CN113516500A - Implementation method and system based on big data business and travel operation platform - Google Patents
Implementation method and system based on big data business and travel operation platform Download PDFInfo
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Abstract
The invention relates to the technical field of big data, in particular to a realization method and a system based on a big data business travel operation platform, wherein the method comprises the following steps: multisource data acquisition, carry out data acquisition in a plurality of passenger traffic production, service system and the extension service system of railway, civil aviation, mainly contain: real-name information, passenger ticket data, ticket booking stub and travel information in the ticketing system; the invention is based on multi-source big data to mine and analyze, and collects multi-dimensional big data such as passenger identity information, passenger flow volume, passenger flow source, stay time, passenger track and the like from railway and civil aviation passenger flow sites to construct a business travel group portrait, and can grasp characteristics such as source province and city, crowd composition, scenic spot preference, hotel selection, traffic preference, shopping tendency and the like of the business travel group, construct a corresponding operation platform, and simultaneously form business travel fusion thematic analysis, thereby providing technical support and convenience for business travel development in related fields.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a realization method and a system based on a big data business and travel operation platform.
Background
The business trip market in China develops rapidly, high-speed rails and civil aviation become mainstream for trip gradually, business trips become important components of passenger transport markets such as high-speed rails and civil aviation gradually, along with rapid popularization of the 'internet plus' business mode, the internet has penetrated various passenger transport service scenes such as railways and civil aviation, business trip users also show explosive growth, and a large amount of user behavior data has been accumulated. For example, since a railway internet ticketing system (12306) is on line, registered users exceed 4 hundred million, riding users exceed 8 million, massive user behavior log data are generated every day, system data of each station of a railway operation network, station car WIFI operation service, an advertisement platform, internet ordering and the like are continuously normalized and collected, operation data of railway passenger transport for many years are included in the data, clear description and positioning of passenger transport products, behavior collection of passengers can reach visual degree, understanding of conversion of extended products and the like, and data value-added application suitable for railway development is urgently sought from thousands of data included by the passenger transport platform, and overall benefit and service level of the railway passenger transport are improved.
The passenger user portrait system is constructed through passenger user behavior data, so that passenger groups can be more accurately mastered, high-quality passengers can be defined, personalized and differentiated marketing and service strategies are formulated for different passenger groups, passenger transport resources are reasonably configured on the basis of passenger with subdivided value levels, and the win-win strategy that the traditional marketing strategy is expanded into the railway benefit maximization and the passenger service quality optimization based on passenger value is realized. Based on multi-source big data mining analysis, the invention collects multi-dimensional big data such as passenger identity information, passenger flow volume, passenger flow source, stay time, passenger track and the like from railway and civil aviation passenger flow sites, constructs a business trip group portrait, strives to master characteristics of the business trip group such as source province and city, crowd composition, scenic spot preference, hotel selection, traffic preference, shopping tendency and the like, constructs a corresponding operation platform, forms business trip fusion thematic analysis at the same time, and provides technical support for business trip development in related fields.
The invention relates to a user portrait label, namely, user purpose, behavior and viewpoint difference are analyzed through data, so that users are divided into types with different typical characteristics, and each type is named, is endowed with a visual photo, some demographic elements and scene description, and a character information prototype is formed.
