CN117078238A - Electronic bill combination method and device, storage medium and electronic equipment - Google Patents
Electronic bill combination method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses an electronic bill combination method, an electronic bill combination device, a storage medium and electronic equipment. Relates to the field of artificial intelligence, and the method comprises the following steps: determining a target account; acquiring a bill weight value corresponding to a target electronic bill held by a target account; under the condition that the number of the target electronic bills is M, determining weight constraint based on bill weight values corresponding to the M target electronic bills respectively, wherein M is an integer greater than or equal to 1; and determining target combination strategies of the M target electronic bills based on the weight constraint and target credit limit required by the target accounts. The application solves the problem of non-ideal electronic bill combination mode in the related technology.
Description
Technical Field
The application relates to the field of artificial intelligence, in particular to an electronic bill combination method, an electronic bill combination device, a storage medium and electronic equipment.
Background
The acceptance bill is a payment and settlement tool, a part of users can hold a large number of bills, the number of the bills is hundreds or more, each bill relates to different bill denominations, the expiration date is that of the acceptance bill, and the new generation bill also comprises different bill surfaces. The cash register is used for meeting the financing demands of ticket holders, and the cash register needs to pay certain cash register interest, and the interest level can be influenced by various factors, such as the ticket amount, the expiration date, the requirements of cash register institutions and the like. For users with a large number of notes, the paste scene is very complex, the time cost for selecting the notes is high, and meanwhile, the problem of non-ideal note selection combination exists.
Aiming at the problem that the electronic bill combination mode is not ideal in the related technology, no effective solution is proposed at present.
Disclosure of Invention
The application mainly aims to provide an electronic bill combination method, an electronic bill combination device, a storage medium and electronic equipment, so as to solve the problem that the electronic bill combination mode is not ideal in the related technology.
In order to achieve the above object, according to one aspect of the present application, there is provided an electronic bill combining method. The method comprises the following steps: determining a target account; acquiring a bill weight value corresponding to a target electronic bill held by the target account; under the condition that the number of the target electronic bills is M, determining weight constraint based on bill weight values corresponding to the M target electronic bills respectively, wherein M is an integer greater than or equal to 1; and determining target combination strategies of the M target electronic bills based on the weight constraint and the target discount amount required by the target account.
To achieve the above object, according to another aspect of the present application, there is provided an electronic bill combining device. The device comprises: the first determining module is used for determining a target account; the first acquisition module is used for acquiring a bill weight value corresponding to the target electronic bill held by the target account; the first constraint module is used for determining weight constraint based on ticket weight values corresponding to the M target electronic tickets respectively under the condition that the target electronic tickets are M, wherein M is an integer greater than or equal to 1; and the first combination module is used for determining target combination strategies of the M target electronic bills based on the weight constraint and the target discount amount required by the target account.
To achieve the above object, according to another aspect of the present application, there is provided a nonvolatile storage medium storing a plurality of instructions adapted to be loaded and executed by a processor for any one of the electronic ticket assembling methods.
To achieve the above object, according to another aspect of the present application, there is provided an electronic device including one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any one of the electronic ticket combining methods.
According to the application, the following steps are adopted: determining a target account; acquiring a bill weight value corresponding to a target electronic bill held by the target account; under the condition that the number of the target electronic bills is M, determining weight constraint based on bill weight values corresponding to the M target electronic bills respectively, wherein M is an integer greater than or equal to 1; and determining target combination strategies of the M target electronic bills based on the weight constraint and the target discount amount required by the target account. The aim of automatic bill selection in a tendency manner is achieved, and the problem that the electronic bill combination mode is not ideal in the related technology is solved. Thereby achieving the effect of improving the cash registering efficiency of bill combination.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of an electronic ticket assembling method provided according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an electronic bill combining device provided according to an embodiment of the present application; and
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of description, the following will describe some terms or terminology involved in the embodiments of the present application:
the bill is accepted, and the payment is guaranteed to be paid by a promised settlement tool sent by a bill holder to a specified date or under specified conditions. The acceptance draft generally requires that the payer be confirmed by the financial institution in advance to ensure the payment capability and reliability of the draft. The ticket holder may sell the acceptance draft to a financial institution for a prior amount of money. The acceptance draft is widely used in commercial transactions for payment of goods, borrowing, financing, etc.
The posting process refers to the transfer of the right to the future due date of the ticket holder holding the ticket holder to a financial institution or other posting institution to obtain the on-demand funds. The cash register calculates cash register rate according to the bill amount and the remaining expiration date when the cash register mechanism registers the cash register draft, pays the cash register amount to the ticket holder, and the ticket holder gives up the right of waiting for the expiration date to collect the full amount of the bill. The method has the function of acquiring funds in advance so as to meet the fund requirements of ticket holders. Mobile funds support may be provided to relieve funds turnover pressure.
