CN105205685A - Method and device for processing virtual commodity data - Google Patents

Abstract

The invention provides a method and a device for processing virtual commodity data. The method comprises the steps of collecting purchase data of multiple virtual commodities; dividing the multiple virtual commodities into two groups according to categories, and determining a purchase intersection point of the two groups of virtual commodities within a first period of time based on the purchase data; and determining inertia selling time of the plurality of virtual commodities based on a plurality of purchase intersection points within a second period of time, wherein the second period of time comprises the first period of time, and the length of the second period of time is multiple times of the length of the first period of time. The invention further provides a virtual commodity data processing device corresponding to the method. Processing can be carried out on the virtual commodity data more accurately and more timely based on the method and the device provided by the invention.

Description

Virtual goods data processing method and treatment facility

Technical field

The present invention relates to virtual goods field, be specifically related to virtual goods data processing method and treatment facility.

Background technology

On current electric business website, for virtual goods (such as, game commodity, to comprise some card, game Key, certificate code, game item etc.) sale, by collecting game commodity purchasing data, the web page browsing amount (PageView of such as game user, PV), order conversion ratio, time buying etc., by data processing personnel, manual analysis is carried out to these data.Such as, by above-mentioned Data Integration in tables of data, present to data processing personnel, data processing personnel manually analyze tables of data.

But, such technology also exists some problems, such as: because virtual goods belongs to fast-moving consumer goods, its quantity upgrades at any time, and have longer production phase and logistics duration unlike physical goods, and data can produce larger fluctuation according to different application (such as different game), carry out analyzing virtual commodity can there is problem inaccurate and not in time with the analysis mode of physical goods.In addition, a burst period can be there is in the sale of virtual goods, a period (as movable in game regular in game) of such as similar " joint ", need to arrange the support of software, hardware and the network bandwidth for this time period, to avoid such as causing systemic breakdown etc. to the impact of network and system.

Also lack at present the sales data of virtual goods is carried out accurately, the effective ways of express-analysis.

Summary of the invention

In order to provide, the sales data of virtual goods is carried out accurately, the effective means of express-analysis, the invention provides a kind of disposal route of virtual goods data and a kind for the treatment of facility of virtual goods data.

According to a first aspect of the invention, provide a kind of disposal route of virtual goods data, comprising: the purchase data collecting multiple virtual goods; Described multiple virtual goods is divided into two groups by category, and based on the purchase point of crossing of two groups of virtual goodses in described purchase data determination first time period; And the sales along the existing trend time of described multiple virtual goods is determined based on the multiple described purchase point of crossing in the second time period, wherein, described second time period comprises described first time period and its length is many times of described first time period.

According to a second aspect of the invention, provide a kind for the treatment of facility of virtual goods data, comprising: information collection module, for collecting the purchase data of multiple virtual goods; Intersection calculations module, for being divided into two groups by described multiple virtual goods by category, and based on the purchase point of crossing of two groups of virtual goodses in described purchase data determination first time period; And inertia determination module, for determining the sales along the existing trend time of described multiple virtual goods based on the multiple described purchase point of crossing in the second time period, wherein, described second time period comprises described first time period and its length is many times of described first time period.

According to said method of the present invention and equipment, comparatively accurately and in time can predict the dden spikes period that virtual goods is sold, thus the support of software, hardware and the network bandwidth can be arranged for this time period, to avoid such as causing systemic breakdown etc. to the impact of network and system.

Accompanying drawing explanation

By the detailed description of carrying out invention below in conjunction with accompanying drawing, above-mentioned feature and advantage of the present invention will be made more obvious, wherein:

Fig. 1 is the outline flowchart of the disposal route illustrated according to embodiments of the invention virtual goods data;

Fig. 2 is the brief block diagram that virtual goods data processing equipment is according to an embodiment of the invention shown; And

Fig. 3 is the schematic diagram illustrating that the point of crossing of the embodiment realizing technical solution of the present invention is determined.

Embodiment

Below, the preferred embodiment of the present invention is described in detail with reference to accompanying drawing.In the accompanying drawings, although be shown in different accompanying drawings, identical Reference numeral is for representing identical or similar assembly.For clarity and conciseness, the detailed description being included in known function and structure here will be omitted, to avoid making theme of the present invention unclear.

