CN105530315A - Data processing method and server - Google Patents

Abstract

The invention discloses a data processing method and a server. The method comprises the following steps of obtaining first data, wherein the first data is used for representing a first characteristic quantity consumed by respectively processing release information through X logical units, and X is a positive integer which is not less than 2; obtaining second data, wherein the second data is used for representing a second characteristic quantity achieved by respectively processing the release information through the the X logical units; determining a first influence factor and a second influence factor which are corresponding to each logical unit in the X logical units according to the first data and the second data; and determining a comprehensive influence factor for influencing a release decision of each logical unit in the X logical units according to the first influence factor and the second influence factor, and sending the comprehensive influence factor to a first terminal.

Description

A kind of data processing method and server

Technical field

The present invention relates to electronic technology, particularly relate to a kind of data processing method and server.

Background technology

Information issuing system based on the Internet is complicated computer system, and generally can comprise multiple stages such as retrieval, thick row and essence row, each stage comprises again multiple logical block.Whether certain releases news wins in the competitive environment of information issuing system, if won, expose this and release news, this process affects by a large amount of factors, the influence degree of each factor also has dividing of priority weight simultaneously, and this proposes stern challenge to reason investigation of the exposure fluctuation that releases news.And be system developer, operation personnel or Information issued master need quick position to cause to release news the reason place of fluctuation, to carry out system improvement and the optimization that releases news.

Summary of the invention

In view of this, the embodiment of the present invention provides a kind of data processing method and server for solving problems of the prior art.

The technical scheme of the embodiment of the present invention is achieved in that

The embodiment of the present invention provides a kind of data processing method, and described method comprises:

Obtain the first data, described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Obtain the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

The first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence is determined according to described first data and described second data;

Determine to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal.

In such scheme, determine to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making, comprising:

Carry out modeling according to the first pre-defined rule based on X logical block said system layer architecture and obtain the first data model;

According to described first data model, described first factor of influence is normalized, obtains the first normalization factor of influence;

Determine to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making.

In such scheme, describedly determine the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data, comprising:

The second data model utilizing described first data and described second data to carry out modeling to obtain for calculating in a described X logical block the corresponding factor of influence of each logical block;

The first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence is determined according to the second pre-defined rule according to described second data model.

In such scheme, describedly determine the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data, comprising:

Extract resource occupation amount spent when X logical block processes respectively, as described first data;

Extract the competitiveness intensity that can reach when X logical block processes respectively, as described second data;

According to described resource occupation amount and described competitiveness intensity determination resource occupation weight, using described resource occupation weight as described first factor of influence;

According to described resource occupation amount and described competitiveness intensity determination competitiveness weight, using described competitiveness weight as described second factor of influence.

In such scheme, described method also comprises:

Obtain the 3rd data, described 3rd data are for N number of third feature amount of when X logical block processes the respectively failed influence degree of corresponding competition of releasing news described in characterizing, and described N is the positive integer of >=2;

N number of three factor of influence corresponding with described N number of third feature amount is determined according to described 3rd data according to the 3rd pre-defined rule.

In such scheme, based on the 4th pre-defined rule, described 3rd factor of influence is normalized according to described first data model, obtains the 3rd normalization factor of influence.

The embodiment of the present invention also provides a kind of server, and described server comprises:

First acquiring unit, for obtaining the first data, described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Second acquisition unit, for obtaining the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

First determining unit, for determining the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data;

Processing unit, issues the integrated contributory factor of decision-making for determining to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence, sends described integrated contributory factor to first terminal.

In such scheme, described processing unit, comprises further:

First modeling subelement, obtains the first data model for carrying out modeling according to the first pre-defined rule based on X logical block said system layer architecture;

First process subelement, for being normalized described first factor of influence according to described first data model, obtains the first normalization factor of influence;

Second process subelement, issues the integrated contributory factor of decision-making for determining to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence.

In such scheme, described first determining unit, comprises further:

Second modeling subelement, the second data model carrying out modeling for utilizing described first data and described second data and obtain for calculating in a described X logical block the corresponding factor of influence of each logical block;

First determines subelement, for determining the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described second data model according to the second pre-defined rule.

In such scheme, described first determining unit, comprises further:

First extracts subelement, for extracting resource occupation amount spent when X logical block processes respectively, as described first data;

Second extracts subelement, for extracting the competitiveness intensity that can reach when X logical block processes respectively, as described second data;

Second determines subelement, for according to described resource occupation amount and described competitiveness intensity determination resource occupation weight, using described resource occupation weight as described first factor of influence;

3rd determines subelement, for according to described resource occupation amount and described competitiveness intensity determination competitiveness weight, using described competitiveness weight as described second factor of influence.

