Flight Delay Risk Early Warning and Response System Based on Big Data Analysis

Abstract

The present invention belongs to the technical field of aviation management. The present invention discloses a flight delay risk early warning and response system based on big data analysis, including: a data collection module for collecting aviation operation data; a data analysis module for obtaining route traffic; a delay prediction module for predicting the probability of flight delay; a delay early warning module for judging whether to generate an early warning instruction according to the probability of flight delay. If an early warning instruction is generated, the flight delay duration is predicted; a compensation payment module for calculating the compensation amount based on the flight delay duration for compensation and judging whether to provide meals; a travel and accommodation arrangement module for judging whether to generate arrangement information based on the flight delay duration. The present invention makes full use of big data and artificial intelligence technologies to realize the intelligent prediction of flight delay risks and refined services, which not only improves the operation efficiency and service efficiency of airlines, but also greatly enhances the travel experience and satisfaction of passengers.

Description

Technical Field

[0001] The present invention relates to the field of aviation management technology, and more specifically, to a flight delay risk warning and response system based on big data analysis. Background Art

[0002] With the rapid development of the global aviation industry and the continuous growth of passenger travel demand, flight delays have become a major challenge affecting airlines' operational efficiency and passengers' travel experience. According to statistics from the International Air Transport Association (IATA), flight delays not only cause additional operating costs for airlines, but also significantly reduce passenger satisfaction and trust, and affect the reputation of airline brands. Traditional manual management and single emergency response methods can no longer meet the needs of modern aviation operations. Especially in the case of large-scale flight delays, how to quickly and accurately provide passengers with compensation, meals, accommodation and transportation arrangements has become a key factor in improving service quality and operational management efficiency.

[0003] With the maturity of technologies such as big data, artificial intelligence, the Internet of Things and cloud computing, the aviation industry is exploring how to deal with flight delays through intelligent and digital means; the patent with publication number CN107657394A discloses a flight delay processing system and method, including: a server-side device and a mobile-side device, wherein: the server-side device is used to publish flight delay information to the mobile-side device; the mobile-side device is used to receive the flight delay information and generate meal arrangement information, car arrangement information and hotel arrangement information according to the flight delay information; this invention solves the existing technical problems of easy errors and low efficiency caused by manual recording, and achieves the technical effect of simply and efficiently handling flight delays;

[0004] However, although the above technology involves relevant service arrangements during flight delays, it only briefly mentions concepts such as generating meal arrangement information, car arrangement information and hotel arrangement information, without elaborating on the specific generation steps and detailed content of this information; this general description makes it difficult for passengers to obtain timely and comprehensive service support during flight delays in actual applications, and their personalized needs cannot be accurately met, which directly affects the passengers' travel experience and satisfaction; at the same time, the lack of a clear service generation process and information management system will also lead to inaccurate or untimely service execution, reducing customer satisfaction during flight delays, and further affecting the evaluation of the airline's brand image and its service quality.

[0005] In view of this, the present invention proposes a flight delay risk warning and response system based on big data analysis to solve the above problems. Summary of the invention

[0006] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A flight delay risk warning and response system based on big data analysis, comprising:

[0007] A data collection module for collecting aviation operation data;

[0008] A data analysis module for analyzing the aviation operation data to obtain route traffic;

[0009] A delay prediction module for analyzing the aviation operation data and the route traffic to predict the flight delay probability;

[0010] A delay warning module for judging whether to generate a warning instruction according to the flight delay probability. If a warning instruction is generated, the flight delay duration is predicted;

[0011] A compensation payment module for calculating and paying compensation amount based on the flight delay duration and judging whether to provide meals;

[0012] A travel accommodation arrangement module for judging whether to generate arrangement information based on the flight delay duration.

[0013] Further, the aviation operation data includes flight data and weather data;

[0014] The method for collecting flight data includes:

[0015] Collecting target flight data, where the target flight data is the flight information of the target flight; the target flight is the flight for which flight delay prediction is performed, and the flight information includes the departure time, arrival time, flight route, and flight distance. The departure time is the time when the flight is scheduled to take off from the departure airport, the arrival time is the time when the flight is scheduled to land at the arrival airport, the flight route is the flight path between the departure airport and the arrival airport of the flight, and the flight distance is the flight distance corresponding to the flight route; obtaining the current time and marking the departure time in the target flight data as the target departure time; obtaining relevant flights according to the current time and the target departure time, where the relevant flights are all flights whose departure times are between the current time and the target departure time; obtaining relevant flight data according to the relevant flights, where the relevant flight data is the flight information of the relevant flights;

[0016] Mark the flight route in the target flight data as the target route, and mark the flight route in the relevant flight data as the relevant route; obtain a world map, draw the target route and the relevant route on the world map, and determine whether there is a route in the relevant route that intersects with the target route; mark the route in the relevant route that intersects with the target route as the associated route, and do not mark the route in the relevant route that does not intersect with the target route; mark the flight corresponding to the associated route as the associated flight, mark the flight information of the associated flight as the associated flight data, and use the associated flight data and the target flight data as the flight data.

[0017] The weather data is the weather information corresponding to the departure airport at the target departure time, and the weather information includes wind speed, precipitation, visibility, and temperature.

[0018] Further, the method for obtaining the route flow includes:

[0019] Mark the departure time in the associated flight data as the associated departure time, and mark the arrival time as the associated arrival time; convert the current time, the target departure time, the associated departure time, and the associated arrival time into a unified unit; subtract the associated departure time of each associated flight from the associated arrival time of each associated flight to obtain the estimated flight time of each associated flight; subtract the associated departure time of each associated flight from the target departure time to obtain the flight time of each associated flight; divide the flight time of each associated flight by the corresponding estimated flight time, and then multiply by the corresponding voyage distance to obtain the flight distance of each associated flight.

