CN106874331B - Data object distribution system and method - Google Patents
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Abstract
The application discloses a data object distribution system and a method. A data object allocation method comprises the following steps: acquiring basic attribute information and user-defined requirement information of a data object requirement party; determining the expected degree of the gain of the demander to the data resource amount according to the user-defined demand information; determining the loss acceptance degree of the demander to the data resource quantity according to the basic attribute information; and querying in a preset data object information base, and distributing the data object corresponding to the query result to the demand side. The technical scheme provided by the application effectively reduces the processing cost, improves the processing efficiency and can be well suitable for various big data application scenes.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data object allocation system and method.
Background
In some professional data distribution fields, there are often a plurality of different types of data objects, and different demanders have different requirements for the data objects, and since the "data object type" and the "requirement" are not simple corresponding relationships, in order to achieve accurate distribution of the data objects, manual participation in processing is often required. Besides the problems of high cost and low efficiency, the more important problem of the manual processing is that the processing capability of the human brain for the data is limited, and when the data volume is too large (for example, the data object types are too many), various problems such as misallocation and missing allocation are inevitable.
Disclosure of Invention
In view of the above technical problems, the present application provides a data object allocation system and method, and the technical scheme is as follows:
according to a first aspect of the present application, there is provided a data object allocation method, comprising:
acquiring basic attribute information and user-defined requirement information of a data object requirement party;
determining the expected degree of the gain of the demander to the data resource amount according to the user-defined demand information;
determining the loss acceptance degree of the demander to the data resource quantity according to the basic attribute information;
inquiring in a preset data object information base, wherein the data object in the data object information base has a resource amount gain attribute and a resource amount loss attribute, and the inquiry result requirement meets the following conditions: the resource amount gain attribute value is matched with the gain expectation degree, and the resource amount loss attribute value is matched with the loss acceptance degree;
and distributing the data object corresponding to the query result to the demand side.
According to a second aspect of the present application, there is provided a method for targeted recommendation of a financial product, the method comprising:
acquiring basic attribute information and user-defined demand information of a target user;
determining the expected gain degree of the target user to the assets according to the user-defined demand information;
determining the loss acceptance degree of the target user to the assets according to the basic attribute information;
inquiring in a preset financial product information base, wherein the financial product information in the financial product information base has an asset gain attribute and an asset loss attribute, and the inquiry result meets the following requirements: an asset gain attribute matching the gain expectation level and an asset loss attribute matching the loss acceptance level;
and recommending the financial products corresponding to the query result to the target user.
According to a third aspect of the present application, there is provided a data object allocation apparatus, the apparatus comprising:
the system comprises a demander information acquisition module, a data object demander information acquisition module and a data object development module, wherein the demander information acquisition module is used for acquiring basic attribute information and user-defined demand information of a data object demander;
the gain expectation degree determining module is used for determining the gain expectation degree of the demander to the data resource amount according to the user-defined demand information;
a loss acceptance degree determining module, configured to determine, according to the basic attribute information, a loss acceptance degree of the data resource amount by the demander;
the query module is used for querying in a preset data object information base, the data objects in the data object information base have a resource amount gain attribute and a resource amount loss attribute, and the query result requirements meet the following conditions: the resource amount gain attribute value is matched with the gain expectation degree, and the resource amount loss attribute value is matched with the loss acceptance degree;
and the distribution module is used for distributing the data object corresponding to the query result to the demand side.
According to a fourth aspect of the present application, there is provided a financial product orientation recommendation apparatus, the apparatus comprising:
the user information acquisition module is used for acquiring basic attribute information and user-defined demand information of a target user;
the gain expectation degree determining module is used for determining the gain expectation degree of the target user to the assets according to the user-defined demand information;
a loss acceptance degree determining module, configured to determine, according to the basic attribute information, a loss acceptance degree of the target user for the asset;
the query module is used for querying in a preset financial product information base, the financial product information in the financial product information base has an asset gain attribute and an asset loss attribute, and the query result meets the requirements of: an asset gain attribute matching the gain expectation level and an asset loss attribute matching the loss acceptance level;
and the recommending module is used for recommending the financial products corresponding to the query result to the target user.
