CN110796471A - Information processing method and device - Google Patents
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- CN110796471A CN110796471A CN201910806576.XA CN201910806576A CN110796471A CN 110796471 A CN110796471 A CN 110796471A CN 201910806576 A CN201910806576 A CN 201910806576A CN 110796471 A CN110796471 A CN 110796471A
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Abstract
The application discloses an information processing method and device, wherein the method comprises the following steps: acquiring user data, wherein the user data comprises: the method comprises the steps that information of a user and payment list data of the user are extracted, the user with the same preset items as the user data of a user to be imaged is taken as a related user of the user to be imaged according to the user data, any one preset item is data in the user data, a related map of the user to be imaged is constructed according to the information of the user to be imaged, the information of the related user and the same preset item, and a transaction index is determined from the payment list data of the user to be imaged, wherein the transaction index comprises the following steps: the word frequency of the words belonging to the preset word set is that the preset word set is the words reflecting the transaction mode of the user to be imaged. The method and the device at least increase the probability of meeting the requirements of more and more service scenes on the information amount in the user image.
Description
Technical Field
The present application relates to the field of electronic information, and in particular, to an information processing method and apparatus.
Background
The merchant image is information for describing a merchant, and specifically, for any one merchant, the merchant image may be transaction index information for reflecting a transaction situation of the merchant, and the like.
The merchant image can help the operator to know about the merchant, for example, whether the merchant has money laundering behavior or not can be judged based on the merchant image.
With the increasing business demands, the current merchant drawings cannot meet the demands of more and more business scenes.
Disclosure of Invention
The application provides an information processing method and device, and aims to provide a merchant portrait capable of meeting more and more business scene requirements.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides an information processing method, which comprises the following steps:
acquiring user data; the user data includes: information of the user and payment slip data of the user;
extracting a user with preset items which are the same as the user data of the user to be imaged as a related user of the user to be imaged according to the user data, wherein any one preset item is one of the user data;
constructing an associated map of the user to be imaged according to the information of the user to be imaged, the information of the associated user and the same preset item;
determining a transaction index from the payment bill data of the user to be imaged; the transaction metrics include: word frequencies of words belonging to a preset word set; the preset word set is a word reflecting the transaction mode of the user to be imaged.
Optionally, the constructing the association map of the user to be imaged according to the information of the user to be imaged, the information of the associated user, and the same preset item includes:
respectively constructing an associated map of each same preset item; the associated map of any one of the same preset items is the information of the user to be imaged, the same preset item and the associated map among the information of the associated users having the same preset item with the user to be imaged;
constructing the associated map of the same preset item; and the associated maps of the same preset items are the information of the user to be imaged, the same preset items and the associated maps among the information of the associated users.
Optionally, the same preset items include: bank card, contact and address.
Optionally, the transaction index further includes: a transaction analysis result;
determining the transaction analysis result from the payment order data of the user to be imaged, wherein the transaction analysis result comprises the following steps:
determining information of a first preset target item belonging to each preset analysis dimension from target information embodied by payment list data of the user to be imaged to obtain a transaction analysis result; the target information is transaction information between a transaction opponent and the merchant to be imaged; the transaction opponent is a user who generates a transaction with the user to be imaged; any one of the preset analysis dimensions is gold output or gold input; the first preset target item represents a preset item of a transaction opponent enabling a merchant to be under the preset analysis dimension; and the preset item reflects the transaction condition between the user and a transaction opponent under the preset analysis dimension.
Optionally, the transaction index further includes: transaction distribution information;
determining the transaction distribution information from the payment order data of the user to be imaged, wherein the transaction distribution information comprises the following steps:
determining data distribution of the users to be imaged under each preset distribution dimension from the payment list data of the users to be imaged; any one preset distribution dimension is any combination of the amount of money for deposit and withdrawal and two dimensions of different preset time periods;
and taking the data distribution of the user to be imaged under each preset distribution dimension as the transaction distribution information.
Optionally, after the obtaining the user data, the method further includes:
under the condition that the content of a second preset target item in the information of the user to be imaged changes, sequentially recording the content of each second preset target item according to the time sequence to obtain the activity track of the user to be imaged; the second preset target item is data in the information of the commercial tenant to be imaged.
