CN111899059A - Navigation driver revenue management dynamic pricing method based on block chain - Google Patents
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Abstract
The invention discloses a block chain-based navigation driver revenue management dynamic pricing method, which comprises the following steps of: s1, constructing a big data platform based on the block chain; s2, constructing a cabin price prediction model by using the big data platform, and predicting the cabin price; s3, taking the cabin price prediction result as input data, and constructing a dynamic pricing model; s4, building a personalized product recommendation model based on deep learning by combining passenger characteristic information provided by the passenger portrait; and S5, optimizing and evaluating the personalized product recommendation model to obtain a yield management decision. According to the invention, the block chain and the big data platform are fused, and a protected data source is provided, so that the data extraction becomes safe and reliable, and the prediction precision of the cabin price is improved; in addition, based on deep learning, through passenger portrait and demand statistics analysis customer demand, realize the accurate capture of target customer crowd's income, satisfy the decision-making needs of actual airline and airport and policy.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to a block chain-based navigation department revenue management dynamic pricing method.
Background
The income management is a process that an airline company selectively accepts passengers of various types to maximize income, is a computer management system integrating manpower and technology, and can estimate demand probability distribution curves of different fares and different slots of each flight through a flight income maximization decision model so as to determine stock control and discount fares and maximize the income.
The key of dynamic pricing lies in maximizing revenue of an airline company through a pricing strategy and determining the optimal time of price change by using a proper method, but most of research is concentrated on revenue management of inventory allocation at present, but the revenue management methods based on price decision are relatively few, and the traditional cabin control model has the defects of limitation on applicability of multiple fare levels and the like because the behavior selected by passengers is not considered, and in addition, the domestic revenue management system has certain defects on data storage and data utilization, and a safer mode is needed for data storage.
Disclosure of Invention
In order to solve the problems, the invention provides a block chain-based navigation driver revenue management dynamic pricing method.
The invention adopts the following technical scheme:
a navigation driver revenue management dynamic pricing method based on a block chain comprises the following steps:
s1, constructing a big data platform based on the block chain;
s2, constructing a cabin price prediction model by using the big data platform, and predicting the cabin price;
s3, taking the cabin price prediction result as input data, and constructing a dynamic pricing model;
s4, building a personalized product recommendation model based on deep learning by combining passenger characteristic information provided by the passenger portrait;
and S5, optimizing and evaluating the personalized product recommendation model to obtain a yield management decision.
Further, the big data platform based on the block chain comprises a block chain consisting of a data layer, a network layer, a consensus layer, an application layer, a contract layer and an incentive layer, wherein the data layer comprises a chain structure, a timestamp, asymmetric encryption, a hash function, an aviation sales data block and an aviation passenger information block, the network layer comprises a P2P network, a propagation mechanism and a verification mechanism, the consensus layer comprises a dynamic data consensus algorithm and a static data consensus algorithm, the application layer comprises an individual user, a government department and an enterprise user, the contract layer comprises a script code, an algorithm mechanism and an intelligent contract, and the incentive layer comprises a resource providing mechanism and a resource adjusting mechanism.