According to the technical scheme, the attributes of the data objects are abstracted into two categories of resource amount gain attributes and resource amount loss attributes, and corresponding data structures and data object information are established and stored based on the two categories of attributes. When data object allocation needs to be carried out on a demand side, the gain expectation degree and the loss acceptance degree of the demand side on the data resource amount are determined according to the user-defined demand information and the basic attribute information of the demand side, and then matched data objects are found in a data object information base and allocated to the demand side. The whole distribution process does not need manual participation in processing, effectively reduces the processing cost, improves the processing efficiency, and can be better applied to various big data application scenes.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be 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 described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a schematic flow chart diagram of a data object allocation method of the present application;
FIG. 2 is a schematic flow chart of a method of financial product recommendation of the present application;
FIG. 3 is a schematic diagram of the structure of the data object distribution apparatus of the present application;
fig. 4 is a schematic structural diagram of the financial product recommendation apparatus of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described in detail 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 embodiments. All other embodiments that can be derived from the embodiments given herein by a person of ordinary skill in the art are intended to be within the scope of the present disclosure.
Fig. 1 is a flowchart of a data object allocation method provided in the present application, where the method may include the following steps:
s101, obtaining basic attribute information and custom requirement information of a data object demander;
s102, determining the expected degree of the gain of a demander to the data resource amount according to the user-defined demand information;
s103, determining the loss acceptance degree of the demander on the data resource quantity according to the basic attribute information;
and S104, inquiring in a preset data object information base. The data objects in the data object information base have a resource amount gain attribute and a resource amount loss attribute, and the query result requirement is satisfied under the following conditions: the resource amount gain attribute value is matched with the expected gain degree, and the resource amount loss attribute value is matched with the loss acceptance degree;
and S105, distributing the data object corresponding to the query result to the demand side.
In the above scheme, the attributes of the data object are abstracted into two categories, namely a "resource amount gain attribute" and a "resource amount loss attribute", and a corresponding data structure is established and data object information is stored based on the two categories of attributes. When data object allocation needs to be carried out on a demand side, the gain expectation degree and the loss acceptance degree of the demand side on the data resource amount are determined according to the user-defined demand information and the basic attribute information of the demand side, and then matched data objects are found in a data object information base and allocated to the demand side. The whole distribution process does not need manual participation in processing, effectively reduces the processing cost, improves the processing efficiency, and can be better applied to various big data application scenes.
Taking the application scenario of financial institutions issuing financial products as an example, ordinary users are not financial experts, and do not know how to invest or control market risks. Although the financial institution investment counselor has such professional ability, the number of investment counselors is far smaller than that of ordinary users, and the manual counseling consumes a lot of time, so that the financial institution mostly adopts a mode of providing manual counseling service to VIP users preferentially at present. In addition, in the actual counseling service providing process, the actual situation of the user is often very complicated, the financial products to be selected may be various, and the investment counselor may be difficult to be in the face of when the amount of information to be processed is too large.
Aiming at the practical problems, by applying the scheme of the application, the implementation cost of financial consultation service can be effectively reduced, the originally high-threshold financial product recommendation service is provided for general users, and the efficient and accurate recommendation of financial products is realized. Fig. 2 is a flowchart illustrating a method for directional recommendation of financial products according to the present application, which may include the following steps:
s201, obtaining basic attribute information and user-defined demand information of a target user;
in this application, an object that needs to provide financial consulting services is called a target user, and in order to recommend a suitable financial product to the target user, two pieces of information for the purpose need to be obtained: basic attribute information and user-defined requirement information.
Basic attribute information: the information that can embody the objective and actual situation of the user is broadly referred to, such as name, age, account flow, etc., in the present application, the basic information that may be used includes: information, family structure information, asset liability information, cash flow information, and the like.
Customizing the requirement information: each user may have their own personalized needs such as buying a house, buying a car, endowment, entrepreneur, child education, etc. The back of these requirements contains specific information such as the user's existing funds, the funds expected to be needed, the projected time to fulfill the requirements, etc., which may determine the basic asset management objectives.
Of course, the specific form of the basic attribute information and the customized requirement information that need to be obtained is not limited in the present application. Those skilled in the art can set the basic attribute information and the custom requirement information type to be obtained according to the actual requirement and the data content actually possessed by the financial system. For example, funds that the user expects to need, a scheduled time to achieve the need may be obtained to calculate the user's desired benefit in subsequent steps, and if the user directly enters the desired benefit information, the desired benefit information may be directly obtained for subsequent processing.
In addition, the information to be obtained may be information that has been filled in by the user in advance and has been stored in a user information base in the system, and in this case, the basic attribute information and the customized requirement information of the target user that are stored in advance may be obtained by reading from the user information base. For information which is not pre-stored, when the financial product is required to be recommended, a corresponding information input operation interface, such as an information form, a question-and-answer interactive interface and the like, can be provided for the user side to guide the current user to fill in the relevant information, so that basic attribute information and custom requirement information required by the recommended financial product are obtained.
