CN108038735A - Data creation method and device - Google Patents
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Abstract
Embodiment of the present invention provides data creation method and device, is related to Computer Applied Technology field.Wherein, data creation method includes：Based on the transaction data of the target trade company collected, the characteristic value of the target trade company is counted；It is regular according to setting corresponding with the type of the characteristic value, determine the feature normalizing value of the characteristic value；The health degree score of the target trade company is generated according to the feature normalizing value.Method provided by the present invention generates according to feature normalizing value the health degree score of trade company according to the feature normalizing value of the definite characteristic value of setting rule corresponding with the type of trade company characteristic value, therefore, it is possible to realize the accurate health degree for portraying trade company.
Description
Technical Field
The present invention relates to the field of computer application technologies, and in particular, to a data generation method and apparatus.
Background
With the increasing popularity and development of the O2O (Online To Offline/Online To Offline) business model, more and more merchants lay out the business model through the O2O platform, for example, restaurant stores implement the O2O business model through an entrance hectometer takeout platform. Since the profits that merchants with different quality levels can bring to the platform are different, it is particularly important for the platform how to accurately characterize the quality levels of the merchants. In the field of merchant images, the quality level of merchants is generally measured by using an index of merchant health.
However, there is no technical solution for accurately generating the health degree of the merchant in the prior art.
Disclosure of Invention
In the existing solution, there is no technical scheme for accurately generating the health degree of the merchant.
In view of the above, embodiments of the present invention provide a data generation method and apparatus, so as to solve the above technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a data generation method.
Specifically, the method comprises the following steps:
counting the characteristic value of the target merchant based on the collected transaction data of the target merchant;
determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value;
and generating a health degree score of the target merchant according to the characteristic normalization value.
In the embodiment, the feature normalization value of the feature value is determined according to the setting rule corresponding to the type of the merchant feature value, and the health score of the merchant is generated according to the feature normalization value, so that the health degree of the merchant can be accurately depicted.
With reference to the first aspect, in some embodiments of the present invention, determining the feature normalization value of the feature value according to a setting rule corresponding to the type of the feature value includes:
if the characteristic value is a qualified characteristic value used for indicating the type of the merchant, acquiring a set numerical value corresponding to the type of the merchant indicated by the characteristic value, wherein the set numerical value is between 0 and 1;
and determining the characteristic normalization value according to the set numerical value.
With reference to the first aspect, in some embodiments of the present invention, determining the feature normalization value of the feature value according to a setting rule corresponding to the type of the feature value includes:
if the characteristic value is a positive correlation quantization characteristic value, sorting the elements in the characteristic value set containing the characteristic value in an ascending order;
and determining the characteristic normalization value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
With reference to the first aspect, in some embodiments of the present invention, determining the feature normalization value of the feature value according to a setting rule corresponding to the type of the feature value includes:
if the characteristic value is a negative correlation quantization characteristic value, sorting elements in the characteristic value set containing the characteristic value in a descending order;
and determining the characteristic normalization value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
With reference to the first aspect, in some embodiments of the invention, generating the health score of the target merchant from the feature normalization value comprises:
carrying out weighting processing on the characteristic normalization value;
generating a merchant score of the target merchant according to the weighted feature normalization value;
sorting the elements in the merchant score set containing the merchant scores in an ascending order;
and generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
In a second aspect, the embodiment of the invention provides a data generation device.
Specifically, the apparatus comprises:
the statistical module is used for counting the characteristic value of the target merchant based on the collected transaction data of the target merchant;
the determining module is used for determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value;
and the generating module is used for generating the health degree score of the target merchant according to the characteristic normalization value.
In the embodiment, the feature normalization value of the feature value is determined according to the setting rule corresponding to the type of the merchant feature value, and the health score of the merchant is generated according to the feature normalization value, so that the health degree of the merchant can be accurately depicted.
With reference to the second aspect, in some embodiments of the invention, the determining module comprises:
the acquiring unit is used for acquiring a set numerical value corresponding to the merchant type indicated by the characteristic value, wherein the set numerical value is between 0 and 1;
and the determining unit is used for determining the characteristic normalization value according to the set numerical value.
