Aviation flow prediction method and related device

Abstract

The invention provides an aviation flow prediction method and a related device. The method comprises the following steps: acquiring first aviation passenger flow data and first aviation cargo flow data of a target airport; the first air passenger flow volume data comprises m air passenger flow volumes; the first aviation cargo flow data comprises m pieces of aviation cargo flow; obtaining m pieces of meteorological data, and determining m meteorological evaluation values according to the m pieces of meteorological data; and according to the m aviation passenger flow volumes, the m aviation cargo flow volumes and the m meteorological evaluation values, determining a first aviation flow volume at the first prediction time. According to the invention, reasonable aviation flow prediction can be realized for an airport.

Description

Technical Field

[0001] The present application relates to the field of big data technology or data processing technology, and specifically to an aviation traffic prediction method and related devices. Background Art

[0002] In practical applications, air traffic forecasting can be understood as the prediction of air traffic volume over a period of time. Air traffic forecasting is crucial for improving flight punctuality, optimizing airport resource allocation, and ensuring flight safety. Currently, it's difficult to reasonably predict air traffic for a single airport. Therefore, achieving reasonable air traffic forecasting for specific airports is an urgent issue. Summary of the Invention

[0003] The embodiments of the present application provide an air traffic forecasting method and related devices, which can achieve reasonable air traffic forecasting for airports.

[0004] In a first aspect, an embodiment of the present application provides an air traffic forecasting method, the method comprising: Obtaining a first predicted time, where the first predicted time is a time point after the current moment; Determining a first duration between the first predicted time and the current moment; Determine a sample time length corresponding to the first time length, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length; Acquire first air passenger flow data and first air cargo flow data of a target airport within a preset time period; the first air passenger flow data includes m air passenger flow data; the first air cargo flow data includes m air cargo flow data; m is an integer greater than 1; Acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; The first air traffic volume at the first prediction time is determined according to the m air passenger traffic volumes, the m air cargo traffic volumes and the m meteorological evaluation values.

[0005] In a second aspect, an embodiment of the present application provides an air traffic prediction device, the device comprising: an acquisition unit and a determination unit; wherein: The acquiring unit is configured to acquire a first predicted time, where the first predicted time is a time point after the current moment; The determining unit is configured to determine a first duration between the first predicted time and the current moment; determine a sample time length corresponding to the first duration, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal in length to the sample time length; The acquisition unit is further configured to acquire first air passenger flow data and first air cargo flow data for a target airport within a preset time period, wherein the first air passenger flow data includes m air passenger flow data; the first air cargo flow data includes m air cargo flow data, where m is an integer greater than 1; acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; The determining unit is further configured to determine the first air traffic flow at the first prediction time based on the m air passenger traffic flows, the m air cargo traffic flows, and the m meteorological evaluation values.

[0006] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the steps in the first aspect of the embodiment of the present application.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0008] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0009] The implementation of the embodiments of the present application has the following beneficial effects: It can be seen that the air traffic prediction method and related devices described in the embodiments of the present application obtain a first prediction time, which is a time point after the current moment, determine a first duration between the first prediction time and the current moment, determine a sample time length corresponding to the first duration, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length, and obtain first air passenger traffic data and first air cargo traffic data of the target airport within the preset time period; the first air passenger traffic data includes m air passenger traffic; the first air cargo traffic data includes m air cargo traffic; m is an integer greater than 1, and meteorological data of the preset time period is obtained to obtain m meteorological data. The method can obtain the first air traffic flow at the first prediction time according to the m air passenger traffic, the m air cargo traffic and the m meteorological evaluation values. Firstly, the method can dynamically obtain the corresponding sample data (the air traffic flow data (air passenger traffic data and air cargo traffic data) corresponding to the preset time period) based on the duration between the first prediction time and the current moment. Secondly, the method can predict the overall air traffic flow of the target airport from the two dimensions of air passenger traffic and air cargo traffic in combination with the meteorological change dynamics of the preset time period to ensure the rationality and accuracy of the air traffic flow prediction. Furthermore, the method can help the target airport understand the flight traffic flow in the future in advance, thereby formulating an effective operation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 This is a flow chart of an air traffic forecasting method provided by an embodiment of the present application; Figure 2 This is a schematic diagram illustrating a scenario of air traffic flow in an air traffic flow prediction method provided in an embodiment of the present application; Figure 3 This is a flow chart of another method for predicting air traffic flow provided by an embodiment of the present application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application; Figure 5 This is a block diagram of the functional units of an air traffic prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may also include steps or elements not listed, or may include other steps or elements inherent to the process, method, product, or apparatus.

[0013] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0014] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] The electronic devices involved in the embodiments of the present application may include any device with computing and / or communication functions, and the device may include but is not limited to: smartphones, tablet computers, intelligent robots, vehicle-mounted equipment, servers, quantum computers, aviation command centers, supercomputers, wearable devices, computing devices or other processing devices connected to wireless modems, as well as various forms of user equipment (UE), mobile stations (MS), terminal devices, etc., without limitation here.

[0016] In the embodiments of the present application, a curve fitting algorithm can be understood as a fitting algorithm for obtaining a fitted curve. The curve fitting algorithm can include at least one of the following: least squares method, principal component analysis, support vector machine, genetic algorithm, neural network algorithm, etc., without limitation herein. Relatedly, a line fitting algorithm can be understood as a fitting algorithm for obtaining a fitted line. The line fitting algorithm can include at least one of the following: least squares method, weighted least squares algorithm, genetic algorithm, neural network algorithm, etc., without limitation herein.

[0017] In this application example, air passenger traffic refers to the volume of cargo and mail handled by an airport within a specific timeframe. Air passenger traffic typically includes passenger throughput and cargo and mail throughput. Passenger throughput refers to the total number of passengers arriving and departing an airport within a specific timeframe, while cargo and mail throughput refers to the volume of cargo and mail handled by an airport within a specific timeframe. Both indicators are important parameters for measuring the scale and workload of an airport's operations.

[0018] In the present application, air cargo volume refers to the total volume of cargo transported by air on a specific route or airport within a specific timeframe. This metric is often used to measure the traffic and scale of the air cargo market. The calculation of air cargo volume typically involves the weight and volume of cargo, as well as the frequency and distance of transport.

[0019] See also Figure 1 , Figure 1 FIG. 1 is a flow chart of an air traffic flow prediction method provided in an embodiment of the present application. As shown in the figure, the air traffic flow prediction method includes: 101. Obtain a first predicted time, where the first predicted time is a time point after the current moment.