Disclosure of Invention
The invention aims to provide a realization method and a system based on a big data business travel operation platform, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a realization method based on big data business travel operation platform comprises the following steps:
(S1), multi-source data acquisition, data acquisition is carried out from a plurality of passenger production, service systems and extended service systems of railway and civil aviation, and the method mainly comprises the following steps: real-name information, passenger ticket data, ticket booking stub and travel information in a ticketing system, user login, inquiry, ticket booking and payment log data in an internet ticketing system, basic information of a user, feedback data of passengers in a travel service related system, a problem processing mode set, registration notification, ticket booking notification and travel notification data in a short message platform, and hotel booking, catering service and station car Wi-Fi operation service related data in extension service;
(S2) forming 7-dimensional portrait labels through multi-dimensional comprehensive analysis of passenger portraits, wherein the characteristics in the portrait labels comprise passenger source market characteristics, demographic characteristics, socioeconomic characteristics, business behavior characteristics, mobile application use behavior information, demand preference characteristics and group sex difference information;
(S3) through the target analysis of the passenger portrait label, the label is finally marked for the user, meanwhile, the defined label can be divided into a fact label, a business label, a model label and a feedback label according to different businesses, the passenger portrait label is used for establishing accurate user portrait, supporting and establishing accurate user group, and an accurate marketing system, a prediction system, a risk identification system and a credit investigation service system are established by utilizing a machine learning algorithm;
(S4), modeling data, providing passenger portrait labels, and calculating user labels, wherein the modeling and the process are a modeling and process, and the modeling mode comprises direct value taking, statistical analysis, business rules and prediction models, and then forming passenger portraits, passenger service product portraits and channel portraits;
(S5) analyzing the mobile data of the portrait label by a mobile data analysis method, adopting a big data acquisition and analysis technology, namely LBS, APP, LBS fusion APP and business rule fusion machine learning artificial intelligence technology, constructing a passenger portrait from multiple aspects based on massive mobile big data, and establishing basic characteristics of a passenger, wherein the basic characteristics comprise five dimensions of passenger source market characteristics, demographic characteristics, social and economic characteristics, business behavior characteristics and mobile application use characteristics;
(S6), calculating a model according to the characteristics of the label, wherein the calculation method of the passenger user portrait system label can be divided into rule calculation, statistical analysis and inductive summarization, the label suitable for rule calculation has gender, age and native place, and is identified by the identity card number, and the statistical analysis class mainly comprises travel times, ticket purchasing times and travel time distribution, and is obtained by statistical calculation according to the business rule, and the user attribute is summarized by a mathematical model according to the user label attribute;
(S7), according to data acquisition of an external system, data processing is carried out on the acquired data and the acquired data are stored in a database, then analysis and mining are carried out on original data according to designed labels, label values of users are calculated and stored in a distributed database, meanwhile, a calculation process scheduling is carried out on an incremental data portrait management module, user labels are continuously updated, and finally different models are designed based on the user labels to group the users and provide services for the outside.
Preferably, in the step (S1), the collected user basic information includes a name, an identification number, a mobile phone number, and a mailbox.
Preferably, in the step (S2), the demographic characteristics include sex, academic history, age, marital and information of children, the socioeconomic characteristics include asset and economic information, and the travel behavior characteristics include eating, living, traveling, swimming, entertaining and purchasing factors.
Preferably, in the step (S2), the mobile application usage behavior information includes frequently used APPs, internet surfing preferences, and usage frequency of social software.
Preferably, in the step (S3), the feedback labels include trip arrival and price sensitivity, the model labels include high-consumption population, goal prediction and potential analysis, the business labels include passenger location, liveness, trip level, trip law, contact point and trip preference, and the fact labels include population attributes, social attributes, member attributes, consumption habits, trip records and trip modes.
Preferably, in the step (S5), the basic information features relate to gender, age, marital status, assets and activities, and the mobile information features relate to tracks, positioning data, bus card tracks and WIFI positioning data, wherein the track comprises check-in tracks in the mobile social network and the number of picture tracks with GPS marks.
Preferably, in the step (S5), the network information is obtained by acquiring various data source data from the fixed network, the mobile and WIFI information network for cleaning, and after deduplication, denoising and consistency processing, performing comprehensive processing based on LBS data, APP data, POI data and third party data to obtain the location information of the mobile terminal user through the radio communication network of the telecom mobile operator or an external location manner.
Preferably, in the step (S5), the characteristics of the tourists that cannot be directly obtained are analyzed by a business rule fusion machine learning artificial intelligence technique.
Preferably, in the step (S6), the calculation method includes a user privacy label desensitization calculation and a statistical class label calculation.
The utility model provides a system based on big data business trip operation platform, includes passenger user and draws together tag system, business trip big data service supporting platform and business trip big data intelligent analysis platform, passenger user draws together tag system and includes data transmission layer, data layer, business supporting layer, core business layer and visit access layer, business trip big data service supporting platform includes data acquisition module, data processing module, label construction module, operation management module and external service module, business trip big data intelligent analysis platform comprises data analysis module and excavation module.
