CN111047167A

CN111047167A - Line data processing method, device, equipment and storage medium

Info

Publication number: CN111047167A
Application number: CN201911205928.2A
Authority: CN
Inventors: 陈凡
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2019-11-29
Filing date: 2019-11-29
Publication date: 2020-04-21
Anticipated expiration: 2039-11-29
Also published as: CN111047167B

Abstract

The embodiment of the application provides a line data processing method, a line data processing device, line data processing equipment and a storage medium, so that the accuracy of line analysis is improved. The method comprises the following steps: obtaining line data from a server of a line service provider; extracting the characteristics of the line data according to the line, and determining the line characteristic information of the corresponding line, wherein the line is determined according to a starting point and an end point; analyzing the line characteristic information of the target line to obtain a line analysis result of the target line; and determining the adjustment information of the delivery vehicle corresponding to the target line according to the line analysis result, and feeding back the adjustment information to the server of the corresponding line service provider. The corresponding delivery vehicles of each circuit are reasonably adjusted, and the accuracy and the reasonability of analysis and circuit adjustment are improved.

Description

Line data processing method, device, equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing line data, an electronic device, and a storage medium.

Background

The route usually refers to a route through which a moving object passes, different vehicles usually correspond to different routes, such as buses, coaches and the like, usually correspond to passenger transport routes, high-speed trains, ordinary trains and the like, usually correspond to railway routes, the flight of the plane also corresponds to a certain route, and the route is a route for the flight of the plane and can also be called an air traffic route.

The service providers corresponding to the various vehicles often have their own routes, for example, each airline company has a certain route, and flights are set for the routes.

However, each service provider generally provides services independently, for example, the airlines have different flights and passenger flows corresponding to each airline, so it is very difficult to determine the actual situation of each route, such as the passenger flow, which causes a certain difficulty for the reasonable layout of the service provider, but only can determine the route situation through market research and subjective evaluation, and the accuracy is low.

Disclosure of Invention

The embodiment of the application provides a line data processing method to improve the accuracy of line analysis.

Correspondingly, the embodiment of the application also provides a line data processing device, an electronic device and a storage medium, which are used for ensuring the implementation and application of the method.

In order to solve the above problem, an embodiment of the present application discloses a line data processing method, where the method includes: obtaining line data from a server of a line service provider; extracting the characteristics of the line data according to the line, and determining the line characteristic information of the corresponding line, wherein the line is determined according to a starting point and an end point; analyzing the line characteristic information of the target line to obtain a line analysis result of the target line; and determining the adjustment information of the delivery vehicle corresponding to the target line according to the line analysis result, and feeding back the adjustment information to the server of the corresponding line service provider.

The embodiment of the application also discloses a line data processing method, which comprises the following steps: the method comprises the steps that line data are obtained from a server side of a line service provider, wherein the line service provider comprises an aviation line service provider; extracting the characteristics of the line data according to the line, and determining the line characteristic information of the corresponding line, wherein the line is determined according to a starting point and an end point; analyzing the line characteristic information of a target route to obtain a route analysis result of the target route; receiving an adjustment request sent by a server of a target line service provider; determining route adjustment information corresponding to the target route according to the route analysis result; and feeding back the route adjustment information to a server of a corresponding target route service provider.

The embodiment of the application also discloses a line data processing device, the device includes: the data acquisition module is used for acquiring line data from a server of a line service provider; the characteristic extraction module is used for extracting the characteristics of the line data according to the line and determining the line characteristic information of the corresponding line, wherein the line is determined according to a starting point and an end point; the line analysis module is used for analyzing the line characteristic information of the target line to obtain a line analysis result of the target line; and the line adjusting module is used for determining the adjusting information of the delivery vehicle corresponding to the target line according to the line analysis result and feeding back the adjusting information to the server of the corresponding line service provider.

The embodiment of the application also discloses a line data processing device, the device includes: the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring line data from a server of a line service provider, and the line service provider comprises an aviation line service provider; the line extraction module is used for extracting the features of the line data according to the lines and determining the line feature information of the corresponding lines, wherein the lines are determined according to a starting point and an end point; the route analysis module is used for analyzing the route characteristic information of the target route to obtain a route analysis result of the target route; the request receiving module is used for receiving an adjustment request sent by a server of a target line service provider; the route adjusting module is used for determining route adjusting information corresponding to the target route according to the route analysis result; and the feedback module is used for feeding back the route adjustment information to a server of a corresponding target route service provider.

The embodiment of the application also discloses an electronic device, which comprises: a processor; and a memory having executable code stored thereon that, when executed, causes the processor to perform a method as described in one or more of the embodiments of the application.

Embodiments of the present application also disclose one or more machine-readable media having executable code stored thereon that, when executed, cause a processor to perform a method as described in one or more of the embodiments of the present application.

Compared with the prior art, the embodiment of the application has the following advantages:

in the embodiment of the application, line data are acquired from a server of a line service provider, so that a data base of big data analysis can be provided, feature extraction is carried out on the line data according to a line, and line feature information of the corresponding line is determined, wherein the line is determined according to a starting point and an end point; analyzing the line characteristic information of the target line to obtain a line analysis result of the target line; the line extraction features based on big data are analyzed, each line can be accurately analyzed, the adjustment information of the delivery vehicle corresponding to the target line is determined according to the line analysis result, the adjustment information is fed back to the server side of the corresponding line service provider, the delivery vehicle corresponding to each line is reasonably adjusted, and the accuracy and the reasonability of analysis and line adjustment are improved.

Drawings

FIG. 1 is a schematic diagram of a line evaluation process according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating steps of an embodiment of a method for processing line data according to the present application;

FIG. 3 is a flow chart of steps in another embodiment of a line data processing method of the present application;

FIG. 4 is a flow chart of steps in yet another embodiment of a method of line data processing according to the present application;

FIG. 5 is a block diagram of an embodiment of a line data processing apparatus according to the present application;

FIG. 6 is a block diagram of an alternative embodiment of a line data processing apparatus according to the present application;

FIG. 7 is a block diagram of another embodiment of a line data processing apparatus according to the present application;

fig. 8 is a schematic structural diagram of an apparatus according to an embodiment of the present application.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.

The method and the device can be applied to the transportation fields of aviation, land transportation, water transportation and the like, provide support for big data in the transportation field, and evaluate and plan the line. The different transportation modes correspond to one or more vehicles, for example, land transportation includes roads and railways, the vehicles for roads include passenger vehicles and small cars such as private cars, taxis and the like, the vehicles for railways include high-speed rails, motor cars, ordinary trains and the like, the vehicles for water transportation include ships, cruise ships, ferry boats and the like, the vehicles for people to take are taken as examples, and the vehicles for transporting goods can be also included in actual processing. Different transportation modes and vehicles can correspond to different line service providers, for example, for the aviation field, each airline company (abbreviated as airline company) and airport company can be the line service provider, for railway transportation, usually, a railway company is the line service provider, for road transportation, each road passenger company, high-speed manager and the like are the line service providers, for the water transportation field, a ship company can be the line service provider, and other ticket service parties can also be the line service providers, and the ticket service party refers to a service party providing ticket purchasing service.

The data can be acquired from each line service provider, so that the support of big data is provided and the analysis is carried out, and the analysis result can be fed back to each line service provider, so that the data service is provided for the line service provider. The analysis of the line in the embodiment of the application can comprise the evaluation and analysis of the existing line, and can also comprise the analysis and planning of the line which is not opened, so that data support can be provided for each line service provider, and the planning, the adjustment of a carrying tool and the like can be carried out on each line. For example, a suggestion of whether to open up a line for non-opening, a vehicle arrangement for a line, a priority setting for a line, and the like, data support can be performed based on different types of line service providers. Wherein the vehicles are different, and the line types are different, and correspond to the vehicles, and the line types include at least one of the following: aviation, railway, highway and water transportation.

