CN106709760A - Single data based data price evaluation method and device - Google Patents
Single data based data price evaluation method and device Download PDFInfo
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Abstract
The invention relates to a single data based data price evaluation method and device. The method comprises the steps of searching historical data with at least one attribute being identical to that of single data; for each attribute contained in an attribute set, determining a basic value of the single data relative to the attribute, wherein the basic value represents the basic price of the single data under the attribute by taking the historical transaction price of the historical data as a benchmark; determining the weight of each attribute in the attribute set relative to the single data; and calculating the price of the single data according to the basic value of each attribute and the weight of the attribute relative to the single data. According to the technical scheme provided by the invention, the data listing price can be reasonably determined based on characteristics of the single data, so that data transaction is effectively promoted to develop towards the direction of minimum dimension standardized supply, the effective utilization rate of a data user for the purchased data is improved, data abuse is avoided, and the security of personal data privacy is improved.
Description
Technical field
The present invention relates to data trade field, more particularly to a kind of data price evaluation method based on single data and
Device.
Background technology
Today's society, global big data industry is just stepping into the accelerated development phase, and annual average compound growth rate, more than 30%, is the same period
7 times of IT industry speedup.The theory of the data creation of value promotes business data demand to face explosive growth, and number is brought therewith
According to flourishing for trade market, data trade realizes interconnecting and being finally applied to each scene for data among enterprises
The value of data is realized, and data trade first has to the problem exactly how fixed a price on data for solving.
Carried out by the way of substantial amounts of bottom or not regular source data batch are packed more than data trade before this, and it is main
To be based on the overall price that the factors such as external application and the trading environment of data consider data acquisition system.For example, Application No.
A kind of computational methods of suitable big data value assessment are disclosed in 201510873584.8 Chinese patent application file, is passed through
The data kind of a data acquisition system to being traded, time span, data depth, data integrity, data sample covering,
Real-time property is combined with application scenarios key element, realizes quantifying the value of a data acquisition system using analytic hierarchy process (AHP)
With qualitative analysis.Therefore this analysis method is only applicable to an overall price assessment for data acquisition system.Again for example, in application
In number for the Chinese patent application file of 201510993443.X, a kind of data assets Valuation Method is disclosed, it is main right
One data acquisition system is according to data integrity, data renewal frequency, data structured degree, data trade frequency, data volume, number
According to publicity, the searched frequency of data, the data assets valency with reference to obtained by cost-or-market method price, liposuction method price, Auction Law price
Lattice are estimated to data assets value, this method be primarily adapted for use in a large amount of conclusion of the business history with same class data to it is related
Information, and primary concern non-data external factor in itself, are achieved in an assessment for data acquisition system overall price.
But, what in most cases data user generally required is only a certain in a certain class data in data acquisition system
The content of data, if the purchase of the data purchaser that still cannot preferably be fitted according to the existing trade mode based on data acquisition system
Demand is bought, causes data trade relatively costly for purchaser.It is more likely to result in data purchaser (or data user)
Data are scalped after data acquisition system is obtained, there are data purchaser abuse data, cause the individual number of data subject
There is the possibility of disclosure risk according to safety.On the other hand, the data form of each data may in not regular data acquisition system
Disunity, while there is likely to be more dirty data (i.e. to the skimble-skamble data of practical business), data purchaser is for supplying
The data structure of side is likely to not enough understanding, and data purchaser may finally be caused fully, reasonably to use data.
The content of the invention
Present invention solves the technical problem that being that existing price is all based on the outer of data acquisition system and data with trade mode
Carried out in factor, data user may be caused to abuse the risk of data.
In order to solve the above technical problems, the embodiment of the present invention provides a kind of data price evaluation side based on single data
Method, comprises the following steps：At least one the attribute identical historical data with the single data is searched, the attribute is included in
Default property set；For each attribute that the property set includes, base of the single data relative to the attribute is determined
Value amount, the base value amount when representing on the basis of the history knock-down price of the historical data the single data under the attribute
Underlying price；Determine weight of each attribute relative to the single data in the property set；According to the base value of each attribute
Amount and the attribute calculate the price for obtaining the single data relative to the weight of the single data.
Optionally, the weight of base value amount and the attribute relative to the single data according to each attribute, calculates
The price of the single data is obtained, is comprised the following steps：The price for obtaining the single data is calculated based on equation below：Wherein, PeIt is the price of the single data；N is the number of attributes in the property set；NiFor described single
Base value amount of the data relative to the i-th attribute；CiWeight for the i-th attribute relative to the single data, 1≤i≤n.
Optionally, determine base value amount of the single data relative to the attribute, comprise the following steps：Determine the list
The evaluating of one data and the historical data relative to the attribute；According to the single data relative to the attribute
Evaluating, the historical data determine the list relative to the evaluating of the attribute and the price of the historical data
Base value amount of one data relative to the attribute.