With reference to the second aspect, in some embodiments of the invention, the determining module comprises:
the first ascending sorting unit is used for ascending sorting the elements in the characteristic value set containing the characteristic value;
and the determining unit is used for determining the characteristic normalization value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
With reference to the second aspect, in some embodiments of the invention, the determining module comprises:
the descending sorting unit is used for carrying out descending sorting on elements in the characteristic value set containing the characteristic value;
and the determining unit is used for determining the characteristic normalization value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
With reference to the second aspect, in some embodiments of the invention, the generating module comprises:
the weighting processing unit is used for weighting the characteristic normalization value;
a first generating unit, configured to generate a merchant score of the target merchant according to the weighted feature normalization value;
the second ascending sorting unit is used for carrying out ascending sorting on elements in the merchant score set containing the merchant scores;
and the second generating unit is used for generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
These and other aspects of the invention will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the description below are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a data generation method according to method embodiment 1 of the present invention;
fig. 2 is a flow chart of a data generation method according to method embodiment 2 of the present invention;
fig. 3 is a flow chart of a data generation method according to method embodiment 3 of the present invention;
fig. 4 is a flow chart of a data generation method according to method embodiment 4 of the present invention;
fig. 5 is a flow chart of a data generation method according to method embodiment 5 of the present invention;
fig. 6 is a schematic configuration diagram of a data generating apparatus according to embodiment 1 of the present invention;
FIG. 7 illustrates one embodiment of the determination module 12 shown in FIG. 6;
FIG. 8 illustrates another embodiment of the determination module 12 shown in FIG. 6;
FIG. 9 illustrates yet another embodiment of the determination module 12 shown in FIG. 6;
FIG. 10 illustrates one embodiment of the generation module 13 shown in FIG. 6;
fig. 11 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
Various aspects of the invention are described in detail below with reference to the figures and the detailed description. Well-known processes, program modules, elements and their interconnections, links, communications or operations, among others, are not shown or described in detail herein in various embodiments of the invention.
Also, the described features, architectures, or functions may be combined in any manner in one or more embodiments.
Furthermore, it should be understood by those skilled in the art that the following embodiments are illustrative only and are not intended to limit the scope of the present invention. Those of skill would further appreciate that the program modules, elements, or steps of the various embodiments described herein and illustrated in the figures may be combined and designed in a wide variety of different configurations.
Technical terms not specifically described in the present specification should be construed in the broadest sense in the art unless otherwise specifically indicated.
In some of the flows described in the present specification and claims and in the above-described figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, the number of operations being labeled as S10, S11, etc., merely to distinguish between various operations, and the sequence number itself does not represent any order of execution. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
[ METHOD EMBODIMENT 1 ]
Fig. 1 is a flowchart of a data generation method according to embodiment 1 of the method of the present invention. Referring to fig. 1, in the present embodiment, the method includes:
s11: and counting the characteristic value of the target merchant based on the collected transaction data of the target merchant.
S12: and determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value.
The characteristic normalization value is a result obtained after normalization processing is carried out on the characteristic value.
S13: and generating a health degree score of the target merchant according to the characteristic normalization value.
In the embodiment, the feature normalization value of the feature value is determined according to the setting rule corresponding to the type of the merchant feature value, and the health score of the merchant is generated according to the feature normalization value, so that the health degree of the merchant can be accurately depicted.
[ METHOD EMBODIMENT 2 ]
Fig. 2 is a flowchart of a data generation method according to embodiment 2 of the method of the present invention. Referring to fig. 2, in the present embodiment, the method includes:
s21: and counting the characteristic value of the target merchant based on the collected transaction data of the target merchant.
S22: determining the feature value as a materialization feature value indicating a merchant type.
The materialized feature value refers to a feature value used for characterizing the merchant (for example, used for indicating the type of the merchant).
S23: and acquiring a set numerical value corresponding to the merchant type indicated by the characteristic value.
Wherein the set value is between 0 and 1.
S24: and determining a characteristic normalization value of the characteristic value according to the set numerical value.
For example, the set value is directly used as the characteristic normalization value. Of course, the set value may be subjected to an operation to obtain the characteristic normalization value.
S25: and generating a health degree score of the target merchant according to the characteristic normalization value.