[0020] In a specific implementation, a first predicted time may be obtained, where the first predicted time is a time point after the current time point. The first predicted time may be a predicted time of a future time point input by the user, for example, 12:20:16 on January 22, 2026.

[0021] The first predicted time may be input by voice or manually.

[0022] 102. Determine a first duration between the first predicted time and the current moment.

[0023] The first duration = the first predicted time - the current moment, that is, the first duration is the length of time between the first predicted time and the current moment.

[0024] 103. Determine a sample time length corresponding to the first time length, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length.

[0025] In a specific implementation, a mapping relationship between a preset duration and a sample time length can be pre-set, and then, a corresponding preset time period can be determined based on the mapping relationship. Specifically, the sample time length corresponding to the first duration can be determined based on the mapping relationship, and the preset time period can be determined based on the sample time length. The preset time period is a time period before the current moment that is equal to the sample time length. That is, the corresponding sample data (air traffic data (air passenger traffic data and air cargo traffic data) corresponding to the preset time period) can be dynamically obtained based on the time length between the first predicted time and the current moment.

[0026] 104. Obtain first air passenger flow data and first air cargo flow data of the target airport within a preset time period; the first air passenger flow data includes m air passenger flows; the first air cargo flow data includes m air cargo flows; m is an integer greater than 1.

[0027] The target airport may be a preset airport or a system default airport. For example, the target airport may be specified by the user. Alternatively, the target airport may include one or more areas of the target airport, which may be specified or system default. Alternatively, the target airport may include one or more routes of the target airport, which may be preset or system default.

[0028] Specifically, each day can correspond to an air passenger flow and air cargo flow, that is, the air passenger flow and air cargo flow can be counted separately for each day. In a specific implementation, first air passenger flow data and first air cargo flow data can be obtained for the target airport within a preset time period. The first air passenger flow data includes m air passenger flow data, and the first air cargo flow data includes m air cargo flow data, where m is an integer greater than 1. m can correspond to m days.

[0029] 105. Obtain meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; the m air passenger flows, the m air cargo flows, and the m meteorological evaluation values ​​all correspond one to one.

[0030] Among them, meteorological data may include at least one of the following: temperature, air pressure, wind direction, wind force, humidity, rainfall, visibility, sunshine duration, radiation conditions, etc., which are not limited here. The meteorological evaluation value can also be a comprehensive meteorological parameter obtained by weighted calculation of at least two types of meteorological parameters among temperature, air pressure, wind direction, wind force, humidity, rainfall, visibility, sunshine duration, and radiation conditions. The comprehensive meteorological parameter is used to evaluate the degree of impact of meteorology on air traffic.

[0031] In a specific implementation, meteorological data for a preset time period can be obtained to obtain m meteorological data. For example, each day can correspond to one meteorological data, and m meteorological evaluation values ​​can be determined based on the m meteorological data. Specifically, each meteorological data can be evaluated accordingly to obtain a corresponding meteorological evaluation value. The meteorological evaluation value represents the degree of impact of meteorology on air traffic.

[0032] There is a one-to-one correspondence between the m air passenger flows, m air cargo flows, and m meteorological evaluation values. That is, each meteorological evaluation value corresponds to one air passenger flow and one air cargo flow. This means that every day corresponds to one air passenger flow, one air cargo flow, and one meteorological evaluation value.

[0033] Optionally, each of the m meteorological data includes multidimensional data; the above step 105 of determining m meteorological evaluation values ​​based on the m meteorological data may be implemented as follows: Evaluate each dimension of first meteorological data to obtain multiple evaluation values, where the first meteorological data is any one of the m meteorological data; A weighted operation is performed on the multiple evaluation values ​​to obtain a meteorological evaluation value corresponding to the first meteorological data.

[0034] In a specific implementation, each of the m meteorological data includes multidimensional data. Specifically, each dimensional data in the first meteorological data can be evaluated to obtain multiple evaluation values. The first meteorological data is any meteorological data among the m meteorological data. For example, each dimensional data can correspond to a mapping relationship between meteorological data and an evaluation value. Then, the evaluation value corresponding to each dimensional data can be determined based on the mapping relationship, that is, multiple evaluation values. The multiple evaluation values ​​are then weighted to obtain the meteorological evaluation value corresponding to the first meteorological data. In this way, the degree of impact of meteorology on air traffic can be accurately evaluated, which helps to ensure the rationality and accuracy of air traffic forecasts.

[0035] 106. When the first duration is within a preset duration range, detect whether the first predicted time belongs to a designated date, where the designated date includes: a holiday date and / or a weekend.

[0036] The preset duration range may be preset or a system default, and the preset duration range may include an upper threshold and a lower threshold, and both the upper threshold and the lower threshold may be preset or a system default.

[0037] In the specific implementation, if the first duration is lower than the lower limit threshold, it means that the predicted time is close to the current time, and there is no need for fitting prediction. It can be directly obtained based on the current air traffic and airport planning. If the first duration is higher than the upper limit threshold, it means that the predicted time is far away from the current time. Considering the accuracy and credibility of meteorological forecasts, the predicted air traffic is of little reference significance. Therefore, the prediction time can be limited to a preset duration range. The preset duration range can be related to the delay rate of the target airport, or the preset duration range can be related to the season, or the preset duration range can be related to the scale or level of the target airport.

[0038] The designated date may include at least one of the following: holidays, weekends, etc., which are not limited here. Holidays may include not only statutory holidays, but also online shopping festivals (for example, "Double 11", "618"), etc., which are not limited here.

[0039] 107. When the first prediction time is not the designated date, the m air passenger flows are screened according to the designated date and a first screening principle to obtain n air passenger flows.

[0040] The first screening principle can be understood as filtering the air traffic data corresponding to a specified date. n is a positive integer less than or equal to m.

[0041] In a specific implementation, when the first prediction time is not a specified date, m air passenger flows can be screened according to the specified date and the first screening principle to obtain n air passenger flow data, that is, the influence of the specified date on the air passenger flow forecast can be excluded.

[0042] 108. Filter the m air cargo flows according to the designated date and the first screening principle to obtain n air cargo flows.

[0043] In a specific implementation, m air cargo flow rates can be screened according to the specified date and the first screening principle to obtain n air cargo flow data, that is, the influence of the specified date on the air cargo flow forecast can be eliminated.

[0044] 109. Determine a first air traffic flow at the first prediction time based on the n air passenger traffic flows, the n air cargo traffic flows, and the m meteorological evaluation values.