In such scheme, described server also comprises:

3rd acquiring unit, for obtaining the 3rd data, described 3rd data are for N number of third feature amount of when X logical block processes the respectively failed influence degree of corresponding competition of releasing news described in characterizing, and described N is the positive integer of >=2;

Second determining unit, for determining N number of three factor of influence corresponding with described N number of third feature amount according to the 3rd pre-defined rule according to described 3rd data.

In such scheme, described first process subelement, is further used for being normalized described 3rd factor of influence based on the 4th pre-defined rule according to described first data model, obtains the 3rd normalization factor of influence.

In the embodiment of the present invention, obtain the first data, described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2; Obtain the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing; The first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence is determined according to described first data and described second data; Determine to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal.So, the embodiment of the present invention by correlation computations accurate analysis release news exposure fluctuation multiple factors of influence, and determine integrated contributory factor according to these factors of influence, each user of information issuing system all can be released news according to described integrated contributory factor quick position and expose the reason place of fluctuation, to carry out system improvement and the optimization that releases news.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of each side's hardware entities of carrying out information interaction in the embodiment of the present invention;

Fig. 2 is the realization flow schematic diagram of the embodiment of the present invention one data processing method;

Fig. 3 is the realization flow schematic diagram that the embodiment of the present invention one determines integrated contributory factor;

Fig. 4 is the terminal of the embodiment of the present invention one and a schematic diagram of server interaction;

Fig. 5 is the composition structural representation of the embodiment of the present invention one first data model;

Fig. 6 is the realization flow schematic diagram of the embodiment of the present invention two data processing method;

Fig. 7 is the terminal of the embodiment of the present invention two and a schematic diagram of server interaction;

Fig. 8 is the composition structural representation of the embodiment of the present invention 2 second data model;

The realization flow schematic diagram of Fig. 9 embodiment of the present invention three data processing method;

Figure 10 is the terminal of the embodiment of the present invention four and a schematic diagram of server interaction;

Figure 11 is that the embodiment of the present invention four adopts stream data handling implement to carry out the schematic flow sheet of real-time data acquisition;

Figure 12 is a composition structural representation of server example of the present invention;

Figure 13 is the exemplary plot of server of the present invention as hardware entities.

Embodiment

Below in conjunction with the drawings and specific embodiments, the technical solution of the present invention is further elaborated.

Fig. 1 is the schematic diagram of each side's hardware entities of carrying out information interaction in the embodiment of the present invention, Fig. 1 comprises: server 11 ... 1n, terminal equipment 21-24, terminal equipment 21-24 carries out information interaction by cable network or wireless network and server, terminal equipment comprises mobile phone, desktop computer, PC, the types such as all-in-one, in an example, server 11 ... 1n can also by network and first kind terminal (as the terminal at advertiser place, or be called the object providing ad material and content to promote) carry out alternately, first kind terminal is (as the terminal at advertiser place, or be called the object providing ad material and content to promote) advertisement wanting to throw in is submitted to after, be stored in server cluster, keeper can be equipped with to first kind terminal (as the terminal at advertiser place, or be called the object providing ad material and content to promote) advertisement of throwing in carries out a series of process such as auditing.Wherein, relative to first kind terminal (as the terminal at advertiser place, or be called the object providing ad material and content to promote), terminal equipment 21-24 can be called that Equations of The Second Kind terminal is (as the terminal at domestic consumer place, or be called the object of advertising display or exposure), for being seen the user of video by Video Applications, user played games etc. can be applied by game.Wherein, all application of installing in terminal equipment or the application of specifying (as game application, Video Applications, navigation application etc.) can add advertisement to show the more recommendation information of user.Adopt the embodiment of the present invention, based on the system shown in above-mentioned Fig. 1, server obtains the first data, and described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2; Obtain the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing; The first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence is determined according to described first data and described second data; Determine to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal (as advertiser or user side), each user of information issuing system all can be released news according to described integrated contributory factor quick position and expose the reason place of fluctuation, to carry out system improvement and the optimization that releases news.

The example of above-mentioned Fig. 1 just realizes a system architecture example of the embodiment of the present invention, and the embodiment of the present invention is not limited to the system configuration described in above-mentioned Fig. 1, based on this system architecture, proposes each embodiment of the present invention.

Embodiment one

Fig. 2 is embodiment of the present invention data processing method schematic flow sheet, and as shown in Figure 2, embodiment of the present invention data processing method comprises:

Step 201, obtains the first data, and described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Step 202, obtains the second data, and described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

Step 203, determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data;

Step 204, determines to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issues the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal.