Taking analysis of a line in the fields of aviation, railways, and the like as an example, as shown in a line analysis processing diagram shown in fig. 1, a line service provider provides data, a line analysis system analyzes the data, and a server of the line service provider feeds back analysis results, adjustment information, and the like. At present, in the aviation category, the department A and the department B have routes between Changchun and Lasa, but all departments do not open the routes between Changchun and Ulimuqi, the routes between Changchun and hong Kong, and the routes between Ulumaqi and hong Kong, and under the railway category, routes between all regions are opened up. According to the embodiment of the application, big data analysis can be performed on the basis of data of various transportation modes such as aviation and railway, the developed and unformed lines are analyzed, the data barrier is broken, analysis results, adjustment suggestions and the like are provided for various line service providers, and the accuracy is high.

In step 102, line data is obtained from a server of a line service provider. The basic data can be obtained from the service terminals of the line service providers such as various airlines, ticket platforms, airports and the like, and the data can also be obtained from the service terminals of the line service providers of other transportation modes such as railway companies and the like to be used as auxiliary data to analyze the airlines. These line service providers may provide a data interface of the service end so that basic data, which may be referred to as line data, including line information between the respective start and end points, may be acquired through the data interface. For the aviation field, the route data may include route data for travel by aviation, such as flight data of each route of each airline department, riding data of each flight, and the like, and in one example, the route data may include air ticket data, air travel data, airline department data, and the like. For example, air ticket data may be acquired from a ticket provider, an airline company, or the like, and air travel data, airline company data, or the like may be acquired from an airline company, an airport, or the like. Based on an actual application scenario, data can be acquired from a server of one or more line service providers, for example, for line analysis and suggestion of a certain line service provider, only line data of the line service provider can be acquired for processing, and for analysis of lines of the whole network, line data can be acquired from each line service provider of the whole network.

The line data can be acquired from the server of each line service provider periodically or in real time or as needed. The method for obtaining the line data in advance can be stored in a corresponding database, corresponding data can be extracted from the database when analysis is needed, corresponding storage rules such as cleaning in excess of term can be set for the line data stored in advance in the database, and the analysis result obtained by each analysis can also be stored and taken out for use when needed, for example, analysis is performed every quarter and every year, and the analysis result in every quarter can be kept for use when annual analysis is performed.

In some other embodiments, the route data corresponding to other transportation modes can also be used as auxiliary data to provide reference data for the target transportation mode for analyzing the route. Therefore, the line data can be acquired from the service end of the corresponding line service provider in other transportation modes according to requirements. In other examples, during the process of analyzing the global routes of various transportation modes, route data of each transportation mode can be acquired separately for analysis, and the route data can be determined according to requirements.

Taking the route data of the aviation mode as an example, the route data comprises air ticket data, aviation travel data and aviation department data. The railway mode is taken as an example, and the line data includes train ticket data, railway trip data, railway data and the like.

The air ticket data includes air ticket data of each flight of each airline company, including flight information, number of air tickets, total price of air tickets, average price of air tickets, number and price information of air tickets of different levels, and the like, and of course, the air ticket data may also include, for each air ticket: the passenger information, such as user name, user identification and other data, may also include ticket service provider information of the ticket service platform that purchased the airline tickets, and aircraft information of the corresponding aircraft, such as model information. The corresponding train ticket data is various information of the train ticket data of each train of the railway company.

The flight travel data refers to travel data from a starting point to an end point, and can include flight information from the starting point to the end point, such as time information including departure time and arrival time, navigation information, airplane information of a used airplane, and the like. The air travel data refers to data of a transportation route between ODs (origin-destination), and may be understood as data of an air travel amount and the like. The train trip data is trip data from a start point to an end point in a train mode, and includes information on each train from the start point to the end point.

The airline company data refers to various data of an airline company (abbreviated as airline company), including routes of each airline company, flight information corresponding to each route, flight information corresponding to each flight, and the like, and further including actual seating information corresponding to each flight, and the like, such as the number of passengers on a designated flight, ticket sales, and the like. The railway data refers to various data of a railway company, including railway lines, train information of each line, and the like.

Thus, it is possible to perform analysis of the line based on various data related to the line provided by each line service provider, including various contents such as the traffic volume and the growth amount of the line. After line data is acquired through the data interface, the data can be cleaned, null data, data which do not accord with data rules and the like are filtered. And then may be based on analytical processing of the line data.

Then, in step 104, feature extraction is performed on the line data according to the line, and line feature information of the corresponding line is determined. Since a route is usually specified in accordance with a start point and an end point, the feature extraction, analysis, and the like of route data are often processed in accordance with the route. Therefore, for the acquired line data, the subsets can be divided according to the line, each line corresponds to one data set, and for the data set of each line, the data set of each line can be further divided according to the line type, each line type corresponds to one data subset, so that the processing can be carried out based on the data subsets and the data sets. The line data in each data subset can be extracted, and the extraction mode can be determined according to corresponding extraction rules, for example, data of some designated fields are extracted, numerical values of some features are counted, and line feature information of each line type under a certain line is obtained.

In an optional embodiment, the performing feature extraction on the line data according to lines to determine line feature information of a corresponding line includes: determining a corresponding line type according to a carrier, and determining a time range; screening the line data corresponding to the line according to the line type and the time range, and determining corresponding sampling line data; and performing feature extraction on the sampled line data to obtain line feature information of at least one line type corresponding to the line.

The type of the line can be determined according to vehicles, such as airplanes, trains and automobiles, and the trains can be divided into high-speed trains (such as high-speed trains and motor train units), ordinary trains and the like. In this case, different vehicles are different in actual routes between a starting point and a terminal point, for example, an aircraft often flies according to a flight route, an automobile usually travels on a highway route, a train travels on a railway route, and in addition, a high-speed train and a general train may travel on different railway routes, so that the types of routes may be classified according to the vehicles.

For the extraction of the features, the processing can be performed according to time, for example, data of last year, half year, 3 months, month, week and the like are extracted, and different features can be counted in different time ranges. For example, a week or a month may be used to extract features such as the number of features, while a half year or a year may be used to extract features such as the amount of growth. Therefore, one or more time ranges of the features to be extracted can be selected, then the line data corresponding to the line is screened according to the line type and the time range, the corresponding sampling line data is determined, then the feature extraction is carried out on the sampling data according to the extraction rule corresponding to the line type and the time range, the line feature information under the line type is obtained, and the line feature information of at least one line type can be obtained according to the extracted line.

In an optional embodiment, the performing feature extraction on the sampled line data to obtain line feature information of at least one line type corresponding to the line includes: determining at least one characteristic dimension according to the line type; and performing feature extraction on the sampled line data according to at least one feature dimension to obtain line feature information of at least one line type corresponding to the line. For each line type, the feature dimensions to be extracted, such as user dimensions, bill dimensions, growth dimensions, quantity dimensions and the like, can be determined, feature extraction can be performed on the sampled line data according to at least one feature dimension, line feature information under each feature dimension is obtained, and line feature information of a plurality of feature dimensions of the line under each line type is obtained.

Wherein, taking an air transportation mode as an example, the characteristic dimension may include at least one of the following: user dimension, airport dimension, volume of bargaining, volume of bargaining dimension, growth rate dimension. A time range, such as 30 days, 60 days, 180 days, etc., or a last month, a quarter, a half year, a year, etc., may be designated, and then features of corresponding line data, such as extracted ticket data, airline travel data, and airline driver data, may be extracted according to the time range and feature dimensions, and features under the time range and feature type may be designated.