[0045] Among them, such as Figure 2 As shown, the air traffic may include air passenger traffic and air cargo traffic, for example, air traffic=air passenger traffic+air cargo traffic.

[0046] In a specific implementation, the first air traffic flow at the first prediction time can be determined based on n air passenger traffic flows, n air cargo traffic flows and m meteorological evaluation values. That is, the overall air traffic flow of the target airport can be predicted from the two dimensions of air passenger traffic flow and air cargo traffic flow in combination with the meteorological change dynamics in the preset time period to ensure the rationality and accuracy of the air traffic flow prediction. Furthermore, it can help the target airport understand the flight traffic flow in the future in advance, and thus formulate an effective operation strategy.

[0047] Optionally, step 109 of determining the first air traffic volume at the first prediction time based on the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values ​​may be implemented as follows: Determining a first air passenger flow curve according to the n air passenger flows, wherein the horizontal axis of the first air passenger flow curve is time and the vertical axis is air passenger flow; determining a first air passenger flow at the first predicted time according to the first air passenger flow curve; Determining a first air cargo flow curve according to the n air cargo flows, wherein the horizontal axis of the first air cargo flow curve is time and the vertical axis is air cargo flow; determining a first air cargo flow rate at the first predicted time according to the first air cargo flow rate curve; Determining a first meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the first meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a first meteorological evaluation value for the first prediction time according to the first meteorological evaluation curve; determining a first weight pair of the first meteorological evaluation value; The first air traffic volume is determined according to the first air passenger traffic volume, the first air cargo traffic volume, and the first weight pair.

[0048] In a specific implementation, each of the n air passenger flows can correspond to a time point, for example, 12:00 a.m. on a given day. The n air passenger flows and their corresponding time points can then be considered n coordinate points, with the horizontal axis corresponding to the coordinate points representing time and the vertical axis representing air passenger flow. Curve fitting is then performed based on these n coordinate points to obtain a first air passenger flow curve, with the horizontal axis representing time and the vertical axis representing air passenger flow. Subsequently, the first air passenger flow at a first predicted time can be determined based on the first air passenger flow curve. Specifically, the first air passenger flow curve can be considered a function, with the first predicted time being the independent variable. Substituting the first predicted time into the function corresponding to the first air passenger flow curve yields the first air passenger flow.

[0049] Accordingly, each of the n air cargo flows can correspond to a time point, for example, 12:00 a.m. on a given day. The n air cargo flows and the time points corresponding to each of the n air cargo flows can be considered n coordinate points, with the horizontal axis corresponding to the coordinate points being time and the vertical axis being the air cargo flow rate. Curve fitting is then performed based on these n coordinate points to obtain a first air cargo flow curve, with the horizontal axis of the first air cargo flow curve being time and the vertical axis being air cargo flow rate. Subsequently, the first air cargo flow rate at the first predicted time can be determined based on the first air cargo flow curve. Specifically, the first air cargo flow curve can be considered a function, with the first predicted time being the independent variable. Substituting the first predicted time into the function corresponding to the first air cargo flow curve yields the first air cargo flow rate.

[0050] Next, each of the m meteorological evaluation values ​​can correspond to a time point, for example, 12 o'clock in a day, then the m meteorological evaluation values ​​and the time point corresponding to each of the m meteorological evaluation values ​​can be regarded as m coordinate points, the horizontal axis corresponding to the coordinate point is time, and the vertical axis is the meteorological evaluation value, and then curve fitting is performed based on the m coordinate points to obtain the first meteorological evaluation curve, the horizontal axis of the first meteorological evaluation curve is time, and the vertical axis is the meteorological evaluation value, and then the first meteorological evaluation value of the first prediction time is determined according to the first meteorological evaluation curve, that is, the first meteorological evaluation curve can be regarded as a function, and the first prediction time can be regarded as an independent variable, and the first prediction time is substituted into the function corresponding to the first meteorological evaluation curve to obtain the first meteorological evaluation value.

[0051] Furthermore, a mapping relationship between preset meteorological evaluation values ​​and weight pairs can be pre-stored, where the weight pair includes a first weight and a second weight, and the value ranges of the first weight and the second weight are both 0~1, and the sum of the first weight and the second weight is between 0~2. Based on the mapping relationship, the first weight pair of the first meteorological evaluation value can be determined, and then the first air traffic flow can be determined based on the first air passenger traffic flow, the first air cargo traffic flow and the first weight pair, then the first air traffic flow = the first weight value × the first air passenger traffic flow + the second weight value × the first air cargo traffic flow.

[0052] In this example, firstly, after excluding the influence of the specified date, the prediction is made based on the continuity of the air passenger flow, and the first air passenger flow corresponding to the first prediction time is obtained. Since the influence of the specified date is excluded, the rationality and accuracy of the air passenger flow prediction can be guaranteed to a certain extent. Secondly, after excluding the influence of the specified date, the prediction is made based on the continuity of the air cargo flow, and the first air cargo flow corresponding to the first prediction time is obtained. Since the influence of the specified date is excluded, the rationality and accuracy of the air cargo flow prediction can be guaranteed to a certain extent. Thirdly, since the weather is not affected by the specified date, in order to ensure the continuity of the weather, the prediction is made based on m weather evaluation values, and the first weather evaluation value for the first prediction time is obtained. The weight pairs corresponding to the weather influence are also determined based on experience, so that the final air traffic is related to time and weather depth, thereby ensuring the rationality and accuracy of the air traffic prediction.

[0053] Optionally, the above step of determining the first meteorological evaluation curve according to the m meteorological evaluation values ​​may be implemented as follows: Determining a first standard deviation of the m meteorological evaluation values; determining a first curve fitting algorithm corresponding to the first standard deviation; Acquire historical meteorological data for the past x years based on the first prediction time and the current time, where x is a positive integer; Determine x meteorological evaluation value sets based on the historical meteorological data of the latest x years; Determining x second standard deviations according to the x meteorological evaluation value sets; Determine the mean of the x second standard deviations to obtain a first mean; determining a first algorithm control parameter of the first curve fitting algorithm corresponding to the first mean; The m meteorological evaluation values ​​are fitted according to the first curve fitting algorithm and the first algorithm control parameters to obtain the first meteorological evaluation curve.

[0054] In a specific implementation, the standard deviation operation can be performed on m meteorological evaluation values ​​to obtain a first standard deviation. The first standard deviation is used to characterize meteorological stability. The mapping relationship between the preset standard deviation and the curve fitting algorithm can be pre-stored. Then, the first curve fitting algorithm corresponding to the first standard deviation can be determined based on the mapping relationship. In this way, a curve fitting algorithm corresponding to the meteorological stability can be obtained, which can ensure the stability and accuracy of the meteorological forecast.

