CN115983572B - Method and device for ordering fund parties, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application discloses a method and a device for ordering fund parties, computer equipment and a storage medium. The method comprises the following steps: acquiring target order information of a target applicant sent by a user terminal; determining a primary feature and a secondary feature corresponding to each preset candidate fund party and a target application party respectively according to the target order information, wherein the influence degree of the secondary feature on the sequencing of the candidate fund parties is smaller than that of the primary feature on the sequencing of the candidate fund parties; dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting; sequencing the candidate fund parties in each subgroup according to the secondary characteristics to obtain the secondary sequencing of the candidate fund parties in each subgroup; determining target ordering of candidate fund parties according to the first order and the second order; and sending the target sequence to the user terminal. By implementing the method provided by the embodiment of the application, the sequencing precision of the candidate fund parties can be improved.
Description
Technical Field
The present application relates to the field of financial technologies, and in particular, to a method and apparatus for ordering funds, a computer device, and a storage medium.
Background
With the continuous progress of science and technology, the internet finance industry is also vigorously developed, and with the change of people's consumption theory, the business of borrowing and lending etc. through the network also becomes the fund turnover mode that people commonly use. After the applicant places a fund application (order), the fund lending platform sorts the plurality of candidate fund parties capable of providing funds so as to obtain the matching degree of each candidate fund party and the current order.
The existing specific sorting process is as follows: firstly, after a user places an order, carrying out pre-matching screening on the user and the order, and removing the fund parties which do not meet the hard rule to obtain a candidate fund party list capable of receiving the order. Then, each candidate fund party in the candidate fund party list is ranked by people based on the historical performance of the candidate fund party, the current fund party sets manual priority, and each list is ranked according to the set priority.
However, the priority is set for the candidate fund party list manually in advance, the priorities of the fund parties are the same for different orders, the fund parties are not flexibly adapted according to the characteristics of the current order, and the ordering is only performed based on the analysis experience of the historical data manually, so that the ordering precision is not high.
Disclosure of Invention
The embodiment of the application provides a method, a device, computer equipment and a storage medium for sequencing fund parties, which can improve the sequencing accuracy of the fund parties.
In a first aspect, an embodiment of the present application provides a method for ordering funding parties, including:
acquiring target order information of a target applicant sent by a user terminal;
determining a primary feature and a secondary feature corresponding to each preset candidate fund party and the target application party respectively according to the target order information, wherein the influence degree of the secondary feature on the sequencing of the candidate fund parties is smaller than that of the primary feature on the sequencing of the candidate fund parties;
dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting;
sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup;
determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
and sending the target sequence to the user terminal, so that a user obtains the target sequence through the user terminal, and determining the matching degree of the target order information and each candidate fund party.
In a second aspect, an embodiment of the present application further provides a sorting apparatus for funding parties, including:
the receiving and transmitting unit is used for acquiring target order information of a target applicant party sent by the user terminal;
the processing unit is used for determining primary characteristics and secondary characteristics of each preset candidate fund party and the target application party respectively according to the target order information, and the influence degree of the secondary characteristics on the sequencing of the candidate fund parties is smaller than that of the primary characteristics on the sequencing of the candidate fund parties; dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting; sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup; determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
the receiving and transmitting unit is further configured to send the target order to the user terminal, so that a user obtains the target order through the user terminal, and determines a matching degree of the target order information and each candidate fund party.
In a third aspect, an embodiment of the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the method when executing the computer program.
In a fourth aspect, embodiments of the present application also provide a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, implement the above-described method.
The embodiment of the application provides a method and a device for ordering fund parties, computer equipment and a storage medium. Wherein the method comprises the following steps: acquiring target order information of a target applicant sent by a user terminal; determining a primary feature and a secondary feature corresponding to each preset candidate fund party and the target application party respectively according to the target order information, wherein the influence degree of the secondary feature on the sequencing of the candidate fund parties is smaller than that of the primary feature on the sequencing of the candidate fund parties; dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting; sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup; determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking; and sending the target sequence to the user terminal, so that a user obtains the target sequence through the user terminal, and determining the matching degree of the target order information and each candidate fund party. On the one hand, the embodiment can determine a plurality of business targets (comprising a first-level feature and a second-level feature) according to the target order information of the current target application party, and sort the candidate fund parties according to the plurality of business targets corresponding to the current target order information without manually setting priority for the candidate fund parties in advance, so that the scheme can flexibly sort the candidate fund parties according to the features of the current target application party, and improve the sorting precision when the fund parties are matched; on the other hand, the application designs double-layer sorting, firstly, the first-layer sorting is carried out by utilizing the first-level characteristics, and the candidate fund sides with similar scores are divided into the same subgroup by utilizing the subgroup division rule in the first-layer sorting, so that the error caused by sorting according to the absolute value is reduced, then, the candidate fund sides in each subgroup are sorted according to the second-level characteristics, the functions of optimizing the main target firstly and then optimizing the secondary target are realized, and the sorting precision is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a framework of an intelligent matching model according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for ordering parties according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating a method for ordering parties according to another embodiment of the present application;
FIG. 4 is a schematic block diagram of a sorting apparatus for funding parties provided by an embodiment of the present application;
fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The embodiment of the application provides a method and a device for ordering fund parties, computer equipment and a storage medium.
