CN115640275A - Automatic generation and updating method for data products and assets - Google Patents
Automatic generation and updating method for data products and assets Download PDFInfo
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Abstract
The invention discloses a method for automatically generating and updating data products and assets, which comprises the following steps: acquiring and dividing a data product and asset data resource library to obtain a minimum data unit which cannot be split; combining the minimum data units which cannot be split according to the value attributes to obtain a minimum value unit; processing the minimum value unit to obtain a data factor with a recombination degree of freedom, a circulation degree of freedom and a value attribute; processing the data factors to obtain a data factor dynamic data value map, processing the data factor dynamic data value map to obtain a data factor recombination freedom function and a data factor circulation freedom function, and processing the data factor recombination freedom function and the data factor circulation freedom function to obtain a user demand function and a value maximization function; and processing the user demand function and the value maximization function to obtain a data factor recombination scheme and a circulation scheme, and automatically generating and updating data products and assets. The method meets the characteristics and requirements of pursuing high value and high liquidity of data products and assets.
Description
Technical Field
The invention relates to the technical field of automatic generation of data products and assets, in particular to an automatic generation and updating method of data products and assets.
Background
At present, a report automatic generation method for data mainly extracts, searches and replaces data, and then establishes an automatic generation report with relatively fixed modes such as detection, medical treatment, document and the like according to a certain scene, professional requirements or a regular template of a typical model through network or specialized software. These methods are passive data report generation methods, and the requirements of report users and the value of reports themselves are not fully reflected in the generation process. Although there is a certain dynamic update on the data values and contents, the method is limited by the regularity template, the possibility of reorganization of organization rules among data is low, the association among data is not fully mined, and the circulation among data and after combination cannot be guaranteed. In short, the current automatic data report generation methods are more suitable for the field application with unchanged scene, stable demand, fixed mode, no pursuit of high value and low circulation requirement.
However, unlike the application areas for which these current methods of automatic generation of data reports are suitable, the characteristics and requirements of data products and data assets are just the opposite. The data products and the data assets have the characteristics and the requirements of uncertain scenes, large demand difference, unfixed modes, pursuit of high value and high circulation requirement. This is because, data products and data assets have economic features, the relevance between the contained data is very important, the organization rule pattern between the data is more flexible, different data combinations can bring different values, and the more combinations, the more applicable scenarios, the higher the possibility of bringing more values. The currency and reorganization of data products and data assets formed between and after the combination of data is not only oriented to the maximization of their diverse and diverse user needs and value, but also closely related to the attributes of the data products and data assets themselves that tend to be of high value. Automatic updating and immediate updated version delivery of data products/assets are also highly necessary due to the characteristics and requirements of data products and data assets, and the high frequency and magnitude of dynamic changes of data resources and data elements.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for automatically generating and updating data products and assets, which can extract and establish data factors with recombination and circulation freedom degrees and value attributes, construct a data factor historical dynamic data value map, establish a data factor recombination and circulation freedom degree function on the basis of the value map, construct a user demand and value maximization function as a demand and value target of data factor recombination and circulation, further determine a data factor recombination and circulation scheme, automatically generate data products and assets, and realize automatic updating of the data products and the assets.
In order to solve the technical problem, the embodiment of the invention discloses a method for automatically generating and updating data products and assets, which comprises the following steps:
s1, acquiring a data product and asset data resource library; the data product and asset data repository is composed of data and has value attributes and liquidity;
the value attribute is the actual value of the data product and the data asset in three dimensions of time, space and path, which accords with the interest of the demand side;
the currency is a social circulation of data products and data assets for their value to be sought and realized;
s2, dividing the data product and asset data resource library to obtain the smallest irreparable data unit;
combining the minimum data units which can not be split according to the value attributes to obtain a minimum value unit;
processing the minimum value unit to obtain a data factor with a recombination degree of freedom, a circulation degree of freedom and a value attribute;
s3, processing the data factors to obtain a data factor dynamic data value map;
s4, processing the data factor dynamic data value map to obtain a data factor recombination freedom function and a data factor circulation freedom function;
s5, processing the data factor recombination freedom function and the data factor circulation freedom function to obtain a user demand function and a value maximization function;
and S6, processing the user demand function and the value maximization function to obtain a data factor recombination scheme and a circulation scheme, and automatically generating and updating data products and assets.
As an optional implementation manner, in an embodiment of the present invention, the processing the minimum value unit to obtain a data factor having a reorganization degree of freedom, a circulation degree of freedom, and a value attribute includes:
when the minimum value unit has the condition of recombining with at least two other minimum value units to form a value, the minimum value unit is called to have a recombination degree of freedom;
when the value represented by the minimum value unit is not directionally supplied to a certain user and can not be only applied to a certain specific scene, the minimum value unit is called to have circulation freedom;
the least valuable unit with the reorganization and circulation freedom is the data factor.
As an optional implementation manner, in an embodiment of the present invention, the processing the data factor to obtain a data factor dynamic data value map includes:
s31, carrying out value marking on the data factors from three dimensions of time, space and path;
s32, mining the association value relation between the data factor and other data factors in three dimensions of time, space and path by using a data value association mining method;
s33, processing the time, space and path three-dimensional values of the data factors to obtain the influence coefficients of the correlation value relationship of the data factors on the time, space and path three-dimensional values;
influence coefficients of the correlation value relations of the data factors in the three dimensions of time, space and path on the three dimensional values of the time, space and path are multiplied by the three dimensional values of the time, space and path corresponding to other data factors respectively to obtain the correlation values of the data factors and the other data factors in the three dimensions of time, space and path;
s34, taking the three-dimensional values of time, space and path of each data factor as the three-dimensional coordinates of each data factor, and marking the associated values of the time, space and path of each data factor and other data factors to obtain a data factor value map of the data factor at a certain time point;
and S35, repeating the steps S31, S32, S33 and S34 at each historical time point to obtain a data factor dynamic data value map.