[0055] Next, the historical meteorological data of the last x years can be obtained based on the first prediction time and the current moment, where x is a positive integer, that is, the historical meteorological data of the same time period between the current moment and the first prediction time in recent years can be obtained. Furthermore, x meteorological evaluation value sets can be determined based on the historical meteorological data of the last x years, that is, the historical meteorological data of each day of each year in the historical meteorological data of the last x years can be regarded as a meteorological evaluation value. Based on this method, x meteorological evaluation value sets can be obtained, and the historical meteorological data of each year can correspond to a meteorological evaluation value set.

[0056] Next, a standard deviation calculation can be performed on each of the x meteorological evaluation value sets to obtain x second standard deviations. A mean calculation can then be performed on the x second standard deviations to obtain a first mean value. The first mean value represents the meteorological stability during the same period as the first prediction time. A mapping relationship between a preset mean value and an algorithm control parameter of a first curve fitting algorithm can be pre-stored. The algorithm control parameter of the first curve fitting algorithm is used to control the fitting effect of the first curve fitting algorithm. The fitting effect can include at least one of the following: fitting speed, fitting accuracy, etc., which are not limited herein. Furthermore, a first algorithm control parameter of the first curve fitting algorithm corresponding to the first mean value can be determined based on the mapping relationship. Then, m meteorological evaluation values ​​can be fitted based on the first curve fitting algorithm and the first algorithm control parameter to obtain a first meteorological evaluation curve. On the one hand, a corresponding curve fitting algorithm can be determined based on the current meteorological stability, thereby ensuring the stability and accuracy of the meteorological forecast. On the other hand, historical meteorological data for the same period as the first prediction time can be considered to evaluate the historical meteorological stability of that period, dynamically constraining the fitting effect so that the fitting effect not only conforms to the meteorological change trend of the preset time period, but also conforms to the historical meteorological change patterns of the same period.

[0057] Optionally, the above step of determining the first air passenger flow curve according to the n air passenger flows may be implemented as follows: Performing linear fitting based on the n air passenger flows to obtain a first air passenger flow straight line, where the horizontal axis of the first air passenger flow straight line is time and the vertical axis is air passenger flow; Obtaining the absolute value of the slope of the first air passenger flow straight line to obtain a first absolute value; determining a second curve fitting algorithm corresponding to the first absolute value; The n air passenger flows are fitted according to the second curve fitting algorithm to obtain the first air passenger flow curve.

[0058] In a specific implementation, each of the n air passenger flows can correspond to a time point, for example, 12 o'clock in a day. Then the n air passenger flows and the time point corresponding to each of the n air passenger flows can be regarded as n coordinate points. The horizontal axis corresponding to the coordinate point is time, and the vertical axis is the air passenger flow. Then, a straight line fitting is performed based on the n coordinate points to obtain the first air cargo flow straight line. The horizontal axis of the first air passenger flow straight line is time, and the vertical axis is air passenger flow.

[0059] Next, the slope of the first air passenger flow straight line can be obtained, and the absolute value of the slope can be obtained to obtain a first absolute value. The first absolute value represents the stability of the air passenger flow, or the changing trend of the air passenger flow. This can be based on a mapping relationship between a preset absolute value and a curve fitting algorithm. Furthermore, a second curve fitting algorithm corresponding to the first absolute value can be determined based on the mapping relationship. According to the second curve fitting algorithm, n air passenger flows are fitted to obtain a first air passenger flow curve. In this way, the corresponding curve fitting algorithm can be dynamically determined based on the stability of the air passenger flow, or the changing trend of the air passenger flow, which can ensure the convergence and volatility of the fitting, and further ensure the rationality and accuracy of the air passenger flow prediction.

[0060] Optionally, the following steps may also be included: When the first prediction time is the designated date, the m air passenger flows are screened according to the designated date and the second screening principle to obtain a pieces of air passenger flow data; Filtering the m air cargo flow data according to the specified date and the second filtering principle to obtain a air cargo flow data; The first air traffic volume at the first prediction time is determined based on the a pieces of air passenger traffic volume data, the a pieces of air cargo traffic volume data, and the m meteorological evaluation values.

[0061] In a specific implementation, the second screening principle can be understood as screening out the air traffic related data corresponding to a specified date. a is a positive integer less than or equal to m.

[0062] Specifically, when the first prediction time is a designated date, m air passenger flow data are filtered according to the designated date and the second filtering principle to obtain a air passenger flow data. This eliminates the impact of non-designated dates on the air passenger flow forecast. Accordingly, m air cargo flow data can be filtered according to the designated date and the second filtering principle to obtain a air cargo flow data. This eliminates the impact of non-designated dates on the air cargo flow forecast. Furthermore, the second air flow for the first prediction time can be determined based on the a air passenger flow data, the a air cargo flow data, and the m meteorological evaluation values. This allows the overall air flow at the target airport to be predicted from the two dimensions of air passenger flow and air cargo flow, combining the meteorological dynamics over a preset time period. This ensures the rationality and accuracy of the air flow forecast, and thus helps the target airport understand flight traffic in the future in advance, thereby formulating effective operational strategies.

[0063] Optionally, the above step of determining the second air traffic flow at the first prediction time based on the a pieces of air passenger traffic data, the a pieces of air cargo traffic data, and the m meteorological evaluation values ​​may be implemented as follows: Determining a second air passenger flow curve based on the a air passenger flow rates, wherein the horizontal axis of the second air passenger flow curve is time and the vertical axis is air passenger flow rate; determining a second air passenger flow at the first predicted time according to the second air passenger flow curve; Determining a second air cargo flow curve based on the a air cargo flow data, wherein the horizontal axis of the second air cargo flow curve is time and the vertical axis is air cargo flow; determining a second air cargo flow rate at the first predicted time according to the second air cargo flow rate curve; determining a second meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the second meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a second meteorological evaluation value for the first prediction time according to the second meteorological evaluation curve; determining a second weight pair of the second meteorological evaluation value; The second air traffic is determined according to the second air passenger traffic, the second air cargo traffic, and the second weight pair.