The execution main body of the fund sorting method can be the fund sorting device provided by the embodiment of the application, or an intelligent matching model integrated with the fund sorting device, or a computer device integrated with the fund sorting device or the intelligent matching model, wherein the fund sorting device can be realized in a hardware or software mode, and the computer device is a server.
In some embodiments, the intelligent matching model in this embodiment includes a ranking module, in other embodiments, referring to fig. 1, the intelligent matching model includes at least one of a planning module, a wind control module, and an underlying module in addition to the ranking module, where the planning module is configured to determine recommended and non-recommended candidate funding parties for global optimal coarse allocation; the sorting module is a core module of the application and is used for sorting candidate fund parties; the wind control module is used for controlling the risks of the distributed wetting parties; the bottom layer module is used for realizing the functions of the planning module, the sequencing module and the wind control module, and the full-flow through is used for bottom layer support, and mainly comprises processing and reading of model real-time characteristic fields, development and deployment of models (comprising a machine learning prediction model, an operation optimizing model, a matching sequencing model and the like), external interaction and the like.
Referring to fig. 2, fig. 2 is a flow chart illustrating a method for ordering funding parties according to an embodiment of the application. As shown in fig. 2, the method includes the following steps S110 to S160.
S110, acquiring target order information of a target applicant sent by the user terminal.
In this embodiment, the target application party is a user who applies for funds to the platform currently, the target order information includes user information and funds application information, the user information includes gender, age, region and occupation of the user, and the funds application information includes application amount and period number of funds.
Specifically, in this embodiment, after the user terminal receives the target order information of the target applicant, which is input by the user, the user terminal sends the target order information to the server.
S120, determining primary characteristics and secondary characteristics of each preset candidate fund party and the target application party respectively corresponding to the target order information.
The influence degree of the secondary features on the candidate fund party sequencing is smaller than that of the primary features on the candidate fund party sequencing.
In some embodiments, the primary characteristics include profit margin, pass rate, and matching, and the secondary characteristics include a split duty cycle (the split duty cycle need not be as large as good based on business considerations, so the application downgrades the split duty cycle into the secondary characteristics).
The following describes the calculation process of each level of features:
profit margin:
specifically, the present embodiment may predict by establishing a profit margin calculation model, and specifically, the model determines the profit margin corresponding to each candidate fund party and each target application party according to a target application credit in the target order information and profit margins corresponding to each candidate fund party and the target application credit.
In some embodiments, the profit margin corresponding to each candidate fund party can be obtained only by inputting the target application credit into the corresponding profit margin calculation model, wherein, for the candidate fund party i, the formula for calculating the profit margin in the model is as follows:
f 1 i ＝L i /gmv i ；
wherein f 1 i For the profit margin of candidate fund party i corresponding to the target applicant party, L i For the profit margin of candidate fund party i corresponding to the target application credit, gmv i And applying for the limit for the target.
The passing rate is as follows:
in some embodiments, the prediction may be performed by establishing a passing rate calculation model, and specifically, the passing rate of each candidate fund party corresponding to the target applicant party is determined according to the historical passing order quantity and the total number of historical orders of each candidate fund party.
In some embodiments, the passage rate calculation model determines the passage rate of the candidate funding party by the following formula:
pass rate corresponding to candidate fund party i, +.>History passing amount of order for candidate fund party i, +.>The history for candidate fund party i does not pass the amount of orders.
Matching degree:
in some embodiments, the matching utility function is based on matching considerations for the three aspects of area, period, and pricing. Specifically, since the matching degree is not quantified well, the embodiment may determine, based on a preset matching degree utility function, the matching degrees of each candidate fund party and the target applicant party respectively corresponding to each candidate fund party according to the target order information; wherein, the matching degree utility function is as follows:
Where rate_diff represents the tariff limit pricing difference (tariff limit pricing maximum-pricing minimum), and term_diff represents the tariff limit period difference. Wherein the pricing is the interest rate of the sponsor.