As an optional implementation manner, in the embodiment of the present invention, the time value includes a time cost and a data aging;
the space value comprises economic cost, resource cost, data content, data reliability and data technology;
the path value comprises a data category, a data positioning, a data association, a data path and a data source.
As an optional implementation manner, in an embodiment of the present invention, the calculating method of the time value, the space value, and the path value of each data factor includes:
the time value, the space value and the path value of each data factor are equal to the correlation value of the data factor and other data factors on the time value, the space value and the path value and the basic value of the data factor on the time value, the space value and the path value;
the basic value is a common factor of values of the data factors in time value, space value and path value at various historical time points.
As an optional implementation manner, in an embodiment of the present invention, the data factor reorganization degree of freedom function includes:
s41, extracting the associated value relation of the same data factor and the time, space and path three-dimensional values of other data factors at each historical time point from the data factor dynamic data value map;
s42, analyzing the correlation value relationship between the same data factor and other data factors in three dimensional values of time, space and path at each historical time point to obtain all non-null correlation value relationships in the correlation value relationship between the same data factor and other data factors in three dimensional values of time, space and path at each historical time point, and marking the non-null correlation value relationships;
s43, combining the data factor and other data factors, and the non-null associated value relation in three dimensions of time, space and path at each historical time point and the corresponding associated value into a non-null associated value set;
s44, regarding all the data factors and other data factors as non-empty associated value sets corresponding to non-empty associated values in the associated values of three dimensions of time, space and path at each historical time point, and referring to the non-empty associated value sets as recombination freedom degree sets of the data factors at each historical time point;
and S45, extracting a historical data factor recombination track function by using the recombination freedom degree set of the data factor at each historical time point to obtain a data factor recombination freedom degree function.
As an optional implementation manner, in an embodiment of the present invention, the data factor circulation degree of freedom function includes:
s46, judging whether the time cost, the data timeliness, the economic cost, the resource cost, the data content, the data reliability, the data technicality, the data category, the data positioning, the data association, the data path and the data source of the data factor in three dimensions of time, space and path at each historical time point are all directionally supplied to a certain user or only can be applied to a certain specific scene;
if so, the data factor has no circulation freedom; if not, marking a plurality of user requirements and a plurality of application scenes corresponding to each item in three dimensions of time, space and path of the data factor at each historical time point to obtain the self circulation freedom degree of the data factor;
s47, extracting other data factors with the recombination freedom degrees of the data factors at the historical time points by using the recombination freedom degree sets of the data factors at the historical time points;
s48, judging the self circulation freedom degree of the data factor at each historical time point, matching the self circulation freedom degree of the data factor at the same historical time point and the self circulation freedom degree of other data factors with recombination freedom degrees;
calculating the correlation value of the data factor and other data factors in the specific dimension with recombination freedom degrees of the data factors and other data factors;
combining the self circulation freedom degree of the data factor at each historical time point, the self circulation freedom degree of other data factors having a recombination freedom degree relation with the self circulation freedom degree, the data factor and the recombination dimension correlation value of other data factors having a recombination freedom degree relation with the data factor into a circulation freedom degree set of the data factor at each historical time point;
and analyzing the circulation freedom set of the data factors at each historical time point, extracting a historical circulation track function of the data factors, and obtaining the circulation freedom function of the data factors.
As an optional implementation manner, in an embodiment of the present invention, the processing is performed on the data factor reorganization degree of freedom function and the data factor circulation degree of freedom function to obtain a user requirement function, where the method includes:
aiming at user requirements, the data products and data assets can be divided into customized data products and data assets determined by buyers and scenes and non-customized data products and data assets determined by buyers and scenes;
respectively determining user demand functions of different types of data products and data asset characteristics;
the user demand function is used as a demand and value target for data factor recombination and circulation;
customizing user demand functions of data products and data assets, which change over time, including specific demands of buyers on time value, space value, path value and determined application scenarios;
the user demand function of the non-customized data products and data assets changes with time, and the specific demands and the potential application scenes of the potential buyers are predicted according to the historical user demands and the overall market demands.
As an optional implementation manner, in an embodiment of the present invention, the processing is performed on the data factor reorganization degree of freedom function and the data factor circulation degree of freedom function to obtain a value maximization function, and the method includes:
processing the data factor recombination freedom function and the data factor circulation freedom function to construct a value maximization function;
the value maximization function is a demand and value target of data factor recombination and circulation;
if all data factors are contained in the data product and the data asset which need to be automatically generated, processing the values of all the data factors on the time value, the space value and the path value, removing the associated values between two repeated data factors in each subentry of the value, and then summing to obtain the value sum of the time value, the space value and the path value of all the data factors contained in the data product and the data asset;
the cost sum maximum is a cost maximization function.
As an optional implementation manner, in an embodiment of the present invention, the processing is performed on the user demand function and the cost maximization function to obtain a data factor reorganization scheme and a circulation scheme, and automatically generate and update data products and assets, where the method includes:
s61, screening the time value, the space value and the path value of each data factor by taking the user demand function as a screening condition, and reserving the data factors which accord with the user demand function in the time value, the space value and the path value;
s62, combining the data factors which accord with the user demand function in the time value, the space value and the path value to obtain the data factors which accord with the circulation freedom degree requirement;
s63, taking the value maximization function as a constraint target of relevance and relevance value between the data factor meeting the requirement of circulation freedom degree and other data factors;
processing the data dynamic data value map and the relevance value between the data factor and other data factors to obtain a data factor recombination scheme which accords with a value maximization function and the requirement of circulation freedom;
the data factor reorganization scheme is automatically generated data products and data assets;
and S64, repeating S61, S62 and S63 when the data factor or the user requirement is changed, and obtaining the updated automatically generated data product and data asset.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
(1) The invention provides a data product and asset automatic generation and updating method based on free recombination and circulation of data factors of a value map, which has the advantages of breaking through the passive generation of the current data report automatic generation method, solving the problems that the requirements of report users and the value of reports are not fully embodied in the generation process, solving the problem that the recombination of organization rules among data is limited by regular templates, and solving the problems that the relevance among data is not fully mined and the circulation among data and after combination cannot be guaranteed. In a word, the method breaks through the limitation of the existing automatic generation method of the current data report, changes the current situation that the automatic generation method of the current data report is suitable for the field application of unchanged scene, stable demand, fixed mode, no pursuit of high value and low circulation requirement, is more suitable for the automatic generation and updating of data products and assets, and meets the characteristics and requirements of uncertain scene, large demand difference, unfixed mode, pursuit of high value and high circulation requirement of the data products and the data assets.