Fig. 1 shows the outline flowchart of the disposal route according to embodiments of the invention virtual goods data.

In the step 110 of Fig. 1, collect the purchase data of multiple virtual goods.

The inventory records data (comprising the sales volume of goods etc. of purchase order amount, purchase) can bought according to all or part client, according to the category of commodity and the minimum inventories unit (StockKeepingUnit of commodity, SKU) carry out classification and matching, carry out statistic of classification according to the time.Said category refers to type of merchandize herein, such as ticketing service class commodity, travelling class commodity, supplements class commodity etc. with money.In some instances, according to consecutive days, data are arranged.Certainly, in other examples, also can by other times unit or same time unit but different time starting point (such as, from every morning 8 to the next morning data of 8) data are arranged.

In some instances, when virtual goods is the virtual objects in game, the virtual objects that a or many moneys can be specified to play is analyzed.Now, first can determine the relevance of this many moneys game, whether such as type or content aspect exist association, and the Data Collection of related game and data analysis subsequently can be put together and carry out.

In the step 120, multiple virtual goods is divided into two groups by category, and based on the purchase point of crossing of two groups of virtual goodses in purchase data determination first time period.

In certain embodiments, first time period can be one month.Certainly, different as the case may be, first time period also can be other times section, such as two months etc.

Fig. 3 shows according to one of purchase point of crossing of the present invention exemplary schematic diagram.As shown in Figure 3, transverse axis coordinate is the time, and ordinate of orthogonal axes is sales volume.Particularly, the ordinate of orthogonal axes in Fig. 3 represents consumption sum, and certainly, ordinate of orthogonal axes also can represent other forms of sales volume, such as consumption sum etc.A time interval on transverse axis can be 1 month.As described in Figure, can there is multiple purchase point of crossing in these two groups of virtual goodses in very first time interval.Now district's order volume or the highest point of crossing of consumption sum purchase point of crossing be can be used as data analysis, and time T and the sales volume at this purchase point of crossing place recorded.As mentioned above, T can be the time representing consecutive days.

In some instances, also may need to reject the order of quantity purchase/amount of money much larger than other orders from collected data.Such as, the big customers such as operating room may order a large amount of virtual goodses once, but this order can not represent the user behavior of general user, and the data rejecting this burst can be analyzed more accurately to user behavior.

In step 130, determine the sales along the existing trend time of multiple virtual goods based on the multiple purchase point of crossing in the second time period, wherein, the second time period comprised first time period and its length is many times of first time period.

Such as, first time period can be 1 month or two months, and the second time period can be half a year or 1 year.

In certain embodiments, following equation (1) can be used to determine the sales volume at purchase point of crossing place of described two groups of virtual goodses in next first time period:

$G_{m+1}=\frac{\underset{n=i}{\overset{m}{\Σ}}{G}_n}{m-i}(m\≥2,i\≥1,m>i)$ Equation (1)

And the purchase point of crossing of described two groups of virtual goodses in next first time period is determined according to following equation (2):

$T_{m+1}=\frac{(T_m+T_{m-1})}{2}(m\≥2)$ Equation (2)

Wherein, G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, a certain first time period before i represents, its value can be determined according to real needs.Such as, when the commodity of certain category have good sale inertia (the purchase point of crossing as multiple first time period is close), the desirable comparatively front value of i.

In further embodiments, can utilize within 1 year, correct with the data of data to this next first time period of this next first time period same period.Such as shown in following equation 3:

$T_{m+1}=\frac{\left|T_m-T_{m-1}\right|}{2}+T_{m+1,y-1}(m\≥2,y\≥2)$ Equation (3)

Wherein, T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, and y represents current year.Although it should be noted that the data that use only the previous year here correct.In certain embodiments, before also can adopting, data for many years correct, and such as, before adopting, average or other forms of data for many years correct.

From above-mentioned equation, the value of the T calculated may not be integer, and now, the value of T can round up, and also can round downwards.The present invention does not limit this.In the operation of reality, the phase sales peak of can thinking that these two time points (value that rounds up and round value downwards) are all potential, need to carry out such as network and hardware supported for these two time points.