S202, determining the gain expectation degree of the target user to the assets according to the user-defined demand information;
one simpler case is: data on the added value expectation degree of the target user on the assets, such as the annual benefit rate of the assets is expected to reach 5%, the income rate of the assets is expected to reach 20% in three years, and the like, can be directly obtained at S201. This situation is typically targeted at users who have some investment experience (e.g., at least knowledge of the underlying profitability concept).
The more complex is: by obtaining one or more other types of customized requirement information, calculating to obtain the expected degree of gain of the target user for the asset, for example, in S201, the target investment amount and the initial investment amount information of the user may be obtained, and then the method may further include:
(target amount of investment-initial investment amount)/initial investment amount, and the income rate of the fund expected by the user is obtained.
In practical applications, the fund return rate in a unit time may be more concerned according to the user or system requirements, so that the calculation can be further performed according to the user-defined investment duration:
according to the following steps: (target amount of investment-initial investment amount)/initial investment amount/investment duration, and the unit duration yield expected by the target user is obtained. For example, the specific form of the investment duration is "investment age", or the duration expressed in any unit (for example, month, week, day, etc.) is converted into the duration expressed in units of years, and the corresponding calculation result is the annual capital benefit rate expected by the target user.
Of course, the above formula is only for the specific type of customized requirement information and the result of the specific calculation, and should not be construed as a limitation to the present application. The skilled person can select a specific gain expectation level determination scheme according to actual situations.
S203, determining the loss acceptance degree of the target user to the assets according to the basic attribute information;
one simpler case is: at S201, data of the loss acceptance degree of the target user for the asset can be directly obtained, for example, 10% of the asset can be borne, 20% of the asset can be borne, or the financing type of the target user is conservative, balanced, aggressive, and so on.
The more complex is: the loss acceptance of the assets by the target user is calculated by obtaining one or more other types of basic attribute information, for example, at S201, the age, family structure condition, asset liability condition, cash flow condition, etc. of the user can be obtained, and these information can reflect the objective risk tolerance of the user, so as to determine the risk control target of asset management, for example, as follows:
age: the age range is 22-55, beyond which asset allocation is not appropriate. Within this range, the greater the age, the less the risk tolerance;
family structure: the more people the family needs to bear, the less risk bearing capacity;
assets and liabilities: the smaller the net value of the assets and liabilities, the smaller the risk bearing capacity;
and (4) cash flow: the smaller the cash flow, the smaller the risk bearing capacity;
in practical application, corresponding scores, namely corresponding risk bearing capacity values, can be given to each specific basic attribute information of the target user according to the preset corresponding relation; and then weighting the risk bearing capacity values corresponding to the one or more basic attribute information according to a preset weight value, thereby obtaining a value for integrally evaluating the loss acceptance degree of the target user to the assets.
Of course, the basic attribute information and the specific algorithm that are actually needed to be used need not be limited in this application, for example, if the consideration is conservative, the risk tolerance level corresponding to each specific basic attribute information of the target user may be determined first, and then the minimum value of the risk tolerance levels may be used as the value that is finally used to evaluate the loss acceptance level of the target user for the asset. For example, the following steps are carried out: based on the age of the target user, the risk tolerance of the user may be determined to be "low"; according to the family structure of the target user, the risk tolerance of the user can be determined to be 'high', and then according to the 'minimum value', the overall risk tolerance of the user can be finally determined to be 'low'.
And S204, inquiring in a preset financial product information base.
According to the scheme of the application, the financial product information in the financial product information base at least has two attributes of 'asset gain attribute' and 'asset loss attribute', wherein the 'asset gain attribute' represents the value-added condition description of a certain financial product, such as 'annual rate of recovery 5%', 'medium income' and the like; an "asset loss attribute" represents a description of a financial product risk profile, such as "maximum asset loss 10%", "low risk", and so forth.
The two attributes of the financial product information correspond to the aforementioned "expected degree of gain" and "acceptable degree of loss", respectively, and accordingly, the query result requires that the conditions be satisfied: the asset gain attribute matches the gain expectation level and the asset loss attribute matches the loss acceptance level.
It should be noted that "match" is not limited to "completely match" in the narrow sense, but should be understood as "satisfy the requirement", that is, two aspects of the financial product can respectively satisfy the requirement of the target user. For example: and if the asset gain attribute value of a certain financial product is not less than the gain expectation degree of the target user and the asset loss attribute value of the certain financial product is not more than the loss acceptance degree of the target user, the certain financial product is considered to meet the user requirements.
