Input page generation method and device, equipment and storage medium thereof

Abstract

The embodiment of the invention belongs to the technical field of artificial intelligence, is applied to a financial product purchase service scene, and relates to an input page generation method, device and equipment and a storage medium thereof.The method comprises the steps that a product recommended to a target user is predicted firstly; predicting target information content which needs to be input when a product is purchased; screening out a front-end input target component in combination with the target information content; and displaying the purchase page and the target component to a front-end interface of the target user together for the user to input the target information content. Specifically, the input page generation method is applied to financial product selection and purchase prediction service, an optimal input component can be screened out by fully combining portrait data and historical input behavior habits of a user, and a selection and purchase page and a target component are displayed on a target front-end interface together, so that the user can input target information content, and the user experience is improved. The user operation process and the page rendering process are simplified when financial service products are purchased, and the service efficiency of financial product purchasing prediction service is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology and is applied to the scenario of financial product selection services, and relates to a method, device, equipment and storage medium for generating an input page. Background Art

[0002] Currently, with the continuous improvement of Internet technology, more and more service industries are transforming or expanding their businesses towards online services. For example: using a financial business product selection platform to realize the online handling or purchase service of newly launched financial business products.

[0003] Currently, based on the product selection platform, the aim is to improve how to accurately display personalized recommendations of the platform to users and recommend more products to users to promote product sales; however, the online financial business product selection platform for providing the online handling or purchase service of newly launched financial business products is significantly different from the conventional product recommendation. Although the online purchase can be carried out by using the conventional e-commerce selection method, in the conventional selection method, usually the product or service name is input first, and then the user flips through the pages for screening. Finally, the target business product is screened out for selection. This method not only has a large number of page rendering processes, but also has a large number of operation processes, and is not simplified and intelligent enough. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a method, device, equipment and storage medium for generating an input page to solve the problem that the product selection method provided by the existing financial business product selection platform not only has a large number of page rendering processes, but also has a large number of operation processes, and is not simplified and intelligent enough.

[0005] In a first aspect, the embodiments of the present application provide a method for generating an input page, adopting the following technical solutions:

[0006] A method for generating an input page includes the following steps:

[0007] Predict the products recommended to the target user according to the portrait data and historical order data of the target user, and the preset risk control strategy;

[0008] Based on the products recommended to the target user, predict the target information content required for the target user to enter when purchasing the recommended products;

[0009] According to the target information content and the pre-generated input restriction conditions, screen out the front-end input target components, where the input restriction conditions are jointly generated according to the historical input behavior of the target user and the historical component screening conditions;

[0010] Render the product selection page and the target component together to the front-end interface of the target user, so that the target user can enter the target information content when purchasing the recommended product.

[0011] In a second aspect, an embodiment of the present application further provides an input page generation device, which adopts the following technical solution:

[0012] An input page generation device includes:

[0013] A recommended product prediction module, configured to predict the products recommended to the target user according to the portrait data and historical order data of the target user, as well as a preset risk control strategy;

[0014] An input information content prediction module, configured to predict the target information content required by the target user when purchasing the recommended product based on the products recommended to the target user;

[0015] A target component screening module, configured to screen out the front-end input target components according to the target information content and pre-generated input restriction conditions, where the input restriction conditions are jointly generated according to the historical input behavior and historical component screening conditions of the target user;

[0016] A front-end interface rendering module, configured to render the product selection page and the target component together to the front-end interface of the target user, so that the target user can enter the target information content when purchasing the recommended product.

[0017] In a third aspect, an embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0018] A computer device includes a memory and a processor. Computer-readable instructions are stored in the memory, and when the processor executes the computer-readable instructions, the steps of the above-mentioned input page generation method are implemented.

[0019] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0020] A computer-readable storage medium, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor, the steps of the input page generation method as described above are implemented.

[0021] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0022] The input page generation method described in the embodiments of the present application predicts the products recommended to the target user based on the portrait data and historical order data of the target user, as well as the preset risk control strategy; based on the products recommended to the target user, predicts the target information content required by the target user when purchasing the recommended products; according to the target information content and the pre-generated input restriction conditions, filters out the front-end input target components; renders the product purchase page and the target components together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products. Applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user and the historical input behavior habits to filter out the most suitable input components. Finally, the product purchase page and the target components are displayed together on the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products, thereby simplifying the operation process of the user and the rendering process of the page when purchasing financial products, and improving the service efficiency of the financial product purchase prediction service. Brief Description of the Drawings

[0023] In order to more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the following described drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0025] Figure 2 is a flowchart of an embodiment of the input page generation method according to the present application;

[0026] Figure 3 is a flowchart of a specific embodiment of constructing relevant processing data in the input page generation method described in the present application;

[0027] Figure 4 is Figure 2 a flowchart of a specific embodiment of step 201;

[0028] Figure 5 is a flowchart of a specific embodiment of classifying and caching the front-end input components in the input page generation method described in the present application;

[0029] Figure 6 is a flowchart of a specific embodiment of pre-generating input restriction conditions for the target user in the input page generation method described in the present application;

[0030] Figure 7 is Figure 2 A flowchart of a specific embodiment of step 203;

[0031] Figure 8 is Figure 2 A flowchart of a specific embodiment of step 204;

[0032] Figure 9 A schematic structural diagram of an embodiment of an input page generation device according to the present application;

[0033] Figure 10 A schematic structural diagram of an embodiment of a computer device according to the present application. Detailed implementation manners

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0035] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0037] As Figure 1 shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0038] Users can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0039] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop portable computer, a desktop computer, etc.