It should be noted that, related information (including, but not limited to, user equipment information, user personal information, etc.) and data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present disclosure are information and data authorized by a user or sufficiently authorized by each party. For example, an interface is provided between the system and the relevant user or institution, before acquiring the relevant information, the system needs to send an acquisition request to the user or institution through the interface, and acquire the relevant information after receiving the consent information fed back by the user or institution.
The particle swarm algorithm (Particle Swarm Optimization, PSO) is a population intelligent optimization algorithm that finds the optimal solution by modeling the position and velocity changes of particles in the solution space. The basic idea of the particle swarm algorithm is to search for the optimal solution by cooperation and information sharing between particles. Each particle represents a potential solution, moves in solution space, and adjusts position and velocity based on its own experience and population experience. The update of the velocity and position of the particles is calculated from the current position and velocity, the individual optimal solution and the global optimal solution. In the particle swarm algorithm, each particle has an fitness function to evaluate the quality of its solution. Each particle updates its own velocity and position based on its fitness and information about neighboring particles. Through continuous iteration, the particle swarm algorithm gradually converges to the vicinity of the optimal solution.
It should be noted that, related information (including, but not limited to, personal information of a bill held by a user, a target account, etc.) and data (including, but not limited to, data for historical posting, required posting target credit, etc.) related to the present disclosure are information and data authorized by the user or sufficiently authorized by each party. For example, an interface is provided between the system and the relevant user or institution, before acquiring the relevant information, the system needs to send an acquisition request to the user or institution through the interface, and acquire the relevant information after receiving the consent information fed back by the user or institution.
Based on the above-mentioned problems, an embodiment of the present application provides an electronic bill combining method, and fig. 1 is a flowchart of the electronic bill combining method according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
step S102, determining a target account;
it will be appreciated that each account will have a different tendency to pick notes due to the different users using the account.
Step S104, acquiring a bill weight value corresponding to the target electronic bill held by the target account;
it can be understood that the target account may hold a large number of notes, and the note weight value corresponding to the target electronic note may represent the selection tendency of the note by the user, and the larger the note weight value, the larger the selection tendency of the target electronic note corresponding to the larger note weight value, and the smaller the note weight value, the smaller the selection tendency of the target electronic note corresponding to the smaller note weight value.
In an optional embodiment, obtaining a ticket weight value corresponding to a target electronic ticket held by a target account includes: determining D bill characteristics corresponding to the target electronic bill and characteristic weight values corresponding to the D bill characteristics respectively, wherein D is an integer greater than or equal to 1; and acquiring the bill weight value corresponding to the target electronic bill based on the feature weight values respectively corresponding to the D bill features.
It can be understood that there may be D bill features in the bill content of the target electronic bill, where the bill features are used to represent information related to the posting process of the bill, and feature weight values corresponding to the D bill features respectively may be used to represent the attention degree of the bill feature to the target account, where a larger feature weight value indicates that the corresponding bill feature is more attention in all the bill features. And on the basis of obtaining the feature weight values corresponding to the D bill features respectively, determining the bill weight value of the whole target electronic bill.
Optionally, the D bill features corresponding to the target electronic bill may include a plurality of: ticket type, ticket face amount, ticket date, expiration date, acceptance agency, etc. Each ticket feature may have a different value, for example, a ticket type, such as a silver ticket or a business ticket.
Alternatively, the target electronic bill may be various, such as an electronic acceptance draft, an electronic check, an electronic promissory note, and the like, and the above-described types of bills may support the feasibility and condition of the posting process. The electronic receipt can also be used as a payment receipt for posting other tickets, without specific limitation.
Optionally, the target electronic bill exists in an electronic form, and is transmitted, stored and approved through an electronic data exchange network.
Optionally, summing the feature weight values corresponding to the D bill features to obtain the bill weight value corresponding to the target electronic bill.
In an alternative embodiment, determining feature weight values corresponding to the D ticket features respectively includes: identifying bill processing behaviors of the target account in a preset time period to obtain historical impression data; based on the historical impression data, determining K historical electronic notes of the target account for impression, wherein K is an integer greater than or equal to 1; determining the occurrence times of any feature in the D bill features in the K historical electronic bills, wherein D is an integer greater than or equal to 1; and determining feature weight values corresponding to the D bill features respectively based on the occurrence times of any features.