(2) The invention provides a data product and asset automatic generation and updating method based on free recombination and circulation of data factors of a value map, which has the advantages that the data factors with the recombination and circulation freedom degrees and the value attributes are extracted and established, the historical dynamic data value map of the data factors is established, and the data factor recombination and circulation freedom degree functions are established on the basis of the value map. Due to the data resources and the data elements, data products and data assets with different modes, higher values, meeting different scene requirements and higher circulation can be formed through the recombination of multiple data. The method develops research around data per se, constructs data factors with recombination freedom and circulation freedom, describes historical evolution situation of the data factors in a value level through construction of a historical dynamic data value map of the data factors, provides flexible recombination conditions and ranges for data product and data asset for data factor recombination, and provides a technical support basis for potential value realization of the data product and the data asset.
(3) The invention provides a data product and asset automatic generation and updating method based on data factor free recombination and circulation of a value map, which has the advantages that a user demand and value maximization function is constructed to serve as a demand and value target of data factor recombination and circulation, and then a data factor recombination and circulation scheme is determined. The currency and reorganization of data products and data assets formed by data to data and after combination are not only oriented to the maximization of their diverse and diverse user needs and values, but also closely related to the attributes of the data products and data assets themselves that tend to be of high value. Therefore, the method constructs a user demand function and a value maximization function aiming at the customized and non-customized data products and data assets respectively, and the value maximization function is used as a constraint condition of the future potential reorganization freedom and the future potential circulation freedom, so that the range of the future potential reorganization freedom and the future potential circulation freedom is further reduced, and the data factor reorganization and circulation scheme is accurately determined.
(4) The invention provides a method for automatically generating and updating data products and assets by freely recombining and circulating data factors based on a value map, which has the advantages of automatically generating the data products and the assets and realizing the automatic updating of the data products and the assets. The data products and the assets have the characteristics of range economy, the relevance between contained data is very important, the organization rule mode among the data is more flexible, different data combinations can bring different values, the more the combinations are, the more applicable scenes are, and the higher the possibility of bringing more values is. Automatic updating and immediate updated version delivery of data products/assets are also highly necessary due to the characteristics and requirements of data products and data assets, and the high frequency and large amplitude of dynamic changes of data resources and data elements. It is to these needs that the present invention is directed.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for automatically generating and updating data products and assets according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may alternatively include other steps or elements not listed or inherent to such process, method, product, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart diagram of a method for automatically generating and updating data products and assets according to an embodiment of the present invention:
s1, acquiring a data product and asset data resource library; the data product and asset data repository is composed of data and has value attributes and liquidity;
the value attribute is the actual value of the data product and the data asset in three dimensions of time, space and path, which accords with the benefit of the demander;
the currency is a social currency of data products and data assets for their value to be sought and realized;
s2, dividing the data product and asset data resource library to obtain the minimum data unit which can not be split;
combining the minimum data units which cannot be split according to the value attributes to obtain minimum value units;
processing the minimum value unit to obtain a data factor with a recombination degree of freedom, a circulation degree of freedom and a value attribute;
s3, processing the data factor to obtain a data factor dynamic data value map;
s4, processing the data factor dynamic data value map to obtain a data factor recombination freedom function and a data factor circulation freedom function;
s5, processing the data factor recombination freedom function and the data factor circulation freedom function to obtain a user demand function and a value maximization function;
and S6, processing the user demand function and the value maximization function to obtain a data factor recombination scheme and a circulation scheme, and automatically generating and updating data products and assets.
Optionally, the minimum value unit is processed to obtain a data factor having a reorganization degree of freedom, a circulation degree of freedom, and a value attribute, and the method includes:
when the minimum value unit has the condition of forming value by recombining with at least two other minimum value units, the minimum value unit is called to have the recombination freedom degree;
when the value represented by the minimum value unit is not directionally supplied to a certain user and can not be only applied to a certain specific scene, the minimum value unit is called to have circulation freedom;
the least valuable unit with the reorganization and circulation freedom is the data factor.
Optionally, the data factor is processed to obtain a data factor dynamic data value map, and the method includes:
s31, carrying out value marking on the data factors from three dimensions of time, space and path;
s32, excavating the association value relation of the data factor and other data factors in three dimensions of time, space and path by using a data value association excavation method;
s33, processing the time, space and path three-dimensional values of the data factors to obtain the influence coefficients of the correlation value relationship of the data factors on the time, space and path three-dimensional values;
influence coefficients of the correlation value relationship of each data factor in three dimensions of time, space and path on the three dimensional values of time, space and path are multiplied by the three dimensional values of time, space and path corresponding to other data factors respectively to obtain the correlation values of the data factors and the other data factors in three dimensions of time, space and path;
s34, taking the three-dimensional values of time, space and path of each data factor as the three-dimensional coordinates of each data factor, and marking the associated values of each data factor and other data factors in the three dimensions of time, space and path to obtain a data factor value map of the data factor at a certain time point;
and S35, repeating the steps S31, S32, S33 and S34 at each historical time point to obtain a data factor dynamic data value map.
Optionally, the time value includes time cost and data aging;
the space value comprises economic cost, resource cost, data content, data reliability and data technology;
the path value comprises a data category, a data positioning, a data association, a data path and a data source.
Optionally, the time value, the space value, and the path value of each data factor are calculated by:
the time value, the space value and the path value of each data factor are equal to the correlation value of the data factor and other data factors on the time value, the space value and the path value plus the basic value of the data factor on the time value, the space value and the path value;
the basic value is a common factor of the values of the data factors in time value, space value and path value at each historical time point.
