CN104991969A - Method and apparatus for generating simulated event result set according to preset template - Google Patents
Method and apparatus for generating simulated event result set according to preset template Download PDFInfo
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- CN104991969A CN104991969A CN201510452410.4A CN201510452410A CN104991969A CN 104991969 A CN104991969 A CN 104991969A CN 201510452410 A CN201510452410 A CN 201510452410A CN 104991969 A CN104991969 A CN 104991969A
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Abstract
The invention discloses a method and an apparatus for generating a simulated event result set according to a preset template and an electronic device. The method comprises: acquiring reference data of multiple associated events; introducing the reference data of multiple associated events in the preset template, recording raw data and a preset algorithm of multiple simulate users by using the preset template, and generating the simulated event result of multiple associated events corresponding to the raw data of each simulated user; collecting the simulated event result of multiple associated events corresponding to the raw data of all simulated users and obtaining a simulated event result set. The method can conveniently generate the simulated event result set, thereby greatly reducing the human cost and time cost on obtaining the event result set.
Description
Technical field
The present invention relates to Internet technical field, be specifically related to a kind of basis and preset the method for template generation modeling event results set, device and electronic equipment.
Background technology
Along with growing with people's living standard of science and technology, data have served very important effect in every field.In a lot of fields, all need to carry out data processing, comprise various raw data analyzed, arrange, calculate, the processing of editor etc. and process.Wherein, raw data also can be described as sample data, and sample data is the basis of carrying out large data processing, all needs to obtain sample data before carrying out large data processing.
Some sample data sets is that the form such as questionnaire obtains by inquiry, when the amount of the sample data that the sample data sets needed comprises is larger, collects sample data sets and will spend a large amount of human costs and time cost.Therefore people wish to find feasible way, to reduce to obtain the human cost and time cost that sample data sets spends, in addition, have important directive function to effective covering of set by the output of net result.
Summary of the invention
In view of the above problems, propose the present invention, to provide one overcome the problems referred to above or solve the problem at least in part, and a kind of basis provided presets the method for template generation modeling event results set, device and electronic equipment.
According to an aspect of the present invention, provide a kind of method that basis presets template generation modeling event results set, the method comprises:
Obtain the reference data of multiple correlating event;
The reference data of multiple correlating event is directed in default template, utilizes raw data and the preset algorithm of the multiple analog subscribers presetting template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber;
Gather the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers, obtain modeling event results set.
According to a further aspect in the invention, provide the device that a kind of basis presets template generation modeling event results set, this device comprises:
Acquisition module, is suitable for the reference data obtaining multiple correlating event;
Generation module, the reference data of multiple correlating event is suitable for be directed in default template, utilize raw data and the preset algorithm of the multiple analog subscribers presetting template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber;
Collection modules, is suitable for gathering the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers that generation module generates, obtains modeling event results set.
According to another aspect of the invention, provide a kind of electronic equipment, this electronic equipment comprises the device that above-mentioned basis presets template generation modeling event results set.
Technical scheme provided by the invention is according to the reference data of multiple correlating event, the raw data of multiple analog subscriber and preset algorithm, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber, then the modeling event result of multiple correlating events corresponding for the raw data of all analog subscribers is gathered, thus obtain modeling event results set.Technical scheme provided by the invention just can generate modeling event results set efficiently and easily according to the reference data of multiple correlating event and default template, considerably reducing to obtain the human cost and time cost that event result set spends, contributing to carrying out of follow-up data work for the treatment of.
Above-mentioned explanation is only the general introduction of technical solution of the present invention, in order to technological means of the present invention can be better understood, and can be implemented according to the content of instructions, and can become apparent, below especially exemplified by the specific embodiment of the present invention to allow above and other objects of the present invention, feature and advantage.
