Value-added product recommendation method and device, equipment, storage medium and program product

Abstract

The embodiment of the invention provides a value-added product recommendation method and device, equipment, a storage medium and a program product, and relates to the field of artificial intelligence or financial science and technology. The method comprises the following steps: acquiring historical resource transfer data of a target user in a first preset time period, determining a resource retention value sequence, a resource increment ratio and a behavior period label in the time period, inputting the data into a long-short-term memory network, and obtaining a predicted total number of residual resources of the user in a second preset time period; and finally, comparing the current residual resource total number value with the predicted residual resource total number value of the user, and if the current value is greater than or equal to the predicted value, determining a value-added product recommendation combination. According to the method, the future resource flow condition of the user is predicted based on the historical resource transfer data, and the value-added product is recommended by combining the comparison of the current resource total number value and the predicted residual resource total number value, so that the problem that idle resources exist in the current account of the user is solved, and the resource utilization rate of the idle resources is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence or the field of financial technology, in particular to a value-added product recommendation method and device, equipment, a storage medium and a program product. BACKGROUND

[0002] With the diversification of financial business, bank accounts have become an important tool for individuals and enterprises to manage funds. Due to the characteristics of flexible access and low risk, current accounts have become the first choice for people to deposit liquid resources and provide basic resource support for various economic activities.

[0003] However, during the use of the current account, micro-increases or resources temporarily not used often occur. Because these resources are small in value and uncertain in use time, users usually do not perform value-added operations, resulting in long-term residence of these resources in the current account, which cannot be fully utilized, thereby forming idle resources. SUMMARY

[0004] The present application provides a value-added product recommendation method, device, equipment, storage medium and program product to solve the problem of idle resources in the current account of the user.

[0005] In a first aspect, the present application provides a value-added product recommendation method, comprising:

[0006] obtaining historical resource transfer data of a target user in a first preset period;

[0007] determining a resource retention value sequence, a resource value-added ratio and a behavior cycle label in the first preset period according to the historical resource transfer data;

[0008] inputting the resource retention value sequence, the resource value-added ratio and the behavior cycle label in the first preset period into a long short-term memory network to obtain a predicted total value of remaining resources of the target user in a second preset period;

[0009] comparing the total value of the current remaining resources of the target user with the predicted total value of the remaining resources, and if the total value of the current remaining resources is greater than or equal to the predicted total value of the remaining resources, determining a value-added product recommendation combination.

[0010] In a possible implementation manner, the determining of the resource retention value sequence, the resource value-added ratio and the behavior cycle label in the first preset period according to the historical resource transfer data comprises:

[0011] performing encryption processing on the historical resource transfer data to obtain encrypted historical resource transfer data;

[0012] According to the historical resource transfer data of the processing completion, a resource retention value sequence, a resource value-added ratio, and a behavior cycle label in the first preset period are determined.

[0013] In a possible implementation, the determining the value-added product recommendation combination comprises:

[0014] An value-added product library is obtained, the value-added product library comprising: a plurality of first value-added products, and a respective annual growth rate, a starting input base amount, and a product type of each of the first value-added products.

[0015] The plurality of first value-added products are sorted based on the respective annual growth rate of each of the first value-added products, to obtain a plurality of first value-added products with a priority order.

[0016] A second value-added product is determined from the plurality of first value-added products with the priority order, the priority of the second value-added product being greater than the priority of other first value-added products, the starting input base amount of the second value-added product being less than or equal to a preset input base amount, and the product type of the second value-added product satisfying a preset product type, the value-added product recommendation combination comprising the second value-added product.

[0017] In a possible implementation, the method further comprises:

[0018] A third value-added product is determined from the plurality of first value-added products with the priority order, the priority of the third value-added product being less than the priority of the second value-added product, the priority of the third value-added product being greater than the priority of other first value-added products, the starting input base amount of the third value-added product being less than or equal to the preset input base amount, the product type of the third value-added product satisfying the preset product type, and the difference between the annual growth rate of the third value-added product and the annual growth rate of the second value-added product being less than or equal to a preset difference, the value-added product recommendation combination comprising the third value-added product.

[0019] In a possible implementation, the method further comprises:

[0020] A remaining quantity of the second value-added product is determined.

[0021] In a case where the remaining quantity of the second value-added product is greater than a preset quantity, a first instruction is executed, the first instruction being used to instruct to obtain the second value-added product.

[0022] In a case where the remaining quantity of the second value-added product is equal to the preset quantity, a second instruction is executed, the second instruction being used to instruct to obtain the third value-added product.

[0023] In a possible implementation, the method further includes:

[0024] If the current total remaining resource value is less than the predicted total remaining resource value, first warning information is generated, the first warning information being used to remind that the current total remaining resource value is insufficient.

[0025] In a possible implementation, the method further includes:

[0026] determining an account status of the target user;

[0027] If the account status indicates an account anomaly, second warning information is generated, the second warning information being used to remind that the account status of the target user is abnormal.