[ METHOD EMBODIMENT 3 ]
Fig. 3 is a flowchart of a data generation method according to embodiment 3 of the method of the present invention. Referring to fig. 3, in the present embodiment, the method includes:
s31: and counting the characteristic value of the target merchant based on the collected transaction data of the target merchant.
S32: and determining the characteristic value as a positive correlation quantization characteristic value.
The positive correlation quantitative characteristic value is a characteristic value which is positively correlated with the health degree score of the commercial tenant and is used for describing the quantity attribute of the commercial tenant.
S33: and sorting the elements in the characteristic value set containing the characteristic value in an ascending order.
S34: and determining a characteristic normalization value of the characteristic value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
Wherein, the positive correlation normalization model is as follows: the input quantity and the output quantity are positively correlated, and the value of the output quantity is between 0 and 1.
S35: and generating a health degree score of the target merchant according to the characteristic normalization value.
[ METHOD EMBODIMENT 4 ]
Fig. 4 is a flowchart of a data generation method according to method embodiment 4 of the present invention. Referring to fig. 4, in the present embodiment, the method includes:
s41: and counting the characteristic value of the target merchant based on the collected transaction data of the target merchant.
S42: and determining the characteristic value as a negative correlation quantization characteristic value.
The negative correlation quantitative characteristic value is a characteristic value which is negatively correlated with the health degree score of the merchant and is used for describing the attribute of the amount of the merchant.
S43: sorting the elements in the set of eigenvalues containing the eigenvalues in descending order.
S44: and determining a characteristic normalization value of the characteristic value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
Wherein, the negative correlation normalization model is as follows: and the input quantity and the output quantity are in negative correlation, and the value of the output quantity is between 0 and 1.
S45: and generating a health degree score of the target merchant according to the characteristic normalization value.
[ METHOD EMBODIMENT 5 ]
Fig. 5 is a flowchart of a data generation method according to method embodiment 5 of the present invention. Referring to fig. 5, in the present embodiment, the method includes:
s51: and counting the characteristic value of the target merchant based on the collected transaction data of the target merchant.
S52: and determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value.
S53: and carrying out weighting processing on the characteristic normalization value.
S54: and generating a merchant score of the target merchant according to the weighted characteristic normalization value.
S55: and sorting the elements in the merchant score set containing the merchant scores in an ascending order.
S56: and generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
[ METHOD EMBODIMENT 6 ]
The data generation method provided in the present embodiment is specifically described below with reference to specific examples. In the present embodiment, the method includes:
(1) and acquiring a merchant list from the e-commerce platform.
(2) And selecting the merchants meeting the following two conditions from the merchant list: 1. ordering data exists in the last month; 2. there is no absence of features (attributes) of the merchant.
(3) And counting the characteristic values of all the characteristics in the table 1 aiming at each selected merchant.
TABLE 1
(4) For each picked merchant, a feature normalization value (normalized feature score) of all features is calculated.
The following takes the feature j of the merchant i as an example, and specifically describes an implementation manner of calculating a feature normalization value (normalized feature score).
A. If the characteristic j is identified as the positive correlation quantization characteristic, the values of the characteristic j of all the merchants selected in the step (2) are sorted in an ascending order, and the sorting position rank of the characteristic j of the merchant i is determinedijA feature normalization value (normalized feature score) of the feature j of the merchant i is calculated by the following formula:
wherein, N represents the number of the merchants selected in the step (2).
B. If the characteristic j is identified as the negative correlation quantization characteristic, the values of the characteristic j of all the merchants selected in the step (2) are sorted in a descending order, and the sorting position rank of the characteristic j of the merchant i is determinedijA feature normalization value (normalized feature score) of the feature j of the merchant i is calculated by the following formula:
wherein, N represents the number of the merchants selected in the step (2).
C. If the characteristic j is identified as a qualified characteristic for indicating the type of the merchant, acquiring a set numerical value (the set numerical value is between 0 and 1) corresponding to the type of the merchant indicated by the characteristic value of the characteristic j, and taking the acquired numerical value as a characteristic normalization value of the characteristic j.
