CN115994821A - Method for establishing financial wind control system based on industrial chain digital scene financial model - Google Patents
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Abstract
The invention relates to the field of data processing, in particular to a method for establishing a financial wind control system based on an industrial chain digital scene financial model, which comprises the following steps: acquiring a user request, user information and history information; judging whether the history information is empty or not; calculating a user information score and a historical information score; calculating a comprehensive score according to the user information and the historical information score; obtaining a first target product according to the type of the request product; and obtaining an actual profit value and a target stage profit value of the first target product according to the user request time, calculating a difference value between the actual profit value and the target stage profit value, obtaining the number of users according to the difference value, adjusting basic data of the product according to a comparison result of the number of users and the preset number, and pushing a second target product obtained by adjustment to the user. The corresponding target product is obtained by utilizing the comprehensive score, and the basic data of the target product is adjusted according to the bisection condition, so that the product is more suitable for users.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method for establishing a financial wind control system based on an industrial chain digital scene financial model.
Background
With the development of the internet, banks and credit agencies become important sites for people to conduct financial transactions, and in order to meet the requirements of improving the efficiency of financial systems, the combination of banking systems and credit agencies with internet technology is an existing key technology.
Patent application number ZN202111548962.7 discloses a method for implementing supply chain financial risk control based on blockchain, comprising: acquiring financial risk data of a supply chain in advance, establishing a risk model, and calibrating an early warning range in a risk scene so as to determine a risk index set of the supply chain; classifying risk models, wherein the risk models include a risk model of a traditional credit business and a risk model specific to supply chain finance; finely classifying a risk model of a traditional credit business; the specific risk models of the supply chain finance include an account receivable financing risk model, a prepaid account financing risk model and an inventory financing risk model; inputting the acquired risk index set as information, and according to the established risk model as an evaluation model, evaluating the overall risk of the supply chain; after the supply chain financial risk is assessed, the supply chain financial risk is controlled.
In the prior art, risks are reduced by carefully classifying acquired financial risks and providing corresponding management modes for each risk, risks are avoided by transferring risks, and financial products provided for users are not analyzed, so that financial products suitable for users cannot be provided.
Disclosure of Invention
Therefore, the invention provides a method for establishing a financial wind control system based on an industrial chain digital scene financial model, which can solve the problem that financial products suitable for users cannot be provided.
In order to achieve the above object, the present invention provides a method for establishing a financial wind control system based on an industrial chain digital scene financial model, the method comprising:
acquiring a user request, user information and history information, wherein the user request comprises a user request name and user request time, and acquiring a request product type according to the user request name;
judging whether the historical information is empty or not, if the historical information is empty, calculating a user information score, and if the historical information is not empty, calculating the user information score and the historical information score respectively;
when the historical information is empty, correcting the user information according to a preset correction coefficient, taking the corrected user information as a comprehensive score, and when the historical information is not empty, adding the corrected user information score and the historical information score to obtain the comprehensive score;
obtaining a first target product according to the type of the request product;
obtaining an actual profit value and a target stage profit value of a first target product according to the user request time, calculating a difference value between the actual profit value and the target stage profit value of the first target product, obtaining the number of users when the difference value of the profit value is smaller than zero or the number of users when the difference value of the first target product is larger than or equal to zero, obtaining a corresponding preset product basic data adjustment coefficient according to a comparison result of the number of users and the preset number, adjusting the product basic data of the first target product according to the obtained preset product basic data adjustment coefficient, and pushing a second target product after the product basic data adjustment to the user.
