CN110659926A - Data value evaluation system and method - Google Patents

Data value evaluation system and method Download PDF

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CN110659926A
CN110659926A CN201810712917.2A CN201810712917A CN110659926A CN 110659926 A CN110659926 A CN 110659926A CN 201810712917 A CN201810712917 A CN 201810712917A CN 110659926 A CN110659926 A CN 110659926A
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段立新
吴燕飞
阮亚芬
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Guoxin Youe Data Co Ltd
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Abstract

The application provides a data value evaluation system and a method, wherein the data value evaluation system comprises: the data acquisition unit is used for acquiring data to be evaluated; the self-value determining unit is used for determining the self-value of the data to be evaluated according to the index value of the data to be evaluated under the self-value index of preset data; the market competition value determining unit is used for determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition values of the similar data; and the value evaluation unit is used for evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated. The data value evaluation system is slightly influenced by human subjective factors in the evaluation process of the data value, and the evaluation result of the service data can be objectively and accurately obtained.

Description

Data value evaluation system and method
Technical Field
The application relates to the technical field of data evaluation, in particular to a data value evaluation system and a data value evaluation method.
Background
Today, as digital information is rapidly developed, the influence of data on enterprises is increasing, and more enterprises need to speak by data. The intangible assets occupy more and more for enterprises, and the importance of the intangible assets such as business data is not a little different except intangible assets such as intellectual property rights such as patents, software copyrights, trademarks and the like. The value of business data sometimes directly determines the value of an enterprise.
When evaluating the value of the business data, the evaluation is generally performed based on the business data; in the prior art, an evaluation service of service data is provided for implementing evaluation of service data. The service data evaluation service provider is mainly an asset evaluation organization; when business data evaluation is carried out, a person to be evaluated needs to be in contact with an asset evaluation mechanism, and both parties communicate evaluation conditions on the spot; after the assessment conditions are closed, the person to be assessed provides the business data to the asset assessment organization, and then the asset assessment expert of the asset assessment organization assesses the business data according to a certain assessment process. Due to the evaluation mode, the evaluation result is not objective and accurate enough because the evaluation process is influenced by human subjective factors.
Disclosure of Invention
In view of this, an object of the embodiments of the present application is to provide a data value evaluation system and method, which are less affected by human subjective factors during an evaluation process and can obtain an evaluation result of service data more objectively and accurately.
In a first aspect, an embodiment of the present application provides a data value evaluation system, where the system includes:
the data acquisition unit is used for acquiring data to be evaluated;
the self-value determining unit is used for determining the self-value of the data to be evaluated according to the index value of the data to be evaluated under the self-value index of preset data;
the market competition value determining unit is used for determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition values of the similar data;
and the value evaluation unit is used for evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated.
In a second aspect, an embodiment of the present application further provides a data value evaluation method, where the method includes:
acquiring data to be evaluated;
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data;
determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition value of the similar data;
and evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated.
According to the method and the device for evaluating the data, the self value of the data to be evaluated is determined according to the index value of the data to be evaluated under the preset data self value index, the market competition value of the data to be evaluated is determined according to the attribute of the data similar to the data to be evaluated and the market competition value of the similar data, and finally the value of the data to be evaluated is evaluated according to the self value and/or the market competition value of the data to be evaluated. The quality of the service data can be determined more objectively and accurately without human intervention in the whole process.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a schematic structural diagram of a data value evaluation system provided by an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating another data value evaluation system provided by an embodiment of the present application;
FIG. 3 is a flow chart of a data value evaluation method provided by an embodiment of the present application;
fig. 4 shows a schematic structural diagram of a computer device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Different from the prior art, in the embodiment of the application, when the data value of the business data is evaluated, the business data (the data to be evaluated in the embodiment of the application) is obtained through the data obtaining unit, the index value of the business data under the preset data value index is determined through the self value determining unit, the market competition value of the data to be evaluated is determined through the market competition value determining unit according to the attribute of the data similar to the data to be evaluated and the market competition value of the similar data, then the value of the data to be evaluated is evaluated through the value evaluating unit according to the self value and/or the market competition value of the data to be evaluated, and the value of the business data can be determined more objectively and accurately without manual intervention in the whole process.
Meanwhile, because human intervention is not needed, the possibility of contact between the business data and people is reduced, the possibility of human leakage of the business data is reduced, and the safety of the business data in the evaluation process is improved.
For the understanding of the present embodiment, the data value evaluation system disclosed in the embodiment of the present application will be described in detail first. It should be noted that the data value evaluation system can determine the value of other data, such as test data, home data, etc., in addition to business data. The technical solution of the present application is described below by taking data to be evaluated as service data as an example.
Referring to fig. 1, a data value evaluation system provided in an embodiment of the present application includes: a data acquisition unit 10, a self-value determination unit 20, a market competition value determination unit 30, and a value evaluation unit 40.
I: a data obtaining unit 10, configured to obtain data to be evaluated.
Here, the data to be evaluated is business data to be subjected to data value evaluation. The data to be evaluated can be acquired in various ways, such as business data crawled from a preset platform, wherein the preset platform comprises an enterprise website, a statistical bureau, a data transaction platform, a button platform and the like; or the data to be evaluated is directly provided by a data source with data evaluation requirements.
Preferably, the object of each implementation of the embodiment of the present application may be a type of data, and if the type of data includes a plurality of data sets, the data value evaluation object of the embodiment of the present application may be one data set.
II: and the self-value determining unit 20 is configured to determine the self-value of the data to be evaluated according to the index value of the data to be evaluated under the self-value index of preset data.
When the method is specifically implemented, the preset data value index comprises one or more of a data consistency index, a data integrity index, a data timeliness index, an information redundancy index, a data scarcity index, a data volume index, a data field classification index, an application scene index and a rights and interests index.
The self-value determining unit 20 in the embodiment of the application is specifically configured to determine index values of data to be evaluated under each preset data self-value index according to the following methods 1 to 9.
1, aiming at the condition that the value indexes of the data comprise data consistency indexes, the data to be evaluated comprises: data content and description information corresponding to the data to be evaluated;
the self-value determining unit 20 is specifically configured to determine a degree of consistency between data content included in the data to be evaluated and description information corresponding to the data to be evaluated; determining an index value of the data to be evaluated under the data consistency index based on the consistency degree, wherein the higher the consistency degree is, the higher the index value representing the data to be evaluated under the data consistency index is;
in a specific implementation, the consistency degree of the data content and the description information of the data to be evaluated can be represented by determining a consistency process between one or more items of data content and the corresponding description information, wherein the higher the consistency degree between any item of data content and the corresponding description information is, the higher the index value of the data consistency index representing the data to be evaluated is.
One is as follows: the data volume contained in the data to be evaluated and the data volume described by the description information of the data to be evaluated.
Here, the data content of the data to be evaluated is carried in a file of a certain format; the data to be evaluated can be composed of a plurality of data entries, and each data entry is composed of a plurality of data elements; where the data elements are the most basic data units that make up the data to be evaluated.
For example, when the data to be evaluated is commodity price data, data elements included in one piece of data to be evaluated are as follows in sequence: commodity name, commodity manufacturer, production place, production time, shelf life, net content, nutrient components, production batch number and sale time.
That is to say, the data to be evaluated is preferably in the form of data entries, and for the case where the data with evaluation requirements is text data, the key information of the text data can be extracted in advance before evaluation, so as to generate the data in the form of data entries. For example: the data with the evaluation requirement is a commodity introduction text, can be extracted into a data entry form according to keywords such as commodity names, commodity manufacturers, production places, production time and the like before evaluation, and the extracted data entry is used as data to be evaluated.