[0020] Obtain the departure airport and the arrival airport of each associated flight, and according to the world map, obtain the longitude and latitude corresponding to the departure airport and the arrival airport of each associated flight, where the longitude and latitude include longitude and latitude; mark the longitude and latitude corresponding to the departure airport as the starting longitude and latitude, and mark the longitude and latitude corresponding to the arrival airport as the ending longitude and latitude; subtract the starting longitude corresponding to each associated flight from the ending longitude of each associated flight to obtain the longitude difference; subtract the starting latitude corresponding to each associated flight from the ending latitude of each associated flight to obtain the latitude difference; calculate the current longitude and latitude corresponding to each associated flight according to the longitude difference, latitude difference, flight distance, and voyage distance corresponding to each associated flight; the expression for the current longitude is: ; in the formula, is the current longitude, is the starting longitude, is the flight distance, is the voyage distance, is the longitude difference; the expression for the current latitude is: ; in the formula, is the current latitude, is the starting latitude, is the latitude difference;

[0021] Divide the airspace of the world map according to a preset division plan to obtain airspaces, where is an integer greater than 1; Mark the position of each associated flight on the world map according to the current latitude and longitude of each associated flight; Mark the airspace passed by the target route on the world map as the target airspace, count the number of associated flights in each target airspace, and mark it as the airspace traffic; Use the airspace traffic corresponding to all target airspaces as the route traffic.

[0022] Furthermore, the method for predicting the flight delay probability includes:

[0023] Use the weather data and route traffic as analysis data, input the analysis data into the trained delay prediction model, and predict the corresponding flight delay probability;

[0024] The method for judging whether to generate a warning instruction includes:

[0025] Set a warning threshold, and compare the flight delay probability with the warning threshold; If the flight delay probability is greater than or equal to the warning threshold, generate a warning instruction; If the flight delay probability is less than the warning threshold, do not generate a warning instruction;

[0026] The steps for predicting the flight delay duration include:

[0027] Step 1: Obtain historical traffic, where the historical traffic is the route traffic obtained at historical times; Use the airspace traffic corresponding to the same target airspace in the historical traffic as a set of traffic collections, and each traffic collection corresponds to a target airspace one by one;

[0028] Step 2: Input each traffic collection into the trained traffic prediction model respectively to predict the airspace traffic at the future T moment, where T is an integer greater than 1;

[0029] Step 3: Use all the predicted airspace traffic as the predicted traffic, obtain the weather data at the future moment, and predict the corresponding flight delay probability according to the weather data and predicted traffic at the future moment, and judge whether to generate a warning instruction;

[0030] Step 4: If a warning instruction is still generated, replace the airspace traffic with the earliest acquisition time in each traffic collection with the predicted airspace traffic, and return to Step 2; If no warning instruction is generated, go to Step 5;

[0031] Step 5: Obtain the number of warning instructions generated in Step 3 and mark it as the delay number, multiply the delay number by T to obtain the flight delay duration, and the unit of the flight delay duration is the same as the unit of the target departure time.

[0032] Further, in the step 2, the traffic prediction model is an RNN neural network model, and the training process of the traffic prediction model includes:

[0033] Continuously collect the airspace traffic in the same airspace in advance and construct a traffic training set, , where

[0034] is the number of airspace traffic in a group of traffic sets; based on the traffic training set, train a traffic prediction model for predicting the airspace traffic at a future time;

[0035] Further, the method for calculating and paying the compensation amount includes:

[0036] Preset a compensation standard, which includes the compensation amounts corresponding to different flight delay durations; obtain the corresponding compensation amount according to the compensation standard and the flight delay duration; obtain the passenger payment information corresponding to the target flight, where the passenger payment information includes the payment type and the payment account, and according to the passenger payment information corresponding to the target flight, pay the compensation amount to all passengers corresponding to the target flight;

[0037] The method for determining whether to distribute meals includes:

[0038] Preset a distribution threshold and compare the flight delay duration with the distribution threshold; if the flight delay duration is greater than or equal to the distribution threshold, distribute meals; if the flight delay duration is less than the distribution threshold, add the current time to the flight delay duration to obtain the estimated takeoff time; preset meal times, including the breakfast period , the lunch period and the dinner period , where ; compare the estimated takeoff time with the meal times; if , distribute meals, where is the estimated takeoff time, means or; if , do not distribute meals;

[0039] If meal distribution is to be carried out, obtain the mobile phone numbers of each target passenger, mark them as push numbers, and the target passengers are the passengers corresponding to the target flight; according to the push numbers of each target passenger, push electronic meal vouchers to each target passenger.

[0040] Further, the method for determining whether to generate arrangement information includes:

[0041] Preset an arrangement threshold, and compare the estimated departure time with the arrangement threshold; if the estimated departure time is greater than or equal to the arrangement threshold, generate arrangement information, and if the estimated departure time is less than the arrangement threshold, do not generate arrangement information;

[0042] The arrangement information includes transportation arrangement information and accommodation arrangement information;

[0043] The method for generating transportation arrangement information includes:

[0044] Obtain the passenger information data of each target passenger, where the passenger information data includes age, cabin type, check-in counter, and check-in time, and the unit of the check-in time is the same as the unit of the current time; according to the check-in counter and check-in time of each target passenger, divide all target passengers into travel groups, where if a passenger travels alone, it is also regarded as a travel group; subtract the check-in time of each target passenger from the current time to obtain the waiting time of each target passenger; calculate the priority corresponding to each travel group according to the age, cabin type, and waiting time of each target passenger; divide all travel groups into group sets according to the priority of each travel group; sequentially incrementally set digital labels for each group set and mark them as group labels; obtain the single round-trip duration, where the single round-trip duration is the duration experienced by the shuttle vehicle to pick up and drop off passengers to the accommodation and then return to the terminal; multiply each single round-trip duration by the group label corresponding to each group set, and then add the current time to obtain the boarding time corresponding to each group set; convert the boarding time corresponding to each group set into the standard time format and use it as the transportation arrangement information, and push the transportation arrangement information corresponding to each group set to the corresponding target passenger.

[0045] Further, the method for dividing all target passengers into travel groups includes:

[0046] Take the target passengers corresponding to each check-in counter as a set of check-in passengers, and the check-in passenger sets correspond one-to-one with the check-in counters; sort each target passenger in each check-in passenger set in descending order according to the corresponding check-in time to generate a check-in sorting list corresponding to each check-in passenger set; calculate the difference between every two adjacent check-in times in each check-in sorting list in turn as the check-in time difference; preset a time difference threshold, and compare each check-in time difference with the time difference threshold respectively; if the check-in time difference is less than or equal to the time difference threshold, then take the two target passengers corresponding to the corresponding check-in time difference as a travel group; if the check-in time difference is greater than the time difference threshold, then take the two target passengers corresponding to the corresponding check-in time difference as a travel group respectively; analyze all travel groups, if there are the same target passengers in two travel groups, then merge the travel groups with the same target passengers until there are no same target passengers in all travel groups;

[0047] The method for calculating the priority corresponding to each travel group includes:

[0048] Set different numerical numbers for different cabin types and mark them as cabin numbers; preset an age threshold and , , ; compare the age corresponding to each target passenger with the age threshold and respectively; if , then assign an age coefficient of 0 to the corresponding target passenger, is the age of the target passenger; if , then assign an age coefficient of to the corresponding target passenger; if , then assign an age coefficient of to the corresponding target passenger;

[0049] The expression for the priority corresponding to the target passenger is: ; in the formula, is the priority, is the age coefficient, is the cabin number, is the waiting time, 、 、 are all preset weight coefficients; count the number of target passengers in each travel group and mark it as the number of passengers; add up the priorities of all target passengers in each travel group in turn and then divide by the corresponding number of passengers to obtain the priority corresponding to each travel group.