Optionally, evaluating, the historical data according to the single data relative to the attribute are relative to institute
The price of the evaluating and the historical data of stating attribute determines base value amount of the single data relative to the attribute,
Comprise the following steps：Calculated based on equation below and obtain base value amount of the single data relative to the attribute：Wherein, NiBase value amount for the single data relative to the i-th attribute, SiIt is the single data relative to institute
State the evaluating of the i-th attribute, Si' it is evaluating of the historical data relative to i-th attribute, PsIt is the history
The price of data.
Optionally, each attribute comprises the following steps relative to the weight of the single data in determining the property set：Adopt
Determine the ratio between the influence of price of any two attributes in the property set for the single data with the mode for comparing in pairs, with
Set up pairwise comparison matrix；The Maximum characteristic root of the pairwise comparison matrix and corresponding is calculated by the way of the calculating of root method
Characteristic vector；The Maximum characteristic root is verified；Determine each attribute relative to the single data according to check results
Weight.
Optionally, carrying out verification to the Maximum characteristic root refers to：At least use coincident indicator, random index and
Cause sex rate carries out consistency desired result to the Maximum characteristic root.
Optionally, determine weight of each attribute relative to the single data according to check results, comprise the following steps：If
Verification passes through, then the weight using the characteristic vector as each attribute relative to the single data.
Optionally, the property set at least includes such as properties：Contents attribute；Sets attribute；Time attribute；Collection category
Property；And machining attribute.
The embodiment of the present invention also provides a kind of data price evaluation device based on single data, including：Searching modul, uses
In at least one the attribute identical historical data with the single data is searched, the attribute is included in default property set；
First determining module, for each attribute that the property set includes, determines base of the single data relative to the attribute
Value amount, the base value amount when representing on the basis of the history knock-down price of the historical data the single data under the attribute
Underlying price；Second determining module, for determining weight of each attribute relative to the single data in the property set；Place
Reason module, for the weight of base value amount and the attribute relative to the single data according to each attribute, calculates and obtains
The price of the single data.
Optionally, the processing module includes：First treatment submodule, the list is obtained for being calculated based on equation below
The price of one data：Wherein, PeIt is the price of the single data；N is the attribute number in the property set
Amount；NiBase value amount for the single data relative to the i-th attribute；CiWeight for the i-th attribute relative to the single data, 1
≤i≤n。
Optionally, first determining module includes：First determination sub-module, for determining single data and described
Evaluating of the historical data relative to the attribute；Second determination sub-module, for according to the single data relative to institute
Evaluating, the historical data for stating attribute are true relative to the evaluating of the attribute and the price of the historical data
Base value amount of the fixed single data relative to the attribute.
Optionally, second determination sub-module includes：Processing unit, the list is obtained for being calculated based on equation below
Base value amount of one data relative to the attribute：Wherein, NiBase for the single data relative to the i-th attribute
Value amount, SiEvaluating for the single data relative to i-th attribute, Si' it is the historical data relative to described the
The evaluating of i attributes, PsIt is the price of the historical data.
Optionally, second determining module includes：3rd determination sub-module, for being determined by the way of comparing in pairs
In the property set any two attributes for the single data the ratio between the influence of price, to set up pairwise comparison matrix；The
Two treatment submodules, for being calculated using root method by the way of calculate the Maximum characteristic root of the pairwise comparison matrix and corresponding
Characteristic vector；Verification submodule, for being verified to the Maximum characteristic root；4th determination sub-module, for according to verification
Result determines weight of each attribute relative to the single data.
Optionally, carrying out verification to the Maximum characteristic root refers to：At least use coincident indicator, random index and
Cause sex rate carries out consistency desired result to the Maximum characteristic root.
Optionally, the 4th determination sub-module includes：Determining unit, if verification passes through, the characteristic vector is made
Weight for each attribute relative to the single data.
Optionally, the property set at least includes such as properties：Contents attribute；Sets attribute；Time attribute；Collection category
Property；And machining attribute.
Compared with prior art, the technical scheme of the embodiment of the present invention has the advantages that：
At least one the attribute identical historical data with single data is searched, and is struck a bargain with the history of the historical data
Base value amount of the single price relative to each attribute in property set is determined on the basis of valency, on this basis, the category is determined
Property concentrate each attribute relative to the weight of the single data, be based ultimately upon the base value amount and the attribute of each attribute relative to
The weight of the single data obtains the price of the single data to calculate.Can only be with the number of the big order of magnitude than prior art
It is unit according to set, and needs just to can determine that the technology of data price with reference to factors such as the external application of data and trading environment
Scheme is compared, the technical scheme of the embodiment of the present invention, can be based on the self-characteristic of individual data rationally to determine individual data
Data price, guiding data trade develop to single data direction, the transaction of data is entered by principle of least unit
OK, it is to avoid data user abuses the risk of data.