Further, it is understood that if the expression form of "gain expectation degree" and "asset gain attribute", "loss acceptance degree" and "asset loss attribute" is not identical, it may be converted into the same expression form according to a corresponding rule, for example, a mutual conversion between "annual rate of return 10%" and "high profit", "a mutual conversion between" affordable investment 50% "and" aggressive type ", and the like.
In practical applications, the final query result is not limited to a single financial product, but may be in the form of a combination of multiple financial products. For example, if a ratio of high-risk low-income financial products a and a ratio of low-risk high-income financial products B are combined, and the overall risk can be matched with the gain expectation degree and the loss acceptance degree of the target user, the combination can be regarded as a query result to be output.
And S205, recommending the financial products corresponding to the query result to the target user.
And outputting and recommending the result meeting the query requirement of the S204 to a target user, and if the final recommendation result contains information of a plurality of financial products (or financial product combinations), sequencing, displaying and outputting according to a certain rule, such as sequencing from high to low according to the profitability, sequencing from low to high according to the risk degree, and the like, or providing an operation interface for user-defined sequencing for the user so as to facilitate the user to view the recommendation result according to the requirement.
Therefore, by applying the scheme of the application, manual service is replaced by machine service, the use threshold of financial consultation service is effectively reduced, and the original services enjoyed by a small number of users are provided for a wider range of common users. The method not only effectively reduces the processing cost and improves the processing efficiency, but also can be better applied to various big data application scenes, and realizes the efficient and accurate recommendation of financial products.
Corresponding to the above method embodiment, the present application further provides a data object allocation apparatus, as shown in fig. 3, the apparatus may include:
a demander information obtaining module 110, configured to obtain basic attribute information and custom requirement information of a data object demander;
a gain expectation degree determining module 120, configured to determine, according to the user-defined requirement information, a gain expectation degree of the data resource amount by the demander;
a loss acceptance degree determining module 130, configured to determine, according to the basic attribute information, a loss acceptance degree of the data resource amount by the demander;
the query module 140 is configured to query a preset data object information base, where a data object in the data object information base has a resource amount gain attribute and a resource amount loss attribute, and a query result requirement satisfies the following conditions: the resource amount gain attribute value is matched with the expected gain degree, and the resource amount loss attribute value is matched with the loss acceptance degree;
and the distribution module 150 is configured to distribute the data object corresponding to the query result to the demand side.
The present application also provides a device for directional recommendation of financial products, as shown in fig. 4, the device may include:
a user information obtaining module 210, configured to obtain basic attribute information and user-defined requirement information of a target user;
the gain expectation degree determining module 220 is configured to determine a gain expectation degree of the target user for the asset according to the user-defined requirement information;
a loss acceptance determining module 230, configured to determine, according to the basic attribute information, a loss acceptance of the target user to the asset;
the query module 240 is configured to query in a preset financial product information base, where the financial product information in the financial product information base has an asset gain attribute and an asset loss attribute, and the query result satisfies the following conditions: the asset gain attribute is matched with the gain expectation degree, and the asset loss attribute is matched with the loss acceptance degree;
and the recommending module 250 is used for recommending the financial products corresponding to the query result to the target user.
In a specific embodiment of the present application, the user information obtaining module 210 may be specifically configured to:
reading pre-stored user-defined requirement information of a target user from a user information base;
or
And providing a user-defined requirement information input operation interface for a user side, and acquiring the user-defined requirement information input by the user through the interface.
In a specific embodiment of the present application, the customized requirement information of the target user may include: the initial investment amount, the target investment amount and the investment duration of the target user.
Accordingly, the gain desirability determination module 220 may be specifically configured to:
and obtaining the expected gain degree of the target user to the assets according to the (target investment amount-initial investment amount)/initial investment amount/investment duration.
In a specific embodiment of the present application, the basic attribute information of the target user may include:
age information, family structure information, asset liability information, cash flow information, and/or risk preference information of the target user.