[0050] Furthermore, the method for dividing all travel groups into group sets includes:

[0051] Obtain the number of feeder vehicles and the passenger capacity of each vehicle, where the passenger capacity of a vehicle is the maximum number of passengers that a single feeder vehicle can transport; sort each travel group in descending order according to the corresponding priority to generate a group sorting table; replace each travel group in the group sorting table with the corresponding number of passengers; add up all the numbers of passengers in front of each number of passengers in the group sorting table to obtain the total number of passengers corresponding to each number of passengers, and replace each number of passengers in the group sorting table with the corresponding total number of passengers; multiply the number of feeder vehicles by the passenger capacity of each vehicle to obtain the maximum passenger capacity per trip; mark the largest total number of passengers in the group sorting table as the maximum number of passengers, divide the maximum number of passengers by the maximum passenger capacity per trip, and round up to obtain the number of trips; multiply the maximum passenger capacity per trip by an increasing connection coefficient to obtain the overall maximum passenger capacities; where the connection coefficient is an integer, and the range of the connection coefficient is , and n is the number of trips; add the overall maximum passenger capacities to the group sorting table, and take all the travel groups corresponding to the total number of passengers between every two overall maximum passenger capacities as a group set to obtain n group sets; the range of the group label is .

[0052] Furthermore, the method for generating accommodation arrangement information includes:

[0053] Count the number of target passengers in each travel group whose age is less than the age threshold , and mark it as the excluded number; subtract the corresponding excluded number from the number of passengers in each travel group to obtain the number of accommodations corresponding to each travel group; divide the number of accommodations in each travel group by 2 to obtain the number of rooms corresponding to each travel group; replace each travel group in the group sorting table with the corresponding number of rooms, arrange the corresponding rooms for each travel group in ascending order, and obtain the room number corresponding to each travel group; use the room number corresponding to each travel group as the accommodation arrangement information, and push the accommodation arrangement information corresponding to each travel group to the corresponding target passengers.

[0054] The technical effects and advantages of the flight delay risk warning and response system based on big data analysis of the present invention:

[0055] By collecting and analyzing aviation operation data, the route flow can be accurately obtained, and deep learning technology can be used to predict the flight delay probability in real time, so as to give early warnings of flight delays in a timely manner. According to the generated warning instructions, accurate compensation, meal distribution, transportation arrangements and accommodation arrangements are provided to effectively deal with flight delays and solve the problem that the needs of passengers cannot be accurately met under traditional manual management and single emergency handling methods. By making full use of big data and artificial intelligence technologies, the intelligent prediction of flight delay risks and refined services are realized, which not only improves the operation efficiency and service efficiency of airlines, but also greatly enhances the travel experience and satisfaction of passengers, providing strong support for the intelligent management of the aviation industry. Brief Description of the Drawings

[0056] Figure 1 Schematic diagram of the flight delay risk warning and response system based on big data analysis according to Embodiment 1 of the present invention;

[0057] Figure 2 Flowchart of the flight delay duration prediction method according to Embodiment 1 of the present invention. Detailed Description of the Invention

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1

[0060] Please refer to Figure 1 As shown, the flight delay risk warning and response system based on big data analysis in this embodiment includes a data collection module, a data analysis module, a delay prediction module, a delay warning module, a compensation distribution module, and a travel and accommodation arrangement module; each module is connected by wired and / or wireless means to realize data transmission between modules;

[0061] The data collection module is used to collect aviation operation data.

[0062] The aviation operation data includes flight data and weather data;

[0063] The methods for collecting flight data include:

[0064] Collect target flight data, where the target flight data is the flight information of the target flight; the target flight is the flight for which flight delay prediction is to be performed, and the flight information includes the departure time, arrival time, flight route, and flight distance. The departure time is the time when the flight is scheduled to take off from the departure airport, the arrival time is the time when the flight is scheduled to land at the arrival airport, the flight route is the flight path between the departure airport and the arrival airport of the flight, and the flight distance is the flight distance corresponding to the flight route; the target flight data is obtained through the official website of the airline corresponding to the target flight; obtain the current time, and mark the departure time in the target flight data as the target departure time; according to the current time and the target departure time, obtain relevant flights, where the relevant flights are all flights whose departure time is between the current time and the target departure time; the relevant flights are obtained by traversing the official websites of all airlines; according to the relevant flights, obtain relevant flight data, where the relevant flight data is the flight information of the relevant flights; the relevant flight data is obtained through the official website of the airline corresponding to the relevant flights;

[0065] Mark the flight route in the target flight data as the target route, and mark the flight route in the relevant flight data as the relevant route; obtain a world map through the Map API, where the world map is a plan view showing the countries, regions, and geographical features on the earth's surface; draw the target route and the relevant routes on the world map, and determine whether there is a route in the relevant routes that intersects with the target route; mark the route in the relevant routes that intersects with the target route as the associated route, and the routes in the relevant routes that do not intersect with the target route are not marked; mark the flights corresponding to the associated routes as associated flights, mark the flight information of the associated flights as associated flight data, and use the associated flight data and the target flight data as flight data;

[0066] The weather data is the weather information corresponding to the departure airport at the target departure time, and the weather information includes wind speed, precipitation, visibility, and temperature; the weather data is obtained through the official website of the meteorological bureau;

[0067] A data analysis module for analyzing aviation operation data to obtain route traffic;

[0068] The method for obtaining route traffic includes:

[0069] Mark the departure time in the associated flight data as the associated departure time, and mark the arrival time as the associated arrival time; convert the current time, the target departure time, the associated departure time, and the associated arrival time into a unified unit. In this embodiment, the preferred unit is minutes. Exemplarily, convert to Minutes; subtract the associated departure time of each associated flight from the associated arrival time to obtain the estimated flight time of each associated flight; subtract the associated departure time of each associated flight from the target departure time to obtain the flight time of each associated flight; divide the flight time of each associated flight by the corresponding estimated flight time, and then multiply by the corresponding voyage distance to obtain the flight distance of each associated flight;

