft onboard avionics devices) to predict the stability or instability of a current approach procedure, during flight.
The presentation module 414 may be configured to present the result of the approach prediction analysis module, such as any predicted stable or unstable approach segment of the aircraft. The presentation may be presented on the EFB and/or the avionics system on the aircraft and operated by aircraft crew or via the display device 416.
The display device 416 may be configured to display various icons, text, and/or graphical elements associated with flight data analysis using a contextual AI model, and the like. In an exemplary embodiment, the display device 416 is communicatively coupled to the at least one processor 402. The at least one processor 402 and the display device 416 are cooperatively configured to display, render, or otherwise convey one or more graphical representations or images associated with aircraft approach prediction using a contextual AI model on the display device 416. It will be appreciated that although the display device 416 may be implemented using a single display, certain embodiments may use additional displays (i.e., a plurality of displays) to accomplish the functionality of the display device 416 described herein.
The process 1300 establishes communication connections to one or more remote servers, via the communication device (step 1302).
The process 1300 obtains historical weather data, the airport and air traffic control (ATC) data, and the aircraft and flight-specific data, applicable to the set of flight data via the communication connections (step 1304). Here, the historical weather data includes historical wind speeds, historical cloud cover conditions, historical visibility conditions, historical precipitation conditions, and historical temperature conditions associated with the set of flight data, wherein the weather data comprises at least the historical weather data, wherein the airport and ATC data includes at least Automatic Terminal Information Service (ATIS) data, airport specifications data, runway length data, airport arrival rate data, airport arrival taxi time data, and airport departure taxi time data, wherein the aircraft and flight-specific data includes at least flight state data from aircraft onboard recorders, flight plan data, aircraft condition data, condition-based maintenance (CBM) data, aircraft specification data, and Notices to Airmen (NOTAMs) data, and wherein the historical weather data, the airport and ATC data, and the aircraft and flight-specific data, are stored by the one or more remote servers.
The process 1300 obtains a first set of pilot-specific data applicable to the set of flight data via the communication connections, wherein the first set of pilot-specific data comprises at least pilot certification data, pilot training hours data, and pilot duty cycle data, and wherein the first set of pilot-specific data is stored by the one or more remote servers (step 1306). The process 1300 derives a second set of pilot-specific data using the first set of pilot-specific data, wherein the second set of pilot-specific data includes a derived skill index and a derived fatigue metric for pilots associated with the set of flight data (step 1308). The process 1300 incorporates the historical weather data, the airport and ATC data, and the aircraft and flight-specific data, into the set of aggregate contextual data, wherein the contextual AI model is created using the set of aggregate contextual data including the historical weather data, the airport and ATC data, and the aircraft and flight-specific data (step 1310). The process 1300 incorporates the first set of pilot-specific data and the second set of pilot-specific data into the set of aggregate contextual data, wherein the human factor data comprises the first set of pilot-specific data and the second set of pilot-specific data, and wherein the contextual AI model is created using the set of aggregate contextual data including the human factor data (step 1312).
Techniques and technologies may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. In practice, one or more processor devices can carry out the described operations, tasks, and functions by manipulating electrical signals representing data bits at memory locations in the system memory, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices.
When implemented in software or firmware, various elements of the systems described herein are essentially the code segments or instructions that perform the various tasks. The program or code segments can be stored in a processor-readable medium or transmitted by a computer data signal embodied in a carrier wave over a transmission medium or communication path. The “computer-readable medium”, “processor-readable medium”, or “machine-readable medium” may include any medium that can store or transfer information. Examples of the processor-readable medium include an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an erasable ROM (EROM), a floppy diskette, a CD-ROM, an optical disk, a hard disk, a fiber optic medium, a radio frequency (RF) link, or the like. The computer data signal may include any signal that can propagate over a transmission medium such as electronic network channels, optical fibers, air, electromagnetic paths, or RF links. The code segments may be downloaded via computer networks such as the Internet, an intranet, a LAN, or the like.
The following description refers to elements or nodes or features being “connected” or “coupled” together. As used herein, unless expressly stated otherwise, “coupled” means that one element/node/feature is directly or indirectly joined to (or directly or indirectly communicates with) another element/node/feature, and not necessarily mechanically. Likewise, unless expressly stated otherwise, “connected” means that one element/node/feature is directly joined to (or directly communicates with) another element/node/feature, and not necessarily mechanically. Thus, although the schematics shown in
For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, network control, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the subject matter.
Some of the functional units described in this specification have been referred to as “modules” in order to more particularly emphasize their implementation independence. For example, functionality referred to herein as a module may be implemented wholly, or partially, as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like. Modules may also be implemented in software for execution by various types of processors. An identified module of executable code may, for instance, comprise one or more physical or logical modules of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together but may comprise disparate instructions stored in different locations that, when joined logically together, comprise the module and achieve the stated purpose for the module. Indeed, a module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.
While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.
This application claims the benefit of priority to U.S. Provisional Application No. 62/771,532, filed Nov. 26, 2018, which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62771532 | Nov 2018 | US |