Compared with the prior art, the invention has the following beneficial effects:
the invention is based on multi-source big data to mine and analyze, and collects multi-dimensional big data such as passenger identity information, passenger flow volume, passenger flow source, stay time, passenger track and the like from railway and civil aviation passenger flow sites to construct a business travel group portrait, and can grasp characteristics such as source province and city, crowd composition, scenic spot preference, hotel selection, traffic preference, shopping tendency and the like of the business travel group, construct a corresponding operation platform, and simultaneously form business travel fusion thematic analysis, thereby providing technical support and convenience for business travel development in related fields.
Drawings
FIG. 1 is a passenger representation tag construction system of the present invention;
FIG. 2 is a schematic illustration of a passenger representation tag in accordance with the present invention;
FIG. 3 is a schematic view of a passenger portrait labeling process according to the present invention;
FIG. 4 is a schematic diagram of an application framework of a business trip operation platform according to the present invention;
fig. 5 is a flowchart of the work flow of the business trip operation platform of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
a realization method based on big data business travel operation platform comprises the following steps:
(S1), multi-source data acquisition, data acquisition is carried out from a plurality of passenger production, service systems and extended service systems of railway and civil aviation, and the method mainly comprises the following steps: real name information, passenger ticket data, ticket booking stub and travel information in a ticketing system, log data of user login, inquiry, ticket booking and payment in an internet ticketing system, basic information of the user, feedback data of passengers in a related system of travel service, a problem processing mode set, registration notification, ticket booking notification and travel notification data in a short message platform, and related data of hotel booking, catering service and station car Wi-Fi operation service in extension service, wherein the acquired basic information of the user comprises a name, an identity card number, a mobile phone number and a mailbox;
(S2) forming a 7-dimensional portrait label through multi-dimensional comprehensive analysis of passenger portrait, wherein the characteristics in the portrait label comprise customer market characteristics, demographic characteristics, socioeconomic characteristics, business behavior characteristics, mobile application use behavior information, demand preference characteristics and group sex difference information, the demographic characteristics comprise sex, academic degree, age, marital and information on children, the socioeconomic characteristics mainly comprise asset and economic information, and the business behavior characteristics relate to eating, living, traveling, swimming, entertainment and purchasing six elements;
(S3) through target analysis of the passenger portrait label, finally, a label is marked for a user, meanwhile, the defined label can be divided into a fact label, a business label, a model label and a feedback label according to different businesses, the passenger portrait label is used for establishing a precise user portrait, supporting and establishing a precise user group, and a machine learning algorithm is used for establishing a precise marketing system, a prediction system, a risk identification system and a credit investigation service system, the feedback label comprises a trip arrival person and price sensitivity, the model label comprises a high-consumption crowd, a purpose prediction and a potential analysis, the business label comprises passenger positioning, liveness, trip grade, trip law, contact points and trip preference, and the fact label comprises population attribute, social attribute, member attribute, consumption habit, trip record and trip mode;
(S4), modeling data, providing passenger portrait labels, and calculating user labels, wherein the modeling and the process are a modeling and process, and the modeling mode comprises direct value taking, statistical analysis, business rules and prediction models, and then forming passenger portraits, passenger service product portraits and channel portraits;
(S5) analyzing mobile data of the portrait label by a mobile data analysis method, and adopting a big data acquisition and analysis technology, namely LBS, APP, LBS fusion APP and business rule fusion machine learning artificial intelligence technology, constructing a passenger portrait from multiple aspects based on massive mobile big data, and establishing basic characteristics of a passenger, wherein the basic characteristics comprise five dimensions of passenger source market characteristics, demographic characteristics, social and economic characteristics, travel behavior characteristics and mobile application use characteristics, the basic information characteristics relate to gender, age, marital conditions, assets and activities, the mobile information characteristics relate to track, positioning data, bus card track and WIFI positioning data, and the mobile information characteristics also comprise sign-in track in a mobile social network and picture track number with GPS marks;
(S6), calculating a model according to the characteristics of the label, wherein the calculation method of the passenger user portrait system label can be divided into rule calculation, statistical analysis and inductive summarization, the label suitable for rule calculation has gender, age and native place, and is identified by the identity card number, and the statistical analysis class mainly comprises travel times, ticket purchasing times and travel time distribution, and is obtained by statistical calculation according to the business rule, and the user attribute is summarized by a mathematical model according to the user label attribute;
(S7), according to data acquisition of an external system, data processing is carried out on the acquired data and the acquired data are stored in a database, then analysis and mining are carried out on original data according to designed labels, label values of users are calculated and stored in a distributed database, meanwhile, a calculation process scheduling is carried out on an incremental data portrait management module, user labels are continuously updated, and finally different models are designed based on the user labels to group the users and provide services for the outside.