[0070] Obtain the departure airport and arrival airport of each associated flight, and according to the world map, obtain the longitude and latitude corresponding to the departure airport and arrival airport of each associated flight, where the longitude and latitude include longitude and latitude; the departure airport and arrival airport of each associated flight are obtained through the official website of the airline corresponding to the associated flight; mark the longitude and latitude corresponding to the departure airport as the starting longitude and latitude, and mark the longitude and latitude corresponding to the arrival airport as the ending longitude and latitude; subtract the starting longitude corresponding to each associated flight from the ending longitude to obtain the longitude difference; subtract the starting latitude corresponding to each associated flight from the ending latitude to obtain the latitude difference; calculate the current longitude and latitude corresponding to each associated flight according to the longitude difference, latitude difference, flight distance and voyage distance corresponding to each associated flight; the expression of the current longitude is: ; in the formula, is the current longitude, is the starting longitude, is the flight distance, is the voyage distance, is the longitude difference; the expression of the current latitude is: ; in the formula, is the current latitude, is the starting latitude, is the latitude difference;

[0071] Divide the world map into airspaces according to a preset division scheme to obtain airspaces, is an integer greater than 1; the division scheme is preset by those skilled in the art according to the actual situation; mark the position of each associated flight on the world map according to the current longitude and latitude of each associated flight; mark the airspace passed by the target route on the world map as the target airspace, count the number of associated flights in each target airspace, and mark it as the airspace traffic; regard the airspace traffic corresponding to all target airspaces as the route traffic;

[0072] The delay prediction module is used to analyze the aviation operation data and route traffic to predict the flight delay probability;

[0073] The method for predicting the flight delay probability includes:

[0074] Using weather data and route traffic as analysis data, input the analysis data into the trained delay prediction model to predict the corresponding flight delay probability; the training process of the delay prediction model includes:

[0075] Pre-collect groups of analysis data, and set corresponding flight delay probabilities for each group of analysis data. is an integer greater than 1. Convert the analysis data and the corresponding flight delay probability into a corresponding group of feature vectors; the flight delay probability corresponding to the analysis data is collected by those skilled in the art during the historical flight delay risk warning process. groups of analysis data. Under the condition of the same group of analysis data, judge whether the flight is delayed multiple times, divide the number of flight delays by the number of times of judging whether the flight is delayed to obtain the flight delay probability corresponding to the corresponding analysis data, and so on, analyze groups of analysis data in turn to obtain the corresponding flight delay probabilities, and set corresponding flight delay probabilities for groups of analysis data in turn.

[0076] Take each group of feature vectors as the input of the delay prediction model. The delay prediction model takes a group of predicted flight delay probabilities corresponding to each group of analysis data as the output, and takes the actual flight delay probability corresponding to each group of analysis data as the prediction target. The actual flight delay probability is the pre-set flight delay probability corresponding to the analysis data; take minimizing the sum of prediction errors of all analysis data as the training target; among them, the calculation formula of the prediction error is: ; in the formula, is the prediction error, is the group number of the feature vector corresponding to the analysis data, is the predicted flight delay probability corresponding to the group of analysis data, is the actual flight delay probability corresponding to the group of analysis data; train the delay prediction model until the sum of prediction errors reaches convergence and stop training.

[0077]

[0078] The delay warning module determines whether to generate a warning instruction according to the flight delay probability. If a warning instruction is generated, the flight delay duration is predicted.

[0079] The method for determining whether to generate a warning instruction includes:

[0080] Set a warning threshold, which is pre-set by those skilled in the art according to the actual situation; compare the flight delay probability with the warning threshold; if the flight delay probability is greater than or equal to the warning threshold, generate a warning instruction, indicating that the target flight will be delayed and flight delay services need to be provided; if the flight delay probability is less than the warning threshold, do not generate a warning instruction, indicating that the target flight will take off normally and there will be no delay situation;

[0081] As Figure 2 shown, the steps for predicting the flight delay duration include:

[0082] Step 1: Obtain historical traffic, where the historical traffic is the route traffic obtained at historical times; the airspace traffic corresponding to the same target airspace in the historical traffic is used as a set of traffic collections, and each traffic collection corresponds to one target airspace;

[0083] Step 2: Input each traffic collection into a trained traffic prediction model respectively to predict the airspace traffic at future T moments, where T is an integer greater than 1; in this embodiment, preferably, the future T moments are future T minutes;

[0084] Step 3: Take all the predicted airspace traffic as the predicted traffic, obtain the weather data at future moments, predict the corresponding flight delay probability based on the weather data at future moments and the predicted traffic, and determine whether to generate a warning instruction;

[0085] Step 4: If a warning instruction is still generated, replace the airspace traffic with the earliest acquisition time in each group of traffic collections with the corresponding predicted airspace traffic, and return to Step 2; if no warning instruction is generated, proceed to Step 5;

[0086] Step 5: Obtain the number of warning instructions generated in Step 3 and mark it as the delay number, multiply the delay number by T as the flight delay duration, and the unit of the flight delay duration is the same as the unit of the target take-off time.

[0087] In the above Step 2, the traffic prediction model is an RNN neural network model, and the training process of the traffic prediction model includes:

[0088] Continuously collect airspace traffic in the same airspace in advance and construct a traffic training set, , being the number of airspace traffic in a set of traffic collections; based on the traffic training set, train a traffic prediction model for predicting the airspace traffic at future moments;

[0089] Preset the sliding step length L and the sliding window length H according to the actual experience of those skilled in the art; convert the airspace traffic in the traffic training set into multiple training samples by using the sliding window method, and each training sample includes H airspace traffic; take each training sample as the input of the traffic prediction model, predict the airspace traffic after the sliding step length L as the output, take the actual subsequent L airspace traffic corresponding to each training sample as the prediction target, evaluate the model accuracy by using the mean absolute percentage error MAPE for the prediction result, and when the calculated MAPE is less than the preset MAPE, the traffic prediction model corresponding to the airspace traffic is trained; among them, the calculation formula of the mean absolute percentage error MAPE is: ; in the formula, is the prediction target corresponding to the th predicted airspace traffic, is the th predicted airspace traffic, is the number of predicted airspace traffic; generate a traffic prediction model for predicting the airspace traffic at future moments according to the airspace traffic.