For example, for the user dimension, feature extraction may be performed on the line data according to the user dimension, and passenger information including the number of passengers may be extracted. The user quantity information corresponding to the route in the specified time range may be counted, for example, the number of passengers per day (or other time range) and per flight of the route, or the number of passengers in a set time period (e.g., 30 days, 60 days, 180 days, etc.) of the route, that is, the number of users taking flights, may be determined. For example, information of each flight corresponding to the airline can be extracted from the air travel data, a time range needing to be counted is determined, then, ticket data of the corresponding flight is inquired according to the time range, the number of passengers corresponding to the flight is determined, and the number of users of the flight in the time range is obtained; or extracting flight information corresponding to the routes within the specified time range from the airline travel data, inquiring the ticket data according to the flight information, determining the number of passengers of the flights corresponding to the routes within the specified time range, and the like. For example, the number of passengers for the last 30 days, 60 days, 180 days, 360 days, etc. of the specified OD is extracted.

For the airport dimension, the number of airports in a specified place or area range can be extracted, for example, the number of airports in each city in China can be counted, and the number of airports can be directly obtained, and can also be obtained based on statistics of each travel data, navigation department data and the like.

For the volume dimension, for the line, the corresponding flight information can be extracted from the airline travel data, then the airline data is inquired according to the flight information, and the volume information of each flight corresponding to the line in the specified time is determined. For the aviation field, the volume of the transaction can be determined according to the number of tickets sold by flights, such as the number of the transactions of the last 30 days, 60 days, 180 days and 360 days between specified ODs, or the number of the transactions of each flight per day between the specified ODs.

For the transaction amount dimension, generally, the transaction amount refers to the total transaction amount, and the transaction amount corresponding to the line may be extracted as aviation characteristic information, where information of each flight corresponding to the line may be extracted from the aviation travel data, and then, the airline data may be queried according to the flight information, and the transaction information of each flight corresponding to the line within a specified time is determined, including cost information of each flight, such as the amount of the air ticket, for example, the cost of each air ticket may be accumulated in one example, the average cost of each air ticket corresponding to each flight may be calculated in other examples, and the transaction amount may be calculated according to the average cost and the transaction amount, so that the transaction amount of each flight per day between specified ODs may be extracted, or the transaction amount of the latest 30 days, 60 days, 180 days, 360 days, and the like between specified ODs may be extracted. The method comprises the steps of extracting the volume of the air traffic, extracting flight information corresponding to lines in a specified time range from air travel data according to a similar extraction mode of the volume of the air traffic, inquiring airline department data according to the flight information, and determining the volume of the lines corresponding to the lines in the specified time range and the cost information of the air tickets, such as the cost of each air ticket, the average cost of the air tickets and the like.

For the dimension of the increase rate, the increase rate may be a volume of deals, an increase rate of the number of passengers, and the like, and may specifically be determined according to the demand, wherein the volume of deals, the volume of passengers, and the number of passengers, corresponding to the lines within more than one specified time range, may be determined, and then, according to the line characteristic information corresponding to the lines within more than one specified time range, the corresponding increase rate information may be determined, and the increase rate information includes at least one item: passenger number growth rate, volume growth rate; after extracting the aviation feature information such as the number of the users, the volume of the deals, and the like, the corresponding growth rate can be determined according to the chronological order, such as the growth rate of each month, each quarter, half a year, one year, and the like, wherein the growth rate can be determined according to the requirements, such as the growth of each month (quarter, year) over the last month (quarter, year) or the growth of the month (quarter) over the same period of the last year, and the like.

The route characteristic information of the aviation is extracted from multiple dimensions by using the characteristics of the aviation travel data in the route data, and the required aviation characteristic information can be determined according to the requirement in the actual processing. The aviation route characteristic information can be used for evaluating the value of the existing airlines of the airlines, such as the value of the existing airlines of the airlines and the value of airlines of other airlines.

In other embodiments, routes are not opened between the starting point and the ending point, and the routes can be analyzed and evaluated in an auxiliary mode based on the existing characteristic information of the routes of other types of routes, for example, the value of the routes is evaluated through a railway train transportation mode, and whether the routes are opened or not is determined.

For the train mode, the dimensionality of the train mode can also comprise a flow dimensionality besides the user dimensionality, the traffic volume dimensionality and the like, wherein when a user takes a train, the user can surf the internet through mobile equipment such as a mobile phone and a tablet personal computer, so that the user condition corresponding to the line can be evaluated through the flow dimensionality, and the value of the air route is further evaluated.

In an embodiment of the present application, the step of extracting, from at least one dimension, corresponding line feature information for the line data corresponding to the line within the specified time range includes at least one of:

extracting train information corresponding to lines within a specified time range from the train travel data, inquiring train ticket data according to the train information, and determining the number of passengers of trains corresponding to the lines within the specified time range; the train information corresponding to the lines in the appointed time range can be extracted from the train travel data, the train travel data can be extracted according to the appointed time range and the lines to obtain the corresponding train information, the train ticket data corresponding to the appointed time range and the lines can be inquired according to the train information, one or more trains and the train ticket data sold by each train can be determined, and therefore the number of passengers of the trains corresponding to the lines in the appointed time range can be obtained. Such as the number of passengers who took the train within the last 30 days, 60 days, 180 days, 360 days, etc., for extracting the specified OD.

Extracting train information corresponding to lines in a specified time range from train travel data, inquiring railway data according to the train information, and determining traffic volume information of trains corresponding to the lines in the specified time range; the train information corresponding to the line in the specified time range can be extracted from the train journey data, the train journey data can be extracted according to the specified time range and the line to obtain corresponding train information, the train data corresponding to the specified time range and the line are inquired according to the train information to obtain one or more trains and ticket data sold by each train, and therefore the traffic information of the train corresponding to the line in the specified time range is obtained, for example, the number of tickets sold in the last 30 days, 60 days, 180 days and 360 days by the specified OD is extracted.

Train information corresponding to the line in the specified time range is extracted from the train travel data, railway data is inquired according to the train information, and mobile signaling information of the train corresponding to the line in the specified time range is determined. The train information corresponding to the line in the specified time range can be extracted from the train travel data, the train travel data can be extracted according to the specified time range and the line to obtain the corresponding train information, the railway data corresponding to the specified time range and the line is inquired according to the train information to obtain the mobile signaling information of the mobile equipment used by the passenger, and for example, the base station information along each train is determined, the mobile signaling information of the mobile equipment used by the passenger is determined based on the base station, and the like.

Therefore, the characteristic extraction can be carried out on the corresponding line data based on the actual line type, and various required line characteristic information can be obtained.

In other examples, since the line data of different line types are usually obtained by different line service providers, after the line data is obtained, feature extraction may be performed according to the line service providers, and the extracted data is clustered according to the lines to obtain line feature information of each line in each line type. The line characteristic information can keep the characteristics of the line, such as the user quantity, the distance and the like.

And 106, analyzing the line characteristic information of the target line to obtain a line analysis result of the target line.

The line can be analyzed according to the characteristics, the analyzed content can be determined according to the requirements of a line service provider, such as the number of annual users, the growth amount, the turnover amount and the like of the line, the service types provided by the line and the like are analyzed, or the possible user amount of the line is estimated for the line which is not opened up, so that the planning of business service is carried out, and the like, and various required line analysis results can be obtained based on the line characteristic information. Taking the aviation field as an example, the route characteristic information of the target route can be analyzed to obtain a route analysis result of the target route.

In an optional embodiment of the present application, analyzing the line characteristic information of the target line to obtain a line analysis result of the target line includes: determining a target line and a target line type; and performing line analysis on the line characteristic information of the target line according to the type of the target line, and determining a line analysis result of the type of the target line under the target line. The method includes determining a target line and a target line type, where the target line corresponding to the target line type may be a developed line or an unformed line, or for different line service providers, some line providers provide services for the line, and some line service providers do not provide services for the line, and the determination may be specifically determined according to requirements.

