WO2023041973A1 - Système et procédé de prédiction de risque pour un vol d'un véhicule aérien sans pilote - Google Patents

Système et procédé de prédiction de risque pour un vol d'un véhicule aérien sans pilote Download PDF

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Publication number
WO2023041973A1
WO2023041973A1 PCT/IB2021/059295 IB2021059295W WO2023041973A1 WO 2023041973 A1 WO2023041973 A1 WO 2023041973A1 IB 2021059295 W IB2021059295 W IB 2021059295W WO 2023041973 A1 WO2023041973 A1 WO 2023041973A1
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Prior art keywords
risk
module
unmanned aerial
aerial vehicle
flight
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PCT/IB2021/059295
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English (en)
Inventor
Sayan Banerjee
Shreya Ghosh
Saptarshi Pal
Sandipan Sen
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Tropogo Limited
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Publication of WO2023041973A1 publication Critical patent/WO2023041973A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0052—Navigation or guidance aids for a single aircraft for cruising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/006—Navigation or guidance aids for a single aircraft in accordance with predefined flight zones, e.g. to avoid prohibited zones
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • G08G5/0091—Surveillance aids for monitoring atmospheric conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the embodiments herein generally relate to unmanned aerial vehicles. More particularly, the disclosure relates to prediction of risk for a flight region of an unmanned aerial vehicle.
  • Unmanned Aerial Vehicle commonly known as a drone, is an aircraft without any human pilot, crew or passengers on board.
  • the drones are remotely operated from the ground by operators. Drones are widely used for various purposes such as surveying, photography, surveillance, research etc. However, the operating conditions for flying the drones vary in each of the purposes based on various factors.
  • the main objective of the present disclosure is to provide a system and method for predicting risk for a flight of an unmanned aerial vehicle.
  • Another objective of the present disclosure is to provide an automated system and method of predicting risk of a flight of an unmanned aerial vehicle for estimating price of insurance for the unmanned aerial vehicle.
  • Still another objective of the present disclosure is to provide a system and method for accurately predicting risk of a flight of an unmanned aerial vehicle based on various factors, for assisting and recommending a user.
  • Yet another objective of the present disclosure is to provide a system and method for ensuring flight safety of unmanned aerial vehicles.
  • an embodiment herein provides a system and method for predicting risk for a flight of an unmanned aerial vehicle.
  • the method for predicting risk for a flight of an unmanned aerial vehicle comprises the steps of providing details including time, date, location and purpose of flying the unmanned aerial vehicle, by a user using a user interface and computing parameters affecting flight of the unmanned aerial vehicle based on the details for determining a risk, by a risk prediction system.
  • the method of computing the parameters includes obtaining weather conditions on the provided time, date and location for determining a first risk, by a weather module. Next, identifying ground assets at the location and attributes of the ground assets for determining a second risk, by a ground module.
  • the crash module transmits the analysed crashes to an artificial intelligence module and the artificial intelligence module updates the parameters for the weather module, ground module, equipment module, purpose module, pilot module and environment module based on the analysed crashes.
  • the weather conditions includes temperature, wind speed, rainfall, visibility, gust, K-index, cloud cover.
  • the method includes comparing the obtained weather conditions with an allowable range of weather conditions and assigning a weightage to the weather conditions based on the comparison for determining the first risk, the risk evaluation module.
  • the ground assets include but not limited to geographical terrain and structures including no-fly zones, tall structures, military structures, government buildings, airports, international boundaries, coastline, dams, power lines, waterways, railways, mobile towers, industrial facilities and forest areas and the attributes include but not limited to height, width, area and allowable flying region of the ground assets.
  • the method includes assigning a weightage to the identified ground assets based on affect of the attributes on the flight of the unmanned aerial vehicle and the proximity of the ground assets to the location of flying the unmanned aerial vehicle for determining the second risk, by the risk evaluation module.
  • the features of the unmanned aerial vehicle include but not limited to battery capacity, weight and capabilities including maximum speed, service ceiling, resistance to wind, operating temperature bandwidth.
  • the method includes but not limited to assigning a weightage to the features and capabilities based on affect on performance of flying the unmanned aerial vehicle, analysing compatibility of the features and capabilities with the weather conditions on the provided date, time and location; and determining the third risk based on the weightage and the compatibility, by the risk evaluation module.