The present application also provides an information processing apparatus including:
the acquisition module is used for acquiring user data; the user data includes: information of the user and payment slip data of the user;
the extraction module is used for extracting a user with preset items which are the same as the user data of the user to be imaged as a related user of the user to be imaged according to the user data, wherein any one preset item is one of the user data;
the construction module is used for constructing the associated map of the user to be imaged according to the information of the user to be imaged, the information of the associated user and the same preset item;
the determining module is used for determining a transaction index from the payment list data of the user to be imaged; the transaction metrics include: word frequencies of words belonging to a preset word set; the preset word set is a word reflecting the transaction mode of the user to be imaged.
Optionally, the constructing unit is configured to construct the association map of the user to be imaged according to the information of the user to be imaged, the information of the associated user, and the same preset item, and includes:
the construction unit is specifically configured to respectively construct an associated map of each of the same preset items; the associated map of any one of the same preset items is the information of the user to be imaged, the same preset item and the associated map among the information of the associated users having the same preset item with the user to be imaged; constructing the associated map of the same preset item; and the associated maps of the same preset items are the information of the user to be imaged, the same preset items and the associated maps among the information of the associated users.
Optionally, the transaction index further includes: a transaction analysis result;
the determining unit is used for determining the transaction analysis result from the payment order data of the user to be imaged, and comprises the following steps:
the determining unit is specifically configured to determine information of a first preset target item belonging to each preset analysis dimension from target information embodied in the payment order data of the user to be imaged to obtain the transaction analysis result; the target information is transaction information between a transaction opponent and the merchant to be imaged; the transaction opponent is a user who generates a transaction with the user to be imaged; any one of the preset analysis dimensions is gold output or gold input; the first preset target item represents a preset item of a transaction opponent enabling a merchant to be under the preset analysis dimension; and the preset item reflects the transaction condition between the user and a transaction opponent under the preset analysis dimension.
Optionally, the transaction index further includes: transaction distribution information;
the determining unit is used for determining the transaction distribution information from the payment order data of the user to be imaged, and comprises the following steps:
the determining unit is specifically configured to determine data distribution of the user to be imaged under each preset distribution dimension from the payment statement data of the user to be imaged; any one preset distribution dimension is any combination of the amount of money for deposit and withdrawal and two dimensions of different preset time periods; and taking the data distribution of the user to be imaged under each preset distribution dimension as the transaction distribution information.
Optionally, the method further includes: the recording module is used for sequentially recording the content of each second preset target item according to the time sequence under the condition that the content of the second preset target item in the information of the user to be imaged is changed after the user data is obtained, so as to obtain the activity track of the user to be imaged; the second preset target item is data in the information of the commercial tenant to be imaged.
The information processing method and the device obtain user data, wherein the user data comprise user information and payment bill data of users, and the users with the same preset items as the users to be imaged are extracted as the associated users of the users to be imaged according to the user data, namely the users associated with the users to be imaged are determined through the same preset items. And constructing the associated map of the user to be imaged according to the information of the user to be imaged, the information of the associated user and the same preset items. And determining a transaction index from the payment order data of the user to be imaged, wherein the transaction index comprises: the word frequencies of words belonging to a preset word set.
In the prior art, it is determined that no payment list data exists in a data source of a user portrait, and the payment list data has more intersection information (information for searching for associated users of users to be pictured), so that the probability that the number of associated users of the users to be pictured in the constructed associated map is increased, and further, the probability that the number of information of the associated users of the users to be pictured in the associated map is increased.
In addition, the preset word set in the payment list data is a word reflecting the transaction mode of the user to be imaged, so that the word frequency of the word belonging to the preset word set and determined from the payment list data of the user to be imaged can reflect the transaction mode of the user to be imaged, and further, the probability that the obtained transaction modes of the user to be imaged are increased is increased.