For example, in the above example, the number of data elements included in a complete piece of data should be nine, and then the data amount corresponding to each data entry is 9; if the data to be evaluated includes 100 data entries, the data size that the data to be evaluated should have is 900, that is, the data size described by the description information is 900; however, in practice, there may be some data elements that are empty, and the empty data elements have no actual content, so that the actual data amount of the data to be evaluated is less than the description data amount.
Taking the number of data entries as an example, the number of data entries included in the data to be evaluated may also be compared with the number of data entries described by the description information of the data to be evaluated.
Therefore, the consistency degree of the data content and the description information of the data to be evaluated can be characterized by determining the consistency degree of the data quantity contained in the data to be evaluated and the data quantity described by the description information of the data to be evaluated.
The second step is as follows: the size of the data to be evaluated and the size of the description information of the data to be evaluated.
Here, the size of the data to be evaluated may be actually regarded as the file size of the file carrying the data to be evaluated. For example, the missing of a data element of a certain data entry (i.e. the data element is empty) may also cause inconsistency between the actual file size of the file data carrying the data to be evaluated and the size described in the description information.
Therefore, the consistency degree of the data content and the description information of the data to be evaluated can be characterized by determining the consistency degree of the size of the data to be evaluated and the size of the description information of the data to be evaluated.
And thirdly: and the data format of the data to be evaluated and the data format described by the description information of the data to be evaluated.
Here, the data format of the data to be evaluated may be a file format of a file carrying the data to be evaluated. The file format carrying the data to be evaluated may be different from the file format described by the description information.
Therefore, the consistency degree of the data content and the description information of the data to be evaluated can be characterized by determining the consistency degree of the data format of the data to be evaluated and the data format described by the description information of the data to be evaluated.
It should be noted that the data content included in the data to be evaluated may be, but is not necessarily limited to, data size, data format, and the like; the description information corresponding to the data to be evaluated is generally data for describing the data to be evaluated, and the description information corresponding to the data to be evaluated also includes contents such as data volume, size, data format and the like.
Specifically, the embodiment of the present application provides a specific method for determining an index value of data to be evaluated under a data consistency index based on a data amount, a data size, and a consistency degree of a data format, where the index value is determined by:
calculating a first difference absolute value of the data quantity contained in the data to be evaluated and the data quantity described by the description information of the data to be evaluated, calculating a second difference absolute value of the size of the data to be evaluated and the size of the description information of the data to be evaluated, if the data format of the data to be evaluated is consistent with the data format described by the description information of the data to be evaluated, determining the consistency of the data to be evaluated as a first preset value, otherwise, determining the consistency as a second preset value, and calculating an index value of a data consistency index according to the first difference absolute value, the second difference absolute value and the consistency.
Here, the first preset value may be set to 0, and the second preset value may be set to 1. Optionally, the first preset value and the second preset value may be set to other values, and a value satisfying the second preset value is larger than a value satisfying the first preset value.
Specifically, the first difference absolute value L1 satisfies: l1 ═ La-Lm|；
Wherein L isaIs the amount of data contained in the data to be evaluated, LmFor the description of the data to be evaluatedThe amount of data contained in the information.
The second difference absolute value L2 satisfies: l2 ═ Sa-Sm|；
Wherein S isaFor the size of the data to be evaluated, SmThe size of the description information for the data to be evaluated.
Then the index value omega of the data to be evaluated under the data consistency index1Satisfies the following conditions:
Figure BDA0001717034480000061
alpha is a calculation coefficient and can take a value between 0 and 1, such as 1/3, 1/4, 1/2 and the like.
ω1The value range is generally [0,1]],ω1The larger the value, the higher the consistency degree of the data to be evaluated.
2. For the case where the data own value indicator comprises a data integrity indicator,
the self-value determining unit 20 is specifically configured to determine a duty ratio of a null value in a data entry included in the data to be evaluated; and determining an index value of the data to be evaluated under the data integrity index based on the duty ratio, wherein the lower the duty ratio is, the higher the data integrity of the data to be evaluated is represented.
In the implementation, the data elements of the data to be evaluated may be missing. In this case, the more data elements that are missing, the worse the integrity of the data to be evaluated.
When determining the empty value proportion in the data entry contained in the data to be evaluated, the self value determination unit 20: sequentially detecting whether data elements in each data entry in the data to be evaluated are empty; according to the detection result, carrying out integrity assignment on each data element to obtain an integrity value of each data element, wherein if the data element is empty, the corresponding integrity value is 0; if the data element is not null, the corresponding integrity value is 1; and taking the ratio of the sum of the integrity values of all the data elements to the number of the data elements as a null value ratio.
The duty ratio of the null value can be directly used as an index value of the data to be evaluated under the data integrity index, for example:
calculating an index value omega of the data to be evaluated under the data integrity index by adopting the following formula2：
Figure BDA0001717034480000071
Wherein, aiAnd N is the total number of the data elements in the data to be evaluated. For the case that the data is in table form, a can also be usediIs denoted by aijI.e. row i and column j data element aijWherein, the values of i and j range from 1 to N.
ω2Has a value range of [0,1]],ω2The larger the value, the better the data integrity of the data to be evaluated.
The index value of the data to be evaluated under the data integrity index can be determined based on the null value proportion based on the positive correlation relationship between the index value and the null value proportion of the data to be evaluated under the data integrity index.
In addition, the self-value determining unit 20 may further adopt the following steps when determining the empty value ratio in the data entry included in the data to be evaluated: counting the total number of the data elements which are empty in all the data entries in the data to be evaluated; and taking the ratio of the total number of the data elements which are empty in all the data entries and the total number of all the data elements in the data to be evaluated as the ratio of the empty values.
Further, the null value occupancy may also be the occupancy of invalid data entries in the data to be evaluated in the total number of data entries. Data entries for which there is a preset number of null data elements may be determined to be invalid data entries. Omega2Is the quotient of the invalid data entry and the total number of data entries.
3. For the case where the value assessment indicator comprises a data timeliness indicator,
the self-value determining unit 20 is specifically configured to determine a time interval spanned between the generation start time and the generation end time of the data to be evaluated, and a time difference between the generation start time of the data to be evaluated and the data to be evaluated provision time; determining an index value of the data to be evaluated under the data timeliness index based on the time interval and the time difference; the larger the time interval span is, the higher the index value representing the data to be evaluated under the data timeliness index is; and the smaller the time difference is, the higher the index value representing the data to be evaluated under the data timeliness index is.
The larger the time interval span is, the higher the index value of the data timeliness index representing the data to be evaluated is; and the smaller the time difference is, the higher the index value of the data timeliness index representing the data to be evaluated is.
In the specific implementation, the time interval spanned by the generation time of the data to be evaluated is the time interval spanned between the generation start time of the data to be evaluated and the generation termination time of the data to be evaluated. The unit of the time interval is specifically set according to the length of the time interval.
Particularly, when the generation starting time and the generation ending time of the data to be evaluated cannot be determined, the generation starting time and the generation ending time can be determined through the description information of the data to be evaluated; the generation time may be a start time and a final time in a time interval spanned by the data to be evaluated, or may be an average time, preferably a start time.
For example, if the length of the time interval is 1 day, the unit of the time interval is set to minutes; if the length of the time interval is 2 months, setting the unit of the time interval as a day; if the length of the time interval is 3 years, the unit of the time interval may be a week. It should be noted that the unit of the set time interval is only an example provided in the embodiment of the present application, and it should not be taken as a limitation to the technical solution of the present application.