Accompanying drawing explanation
By reading hereafter detailed description of the preferred embodiment, various other advantage and benefit will become cheer and bright for those of ordinary skill in the art.Accompanying drawing only for illustrating the object of preferred implementation, and does not think limitation of the present invention.And in whole accompanying drawing, represent identical parts by identical reference symbol.In the accompanying drawings:
Fig. 1 shows according to an embodiment of the invention according to the schematic flow sheet of the method for default template generation modeling event results set;
Fig. 2 is the selection schematic diagram of 2015103 phases victory or defeat coloured silk;
Fig. 3 shows in accordance with another embodiment of the present invention according to the schematic flow sheet of the method for default template generation modeling event results set;
Fig. 4 shows and illustrates according to the functional structure of the device presetting template generation modeling event results set according to an embodiment of the invention;
Fig. 5 shows in accordance with another embodiment of the present invention according to the illustrative view of functional configuration of the device of default template generation modeling event results set.
Embodiment
Below with reference to accompanying drawings exemplary embodiment of the present disclosure is described in more detail.Although show exemplary embodiment of the present disclosure in accompanying drawing, however should be appreciated that can realize the disclosure in a variety of manners and not should limit by the embodiment set forth here.On the contrary, provide these embodiments to be in order to more thoroughly the disclosure can be understood, and complete for the scope of the present disclosure can be conveyed to those skilled in the art.
Fig. 1 shows according to an embodiment of the invention according to the schematic flow sheet of the method for default template generation modeling event results set, and wherein, modeling event results set can be regarded as analog sample data acquisition, and as shown in Figure 1, the method comprises the steps:
Step S100, obtains the reference data of multiple correlating event.
If multiple different event is the set element in same setting event sets, then can think that multiple event has incidence relation each other, or a result is determined jointly by multiple different event, also can think that multiple event has incidence relation each other, be correlating event by the event definition with incidence relation in the present invention.Such as, the Football World Championship match having branched team to participate in, then can regard the match between any two of branched team as multiple different event, multiple different event all belongs to the set element that Football World Championship is competed in this setting event sets, and the match between any two of so branched team can be called multiple correlating event.And for example, " victory or defeat is color " in soccer lottery, namely to guess the results of the match of 14 matches undertaken by 28 teams, when the result of the match of all plays match of hitting it, then the first prize in, when the result of the match of wherein any 13 matches of hitting it, then middle second prize, because the net result of victory or defeat coloured silk is determined jointly by 14 matches, therefore, these 14 matches can be described as 14 correlating events with incidence relation.For another example, the result of the match needing guess two to compete in soccer lottery " two strings one ", if hit it two results of the match of competing simultaneously, then gets the winning number in a bond, so regards these two matches as two correlating events.
Wherein, the reference data of each correlating event comprises: one or more event result offsets that one or more event result of this correlating event are corresponding.Event result offset is then for representing the compensation rate when a kind of event result of a correlating event occurs.
Suppose that correlating event 1 is Brazilian VS Holland in soccer lottery " two strings one ", correlating event 2 is German VS Italy.Every bout all has 3 kinds of results of the match, namely 3 kinds of event result: host team's victory (representing that host team wins with 3); Host team's flat (representing that host team puts down with 1); Host team's negative (representing that host team bears with 0).For correlating event 1, represent Brazil's victory with 3, represent that Brazil is flat with 1, represent that Brazil is negative with 0.
But before the end of match of correlating event 1 correspondence, all cannot determine that final event result is 3, is 1 or 0.Generally, many third parties can provide 3 the event result offsets corresponding with 3 of correlating event 1 kinds of event result in advance.Such as, according to history result of the match, in the match of nearest 10 Brazilian VS Holland, there are 8 results of the match of competing to be all Brazil's victory, then can predict that the final event result of above-mentioned correlating event 1 is the possibility that the Brazilian possibility won is greater than Holland's victory.Now, corresponding 3 the event result offsets of 3 kinds of event result of the correlating event 1 that third party generally provides respectively: the event result offset of Brazil's victory is 1, and the flat event result offset of Brazil is 3, and the negative event result offset of Brazil is 6.That is, if having selected the final event result of Brazil's this event result of victory as correlating event 1 in advance, then when the event result when correlating event 1 finally completes is Brazil's victory, then compensate with the compensation rate of X*1; If have selected the final event result of Brazil's this event result negative as correlating event 1 in advance, then when the event result when correlating event 1 finally completes is Brazilian bearing, then compensate with the compensation rate of X*6.Wherein, X determines according to actual conditions, can but be not limited only to be at least one in amount of money value, integrated value and virtual game currency value.