Further, by formulaThe price of the single data is calculated, wherein, PeIt is the single number
According to price；N is the number of attributes in the property set；NiBase value amount for the single data relative to the i-th attribute；CiFor
I-th attribute relative to the single data weight, 1≤i≤n.Further, the property set at least include contents attribute,
Sets attribute, time attribute, acquisition attributes and machining attribute, the embodiment of the present invention are come to institute from the angle of above-mentioned five attributes
Stating the self-characteristic of single data carries out comprehensive, multi-angle analysis and evaluation, builds multi-sector model, and then determine the list
The price of one data.
Brief description of the drawings
Fig. 1 is a kind of flow chart of data price evaluation method based on single data of the first embodiment of the present invention；
Fig. 2 is a kind of flow chart of data price evaluation method based on single data of the second embodiment of the present invention；
Fig. 3 is a kind of flow chart of data price evaluation method based on single data of the third embodiment of the present invention；
Fig. 4 is a kind of structural representation of data price evaluation device based on single data of the fourth embodiment of the present invention
Figure.
Specific embodiment
It will be appreciated by those skilled in the art that when data trade is carried out, on the one hand, prior art still is limited to the big order of magnitude
Data acquisition system be unit, and current data trade market have begun to smallest dimension standardized data transaction direction send out
Exhibition, then transaction value evaluation profile of the prior art in units of traditional data acquisition system is possibly cannot meet user's request；Separately
On the one hand, the focal point that existing price evaluation pattern will be studied mostly excessively combines number in the knock-down price of data
According to external application and the factor such as trading environment.And although existing relatively new type cost-or-market method pricing model is seemed before can solve the problem that
Problem is stated, but in practical application, the cost such as its collection, storage, treatment is actual hiding for enterprise itself deposition data
During its day-to-day operations, it is difficult to individually distinguish, cause using this pricing model it is difficult to accurate evaluation obtains the reality of data
Border input value (namely data cost price in itself).
In order to solve this technical problem, technical scheme of the present invention is based on the self-characteristic of individual data come rationally true
Fixed number, with reference to the evolution of market trend that data trade develops to single data direction, improves data purchaser to its institute according to price
The availability of data is bought, data is effectively reduced by the risk of unreasonable abuse, privacy of user safety is improved.Further
Ground, because technical scheme described in the embodiment of the present invention is not it is determined that rely on during data price external residing for the individual data
Environment or market are applied, and when the external information of data is not enough, data supplier is based on technical side described in the embodiment of the present invention
Case remains to more precisely, reasonably determine the data price (alternatively referred to as sticker price) of the individual data.
In a preferred embodiment of the invention, by searching at least one the attribute identical history with single data
Data, so as to determine the single price relative to each category in property set on the basis of the history knock-down price of the historical data
The base value amount of property, on this basis, determines the weight of each attribute in the property set relative to the single data, eventually through
FormulaThe price of the single data is calculated, wherein, PeIt is the price of the single data；N is the attribute
The number of attributes of concentration；NiBase value amount for the single data relative to the i-th attribute；CiFor the i-th attribute is single relative to described
The weight of data, 1≤i≤n.Further, the property set at least includes contents attribute, sets attribute, time attribute, collection
Attribute and machining attribute, are carried out comprehensive, polygonal from this five angles of attribute come the self-characteristic to the single data
The analysis and evaluation of degree, builds multi-sector model, and then determine the price of the single data.
It is understandable to enable above-mentioned purpose of the invention, feature and beneficial effect to become apparent, below in conjunction with the accompanying drawings to this
The specific embodiment of invention is described in detail.
Fig. 1 is a kind of flow chart of data price evaluation method based on single data of the first embodiment of the present invention.
Wherein, the single data can be understood as can for data user provide information needed data least unit；It is described
Data price is used for the cognition of the unified each side for participating in data trade, is determined based on technical scheme described in the embodiment of the present invention described
After data price, the sticker price of the single data is can serve as in follow-up data trade with directive function, can also be
Price is further adjusted according to market application in follow-up process of exchange.
Specifically, in the present embodiment, step S101 is first carried out, at least one attribute with the single data is searched
Identical historical data, the attribute is included in default property set.More specifically, the property set at least belongs to including content
Property, sets attribute, time attribute, acquisition attributes and machining attribute.In a preference, search and the single data
Contents attribute identical historical data, if multiple contents attribute identical history numbers with the single data can be found
According to then selecting wherein to strike a bargain records the historical data that nearest historical data finds as this step, and obtains described
The content and history knock-down price of historical data.It will be appreciated by those skilled in the art that identical described in this step can be exponential quantity meaning
Equal within default error range, or refer to identical or approximate on noun meaning in justice.
Performed subsequently into step S102, for each attribute that the property set includes, determine the single data phase
For the base value amount of the attribute, the base value amount is described single when representing on the basis of the history knock-down price of the historical data
Underlying price of the data under the attribute.In a preference, for the step S101 in five attributes, point
It is other that the single data and the sample data are compared, according to the single data under each attribute than the sample number
According to good and bad degree, determine base value amount of the single data relative to the attribute.