After the target line and the type of the target line are determined, line analysis can be performed on line characteristic information of the target line, wherein the line analysis is performed on the line characteristic information of the type of the target line corresponding to the target line, and the analysis also includes the analysis performed by adopting line characteristics of other line types to assist the analysis of the target line or estimate the condition of the target line and the like, so that a required line analysis result is obtained. For the above-mentioned line analysis based on the features, the analysis can be performed by a machine learning method such as supervised learning.

In an optional embodiment, the performing, according to the target line type, line analysis on the line characteristic information of the target line to determine a line analysis result of the target line type under the target line includes: and performing line analysis on the line characteristic information of the target line corresponding to the target line type in a supervised learning mode, and determining a line analysis result of the target line type under the target line. The method for performing line analysis on the line characteristic information of the target line type corresponding to the target line in a supervised learning mode to determine the line analysis result of the target line type under the target line comprises the following steps: and inputting the line characteristic information of the target line corresponding to the type of the target line into a first line analyzer to obtain a line analysis result output by the first line analyzer, wherein the first line analyzer is obtained by training in a supervised learning mode.

The required line analyzer may be trained before analysis, where the line analyzer is an analyzer trained in a machine learning manner, and the line analyzer may also be referred to as a line analysis model, line mapping information, a line data set, and the like, and is obtained by training a mathematical model, for example, training a first line analyzer by using a supervised learning model such as an eXtreme Gradient boost (XGBoost) model and a decision tree model, or training a second line analyzer in an unsupervised learning manner. The mathematical model is a scientific or engineering model constructed by using a mathematical logic method and a mathematical language, and is a mathematical structure which is generally or approximately expressed by using the mathematical language aiming at the characteristic or quantity dependency relationship of a certain object system, and the mathematical structure is a pure relationship structure of a certain system which is described by means of mathematical symbols. The mathematical model may be one or a set of algebraic, differential, integral or statistical equations, and combinations thereof, by which the interrelationships or causal relationships between the variables of the system are described quantitatively or qualitatively. In addition to mathematical models described by equations, there are also models described by other mathematical tools, such as algebra, geometry, topology, mathematical logic, etc. Mathematical models describe the behavior and characteristics of a system rather than the actual structure of the system.

For the supervised learning training, the line feature information may be extracted from the historical data, in a manner similar to that in step 104, the historical line feature information of each line under each line type is extracted from the historical data, the historical line feature information is used as training data, and the training data is input into the supervised learning mathematical model for model training, so as to obtain a corresponding first line analyzer. After the historical line characteristic information in the training data is input into the selected mathematical model, corresponding output data can be obtained, such as the score of a certain specified evaluation information of the output data, and such as the score of a certain classification result or classification result of the output data, and the like, then the output data can be calculated based on loss functions, backward propagation and other modes, the parameters of the mathematical model are adjusted until the obtained output data meets the corresponding model analysis standard, such as a certain threshold value, and the required first line analyzer can be obtained. For example, the analysis result is the total amount of the transaction, the number of the users, and the like corresponding to a certain time.

The first route analyzer may be trained to analyze the route characteristic information, so that the route characteristic information of the target route type corresponding to the target route extracted in step 104 may be input into the first route analyzer, and processed by the first route analyzer, so as to obtain corresponding route evaluation information, such as an analysis result of a total volume of a year, a monthly passenger flow, and various analysis results, such as an average fare, a number of vehicles, and a time distribution. An assessment of the route may be made based on the route characteristic information. As shown in fig. 1, currently, the department a and department B have routes from vinpoch to losa, and feature extraction and route analysis can be performed based on the airline travel data of the department a and department B, so as to determine that the route evaluation score of the route between vinpoch and losa is S2.

In other embodiments, if the line is not opened up under some target line type, the result of the target line type can be estimated by the characteristics of other line types, the possibility under the target line type is analyzed, and a suggestion is made, etc. The line characteristic information can be subjected to line analysis in an unsupervised learning mode, and a corresponding line analysis result is determined. Among them, unsupervised learning is also a machine learning method, and its difference with supervised learning is that training data has no label, and after inputting training data into unsupervised learning mathematical model, the mathematical model can automatically learn, such as unsupervised learning by cluster analysis and other methods.

In an optional embodiment of the present application, the performing, according to the target line type, line analysis on the line characteristic information of the target line to determine a line analysis result of the target line type under the target line includes: and performing line analysis on the line characteristic information of the target line corresponding to other line types in an unsupervised learning mode, and determining a line analysis result of the target line type under the target line, wherein the other line types comprise line types except the target line type. The line analysis is performed on the line characteristic information of the target line corresponding to other line types in a supervised learning mode, and the line analysis result of the line type under the target line is determined, wherein the line analysis result comprises the following steps: and inputting the line characteristic information of the target line corresponding to other line types into a second line analyzer to obtain a line analysis result output by the second line analyzer, wherein the second line analyzer is obtained by training in an unsupervised learning mode.

For the unsupervised learning mode, historical line characteristic information can be obtained based on historical data, the characteristic extraction step is similar to the step 104, then the historical line characteristic information can be input into a mathematical model of unsupervised learning, an analysis result between the ODs is obtained through processing, the mathematical model is fed back through a reward and punishment mechanism, the model is trained and adjusted continuously until a standard meeting the corresponding model analysis is obtained, and a corresponding second line analyzer is obtained. The line characteristic information may then be input into the second line analyzer in step 106 to obtain a corresponding line analysis result. As shown in fig. 1, at present, no routes between vinpocetine and wuluqi, routes between vinpocetine and hong kong, and routes between wuluqi and hong kong are opened up, and feature extraction and analysis processing of route data can be performed based on transportation modes such as trains, so that the route evaluation score of the route between vinpocetine and wuluqi is determined to be S1, the route evaluation score of the route between vinpocetine and hong kong is S3, the route evaluation score of the route between wuluzuqi and hong kong is S4, each evaluation score is used as a route analysis result, and it can be determined whether to open the route based on the evaluation score, and flight class recommendation and the like corresponding to the route.

In addition, for routes such as routes and railways which are not opened up, analysis can be performed based on characteristics of other route types of transitions and sections to obtain a route analysis result, and the route analysis result can be used for determining whether to open a corresponding route or not.

In one example, the output of the second line analyzer is a composite score for a line, Y w1 first line characteristic + w2 second line characteristic + w3 third line characteristic; wherein w1, w2 and w3 are weights occupied by the characteristics of each line and can be determined through experimental tests. Then by weighted calculation of the plurality of line characteristics, a composite score of the line can be obtained as a line analysis result.

Taking the analysis applied to the railway characteristics as an example, the composite score Y ═ w1 ═ train number of tickets + w2 · train number of tickets sold + w3 · mobile phone signaling traffic. Based on the line characteristic information corresponding to the railway mode, under the condition that the line is not opened correspondingly in the modes of aviation, highway and the like, the estimation result of the line corresponding to other line types provides a suggestion for whether the line is opened up or not in other modes.

And 108, determining the adjustment information of the carrier corresponding to the target route according to the route analysis result, and feeding back the adjustment information to the server of the corresponding route service provider.

After the line is analyzed through modes of supervised learning, unsupervised learning and the like, a line analysis result can be obtained, and then the adjustment information of the target line corresponding to the carrier can be determined according to the line analysis result. The line analysis result can be used to determine the value of the line, such as the passenger flow volume of the line, the ticket price corresponding to the line, the operation cost, the profit, the customer growth situation, and the like. Therefore, based on the line analysis result, the adjustment of the line, the vehicle and the like corresponding to each line service provider can be performed, including the adjustment of various angles, for example, for a line which is not opened under certain types, whether to open the line can be determined based on the line analysis result, and for a line which is opened under certain types, the layout of the vehicle, the priority arrangement of the line and the corresponding pricing of the line can be performed based on the line analysis result.