Optionally, the data factor is a recombination degree of freedom function, and the calculation method includes:
s41, extracting the associated value relation of the same data factor and the time, space and path three-dimensional values of other data factors at each historical time point from the data factor dynamic data value map;
s42, analyzing the correlation value relationship between the same data factor and other data factors in three dimensional values of time, space and path at each historical time point to obtain all non-null correlation value relationships in the correlation value relationship between the same data factor and other data factors in three dimensional values of time, space and path at each historical time point, and marking the non-null correlation value relationships;
s43, combining the data factor and other data factors, and combining the non-null association value relationship in three dimensions of time, space and path at each historical time point and the corresponding association value into a non-null association value set;
s44, regarding all the data factors and other data factors, and regarding the non-null associated value sets corresponding to the non-null associated values in the associated values of three dimensions of time, space and path at each historical time point as recombination freedom degree sets of the data factors at each historical time point;
and S45, extracting a historical data factor recombination track function by using the recombination freedom set of the data factor at each historical time point to obtain a data factor recombination freedom function.
Optionally, the data factor circulation degree of freedom function, the calculation method includes:
s45, judging whether time cost, data timeliness, economic cost, resource cost, data content, data reliability, data technology, data category, data positioning, data association, data path and data source of the data factor in three dimensions of time, space and path at each historical time point are all directionally supplied to a certain user or can only be applied to a certain specific scene;
if so, the data factor has no circulation freedom; if not, marking a plurality of user requirements and a plurality of application scenes corresponding to each item of the data factor in three dimensions of time, space and path at each historical time point to obtain the self circulation freedom degree of the data factor;
s46, extracting other data factors with recombination freedom degrees of the data factors at each historical time point by using the recombination freedom degree sets of the data factors at each historical time point;
s47, judging the self circulation freedom degree of the data factor at each historical time point, matching the self circulation freedom degree of the data factor at the same historical time point and the self circulation freedom degree of other data factors with recombination freedom degrees;
calculating the correlation value of the data factor and other data factors in the specific dimension with recombination freedom degrees of the data factors and other data factors;
combining the self circulation freedom degree of the data factor at each historical time point, the self circulation freedom degree of other data factors having a recombination freedom degree relation with the self circulation freedom degree, the data factor and the recombination dimension correlation value of other data factors having a recombination freedom degree relation with the data factor into a circulation freedom degree set of the data factor at each historical time point;
and analyzing the circulation freedom set of the data factors at each historical time point, extracting a historical circulation track function of the data factors, and obtaining the circulation freedom function of the data factors.
Optionally, the processing is performed on the data factor reorganization degree of freedom function and the data factor circulation degree of freedom function to obtain a user demand function, and the method includes:
aiming at user requirements, the data products and data assets can be divided into customized data products and data assets determined by buyers and scenes and non-customized data products and data assets determined by buyers and scenes;
respectively determining user demand functions of different types of data products and data asset characteristics;
the user demand function is used as a demand and value target for data factor recombination and circulation;
customizing user demand functions of data products and data assets, which change over time, including specific demands of buyers on time value, space value, path value and determined application scenarios;
the user demand function of the non-customized data products and data assets changes with time, and the specific demands and the potential application scenes of the potential buyers are predicted according to the historical user demands and the overall market demands.
Optionally, the data factor recombination degree of freedom function and the data factor circulation degree of freedom function are processed to obtain a value maximization function, and the method includes:
processing the data factor recombination freedom function and the data factor circulation freedom function to construct a value maximization function;
the value maximization function is a demand and value target of data factor recombination and circulation;
if all data factors are contained in the data product and the data asset which need to be automatically generated, processing the values of all the data factors on the time value, the space value and the path value, removing the associated values between two repeated data factors in each subentry of the value, and then summing to obtain the value sum of the time value, the space value and the path value of all the data factors contained in the data product and the data asset;
the cost sum maximum is a cost maximization function.
Optionally, the user demand function and the value maximization function are processed to obtain a data factor reorganization scheme and a circulation scheme, and data products and assets are automatically generated and updated, wherein the method includes:
s61, screening the time value, the space value and the path value of each data factor by taking the user demand function as a screening condition, and reserving the data factors which accord with the user demand function in the time value, the space value and the path value;
s62, combining the data factors which accord with the user demand function in the time value, the space value and the path value to obtain the data factors which accord with the circulation freedom degree requirement;
s63, taking the value maximization function as a constraint target of relevance and relevance value between the data factor meeting the circulation freedom degree requirement and other data factors;
processing the data dynamic data value map and the relevance value between the data factor and other data factors to obtain a data factor recombination scheme which accords with a value maximization function and the requirement of circulation freedom;
the data factor reorganization scheme is automatically generated data products and data assets;
and S64, repeating S61, S62 and S63 when the data factor or the user requirement is changed, and obtaining the updated automatically generated data product and data asset.
Example two
Data products and assets are made up of data, the data types including numbers, text, and the like. Data products and data assets have value attributes and currency, and thus the data that constitutes them also has value attributes and currency. The data is used as data resources and data elements, and data products and data assets which have different modes, higher values, meet different scene requirements and have higher circulation can be formed through recombination of multiple data. In order to meet the characteristics and requirements of data products and data assets, such as uncertain scenes, large demand difference, unfixed modes, pursuit of high value and high circulation requirements, a data product and asset automatic generation and updating method based on free recombination and circulation of data factors of a value map is provided, and the method comprises the following steps: extracting and establishing data factors with recombination and circulation freedom degrees and value attributes, constructing a historical dynamic data value map of the data factors, establishing data factor recombination and circulation freedom degree functions on the basis of the value map, constructing user demand and value maximization functions as demand and value targets of data factor recombination and circulation, further determining a data factor recombination and circulation scheme, automatically generating data products and assets, and realizing automatic updating of the data products and the assets.