[0090] Exemplarily, the traffic training set A contains 10 airspace traffic, , is the th airspace traffic, , define the length of the sliding window as 3 and the sliding step length L as 1, use the sliding window to construct 7 training samples, each training sample contains 3 consecutive airspace traffic, and take the next airspace traffic of the 3 consecutive airspace traffic as the prediction target; for example: the training sample , the prediction target corresponding to the training sample is ; the training sample , the prediction target corresponding to the training sample is ; and so on, which is used to train the traffic prediction model.

[0091] The compensation payment module calculates the compensation amount for compensation based on the flight delay duration and determines whether to provide meal service.

[0092] The methods for calculating the compensation amount for compensation include:

[0093] Preset the compensation standard, which is pre-set by those skilled in the art according to the actual situation, and the compensation standard includes the compensation amount corresponding to different flight delay durations; exemplarily, if the flight delay duration is less than 60 minutes, no compensation is provided; if the flight delay duration is between 60 minutes and 460 minutes, the compensation amount is 200 yuan; if the flight delay duration exceeds 460 minutes, the compensation amount is 400 yuan; obtain the corresponding compensation amount according to the compensation standard and the flight delay duration;

[0094] Obtain the passenger payment information corresponding to the target flight. The passenger payment information includes the payment type and the payment account. The passenger payment information is obtained through the ticketing system of the airline corresponding to the target flight. The payment type is the payment method selected by the passenger when purchasing the air ticket, such as credit card payment, debit card, WeChat payment, Alipay payment, etc. The payment account is the account information associated with the payment type when the passenger purchases the air ticket, such as the mobile phone number, bank card number, etc. According to the passenger payment information corresponding to the target flight, pay the compensation amount to all passengers corresponding to the target flight.

[0095] The method for determining whether to provide meals includes:

[0096] Preset a release threshold, which is preset by those skilled in the art according to the actual situation. Compare the flight delay duration with the release threshold. If the flight delay duration is greater than or equal to the release threshold, then provide meals. The reason is that the long waiting process will make passengers anxious and uneasy, and feel hungry. Providing meals helps soothe the passengers' emotions and reduce hunger. If the flight delay duration is less than the release threshold, then add the current time to the flight delay duration to obtain the estimated departure time. Preset meal times, which include breakfast time , lunch time and dinner time , where ; the meal times are preset by those skilled in the art according to the actual situation. Compare the estimated departure time with the meal times. If , then provide meals, indicating that passengers will experience meal times during the waiting process, resulting in hunger and discomfort for passengers. Therefore, it is necessary to provide meals to ensure that passengers can obtain necessary dietary supplements during the waiting process, where is the estimated departure time, represents "or"; if , then do not provide meals, indicating that the waiting process experienced by passengers is relatively short and they have not passed through meal times, and providing meal service will cause waste of resources.

[0097] If meals are provided, obtain the mobile phone number of each target passenger and mark it as the push number. The target passengers are the passengers corresponding to the target flight. The mobile phone number of each target passenger is obtained through the ticketing system of the airline corresponding to the target flight. According to the push number of each target passenger, push electronic meal vouchers to each target passenger. The method of pushing electronic meal vouchers is, for example, pushing through WeChat mini-programs, text messages, etc. The electronic meal voucher is a digital meal voucher, and passengers can use the electronic meal voucher to choose their own meals in the terminal restaurants, thus solving the problems of long waiting time, single taste, limited distribution time, etc. in the traditional flight delay meal service, and further meeting the diverse dining needs of passengers.

[0098] The accommodation arrangement module determines whether to generate arrangement information based on the flight delay duration.

[0099] The method for determining whether to generate arrangement information includes:

[0100] Preset an arrangement threshold, which is pre-set by those skilled in the art according to the actual situation; compare the estimated departure time with the arrangement threshold; if the estimated departure time is greater than or equal to the arrangement threshold, generate arrangement information, indicating that the estimated departure time of the flight is relatively late and the passengers cannot reach the destination on the same day, so the airline needs to arrange accommodation and transportation for the target passengers; if the estimated departure time is less than the arrangement threshold, no arrangement information is generated.

[0101] The arrangement information includes transportation arrangement information and accommodation arrangement information;

[0102] The method for generating transportation arrangement information includes:

[0103] Obtain the passenger information data of each target passenger. The passenger information data includes age, cabin type, check-in counter, and check-in time; the cabin type is the class of the ticket purchased by the passenger, the check-in counter is the service window where the passenger goes through the boarding procedures, and the check-in time is the time when the passenger goes through the boarding procedures before the flight takes off. The unit of the check-in time is the same as the unit of the current time; the cabin type includes first class, business class, and economy class; the passenger information data of each target passenger is obtained through the check-in system of the airline corresponding to the target flight; divide all target passengers into travel groups according to the check-in counter and check-in time of each target passenger. If a passenger travels alone, they are also regarded as a travel group.

[0104] Subtract the check-in time of each target passenger from the current time to obtain the waiting time of each target passenger; calculate the priority corresponding to each travel group according to the age, cabin type, and waiting time of each target passenger; divide all travel groups into group sets according to the priority of each travel group; sequentially incrementally set a digital label for each group set and mark it as a group label; obtain the single round-trip duration, which is the duration experienced by the shuttle vehicle to pick up and drop off passengers to the accommodation and then return to the terminal building; multiply each single round-trip duration by the group label corresponding to each group set, and then add the current time to obtain the boarding time corresponding to each group set; convert the boarding time corresponding to each group set into the standard time format (24-hour system) and use it as the transportation arrangement information, and push the transportation arrangement information corresponding to each group set to the corresponding target passenger. The method of pushing the transportation arrangement information is the same as the method of pushing the electronic meal voucher.

[0105] The method for dividing all target passengers into travel groups includes:

[0106] Take the target passengers corresponding to each check-in counter as a set of check-in passengers, and the set of check-in passengers corresponds one-to-one with the check-in counter; sort each target passenger in each set of check-in passengers in descending order according to the corresponding check-in time to generate a check-in sorting table corresponding to each set of check-in passengers; calculate the difference between every two adjacent check-in times in each check-in sorting table in turn as the check-in time difference; preset a time difference threshold, and the time difference threshold is preset by those skilled in the art according to the actual situation; compare each check-in time difference with the time difference threshold respectively; if the check-in time difference is less than or equal to the time difference threshold, then take the two target passengers corresponding to the corresponding check-in time difference as a travel group; if the check-in time difference is greater than the time difference threshold, then take the two target passengers corresponding to the corresponding check-in time difference as a travel group respectively; analyze all travel groups, if there are the same target passengers in two travel groups, then merge the travel groups with the same target passengers until there are no same target passengers in all travel groups.