For example, if the value of the feature j indicates that the merchant i is a platform-independent merchant, the feature normalization value is 1, if the value of the feature j indicates that the merchant i is not a platform-independent merchant, the feature normalization value is 0, if the value of the feature j indicates that the merchant i is an NKA merchant, the feature normalization value is 1, if the value of the feature j indicates that the merchant i is an LKA merchant, the feature normalization value is 0.5, and if the value of the feature j indicates that the merchant i is not a KA merchant, the feature normalization value is 0.
(5) And calculating the merchant score according to all the characteristic normalization values aiming at each selected merchant.
For example, the merchant Score of merchant i is calculated by the following formulai：
Wherein, wjOf feature jThe weight, n, represents the number of features.
(6) For each of the selected merchants, a normalized merchant score is calculated.
The following takes merchant i as an example, and specifically describes an implementation manner of calculating the normalized merchant score.
Sorting the merchant scores of all the merchants selected in the step (2) in an ascending order to determine the merchant Score of the merchant iiRank (Score) ofi) Calculating the normalized merchant score UScore of the merchant i by the following formulai：
Wherein, N represents the number of the merchants selected in the step (2).
(7) And aiming at each selected merchant, taking the normalized merchant score as the merchant health score.
[ PRODUCT EMBODIMENT 1 ]
Fig. 6 is a schematic configuration diagram of a data generating apparatus according to embodiment 1 of the present invention. Referring to fig. 6, in the present embodiment, the data generation apparatus 10 includes: the statistical module 11, the determining module 12 and the generating module 13 specifically:
the statistic module 11 is configured to count the feature value of the target merchant based on the collected transaction data of the target merchant.
The determining module 12 is configured to determine a feature normalization value of the feature value according to a setting rule corresponding to the type of the feature value counted by the counting module 11.
The generating module 13 is configured to generate a health score of the target merchant according to the feature normalization value determined by the determining module 12.
In the embodiment, the feature normalization value of the feature value is determined according to the setting rule corresponding to the type of the merchant feature value, and the health score of the merchant is generated according to the feature normalization value, so that the health degree of the merchant can be accurately depicted.
[ PRODUCT EMBODIMENT 2 ]
The data generating apparatus provided in this embodiment includes all the contents in product embodiment 1, and is not described herein again. As shown in fig. 7, in the present embodiment, the determination module 12 includes: the acquisition unit 121 and the determination unit 122, specifically:
the obtaining unit 121 is configured to obtain a setting value corresponding to the merchant type indicated by the feature value.
Wherein the set value is between 0 and 1.
The determining unit 122 is configured to determine the feature normalization value according to the numerical value acquired by the acquiring unit 121.
[ PRODUCT EMBODIMENT 3 ]
The data generating apparatus provided in this embodiment includes all the contents in product embodiment 1, and is not described herein again. As shown in fig. 8, in the present embodiment, the determination module 12 includes: a first ascending sorting unit 121 'and a determination unit 122', specifically:
the first ascending sorting unit 121' is configured to sort the elements in the eigenvalue set containing the eigenvalue in ascending order.
The determining unit 122 'is configured to determine the feature normalization value according to the sorting position of the feature value in the ascending sorting performed by the first ascending sorting unit 121' and the set positive correlation normalization model.
[ PRODUCT EMBODIMENT 4 ]
The data generating apparatus provided in this embodiment includes all the contents in product embodiment 1, and is not described herein again. As shown in fig. 9, in the present embodiment, the determination module 12 includes: the descending order unit 121 ″ and the determination unit 122 ″, specifically:
the descending order unit 121 ″ is configured to sort the elements in the eigenvalue set containing the eigenvalue in descending order.
The determining unit 122 ″ is configured to determine the feature normalization value according to a sorting position of the feature value in the descending sorting performed by the descending sorting unit 121 ″ and a set negative correlation normalization model.
[ PRODUCT EMBODIMENT 5 ]
The data generating apparatus provided in this embodiment includes all of the contents of any one of product embodiments 1 to 4, and is not described herein again. As shown in fig. 10, in the present embodiment, the generation module 13 includes: the weighting processing unit 131, the first generating unit 132, the second ascending sort unit 133, and the second generating unit 134, specifically:
the weighting processing unit 131 is configured to perform weighting processing on the feature normalization value.