Therefore, by adopting the scheme provided by the application, through the payment order data of the to-be-imaged merchant, the probability of increasing the information of the associated user of the to-be-imaged user in the constructed associated map can be increased, and the probability of increasing the transaction modes of the to-be-imaged user can be increased, so that the application at least increases the probability of meeting the requirements of more and more service scenes on the information amount in the user image.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an information processing method according to an embodiment of the present application;
FIG. 2 is a schematic illustration of an association map disclosed in an embodiment of the present application;
fig. 3 is a word frequency statistical diagram of a merchant to be imaged, which is disclosed in the embodiment of the present application;
FIG. 4 is a schematic diagram of a transaction analysis result disclosed in an embodiment of the present application;
FIG. 5(a) is a schematic diagram illustrating a time distribution of a cash amount paid by a merchant to be portrait in a near year according to an embodiment of the present application;
FIG. 5(b) is a schematic diagram illustrating the time distribution of the amount of money deposited in the last year by the merchant to be portrait according to the embodiment of the present application;
FIG. 5(c) is a schematic diagram illustrating the time distribution of the cash amounts paid out by the merchants to be portrait in the last three months according to the embodiment of the present application;
FIG. 5(d) is a schematic diagram illustrating the time distribution of the amount of money deposited by the merchant to be portrait in three months according to the embodiment of the present application;
fig. 6 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present application.
Detailed Description
In the technical solution disclosed in the embodiment of the present application, the processing object is user data, for example, user data generated in a payment service, where a user refers to a main body that has generated payment order data, and a merchant is taken as an example for description below.
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 of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is an information processing method provided in an embodiment of the present application, including the following steps:
s101, acquiring merchant data.
In this step, the merchant data includes: merchant information, merchant contact data, and merchant payment slip data. The merchant data may include, among other things, merchant data for a plurality of merchants.
The information of any merchant may include: the person (e.g., a worker) involved in the merchant, the bank card number, the contact and address, etc. The contact information may include a telephone number, a QQ number, a mailbox address, and the like. The contact data of any one merchant is the information of the person who has contact with the merchant.
The payment data of any merchant is the transaction data generated by the merchant and the counterparty. The transaction opponent of the merchant is a merchant that generates a transaction with the merchant, and in general, the transaction opponent of the merchant is a customer of the merchant. Specifically, the payment slip data may include: the name of the transaction opponent, the contact way of the transaction opponent, the bank card information of the transaction opponent, the address of the transaction opponent, the name of the merchant, the identity card information of the merchant, the contact way of the merchant, the bank card information of the merchant, the transaction amount and the like.
Specifically, in this step, merchant data may be obtained from different preset data sources by using spark, and a specific obtaining process is the prior art and is not described herein again. The spark is an open source computing engine, and the open source computing engine is mainly used for rapidly processing large-scale data.
S102, according to the merchant data, the merchant with the preset item same as the merchant data of the merchant to be imaged is extracted as the associated merchant of the merchant to be imaged.
In this embodiment, a merchant to be imaged is a merchant needing to perform an image, specifically, which merchant is the merchant to be imaged needs to be determined according to specific situations, which merchant is not specifically limited in this embodiment, and in this embodiment, a process of performing an image on the merchant to be imaged is described by taking one merchant to be imaged as an example.
In this step, a preset item is a data in the merchant data, and the preset item may include: the system comprises a bank card, a contact way and an address, wherein the contact way can comprise: a mobile phone number, a QQ number, a mailbox address, etc., and the embodiment does not limit the specific content of the contact.
In this step, the merchant data of the merchant to be imaged is the information of the merchant to be imaged, the contact data and the payment order data in the acquired merchant data. In the step, the commercial tenant with the same preset item as the commercial tenant data of the commercial tenant to be imaged is extracted from the commercial tenant data and is the associated commercial tenant of the commercial tenant to be imaged.
For example, when the preset item is a bank card, in this step, a merchant having the same preset item as the merchant data of the merchant to be imaged is extracted, that is, a merchant having the same bank card number as the merchant data of the merchant to be imaged is extracted as an associated merchant of the merchant to be imaged, that is, an associated merchant of the merchant to be imaged is obtained through the same bank card number.
The specific implementation process of this step is the prior art, and is not described herein again.
S103, constructing an association map of the user to be imaged according to the information of the merchant to be imaged, the information of the associated merchant and the same preset item.
In this embodiment, there may be multiple items of the same preset item, and in this step, the process of constructing the associated map of the user to be imaged includes two parts. The first part is used for constructing information of merchants to be imaged, all preset items in the same preset items and an association map among information of associated merchants. The second part is that a correlation map is respectively constructed according to each same preset item, wherein for any one same preset item, the construction of the correlation map of the preset item refers to the following steps: and constructing the information of the commercial tenant to be imaged, the same preset item and an associated atlas between the information of the associated commercial tenant with the same preset item with the user to be imaged.