  • the capability includes but not limited to interchangeable payload of the unmanned aerial vehicle, analysing an affect of the interchangeable payload on performance of the unmanned aerial vehicle and assigning a weightage to the capability of interchangeable payload based on the affect for determining the third risk, by the risk evaluation module.
  • the purpose includes but not limited to emergency services, rescue, security, sporting events, entertainment, photography, logistics, survey, research, agriculture, hardware testing, training, recreational, analysing safety of the purpose and comparing the assessed features and capabilities of the unmanned aerial vehicle with features and capabilities required for the purpose and assigning a weightage to the purpose based on the safety and the comparison for determining the fourth risk, by the risk evaluation module.
  • the abilities of the pilot estimated based on age, years of experience, history of crash incidents and the method includes assigning weightage to the abilities of the pilot for flying the unmanned aerial vehicle safely, for determining the fifth risk by the risk evaluation module.
  • the variables at the location include but not limited to GPS strength and routes of aerial vehicles at the location of flying wherein assessing the variables includes identifying the GPS strength at the location based on the obtained weather conditions and identifying the routes of aerial vehicles at the date and time of flying and assigning weightage to the variables based on the identified GPS strength and proximity to the routes of the aerial vehicles at the location for determining the sixth risk, by the risk evaluation module.
  • analysing the crashes inclues identifying the parameters of the flight of the crashed unmanned aerial vehicles, identifying damage on the unmanned aerial vehicle based on the parameters and comparing the parameters of the crash with the parameters of the flight of the unmanned aerial vehicle to identify similarities, and assigning a weightage to the flight of the UAV based on the identified similar parameters and damage to the crashed unmanned aerial vehicle, for determining a seventh risk, by the risk evaluation module.
  • the method includes combining the first risk, second risk, third risk, fourth risk, fifth risk, sixth risk and seventh risk for predicting the total risk, by the prediction module.
  • the method includes estimating a price for insurance of the flight of the unmanned aerial vehicle based on the predicted total risk, by a dynamic pricing unit.
  • the method includes providing flying recommendations to the user for reducing risk based on the predicted total risk, by a user recommendation unit.
  • the risk evaluation module assigns a weightage to the parameters proportional to risk contributed by the parameters for the flight of the unmanned aerial vehicle.
  • the parameters includes weather conditions, ground assets, capabilities and features of the unmanned aerial vehicle, purpose, abilities of the pilot, variables at the location and crashes of unmanned aerial vehicles.
  • the system for predicting risk for the flight of an unmanned aerial vehicle comprises a risk prediction system for predicting the risk, a user interface provided in a user device for allowing a user to provide details including date, time, location and purpose of flying the unmanned aerial vehicle to the risk prediction system.
  • the computing module is provided for computing parameters affecting the flight of the unmanned aerial vehicle, wherein the computing module includes a weather module, a ground module, an equipment module, a purpose module, a pilot module, an environment module, and a crash module.
  • the weather module is provided for obtaining weather conditions at the provided date, time and location
  • the ground module is provided for identifying ground assets and attributes of the ground assets at the provided location
  • the equipment module is provided for assessing features and capabilities of the unmanned aerial vehicle
  • the purpose module is provided for analysing feasibility of the purpose
  • the pilot module is provided for estimating abilities of a pilot
  • the crash module is provided for analysing crashes of unmanned aerial vehicles at the location
  • the environment module is provided for assessing variables at the location of flying the unmanned aerial vehicle.
  • the computing module transmits the computed parameters to the risk evaluation module and the artificial intelligence module.
  • the artificial intelligence module is provided for updating parameters for computing by the computing module based on the analysed crashes
  • the risk evaluation module is provided for assigning a weightage to the parameters and determining a first risk, second risk, third risk, the fourth risk, fifth risk, sixth risk and seventh risk
  • the prediction module module is provided for predicting a total risk of the flight of the unmanned aerial vehicle by combining the first risk, the second risk, the third risk, the fourth risk, the fifth risk, the sixth risk and the seventh risk.
  • the dynamic pricing unit is provided for estimating price of insurance of the unmanned aerial vehicle based on the total risk predicted by the prediction module.
  • the user recommendation unit is provided for recommending flight of the unmanned aerial vehicle to the user based on the total risk predicted by the prediction module.