It can thus be appreciated that, the size of event result offset reflects the probability of event result generation corresponding with it indirectly, particularly, event result offset is larger, and illustrate that the net result of correlating event is that the possibility of this event result is less, event result offset is less, illustrates that the net result of correlating event is that the possibility of this event result is larger.
Suppose that the modeling event results set that will obtain is the stake set about 2015103 phases victory or defeat coloured silk.Fig. 2 is the selection schematic diagram of 2015103 phases victory or defeat coloured silk, as shown in Figure 2,3 kinds of event result (namely 3,1 and 0) of every bout have corresponding average odds, wherein, bout corresponds to a correlating event, and can using average odds as event result offset, therefore, in order to generate this modeling event results set, need the reference data first obtaining these 14 correlating events.
Step S101, the reference data of multiple correlating event is directed in default template, utilize raw data and the preset algorithm of the multiple analog subscribers presetting template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber.
Suppose to preset the pre-recorded raw data having 10000 analog subscribers in template, the raw data of each analog subscriber comprises to be selected the stake of 14 matches, in conjunction with preset algorithm, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber, namely generate 10000 results about the simulated race of multiple correlating event.
Step S102, gathers the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers, obtains modeling event results set.
The modeling event result of multiple correlating events that the raw data of all analog subscribers generated by step S101 is corresponding is gathered, thus obtains modeling event results set, for reference or use.
The method according to default template generation modeling event results set that the present embodiment provides, according to the reference data of multiple correlating event, the raw data of multiple analog subscriber and preset algorithm, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber, then the modeling event result of multiple correlating events corresponding for the raw data of all analog subscribers is gathered, thus obtain modeling event results set.Technical scheme provided by the invention just can generate modeling event results set efficiently and easily according to the reference data of multiple correlating event and default template, considerably reducing to obtain the human cost and time cost that event result set spends, contributing to carrying out of follow-up data work for the treatment of.
Fig. 3 shows in accordance with another embodiment of the present invention according to the schematic flow sheet of the method for default template generation modeling event results set, and as shown in Figure 3, the method comprises the steps:
Step S300, obtains the reference data of multiple correlating event respectively from multiple third party, average multiple sample compensation values corresponding for the same event result of the same correlating event got as the event result offset of correspondence.
Wherein, the reference data of each correlating event comprises: one or more event result offsets that one or more event result of this correlating event are corresponding.Event result offset is then for representing the compensation rate when a kind of event result of a correlating event occurs.
Color for the victory or defeat in soccer lottery, multiple correlating event comprises: 14 correlating events with incidence relation, and 14 correlating events corresponding 3 kinds of event result separately.These 14 correlating events are 14 matches, corresponding host team's victory (representing that host team wins with 3) of each correlating event, host team's flat (representing that host team puts down with 1) and host team's negative (representing that host team bears with 0) 3 kinds of event result altogether.Suppose that user goes for the modeling event results set of 2015103 phases victory or defeat coloured silk stake, then need the reference data of 14 correlating events obtaining this phase from third party, the reference data of each correlating event comprises: the event result offset corresponding with 3 kinds of event result of this correlating event.
In order to make the reference data of multiple correlating events of acquisition more accurate, the reference data of multiple correlating event can be obtained respectively from multiple third party.When the same event result of obtained same correlating event has multiple corresponding sample compensation value, get the event result offset of mean value as correspondence of multiple sample compensation value, thus make event result offset more accurate.Also process by multiple sample compensation values that other the same event result of mode to the same correlating event got is corresponding, thus determine corresponding event result offset.
Step S301, according to one or more event result offsets of each correlating event, sorts to multiple correlating event.