It can be understood that the feature weight value corresponding to each bill feature is not subjectively set, and the historical impression data of the target account can be obtained by identifying and extracting according to the bill processing behavior of the target account, so that the past selection of the target account on the impression bill can be reflected. The method comprises the steps of determining K historical electronic notes based on historical impression data, determining the frequency of note features to be selected according to the occurrence times of any feature in the K historical electronic notes in the D note features, and further determining feature weight values corresponding to each note feature according to the occurrence times of any feature, wherein the selection times are more and the target account preference and the note feature can be reflected. Through the processing, the preference for bill selection can be reflected based on the historical impression data of the target account, and then the weight of the bill characteristic is given, so that the method has better accuracy compared with subjective weighting.
Optionally, the bill processing behavior of the user within a predetermined period of time is identified, and corresponding processing data is extracted. First, it is necessary to collect bill handling actions of the target account within a predetermined period of time, which may be achieved by recording an account operation log of the user, transaction records, or the like. The bill processing behavior is defined, and behavior recognition is carried out on the bill processing behavior by using methods such as machine learning or statistical analysis. The behavior recognition can be performed by using a supervised learning algorithm, using a labeled data training model, or by using an unsupervised learning algorithm, by clustering, etc. Once the user's ticket processing actions are identified, relevant processing data can be extracted as needed. For example, data such as a demand impression amount and an impression count may be extracted as history impression data.
For ease of understanding, examples are: w (w) mdy The value of the y characteristic value in the d bill characteristic of the mth target electronic bill is represented, for example, the bill type is the first bill characteristic, d=1, the bill type is classified into silver bill and business bill, and the y characteristic value in the bill type can be silver bill or business bill.
In an alternative embodiment, determining feature weight values corresponding to the D ticket features respectively based on the number of occurrences of any feature includes: determining the proportion of the occurrence times of any feature to the total ticket number of the K historical electronic tickets; sorting the D bill features based on the proportion corresponding to any feature respectively to obtain a sorting result; and determining feature weight values corresponding to the D bill features respectively based on the sorting result.
It can be understood that the method can be used as a normalization processing mode according to the proportion corresponding to any feature, and then sorting is performed according to the proportion of the total ticket number, so as to obtain a sorting result, and further determine the feature weight value of the corresponding ticket feature.
For ease of understanding, examples are: the target account holds 8 electronic notes, 6 electronic notes are transacted, the measurement and calculation are carried out on the 6 electronic notes, the types of the notes are 4 times of appearance of the silver notes, the weight of the silver notes is 4 when the business notes appear 2 times, the weight of the business notes is 2, normalization processing is carried out, the weight of the silver notes is 4/6, the weight of the business notes is 2/6, and the characteristics of the rest notes are similar.
Step S106, determining weight constraint based on ticket weight values corresponding to M target electronic tickets respectively under the condition that the target electronic tickets are M, wherein M is an integer greater than or equal to 1;
it can be understood that the number of the target electronic bills held by the target account can be M, and the more the number is, the more obvious the bill combination efficiency improving effect is. In order to determine the condition that too many feature dimensions cause too complex bill selection, the weight constraint is determined by adopting bill weight values corresponding to M target electronic bills respectively. The determined bill combination can show the tendency of the user.
Step S108, determining target combination strategies of M target electronic bills based on the weight constraint and target discount amount required by the target account.
It can be appreciated that the target account at least needs to satisfy the target credit by selecting and combining the obtained total credit (i.e., the first credit) in the M target electronic notes. However, due to the nature of the ticketing process, the amount to be applied by redemption is typically less than the nominal amount, as the applied financial institution needs to collect a certain applied interest. The discount loss may be related to a variety of factors, such as, for example, determined based on market interest, the remaining deadline of the ticket, the acceptance agency's specifications, and so forth. Therefore, the selected target combination strategy not only needs to meet the target discount amount and the weight constraint, but also needs to consider the loss caused by the discount behavior reduction, so that the obtained target combination strategy has the highest discount efficiency.
In an alternative embodiment, determining a target combination policy for the M target electronic tickets based on the weight constraints and the target credit required for the target account includes: based on weight constraint, determining target particles with a minimum target function by adopting a particle swarm algorithm, wherein the target function is used for representing a discount value of which a first discount amount exceeds a target discount amount, any particles included in the particle swarm algorithm represent selection states respectively corresponding to M target electronic notes, and the first discount amount is a discount total amount obtained based on the M target electronic notes; and determining a target combination strategy based on the selection states respectively corresponding to the M target electronic notes indicated by the target particles.