The principle of constructing the association map of the merchant to be imaged is the same no matter in the first part or the second part, and this embodiment takes as an example a process of constructing the association map of the merchant to be imaged by using any one of the same preset items.
Specifically, the information of the merchant to be imaged is obtained from the merchant data, the information of the associated merchant having the same preset item with the merchant data of the merchant to be imaged and the same preset item are used as a data set, the data set is converted into a data structure of a graph, specifically, the data structure of the associated graph can be converted into the data structure of the associated graph, the associated graph between the merchant to be imaged and the associated merchant having the same preset item with the merchant to be imaged is obtained, and the data structure converted into the graph can be written into a preset graph database. The data structure process of converting the data in the data set into the graph is the prior art, and is not described herein again.
In this embodiment, for any one same preset item, the relationship between the same preset item and the to-be-imaged business and the associated business may also be determined. For example, the same preset item is a mobile phone, and the merchants related to the to-be-imaged merchant determined by the mobile phone are a first merchant and a second merchant respectively, where the relationship between the mobile phone and the to-be-imaged merchant is that the mobile phone is a mobile phone of a general manager of the to-be-imaged merchant, the relationship between the mobile phone and the first merchant is that the mobile phone is a mobile phone of a sales chief of the first merchant, and the relationship between the mobile phone and the second merchant is that the mobile phone is a mobile phone of a financial chief of the second merchant.
Specifically, the association map obtained by the present step may be as shown in fig. 2, where the upper part in fig. 2 is the specific content of the preset item, and the sequence from left to right is as follows: credentials, customer, cell phone, address, merchant, QQ, mailbox, personnel, and bank card. The left side of fig. 2 is, from top to bottom, "all relationships", "merchant and bank card", "merchant and mobile phone", "merchant and certificate", "merchant and address", "merchant and mailbox", "merchant and QQ", "merchant and person", and "customer and merchant".
Wherein "all relationships" represent: the information of the commercial tenant, the information of all the related commercial tenants, and all the related maps corresponding to the same preset items. Corresponding to the first part of the process of constructing the associated map.
Any one of the merchant and bank card, the merchant and mobile phone, the merchant and certificate, the merchant and address, the merchant and mailbox, the merchant and QQ, the merchant and person and the customer and merchant represents the information of the associated merchant determined by the same preset item, the same preset item and the associated map among the information of the merchant. Corresponding to the second part of the process of constructing the associated map. For example, "merchant and bank card" mean that the same preset item is the information of the merchant related to the merchant determined by the bank card, and the association map formed by the information of the merchant.
The right side of fig. 2 is an example of an association map corresponding to "business and mobile phone" given when the user clicks "business and mobile phone" on the left side, and the association map includes three businesses, which are a first business, a second business and a third business, respectively, and the same preset item is a middle mobile phone. Assume that the merchant at the lower left is the first merchant, the merchant at the upper right is the second merchant, and the merchant at the lower right is the third merchant. When the computer pointer is placed on a connection line between any commercial tenant and a preset item, the relation between the mobile phone and the commercial tenant is displayed on the connection line.
The display level at the upper left corner of fig. 2 is 3, and the display level represents the number of relation level levels of the required display merchants. Taking the association map given at the right side of fig. 2 as an example, taking the first business as an example, the first business is a zeroth level relationship, and the content indicated from the zeroth level relationship to the direction away from the first business is the first level relationship, that is, the information of the first business and the same preset item are the first level relationship of the first business. The content indicated from the first hierarchical relationship to the direction away from the first merchant is a second hierarchical relationship, that is, the second merchant and the third merchant are both the second hierarchical relationship of the first merchant. The content indicated from the second hierarchical relationship to the direction away from the first merchant is a third hierarchical relationship, that is, the information of the second merchant and the information of the third merchant are both the third hierarchical relationship of the first merchant.
At the bottom of FIG. 2, there is also a node list including nodes of, from left to right, customer, merchant, person, certificate, cell phone, mailbox, address, bank card and QQ, where "merchant" in the node represents the merchant of the representation, which is the merchant queried by the user for the representation. Each node except the 'merchant' in the nodes respectively represents the same preset item for determining the associated merchant of the merchant. In the case where any one node is clicked, the contents included in the node are displayed in the node list.