[0028] In a second aspect, the present application provides a value-added product recommendation device, comprising:

[0029] an acquisition module configured to acquire historical resource transfer data of a target user in a first preset time period;

[0030] a determination module configured to determine, according to the historical resource transfer data, a resource retention value sequence, a resource value-added ratio, and a behavior cycle label in the first preset time period;

[0031] an input module configured to input the resource retention value sequence, the resource value-added ratio, and the behavior cycle label in the first preset time period into a long short-term memory network to obtain a predicted total remaining resource value of the target user in a second preset time period;

[0032] The determination module is further configured to compare a current total remaining resource value of the target user with the predicted total remaining resource value, and if the current total remaining resource value is greater than or equal to the predicted total remaining resource value, determine a value-added product recommendation combination.

[0033] In a possible implementation, the device further includes an encryption module.

[0034] The encryption module is configured to perform encryption processing on the historical resource transfer data to obtain encrypted historical resource transfer data.

[0035] The determination module is specifically configured to determine, according to the processed historical resource transfer data, the resource retention value sequence, the resource value-added ratio, and the behavior cycle label in the first preset time period.

[0036] In a possible implementation, the acquisition module is further configured to acquire a value-added product library, the value-added product library including a plurality of first value-added products, and an annual growth rate, a starting input base amount, and a product type of each first value-added product.

[0037] The apparatus further comprises an ordering module;

[0038] The ordering module is configured to order the first value-added products based on respective annual growth rates of the first value-added products, to obtain first value-added products with priority orders.

[0039] The determining module is specifically configured to determine a second value-added product from the first value-added products with priority orders, the priority of the second value-added product being higher than that of other first value-added products, the initial input base amount of the second value-added product being less than or equal to the preset input base amount, and the product type of the second value-added product satisfying a preset product type, the value-added product recommendation combination comprising the second value-added product.

[0040] In a possible implementation, the determining module is specifically configured to determine a third value-added product from the first value-added products with priority orders, the priority of the third value-added product being lower than that of the second value-added product, the priority of the third value-added product being higher than that of other first value-added products, the initial input base amount of the third value-added product being less than or equal to the preset input base amount, the product type of the third value-added product satisfying a preset product type, and the difference between the annual growth rate of the third value-added product and the annual growth rate of the second value-added product being less than or equal to a preset difference, the value-added product recommendation combination comprising the third value-added product.

[0041] In a possible implementation, the determining module is further configured to determine a remaining quantity of the second value-added product.

[0042] The apparatus further comprises an executing module.

[0043] The executing module is configured to execute a first instruction in a case where the remaining quantity of the second value-added product is greater than a preset quantity, the first instruction being used to instruct to acquire the second value-added product.

[0044] The executing module is further configured to execute a second instruction in a case where the remaining quantity of the second value-added product is equal to the preset quantity, the second instruction being used to instruct to acquire the third value-added product.

[0045] In a possible implementation, the apparatus further comprises a generating module.

[0046] The generating module is configured to generate first early warning information in a case where the current total remaining resource value is less than the predicted total remaining resource value, the first early warning information being used to remind that the current total remaining resource value is insufficient.

[0047] In a possible implementation mode, the determining module is further configured to determine an account state of the target user.

[0048] The generating module is further configured to generate second early warning information in a case where the account state indicates an account anomaly, the second early warning information being used to remind that the account state of the target user is abnormal.

[0049] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor.

[0050] The memory stores computer execution instructions.

[0051] The processor executes the computer execution instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation modes of the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the first aspect and / or various possible implementation modes of the first aspect.

[0053] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and the computer program is executed by a processor to implement the first aspect and / or various possible implementation modes of the first aspect.

[0054] The value-added product recommendation method, device, equipment, storage medium and program product provided by the present application obtain historical resource transfer data of a target user in a first preset period, then input the historical resource transfer data, resource retention value sequence in the first preset period, resource value-added ratio and behavior cycle label into a long short-term memory network for prediction to obtain a total value of remaining resources of the target user in a second preset period, and finally compare the current total value of remaining resources with the total value of remaining resources, and if the current total value of remaining resources is greater than or equal to the predicted total value of remaining resources, recommend a value-added product combination. The method predicts the future resource flow of the user based on the historical resource transfer data, and recommends a value-added product by comparing the current total value of resources with the predicted total value of remaining resources, solves the problem of idle resources in the current account of the user, and improves the utilization rate of idle resources. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0056] Figure 1 Flowchart of the value-added product recommendation method provided by the present applicationFigure 1 ;

[0057] Figure 2 The flowchart of the value-added product recommendation method provided in the present application Figure 2 ;

[0058] Figure 3 The structure diagram of the value-added product recommendation device provided in the present application

[0059] Figure 4 The structure diagram of the value-added product recommendation device provided in the present application

[0060] The specific embodiments of the present application have been shown in the above-described drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0061] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same or similar elements throughout the drawings. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they only represent examples of apparatuses and methods consistent with some aspects of the present application, as detailed in the appended claims.