Further, when calculating the user information score and the historical information score, the user information comprises a plurality of user basic information items and corresponding user basic information item values, the historical information comprises a plurality of historical basic information items and corresponding historical basic information item values, all user basic information item scores in the user information are obtained according to the user basic information item values in the user information and a preset user basic information item value range, all user basic information item scores are added to obtain the user information score, all historical basic information item scores in the historical information are obtained according to the historical basic information item values in the historical information and the preset historical basic information item value range, and all historical basic information item scores are added to obtain the historical information score;
when the comprehensive score is obtained, if the history information is empty, a preset correction coefficient p is obtained, and the user information score is multiplied by the correction coefficient p to obtain the comprehensive score;
if the historical information is not empty, a preset correction coefficient p is obtained, the user information score is multiplied by the preset correction coefficient p to obtain a corrected user information score, and the corrected user information score and the historical information score are added to obtain a comprehensive score; wherein, 0.8 < preset correction coefficient p < 0.9.
Further, when obtaining the first target product according to the requested product type, the method includes:
screening the products according to the types of the requested products to obtain a plurality of target type products;
obtaining actual profit values and corresponding target profit values of all target type products in a previous period, and respectively adding the actual profit values and the corresponding target profit values of all the target type products to obtain an actual total profit value and a target total profit value;
calculating a total difference between the actual total profit value and the target total profit value;
comparing the calculated total difference value with a preset total difference value, and grading the product score of the target type product according to the comparison result;
obtaining a preset grade score range corresponding to each product grade, matching the comprehensive score with the preset grade score range, taking the product grade corresponding to the matched preset grade score range as a target product grade, and taking the product in the target product grade as a first target product.
Further, when the first target product is obtained according to the type of the request product, the method further comprises: obtaining a preset product type subclass corresponding to a target product score grade according to the preset product type subclass list to serve as a target product type subclass, wherein the target product score grade is associated with any preset product type subclass, and obtaining a first target product according to the request product type and the target product type subclass.
Further, when calculating the total difference between the actual total profit value and the target total profit value, judging whether the total difference DeltaW is larger than zero and classifying the grades, when DeltaW is more than 0, comparing the total difference DeltaW with a preset total difference DeltaW 1, if DeltaW is more than or equal to DeltaW 1, classifying the grade of the product score of the target type product into a grade A, and if DeltaW is less than DeltaW 1, classifying the grade of the product score of the target type product into a grade B;
when DeltaW is less than or equal to 0, comparing the total difference DeltaW with a preset total difference DeltaW 2, if DeltaW is more than or equal to DeltaW 1, dividing the product fraction grade of the target type product into C grade, and if DeltaW is less than DeltaW 1, dividing the product fraction grade of the target type product into D grade, wherein the grade is A grade > B grade > C grade > D grade.
Further, after determining the target product type subclass of the first target product, acquiring an actual benefit value and a target stage benefit value of the first target product of the target product type subclass within a period time deadline before and after the user request time, calculating a time T between the request time and the latest period time, calculating a duty ratio P of the time T to the period time T, wherein the target stage benefit value = target benefit value x P, calculating a difference value Deltaw between the actual benefit value and the target stage benefit value of the first target product, if Deltaw is less than 0, acquiring the number M of users corresponding to the first target product, comparing the number M with a preset number M0, if M is more than or equal to M0, adjusting the product basic data of the first target product, and if M is less than M0, not adjusting the basic data of the first target product;
if Deltaw is more than or equal to 0, the number N of users corresponding to the first target product is obtained, the number N is compared with the preset number N0, if N is more than or equal to N0, the product basic data of the first target product is adjusted, and if N is less than N0, the product basic data of the first target product is not adjusted.
Further, when Deltaw is less than 0 and M is more than or equal to M0, reducing the product basic data of the first target product according to the first preset product basic data adjustment coefficient;
and when Deltaw is more than or equal to 0 and N is more than or equal to N0, the product basic data of the first target product is improved according to the second preset product basic data adjustment coefficient.
Further, when the product basic data of the first target product is adjusted, the product basic data of the first target product is a fixed value, the first target product comprises a plurality of items of product basic data, when the product basic data of the first target product is reduced, any item of product basic data is multiplied by a first preset product basic data adjustment coefficient to obtain any item of product actual basic data, when the product basic data of the first target product is increased, any item of product basic data is multiplied by a second preset product basic data adjustment coefficient to obtain any item of product actual basic data, and all items of product actual basic data are combined to form the second target product.
