CN106875218B - Price prediction method and device for data flow product - Google Patents
Price prediction method and device for data flow product Download PDFInfo
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Abstract
The invention discloses a price prediction method of a data flow product. The invention also discloses a price prediction device of the data flow product. The method determines a corresponding current price prediction model according to current position information and current time information, estimates the order entry speed corresponding to different proposed set prices of the data flow product through the current price prediction model, calculates the total income generated by the different proposed set prices and the corresponding order entry speed, and takes the proposed set price with the highest total income as a predicted price, so that market managers can be helped to better regulate and control the market and allocate resources as much as possible, and the workload of the market managers is reduced.
Description
Technical Field
The invention relates to the technical field of websites, in particular to a price prediction method and device for a data flow product.
Background
The data traffic product is a resource type product generated along with the rapid development of the mobile internet and the popularization of the smart phone in recent years, on one hand, the data traffic product is a requirement of a smart phone user on network traffic application, on the other hand, the data traffic product is a service provided by integrating bandwidth resources by an operator company, and the data traffic product is a service type product which occupies network resources essentially.
The service exists along with the demand, the demand is estimated and calculated in advance, and corresponding resource capacity is provided for supporting, so that the demand and perfect experience of the user can be met, and waste caused by configuration of excessive resources is avoided. For example, in the market in the early month, the demand for large-size traffic packets is larger, but in the end of the month, small-size traffic packets are more favored by the market, and the experience requirement for network response is higher, which is also determined by the order distribution speed of the system.
Therefore, how to provide a price prediction method for data traffic products (traffic packets) in an e-commerce platform to accurately predict the distribution of price and order entry speed, and further help market managers to better regulate and control the market and configure resources, thereby reducing the workload of the market managers becomes a technical problem to be solved urgently by practitioners in the field.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a price prediction method and device for a data flow product, and aims to solve the technical problem of reducing the workload of market managers.
To achieve the above object, the present invention provides a price prediction method for data flow products, comprising the steps of:
acquiring current position information and current time information;
determining a corresponding current price prediction model according to the current position information and the current time information;
estimating the order entry speed corresponding to different proposed set prices of the data flow product through the current price prediction model;
calculating the total income generated by different preset prices and corresponding order entering speeds;
and taking the planned price with the highest total income as a predicted price.
Preferably, before the obtaining of the current location information and the current time information, the method further includes:
acquiring historical data of the e-commerce platform within a preset region range and a preset time period, and establishing a price prediction model of the preset region range and the preset time period according to the acquired historical data of the e-commerce platform so as to obtain price prediction models of different region ranges and time periods;
correspondingly, the determining a corresponding current price prediction model according to the current location information and the current time information specifically includes:
and acquiring a current region range to which the current position information belongs, acquiring a current time period to which the current time information belongs, and taking a price prediction model of the current region range and the current time period as a current price prediction model.
Preferably, the price prediction model is
P＝aV+b
Wherein V is the speed of advancing, P is the price of the data flow product, a is the primary term of the relation, and b is the line term.
Preferably, the establishing of the price prediction model of the preset region range and the preset time period according to the obtained historical data of the e-commerce platform specifically includes:
and determining a and b in the price prediction model according to the acquired e-commerce platform historical data in a linear fitting mode, and taking the price prediction model substituted into the a and b as the price prediction model of the preset region range and the preset time period.
Preferably, the e-commerce platform history data comprises: historical prices and historical rates of entry for data flow products.
In addition, to achieve the above object, the present invention provides a price forecasting apparatus for a data flow product, the apparatus including:
the data acquisition module is used for acquiring current position information and current time information;
the model determining module is used for determining a corresponding current price prediction model according to the current position information and the current time information;
the speed estimation module is used for estimating the order entry speed corresponding to different proposed set prices of the data flow product through the current price estimation model;
the income calculation module is used for calculating total income generated by different prices to be set and corresponding order entering speeds;
and the price prediction module is used for taking the planned set price with the highest total income as the predicted price.
Preferably, the apparatus further comprises:
the model establishing module is used for acquiring historical data of the e-commerce platform within a preset region range and a preset time period, and establishing a price prediction model of the preset region range and the preset time period according to the acquired historical data of the e-commerce platform so as to obtain price prediction models of different region ranges and time periods;
correspondingly, the model determining module is further configured to obtain a current region range to which the current location information belongs, obtain a current time period to which the current time information belongs, and use a price prediction model of the current region range and the current time period as a current price prediction model.