Adopt the embodiment of the present invention, by step 201-204, server determines by the first data of obtaining and the second data the first factor of influence and the second factor of influence that in a described X logical block, each logical block is corresponding, then obtains affecting the integrated contributory factor of the issue of each logical block in described X logical block decision-making according to determined first factor of influence and the second factor of influence.Like this, after the integrated contributory factor obtained is sent to first terminal (originating end released news) by server, described releasing news just can be pushed to the second terminal (receiving terminal released news) and carry out Information issued according to described integrated contributory factor by described first terminal; Thus multiple factors of influence of the exposure fluctuation that released news by correlation computations accurate analysis, and determine integrated contributory factor according to these factors of influence, each user of information issuing system all can be released news according to described integrated contributory factor quick position and expose the reason place of fluctuation, to carry out system improvement and the optimization that releases news.

In the embodiment of the present invention one execution mode, step 203 determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data, comprise: extract resource occupation amount spent when X logical block processes respectively, as described first data; Extract the competitiveness intensity that can reach when X logical block processes respectively, as described second data; According to described resource occupation amount and described competitiveness intensity determination resource occupation weight, using described resource occupation weight as described first factor of influence; According to described resource occupation amount and described competitiveness intensity determination competitiveness weight, using described competitiveness weight as described second factor of influence.

Here, described first factor of influence is respectively the weighing factor corresponding with resource occupation and competitiveness with described second factor of influence.

In the embodiment of the present invention one execution mode, as shown in Figure 3, step 204 specifically comprises:

Step 2041: carry out modeling according to the first pre-defined rule based on X logical block said system layer architecture and obtain the first data model;

Step 2042: be normalized described first factor of influence according to described first data model, obtains the first normalization factor of influence;

Step 2043: determine to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making.

Being illustrated in figure 4 the schematic diagram of corresponding first terminal and server interaction, in Fig. 4, in server, being mounted with the first data model for calculating the first normalization factor of influence.The handling process that first terminal obtains integrated contributory factor based on this first data model and the second factor of influence comprises:

Step a1, server carries out modeling according to the first pre-defined rule based on X logical block said system layer architecture and obtains the first data model, as shown in Figure 5.

Particularly, generally be made up of parts such as retrieval, thick row, essence rows based on X logical block said system layer architecture, logical block can be comprised again in each part, in addition abstract by the mode of hierarchical tree cascade between the logical block of these different pieces, obtain the first data model as shown in Figure 5.In described first data model, comprise one-level cause of fluctuation and the secondary cause of fluctuation of each logical block corresponding.

Certainly, in actual applications, also other modes can be adopted in addition abstract based on X logical block said system layer architecture, as level is incessantly two-layer or adopt the mode modeling of series connection in parallel.

Step a2, server is normalized described first factor of influence according to described first data model, obtains the first normalization factor of influence;

Here, based on the first data model as shown in Figure 5, these logical blocks are each via the first factor of influence of obtaining of coupling decomposition algorithm, and one-level cause of fluctuation A, B, C, D etc. in corresponding diagram 5, obtain the first normalization factor of influence by the cascade normalization that is multiplied; Wherein,

Here, according to hierarchical tree model as shown in Figure 5, the one-level cause of fluctuation weight of every one deck, namely the first factor of influence sum is 1;

weight_A+weight_B+weight_C＝1

Secondary cause of fluctuation weight sum below one-level cause of fluctuation is also 1;

weight_α+weight_β+weight_γ＝1

Further, current layer first normalization factor of influence is that the first factor of influence is multiplied by the first normalization factor of influence corresponding to upper strata:

normalized_weight_C＝wegiht_C×normalized_weight_D

When being normalized secondary cause of fluctuation weight, secondary cause of fluctuation weight is multiplied by current layer first normalization factor of influence:

normalized_weight_α＝weight_α×normalized_wegiht_C

normalized_weight_β＝weight_β×normalized_wegiht_C

normalized_weight_γ＝weight_γ×normalized_wegiht_C

Here, when described server performs the processing procedure of step a1 ~ a2, central processing unit (CPU can be adopted, CentralProcessingUnit), digital signal processor (DSP, DigitalSingnalProcessor) or programmable logic array (FPGA, Field-ProgrammableGateArray) realize.

Step a3, server is determined to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence and is issued the integrated contributory factor of decision-making, and output to first terminal, according to described integrated contributory factor by first terminal described releasing news is pushed to the second terminal (receiving terminal released news) and carry out Information issued.

In the embodiment of the present invention, server is determined on the basis of the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence by the first data of obtaining and the second data, by step 2041 ~ 2043, carry out modeling according to the 3rd pre-defined rule based on X logical block said system layer architecture and obtain the second data model; According to described second data model, described first factor of influence is normalized, obtains the first normalization factor of influence; Determine to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making.Like this, after the integrated contributory factor obtained is sent to first terminal (originating end released news) by server, described releasing news just can be pushed to the second terminal (receiving terminal released news) and carry out Information issued according to described integrated contributory factor by described first terminal.