Next step S103 is performed, weight of each attribute relative to the single data in the property set is determined.This
Art personnel understand that each attribute that the property set includes is not fully identical to the influence degree of the single data,
It is then desired to determine the weight of each attribute in the property set relative to the single data, so that more accurate obtain described
The price of single data.
Step S104 is finally performed, the base value amount and the attribute according to each attribute are relative to the single data
Weight, calculates the price for obtaining the single data.In a preference, calculated based on equation below and obtain the single number
According to price：
Wherein, PeIt is the price of the single data；N is the number of attributes in the property set；NiIt is the single data
Relative to the base value amount of the i-th attribute；CiWeight for the i-th attribute relative to the single data, 1≤i≤n.Preferably for
5 attributes in the step S101, the then n=5.
Further, the content factors are it is to be understood that identifiability and the content type factor of data, wherein, institute
State content type factor mainly can be considered from primary attribute, behavior property, prediction attributive classification；The identifiability can
With mainly from being directly linked to particular person, indirect association to particular person or particular person cannot be associated with considered.This area
Technical staff understands that generally the contents attribute of the sample data is close with the contents attribute of the single data.
For example, in reference application field, if the contents attribute of the single data and the sample data is used to describe user's
Age, then can be understood as the single data has comparativity with the sample data, and both contents attributes are approached；Enter one
Step ground, if the age of the single data is obtained come match query by the phone number of user, and the sample data
Age be to be obtained come match query by the broadband account of user, then it is described single it is considered that on institute's contents attribute
Data are better than the sample data.
Further, the sets attribute is it is to be understood that accurate produced by the different assignment mode of data content
Degree factor, similar to the granularity of mosaic, granularity is smaller, and data are more accurate, is worth higher.For example, in reference application field,
The content of the single data is the age data of user, then the range section at age is smaller, and its assignment factor is more excellent.
Further, the time attribute is it is to be understood that time shaft change speed and the data sampling time phase of data
With reference to the result for producing, to the changing factor of data value.For example, the content of data checks automobile model recently including user
Data, then the sampling time of the data more near then described time attribute is more excellent, it will be appreciated by those skilled in the art that ordinary circumstance
Under, the purchase intention of user can axle iteratively faster over time, if then wanting, understanding certain user can or can not have purchase car wish recently,
If the data are the data that the user browses car website the year before or before month, the data are more bad；And if described
Data are the data that user's other day browses car website, then the data are more excellent.Wherein, the time shaft change speed can
To be interpreted as, when time shaft changes, the situation of change of the data, for example, in the application scenarios by taking wisdom commercial circle as an example, business
It is layered on when carrying out addressing, situation about can be changed with time shaft with the flow of the people in market is carried out to consider；And in the application of reference
In field, provinces and cities where personal residence are then less to change with time shaft.The data sampling time is it is to be understood that data
Acquisition time, and feedback time.It will be appreciated by those skilled in the art that the time shaft with reference to the data changes speed, if described
The time shaft of data quickly changes and the sampling time is from now close, then it is considered that the data are more excellent；If the data
Time shaft quickly change and the sampling time from now it is far, then it is considered that the data it is more bad；If the time shaft of the data
Then the sampling time is smaller to the price of the data for change relatively slow or being basically unchanged.
Further, the acquisition attributes are it is to be understood that the data of the different acquisition mode generation of data content are accurate
True sexual factor.Specifically, the different acquisition mode can include directly collection and collection indirectly, wherein, directly collection is obtained
The data for obtaining are better than the data that collection is obtained indirectly.The mode of the direct collection can be including own business deposition data etc.；
The mode of the indirect collection can include purchase collection etc..For example, in reference application field, search website is obtained in that use
The merchandise news that family is inquired about on its site, then thus obtained data are more excellent；And if these data are changed hands from other approach
Obtain, then confidence level reduction, these data are just more bad.
Further, the processing factors it is to be understood that data mart modeling mode the different data accuracies for producing because
Element.For example, by the indirect consequence obtained with predictive models treatment based on model, algorithm etc., and by collecting, integrating
The direct result of acquisition, then the latter is more excellent in this factor.
By upper, using the scheme of first embodiment, than prior art can only in units of the data acquisition system of the big order of magnitude,
And need the factors such as external application with reference to data and trading environment just to can determine that the technical scheme of data price is compared, this hair
The technical scheme of bright embodiment, can be based on the self-characteristic of individual data rationally to determine data price, improve data purchase
Side buys the availability of data to it, so as to effectively reduce data by the risk of unreasonable abuse, improves privacy of user
Safety.Further, the property set at least includes contents attribute, sets attribute, time attribute, acquisition attributes and processing category
Property, the embodiment of the present invention carries out comprehensive, polygonal from the angle of above-mentioned five attributes come the self-characteristic to the single data
The analysis and evaluation of degree, builds multi-sector model, and then determine the price of the single data.Further, due to of the invention real
The example technical scheme is applied it is determined that not relying on external environment residing for the individual data during data price or market should
With, when the external information of data is not enough, data supplier be based on technical scheme described in the embodiment of the present invention remain to it is more accurate,
Reasonably determine the data price (i.e. sticker price) of the individual data.
