Business promotion strategy determination method and apparatus, and electronic device

Abstract

The invention discloses a business promotion strategy determination method and device and electronic equipment. The method relates to the field of big data, and comprises the following steps: acquiring historical business data of a target user under multiple businesses to obtain a historical business data set; model parameters of the initial prediction model are determined through the sample data set and a preset optimization algorithm, a target prediction model is obtained, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; inputting the historical business data set into a target prediction model to obtain a prediction result of whether the target user handles the target business; and determining a promotion strategy for promoting the target service to the target user according to the prediction result. Through the application, the problem that the matching degree of business promotion to the user according to the business information is low due to the fact that the business promoted to the user is determined according to artificial experience in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the field of big data, and more specifically, to a method, device and electronic device for determining a business promotion strategy. Background Art

[0002] As the number of businesses in financial institutions continues to increase, the number of users who handle business in financial institutions is also increasing. In order to improve the degree of user understanding of the business and to promote the business, financial institutions usually carry out business promotion. The current method of business promotion is to determine new business suitable for the user based on the user's historical business handling, and promote the new business to the user.

[0003] However, for new users of financial institutions, since the users have no history of handling business or have little historical business handling information, when promoting business to the users, the method usually adopted is to determine the business to promote to the users based on manual experience. However, this method cannot accurately determine the business that is suitable for promotion to the users, which leads to a poor match between the promoted business and the users, resulting in low business promotion efficiency.

[0004] Currently, no effective solution has been proposed to address the problem that in related technologies, the services promoted to users are determined based on manual experience, resulting in a low matching degree of the services promoted to users based on the service information. Summary of the invention

[0005] The main purpose of the present application is to provide a method, device and electronic device for determining a business promotion strategy, so as to solve the problem in the related art that the business promoted to the user is determined based on manual experience, resulting in a low matching degree of the business promoted to the user based on the business information.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining a business promotion strategy is provided. The method includes: obtaining the historical business data of the target user under multiple businesses to obtain a historical business data set; determining the model parameters of the initial prediction model through a sample data set and a preset optimization algorithm to obtain a target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes the business data of the historical user under the sample historical period, and the handling results of the historical user handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; inputting the historical business data set into the target prediction model to obtain a prediction result of whether the target user handles the target business; and determining a promotion strategy for promoting the target business to the target user based on the prediction result.

[0007] Optionally, the model parameters of the initial prediction model are determined by a sample data set and a preset optimization algorithm, and obtaining the target prediction model includes: setting the initial parameters of the preset optimization algorithm, and generating initial values ​​for each target individual to obtain n initial value sets, wherein the initial parameters include: the total number of iterations of the preset optimization algorithm, the number of individuals in the population of the spider bee optimization algorithm n, each initial value set contains multiple initial values ​​that are the same as the number of model parameters, and the initial values ​​are used to determine the model parameters, and n is a positive integer; obtaining the target initial value set, calculating the prediction accuracy of each initial value set, and determining the target initial value in the current iteration cycle according to the prediction accuracy to obtain an updated target initial value set, wherein the target initial value is the initial value set with the highest prediction accuracy in the previous iteration cycle; judging whether the current number of iterations is equal to the total number of iterations; if the current number of iterations is not equal to the total number of iterations, continuing to execute the step of generating initial values ​​for each target individual until the current number of iterations is equal to the total number of iterations; if the current number of iterations is equal to the total number of iterations, determining the updated target initial value set as the model parameters of the initial prediction model to obtain the target prediction model.

[0008] Optionally, a target initial value set is obtained, the prediction accuracy of each initial value set is calculated, and the target initial value in the current iteration cycle is determined according to the prediction accuracy, and the updated target initial value set is obtained, including: determining the initial value set with the highest prediction accuracy from n initial value sets by the spider bee optimization algorithm to obtain a first initial value set; updating the first initial value set according to the target initial value set to obtain a second initial value set, wherein the target initial value set is the initial value set with the highest prediction accuracy in the previous iteration cycle; selecting a candidate initial value set from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy, and processing the candidate initial value set through a preset search strategy to obtain a third initial value set; selecting the target initial value set in the current iteration cycle from the candidate initial value set and the third initial value set according to the prediction accuracy to obtain an updated target initial value set.

[0009] Optionally, determining the initial value set with the highest prediction accuracy from n initial value sets through the spider bee optimization algorithm to obtain the first initial value set includes: updating each initial value set through the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets; inputting each updated initial value set as a model parameter into the initial prediction model in turn to obtain n first prediction models; determining the prediction accuracy of each first prediction model through a sample data set to obtain n first prediction accuracy rates; and determining the updated initial value set corresponding to the highest accuracy among the n first prediction accuracy rates as the first initial value set.

[0010] Optionally, the initial parameters include a range of initial values, and each initial value set is updated by the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets, including: updating each initial value set by the initial parameters carried by the spider bee optimization algorithm to obtain n fourth initial value sets; determining whether there are initial values ​​outside the range in the n fourth initial value sets; if there are initial values ​​outside the range, if the initial value is greater than the upper limit of the range, changing the initial value to the upper limit; if the initial value is less than the lower limit of the range, changing the initial value to the lower limit.

[0011] Optionally, selecting a candidate initial value set from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy includes: substituting the second initial value set as a model parameter into the initial prediction model to obtain a second prediction model; substituting the target initial value set as a model parameter into the initial prediction model to obtain a third prediction model; substituting the first initial value set as a model parameter into the initial prediction model to obtain a fourth prediction model; determining the prediction accuracy of the second prediction model through a sample data set to obtain a second prediction accuracy; determining the prediction accuracy of the third prediction model through a sample data set to obtain a third prediction accuracy; determining the prediction accuracy of the fourth prediction model through a sample data set to obtain a fourth prediction accuracy; and determining the initial value set corresponding to the larger prediction accuracy among the second prediction accuracy, the third prediction accuracy and the fourth prediction accuracy as the candidate initial value set.