Bout is a correlating event, every game has 3 event result offsets, minimum event result offset can be obtained from the event result offset of 3 of every game, then according to these minimum event result offsets order from small to large, 14 matches are sorted, thus complete the sequence of multiple correlating event.1st match is called correlating event 1, and the 2nd match is called correlating event 2, by that analogy.As shown in Figure 2, the minimum event result offset of correlating event 1 is 2.00, the minimum event result offset of correlating event 2 is 2.28, the minimum event result offset of correlating event 3 is 1.78, the minimum event result offset of correlating event 4 is 1.88, the minimum event result offset of correlating event 5 is 1.54, the minimum event result offset of correlating event 6 is 1.87, the minimum event result offset of correlating event 7 is 2.50, the minimum event result offset of correlating event 8 is 1.35, the minimum event result offset of correlating event 9 is 1.42, the minimum event result offset of correlating event 10 is 2.09, the minimum event result offset of correlating event 11 is 2.49, the minimum event result offset of correlating event 12 is 2.55, the minimum event result offset of correlating event 13 is 1.90, the minimum event result offset of correlating event 14 is 1.96, according to these minimum event result offsets order from small to large, 14 correlating events are sorted, wherein correlating event 8 comes the 1st, correlating event 9 comes the 2nd.
Step S302, is directed into the reference data of multiple correlating event in default template, according to preset algorithm, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event.
Particularly, for a correlating event, according to the incidence relation of raw data and event result offset, each analog subscriber is mapped as the modeling event result corresponding with event result offset of this correlating event about the raw data of this correlating event.
Wherein, pre-recorded raw data and the preset algorithm having multiple analog subscriber in template is preset.Such as, the raw data of analog subscriber 1 comprises to be selected the stake of 14 matches after step S301 sequence, and the stake of every game selects 3 event result offsets of all competing with this to have incidence relation, particularly, for the match coming the 1st, i.e. correlating event 8, the event result that what analog subscriber 1 was selected is minimum event result offset in 3 event result offsets is corresponding, composition graphs 2 is known, and the event result that the minimum event result offset of correlating event 8 is corresponding is host team negative (namely 0); For the match coming the 2nd, i.e. correlating event 9, the event result that what analog subscriber 1 was selected is minimum event result offset in 3 event result offsets is corresponding with maximum event result offset, composition graphs 2 is known, and the event result that the minimum event result offset of correlating event 9 is corresponding with maximum event result offset is host team's victory (namely 3) and host team negative (namely 0).Obtain the event result of 14 matches according to the method described above, thus complete the raw data of analog subscriber 1 about 14 correlating events to the mapping of modeling event result.
According to method of the same race, each analog subscriber is mapped as the modeling event result of 14 correlating events about the raw data of 14 correlating events.
Step S303, according to one or more event result offsets of each correlating event, sorts to the modeling event result of multiple correlating events corresponding with the raw data of each analog subscriber.
Due to the one or more event result offsets according to each correlating event in step S301, sequence was carried out to multiple correlating event.Therefore, need to reduce the order before sorting in step S303, thus be user-friendly to these modeling event results.
Step S304, the modeling event result according to the multidate information pair multiple correlating events corresponding with the raw data of one or more analog subscriber adjusts.
In order to obtain modeling event results set more accurately, also need to adjust according to the modeling event result of multidate information pair multiple correlating events corresponding with the raw data of one or more analog subscriber.For the example of soccer lottery victory or defeat coloured silk, multidate information can comprise: cross swords record, the fighting will of team, agreement ball, world rankings and main force's wound of weather conditions, home-away factor, history is stopped.Such as, main force's wound of the host team in certain match is stopped, the event result of this originally corresponding with the raw data of analog subscriber 1 correlating event is that host team wins, consider that the multidate information stopped is hindered by the main force, the event result of this correlating event in the modeling event result corresponding with the raw data of analog subscriber 1 can be adjusted to host team and put down.
Step S305, gathers the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers, obtains modeling event results set.
The modeling event result of multiple correlating events corresponding for the raw data of all analog subscribers obtained through above-mentioned steps is gathered, just can obtain modeling event results set, for reference or use.
According to the method according to default template generation modeling event results set that the present embodiment provides, the reference data of multiple correlating event is obtained respectively from multiple third party, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event, and according to multidate information, it is adjusted, then the modeling event result of multiple correlating events corresponding for the raw data of all analog subscribers is gathered, thus obtain modeling event results set.Not only modeling event results set can be generated efficiently and easily according to technical scheme provided by the invention, considerably reduce to obtain the human cost and time cost that event result set spends, contribute to carrying out of follow-up data work for the treatment of, and reference data and the multidate information of multiple correlating event are provided by considering multiple third party, make the modeling event results set of acquisition more accurately, more have reference value.