Further, when the request product type is obtained, the user request name is matched with a preset product type keyword, and the matched preset product type keyword is used as the request product type.
Further, when calculating the user information score, matching any user basic information item value with a corresponding preset user basic information item value range, obtaining the score corresponding to the matched preset user basic information item value range as the user basic information item score of the user basic information item, and adding all the user basic information item scores of the user basic information items to obtain the user information score;
when the historical information score is calculated, matching any historical basic information item value with a corresponding preset historical basic information item value range, obtaining the score corresponding to the matched preset historical basic information item value range as the historical basic information item score of the historical basic information item, and adding all the historical basic information item scores of the historical basic information items to obtain the historical information score.
Compared with the prior art, the method has the beneficial effects that the comprehensive score is obtained by scoring the user information and the historical information, the obtained comprehensive score is more accurate, the corresponding first target product is obtained according to the product request type, the basic data of the product is adjusted according to the income condition of the product in the preset time before the user request time to obtain the second target product, the basic data of the product is adjusted in time according to the income condition, the product is more suitable for users, and the accuracy of the product is improved.
In particular, the corrected user information score is used as the comprehensive score according to the condition that the historical information is empty, and the corrected user information score and the historical information score are added to obtain the comprehensive score when the historical information is not empty.
In particular, the embodiment of the invention reduces the data volume by screening the products of the corresponding types according to the request types, analyzes the product score grade of the comprehensive score obtained by the user according to the preset product score grade, ensures that the user requests the products more accurately, and ensures that the optimized setting of the products more accords with the user requirements.
In particular, the total difference DeltaW is calculated by acquiring the actual total profit value and the target total profit value of the target type product, whether DeltaW is larger than zero or not is judged, and the grade is classified for the product score grade of the target type product, further, the product score grade is classified for each target type product according to historical data, and further, the grade of the target type product is acquired according to the comprehensive score and the score range of the corresponding grade, so that the product requested by a user is more accurate, and the optimal setting is more in accordance with the product required by the user.
In particular, the product basic data of the first target product is correspondingly adjusted according to the number of users in the difference classification, so that each product is provided with the basic data according to the actual historical conditions, the product is adjustable, and further the product meets the user requirements better.
Drawings
FIG. 1 is a flow chart of a method for establishing a financial wind control system based on an industrial chain digital scene financial model according to an embodiment of the invention;
fig. 2 is a flowchart of a method for establishing a financial wind control system based on an industrial chain digital scene financial model according to another embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a method for establishing a financial wind control system based on an industrial chain digital scene financial model according to an embodiment of the present invention includes:
step S110, obtaining a user request, user information and history information, wherein the user request comprises a user request name and user request time, and obtaining a request product type according to the user request name;
step S120, judging whether the history information is empty, if so, calculating a user information score, and if not, calculating the user information score and the history information score respectively;
step S130, when the historical information is empty, correcting the user information according to a preset correction coefficient, taking the corrected user information as a comprehensive score, and when the historical information is not empty, adding the corrected user information score and the historical information score to obtain the comprehensive score;
step S140, obtaining a first target product according to the type of the request product;
step S150, obtaining an actual profit value and a target stage profit value of the first target product according to the user request time, calculating a difference value between the actual profit value and the target stage profit value of the first target product, obtaining the number of users when the difference value of the first target product is smaller than zero or the number of users when the difference value of the first target product is larger than or equal to zero, obtaining a corresponding preset product basic data adjustment coefficient according to a comparison result of the number of users and the preset number, adjusting the product basic data of the first target product according to the obtained preset product basic data adjustment coefficient, and pushing the second target product after the product basic data adjustment to the user.
