Smart airport passenger travel experience optimization method and system based on digital twinning

Abstract

The invention relates to a smart airport passenger travel experience optimization method and system based on digital twinning, and is applied to the technical field of airport services, and the method comprises the steps: obtaining the physical parameters and people flow monitoring data of an airport; constructing a digital twinborn model of the airport based on the physical parameters and the human traffic monitoring data; passenger information and flight information are obtained, wherein the passenger information comprises position information of passengers; performing human flow simulation based on the human flow monitoring data, the flight information and a digital twin model to obtain human flow data; planning a passenger passage path based on the position information, the flight information and the passenger flow data; determining an abnormal condition based on the visitor flow data and the flight information; determining an abnormal response strategy based on the abnormal condition; and determining an experience optimization strategy based on the passenger passing path and the abnormal response strategy. The method has the effect of improving the travel experience of airport passengers.

Description

Technical Field

[0001] The present application relates to the technical field of airport services, and in particular to a method and system for optimizing the travel experience of passengers at a smart airport based on digital twins. Background Art

[0002] With the rapid development of the aviation industry, airports, as important hubs for passenger travel, are increasingly concerned about their service quality and passenger experience. At present, some airports have begun to try to use digital technology to improve passenger service efficiency, but they still face problems such as data islands and insufficient real-time response.

[0003] Currently, airports often collect and analyze passenger data through a single passenger information system or monitoring system to provide basic service support. They often lack the ability to integrate and deeply analyze multi-dimensional data, are unable to provide refined and personalized passenger services, and have a slow response speed when dealing with emergencies, affecting passengers' travel experience. Summary of the invention

[0004] In order to improve the travel experience of airport passengers, the present application provides a method and system for optimizing the travel experience of smart airport passengers based on digital twins.

[0005] In the first aspect, the present application provides a method for optimizing the travel experience of passengers at a smart airport based on digital twins, which adopts the following technical solutions: A method for optimizing the passenger travel experience of a smart airport based on digital twins, comprising: Obtain physical parameters of the airport and passenger flow monitoring data; Building a digital twin model of the airport based on the physical parameters and the passenger flow monitoring data; Acquire passenger information and flight information, wherein the passenger information includes the passenger's location information, and the flight information includes the flight time period; Performing a crowd flow simulation based on the crowd flow monitoring data, the flight information, and the digital twin model to obtain crowd flow data, where the crowd flow data is used to characterize the crowd flow situation at various locations in the airport; Planning a passenger travel path based on the location information, the flight information, and the passenger flow data; Determine an abnormal situation based on the passenger flow data and the flight information, where the abnormal situation includes an abnormal type; Determining an abnormal response strategy based on the abnormal situation; An experience optimization strategy is determined based on the passenger travel path and the abnormal response strategy.

[0006] By adopting the above technical solution, a digital twin model is constructed by acquiring the physical parameters of the airport and the passenger flow monitoring data. The passenger flow simulation is performed through the digital twin model to obtain the passenger flow data at various locations of the airport. The personalized passenger travel path is planned according to the passenger's location information, flight information and passenger flow data, that is, the passenger travel path is planned by the passenger flow data obtained through the passenger flow simulation, which improves the reliability of the passenger travel path, thereby improving the passenger travel efficiency in the airport, and determines abnormal situations through passenger flow data and flight information, thereby determining the abnormal response strategy, and improving the abnormal response efficiency, that is, improving the airport's ability to respond to emergencies, and formulating an experience optimization strategy in combination with the passenger travel path and the abnormal response strategy, which effectively improves the travel experience of airport passengers.

[0007] Optionally, the performing of human flow simulation based on the human flow monitoring data, the flight information, and the digital twin model to obtain human flow data includes: Obtaining ticket sales information, the ticket sales information including the number of tickets sold for each flight and the flight type; Determine the arrival passenger flow and the departure passenger flow at each time based on the flight type, the flight time period and the first preset association; Calculate a first predicted human flow at each moment based on the human flow monitoring data, the arriving human flow, and the leaving human flow; Get historical traffic information; Analyze the historical passenger flow information to obtain a first passenger flow level on each date and a second passenger flow level at each moment; Obtaining a first weight based on the first pedestrian flow level; Obtaining a second weight based on the second pedestrian flow level; Calculate a second predicted passenger flow based on the first weight, the second weight, and the first predicted passenger flow; The second predicted passenger flow is input into the digital twin model to perform passenger flow simulation to obtain passenger flow data at each location.

[0008] By adopting the above technical solution, the arrival and departure passenger flow at each moment is determined according to the flight type, flight time period and the first preset association, so that the first predicted passenger flow at each moment can be calculated, and the first weight and the second weight are obtained by analyzing the historical passenger flow. The first predicted passenger flow is adjusted by the first weight and the second weight, which reduces the impact of the uncertainty of the passenger flow on the prediction and improves the accuracy of the passenger flow prediction.

[0009] Optionally, the planning of a passenger travel path based on the location information, the flight information, and the passenger flow data includes: Determine the travel status of the passenger based on the location information and the flight information, where the travel status includes check-in, transfer, and departure from the airport; Acquire the travel process of the passenger based on the location information and the travel status, where the travel process is the waypoints and / or destinations that the passenger needs to reach later; Calculate the latest arrival time at the destination based on the flight time period and the first preset duration; Acquire a first time duration corresponding to each waypoint in the travel process based on the passenger flow data; Calculate the leisure time based on the current time, the first spent time and the latest arrival time at the destination; Determine the leisure items and the segment durations corresponding to the leisure items based on the leisure duration and the passenger's preferences; The passenger travel path is determined based on the travel process, the first spent time, the leisure items and the segmented time.

[0010] By adopting the above technical solution, the waypoints and / or destinations that the passenger needs to reach later are obtained through the passenger's location information and travel status, and the leisure time is calculated according to the latest arrival time at the destination and the first spent time at each waypoint. In this way, leisure projects and the corresponding segmented durations of the leisure projects are recommended to the passenger based on the leisure time and the passenger's preferences, thereby improving the passenger's travel experience.