The first generating unit 132 is configured to generate a merchant score of the target merchant according to the feature normalization value weighted by the weighting processing unit 131.
The second ascending sorting unit 133 is configured to sort the elements in the merchant score set containing the merchant scores generated by the first generating unit 132 in an ascending order.
The second generating unit 134 is configured to generate the health score of the target merchant according to the ranking position of the merchant score in the ascending ranking performed by the second ascending ranking unit 133 and the number of elements of the merchant score set.
As shown in fig. 11, the embodiment of the present invention also provides a terminal device, including a memory 21 and a processor 22; wherein,
the memory 21 is configured to store one or more computer instructions which, when executed by the processor 22, are capable of implementing the method as described in any one of method embodiments 1-6.
In the embodiment, the feature normalization value of the feature value is determined according to the setting rule corresponding to the type of the merchant feature value, and the health score of the merchant is generated according to the feature normalization value, so that the health degree of the merchant can be accurately depicted.
Furthermore, embodiments of the present invention also provide a computer storage medium for storing one or more computer instructions, wherein the one or more computer instructions, when executed, enable implementation of the method according to any one of method embodiment 1 to method embodiment 6.
In the embodiment, the feature normalization value of the feature value is determined according to the setting rule corresponding to the type of the merchant feature value, and the health score of the merchant is generated according to the feature normalization value, so that the health degree of the merchant can be accurately depicted.
Those skilled in the art will clearly understand that the present invention may be implemented entirely in software, or by a combination of software and a hardware platform. Based on such understanding, all or part of the technical solutions of the present invention contributing to the background may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, a smart phone, a network device, etc.) to execute the method according to each embodiment or some parts of the embodiments of the present invention.
As used herein, the term "software" or the like refers to any type of computer code or set of computer-executable instructions in a general sense that is executed to program a computer or other processor to perform various aspects of the present inventive concepts as discussed above. Furthermore, it should be noted that according to one aspect of the embodiment, one or more computer programs implementing the method of the present invention when executed do not need to be on one computer or processor, but may be distributed in modules in multiple computers or processors to execute various aspects of the present invention.
Computer-executable instructions may take many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In particular, the operations performed by the program modules may be combined or separated as desired in various embodiments.
Also, technical solutions of the present invention may be embodied as a method, and at least one example of the method has been provided. The actions may be performed in any suitable order and may be presented as part of the method. Thus, embodiments may be configured such that acts may be performed in an order different than illustrated, which may include performing some acts simultaneously (although in the illustrated embodiments, the acts are sequential).
The definitions given and used herein should be understood with reference to dictionaries, definitions in documents incorporated by reference, and/or their ordinary meanings.
In the claims, as well as in the specification above, all transitional phrases such as "comprising," "having," "containing," "carrying," "having," "involving," "consisting essentially of …," and the like are to be understood to be open-ended, i.e., to include but not limited to.
The terms and expressions used in the specification of the present invention have been set forth for illustrative purposes only and are not meant to be limiting. It will be appreciated by those skilled in the art that changes could be made to the details of the above-described embodiments without departing from the underlying principles thereof. The scope of the invention is, therefore, indicated by the appended claims, in which all terms are intended to be interpreted in their broadest reasonable sense unless otherwise indicated.
While various embodiments of the present invention have been described above with particularity, various aspects or features of the teachings of embodiments of the present invention are described below in another form and are not limited to the following series of paragraphs, some or all of which may be assigned alphanumeric characters for the sake of clarity. Each of these paragraphs may be combined with the contents of one or more other paragraphs in any suitable manner. Without limiting examples of some of the suitable combinations, some paragraphs hereinafter make specific reference to and further define other paragraphs.
A1, a data generation method, the method comprising:
counting the characteristic value of the target merchant based on the collected transaction data of the target merchant;
determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value;
and generating a health degree score of the target merchant according to the characteristic normalization value.
The method of a2, as defined in a1, wherein determining the eigenvalue normalization value of the eigenvalue according to the setting rule corresponding to the type of the eigenvalue comprises:
if the characteristic value is a qualified characteristic value used for indicating the type of the merchant, acquiring a set numerical value corresponding to the type of the merchant indicated by the characteristic value, wherein the set numerical value is between 0 and 1;
and determining the characteristic normalization value according to the set numerical value.