Preferably, the price prediction model is
P＝aV+b
Wherein V is the speed of advancing, P is the price of the data flow product, a is the primary term of the relation, and b is the line term.
Preferably, the model establishing module is further configured to determine a and b in the price prediction model according to the obtained e-commerce platform historical data in a linear fitting manner, and use the price prediction model substituted into a and b as the price prediction model of the preset region range and the preset time period.
Preferably, the e-commerce platform history data comprises: historical prices and historical rates of entry for data flow products.
The method determines a corresponding current price prediction model according to current position information and current time information, estimates the order entry speed corresponding to different proposed set prices of the data flow product through the current price prediction model, calculates the total income generated by the different proposed set prices and the corresponding order entry speed, and takes the proposed set price with the highest total income as a predicted price, so that market managers can be helped to better regulate and control the market and allocate resources as much as possible, and the workload of the market managers is reduced.
Drawings
FIG. 1 is a schematic flow chart illustrating a price forecasting method for data traffic products according to a first embodiment of the present invention;
FIG. 2 is a flow chart illustrating a price forecasting method for data flow products according to a second embodiment of the present invention;
FIG. 3 is a functional block diagram of a price forecasting apparatus for data flow products according to a first embodiment of the present invention;
fig. 4 is a functional block diagram of a price forecasting device of a data flow product according to a second embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a first embodiment of the present invention provides a price prediction method for a data flow product, the method including:
s10: acquiring current position information and current time information;
it should be noted that the main execution body of the method of this embodiment is a user device, where the user device is a device used by a market manager of a store in an e-commerce platform, and may be a device such as a mobile phone, a tablet computer, a PC, a notebook computer, or a PDA, which is not limited in this embodiment.
It can be understood that the current location information is the current location information of the ue, and the current time information is information reflecting the current time.
S20: determining a corresponding current price prediction model according to the current position information and the current time information;
in a specific implementation, price prediction models corresponding to different locations and different times may be different, for example: in the e-commerce platform, the e-commerce platform has a large demand on large-specification flow packets at the beginning of the month, but at the end of the month, small-specification flow packets are favored by the market, and the experience requirement on network response is high, so the order entry speed is different.
S30: estimating the order entry speed corresponding to different proposed set prices of the data flow product through the current price prediction model;
it can be understood that the current price prediction model can reflect the relationship between the price of the data flow product and the order entry speed, so that the order entry speed corresponding to different proposed set prices of the data flow product can be predicted through the current price prediction model.
S40: calculating the total income generated by different preset prices and corresponding order entering speeds;
it can be understood that, since the selling price is the cost price + the electric company credit + the product profit, the selling price is the standard price × discount, the order input speed is the unit input amount/unit time, and the cost price and the electric company credit are fixed values, the total profit can be calculated after the price and the order input speed are determined, and the total profit is the product profit × the order input speed.
S50: and taking the planned price with the highest total income as a predicted price.
In a specific implementation, the planned set price with the highest total profit is the predicted price, and the predicted price can be displayed for informing the user conveniently, or certainly, corresponding curves between different planned set prices and corresponding total profits can be displayed.
The method comprises the steps of determining a corresponding current price prediction model according to current position information and current time information, predicting order entry speeds corresponding to different proposed set prices of a data flow product through the current price prediction model, calculating total benefits generated by the different proposed set prices and the corresponding order entry speeds, and using the proposed set price with the highest total benefit as a predicted price to help market managers to better regulate and control markets and configure resources as much as possible, so that the workload of the market managers is reduced.
Referring to fig. 2, fig. 2 is a schematic flow chart of the price prediction method of the data flow product according to the present invention, and a second embodiment of the price prediction method of the data flow product according to the present invention is proposed based on the embodiment shown in fig. 1.
In this embodiment, before the step S10, the method further includes:
s00: acquiring historical data of the e-commerce platform within a preset region range and a preset time period, and establishing a price prediction model of the preset region range and the preset time period according to the acquired historical data of the e-commerce platform so as to obtain price prediction models of different region ranges and time periods;
because the price prediction models in different regions and time periods usually have differences, the price prediction models in different regions and time periods can be respectively established in the embodiment.