The calculation process of the secondary features is described below:
and (5) distributing the self-guarantee risks of the sponsors, and proportionally or fixedly receiving the return commissions. The candidate fund party needs the platform to bear the risk, and for the risk control, a certain proportion (such as 5%) of the guarantee is collected from the platform to cope with bad account loss while receiving the asset. In order to relieve the pressure of the guarantee and obtain higher operation profits, the platform has certain requirements on the wetting ratio, and in the embodiment, a wetting ratio utility function is set, wherein the smaller the absolute value of the difference between the real-time wetting ratio of the candidate fund party and the target value (namely the preset wetting ratio), the higher the corresponding wetting ratio score is.
And S130, dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and a preset subgroup dividing rule, and sorting the subgroups to obtain a first-level sorting.
Specifically, the application performs ranking of candidate fund parties through a ranking module, and in some embodiments, the ranking module in the intelligent matching model comprises an optimization layer and a constraint layer, wherein the optimization layer performs ranking by using primary features, and the constraint layer performs ranking by using secondary features.
Regarding the optimization layer:
step a and step b are performed separately:
and a step a of determining a first-level matching score corresponding to each candidate fund party according to the first-level features and the preset feature weights corresponding to the first-level features.
Specifically, the candidate fund parties respectively determine the first-order matching scores of the target application party and each candidate fund party in the optimization layer according to the following formula:
wherein Y is i 1 For the first-order matching score corresponding to candidate fund party i in the optimization layer, f 1 i Profit margin, w, of candidate fund party i corresponding to target applicant party 1 For the weight corresponding to the profit margin,the corresponding passing rate of the candidate fund party i is obtained; w (w) 2 F is the weight corresponding to the passing rate 3 i For the matching degree, w, of the target application party and the candidate fund party i 3 And the weight corresponding to the matching degree.
Wherein the characteristic weight is obtained according to subjective and objective fusion weighting method (fusion subjective weighting method and objective weighting method), and w 1 、w 2 Or w 3 The weighting process is specifically as follows:
w＝λ*w z +(1-λ)*w k ；
wherein lambda E (0, 1) is a constant, w z For weights determined by subjective weighting, w k Is a weight determined by an objective weighting method.
The objective of determining the characteristic weight through the subjective and objective fusion weighting method is mainly to take advantage of the subjective weighting method, which is determined artificially and subjectively and reflects the intention of a decision maker, but is static once set. And the objective weighting result is that the data itself reflects the weight information, and the data is more objective and dynamically changed, so that the data and the weight information are combined.
Subjective weighting method:
by adopting an analytic hierarchy process (Analytic Hierarchy Process, AHP for short), the problem that a business party cannot intuitively scale the weights can be solved, and the relative importance between the targets is only required to be determined by the method, and consistency check can be carried out to see whether the weight setting is reasonable or not. The method comprises the following four steps:
(1) establishing a hierarchical model; (2) constructing a judgment matrix (3) for consistency test CI; (4) computing feature vectors
Objective weighting method:
the entropy weight method is adopted, and the method is based on the following principle: the smaller the degree of variation of the index, the smaller the amount of information reflected, and the lower the corresponding weight should be. An extreme example is: if this indicator is the same value for all tariffs, we can consider the weight of this indicator to be 0, i.e. this indicator does not contribute to our overall evaluation.
The calculation steps of the entropy weight method are as follows:
1) Determining an index system;
2) Data preprocessing: redundant data processing, outlier processing and the like;
3) Normalization: homomorphism of indices of different dimensions (linear normalization or z-score method);
wherein, the linear normalization:
z-score method:
wherein x is ij The value of the jth index is the ith sponsor in the sponsor sample; max (x) j ) Is the maximum value of the j index in the sponsor sample, min (x j ) Is the minimum value of the j index in the tariff sample,the average value of the jth sample in the sponsor samples is taken, and S is the standard deviation of the jth sample in the sponsor samples.
4) Calculating entropy and weight of the index:
(1) firstly, calculating the specific gravity of the j index of the i-th sponsor:
(2) calculating information entropy of the j-th index:
(3) calculating the weight of the j index:
wherein, w is different according to the index j May be w 1 、w 2 Or w 3 。
Although the entropy weight method can objectively reflect the index distinguishing force from the data to a certain extent, the dependence on the sample data is larger, such as no service experience guidance, and the weight may be distorted, so that the final attribute weight combines subjective expert scoring and objective data entropy weight, and the advantages of the entropy weight method can be better exerted after the subjective expert scoring and objective data entropy weight are fused together.
And b, dividing the candidate fund party into a plurality of subgroups according to the primary matching score and the subgroup dividing rule, and sorting the subgroups according to the score interval size corresponding to each subgroup in the subgroup dividing rule to obtain the primary sorting.
In this embodiment, after determining the first-level matching score, dividing the candidate fund party into a plurality of subgroups according to a subgroup dividing rule, and sorting the subgroups according to the score interval size corresponding to each subgroup in the subgroup dividing rule, so as to obtain the first-level sorting.