Example two:
a realization method based on big data business travel operation platform comprises the following steps:
(S1), multi-source data acquisition, data acquisition is carried out from a plurality of passenger production, service systems and extended service systems of railway and civil aviation, and the method mainly comprises the following steps: real name information, passenger ticket data, ticket booking stub and travel information in a ticketing system, log data of user login, inquiry, ticket booking and payment in an internet ticketing system, basic information of the user, feedback data of passengers in a related system of travel service, a problem processing mode set, registration notification, ticket booking notification and travel notification data in a short message platform, and related data of hotel booking, catering service and station car Wi-Fi operation service in extension service, wherein the acquired basic information of the user comprises a name, an identity card number, a mobile phone number and a mailbox;
(S2) forming a 7-dimensional portrait label through multi-dimensional comprehensive analysis of passenger portrait, wherein the characteristics in the portrait label comprise customer market characteristics, demographic characteristics, socioeconomic characteristics, business behavior characteristics, mobile application use behavior information, demand preference characteristics and group sex difference information, the demographic characteristics comprise information on gender, academic calendar, age, marital and children, the socioeconomic characteristics mainly comprise information on assets and economy, the business behavior characteristics relate to the aspects of eating, living, walking, swimming, entertainment and purchasing, and the mobile application use behavior information comprises frequently-used APP, internet surfing preference and the use frequency of social software;
(S3) through target analysis of the passenger portrait label, finally, a label is marked for a user, meanwhile, the defined label can be divided into a fact label, a business label, a model label and a feedback label according to different businesses, the passenger portrait label is used for establishing a precise user portrait, supporting and establishing a precise user group, and a machine learning algorithm is used for establishing a precise marketing system, a prediction system, a risk identification system and a credit investigation service system, the feedback label comprises a trip arrival person and price sensitivity, the model label comprises a high-consumption crowd, a purpose prediction and a potential analysis, the business label comprises passenger positioning, liveness, trip grade, trip law, contact points and trip preference, and the fact label comprises population attribute, social attribute, member attribute, consumption habit, trip record and trip mode;
(S4), modeling data, providing passenger portrait labels, and calculating user labels, wherein the modeling and the process are a modeling and process, and the modeling mode comprises direct value taking, statistical analysis, business rules and prediction models, and then forming passenger portraits, passenger service product portraits and channel portraits;
(S5), analyzing the mobile data of the portrait label by a mobile data analysis method, adopting big data acquisition and analysis technology, namely LBS, APP, LBS fusion APP and business rule fusion machine learning artificial intelligence technology, constructing a passenger portrait from multiple aspects based on mass mobile big data, establishing basic characteristics of the passenger, including five dimensions of passenger source market characteristics, demographic characteristics, social and economic characteristics, business behavior characteristics and mobile application use characteristics, wherein the basic information characteristics relate to sex, age, marital conditions, assets and activities, the mobile information characteristics relate to track, positioning data, bus card track and WIFI positioning data, the sign-in track in a mobile social network and the number of picture tracks with GPS marks are also included, and the network information is to obtain various data source data from a fixed network, a mobile and WIFI information network for cleaning, after the duplication removal, denoising and consistency processing, comprehensive processing is carried out based on LBS data, APP data, POI data and third-party data to obtain the LBS data, and the LBS data is obtained by the position information of the mobile terminal user through a radio communication network of a telecom mobile operator or an external positioning mode;
(S6), calculating a model according to the characteristics of the label, wherein the calculation method of the passenger user portrait system label can be divided into rule calculation, statistical analysis and inductive summarization, the label suitable for rule calculation has gender, age and native place, and is identified by the identity card number, and the statistical analysis class mainly comprises travel times, ticket purchasing times and travel time distribution, and is obtained by statistical calculation according to the business rule, and the user attribute is summarized by a mathematical model according to the user label attribute;
(S7), according to data acquisition of an external system, data processing is carried out on the acquired data and the acquired data are stored in a database, then analysis and mining are carried out on original data according to designed labels, label values of users are calculated and stored in a distributed database, meanwhile, a calculation process scheduling is carried out on an incremental data portrait management module, user labels are continuously updated, and finally different models are designed based on the user labels to group the users and provide services for the outside.