Embodiment two

Fig. 6 is embodiment of the present invention data processing method schematic flow sheet, and as shown in Figure 6, embodiment of the present invention data processing method comprises:

Step 201, obtains the first data, and described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Step 202, obtains the second data, and described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

Step 2031, the second data model utilizing described first data and described second data to carry out modeling to obtain for calculating in a described X logical block the corresponding factor of influence of each logical block;

Step 2032, determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described second data model according to the second pre-defined rule;

Step 204, determines to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issues the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal.

Be illustrated in figure 7 the schematic diagram of corresponding first terminal and server interaction, the second data model for calculating the corresponding factor of influence of each logical block in a described X logical block is mounted with in server in Fig. 7, such as calculate first factor of influence corresponding with resource occupation, second factor of influence corresponding with competitiveness, and the second data model of integrated contributory factor.The handling process that first terminal obtains integrated contributory factor based on this second data model comprises:

Step b1, the first data described in server by utilizing and described second data carry out the second data model that modeling obtains for calculating in a described X logical block the corresponding factor of influence of each logical block;

Particularly, resource occupation amount res spent when X logical block processes respectively is extracted _yand res _tas the first data, wherein, res _yrepresent that certain releases news (as advertisement) in the y time period and enter the floating resources of logical block, res _trepresent the floating resources of t time; Extract the competitiveness intensity pro that can reach when X logical block processes respectively _yand pro _tas the second data, wherein, pro _yrepresent that the y time releases news (as advertisement) competitiveness intensity by this logical block, pro _trepresent the competitiveness intensity that the t time releases news; Afterwards, carry out modeling according to described first data and described second data and obtain the second data model as shown in Figure 8.

Step b2, server determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described second data model according to the second pre-defined rule;

Here, based on the second data model as shown in Figure 8, can learn, the y moment is by the floating resources flow of logical block _y=res _y× pro _y, t is by the floating resources flow of logical block _t=res _t× pro _t, so output wave momentum flow=flow in t and y moment _t-flow _y=A ₁+ A ₂+ A ₃, wherein:

A ₁＝res _y×(pro _t-pro _y)，

A ₂＝pro _y×(res _t-res _y)，

A ₃＝(pro _t-pro _y)×(res _t-res _y)；

Server determines the first factor of influence weight_res that in a described X logical block, each logical block is corresponding and the second factor of influence weight_pro according to the second data model as shown in Figure 8 according to coupling decomposition algorithm, and it is calculated as follows:

1) A is worked as ₁with A ₂when symbol is identical,

weight_res＝A ₂+(A ₂/(A ₁+A ₂))×A ₃，weight_pro＝A ₁+(A ₁/(A ₁+A ₂))×A ₃；

2) A is worked as ₁with A ₃when symbol is identical,

weight_res＝A ₁+A ₃，weight_pro＝A ₂；

3) A is worked as ₂with A ₃when symbol is identical,

weight_res＝A ₁，weight_pro＝A ₂+A ₃。

Here, when described server performs the processing procedure of step b1 and b2, central processing unit, digital signal processor or programmable logic array can be adopted to realize.

Step b3, server is determined to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and is issued the integrated contributory factor of decision-making, and output to first terminal, according to described integrated contributory factor by first terminal described releasing news is pushed to the second terminal (receiving terminal released news) and carry out Information issued.

Here, in step b3, server can be weighted average computation by described second data model to described first factor of influence and described second factor of influence, to determine that in the described X of an impact logical block, each logical block issues the integrated contributory factor of decision-making.Certainly, by weighted average calculation, the embodiment of the present invention determines that the mode of integrated contributory factor is only an exemplary description, is not intended to limit the present invention for determining the mode of integrated contributory factor.

Embodiment three

Fig. 9 is embodiment of the present invention data processing method schematic flow sheet, and as shown in Figure 9, embodiment of the present invention data processing method comprises:

Step 201, obtains the first data, and described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Step 202, obtains the second data, and described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

Step 203, determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data;

Step 204, determines to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issues the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal;

Step 901, obtains the 3rd data, and described 3rd data are for the corresponding N number of third feature amount of competing failed influence degree when X logical block processes respectively that releases news described in characterizing, and described N is the positive integer of >=2;

Step 902, determines N number of three factor of influence corresponding with described N number of third feature amount according to the 3rd pre-defined rule according to described 3rd data.