[0062] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary security measures are taken, public order and good customs are not violated, and appropriate operation portals are provided for the user to choose to authorize or refuse.

[0063] And the present application involves big data analysis of user information (including but not limited to personal biological characteristics, identity data, consumption data, asset data, electronic terminal operation data, etc.), and uses artificial intelligence technology for automatic decision-making, and provides appropriate operation portals for the user to choose to agree or refuse the automatic decision-making result based on the automatic decision-making result to make a technical solution that has a significant impact on personal rights and interests; if the user chooses to refuse, the expert decision-making process is entered.

[0064] It should be noted that the value-added product recommendation method, device, equipment, storage medium and program product provided by the present application can be used in the field of artificial intelligence or the field of financial technology, and can also be used in any field other than the field of artificial intelligence or the field of financial technology. The application field of the value-added product recommendation method, device, equipment, storage medium and program product in the present application is not limited.

[0065] With the diversification of modern financial business, bank accounts have become an important tool for individuals and enterprises to manage funds. There are various types of accounts, and current accounts meet the needs of more and more users for liquidity due to their flexibility and convenience.

[0066] However, during the use of the current account, micro-increases or resources temporarily not used may occur. Because these resources are small in value and uncertain in use time, users usually do not perform value-added operations, resulting in long-term residence of these resources in the current account, which cannot be fully utilized, thereby forming idle resources.

[0067] To solve the above problems, the present application provides a value-added product recommendation method, which first acquires historical resource transfer data of a target user in a first preset period, then determines resource retention value sequence, resource value-added ratio and behavior cycle label in the period according to the data, subsequently inputs these data into a long short-term memory network to obtain a predicted total value of remaining resources of the user in a second preset period, and finally compares the current total value of remaining resources of the user with the predicted total value of remaining resources. If the current value is greater than or equal to the predicted value, a value-added product recommendation combination is determined. The method predicts the future resource flow of the user by analyzing the historical resource transfer data of the user's current account, and recommends appropriate value-added products for the user, effectively solving the problem of idle resources in the user's current account, thereby ensuring the liquidity of funds while improving resource income.

[0068] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0069] Figure 1 Flowchart of the value-added product recommendation method provided by the present application Figure 1 As shown in Figure 1 , the method comprises.

[0070] S101, acquiring historical resource transfer data of a target user in a first preset period.

[0071] The first preset period may be, for example, the past three months or the past week. The present application does not have special restrictions on this.

[0072] The historical resource transfer data refers to detailed record information of the target user within the first preset time period, involving the resource flow between the current account and other accounts. The historical resource transfer data includes but is not limited to: resource transfer time, resource transfer value, resource transfer direction (such as transfer-in or transfer-out).

[0073] The purpose of this step is to obtain the historical resource transfer record of the target user in the past period of time.

[0074] The acquisition method of this step may be obtained by calling the API interface provided by the related data platform or system, or may be obtained by accessing the related device authorized by the target user under the premise of user authorization. The present application does not make special limitation.

[0075] Optionally, the present application provides an implementation manner for determining the account status of the target user, comprising:

[0076] First, determine the account status of the target user.

[0077] The account status includes: account normal, account abnormal. The account normal is used to indicate that the account of the target user is running normally, without freezing, loss, compliance risk and other abnormal situations, and can normally carry out resource transfer and other operations. The account abnormal is used to indicate that the account of the target user is running abnormally, with freezing, loss, incomplete information or compliance risk and other abnormal situations, and cannot normally carry out resource transfer and other operations.

[0078] It can be understood that by determining the account status of the target user, it can be determined whether the account status of the target user is normal, avoiding unnecessary invalid processing of abnormal accounts, such as avoiding normal business operation process for accounts that have been frozen, have security risks and other abnormal situations, so as to ensure that the subsequent value-added product recommendation process can proceed smoothly.

[0079] Second, in the case that the account status indicates that the account is abnormal, a second warning information is generated, and the second warning information is used to remind that the account status of the target user is abnormal.

[0080] The purpose of this step is to timely remind the user and the staff, and inform the user that the account status is abnormal.

[0081] It can be understood that the account abnormality will directly affect the resource use of the target user, such as being unable to carry out resource exchange or complete related operations. If the account problem is not handled in time, it may cause resource security risk, even cause fund loss or unable to normally execute transaction.

[0082] Therefore, by generating the second early warning information, not only can the target user be reminded to discover and solve the account problem as early as possible to avoid repeated operation failures due to unawareness, but also the staff can take corresponding measures on the account of the target user in time to avoid the continuous expansion of the account problem, further protecting the user's fund safety and account use rights and interests.

[0083] S102, determine the resource retention value sequence, resource value-added ratio and behavior cycle label in the first preset period according to the historical resource transfer data.

[0084] The resource retention value sequence refers to a sequence of values of resources retained in the account within the first preset period, which is obtained by statistical analysis at a specific time interval (such as daily, weekly, monthly, etc.). For example, assuming that the first preset period is the past 7 days, the resource retention value sequence of the target user can be obtained by determining the account resource retention value of the target user every day within 7 days and arranging the corresponding 7 values in chronological order.