Example three:
a realization method based on big data business travel operation platform comprises the following steps:
(S1), multi-source data acquisition, data acquisition is carried out from a plurality of passenger production, service systems and extended service systems of railway and civil aviation, and the method mainly comprises the following steps: real name information, passenger ticket data, ticket booking stub and travel information in a ticketing system, log data of user login, inquiry, ticket booking and payment in an internet ticketing system, basic information of the user, feedback data of passengers in a related system of travel service, a problem processing mode set, registration notification, ticket booking notification and travel notification data in a short message platform, and related data of hotel booking, catering service and station car Wi-Fi operation service in extension service, wherein the acquired basic information of the user comprises a name, an identity card number, a mobile phone number and a mailbox;
(S2) forming a 7-dimensional portrait label through multi-dimensional comprehensive analysis of passenger portrait, wherein the characteristics in the portrait label comprise customer market characteristics, demographic characteristics, socioeconomic characteristics, business behavior characteristics, mobile application use behavior information, demand preference characteristics and group sex difference information, the demographic characteristics comprise information on gender, academic calendar, age, marital and children, the socioeconomic characteristics mainly comprise information on assets and economy, the business behavior characteristics relate to the aspects of eating, living, walking, swimming, entertainment and purchasing, and the mobile application use behavior information comprises frequently-used APP, internet surfing preference and the use frequency of social software;
(S3) through target analysis of the passenger portrait label, finally, a label is marked for a user, meanwhile, the defined label can be divided into a fact label, a business label, a model label and a feedback label according to different businesses, the passenger portrait label is used for establishing a precise user portrait, supporting and establishing a precise user group, and a machine learning algorithm is used for establishing a precise marketing system, a prediction system, a risk identification system and a credit investigation service system, the feedback label comprises a trip arrival person and price sensitivity, the model label comprises a high-consumption crowd, a purpose prediction and a potential analysis, the business label comprises passenger positioning, liveness, trip grade, trip law, contact points and trip preference, and the fact label comprises population attribute, social attribute, member attribute, consumption habit, trip record and trip mode;
(S4), modeling data, providing passenger portrait labels, and calculating user labels, wherein the modeling and the process are a modeling and process, and the modeling mode comprises direct value taking, statistical analysis, business rules and prediction models, and then forming passenger portraits, passenger service product portraits and channel portraits;
(S5), analyzing the mobile data of the portrait label by a mobile data analysis method, adopting big data acquisition and analysis technology, namely LBS, APP, LBS fusion APP and business rule fusion machine learning artificial intelligence technology, constructing a passenger portrait from multiple aspects based on mass mobile big data, establishing basic characteristics of the passenger, including five dimensions of passenger source market characteristics, demographic characteristics, social and economic characteristics, business behavior characteristics and mobile application use characteristics, wherein the basic information characteristics relate to sex, age, marital conditions, assets and activities, the mobile information characteristics relate to track, positioning data, bus card track and WIFI positioning data, the sign-in track in a mobile social network and the number of picture tracks with GPS marks are also included, and the network information is to obtain various data source data from a fixed network, a mobile and WIFI information network for cleaning, after the duplication removal, the denoising and the consistency processing, comprehensive processing is carried out on the basis of LBS data, APP data, POI data and third-party data to obtain the LBS data, the LBS data is obtained by obtaining the position information of a mobile terminal user through a radio communication network or an external positioning mode of a telecom mobile operator, and the characteristics of tourists which cannot be directly obtained are analyzed through a business rule fusion machine learning artificial intelligence technology;
(S6), calculating a model according to the characteristics of the label, wherein the calculation method of the passenger user portrait system label can be divided into rule calculation, statistical analysis and inductive summary, the label suitable for rule calculation has gender, age and native place, and is identified by the identity card number, the statistical analysis class mainly comprises travel times, ticket purchasing times and travel time distribution, meanwhile, the statistical analysis class is obtained by statistical calculation according to the business rule, and the user attribute is further summarized and summarized by a mathematical model according to the user label attribute, and the calculation method comprises desensitization calculation of a user privacy label and calculation of a statistical class label;
(S7), according to data acquisition of an external system, data processing is carried out on the acquired data and the acquired data are stored in a database, then analysis and mining are carried out on original data according to designed labels, label values of users are calculated and stored in a distributed database, meanwhile, a calculation process scheduling is carried out on an incremental data portrait management module, user labels are continuously updated, and finally different models are designed based on the user labels to group the users and provide services for the outside.