Fig. 4 shows according to an embodiment of the invention according to the illustrative view of functional configuration of the device of default template generation modeling event results set, and as shown in Figure 4, this device comprises: acquisition module 401, generation module 402 and collection modules 403.
Acquisition module 401, is suitable for the reference data obtaining multiple correlating event.
Wherein, the reference data of each correlating event comprises: one or more event result offsets that one or more event result of this correlating event are corresponding.Event result offset is then for representing the compensation rate when a kind of event result of a correlating event occurs.
Generation module 402, the reference data of multiple correlating event is suitable for be directed in default template, utilize raw data and the preset algorithm of the multiple analog subscribers presetting template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber.
Suppose to preset the pre-recorded raw data having 10000 analog subscribers in template, the raw data of each analog subscriber comprises to be selected the stake of 14 matches, in conjunction with preset algorithm, generation module 402 generates the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber, namely generates 10000 modeling event results about multiple correlating event.
Collection modules 403, is suitable for gathering the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers that generation module generates, obtains modeling event results set.
The modeling event result of multiple correlating events that the raw data of all analog subscribers that generation module 402 generates by collection modules 403 is corresponding is gathered, thus obtains modeling event results set, for reference or use.
According to the device according to default template generation modeling event results set that the present embodiment provides, the reference data of multiple correlating event is obtained by acquisition module, and the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber is generated by generation module, the modeling event result of multiple correlating events that the raw data of all analog subscribers then generated by generation module by integration module is corresponding is gathered, thus obtains modeling event results set.Technical scheme provided by the invention just can generate modeling event results set efficiently and easily according to the reference data of multiple correlating event and default template, considerably reducing to obtain the human cost and time cost that event result set spends, contributing to carrying out of follow-up data work for the treatment of.
Fig. 5 shows in accordance with another embodiment of the present invention according to the illustrative view of functional configuration of the device of default template generation modeling event results set, as shown in Figure 5, this device comprises: acquisition module 501, first order module 502, generation module 503, second order module 504, adjusting module 505 and collection modules 506.
Acquisition module 501, is suitable for the reference data obtaining multiple correlating event from multiple third party respectively, averages multiple sample compensation values corresponding for the same event result of the same correlating event got as the event result offset of correspondence.
In order to make the reference data of multiple correlating events of acquisition more accurate, acquisition module 501 can obtain the reference data of multiple correlating event respectively from multiple third party.When the same event result of obtained same correlating event has multiple corresponding sample compensation value, get the event result offset of mean value as correspondence of multiple sample compensation value, thus make event result offset more accurate.Acquisition module 501 also processes by multiple sample compensation values that other the same event result of mode to the same correlating event got is corresponding, thus determines corresponding event result offset.
First order module 502, is suitable for the one or more event result offsets according to each correlating event, sorts to multiple correlating event.
Color for soccer lottery victory or defeat, multiple correlating event comprises: 14 correlating events with incidence relation, and 14 correlating events corresponding 3 kinds of event result separately.14 matches are 14 correlating events, and 3 kinds of event result are respectively host team's victory, gentle host team of host team bears.First order module 502 according to the minimum event result offset of each correlating event, can sort to 14 correlating events according to order from small to large.
Generation module 503, is suitable for the reference data of multiple correlating event to be directed in default template, according to preset algorithm, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event.
Generation module 503 is further adapted for: for a correlating event, according to the incidence relation of raw data and event result offset, each analog subscriber is mapped as the modeling event result corresponding with event result offset of this correlating event about the raw data of this correlating event.
Such as, the raw data of analog subscriber 1 comprises to be selected the sort stakes of 14 matches obtained of the first order module 502, and the stake of every game selects 3 event result offsets of all competing with this to have incidence relation, for a correlating event, according to the incidence relation of raw data and event result offset, analog subscriber 1 is mapped as the modeling event result corresponding with event result offset of this correlating event about the raw data of this correlating event.Equally, each analog subscriber is mapped as the modeling event result of 14 correlating events about the raw data of 14 correlating events.
Second order module 504, is suitable for the one or more event result offsets according to each correlating event, sorts to the modeling event result of multiple correlating events corresponding with the raw data of each analog subscriber.