It is to be understood that the e-commerce platform history data includes: the historical price and historical order entry speed of the data flow product may, of course, also include: the e-commerce account distribution of data traffic products with different specification attributes comprises operator brands, regions, traffic packet specifications and product application ranges, and for example, a certain data traffic product can be specifically a 100MB intra-provincial traffic packet moved in northwest of Hubei according to the minimum granularity.
To reduce the complexity of the model, in a specific implementation, the price prediction model is
P＝aV+b
Wherein V is the speed of advancing, P is the price of the data flow product, a is the primary term of the relation, and b is the line term.
Correspondingly, the establishing of the price prediction model of the preset region range and the preset time period according to the obtained historical data of the e-commerce platform may specifically include: and determining a and b in the price prediction model according to the acquired e-commerce platform historical data in a linear fitting mode, and taking the price prediction model substituted into the a and b as the price prediction model of the preset region range and the preset time period.
Correspondingly, the step S20 specifically includes:
s21: and acquiring a current region range to which the current position information belongs, acquiring a current time period to which the current time information belongs, and taking a price prediction model of the current region range and the current time period as a current price prediction model.
Referring to fig. 3, a first embodiment of the present invention provides a price prediction apparatus for a data flow product, the apparatus including:
a data obtaining module 10, configured to obtain current position information and current time information;
it should be noted that the apparatus of this embodiment is deployed on a user device, where the user device is a device used by a market manager of a store in an e-commerce platform, and may be a mobile phone, a tablet computer, a PC, a notebook computer, or a PDA, which is not limited in this embodiment.
It can be understood that the current location information is the current location information of the ue, and the current time information is information reflecting the current time.
A model determining module 20, configured to determine a corresponding current price prediction model according to the current location information and the current time information;
in a specific implementation, price prediction models corresponding to different locations and different times may be different, for example: in the e-commerce platform, the e-commerce platform has a large demand on large-specification flow packets at the beginning of the month, but at the end of the month, small-specification flow packets are favored by the market, and the experience requirement on network response is high, so the order entry speed is different.
The speed estimation module 30 is used for estimating the order entry speed corresponding to different proposed set prices of the data flow products through the current price estimation model;
it can be understood that the current price prediction model can reflect the relationship between the price of the data flow product and the order entry speed, so that the order entry speed corresponding to different proposed set prices of the data flow product can be predicted through the current price prediction model.
The profit calculation module 40 is used for calculating the total profit generated by different proposed set prices and corresponding order entry speeds;
it can be understood that, since the selling price is the cost price + the electric company credit + the product profit, the selling price is the standard price × discount, the order input speed is the unit input amount/unit time, and the cost price and the electric company credit are fixed values, the total profit can be calculated after the price and the order input speed are determined, and the total profit is the product profit × the order input speed.
And the price prediction module 50 is used for taking the planned price with the highest total income as the predicted price.
In a specific implementation, the planned set price with the highest total profit is the predicted price, and the predicted price can be displayed for informing the user conveniently, or certainly, corresponding curves between different planned set prices and corresponding total profits can be displayed.
The method comprises the steps of determining a corresponding current price prediction model according to current position information and current time information, predicting order entry speeds corresponding to different proposed set prices of a data flow product through the current price prediction model, calculating total benefits generated by the different proposed set prices and the corresponding order entry speeds, and using the proposed set price with the highest total benefit as a predicted price to help market managers to better regulate and control markets and configure resources as much as possible, so that the workload of the market managers is reduced.
Referring to fig. 4, fig. 4 is a functional block diagram of a price forecasting device of a data flow product according to the present invention, and a second embodiment of the price forecasting device of a data flow product according to the present invention is proposed based on the embodiment shown in fig. 3.
In this embodiment, the apparatus further includes:
the model establishing module 00 is used for acquiring historical data of the e-commerce platform within a preset region range and a preset time period, and establishing a price prediction model of the preset region range and the preset time period according to the acquired historical data of the e-commerce platform so as to obtain price prediction models of different region ranges and time periods;
because the price prediction models in different regions and time periods usually have differences, the price prediction models in different regions and time periods can be respectively established in the embodiment.