In addition, the method can also comprise: for each first time period (for 1 month) of (for 1 year) in the second time period, calculate the matching factor of current first time period and a upper first time period.

Following equation (4) can be used to calculate matching factor:

$f=\frac{G_m{T}_m}{G_{m-1}{T}_{m-1}}$ Equation (4)

Wherein, f represents matching factor, and G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period.Such as, suppose that the purchase point of crossing in current first time period is March 23, then current first time period is designated as T ₃, value is 23, and the sales volume of current first time period is designated as G ₃.

Thus, such as, after the matching factor knowing the current moon and the previous moon, when carrying out the prediction in lower January, the matching factor of the current moon and the previous moon can be averaged simply and obtaining the matching factor in lower January.When the matching factor value obtained is more close to 1, then show the data prediction in this lower January more normal, when matching factor value from 1 more away from, then show that the confidence level of the prediction to this lower January is lower.

In certain embodiments, can by the matching factor in the second time period closest to the best sales along the existing trend time that the time point at the matching factor place of 1 is defined as.Such as, if March 23, corresponding matching factor was the matching factor closest to 1 in whole a year, then (so far) the best sales along the existing trend time in this year was on March 23.This best sales along the existing trend time can show in this time, large-scale virtual goods buying behavior all may occur such as every year, and needing provides sufficient software, hardware and network bandwidth support, to avoid the unfavorable impact to network and system for the behavior.

Next, with reference to Fig. 2, the mode shown in composition graphs 1 describes the treatment facility of the virtual goods data corresponding with the method for Fig. 1 in detail.

As shown in Figure 2, at least comprise according to the virtual goods data processing equipment of the embodiment of the present invention:

Information collection module 210, for collecting the purchase data of multiple virtual goods;

Intersection calculations module 220, for being divided into two groups by the plurality of virtual goods by category, and based on the purchase point of crossing of two groups of virtual goodses in purchase data determination first time period; And

Inertia determination module 230, for determining the sales along the existing trend time of the plurality of virtual goods based on the multiple purchase point of crossing in the second time period, wherein, the second time period comprised first time period and its length is many times of first time period.

This virtual goods data processing equipment can also comprise abnormity removing module 240, for rejecting the order of quantity purchase/amount of money much larger than other orders from collected purchase data.

This virtual goods data processing equipment can also comprise memory module (not shown), for store above-mentioned module process in need the data that store.This memory module can realize by the various technology that those skilled in the art are known.

In some instances, intersection calculations module 220 also can be used for: determine that described two groups of virtual goodses have the time point of identical sales volume on a timeline as purchase point of crossing.

In some instances, intersection calculations module 220 also can be used for: if there is multiple time point with identical sales volume in first time period, gets the most salable time point as purchase point of crossing.

In some instances, this treatment facility can also comprise:

Matching factor computing module 250: for for each first time period in the second time period, calculate the matching factor of current first time period and a upper first time period; And

In some instances, matching factor computing module 250 can also be used for:

The matching factor of current first time period and a upper first time period is calculated according to following equation:

$f=\frac{G_m{T}_m}{G_{m-1}{T}_{m-1}}$

Wherein, G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period.

In some instances, inertia determination module 230 also can be used for:

The sales volume at purchase point of crossing place of two groups of virtual goodses in next first time period is determined according to following equation:

$G_{m+1}=\frac{\underset{n=i}{\overset{m}{\Σ}}{G}_n}{m-i}(m\≥2,i\≥1,m>i)$

And the purchase point of crossing of two groups of virtual goodses in next first time period is determined according to following equation:

$T_{m+1}=\frac{(T_m+T_{m-1})}{2}(m\≥2)$

Wherein, G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, a certain first time period before i represents, its value can be determined according to real needs.

In some instances, the data of data to next predicted first time period in the past can be used to correct.Such as, inertia determination module 230 can also be used for: utilize within 1 year, correct with the data of data to this next first time period of this next first time period same period.Particularly, in some instances, inertia determination module 230 can determine the purchase point of crossing of two groups of virtual goodses in next first time period according to following equation:

$T_{m+1}=\frac{\left|T_m-T_{m-1}\right|}{2}+T_{m+1,y-1}(m\≥2,y\≥2)$

Wherein, T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, and y represents current year.Although it should be noted that the data that use only the previous year here correct.In certain embodiments, before also can adopting, data for many years correct, and such as, before adopting, average or other forms of data for many years correct.