In the following embodiment, the group division rule is as shown in table 1:
TABLE 1
| Score interval | [0，2) | [2，4) | [4，6) | [6，8) | [8，10] |
| Group of | 1 | 2 | 3 | 4 | 5 |
For example, if the first-order matching score corresponding to a candidate fund party is 3, the candidate fund party is divided into the group 2.
And S140, sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup.
The implementation step of determining the secondary ordering of the candidate fund parties in the group is implemented in a constraint layer, and specifically comprises the following steps:
regarding the constraint layer:
secondary optimization objectives of constraint layer degradation, such as the fraction of the wetting, similar to the optimization layer, are also weighted and summed to calculate a constraint layer score Y i 2 If the constraint layer has only a wetting duty ratio (corresponding to the second-level matching score of the candidate fund party i in the constraint layer), the value corresponding to the wetting duty ratio is multiplied by the corresponding feature weight, namely the corresponding second-level matching score.
And S150, determining the target ordering of the candidate fund party according to the primary ordering and the secondary ordering.
In this embodiment, the comprehensive ordering of the candidate fund parties is determined according to the first order and the second order of the candidate fund parties, and when the comprehensive evaluation ordering is finally performed, the scores of the constraint layers are ordered according to the hierarchical ordering of the scores of the optimization layers.
Therefore, the application designs double-layer sequencing, on one hand, the main target can be optimized firstly, and then the secondary target can be optimized, on the other hand, the characteristic value of target quantization is actually obtained from the result of a utility function or a prediction model and is not completely accurate, so that the similarity score is divided into the same level, and the error influence caused by sequencing according to the absolute value can be reduced.
And S160, sending the target sequence to the user terminal.
In this embodiment, after the server calculates the target rank of each candidate fund party, the target rank is sent to the user terminal, so that the user obtains the target rank through the user terminal, and determines the matching degree of the target order information and each candidate fund party, where in the target rank, the matching degree of the candidate fund party and the target order information that are earlier in the target rank is higher.
In this embodiment, the intelligent matching model is further provided with a risk layer, and specifically, in this layer, risk scores of the candidate fund parties may be obtained; and then adding a risk label containing a corresponding risk score for each candidate fund party. In this embodiment, a risk score threshold may be further set, and then candidate funds parties with risk scores greater than the risk score threshold may be filtered out, so as to reduce recommended risks.
In summary, on the one hand, the present embodiment may determine a plurality of business targets (including a primary feature and a secondary feature) according to the target order information of the current target applicant, and sort the candidate fund parties according to the plurality of business targets corresponding to the current target order information, without manually setting priority for the candidate fund parties in advance, so that the present embodiment may flexibly sort the candidate fund parties according to the feature of the current order, and the sorting precision is high; on the other hand, the application designs double-layer sorting, firstly, the first-layer sorting is carried out by utilizing the first-level characteristics, and the candidate fund sides with similar scores are divided into the same subgroup by utilizing the subgroup division rule in the first-layer sorting, so that the error caused by sorting according to the absolute value is reduced, then, the candidate fund sides in each subgroup are sorted according to the second-level characteristics, the functions of optimizing the main target firstly and then optimizing the secondary target are realized, and the sorting precision is further improved.
Fig. 3 is a flow chart of a method for ordering funding parties according to another embodiment of the present application. As shown in fig. 3, the sorting method of the funding party of the present embodiment includes steps S210 to S2100.
Compared with the corresponding embodiment of fig. 2, the present embodiment adds the function of the planning module, wherein the function of the planning module is global optimal coarse allocation, and the adding of the planning module mainly considers that the decision of a single order is optimal, but not the optimal decision under the global condition. Although the action is a pen order to make decisions, the eye is aimed globally. Therefore, an optimized rough allocation is firstly made on the basis of the global, so that the accuracy of the sorting is further improved.
S210, acquiring target order information of a target applicant sent by a user terminal.
In this embodiment, the target application party is a user who applies for funds to the platform currently, the target order information includes user information and funds application information, the user information includes gender, age, region and occupation of the user, and the funds application information includes application amount and period number of funds.
S220, adding pre-allocation labels to each candidate fund party according to the target order information based on a preset global optimization model, wherein the pre-allocation labels comprise recommended labels and non-recommended labels.
In this embodiment, the global optimization model is built by the following 3 steps:
step 1, defining service problems:
the service problem of the current scene is defined: based on the data of the position of the fund, the budget of the asset, etc., how to allocate can the global optimization be realized.
Step 2, modeling and solving:
based on the business problem, converting into a mathematical language, establishing an optimization model through a linear programming algorithm, and respectively determining three important factors: decision variables, objective functions, and constraints.