Accordingly, the loss acceptance determination module 230 may be specifically configured to;
acquiring a risk bearing capacity value corresponding to the basic attribute information of the target user according to a preset corresponding relation;
and weighting the risk bearing capacity values corresponding to the obtained one or more kinds of basic attribute information to obtain the loss acceptance degree of the target user to the assets.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented 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 several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above-described apparatus embodiments are merely illustrative, and the modules described as separate components may or may not be physically separate, and the functions of the modules may be implemented in one or more software and/or hardware when implementing the solution of the present application. And part or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The foregoing is directed to embodiments of the present application and it is noted that numerous modifications and adaptations may be made by those skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
Claims (9)
1. A method for data object allocation, the method comprising:
acquiring basic attribute information and user-defined requirement information of a data object requirement party;
determining the expected degree of the gain of the demander to the data resource amount according to the user-defined demand information;
determining the loss acceptance degree of the demander to the data resource quantity according to the basic attribute information;
inquiring in a preset data object information base, wherein the data object in the data object information base has a resource amount gain attribute and a resource amount loss attribute, and the inquiry result requirement meets the following conditions: the resource amount gain attribute value is not less than the gain expectation degree, and the resource amount loss attribute value is not more than the loss acceptance degree;
and distributing the data object corresponding to the query result to the demander so that the demander can obtain the data object meeting the requirement of the demander.
2. A method for targeted recommendation of a financial product, the method comprising:
acquiring basic attribute information and user-defined demand information of a target user;
determining the expected gain degree of the target user to the assets according to the user-defined demand information;
determining the loss acceptance degree of the target user to the assets according to the basic attribute information;
inquiring in a preset financial product information base, wherein the financial product information in the financial product information base has an asset gain attribute and an asset loss attribute, and the inquiry result meets the following requirements: an asset gain attribute is not less than the gain desirability and an asset loss attribute is not greater than the loss acceptance;
and recommending the financial products corresponding to the query result to the target user so that the target user can obtain the financial products meeting the requirements of the target user.
3. The method of claim 2, wherein the obtaining of the basic attribute information and the customized requirement information of the target user comprises:
reading pre-stored basic attribute information and user-defined demand information of a target user from a user information base;
and/or
And providing an information input operation interface for a user side, and acquiring the basic attribute information and the user-defined requirement information input by the user through the interface.
4. The method according to claim 2 or 3,
the user-defined requirement information of the target user comprises the following steps: the initial investment amount, the target investment amount and the investment duration of the target user.
5. The method of claim 4, wherein determining the target user's desired level of gain for an asset based on the customized requirements information comprises:
and obtaining the expected gain degree of the target user to the assets according to the (target investment amount-initial investment amount)/initial investment amount.
6. The method of claim 2, wherein the basic attribute information of the target user comprises:
age information, family structure information, asset liability information, and/or cash flow information of the target user.
7. The method of claim 6, wherein the target user's attrition acceptance of an asset is determined based on the basic attribute information;
acquiring a risk bearing capacity value corresponding to the basic attribute information of the target user according to a preset corresponding relation;
and weighting the risk bearing capacity values corresponding to the obtained one or more kinds of basic attribute information to obtain the loss acceptance degree of the target user to the assets.
8. An apparatus for data object distribution, the apparatus comprising:
the system comprises a demander information acquisition module, a data object demander information acquisition module and a data object development module, wherein the demander information acquisition module is used for acquiring basic attribute information and user-defined demand information of a data object demander;
the gain expectation degree determining module is used for determining the gain expectation degree of the demander to the data resource amount according to the user-defined demand information;
a loss acceptance degree determining module, configured to determine, according to the basic attribute information, a loss acceptance degree of the data resource amount by the demander;
the query module is used for querying in a preset data object information base, the data objects in the data object information base have a resource amount gain attribute and a resource amount loss attribute, and the query result requirements meet the following conditions: the resource amount gain attribute value is not less than the gain expectation degree, and the resource amount loss attribute value is not more than the loss acceptance degree;
and the distribution module is used for distributing the data object corresponding to the query result to the demander so as to enable the demander to obtain the data object meeting the demand of the demander.
9. An apparatus for targeted recommendation of a financial product, the apparatus comprising:
the user information acquisition module is used for acquiring basic attribute information and user-defined demand information of a target user;
the gain expectation degree determining module is used for determining the gain expectation degree of the target user to the assets according to the user-defined demand information;
a loss acceptance degree determining module, configured to determine, according to the basic attribute information, a loss acceptance degree of the target user for the asset;
the query module is used for querying in a preset financial product information base, the financial product information in the financial product information base has an asset gain attribute and an asset loss attribute, and the query result meets the requirements of: an asset gain attribute is not less than the gain expectation level and an asset loss attribute is not greater than the loss acceptance level;
and the recommending module is used for recommending the financial products corresponding to the query result to the target user so that the target user can obtain the financial products meeting the requirements of the target user.
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| CN108257007A (en) * | 2018-01-05 | 2018-07-06 | 保均（厦门）金融信息技术服务有限公司 | Intelligent Matching system based on Internet of Things finance investment and financing platform |
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