In some instances, matching factor computing module 250 can also be used for by the matching factor in the second time period closest to the best sales along the existing trend time that the time point at the matching factor place of 1 is defined as.As mentioned above, this best sales along the existing trend time can show in this time, large-scale virtual goods buying behavior all may occur such as every year, need to provide sufficient software, hardware and network bandwidth support, to avoid the unfavorable impact to network and system for the behavior.

As mentioned above, the value of the T calculated may not be integer, and now, the value of T can round up, and also can round downwards.The present invention does not limit this.In the operation of reality, the phase sales peak of can thinking that these two time points (value that rounds up and round value downwards) are all potential, need to carry out such as network and hardware supported for these two time points.

In addition, it should be noted that the equation provided in the present invention is only the example provided to understand the present invention.Those skilled in the art also can instruct according to according to the present invention the equation adopting other.Such as, in above-mentioned equation 2 and 3, use only the data of two first time period to predict the data of next first time period, but those skilled in the art also can adopt the data of more first time period to predict.Again such as, have employed current first time period in above-mentioned equation 1 and 2 to predict with the simple arithmetic mean of the data of one or more first time period before, but according to embodiments of the invention, the mathematical expression that the weighted mean using matching factor as weighting coefficient or other those skilled in the art also can be adopted to expect is predicted.In addition, the T in equation 3 _mand T _m-1also weighting scheme can be adopted to process.

It should be noted that; the virtual goods data processing equipment described in fig. 2 is only the figure done to make those skilled in the art more clearly understand the present invention; wherein eliminate some to understanding the unnecessary modules/components of the present invention, protection scope of the present invention should not limit by the detail of these accompanying drawings.Such as, more modules/components can be comprised, as display, Operation and Maintenance interface, IO interface etc. in actual equipment.The present invention does not limit these.

In the above embodiment of the present invention, by collecting the purchase data of multiple virtual goods and analyzing to determine sales along the existing trend time of the plurality of virtual goods to it, can make prediction to the sale of virtual goods effectively, immediately and exactly, thus avoid the impact of the software to virtual goods purchase system, hardware and the network caused because of the appearance of the buying behavior of a large amount of virtual goods, avoid the appearance of the unfavorable phenomenons such as such as systemic breakdown.

Description is above only for realizing embodiments of the present invention; it should be appreciated by those skilled in the art; the any modification or partial replacement do not departed from the scope of the present invention; the scope that all should belong to claim of the present invention to limit; therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.

Claims (22)

1. a disposal route for virtual goods data, comprising:

Collect the purchase data of multiple virtual goods;

Described multiple virtual goods is divided into two groups by category, and based on the purchase point of crossing of two groups of virtual goodses in described purchase data determination first time period; And

Determine the sales along the existing trend time of described multiple virtual goods based on the multiple described purchase point of crossing in the second time period, wherein, described second time period comprises described first time period and its length is many times of described first time period.

2. disposal route according to claim 1, also comprises:

The order of quantity purchase/amount of money much larger than other orders is rejected from collected purchase data.

3. disposal route according to claim 1, wherein, determines that described purchase point of crossing comprises: determine that described two groups of virtual goodses have the time point of identical sales volume on a timeline.

4. disposal route according to claim 3, wherein, if there is multiple time point with identical sales volume in described first time period, gets the most salable time point as described purchase point of crossing.

5. disposal route according to claim 1, also comprises: the sales volume at purchase point of crossing place determining described two groups of virtual goodses in next first time period according to following equation:

$G_{m+1}=\frac{\underset{n=i}{\overset{m}{\Σ}}{G}_n}{m-i}(m\≥2,i\≥1,m>i)$

And the purchase point of crossing of described two groups of virtual goodses in next first time period is determined according to following equation:

$T_{m+1}=\frac{(T_m+T_{m-1})}{2}(m\≥2)$

Wherein, G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, a certain first time period before i represents.