[0085] The resource value-added ratio refers to the proportion of resources that are increased in value through resource allocation, resource investment, resource deployment, etc. within the first preset period. For example, assuming that the initial resource value of the target user is 1000, and after 7 days, the initial resource value increases from 1000 to 1200, then based on the above information, the resource value-added ratio can be determined as (1200-1000) ÷ 1000 × 100% = 20%.

[0086] The behavior cycle label refers to a label for classifying the resource flow behavior of the target user within the first preset period. The behavior cycle label includes but is not limited to: purchase behavior, resource transfer, resource deposit.

[0087] The purpose of this step is to determine the change trend of the resources owned by the target user in the past period, the resource value-added efficiency, and the behavior cycle label that can reflect the user's resource flow regularity.

[0088] It can be understood that the resource retention value sequence can represent the change trend of the resources of the target user within the first preset period, the resource value-added ratio can represent the value-added income level of the resources within the period, and the behavior cycle label can represent the behavior characteristics of the target user using the resources. Therefore, by determining the resource retention value sequence, the resource value-added ratio and the behavior cycle label, the resource status and its change trend of the target user can be comprehensively understood.

[0089] S103, input the resource retention value sequence, resource value-added ratio and behavior cycle label in the first preset period into the long short-term memory network to obtain the predicted total resource value of the target user in the second preset period.

[0090] The second preset time period can be, for example, three months in the future or one week in the future. The application does not make special limitations on this.

[0091] The purpose of this step is to predict the total value of the remaining resources of the target user in a future period of time by using the resource retention value sequence, the resource appreciation ratio, and the behavior cycle label of the target user in a historical period.

[0092] It can be understood that the long short-term memory network has an advantage of processing time series data and can capture the time regularity, behavior pattern, and appreciation trend of the resource flow of the target user. Therefore, by taking the resource retention value sequence, the resource appreciation ratio, and the behavior cycle label in the first preset time period as inputs and inputting them into the long short-term network, the predicted total value of the remaining resources of the target user in the second preset time period can be obtained by means of the mining and analysis capability of the long short-term memory network on time series related information.

[0093] S104, comparing the total value of the current remaining resources of the target user with the predicted total value of the remaining resources, and if the total value of the current remaining resources is greater than or equal to the predicted total value of the remaining resources, determining the value-added product recommendation combination.

[0094] The total value of the current remaining resources refers to the total amount of resources remaining in the account of the target user in the current time or period.

[0095] The purpose of this step is to compare the total value of the current remaining resources of the target user with the predicted total value of the remaining resources in order to determine whether there is idle resource in the target user's demand account.

[0096] If the total value of the current remaining resources of the target user is greater than or equal to the predicted total value of the remaining resources, it indicates that there is idle resource in the target user's demand account. At this time, the value-added product recommendation combination can be determined.

[0097] It can be understood that, first, the total value of the current remaining resources is the total amount of resources actually possessed and available for disposal by the target user in the current time, which can reflect the real resource strength of the target user at present.

[0098] Secondly, the predicted total value of the remaining resources is determined according to the historical resource transfer data of the target user in the first preset time period, which can predict the resource situation of the target user in a future period of time.

[0099] Therefore, in the case where the total value of the current remaining resources is greater than or equal to the predicted total value of the remaining resources, in order to avoid the waste caused by the idle resources of the target user and help the target user to realize the preservation and appreciation of resources, it is necessary to determine the value-added product recommendation combination for the target user.

[0100] Optionally, the application provides a possible implementation manner in the case that the current total remaining resource value is less than the predicted total remaining resource value, comprising: generating first warning information, the first warning information being used to remind that the current total remaining resource value is insufficient.

[0101] If the current total remaining resource value of the target user is less than the predicted total remaining resource value, it indicates that there is no idle resource in the target user's current account. At this time, the first warning information can be generated.

[0102] The purpose of this step is to provide a warning mechanism for the target user, so that the user can discover the resource shortage in time when the current total remaining resource value is less than the predicted total remaining resource value.

[0103] It can be understood that by generating the first warning information, the target user can quickly know that the resources may face the risk of shortage, and make preparations in advance to avoid affecting the normal life, business development or other important matters due to insufficient resources, and ensure the stability and continuity of the user's resource use.

[0104] The value-added product recommendation method provided by the embodiment first acquires the historical resource transfer data of the target user in the first preset period, then determines the resource retention value sequence, the resource value-added ratio and the behavior cycle label in the period according to the historical resource transfer data, then inputs these data into the long short-term memory network to obtain the predicted total remaining resource value of the user in the second preset period, and finally compares the current total remaining resource value of the user with the predicted total remaining resource value. If the current value is greater than or equal to the predicted value, the value-added product recommendation combination is determined. The method predicts the future resource flow of the user, and recommends the value-added product by comparing the current total resource value with the predicted total remaining resource value, solves the problem of idle resources in the user's current account, and improves the utilization rate of idle resources.