It is to be understood that the e-commerce platform history data includes: the historical price and historical order entry speed of the data flow product may, of course, also include: the e-commerce account distribution of data traffic products with different specification attributes comprises operator brands, regions, traffic packet specifications and product application ranges, and for example, a certain data traffic product can be specifically a 100MB intra-provincial traffic packet moved in northwest of Hubei according to the minimum granularity.
To reduce the complexity of the model, in a specific implementation, the price prediction model is
P＝aV+b
Wherein V is the speed of advancing, P is the price of the data flow product, a is the primary term of the relation, and b is the line term.
Correspondingly, the establishing of the price prediction model of the preset region range and the preset time period according to the obtained historical data of the e-commerce platform may specifically include: and determining a and b in the price prediction model according to the acquired e-commerce platform historical data in a linear fitting mode, and taking the price prediction model substituted into the a and b as the price prediction model of the preset region range and the preset time period.
Correspondingly, the model determining module 21 is further configured to obtain a current region range to which the current location information belongs, obtain a current time period to which the current time information belongs, and use a price prediction model of the current region range and the current time period as a current price prediction model.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (4)
1. A method for price forecasting of a data traffic product, the method comprising the steps of:
the method comprises the steps that user equipment obtains e-commerce platform historical data within a preset region range and a preset time period, a and b in a price prediction model are determined in a linear fitting mode according to the e-commerce platform historical data, and the price prediction model substituted into the a and b is used as the price prediction model of the preset region range and the preset time period;
the price prediction model is
P＝aV+b
Wherein V is the speed of advancing the order, P is the price of the data flow product, a is the first order term of the relation, b is the line number term;
acquiring current position information and current time information;
acquiring a current region range to which the current position information belongs, acquiring a current time period to which the current time information belongs, and taking a price prediction model of the current region range and the current time period as a current price prediction model;
estimating the order entry speed corresponding to different proposed set prices of the data flow product through the current price prediction model;
calculating the total income generated by different preset prices and corresponding order entering speeds;
taking the planned price with the highest total income as a predicted price;
the current position information is the current position information of the user equipment, and the current time information is information reflecting the current time.
2. The method of claim 1, wherein the e-commerce platform history data comprises: historical prices and historical rates of entry for data flow products.
3. An apparatus for price forecasting of a data flow product, the apparatus comprising:
the data acquisition module is used for acquiring current position information and current time information by the user equipment;
the model determining module is used for determining a corresponding current price prediction model according to the current position information and the current time information;
the speed estimation module is used for estimating the order entry speed corresponding to different proposed set prices of the data flow product through the current price estimation model;
the income calculation module is used for calculating total income generated by different prices to be set and corresponding order entering speeds;
the price prediction module is used for taking the planned set price with the highest total income as a predicted price;
the current position information is the current position information of the user equipment, and the current time information is information reflecting the current time;
the model establishing module is used for acquiring historical data of the e-commerce platform within a preset region range and a preset time period, and establishing a price prediction model of the preset region range and the preset time period according to the acquired historical data of the e-commerce platform so as to obtain price prediction models of different region ranges and time periods;
the model determining module is further configured to obtain a current region range to which the current location information belongs, obtain a current time period to which the current time information belongs, and use a price prediction model of the current region range and the current time period as a current price prediction model;
the price prediction model is
P＝aV+b
Wherein V is the speed of advancing the order, P is the price of the data flow product, a is the first order term of the relation, b is the line number term;
the model establishing module is further used for determining a and b in the price prediction model according to the acquired e-commerce platform historical data in a linear fitting mode, and taking the price prediction model substituted into the a and b as the price prediction model of the preset region range and the preset time period.
4. The apparatus of claim 3, wherein the e-commerce platform history data comprises: historical prices and historical rates of entry for data flow products.
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| CN102324082A (en) * | 2011-09-08 | 2012-01-18 | 上海烟草集团有限责任公司 | Cigarette market price prediction method based on multiple linear regression |
| CN105205701A (en) * | 2015-09-22 | 2015-12-30 | 创点客（北京）科技有限公司 | Network dynamic pricing method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102324082A (en) * | 2011-09-08 | 2012-01-18 | 上海烟草集团有限责任公司 | Cigarette market price prediction method based on multiple linear regression |
| CN105205701A (en) * | 2015-09-22 | 2015-12-30 | 创点客（北京）科技有限公司 | Network dynamic pricing method and system |
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