[0064] In a specific implementation, each of the a air passenger flow rates can correspond to a time point, for example, 12:00 a.m. The a air passenger flow rates and their corresponding time points can be considered as a coordinate point, with the horizontal axis corresponding to the coordinate point being time and the vertical axis being air passenger flow rate. A curve fitting is then performed based on the a coordinate points to obtain a second air passenger flow curve, with the horizontal axis being time and the vertical axis being air passenger flow rate. Subsequently, the second air passenger flow rate at the first predicted time can be determined based on the second air passenger flow curve. Specifically, the second air passenger flow curve can be considered a function, with the first predicted time being the independent variable. Substituting the first predicted time into the function corresponding to the second air passenger flow curve yields the second air passenger flow rate.

[0065] Accordingly, each of the a air cargo flows can correspond to a time point, for example, 12:00 a.m., then the a air cargo flows and the time points corresponding to each of the a air cargo flows can be considered as a coordinate points, with the horizontal axis corresponding to the coordinate points being time and the vertical axis being the air cargo flow rate. Curve fitting is then performed based on these a coordinate points to obtain a second air cargo flow curve, with the horizontal axis of the second air cargo flow curve being time and the vertical axis being air cargo flow rate. Subsequently, the second air cargo flow curve can be used to determine the second air cargo flow rate at the first predicted time. That is, the second air cargo flow curve can be considered a function, with the first predicted time being the independent variable. Substituting the first predicted time into the function corresponding to the second air cargo flow curve yields the second air cargo flow rate.

[0066] Next, each of the m meteorological evaluation values ​​can correspond to a time point, for example, 12 o'clock in a day, then the m meteorological evaluation values ​​and the time point corresponding to each of the m meteorological evaluation values ​​can be regarded as m coordinate points, the horizontal axis corresponding to the coordinate point is time, and the vertical axis is the meteorological evaluation value, and then curve fitting is performed based on the m coordinate points to obtain a second meteorological evaluation curve, the horizontal axis of the second meteorological evaluation curve is time, and the vertical axis is the meteorological evaluation value, and then the second meteorological evaluation value of the first prediction time is determined according to the second meteorological evaluation curve, that is, the second meteorological evaluation curve can be regarded as a function, and the first prediction time can be regarded as an independent variable, and the first prediction time is substituted into the function corresponding to the second meteorological evaluation curve to obtain the second meteorological evaluation value.

[0067] Furthermore, a mapping relationship between preset meteorological evaluation values ​​and weight pairs can be pre-stored, where the weight pair includes a first weight and a second weight, and the value ranges of the first weight and the second weight are both 0~1, and the sum of the first weight and the second weight is between 0~2. Based on the mapping relationship, the second weight pair of the second meteorological evaluation value can be determined, and then the second air traffic flow can be determined based on the second air passenger traffic flow, the second air cargo traffic flow and the second weight pair. Then, the second air traffic flow = the first weight value × the second air passenger traffic flow + the second weight value × the second air cargo traffic flow.

[0068] In this example, firstly, after excluding the influence of non-designated dates, the prediction is made based on the continuity of air passenger flow, and the second air passenger flow corresponding to the first prediction time is obtained. Since the influence of non-designated dates is excluded, the rationality and accuracy of the air passenger flow prediction can be guaranteed to a certain extent. Secondly, after excluding the influence of non-designated dates, the prediction is made based on the continuity of air cargo flow, and the second air cargo flow corresponding to the first prediction time is obtained. Since the influence of non-designated dates is excluded, the rationality and accuracy of the air cargo flow prediction can be guaranteed to a certain extent. Thirdly, since the weather is not affected by the designated date, in order to ensure the continuity of the weather, the prediction is made based on m weather evaluation values, and the second weather evaluation value for the first prediction time is obtained. The weight pair corresponding to the weather influence is also determined based on experience, so that the final air traffic is related to time and weather depth, thereby ensuring the rationality and accuracy of the air traffic prediction.

[0069] It can be seen that the air traffic prediction method described in the embodiment of the present application obtains a first prediction time, which is a time point after the current moment, determines the first duration between the first prediction time and the current moment, determines the sample time length corresponding to the first duration, and determines a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length, and obtains the first air passenger traffic data and the first air cargo traffic data of the target airport within the preset time period; the first air passenger traffic data includes m air passenger traffic; the first air cargo traffic data includes m air cargo traffic; m is an integer greater than 1, and meteorological data of the preset time period is obtained to obtain m meteorological data, and m meteorological evaluation values ​​are determined based on the m meteorological data; there is a one-to-one correspondence between the m air passenger traffic, the m air cargo traffic, and the m meteorological evaluation values. When the first duration is within a preset duration range, it is detected whether the first prediction time falls on a specified date, where the specified date includes holidays and / or weekends. When the first prediction time does not fall on a specified date, the m air passenger flows are screened according to the specified date and the first screening principle to obtain n air passenger flows. The m air cargo flows are screened according to the specified date and the first screening principle to obtain n air cargo flows. The first air flow at the first prediction time is determined based on the n air passenger flows, the n air cargo flows, and the m meteorological evaluation values. Firstly, corresponding sample data (air flow data (air passenger flow data and air cargo flow data) corresponding to the preset time period) can be dynamically obtained based on the duration between the first prediction time and the current moment. Secondly, the rationality of the first prediction time (whether it falls within the preset duration range) and the particularity (whether it is a specified date) can be combined. , the overall air traffic of the target airport can be predicted from the two dimensions of air passenger traffic and air cargo traffic in combination with the meteorological changes in the preset time period to ensure the rationality and accuracy of the air traffic forecast. Furthermore, it can help the target airport understand the flight traffic in the future in advance and formulate effective operation strategies.

[0070] See also Figure 3 , Figure 3 FIG. 1 is a flow chart of another method for predicting air traffic flow provided in an embodiment of the present application. As shown in the figure, the method for predicting air traffic flow includes: 301. Obtain a first predicted time, where the first predicted time is a time point after the current moment.

[0071] 302. Determine a first duration between the first predicted time and the current moment.

[0072] 303. Determine a sample time length corresponding to the first time length, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length.

[0073] 304. Obtain first air passenger flow data and first air cargo flow data of the target airport within a preset time period; the first air passenger flow data includes m air passenger flows; the first air cargo flow data includes m air cargo flows; m is an integer greater than 1.

[0074] 305. Acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data.

[0075] 306. Determine a first air traffic flow at the first prediction time based on the m air passenger traffic flows, the m air cargo traffic flows, and the m meteorological evaluation values.

[0076] The detailed description of steps 301 to 306 can refer to the above Figure 1 The steps of the described method for predicting air traffic volume are not limited here.