The invention is based on multi-source big data to mine and analyze, and collects multi-dimensional big data such as passenger identity information, passenger flow volume, passenger flow source, stay time, passenger track and the like from railway and civil aviation passenger flow sites to construct a business travel group portrait, and can grasp characteristics such as source province and city, crowd composition, scenic spot preference, hotel selection, traffic preference, shopping tendency and the like of the business travel group, construct a corresponding operation platform, and simultaneously form business travel fusion thematic analysis, thereby providing technical support and convenience for business travel development in related fields.
1. Passenger user portrait labeling system
(1) The data transmission layer is mainly used for data acquisition and data service provided by data to a business system, acquiring data in each passenger transport information system to the data layer of the passenger transport user figure system in real time or quasi-real time, and outputting the deeply analyzed data to an external system for marketing decision or risk control of the external system;
(2) the data layer is used for storing and managing related data of the system, acquiring external data through an interface of the data transmission layer and providing data support for the service layer;
(3) the business support layer mainly comprises an application development platform (such as a report tool, an OLAP engine, a data analysis engine, an ETL tool and the like) and middleware (an application server, a message middleware, a WEB server, a workflow and the like). The integration of data and application is realized by applying a system for supporting passenger user portrait;
(4) the core business layer comprises functional modules in four aspects of data, labels, operation and service, such as data cleaning and importing, label management, resource management, scheduling management, service management and the like;
(5) and accessing the access layer, and providing application functions for various business personnel in the forms of a business portal, interface service and a client.
2. Business and travel big data service support platform
On the basis of a passenger portrait label system, seven functional modules of data acquisition, data processing, analysis mining, label construction, portrait management, portrait analysis and external service are realized;
the data acquisition module is mainly used for acquiring data in real time or quasi-real time aiming at a passenger transport information system;
the data processing module carries out transcoding, cleaning, desensitization, warehousing, association and other processing on the data acquired by the passenger representation system, and provides a data basis for assignment of user labels;
the label construction module is used for managing labels of the passenger user representation system, and comprises a user label on-line and off-line, a calculation method, a statistical period and the like;
the operation management module is used for automatically acquiring and identifying the newly added production data of the passenger ticket system and the marketing system every day, and recalculating and assigning the user tags according to the tag calculation period;
the external service module is a query service for providing user portrait labels for users or other business systems.
3. Intelligent business and travel big data analysis platform
And data analysis and mining, namely establishing a multi-dimensional database, providing a data analysis method and a tool, mining the interrelation hidden in data, finding out a rule by using the interrelation of the data, establishing a model, predicting the future data trend by using the model, and finally providing a basis for leader decision making.