(1) Extracting and establishing data factors with recombination and circulation freedom degrees and value attributes
Suppose the smallest unit of data a (n) in the data repository that is meaningfully non-separable (n =1,2, \8230;, n). According to the value property, a minimum value unit v ({ a (n ') }) is constructed by aggregating partial minimum data units (n' is the partial code set in n). When the minimum worth unit v ({ a (n') }) has at least two or more minimum worth units with the others
The least valuable unit v ({ a (n') }) is said to have the freedom to recombine, when the conditions for recombination to form value are present. The minimum value unit v ({ a (n ') }) is said to have a circulation degree of freedom when the value represented by the minimum value unit v ({ a (n') }) is not targeted to a certain user and is not applicable only to a certain scenario. The least valued unit v ({ a (n') }) with the reorganization and circulation freedom is called the data factor. Data of non-data factors cannot be recombined and can only be used for targeted provisioning or specific scenario applications.
(2) Construction of historical dynamic data value map of data factor
Values are marked for data factor v ({ a (n ') }) from three dimensions, time value tv (v ({ a (n') })), space value sv (v ({ a (n ') })), path value pv (v ({ a (n') })). Among them, the time value tv (v ({ a (n ') })) includes a time cost tc (v ({ a (n ') })) and a data age da (v ({ a (n ') })). The space value sv (v ({ a (n ') })) includes economic cost ec (v ({ a (n') })), resource cost rc (v ({ a (n ') })), data content dc (v ({ a (n') })), data reliability dr (v ({ a (n ') })), data technology dt (v ({ a (n') })). The path value pv (v ({ a (n ') })) includes a data category dc (v ({ a (n') })), a data location dl (v ({ a (n ') })), a data association da (v ({ a (n') })), a data path dp (v ({ a (n ') })), and a data source ds (v ({ a (n') })).
The data factor v ({ a (n') }) and other data factors are excavated by adopting a method of data value association mining, such as clustering, a grey association analysis method, an Apriori algorithm and the like
An associated value relationship RVT (tv (v ({ a (n ') })))) over a time value tv (v ({ a (n') })),
an associated value relationship RVS (sv (v ({ a (n ') })))) in the space value sv (v ({ a (n') })),
an associated value RVP (pv (v ({ a (n ') })))) on the path value pv (v ({ a (n') }))),
a time-value relationship RVT (tv (V ({ a (n ') })) for each data factor V ({ a (n ') })) can be derived from the time value, space value, and path value V (V ({ a (n ') })) = (VT (V ({ a (n ') })), VS (V ({ a (n ') })), VP (V ({ a (n ')))))))) for each data factor V ({ a (n ') }),
the coefficient of influence on value kt (RVT (tv (v ({ a (n') })),
spatial value relationship RVS (sv ({ a (n') })), sv
Value-influencing factor ks (RVS (sv (v ({ a (n') })),
the path-value relationship RVP (pv (v ({ a (n') })),
the coefficient of influence on value kp (RVP (pv (v ({ a (n'))))))))),
the formula is found as follows:
where N is the total number of data factors, and VT0 (v ({ a (N ') })), VS0 (v ({ a (N') })), VP0 (v ({ a (N ') })) is the underlying value of the data factor v ({ a (N') }) in terms of time value, space value, and path value. These base value values are a common factor of the value of the data factor v ({ a (n') }) in three dimensions at various time points in history, and are usually obtained by a factor analysis method.
The influence coefficient kt (RVT (tv ({ a (n ') })) of the correlation between the time value, the space value and the path value of each data factor v ({ a (n') }) on the value,
ks(RVS(sv(v({a(n’)}))，
kp(RVP(pv(v({a(n’)}))，
the values are multiplied by the time value tv (v ({ a (n ') })), the space value sv (v ({ a (n') }) and the path value pv (v ({ a (n ') })) corresponding to each data factor v ({ a (n') }), to obtain the data factor v ({ a (n ') }) and other data factors v (v) (a (n') })
The correlation value RVDT ((tv (v ({ a (n'))))))))))),
the relevance value in terms of spatial value RVDS ((sv (v ({ a (n')))))))))),
the correlation value RVDP in path value ((pv (v ({ a (n') })),
the time value, the space value and the path value of each data factor v ({ a (n ') }) are used as the three-dimensional coordinates of each data factor v ({ a (n ') }), and each data factor v ({ a (n ') }) and other data factors are marked
The time value, the space value and the path value of the data factor value map VM { v ({ a (n ') }), v (n') }) are constructed according to the correlation value{-a(n’)}))}。
The data factor value map constructed at a certain time point is obtained through the steps. And repeating the steps at each historical time point, constructing a data factor value map at each historical time point, and obtaining a data factor historical dynamic data value map VMT { v ({ a (n ') }), v ({ -a (n') })), t } (t =1,2, \8230;, n) according to a time clue.