Further, the specific steps of constructing the cabin price prediction model are as follows:
s21, analyzing and processing the air ticket data: extracting relevant data for constructing a cabin price prediction model from the flight basic information table and the historical data of the flight fare information table; through exploratory analysis, the distribution condition and the importance of passengers on labels such as comfort degree, basic attribute, time sequence characteristic and other additional attribute data are explored; extracting main characteristics possibly influencing the pricing of the fare of the airplane by carrying out preliminary data cleaning and correlation analysis on the original data, and classifying the main characteristics into comfort degree, traffic attributes or time characteristics;
s22, improving the prediction algorithm: combining five models of random forests, support vector machines, gradient lifting trees, artificial neural networks and deep learning into a strong learner to carry out comprehensive learning and judgment; the data set of model training is divided into a training set and a verification set, and the training and the tuning of the model are carried out on the training set; the model is analyzed and calculated by adopting a gradient lifting tree algorithm; the index of evaluation isR mapping goodness of fit of model2And the absolute value of the deviation of all individual observations from the arithmetic mean;
s23, implementation of the cabin price prediction model: importing training set data into a model for training, and adjusting corresponding parameters of an algorithm to achieve the optimal parameters; diagnosing the trained model by using the evaluation indexes, and judging the fitting state of the trained model; carrying out further optimization on the diagnosed model, diagnosing the optimized new model again, and repeatedly iterating and continuously approaching to achieve the optimal state;
s24, price prediction is carried out by utilizing the built cabin price prediction model, the prediction result is stored in a big data platform, the model effect is evaluated through the business indexes of passenger behaviors or enterprise profits and the operation indexes of online operation speed, resource consumption degree and stability of the model, the business indexes of the passenger behaviors comprise high evaluation rate, the business indexes of the enterprise profits comprise profit, high probability and passenger source amplification, the online operation speed of the model is time complexity, and the resource consumption degree is space complexity.
Further, the building of the dynamic pricing model specifically includes:
s31, preprocessing data: extracting a prediction result of the cabin price prediction model from a big data platform as input data, wherein the input data comprises four types: TCN data, INV data, flight information data of the co-flying company and other basic data, and the data cleaning is carried out on the input data, and the non-limiting processing is carried out on the historical booking data;
s32, prediction: selecting a prediction model with the best effect in the current period from the cabin price prediction models, extracting historical cabin or ticket outlet people flow statistical data, estimating the whole market demand scale by adopting an empirical method and a joint estimation method, performing prediction correction on data loss, correcting the prediction data provided by a cabin price prediction module, performing price optimization by combining passenger behavior analysis, and obtaining a passenger sensitivity behavior analysis result through passenger portrait and demand statistics;
s33, solving the dynamic pricing model: the optimal strategy of the control cabin is actively controlled by utilizing an optimal solution algorithm, and meanwhile, manual intervention measures are taken, wherein the manual intervention comprises the consideration of suboptimal targets and the control adjustment of rules,
s34, outputting the result: and obtaining an output result by the dynamic pricing model, wherein the output result comprises a cabin position control cabin instruction and an analysis chart, the cabin position control cabin instruction is used for cabin position control, cabin position opening quantity change and cabin position nesting change, the analysis chart comprises a data analysis chart and a flight early warning chart, the data analysis chart provides a sale price curve for the navigation department, and the flight early warning chart provides seat booking and capacity change warning for the navigation department.
Further, the TCN data includes flight booking data including historical data for estimating market demand and real-time data for performing a comparably comparative analysis; the INV data comprises bay data comprising historical data for estimating market demand and real-time data for supporting subsequent algorithm decisions; the flight information data of the co-flight company and other basic data mainly describe basic information related to flights, including flight numbers, moments, machine types, real-time freight rates and flight basic cabin freight rate tables, and the INV data are further processed to generate passenger seat rate data and output to be a flight passenger seat rate growth curve for airline revenue assessment of a navigation department.
Further, the personalized product recommendation model is modeled by combining passenger characteristic information provided by the passenger portrait, deep learning modeling is performed by adopting a deep cyclic neural network and a convolutional network during modeling, and finally a commodity recommendation list is generated.
Further, the passenger portrait is a customer portrait of an airline passenger, and comprises demographic attributes, ticket buying behaviors, check-in behaviors and boarding behaviors, wherein the demographic attributes comprise the sex, age, source of the passenger, occupation and cultural degree of the customer; the ticket buying behavior comprises a ticket buying advance, a ticket buying channel and a ticket refunding and changing condition; the check-in behavior comprises a check-in mode; the boarding behaviors include a boarding period, a boarding moment, a boarding seat, a boarding cabin space, and meal preferences.