[0107] The method for calculating the priority corresponding to each travel group includes:

[0108] Set different digital numbers for different cabin types and mark them as cabin numbers; the cabin numbers are preset by those skilled in the art according to the prices corresponding to different cabin types, and the cabin type with a higher price corresponds to a larger cabin number, and the cabin type with a lower price corresponds to a smaller cabin number. Therefore, the cabin number corresponding to the first class is greater than the cabin number corresponding to the business class is greater than the cabin number corresponding to the economy class; preset an age threshold and , , ; compare the age corresponding to each target passenger with the age threshold and respectively; if , then assign an age coefficient of 0 to the corresponding target passenger, is the age of the target passenger; if , then assign an age coefficient of to the corresponding target passenger; if , then assign an age coefficient of to the corresponding target passenger;

[0109] The expression for the priority corresponding to the target passenger is: ; in the formula, is the priority, is the age coefficient, is the cabin number, is the waiting time, 、 、 are all preset weight coefficients; the specific values ​​of the weight coefficients can be set according to actual conditions, and the weight coefficients reflect the influence of the age coefficient, cabin number and waiting time on the priority. Technical personnel in this field can preset corresponding weight coefficients according to the actual influence of the age coefficient, cabin number and waiting time on the priority, so as to accurately evaluate the priority of different target passengers.

[0110] It should be understood that the age coefficient, cabin number and waiting time are all factors affecting priority; among them, the larger the age coefficient, the younger the child or the older the elderly, who usually have weaker physical and mental adaptability to travel and need more care and attention, so they need to be given priority when arranging accommodation and transportation; the larger the cabin number, the more the passenger pays for the ticket and should enjoy higher service standards, so they should enjoy priority; the longer the waiting time, the lower the passenger's patience and comfort level tend to be, and long waits can cause passengers to feel uneasy or tired, so giving priority to these passengers can effectively improve the passenger experience.

[0111] Count the number of target passengers in each travel group and mark it as the number of passengers; add up the priorities of all target passengers in each travel group in turn, and then divide it by the corresponding number of passengers to obtain the priority corresponding to each travel group.

[0112] Methods for dividing all travel groups into groups include:

[0113] Get the number of shuttle vehicles and the vehicle passenger capacity. Shuttle vehicles are transportation tools (such as airport buses, vans, taxis, etc.) that transport passengers to accommodation places (such as hotels, inns, homestays, etc.), and the vehicle passenger capacity is the maximum number of passengers that a single shuttle vehicle can transport. The number of shuttle vehicles and the vehicle passenger capacity are manually reported by the relevant staff of the airline. Sort each travel group from large to small according to the corresponding priority to generate a group sorting table. Replace each travel group in the group sorting table with the corresponding number of passengers. Add all the passengers in front of the corresponding number of passengers to each passenger number in the group sorting table to obtain the total number of passengers corresponding to each passenger number, and replace each passenger number in the group sorting table with the corresponding total number of passengers. Multiply the number of shuttle vehicles by the vehicle passenger capacity to obtain the maximum single passenger capacity. Mark the total number of passengers with the largest value in the group sorting table as the maximum number of passengers. Divide the maximum number of passengers by the maximum single passenger capacity and round up to obtain the number of connections. Multiply the maximum single passenger capacity by the increasing connection coefficient to obtain The overall maximum passenger capacity; where the connection coefficient is an integer and the range of the connection coefficient is , n is the number of connections; The total maximum passenger capacity is added to the group ranking table. All passenger groups corresponding to the total number of passengers between every two total maximum passenger capacities are taken as a group set, and n group sets are obtained. The range of the group label is .

[0114] The method for generating accommodation arrangement information includes:

[0115] Count the number of target passengers whose age is less than the age threshold in each travel group, and mark it as the exclusion number; subtract the corresponding exclusion number from the number of passengers in each travel group to obtain the accommodation number corresponding to each travel group; divide the accommodation number of each travel group by 2 to obtain the room number corresponding to each travel group; replace each travel group in the group ranking table with the corresponding room number, arrange the corresponding rooms for each travel group in ascending order, and obtain the room number corresponding to each travel group; use the room number corresponding to each travel group as the accommodation arrangement information, and push the accommodation arrangement information corresponding to each travel group to the corresponding target passengers. The method for pushing the accommodation arrangement information is the same as the method for pushing the electronic meal voucher.

[0116] In this embodiment, by collecting and analyzing airline operation data, the route flow can be accurately obtained, and the deep learning technology is used to predict the flight delay probability in real time, so as to timely issue a flight delay risk warning; according to the generated warning instructions, accurate compensation, meal distribution, transportation arrangement and accommodation arrangement are provided to effectively respond to flight delays, and solve the problem that the traditional manual management and single emergency handling method cannot accurately meet the needs of passengers; make full use of big data and artificial intelligence technologies to realize the intelligent prediction of flight delay risks and refined services, which not only improves the operation efficiency and service efficiency of airlines, but also greatly enhances the travel experience and satisfaction of passengers, providing strong support for the intelligent management of the aviation industry.

[0117] Embodiment 2

[0118] This application also provides an electronic device. The electronic device may include one or more processors and one or more memories. Among them, computer-readable code is stored in the memory, and when the computer-readable code is run by one or more processors, it can execute the flight delay risk warning and response system based on big data analysis as described above.

[0119] The method or system according to an embodiment of the present application can also be implemented by means of the architecture of the electronic device shown in the present application. The electronic device may include a bus, one or more CPUs, a ROM, a RAM, a communication port connected to a network, an input / output, a hard disk, etc. The storage device in the electronic device, such as the ROM or the hard disk, can store the flight delay risk warning and response system based on big data analysis provided by the present application. Further, the electronic device may further include a user interface. Of course, the architecture shown in the present application is only exemplary. When implementing different devices, one or more components shown in the electronic device of the present application can be omitted according to actual needs.