(3) Establishing data factor reorganization and circulation freedom function
(1) Establishing a data factor reorganization degree of freedom function
From the data factor historical dynamic data value map VMT v ({ a (n') }),
t } the same data factor v ({ a (n') }) is extracted at various historical time points with other data factors v (n }){-a(n’)}) A correlation value relationship RVT (tv (v ({ a (n'))))))))))),
the relevance value relationship RVS (sv (v ({ a (n')))))))))),
relevance value relationship on path value RVP (pv (v ({ a (n'))))))))),
analyzing the above associated value relationship RVT (tv (v ({ a (n') })) in time value at each historical time point,
the relevance value relationship RVS (sv (v ({ a (n')))))))))),
relevance value relationship in Path value RVP (pv (v ({ a (n') })),
extracting data factors v ({ a (n') }) and other data factors at various historical time points
Marking all non-null associated value relations among the associated value relations on the time value, the space value and the path value as RVT p (tv(v({a(n’)}))，
RVS p (sv(v({a(n’)}))，
RVP p (pv(v({a(n’)}))，
And obtaining the data factor v ({ a (n') }) corresponding to the non-null association value relationship and other data factors at each historical time point
The correlation value RVDT on the time value of p ((tv(v({a(n’)}))，
Relevance value RVDS on space value p ((sv(v({a(n’)}))，
Relevance value RVDP on Path value p ((pv(v({a(n’)}))，
The data factor v ({ a (n') }) is compared with other data factors
The non-null associated value relationship among the time value, the space value and the path value at each historical time point and the corresponding associated value set are a non-null associated value set { (RVTp (v ({ a (n') })),
RVDTp((tv(v({a(n’)}))，
(RVSp(sv(v({a(n’)}))，sv(v({-a(n’)})),t)，RVDSp((sv(v({a(n’)}))，
(RVPp(pv(v({a(n’)}))，pv(v({-a(n’)})),t)，RVDPp((pv(v({a(n’)}))，pv(v({-a(n’)}))),t)))}。
judging the data factor v ({ a (n ') }) and other data factors according to the definition of the recombination degree of freedom of the data factor v ({ a (n') })
The non-null associated value set of time value, space value, path value at each historical time point, and all data factors v ({ a (n') }) and other data factors
The correlation value RVDTp in time value, space value and path value at each historical time point ((tv (v ({ a (n'))))))))),
RVDSp((sv(v({a(n’)}))，
RVDPp((pv(v({a(n’)}))，
the set of non-null associated value set elements corresponding to the associated value with non-null time value, space value and path value is called as a recombination degree of freedom set (RVTpo (tv ({ a (n ') })) of a data factor v ({ a (n') }) at each historical time point,
RVDTpo((tv(v({a(n’)}))，
(RVSpo(sv(v({a(n’)}))，
RVDSpo((sv(v({a(n’)}))，
(RVPp(pv(v({a(n’)}))，
RVDPpo((pv(v({a(n’)}))，
according to the recombination freedom degree set of the data factor v ({ a (n') }) at each historical time point, a numerical analysis method, a data mining method, a factor analysis method and other methods are adopted to extract a historical recombination track function of the data factor, and the freedom degree range of the future potential recombination of the data factor is determined.
(2) Establishing a data factor circulation degree of freedom function
It is determined whether the time cost tc (v ({ a (n ') })), the data age da (v ({ a (n') })), the economic cost ec (v ({ a (n ') })), the resource cost rc (v ({ a (n') })), the data content dc (v ({ a (n ') })), the data reliability dr (v ({ a (n') })), the data technical dt (v ({ a (n ') })), the data category dc (v ({ a (n') })), the data location dl (v ({ a (n ')), the data association da (v ({ a (n') })), the data pathway (v) }) ({ a (n ')), the data source (v) }) or the data cost pv ({ a (n')), at each historical time point, is applicable to a specific user orientation or a specific user orientation. If so, the data factor v ({ a (n') }) is said to have no flow-through freedom. Otherwise, a plurality of user demands and a plurality of application scenarios corresponding to each of time values tv (v ({ a (n ') })), space values sv (v ({ a (n') })), and path values pv (v ({ a (n ') })) of the data factor v ({ a (n') }) at each historical time point are marked, and the user demands and the application scenarios are called as self circulation degrees of freedom DFC (dfct (tv ({ a (n ') }))), DFCs (sv (a ({ n') })), dfcv (pv ({ a (n ') })), t) of the data factor v ({ a (n') }).
Extracting other data factors with recombination freedom degrees at the historical time points with the data factor v ({ a (n ') }) according to the recombination freedom degree set of the data factor v ({ a (n') }) at the historical time points
Judging the other data factors according to the previous step
Self-circulation freedom at various historical time points. Self-flux freedom degrees matching data factor v ({ a (n ') }) at the same historical time point, self-flux freedom degrees DFC (dfct (tv (v ({ -a (n ') }))) of other data factors v ({ -a (n ') })) with which there is a recombination freedom degree,
and according to the data factor v ({ a (n') }) and these other data factors
Associated values in temporal value RVDTp ((tv (v ({ a (n ') })), tv (v ({ -a (n') }))), t), associated values in spatial value RVDSp ((sv (v ({ a (n ') })), sv (v ({ -a (n') })), t), associated values in path value RVDPp ((pv (v ({ a (n ') })), pv ({ -a (n')))), t) with reorganization freedom. The self-circulation freedom degree of the data factor v ({ a (n ') }) at various historical time points is heavily communicated with the data factor v ({ a (n') }) at various historical time pointsSelf-circulation freedom of other data factors v ({ -a (n ') }) in group-degree-of-freedom relationship, data factor v ({ a (n') }) and other data factors having a reorganization freedom relationship therewith
The set is the flow-through freedom set { DFC (dfct (tv ({ a (n ') }))), DFCs (sv (v ({ a (n') }))), dfcv (pv ({ a (n ') }))), t) of the data factor v ({ a (n') }) at various time points in the history,
dfcv(pv(v({-a(n’)}))))，t)，RVDTp((tv(v({a(n’)}))，
RVDSp((sv(v({a(n’)}))，
RVDPp((pv(v({a(n’)}))，
and analyzing the circulation freedom degree set of the data factor v ({ a (n') }) at each historical time point by adopting a numerical analysis method, a data mining method, a factor analysis method and other methods, extracting a historical circulation track function of the data factor, and determining the freedom degree range of the future potential circulation of the data factor.
(4) Constructing a user demand and value maximization function as a demand and value target of data factor recombination and circulation
Due to the data resources and the data elements, data products and data assets with different modes, higher value, accordance with different scene requirements and higher circulation can be formed through the recombination of multiple data.
And establishing a recombination and circulation freedom degree function of the data factor v ({ a (n') }), and constructing a user demand and value maximization function as a demand and value target of data factor recombination and circulation on the basis of the two functions.
(1) Building user demand functions
Data products and data assets can be divided into two categories for user needs, namely, buyer and scenario specific customized data products and data assets and buyer and scenario specific non-customized data products and data assets.
The user requirements for the customized data products and data assets may change over time, and thus the user requirement target is time-dependent, labeled (UN (tg, sg, pg), SR, t). UN is the specific requirement of the buyer, tg, sg and pg are the specific requirements of the buyer in three dimensions of time, space and path, and SR is the determined application scenario.