It can be understood that when the bill combination mode for exchanging the target total amount is processed, any particle included in the particle swarm algorithm is a selected state representing that M target electronic bills respectively correspond, namely, a candidate combination mode, under which the solving quality of a possible target function is not ideal, and is an adaptive value in the particle swarm algorithm in the particle searching process, until the set particle obtains the self optimal position through iteration, and updates the global optimal position of the whole particle swarm, finally, the target function is obtained, the matching difference value exceeding the target matching limit is minimum, and the weight constraint is satisfied. Through the processing, the target combination strategy with high posting efficiency and meeting the tendency requirement of the target account can be determined according to the selection states respectively corresponding to the M target electronic bills indicated by the target particles.
Alternatively, the particle swarm algorithm may be applied as follows in processing the bill combination scheme for redeeming the target total amount. And comparing the target cash register amount required to be exchanged by the target account with the first cash register amount corresponding to the bill combination mode of each particle, and calculating the fitness value. The fitness function is designed to be the same as the objective function, and for example, a difference between the target present amount and the first present amount may be used as the fitness value.
First initializing a particle swarm: a certain number of particles are randomly generated, each particle represents a bill combination mode, namely, each particle represents whether any bill in the M target electronic bills is selected or not selected. The position of the particle indicates the selection condition of the bill under the current iteration, for example, a binary string can be used to indicate the selection state of each target electronic bill, wherein the number of characters in {0,1, …,0} is M, the number of characters is the same as the total number of the target electronic bills, 0 represents that the target electronic bill is not selected, and 1 represents that the target electronic bill is selected. Based on the current position and velocity of the particle, a new velocity and position are calculated and iterated continuously. The updating of the speed takes into account the historical optimal position of the particle itself and the historical optimal position of the whole population, as well as certain random factors. The update of the position is then adjusted according to the speed (also called the flight speed). In the searching process of the particles, after each position update of each particle, judging the quality degree of the current solution through a constraint function (comprising weight constraint), adjusting the position and the speed through the constraint function, calculating the fitness value for a plurality of times and updating the historical optimal position until a stopping condition is met, for example, the maximum iteration number is reached or the fitness value of the particle swarm reaches a certain threshold value, and finally, the found optimal solution is the target particle.
Alternatively, the formula of the particle swarm algorithm that may be used is as follows:
v i (t+1)＝w×v i (t)+c 1 ×rand 1 ×(p i (t)-x i (t))+c 2 ×rand 2 ×(p g (t)-x i (t))
wherein v is i (t+1) represents the speed of the ith particle at t+1 iterations, v i (t) represents the speed of the ith particle at t iterations, x i (t) represents the position of the ith particle at t iterations, p i (t) represents the individual optimal position, p, of the ith particle at t iterations g (t) represents the globally optimal position, rand, in the whole population of particles at t iterations 1 、rand 2 Respectively represent different random numbers c 1 、c 2 Respectively representing different learning factors, w being the inertial weight.
The manner in which the particles are updated is as follows:
x i (t+1)＝x i (t)+v i (t+1)
by adding the speed of the ith particle in t+1 iterations based on the t iteration positions of the ith particle, the new position of the ith particle in t+1 iterations can be updated.
The position for the ith particle is x i ＝(x i1 ,x i2 ,x i3 ,…x im ) Can represent the selection state of M target electronic bills given by the ith particle, x i1 ,x i2 ,x i3 ,…x im And representing the selection states respectively corresponding to the 1 st to the M-th target electronic bills in the M target electronic bills. Similarly, the velocity of the ith particle is denoted v i ＝(v i1 ,v i2 ,v i3 ,...v im )，v i1 ,v i2 ,v i3 ,...v im And the speeds corresponding to the 1 st to the M-th target electronic bills in the M target electronic bills are represented. The individual optimum position of the ith particle is p i ＝(p i1 ,p i2 ,p i3 ,…p im )，p i1 ,p i2 ,p i3 ,…p im And the optimal individual positions corresponding to the 1 st to the M-th target electronic bills in the M target electronic bills are indicated.
Further, for each target electronic bill there are D bill features, and the mth target electronic bill in the ith particle includes the D bill feature, where the feature weight may be expressed as x imd . The weight constraint may be implemented in a variety of ways, for example, as follows:
wherein T represents a weight constraint, max (…) represents a maximum value, h im The mth target electronic bill representing the ith particle is in a selected state, is selected as 1, and is otherwise 0.w (w) d Feature weights representing the d-th feature. By the above method, the bill weight value of each target electronic bill can be accumulated, and the total weight of all target electronic bills contained in the ith particle is further accumulated, and it can be understood that the weightThe larger the weight is, the better the tendency of the target account to select is shown, the weight constraint can maximize the weight of the selected particles, and the selection preference of the target account is favorably met.