The data providing time refers to the time when the data obtaining module 10 of the data value evaluation system obtains the data to be evaluated. It should be noted here that, because the data to be evaluated has a certain data amount, the data obtaining module cannot actually obtain all the data to be evaluated from scratch at a certain time point, and therefore, the data providing time may be a starting time when the data obtaining module 10 obtains the data to be evaluated, or may be an ending time when the data obtaining module 10 obtains the data to be evaluated; in addition, since the data obtaining module 10 transmits the data to be evaluated to the index determining module 20 for processing in a short time after obtaining the data to be evaluated, and the time difference between the starting time or the ending time of obtaining the data to be evaluated by the data obtaining module 10 and the current time of determining the index value under the timeliness index by the value evaluating module 20 is very small, the current time of determining the index value under the timeliness index by the value evaluating module 20 to the data to be evaluated can also be used as the data providing time.
For example, 100 data entries are included in the data to be evaluated; of the 100 data entries, the generation time (i.e., the generation start time of the data to be evaluated) at which the oldest data entry is generated is 3/15 in 2018; the generation time of the data entry with the latest generation time (i.e., the generation termination time of the data to be evaluated) is 2018, 4 and 17 days; the time span spanned by the time of generation of the data to be evaluated is 33 days. If the data to be evaluated is provided for 5, month and 10 days in 2018, the time difference between the data generation time to be evaluated and the data provision time to be evaluated is the time difference between 3, month and 15 days in 2018 and 5, month and 10 days in 2018.
When the index value of the data to be evaluated under the data timeliness index is determined based on the time interval and the time difference, the ratio of the time interval and the time difference can be used as the index value of the data to be evaluated under the data timeliness index.
For example, the index value ω of the data to be evaluated under the timeliness index can be calculated by adopting the following formula3：
Figure BDA0001717034480000091
TfGenerating time for the data to be evaluated, and if the data to be evaluated can not determine the final time, using the corresponding description of the data to be evaluatedThe final time of the message; t issGenerating starting time for the data to be evaluated, and if the data to be evaluated can not determine the generating starting time, using the generating starting time of the description information corresponding to the data to be evaluated; t isnThe time of provision of the data to be evaluated.
ω3The value range is [0,1]],ω3The larger the value is, the stronger the timeliness of the data to be evaluated is.
4. For the case that the data self-value index comprises a data redundancy index,
the self-value determining unit 20 is specifically configured to determine a proportion of duplicate entries in data entries included in the data to be evaluated; and determining an index value of the data to be evaluated under the data redundancy index based on the occupation ratio of the repeated entries, wherein the lower the occupation ratio of the repeated entries is, the lower the data redundancy representing the data to be evaluated is.
In particular implementations, data redundancy is the rate at which duplicate data is calculated to appear. In one data set, the repeated data becomes data redundancy, and the higher the information redundancy is, the lower the data quality is.
Specifically, the self-value determining unit 20 may determine the index value of the data to be evaluated under the data redundancy index in any one of the following manners:
one is as follows: counting the repeated occurrence times of each data entry in the data to be evaluated according to the data element included in each data entry; determining the repeated occurrence ratio of the data items, namely the repeated occurrence ratio of the repeated items according to the repeated occurrence times of all the data items in the data to be evaluated and the total number of the data items; i.e. the proportion of duplicate entries in the data entries contained in the data to be evaluated. Calculating a value evaluation value of the data to be evaluated under the information redundancy index based on the repeated occurrence rate of the data items; and the value evaluation value of the data to be evaluated under the information redundancy index and the repeated occurrence rate of the data items form negative correlation.
When counting the repeated occurrence times of each data entry in the data to be evaluated, sequentially detecting whether each data entry appears at the front edge according to the arrangement sequence of the data entries; the contents of the data elements in the two identical data entries are completely consistent, or the number of the data elements with consistent contents or similar contents reaches a preset threshold value. If the ith data entry is detected and appears for the first time, the statistical number is unchanged; if the ith data entry is not the first occurrence, the statistical number is increased by 1.
The second step is as follows: the self-value determining unit 20 sequentially detects whether each data entry in the data to be evaluated is a repeated data entry; and performing repeated assignment on each data item according to the detection result to obtain a repeated value corresponding to each data item. If the data entry is a data entry which appears repeatedly, namely another data entry which is the same as the current data entry is detected before the current data entry is detected, the corresponding repeatability value is 1; if the data entry is not a repeated data entry, that is, before the current data entry is detected, another data entry which is the same as the current data entry is not detected, the corresponding repeatability value is 0, and the ratio of the sum of the repeatability values of all the data entries to the number of the data entries is used as the proportion of repeated entries in the data entries contained in the data to be evaluated.
For example, ω of the data to be evaluated under the data redundancy index can be calculated by the following formula4Index value omega4：
Figure BDA0001717034480000101
Wherein, biThe repeatability value of the ith data entry in the data to be evaluated is shown, and N is the total number of the data entries in the data to be evaluated.
ω4The value range is [0,1]],ω4The larger the value, the less data repeatability of the data to be evaluated, and the higher the corresponding data value.
For example, the data to be evaluated includes 5 data entries, respectivelya. b, c, d and e, wherein a, b and e are the same, c and d are the same, and whether each data item is a repeated data item is detected from a to e in sequence; a first occurrence with a repeatability value of 0; b and a are the same and are data items which repeatedly appear, so that the repeatability value of b is 1, and the repeatability value of c which appears for the first time is 0; d and c are the same and are repeated data items, and the repeatability value of the data items is 1; e is the same as a, is a repeated data entry, the repeatability value of the repeated data entry is 1, and the proportion of the repeated entry in the data entry contained in the finally obtained data to be evaluated is 0.6. From the above formula, it can be known that the index value ω of the finally obtained specified data under the data redundancy index4Is 0.4.
5. For the case that the data self value index comprises a data scarcity index,
the self-value determining unit 20 is specifically configured to determine the occurrence times of the data to be evaluated and similar data similar to the data to be evaluated on a preset platform; and determining an index value of the data to be evaluated under the data scarcity index based on the occurrence number, wherein the less the occurrence number is, the higher the scarcity of the data to be evaluated is represented.
In order to determine similar data similar to the data to be evaluated in the specific implementation, referring to fig. 2, the embodiment of the present application further includes: a first similar data determining unit 50.
At this time, the data acquiring unit is further configured to: crawling a plurality of data sets from a preset platform;
the first similar data determining unit 50 may determine similar data for the data to be evaluated in two ways:
one is as follows: the first similar data determining unit 50 is configured to analyze the data to be evaluated and the multiple data sets, and determine the vocabulary characteristics of the data to be evaluated and the data sets; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; determining a data set with text similarity reaching a preset similarity threshold as similar data of the data to be evaluated;
specifically, the scarcity is the degree of scarcity of the data calculated according to the collected preset platform and the providing condition of the data information of the platform for the same type of data; the more homogeneous data, the lower the scarcity; the less homogeneous data, the higher the scarcity; the higher the scarcity of the data to be evaluated, the higher the quality and value of the data.
In the specific implementation, the preset platform can be a data transaction platform or other data platforms; taking the data transaction platform as an example, each data transaction corresponds to at least one type of business data to be transacted. When crawling a data set from a preset platform, crawling one data set for each data transaction; each data set includes a plurality of data entries.
When the first similar data determining unit 50 performs data crawling, the data set may be crawled through technologies such as a crawler and a crawling tool, which is not limited in this application.
In a specific implementation, the first similar data determining unit 50 may determine the data to be evaluated and the lexical characteristics of each data set by:
performing word segmentation processing on each acquired data set to obtain first vocabulary data after word segmentation processing; screening out a preset number of first vocabulary data according to the sequence of the frequency of appearance of each first vocabulary data after word segmentation in a corresponding data set from high to low, and determining the vocabulary characteristics of each data according to the frequency of appearance of each screened first vocabulary data in the data set aiming at each data of the data set.
Performing word segmentation on the data to be evaluated to obtain second vocabulary data after word segmentation; screening out a preset number of second vocabulary data according to the sequence of the occurrence frequency of each second vocabulary data after word segmentation in the data to be evaluated from high to low, and determining the vocabulary characteristics of the data according to the occurrence frequency of each screened second vocabulary data in the data to be evaluated aiming at each data in the data to be evaluated.