(1) Defining variables:
decision variables: the group i of assets is assigned to the magnitude x of the sponsor j ij
Constant is known: e.g., C (n) is the total position of the current month borrowing for tariff n, the z_i asset group i budget magnitude, etc
(2) Defining an objective function: the optimization formula is as follows, where t is the profit margin, g is the transaction amount gmv, d1 and d2 are preset weights, and the objective function is as follows:
(3) setting constraint conditions: the formula is shown in the following set, and mainly comprises: the position of the fund, the rule of the fund and the budget of the fund, the constraint of the large disc division and lubrication ratio, and the like.
φ(y)≤G
(4) Model solving: carrying out optimization solution on the established model to obtain an optimal solution of the model
Step 3, model application
And finally, applying the result of the global optimization model to intelligent matching, and acquiring a recommended sponsor list of the current order in real time under global optimal coarse distribution.
So far, through a first planning module, we divide the fund candidates into 2 large queues, and respectively add pre-allocation labels for each candidate fund party, wherein the pre-allocation labels comprise recommended labels and non-recommended labels, namely, whether the recommended fund party is under global optimal rough allocation or not.
And S230, determining the candidate fund party corresponding to the recommended label as a first candidate fund party, and determining the candidate fund party corresponding to the non-recommended label as a second candidate fund party.
In this embodiment, the candidate fund party corresponding to the recommended label is determined as a first candidate fund party (classified into one column), the candidate fund party corresponding to the non-recommended label is determined as a second candidate fund party (classified into another column), and finally two columns of candidate fund parties are obtained, and the subsequent sorting module sorts the candidate fund parties for each column.
S240, determining primary characteristics and secondary characteristics of each preset candidate fund party and the target application party respectively corresponding to the target order information.
The influence degree of the secondary features on the candidate fund party sequencing is smaller than that of the primary features on the candidate fund party sequencing.
In this embodiment, step S240 is similar to step S120 in the corresponding embodiment of fig. 2, and detailed descriptions thereof are omitted here.
S250, dividing the first candidate fund party into a plurality of first subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the first subgroups to obtain a first-level sorting.
In this embodiment, after the candidate funding party in step S130 is replaced with the first candidate funding party, the step is similar to S130, and the detailed sorting process is not described here.
And S260, dividing the second candidate fund party into a plurality of second subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the second subgroups to obtain a second first-level sorting.
In this embodiment, after the candidate fund party in step S130 is replaced with the second candidate fund party, the step is similar to step S130, and the detailed sorting process is not described here.
S270, sorting the candidate fund parties in each first subgroup according to the secondary characteristics, and obtaining first secondary sorting of the candidate fund parties in each first subgroup.
In this embodiment, after the group in step S140 is replaced with the first group, the step is similar to S140, and the specific secondary sorting process is not described here.
S280, sorting the candidate fund parties in each second subgroup according to the secondary characteristics to obtain second-level sorting of the candidate fund parties in each second subgroup.
In this embodiment, after the group in step S140 is replaced with the second group, the step is similar to S140, and the specific secondary sorting process is not described here.
S290, determining the target sequence according to the first one-level sequence, the first two-level sequence, the second one-level sequence and the second two-level sequence.
The present embodiment combines the first one-level ordering and the first one-level ordering of the first candidate fund party (recommended fund party) and the second one-level ordering of the second candidate fund party (non-recommended fund party).
Specifically, the first candidate fund party is ordered according to the first one-level ordering and the first secondary feature, so that a first ordering is obtained; i.e. determining a first ranking for the rankings between the first subgroups and the rankings of the first candidate fund parties within each first subgroup, the resulting first ranking being the ranking between the first candidate fund parties.
Meanwhile, sorting the second candidate fund parties according to the second primary sorting and the second secondary sorting to obtain a second sorting; i.e. determining a second rank for ranks among the second subgroups and ranks of the second candidate fund parties within each second subgroup, the resulting second rank being the rank among the second candidate fund parties.
Finally, determining the target sequence according to the first sequence and the second sequence; in this embodiment, the first rank is ranked before the second rank, i.e., the first candidate fund party is ranked before and the second candidate fund party is ranked after.
And S2100, sending the target sequence to the user terminal.
In this embodiment, after the server calculates the target rank of each candidate fund party, the target rank is sent to the user terminal, so that the user obtains the target rank through the user terminal, and determines the matching degree of the target order information and each candidate fund party, where in the target rank, the matching degree of the candidate fund party and the target order information that are earlier in the target rank is higher.
In summary, the present embodiment includes the following beneficial effects:
1. compared with the prior art, the priority is manually set, and the intelligent matching model with a plurality of modules is built to realize simultaneous optimization of a plurality of business targets (profit margin, passing rate, matching degree, wetting proportion and sponsor risk), so that the intelligent business system is more time-saving, labor-saving, efficient and intelligent.