6. disposal route according to claim 1, also comprises:

Utilize within 1 year, correct with the data of data to next first time period described of described next first time period same period.

7. disposal route according to claim 6, wherein, determine the purchase point of crossing of described two groups of virtual goodses in next first time period according to following equation:

$T_{m+1}=\frac{|T_m-T_{m-1}|}{2}+T_{m+1,y-1}(m\≥2,y\≥2)$

Wherein, T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, _yrepresent current year.

8. disposal route according to claim 1, also comprises:

For each described first time period in described second time period, calculate the matching factor of current first time period and a upper first time period.

9. disposal route according to claim 8, wherein, calculates the matching factor of current first time period and a upper first time period according to following equation:

$f=\frac{G_m{T}_m}{G_{m-1}{T}_{m-1}}$

Wherein, f represents matching factor, and G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period.

10. disposal route according to claim 9, also comprises:

By in the matching factor in described second time period closest to the best sales along the existing trend time that the time point at the matching factor place of 1 is defined as.

11. disposal routes according to any one of claim 1 to 10, wherein, described first time period is one month, and described second time period is half a year or 1 year, and described time point is one day.

The treatment facility of 12. 1 kinds of virtual goods data, comprising:

Information collection module, for collecting the purchase data of multiple virtual goods;

Intersection calculations module, for being divided into two groups by described multiple virtual goods by category, and based on the purchase point of crossing of two groups of virtual goodses in described purchase data determination first time period; And

Inertia determination module, for determining the sales along the existing trend time of described multiple virtual goods based on the multiple described purchase point of crossing in the second time period, wherein, described second time period comprises described first time period and its length is many times of described first time period.

13. treatment facilities according to claim 12, also comprise:

Abnormity removing module, for rejecting the order of quantity purchase/amount of money much larger than other orders from collected purchase data.

14. treatment facilities according to claim 12, wherein, described intersection calculations module also for: determine that described two groups of virtual goodses have the time point of identical sales volume on a timeline as described purchase point of crossing.

15. treatment facilities according to claim 14, wherein, described intersection calculations module also for: if there is multiple time point with identical sales volume in described first time period, get the most salable time point as described purchase point of crossing.

16. treatment facilities according to claim 12, wherein, described inertia determination module also for:

The sales volume at purchase point of crossing place of described two groups of virtual goodses in next first time period is determined according to following equation:

$G_{m+1}=\frac{\underset{n=i}{\overset{m}{\Σ}}{G}_n}{m-i}(m\≥2,i\≥1,m>i)$

And the purchase point of crossing of described two groups of virtual goodses in next first time period is determined according to following equation:

$T_{m+1}=\frac{(T_m+T_{m-1})}{2}(m\≥2)$

Wherein, G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, a certain first time period before i represents.

17. treatment facilities according to claim 12, wherein, described inertia determination module also for:

Utilize within 1 year, correct with the data of data to next first time period described of described next first time period same period.

18. treatment facilities according to claim 17, wherein, described inertia determination module also for:

The purchase point of crossing of described two groups of virtual goodses in next first time period is determined according to following equation:

$T_{m+1}=\frac{|T_m-T_{m-1}|}{2}+T_{m+1,y-1}(m\≥2,y\≥2)$

Wherein, T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period, and m+1 represents next first time period, and y represents current year.

19. treatment facilities according to claim 12, also comprise:

Matching factor computing module: for for each described first time period in described second time period, calculate the matching factor of current first time period and a upper first time period.

20. treatment facilities according to claim 19, wherein, described matching factor computing module is used for:

The matching factor between current first time period and a upper very first time was calculated according to following equation:

$f=\frac{G_m{T}_m}{G_{m-1}{T}_{m-1}}$

Wherein, f represents matching factor, and G represents the sales volume buying point of crossing place, and T represents purchase point of crossing, and m represents current first time period, and m-1 represents a first time period.

21. treatment facilities according to claim 19, described matching factor computing module is used for:

By in the matching factor in described second time period closest to the best sales along the existing trend time that the time point at the matching factor place of 1 is defined as.

22. according to claim 12 to the treatment facility according to any one of 21, and wherein, described first time period is one month, and described second time period is half a year or 1 year, and described time point is one day.