[0011] Optionally, determining the leisure items and the segment durations corresponding to the leisure items based on the leisure duration and the passenger's preference includes: Acquire a historical behavior trajectory of a passenger, wherein the historical behavior trajectory includes leisure items; Combining the leisure items by exhaustive method to obtain multiple item combinations; Counting the first occurrence times of each item combination in the historical behavior track; Counting the number of trajectories of the historical behavior trajectories; Calculating the recommendation degree of each item combination based on the number of trajectories and the first number of occurrences; Determining the number of recommended items based on the leisure time and a first preset rule; Screening the project combination based on the number of recommended projects and the recommendation degree to obtain a candidate project combination; Searching for an item to be replaced in the candidate item combination based on the traffic data; Determine a recommended replacement item based on the passenger flow data and the passenger flow that can be accommodated by the leisure item; Obtaining a matching degree between the item to be replaced and the recommended replacement item; replacing the item to be replaced with the recommended replacement item based on the matching degree; Score each candidate item combination based on the traffic data to obtain a combination score; Determine a recommended project combination based on the combination score and the candidate project combination; The segment durations corresponding to the leisure items are determined based on the leisure duration, the passenger preferences and the recommended item combinations.

[0012] By adopting the above technical solution, when recommending leisure projects for passengers, the recommendation degree of each project combination is obtained by analyzing the historical behavior trajectory, the number of recommended projects is determined according to the leisure time, and the candidate project combination is obtained according to the number of recommended projects and the recommendation degree, and the leisure projects in the candidate project combination are replaced according to the flow of people data, and the replaced candidate project combination is scored according to the flow of people data to obtain the recommended project combination. That is, when determining the leisure projects recommended to passengers, the passenger preferences, the number of recommended projects and the flow of people data of each leisure project are fully considered, thereby improving the passenger satisfaction with the leisure projects.

[0013] Optionally, the determining the segment durations corresponding to the leisure items based on the leisure duration, the passenger preference and the recommended item combination includes: Obtaining the recommended duration of each leisure item in the recommended item combination; Calculate the sum of the recommended durations to obtain a recommended duration sum; If the recommended duration is less than the leisure duration, the remaining duration is calculated, where the remaining duration = the leisure duration - the recommended duration. Counting the second occurrence number of each leisure item in the recommended item combination in the historical behavior track; Calculate the increased duration corresponding to each of the leisure items based on the second number of occurrences and the remaining duration; The segmented duration of each of the leisure items is calculated based on the increased duration and the recommended duration.

[0014] By adopting the above technical solution, corresponding segment duration is allocated to each leisure item according to the recommended duration of each leisure item and the second occurrence number in the historical behavior trajectory, so that passengers can have a better leisure experience.

[0015] Optionally, determining an abnormal response strategy based on the abnormal situation includes: If the abnormal type is abnormal human flow, determining an abnormal area, first human flow data and an abnormal time period based on the abnormal situation, wherein the first human flow data is the human flow data of the abnormal area in the abnormal time period; Determine the number of required channels and the number of required staff in the abnormal area within the abnormal time period based on the first human flow data and the second preset rule; Calculate the number of channels to be added based on the first number of channels and the required number of channels, where the first number of channels is the number of channels in the abnormal area during the abnormal time period; Calculate the number of staff to be added based on the first number of staff and the required number of staff, where the first number of staff is the number of staff in the abnormal area during the abnormal time period; Acquire the second passenger flow data at each location of the airport during the abnormal time period and the number of second staff members at each location; Determine the number of dispatchable personnel at each location based on the second preset rule, the second human flow data, and the second number of staff members; Determine a personnel scheduling strategy based on the number of dispatchable personnel and the number of staff to be added; An abnormal response strategy is determined based on the number of channels to be added and the personnel scheduling strategy.

[0016] By adopting the above technical solution, when the flow of people in an area is abnormal, the number of required channels and the number of required staff are determined, so as to obtain the number of channels to be added and the number of staff to be added in the abnormal area during the abnormal time period, and the number of dispatchable personnel at each position is calculated to determine the personnel dispatch strategy, thereby improving the efficiency of abnormal handling and enhancing the travel experience of passengers.

[0017] Optionally, the determining of the personnel scheduling strategy based on the number of schedulable personnel and the number of personnel to be added includes: Determine the location where the number of dispatchable personnel is greater than 0 as a dispatchable location; Acquire a first work content of the dispatchable position and a second work content of the abnormal area; Determining a matching degree between the first work content and the second work content based on a third preset rule; Sorting the schedulable positions based on the matching degree to obtain a first sorting result; Adjusting the first sorting result based on the first distance between the dispatchable position and the abnormal area and the number of dispatchable personnel to obtain a second sorting result; Sorting the abnormal areas based on the number of staff to be added to obtain a third sorting result; A personnel adjustment strategy is determined based on the second sorting result, the number of dispatchable personnel, the third sorting result, and the number of staff to be added.

[0018] By adopting the above technical solution, the abnormal areas and the dispatchable positions are sorted respectively, and the personnel adjustment strategy is determined according to the second sorting result and the third sorting result, so that the personnel scheduling strategy is more reasonable.

[0019] In the second aspect, this application provides a smart airport passenger travel experience optimization system based on digital twins, which adopts the following technical solutions: A smart airport passenger travel experience optimization system based on digital twins, including: A first data acquisition module, used to acquire physical parameters of the airport and passenger flow monitoring data; A twin model construction module, used to construct a digital twin model of the airport based on the physical parameters and the passenger flow monitoring data; A second data acquisition module is used to acquire passenger information and flight information, wherein the passenger information includes the passenger's location information, and the flight information includes the flight time period; A flow data simulation module, used to simulate the flow of people based on the flow monitoring data, the flight information and the digital twin model to obtain flow data, where the flow data is used to characterize the flow of people at various locations in the airport; A travel path planning module, used to plan a passenger travel path based on the location information, the flight information and the passenger flow data; An abnormal situation determination module, used to determine an abnormal situation based on the passenger flow data and the flight information, wherein the abnormal situation includes an abnormal type; A response strategy determination module, used to determine an abnormal response strategy based on the abnormal situation; An optimization strategy determination module is used to determine an experience optimization strategy based on the passenger travel path and the abnormal response strategy.