Specifically, the product wind control system is divided into pre-credit admittance, product admittance, limit approval, product optimization, product prediction and post-credit monitoring, and the method is mainly in the step of product optimization, wherein the product basic data can be product quantity, product limit, product expiration date, product interest rate, product overdue interest rate and the like.
Specifically, the embodiment of the invention obtains the comprehensive score by scoring the user information and the historical information, obtains the comprehensive score more accurately, obtains the corresponding first target product according to the product request type, obtains the second target product by adjusting the basic data of the product according to the income condition of the product in the preset time before the user request time, and timely adjusts the basic data of the product according to the income condition, so that the product is more suitable for users and the accuracy of the product is improved.
Specifically, when the request product type is acquired, the user request name is matched with a preset product type keyword, and the matched preset product type keyword is used as the request product type.
Specifically, the user information comprises a plurality of user basic information items and corresponding user basic information item values, the history information comprises a plurality of history basic information items and corresponding history basic information item values, when the user information score is calculated, any user basic information item value is matched with a corresponding preset user basic information item value range, the score corresponding to the matched preset user basic information item value range is obtained to be used as the user basic information item score of the user basic information item, and the user basic information item scores of all the user basic information items are added and calculated to obtain the user information score;
when the historical information score is calculated, matching any historical basic information item value with a corresponding preset historical basic information item value range, obtaining the score corresponding to the matched preset historical basic information item value range as the historical basic information item score of the historical basic information item, and adding all the historical basic information item scores of the historical basic information items to obtain the historical information score.
Specifically, the user basic information item value corresponding to the user basic information item and the history basic information item value corresponding to the history basic information item may be null, when the user basic information item is a user name, the corresponding user name value is null, and when the history basic information item is a history product name, the corresponding history product name value is null.
Specifically, the user information user basic information item may be registered capital data, annual operating data, annual tax payment data and liability data of the user, the historical basic information item may be historical loan products of the user, loan amount data, loan duration, overdue payment times and passive times of exhibition of the corresponding products, if the user basic information item is annual profit of the user and annual tax payment of the user, corresponding values thereof are 500 ten thousand and 190 ten thousand respectively, the range of the preset annual profit value of the user at which the annual profit value of the user is 500 ten thousand is 450-550, corresponding score thereof is 75 score, and the obtained annual tax payment value of the user corresponds to 50 score, the user information score is 75+50=125 score.
Specifically, the embodiment of the invention obtains the preset score of the corresponding range according to the preset numerical range of the numerical values corresponding to the user basic information items and the historical basic information items, respectively adds all the user basic information items and the historical basic information items to obtain the user information score and the historical information score, further obtains the comprehensive score, and obtains the target product according to the comprehensive score, so that the product is more suitable for users.
Specifically, when the comprehensive score is obtained, if the history information is empty, a preset correction coefficient p is obtained, and the user information score is multiplied by the correction coefficient p to obtain the comprehensive score;
if the historical information is not empty, a preset correction coefficient p is obtained, the user information score is multiplied by the preset correction coefficient p to obtain a corrected user information score, and the corrected user information score and the historical information score are added to obtain a comprehensive score; wherein, 0.8 < preset correction coefficient p < 0.9.
Specifically, according to the embodiment of the invention, the corrected user information score is used as the comprehensive score when the historical information is empty, and the corrected user information score and the historical information score are added to obtain the comprehensive score when the historical information is not empty, and the user information can be corrected, so that the data reliability is lower, the influence caused by subjective factors is reduced through the corresponding correction score coefficient, the risk is reduced, the final score data is more accurate, the product is more suitable for users, and the accuracy of the product is improved.