And calculating the text similarity between the vocabulary features in each data set and the vocabulary features in the data to be evaluated respectively aiming at each vocabulary feature in each data set. And determining the data set with the text similarity larger than or equal to a preset similarity threshold as the similar data of the data to be evaluated.
Furthermore, under the condition that a plurality of characteristic vocabularies are determined according to the data to be evaluated and the data set, for each characteristic vocabulary of the data to be evaluated, text similarity comparison can be performed between the characteristic vocabulary and each characteristic vocabulary of the data set, the characteristic vocabulary with the similarity reaching a first preset similarity threshold is determined as the similar vocabulary of the characteristic vocabulary, and when the number of the similar vocabularies reaches a second preset threshold, the data to be evaluated and the data set are determined as the similar data.
Further, for the situation that the data to be evaluated and the data set have marked industry tags, the industry tags can also be directly used as the feature words of the corresponding data, and the feature words are directly subjected to similarity comparison.
The second step is as follows: a first similar data determining unit 50, configured to determine attribute tag information of the data to be evaluated and the multiple data sets; respectively determining the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set; and determining the attribute label similarity as the similar data of the data to be evaluated according to the data set with the attribute label similarity meeting a preset attribute similarity threshold.
Here, the process of determining the similar data of the data to be evaluated by the first similar data determining unit 50 is similar to the process of determining the similar data of the data to be evaluated by the second similar data determining unit 70 described below, and thus, the description thereof is omitted.
After the first similar data determining unit 50 determines similar data of the data to be evaluated from the plurality of crawled data sets, an index value of the data to be evaluated under the data scarcity index is determined according to the number of times of appearance of the similar data on a preset platform.
Specifically, the index value of the data to be evaluated under the scarcity index can be calculated by adopting the following steps:
determining the number of data sets of the similar data similar to the data to be evaluated;
calculating index values of the data to be evaluated under the scarcity index based on the total number of the crawled data sets and the number of the data sets of the similar data similar to the data to be evaluated;
for example, the index value omega of the data to be evaluated under the data scarcity index is calculated by the following formula5：
Figure BDA0001717034480000131
Wherein x is the occurrence frequency of the data to be evaluated and similar data of the data to be evaluated on a preset platform, and y is the total number of the crawled data sets.
ω5Has a value range of [0,1]]When ω is5Close to 1, the more similar data occurs, indicating that the less scarcity the data under evaluation, ω5The closer to 0, the less similar data indicating the data to be evaluated appears, the higher the scarcity of the data to be evaluated.
In addition, the index value omega of the data to be evaluated under the data scarcity index can be calculated by adopting the following formula5：
ω5＝1-e-x/y
Wherein x is the occurrence frequency of the data to be evaluated and the similar data of the data to be evaluated on the preset platform, and y is the total number of the preset platform.
ω5Has a value range of [0,1]]When ω is5Close to 1, it shows that similar data exist in each preset platform, and the lower the scarcity of the data to be evaluated is, omega5And if the number is equal to 0, the preset platforms do not have similar data, and the scarcity of the data to be evaluated is higher.
6. For the case where the data own value index includes a data amount index,
the self-value determining unit 20 is specifically configured to determine a data amount included in the data to be evaluated; determining an index value of the data to be evaluated under the data quantity index based on the data quantity, wherein the larger the data quantity is, the higher the index value representing the data to be evaluated under the data quantity index is;
in the specific implementation, the index value of the data to be evaluated under the data volume index can be determined by any one of the following two methods:
firstly, the ratio of the calculated data amount of the data to be evaluated to the total data amount of the data of each preset platform can be used as an index value of the data amount index, or the data amount of the data to be evaluated can be directly used as the index value of the data amount index, and the index value can be determined according to actual situations.
For example, when the ratio of the data amount of the data to be evaluated to the total data amount of the data of each preset platform is used as the index value of the data amount index, the index value ω of the data amount index may be calculated by using the following formula6：
And N is the data volume of the data in the data to be evaluated, and P is the total data volume of the data of each preset platform.
ω6Is a value of [0,1]When ω is6When the value is 0, the data size of the data to be evaluated is small, and conversely, the data size is large.
Secondly, based on the committed data volume carried in the description information of the data to be evaluated and the data volume described by the description information; the data volume of the data to be evaluated and the similar data volume similar to the data to be evaluated, which is obtained by carrying out data acquisition on the data of the preset platform, are obtained, and the index value of the data to be evaluated under the data volume index is calculated.
The committed data volume is the data volume of the data to be evaluated which is expected to be provided when the user provides the data to be evaluated.
The data volume included in the data to be evaluated is the data volume of the valid data elements included in the data to be evaluated.
The data acquisition of the data of the preset platform is carried out to obtain similar data quantity similar to the data to be evaluated, and the acquisition process of the data to be evaluated is similar to the acquisition process of the similar data when the index value of the data to be evaluated under the data scarcity index is determined. The specific process is as follows:
the data acquisition unit 10 crawls a plurality of data sets from the preset platform; the first similar data determining unit 50 is configured to analyze the data to be evaluated and the multiple data sets, and determine the vocabulary characteristics of the data to be evaluated and the data sets; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; determining a data set with text similarity reaching a preset similarity threshold as similar data of the data to be evaluated; and performing data quantity determination operation on the determined similar data so as to obtain the similar data quantity similar to the data to be evaluated.
Specifically, the index value of the data to be evaluated under the data amount index can be calculated by adopting the following formula:
Figure BDA0001717034480000151
wherein m represents the data volume contained in the data to be evaluated; n is a radical of1Representing similar data quantity similar to the data to be evaluated, obtained by data acquisition of the data of a preset platform; n is a radical of2Data described by the representation description information; n is a radical of3Representing the committed data volume.
7. For the case where the data value indicators themselves include data domain classification indicators,
the self-value determining unit 20 is specifically configured to determine a ratio of the number of industry domain tags corresponding to the data set to which the data to be evaluated belongs to the number of industry domain tags corresponding to the data category to which the data to be evaluated belongs; and determining an index value of the domain classification index of the data to be evaluated based on the ratio, wherein the larger the ratio is, the larger the index value of the domain classification index representing the data to be evaluated is.
In the specific implementation, the industry field label is a classification to which the data to be evaluated can belong when the data to be evaluated is classified in the industry field to which the data to be evaluated belongs. For example, the data to be evaluated is sales data of a certain product, including related attributes of the product, such as name, size, usage, price, manufacturer related information and other attributes; the system also comprises buyer information for purchasing the product, such as the name of the buyer, the time for purchasing the product, a payment account number, a payment amount, a delivery address, a contact phone and the like; also included are after-market information for the product, such as warranty time, associated repairs during the warranty period, and the like. The attribute name may be used as an attribute tag for the corresponding data.
The ratio of the number of the industry domain tags corresponding to the data set to which the data to be evaluated belongs to the number of the industry domain tags corresponding to the data category to which the data to be evaluated belongs is the ratio of the number of the tags of the data to be evaluated to the number of the total tags of the industry to which the data to be evaluated belongs.
For example, the index value ω of the data to be evaluated under the data field classification index is calculated by the following formula7：
Figure BDA0001717034480000152
Wherein m refers to the number of industry field tags corresponding to a data set to which data to be evaluated belongs; and n is the number of the industry field labels corresponding to the data category to which the data to be evaluated belongs.
8. For the case where the data value index itself includes an application scenario index,
the self-value determining unit 20 is specifically configured to determine an index value of the data to be evaluated under an application scene index according to the number of scenes in which the data to be evaluated can be applied; and the more scenes that the data to be evaluated can be applied, the higher the index value representing the data to be evaluated under the application scene index is.