[0020] By adopting the above technical solution, a digital twin model is constructed by acquiring the physical parameters of the airport and the passenger flow monitoring data. The passenger flow simulation is performed through the digital twin model to obtain the passenger flow data at various locations of the airport. The personalized passenger travel path is planned according to the passenger's location information, flight information and passenger flow data, that is, the passenger travel path is planned by the passenger flow data obtained through the passenger flow simulation, which improves the reliability of the passenger travel path, thereby improving the passenger travel efficiency in the airport, and determines abnormal situations through passenger flow data and flight information, thereby determining the abnormal response strategy, and improving the abnormal response efficiency, that is, improving the airport's ability to respond to emergencies, and formulating an experience optimization strategy in combination with the passenger travel path and the abnormal response strategy, which effectively improves the travel experience of airport passengers.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a processor, wherein the processor is coupled to a memory; The memory stores a computer program that can be loaded by the processor and executes the method for optimizing the travel experience of smart airport passengers based on digital twins as described in any one of the first aspects.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method for optimizing the travel experience of smart airport passengers based on digital twins as described in any one of the first aspects.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. By acquiring the physical parameters of the airport and the crowd flow monitoring data to build a digital twin model, and simulating the crowd flow through the digital twin model, the crowd flow data of each location in the airport can be obtained. According to the passenger's location information, flight information and crowd flow data, a personalized passenger passage path is planned, that is, the passenger passage path is planned through the crowd flow data obtained by the crowd flow simulation, which improves the reliability of the passenger passage path, thereby improving the passenger's passage efficiency in the airport. Abnormal situations are determined through crowd flow data and flight information, and the abnormal response strategy is determined accordingly, which improves the abnormal response efficiency, that is, the airport's ability to respond to emergencies is improved. In combination with the passenger passage path and the abnormal response strategy, an experience optimization strategy is formulated, which effectively improves the travel experience of airport passengers; 2. The arriving passenger flow and the departing passenger flow at each moment are determined according to the flight type, flight time period and the first preset association, so that the first predicted passenger flow at each moment can be calculated. By analyzing the historical passenger flow, the first weight and the second weight are obtained. The first predicted passenger flow is adjusted by the first weight and the second weight, which reduces the impact of the uncertainty of the passenger flow on the prediction and improves the accuracy of the passenger flow prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for optimizing the travel experience of passengers at a smart airport based on digital twins provided in an embodiment of the present application.

[0025] Figure 2 It is a structural block diagram of a smart airport passenger travel experience optimization system based on digital twins provided in an embodiment of the present application.

[0026] Figure 3 It is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The present application is further described in detail below in conjunction with the accompanying drawings.

[0028] The embodiment of the present application provides a method for optimizing the travel experience of passengers at a smart airport based on digital twins. The method for optimizing the travel experience of passengers at a smart airport based on digital twins can be executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0031] like Figure 1 As shown, a method for optimizing the travel experience of smart airport passengers based on digital twins is described as follows (steps S101 to S108): Step S101: Acquire the physical parameters of the airport and the passenger flow monitoring data.

[0032] Obtain the physical parameters of the airport from the staff or database, such as the spatial layout and channel width of the airport, and obtain the airport's passenger flow monitoring data through sensors such as cameras and infrared thermal imagers.

[0033] Step S102: construct a digital twin model of the airport based on physical parameters and passenger flow monitoring data.

[0034] The physical parameters of the airport and the passenger flow monitoring data are input into the digital twin model building tool to obtain the digital twin model of the airport. The digital twin model building tool can be any tool with digital twin model building and simulation functions, and is not specifically limited here.

[0035] Step S103: Obtain passenger information and flight information, where the passenger information includes the passenger's location information.

[0036] Passenger information is obtained from the airport database and the passenger's communication terminal. The passenger information also includes the passenger's flight number and flight time period. The flight information includes the flight number, flight time period and flight type of all flights in the airport. The flight time period is the time period between the aircraft take-off time and the aircraft landing time.

[0037] Step S104: Perform a crowd flow simulation based on crowd flow monitoring data, flight information, and the digital twin model to obtain crowd flow data.

[0038] Specifically, based on the crowd flow monitoring data, flight information and the digital twin model, crowd flow simulation is performed to obtain crowd flow data, including: obtaining ticket sales information, the ticket sales information includes the number of tickets sold for each flight and the flight type; determining the arrival crowd flow and the departure crowd flow at each moment based on the flight type, the flight time period and the first preset association; calculating the first predicted crowd flow at each moment based on the crowd flow monitoring data, the arrival crowd flow and the departure crowd flow; obtaining historical crowd flow information; analyzing the historical crowd flow information to obtain the first crowd flow level for each date and the second crowd flow level for each moment; obtaining a first weight based on the first crowd flow level; obtaining a second weight based on the second crowd flow level; calculating the second predicted crowd flow based on the first weight, the second weight and the first predicted crowd flow; inputting the second predicted crowd flow into the digital twin model for crowd flow simulation to obtain crowd flow data for each location.

[0039] In this embodiment, ticket information of each flight is obtained from a database according to the flight number. The ticket information includes the number of tickets sold for each flight and the flight type. The time required for passengers to arrive at the airport in advance may be different for different flight types. For example, international flights require more procedures before boarding, so passengers need to arrive at the airport earlier. A first preset association is obtained from the database. The first preset association is the correspondence between the flight type, the time to arrive at the airport in advance, and the boarding stop time. The time when the passenger arrives at the airport = the time when the plane takes off - the time to arrive at the airport in advance. The time when the passenger leaves the airport is the time when the plane stops boarding. The time when the plane stops boarding = the time when the plane takes off - the boarding stop time. According to the time when the passenger arrives at the airport, the arrival flow at each time, that is, the number of passengers arriving at the airport, is counted. According to the time when the passenger leaves the airport, the departure flow at each time is counted. The first predicted flow at each time = the current flow monitoring data + the arrival flow - the departure flow. When calculating the first predicted flow at a later time, the "current flow monitoring data" is actually the first predicted flow at a previous time. The historical flow information is obtained from the database. The historical flow information includes the flow at each time on each date. Data, statistical analysis is performed on historical passenger flow information to obtain the first passenger flow data of each date and the second passenger flow data of each moment. The statistical analysis method can be to find the average value, that is, take one year as a cycle, find the average value of the passenger flow data on the same date of each year, and obtain the first passenger flow data of each date; take one day as a cycle, find the average value of the passenger flow data at the same time of each day, and obtain the second passenger flow data at each moment. The database stores the correspondence between the passenger flow data and the passenger flow level and the correspondence between the passenger flow level and the weight. The first passenger flow level of each date is obtained from the database according to the first passenger flow data, and the second passenger flow level of each moment is obtained from the database according to the second passenger flow data. The first weight is obtained from the database according to the first passenger flow level, and the second weight is obtained from the database according to the second passenger flow level. The second predicted passenger flow = the first predicted passenger flow × the first weight × the second weight. The second predicted passenger flow is input into the digital twin model to perform passenger flow simulation to obtain passenger flow data at each location. When performing passenger flow simulation, passengers flow according to preset rules. The preset rules can be that passengers flow according to the locations corresponding to the travel process, which are not specifically limited here.