Fig. 2 is a kind of flow chart of data price evaluation method based on single data of the second embodiment of the present invention.
Specifically, in the present embodiment, step S201 is first carried out, lookup is gone through with least one attribute identical of the single data
History data, the attribute is included in default property set.More specifically, the property set at least includes contents attribute, assignment
Attribute, time attribute, acquisition attributes and machining attribute.Further, those skilled in the art may be referred to above-mentioned Fig. 1 institutes
Show step S101 described in embodiment, will not be described here.
Subsequently into step S202 perform, for each attribute that the property set includes, determine the single data and
Evaluating of the historical data relative to the attribute.Specifically, can embody same with the height of the evaluating
The good and bad degree of the single data and the sample data under one attribute.For example, the single data or the sample number
According to more excellent, the evaluating is higher.
Next step S203, evaluating, the history according to the single data relative to the attribute are performed
Data determine the single data relative to described relative to the evaluating of the attribute and the price of the historical data
The base value amount of attribute.In a preference, calculated based on equation below and obtain the single data relative to the attribute
Base value amount：
Wherein, NiBase value amount for the single data relative to the i-th attribute, SiIt is the single data relative to described
The evaluating of the i-th attribute, Si' it is evaluating of the historical data relative to i-th attribute, PsIt is the history number
According to price.
Next step S204 is performed, weight of each attribute relative to the single data in the property set is determined.This
Art personnel understand that each attribute that the property set includes is not fully identical to the influence degree of the single data,
It is then desired to determine the weight of each attribute in the property set relative to the single data, so that more accurate obtain described
The price of single data.
Step S205 is finally performed, the base value amount and the attribute according to each attribute are relative to the single data
Weight, calculates the price for obtaining the single data.Specifically, those skilled in the art may be referred to implement shown in above-mentioned Fig. 1
Step S104 described in example, will not be described here.
The step S202 is performed it is possible to further the instruction of the provider according to the single data, to determine
Evaluating of the single data with the sample data on each attribute in the property set；Or, can also build in advance
Vertical database, the factor of the influence evaluating occurred to history carries out classification storage, so as to perform the step
During S202, Keywords matching can be based on, conveniently determine the single data with the sample data in the attribute
Concentrate the evaluating on each attribute.
By upper, using the scheme of second embodiment, the step S202 and step S203 can be understood as above-mentioned
One specific embodiment of step S102 described in embodiment illustrated in fig. 1, by determining each attribute that the property set includes
Under, the single data and the respective evaluating of the sample data, more accurately to determine the base value of the single data
Amount, and then obtain the price of the single data.
Fig. 3 is a kind of flow chart of data price evaluation method based on single data of the third embodiment of the present invention.
Specifically, in the present embodiment, step S301 is first carried out, lookup is gone through with least one attribute identical of the single data
History data, the attribute is included in default property set.More specifically, those skilled in the art may be referred to above-mentioned Fig. 1 institutes
Show step S101 described in embodiment, will not be described here.
Subsequently into step S302 perform, for each attribute that the property set includes, determine the single data and
Evaluating of the historical data relative to the attribute.Specifically, those skilled in the art may be referred to shown in above-mentioned Fig. 2
Step S202 described in embodiment, will not be described here.
Next step S303, evaluating, the history according to the single data relative to the attribute are performed
Data determine the single data relative to described relative to the evaluating of the attribute and the price of the historical data
The base value amount of attribute.Specifically, those skilled in the art may be referred to step S203 described in above-mentioned embodiment illustrated in fig. 2,
It will not go into details for this.
Subsequently into step S304 perform, in the property set is determined by the way of comparing in pairs any two attributes for
The ratio between influence of price of the single data, to set up pairwise comparison matrix.Specifically, from operational research angle, according to Satie
(Saaty) 1-9 and its inverse as scale, the importance of data price evaluation described in the embodiment of the present invention is carried out in contrast with
Compared with.In a preference, a (is denoted as with scalejk) represent the property set in any two attributes (be denoted as CjAnd Ck) to described
The ratio between influence of price of single data, and then pairwise comparison matrix A is automatically formed, wherein, A=(ajk) n × n and ajj=1 and
ajk=1/akj。
Table 1
| Implication | Scale |
| CjFactor and CkFactor is of equal importance | 1 |
| CjFactor compares CkFactor is somewhat important | 3 |
| CjFactor compares CkFactor is relatively strong important | 5 |
| CjFactor compares CkFactor is strongly important | 7 |
| CjFactor compares CkFactor is extremely important | 9 |
| Between the median of above-mentioned two adjacent judgements | 2,4,6,8 |
Scale a in the pairwise comparison matrix A is listed in table 1jkReally calibrate accurate, it should be pointed out that this area skill
Art personnel can also according to actual needs to the scale ajkConcrete numerical value or correspondence implication be changed, this has no effect on this
The technology contents of invention.