After adopting the technical scheme, compared with the background technology, the invention has the following advantages:
the navigation driver profit management dynamic pricing method solves the data security problem by utilizing a decentralized bottom database which is a block chain technology, fuses a block chain and a large data platform, provides a protected data source, ensures that data extraction becomes safe and reliable, and improves the prediction precision of the cabin price; help model implementation optimization while maintaining data privacy and security; a multi-dimensional and multi-angle analysis is provided by a revenue system considering various service scenes, so that the statistical analysis is conveniently carried out on the result, the personnel are assisted to make the next-stage plan, and the previous complex manual operation is reduced; in addition, based on deep learning, through passenger portrait and demand statistics analysis customer demand, realize the accurate capture of target customer crowd's income, satisfy the decision-making needs of actual airline and airport and policy.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a block chain based basic architecture diagram of a large data platform;
FIG. 3 is a diagram of a dynamic pricing model.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
As shown in fig. 1, a block chain-based airline driver revenue management dynamic pricing method includes the following steps:
s1, constructing a big data platform based on the block chain;
s2, constructing a cabin price prediction model by using the big data platform, and predicting the cabin price;
s3, taking the cabin price prediction result as input data, and constructing a dynamic pricing model;
s4, building a personalized product recommendation model based on deep learning by combining passenger characteristic information provided by the passenger portrait;
and S5, optimizing and evaluating the personalized product recommendation model to obtain a yield management decision.
As shown in fig. 2, the big data platform based on the block chain comprises a block chain consisting of a data layer, a network layer, a consensus layer, an application layer, a contract layer and an incentive layer, wherein the data layer comprises a chain structure, a timestamp, asymmetric encryption, a hash function, an aviation sales data block and an aviation passenger information block, the network layer comprises a P2P network, a propagation mechanism and a verification mechanism, the consensus layer comprises a dynamic data consensus algorithm and a static data consensus algorithm, the application layer comprises an individual user, a government department and an enterprise user, the contract layer comprises a script code, an algorithm mechanism and an intelligent contract, and the incentive layer comprises a resource providing mechanism and a resource adjusting mechanism.
The big information data such as the airline flights has the characteristics of multiple scenes, large data volume, complex structure and difficulty in collection and integration. For the model construction driven by big data, no matter the algorithm development in the early stage or the optimization iteration in the later stage, the model construction needs to be completed through a large amount of data collection and training. Under the condition that modeling needs to be carried out by combining different trip scene data, a big data architecture for guaranteeing safety and privacy is needed. The blockchain is used as a decentralized bottom database to solve the safety problem: it will enable information security techniques to be consolidated and enhanced from this, which has never been done before, helping to achieve optimization of all models.
The specific steps of constructing the cabin price prediction model are as follows:
s21, analyzing and processing the air ticket data: extracting relevant data for constructing a cabin price prediction model from the flight basic information table and the historical data of the flight fare information table; through exploratory analysis, the distribution condition and the importance of passengers on labels such as comfort degree, basic attribute, time sequence characteristic and other additional attribute data are explored; extracting main characteristics possibly influencing the pricing of the fare of the airplane by carrying out preliminary data cleaning and correlation analysis on the original data, and classifying the main characteristics into comfort degree, traffic attributes or time characteristics;
s22, improving the prediction algorithm: combining five models of random forests, support vector machines, gradient lifting trees, artificial neural networks and deep learning into a strong learner to carry out comprehensive learning and judgment; the data set of model training is divided into a training set and a verification set, and the training and the tuning of the model are carried out on the training set; the model is analyzed and calculated by adopting a gradient lifting tree algorithm; the index evaluated is R reflecting the goodness of fit of the model2And the absolute value of the deviation of all individual observations from the arithmetic mean;
s23, implementation of the cabin price prediction model: importing training set data into a model for training, and adjusting corresponding parameters of an algorithm to achieve the optimal parameters; diagnosing the trained model by using the evaluation indexes, and judging the fitting state of the trained model; carrying out further optimization on the diagnosed model, diagnosing the optimized new model again, and repeatedly iterating and continuously approaching to achieve the optimal state;
s24, price prediction is carried out by utilizing the built cabin price prediction model, the prediction result is stored in a big data platform, the model effect is evaluated through the business indexes of passenger behaviors or enterprise profits and the operation indexes of online operation speed, resource consumption degree and stability of the model, the business indexes of the passenger behaviors comprise high evaluation rate, the business indexes of the enterprise profits comprise profit, high probability and passenger source amplification, the online operation speed of the model is time complexity, and the resource consumption degree is space complexity.