It is 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 apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A realization method based on big data business travel operation platform is characterized in that: the method comprises the following steps:
(S1), multi-source data acquisition, data acquisition is carried out from a plurality of passenger production, service systems and extended service systems of railway and civil aviation, and the method mainly comprises the following steps: real-name information, passenger ticket data, ticket booking stub and travel information in a ticketing system, user login, inquiry, ticket booking and payment log data in an internet ticketing system, basic information of a user, feedback data of passengers in a travel service related system, a problem processing mode set, registration notification, ticket booking notification and travel notification data in a short message platform, and hotel booking, catering service and station car Wi-Fi operation service related data in extension service;
(S2) forming 7-dimensional portrait labels through multi-dimensional comprehensive analysis of passenger portraits, wherein the characteristics in the portrait labels comprise passenger source market characteristics, demographic characteristics, socioeconomic characteristics, business behavior characteristics, mobile application use behavior information, demand preference characteristics and group sex difference information;
(S3) through the target analysis of the passenger portrait label, the label is finally marked for the user, meanwhile, the defined label can be divided into a fact label, a business label, a model label and a feedback label according to different businesses, the passenger portrait label is used for establishing accurate user portrait, supporting and establishing accurate user group, and an accurate marketing system, a prediction system, a risk identification system and a credit investigation service system are established by utilizing a machine learning algorithm;
(S4), modeling data, providing passenger portrait labels, and calculating user labels, wherein the modeling and the process are a modeling and process, and the modeling mode comprises direct value taking, statistical analysis, business rules and prediction models, and then forming passenger portraits, passenger service product portraits and channel portraits;
(S5) analyzing the mobile data of the portrait label by a mobile data analysis method, adopting a big data acquisition and analysis technology, namely LBS, APP, LBS fusion APP and business rule fusion machine learning artificial intelligence technology, constructing a passenger portrait from multiple aspects based on massive mobile big data, and establishing basic characteristics of a passenger, wherein the basic characteristics comprise five dimensions of passenger source market characteristics, demographic characteristics, social and economic characteristics, business behavior characteristics and mobile application use characteristics;
(S6), calculating a model according to the characteristics of the label, wherein the calculation method of the passenger user portrait system label can be divided into rule calculation, statistical analysis and inductive summarization, the label suitable for rule calculation has gender, age and native place, and is identified by the identity card number, and the statistical analysis class mainly comprises travel times, ticket purchasing times and travel time distribution, and is obtained by statistical calculation according to the business rule, and the user attribute is summarized by a mathematical model according to the user label attribute;
(S7), according to data acquisition of an external system, data processing is carried out on the acquired data and the acquired data are stored in a database, then analysis and mining are carried out on original data according to designed labels, label values of users are calculated and stored in a distributed database, meanwhile, a calculation process scheduling is carried out on an incremental data portrait management module, user labels are continuously updated, and finally different models are designed based on the user labels to group the users and provide services for the outside.
2. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S1), the collected user basic information includes a name, an identification number, a mobile phone number, and a mailbox.
3. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S2), the demographic characteristics include sex, academic history, age, marital and information of children, the socioeconomic characteristics include asset and economic information, and the travel behavior characteristics relate to six factors of eating, living, traveling, swimming, entertainment and shopping.
4. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S2), the mobile application usage behavior information includes frequently used APPs, internet preferences, and usage frequency of social software.
5. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S3), the feedback labels include trip arrival and price sensitivity, the model labels include high-consumption population, purpose prediction and potential analysis, the business labels include passenger location, liveness, trip level, trip rule, contact point and trip preference, and the fact labels include population attributes, social attributes, member attributes, consumption habits, trip records and trip modes.
6. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S5), the basic information features relate to gender, age, marital status, assets and activities, and the mobile information features relate to track, positioning data, bus card track and WIFI positioning data, wherein the track comprises check-in track in the mobile social network and the number of picture tracks with GPS tags.
7. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S5), the network information is obtained by acquiring various data source data from the fixed network, the mobile and WIFI information network for cleaning, and after deduplication, denoising and consistency processing, performing comprehensive processing based on LBS data, APP data, POI data and third party data, and the LBS data is obtained by acquiring the location information of the mobile terminal user through the radio communication network of the telecom mobile operator or an external location manner.
8. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S5), the characteristics of the tourists that cannot be directly obtained are analyzed by a business rule fusion machine learning artificial intelligence technique.
9. The implementation method of claim 1, wherein the implementation method is based on a big data business travel operation platform, and comprises the following steps: in the step (S6), the calculation method includes a user privacy label desensitization calculation and a statistical class label calculation.
10. The big data business travel operation platform-based system according to any one of claims 1 to 9, comprising a passenger user portrait labeling system, a business travel big data service support platform and a business travel big data intelligent analysis platform, wherein: the passenger user portrait labeling system comprises a data transmission layer, a data layer, a service supporting layer, a core service layer and an access layer, the business trip big data service supporting platform comprises a data acquisition module, a data processing module, a label building module, an operation management module and an external service module, and the business trip big data intelligent analysis platform comprises a data analysis module and a mining module.
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