For ease of understanding, the selection states of notes in the particles are shown in table 1, where table 1 illustrates the selection states included in the 1 st … … i particle in the same iteration, 0 represents that the corresponding target electronic note is not selected, and 1 represents that the target electronic note is selected. Taking particle 1 as an example, the 2 nd and m th target electronic notes are selected, wherein D note features are shown by note denominations and due dates, the note denomination of the 2 nd target electronic note is 20-40 yuan, the weight of the note feature is 0.6, the note denomination of the m th target electronic note is 10-20 yuan, the weight of the note feature is 0.3, and other target electronic notes and note features thereof are analogized in the same way.
TABLE 1
In an alternative embodiment, determining a target combination policy for the M target electronic tickets based on the weight constraints and the target credit required for the target account includes: determining a single ticket loss constraint based on the expiration dates and ticket exchange interest rates respectively corresponding to the M target electronic tickets; determining a total discount constraint based on a first discount amount and an expected total discount amount, wherein the expected total discount amount is determined based on bill denominations corresponding to the M target electronic bills respectively, and the first discount amount is a discount total obtained based on the M target electronic bills; and determining a target combination strategy according to the ticket loss constraint, the total loss constraint, the weight constraint and the target discount amount.
It can be understood that other constraints can be performed in addition to the weight constraint, so that the discount efficiency of the target combination strategy is improved, a certain loss is generated due to discount, and in order to ensure that the discount efficiency is maximum, the loss generated by each target electronic bill and the total loss generated by the target combination strategy are constrained. And determining the loss constraint of the ticket based on the expiration dates and the ticket exchange interest rates respectively corresponding to the M target electronic tickets. And determining the total loss constraint of the cash register according to the cash register total amount obtained by the M target electronic notes, namely the first cash register total amount and the expected total amount of the M target electronic notes after the normal expiration. By adopting the single ticket loss constraint, the total loss constraint is attached, the weight constraint is combined and constraint is carried out, and a target combination strategy meeting the target attached limit is determined.
Alternatively, the particle swarm algorithm may be used to determine the target particles that minimize the objective function based on the ticket loss constraint, the fit total loss constraint, and the weight constraint.
Alternatively, the ticket penalty constraint, the post total penalty constraint may be set as a penalty function, and the weight constraint set as a correction factor. In particle swarm optimization, there are generally two processing methods for adding constraints to the objective function, including a penalty function method and a correction factor method. The penalty function acts to make an appropriate penalty to the objective function in the event of a violation of a constraint to enable the optimization process to better follow the constraint. Common penalty functions are linear penalty functions, quadratic penalty functions, and the like. When the particles violate the constraint conditions, the positions and the speeds of the particles can be adjusted to meet the constraint conditions. The correction factor method typically requires restrictions on the position and velocity of the particles to ensure legal movement of the particles within the search space.
In an alternative embodiment, after determining the target combination policy of the M target electronic notes, the apparatus further includes: displaying the target combination strategy; and under the condition that the correction instruction is received, generating a correction combination strategy in response to the correction instruction, and generating prompt information based on a comparison result of the correction combination strategy and the target combination strategy.
It can be understood that after the target combination policy is obtained, the target account can be manually fine-tuned, personalized setting is further performed, and the correction combination policy is generated through the received correction instruction. It is readily appreciated that modifying the combining policy may be more indicative of user preferences, but may not be an optimal combining approach, requiring comparison with the target combining policy, prompting the target account for an indication of the effect of modifying the combining policy. Through the processing, under the condition that the optimal target combination strategy is determined, adjustment can be further carried out, and the flexibility of bill combination is improved.
Optionally, the prompt information may indicate various information based on the comparison result, for example, correcting the extra loss generated by the combination policy compared to the target combination policy.
According to the electronic bill combination method provided by the embodiment of the application, the purpose of automatic bill selection in a tendency manner is achieved through the steps S102 to S108, and the problem that the electronic bill combination mode is not ideal in the related technology is solved. Thereby achieving the effect of improving the cash registering efficiency of bill combination.
Based on the above embodiment and the optional embodiment, the present application proposes an optional implementation, specifically the following steps:
And S1, performing the impression behavior feature recognition based on the historical bill processing behavior of the target account to obtain historical impression data. Based on the history impression data, feature weight values corresponding to the D bill features can be obtained.
And S2, the target account currently holds M target electronic bills, weight constraint is established based on bill weight values corresponding to the M target electronic bills respectively, and target particles are determined by adopting a particle swarm algorithm, so that a target combination strategy with the minimum value of the discount exceeding the target discount limit can be obtained under the condition that the limitation of the weight constraint is met. Besides the weight constraint, a single ticket loss constraint can be established, and the total loss constraint is posted, so that the cost loss caused by the posting process is further reduced.