[0012] Optionally, determining a promotion strategy for promoting the target service to the target user based on the prediction result includes: when the prediction result indicates that the target user has handled the target service, determining the promotion strategy to promote the target service to the target user according to a first promotion frequency; when the prediction result indicates that the target user has not handled the target service, determining the promotion strategy to promote the target service to the target user according to a second promotion frequency, wherein the first promotion frequency is greater than the second promotion frequency.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for determining a business promotion strategy is provided. The device includes: an acquisition unit, which is used to acquire the historical business data of the target user under multiple businesses and obtain a historical business data set; a first determination unit, which is used to determine the model parameters of the initial prediction model through a sample data set and a preset optimization algorithm to obtain a target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes the business data of the historical user under the sample historical period, and the handling results of the historical user handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; a prediction unit, which is used to input the historical business data set into the target prediction model to obtain a prediction result of whether the target user handles the target business; a second determination unit, which is used to determine the promotion strategy for promoting the target business to the target user based on the prediction result.

[0014] In an embodiment of the present application, historical business data of a target user under multiple businesses is obtained to obtain a historical business data set; model parameters of an initial prediction model are determined through a sample data set and a preset optimization algorithm to obtain a target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes business data of historical users in a sample historical period, and handling results of target businesses handled by historical users, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; the historical business data set is input into the target prediction model to obtain a prediction result of whether the target user handles the target business; and a promotion strategy for promoting the target business to the target user is determined based on the prediction result. By setting an initial prediction model and adjusting the model parameters of the initial prediction model through a sample data set and a preset optimization algorithm, a more accurate target prediction model is obtained. By obtaining the historical business data of the target user under only a few businesses, the matching degree between the user and the target business is predicted based on the historical business data and the target prediction model, and whether the user is a promoted user of the target business is judged based on the prediction result. The technical effect of accurately determining the matching degree between the target user and the target business is achieved, thereby solving the problem in the related technology of determining the business to be promoted to the user based on manual experience, resulting in a low matching degree of promoting the business to the user based on business information. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for determining a business promotion strategy is shown;

[0017] Figure 2is a flow chart of a method for determining a business promotion strategy provided in Example 1 of the present application;

[0018] Figure 3 is a schematic diagram of a device for determining a business promotion strategy provided in Example 2 of the present application;

[0019] Figure 4 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] It should be noted that the method, device and electronic device for determining the business promotion strategy determined in the present disclosure can be used in the big data field, and can also be used in any field other than the big data field. The application field of the method, device and electronic device for determining the business promotion strategy determined in the present disclosure is not limited.

[0024] It should be noted that the collected information, user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) used in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with the relevant laws, regulations and standards of the relevant regions, necessary confidentiality measures are taken, and public order and good customs are not violated. Corresponding operation entrances are provided for users to choose to authorize use or refuse use. If the user chooses to refuse, the expert decision-making process is entered. For example, an interface is set between this system and relevant users or institutions. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or institution through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or institution.

[0025] The embodiments or examples of the present disclosure are not exhaustive, but are only illustrative of some embodiments or examples, and are not intended to be specific limitations on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment or example can be implemented as an independent example, and the steps can be combined arbitrarily. For example, the scheme after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be arbitrarily exchanged. In addition, the optional methods or optional examples in a certain embodiment or example can be combined arbitrarily; in addition, the various embodiments or examples can be combined arbitrarily, for example, some or all steps of different embodiments or examples can be combined arbitrarily, and a certain embodiment or example can be combined arbitrarily with the optional methods or optional examples of other embodiments or examples.

[0026] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0027] LS-SVM (Least Squares Support Vector Machine): A new type of support vector machine algorithm proposed based on the traditional SVM (Support Vector Machine) model. Its main principle is to change the inequality constraints in the SVM model into equality constraints, and use the error square loss function as the empirical loss of the training set. Under the premise of ensuring the calculation accuracy, the quadratic optimization problem is transformed into the problem of solving a linear system of equations. Least Squares Support Vector Machine (LS-SVM) is an effective classification algorithm, but the relevant parameters need to be manually selected, which has a great impact on the results.

[0028] Reverse learning strategy: Based on the current solution, the corresponding reverse solution is found through the reverse learning mechanism, and then evaluated and compared, and the better solution is saved, so as to expand the search area of ​​the algorithm.

[0029] Spider Bee Optimization Algorithm: It is an optimization algorithm based on bionics. The Spider Bee Optimization Algorithm has good global search capabilities, but it is easy to fall into the local optimal solution.

[0030] Example 1

[0031] According to an embodiment of the present application, a method embodiment of a method for determining a business promotion strategy is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for determining a business promotion strategy. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0034] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the business promotion strategy in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the method for determining the business promotion strategy described above is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0037] Under the above operating environment, this application provides Figure 2 The method for determining the business promotion strategy shown in FIG. Figure 2 is a flow chart of a method for determining a business promotion strategy according to Example 1 of the present application, such as Figure 2 As shown, the method includes:

[0038] Step S201, obtaining historical service data of a target user under multiple services to obtain a historical service data set.

[0039] Specifically, the historical business data may be a small amount of historical business handled by the user or historical business data at other financial institutions, wherein the historical business data includes not only data related to the handled business, but may also include the user's financial information behavior data, such as the user's deposit amount, the user's level in the financial institution, and other data unrelated to the business, and the acquired data is further combined into data to obtain a historical business data set.

[0040] For example, the relevant data of the target user is obtained, and the historical business data set is obtained as M={M 1 ,M 2 ,M 3 ...M 18}, where M1 can be the third-party payment transaction amount in the past three months, M2 can be the total assets, M3 can be the contribution star rating of the entire bank, M4 can be the average daily deposit in the past three months, M5 can be the debit card transaction amount in the past three months, M6 can be the average daily assets in the past three months, M7 can be the total star point value, M8 can be the average daily deposit in the past year, M9 can be the average daily deposit in the past six months, M10 can be the service star rating of the entire bank, M11 can be the total liabilities, M12 can be the bank card balance, M13 can be the total deposits, M14 can be the number of debit card transactions in the past three months, M15 can be the number of credit card transactions in the past three months, M16 can be the number of third-party transactions in the past three months, M17 can be the total loans, and M18 can be the average daily assets in the past year.

[0041] Step S202, determine the model parameters of the initial prediction model through the sample data set and the preset optimization algorithm to obtain the target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes the business data of historical users in the sample historical period, and the processing results of the historical users in handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy.