[0105] Figure 2 Flowchart of the value-added product recommendation method provided by the application Figure 2 As shown in the flowchart, the embodiment is based on the embodiment, and the value-added product recommendation method is described in detail, which comprises: Figure 1 Figure 3 The method comprises the following steps:

[0106] S201, acquiring historical resource transfer data of a target user in a first preset period.

[0107] The explanation of step S201 is similar to the explanation of step S101, and will not be repeated here.

[0108] S202, encrypting the historical resource transfer data to obtain encrypted historical resource transfer data.

[0109] ​The step is to encrypt sensitive data in the historical resource transfer data.

[0110] It can be understood that the historical resource transfer data contains sensitive data such as resource transfer-in and transfer-out value size, time, and account association information of the target user. If the historical resource transfer data is directly used, it may lead to resource leakage of the target user.

[0111] Therefore, by encrypting the sensitive data in the historical resource transfer data through encryption technology, the original and directly readable user resource flow record can be converted into uncrackable encrypted data, thereby preventing data from being tampered with and stolen during storage, transmission or subsequent processing, and protecting the security of the user's resource information.

[0112] S203, according to the processed historical resource transfer data, determine the resource retention value sequence, the resource value-added ratio and the behavior cycle label in the first preset period.

[0113] It can be understood that the processed historical resource transfer data is a standardized fund flow record after encryption operation, which not only guarantees the privacy security of the target user, but also eliminates redundant and error information, and completely retains the core information of the target user's past resource transfer-in and transfer-out.

[0114] Therefore, by considering the processed historical resource transfer data, not only the resource change trend and value-added income level of the resources owned by the target user in the past period of time can be obtained, but also the behavior cycle label of the target user in the past period of time can be determined.

[0115] S204, input the resource retention value sequence, the resource value-added ratio and the behavior cycle label in the first preset period into the long short-term memory network to obtain the predicted remaining resource total value of the target user in the second preset period.

[0116] The explanation of step S204 is similar to that of step S103, and will not be repeated here.

[0117] S205, compare the current remaining resource total value of the target user with the predicted remaining resource total value. If the current remaining resource total value is greater than or equal to the predicted remaining resource total value, obtain a value-added product library, and the value-added product library includes: a plurality of first value-added products, and the annual growth rate, the initial input base amount and the product type of each first value-added product.

[0118] The first value-added product refers to a product with a specific value-added function provided to the user in the value-added product library.

[0119] The annual growth rate of the first value-added product refers to the growth rate of the first value-added product in the past year. The annual growth rate can be expressed in percentage, for example. If the annual growth rate of a first value-added product is 70%, the growth rate of the product in the past year is 70%.

[0120] The initial input basis of the first value-added product refers to the minimum resource value required to obtain the product. For example, if the initial input basis of a first value-added product is 100 yuan, the minimum resource value required to obtain the product is 100 yuan.

[0121] The product type of the first value-added product refers to the classification of the first value-added product according to its essential characteristics and operation mode.

[0122] It can be understood that by comparing the current total value of the target user's remaining resources and the predicted total value of the remaining resources, it can be determined whether there are idle resources in the target user's current account. If the current total value of the target user's remaining resources is greater than or equal to the predicted total value of the remaining resources, it indicates that the target user's current account has resource constraints, and at this time, the value-added product library can provide diversified value-added options to help the target user choose appropriate value-added products according to their own resource conditions.

[0123] S206, based on the annual growth rate of each first value-added product, the plurality of first value-added products are sorted to obtain a plurality of first value-added products with priority order.

[0124] The purpose of this step is to determine the priority order of each first value-added product.

[0125] It can be understood that

[0126] It can be understood that the annual growth rate of the first value-added product can reflect the popularity of the product in the past year. If the annual growth rate of a first value-added product is greater, it indicates that the product is more popular in the past year, and vice versa. If the annual growth rate of a first value-added product is smaller, it indicates that the product is less popular in the past year.

[0127] Therefore, by sorting the plurality of first value-added products according to the annual growth rate of each first value-added product, such as ascending order processing, the first value-added product with higher popularity can be placed in the front row.

[0128] S207, determining a second value-added product from the plurality of first value-added products with priority orders, the priority of the second value-added product being higher than the priorities of the other first value-added products, the starting input base amount of the second value-added product being less than or equal to the preset input base amount, and the product type of the second value-added product meeting the preset product type, the value-added product recommendation combination including the second value-added product.

[0129] For example, the preset input base amount can be 0. For example, the preset product type can be an existing resource management type.

[0130] The purpose of this step is to determine a second value-added product with the highest priority and meeting the input and product type requirements from the sorted first value-added products.

[0131] It can be understood that, first, the priority order can reflect the popularity of the first value-added product in the user group. If the priority order of a first value-added product is higher, it means that the value-added product is more popular with users.

[0132] Secondly, the preset input base amount can ensure that the target user has enough resources to participate in the investment of the product, avoiding the situation that the user cannot invest due to insufficient resources.