The method predicts the specific price of the air ticket of the given air route at one month or a plurality of time points in the future, and has great positive promotion effect on grasping the market trend of the airline company and adjusting the strategy in time. The air ticket price prediction research has special complexity, which is reflected in the volatility: frequently fluctuating along with time evolution, leading hidden factors (remaining seat number of flight, sales condition and the like), diversity: the prices of different flight routes exhibit a high diversity even at the same point in time, and the distribution and the law followed of the ticket prices as time-sensitive data also vary over time. Therefore, when a cabin price prediction model is constructed, the prediction calculation is improved, and various models are combined into a strong learner by utilizing the idea of integrated learning to carry out comprehensive learning and judgment, so that the accuracy of the model is improved.
As shown in fig. 3, the building of the dynamic pricing model specifically includes:
s31, preprocessing data: extracting a prediction result of the cabin price prediction model from a big data platform as input data, wherein the input data comprises four types: TCN data, INV data, flight information data of the co-flying company and other basic data, and the data cleaning is carried out on the input data, and the non-limiting processing is carried out on the historical booking data;
s32, prediction: selecting a prediction model with the best effect in the current period from the cabin price prediction models, extracting historical cabin or ticket outlet people flow statistical data, estimating the whole market demand scale by adopting an empirical method and a joint estimation method, performing prediction correction on data loss, correcting the prediction data provided by a cabin price prediction module, performing price optimization by combining passenger behavior analysis, and obtaining a passenger sensitivity behavior analysis result through passenger portrait and demand statistics;
s33, solving the dynamic pricing model: the optimal strategy of the control cabin is actively controlled by utilizing an optimal solution algorithm, and meanwhile, manual intervention measures are taken, wherein the manual intervention comprises the consideration of suboptimal targets and the control adjustment of rules,
s34, outputting the result: and obtaining an output result by the dynamic pricing model, wherein the output result comprises a cabin position control cabin instruction and an analysis chart, the cabin position control cabin instruction is used for cabin position control, cabin position opening quantity change and cabin position nesting change, the analysis chart comprises a data analysis chart and a flight early warning chart, the data analysis chart provides a sale price curve for the navigation department, and the flight early warning chart provides seat booking and capacity change warning for the navigation department.
In the dynamic pricing model, price optimization is carried out by combining passenger behavior analysis, behavior analysis results such as passenger sensitivity and the like are obtained through analysis of passenger portraits, flexible manual intervention measures are taken, suboptimal objectives are considered, control and adjustment of rules are achieved, intelligent cabin control is achieved, output results are obtained through the dynamic pricing model, flight quantity of a navigation department in a long time span is managed systematically, real-time price information is provided for the navigation department, a price list and an opening instruction file are generated, and the real-time price information is uploaded to a background for the navigation department to sell in the market.
The TCN data comprises flight booking data comprising historical data for estimating market demand and real-time data for performing a comparably comparative analysis; the INV data comprises bay data comprising historical data for estimating market demand and real-time data for supporting subsequent algorithm decisions; the flight information data of the co-flight company and other basic data mainly describe basic information related to flights, including flight numbers, moments, machine types, real-time freight rates and flight basic cabin freight rate tables, and the INV data are further processed to generate passenger seat rate data and output to be a flight passenger seat rate growth curve for airline revenue assessment of a navigation department.