The method of A3, as defined in a1, wherein determining the eigenvalue normalization value of the eigenvalue according to the setting rule corresponding to the type of the eigenvalue comprises:
if the characteristic value is a positive correlation quantization characteristic value, sorting the elements in the characteristic value set containing the characteristic value in an ascending order;
and determining the characteristic normalization value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
The method of a4, as defined in a1, wherein determining the eigenvalue normalization value of the eigenvalue according to the setting rule corresponding to the type of the eigenvalue comprises:
if the characteristic value is a negative correlation quantization characteristic value, sorting elements in the characteristic value set containing the characteristic value in a descending order;
and determining the characteristic normalization value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
A5, the method of any one of a 1-a 4, wherein generating the health score for the target merchant from the feature normalization value comprises:
carrying out weighting processing on the characteristic normalization value;
generating a merchant score of the target merchant according to the weighted feature normalization value;
sorting the elements in the merchant score set containing the merchant scores in an ascending order;
and generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
B6, an apparatus for generating data, the apparatus comprising:
the statistical module is used for counting the characteristic value of the target merchant based on the collected transaction data of the target merchant;
the determining module is used for determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value;
and the generating module is used for generating the health degree score of the target merchant according to the characteristic normalization value.
B7, the apparatus of B6, wherein the determining module comprises:
the acquiring unit is used for acquiring a set numerical value corresponding to the merchant type indicated by the characteristic value, wherein the set numerical value is between 0 and 1;
and the determining unit is used for determining the characteristic normalization value according to the set numerical value.
B8, the apparatus of B6, wherein the determining module comprises:
the first ascending sorting unit is used for ascending sorting the elements in the characteristic value set containing the characteristic value;
and the determining unit is used for determining the characteristic normalization value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
B9, the apparatus of B6, wherein the determining module comprises:
the descending sorting unit is used for carrying out descending sorting on elements in the characteristic value set containing the characteristic value;
and the determining unit is used for determining the characteristic normalization value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
B10, the apparatus of any one of B6-B9, the means for generating comprising:
the weighting processing unit is used for weighting the characteristic normalization value;
a first generating unit, configured to generate a merchant score of the target merchant according to the weighted feature normalization value;
the second ascending sorting unit is used for carrying out ascending sorting on elements in the merchant score set containing the merchant scores;
and the second generating unit is used for generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
C11, a terminal device comprising a memory and a processor; wherein,
the memory is to store one or more computer instructions that, when executed by the processor, are capable of implementing the method as any one of A1-A5.
D12, a computer storage medium storing one or more computer instructions which, when executed, are capable of implementing the method of any one of a 1-a 5.
Claims (10)
1. A method of data generation, the method comprising:
counting the characteristic value of the target merchant based on the collected transaction data of the target merchant;
determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value;
and generating a health degree score of the target merchant according to the characteristic normalization value.
2. The method of claim 1, wherein determining the feature normalization value for the feature value according to a set rule corresponding to the type of the feature value comprises:
if the characteristic value is a qualified characteristic value used for indicating the type of the merchant, acquiring a set numerical value corresponding to the type of the merchant indicated by the characteristic value, wherein the set numerical value is between 0 and 1;
and determining the characteristic normalization value according to the set numerical value.
3. The method of claim 1, wherein determining the feature normalization value for the feature value according to a set rule corresponding to the type of the feature value comprises:
if the characteristic value is a positive correlation quantization characteristic value, sorting the elements in the characteristic value set containing the characteristic value in an ascending order;
and determining the characteristic normalization value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
4. The method of claim 1, wherein determining the feature normalization value for the feature value according to a set rule corresponding to the type of the feature value comprises:
if the characteristic value is a negative correlation quantization characteristic value, sorting elements in the characteristic value set containing the characteristic value in a descending order;
and determining the characteristic normalization value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
5. The method of any of claims 1-4, wherein generating the health score for the target merchant from the feature normalization value comprises:
carrying out weighting processing on the characteristic normalization value;
generating a merchant score of the target merchant according to the weighted feature normalization value;
sorting the elements in the merchant score set containing the merchant scores in an ascending order;
and generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
6. An apparatus for generating data, the apparatus comprising:
the statistical module is used for counting the characteristic value of the target merchant based on the collected transaction data of the target merchant;
the determining module is used for determining a characteristic normalization value of the characteristic value according to a set rule corresponding to the type of the characteristic value;
and the generating module is used for generating the health degree score of the target merchant according to the characteristic normalization value.