Because the first order module 502 is according to one or more event result offsets of each correlating event, sequence was carried out to multiple correlating event.Therefore, the order before needing by the second order module 504 reduction sequence, thus be user-friendly to these modeling event results.
Adjusting module 505, is suitable for adjusting according to the modeling event result of multidate information pair multiple correlating events corresponding with the raw data of one or more analog subscriber.
In order to obtain modeling event results set more accurately, also need to be adjusted by the modeling event result of adjusting module 505 according to multidate information pair multiple correlating events corresponding with the raw data of one or more analog subscriber.For the example of soccer lottery victory or defeat coloured silk, multidate information can comprise: cross swords record, the fighting will of team, agreement ball, world rankings and main force's wound of weather conditions, home-away factor, history is stopped.Such as, main force's wound of the host team in certain match is stopped, the event result of this originally corresponding with the raw data of analog subscriber 1 correlating event is that host team wins, consider that the multidate information stopped is hindered by the main force, the event result of this correlating event in the modeling event result corresponding with the raw data of analog subscriber 1 can be adjusted to host team and put down.
Collection modules 506, is suitable for gathering the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers that generation module 503 generates, obtains modeling event results set.
The modeling event result of multiple correlating events corresponding for the raw data of all analog subscribers is gathered by collection modules 506, just can obtain modeling event results set, for reference or use.
According to the device according to default template generation modeling event results set that the present embodiment provides, obtained the reference data of multiple correlating event respectively from multiple third party by acquisition module, and by generation module, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event, then according to multidate information, it is adjusted by adjusting module, obtain modeling event results set finally by collection modules.Not only modeling event results set can be generated efficiently and easily according to technical scheme provided by the invention, considerably reduce to obtain the human cost and time cost that event result set spends, contribute to carrying out of follow-up data work for the treatment of, and reference data and the multidate information of multiple correlating event are provided by considering multiple third party, make the modeling event results set of acquisition more accurately, more have reference value.
Present invention also offers a kind of electronic equipment, this electronic equipment comprises the device that above-mentioned basis presets template generation modeling event results set.The method of template generation modeling event results set preset by this electronic equipment for realizing above-mentioned basis, and has the beneficial effect of corresponding method, do not repeat them here.
Intrinsic not relevant to any certain computer, virtual system or miscellaneous equipment with display at this algorithm provided.Various general-purpose system also can with use based on together with this teaching.According to description above, the structure constructed required by this type systematic is apparent.In addition, the present invention is not also for any certain programmed language.It should be understood that and various programming language can be utilized to realize content of the present invention described here, and the description done language-specific is above to disclose preferred forms of the present invention.
In instructions provided herein, describe a large amount of detail.But can understand, embodiments of the invention can be put into practice when not having these details.In some instances, be not shown specifically known method, structure and technology, so that not fuzzy understanding of this description.
Similarly, be to be understood that, in order to simplify the disclosure and to help to understand in each inventive aspect one or more, in the description above to exemplary embodiment of the present invention, each feature of the present invention is grouped together in single embodiment, figure or the description to it sometimes.But, the method for the disclosure should be construed to the following intention of reflection: namely the present invention for required protection requires feature more more than the feature clearly recorded in each claim.Or rather, as claims below reflect, all features of disclosed single embodiment before inventive aspect is to be less than.Therefore, the claims following embodiment are incorporated to this embodiment thus clearly, and wherein each claim itself is as independent embodiment of the present invention.
Those skilled in the art are appreciated that and adaptively can change the module in the equipment in embodiment and they are arranged in one or more equipment different from this embodiment.Module in embodiment or unit or assembly can be combined into a module or unit or assembly, and multiple submodule or subelement or sub-component can be put them in addition.Except at least some in such feature and/or process or unit be mutually repel except, any combination can be adopted to combine all processes of all features disclosed in this instructions (comprising adjoint claim, summary and accompanying drawing) and so disclosed any method or equipment or unit.Unless expressly stated otherwise, each feature disclosed in this instructions (comprising adjoint claim, summary and accompanying drawing) can by providing identical, alternative features that is equivalent or similar object replaces.