Next step S305 is performed, the Maximum characteristic root of the pairwise comparison matrix is calculated by the way of the calculating of root method
And corresponding characteristic vector.It will be appreciated by those skilled in the art that calculating the pairwise comparison matrix A using root method described in this step
Maximum characteristic root and the method for corresponding characteristic vector belong to prior art, will not be described here.
Performed subsequently into step S306, the Maximum characteristic root is verified.Specifically, at least referred to using uniformity
Mark, random index and Consistency Ratio carry out consistency desired result to the Maximum characteristic root.It will be appreciated by those skilled in the art that adopting
It is more normal prior art to carry out consistency desired result to the result of calculation of the step S305 with the method for above-mentioned three kinds or more
A kind of consistency desired result method, will not be described here.
Next step S307 is performed, weight of each attribute relative to the single data is determined according to check results.
In one preference, if verification passes through, the weight using the characteristic vector as each attribute relative to the single data；If
Verification does not pass through, then repeating said steps S304 rebuilds the pairwise comparison matrix to the step S306, until described
The result of the consistency desired result of step S306 passes through for verification.
Step S308 is finally performed, the base value amount and the attribute according to each attribute are relative to the single data
Weight, calculates the price for obtaining the single data.Specifically, those skilled in the art may be referred to implement shown in above-mentioned Fig. 1
Step S104 described in example, will not be described here.
By upper, using the scheme of 3rd embodiment, the step S304, the step S305, the step S306 and
The step S307 can be understood as a specific reality of step S103 described in above-mentioned Fig. 1 or step S204 described in above-mentioned Fig. 2
Apply mode, on the basis of searching and obtaining the historical data, by building multi-sector model, and based on the single data and
Difference of the historical data on same alike result is calculated and obtains base value amount, and because different attribute is for the single data
The influence degree of price is different, and each attribute is described single during the property set is precisely determined by building pairwise comparison matrix
The weight of the price of data, it is final to calculate the price for obtaining the single data.
The data price evaluation method of above-mentioned the first to 3rd embodiment can be used in data publication method, it is specific and
Speech, the data publication method includes：At least one single data are provided；Use any one data in the first to 3rd embodiment
Price evaluation method determines the price of each single data；Based on the price of each single data, to above-mentioned at least one
Single data are issued.Additionally, can also be arranged according to the price of different single data according to preset configuration in issue
Sequence or screening.Because the price of data is directed to what single data determined, therefore can be according to the demand of user in issue
Accurately sorted or screened, be conducive to improving the accuracy and convenience of user-selected number evidence.
Fig. 4 is a kind of structural representation of data price evaluation device based on single data of the fourth embodiment of the present invention
Figure.It will be appreciated by those skilled in the art that the data price evaluation device 4 based on single data described in the present embodiment is used to implement above-mentioned
Fig. 1 is to the method and technology scheme described in embodiment illustrated in fig. 3.Specifically, in the present embodiment, it is described based on single data
Data price evaluation device 4 includes searching modul 41, is gone through with least one attribute identical of the single data for searching
History data, the attribute is included in default property set；First determining module 42, for each category that the property set includes
Property, determining base value amount of the single data relative to the attribute, the base value amount is represented with the history of the historical data
Underlying price of the single data under attribute when on the basis of knock-down price；Second determining module 43, it is described for determining
Weight of each attribute relative to the single data in property set；And processing module 44, for the base value according to each attribute
Amount and the attribute calculate the price for obtaining the single data relative to the weight of the single data.
Further, the processing module 44 includes the first treatment submodule 441, is obtained for being calculated based on equation below
The price of the single data：
Wherein, PeIt is the price of the single data；N is the number of attributes in the property set；NiIt is the single data
Relative to the base value amount of the i-th attribute；CiWeight for the i-th attribute relative to the single data, 1≤i≤n.
Further, first determining module 42 includes the first determination sub-module 421, for determining the single data
Evaluating with the historical data relative to the attribute；Second determination sub-module 422, for according to the single data
The evaluating and the historical data of evaluating, the historical data relative to the attribute relative to the attribute
Price determine base value amount of the single data relative to the attribute.
Preferably, second determination sub-module 422 includes processing unit 4221, is obtained for being calculated based on equation below
Base value amount of the single data relative to the attribute：
Wherein, NiBase value amount for the single data relative to the i-th attribute, SiIt is the single data relative to described
The evaluating of the i-th attribute, Si' it is evaluating of the historical data relative to i-th attribute, PsIt is the history number
According to price.
Further, second determining module 43 includes the 3rd determination sub-module 431, for using the side for comparing in pairs
Formula determines the ratio between the influence of price of any two attributes in the property set for the single data, and square is compared in pairs to set up
Battle array；Second processing submodule 432, the mode for using root method to calculate calculate the Maximum characteristic root of the pairwise comparison matrix with
And corresponding characteristic vector；Verification submodule 433, for being verified to the Maximum characteristic root；And the 4th determine submodule
Block 434, for determining weight of each attribute relative to the single data according to check results.