[0133] Finally, the preset product type can ensure that the product meets the user's existing resource management needs or investment direction.

[0134] Therefore, by starting from multiple dimensions such as priority, preset input base amount, and preset product type, more accurate value-added product recommendations can be provided for target users, thereby enhancing the trust and dependence of target users on the recommendation service.

[0135] S208, determining a third value-added product from the plurality of first value-added products with priority orders, the priority of the third value-added product being lower than the priority of the second value-added product, the priority of the third value-added product being higher than the priorities of the other first value-added products, the starting input base amount of the third value-added product being less than or equal to the preset input base amount, the product type of the third value-added product meeting the preset product type, and the difference between the annual growth rate of the third value-added product and the annual growth rate of the second value-added product being less than or equal to a preset difference, the value-added product recommendation combination including the third value-added product.

[0136] The purpose of this step is to determine a third value-added product with a lower priority than the second value-added product from the sorted first value-added products, and to ensure that the product meets the preset input base amount and product type requirements.

[0137] For example, the preset difference can be 0.1%, or 0.2%. The present application does not make special limitations on this.

[0138] It can be understood that, first, in terms of priority order, the priority of the third value-added product is less than that of the second value-added product, but greater than that of other first value-added products. This means that among the remaining first value-added products, the third value-added product is relatively more popular with users, and is in an intermediate position in terms of popularity among a large number of products, and is the product with the highest priority except for the second value-added product.

[0139] Second, in terms of preset product type, the preset product type requires that the product type of the third value-added product must meet the pre-set type. For example, if the preset product type is the existing resource management type, then the third value-added product must belong to this category, so as to ensure that the product meets the user's existing resource management needs or investment direction.

[0140] Next, in terms of the preset input base amount, the starting input base amount of the third value-added product is less than or equal to the preset input base amount. For example, the preset input base amount is 0, which can ensure that the target user has sufficient resources to participate in investing in the product and will not be unable to invest due to insufficient resources.

[0141] Finally, in terms of the preset difference, the preset difference must satisfy the difference between the annual growth rate of the third value-added product and the annual growth rate of the second value-added product, which is less than or equal to the preset difference, which can ensure that the third value-added product has a certain value-added potential, while the growth difference between different products is within a reasonable range.

[0142] Therefore, by comprehensively considering the priority order, the preset product type, the preset input base amount, and the preset difference, a relatively popular, user resource management demand and investment capacity, and a reasonable difference in annual growth rate with the second value-added product can be determined. The selected value-added product is included in the value-added product recommendation combination as the third value-added product.

[0143] S209, determine the remaining number of the second value-added product.

[0144] Among them, the remaining number refers to the number of unoccupied quotas or shares that the second value-added product currently provides for users to newly acquire or participate in, which is the remaining part after deducting the shares that have been acquired by other users from the total available shares.

[0145] It can be understood that by determining the remaining number of the second value-added product, it can be determined whether there is enough quantity of the second value-added product for the target user to acquire, avoiding recommending a product to the target user that does not have enough quantity available for acquisition, thereby improving the effectiveness of the recommendation and the trust of the user in the recommendation service.

[0146] For example, if the total available shares of a second value-added product are 10000, and 8000 shares have been acquired by other users, then the remaining number of the second value-added product can be determined as 2000.

[0147] S210, if the remaining number of the second value-added product is greater than the preset number, executing a first instruction, the first instruction being used to instruct to acquire the second value-added product.

[0148] The preset number can be 0, for example. The present application does not make special limitations on this.

[0149] The purpose of determining whether the remaining number of the second value-added product is greater than the preset number is to determine whether the second value-added product can be acquired by the target user.

[0150] If the remaining number of the second value-added product is greater than the preset number, it indicates that the second value-added product can be acquired by the target user. In this case, the first instruction can be executed.

[0151] It can be understood that when the remaining number of the second value-added product is greater than the preset number, it indicates that there are enough resources to meet the demand of the target user for the product. In this case, the first instruction is executed, which allows the target user to successfully obtain the recommended product and enhances the satisfaction of the target user for the recommendation service.

[0152] S211, if the remaining number of the second value-added product is equal to the preset number, executing a second instruction, the second instruction being used to instruct to acquire a third value-added product.

[0153] If the remaining number of the second value-added product is equal to the preset number, it indicates that the second value-added product cannot be acquired by the target user. In this case, the second instruction can be executed.

[0154] It can be understood that when the remaining number of the second value-added product is equal to the preset number, it means that the second value-added product has been sold out and cannot be acquired by the target user. In this case, in order to avoid the interruption of the recommendation service due to the unavailability of the second value-added product, the second instruction needs to be executed. By executing the instruction to acquire the third value-added product, an alternative value-added product selection is provided for the user, ensuring that the recommendation service can continue and effectively, and improving the satisfaction of the user for the recommendation service.