And S3, displaying the target combination strategy, generating a correction combination strategy under the condition that a correction instruction exists, and prompting a comparison result of the correction combination strategy and the target combination strategy. And determining a final selection result of the target account, and taking the modified combination strategy or the target electronic bill selection state indicated by the target combination strategy as a new bill processing behavior for data updating. And restarting the evaluation of the new weight round, so as to accurately restrict the weight and achieve the aim of optimizing the weight restriction.
At least the following effects are achieved by the above alternative embodiments: on the basis of analysis of historical bill processing behaviors, the bill with high selection probability is used as a large-weight bill to reflect the selection tendency of a target account, a particle swarm algorithm is introduced to carry out multi-objective solution optimization, so that optimization can be carried out quickly, the calculation speed is increased, and a feasible landing scene is provided.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment of the application also provides an electronic bill combining device, and the electronic bill combining device can be used for executing the electronic bill combining method provided by the embodiment of the application. The electronic bill combining device provided by the embodiment of the application is described below.
Fig. 2 is a schematic view of an electronic bill combining apparatus according to an embodiment of the present application. As shown in fig. 2, the apparatus includes: the first determining module 202, the first obtaining module 204, the first constraint module 206, and the first combining module 208 are specifically described below:
A first determining module 202, configured to determine a target account;
the first obtaining module 204 is connected with the first determining module 202, and is configured to obtain a ticket weight value corresponding to a target electronic ticket held by a target account;
the first constraint module 206 is connected with the first acquisition module 204, and is configured to determine a weight constraint based on ticket weight values corresponding to M target electronic tickets respectively when the target electronic tickets are M, where M is an integer greater than or equal to 1;
the first combination module 208 is connected to the first constraint module 206, and is configured to determine a target combination policy of the M target electronic tickets based on the weight constraint and the target credit required by the target account.
The electronic bill combination device provided by the embodiment of the application is used for determining a target account through the first determining module 202; the first obtaining module 204 is connected with the first determining module 202, and is configured to obtain a ticket weight value corresponding to a target electronic ticket held by a target account; the first constraint module 206 is connected with the first acquisition module 204, and is configured to determine a weight constraint based on ticket weight values corresponding to M target electronic tickets respectively when the target electronic tickets are M, where M is an integer greater than or equal to 1; the first combination module 208 is connected to the first constraint module 206, and is configured to determine a target combination policy of the M target electronic tickets based on the weight constraint and the target credit required by the target account. The aim of automatic bill selection in a tendency manner is achieved, and the problem that the electronic bill combination mode is not ideal in the related technology is solved. Thereby achieving the effect of improving the cash registering efficiency of bill combination.
Optionally, in the electronic bill combining device provided in the embodiment of the present application, the first combining module includes: the second constraint module is used for determining target particles with the smallest target function by adopting a particle swarm algorithm based on weight constraint, wherein the target function is used for representing a discount value of which the first discount amount exceeds the target discount amount, any particle included in the particle swarm algorithm represents the selection state corresponding to each of M target electronic notes, and the first discount amount is the discount total amount obtained based on the M target electronic notes; and the combination strategy determining module is used for determining a target combination strategy based on the selection states respectively corresponding to the M target electronic bills indicated by the target particles.
Optionally, in the electronic bill combining device provided in the embodiment of the present application, the first combining module includes: the second constraint module is used for determining single ticket loss constraint based on the expiration date and ticket exchange interest rate corresponding to the M target electronic tickets respectively; the third constraint module is used for determining a total discount constraint based on a first discount amount and an expected total discount amount, wherein the expected total discount amount is determined based on bill denominations corresponding to the M target electronic bills respectively, and the first discount amount is obtained based on the M target electronic bills; and the second combination module is used for determining a target combination strategy according to the ticket loss constraint, the total loss constraint, the weight constraint and the target discount amount.
Optionally, in the electronic bill combining device provided in the embodiment of the present application, the first obtaining module includes: the second determining module is used for determining D bill characteristics corresponding to the target electronic bill and characteristic weight values corresponding to the D bill characteristics respectively, wherein D is an integer greater than or equal to 1; and the second acquisition module is used for acquiring the bill weight value corresponding to the target electronic bill based on the feature weight values respectively corresponding to the D bill features.
Optionally, in the electronic bill combining device provided in the embodiment of the present application, the second determining module includes: the identification module is used for identifying bill processing behaviors of the target account in a preset time period to obtain historical impression data; the third determining module is used for determining K historical electronic notes posted by the target account based on the historical posting data, wherein K is an integer greater than or equal to 1; a fourth determining module, configured to determine the number of occurrences of any feature of the D ticket features in the K historical electronic tickets, where D is an integer greater than or equal to 1; and a fifth determining module, configured to determine feature weight values corresponding to the D ticket features respectively based on the number of occurrences of any feature.