[0042] It should be noted that the initial prediction model can be a least squares support vector machine model, and the parameters that need to be set in the model are optimized through a preset optimization algorithm to obtain a prediction model with higher accuracy, wherein the model parameters include at least one of the following: optimal penalty coefficient, kernel function parameters.

[0043] Specifically, when optimizing the parameters of the initial prediction model, it is necessary to set a sample data set and a preset optimization algorithm for optimizing the parameters, so that it is possible to determine whether the accuracy of the model parameters optimized by the preset optimization algorithm meets the requirements based on the sample data set, thereby ensuring the accuracy of the model optimization operation.

[0044] It should be noted that when setting the sample data set, it is necessary to obtain multiple sample data, and divide the multiple sample data into training group and test group according to preset proportions. The model parameters are optimized through the training group, and the model accuracy is determined using the test group, thereby ensuring the accuracy of the model in predicting the number of business transactions.

[0045] It should be noted that when obtaining sample data, each sample data needs to include the business data of multiple historical users, as well as the actual processing results (including processing and non-processing) of the historical users after promoting the target business to the historical users. The target business processing results of multiple historical users are used as samples to train the model, thereby ensuring that the model can accurately predict the processing results of the user's target business based on the user's business data.

[0046] Step S203: input the historical business data set into the target prediction model to obtain a prediction result of whether the target user handles the target business.

[0047] Specifically, after optimizing the model parameters of the initial prediction model through the sample data set and the preset optimization algorithm, a target prediction model with a more accurate prediction accuracy is obtained. At this time, the historical business data set of the target user can be input into the target prediction model, and the historical business data set can be analyzed and predicted according to the target prediction model to obtain a prediction result of whether the target user will handle the target business, and then it can be determined whether to perform business promotion operations to the user and how to perform business promotion operations to the target user based on the prediction results.

[0048] It should be noted that the prediction result can be a single result, for example, the prediction result is that the target user handles the target service or the target user does not handle the target service. The prediction result can also be a probability value, for example, the prediction result is that the target user has a 65% probability of handling the target service. This application does not limit the content of the prediction result.

[0049] Step S204: determining a promotion strategy for promoting the target service to the target users according to the prediction result.

[0050] Specifically, when the prediction results are obtained, the promotion strategy for promoting the target business to the target users can be determined based on the prediction results, thereby ensuring the match between the promotion strategy and the target users, and further ensuring a high match between the target business promoted to the target users and the target users, while ensuring the user experience and improving the business promotion efficiency.

[0051] For example, when the prediction result is that the target user has an 80% probability of handling the target business, the promotion strategy may be to send promotional information of the target business to the target user according to the first promotion cycle; when the prediction result is that the target user has a 50% probability of handling the target business, the promotion strategy may be to send promotional information of the target business to the target user according to the second promotion cycle, the second promotion cycle being longer than the first promotion cycle; when the prediction result is that the target user has a 10% probability of handling the target business, the promotion strategy may be not to promote the target business to the target user, thereby ensuring the accuracy and rationality of the target business promoted to the target user.

[0052] The method for determining a business promotion strategy provided in an embodiment of the present application obtains historical business data of a target user under multiple businesses to obtain a historical business data set; determines model parameters of an initial prediction model through a sample data set and a preset optimization algorithm to obtain a target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes business data of historical users in a sample historical period, and handling results of historical users handling target businesses, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; inputs the historical business data set into the target prediction model to obtain a prediction result of whether the target user handles the target business; and determines a promotion strategy for promoting the target business to the target user based on the prediction result. By setting an initial prediction model and adjusting the model parameters of the initial prediction model through a sample data set and a preset optimization algorithm, a more accurate target prediction model is obtained. By obtaining the historical business data of the target user under only a few businesses, the matching degree between the user and the target business is predicted based on the historical business data and the target prediction model, and whether the user is a promoted user of the target business is judged based on the prediction result. The technical effect of accurately determining the matching degree between the target user and the target business is achieved, thereby solving the problem in the related technology of determining the business to be promoted to the user based on manual experience, resulting in a low matching degree of promoting the business to the user based on business information.

[0053] In order to obtain a target prediction model with a high prediction accuracy, optionally, in the method for determining the business promotion strategy provided in the embodiment of the present application, the model parameters of the initial prediction model are determined by the sample data set and the preset optimization algorithm, and the target prediction model is obtained by: setting the initial parameters of the preset optimization algorithm, and generating initial values ​​of each target individual, and obtaining n initial value sets, wherein the initial parameters include: the total number of iterations of the preset optimization algorithm, the number of individuals in the population of the spider bee optimization algorithm n, each initial value set contains a plurality of initial values ​​that are the same as the number of model parameters, and the initial values ​​are used to determine the model parameters, and n is a positive integer; obtaining the target initial value set Combine, calculate the prediction accuracy of each initial value set, and determine the target initial value in the current iteration cycle according to the prediction accuracy, and obtain an updated target initial value set, wherein the target initial value is the initial value set with the highest prediction accuracy in the previous iteration cycle; determine whether the current number of iterations is equal to the total number of iterations; when the current number of iterations is not equal to the total number of iterations, continue to execute the steps of generating the initial value of each target individual until the current number of iterations is equal to the total number of iterations; when the current number of iterations is equal to the total number of iterations, determine the updated target initial value set as the model parameters of the initial prediction model to obtain the target prediction model.

[0054] Specifically, the preset optimization algorithm may be a spider bee optimization algorithm. When optimizing the model parameters of the initial prediction model, it is first necessary to set the initial parameters of the preset optimization algorithm, including the total number of optimization iterations t max , preset the number of target individuals and initial values ​​in the optimization algorithm, thereby obtaining n initial value sets, and selecting the optimal model parameters from the n initial value sets in the first iteration operation, wherein each target individual corresponds to a set of model parameters, and the number of model parameters to be determined is the same as the number of initial values ​​in each target individual. For example, n can be 5, and when the model parameters are 2 parameters, the 5 initial value sets obtained can be: [a1, b1], [a2, b2], [a3, b3], [a4, b4], [a5, b5].