In the embodiment of the present invention three execution mode, integrating step 901 ~ 902, for N value 3, the failed influence degree of corresponding competition that to release news described in extraction when X logical block processes respectively N number of third feature amount estimates Probability p ctr, bid bid and the quality quality that releases news, as the 3rd data; One for the expectation maximum return ecpm=pctr*bid*quality released news, in the sequencing queue of the request of releasing news each time, what ecpm score was high releases news and will expose.Wherein, the expectation maximum return ecpm released news described in refers to the income that Information issued each time can obtain; Described estimate Probability p crt refer to release news issue after may the probability clicked by user side; Described bid bid refers to release news often clicked bid once or expense; The described quality quality that releases news refers to that the system of releasing news carries out the marking situation after overall merit to release news related materials, content etc.

Here, minimum the releasing news as winning of the middle ecpm that releases news exposed will be obtained, then compete releasing news and always have 1 ~ 3 factor of influence absolute value to be less than to win and to release news unsuccessfully.Note bigger_pctr _i, bigger_bid _i, bigger_quality _irespectively as certain weighing factor of three third feature amount pctr, bid, quality that to release news when the i-th minor sort is competed unsuccessfully; Wherein,

1) if described third feature amount pctr, bid, quality are all less than to win and release news, then

bigger_pctr _i＝1/3，bigger_bid _i＝1/3，bigger_quality _i＝1/3；

2) if described third feature amount pctr, bid are less than to win release news, quality is not less than to win and releases news, then

bigger_pctr _i＝1/2，bigger_bid _i＝1/2，bigger_quality _i＝0；

3) if described third feature amount pctr is less than to win release news, bid, quality are not less than to win and release news, then

bigger_pctr _i＝1，bigger_bid _i＝0，bigger_quality _i＝0；

Based on the bigger_pctr that above-mentioned three kinds of situations are determined _i, bigger_bid _i, bigger_quality _i, finally can determine to correspond respectively to described third feature amount pctr, the value of the 3rd factor of influence of bid, quality is:

$\mathrm{\α}=\frac{\underset{1}{\overset{N}{\Σ}}bigger\_{\mathrm{pctr}}_i}{N},\mathrm{\β}=\frac{\underset{1}{\overset{N}{\Σ}}bigger\_{\mathrm{bid}}_i}{N},\mathrm{\γ}=\frac{\underset{1}{\overset{N}{\Σ}}bigger\_{\mathrm{quality}}_i}{N};$

$N=\underset{1}{\overset{N}{\Σ}}bigger\_{\mathrm{pctr}}_i+\underset{1}{\overset{N}{\Σ}}bigger\_{\mathrm{bid}}_i+\underset{1}{\overset{N}{\Σ}}bigger\_{\mathrm{quality}}_i$

Here, N is certain number of times that corresponding competition is failed when X logical block processes respectively that releases news.

Adopt the embodiment of the present invention three, on the basis determining the first factor of influence and the second factor of influence, determine by the 3rd data obtained N number of 3rd factor of influence that described N number of third feature amount is corresponding, like this, after the integrated contributory factor obtained is sent to first terminal (originating end released news) by server, described releasing news just can be pushed to the second terminal (receiving terminal released news) and carry out Information issued according to described integrated contributory factor by described first terminal, make each user of information issuing system all can release news on the basis at the reason place exposing fluctuation according to described integrated contributory factor quick position, the improvement that server can also carry out information issuing system according to described 3rd factor of influence and the optimization etc. released news.

Embodiment four

On the basis of the embodiment of the present invention one to three, the embodiment of the present invention also comprises: be normalized described 3rd factor of influence based on the 4th pre-defined rule according to described first data model, obtain the 3rd normalization factor of influence.

Being the schematic diagram of corresponding first terminal and server interaction as shown in Figure 10, in Figure 10, in server, being mounted with the first data model for calculating the first normalization factor of influence and the 3rd normalization factor of influence; Also be mounted with the first data model calculating the corresponding factor of influence of each logical block in a described X logical block, such as calculate first factor of influence corresponding with resource occupation, second factor of influence corresponding with competitiveness, 3rd factor of influence, and the second data model of integrated contributory factor; The handling process that first terminal obtains integrated contributory factor based on this first data model and the second factor of influence comprises:

Step c1, server obtains the first data, the second data and the 3rd data;

Particularly, server, in the process of acquisition first data, the second data and the 3rd data, in order to ensure the accuracy of fluction analysis, needs by the first data, the second data and the 3rd data described in server Real-time Collection.Based on this, information issuing system uses the stream data handling implement such as Storm to realize the relevant retrieval that releases news of backflow in real time and sorting data, to release news the reason that exposure fluctuates with assist server rapid analysis.Here, the embodiment of the present invention uses the stream data handling implements such as Storm to carry out real time data processing flow process as shown in figure 11.