Specifically, when obtaining the first target product according to the requested product type, the method includes:
step S141, screening the products according to the types of the requested products to obtain a plurality of target type products;
step S142, obtaining the actual profit value and the corresponding target profit value of each target type product in the previous period, and respectively adding the actual profit values and the corresponding target profit values of all the target type products to obtain the actual total profit value and the target total profit value;
step S143, calculating the total difference between the actual total profit value and the target total profit value;
step S144, comparing the calculated total difference with a preset total difference, and grading the product score of the target type product according to the comparison result;
step S145, a preset grade score range corresponding to each product grade is obtained, the comprehensive score is matched with the preset grade score range, the product grade corresponding to the matched preset grade score range is used as a target product grade, and the product in the target product grade is a first target product.
Specifically, the embodiment of the invention screens the products of the corresponding types according to the request types, so that the data volume is reduced, and the product score grade of the comprehensive score obtained by the user is analyzed according to the preset product score grade, so that the user requests the products more accurately, and the optimized setting of the products more meeting the requirements of the user is realized.
Specifically, when the first target product is obtained according to the request product type, the method further comprises: obtaining a preset product type subclass corresponding to a target product score grade according to the preset product type subclass list to serve as a target product type subclass, wherein the target product score grade is associated with any preset product type subclass, and obtaining a first target product according to the request product type and the target product type subclass.
Specifically, if the requested product type is a loan, the preset product type subclass list includes a long-term loan, a medium-term loan, and a short-term loan, and the first target product is obtained according to the target product type subclass corresponding to the target product score level, where the first target product may be a short-term loan product.
Specifically, when calculating the total difference between the actual total profit value and the target total profit value, judging whether the total difference DeltaW is larger than zero and classifying the grades, when DeltaW is larger than 0, comparing the total difference DeltaW with a preset total difference DeltaW 1, if DeltaW is larger than or equal to DeltaW 1, classifying the grade of the product score of the target type product into a grade A, and if DeltaW is less than DeltaW 1, classifying the grade of the product score of the target type product into a grade B;
when DeltaW is less than or equal to 0, comparing the total difference DeltaW with a preset total difference DeltaW 2, if DeltaW is more than or equal to DeltaW 1, dividing the product fraction grade of the target type product into C grade, and if DeltaW is less than DeltaW 1, dividing the product fraction grade of the target type product into D grade, wherein the grade is A grade > B grade > C grade > D grade.
Specifically, the embodiment of the invention calculates the total difference delta W by acquiring the actual total profit value and the target total profit value of the target type product, judges whether delta W is larger than zero and classifies the product score grades of the target type product, further classifies the product score grades of each target type product according to historical data, further acquires the grade of the target type product according to the comprehensive score and the score range of the corresponding grade, ensures that the user requests the product to be more accurate, and ensures that the optimization setting of the product more accords with the user requirement.
Specifically, after determining the target product type subclass of the first target product, acquiring an actual benefit value and a target stage benefit value of the first target product of the target product type subclass within a period of time deadline before and after a user request time, calculating a time T between the request time and the latest period time, calculating a duty ratio P of the time T to the period time T, wherein the target stage benefit value = target benefit value x P, calculating a difference value Deltaw between the actual benefit value and the target stage benefit value of the first target product, if Deltaw is less than 0, acquiring the number M of users corresponding to the first target product, comparing the number M with a preset number M0, if M is more than or equal to M0, adjusting product basic data of the first target product, and if M is less than M0, not adjusting the basic data of the first target product;
if Deltaw is more than or equal to 0, the number N of users corresponding to the first target product is obtained, the number N is compared with the preset number N0, if N is more than or equal to N0, the product basic data of the first target product is adjusted, and if N is less than N0, the product basic data of the first target product is not adjusted;
when Deltaw is less than 0 and M is more than or equal to M0, reducing the product basic data of the first target product according to a first preset product basic data adjustment coefficient;
and when Deltaw is more than or equal to 0 and N is more than or equal to N0, the product basic data of the first target product is improved according to the second preset product basic data adjustment coefficient.
Specifically, according to the user request time, the actual benefit value and the target stage benefit value of the first target product of the target product type subclass in the period between the time before the user request time and the expiration time of the latest period are obtained, and the time t between the request time and the latest period is calculated, for example, the user request time is in one period, and the time t is the time difference between the user request time and the last period end time.