  • FIG. 1 illustrates a system for predicting risk for a flight of an unmanned aerial vehicle, according to an embodiment herein;
  • Fig.2 illustrates a flow chart of a method of for predicting risk for a flight of an unmanned aerial vehicle, according to an embodiment herein;
  • FIG. 3a illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle, according to an embodiment herein;
  • FIG. 3b illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle, according to an embodiment herein
  • Fig. 3c illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle, according to an embodiment herein;
  • FIG. 4 illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle, according to an embodiment herein.
  • Fig. 1 illustrates a system for predicting risk for a flight of an unmanned aerial vehicle.
  • the system includes a user device 101 and a risk prediction system 105.
  • a user interface 102 is provided in the user device 101.
  • the user interface 102 is provided for allowing a user to communicate with the risk prediction system 105.
  • the user interface 102 allows the user for providing details including but not limited type of unmanned aerial vehicle, date of flying, time of flying, location of flying and purpose of flying the unmanned aerial vehicle.
  • the risk prediction system 105 includes a computing module 106, an artificial intelligence module 120, a risk evaluation module 121, a prediction module 123, a dynamic pricing unit 125 and a user recommendation unit 127.
  • the computing module 106 is provided for computing parameters affecting the flight of the unmanned aerial vehicle.
  • the parameters including but not limited to weather conditions, ground assets, capabilities and features of the unmanned aerial vehicle, purpose of flight of the unmanned aerial vehicle, abilities of a pilot operating the unmanned aerial vehicle, variables at the location of flying the unmanned aerial vehicle and crashes of various unmanned aerial vehicles.
  • the computing module 106 includes a weather module 107, a ground module 109, an equipment module 111, a purpose module 113, a pilot module 115, an environment module 117, and a crash module 119.
  • the weather module 107 is provided for obtaining weather conditions at the provided date, time and location from the weather database.
  • the weather conditions including but not limited to temperature, wind speed, wind speed at variable height, rainfall, visibility, gust, K-index, cloud cover, humidity, pressure.
  • the weather module 107 includes a weather database having historical data of weather conditions, present data of weather conditions and forecast data of weather conditions at any location.
  • the weather database is an external database.
  • the weather module 107 retrieves the weather conditions at the date, time and location from the weather database and the obtained weather conditions are transmitted to the risk evaluation module 121 for determining a first risk.
  • the risk evaluation module 121 compares the weather conditions received from the weather module 107 with an allowable range of weather conditions for flying the UAV.
  • the allowable range of weather conditions is set by the artificial intelligence module 120, based on weather conditions suitable for safe flying of the UAV.
  • the allowable range of weather conditions is different based on model of the UAV.
  • the risk evaluation module 121 assigns a weightage to the weather conditions based on the comparison with the allowable range for determining the first risk.
  • the weightage is assigned by measuring deviation of the weather conditions from the allowable range, wherein the weather conditions with higher deviation is assigned a higher weightage and weather conditions with lower deviation is assigned a lower weightage.
  • the ground module 109 is provided for identifying ground assets at the provided location wherein the ground assets including but not limited to geographical terrain, no-fly zones, tall structures, multi-storied buildings, military structures, sensitive government buildings, airports, international boundaries, coastline, dams, power lines, waterways, railways, government offices, mobile towers, industrial facilities and forest areas.
  • the ground assets at the location are capable of obstructing and limiting the flying of the drones and the UAVs, thereby adding to the risk of flying the UAV. Flying of UAVs is restricted near identified no-fly zones, military structures, international boundaries and sensitive government buildings. Dams, power lines, waterways, railways, forest areas and certain geographical terrain add considerable risk for flying the UAV.
  • the ground module 109 identifies attributes of the ground assets at the location. In an embodiment, the attributes are parameters of the ground assets affecting the flight of the UAV. The attributes including but not limited to height, width, area and allowable flying region.
  • the identified ground assets and the attributes are transmitted to the risk evaluation module 121.
  • the risk evaluation module 121 assigns a weightage to the identified ground assets based on the attributes of the ground assets in the flight region of the unmanned aerial vehicle.
  • the risk evaluation module 121 analyses the attributes overlapping and affecting the flight location of the UAV, wherein the attributes causing higher affect on the flight of the UAV and/or proximal to the flight region of the UAV are assigned a higher weightage.