Step c2, the first data described in server by utilizing and described second data carry out the second data model that modeling obtains for calculating in a described X logical block the corresponding factor of influence of each logical block;

Particularly, resource occupation amount res spent when X logical block processes respectively is extracted _yand res _tas the first data, wherein, res _yrepresent that certain releases news (as advertisement) in the y time period and enter the floating resources of logical block, res _trepresent the floating resources of t time; Extract the competitiveness intensity pro that can reach when X logical block processes respectively _yand pro _tas the second data, wherein, pro _yrepresent that the y time releases news (as advertisement) competitiveness intensity by this logical block, pro _trepresent the competitiveness intensity that the t time releases news; Afterwards, carry out modeling according to described first data and described second data and obtain the second data model as shown in Figure 8.

Step c3, server determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described second data model according to the second pre-defined rule;

Here, based on the second data model as shown in Figure 8, can learn, the y moment is by the floating resources flow of logical block _y=res _y× pro _y, t is by the floating resources flow of logical block _t=res _t× pro _t, so output wave momentum flow=flow in t and y moment _t-flow _y=A ₁+ A ₂+ A ₃, wherein:

A ₁＝res _y×(pro _t-pro _y)，

A ₂＝pro _y×(res _t-res _y)，

A ₃＝(pro _t-pro _y)×(res _t-res _y)；

Server determines the first factor of influence weight_res that in a described X logical block, each logical block is corresponding and the second factor of influence weight_pro according to the second data model as shown in Figure 8 according to coupling decomposition algorithm, and it is calculated as follows:

1) A is worked as ₁with A ₂when symbol is identical,

weight_res＝A ₂+(A ₂/(A ₁+A ₂))×A ₃，weight_pro＝A ₁+(A ₁/(A ₁+A ₂))×A ₃；

2) A is worked as ₁with A ₃when symbol is identical,

weight_res＝A ₁+A ₃，weight_pro＝A ₂；

3) A is worked as ₂with A ₃when symbol is identical,

weight_res＝A ₁，weight_pro＝A ₂+A ₃。

Step c4, server carries out modeling according to the first pre-defined rule based on X logical block said system layer architecture and obtains the first data model, as shown in Figure 5.

Particularly, generally be made up of parts such as retrieval, thick row, essence rows based on X logical block said system layer architecture, logical block can be comprised again in each part, in addition abstract by the mode of hierarchical tree cascade between the logical block of these different pieces, obtain the first data model as shown in Figure 5.In described first data model, comprise one-level cause of fluctuation and the secondary cause of fluctuation of each logical block corresponding.

Certainly, in actual applications, also other modes can be adopted in addition abstract based on X logical block said system layer architecture, as level is incessantly two-layer or adopt the mode modeling of series connection in parallel.

Step c5, server is normalized described first factor of influence according to described first data model, obtains the first normalization factor of influence;

Step c6, server determines N number of three factor of influence corresponding with described N number of third feature amount according to the 3rd pre-defined rule according to the 3rd data obtained, based on the 4th pre-defined rule, described 3rd factor of influence is normalized according to described first data model, obtains the 3rd normalization factor of influence.

Here, based on the first data model as shown in Figure 5, these logical blocks are each via the first factor of influence weight_res of obtaining of coupling decomposition algorithm, and one-level cause of fluctuation A, B, C, D etc. in corresponding diagram 5, obtain the first normalization factor of influence by the cascade normalization that is multiplied; Wherein,

Here, according to hierarchical tree model as shown in Figure 5, the one-level cause of fluctuation weight of every one deck, namely the first factor of influence sum is 1;

weight_A+weight_B+weight_C＝1

Secondary cause of fluctuation weight below one-level cause of fluctuation, namely the 3rd factor of influence sum is also 1;

weight_α+weight_β+weight_γ＝1

Further, current layer first normalization factor of influence is that the first factor of influence is multiplied by the first normalization factor of influence corresponding to upper strata:

normalized_weight_C＝wegiht_C×normalized_weight_D

3rd normalization factor of influence is that the 3rd factor of influence is multiplied by current layer first normalization factor of influence:

normalized_weight_α＝weight_α×normalized_wegiht_C

normalized_weight_β＝weight_β×normalized_wegiht_C

normalized_weight_γ＝weight_γ×normalized_wegiht_C

Here, when described server performs the processing procedure of step c1 ~ c6, the realizations such as central processing unit, digital signal processor or programmable logic array can be adopted.

Step c7, server is determined to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence and is issued the integrated contributory factor of decision-making, and output to first terminal, according to described integrated contributory factor by first terminal described releasing news is pushed to the second terminal (receiving terminal released news) and carry out Information issued.