[0040] Step S105: planning passenger travel routes based on location information, flight information, and passenger flow data.

[0041] Specifically, planning passenger travel routes based on location information, flight information and passenger flow data includes: determining the passenger's travel status based on the location information and flight information, the travel status including check-in, transfer and leaving the airport; obtaining the passenger's travel process based on the location information and travel status, the travel process being the way points and / or destinations that the passenger needs to reach later; calculating the latest arrival time at the destination based on the flight time period and the first preset duration; obtaining the first spent duration corresponding to each way point in the travel process based on the passenger flow data; calculating the leisure time based on the current time, the first spent duration and the latest arrival time at the destination; determining the leisure items and the segmented duration corresponding to the leisure items based on the leisure time and passenger preferences; determining the passenger's travel route based on the travel process, the first spent duration, the leisure items and the segmented duration.

[0042] In this embodiment, if the passenger has information about flights that he / she has not taken, the travel status of the passenger is determined according to the passenger's current location. If the passenger does not have information about flights that he / she has not taken, that is, the passenger has completed the trip, then the travel status of the passenger is leaving the airport. The database stores the correspondence between the travel status and the travel process. The travel process of the passenger is obtained from the database according to the passenger's current location and travel status, that is, the waypoints and / or destinations that the passenger needs to reach later. Waypoints include check-in counters, security checkpoints, waiting rooms, etc. The first preset time is the boarding stop time. The first preset time corresponding to different flights may be different. The first preset time corresponding to each flight is obtained from the database. The latest arrival time at the destination (Same as the time when the plane stops boarding as mentioned above) = the time when the plane takes off - the first preset duration. When the passenger flow data is different, the first expended time corresponding to each waypoint is different. The database stores the correspondence between the passenger flow data, waypoints and the first expended time. The first expended time corresponding to each waypoint in the travel process is obtained from the database according to the passenger flow data; leisure time = the latest arrival time at the destination - the current time - the sum of the first expended times of each waypoint. By analyzing the leisure time and passenger preferences, the leisure items recommended for the passengers and the corresponding segmented duration of each leisure item are obtained; the passenger's travel path is determined based on the travel process, the first expended time, the leisure items and the segmented duration.

[0043] If the passenger's travel status is leaving the airport, it is only necessary to obtain the passenger's travel process and obtain the first time spent at each waypoint based on the travel process. The order of the waypoints is the passenger's travel path, and the waypoints are marked according to the first time spent. There is no need to determine the leisure items.

[0044] Specifically, leisure items and the corresponding segment durations of the leisure items are determined based on the leisure duration and the passenger's preference, including: obtaining the passenger's historical behavior trajectory, the historical behavior trajectory including leisure items; combining the leisure items by exhaustive method to obtain multiple item combinations; counting the first occurrence times of each item combination in the historical behavior trajectory; counting the number of trajectories in the historical behavior trajectory; calculating the recommendation degree of each item combination based on the number of trajectories and the first occurrence times; determining the number of recommended items based on the leisure duration and the first preset rule; screening the item combinations based on the number of recommended items and the recommendation degrees to obtain candidate item combinations; searching for items to be replaced in the candidate item combinations based on the flow of people data; determining the recommended replacement items based on the flow of people data and the accommodating flow of people of the leisure items; obtaining the matching degree between the items to be replaced and the recommended replacement items; replacing the items to be replaced with the recommended replacement items based on the matching degree; scoring each candidate item combination based on the flow of people data to obtain a combination score; determining the recommended item combination based on the combination score and the candidate item combination; determining the corresponding segment durations of the leisure items based on the leisure duration, the passenger's preference and the recommended item combination.

[0045] In this embodiment, the historical behavior trajectory of the passenger is obtained from the airport database or the passenger, the historical behavior trajectory is a trajectory composed of at least one leisure item, and the leisure items in each historical behavior trajectory are combined by exhaustive method to obtain multiple item combinations, the number of leisure items in each item combination is greater than or equal to 1, the first appearance number of each item combination in all historical behavior trajectories, that is, the number of each item combination, is counted, the number of trajectories of the historical behavior trajectory is counted, and the recommendation degree of each item combination = the first appearance number / the number of trajectories.

[0046] In order to enable passengers to have a better leisure experience, the number of projects recommended to passengers is different when the leisure time is different. The first preset rule is obtained from the database. The first preset rule is the correspondence between the leisure time and the number of recommended projects. The number of recommended projects for passengers is determined according to the leisure time and the first preset rule. The number of recommended projects can be a fixed value or an interval, which is not specifically limited here. A project combination whose number of leisure projects meets the number of recommended projects and whose recommendation degree is greater than the preset recommendation degree is determined as a candidate project combination. The recommended flow and the accommodating flow of each leisure project are obtained from the database. If the recommended flow is less than the accommodating flow, the leisure projects in the candidate project combination whose flow data is greater than the recommended flow are determined as projects to be replaced.

[0047] A passenger flow ratio interval is obtained from the database. The passenger flow ratio interval is an interval consisting of two values ​​greater than 0 and less than 1. For example, the passenger flow ratio interval is (1 / 3, 2 / 3). The two values ​​of the passenger flow ratio interval are multiplied by the accommodated passenger flow to obtain a recommended replacement passenger flow interval. For example, if the accommodated passenger flow is 10 and the passenger flow ratio interval is (1 / 3, 2 / 3), the recommended replacement passenger flow interval is (10 / 3, 20 / 3). Leisure items with passenger flow data in the recommended replacement passenger flow interval are determined as recommended replacement items. The database stores the matching degree between each two leisure items. The matching degree between the item to be replaced and all the recommended replacement items is obtained from the database. For a candidate item combination, the recommended replacement item that does not exist in the candidate item combination and has the highest matching degree with the item to be replaced is determined as the actual replacement item, and the corresponding item to be replaced is replaced with the actual replacement item to obtain a new candidate item combination.