[0120] Embodiment 3

[0121] An embodiment of the present application discloses a computer-readable storage medium. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are run by a processor, the flight delay risk warning and response system based on big data analysis according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0122] In addition, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium that stores machine-readable instructions that can be run by a processor to execute instructions corresponding to the method steps provided by the present application, such as: the flight delay risk warning and response system based on big data analysis. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0123] The above is only the specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0124] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. Flight delay risk warning and response system based on big data analysis, characterized by: include: Data collection module, used to collect aviation operation data; The aviation operation data includes flight data and weather data; Flight data collection methods include: Collect target flight data, the target flight data is the flight information of the target flight, and the target flight is the flight for which flight delay prediction is performed; obtain the current time, and mark the take-off time in the target flight data as the target take-off time; according to the current time and the target take-off time, obtain related flights, and the related flights are all flights whose take-off times are between the current time and the target take-off time; mark the flight routes in the target flight data as the target routes, and mark the flight routes in the related flight data as the related routes; obtain a world map, draw the target routes and the related routes on the world map, and determine whether there are routes in the related routes that intersect with the target routes; mark the routes in the related routes that intersect with the target routes as associated routes, mark the flights corresponding to the associated routes as associated flights, mark the flight information of the associated flights as associated flight data, and use the associated flight data and the target flight data as flight data; The data analysis module is used to analyze the aviation operation data and obtain the route traffic. The method of obtaining the route traffic includes: Divide the world map into airspace according to the preset division scheme to obtain airspace, is an integer greater than 1; according to the current longitude and latitude of each associated flight, mark the position of each associated flight on the world map; mark the airspace passed by the target route on the world map as the target airspace, count the number of associated flights in each target airspace, and mark it as airspace flow; take the airspace flow corresponding to all target airspaces as route flow; The delay prediction module is used to analyze aviation operation data and route traffic to predict the probability of flight delay; the method for predicting the probability of flight delay includes: Use weather data and route traffic as analysis data, input the analysis data into the trained delay prediction model, and predict the corresponding flight delay probability; The delay warning module determines whether to generate a warning instruction based on the probability of flight delay. If a warning instruction is generated, the flight delay duration is predicted; The method for determining whether to generate an early warning instruction includes: Set a warning threshold and compare the flight delay probability with the warning threshold; if the flight delay probability is greater than or equal to the warning threshold, a warning instruction is generated; if the flight delay probability is less than the warning threshold, no warning instruction is generated; The step of predicting flight delay duration includes: Step 1: Obtain historical traffic, which is the route traffic obtained at historical time; take the airspace traffic corresponding to the same target airspace in the historical traffic as a set of traffic sets, and the traffic set corresponds to the target airspace one by one; Step 2: Input each traffic set into the trained traffic prediction model to predict the airspace traffic at the future time T, where T is an integer greater than 1; Step 3: Take all predicted airspace traffic as predicted traffic, obtain weather data at future times, predict the corresponding flight delay probability based on the weather data at future times and predicted traffic, and determine whether to generate an early warning instruction; Step 4: If a warning instruction is still generated, replace the earliest airspace traffic in each group of traffic sets with the corresponding predicted airspace traffic, and return to step 2; if no warning instruction is generated, go to step 5; Step 5: Get the number of warning instructions generated in step 3 and mark it as the delay number. Multiply the delay number by T to get the flight delay duration. The unit of the flight delay duration is consistent with the unit of the target take-off time. The compensation payment module calculates the compensation amount based on the flight delay duration and determines whether to pay meals. The accommodation arrangement module determines whether to generate arrangement information based on the flight delay duration.

2. The flight delay risk warning and response system based on big data analysis according to claim 1 is characterized in that: Flight information includes take-off time, landing time, flight route and flight distance. The take-off time is the time when the flight is scheduled to take off from the departure airport, the landing time is the time when the flight is scheduled to land at the arrival airport, the flight route is the flight path between the departure airport and the arrival airport, and the flight distance is the flight distance corresponding to the flight route; according to the relevant flight, obtain the relevant flight data, and the relevant flight data is the flight information of the relevant flight; The routes among the related routes that do not intersect with the target route will not be marked; The weather data is the weather information corresponding to the departure airport at the target take-off time, including wind speed, precipitation, visibility and temperature.

3. The flight delay risk warning and response system based on big data analysis according to claim 2 is characterized in that: Mark the take-off time in the associated flight data as the associated take-off time, and the landing time as the associated landing time; convert the current time, target take-off time, associated take-off time, and associated landing time into a unified unit; subtract the corresponding associated take-off time from the associated landing time of each associated flight to obtain the estimated flight time of each associated flight; subtract the associated take-off time of each associated flight from the target take-off time to obtain the elapsed flight time of each associated flight; divide the elapsed flight time of each associated flight by the corresponding estimated flight time, and then multiply by the corresponding flight distance to obtain the elapsed flight distance of each associated flight; Obtain the departure airport and landing airport of each associated flight, and obtain the longitude and latitude corresponding to the departure airport and landing airport of each associated flight according to the world map, which include longitude and latitude; mark the longitude and latitude corresponding to the departure airport as the starting longitude and latitude, and mark the longitude and latitude corresponding to the landing airport as the ending longitude and latitude; subtract the corresponding starting longitude from the ending longitude of each associated flight to obtain the longitude difference; subtract the corresponding starting latitude from the ending latitude of each associated flight to obtain the latitude difference; calculate the current longitude and latitude corresponding to each associated flight according to the longitude difference, latitude difference, flown distance and flight distance corresponding to each associated flight; The expression for the current longitude is: ; In the formula, is the current longitude, is the starting point longitude, is the distance flown, is the flight distance, is the longitude difference; the expression for the current latitude is: ; In the formula, is the current latitude, is the starting point latitude, is the latitude difference.

4. The flight delay risk warning and response system based on big data analysis according to claim 3 is characterized in that: In step 2, the traffic prediction model is an RNN neural network model, and the training process of the traffic prediction model includes: Pre-collect the same airspace continuously airspace traffic and build a traffic training set. , is the number of airspace flows in a set of flow sets; based on the flow training set, a flow prediction model for predicting airspace flows at future moments is trained; Preset the sliding step size L and the sliding window length H; use the sliding window method to convert the airspace traffic in the traffic training set into multiple training samples, each training sample includes H airspace traffic; use each training sample as the input of the traffic prediction model, predict the airspace traffic after the sliding step size L as the output, and use the actual L airspace traffic corresponding to each training sample as the prediction target. Use the mean absolute percentage error MAPE to evaluate the model accuracy of the prediction results. When the calculated MAPE is less than the preset MAPE, the traffic prediction model training corresponding to the airspace traffic is completed; generate a traffic prediction model that predicts the airspace traffic at future times based on the airspace traffic.