Specifically, according to the embodiment of the invention, the product basic data of the first target product is correspondingly adjusted according to the number of the users in the difference classification, so that each product is provided with the basic data according to the actual historical conditions, the adjustment is realized, and the product meets the requirements of the users more.
Specifically, when the product basic data of the first target product is regulated, the product basic data of the first target product is a fixed value, the first target product comprises a plurality of items of product basic data, when the product basic data of the first target product is reduced, any item of product basic data is multiplied by a first preset product basic data regulating coefficient to obtain any item of product actual basic data, when the product basic data of the first target product is increased, any item of product basic data is multiplied by a second preset product basic data regulating coefficient to obtain any item of product actual basic data, and all items of product actual basic data are combined to form the second target product.
Specifically, according to the embodiment of the invention, the historical income situation of the first target product in a certain time is analyzed by dividing the situation, and the income situation of the product is not analyzed yet when the product request time is positioned in the next period time because the period time of the product fraction grade is fixed, so that the income situation in the period time from the last period time is analyzed to adaptively adjust the product basic data of the product, the product basic data can be adjusted according to the specific situation, the accuracy of the weight is improved, and the product meets the requirements of users more.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for establishing the financial wind control system based on the industrial chain digital scene financial model is characterized by comprising the following steps of:
acquiring a user request, user information and history information, wherein the user request comprises a user request name and user request time, and acquiring a request product type according to the user request name;
judging whether the historical information is empty or not, if the historical information is empty, calculating a user information score, and if the historical information is not empty, calculating the user information score and the historical information score respectively;
when the historical information is empty, correcting the user information according to a preset correction coefficient, taking the corrected user information as a comprehensive score, and when the historical information is not empty, adding the corrected user information score and the historical information score to obtain the comprehensive score;
obtaining a first target product according to the type of the request product;
obtaining an actual profit value and a target stage profit value of a first target product according to the user request time, calculating a difference value between the actual profit value and the target stage profit value of the first target product, obtaining the number of users when the difference value of the profit value is smaller than zero or the number of users when the difference value of the first target product is larger than or equal to zero, obtaining a corresponding preset product basic data adjustment coefficient according to a comparison result of the number of users and the preset number, adjusting the product basic data of the first target product according to the obtained preset product basic data adjustment coefficient, and pushing a second target product after the product basic data adjustment to the user.
2. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 1, wherein when calculating a user information score and a history information score, the user information comprises a plurality of user basic information items and corresponding user basic information item values, the history information comprises a plurality of history basic information items and corresponding history basic information item values, all user basic information item scores in the user information are obtained according to the user basic information item values and a preset user basic information item value range in the user information, all user basic information item scores are added to obtain a user information score, all history basic information item scores in the history information are obtained according to the history basic information item values and the preset history basic information item value range in the history information, and all history basic information item scores are added to obtain a history information score;
when the comprehensive score is obtained, if the history information is empty, a preset correction coefficient p is obtained, and the user information score is multiplied by the correction coefficient p to obtain the comprehensive score;
if the historical information is not null, a preset correction coefficient p is obtained, the user information score is multiplied by the preset correction coefficient p to obtain a corrected user information score, and the corrected user information score and the historical information score are added to obtain a comprehensive score;
wherein, 0.8 < preset correction coefficient p < 0.9.
3. The method of building a financial pneumatic control system based on an industrial chain digitized scene financial model of claim 2, wherein when obtaining a first target product from said requested product type comprises:
screening the products according to the types of the requested products to obtain a plurality of target type products;
obtaining actual profit values and corresponding target profit values of all target type products in a previous period, and respectively adding the actual profit values and the corresponding target profit values of all the target type products to obtain an actual total profit value and a target total profit value;
calculating a total difference between the actual total profit value and the target total profit value;
comparing the calculated total difference value with a preset total difference value, and grading the product score of the target type product according to the comparison result;
obtaining a preset grade score range corresponding to each product grade, matching the comprehensive score with the preset grade score range, taking the product grade corresponding to the matched preset grade score range as a target product grade, and taking the product in the target product grade as a first target product.