[0077] It can be seen that the air traffic prediction method described in the embodiment of the present application obtains a first prediction time, which is a time point after the current moment, determines a first duration between the first prediction time and the current moment, determines a sample time length corresponding to the first duration, and determines a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length, obtains the first air passenger traffic data and the first air cargo traffic data of the target airport within the preset time period; the first air passenger traffic data includes m air passenger traffic; the first air cargo traffic data includes m air cargo traffic; m is an integer greater than 1, obtains meteorological data for the preset time period, and obtains m meteorological data , and determine m meteorological evaluation values ​​based on m meteorological data; determine the first air traffic at the first prediction time based on the m air passenger traffic, m air cargo traffic and m meteorological evaluation values. Firstly, the corresponding sample data (air traffic data corresponding to the preset time period (air passenger traffic data and air cargo traffic data)) can be dynamically obtained based on the time between the first prediction time and the current moment. Secondly, the overall air traffic of the target airport can be predicted from the two dimensions of air passenger traffic and air cargo traffic in combination with the meteorological change dynamics of the preset time period to ensure the rationality and accuracy of the air traffic prediction. Furthermore, it can help the target airport understand the flight traffic in the future in advance, so as to formulate an effective operation strategy.

[0078] In accordance with the above embodiment, please refer to Figure 4 , Figure 41 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in the figure, the electronic device includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for performing the following steps: Obtaining a first predicted time, where the first predicted time is a time point after the current moment; Determining a first duration between the first predicted time and the current moment; Determine a sample time length corresponding to the first time length, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length; Acquire first air passenger flow data and first air cargo flow data of a target airport within a preset time period; the first air passenger flow data includes m air passenger flow data; the first air cargo flow data includes m air cargo flow data; m is an integer greater than 1; Acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; The first air traffic volume at the first prediction time is determined according to the m air passenger traffic volumes, the m air cargo traffic volumes and the m meteorological evaluation values.

[0079] Optionally, in determining the first air traffic volume at the first predicted time based on the m air passenger traffic volumes, the m air cargo traffic volumes, and the m meteorological evaluation values, the program includes instructions for performing the following steps: When the first duration is within a preset duration range, detecting whether the first predicted time belongs to a specified date, the specified date including: a holiday date and / or a weekend; When the first prediction time is not the designated date, screening the m air passenger flows according to the designated date and a first screening principle to obtain n air passenger flows; Filtering the m air cargo flows according to the designated date and the first filtering principle to obtain n air cargo flows; The first air traffic volume at the first prediction time is determined according to the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values.

[0080] Optionally, in determining the first air traffic volume at the first predicted time based on the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values, the program includes instructions for performing the following steps: Determining a first air passenger flow curve according to the n air passenger flows, wherein the horizontal axis of the first air passenger flow curve is time and the vertical axis is air passenger flow; determining a first air passenger flow at the first predicted time according to the first air passenger flow curve; Determining a first air cargo flow curve according to the n air cargo flows, wherein the horizontal axis of the first air cargo flow curve is time and the vertical axis is air cargo flow; determining a first air cargo flow rate at the first predicted time according to the first air cargo flow rate curve; Determining a first meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the first meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a first meteorological evaluation value for the first prediction time according to the first meteorological evaluation curve; determining a first weight pair of the first meteorological evaluation value; The first air traffic volume is determined according to the first air passenger traffic volume, the first air cargo traffic volume, and the first weight pair.

[0081] Optionally, in determining the first meteorological evaluation curve according to the m meteorological evaluation values, the program includes instructions for executing the following steps: Determining a first standard deviation of the m meteorological evaluation values; determining a first curve fitting algorithm corresponding to the first standard deviation; Acquire historical meteorological data for the past x years based on the first prediction time and the current time, where x is a positive integer; Determine x meteorological evaluation value sets based on the historical meteorological data of the latest x years; Determining x second standard deviations according to the x meteorological evaluation value sets; Determine the mean of the x second standard deviations to obtain a first mean; determining a first algorithm control parameter of the first curve fitting algorithm corresponding to the first mean; The m meteorological evaluation values ​​are fitted according to the first curve fitting algorithm and the first algorithm control parameters to obtain the first meteorological evaluation curve.

[0082] Optionally, the program further includes instructions for executing the following steps: When the first prediction time is the designated date, the m air passenger flows are screened according to the designated date and the second screening principle to obtain a pieces of air passenger flow data; Filtering the m air cargo flows according to the designated date and the second filtering principle to obtain a air cargo flows; The second air traffic flow at the first prediction time is determined based on the a air passenger traffic flows, the a air cargo traffic flows, and the m meteorological evaluation values.

[0083] Optionally, in determining the second air traffic flow at the first predicted time based on the a air passenger traffic flow, the a air cargo traffic flow, and the m meteorological evaluation values, the program includes instructions for performing the following steps: Determining a second air passenger flow curve based on the a air passenger flow rates, wherein the horizontal axis of the second air passenger flow curve is time and the vertical axis is air passenger flow rate; determining a second air passenger flow at the first predicted time according to the second air passenger flow curve; Determining a second air cargo flow curve based on the a air cargo flow rates, wherein the horizontal axis of the second air cargo flow curve is time and the vertical axis is air cargo flow rate; determining a second air cargo flow rate at the first predicted time according to the second air cargo flow rate curve; determining a second meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the second meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a second meteorological evaluation value for the first prediction time according to the second meteorological evaluation curve; determining a second weight pair of the second meteorological evaluation value; The second air traffic is determined according to the second air passenger traffic, the second air cargo traffic, and the second weight pair.

[0084] Optionally, each of the m meteorological data includes multidimensional data; and in determining the m meteorological evaluation values ​​based on the m meteorological data, the program includes instructions for executing the following steps: Evaluate each dimension of first meteorological data to obtain multiple evaluation values, where the first meteorological data is any one of the m meteorological data; A weighted operation is performed on the multiple evaluation values ​​to obtain a meteorological evaluation value corresponding to the first meteorological data.