5. The flight delay risk warning and response system based on big data analysis according to claim 4 is characterized in that: The method for calculating the compensation amount and making compensation payment includes: Preset compensation standards, including compensation amounts corresponding to different flight delay durations; obtain corresponding compensation amounts according to the compensation standards and flight delay durations; obtain passenger payment information corresponding to the target flight, including payment type and payment account number, and pay the compensation amount to all passengers corresponding to the target flight according to the passenger payment information corresponding to the target flight; The method for determining whether to distribute meals includes: Preset the delivery threshold and compare the flight delay duration with the delivery threshold; if the flight delay duration is greater than or equal to the delivery threshold, the meal will be delivered; if the flight delay duration is less than the delivery threshold, the current time will be added to the flight delay duration to obtain the estimated departure time; preset meal time, meal time includes breakfast time , lunch time and dinner time ,in, ; Compare the estimated departure time with the meal time; if , meals will be distributed, including is the estimated time of departure, means or; if , no meals will be distributed; If meals are distributed, the mobile phone number of each target passenger is obtained and marked as a push number. The target passengers are passengers corresponding to the target flights. Electronic meal coupons are pushed to each target passenger based on the push number of each target passenger.

6. The flight delay risk warning and response system based on big data analysis according to claim 5 is characterized in that: The method for determining whether to generate arrangement information includes: A scheduling threshold is preset, and the estimated take-off time is compared with the scheduling threshold; if the estimated take-off time is greater than or equal to the scheduling threshold, scheduling information is generated; if the estimated take-off time is less than the scheduling threshold, scheduling information is not generated; The arrangement information includes transportation arrangement information and accommodation arrangement information; Methods for generating traffic arrangement information include: Obtain the passenger information data of each target passenger, which includes age, cabin type, check-in counter and check-in time, and the unit of check-in time is consistent with the unit of current time; divide all target passengers into travel groups according to the check-in counter and check-in time of each target passenger, where if only one passenger travels, it is also considered as a travel group; subtract the check-in time of each target passenger from the current time to obtain the waiting time of each target passenger; calculate the priority corresponding to each travel group according to the age, cabin type and waiting time of each target passenger; divide all travel groups into group sets according to the priority of each travel group; set digital labels for each group set in increasing order and mark them as group labels; obtain the single round-trip time, which is the time it takes for the shuttle vehicle to pick up the passenger to the accommodation and then return to the terminal; multiply each single round-trip time by the group label corresponding to each group set, and add the current time to obtain the boarding time corresponding to each group set; convert the boarding time corresponding to each group set into a standard time format, and use it as traffic arrangement information, and push the traffic arrangement information corresponding to each group set to the corresponding target passenger.

7. The flight delay risk warning and response system based on big data analysis according to claim 6 is characterized in that: The method for dividing all target passengers into travel groups comprises: The target passengers corresponding to each check-in counter are regarded as a group of check-in sets, and the check-in sets correspond to the check-in counters one by one; each target passenger in each check-in set is sorted from large to small according to the corresponding check-in time, and a check-in sorting table corresponding to each check-in set is generated; the difference between each two adjacent check-in times in each check-in sorting table is calculated in turn as the check-in time difference; a time difference threshold is preset, and each check-in time difference is compared with the time difference threshold respectively; if the check-in time difference is less than or equal to the time difference threshold, the two target passengers corresponding to the corresponding check-in time difference are regarded as a travel group; if the check-in time difference is greater than the time difference threshold, the two target passengers corresponding to the corresponding check-in time difference are regarded as a travel group respectively; all travel groups are analyzed, and if there are the same target passengers in two travel groups, the travel groups with the same target passengers are merged until there are no same target passengers in all travel groups; The method for calculating the priority corresponding to each travel group includes: Set different digital numbers for different cabin types and mark them as cabin numbers; preset age thresholds and , , ; Compare the age of each target passenger with the age threshold and For comparison; if , then the age coefficient assigned to the corresponding target passenger is 0, is the age of the target passenger; if , then the age coefficient assigned to the corresponding target passenger is ;like , then the age coefficient assigned to the corresponding target passenger is ; The expression of the priority corresponding to the target passenger is: ; In the formula, For priority, is the age coefficient, is the cabin number, The waiting time is , , are all preset weight coefficients; the number of target passengers in each travel group is counted and marked as the number of passengers; the priorities of all target passengers in each travel group are added up in turn, and then divided by the corresponding number of passengers to obtain the priority corresponding to each travel group.

8. The flight delay risk warning and response system based on big data analysis according to claim 7 is characterized in that: The method for dividing all travel groups into group sets includes: Get the number of shuttle vehicles and the vehicle passenger capacity, where the vehicle passenger capacity is the maximum number of passengers that a single shuttle vehicle can transport; sort each travel group from large to small according to the corresponding priority to generate a group sorting table; replace each travel group in the group sorting table with the corresponding number of passengers; add each passenger number in the group sorting table to all the passengers in front of the corresponding passenger number to obtain the total number of passengers corresponding to each passenger number, and replace each passenger number in the group sorting table with the corresponding total number of passengers; multiply the number of shuttle vehicles by the vehicle passenger capacity to obtain the maximum single passenger capacity; mark the total number of passengers with the largest value in the group sorting table as the maximum number of passengers, divide the maximum number of passengers by the maximum single passenger capacity, and round up to obtain the number of connections; multiply the maximum single passenger capacity by the increasing connection coefficient to obtain The overall maximum passenger capacity; where the connection coefficient is an integer and the range of the connection coefficient is , n is the number of connections; The total maximum passenger capacity is added to the group sorting table, and the travel group corresponding to the total number of passengers between every two total maximum passenger capacities is taken as a group set to obtain n group sets; the range of group labels is .

9. The flight delay risk warning and response system based on big data analysis according to claim 8 is characterized in that: Methods for generating accommodation information include: Count the number of people in each travel group who are younger than the age threshold The target number of passengers is obtained and marked as the exclusion number; the number of passengers in each travel group is subtracted from the corresponding exclusion number to obtain the number of accommodations corresponding to each travel group; the number of accommodations for each travel group is divided by 2 to obtain the number of rooms corresponding to each travel group; each travel group in the group sorting table is replaced with the corresponding number of rooms, and the corresponding rooms are arranged for each travel group in positive order, and the room number corresponding to each travel group is obtained; the room number corresponding to each travel group is used as the accommodation arrangement information, and the accommodation arrangement information corresponding to each travel group is pushed to the corresponding target passengers.

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