  • the ground module 109 determines height of the terrain, wherein a higher terrain is assigned a higher weightage due to higher chances of losing contact with the UAV during flying.
  • the ground module 109 identifies nearest Air Traffic Control (ATC), nearest police station, nearest fire station to the location and transmits to the user interface 102 for assisting the user.
  • ATC Air Traffic Control
  • the equipment module 111 is provided for assessing features and capabilities of the unmanned aerial vehicle selected by the user for flying.
  • the equipment module 111 including a database of existing unmanned aerial vehicles with their respective features and capabilities.
  • the features including but not limited to battery capacity, weight and the capabilities including but not limited to interchangeable payload, return to home based on GPS, maximum speed, service ceiling, types of payload, resistance to the wind, operating temperature bandwidth.
  • the equipment module 111 transmits a notification to the user interface 102 for requesting the user to provide features and capabilities of the unmanned aerial vehicle.
  • the equipment module 111 transmits the assessed features and capabilities of the unmanned aerial vehicle to the risk evaluation module 121 for determining a third risk.
  • the risk evaluation module 121 compares the features and capabilities of the provided UAV with an allowable range of features and capabilities.
  • the allowable range of features and capabilities are set by the artificial intelligence module 120 based on performance of the UAV.
  • the risk evaluation module 121 analyses compatibility of the features and capabilities with the weather conditions at the date, time and location of flying the UAV. The compatibility is analysed by comparing the features of the UAV and features required for the weather condition, provided in the database.
  • the risk evaluation module 121 assigns a weightage to the features and capabilities of the UAV based on the comparison with the allowable range of features and capabilities and compatibility of the UAV with the weather conditions at the location of flying the UAV, for determining the third risk. A higher weightage is assigned to the features and capabilities having higher deviation from the allowable range of features and less similarities in the comparison.
  • risk evaluation module 121 assigns a weightage based the capability of interchangeable/ swappable payloads and affect in performance of the UAV due to interchangeable payloads. For example, in UAVs carrying fertilizers, payload changes during the flight and may be swapped during duration of the flight. The interchange-ability increases or decreases risk of the flight of the UAV. The capability of interchange-ability changes performance of the UAV including but not limited to battery drainage, power usage and flying characteristics. The weightage for interchange-ability is assigned based on the performance of the UAV
  • the purpose module 113 is provided for analysing the purpose provided by the user.
  • the purpose module 113 analyses feasibility of the purpose based on safety of the purpose for flying the unmanned aerial vehicle and comparing assessed features and capabilities of the UAV with features and capabilities required for the purpose.
  • the safety is based on ease of flying the UAV, without leading to considerable damage or loss of control.
  • the artificial intelligence module 120 sets a safety score to a plurality of purposes based on ease of flying the UAV for the purpose.
  • the features and capabilities of the UAV required for various purposes are set by the artificial intelligence module 120.
  • the purpose including but not limited to emergency services, rescue, security, sporting events, entertainment, photography, logistics, survey, research, agriculture, hardware testing, training, and recreational activities.
  • the unmanned aerial vehicle used for a sporting event includes greater risk compared to the unmanned aerial vehicle used for agricultural purpose.
  • flying an UAV designed for agriculturalpurpose for the purpose of sports or entertainment is risky as an agri cultural drone is suitable for carrying high payloads for spraying fertilizers.
  • UAVs outside the purpose is a potential case of fraud in insurance or other illegal activities.
  • the purpose module 113 transmits the analyzed feasibility of the purpose to the risk evaluation module 121 for determining a fourth risk.
  • the risk evaluation module 121 assigns a weightage to the purpose of flying of the UAV based on the safety of flying the UAV for the purpose and the comparison of the features and capabilities of the UAV required for the purpose.
  • the risk evaluation module assigns weightage, wherein lower safety and large differences in the features and capability of the UAV to the purpose contributes to higher weightage and higher risk.
  • the pilot module 115 provided for estimating abilities of a pilot based on details of the pilot flying the unmanned aerial vehicle. The details including but not limited to name, age, experience, flying history and crash history.
  • the user transmits details of the pilot flying the unmanned aerial vehicle to the pilot module 115 using the user interface 102.
  • the pilot module 115 estimates the abilities wherein the pilot with having higher age, experience and less number of crashes in crash history has higher ability.