In addition, those skilled in the art can understand, although embodiments more described herein to comprise in other embodiment some included feature instead of further feature, the combination of the feature of different embodiment means and to be within scope of the present invention and to form different embodiments.Such as, in the following claims, the one of any of embodiment required for protection can use with arbitrary array mode.
All parts embodiment of the present invention with hardware implementing, or can realize with the software module run on one or more processor, or realizes with their combination.It will be understood by those of skill in the art that the some or all functions that microprocessor or digital signal processor (DSP) can be used in practice to realize according to the some or all parts in the embodiment of the present invention.The present invention can also be embodied as part or all equipment for performing method as described herein or device program (such as, computer program and computer program).Realizing program of the present invention and can store on a computer-readable medium like this, or the form of one or more signal can be had.Such signal can be downloaded from internet website and obtain, or provides on carrier signal, or provides with any other form.
The present invention will be described instead of limit the invention to it should be noted above-described embodiment, and those skilled in the art can design alternative embodiment when not departing from the scope of claims.In the claims, any reference symbol between bracket should be configured to limitations on claims.Word " comprises " not to be got rid of existence and does not arrange element in the claims or step.Word "a" or "an" before being positioned at element is not got rid of and be there is multiple such element.The present invention can by means of including the hardware of some different elements and realizing by means of the computing machine of suitably programming.In the unit claim listing some devices, several in these devices can be carry out imbody by same hardware branch.Word first, second and third-class use do not represent any order.Can be title by these word explanations.
The invention discloses:
A1, a kind of basis preset the method for template generation modeling event results set, and described method comprises:
Obtain the reference data of multiple correlating event;
The reference data of described multiple correlating event is directed in default template, utilize raw data and the preset algorithm of multiple analog subscribers of described default template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber;
Gather the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers, obtain modeling event results set.
A2, method according to A1, wherein, the reference data of each correlating event comprises: described one or more event result offsets that one or more event result of this correlating event are corresponding.
A3, method according to A2, wherein, described event result offset is for representing the compensation rate when a kind of event result of a correlating event occurs.
A4, method according to A2, wherein, the reference data of the multiple correlating event of described acquisition comprises further: the reference data obtaining multiple correlating event from multiple third party respectively, averages multiple sample compensation values corresponding for the same event result of the same correlating event got as the event result offset of correspondence.
A5, method according to A2, wherein, after the reference data of the multiple correlating event of described acquisition, described method also comprises:
According to one or more event result offsets of each correlating event, described multiple correlating event is sorted.
A6, method according to A5, wherein, raw data and the preset algorithm of multiple analog subscribers of template record are preset in described utilization, and the modeling event result generating the multiple correlating events corresponding with the raw data of each analog subscriber comprises further:
According to preset algorithm, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event.
A7, method according to A6, wherein, describedly to comprise the modeling event result that each analog subscriber is mapped as multiple correlating event about the raw data of multiple correlating event further:
For a correlating event, according to the incidence relation of raw data and event result offset, each analog subscriber is mapped as the modeling event result corresponding with event result offset of this correlating event about the raw data of this correlating event.
A8, method according to A2, wherein, in the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers of described set, before obtaining modeling event results set, described method also comprises: according to one or more event result offsets of each correlating event, sorts to the modeling event result of multiple correlating events corresponding to raw data that is described and each analog subscriber.
A9, method according to A2, wherein, in the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers of described set, before obtaining modeling event results set, described method also comprises: the modeling event result according to the multidate information pair multiple correlating events corresponding with the raw data of one or more analog subscriber adjusts.
A10, method according to A1, wherein, described multiple correlating event comprises: 14 correlating events with incidence relation; Described 14 correlating events are corresponding 3 kinds of event result separately.
B11, a kind of basis preset the device of template generation modeling event results set, and described device comprises:
Acquisition module, is suitable for the reference data obtaining multiple correlating event;
Generation module, the reference data of described multiple correlating event is suitable for be directed in default template, utilize raw data and the preset algorithm of multiple analog subscribers of described default template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber;
Collection modules, is suitable for gathering the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers that described generation module generates, obtains modeling event results set.
B12, device according to B11, wherein, the reference data of each correlating event comprises: described one or more event result offsets that one or more event result of this correlating event are corresponding.