Preferably, carrying out verification to the Maximum characteristic root refers to：At least use coincident indicator, random index and
Cause sex rate carries out consistency desired result to the Maximum characteristic root.
Further, the 4th determination sub-module 434 includes determining unit 4341, if verification passes through, by the spy
Levy weight of the vector as each attribute relative to the single data.
Preferably, the property set at least includes contents attribute, sets attribute, time attribute, acquisition attributes and processing
Attribute.
Operation principle, more contents of working method on the data price evaluation device 4 based on single data,
The associated description in Fig. 1 to Fig. 3 is referred to, is repeated no more here.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can
Completed with instructing the hardware of correlation by program, the program can be stored in computer-readable recording medium, to store
Medium can include：ROM, RAM, disk or CD etc..
Although present disclosure is as above, the present invention is not limited to this.Any those skilled in the art, are not departing from this
In the spirit and scope of invention, can make various changes or modifications, therefore protection scope of the present invention should be with claim institute
The scope of restriction is defined.
Claims (16)
1. a kind of data price evaluation method based on single data, it is characterised in that comprise the following steps：
At least one the attribute identical historical data with the single data is searched, the attribute is included in default attribute
Collection；
For each attribute that the property set includes, base value amount of the single data relative to the attribute is determined, it is described
Base value amount represents basic valency of the single data under attribute when on the basis of the history knock-down price of the historical data
Lattice；
Determine weight of each attribute relative to the single data in the property set；
The weight of base value amount and the attribute relative to the single data according to each attribute, calculates and obtains described single
The price of data.
2. the data price evaluation method based on single data according to claim 1, it is characterised in that according to each category
Property base value amount and the attribute relative to the weight of the single data, calculate the price for obtaining the single data, bag
Include following steps：
The price for obtaining the single data is calculated based on equation below：
Wherein, PeIt is the price of the single data；N is the number of attributes in the property set；NiFor the single data are relative
In the base value amount of the i-th attribute；CiWeight for the i-th attribute relative to the single data, 1≤i≤n.
3. the data price evaluation method based on single data according to claim 1, it is characterised in that determine the list
One data comprise the following steps relative to the base value amount of the attribute：
Determine the evaluating of the single data and the historical data relative to the attribute；
According to the single data relative to the evaluating of the attribute, the historical data relative to the attribute evaluation
The price of parameter and the historical data determines base value amount of the single data relative to the attribute.
4. the data price evaluation method based on single data according to claim 3, it is characterised in that according to the list
One data relative to the evaluating of the attribute, the historical data relative to the attribute evaluating and described go through
The price of history data determines base value amount of the single data relative to the attribute, comprises the following steps：
Calculated based on equation below and obtain base value amount of the single data relative to the attribute：
Wherein, NiBase value amount for the single data relative to the i-th attribute, SiIt is that the single data belong to relative to described i-th
The evaluating of property, Si' it is evaluating of the historical data relative to i-th attribute, PsIt is the valency of the historical data
Lattice.
5. the data price evaluation method based on single data according to claim 1, it is characterised in that determine the category
Property concentrate each attribute relative to the weight of the single data, comprise the following steps：
Influence of any two attributes for the price of the single data in the property set is determined by the way of comparing in pairs
The ratio between, to set up pairwise comparison matrix；
The Maximum characteristic root and corresponding characteristic vector of the pairwise comparison matrix are calculated by the way of the calculating of root method；
The Maximum characteristic root is verified；
Determine weight of each attribute relative to the single data according to check results.
6. the data price evaluation method based on single data according to claim 5, it is characterised in that to the maximum
Characteristic root carries out verification：At least using coincident indicator, random index and Consistency Ratio to the Maximum characteristic root
Carry out consistency desired result.
7. the data price evaluation method based on single data according to claim 5, it is characterised in that tied according to verification
Fruit determines weight of each attribute relative to the single data, comprises the following steps：
If verification passes through, the weight using the characteristic vector as each attribute relative to the single data.
8. the data price evaluation method based on single data according to any one of claim 1 to 7, it is characterised in that
The property set at least includes such as properties：
Contents attribute；
Sets attribute；
Time attribute；
Acquisition attributes；And
Machining attribute.
9. a kind of data price evaluation device based on single data, it is characterised in that including：
Searching modul, for searching at least one the attribute identical historical data with the single data, the attribute includes
In default property set；
First determining module, for each attribute that the property set includes, determines the single data relative to the attribute
Base value amount, the base value amount when representing on the basis of the history knock-down price of the historical data the single data in the category
Underlying price under property；
Second determining module, for determining weight of each attribute relative to the single data in the property set；
Processing module, for the weight of base value amount and the attribute relative to the single data according to each attribute, meter
Calculate the price for obtaining the single data.