Here, the abbreviation related to the embodiment of the present invention and Key Term definition illustrate as follows:

1) floating resources: a request amount that releases news, enter thick ejaculation row number of times etc.;

2) competitiveness: release news own bid, industrial characteristic, quality degree etc.

Here it is to be noted: the description of following server entry, it is similar for describing with said method, and the beneficial effect with method describes, and does not repeat.For the ins and outs do not disclosed in server example of the present invention, please refer to the description of the inventive method embodiment.

Embodiment five:

Figure 12 is the composition structural representation of embodiment of the present invention server, and as shown in figure 12, embodiment of the present invention server comprises:

First acquiring unit 110, for obtaining the first data, described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Second acquisition unit 120, for obtaining the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

First determining unit 130, for determining the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data;

Processing unit 140, issues the integrated contributory factor of decision-making for determining to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence, sends described integrated contributory factor to first terminal.

In embodiment of the present invention execution mode, described processing unit, comprises further:

First modeling subelement, obtains the first data model for carrying out modeling according to the first pre-defined rule based on X logical block said system layer architecture;

First process subelement, for being normalized described first factor of influence according to described first data model, obtains the first normalization factor of influence;

Second process subelement, issues the integrated contributory factor of decision-making for determining to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence.

In embodiment of the present invention execution mode, described first determining unit, comprises further:

Second modeling subelement, the second data model carrying out modeling for utilizing described first data and described second data and obtain for calculating in a described X logical block the corresponding factor of influence of each logical block;

3rd determines subelement, for determining the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described second data model according to the second pre-defined rule.

In embodiment of the present invention execution mode, described first determining unit, comprises further:

First extracts subelement, for extracting resource occupation amount spent when X logical block processes respectively, as described first data;

Second extracts subelement, for extracting the competitiveness intensity that can reach when X logical block processes respectively, as described second data;

Second determines subelement, for according to described resource occupation amount and described competitiveness intensity determination resource occupation weight, using described resource occupation weight as described first factor of influence;

3rd determines subelement, for according to described resource occupation amount and described competitiveness intensity determination competitiveness weight, using described competitiveness weight as described second factor of influence.

In embodiment of the present invention execution mode, described server also comprises:

3rd acquiring unit, for obtaining the 3rd data, described 3rd data are for N number of third feature amount of when X logical block processes the respectively failed influence degree of corresponding competition of releasing news described in characterizing, and described N is the positive integer of >=2;

Second determining unit, for determining N number of three factor of influence corresponding with described N number of third feature amount according to the 3rd pre-defined rule according to described 3rd data.

In embodiment of the present invention execution mode, described first process subelement, is further used for being normalized described 3rd factor of influence based on the 4th pre-defined rule according to described first data model, obtains the 3rd normalization factor of influence.

Above-mentioned server as hardware entities S50 an example as shown in figure 13.Described server comprises processor 51, storage medium 52 and at least one external communication interface 53; Described processor 51, storage medium 52 and external communication interface 53 are all connected by bus 54.

In several embodiments that the application provides, should be understood that disclosed equipment and method can realize by another way.Apparatus embodiments described above is only schematic, such as, the division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, and as: multiple unit or assembly can be in conjunction with, maybe can be integrated into another system, or some features can be ignored, or do not perform.In addition, the coupling each other of shown or discussed each part or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of equipment or unit or communication connection can be electrical, machinery or other form.

The above-mentioned unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location; Both can be positioned at a place, also can be distributed in multiple network element; Part or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.

In addition, each functional unit in various embodiments of the present invention can all be integrated in a processing unit, also can be each unit individually as a unit, also can two or more unit in a unit integrated; Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form that hardware also can be adopted to add SFU software functional unit realizes.

One of ordinary skill in the art will appreciate that: all or part of step realizing said method embodiment can have been come by the hardware that program command is relevant, aforesaid program can be stored in computer read/write memory medium, this program, when performing, performs the step comprising said method embodiment; And aforesaid storage medium comprises: movable storage device, read-only memory (Read-OnlyMemory, ROM), random access memory (RandomAccessMemory, RAM), magnetic disc or CD etc. various can be program code stored medium.

Or, if the above-mentioned integrated unit of the present invention using the form of software function module realize and as independently production marketing or use time, also can be stored in a computer read/write memory medium.Based on such understanding, the technical scheme of the embodiment of the present invention can embody with the form of software product the part that prior art contributes in essence in other words, this computer software product is stored in a storage medium, comprises some instructions and performs all or part of of method described in each embodiment of the present invention in order to make a computer equipment (can be personal computer, server or the network equipment etc.).And aforesaid storage medium comprises: movable storage device, ROM, RAM, magnetic disc or CD etc. various can be program code stored medium.

The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; change can be expected easily or replace, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of described claim.