2. During sorting, a planning module is introduced, a global optimal rough allocation is firstly made, and then sorting decision is made for single orders. And organically combining the offline global optimum algorithm with the real-time greedy algorithm.
3. The multi-attribute utility function method of the multi-objective optimization algorithm is innovated based on the service scene, the objective design is innovated, meanwhile, the double-layer ordering is designed, the hierarchical level of the optimizing layer is arranged firstly, the constraint layer is arranged secondly, the error caused by the absolute value ordering is reduced, and meanwhile, the functions of optimizing the main objective firstly and then optimizing the secondary objective can be realized.
4. For business targets (matching degree and wetting ratio) which are not easy to directly predict and difficult to describe artificially are converted into model language, a series of utility functions are designed based on business functions, and the business targets are skillfully included in an optimization target for matching and sequencing according to local conditions.
Fig. 4 is a schematic block diagram of a sorting apparatus for funding parties according to an embodiment of the present application. As shown in fig. 4, the present application further provides a sorting device for the fund party, corresponding to the above sorting method for the fund party. The arrangement of the parties comprises means for performing the above-described method of ordering parties, which arrangement may be arranged in a server. Specifically, referring to fig. 4, the sorting apparatus 400 of the fund party includes a transceiver unit 401 and a processing unit 402.
A transceiver unit 401, configured to obtain target order information of a target applicant sent by a user terminal;
The processing unit 402 is configured to determine, according to the target order information, a primary feature and a secondary feature that respectively correspond to each preset candidate fund party and the target applicant party, where the influence of the secondary feature on the ranking of the candidate fund parties is smaller than the influence of the primary feature on the ranking of the candidate fund parties; dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting; sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup; determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
the transceiver 401 is further configured to send the target order to the user terminal, so that a user obtains the target order through the user terminal, and determines a matching degree of the target order information and each candidate fund party.
In some real-time, after the transceiver unit 401 performs the step of obtaining the target order information of the target applicant sent by the user terminal, the processing unit 402 is further configured to:
Based on a preset global optimization model, adding pre-allocation labels for each candidate fund party according to the target order information, wherein the pre-allocation labels comprise recommended labels and non-recommended labels;
and determining the candidate fund party corresponding to the recommended label as a first candidate fund party, and determining the candidate fund party corresponding to the non-recommended label as a second candidate fund party.
In some real-time, when executing the step of classifying the candidate fund party into a plurality of subgroups according to the first-level feature and a preset subgroup classification rule, the processing unit 402 is specifically configured to:
dividing the first candidate fund party into a plurality of first subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the first subgroups to obtain a first-level sorting;
dividing the second candidate fund party into a plurality of second subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the second subgroups to obtain a second first-level sorting;
in some real-time, the processing unit 402 is specifically configured to, when executing the step of sorting the candidate fund parties in each of the subgroups according to the secondary characteristics to obtain the secondary sorting of the candidate fund parties in each of the subgroups:
Sorting the candidate fund parties in each first subgroup according to the secondary characteristics to obtain first secondary sorting of the candidate fund parties in each first subgroup;
sorting the candidate fund parties in each second subgroup according to the secondary characteristics to obtain second secondary sorting of the candidate fund parties in each second subgroup;
in some real-time, the processing unit 402, when executing the target ranking step of determining the candidate fund party according to the primary ranking and the secondary ranking, is specifically configured to:
sorting the first candidate fund parties according to the first one-level sorting and the first secondary characteristics to obtain a first sorting;
sorting the second candidate fund parties according to the second primary sorting and the second secondary sorting to obtain a second sorting;
and determining the target sequence according to the first sequence and the second sequence.
In some real-time, the primary characteristics include profit margin, pass rate, and match, and the secondary characteristics include a lubrication duty cycle.
In some real-time, the processing unit 402 is specifically configured to, when executing the steps of determining, according to the target order information, the primary feature and the secondary feature of each preset candidate fund party corresponding to the target applicant party, respectively:
Determining the profit margin corresponding to each candidate fund party and each target application party according to the target application amount in the target order information and the profit margin corresponding to each candidate fund party and the target application amount;
determining the passing rate of each candidate fund party and the corresponding target application party according to the history passing order quantity and the total number of history orders of each candidate fund party;
determining the matching degree of each candidate fund party and the corresponding target application party according to the target order information based on a preset matching degree utility function;
and determining the distribution and lubrication ratio of each candidate fund party and the target application party respectively corresponding to the candidate fund party according to the target order information based on a preset distribution and lubrication ratio utility function.