The personalized product recommendation model is modeled by combining passenger characteristic information provided by the passenger portrait, deep learning modeling is carried out by adopting a deep circulation neural network and a convolution network during modeling, and finally a commodity recommendation list is generated.
The passenger portrait is a customer portrait of an aviation passenger, and comprises demographic attributes, ticket buying behaviors, check-in behaviors and airplane riding behaviors, wherein the demographic attributes comprise sex, age, passenger source, occupation and cultural degree of the customer; the ticket buying behavior comprises a ticket buying advance, a ticket buying channel and a ticket refunding and changing condition; the check-in behavior comprises a check-in mode; the boarding behaviors include a boarding period, a boarding moment, a boarding seat, a boarding cabin space, and meal preferences.
The aviation department revenue management dynamic pricing method of the embodiment solves the data security problem by utilizing a decentralized bottom database, namely a block chain technology, and fuses a block chain and a large data platform to provide a protected data source, so that data extraction becomes safe and reliable, and the prediction precision of the cabin price is improved; help model implementation optimization while maintaining data privacy and security; a multi-dimensional and multi-angle analysis is provided by a revenue system considering various service scenes, so that the statistical analysis is conveniently carried out on the result, the personnel are assisted to make the next-stage plan, and the previous complex manual operation is reduced; in addition, based on deep learning, through passenger portrait and demand statistics analysis customer demand, realize the accurate capture of target customer crowd's income, satisfy the decision-making needs of actual airline and airport and policy.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A navigation department revenue management dynamic pricing method based on a block chain is characterized in that: the method comprises the following steps:
s1, constructing a big data platform based on the block chain;
s2, constructing a cabin price prediction model by using the big data platform, and predicting the cabin price;
s3, taking the cabin price prediction result as input data, and constructing a dynamic pricing model;
s4, building a personalized product recommendation model based on deep learning by combining passenger characteristic information provided by the passenger portrait;
and S5, optimizing and evaluating the personalized product recommendation model to obtain a yield management decision.
2. The block chain-based airline department revenue management dynamic pricing method of claim 1, wherein: the big data platform based on the block chain comprises a block chain consisting of a data layer, a network layer, a consensus layer, an application layer, a contract layer and an incentive layer, wherein the data layer comprises a chain structure, a timestamp, asymmetric encryption, a hash function, an aviation sales data block and an aviation passenger information block, the network layer comprises a P2P network, a propagation mechanism and a verification mechanism, the consensus layer comprises a dynamic data consensus algorithm and a static data consensus algorithm, the application layer comprises an individual user, a government department and an enterprise user, the contract layer comprises a script code, an algorithm mechanism and an intelligent contract, and the incentive layer comprises a resource providing mechanism and a resource adjusting mechanism.
3. The block chain-based airline department revenue management dynamic pricing method of claim 1, wherein: the specific steps of constructing the cabin price prediction model are as follows:
s21, analyzing and processing the air ticket data: extracting relevant data for constructing a cabin price prediction model from the flight basic information table and the historical data of the flight fare information table; through exploratory analysis, the distribution condition and the importance of passengers on labels such as comfort degree, basic attribute, time sequence characteristic and other additional attribute data are explored; extracting main characteristics possibly influencing the pricing of the fare of the airplane by carrying out preliminary data cleaning and correlation analysis on the original data, and classifying the main characteristics into comfort degree, traffic attributes or time characteristics;
s22, improving the prediction algorithm: combining five models of random forests, support vector machines, gradient lifting trees, artificial neural networks and deep learning into a strong learner to carry out comprehensive learning and judgment; the data set of model training is divided into a training set and a verification set, and the training and the tuning of the model are carried out on the training set; the model is analyzed and calculated by adopting a gradient lifting tree algorithm; the index evaluated is R reflecting the goodness of fit of the model2And the absolute value of the deviation of all individual observations from the arithmetic mean;
s23, implementation of the cabin price prediction model: importing training set data into a model for training, and adjusting corresponding parameters of an algorithm to achieve the optimal parameters; diagnosing the trained model by using the evaluation indexes, and judging the fitting state of the trained model; carrying out further optimization on the diagnosed model, diagnosing the optimized new model again, and repeatedly iterating and continuously approaching to achieve the optimal state;
s24, price prediction is carried out by utilizing the built cabin price prediction model, the prediction result is stored in a big data platform, the model effect is evaluated through the business indexes of passenger behaviors or enterprise profits and the operation indexes of online operation speed, resource consumption degree and stability of the model, the business indexes of the passenger behaviors comprise high evaluation rate, the business indexes of the enterprise profits comprise profit, high probability and passenger source amplification, the online operation speed of the model is time complexity, and the resource consumption degree is space complexity.