B13, device according to B12, wherein, described event result offset is for representing the compensation rate when a kind of event result of a correlating event occurs.
B14, device according to B12, wherein, described acquisition module is further adapted for: the reference data obtaining multiple correlating event from multiple third party respectively, averages multiple sample compensation values corresponding for the same event result of the same correlating event got as the event result offset of correspondence.
B15, device according to B12, wherein, described device also comprises: the first order module, is suitable for the one or more event result offsets according to each correlating event, sorts to described multiple correlating event.
B16, device according to B15, wherein, described generation module is further adapted for: according to preset algorithm, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event.
B17, device according to B16, wherein, described generation module is further adapted for: for a correlating event, according to the incidence relation of raw data and event result offset, each analog subscriber is mapped as the modeling event result corresponding with event result offset of this correlating event about the raw data of this correlating event.
B18, device according to B12, wherein, described device also comprises: the second order module, is suitable for the one or more event result offsets according to each correlating event, sorts to the modeling event result of multiple correlating events corresponding to raw data that is described and each analog subscriber.
B19, device according to B12, wherein, described device also comprises: adjusting module, is suitable for adjusting according to the modeling event result of multidate information pair multiple correlating events corresponding with the raw data of one or more analog subscriber.
B20, device according to B11, wherein, described multiple correlating event comprises: 14 correlating events with incidence relation; Described 14 correlating events are corresponding 3 kinds of event result separately.
C21, a kind of electronic equipment, comprise the device according to default template generation modeling event results set as described in any one of claim 11-20.
Claims (10)
1. basis presets a method for template generation modeling event results set, and described method comprises:
Obtain the reference data of multiple correlating event;
The reference data of described multiple correlating event is directed in default template, utilize raw data and the preset algorithm of multiple analog subscribers of described default template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber;
Gather the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers, obtain modeling event results set.
2. method according to claim 1, wherein, the reference data of each correlating event comprises: described one or more event result offsets that one or more event result of this correlating event are corresponding.
3. method according to claim 2, wherein, described event result offset is for representing the compensation rate when a kind of event result of a correlating event occurs.
4. method according to claim 2, wherein, the reference data of the multiple correlating event of described acquisition comprises further: the reference data obtaining multiple correlating event from multiple third party respectively, averages multiple sample compensation values corresponding for the same event result of the same correlating event got as the event result offset of correspondence.
5. method according to claim 2, wherein, after the reference data of the multiple correlating event of described acquisition, described method also comprises:
According to one or more event result offsets of each correlating event, described multiple correlating event is sorted.
6. method according to claim 5, wherein, raw data and the preset algorithm of multiple analog subscribers of template record are preset in described utilization, and the modeling event result generating the multiple correlating events corresponding with the raw data of each analog subscriber comprises further:
According to preset algorithm, each analog subscriber is mapped as the modeling event result of multiple correlating event about the raw data of multiple correlating event.
7. method according to claim 6, wherein, describedly to comprise the modeling event result that each analog subscriber is mapped as multiple correlating event about the raw data of multiple correlating event further:
For a correlating event, according to the incidence relation of raw data and event result offset, each analog subscriber is mapped as the modeling event result corresponding with event result offset of this correlating event about the raw data of this correlating event.
8. method according to claim 2, wherein, in the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers of described set, before obtaining modeling event results set, described method also comprises: according to one or more event result offsets of each correlating event, sorts to the modeling event result of multiple correlating events corresponding to raw data that is described and each analog subscriber.
9. basis presets a device for template generation modeling event results set, and described device comprises:
Acquisition module, is suitable for the reference data obtaining multiple correlating event;
Generation module, the reference data of described multiple correlating event is suitable for be directed in default template, utilize raw data and the preset algorithm of multiple analog subscribers of described default template record, generate the modeling event result of the multiple correlating events corresponding with the raw data of each analog subscriber;
Collection modules, is suitable for gathering the modeling event result of multiple correlating events corresponding to the raw data of all analog subscribers that described generation module generates, obtains modeling event results set.
10. an electronic equipment, comprises the device according to presetting template generation modeling event results set as claimed in claim 9.
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