In some real-time, when executing the step of classifying the candidate fund party into a plurality of subgroups according to the first-level feature and a preset subgroup classification rule, the processing unit 402 is specifically configured to:
determining primary matching scores corresponding to the candidate fund parties respectively according to the primary characteristics and preset characteristic weights corresponding to the primary characteristics respectively, wherein the characteristic weights are obtained according to a subjective and objective fusion weighting method;
Dividing the candidate fund party into a plurality of subgroups according to the primary matching score and the subgroup dividing rule, and sorting the subgroups according to the score interval size corresponding to each subgroup in the subgroup dividing rule to obtain the primary sorting.
In some real-time, the processing unit 402 is further configured to:
acquiring risk scores of the candidate funding parties through the transceiving unit 401;
and adding a risk label containing the corresponding risk score for each candidate fund party.
It should be noted that, as those skilled in the art can clearly understand, the above-mentioned sorting device of the fund party and the specific implementation process of each unit may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, the description is omitted here.
The above-described ordering means of the parties may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 5.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, which may be a stand-alone server or a server cluster formed by a plurality of servers.
With reference to FIG. 5, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a method of ordering parties.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the non-volatile storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform a method of ordering parties.
The network interface 505 is used for network communication with other devices. It will be appreciated by those skilled in the art that the architecture shown in fig. 5 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 500 to which the present inventive arrangements may be implemented, as a particular computer device 500 may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
Wherein the processor 502 is configured to execute a computer program 5032 stored in a memory to implement the steps of:
acquiring target order information of a target applicant sent by a user terminal;
determining a primary feature and a secondary feature corresponding to each preset candidate fund party and the target application party respectively according to the target order information, wherein the influence degree of the secondary feature on the sequencing of the candidate fund parties is smaller than that of the primary feature on the sequencing of the candidate fund parties;
dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting;
sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup;
determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
and sending the target sequence to the user terminal, so that a user obtains the target sequence through the user terminal, and determining the matching degree of the target order information and each candidate fund party.
It should be appreciated that in an embodiment of the application, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Those skilled in the art will appreciate that all or part of the flow in a method embodying the above described embodiments may be accomplished by computer programs instructing the relevant hardware. The computer program comprises program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present application also provides a storage medium. The storage medium may be a computer readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. The program instructions, when executed by the processor, cause the processor to perform the steps of:
Acquiring target order information of a target applicant sent by a user terminal;
determining a primary feature and a secondary feature corresponding to each preset candidate fund party and the target application party respectively according to the target order information, wherein the influence degree of the secondary feature on the sequencing of the candidate fund parties is smaller than that of the primary feature on the sequencing of the candidate fund parties;
dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting;
sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup;
determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
and sending the target sequence to the user terminal, so that a user obtains the target sequence through the user terminal, and determining the matching degree of the target order information and each candidate fund party.
The storage medium may be a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, or other various computer-readable storage media that can store program codes.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The integrated unit may be stored in a storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a terminal, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (8)
1. A method of ordering parties for funds, the method being applied to a server, the method comprising:
acquiring target order information of a target applicant sent by a user terminal;
determining primary characteristics and secondary characteristics of each preset candidate fund party and the target applicant party respectively corresponding to the target order information;
dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting;
sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup;
determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
sending the target sequence to the user terminal, so that a user obtains the target sequence through the user terminal, and determining the matching degree of the target order information and each candidate fund party;
after the target order information of the target applicant sent by the user terminal is obtained, the method further includes:
based on a preset global optimization model, adding pre-allocation labels for each candidate fund party according to the target order information, wherein the pre-allocation labels comprise recommended labels and non-recommended labels;
Determining a candidate fund party corresponding to the recommended label as a first candidate fund party, and determining a candidate fund party corresponding to the non-recommended label as a second candidate fund party;
dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and a preset subgroup dividing rule, and sorting the subgroups to obtain a first-level sorting, including:
dividing the first candidate fund party into a plurality of first subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the first subgroups to obtain a first-level sorting;
dividing the second candidate fund party into a plurality of second subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the second subgroups to obtain a second first-level sorting;
the sorting of the candidate fund parties in each subgroup according to the secondary characteristics respectively, to obtain a secondary sorting of the candidate fund parties in each subgroup, includes:
sorting the candidate fund parties in each first subgroup according to the secondary characteristics to obtain first secondary sorting of the candidate fund parties in each first subgroup;
Sorting the candidate fund parties in each second subgroup according to the secondary characteristics to obtain second secondary sorting of the candidate fund parties in each second subgroup;
the determining the target rank of the candidate fund party according to the primary rank and the secondary rank comprises the following steps:
sorting the first candidate fund parties according to the first one-level sorting and the first secondary characteristics to obtain a first sorting;
sorting the second candidate fund parties according to the second primary sorting and the second secondary sorting to obtain a second sorting;
and determining the target sequence according to the first sequence and the second sequence.