  • the estimated ability is transmitted to the risk evaluation module 121 by the pilot module 115 for determining a fifth risk.
  • the risk evaluation module 121 assigns weightage to the abilities of the pilot for flying the unmanned aerial vehicle safely, wherein the pilot having higher ability is assigned a lower weightage thereby contributing to lower risk of flying the UAV.
  • the environment module 117 is provided for assessing variables at the location restricting the flying of the UAV.
  • the variables including but not limited to GPS strength and routes of aerial vehicles at the date and time of flying.
  • GPS strength at the location of flying the UAV is determined by the environment module. Lower GPS strength contributes to higher risk, as there are higher chances of losing the UAV during flying and difficulty in locating the UAV.
  • the GPS strength is identified at the location, and based on the obtained weather conditions at the date and time of flying the UAV.
  • the environment module identifies routes and time of aerial vehicles including manned aircrafts, aeroplanes at the location of flying the UAV.
  • the environment module 117 identifies time and routes of take-off and landing of the aerial vehicles at plurality of locations and determines proximity of the location of flying the UAV to the take-off and landing routes of the aerial vehicles at the date, time provided by the user.
  • the environment module 117 transmits the identified GPS strength and proximity of the location of flying the UAV to the routes of the aerial vehicles, to the risk evaluation module 121 for determining a sixth risk.
  • the risk evaluation module 121 assigns weightage to the variables including the GPS strength and the proximity to the routes of the aerial vehicles.
  • the location including lower GPS strength and proximal to the route of the aerial vehicles is assigned a higher weightage, thereby contributing to a higher risk of flying the UAV.
  • a crash module 119 is provided for analysing crashes of unmanned aerial vehicles at the location provided by the user.
  • the crash module 119 includes a database for storing the crashes, parameters of the crashes, reasons of the crashes and damage caused to the unmanned aerial vehicle due to the crashes.
  • the parameters including but not limited to weather conditions, ground assets, capabilities and features of the unmanned aerial vehicle, purpose, abilities of the pilot, variables at the location.
  • the crash module 119 identifies the parameters of crashes at the location, damages of the unmanned aerial vehicles due to the crash and compares the parameters of the UAV of the user for flying at the location for identifying similarities.
  • the identified similarities and the damage to the crashed UAV due to the parameters are transmitted to the risk evaluation module 121.
  • the risk evaluation module 121 assigns a weightage to identified similarities of the parameters of the UAV of the user and the crashed UAV, and damage on the crashed UAV due to the parameters. Based on the weightage, the risk evaluation module 121 determines the seventh risk.
  • the crash module 119 transmits the analysed crashes including parameter of the crashes, damage caused to the crashed UAVs and time, date and location of the crashes to the artificial intelligence module 120.
  • the risk evaluation module 121 determines the first risk, the second risk, the third risk, the fourth risk, the fifth risk, the sixth risk and the seventh risk based on the assigned weightages on the parameters including weather condition, ground assets and attributes of ground assets, features and capabilities, feasibility of purpose, ability of the pilot, variables at the location and crashes received from the weather module 107, ground module 109, equipment module 111, purpose module 113, pilot module 115, environment module 117 and the crash module 119.
  • the risk evaluation module 121 transmits the determined first risk, the second risk, the third risk, the fourth risk, the fifth risk, the sixth risk and the seventh risk to the prediction module 123.
  • the weightage assigned by the risk evaluation module 121 to the parameters is proportional to the risk contributed by the parameters to a flight of the UAV. Parameters corresponding to higher risk of the flight of the UAV are assigned higher weightage, by the risk evaluation module 121.
  • the artificial intelligence module 120 updates the parameters of the computing module 106 based on the parameters of the analysed crashes for increasing accuracy of predicting risk of flight of an UAV.
  • the artificial intelligence module 120 updates the weightage to be assigned by the risk evaluation module, based on the analysed crashes.
  • the prediction module 123 is provided for predicting total risk of the flight of the unmanned aerial vehicle by combining the first risk, the second risk, the third risk, the fourth risk, the fifth risk, the sixth risk and the seventh risk determined by the risk evaluation module 121.
  • the prediction module 123 is connected to the dynamic pricing unit 125 and the user recommendation unit 127.
  • the dynamic pricing unit 125 estimates a price for the flight of the unmanned aerial vehicle based on the predicted risk.