[0048] Calculate the project passenger flow ratio of each leisure project, project passenger flow ratio = leisure project passenger flow data / recommended passenger flow of leisure projects, obtain the corresponding relationship between the project passenger flow ratio and the leisure project score from the database, find the leisure project score of each leisure project from the corresponding relationship according to the project passenger flow ratio of each leisure project, average the leisure project scores of all leisure projects in a candidate project combination to obtain the combined score of the candidate project combination, determine the candidate project combination with the highest combined score as the recommended project combination, and determine the corresponding segment duration of each leisure project in the recommended project combination according to the leisure duration, passenger preferences and the recommended project combination.

[0049] Specifically, the segmented duration corresponding to the leisure items is determined based on the leisure duration, passenger preferences and the recommended item combination, including: obtaining the recommended duration of each leisure item in the recommended item combination; calculating the sum of the recommended durations to obtain the sum of the recommended durations; if the sum of the recommended durations is less than the leisure duration, calculating the remaining duration, which is the leisure duration minus the sum of the recommended durations; counting the second occurrence times of each leisure item in the recommended item combination in the historical behavior trajectory; calculating the increased duration corresponding to each leisure item based on the second occurrence times and the remaining duration; and calculating the segmented duration of each leisure item based on the increased duration and the recommended duration.

[0050] In this embodiment, the recommended duration of each leisure item in the recommended item combination is obtained from the database, and the recommended duration sum is the sum of the recommended durations of each leisure item in the recommended item combination.

[0051] If the recommended duration is less than the leisure duration, the remaining duration = leisure duration - the recommended duration; count the second occurrence times of each leisure item in the recommended item combination in all historical behavior trajectories, the increased duration of the leisure item = the second occurrence times of the leisure item / the sum of the second occurrence times of all leisure items in the recommended item combination × the remaining duration, and the segmented duration of each leisure item = the increased duration + the recommended duration.

[0052] If the recommended duration is greater than the leisure time, the excess time is calculated: excess time = sum of recommended duration - leisure time; the reduced duration of each leisure item = excess duration / number of leisure items in the recommended item combination; the segmented duration of each leisure item = recommended duration - reduced duration of each leisure item.

[0053] If the recommended duration is equal to the leisure duration, then the segmented duration of each leisure item = the recommended duration.

[0054] Specifically, the passenger's travel path is determined based on the travel process, the first spent time, leisure items and segmented duration, including: obtaining at least one first position corresponding to each waypoint in the travel process; filtering the first position based on flight information to obtain the second position corresponding to the flight; filtering the second position based on passenger flow monitoring data to obtain the third position of each passenger at each waypoint; sorting the third position and the positions corresponding to the leisure items based on the travel process, and calculating the time at each position according to the first spent time and the segmented duration to obtain the passenger's travel path.

[0055] In this embodiment, the position corresponding to each waypoint is fixed, and at least one first position corresponding to each waypoint in the travel process is obtained from the staff or the database. The first positions corresponding to different flights may be different. For example, different types of flights correspond to different check-in counters. The first position corresponding to the passenger's flight information is determined as the second position, and the second position is screened according to the passenger flow monitoring data of each second position. The second position with the least passengers is determined as the third position of the passenger at the waypoint. The third position and the positions corresponding to the leisure items are sorted according to the travel process. If the positions corresponding to two or more leisure items are adjacent in the sorting, the positions corresponding to the leisure items in the sorting are adjusted from high to low according to the leisure item scores. The time period at each position is calculated in turn according to the sorting, the first spent time and the segmented time to obtain the passenger's passage path. The passenger's passage path is, for example, the first check-in counter (AB), the third security check channel (BC), the first convenience store (CD), and the second waiting room (DE), where A, B, C, D, and E are all time points.

[0056] Step S106: Determine abnormal conditions based on passenger flow data and flight information.

[0057] Among them, the abnormal situation includes the abnormal type. If the abnormal type is abnormal human flow, the abnormal situation also includes the abnormal area, abnormal time period, and human flow data of the abnormal area in the abnormal time period. If the abnormal type is abnormal flight delay, the abnormal situation includes the abnormal flight, the standard take-off time of the abnormal flight, the delayed take-off time, the number of affected people, the flight type, etc. The number of affected people is the number of tickets sold for the abnormal flight.

[0058] When the passenger flow data of a location exceeds the preset passenger flow value, there is a passenger flow abnormality at that location. The preset passenger flow values ​​of different locations are different and are pre-set according to actual conditions. No specific limitation is made here. Flight information also includes aircraft delay information. When a flight is delayed, the abnormality type is aircraft delay abnormality.

[0059] Step S107: Determine an abnormal response strategy based on the abnormal situation.

[0060] Specifically, determining an abnormal response strategy based on an abnormal situation includes: if the abnormal type is abnormal human flow, determining an abnormal area, first human flow data and an abnormal time period based on the abnormal situation, the first human flow data being the human flow data of the abnormal area in the abnormal time period; determining the number of required channels and the number of required staff in the abnormal area in the abnormal time period based on the first human flow data and a second preset rule; calculating the number of channels to be added based on the first number of channels and the number of required channels, the first number of channels being the number of channels in the abnormal area in the abnormal time period; calculating the number of staff to be added based on the first number of staff and the number of required staff, the first number of staff being the number of staff in the abnormal area in the abnormal time period; obtaining second human flow data and the number of second staff at each location of the airport in the abnormal time period; determining the number of dispatchable personnel at each location based on the second preset rule, the second human flow data and the second number of staff; determining a staff scheduling strategy based on the number of dispatchable personnel and the number of staff to be added; determining an abnormal response strategy based on the number of channels to be added and the staff scheduling strategy.