In the specific implementation, the more the number of the scenes in which the data to be evaluated can be applied is, the higher the value of the data to be evaluated is proved to be. Therefore, the index value of the data to be evaluated under the application scene index can be determined according to the number of the scenes in which the data to be evaluated can be applied.
For example, the following formula is adopted to calculate the index of the data to be evaluated under the application scene indexValue omega8：
ω8＝s。
Wherein s refers to the number of scenes to which the data to be evaluated can be applied.
9. For the case where the data own value indicator includes an equity property indicator,
the self-value determining unit 20 is specifically configured to determine, according to the accessibility of the data to be evaluated, an index value of the data to be evaluated under the equity property index; and the usability of the data to be evaluated is the index value of the data to be evaluated under the equity index when the data to be evaluated can be traded, and is higher than the index value of the data to be evaluated under the equity index when the data to be evaluated is not traded.
Here, the accessibility of the data to be evaluated means whether the data to be evaluated is allowed to be traded; when the evaluation data of the current generation is not allowed to trade, the evaluation data cannot be directly converted into market competition value, and therefore the value of the data to be evaluated is influenced to a certain extent.
For example, the index value ω of the data to be evaluated under the equity property index is calculated by the following formula9：
Figure BDA0001717034480000161
Wherein k is1Is an index value when the exchangeability of the data to be evaluated is tradable; k is a radical of2Is an index value when the non-tradable nature of the data to be evaluated is tradable. And k is1Greater than k2。
When determining the self-value of the data to be evaluated according to the index value of the data to be evaluated under the self-value index of the preset data, the self-value determining unit 20 may perform weighted summation processing on the index value of the data to be evaluated under the self-value index of the preset data according to the weight coefficient of the self-value index of the preset data, so as to obtain the self-value of the data to be evaluated.
Here, the process of performing weighted summation processing on the index values of the data to be evaluated under the value index of the preset data is actually a process of determining the value evaluation result of the data to be evaluated according to different data quantity indexes and different quality influence degrees of the data to be evaluated.
The weighting coefficients corresponding to different types of data to be evaluated may be the same or different.
For example, for the case that the value evaluation index includes a data consistency index, a data integrity index, an information redundancy index, a data timeliness index, a data scarcity index, a data volume index, a data field classification index, an application scenario index, and a rights and interests index, the value P of the data to be evaluated can be calculated according to the following formula1：
Figure BDA0001717034480000171
Wherein, a1To a9And sequentially obtaining weight coefficients corresponding to a data consistency index, a data integrity index, an information redundancy index, a data timeliness index, a data scarcity index, a data volume index, a data field classification index, an application scene index and a rights and interests index. Omega1To omega9The index values respectively correspond to a data consistency index, a data integrity index, an information redundancy index, a data timeliness index, a data scarcity index, a data quantity index, a data field classification index, an application scene index and a rights and interests index in sequence.
And secondly, calculating the value of the data to be evaluated according to a self value regression model obtained by pre-training.
At this time, referring to fig. 2, the system further includes: a model training unit 60, configured to obtain an intrinsic value regression model through the following training method:
constructing an evaluation model for evaluating the value of the user; the evaluation model takes the value index of the data to be evaluated in the preset data as an explanatory variable, and takes the value of the data to be evaluated as an explained variable;
acquiring a training data set; the training dataset comprises: a plurality of groups of training evaluation data and self values corresponding to each group of training evaluation data;
acquiring index values of each group of training evaluation data under the value indexes of the preset data;
and taking the index value of each group of training evaluation data under the self-value index of the preset data as the value of the interpretation variable, substituting the self-value corresponding to each group of training evaluation data as the value of the interpreted variable into the evaluation model, training the evaluation model, and taking the trained evaluation model as the self-value regression model.
In specific implementation, when the model training module constructs an evaluation model for evaluating the value of the model training module, it is necessary to determine an explanatory variable and an explained variable in the model, and determine a link between the explanatory variable and the explained variable through a subsequent model training process. And if the factors influencing the value of the data to be evaluated are several, taking the several factors as corresponding data value indexes, taking the data quality coordinate as an explanatory variable, and taking the value evaluation result of the data to be evaluated as the explained variable to construct an evaluation model.
In the embodiment of the present application, the constructed model includes but is not limited to: the system comprises an autoregressive model, a moving average model, an autoregressive moving average model, an integrated moving average autoregressive model and a generalized autoregressive conditional variance model.
After the evaluation model is constructed, the evaluation model is trained. Training data used in training can be acquired through a data acquisition module; here, it should be noted that the acquired training data may be data that has been subjected to value evaluation or data that has not been subjected to value evaluation.
And for the data which is subjected to value evaluation, the index determining module is not required to perform value evaluation on the data. For data which is not subjected to value evaluation, an index determining module is required to perform value evaluation on the data, and an index value of the data under a preset data value index and a value evaluation result of training data are obtained.
Here, the evaluation result of the value of the training data may be a grade of the data quality or a score of the data quality, and may be specifically set according to an actual requirement.
Specifically, when the value evaluation method provided by the embodiment of the application is used to determine the value evaluation result of the data to be evaluated, if the value evaluation result of the data to be evaluated is a score, the result of performing weighted summation processing on the index value of the data to be evaluated under the preset data value index can be directly used as the score, the value range of the score is [0,1], the result of processing the result of the weighted summation can also be used as the score, and for example, the value obtained by multiplying the result of the weighted summation by 100 is used as the score of the quality of the data to be evaluated. If the value evaluation result of the data to be evaluated is a grade, the index value of the data to be evaluated under the value index of the preset data can be subjected to weighted summation processing according to a preset conversion rule, and the result is converted into a corresponding grade.
For example, 5 levels, A, B, C, D, E respectively, are set, and the quality of the data to be evaluated corresponding to a is lower than the value of the data to be evaluated corresponding to E. And the smaller the result of the weighted summation processing of the index values of the data to be evaluated under the value indexes of the preset data per se is, the lower the grade is. The value ranges of the results of the weighted summation processing respectively corresponding to the grades A-E are as follows: [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), [0.8, 1 ]. Based on the value range, the result of the weighting process can be converted into the grade corresponding to the data to be evaluated.
The model is trained by using the training data, namely, the model parameters are continuously adjusted according to the index values of the training data and the corresponding value evaluation results, so that when the model calculates the value evaluation results based on the index values of each training data under the preset data quality, the calculated value evaluation results are consistent with the value evaluation results corresponding to the training data.
III: market competition value determination unit 30: and the method is used for determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition value of the similar data.
In the specific implementation, similar data similar to the data to be evaluated is acquired in order to determine the market competition value of the data to be evaluated.
In order to obtain similar data similar to the data to be evaluated, the data obtaining unit 10 is further configured to: a plurality of data sets is crawled from a pre-set platform.
Here, the preset platform may be a data transaction platform, or may be another data platform; taking the data transaction platform as an example, each data transaction corresponds to at least one type of business data to be transacted. When crawling a data set from a preset platform, crawling one data set for each data transaction; each data set includes a plurality of data entries.
Here, the plurality of data sets crawled by the data acquisition unit 10 may be the same as or different from the data crawled by the data acquisition unit 10 in the case where the value index of the data itself includes the data scarcity index.
Referring to fig. 2, the data value evaluation system provided in the embodiment of the present application further includes: the second similar data determining unit 70.