  • the dynamic pricing unit 125 estimates a higher insurance price for the UAV associated with higher predicted risk and a lower insurance price for the UAV associated with lower predicted risk.
  • the user recommendation unit 127 is provided for recommending flying tips to the user based on the predicted risk and computed parameters. The flying tips are recommended for reducing the risk by avoiding the parameters with higher predicted risk.
  • a user database is provided in the risk prediction system 105 for storing the details of plurality of users, tracking details of the UAVs flown by the users, flight history of the users including crash incidents, rules followed and broken by the user while flying the UAV, negligence of the users while flying the UAV, predicted risk of the UAV of the users, parameters of the UAVs.
  • the artificial intelligence module 120 provides rating to the plurality of users based on the flight history of the users and the negligence of the users.
  • Fig.2 illustrates a flow chart of a method of for predicting risk for a flight of an unmanned aerial vehicle.
  • the method includes a user providing 201 details for flying an unmanned aerial vehicle through the user interface provided in the user device.
  • the details including but not limited to date, time, location and purpose for flying the Unmanned Aerial Vehicle.
  • Computing parameters includes, obtaining 203 weather conditions at the location and vicinity of the location based on date and time of flying the Unmanned Aerial Vehicle, by the weather module.
  • weather conditions including but not limited to temperature, rainfall, visibility, wind speed, gust, k-index, cloud cover, pressure, humidity.
  • Obtaining the weather conditions including retrieving the weather conditions from a weather database at the time, date and location provided by the user.
  • the weather module transmits the obtained weather condition at the date and time of the location to a risk evaluation module.
  • the risk evaluation module determines a first risk by assigned weightage to the weather conditions based on comparison of the analyzed weather conditions with an allowable range of weather conditions for ensuring safe flying of the UAV.
  • the risk evaluation module compares, wherein weather conditions exceeding or below the allowable range of weather conditions provide a higher risk for flying the unmanned aerial vehicle.
  • ground assets including but not limited to geographical terrains, tall structures, no-fly zones, multi-storied buildings, military structures, sensitive government buildings, airports, international boundaries, coastline, dams, power lines, waterways, railways, government offices, mobile towers, industrial facilities and forest areas.
  • the attributes of the ground assets including but not limited to height, width, area and allowable flying region are identified. Identifying the ground assets and the attributes of the ground assets based on a database and a map of the ground assets and attributes at a plurality of locations.
  • the ground module transmits the identified ground assets and the attributes of the ground assets in the location to the risk evaluation module.
  • the risk evaluation module determines a second risk by assigning weightage to the ground assets based on affect of the attributes of the ground assets on the flight of the UAV and/or proximity of the ground assets to the location of the flight of the unmanned aerial vehicle.
  • the features including but not limited to battery, weight, safety provided in the Unmanned Aerial Vehicle.
  • the capabilities including but not limited to, maximum speed, service ceiling, types of payload, resistance to the wind, operating temperature bandwidth. Assessing the features and capabilities of the equipment module based on a model of the Unmanned Aerial Vehicle selected by the user and affect of the features and capabilities on performance of flight of the UAV. Next, comparing the features and capabilities to an allowable range of features and capabilities, by the equipment module. The assessed features and the capabilities are transmitted to the risk evaluation module.
  • the risk evaluation module analyses the features and the capabilities and for determining a third risk.
  • the risk evaluation module assigns weightage to the UAV based on performance achieved by the features and the capabilities of the UAV and compatibility of the features and capabilities for safe flying of the UAV in the weather conditions on the provided date, time and location.
  • the risk evaluation module identifying the capability of interchangeable payload of the unmanned aerial vehicle, wherein the interchangeable payloads affect battery drainage, power usages and flying characteristics of a drone, for determining the third risk.
  • the purpose module analyses feasibility of the purpose based on safe flying of the UAV and ease of flying the UAV for the purpose.
  • the purpose module compares the capabilities and features of the UAV required for the purpose and the capabilities and the features present in the selected UAV.
  • the feasibility and the comparison is transmitted by the purpose module to the risk evaluation module.
  • the risk evaluation module assigns a weightage to the feasibility and similarities of the capabilities and features of the selected UAV and the capabilities and features required for the purpose, for determining a fourth risk.
  • the purpose module recommends a suitable UAV to the user, from an existing database based on the purpose.