In one example, the determining, according to the line analysis result, adjustment information of a vehicle corresponding to the target line includes: and according to the line analysis result corresponding to the line type, laying out the delivery vehicle corresponding to the target line, and determining corresponding tool arrangement information. When the carrier is laid out, the line service providers corresponding to the line types, such as the airline department corresponding to the airline class and the transportation company corresponding to the highway class, can be determined, so that the carrier is laid out according to the line analysis result for the target line by the line service providers selected under the line types, including the arrangement of the shift and the operation time of the carrier, different layouts are also corresponding to different types, for example, the arrangement of the direct layout, the stop and the transfer can be performed for the airline class, the corresponding stop position, the transfer flight and the like are also determined for the stop and the transfer, the types of the flights are also included, and the number of the carrying users corresponding to the flights of different types is different. For example, for a railway class, a shift event of each type of train such as a high-speed rail and a normal train, a station at which the train stops in the middle, and the like can be determined, and the number of users that can be carried by each train, and the like can be obtained, so that based on the layout of the carriers, tool arrangement information including various information such as a tool model, shift information, stop information, the number of users that can be carried, and tool arrangement information of a corresponding line service provider in a certain time (such as every day, every week, and every month) can be obtained, and tool arrangement information including tool arrangement information of an individual target line, tool arrangement information of a whole network line, and the like can be.

In another example, the determining, according to the line analysis result, vehicle adjustment information corresponding to the target line includes: and formulating cost information corresponding to the target line according to the line analysis result corresponding to the line type. For the line analysis result corresponding to each line type, various cost information such as ticket price, cost, income and the like of the line in a certain time and traffic information such as passenger flow and the like can be determined, so that the cost information of a target line can be specified based on the line analysis result, such as the ticket price corresponding to carriers of different types, the ticket price of seats of different levels under the carriers and the like, the cost information such as different running time of the carriers under the target line, the ticket price corresponding to a stop and the like can also be specified, and the promotion information of ticket business and the like in a certain time can be further specified, so that the cost information of the carriers can be dynamically adjusted based on the line analysis result, and the planning of the line can be reasonably arranged.

In another example, the determining, according to the line analysis result, vehicle adjustment information corresponding to the target line includes: opening up a target line corresponding to the line type according to the line analysis result; and determining the tool arrangement information of the vehicle corresponding to the target line. In the embodiment of the application, for a line not opened by some vehicles or a line not opened by some line service providers, the analysis of the target line can be performed based on the characteristics of the line type corresponding to other vehicles or the characteristics of the line type corresponding to the vehicle by other line service providers, so that a data base can be provided based on data of the whole network. Correspondingly, for a line which is not opened up by a certain line type or an un-opened line which corresponds to a line service provider by a certain carrier, whether the line can be opened up or not can be determined based on the line analysis result, the passenger flow rate which is estimated corresponding to the opened-up line and the like can be opened up, the line service provider which needs the line opening-up analysis can also set a corresponding opening-up rule, the suggestion of the line can be provided for the line service provider by meeting a certain opening-up rule, for example, the passenger flow rate is estimated to reach a certain user threshold value and the like, then the target line which corresponds to the line type can be determined to be opened up, the target line can be laid out based on the line analysis result, the tool arrangement information which corresponds to the carrier can be determined, for example, the number of the scheduled fine and smooth, the model of the carrier which corresponds to each scheduled, the line duration information, revenue, etc.

In another example, the determining, according to the line analysis result, vehicle adjustment information corresponding to the target line includes: sequencing the target lines according to line analysis results of the target lines to obtain line sequencing results; and configuring priority information of the corresponding vehicles according to the line sequencing result. For each line service provider, by serving multiple lines under the line type served, therefore, for each line service provider, the line of the whole network can be sorted and the priority information of the line can be set, the lines of the entire network of the line service provider can be sorted according to the corresponding line analysis results, wherein, the sorting can be carried out according to the weighted result of one or more contents in the analysis result, which can be determined according to the requirement, thereby obtaining the line sorting result, priority information for the lines may then be determined according to the line sequencing results, and may be configured as priority information for line allocation vehicles, etc., such as priority information provided to each line by various models of vehicles, so that the setting of priority of the intelligence based on the line analysis, the configuration of the vehicle, and the like can be made.

On the basis of the above embodiments, the embodiments of the present application further provide a line data analysis method, which can provide corresponding help for each line service provider by analyzing the characteristics of a line through big data based on data of the line service provider.

Referring to fig. 2, a flowchart illustrating steps of an embodiment of a method for analyzing line data according to the present application is shown.

Step 202, obtaining line data from a server of a line service provider. The line service provider may be one or more, and may specifically be determined according to a requirement, an application scenario, and the like, for example, for analysis of its own line, the line service provider's server may provide line data, and for analysis of the line of the whole network, the line service provider's server may provide line data.

Step 204, determining the corresponding line type according to the vehicle, and determining the time range.

And step 206, screening the line data corresponding to the line according to the line type and the time range, and determining corresponding sampling line data.

And 208, performing feature extraction on the sampled line data to obtain line feature information of at least one line type corresponding to the line.

Wherein, extracting the characteristics of the sampled line data to obtain the line characteristic information of at least one line type corresponding to the line comprises: determining at least one characteristic dimension according to the line type; and performing feature extraction on the sampled line data according to at least one feature dimension to obtain line feature information of at least one line type corresponding to the line.

At step 210, a target route and a target route type are determined.

And 212, performing line analysis on the line characteristic information of the target line according to the type of the target line, and determining a line analysis result of the type of the target line under the target line.

In an optional embodiment, the performing, according to the target line type, line analysis on the line characteristic information of the target line to determine a line analysis result of the target line type under the target line includes: and performing line analysis on the line characteristic information of the target line corresponding to the target line type in a supervised learning mode, and determining a line analysis result of the target line type under the target line. Further comprising: and inputting the line characteristic information of the target line corresponding to the type of the target line into a first line analyzer to obtain a line analysis result output by the first line analyzer, wherein the first line analyzer is obtained by training in a supervised learning mode.

In another optional embodiment, the performing, according to the target line type, line analysis on the line characteristic information of the target line to determine a line analysis result of the target line type under the target line includes: and performing line analysis on the line characteristic information of the target line corresponding to other line types in an unsupervised learning mode, and determining a line analysis result of the target line type under the target line, wherein the other line types comprise line types except the target line type. Further comprising: and inputting the line characteristic information of the target line corresponding to other line types into a second line analyzer to obtain a line analysis result output by the second line analyzer, wherein the second line analyzer is obtained by training in an unsupervised learning mode.

Step 214, determining the adjustment information of the vehicle corresponding to the target route according to the route analysis result.

In one example, according to a line analysis result corresponding to the line type, a vehicle corresponding to the target line is laid out, and corresponding tool arrangement information is determined.

In another example, the cost information corresponding to the target route is formulated according to a route analysis result corresponding to the route type.

In another example, according to the line analysis result, opening up a target line corresponding to the line type; and determining the tool arrangement information of the vehicle corresponding to the target line.

In another example, the target lines are sorted according to line analysis results of the target lines to obtain line sorting results; and configuring priority information of the corresponding vehicles according to the line sequencing result.