And S104, determining a transaction index value from the payment order data of the merchant to be imaged.
In this step, the transaction metric value comprises: the word frequency, the transaction analysis result and the transaction distribution information of the words belonging to the preset word set.
The preset term set is a term reflecting a transaction mode of a merchant to be imaged, wherein each term in the preset term set may be determined according to an actual scene, for example, the term may include "purchase", "distribution", and the like, and the specific content of the term included in the preset term set is not limited in this embodiment.
Specifically, in the process of determining the transaction index value from the payment order data of the merchant to be imaged, statistics can be performed by using various operators of spark and spark sql.
Specifically, the process of determining the word frequency of the word belonging to the preset word set from the payment list data of the merchant to be imaged comprises the following steps: determining words belonging to a preset word set from payment order data of a merchant to be imaged as target words, counting the occurrence times of the target words to obtain the respective occurrence times of the target words, namely the word frequency of each target word, storing each target word and the corresponding word frequency in a database, converting the word frequency data of each target word into a data structure of a graph with a preset pattern, and storing the data structure in the graph database.
Specifically, as shown in fig. 3, a word frequency statistical diagram of a to-be-imaged merchant is provided in the embodiment of the present application. The ordinate in fig. 3 represents the target word belonging to the preset word set, the abscissa represents the number of times the word appears, and each horizontal bar represents the number of times each target word appears, i.e., the word frequency of each target word.
In this embodiment, the risk word frequency may also be determined according to the word frequency of each target word, specifically, a word whose occurrence frequency is greater than a preset threshold may be used as the risk word frequency, and the preset threshold of the specific risk word frequency is not limited in this embodiment. For example, the frequency of occurrence of the term "funding" is 35, and if the preset threshold is 30, the term "funding" is the frequency of risk terms.
The process of determining the transaction analysis result from the payment slip data of the merchant to be imaged may include steps a1 to a 6:
a1, determining the transaction information of the transaction opponent of the merchant to be imaged from the payment sheet data of the merchant to be imaged.
Since the transaction opponent is a user transacting with the to-be-imaged merchant, the transaction condition between the transaction opponent of the to-be-imaged merchant and the to-be-imaged merchant can be reflected from the payment sheet data of the to-be-imaged merchant, and further the transaction information of the transaction opponent of the to-be-imaged merchant can be determined from the payment sheet data of the to-be-imaged merchant.
A2, extracting information belonging to each preset analysis dimension from the transaction information of the transaction opponent.
In this step, the preset analysis dimension may include: "deposit", "withdrawal", "payment collection" and "pay payment".
Wherein, the meaning of "deposit" is that the merchant collects money through the bank channel, the meaning of "deposit out" is that the merchant pays the money through the bank channel, the meaning of "payment collection" is that the merchant collects the money to the transaction object through the third party payment company, and the meaning of "payment" is that the merchant pays the transaction object through the third party payment company.
In this step, the information belonging to each preset analysis dimension in the transaction information of the counterparty is: and enabling the merchant to be in the information of the transaction opponent under the preset analysis dimension. I.e., information of a transaction opponent that makes the merchant make a deposit, information of a transaction opponent that makes the merchant pay a charge, and information of a transaction opponent that makes the merchant pay a payment. Taking the information of the transaction opponent making the merchant pay out as an example, the information of the transaction opponent receiving the payment from the merchant through the bank channel is extracted, for example, the transaction opponent a receives the payment of the merchant B through the bank channel in 2019, 4 and 5 days.
And A3, determining the information of the first preset target item of the counterparty from the information belonging to each preset analysis dimension respectively.
In this embodiment, the transaction condition of the merchant in each preset analysis dimension is embodied through the information of the transaction opponent in each preset analysis dimension. Specifically, the information of the first preset target item is determined from the information belonging to each preset analysis dimension in the transaction information of the transaction opponent.
In this embodiment, the first preset target item represents a preset item of the counterparty which enables the merchant to be under a preset analysis dimension. The preset items reflect the transaction conditions between the commercial tenant and the transaction opponent under the preset analysis dimension. Taking the preset analysis dimension as "pay out" as an example, the first preset target item represents a preset item of a transaction opponent enabling the merchant to be under the dimension of "pay out".