[0055] It should be noted that after obtaining n initial value sets, it is necessary to first determine whether there is a previous iteration cycle. In the absence of a previous iteration cycle, the initial value set that makes the initial prediction model most accurate can be directly selected from the n initial value sets as the target initial value set, and the next iteration is performed. In the case of a previous iteration cycle, it is necessary to obtain the target initial value set of the previous iteration cycle, and select the initial value set with the highest accuracy from the target initial value set and the n initial value sets, so as to obtain the updated target initial value set, and then complete the selection operation of the target initial value set.

[0056] It should be noted that after obtaining the updated target initial value set, it indicates that the current iteration calculation has been completed. At this time, it is necessary to determine whether the current number of iterations is equal to the total number of iterations t max , and when it is equal to the total number of iterations, it represents the completion of the optimization operation, and the current optimal solution, that is, the updated target initial value set obtained in the current iteration cycle is used as the model parameters of the initial prediction model, and then the target prediction model is obtained.

[0057] When the current number of iterations is not equal to the total number of iterations, the representation still needs to perform the next round of operations. At this time, it is necessary to re-execute the steps of generating the initial values ​​of each target individual and recalculate the new updated target initial value set until the number of iterations is equal to the total number of iterations, thereby ensuring the accuracy of the determination of the model parameters.

[0058] This embodiment optimizes n initial value sets by using a preset optimization algorithm, and optimizes the target initial value set obtained in each round of iteration through multiple iterative calculations, thereby ensuring the accuracy of the model parameters of the prediction model.

[0059] In order to ensure the accuracy of the updated target initial value set, in the method for determining the business promotion strategy provided in the embodiment of the present application, the target initial value set is obtained, the prediction accuracy of each initial value set is calculated, and the target initial value in the current iteration cycle is determined according to the prediction accuracy, and the updated target initial value set is obtained, including: determining the initial value set with the highest prediction accuracy from n initial value sets through the spider bee optimization algorithm to obtain a first initial value set; updating the first initial value set according to the target initial value set to obtain a second initial value set, wherein the target initial value set is the initial value set with the highest prediction accuracy in the previous iteration cycle; selecting a candidate initial value set from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy, and processing the candidate initial value set through a preset search strategy to obtain a third initial value set; selecting the target initial value set in the current iteration cycle from the candidate initial value set and the third initial value set according to the prediction accuracy to obtain an updated target initial value set.

[0060] Specifically, first, the spider bee optimization algorithm is used to determine the initial value set with the highest prediction accuracy from n initial value sets to obtain the first initial value set. When the first initial value set is determined by the spider bee optimization algorithm, it is necessary to first generate a first random number and a second random number, and determine an optimized calculation formula based on the first random number and the second random number, so as to update the n initial value sets and obtain n updated initial value sets.

[0061] It should be noted that the first random number and the second random number may be p and k, where p is a random number in [0, 1], and k is calculated by the following formula (1):

[0062]

[0063] Among them, t is the current iteration number, t max is the total number of iterations.

[0064] 1. When p is less than k, each initial value set can be updated by the following calculation process:

[0065]

[0066] Among them, r3 and r4 are two random numbers in [0,1]. is the set of initial values ​​after the i-th update.

[0067] Formula (2) is as follows:

[0068]

[0069] in, and are any two initial value sets among the n initial value sets, is the i-th initial value set currently being optimized, is the i-th initial value set after optimization, μ 1 It can be calculated by formula (3):

[0070] μ 1 =|rn|*r 1 (3)

[0071] Among them, r 1 is a random number between 0 and 1, and rn is a random number generated according to a normal distribution.

[0072] Formula (4) is as follows:

[0073]

[0074] Among them, H and L are the lower and upper limits of the initial values ​​of each model parameter dimension, and r2 is a random number between 0 and 1. is the i-th initial value set currently being optimized, is the i-th initial value set after optimization, μ 2 It can be calculated by formula (5):

[0075] μ 2 =B*cos(2πl) (5)

[0076] Where l is a randomly generated number between 1 and -2, and B can be calculated using formula (6):

[0077]

[0078] 2. When p is greater than or equal to k, each initial value set can be updated by the following calculation process:

[0079]

[0080] Among them, r3 and r4 are two random numbers in [0,1]. is the set of initial values ​​after the i-th update.

[0081] Formula (7) is as follows:

[0082]

[0083] in, are any initial value sets among the n initial value sets, is the i-th initial value set currently being optimized, To complete the optimized i-th initial value set, r5 is a vector representing a randomly generated value in the interval [0, 1], and C is calculated by formula (8):

[0084]

[0085] Where r6 is a random number in the interval [0, 1], t is the current iteration number, and tmax is the total iteration number.

[0086] Formula (9) is as follows:

[0087]

[0088] in, is the i-th initial value set currently being optimized, To complete the optimized i-th initial value set, vc is a vector generated between k and -k according to the normal distribution, and k can be calculated by formula (1).

[0089] It should be noted that after obtaining n updated initial value sets, an optimal updated initial value set may be selected therefrom to obtain a first initial value set.

[0090] Further, after obtaining the first initial value set, the first initial value set can be updated by the optimal initial value set in the previous iteration cycle, that is, the target initial value set, to obtain the second initial value set. The first initial value set can be updated by the following process:

[0091] First, the calculation formula used to update the first initial value set can be determined as follows:

[0092]

[0093] in, is the second initial value set, formula (10) can be shown as follows:

[0094]

[0095] in, is the target initial value set, is the first initial value set, is the second initial value set, where l is a randomly generated number between 1 and -2.

[0096] Formula (11) can be expressed as follows:

[0097]

[0098] Among them, r3 is a random number created in the interval [0,1], is the first initial value set, is the second initial value set, γ is a number generated according to the Levy flight, and are three sets randomly selected from the n updated initial value sets, U is a binary vector used to determine when to apply a step size to avoid building two nests at the same location, and U is determined according to the following formula (12):

[0099]

[0100] Among them, r 4 and r 5 are two vectors representing random values ​​in the interval [0, 1].