The second similar data determining unit 70 is configured to determine similar data of the data to be evaluated by any one of the following two ways:
one is as follows: a second similar data determining unit 70, configured to determine attribute tag information of the data to be evaluated and the multiple data sets; respectively determining the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set; determining the attribute label similarity as the similar data of the data to be evaluated according to the data set with the attribute label similarity meeting a preset attribute similarity threshold;
the attribute tag information may be already possessed by the data to be evaluated and the plurality of data sets, or may be added to the data to be evaluated and the plurality of data sets immediately according to the attributes of the data to be evaluated and the plurality of data sets. Taking the data to be evaluated as an example, the attribute tag information is used for identifying the attribute of the data to be evaluated, and the attribute of the data with higher similarity is more similar, so that the similarity between the data to be evaluated and each data set can be characterized through the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set.
Therefore, the data set with the similarity meeting the preset attribute similarity threshold can be determined as the similar data of the data to be evaluated.
Specifically, the second similar data determining unit 70 is specifically configured to determine the similarity between the data to be evaluated and the attribute tag information of any data set according to the following steps:
acquiring the quantity of the same attribute tags in the attribute tags of the data to be evaluated and the attribute tags of any data set; and the total number of unrepeated attribute tags in the attribute tags of the data to be evaluated and the attribute tags of any data set;
and determining the similarity of the data to be evaluated and the attribute tag information of any data set according to the number of the same attribute tags and the total number, wherein the similarity of the attribute tag information representing the data to be evaluated and any data set is larger when the number of the same attribute tags is larger.
Specifically, the following formula can be adopted to calculate the similarity S of the attribute tag information of the data to be evaluated and the ith data seti：
Wherein x isiThe number of the attribute tags representing the data to be evaluated and the number of the same attribute tags in the attribute tags of any data set; y isiAnd the total number of the attribute labels which represent the data to be evaluated and do not overlap in the attribute labels of any data set. SiIs between 0 and 1.
The second step is as follows: a second similar data determining unit 70, configured to analyze the data to be evaluated and the multiple data sets, respectively, and determine the vocabulary characteristics of the data to be evaluated and each data set; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; and determining the data set with the text similarity reaching a preset similarity threshold as the similar data of the data to be evaluated. For a detailed description, reference may be made to relevant sections of the first similar data determining unit 50, which are not described herein.
Aiming at the condition that the data to be evaluated and the data set have marked industry labels, the industry labels can also be directly used as the feature words of the corresponding data, and the feature words are directly subjected to similarity comparison.
After determining the similar data of the data to be evaluated, the market competition value determining unit 30 can determine the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition values of the similar data.
Specifically, an embodiment of the present application further provides a specific method for determining a market competition value of the to-be-evaluated data according to an attribute of data similar to the to-be-evaluated data and the market competition value of the similar data, including:
and taking the similarity of the attribute label information between the data to be evaluated and the similar data as a corresponding similar data weight coefficient, carrying out weighted summation on the market competition value of the similar data, and taking the result of the weighted summation as the market competition value of the data to be evaluated.
Here, the market competition value of the similar data can be directly and correspondingly obtained when the data set is obtained, and at this time, the market competition value is an inherent attribute of the similar data, and for the similar data which has already been traded, the market competition value can be a specific actual trading value, a selling value proposed by a seller, a buying value proposed by a buyer, or an average value of the selling value proposed by the seller and the buying value proposed by the buyer; for similar data that is not transacted, the market competition value can be the sell value proposed by the seller, the buy value proposed by the buyer, or an average of the sell value proposed by the seller and the buy value proposed by the buyer.
When the market competition values of the similar data are weighted and summed according to the attribute tag information similarity between the data to be evaluated and the similar data as the corresponding similar data weight coefficient, the attribute tag information similarity between all the data to be evaluated and all the similar data can be normalized for convenience of calculation.
Specifically, the similarity of the attribute label information of the data to be processed and all similar data can be normalized through the following formula:
Figure BDA0001717034480000211
wherein v isiExpressing the result of normalization processing on the similarity of the attribute label information of the data to be processed and the ith similar data; m represents the number of similar data.
Then, the market competition value P of the data to be evaluated2Satisfies the following formula:
Figure BDA0001717034480000221
wherein in the formula, viExpressing the result of normalization processing on the similarity of the attribute label information of the data to be processed and the ith similar data; m represents the number of similar data. f. ofiIndicating the market competition value of the ith similar data.
IV: the value evaluation unit 40: and the evaluation module is used for evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated.
In the specific implementation, the value of the data to be evaluated can be evaluated through the value of the data to be evaluated and/or the market competition value.
Taking as an example that the value of the data to be evaluated can be evaluated through the self value and the market competition value:
and according to a preset value weight coefficient, carrying out weighted summation on the self value and the market competition value of the data to be evaluated, and determining the value of the data to be evaluated.
Here, the preset value weight coefficient may be obtained by using a method of expert scoring. The specific process of the expert scoring method is as follows:
(1) an expert is selected.
Here, the expert should be an expert having a deep understanding of the field to which the data to be evaluated belongs.
(2) Two factors that influence the evaluation price are determined: data value itself and market competition value.
(3) Providing background information to the expert to solicit the expert's opinion in an anonymous manner.
(4) And analyzing and summarizing the expert opinions, and feeding back the statistical result to the expert.
(5) And the expert corrects own opinions according to the feedback result.
(6) And forming a final analysis conclusion through multiple rounds of anonymous inquiry and opinion feedback.
After determining the weight coefficients of the self value and the market competitive value of the data to be evaluated by the expert scoring method, storing the weight coefficients corresponding to the self value and the market competitive value of the data to be evaluated respectively; then, according to the preset value weight coefficient, the self value and the market competition value of the data to be evaluated are subjected to weighted summation to determine the value of the data to be evaluated.
For example, the value P of the data to be evaluated satisfies the following formula:
P＝b1×P1+b2×P2。
wherein, b1And b2And the weight coefficients respectively correspond to the self value and the market competition value of the data to be evaluated. P1The value of the data to be evaluated is the value of the data to be evaluated; p2Is a market competitive value.
In the embodiment of the application, data to be evaluated are obtained through a data obtaining unit; determining the value of the data to be evaluated by a value determination unit according to the index value of the data to be evaluated under the value index of preset data; and determining the market competition value of the data to be evaluated through a market competition value determining unit according to the attributes of the data similar to the data to be evaluated and the market competition values of the similar data. And finally, evaluating the value of the data to be evaluated through a value evaluation unit according to the self value and/or the market competition value of the data to be evaluated. The quality of the service data can be determined more objectively and accurately without human intervention in the whole process.
Based on the same inventive concept, the embodiment of the present application further provides a data value evaluation method corresponding to the data value evaluation system, and as the principle of solving the problem of the method in the embodiment of the present application is similar to that of the system in the embodiment of the present application, the implementation of the apparatus can refer to the implementation of the method, and repeated details are not repeated.
Referring to fig. 3, the data value evaluation method provided by the embodiment of the present application includes:
s301: acquiring data to be evaluated;
s302: determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data;
s303: determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition value of the similar data;
s304: and evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated.
According to the method and the device for evaluating the data, the self value of the data to be evaluated is determined according to the index value of the data to be evaluated under the preset data self value index, the market competition value of the data to be evaluated is determined according to the attribute of the data similar to the data to be evaluated and the market competition value of the similar data, and finally the value of the data to be evaluated is evaluated according to the self value and/or the market competition value of the data to be evaluated. The quality of the service data can be determined more objectively and accurately without human intervention in the whole process.
Optionally, the data self-value index includes: one or more of a data consistency index, a data integrity index, an information redundancy index, a data timeliness index, a data scarcity index, a data volume index, a data field classification index, an application scenario index, and a rights and interests index.