2. The method of claim 1, wherein the primary characteristics include profit margin, pass rate, and match, and the secondary characteristics include a lubrication duty cycle.
3. The method according to claim 2, wherein determining the primary and secondary characteristics of each preset candidate fund party and the target applicant party according to the target order information includes:
determining the profit margin corresponding to each candidate fund party and each target application party according to the target application amount in the target order information and the profit margin corresponding to each candidate fund party and the target application amount;
Determining the passing rate of each candidate fund party and the corresponding target application party according to the history passing order quantity and the total number of history orders of each candidate fund party;
determining the matching degree of each candidate fund party and the corresponding target application party according to the target order information based on a preset matching degree utility function;
and determining the distribution and lubrication ratio of each candidate fund party and the target application party respectively corresponding to the candidate fund party according to the target order information based on a preset distribution and lubrication ratio utility function.
4. A method according to any one of claims 1 to 3, wherein the dividing the candidate fund party into a plurality of subgroups according to the primary characteristic and a preset subgroup dividing rule, and sorting the subgroups to obtain a primary sorting comprises:
determining primary matching scores corresponding to the candidate fund parties respectively according to the primary characteristics and preset characteristic weights corresponding to the primary characteristics respectively, wherein the characteristic weights are obtained according to a subjective and objective fusion weighting method;
dividing the candidate fund party into a plurality of subgroups according to the primary matching score and the subgroup dividing rule, and sorting the subgroups according to the score interval size corresponding to each subgroup in the subgroup dividing rule to obtain the primary sorting.
5. A method according to any one of claims 1 to 3, further comprising:
acquiring risk scores of the candidate fund parties;
and adding a risk label containing the corresponding risk score for each candidate fund party.
6. A funding party ordering apparatus, comprising:
the receiving and transmitting unit is used for acquiring target order information of a target applicant party sent by the user terminal;
the processing unit is used for determining primary characteristics and secondary characteristics of each preset candidate fund party and the target application party respectively according to the target order information, and the influence degree of the secondary characteristics on the sequencing of the candidate fund parties is smaller than that of the primary characteristics on the sequencing of the candidate fund parties; dividing the candidate fund party into a plurality of subgroups according to the first-level characteristics and preset subgroup dividing rules, and sorting the subgroups to obtain a first-level sorting; sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain secondary sorting of the candidate fund parties in each subgroup; determining a target ranking of the candidate fund party according to the primary ranking and the secondary ranking;
The receiving and transmitting unit is further configured to send the target order to the user terminal, so that a user obtains the target order through the user terminal, and determines a matching degree of the target order information and each candidate fund party;
after the receiving and sending unit executes the step of obtaining the target order information of the target applicant sent by the user terminal, the processing unit is further configured to:
based on a preset global optimization model, adding pre-allocation labels for each candidate fund party according to the target order information, wherein the pre-allocation labels comprise recommended labels and non-recommended labels; determining a candidate fund party corresponding to the recommended label as a first candidate fund party, and determining a candidate fund party corresponding to the non-recommended label as a second candidate fund party;
the processing unit is specifically configured to, when executing the step of first-order sorting, divide the candidate fund party into a plurality of subgroups according to the first-order feature and a preset subgroup dividing rule, and sort the subgroups to obtain a first-order sorting step:
dividing the first candidate fund party into a plurality of first subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the first subgroups to obtain a first-level sorting; dividing the second candidate fund party into a plurality of second subgroups according to the first-level characteristics and the subgroup dividing rule, and sorting the second subgroups to obtain a second first-level sorting;
The processing unit is specifically configured to, when executing the step of sorting the candidate fund parties in each subgroup according to the secondary characteristics to obtain the secondary sorting of the candidate fund parties in each subgroup:
sorting the candidate fund parties in each first subgroup according to the secondary characteristics to obtain first secondary sorting of the candidate fund parties in each first subgroup; sorting the candidate fund parties in each second subgroup according to the secondary characteristics to obtain second secondary sorting of the candidate fund parties in each second subgroup;
the processing unit is specifically configured to, when executing the target ranking step of determining the candidate fund party according to the first-order ranking and the second-order ranking:
sorting the first candidate fund parties according to the first one-level sorting and the first secondary characteristics to obtain a first sorting; sorting the second candidate fund parties according to the second primary sorting and the second secondary sorting to obtain a second sorting; and determining the target sequence according to the first sequence and the second sequence.
7. A computer device, characterized in that it comprises a memory and a processor, on which a computer program is stored, which processor implements the method according to any of claims 1-5 when executing the computer program.
8. A computer readable storage medium, characterized in that the storage medium stores a computer program comprising program instructions which, when executed by a processor, can implement the method of any of claims 1-5.
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