[0061] In this embodiment, if the abnormal type is abnormal human flow, the abnormal area, the first human flow data of the abnormal area in the abnormal time period, and the abnormal time period are searched from the abnormal situation, and the second preset rule is obtained from the database. The second preset rule is the correspondence between the human flow data and the number of required channels and the number of required staff. The number of required channels and the number of required staff corresponding to the first human flow data are searched from the second preset rule, that is, the number of required channels and the number of required staff in the abnormal area in the abnormal time period. The staff scheduling and the setting of the number of channels in each time period are different. The originally set first channel number and the first staff number of the abnormal area in the abnormal time period are obtained from the staff. The number of channels to be added = the number of required channels - the first channel The number of staff to be added = the number of required staff - the number of first staff. The second passenger flow data at various locations of the airport during the abnormal time period is searched from the predicted passenger flow data, and the number of second staff at various locations of the airport during the abnormal time period is obtained from the staff. The number of required staff corresponding to the second passenger flow data is searched from the second preset rule. The number of dispatchable staff = the number of second staff - the number of required staff corresponding to the second passenger flow data. The staff dispatch strategy is determined according to the number of dispatchable staff and the number of staff to be added. The number of channels to be added and the staff dispatch strategy are jointly determined as the abnormal response strategy, so that the staff can increase the number of channels in the abnormal area during the abnormal time period according to the abnormal response strategy, and conduct personnel dispatch.

[0062] Furthermore, a personnel scheduling strategy is determined based on the number of dispatchable personnel and the number of staff to be added, including: determining a position where the number of dispatchable personnel is greater than 0 as a dispatchable position; obtaining a first work content at the dispatchable position and a second work content in the abnormal area; determining a matching degree between the first work content and the second work content based on a third preset rule; sorting the dispatchable positions based on the matching degree to obtain a first sorting result; adjusting the first sorting result based on a first distance between the dispatchable position and the abnormal area and the number of dispatchable personnel to obtain a second sorting result; sorting the abnormal area based on the number of staff to be added to obtain a third sorting result; and determining a personnel adjustment strategy based on the second sorting result, the number of dispatchable personnel, the third sorting result, and the number of staff to be added.

[0063] In this embodiment, a position where the number of dispatchable personnel is greater than 0 is determined as a dispatchable position, a first work content of the dispatchable position and a second work content of the abnormal area are obtained from the staff, a third preset rule is obtained from the database, the third preset rule includes a matching degree between each two work contents, the matching degree of the first work content and the second work content is found from the third preset rule, the dispatchable positions are sorted from high to low according to the matching degree, and a first sorting result is obtained, each abnormal area corresponds to a first sorting result, and a first distance between the dispatchable position and the abnormal area is calculated. When calculating the first distance, any position in the abnormal area can be selected, which is not specifically limited here. When there are multiple dispatchable positions with the same matching degree or the difference between them is less than the first preset difference (pre-set, not specifically limited here), the dispatchable positions are sorted from small to large according to the first distance. When there are multiple dispatchable positions with the same first distance or the difference between them is less than the second preset difference (pre-set), the dispatchable positions are sorted from small to large according to the first distance. When a scheduling position is selected and the number of staff to be added is not specifically limited here), the dispatchable positions are sorted from large to small according to the number of dispatchable personnel, so as to adjust the first sorting result to obtain a second sorting result. Each abnormal area corresponds to a second sorting result, and the abnormal areas are sorted from large to small according to the number of staff to be added to obtain a third sorting result. Dispatching personnel are arranged for each abnormal area in turn according to the third sorting result. When arranging dispatching personnel for an abnormal area, dispatching personnel are selected in turn from the corresponding second sorting result until the total number of selected dispatchable personnel is equal to the number of staff to be added, and the number of dispatchable personnel of the dispatchable position is modified accordingly, and then the second sorting result of the next abnormal area in the third sorting result is determined, and the personnel dispatch of the next abnormal area in the third sorting result is performed. The personnel adjustment strategy is to dispatch personnel in the abnormal area according to the selected dispatchable personnel. It is worth noting that the above scheduling only determines the number of staff dispatched from each dispatchable position to the abnormal area, and the specific personnel are not specifically limited here.

[0064] If the abnormality type is an aircraft delay abnormality, the delay duration is calculated, and the delay duration = delayed take-off time - standard take-off time. The database stores the correspondence between the delay duration, flight type, number of affected personnel and delay level. The delay level is searched from the database according to the delay duration, flight type and number of affected personnel. Different delay levels correspond to different abnormal response strategies. The abnormal response strategy corresponding to the delay level of the abnormal flight is obtained from the database.

[0065] Step S108: Determine an experience optimization strategy based on the passenger's travel path and the abnormal response strategy.

[0066] The passenger travel path and exception response strategy are jointly determined as the experience optimization strategy.

[0067] Figure 2 A structural block diagram of a digital twin-based smart airport passenger travel experience optimization system 200 provided in an embodiment of the present application.

[0068] like Figure 2 As shown, the smart airport passenger travel experience optimization system 200 based on digital twins mainly includes: The first data acquisition module 201 is used to acquire physical parameters of the airport and passenger flow monitoring data; A twin model building module 202 is used to build a digital twin model of the airport based on physical parameters and passenger flow monitoring data; The second data acquisition module 203 is used to acquire passenger information and flight information, where the passenger information includes the passenger's location information and the flight information includes the flight time period; The flow data simulation module 204 is used to simulate the flow of people based on the flow monitoring data, flight information and the digital twin model to obtain flow data, which is used to characterize the flow of people at various locations in the airport; A travel path planning module 205 is used to plan a travel path for passengers based on location information, flight information, and passenger flow data; An abnormal situation determination module 206, used to determine an abnormal situation based on the passenger flow data and flight information, the abnormal situation including the abnormal type; A response strategy determination module 207, configured to determine an abnormal response strategy based on an abnormal situation; The optimization strategy determination module 208 is used to determine the experience optimization strategy based on the passenger's travel path and the abnormal response strategy.

[0069] As an optional implementation of this embodiment, the traffic data simulation module 204 is also specifically used to simulate human flow based on human flow monitoring data, flight information and a digital twin model to obtain human flow data, including: obtaining ticket sales information, the ticket sales information including the number of tickets sold for each flight and the flight type; determining the arrival human flow and departure human flow at each moment based on the flight type, flight time period and a first preset association; calculating the first predicted human flow at each moment based on the human flow monitoring data, the arrival human flow and the departure human flow; obtaining historical human flow information; analyzing the historical human flow information to obtain the first human flow level for each date and the second human flow level for each moment; obtaining a first weight based on the first human flow level; obtaining a second weight based on the second human flow level; calculating the second predicted human flow based on the first weight, the second weight and the first predicted human flow; inputting the second predicted human flow into the digital twin model for human flow simulation to obtain human flow data for each location.