[0101] After obtaining the second initial value set, an optimal solution can be selected from the second initial value set, the first initial value set and the target initial value set to obtain a candidate initial value set, and the candidate initial value set is processed by a preset search strategy to obtain a third initial value set, wherein the preset search strategy can be an elite reverse search strategy, wherein the candidate initial value set can be processed by formula (13) to obtain the third initial value set:

[0102] x ij (t)′=m(l ij (t)+u ij (t))-x ij (t) (13)

[0103] Among them, x ij (t) is the candidate initial value set, x ij (t)′ is the third initial value set, l ij (t) is the upper limit value of each parameter in the initial value set, u ij (t) is the lower limit value of each parameter in the initial value set, and m is a random number between 0 and 1.

[0104] After the third initial value set is obtained, the optimal solution can be selected from the third initial value set and the candidate initial value set as the updated target initial value set, thereby improving the accuracy of the operation of selecting model parameters.

[0105] This embodiment achieves the technical effect of improving the accuracy of selecting the updated target initial value set by calculating the first initial value set, updating the first initial value set according to the target initial value set, determining the third initial value set through an elite reverse search strategy after selecting the candidate initial value set, and determining the updated target initial value set from the candidate initial value set and the third initial value set.

[0106] In order to ensure accurate selection of the first initial value set, optionally, in the method for determining the business promotion strategy provided in the embodiment of the present application, an initial value set with the highest prediction accuracy is determined from n initial value sets by a spider bee optimization algorithm, and obtaining the first initial value set includes: updating each initial value set by the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets; inputting each updated initial value set as a model parameter into the initial prediction model in turn to obtain n first prediction models; determining the prediction accuracy of each first prediction model respectively by a sample data set to obtain n first prediction accuracy rates; and determining the updated initial value set corresponding to the highest accuracy rate among the n first prediction accuracy rates as the first initial value set.

[0107] Specifically, after obtaining n updated initial value sets, it is necessary to substitute each updated initial value set as a model parameter into the initial prediction model to obtain n first prediction models, and process each first prediction model through a sample data set, so that the prediction accuracy of each first prediction model can be judged according to the prediction accuracy of the first prediction model for the sample data, that is, the prediction accuracy of the n models is obtained, and then according to the prediction accuracy of the n models, the initial value set with the highest accuracy is selected from the n updated initial value sets to obtain the first initial value set, thereby ensuring the accurate selection of the first initial value set.

[0108] This embodiment obtains the prediction accuracy of each initial value set by substituting each initial value set as a model parameter into the initial prediction model, thereby achieving the technical effect of ensuring the accuracy of selecting the first initial value set.

[0109] Optionally, in the method for determining the business promotion strategy provided in the embodiment of the present application, the initial parameters include a value range of the initial value, and each initial value set is updated by the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets, including: updating each initial value set by the initial parameters carried by the spider bee optimization algorithm to obtain n fourth initial value sets; determining whether there are initial values ​​outside the value range in the n fourth initial value sets; if there are initial values ​​outside the value range, if the initial value is greater than the upper limit value of the value range, changing the initial value to the upper limit value; if the initial value is less than the lower limit value of the value range, changing the initial value to the lower limit value.

[0110] Specifically, when n updated initial value sets are obtained, after each initial value set is updated, n fourth initial value sets are obtained. At this time, there may be abnormal values ​​in the n fourth initial value sets, such as an initial value set in which a certain initial value exceeds the value interval. At this time, it is necessary to change each initial value in each fourth initial value set according to the value range of each model parameter, so that the initial value in each initial value set is within the corresponding value range, thereby ensuring the accuracy of the updated initial value set.

[0111] It should be noted that after each initial value is changed to be within the corresponding value range, the n changed fourth initial value sets can be determined as the n changed initial value sets, thereby completing the update operation of the initial value set.

[0112] This embodiment uses the value range of each model parameter to limit the value of each initial value in the initial value set, thereby achieving the technical effect of ensuring the accuracy of the initial values ​​in the updated initial value set.

[0113] Optionally, in the method for determining the business promotion strategy provided in the embodiment of the present application, selecting a candidate initial value set from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy includes: substituting the second initial value set as a model parameter into the initial prediction model to obtain a second prediction model; substituting the target initial value set as a model parameter into the initial prediction model to obtain a third prediction model; substituting the first initial value set as a model parameter into the initial prediction model to obtain a fourth prediction model; determining the prediction accuracy of the second prediction model through a sample data set to obtain a second prediction accuracy; determining the prediction accuracy of the third prediction model through a sample data set to obtain a third prediction accuracy; determining the prediction accuracy of the fourth prediction model through a sample data set to obtain a fourth prediction accuracy; and determining the initial value set corresponding to the larger prediction accuracy among the second prediction accuracy, the third prediction accuracy and the fourth prediction accuracy as the candidate initial value set.

[0114] Specifically, after obtaining the first initial value set, the second initial value set and the target initial value set, it is necessary to select the optimal solution therefrom, so as to use the optimal solution to execute subsequent calculation processes, thereby ensuring the accuracy of the final solution selected.

[0115] Therefore, after obtaining the first initial value set, the second initial value set and the target initial value set, it is necessary to substitute the first initial value set, the second initial value set and the target initial value set as model parameters into the initial prediction model respectively to obtain the second prediction model, the third prediction model and the fourth prediction model, and test each prediction model respectively through the sample data set to obtain the prediction accuracy of each prediction model for the sample data set, and then according to the prediction accuracy of each prediction model, select the initial value set with the highest accuracy from the first initial value set, the second initial value set and the target initial value set to obtain the candidate initial value set.

[0116] This embodiment achieves the technical effect of ensuring the accuracy of selecting a candidate initial value set by calculating the prediction accuracy of the first initial value set, the second initial value set, and the target initial value set.

[0117] Optionally, in the method for determining a business promotion strategy provided in an embodiment of the present application, determining a promotion strategy for promoting a target business to a target user based on a prediction result includes: when the prediction result indicates that the target user handles the target business, determining the promotion strategy to promote the target business to the target user according to a first promotion frequency; when the prediction result indicates that the target user does not handle the target business, determining the promotion strategy to promote the target business to the target user according to a second promotion frequency, wherein the first promotion frequency is greater than the second promotion frequency.

[0118] Specifically, after obtaining the prediction result, when the prediction result indicates that the target user has the intention to handle the target service, the target service can be promoted to the target user according to the first promotion frequency, so that the user can be prompted to handle the target service, and promotion according to the first promotion frequency can increase the probability of the target user handling the target service.