The user demand of the non-customized data products and data assets is also a function of time, and the specific demand UNm (tg (PB (m)), sg (PB (m)), pg (PB (m))) and the potential application scenario SR (c) (c =1,2, 8230; n) of the potential buyers PB (m) (m =1,2, \ 8230;, n) are predicted according to the historical user demand and the overall demand of the market by adopting methods such as data mining, predictive analysis, time series analysis, causal relation prediction and the like.
(2) Constructing a value maximization function
Assuming that the data products and data assets to be automatically generated are DPAs, all the data factors contained therein are assumed to be vDPA ({ a (n ') }), and the association relationship value between these data factors can be expressed according to step 2), so as to obtain the cost functions VT (vDPA ({ a (n ') })) in three dimensions, VS (vDPA ({ a (n ') })), VP (vDPA ({ a (n ') })) in three dimensions of all the data factors vDPA ({ a (n ') }). The associated merit values between the two repeated data factors in each of the components of the merit function are redundantly removed and summed to obtain the three-dimensional sum of the merit values of all the data factors vDPA ({ a (n') }) included in the data product and data asset DPA. This maximum sum is the value maximization objective.
(5) Determining data factor reorganization and circulation scheme, and automatically generating or updating data products and assets
The user demand and value maximization function is equivalent to the constraint conditions of the future potential recombination freedom degree and the future potential circulation freedom degree of the data factor v ({ a (n') }), and further narrows the range of the future potential recombination freedom degree and the future potential circulation freedom degree.
The user demand function is used as a condition for screening the value of three dimensions of each data factor, the data factors meeting the user demand function in three dimensions are reserved according to the actual user demand target, and the data factors meeting the circulation freedom degree requirement are determined for the data factor set meeting the screening of the value of the three dimensions of the data factors.
And taking the value maximization function as a constraint target of the relevance and the relevance value between the data factor meeting the circulation freedom requirement and other data factors. And according to the data factor value map and the relevance value between the data factor and other data factors, solving a data factor recombination scheme which maximizes the value function and meets the requirement of circulation freedom. The data factor reorganization scheme is the automatically generated data product and data asset. When the data factor or the user requirement is changed, the steps are repeated, and the data product and the data asset which are automatically generated after updating can be obtained.
Through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, wherein the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc-Read-Only Memory (CD-ROM) or other Memory capable of storing data, a magnetic tape, or any other computer-readable medium capable of storing data.
Finally, it should be noted that: the method for automatically generating and updating data products and assets disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention, and is only used for illustrating the technical solutions of the present invention, not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for automatically generating and updating data products and assets, the method comprising:
s1, acquiring a data product and asset data resource library; the data product and asset data repository is composed of data and has value attributes and liquidity;
the value attribute is the actual value of the data product and the data asset in three dimensions of time, space and path, which accords with the interest of the demand side;
the currency is a social currency of data products and data assets for their value to be sought and realized;
s2, dividing the data product and asset data resource library to obtain the smallest irreparable data unit;
combining the minimum data units which cannot be split according to the value attributes to obtain minimum value units;
processing the minimum value unit to obtain a data factor with a recombination degree of freedom, a circulation degree of freedom and a value attribute;
s3, processing the data factor to obtain a data factor dynamic data value map;
s4, processing the data factor dynamic data value map to obtain a data factor recombination freedom function and a data factor circulation freedom function;
s5, processing the data factor recombination freedom function and the data factor circulation freedom function to obtain a user demand function and a value maximization function;
and S6, processing the user demand function and the value maximization function to obtain a data factor recombination scheme and a circulation scheme, and automatically generating and updating data products and assets.
2. The method of claim 1, wherein said processing of said least valued units results in data factors having reorganization degrees of freedom, circulation degrees of freedom, and value attributes, the method comprising:
when the minimum value unit has the condition of forming value by recombining with at least two other minimum value units, the minimum value unit is called to have the recombination freedom degree;
when the value represented by the minimum value unit is not directionally supplied to a certain user and can not be only applied to a certain specific scene, the minimum value unit is called to have circulation freedom;
the least valuable unit with the reorganization freedom and the circulation freedom is a data factor.
3. The method of claim 1 for automatically generating and updating data products and assets, wherein the processing of the data factors to obtain a data factor dynamic data value graph comprises:
s31, carrying out value marking on the data factors from three dimensions of time, space and path;
s32, mining the association value relation between the data factor and other data factors in three dimensions of time, space and path by using a data value association mining method;
s33, processing the time, space and path three-dimensional values of the data factors to obtain the influence coefficients of the correlation value relationship of the data factors on the time, space and path three-dimensional values;
influence coefficients of the correlation value relations of the data factors in the three dimensions of time, space and path on the three dimensional values of the time, space and path are multiplied by the three dimensional values of the time, space and path corresponding to other data factors respectively to obtain the correlation values of the data factors and the other data factors in the three dimensions of time, space and path;
s34, taking the three-dimensional values of time, space and path of each data factor as the three-dimensional coordinates of each data factor, and marking the associated values of each data factor and other data factors in the three dimensions of time, space and path to obtain a data factor value map of the data factor at a certain time point;
and S35, repeating the steps S31, S32, S33 and S34 at each historical time point to obtain a data factor dynamic data value map.
4. The method of claim 3, wherein the time value includes time cost and data age;
the space value comprises economic cost, resource cost, data content, data reliability and data technology;
the path value comprises a data category, a data positioning, a data association, a data path and a data source.
5. The method of claim 3, wherein the computing method comprises the steps of:
the time value, the space value and the path value of each data factor are equal to the correlation value of the data factor and other data factors on the time value, the space value and the path value plus the basic value of the data factor on the time value, the space value and the path value;
the basic value is a common factor of values of the data factors in time value, space value and path value at various historical time points.