[0070] As an optional implementation of this embodiment, the travel path planning module 205 is also specifically used to plan the passenger travel path based on location information, flight information and passenger flow data, including: determining the passenger's travel status based on the location information and flight information, the travel status including check-in, transfer and leaving the airport; obtaining the passenger's travel process based on the location information and travel status, the travel process is the way points and / or destination that the passenger needs to reach later; calculating the latest arrival time at the destination based on the flight time period and the first preset duration; obtaining the first spent time corresponding to each way point in the travel process based on the passenger flow data; calculating the leisure time based on the current time, the first spent time and the latest arrival time at the destination; determining the leisure time based on the leisure time and the passenger's preferences and the corresponding segmented time of the leisure items; determining the passenger's travel path based on the travel process, the first spent time, the leisure items and the segmented time.

[0071] As an optional implementation of this embodiment, the travel path planning module 205 is also specifically used to determine leisure items and the corresponding segment durations of the leisure items based on the leisure time and the passenger's preference, including: obtaining the passenger's historical behavior trajectory, the historical behavior trajectory includes leisure items; combining the leisure items by exhaustive method to obtain multiple item combinations; counting the first occurrence times of each item combination in the historical behavior trajectory; counting the number of trajectories in the historical behavior trajectory; calculating the recommendation degree of each item combination based on the number of trajectories and the first occurrence times; determining the number of recommended items based on the leisure time and the first preset rule; screening the item combinations based on the number of recommended items and the recommendation degrees to obtain candidate item combinations; searching for items to be replaced in the candidate item combinations based on the flow of people data; determining recommended replacement items based on the flow of people data and the accommodating flow of people for the leisure items; obtaining the matching degree between the items to be replaced and the recommended replacement items; replacing the items to be replaced with the recommended replacement items based on the matching degree; scoring each candidate item combination based on the flow of people data to obtain a combination score; determining a recommended item combination based on the combination score and the candidate item combination; determining the corresponding segment durations of the leisure items based on the leisure time, the passenger's preference and the recommended item combination.

[0072] As an optional implementation of this embodiment, the travel path planning module 205 is also specifically used to determine the segmented duration corresponding to the leisure items based on the leisure duration, passenger preferences and recommended item combinations, including: obtaining the recommended duration of each leisure item in the recommended item combination; calculating the sum of the recommended durations to obtain the sum of the recommended durations; if the sum of the recommended durations is less than the leisure duration, calculating the remaining duration, the remaining duration = leisure duration - sum of the recommended durations; counting the second occurrence number of each leisure item in the recommended item combination in the historical behavior trajectory; calculating the increased duration corresponding to each leisure item based on the second occurrence number and the remaining duration; and calculating the segmented duration of each leisure item based on the increased duration and the recommended duration.

[0073] As an optional implementation of this embodiment, the response strategy determination module 207 is also specifically used to determine an abnormal response strategy based on an abnormal situation, including: if the abnormal type is abnormal human flow, then determining the abnormal area, the first human flow data and the abnormal time period based on the abnormal situation, the first human flow data being the human flow data of the abnormal area in the abnormal time period; determining the number of required channels and the number of required staff in the abnormal area in the abnormal time period based on the first human flow data and the second preset rule; calculating the number of channels to be added based on the first number of channels and the number of required channels, the first number of channels being the number of channels in the abnormal area in the abnormal time period; calculating the number of staff to be added based on the first number of staff and the number of required staff, the first number of staff being the number of staff in the abnormal area in the abnormal time period; obtaining the second human flow data and the number of second staff at each location of the airport in the abnormal time period; determining the number of dispatchable personnel at each location based on the second preset rule, the second human flow data and the second number of staff; determining the personnel scheduling strategy based on the number of dispatchable personnel and the number of staff to be added; determining the abnormal response strategy based on the number of channels to be added and the personnel scheduling strategy.

[0074] As an optional implementation of this embodiment, the response strategy determination module 207 is also specifically used to determine the personnel scheduling strategy based on the number of dispatchable personnel and the number of staff to be added, including: determining a position where the number of dispatchable personnel is greater than 0 as a dispatchable position; obtaining a first work content at the dispatchable position and a second work content in the abnormal area; determining a matching degree between the first work content and the second work content based on a third preset rule; sorting the dispatchable positions based on the matching degree to obtain a first sorting result; adjusting the first sorting result based on a first distance between the dispatchable position and the abnormal area and the number of dispatchable personnel to obtain a second sorting result; sorting the abnormal area based on the number of staff to be added to obtain a third sorting result; determining a personnel adjustment strategy based on the second sorting result, the number of dispatchable personnel, the third sorting result and the number of staff to be added.

[0075] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0076] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] Figure 3 A structural block diagram of an electronic device 300 provided in an embodiment of the present application.

[0079] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0080] Among them, the processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps of the above-mentioned smart airport passenger travel experience optimization method based on digital twins; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method for operating on the electronic device 300, and application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), programmable read-only memory (Programmable Read-Only Memory, PROM), read-only memory (Read-Only Memory, ROM), magnetic memory, flash memory, disk or optical disk. One or more.

[0081] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0082] The electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the digital twin-based smart airport passenger travel experience optimization method given in the above embodiment.

[0083] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0084] The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and may also be servers, etc.

[0085] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the travel experience of smart airport passengers based on digital twins are implemented.

[0086] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0087] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0088] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for optimizing the travel experience of smart airport passengers based on digital twins, characterized in that: include: Obtain physical parameters of the airport and passenger flow monitoring data; Building a digital twin model of the airport based on the physical parameters and the passenger flow monitoring data; Acquire passenger information and flight information, wherein the passenger information includes the passenger's location information, and the flight information includes the flight time period; Performing a crowd flow simulation based on the crowd flow monitoring data, the flight information, and the digital twin model to obtain crowd flow data, where the crowd flow data is used to characterize the crowd flow situation at various locations in the airport; Planning a passenger travel path based on the location information, the flight information, and the passenger flow data; Determine an abnormal situation based on the passenger flow data and the flight information, where the abnormal situation includes an abnormal type; Determining an abnormal response strategy based on the abnormal situation; An experience optimization strategy is determined based on the passenger travel path and the abnormal response strategy.