[0119] Furthermore, when the prediction result indicates that the target user has no intention of handling the target service, the service can also be promoted to the user, but the service can be promoted to the target user at a second promotion frequency, so that when the service is promoted to the target user, the user will not generate negative feedback, thereby completing the promotion operation of the target service while ensuring the user experience.

[0120] It should be noted that when the promotion operation to users is complex or costly, the target service can be promoted to the target user only when the prediction result shows that the target user has the intention to handle the target service, thereby reducing the service promotion cost and improving the service promotion efficiency.

[0121] This embodiment achieves the technical effect of improving the efficiency of business promotion by promoting the business to target users in different ways according to the prediction results.

[0122] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0123] Example 2

[0124] The embodiment of the present application also provides a device for determining a business promotion strategy. It should be noted that the device for determining a business promotion strategy in the embodiment of the present application can be used to execute the method for determining a business promotion strategy provided in the embodiment of the present application. The device for determining a business promotion strategy provided in the embodiment of the present application is introduced below.

[0125] According to an embodiment of the present application, a device for implementing the above-mentioned method for determining a business promotion strategy is also provided. Figure 3 is a schematic diagram of a device for determining a business promotion strategy according to Example 2 of the present application, such as Figure 3 As shown, the device comprises:

[0126] The acquisition unit 31 is used to acquire the historical service data of the target user under multiple services to obtain a historical service data set.

[0127] The first determination unit 32 is used to determine the model parameters of the initial prediction model through the sample data set and the preset optimization algorithm to obtain the target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes the business data of historical users in the sample historical period, and the processing results of the historical users in handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy.

[0128] The prediction unit 33 is used to input the historical service data set into the target prediction model to obtain the prediction result of whether the target user handles the target service.

[0129] The second determining unit 34 is used to determine a promotion strategy for promoting the target service to the target user according to the prediction result.

[0130] The device for determining the business promotion strategy provided in the embodiment of the present application obtains the historical business data of the target user under multiple businesses through the acquisition unit 31 to obtain a historical business data set; the first determination unit 32 determines the model parameters of the initial prediction model through the sample data set and the preset optimization algorithm to obtain the target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes the business data of the historical users in the sample historical period, and the handling results of the historical users handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; the prediction unit 33 inputs the historical business data set into the target prediction model to obtain a prediction result of whether the target user handles the target business; the second determination unit 34 determines the promotion strategy for promoting the target business to the target user based on the prediction result. By setting an initial prediction model and adjusting the model parameters of the initial prediction model through a sample data set and a preset optimization algorithm, a more accurate target prediction model is obtained. By obtaining the historical business data of the target user under only a few businesses, the matching degree between the user and the target business is predicted based on the historical business data and the target prediction model, and whether the user is a promoted user of the target business is judged based on the prediction result. The technical effect of accurately determining the matching degree between the target user and the target business is achieved, thereby solving the problem in the related technology of determining the business to be promoted to the user based on manual experience, resulting in a low matching degree of promoting the business to the user based on business information.

[0131] Optionally, in the device for determining the business promotion strategy provided in the embodiment of the present application, the first determination unit 32 includes: a setting subunit, which is used to set the initial parameters of the preset optimization algorithm, and generate the initial values ​​of each target individual to obtain n initial value sets, wherein the initial parameters include: the total number of iterations of the preset optimization algorithm, the number of population individuals n of the spider bee optimization algorithm, each initial value set contains a plurality of initial values ​​that are the same as the number of model parameters, the initial values ​​are used to determine the model parameters, and n is a positive integer; an acquisition subunit, which is used to acquire the target initial value set, calculate the prediction accuracy of each initial value set, and determine the current target according to the prediction accuracy. The target initial value in the previous iteration cycle is used to obtain an updated target initial value set, wherein the target initial value is the initial value set with the highest prediction accuracy in the previous iteration cycle; a judgment subunit is used to judge whether the current number of iterations is equal to the total number of iterations; an execution subunit is used to continue to execute the steps of generating initial values ​​of each target individual when the current number of iterations is not equal to the total number of iterations until the current number of iterations is equal to the total number of iterations; a first determination subunit is used to determine the updated target initial value set as the model parameters of the initial prediction model when the current number of iterations is equal to the total number of iterations, so as to obtain the target prediction model.

[0132] Optionally, in the device for determining the business promotion strategy provided in the embodiment of the present application, the acquisition subunit includes: a determination module, which is used to determine the initial value set with the highest prediction accuracy from n initial value sets through the spider bee optimization algorithm to obtain a first initial value set; an update module, which is used to update the first initial value set according to the target initial value set to obtain a second initial value set, wherein the target initial value set is the initial value set with the highest prediction accuracy in the previous iteration cycle; a first selection module, which is used to select candidate initial value sets from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy, and process the candidate initial value sets through a preset search strategy to obtain a third initial value set; a second selection module, which is used to select the target initial value set in the current iteration cycle from the candidate initial value set and the third initial value set according to the prediction accuracy to obtain an updated target initial value set.

[0133] Optionally, in the device for determining the business promotion strategy provided in the embodiment of the present application, the determination module includes: a first updating sub-module, used to update each initial value set through the initial parameters carried by the spider bee optimization algorithm, and obtain n updated initial value sets; an input sub-module, used to input each updated initial value set as a model parameter into the initial prediction model in turn, and obtain n first prediction models; a first determination sub-module, used to determine the prediction accuracy of each first prediction model respectively through a sample data set, and obtain n first prediction accuracy rates; a second determination sub-module, used to determine the updated initial value set corresponding to the highest accuracy among the n first prediction accuracy rates as the first initial value set.

[0134] Optionally, in the device for determining the business promotion strategy provided in the embodiment of the present application, the initial parameters include a value range of the initial value, and the first update submodule includes: a second update submodule, used to update each initial value set through the initial parameters carried by the spider bee optimization algorithm to obtain n fourth initial value sets; a judgment submodule, used to judge whether there are initial values ​​outside the value range in the n fourth initial value sets; a first change submodule, used to change the initial value to the upper limit value when there is an initial value outside the value range and the initial value is greater than the upper limit value of the value range; a second change submodule, used to change the initial value to the lower limit value when the initial value is less than the lower limit value of the value range.