[0155] The value-added product recommendation method provided in the embodiment first acquires historical resource transfer data of a target user in a first preset time period and encrypts the historical resource transfer data, then determines a resource retention value sequence, a resource value-added ratio and a behavior cycle label of the time period according to the encrypted data, inputs the data into a long short-term memory network to obtain a predicted total remaining resource value of a second preset time period. If a current total remaining resource value is greater than or equal to the predicted total remaining resource value, the second and third value-added products that meet the requirements of a starting input base amount and a product type are selected from a value-added product library containing a plurality of first value-added products in order of annual growth rate, and an instruction to acquire the second or third value-added product is executed according to a remaining quantity of the second value-added product.

[0156] The method predicts future resource conditions by analyzing historical resource data of a user, filters and recommends suitable value-added products for the user according to a comparison between current resources and predicted resources of the user, solves the problem of idle resources in a current account of the user, improves the utilization rate of idle resources, and increases the income of the user.

[0157] Figure 3 A structural diagram of a value-added product recommendation device provided in the application is shown in FIG. 3, which includes the following components. Figure 4

[0158] The acquisition module 301 is configured to acquire historical resource transfer data of a target user in a first preset time period.

[0159] The determination module 302 is configured to determine a resource retention value sequence, a resource value-added ratio and a behavior cycle label in the first preset time period according to the historical resource transfer data.

[0160] The input module 303 is configured to input the resource retention value sequence, the resource value-added ratio and the behavior cycle label in the first preset time period into a long short-term memory network to obtain a predicted total remaining resource value of a target user in a second preset time period.

[0161] The determination module 302 is further configured to compare a current total remaining resource value of the target user with the predicted total remaining resource value, and if the current total remaining resource value is greater than or equal to the predicted total remaining resource value, determine a value-added product recommendation combination.

[0162] In a possible implementation manner, the device further includes an encryption module 304.

[0163] The encryption module 304 is configured to perform encryption processing on the historical resource transfer data to obtain encrypted historical resource transfer data.

[0164] ​The determining module 302 is specifically configured to determine, according to the historical resource transfer data of the processing completion, a resource retention value sequence, a resource value-added ratio and a behavior cycle label in a first preset time period.

[0165] In a possible implementation, the acquiring module 301 is further configured to acquire a value-added product library, the value-added product library including a plurality of first value-added products, and a respective annual growth rate, a starting input base amount and a product type of each first value-added product.

[0166] The apparatus further includes an ordering module 305.

[0167] The ordering module 305 is configured to perform ordering processing on the plurality of first value-added products based on the respective annual growth rate of each first value-added product, to obtain a plurality of first value-added products with a priority order.

[0168] The determining module 302 is specifically configured to determine, from the plurality of first value-added products with the priority order, a second value-added product, the priority of the second value-added product being greater than the priority of the other first value-added products, the starting input base amount of the second value-added product being less than or equal to a preset input base amount, and the product type of the second value-added product satisfying a preset product type, the value-added product recommendation combination including the second value-added product.

[0169] In a possible implementation, the determining module 302 is specifically configured to determine, from the plurality of first value-added products with the priority order, a third value-added product, the priority of the third value-added product being less than the priority of the second value-added product, the priority of the third value-added product being greater than the priority of the other first value-added products, the starting input base amount of the third value-added product being less than or equal to the preset input base amount, the product type of the third value-added product satisfying the preset product type, and the difference between the annual growth rate of the third value-added product and the annual growth rate of the second value-added product being less than or equal to a preset difference, the value-added product recommendation combination including the third value-added product.

[0170] In a possible implementation, the determining module 302 is further configured to determine a remaining quantity of the second value-added product.

[0171] The apparatus further includes an executing module 306.

[0172] The executing module 306 is configured to execute a first instruction in a case where the remaining quantity of the second value-added product is greater than a preset quantity, the first instruction being used to instruct to acquire the second value-added product.

[0173] The executing module 306 is further configured to execute a second instruction in a case where the remaining quantity of the second value-added product is equal to the preset quantity, the second instruction being used to instruct to acquire the third value-added product.

[0174] In a possible implementation, the apparatus further includes a generating module 307.

[0175] The generating module 307 is configured to generate first warning information in a case where the current total remaining resource value is less than the predicted total remaining resource value, the first warning information being used to remind that the current total remaining resource value is insufficient.

[0176] In a possible implementation, the determining module 307 is further configured to determine the account status of the target user.

[0177] The generating module is further configured to generate second warning information in a case where the account status indicates that the account is abnormal, the second warning information being used to remind that the account status of the target user is abnormal.

[0178] The value-added product recommendation apparatus provided in this embodiment can execute the method provided in the method embodiments, and has similar implementation principles and technical effects, which will not be described here again in this embodiment.

[0179] Figure 4 FIG. 1 is a structural schematic diagram of a value-added product recommendation apparatus provided in this embodiment. As shown in the figure, the electronic device 400 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 400 further includes a communication component 403. The processor 401, the memory 402, and the communication component 403 are connected through a bus 404. ​

[0180] In the specific implementation process, the at least one processor 401 executes the computer execution instructions stored in the memory 402, so that the at least one processor 401 executes the method described above.