[0085] It can be seen that the electronic device described in the embodiment of the present application obtains a first predicted time, which is a time point after the current moment, determines a first duration between the first predicted time and the current moment, determines a sample time length corresponding to the first duration, and determines a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length, obtains first air passenger flow data and first air cargo flow data of the target airport within the preset time period; the first air passenger flow data includes m air passenger flows; the first air cargo flow data includes m air cargo flows; m is an integer greater than 1, obtains meteorological data for the preset time period, obtains m meteorological data, and m meteorological evaluation values ​​are determined based on m meteorological data; a first air traffic at a first prediction time is determined based on m air passenger traffic, m air cargo traffic and m meteorological evaluation values. Firstly, corresponding sample data (air traffic data corresponding to a preset time period (air passenger traffic data and air cargo traffic data)) can be dynamically obtained based on the duration between the first prediction time and the current moment. Secondly, the overall air traffic of the target airport can be predicted from the two dimensions of air passenger traffic and air cargo traffic in combination with the meteorological changes in the preset time period to ensure the rationality and accuracy of the air traffic prediction. Furthermore, this can help the target airport understand the flight traffic in the future in advance, thereby formulating an effective operation strategy.

[0086] Figure 5 : is a functional unit block diagram of an air traffic prediction device 500 involved in an embodiment of the present application. The air traffic prediction device 500 includes: an acquisition unit 501 and a determination unit 502; wherein, The acquiring unit 501 is configured to acquire a first predicted time, where the first predicted time is a time point after the current moment; The determining unit 502 is configured to determine a first duration between the first predicted time and the current moment; determine a sample time length corresponding to the first duration, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length; The acquisition unit 501 is further configured to acquire first air passenger flow data and first air cargo flow data for a target airport within a preset time period, wherein the first air passenger flow data includes m air passenger flow data; the first air cargo flow data includes m air cargo flow data, where m is an integer greater than 1; acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; The determining unit 502 is further configured to determine the first air traffic volume at the first prediction time based on the m air passenger traffic volumes, the m air cargo traffic volumes, and the m meteorological evaluation values.

[0087] Optionally, in determining the first air traffic flow at the first prediction time according to the m air passenger traffic flows, the m air cargo traffic flows, and the m meteorological evaluation values, the determining unit 502 is specifically configured to: When the first duration is within a preset duration range, detecting whether the first predicted time belongs to a specified date, the specified date including: a holiday date and / or a weekend; When the first prediction time is not the designated date, screening the m air passenger flows according to the designated date and a first screening principle to obtain n air passenger flows; Filtering the m air cargo flows according to the designated date and the first filtering principle to obtain n air cargo flows; The first air traffic volume at the first prediction time is determined according to the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values.

[0088] Optionally, in determining the first air traffic volume at the first prediction time according to the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values, the determining unit 502 is specifically configured to: Determining a first air passenger flow curve according to the n air passenger flows, wherein the horizontal axis of the first air passenger flow curve is time and the vertical axis is air passenger flow; determining a first air passenger flow at the first predicted time according to the first air passenger flow curve; Determining a first air cargo flow curve according to the n air cargo flows, wherein the horizontal axis of the first air cargo flow curve is time and the vertical axis is air cargo flow; determining a first air cargo flow rate at the first predicted time according to the first air cargo flow rate curve; Determining a first meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the first meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a first meteorological evaluation value for the first prediction time according to the first meteorological evaluation curve; determining a first weight pair of the first meteorological evaluation value; The first air traffic volume is determined according to the first air passenger traffic volume, the first air cargo traffic volume, and the first weight pair.

[0089] Optionally, in determining the first meteorological evaluation curve according to the m meteorological evaluation values, the determining unit 502 is specifically configured to: Determining a first standard deviation of the m meteorological evaluation values; determining a first curve fitting algorithm corresponding to the first standard deviation; Acquire historical meteorological data for the past x years based on the first prediction time and the current time, where x is a positive integer; Determine x meteorological evaluation value sets based on the historical meteorological data of the latest x years; Determining x second standard deviations according to the x meteorological evaluation value sets; Determine the mean of the x second standard deviations to obtain a first mean; determining a first algorithm control parameter of the first curve fitting algorithm corresponding to the first mean; The m meteorological evaluation values ​​are fitted according to the first curve fitting algorithm and the first algorithm control parameters to obtain the first meteorological evaluation curve.

[0090] Optionally, the air traffic prediction device 500 is further specifically configured to: When the first prediction time is the designated date, the m air passenger flows are screened according to the designated date and the second screening principle to obtain a pieces of air passenger flow data; Filtering the m air cargo flows according to the designated date and the second filtering principle to obtain a air cargo flows; The second air traffic flow at the first prediction time is determined based on the a air passenger traffic flows, the a air cargo traffic flows, and the m meteorological evaluation values.

[0091] Optionally, in determining the second air traffic flow at the first prediction time based on the a air passenger flow, the a air cargo flow, and the m meteorological evaluation values, the air traffic flow prediction device 500 is specifically configured to: Determining a second air passenger flow curve based on the a air passenger flow rates, wherein the horizontal axis of the second air passenger flow curve is time and the vertical axis is air passenger flow rate; determining a second air passenger flow at the first predicted time according to the second air passenger flow curve; Determining a second air cargo flow curve based on the a air cargo flow rates, wherein the horizontal axis of the second air cargo flow curve is time and the vertical axis is air cargo flow rate; determining a second air cargo flow rate at the first predicted time according to the second air cargo flow rate curve; determining a second meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the second meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a second meteorological evaluation value for the first prediction time according to the second meteorological evaluation curve; determining a second weight pair of the second meteorological evaluation value; The second air traffic is determined according to the second air passenger traffic, the second air cargo traffic, and the second weight pair.

[0092] Optionally, each of the m meteorological data includes multidimensional data; in determining the m meteorological evaluation values ​​based on the m meteorological data, the acquiring unit 501 is specifically configured to: Evaluate each dimension of first meteorological data to obtain multiple evaluation values, where the first meteorological data is any one of the m meteorological data; A weighted operation is performed on the multiple evaluation values ​​to obtain a meteorological evaluation value corresponding to the first meteorological data.

[0093] It can be seen that the air traffic prediction device described in the embodiment of the present application obtains a first prediction time, which is a time point after the current moment, determines a first duration between the first prediction time and the current moment, determines a sample time length corresponding to the first duration, and determines a preset time period according to the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length, obtains the first air passenger traffic data and the first air cargo traffic data of the target airport within the preset time period; the first air passenger traffic data includes m air passenger traffic; the first air cargo traffic data includes m air cargo traffic; m is an integer greater than 1, obtains meteorological data for the preset time period, and obtains m meteorological data , and determine m meteorological evaluation values ​​based on m meteorological data; determine the first air traffic at the first prediction time based on the m air passenger traffic, m air cargo traffic and m meteorological evaluation values. Firstly, the corresponding sample data (air traffic data corresponding to the preset time period (air passenger traffic data and air cargo traffic data)) can be dynamically obtained based on the time between the first prediction time and the current moment. Secondly, the overall air traffic of the target airport can be predicted from the two dimensions of air passenger traffic and air cargo traffic in combination with the meteorological change dynamics of the preset time period to ensure the rationality and accuracy of the air traffic prediction. Furthermore, it can help the target airport understand the flight traffic in the future in advance, so as to formulate an effective operation strategy.