4. The block chain-based airline department revenue management dynamic pricing method of claim 1, wherein: the method for constructing the dynamic pricing model specifically comprises the following steps:
s31, preprocessing data: extracting a prediction result of the cabin price prediction model from a big data platform as input data, wherein the input data comprises four types: TCN data, INV data, flight information data of the co-flying company and other basic data, and the data cleaning is carried out on the input data, and the non-limiting processing is carried out on the historical booking data;
s32, prediction: selecting a prediction model with the best effect in the current period from the cabin price prediction models, extracting historical cabin or ticket outlet people flow statistical data, estimating the whole market demand scale by adopting an empirical method and a joint estimation method, performing prediction correction on data loss, correcting the prediction data provided by a cabin price prediction module, performing price optimization by combining passenger behavior analysis, and obtaining a passenger sensitivity behavior analysis result through passenger portrait and demand statistics;
s33, solving the dynamic pricing model: the optimal strategy of the control cabin is actively controlled by utilizing an optimal solution algorithm, and meanwhile, manual intervention measures are taken, wherein the manual intervention comprises the consideration of suboptimal targets and the control adjustment of rules,
s34, outputting the result: and obtaining an output result by the dynamic pricing model, wherein the output result comprises a cabin position control cabin instruction and an analysis chart, the cabin position control cabin instruction is used for cabin position control, cabin position opening quantity change and cabin position nesting change, the analysis chart comprises a data analysis chart and a flight early warning chart, the data analysis chart provides a sale price curve for the navigation department, and the flight early warning chart provides seat booking and capacity change warning for the navigation department.
5. The block chain-based airline department revenue management dynamic pricing method of claim 4, wherein: the TCN data comprises flight booking data comprising historical data for estimating market demand and real-time data for performing a comparably comparative analysis; the INV data comprises bay data comprising historical data for estimating market demand and real-time data for supporting subsequent algorithm decisions; the flight information data of the co-flight company and other basic data mainly describe basic information related to flights, including flight numbers, moments, machine types, real-time freight rates and flight basic cabin freight rate tables, and the INV data are further processed to generate passenger seat rate data and output to be a flight passenger seat rate growth curve for airline revenue assessment of a navigation department.
6. The block chain-based airline department revenue management dynamic pricing method of claim 1, wherein: the personalized product recommendation model is modeled by combining passenger characteristic information provided by the passenger portrait, deep learning modeling is carried out by adopting a deep circulation neural network and a convolution network during modeling, and finally a commodity recommendation list is generated.
7. The block chain-based airline department revenue management dynamic pricing method of claim 6, wherein: the passenger portrait is a customer portrait of an aviation passenger, and comprises demographic attributes, ticket buying behaviors, check-in behaviors and airplane riding behaviors, wherein the demographic attributes comprise sex, age, passenger source, occupation and cultural degree of the customer; the ticket buying behavior comprises a ticket buying advance, a ticket buying channel and a ticket refunding and changing condition; the check-in behavior comprises a check-in mode; the boarding behaviors include a boarding period, a boarding moment, a boarding seat, a boarding cabin space, and meal preferences.
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