10. the data price evaluation device based on single data according to claim 9, it is characterised in that the treatment
Module includes：
First treatment submodule, the price of the single data is obtained for being calculated based on equation below：
Wherein, PeIt is the price of the single data；N is the number of attributes in the property set；NiFor the single data are relative
In the base value amount of the i-th attribute；CiWeight for the i-th attribute relative to the single data, 1≤i≤n.
The 11. data price evaluation devices based on single data according to claim 9, it is characterised in that described first
Determining module includes：
First determination sub-module, for determining that the single data and the historical data are joined relative to the evaluation of the attribute
Number；
Second determination sub-module, for the evaluating according to the single data relative to the attribute, the historical data
Determine the single data relative to the attribute relative to the evaluating of the attribute and the price of the historical data
Base value amount.
The 12. data price evaluation devices based on single data according to claim 11, it is characterised in that described second
Determination sub-module includes：
Processing unit, base value amount of the single data relative to the attribute is obtained for being calculated based on equation below：
Wherein, NiBase value amount for the single data relative to the i-th attribute, SiIt is that the single data belong to relative to described i-th
The evaluating of property, Si' it is evaluating of the historical data relative to i-th attribute, PsIt is the valency of the historical data
Lattice.
The 13. data price evaluation devices based on single data according to claim 9, it is characterised in that described second
Determining module includes：
3rd determination sub-module, for any two attributes in the property set is determined by the way of comparing in pairs for the list
The ratio between influence of price of one data, to set up pairwise comparison matrix；
Second processing submodule, for using root method calculating by the way of calculate the pairwise comparison matrix Maximum characteristic root and
Corresponding characteristic vector；
Verification submodule, for being verified to the Maximum characteristic root；
4th determination sub-module, for determining weight of each attribute relative to the single data according to check results.
The 14. data price evaluation devices based on single data according to claim 13, it is characterised in that to it is described most
Big characteristic root carries out verification and refers to：At least using coincident indicator, random index and Consistency Ratio to the maximum feature
Root carries out consistency desired result.
The 15. data price evaluation devices based on single data according to claim 13, it is characterised in that the described 4th
Determination sub-module includes：
Determining unit, if verification passes through, the weight using the characteristic vector as each attribute relative to the single data.
The 16. data price evaluation device based on single data according to any one of claim 9 to 15, its feature exists
In the property set at least includes such as properties：
Contents attribute；
Sets attribute；
Time attribute；
Acquisition attributes；And
Machining attribute.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105992015A (en) * | 2015-02-02 | 2016-10-05 | 腾讯科技（北京）有限公司 | Information processing method and device |
| CN107688901A (en) * | 2017-08-24 | 2018-02-13 | 北京小度信息科技有限公司 | Data adjustment method and device |
| CN107748792A (en) * | 2017-11-01 | 2018-03-02 | 上海数据交易中心有限公司 | Data retrieval method and device, terminal |
| CN109345301A (en) * | 2018-09-26 | 2019-02-15 | 国信优易数据有限公司 | A kind of data price-determining system and determining method |
| CN109432786A (en) * | 2018-10-08 | 2019-03-08 | 腾讯科技（深圳）有限公司 | The price control method and device of game item, electronic equipment, storage medium |
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| CN110858368A (en) * | 2018-08-24 | 2020-03-03 | 国信优易数据有限公司 | Data evaluation service value determination system and method |
| CN111340521A (en) * | 2018-12-19 | 2020-06-26 | 掌阅科技股份有限公司 | Book production price processing method, electronic device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105992015A (en) * | 2015-02-02 | 2016-10-05 | 腾讯科技（北京）有限公司 | Information processing method and device |
| CN105992015B (en) * | 2015-02-02 | 2019-12-13 | 腾讯科技（北京）有限公司 | information processing method and device |
| CN107688901A (en) * | 2017-08-24 | 2018-02-13 | 北京小度信息科技有限公司 | Data adjustment method and device |
| CN107688901B (en) * | 2017-08-24 | 2021-05-11 | 北京星选科技有限公司 | Data adjusting method and device |
| CN107748792A (en) * | 2017-11-01 | 2018-03-02 | 上海数据交易中心有限公司 | Data retrieval method and device, terminal |
| CN110766429A (en) * | 2018-07-26 | 2020-02-07 | 国信优易数据有限公司 | Data value evaluation system and method |
| CN110858368A (en) * | 2018-08-24 | 2020-03-03 | 国信优易数据有限公司 | Data evaluation service value determination system and method |
| CN109345301A (en) * | 2018-09-26 | 2019-02-15 | 国信优易数据有限公司 | A kind of data price-determining system and determining method |
| CN109432786A (en) * | 2018-10-08 | 2019-03-08 | 腾讯科技（深圳）有限公司 | The price control method and device of game item, electronic equipment, storage medium |
| CN111340521A (en) * | 2018-12-19 | 2020-06-26 | 掌阅科技股份有限公司 | Book production price processing method, electronic device and storage medium |
| CN111340521B (en) * | 2018-12-19 | 2023-09-01 | 掌阅科技股份有限公司 | Book production price processing method, electronic device and storage medium |
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