Optionally, for a case that the data value index itself includes a data consistency index, the data to be evaluated includes: data content and description information corresponding to the data to be evaluated;
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps:
the consistency degree of the data content contained in the data to be evaluated and the corresponding description information of the data to be evaluated is determined; determining an index value of the data to be evaluated under the data consistency index based on the consistency degree, wherein the higher the consistency degree is, the higher the index value representing the data to be evaluated under the data consistency index is;
for the case where the data own value indicator comprises a data integrity indicator,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: determining the empty value ratio in the data entries contained in the data to be evaluated; determining an index value of the data to be evaluated under the data integrity index based on the duty ratio, wherein the lower the duty ratio is, the higher the data integrity of the data to be evaluated is represented;
for the case that the data self-value index comprises a data redundancy index,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: determining the proportion of repeated entries in data entries contained in the data to be evaluated; determining an index value of the data to be evaluated under the data redundancy index based on the proportion of the repeated entries, wherein the lower the proportion of the repeated entries is, the lower the data redundancy representing the data to be evaluated is;
for the case that the data self value index comprises a data timeliness index,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: determining a time interval spanned between the generation starting time and the generation ending time of the data to be evaluated and a time difference between the generation starting time of the data to be evaluated and the providing time of the data to be evaluated; determining an index value of the data to be evaluated under the data timeliness index based on the time interval and the time difference; the larger the time interval span is, the higher the index value representing the data to be evaluated under the data timeliness index is; and the smaller the time difference is, the higher the index value representing the data to be evaluated under the data timeliness index is;
for the case that the data self value index comprises a data scarcity index,
further comprising: crawling a plurality of data sets from a preset platform;
analyzing the data to be evaluated and the data sets respectively to determine the vocabulary characteristics of the data to be evaluated and the data sets; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; determining a data set with text similarity reaching a preset similarity threshold as similar data of the data to be evaluated; or
Determining attribute tag information of the data to be evaluated and the multiple data sets; respectively determining the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set; and determining the attribute label similarity as the similar data of the data to be evaluated according to the data set with the attribute label similarity meeting a preset attribute similarity threshold.
Determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: the data processing device is used for determining the occurrence frequency of the data to be evaluated and similar data similar to the data to be evaluated on a preset platform; determining an index value of the data to be evaluated under the data scarcity index based on the occurrence frequency, wherein the less the occurrence frequency is, the higher the scarcity of the data to be evaluated is represented;
for the case where the data own value index includes a data amount index,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: determining the data volume contained in the data to be evaluated; determining an index value of the data to be evaluated under the data quantity index based on the data quantity, wherein the larger the data quantity is, the higher the index value representing the data to be evaluated under the data quantity index is;
for the case where the data value indicators themselves include data domain classification indicators,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: the evaluation system is used for determining the ratio of the number of the industry field tags corresponding to the data set to which the data to be evaluated belongs to the number of the industry field tags corresponding to the data category to which the data to be evaluated belongs; determining an index value of the domain classification index of the data to be evaluated based on the ratio, wherein the larger the ratio is, the larger the index value of the domain classification index representing the data to be evaluated is;
for the case where the data value index itself includes an application scenario index,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: according to the number of scenes in which the data to be evaluated can be applied, determining an index value of the data to be evaluated under an application scene index; the more scenes in which the data to be evaluated can be applied, the higher the index value representing the data to be evaluated under the application scene index is;
for the case where the data own value indicator includes an equity property indicator,
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data, specifically comprising the following steps: according to the accessibility of the data to be evaluated, determining an index value of the data to be evaluated under the equity property index; and the usability of the data to be evaluated is the index value of the data to be evaluated under the equity index when the data to be evaluated can be traded, and is higher than the index value of the data to be evaluated under the equity index when the data to be evaluated is not traded.
Optionally, the determining the self-value of the data to be evaluated according to the index value of the data to be evaluated under the preset data self-value index specifically includes: and according to the weight coefficient of the self value index of the preset data, carrying out weighted summation processing on the index value of the self value index of the preset data of the data to be evaluated to obtain the self value of the data to be evaluated.
Optionally, the method further comprises: training by the following method to obtain an intrinsic value regression model:
constructing an evaluation model for evaluating the value of the user; the evaluation model takes the value index of the data to be evaluated in the preset data as an explanatory variable, and takes the value of the data to be evaluated as an explained variable;
acquiring a training data set; the training dataset comprises: a plurality of groups of training evaluation data and self values corresponding to each group of training evaluation data;
acquiring index values of each group of training evaluation data under the value indexes of the preset data;
and taking the index value of each group of training evaluation data under the self-value index of the preset data as the value of the interpretation variable, substituting the self-value corresponding to each group of training evaluation data as the value of the interpreted variable into the evaluation model, training the evaluation model, and taking the trained evaluation model as the self-value regression model.
Optionally, the method further comprises: crawling a plurality of data sets from a preset platform;
determining attribute tag information of the data to be evaluated and the multiple data sets; respectively determining the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set; determining the attribute label similarity as the similar data of the data to be evaluated according to the data set with the attribute label similarity meeting a preset attribute similarity threshold; or
Analyzing the data to be evaluated and the data sets respectively to determine the vocabulary characteristics of the data to be evaluated and the data sets; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; and determining the data set with the text similarity reaching a preset similarity threshold as the similar data of the data to be evaluated.
Optionally, determining the similarity of the attribute tag information of the data to be evaluated and any data set according to the following steps:
acquiring the quantity of the same attribute tags in the attribute tags of the data to be evaluated and the attribute tags of any data set; and the total number of unrepeated attribute tags in the attribute tags of the data to be evaluated and the attribute tags of any data set;
and determining the similarity of the data to be evaluated and the attribute tag information of any data set according to the number of the same attribute tags and the total number, wherein the similarity of the attribute tag information representing the data to be evaluated and any data set is larger when the number of the same attribute tags is larger.
Optionally, determining the market competition value of the data to be evaluated according to the attribute of the data similar to the data to be evaluated and the market competition value of the similar data, specifically including: and taking the similarity of the attribute label information between the data to be evaluated and the similar data as a corresponding similar data weight coefficient, carrying out weighted summation on the market competition value of the similar data, and taking the result of the weighted summation as the market competition value of the data to be evaluated.
Optionally, the evaluating the value of the data to be evaluated according to the value of the data to be evaluated and/or the market competition value includes: and according to a preset value weight coefficient, carrying out weighted summation on the self value and the market competition value of the data to be evaluated, and determining the value of the data to be evaluated.
Corresponding to the data value evaluation method in fig. 3, an embodiment of the present application further provides a computer device, as shown in fig. 4, the device includes a memory 1000, a processor 2000 and a computer program stored in the memory 1000 and executable on the processor 2000, wherein the processor 2000 implements the steps of the data value evaluation method when executing the computer program.
Specifically, the memory 1000 and the processor 2000 can be general memories and general processors, which are not specifically limited herein, and when the processor 2000 runs a computer program stored in the memory 1000, the data value evaluation method can be executed, so that the problem that an evaluation result is not objective and accurate due to human participation in a value evaluation process is solved, and an effect that the evaluation result of the service data can be objectively and accurately obtained due to small influence of human subjective factors in the evaluation process is achieved.
Corresponding to the data value evaluation method in fig. 1, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps of the data value evaluation method.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, and the like, and when a computer program on the storage medium is run, the data value evaluation method can be executed, so that the problem that an evaluation result is not objective and accurate due to human participation in a value evaluation process is solved, and the effects that the evaluation result is less influenced by human subjective factors in the evaluation process and can objectively and accurately obtain the evaluation result of the service data are achieved.
The computer program product of the data value evaluation system and method provided in the embodiments of the present application includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A data value evaluation system, the system comprising:
the data acquisition unit is used for acquiring data to be evaluated;
the self-value determining unit is used for determining the self-value of the data to be evaluated according to the index value of the data to be evaluated under the self-value index of preset data;
the market competition value determining unit is used for determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition values of the similar data;
and the value evaluation unit is used for evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated.