Optionally, in the electronic bill combining device provided in the embodiment of the present application, the fifth determining module includes: a sixth determining module, configured to determine a proportion of the number of occurrences of any feature to a total ticket number of the K historical electronic tickets; the sorting module is used for sorting the D bill features based on the proportion corresponding to any feature respectively to obtain a sorting result; and the weight determining module is used for determining feature weight values corresponding to the D bill features respectively based on the sorting result.
Optionally, in the electronic bill combining device provided by the embodiment of the present application, the device further includes: the display module is used for displaying the target combination strategy; and the correction module is used for responding to the correction instruction under the condition of receiving the correction instruction, generating a correction combination strategy and generating prompt information based on the comparison result of the correction combination strategy and the target combination strategy.
It should be noted that each of the above modules may be implemented by software or hardware, for example, in the latter case, it may be implemented by: the above modules may be located in the same processor; alternatively, the various modules described above may be located in different processors in any combination.
It should be noted that, the first determining module 202, the first obtaining module 204, the first constraint module 206, and the first combining module 208 correspond to steps S102 to S108 in the embodiment, and the modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the embodiment. It should be noted that the above modules may be run in a computer terminal as part of the apparatus.
It should be noted that, the optional or preferred implementation manner of this embodiment may be referred to the related description in the embodiment, and will not be repeated herein.
The electronic bill combining device comprises a processor and a memory, wherein the units and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel may set one or more, and the electronic bill combination is performed by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the application provides a nonvolatile storage medium, and a program is stored on the nonvolatile storage medium, and when the program is executed by a processor, the program realizes an electronic bill combination method.
The embodiment of the application provides a processor, which is used for running a program, wherein the program runs to execute an electronic bill combination method.
As shown in fig. 3, an embodiment of the present application provides an electronic device, where the electronic device 10 includes a processor, a memory, and a program stored on the memory and executable on the processor, and the processor implements the following steps when executing the program: determining a target account; acquiring a bill weight value corresponding to a target electronic bill held by a target account; under the condition that the number of the target electronic bills is M, determining weight constraint based on bill weight values corresponding to the M target electronic bills respectively, wherein M is an integer greater than or equal to 1; and determining target combination strategies of the M target electronic bills based on the weight constraint and target credit limit required by the target accounts. The device herein may be a server, PC, PAD, cell phone, etc.
The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: determining a target account; acquiring a bill weight value corresponding to a target electronic bill held by a target account; under the condition that the number of the target electronic bills is M, determining weight constraint based on bill weight values corresponding to the M target electronic bills respectively, wherein M is an integer greater than or equal to 1; and determining target combination strategies of the M target electronic bills based on the weight constraint and target credit limit required by the target accounts.
Optionally, the above computer program product is further adapted to execute a program initialized with the method steps of: based on weight constraint, determining target particles with a minimum target function by adopting a particle swarm algorithm, wherein the target function is used for representing a discount value of which a first discount amount exceeds a target discount amount, any particles included in the particle swarm algorithm represent selection states respectively corresponding to M target electronic notes, and the first discount amount is a discount total amount obtained based on the M target electronic notes; and determining a target combination strategy based on the selection states respectively corresponding to the M target electronic notes indicated by the target particles.
Optionally, the above computer program product is further adapted to execute a program initialized with the method steps of: determining a single ticket loss constraint based on the expiration dates and ticket exchange interest rates respectively corresponding to the M target electronic tickets; determining a total discount constraint based on a first discount amount and an expected total discount amount, wherein the expected total discount amount is determined based on bill denominations corresponding to the M target electronic bills respectively, and the first discount amount is a discount total obtained based on the M target electronic bills; and determining a target combination strategy according to the ticket loss constraint, the total loss constraint, the weight constraint and the target discount amount.
Optionally, the above computer program product is further adapted to execute a program initialized with the method steps of: determining D bill characteristics corresponding to the target electronic bill and characteristic weight values corresponding to the D bill characteristics respectively, wherein D is an integer greater than or equal to 1; and acquiring the bill weight value corresponding to the target electronic bill based on the feature weight values respectively corresponding to the D bill features.
Optionally, the above computer program product is further adapted to execute a program initialized with the method steps of: identifying bill processing behaviors of the target account in a preset time period to obtain historical impression data; based on the historical impression data, determining K historical electronic notes of the target account for impression, wherein K is an integer greater than or equal to 1; determining the occurrence times of any feature in the D bill features in the K historical electronic bills, wherein D is an integer greater than or equal to 1; and determining feature weight values corresponding to the D bill features respectively based on the occurrence times of any features.