Specifically, the first preset target item may include "name of a transaction opponent", "bank card number", "bank account opening", "total amount", "maximum amount for a single stroke", "minimum amount for a single stroke", "total number for a single stroke", and "first payment single number". Of course, in practice, the first preset target item may also be other contents, this step is only an example, and the specific content of the first preset target item is not limited in this embodiment.
Taking the information of a transaction opponent making the merchant pay money as an example, specifically, taking the transaction opponent 'a', receiving the payment of the merchant B10000 yuan through a bank channel in 2019, 4 and 5 months. In this step, the extracted information of the first preset target item includes: the "bank card number" of "A", "bank card" of "A", "A" receives "total amount" paid by the merchant B "," A "receives" single maximum amount "paid by the merchant B", "A" receives "single minimum amount" paid by the merchant B "," A "receives" total amount "paid by the merchant B", and "A" receives "single number successful in first payment" paid by the merchant B.
And A4, using the information of the determined first preset target item as a transaction analysis result.
And A5, storing the content of the transaction analysis result in a database.
A6, converting the content of the transaction analysis into the structural data of the preset style chart.
Specifically, fig. 4 is a schematic diagram of a transaction analysis result provided in the embodiment of the present application, and fig. 4 is a data structure of a style, in practice, the style of the graph may be other, the embodiment does not limit the style of the graph, and a process of converting the content of the transaction analysis result into the data structure of the graph of the preset style is the prior art and is not described herein again.
Four aspects of "principal transaction opponents for deposit", "principal transaction opponents for payment receipt", and "principal transaction opponents for payment and payment" are given above fig. 4. The results for this aspect of "principal trading opponents in deposit" include: the name of the transaction opponent, the bank card number of the transaction opponent, the bank of the transaction opponent, the total amount of the transaction opponent and the merchant, the single maximum amount of the transaction opponent and the merchant, the single minimum amount of the transaction opponent and the merchant, the total amount of the transaction opponent and the merchant, the single number paid by the transaction opponent and the merchant for the first time and the like.
The process of determining transaction distribution information from the payment slip data to be imaged to the merchant may include steps B1 to B4:
and B1, determining distribution data of the merchants to be imaged under each preset distribution dimension from the payment list data of the merchants to be imaged.
In this step, the preset distribution dimension may include a plurality of dimensions, and any one of the preset distribution dimensions is any combination of the amount of money deposited and two dimensions of different preset time periods.
Assume that the different preset time periods include: in the last year and last three months, the preset distribution dimensions include: "the amount of money deposited in the last year", "the amount of money deposited in the last three months", and "the amount of money deposited in the last three months".
In this step, from the payment order data of the merchant to be imaged, it is determined that the corresponding relationship of the merchant to be imaged in the distribution dimension is data distribution in the distribution dimension. For example, if the distribution dimension is the payout amount of the last year, the correspondence between the payout amount of the last year and the time is referred to as distribution data in the distribution dimension.
And B2, taking the distribution data of the merchants to be imaged under each preset distribution dimension as transaction distribution information.
In this step, the distribution data of the image merchants in each distribution dimension is used as transaction distribution information.
And B3, storing the content of the transaction distribution information in a database.
B4, converting the content of the transaction distribution information into the structure data of the graph with the preset style, obtaining the data distribution under each preset distribution dimension, and storing the structure data of the graph in the preset graph database.
For example, the corresponding relationship between the money amount of the last year and the time is converted into a data structure of a preset pattern diagram, as shown in fig. 5, which is a schematic diagram of a distribution structure of distribution data in a distribution dimension. Fig. 5(a) is a schematic diagram of time distribution of the money amount paid by the to-be-imaged merchant in the last year according to the embodiment of the present application; FIG. 5(b) is a schematic diagram of the time distribution of the amount of money deposited by the merchant in the last year according to the embodiment of the present application; fig. 5(c) is a schematic diagram of time distribution of the money amounts paid by the merchants to be imaged in the last three months according to the embodiment of the present application; fig. 5(d) is a schematic diagram of time distribution of deposit amount of the to-be-imaged merchant in nearly three months according to the embodiment of the present application.
It should be noted that, in the present embodiment, the data of different transaction indexes are stored in different databases.
In this embodiment, under the condition that the content of the second preset target item changes, the content of each second preset target item is sequentially recorded according to the time sequence, so as to obtain the activity track of the merchant to be portrait. The second preset target item is data in the information of the merchant to be imaged, and specifically, the second preset target item may include: the registration address, the office address, the transaction IP address, and the like, in practice, the second preset target item may also be other content, and the embodiment does not limit the specific content of the second preset target item.