7. The apparatus of claim 6, wherein the determining module comprises:
the acquiring unit is used for acquiring a set numerical value corresponding to the merchant type indicated by the characteristic value, wherein the set numerical value is between 0 and 1;
and the determining unit is used for determining the characteristic normalization value according to the set numerical value.
8. The apparatus of claim 6, wherein the determining module comprises:
the first ascending sorting unit is used for ascending sorting the elements in the characteristic value set containing the characteristic value;
and the determining unit is used for determining the characteristic normalization value according to the sequencing position of the characteristic value and a set positive correlation normalization model.
9. The apparatus of claim 6, wherein the determining module comprises:
the descending sorting unit is used for carrying out descending sorting on elements in the characteristic value set containing the characteristic value;
and the determining unit is used for determining the characteristic normalization value according to the sequencing position of the characteristic value and a set negative correlation normalization model.
10. The apparatus of any of claims 6 to 9, wherein the generating module comprises:
the weighting processing unit is used for weighting the characteristic normalization value;
a first generating unit, configured to generate a merchant score of the target merchant according to the weighted feature normalization value;
the second ascending sorting unit is used for carrying out ascending sorting on elements in the merchant score set containing the merchant scores;
and the second generating unit is used for generating the health degree score of the target merchant according to the ranking position of the merchant score and the number of elements of the merchant score set.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109658193A (en) * | 2018-12-20 | 2019-04-19 | 拉扎斯网络科技（上海）有限公司 | Method and device for determining importance of system object, electronic equipment and storage medium |
| CN112561550A (en) * | 2019-09-26 | 2021-03-26 | 中移动信息技术有限公司 | Method, device, equipment and storage medium for classifying health degrees of merchants |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103716351A (en) * | 2012-09-29 | 2014-04-09 | 阿里巴巴集团控股有限公司 | Information display method and server |
| US20150019290A1 (en) * | 2013-07-12 | 2015-01-15 | Capital One Financial Corporation | Systems and methods for representing consumer behavior |
| CN105894360A (en) * | 2016-03-31 | 2016-08-24 | 百度在线网络技术（北京）有限公司 | Cheated order identification method, device and system |
| CN106980988A (en) * | 2016-12-29 | 2017-07-25 | 中国银联股份有限公司 | Trade company's value assessment method |
| CN107133805A (en) * | 2017-05-09 | 2017-09-05 | 北京小度信息科技有限公司 | Method of adjustment, device and the equipment of user's cheating category forecasting Model Parameter |
-
2017
- 2017-12-26 CN CN201711431025.7A patent/CN108038735A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103716351A (en) * | 2012-09-29 | 2014-04-09 | 阿里巴巴集团控股有限公司 | Information display method and server |
| US20150019290A1 (en) * | 2013-07-12 | 2015-01-15 | Capital One Financial Corporation | Systems and methods for representing consumer behavior |
| CN105894360A (en) * | 2016-03-31 | 2016-08-24 | 百度在线网络技术（北京）有限公司 | Cheated order identification method, device and system |
| CN106980988A (en) * | 2016-12-29 | 2017-07-25 | 中国银联股份有限公司 | Trade company's value assessment method |
| CN107133805A (en) * | 2017-05-09 | 2017-09-05 | 北京小度信息科技有限公司 | Method of adjustment, device and the equipment of user's cheating category forecasting Model Parameter |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109658193A (en) * | 2018-12-20 | 2019-04-19 | 拉扎斯网络科技（上海）有限公司 | Method and device for determining importance of system object, electronic equipment and storage medium |
| CN112561550A (en) * | 2019-09-26 | 2021-03-26 | 中移动信息技术有限公司 | Method, device, equipment and storage medium for classifying health degrees of merchants |
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