[0135] Optionally, in the device for determining the business promotion strategy provided in the embodiment of the present application, the first selection module includes: a first substitution submodule, used to substitute the second initial value set as a model parameter into the initial prediction model to obtain a second prediction model; a second substitution submodule, used to substitute the target initial value set as a model parameter into the initial prediction model to obtain a third prediction model; a third substitution submodule, used to substitute the first initial value set as a model parameter into the initial prediction model to obtain a fourth prediction model; a third determination submodule, used to determine the prediction accuracy of the second prediction model through a sample data set to obtain a second prediction accuracy; a fourth determination submodule, used to determine the prediction accuracy of the third prediction model through a sample data set to obtain a third prediction accuracy; a fifth determination submodule, used to determine the prediction accuracy of the fourth prediction model through a sample data set to obtain a fourth prediction accuracy; and a sixth determination submodule, used to determine the initial value set corresponding to the larger prediction accuracy among the second prediction accuracy, the third prediction accuracy and the fourth prediction accuracy as a candidate initial value set.

[0136] Optionally, in the device for determining the business promotion strategy provided in the embodiment of the present application, the second determination unit 34 includes: a second determination sub-unit, used to determine the promotion strategy as promoting the target business to the target user according to a first promotion frequency when the prediction result indicates that the target user handles the target business; and a third determination sub-unit, used to determine the promotion strategy as promoting the target business to the target user according to a second promotion frequency when the prediction result indicates that the target user does not handle the target business, wherein the first promotion frequency is greater than the second promotion frequency.

[0137] It should be noted that the acquisition unit 31, the first determination unit 32, the prediction unit 33, and the second determination unit 34 correspond to steps S201 to S204 in Embodiment 1, and the two modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the modules may also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0138] Example 3

[0139] An embodiment of the present application may provide an electronic device, Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4(only one is shown) processor 1002, memory 1004, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0140] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain the historical business data of the target user under multiple businesses to obtain a historical business data set; determine the model parameters of the initial prediction model through the sample data set and the preset optimization algorithm to obtain the target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes the business data of the historical user in the sample historical period, and the processing results of the historical user in handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; input the historical business data set into the target prediction model to obtain a prediction result of whether the target user handles the target business; determine a promotion strategy for promoting the target business to the target user based on the prediction result.

[0142] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determine the model parameters of the initial prediction model through the sample data set and the preset optimization algorithm, and obtain the target prediction model, including: setting the initial parameters of the preset optimization algorithm, and generating the initial values ​​of each target individual to obtain n initial value sets, wherein the initial parameters include: the total number of iterations of the preset optimization algorithm, the number of population individuals n of the spider bee optimization algorithm, each initial value set contains multiple initial values ​​that are the same as the number of model parameters, and the initial values ​​are used to determine the model parameters, and n is a positive integer; obtain the target initial value set, calculate the prediction accuracy of each initial value set, and determine the target initial value in the current iteration cycle according to the prediction accuracy to obtain an updated target initial value set, wherein the target initial value is the initial value set with the highest prediction accuracy in the previous iteration cycle; determine whether the current number of iterations is equal to the total number of iterations; if the current number of iterations is not equal to the total number of iterations, continue to execute the step of generating the initial values ​​of each target individual until the current number of iterations is equal to the total number of iterations; if the current number of iterations is equal to the total number of iterations, determine the updated target initial value set as the model parameter of the initial prediction model to obtain the target prediction model.

[0143] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain the target initial value set, calculate the prediction accuracy of each initial value set, and determine the target initial value in the current iteration cycle according to the prediction accuracy, and obtain the updated target initial value set including: determining the initial value set with the highest prediction accuracy from n initial value sets through the spider bee optimization algorithm to obtain a first initial value set; updating the first initial value set according to the target initial value set to obtain a second initial value set, wherein the target initial value set is the initial value set with the highest prediction accuracy in the previous iteration cycle; selecting a candidate initial value set from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy, and processing the candidate initial value set through a preset search strategy to obtain a third initial value set; selecting the target initial value set in the current iteration cycle from the candidate initial value set and the third initial value set according to the prediction accuracy to obtain an updated target initial value set.

[0144] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: determine the initial value set with the highest prediction accuracy from n initial value sets through the spider bee optimization algorithm, and obtain the first initial value set, including: updating each initial value set through the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets; inputting each updated initial value set as a model parameter into the initial prediction model in turn to obtain n first prediction models; determining the prediction accuracy of each first prediction model through the sample data set to obtain n first prediction accuracy rates; determining the updated initial value set corresponding to the highest accuracy among the n first prediction accuracy rates as the first initial value set.

[0145] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: the initial parameters include the value range of the initial value, and each initial value set is updated by the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets, including: each initial value set is updated by the initial parameters carried by the spider bee optimization algorithm to obtain n fourth initial value sets; it is determined whether there are initial values ​​outside the value range in the n fourth initial value sets; if there are initial values ​​outside the value range, if the initial value is greater than the upper limit value of the value range, the initial value is changed to the upper limit value; if the initial value is less than the lower limit value of the value range, the initial value is changed to the lower limit value.

[0146] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: selecting a candidate initial value set from the first initial value set, the second initial value set and the target initial value set according to the prediction accuracy, including: substituting the second initial value set as a model parameter into the initial prediction model to obtain a second prediction model; substituting the target initial value set as a model parameter into the initial prediction model to obtain a third prediction model; substituting the first initial value set as a model parameter into the initial prediction model to obtain a fourth prediction model; determining the prediction accuracy of the second prediction model through a sample data set to obtain a second prediction accuracy; determining the prediction accuracy of the third prediction model through a sample data set to obtain a third prediction accuracy; determining the prediction accuracy of the fourth prediction model through a sample data set to obtain a fourth prediction accuracy; determining the initial value set corresponding to the larger prediction accuracy among the second prediction accuracy, the third prediction accuracy and the fourth prediction accuracy as the candidate initial value set.

[0147] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determining a promotion strategy for promoting the target service to the target user based on the prediction result, including: when the prediction result indicates that the target user handles the target service, determining the promotion strategy to promote the target service to the target user according to a first promotion frequency; when the prediction result indicates that the target user does not handle the target service, determining the promotion strategy to promote the target service to the target user according to a second promotion frequency, wherein the first promotion frequency is greater than the second promotion frequency.