Fig. 6 is an information processing apparatus according to an embodiment of the present application, including: an acquisition module 601, an extraction module 602, a construction module 603, and a determination module 604.
The obtaining module 601 is configured to obtain user data, where the user data includes: information of the user and payment slip data of the user. The extracting module 602 is configured to extract, according to the user data, a user having a preset item that is the same as the user data of the user to be imaged as an associated user of the user to be imaged, where any preset item is one of the user data. The building module 603 is configured to build an association map of the user to be imaged according to the information of the user to be imaged, the information of the associated user, and the same preset item. The determining module 604 is configured to determine a transaction index from the payment order data of the user to be imaged, where the transaction index includes: the word frequency of the words belonging to the preset word set is that the preset word set is the words reflecting the transaction mode of the user to be imaged.
Optionally, the constructing unit 603 is configured to construct the association map of the user to be imaged according to the information of the user to be imaged, the information of the associated user, and the same preset item, and includes:
the constructing unit 603 is specifically configured to respectively construct an association map of each identical preset item, where an association map of any identical preset item is information of a user to be imaged, the identical preset item, and an association map between information of associated users having the identical preset item with the user to be imaged, and construct an association map of the identical preset item, and an association map of the identical preset item is information of the user to be imaged, the identical preset item, and an association map between information of associated users.
Optionally, the same preset items include: bank card, contact and address.
Optionally, the transaction index further includes: a transaction analysis result;
the determining unit 604 is configured to determine a transaction analysis result from the payment order data of the user to be imaged, and includes: the determining unit 604 is specifically configured to determine, from target information embodied by payment slip data of a user to be imaged, information of a first preset target item belonging to each preset analysis dimension, to obtain a transaction analysis result, where the target information is transaction information between a transaction opponent and a merchant to be imaged, the transaction opponent is a user who makes a transaction with the user to be imaged, any one preset analysis dimension is a yield or an income, and the first preset target item represents a preset item of the transaction opponent that enables the merchant to be under the preset analysis dimension; the preset items reflect the transaction condition between the user and the transaction opponent under the preset analysis dimension.
Optionally, the transaction index further comprises: transaction distribution information. The determining unit 604 is configured to determine transaction distribution information from the payment slip data of the user to be imaged, and includes:
the determining unit 604 is specifically configured to determine, from the payment receipt data of the user to be imaged, data distributions of the user to be imaged under respective preset distribution dimensions, where any preset distribution dimension is any combination of the amount of money deposited and paid out and two dimensions of different preset time periods, and the data distributions of the user to be imaged under the respective preset distribution dimensions are used as transaction distribution information.
Optionally, the apparatus embodiment further includes a recording module 605, configured to, after obtaining the user data, sequentially record the content of each second preset target item according to a time sequence when the content of the second preset target item in the information of the user to be imaged changes, so as to obtain an activity track of the user to be imaged, where the second preset target item is one of the information of the merchant to be imaged.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (11)
1. An information processing method characterized by comprising:
acquiring user data; the user data includes: information of the user and payment slip data of the user;
extracting a user with preset items which are the same as the user data of the user to be imaged as a related user of the user to be imaged according to the user data, wherein any one preset item is one of the user data;
constructing an associated map of the user to be imaged according to the information of the user to be imaged, the information of the associated user and the same preset item;
determining a transaction index from the payment bill data of the user to be imaged; the transaction metrics include: word frequencies of words belonging to a preset word set; the preset word set is a word reflecting the transaction mode of the user to be imaged.
2. The method according to claim 1, wherein the constructing the association map of the user to be imaged according to the information of the user to be imaged, the information of the associated user, and the same preset item comprises:
respectively constructing an associated map of each same preset item; the associated map of any one of the same preset items is the information of the user to be imaged, the same preset item and the associated map among the information of the associated users having the same preset item with the user to be imaged;
constructing the associated map of the same preset item; and the associated maps of the same preset items are the information of the user to be imaged, the same preset items and the associated maps among the information of the associated users.