[0094] It can be understood that the functions of each program module of the air traffic prediction device of this embodiment can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment and will not be repeated here.

[0095] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0096] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0097] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0098] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0100] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0102] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program code.

[0103] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing related hardware. The program can be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0104] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for predicting air traffic flow, characterized in that: The method comprises: Obtaining a first predicted time, where the first predicted time is a time point after the current moment; Determining a first duration between the first predicted time and the current moment; Determine a sample time length corresponding to the first time length, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal to the sample time length; Acquire first air passenger flow data and first air cargo flow data of a target airport within a preset time period; the first air passenger flow data includes m air passenger flow data; the first air cargo flow data includes m air cargo flow data; m is an integer greater than 1; Acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; The first air traffic volume at the first prediction time is determined according to the m air passenger traffic volumes, the m air cargo traffic volumes and the m meteorological evaluation values.

2. The method according to claim 1, characterized in that The determining the first air traffic flow at the first prediction time according to the m air passenger traffic flows, the m air cargo traffic flows, and the m meteorological evaluation values ​​includes: When the first duration is within a preset duration range, detecting whether the first predicted time belongs to a specified date, the specified date including: a holiday date and / or a weekend; When the first prediction time is not the designated date, screening the m air passenger flows according to the designated date and a first screening principle to obtain n air passenger flows; Filtering the m air cargo flows according to the designated date and the first filtering principle to obtain n air cargo flows; The first air traffic volume at the first prediction time is determined according to the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values.

3. The method according to claim 2, characterized in that The determining the first air traffic volume at the first prediction time according to the n air passenger traffic volumes, the n air cargo traffic volumes, and the m meteorological evaluation values ​​includes: Determining a first air passenger flow curve according to the n air passenger flows, wherein the horizontal axis of the first air passenger flow curve is time and the vertical axis is air passenger flow; determining a first air passenger flow at the first predicted time according to the first air passenger flow curve; Determining a first air cargo flow curve according to the n air cargo flows, wherein the horizontal axis of the first air cargo flow curve is time and the vertical axis is air cargo flow; determining a first air cargo flow rate at the first predicted time according to the first air cargo flow rate curve; Determining a first meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the first meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a first meteorological evaluation value for the first prediction time according to the first meteorological evaluation curve; determining a first weight pair of the first meteorological evaluation value; The first air traffic volume is determined according to the first air passenger traffic volume, the first air cargo traffic volume, and the first weight pair.

4. The method according to claim 3, characterized in that Determining a first meteorological evaluation curve according to the m meteorological evaluation values ​​includes: Determining a first standard deviation of the m meteorological evaluation values; determining a first curve fitting algorithm corresponding to the first standard deviation; Acquire historical meteorological data for the past x years based on the first prediction time and the current time, where x is a positive integer; Determine x meteorological evaluation value sets based on the historical meteorological data of the latest x years; Determining x second standard deviations according to the x meteorological evaluation value sets; Determine the mean of the x second standard deviations to obtain a first mean; determining a first algorithm control parameter of the first curve fitting algorithm corresponding to the first mean; The m meteorological evaluation values ​​are fitted according to the first curve fitting algorithm and the first algorithm control parameters to obtain the first meteorological evaluation curve.

5. The method according to claims 2-4, characterized in that The method further comprises: When the first prediction time is the designated date, the m air passenger flows are screened according to the designated date and the second screening principle to obtain a pieces of air passenger flow data; Filtering the m air cargo flows according to the designated date and the second filtering principle to obtain a air cargo flows; The second air traffic flow at the first prediction time is determined based on the a air passenger traffic flows, the a air cargo traffic flows, and the m meteorological evaluation values.

6. The method according to claim 5, characterized in that The determining the second air traffic flow at the first prediction time according to the a air passenger traffic flow, the a air cargo traffic flow, and the m meteorological evaluation values ​​includes: Determining a second air passenger flow curve based on the a air passenger flow rates, wherein the horizontal axis of the second air passenger flow curve is time and the vertical axis is air passenger flow rate; determining a second air passenger flow at the first predicted time according to the second air passenger flow curve; Determining a second air cargo flow curve based on the a air cargo flow rates, wherein the horizontal axis of the second air cargo flow curve is time and the vertical axis is air cargo flow rate; determining a second air cargo flow rate at the first predicted time according to the second air cargo flow rate curve; determining a second meteorological evaluation curve according to the m meteorological evaluation values, wherein the horizontal axis of the second meteorological evaluation curve is time and the vertical axis is the meteorological evaluation value; determining a second meteorological evaluation value for the first prediction time according to the second meteorological evaluation curve; determining a second weight pair of the second meteorological evaluation value; The second air traffic is determined according to the second air passenger traffic, the second air cargo traffic, and the second weight pair.

7. The method according to any one of claims 1 to 4, characterized in that Each of the m meteorological data includes multidimensional data; and determining m meteorological evaluation values ​​based on the m meteorological data includes: Evaluate each dimension of first meteorological data to obtain multiple evaluation values, where the first meteorological data is any one of the m meteorological data; A weighted operation is performed on the multiple evaluation values ​​to obtain a meteorological evaluation value corresponding to the first meteorological data.

8. An air traffic forecasting device, characterized in that: The device includes: an acquisition unit and a determination unit; wherein, The acquiring unit is configured to acquire a first predicted time, where the first predicted time is a time point after the current moment; The determining unit is configured to determine a first duration between the first predicted time and the current moment; determine a sample time length corresponding to the first duration, and determine a preset time period based on the sample time length; the preset time period is a time period before the current moment that is equal in length to the sample time length; The acquisition unit is further configured to acquire first air passenger flow data and first air cargo flow data for a target airport within a preset time period, wherein the first air passenger flow data includes m air passenger flow data; the first air cargo flow data includes m air cargo flow data, where m is an integer greater than 1; acquire meteorological data for the preset time period to obtain m meteorological data, and determine m meteorological evaluation values ​​based on the m meteorological data; The determining unit is further configured to determine the first air traffic flow at the first prediction time based on the m air passenger traffic flows, the m air cargo traffic flows, and the m meteorological evaluation values.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

Images