  • the pilot module estimates the ability by comparing the age, years of experience and history of crash incidents with a threshold age, years of experience and number of crash incidents.
  • the pilot module estimates a higher ability for higher age, higher number of years of experience and lower number of crash incidents and lower ability based on higher number of crash incidents and less years of experience.
  • the pilot module transmits the estimated ability to the risk evaluation module.
  • the risk evaluation module assigns a weightage to the abilities of the pilot based on the difference in the abilities of the pilot and threshold abilities required for a pilot. A fifth risk is determined based on the assigned weightage, by the risk evaluation module.
  • assessing 208 variables at the location restricting the flying of the unmanned aerial vehicle for determining a sixth risk by an environment module.
  • the variables including but not limited to GPS strength and routes of aerial vehicles.
  • the assessed variables are transmitted to the risk evaluation module.
  • the risk evaluation module determines the sixth risk by assigning weightage to the variables based on the identified GPS strength and proximity to the routes of the aerial vehicles at the location.
  • the risk evaluation module determines 210 the first risk, second risk, third risk, fourth risk, fifth risk, sixth risk and seventh risk and transmits to a risk prediction module. [000101] Next, predicting 211 a total risk by combining the first risk, second risk, third risk, fourth risk, fifth risk, sixth risk and seventh risk, by a prediction module.
  • FIG. 3a illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle.
  • 301a displays a screen in the user interface for a user to provide location of flying the unmanned aerial vehicle.
  • 301b displays a screen in the user interface for a user to provide date and time of flying the unmanned aerial vehicle.
  • FIG. 3b illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle.
  • 300b displays a screen in the user interface for a user to select a purpose of flying the unmanned aerial vehicle from a list including plurality of purposes.
  • FIG. 4 illustrates screenshots of a user interface of the system for predicting risk of flight of an unmanned aerial vehicle. 400 displays a screen in the user interface displaying the total risk (100/100) predicted by the prediction module 123, based on the date, time, location and purpose of flying the unmanned aerial vehicle provided by the user.
  • a main advantage of the present disclosure is that the system and method predicts a risk for a flight of an unmanned aerial vehicle.
  • system and method provides individual risk scores based on specific criteria of the module such as ground risk, weather risk.
  • Still another advantage of the present disclosure is that the system and method reduces crashes of unmanned aerial vehicles by predicting risk of a flight of an unmanned aerial vehicle.
  • Yet another advantage of the present disclosure is that the system and method improves safety of flying unmanned aerial vehicles.
  • Still another advantage of the present disclosure is that the system and method provides accurate and efficient prediction of risk of a flight of an unmanned aerial vehicle.
  • Yet another advantage of the present disclosure is that the system and method for predicting risk of flight of an UAV aids insurers for determining premium pricing of the UAV.

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Abstract

La présente invention concerne un système et un procédé de prédiction de risque pour un vol d'un véhicule aérien sans pilote (UAV). Le système comprend un dispositif utilisateur (101) comprenant une interface utilisateur (102), connectée à un système de prédiction de risque (105). Le système de prédiction de risque (105) comprend un module de calcul (106), un module d'intelligence artificielle (120), un module d'évaluation de risque (121), un module de prédiction (123), une unité de tarification dynamique (125) et une unité de recommandation d'utilisateur (127). Le procédé comprend les étapes consistant à fournir (201) des détails de vol du véhicule aérien sans pilote, par un utilisateur à l'aide de l'interface utilisateur, calculer (202) des paramètres affectant le vol du véhicule aérien sans pilote sur la base des détails, par le module de calcul (106), déterminer des risques sur la base des paramètres calculés, par le module d'évaluation de risque (121) et prédire un risque total de vol de l'UAV sur la base des risques déterminés, par le module de prédiction (123).
PCT/IB2021/059295 2021-09-15 2021-10-11 Système et procédé de prédiction de risque pour un vol d'un véhicule aérien sans pilote WO2023041973A1 (fr)

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CN117173935A (zh) * 2023-07-18 2023-12-05 北京锐士装备科技有限公司 一种针对无人机提供鉴权服务的鉴权方法及系统

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117173935A (zh) * 2023-07-18 2023-12-05 北京锐士装备科技有限公司 一种针对无人机提供鉴权服务的鉴权方法及系统
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