Optionally, the above computer program product is further adapted to execute a program initialized with the method steps of: determining the proportion of the occurrence times of any feature to the total ticket number of the K historical electronic tickets; sorting the D bill features based on the proportion corresponding to any feature respectively to obtain a sorting result; and determining feature weight values corresponding to the D bill features respectively based on the sorting result.
Optionally, the above computer program product is further adapted to execute a program initialized with the method steps of: displaying the target combination strategy; and under the condition that the correction instruction is received, generating a correction combination strategy in response to the correction instruction, and generating prompt information based on a comparison result of the correction combination strategy and the target combination strategy.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. An electronic bill combining method, comprising:
determining a target account;
acquiring a bill weight value corresponding to a target electronic bill held by the target account;
under the condition that the number of the target electronic bills is M, determining weight constraint based on bill weight values corresponding to the M target electronic bills respectively, wherein M is an integer greater than or equal to 1;
And determining target combination strategies of the M target electronic bills based on the weight constraint and the target discount amount required by the target account.
2. The method of claim 1, wherein the determining the target combination policy for the M target electronic tickets based on the weight constraints and the target credit required for the target account comprises:
determining target particles with a minimum target function by adopting a particle swarm algorithm based on the weight constraint, wherein the target function is used for expressing a discount value of a first discount amount exceeding the target discount amount, any particle included in the particle swarm algorithm expresses the selection states respectively corresponding to the M target electronic notes, and the first discount amount is the discount total amount obtained based on the M target electronic notes;
and determining the target combination strategy based on the selection states respectively corresponding to the M target electronic notes indicated by the target particles.
3. The method of claim 1, wherein the determining the target combination policy for the M target electronic tickets based on the weight constraints and the target credit required for the target account comprises:
Determining a single ticket loss constraint based on the expiration dates and ticket exchange interest rates respectively corresponding to the M target electronic tickets;
determining a total discount constraint based on a first discount amount and an expected total discount amount, wherein the expected total discount amount is determined based on bill denominations respectively corresponding to the M target electronic bills, and the first discount amount is a discount total obtained based on the M target electronic bills;
and determining the target combination strategy according to the ticket loss constraint, the total loss constraint, the weight constraint and the target credit limit.
4. A method according to any one of claims 1 to 3, wherein the obtaining a ticket weight value corresponding to a target electronic ticket held by the target account includes:
determining D bill characteristics corresponding to the target electronic bill and characteristic weight values corresponding to the D bill characteristics respectively, wherein D is an integer greater than or equal to 1;
and acquiring the bill weight value corresponding to the target electronic bill based on the feature weight values respectively corresponding to the D bill features.
5. The method of claim 4, wherein determining feature weight values for the D ticket features, respectively, comprises:
Identifying bill processing behaviors of the target account in a preset time period to obtain historical impression data;
based on the historical impression data, determining K historical electronic notes which are to be impression of the target account, wherein K is an integer greater than or equal to 1;
determining the occurrence times of any feature in the D bill features in the K historical electronic bills, wherein D is an integer greater than or equal to 1;
and determining the feature weight values corresponding to the D bill features respectively based on the occurrence times of the arbitrary features.
6. The method of claim 5, wherein determining feature weight values for the D ticket features based on the number of occurrences of the arbitrary feature, respectively, comprises:
determining the proportion of the occurrence times of the optional features to the total ticket number of the K historical electronic tickets;
sorting the D bill features based on the proportions corresponding to the arbitrary features respectively to obtain a sorting result;
and determining feature weight values corresponding to the D bill features respectively based on the sorting result.
7. The method of claim 4, wherein after said determining the target combining policy for the M target electronic tickets, the method further comprises:
Displaying the target combination strategy;
and under the condition that a correction instruction is received, generating a correction combination strategy in response to the correction instruction, and generating prompt information based on a comparison result of the correction combination strategy and the target combination strategy.
8. An electronic bill combining apparatus, comprising:
the first determining module is used for determining a target account;
the first acquisition module is used for acquiring a bill weight value corresponding to the target electronic bill held by the target account;
the first constraint module is used for determining weight constraint based on ticket weight values corresponding to the M target electronic tickets respectively under the condition that the target electronic tickets are M, wherein M is an integer greater than or equal to 1;
and the first combination module is used for determining target combination strategies of the M target electronic bills based on the weight constraint and the target discount amount required by the target account.
9. A non-volatile storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the electronic ticket assembling method of any of claims 1 to 7.
10. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the electronic ticket combining method of any of claims 1 to 7.
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| CN202311063042.5A CN117078238A (en) | 2023-08-22 | 2023-08-22 | Electronic bill combination method and device, storage medium and electronic equipment |
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