2. The method according to claim 1, characterized in that The human flow simulation is performed based on the human flow monitoring data, the flight information and the digital twin model to obtain human flow data, including: Obtaining ticket sales information, the ticket sales information including the number of tickets sold for each flight and the flight type; Determine the arrival passenger flow and the departure passenger flow at each time based on the flight type, the flight time period and the first preset association; Calculate a first predicted human flow at each moment based on the human flow monitoring data, the arriving human flow, and the leaving human flow; Get historical traffic information; Analyze the historical passenger flow information to obtain a first passenger flow level on each date and a second passenger flow level at each moment; Obtaining a first weight based on the first pedestrian flow level; Obtaining a second weight based on the second pedestrian flow level; Calculate a second predicted passenger flow based on the first weight, the second weight, and the first predicted passenger flow; The second predicted passenger flow is input into the digital twin model to perform passenger flow simulation to obtain passenger flow data at each location.

3. The method according to claim 1, characterized in that The planning of the passenger travel path based on the location information, the flight information and the passenger flow data includes: Determine the travel status of the passenger based on the location information and the flight information, where the travel status includes check-in, transfer, and departure from the airport; Acquire the travel process of the passenger based on the location information and the travel status, where the travel process is the waypoints and / or destinations that the passenger needs to reach later; Calculate the latest arrival time at the destination based on the flight time period and the first preset duration; Acquire a first time duration corresponding to each waypoint in the travel process based on the passenger flow data; Calculate the leisure time based on the current time, the first spent time and the latest arrival time at the destination; Determine the leisure items and the segment durations corresponding to the leisure items based on the leisure duration and the passenger's preferences; The passenger travel path is determined based on the travel process, the first spent time, the leisure items and the segmented time.

4. The method according to claim 3, characterized in that The determining of leisure items and the segment durations corresponding to the leisure items based on the leisure duration and the passenger's preference includes: Acquire a historical behavior trajectory of a passenger, wherein the historical behavior trajectory includes leisure items; Combining the leisure items by exhaustive method to obtain multiple item combinations; Counting the first occurrence times of each item combination in the historical behavior track; Counting the number of trajectories of the historical behavior trajectories; Calculating the recommendation degree of each item combination based on the number of trajectories and the first number of occurrences; Determining the number of recommended items based on the leisure time and a first preset rule; Screening the project combination based on the number of recommended projects and the recommendation degree to obtain a candidate project combination; Searching for an item to be replaced in the candidate item combination based on the traffic data; Determine a recommended replacement item based on the passenger flow data and the passenger flow that can be accommodated by the leisure item; Obtaining a matching degree between the item to be replaced and the recommended replacement item; replacing the item to be replaced with the recommended replacement item based on the matching degree; Score each candidate item combination based on the traffic data to obtain a combination score; Determine a recommended project combination based on the combination score and the candidate project combination; The segment durations corresponding to the leisure items are determined based on the leisure duration, the passenger preferences and the recommended item combinations.

5. The method according to claim 4, characterized in that The determining of the segment duration corresponding to the leisure item based on the leisure duration, the passenger preference and the recommended item combination includes: Obtaining the recommended duration of each leisure item in the recommended item combination; Calculate the sum of the recommended durations to obtain a recommended duration sum; If the recommended duration is less than the leisure duration, the remaining duration is calculated, where the remaining duration = the leisure duration - the recommended duration. Counting the second occurrence number of each leisure item in the recommended item combination in the historical behavior track; Calculate the increased duration corresponding to each of the leisure items based on the second number of occurrences and the remaining duration; The segmented duration of each of the leisure items is calculated based on the increased duration and the recommended duration.

6. The method according to claim 1, characterized in that The determining of an abnormal response strategy based on the abnormal situation includes: If the abnormal type is abnormal human flow, then determining an abnormal area, first human flow data and an abnormal time period based on the abnormal situation, wherein the first human flow data is the human flow data of the abnormal area in the abnormal time period; Determine the number of required channels and the number of required staff in the abnormal area within the abnormal time period based on the first human flow data and the second preset rule; Calculate the number of channels to be added based on the first number of channels and the required number of channels, where the first number of channels is the number of channels in the abnormal area during the abnormal time period; Calculate the number of staff to be added based on the first number of staff and the required number of staff, where the first number of staff is the number of staff in the abnormal area during the abnormal time period; Acquire the second passenger flow data at each location of the airport during the abnormal time period and the number of second staff members at each location; Determine the number of dispatchable personnel at each location based on the second preset rule, the second human flow data, and the second number of staff members; Determine a personnel scheduling strategy based on the number of dispatchable personnel and the number of staff to be added; An abnormal response strategy is determined based on the number of channels to be added and the personnel scheduling strategy.

7. The method according to claim 6, characterized in that The determining of the personnel scheduling strategy based on the number of schedulable personnel and the number of personnel to be added includes: Determine the location where the number of dispatchable personnel is greater than 0 as a dispatchable location; Acquire a first work content of the dispatchable position and a second work content of the abnormal area; Determining a matching degree between the first work content and the second work content based on a third preset rule; Sorting the schedulable positions based on the matching degree to obtain a first sorting result; Adjusting the first sorting result based on the first distance between the dispatchable position and the abnormal area and the number of dispatchable personnel to obtain a second sorting result; Sorting the abnormal areas based on the number of staff to be added to obtain a third sorting result; A personnel adjustment strategy is determined based on the second sorting result, the number of dispatchable personnel, the third sorting result, and the number of staff to be added.

8. A smart airport passenger travel experience optimization system based on digital twins, characterized in that: include: A first data acquisition module, used to acquire physical parameters of the airport and passenger flow monitoring data; A twin model construction module, used to construct a digital twin model of the airport based on the physical parameters and the passenger flow monitoring data; A second data acquisition module is used to acquire passenger information and flight information, wherein the passenger information includes the passenger's location information, and the flight information includes the flight time period; A flow data simulation module, used to simulate the flow of people based on the flow monitoring data, the flight information and the digital twin model to obtain flow data, where the flow data is used to characterize the flow of people at various locations in the airport; A travel path planning module, used to plan a passenger travel path based on the location information, the flight information and the passenger flow data; An abnormal situation determination module, used to determine an abnormal situation based on the passenger flow data and the flight information, wherein the abnormal situation includes an abnormal type; A response strategy determination module, used to determine an abnormal response strategy based on the abnormal situation; An optimization strategy determination module is used to determine an experience optimization strategy based on the passenger travel path and the abnormal response strategy.

9. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

Images