2. The system of claim 1, wherein the data worth indices include: one or more of a data consistency index, a data integrity index, an information redundancy index, a data timeliness index, a data scarcity index, a data volume index, a data field classification index, an application scenario index, and a rights and interests index.
3. The system of claim 2, wherein:
aiming at the condition that the value indexes of the data comprise data consistency indexes, the data to be evaluated comprise: data content and description information corresponding to the data to be evaluated;
the self-value determining unit is specifically configured to determine a degree of consistency between data content included in the data to be evaluated and description information corresponding to the data to be evaluated; determining an index value of the data to be evaluated under the data consistency index based on the consistency degree, wherein the higher the consistency degree is, the higher the index value representing the data to be evaluated under the data consistency index is;
for the case where the data own value indicator comprises a data integrity indicator,
the self-value determining unit is specifically configured to determine a duty ratio of a null value in a data entry included in the data to be evaluated; determining an index value of the data to be evaluated under the data integrity index based on the duty ratio, wherein the lower the duty ratio is, the higher the data integrity of the data to be evaluated is represented;
for the case that the data self-value index comprises a data redundancy index,
the self-value determining unit is specifically configured to determine a proportion of duplicate entries in data entries included in the data to be evaluated; determining an index value of the data to be evaluated under the data redundancy index based on the proportion of the repeated entries, wherein the lower the proportion of the repeated entries is, the lower the data redundancy representing the data to be evaluated is;
for the case that the data self value index comprises a data timeliness index,
the self-value determining unit is specifically configured to determine a time interval spanned between the generation start time and the generation end time of the data to be evaluated, and a time difference between the generation start time of the data to be evaluated and the data to be evaluated provision time; determining an index value of the data to be evaluated under the data timeliness index based on the time interval and the time difference; the larger the time interval span is, the higher the index value representing the data to be evaluated under the data timeliness index is; and the smaller the time difference is, the higher the index value representing the data to be evaluated under the data timeliness index is;
for the case that the data self value index comprises a data scarcity index,
the data acquisition unit is further configured to: crawling a plurality of data sets from a preset platform;
the system, still include: a first similar data determination unit;
the first similar data determining unit is used for analyzing the data to be evaluated and the plurality of data sets respectively and determining the vocabulary characteristics of the data to be evaluated and the data sets; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; determining a data set with text similarity reaching a preset similarity threshold as similar data of the data to be evaluated; or
Attribute tag information for determining the data to be evaluated and the plurality of data sets; respectively determining the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set; and determining the attribute label similarity as the similar data of the data to be evaluated according to the data set with the attribute label similarity meeting a preset attribute similarity threshold.
The self-value determining unit is specifically configured to determine the occurrence times of the data to be evaluated and similar data similar to the data to be evaluated on a preset platform; determining an index value of the data to be evaluated under the data scarcity index based on the occurrence frequency, wherein the less the occurrence frequency is, the higher the scarcity of the data to be evaluated is represented;
for the case where the data own value index includes a data amount index,
the self-value determining unit is specifically configured to determine a data amount included in the data to be evaluated; determining an index value of the data to be evaluated under the data quantity index based on the data quantity, wherein the larger the data quantity is, the higher the index value representing the data to be evaluated under the data quantity index is;
for the case where the data value indicators themselves include data domain classification indicators,
the self-value determining unit is specifically configured to determine a ratio of the number of industry domain tags corresponding to the data set to which the data to be evaluated belongs to the number of industry domain tags corresponding to the data category to which the data to be evaluated belongs; determining an index value of the domain classification index of the data to be evaluated based on the ratio, wherein the larger the ratio is, the larger the index value of the domain classification index representing the data to be evaluated is;
for the case where the data value index itself includes an application scenario index,
the self-value determining unit is specifically configured to determine an index value of the data to be evaluated under an application scene index according to the number of scenes in which the data to be evaluated can be applied; the more scenes in which the data to be evaluated can be applied, the higher the index value representing the data to be evaluated under the application scene index is;
for the case where the data own value indicator includes an equity property indicator,
the self-value determining unit is specifically configured to determine an index value of the data to be evaluated under the equity property index according to the accessibility of the data to be evaluated; and the usability of the data to be evaluated is the index value of the data to be evaluated under the equity index when the data to be evaluated can be traded, and is higher than the index value of the data to be evaluated under the equity index when the data to be evaluated is not traded.
4. The system according to any one of claims 1 to 3, wherein the self-value determining unit is specifically configured to perform weighted summation processing on the index value of the self-value index of the to-be-evaluated data in the preset data according to a weight coefficient of the self-value index of the preset data, so as to obtain the self-value of the to-be-evaluated data.
5. The system of any one of claims 1-3, further comprising:
the model training module is used for obtaining a self value regression model through the following training methods:
constructing an evaluation model for evaluating the value of the user; the evaluation model takes the value index of the data to be evaluated in the preset data as an explanatory variable, and takes the value of the data to be evaluated as an explained variable;
acquiring a training data set; the training dataset comprises: a plurality of groups of training evaluation data and self values corresponding to each group of training evaluation data;
acquiring index values of each group of training evaluation data under the value indexes of the preset data;
and taking the index value of each group of training evaluation data under the self-value index of the preset data as the value of the interpretation variable, substituting the self-value corresponding to each group of training evaluation data as the value of the interpreted variable into the evaluation model, training the evaluation model, and taking the trained evaluation model as the self-value regression model.
6. The system of claim 1, further comprising: a second similar data determination unit;
the data acquisition unit is further configured to: crawling a plurality of data sets from a preset platform;
the second similar data determining unit is used for determining the data to be evaluated and the attribute label information of the data sets; respectively determining the similarity between the attribute tag information of the data to be evaluated and the attribute tag information of each data set; determining the attribute label similarity as the similar data of the data to be evaluated according to the data set with the attribute label similarity meeting a preset attribute similarity threshold; or
The system comprises a plurality of data sets, a database and a database, wherein the data sets are used for storing data to be evaluated and a plurality of data sets; respectively carrying out text similarity matching on the vocabulary characteristics of the data to be evaluated and the vocabulary characteristics of each data set; and determining the data set with the text similarity reaching a preset similarity threshold as the similar data of the data to be evaluated.
7. The system according to claim 6, wherein the second similarity data determining unit is specifically configured to determine the similarity of the attribute label information of the data to be evaluated and any data set according to the following steps:
acquiring the quantity of the same attribute tags in the attribute tags of the data to be evaluated and the attribute tags of any data set; and the total number of unrepeated attribute tags in the attribute tags of the data to be evaluated and the attribute tags of any data set;
and determining the similarity of the data to be evaluated and the attribute tag information of any data set according to the number of the same attribute tags and the total number, wherein the similarity of the attribute tag information representing the data to be evaluated and any data set is larger when the number of the same attribute tags is larger.
8. The system according to any one of claims 1 and 6 to 7, wherein the market competition value determining unit is specifically configured to use attribute tag information similarity between the data to be evaluated and the similar data as a corresponding similar data weight coefficient, perform weighted summation on the market competition values of the similar data, and use a result of the weighted summation as the market competition value of the data to be evaluated.
9. The system of claim 1, wherein the value assessment unit is specifically configured to:
and according to a preset value weight coefficient, carrying out weighted summation on the self value and the market competition value of the data to be evaluated, and determining the value of the data to be evaluated.
10. A method for evaluating data value, the method comprising:
acquiring data to be evaluated;
determining the value of the data to be evaluated according to the index value of the data to be evaluated under the value index of the preset data;
determining the market competition value of the data to be evaluated according to the attributes of the data similar to the data to be evaluated and the market competition value of the similar data;
and evaluating the value of the data to be evaluated according to the self value and/or the market competition value of the data to be evaluated.
CN201810712917.2A 2018-06-29 2018-06-29 Data value evaluation system and method Pending CN110659926A (en)

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