[0181] The specific implementation process of the processor 401 can refer to the method embodiments described above, and has similar implementation principles and technical effects, which will not be described here again in this embodiment.

[0182] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0183] ​The memory can include a Random Access Memory (RAM) and can also include a Non-volatile Memory (NVM), such as at least one disk memory.

[0184] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0185] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above method.

[0186] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when a processor executes the computer execution instructions, the above method is implemented.

[0187] The above readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0188] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0189] The division of units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0190] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0191] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0192] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0193] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The aforementioned program can be stored in a computer readable storage medium. The program executes the steps including the above-mentioned method embodiments when executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, and various program code storage media.

[0194] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0195] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0196] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0197] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0198] If the integrated units / modules are implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. Unless otherwise specified, the storage unit can be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0199] If the integrated units / modules are implemented in the form of software program modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the essential part or all or part of the technical solutions that make contributions to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0200] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0201] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0202] It is to be understood that the application is not limited to the precise construction herein disclosed and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is limited only by the claims that follow.

Claims

1. A method of recommending a value-added product, characterized by, The method comprises: obtaining historical resource transfer data of a target user within a first preset period; determining a resource retention value sequence, a resource value-added ratio, and a behavior cycle label within the first preset period according to the historical resource transfer data; inputting the resource retention value sequence, the resource value-added ratio, and the behavior cycle label within the first preset period into a long short-term memory network to obtain a predicted total remaining resource value of the target user within a second preset period; comparing a current total remaining resource value of the target user with the predicted total remaining resource value, and if the current total remaining resource value is greater than or equal to the predicted total remaining resource value, determining a value-added product recommendation combination.

2. The method of claim 1, wherein, The determining of the resource retention value sequence, the resource value-added ratio, and the behavior cycle label within the first preset period according to the historical resource transfer data comprises: performing encryption processing on the historical resource transfer data to obtain encrypted historical resource transfer data; determining the resource retention value sequence, the resource value-added ratio, and the behavior cycle label within the first preset period according to the processed historical resource transfer data.

3. The method of claim 1, wherein, The determining of the value-added product recommendation combination comprises: obtaining a value-added product library, the value-added product library comprising: a plurality of first value-added products, and an annual growth rate, a starting input base amount, and a product type of each first value-added product; performing sorting processing on the plurality of first value-added products based on the annual growth rate of each first value-added product to obtain a plurality of first value-added products with priority orders; determining a second value-added product from the plurality of first value-added products with priority orders, the priority of the second value-added product being greater than the priority of other first value-added products, the starting input base amount of the second value-added product being less than or equal to a preset input base amount, and the product type of the second value-added product satisfying a preset product type, the value-added product recommendation combination comprising the second value-added product.

4. The method of claim 3, wherein, The method further comprises: determining a third value-added product from the plurality of first value-added products with priority orders, the priority of the third value-added product being less than the priority of the second value-added product, the priority of the third value-added product being greater than the priority of other first value-added products, the starting input base amount of the third value-added product being less than or equal to the preset input base amount, the product type of the third value-added product satisfying the preset product type, and the difference between the annual growth rate of the third value-added product and the annual growth rate of the second value-added product being less than or equal to a preset difference, the value-added product recommendation combination comprising the third value-added product.

5. The method of claim 4, wherein, The method further comprises: determining a remaining quantity of the second value-added product; in a case where the remaining quantity of the second value-added product is greater than a preset quantity, executing a first instruction for instructing to obtain the second value-added product; in a case where the remaining quantity of the second value-added product is equal to the preset quantity, executing a second instruction for instructing to obtain the third value-added product.

6. The method of claim 1, wherein, The method further comprises: If the current total remaining resource value is less than the predicted total remaining resource value, first warning information is generated, the first warning information being used to remind that the current total remaining resource value is insufficient.

7. The method of claim 1, wherein, The method further includes: determining an account state of the target user; if the account state indicates an account anomaly, generating second warning information, the second warning information being used to remind that the account state of the target user is abnormal.

8. A value-added product recommendation device, characterized by, comprising: an acquisition module, configured to acquire historical resource transfer data of a target user within a first preset time period; a determination module, configured to determine, according to the historical resource transfer data, a resource retention value sequence, a resource value-added ratio, and a behavior cycle label within the first preset time period; an input module, configured to input the resource retention value sequence, the resource value-added ratio, and the behavior cycle label within the first preset time period into a time sequence network to obtain a predicted total remaining resource value of the target user within a second preset time period; the determination module is further configured to compare a current total remaining resource value of the target user with the predicted total remaining resource value, and if the current total remaining resource value is greater than or equal to the predicted total remaining resource value, determine a value-added product recommendation combination.

9. An electronic device, comprising: comprising: a memory, a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method according to any one of claims 1-7.

11. A computer program product, characterised in that, comprising a computer program, which, when executed by the processor, implements the method according to any one of claims 1-7.

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