[0148] It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 4 The structure of the electronic device is not limited. Figure 4 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 4 Different configurations are shown.

[0149] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0150] Example 4

[0151] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for determining the business promotion strategy provided in the first embodiment.

[0152] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0153] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the method for determining a business promotion strategy.

[0154] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0155] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0160] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a business promotion strategy, characterized in that: include: Obtain the historical business data of the target user under multiple businesses and obtain a historical business data set; The model parameters of the initial prediction model are determined by a sample data set and a preset optimization algorithm to obtain a target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes business data of historical users in a sample historical period, and the handling results of the historical users handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; Inputting the historical business data set into a target prediction model to obtain a prediction result of whether the target user handles the target business; A promotion strategy for promoting the target service to the target user is determined according to the prediction result.

2. The method according to claim 1, characterized in that The model parameters of the initial prediction model are determined through the sample data set and the preset optimization algorithm, and the target prediction model is obtained, including: Setting initial parameters of the preset optimization algorithm and generating initial values ​​of each target individual to obtain n initial value sets, wherein the initial parameters include: the total number of iterations of the preset optimization algorithm, the number n of population individuals of the spider bee optimization algorithm, each initial value set contains a plurality of initial values ​​that are the same as the number of the model parameters, and the initial values ​​are used to determine the model parameters, and n is a positive integer; Obtain a target initial value set, calculate the prediction accuracy of each initial value set, and determine the target initial value in the current iteration cycle according to the prediction accuracy, to obtain an updated target initial value set, wherein the target initial value is the initial value set with the highest prediction accuracy in the previous iteration cycle; Determine whether the current number of iterations is equal to the total number of iterations; When the current number of iterations is not equal to the total number of iterations, continue to execute the step of generating the initial value of each target individual until the current number of iterations is equal to the total number of iterations; When the current number of iterations is equal to the total number of iterations, the updated target initial value set is determined as the model parameters of the initial prediction model to obtain the target prediction model.

3. The method according to claim 2, characterized in that Obtain a target initial value set, calculate the prediction accuracy of each initial value set, and determine the target initial value in the current iteration cycle according to the prediction accuracy, and obtain an updated target initial value set including: Determine the initial value set with the highest prediction accuracy from the n initial value sets by using the spider bee optimization algorithm to obtain a first initial value set; The first initial value set is updated according to the target initial value set to obtain a second initial value set, wherein the target initial value set is the initial value set with the highest prediction accuracy in the previous iteration cycle; Selecting a candidate initial value set from the first initial value set, the second initial value set, and the target initial value set according to the prediction accuracy, and processing the candidate initial value set by using the preset search strategy to obtain a third initial value set; A target initial value set in the current iteration cycle is selected from the candidate initial value set and the third initial value set according to the prediction accuracy to obtain the updated target initial value set.

4. The method according to claim 3, characterized in that The spider bee optimization algorithm is used to determine the initial value set with the highest prediction accuracy from the n initial value sets, and the first initial value set is obtained, including: Each initial value set is updated by using the initial parameters carried by the spider bee optimization algorithm to obtain n updated initial value sets; Inputting each updated initial value set as a model parameter into the initial prediction model in turn to obtain n first prediction models; Determine the prediction accuracy of each first prediction model respectively through the sample data set to obtain n first prediction accuracy rates; The updated initial value set corresponding to the highest accuracy rate among the n first prediction accuracy rates is determined as the first initial value set.

5. The method according to claim 4, characterized in that The initial parameters include the value range of the initial value. Each initial value set is updated by the initial parameters carried by the spider bee optimization algorithm, and n updated initial value sets are obtained, including: Each initial value set is updated by using the initial parameters carried by the spider bee optimization algorithm to obtain n fourth initial value sets; Determine whether there is an initial value outside the value range in the n fourth initial value sets; If there is an initial value outside the value range, and if the initial value is greater than an upper limit value of the value range, changing the initial value to the upper limit value; When the initial value is smaller than the lower limit of the value range, the initial value is changed to the lower limit.

6. The method according to claim 3, characterized in that Selecting a candidate initial value set from the first initial value set, the second initial value set, and the target initial value set according to the prediction accuracy includes: Substituting the second initial value set as the model parameters into the initial prediction model to obtain a second prediction model; Substituting the target initial value set as the model parameter into the initial prediction model to obtain a third prediction model; Substituting the first initial value set as the model parameters into the initial prediction model to obtain a fourth prediction model; Determining the prediction accuracy of the second prediction model through the sample data set to obtain a second prediction accuracy; Determining the prediction accuracy of the third prediction model through the sample data set to obtain a third prediction accuracy; Determining the prediction accuracy of the fourth prediction model through the sample data set to obtain a fourth prediction accuracy; An initial value set corresponding to a larger prediction accuracy rate among the second prediction accuracy rate, the third prediction accuracy rate and the fourth prediction accuracy rate is determined as the candidate initial value set.

7. The method according to claim 1, characterized in that Determining a promotion strategy for promoting the target service to the target user according to the prediction result includes: In a case where the prediction result indicates that the target user handles the target service, determining the promotion strategy to promote the target service to the target user at a first promotion frequency; When the prediction result indicates that the target user has not applied for the target service, the promotion strategy is determined to promote the target service to the target user at a second promotion frequency, wherein the first promotion frequency is greater than the second promotion frequency.

8. A device for determining a business promotion strategy, characterized in that: include: An acquisition unit, used to acquire historical service data of a target user under multiple services, and obtain a historical service data set; A first determination unit is used to determine the model parameters of the initial prediction model through a sample data set and a preset optimization algorithm to obtain a target prediction model, wherein the sample data set is composed of multiple groups of sample data, each group of sample data includes business data of historical users in a sample history period, and the handling results of the historical users handling the target business, and the preset optimization algorithm is a spider bee optimization algorithm combined with a preset search strategy; A prediction unit, used for inputting the historical service data set into a target prediction model to obtain a prediction result of whether the target user handles the target service; The second determining unit is used to determine a promotion strategy for promoting the target service to the target user according to the prediction result.

9. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for determining the business promotion strategy described in any one of claims 1 to 7.

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