6. The method of claim 1, wherein the data factors reorganize the degree of freedom function, the calculation method comprising:
s41, extracting the associated value relation of the same data factor with the time, space and path values of other data factors at each historical time point from the data factor dynamic data value map;
s42, analyzing the correlation value relationship between the same data factor and the three dimensional values of time, space and path of other data factors at each historical time point to obtain all non-null correlation value relationships among the correlation value relationships between the same data factor and the other data factors at the three dimensional values of time, space and path at each historical time point, and marking the non-null correlation value relationships;
s43, combining the data factor and other data factors, and the non-null associated value relation in three dimensions of time, space and path at each historical time point and the corresponding associated value into a non-null associated value set;
s44, regarding all the data factors and other data factors as non-empty associated value sets corresponding to non-empty associated values in the associated values of three dimensions of time, space and path at each historical time point, and referring to the non-empty associated value sets as recombination freedom degree sets of the data factors at each historical time point;
and S45, extracting a historical data factor recombination track function by using the recombination freedom set of the data factor at each historical time point to obtain a data factor recombination freedom function.
7. The method of claim 1, wherein the data factor currency freedom function is calculated by:
s46, judging whether time cost, data timeliness, economic cost, resource cost, data content, data reliability, data technology, data category, data positioning, data association, data path and data source of the data factor in three dimensions of time, space and path at each historical time point are all directionally supplied to a certain user or can only be applied to a certain specific scene;
if so, the data factor has no circulation freedom; if not, marking a plurality of user requirements and a plurality of application scenes corresponding to each item in three dimensions of time, space and path of the data factor at each historical time point to obtain the self circulation freedom degree of the data factor;
s47, extracting other data factors with the recombination freedom degrees of the data factors at the historical time points by using the recombination freedom degree sets of the data factors at the historical time points;
s48, judging the self circulation freedom degree of the data factor at each historical time point, matching the self circulation freedom degree of the data factor at the same historical time point and the self circulation freedom degree of other data factors with recombination freedom degrees;
calculating the correlation value of the data factor and other data factors in the specific dimension with recombination freedom degrees of the data factors and other data factors;
combining the self circulation freedom degree of the data factor at each historical time point, the self circulation freedom degree of other data factors having a recombination freedom degree relation with the self circulation freedom degree, the data factor and the recombination dimension correlation value of other data factors having a recombination freedom degree relation with the data factor into a circulation freedom degree set of the data factor at each historical time point;
and analyzing the circulation freedom set of the data factors at each historical time point, extracting a historical circulation track function of the data factors, and obtaining the circulation freedom function of the data factors.
8. The method of claim 1, wherein the processing of the data factor reorganization degree of freedom function and the data factor currency degree of freedom function to obtain a user demand function comprises:
aiming at user requirements, the data products and data assets can be divided into customized data products and data assets determined by buyers and scenes and non-customized data products and data assets determined by buyers and scenes;
respectively determining user demand functions of different types of data products and data asset characteristics;
the user demand function is used as a demand and value target for data factor recombination and circulation;
customizing user demand functions of data products and data assets, which change over time, including specific demands of buyers on time value, space value, path value and determined application scenarios;
the user demand function of the non-customized data products and data assets changes with time, and the specific demands and the potential application scenes of the potential buyers are predicted according to the historical user demands and the overall market demands.
9. The method of claim 1, wherein the processing of the data factor reorganization degree of freedom function and the data factor currency degree of freedom function to obtain a cost maximization function comprises:
processing the data factor recombination freedom function and the data factor circulation freedom function to construct a value maximization function;
the value maximization function is a demand and value target of data factor recombination and circulation;
if all data factors are contained in the data products and the data assets which need to be automatically generated, processing the values of all the data factors on time value, space value and path value, removing the associated values between two repeated data factors in each subentry of the value, and summing to obtain the value sum of the time value, the space value and the path value of all the data factors contained in the data products and the data assets;
the cost sum maximum is a cost maximization function.
10. The method of claim 1, wherein the user demand function and the cost maximization function are processed to obtain a data factor reorganization scheme and a circulation scheme, and the method of automatically generating and updating data products and assets comprises:
s61, taking the user demand function as a screening condition, screening the time value, the space value and the path value of each data factor, and reserving the data factors which accord with the user demand function in the time value, the space value and the path value;
s62, combining the data factors which accord with the user demand function in the time value, the space value and the path value to obtain the data factors which accord with the circulation freedom degree requirement;
s63, taking the value maximization function as a constraint target of relevance and relevance value between the data factor meeting the circulation freedom degree requirement and other data factors;
processing the data dynamic data value map and the relevance value between the data factor and other data factors to obtain a data factor recombination scheme which accords with a value maximization function and the requirement of circulation freedom;
the data factor reorganization scheme is automatically generated data products and data assets;
and S64, repeating S61, S62 and S63 when the data factor or the user requirement is changed, and obtaining the updated automatically generated data product and data asset.
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| US20190019119A1 (en) * | 2017-07-17 | 2019-01-17 | Theodore J Lucas | System for automated resource set multi-factor risk analysis |
| US20200242697A1 (en) * | 2019-01-25 | 2020-07-30 | Franklin Advisers, Inc. | Systems and methods for dynamic fund allocation in goals-based wealth management portfolio |
| CN111178940A (en) * | 2019-12-11 | 2020-05-19 | 北京明略软件系统有限公司 | Method and system for automatically generating sales call technology map |
| KR102270683B1 (en) * | 2019-12-23 | 2021-06-29 | 주식회사 현대케피코 | Engine ignition timing efficiency determination method |
| CN112308173A (en) * | 2020-12-28 | 2021-02-02 | 平安科技（深圳）有限公司 | Multi-target object evaluation method based on multi-evaluation factor fusion and related equipment thereof |
| CN113643078A (en) * | 2021-10-14 | 2021-11-12 | 北京华宜信科技有限公司 | Block chain-based information value marking method, device, equipment and medium |
| CN113886484A (en) * | 2021-10-26 | 2022-01-04 | 北京华宜信科技有限公司 | Method for automatically generating and updating scientific and technological achievement evaluation report based on block chain |
| CN114490644A (en) * | 2022-01-10 | 2022-05-13 | 阿里云计算有限公司 | Data storage method, device and storage medium |
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