In some optional embodiments of the present application, the system performs feature extraction in advance based on line data of each type of line service provider, so as to obtain line feature information corresponding to each line type, and may train the line analyzer based on the line feature information, so as to obtain the first line analyzer and the second line analyzer. Therefore, the analysis service for the line can be provided based on the line analyzer, and the adjustment information related to the line can be determined based on the analysis result, so that the suggestion can be provided to the line service provider. Therefore, in some scenarios, a line analysis interface may be further configured, and the line analysis result parameter setting control may set a line analysis parameter, such as a line type, a time range of required line characteristic information, and the like, and may also set a required characteristic type, a result type of required analysis, and the like, so as to invoke corresponding line characteristic information based on the setting of the parameter, and perform analysis by using a corresponding line analyzer, thereby obtaining a corresponding line analysis result. The line service provider can directly return the line analysis result, and the required analysis is carried out on the basis of the line analysis result. And a required adjustment suggestion type can be set, and the line adjustment information can be further analyzed and displayed based on the adjustment suggestion type and the line analysis result, so that a user can obtain the required line adjustment information.

Therefore, through the data extraction and analysis processes, the analysis and the adjustment can be realized, and the value of the airline can be evaluated in various aspects including the value evaluation of the existing airline of the airline department by taking the aviation field as an example; the airline value evaluation which is not provided by the airline department and is provided by other airline departments; and the value evaluation of routes not opened by all navigation departments.

For the developed lines, the total volume of the future one year of each line is predicted as a target, the line characteristic information extracted through the characteristic engineering processing is used as an influence factor, and a prediction model is constructed, so that the value of each route is obtained.

For the lines which are not opened up, line characteristic information of other line types can be adopted, and then experience evaluation is carried out in an unsupervised learning mode, so that line analysis results such as line values and the like are obtained.

The line analysis result such as the lane evaluation value can be obtained through the analysis processing, then the analysis and adjustment of the corresponding line can be carried out based on the line analysis result, and the analysis and adjustment can be fed back to the service end of each line service provider, and the line service provider can carry out business processing according to the line adjustment information. For example, the airline department can determine whether to open a new airline according to the evaluation result of the airline evaluation information, and also determine whether to close certain airlines and deal with the promotion business adopted by different airlines.

On the basis of the above embodiment, the embodiment of the application further provides a route processing method, which can evaluate the existing routes of the driver or the driver without routes and other routes.

Referring to FIG. 3, a flow chart of steps of a lane processing method embodiment of the present application is shown.

Step 302, obtaining line data from a server of a line service provider, where the line service provider includes an aviation line service provider.

And 304, performing feature extraction on the line data according to the line, and determining line feature information of the corresponding line. Wherein, a starting point and an end point can be selected, and a corresponding target route and the like are determined.

And 306, inputting the line characteristic information of the aviation class corresponding to the target route into a first route analyzer aiming at the opened route to obtain a route analysis result output by the first route analyzer, wherein the first route analyzer is obtained by training in a supervised learning mode.

And 308, inputting the line characteristic information of the target route corresponding to other line types into a second line analyzer aiming at the route which is not opened, so as to obtain a route analysis result output by the second line analyzer, wherein the other line types comprise the line types except the aviation type, and the second line analyzer is obtained by training in a supervision and learning mode.

Step 310, receiving an adjustment request sent by a server of a target line service provider.

And step 312, determining route adjustment information corresponding to the target route according to the route analysis result.

And step 314, feeding back the route adjustment information to a server of a corresponding target route service provider.

Determining route adjustment information corresponding to the target route according to the route analysis result, wherein the route adjustment information comprises at least one of the following:

according to the airline analysis result, arranging flights of the target line service provider, and determining corresponding flight arrangement information;

determining flight recommendation information of the target line service provider according to the route analysis result;

according to the lane analysis result, designating ticket price grade information corresponding to the target line service provider;

determining corresponding route sequencing results according to route analysis results of a plurality of target routes of the target route service provider; and configuring corresponding route priority information according to the route sequencing result.

The steps of the embodiments of the present application are similar to those of the embodiments described above, and specific reference may be made to the description of the embodiments described above.

On the basis of the above embodiments, the present embodiment may also take the route as a starting point, analyze the existing route of the route service provider, and predict the route that is not opened by the route service provider.

Referring to FIG. 4, a flow chart of steps of yet another lane processing method embodiment of the present application is shown.

Step 402, determining a target line and a line type according to a starting point and an end point. A starting point and an ending point may be selected for the route and the route may then be analyzed.

Step 404, judging whether the target line has line characteristic information of the line type; if yes, i.e. the line feature information of the line type is available, step 406 can be executed; if not, i.e., does not have the line profile information for the line type, step 408 may be performed.

And 406, analyzing the target line through the first mode analysis and the line characteristic information of the line type to obtain a corresponding line analysis result.

The first analysis mode may include a supervised learning mode, so that the first line analyzer may analyze the line characteristic information of the line type to obtain a corresponding line analysis result.

And step 408, analyzing the target line through the second mode and the line characteristic information of other line types to obtain a corresponding line analysis result.

The second analysis mode may include an unsupervised learning mode, so that the second line analyzer may analyze the line characteristic information of the other line types to obtain corresponding line analysis results.

According to the embodiment of the application, the line analysis and adjustment corresponding to various line types can be carried out by relying on big data, and the value evaluation and the characteristic analysis can be carried out on various lines such as air routes, railway lines and the like more comprehensively by combining third-party line data in the whole industry and integrating data. And the method can be used for carrying out algorithm modeling from practical problems in the industry through multiple angles such as machine learning prediction and experience, and the evaluation of the course value is realized.

Compared with the market research and subjective evaluation mode adopted at present, the embodiment of the application can carry out objective data evaluation, analysis and line adjustment. The line analysis system can construct an evaluation engine, and a line value evaluation algorithm is constructed on the basis of big data by relying on a public transportation mode, so that a quantitative line analysis processing method is provided for an airline company from a field angle of the whole city. The method can be used as one of important tasks of future development strategies of a line service provider, namely planning a line through line value evaluation and analysis, and accordingly future income and market competitiveness are determined.

The line analysis of the embodiment of the application is the data product incubation of the line service provider on the basis of the public travel project of the intelligent data product, and the algorithm is mainly based on big data, independently researched and developed and provides product service for the line service provider and other partners in an API (application programming interface) mode.

It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.

On the basis of the above embodiments, the present embodiment further provides a line data processing apparatus, which is applied to electronic devices such as a terminal device and a server.

Referring to fig. 5, a block diagram of a line data processing apparatus according to an embodiment of the present application is shown, which may specifically include the following modules:

a data obtaining module 502, configured to obtain line data from a server of a line service provider.

A feature extraction module 504, configured to perform feature extraction on the line data according to a line, and determine line feature information of a corresponding line, where the line is determined according to a starting point and an end point.

The line analysis module 506 is configured to analyze the line characteristic information of the target line to obtain a line analysis result of the target line.

And a route adjusting module 508, configured to determine, according to the route analysis result, adjustment information of a vehicle corresponding to the target route, and feed back the adjustment information to a server of a corresponding route service provider.

In summary, line data is acquired from the service terminals of a plurality of line service providers, so that a data base for big data analysis can be provided, feature extraction is performed on the line data according to lines, and line feature information of corresponding lines is determined, wherein the lines are determined according to a starting point and an end point; analyzing the line characteristic information of the target line to obtain a line analysis result of the target line; the line extraction features based on big data are analyzed, each line can be accurately analyzed, the adjustment information of the delivery vehicle corresponding to the target line is determined according to the line analysis result, the adjustment information is fed back to the server side of the corresponding line service provider, the delivery vehicle corresponding to each line is reasonably adjusted, and the accuracy and the reasonability of analysis and line adjustment are improved.

Referring to fig. 6, a block diagram of an alternative embodiment of a line data processing apparatus according to the present application is shown, and specifically, the structure may include the following modules:

Wherein the feature extraction module 504 includes: a data determination sub-module 5042 and an extraction sub-module 5044;

the data determination submodule 5042 is configured to determine a corresponding line type according to a vehicle, and determine a time range; and screening the line data corresponding to the line according to the line type and the time range, and determining the corresponding sampling line data.