Claims (12)

1. a data processing method, is characterized in that, described method comprises:

Obtain the first data, described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Obtain the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

The first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence is determined according to described first data and described second data;

Determine to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making, send described integrated contributory factor to first terminal.

2. method according to claim 1, is characterized in that, determines to affect each logical block in a described X logical block and issues the integrated contributory factor of decision-making, comprising according to described first factor of influence and described second factor of influence:

Carry out modeling according to the first pre-defined rule based on X logical block said system layer architecture and obtain the first data model;

According to described first data model, described first factor of influence is normalized, obtains the first normalization factor of influence;

Determine to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence and issue the integrated contributory factor of decision-making.

3. method according to claim 1, is characterized in that, describedly determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data, comprising:

The second data model utilizing described first data and described second data to carry out modeling to obtain for calculating in a described X logical block the corresponding factor of influence of each logical block;

The first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence is determined according to the second pre-defined rule according to described second data model.

4. method according to claim 1, is characterized in that, describedly determines the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data, comprising:

Extract resource occupation amount spent when X logical block processes respectively, as described first data;

Extract the competitiveness intensity that can reach when X logical block processes respectively, as described second data;

According to described resource occupation amount and described competitiveness intensity determination resource occupation weight, using described resource occupation weight as described first factor of influence;

According to described resource occupation amount and described competitiveness intensity determination competitiveness weight, using described competitiveness weight as described second factor of influence.

5. the method according to any one of Claims 1-4, is characterized in that, described method also comprises:

Obtain the 3rd data, described 3rd data are for N number of third feature amount of when X logical block processes the respectively failed influence degree of corresponding competition of releasing news described in characterizing, and described N is the positive integer of >=2;

N number of three factor of influence corresponding with described N number of third feature amount is determined according to described 3rd data according to the 3rd pre-defined rule.

6. method according to claim 5, is characterized in that, described method also comprises:

Based on the 4th pre-defined rule, described 3rd factor of influence is normalized according to described first data model, obtains the 3rd normalization factor of influence.

7. a server, is characterized in that, described server comprises:

First acquiring unit, for obtaining the first data, described first data are for characterizing the fisrt feature amount spent when X logical block processes respectively that releases news, and described X is the positive integer of >=2;

Second acquisition unit, for obtaining the second data, described second data are for the second feature amount that can reach when X logical block processes respectively that releases news described in characterizing;

First determining unit, for determining the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described first data and described second data;

Processing unit, issues the integrated contributory factor of decision-making for determining to affect each logical block in a described X logical block according to described first factor of influence and described second factor of influence, sends described integrated contributory factor to first terminal.

8. server according to claim 7, is characterized in that, described processing unit, comprises further:

First modeling subelement, obtains the first data model for carrying out modeling according to the first pre-defined rule based on X logical block said system layer architecture;

First process subelement, for being normalized described first factor of influence according to described first data model, obtains the first normalization factor of influence;

Second process subelement, issues the integrated contributory factor of decision-making for determining to affect each logical block in a described X logical block according to described first normalization factor of influence and described second factor of influence.

9. server according to claim 7, is characterized in that, described first determining unit, comprises further:

Second modeling subelement, the second data model carrying out modeling for utilizing described first data and described second data and obtain for calculating in a described X logical block the corresponding factor of influence of each logical block;

First determines subelement, for determining the first factor of influence that in a described X logical block, each logical block is corresponding and the second factor of influence according to described second data model according to the second pre-defined rule.

10. server according to claim 7, is characterized in that, described first determining unit, comprises further:

First extracts subelement, for extracting resource occupation amount spent when X logical block processes respectively, as described first data;

Second extracts subelement, for extracting the competitiveness intensity that can reach when X logical block processes respectively, as described second data;

Second determines subelement, for according to described resource occupation amount and described competitiveness intensity determination resource occupation weight, using described resource occupation weight as described first factor of influence;

3rd determines subelement, for according to described resource occupation amount and described competitiveness intensity determination competitiveness weight, using described competitiveness weight as described second factor of influence.

11. servers according to any one of claim 7 to 10, it is characterized in that, described server also comprises:

3rd acquiring unit, for obtaining the 3rd data, described 3rd data are for N number of third feature amount of when X logical block processes the respectively failed influence degree of corresponding competition of releasing news described in characterizing, and described N is the positive integer of >=2;

Second determining unit, for determining N number of three factor of influence corresponding with described N number of third feature amount according to the 3rd pre-defined rule according to described 3rd data.

12. servers according to claim 11, is characterized in that,

Described first process subelement, is further used for being normalized described 3rd factor of influence based on the 4th pre-defined rule according to described first data model, obtains the 3rd normalization factor of influence.