3. The method of claim 1, wherein the same preset items comprise: bank card, contact and address.
4. The method of claim 1, wherein the transaction metrics further comprise: a transaction analysis result;
determining the transaction analysis result from the payment order data of the user to be imaged, wherein the transaction analysis result comprises the following steps:
determining information of a first preset target item belonging to each preset analysis dimension from target information embodied by payment list data of the user to be imaged to obtain a transaction analysis result; the target information is transaction information between a transaction opponent and the merchant to be imaged; the transaction opponent is a user who generates a transaction with the user to be imaged; any one of the preset analysis dimensions is gold output or gold input; the first preset target item represents a preset item of a transaction opponent enabling a merchant to be under the preset analysis dimension; and the preset item reflects the transaction condition between the user and a transaction opponent under the preset analysis dimension.
5. The method of claim 1, wherein the transaction metrics further comprise: transaction distribution information;
determining the transaction distribution information from the payment order data of the user to be imaged, wherein the transaction distribution information comprises the following steps:
determining data distribution of the users to be imaged under each preset distribution dimension from the payment list data of the users to be imaged; any one preset distribution dimension is any combination of the amount of money for deposit and withdrawal and two dimensions of different preset time periods;
and taking the data distribution of the user to be imaged under each preset distribution dimension as the transaction distribution information.
6. The method of claim 1, further comprising, after said obtaining user data:
under the condition that the content of a second preset target item in the information of the user to be imaged changes, sequentially recording the content of each second preset target item according to the time sequence to obtain the activity track of the user to be imaged; the second preset target item is data in the information of the commercial tenant to be imaged.
7. An information processing apparatus characterized by comprising:
the acquisition module is used for acquiring user data; the user data includes: information of the user and payment slip data of the user;
the extraction module is used for extracting a user with preset items which are the same as the user data of the user to be imaged as a related user of the user to be imaged according to the user data, wherein any one preset item is one of the user data;
the construction module is used for constructing the associated map of the user to be imaged according to the information of the user to be imaged, the information of the associated user and the same preset item;
the determining module is used for determining a transaction index from the payment list data of the user to be imaged; the transaction metrics include: word frequencies of words belonging to a preset word set; the preset word set is a word reflecting the transaction mode of the user to be imaged.
8. The apparatus according to claim 7, wherein the constructing unit is configured to construct the association map of the user to be imaged according to the information of the user to be imaged, the information of the associated user, and the same preset item, and includes:
the construction unit is specifically configured to respectively construct an associated map of each of the same preset items; the associated map of any one of the same preset items is the information of the user to be imaged, the same preset item and the associated map among the information of the associated users having the same preset item with the user to be imaged; constructing the associated map of the same preset item; and the associated maps of the same preset items are the information of the user to be imaged, the same preset items and the associated maps among the information of the associated users.
9. The apparatus of claim 7, wherein the transaction metrics further comprise: a transaction analysis result;
the determining unit is used for determining the transaction analysis result from the payment order data of the user to be imaged, and comprises the following steps:
the determining unit is specifically configured to determine information of a first preset target item belonging to each preset analysis dimension from target information embodied in the payment order data of the user to be imaged to obtain the transaction analysis result; the target information is transaction information between a transaction opponent and the merchant to be imaged; the transaction opponent is a user who generates a transaction with the user to be imaged; any one of the preset analysis dimensions is gold output or gold input; the first preset target item represents a preset item of a transaction opponent enabling a merchant to be under the preset analysis dimension; and the preset item reflects the transaction condition between the user and a transaction opponent under the preset analysis dimension.
10. The apparatus of claim 7, wherein the transaction metrics further comprise: transaction distribution information;
the determining unit is used for determining the transaction distribution information from the payment order data of the user to be imaged, and comprises the following steps:
the determining unit is specifically configured to determine data distribution of the user to be imaged under each preset distribution dimension from the payment statement data of the user to be imaged; any one preset distribution dimension is any combination of the amount of money for deposit and withdrawal and two dimensions of different preset time periods; and taking the data distribution of the user to be imaged under each preset distribution dimension as the transaction distribution information.
11. The apparatus of claim 7, further comprising: the recording module is used for sequentially recording the content of each second preset target item according to the time sequence under the condition that the content of the second preset target item in the information of the user to be imaged is changed after the user data is obtained, so as to obtain the activity track of the user to be imaged; the second preset target item is data in the information of the commercial tenant to be imaged.
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