The extraction submodule 5044 is configured to perform feature extraction on the sampling line data to obtain line feature information of at least one line type corresponding to the line.

The extraction submodule 5044 is configured to determine at least one feature dimension according to a line type; and performing feature extraction on the sampled line data according to at least one feature dimension to obtain line feature information of at least one line type corresponding to the line.

The line analysis module 506 is configured to determine a target line and a target line type; and performing line analysis on the line characteristic information of the target line according to the type of the target line, and determining a line analysis result of the type of the target line under the target line.

Optionally, the line analysis module 506 includes: a first analysis sub-module 5062 and a second analysis sub-module 5064, wherein:

the first analysis submodule 5062 is configured to perform line analysis on the line characteristic information of the target line corresponding to the target line type in a supervised learning manner, and determine a line analysis result of the target line type under the target line.

The second analysis submodule 5064 is configured to perform line analysis on the line characteristic information of the target line corresponding to other line types in an unsupervised learning manner, and determine a line analysis result of the target line type under the target line, where the other line types include line types other than the target line type.

Optionally, the first analysis sub-module 5062 is configured to input the line characteristic information of the target line corresponding to the target line type into a first line analyzer to obtain a line analysis result output by the first line analyzer, where the first line analyzer is obtained by training in a supervised learning manner.

The second analysis submodule 5064 is configured to input the line feature information of the target line corresponding to the other line types into a second line analyzer to obtain a line analysis result output by the second line analyzer, where the second line analyzer is obtained through unsupervised learning training.

The line adjusting module 508 includes: layout sub-module 5082, formulation sub-module 5084, open sub-module 5086, and priority settings sub-module 5088, wherein:

the layout submodule 5082 is configured to, according to the line analysis result corresponding to the line type, layout the vehicle corresponding to the target line, and determine corresponding tool arrangement information.

The formulating submodule 5084 is configured to formulate cost information corresponding to the target line according to a line analysis result corresponding to the line type.

The opening submodule 5086 is used for opening a target line corresponding to the line type according to the line analysis result; and determining the tool arrangement information of the vehicle corresponding to the target line.

The priority setting submodule 5088 is configured to sort the multiple target lines according to line analysis results of the multiple target lines, so as to obtain a line sorting result; and configuring priority information of the corresponding vehicles according to the line sequencing result.

The line type includes at least one of: aviation, railway, highway and water transportation.

On the basis of the above embodiments, the present embodiment further provides a line data processing apparatus, which is applied to electronic devices such as terminal devices and servers in the field of aviation.

Referring to fig. 7, a block diagram of another embodiment of the line data processing apparatus of the present application is shown, which may specifically include the following modules:

an obtaining module 702 is configured to obtain line data from a server of a line service provider, where the line service provider includes an aviation line service provider.

A line extraction module 704, configured to perform feature extraction on the line data according to a line, and determine line feature information of the corresponding line, where the line is determined according to a starting point and an end point.

And the route analysis module 706 is configured to analyze the route characteristic information of the target route to obtain a route analysis result of the target route.

The request receiving module 708 is configured to receive an adjustment request sent by a server of a target line service provider.

And the route adjusting module 710 is configured to determine route adjustment information corresponding to the target route according to the route analysis result.

And the feedback module 712 is configured to feed back the lane adjustment information to a server of a corresponding target line service provider.

In an optional embodiment, the route analysis module 706 is configured to, for a route that has been opened, input route feature information of a route class corresponding to the target route into a first route analyzer to obtain a route analysis result output by the first route analyzer, where the first route analyzer is obtained by training in a supervised learning manner.

In another optional embodiment, the route analysis module 706 is configured to, for an un-opened route, input route feature information of other route types corresponding to the target route into a second route analyzer to obtain a route analysis result output by the second route analyzer, where the other route types include a route type other than the aviation type, and the second route analyzer is obtained by training in a supervised learning manner.

The route adjusting module 710 is configured to lay out flights of the target route service provider according to the route analysis result, and determine corresponding flight arrangement information; and/or determining flight recommendation information of the target line service provider according to the route analysis result; and/or, according to the airline analysis result, assigning fare grade information corresponding to the target route service provider. And/or determining corresponding route sequencing results according to route analysis results of a plurality of target routes of the target route service provider; and configuring corresponding route priority information according to the route sequencing result.

The present application further provides a non-transitory, readable storage medium, where one or more modules (programs) are stored, and when the one or more modules are applied to a device, the device may execute instructions (instructions) of method steps in this application.

Embodiments of the present application provide one or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an electronic device to perform the methods as described in one or more of the above embodiments. In the embodiment of the present application, the electronic device includes various types of devices such as a terminal device and a server (cluster).

Embodiments of the present disclosure may be implemented as an apparatus, which may include electronic devices such as a terminal device, a server (cluster), etc., using any suitable hardware, firmware, software, or any combination thereof, to perform a desired configuration. Fig. 8 schematically illustrates an example apparatus 800 that may be used to implement various embodiments described herein.

For one embodiment, fig. 8 illustrates an example apparatus 800 having one or more processors 802, a control module (chipset) 804 coupled to at least one of the processor(s) 802, a memory 806 coupled to the control module 804, a non-volatile memory (NVM)/storage 808 coupled to the control module 804, one or more input/output devices 810 coupled to the control module 804, and a network interface 812 coupled to the control module 804.

The processor 802 may include one or more single-core or multi-core processors, and the processor 802 may include any combination of general-purpose or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, the apparatus 800 can be used as a terminal device, a server (cluster), or the like in the embodiments of the present application.

In some embodiments, the apparatus 800 may include one or more computer-readable media (e.g., the memory 806 or the NVM/storage 808) having instructions 814 and one or more processors 802 that, in conjunction with the one or more computer-readable media, are configured to execute the instructions 814 to implement modules to perform the actions described in this disclosure.

For one embodiment, the control module 804 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 802 and/or any suitable device or component in communication with the control module 804.

The control module 804 may include a memory controller module to provide an interface to the memory 806. The memory controller module may be a hardware module, a software module, and/or a firmware module.

The memory 806 may be used, for example, to load and store data and/or instructions 814 for the apparatus 800. For one embodiment, memory 806 may include any suitable volatile memory, such as suitable DRAM. In some embodiments, the memory 806 may comprise a double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

For one embodiment, the control module 804 may include one or more input/output controllers to provide an interface to the NVM/storage 808 and input/output device(s) 810.

For example, the NVM/storage 808 may be used to store data and/or instructions 814. NVM/storage 808 may include any suitable non-volatile memory (e.g., flash memory) and/or may include any suitable non-volatile storage device(s) (e.g., one or more Hard Disk Drives (HDDs), one or more Compact Disc (CD) drives, and/or one or more Digital Versatile Disc (DVD) drives).

The NVM/storage 808 may include storage resources that are physically part of the device on which the apparatus 800 is installed, or it may be accessible by the device and may not necessarily be part of the device. For example, the NVM/storage 808 may be accessible over a network via the input/output device(s) 810.

Input/output device(s) 810 may provide an interface for apparatus 800 to communicate with any other suitable device, input/output devices 810 may include communication components, audio components, sensor components, and so forth. The network interface 812 may provide an interface for the device 800 to communicate over one or more networks, and the device 800 may wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and/or protocols, such as access to a communication standard-based wireless network, such as WiFi, 2G, 3G, 4G, 5G, etc., or a combination thereof.

For one embodiment, at least one of the processor(s) 802 may be packaged together with logic for one or more controller(s) (e.g., memory controller module) of the control module 804. For one embodiment, at least one of the processor(s) 802 may be packaged together with logic for one or more controller(s) of the control module 804 to form a System In Package (SiP). For one embodiment, at least one of the processor(s) 802 may be integrated on the same die with logic for one or more controller(s) of the control module 804. For one embodiment, at least one of the processor(s) 802 may be integrated on the same die with logic of one or more controllers of the control module 804 to form a system on a chip (SoC).