4. The method of claim 3, further comprising, when obtaining the first target product according to the requested product type: obtaining a preset product type subclass corresponding to a target product score grade according to the preset product type subclass list to serve as a target product type subclass, wherein the target product score grade is associated with any preset product type subclass, and obtaining a first target product according to the request product type and the target product type subclass.
5. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 4, wherein when calculating the total difference between the actual total profit value and the target total profit value, judging whether the total difference DeltaW is larger than zero and classifying, wherein when DeltaW is larger than 0, comparing the total difference DeltaW with a preset total difference DeltaW 1, if DeltaW is larger than or equal to DeltaW 1, classifying the product score grade of the target type product as A, and if DeltaW < DeltaW1, classifying the product score grade of the target type product as B;
when DeltaW is less than or equal to 0, comparing the total difference DeltaW with a preset total difference DeltaW 2, if DeltaW is more than or equal to DeltaW 1, dividing the product fraction grade of the target type product into C grade, and if DeltaW is less than DeltaW 1, dividing the product fraction grade of the target type product into D grade, wherein the grade is A grade > B grade > C grade > D grade.
6. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 5, wherein after determining a target product type subclass of the first target product, acquiring an actual profit value and a target stage profit value of the first target product of the target product type subclass within a period of time between a time before a user request and a time after a latest period of time according to a user request, calculating a time T between the request time and the latest period of time, calculating a duty ratio P of the time T to the period of time T, wherein the target stage profit value = target profit value x P, calculating a difference Δw between the actual profit value and the target stage profit value of the first target product, if Δw is less than 0, acquiring a number M of users corresponding to the first target product, comparing the number M with a preset number M0, if M is greater than or equal to M0, adjusting product basic data of the first target product, and if M is less than M0, not adjusting basic data of the first target product;
if Deltaw is more than or equal to 0, the number N of users corresponding to the first target product is obtained, the number N is compared with the preset number N0, if N is more than or equal to N0, the product basic data of the first target product is adjusted, and if N is less than N0, the product basic data of the first target product is not adjusted.
7. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 6, wherein when Δw is less than 0 and M is greater than or equal to M0, the product basic data of the first target product is reduced according to a first preset product basic data adjustment coefficient;
and when Deltaw is more than or equal to 0 and N is more than or equal to N0, the product basic data of the first target product is improved according to the second preset product basic data adjustment coefficient.
8. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 7, wherein when the product basic data of the first target product is adjusted, the product basic data of the first target product is a fixed value, the first target product comprises a plurality of product basic data, when the product basic data of the first target product is reduced, any product basic data is multiplied by a first preset product basic data adjustment coefficient to obtain any product actual basic data, when the product basic data of the first target product is increased, any product basic data is multiplied by a second preset product basic data adjustment coefficient to obtain any product actual basic data, and all the product actual basic data are combined to form the second target product.
9. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 8, wherein when the request product type is obtained, the user request name is matched with a preset product type keyword, and the matched preset product type keyword is used as the request product type.
10. The method for building a financial wind control system based on an industrial chain digital scene financial model according to claim 9, wherein when calculating user information scores, matching any user basic information item value with a corresponding preset user basic information item value range, obtaining the score corresponding to the matched preset user basic information item value range as the user basic information item score of the user basic information item, and adding all the user basic information item scores of the user basic information items to obtain the user information score;
when the historical information score is calculated, matching any historical basic information item value with a corresponding preset historical basic information item value range, obtaining the score corresponding to the matched preset historical basic information item value range as the historical basic information item score of the historical basic information item, and adding all the historical basic information item scores of the historical basic information items to obtain the historical information score.
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