In various embodiments, the apparatus 800 may be, but is not limited to being: a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.), among other terminal devices. In various embodiments, the apparatus 800 may have more or fewer components and/or different architectures. For example, in some embodiments, device 800 includes one or more cameras, a keyboard, a Liquid Crystal Display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an Application Specific Integrated Circuit (ASIC), and speakers.

The detection device can adopt a main control chip as a processor or a control module, sensor data, position information and the like are stored in a memory or an NVM/storage device, a sensor group can be used as an input/output device, and a communication interface can comprise a network interface.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.

The above detailed description is provided for a line data processing method and apparatus, an electronic device and a storage medium, and specific examples are applied herein to explain the principles and embodiments of the present application, and the descriptions of the above embodiments are only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A method for processing line data, the method comprising:

obtaining line data from a server of a line service provider;

extracting the characteristics of the line data according to the line, and determining the line characteristic information of the corresponding line, wherein the line is determined according to a starting point and an end point;

analyzing the line characteristic information of the target line to obtain a line analysis result of the target line;

and determining the adjustment information of the delivery vehicle corresponding to the target line according to the line analysis result, and feeding back the adjustment information to the server of the corresponding line service provider.

2. The method of claim 1, wherein the performing feature extraction on the line data according to lines to determine line feature information of corresponding lines comprises:

determining a corresponding line type according to a carrier, and determining a time range;

screening the line data corresponding to the line according to the line type and the time range, and determining corresponding sampling line data;

and performing feature extraction on the sampled line data to obtain line feature information of at least one line type corresponding to the line.

3. The method of claim 2, wherein performing feature extraction on the sampled line data to obtain line feature information of the line corresponding to at least one line type comprises:

determining at least one characteristic dimension according to the line type;

and performing feature extraction on the sampled line data according to at least one feature dimension to obtain line feature information of at least one line type corresponding to the line.

4. The method of claim 2, wherein analyzing the line characteristic information of the target line to obtain a line analysis result of the target line comprises:

determining a target line and a target line type;

and performing line analysis on the line characteristic information of the target line according to the type of the target line, and determining a line analysis result of the type of the target line under the target line.

5. The method of claim 4, wherein performing line analysis on the line characteristic information of the target line according to the target line type to determine a line analysis result of the target line type under the target line comprises:

and performing line analysis on the line characteristic information of the target line corresponding to the target line type in a supervised learning mode, and determining a line analysis result of the target line type under the target line.

6. The method according to claim 5, wherein the performing line analysis on the line characteristic information of the target line corresponding to the target line type in a supervised learning manner to determine a line analysis result of the target line type under the target line comprises:

and inputting the line characteristic information of the target line corresponding to the type of the target line into a first line analyzer to obtain a line analysis result output by the first line analyzer, wherein the first line analyzer is obtained by training in a supervised learning mode.

7. The method of claim 4, wherein performing line analysis on the line characteristic information of the target line according to the target line type to determine a line analysis result of the target line type under the target line comprises:

and performing line analysis on the line characteristic information of the target line corresponding to other line types in an unsupervised learning mode, and determining a line analysis result of the target line type under the target line, wherein the other line types comprise line types except the target line type.

8. The method according to claim 7, wherein the performing line analysis on the line characteristic information of the target line corresponding to other line types in a supervised learning manner to determine the line analysis result of the line type under the target line comprises:

and inputting the line characteristic information of the target line corresponding to other line types into a second line analyzer to obtain a line analysis result output by the second line analyzer, wherein the second line analyzer is obtained by training in an unsupervised learning mode.

9. The method of claim 1, wherein determining the vehicle adjustment information corresponding to the target route according to the route analysis result comprises:

and according to the line analysis result corresponding to the line type, laying out the delivery vehicle corresponding to the target line, and determining corresponding tool arrangement information.

10. The method of claim 1, wherein determining the vehicle adjustment information corresponding to the target route according to the route analysis result comprises:

and formulating cost information corresponding to the target line according to the line analysis result corresponding to the line type.

11. The method of claim 1, wherein determining the vehicle adjustment information corresponding to the target route according to the route analysis result comprises:

opening up a target line corresponding to the line type according to the line analysis result;

and determining the tool arrangement information of the vehicle corresponding to the target line.

12. The method of claim 1, wherein determining the vehicle adjustment information corresponding to the target route according to the route analysis result comprises:

sequencing the target lines according to line analysis results of the target lines to obtain line sequencing results;

and configuring priority information of the corresponding vehicles according to the line sequencing result.

13. The method of any of claims 1-12, wherein the line type comprises at least one of: aviation, railway, highway and water transportation.

14. A method for processing line data, the method comprising:

the method comprises the steps that line data are obtained from a server side of a line service provider, wherein the line service provider comprises an aviation line service provider;

analyzing the line characteristic information of a target route to obtain a route analysis result of the target route;

receiving an adjustment request sent by a server of a target line service provider;

determining route adjustment information corresponding to the target route according to the route analysis result;

and feeding back the route adjustment information to a server of a corresponding target route service provider.

15. The method of claim 14, wherein analyzing the route characteristic information of the target route to obtain a route analysis result of the target route comprises:

and aiming at the developed route, inputting the route characteristic information of the corresponding aviation class of the target route into a first route analyzer to obtain a route analysis result output by the first route analyzer, wherein the first route analyzer is obtained by training in a supervision and learning mode.

16. The method of claim 14, wherein analyzing the route characteristic information of the target route to obtain a route analysis result of the target route comprises:

and inputting the line characteristic information of the target route corresponding to other line types into a second line analyzer aiming at the route which is not opened, and obtaining a route analysis result output by the second line analyzer, wherein the other line types comprise the line types except the aviation type, and the second line analyzer is obtained by training in a supervision and learning mode.

17. The method of claim 14, wherein determining the course adjustment information corresponding to the target course according to the course analysis result comprises at least one of:

and according to the airline analysis result, assigning fare grade information corresponding to the target route service provider.

18. The method of claim 14, wherein determining course adjustment information corresponding to the target course according to the course analysis result comprises:

determining corresponding route sequencing results according to route analysis results of a plurality of target routes of the target route service provider;

and configuring corresponding route priority information according to the route sequencing result.

19. A line data processing apparatus, characterized in that the apparatus comprises:

the data acquisition module is used for acquiring line data from a server of a line service provider;

the characteristic extraction module is used for extracting the characteristics of the line data according to the line and determining the line characteristic information of the corresponding line, wherein the line is determined according to a starting point and an end point;

the line analysis module is used for analyzing the line characteristic information of the target line to obtain a line analysis result of the target line;

and the line adjusting module is used for determining the adjusting information of the delivery vehicle corresponding to the target line according to the line analysis result and feeding back the adjusting information to the server of the corresponding line service provider.

20. A line data processing apparatus, characterized in that the apparatus comprises:

the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring line data from a server of a line service provider, and the line service provider comprises an aviation line service provider;

the line extraction module is used for extracting the features of the line data according to the lines and determining the line feature information of the corresponding lines, wherein the lines are determined according to a starting point and an end point;

the route analysis module is used for analyzing the route characteristic information of the target route to obtain a route analysis result of the target route;

the request receiving module is used for receiving an adjustment request sent by a server of a target line service provider;

the route adjusting module is used for determining route adjusting information corresponding to the target route according to the route analysis result;

and the feedback module is used for feeding back the route adjustment information to a server of a corresponding target route service provider.

21. An electronic device, comprising: a processor; and

memory having stored thereon executable code which, when executed, causes the processor to perform the method of one or more of claims 1-18.

22. One or more machine-readable media having executable code stored thereon that, when executed, causes a processor to perform the method of one or more of claims 1-18.