[0040] The server 103 can be a server providing various services, such as a background server supporting the pages displayed on the terminal device 101.

[0041] It should be noted that the input page generation method provided by the embodiments of the present application is generally executed by the server. Correspondingly, the input page generation device is generally set in the server.

[0042] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0043] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. Figure 2 Continuing to refer to

[0044] Step 201, predict the products recommended to the target user according to the portrait data and historical order data of the target user, and the preset risk control strategy.

[0045] In this embodiment, the portrait data of the target user refers to the portrait data constructed based on the personal information of the target user and the financial product purchase preferences; and the preset risk control strategy specifically refers to the risk control strategy for the target user when purchasing relevant financial products constructed based on the financial product purchase preferences. The historical order data refers to the historical financial product purchase data of the target user. It should be understood that when a financial institution launches a new business product and there is a situation of recommended purchase, through the above portrait data, historical order data, and the preset risk control strategy, the purchase interest and purchase behavior of the target user can be predicted. It realizes product recommendation by combining the historical purchase situation of the target user, making it more intelligent.

[0046] In addition, the portrait data of the target user can also refer to the portrait data constructed based on the disease information, age information, and purchase risk data of the target user; and the preset risk control strategy specifically refers to the risk control strategy for the target user when purchasing relevant products constructed based on the purchase risk data. The historical order data refers to the historical drug purchase data or doctor's prescription data of the target user.

[0047] In this embodiment, the product recommended to the target user is predicted based on the portrait data, historical order data, and the preset risk control strategy of the target user. It should be understood that in the case of chronic disease medication, which involves long-term medication, through the above portrait data, historical order data, and the preset risk control strategy, the drugs and the total amount of drugs that the target user will purchase during the next medication cycle can be predicted. It realizes product recommendation by combining the chronic disease drug purchase situation and periodic habits of the target user, making it more intelligent.

[0048] Step 202: Based on the product recommended to the target user, predict the target information content that the target user needs to enter when purchasing the recommended product.

[0049] Specifically, for example: when a financial business product purchase platform promotes relevant financial products to a target user, generally, the target information content that the target user needs to enter when purchasing includes the personal information of the purchasing user. For example, when a user handles a stock trading account opening business, at this time, the user is also required to provide information such as the bound bank card number, as well as the ticking of relevant laws and regulations and the personal signature.

[0050] Specifically, for example, when a medical drug online purchase service platform recommends relevant drugs to target users, generally, the target information content that the target user needs to enter during the purchase selection includes conventional information content such as the purchase quantity of the drug, the contact information of the purchaser, and the mailing address. For some unconventional drugs, the target user also needs to enter a usage commitment letter (for example: this drug is purchased by XXX for medical use and cannot be used for other purposes) or a commitment letter regarding relevant laws and regulations when entering information.

[0051] The above step 201 can be implemented by using an artificial intelligence prediction model. Specifically, the portrait data and historical order data of the target user are used as the prediction input data, the preset risk control strategy is used as the output control data, and combined with the preset prediction model, the product recommended to the target user is predicted. Among them, the preset prediction model includes a prediction model pre-trained based on the portrait data and historical order data of the target user. Similarly, for step 202, it is also possible to first obtain the content information that needs to be entered when the user selects different products for training. After training the corresponding prediction model, then according to the product selected by the target user, the corresponding entry content information is output.

[0052] In step 203, according to the target information content and the pre-generated entry restriction conditions, the front-end entry target components are screened out, where the entry restriction conditions are jointly generated based on the historical entry behavior of the target user and the historical component screening conditions.

[0053] Specifically, according to the target information content that the target user is about to enter and the pre-generated entry restriction conditions, the front-end entry target components are flexibly screened out, so as to screen out entry components that are more in line with the user's entry habits and applicable to the target information content, avoiding the rigid implantation of entry components in the existing mode, which is not conducive to the user's purchase and usage experience.

[0054] In step 204, the purchase page of the product and the target components are rendered together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended product.

[0055] In this embodiment, the purchase page of the product and the target components are rendered together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended product. Compared with the existing purchase system that first clicks on the purchase page, then generates an order, and then uses front-end component rendering for entry, it improves the business recommendation and processing efficiency.

[0056] It should be understood that applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user and the historical input behavior habits (the historical input methods often selected) to screen out the most suitable input components. Finally, the purchase page of the product and the target components are rendered onto the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended product, thereby simplifying the operation process of the user and the rendering process of the page during the purchase of financial products and improving the service efficiency of the financial product purchase prediction service.

[0057] In this embodiment, based on the portrait data and historical order data of the target user, as well as the preset risk control strategy, the products recommended to the target user are predicted; based on the products recommended to the target user, the target information content required for the target user to enter when purchasing the recommended products is predicted; according to the target information content and the pre-generated input restriction conditions, the front-end input target components are screened out; the purchase page of the product and the target components are rendered onto the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products. Applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user and the historical input behavior habits to screen out the most suitable input components. Finally, the purchase page of the product and the target components are displayed onto the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products, thereby simplifying the operation process of the user and the rendering process of the page during the purchase of financial products and improving the service efficiency of the financial product purchase prediction service.

[0058] Continue to refer to Figure 3 , in some optional implementation manners, before executing step 201, the method further includes a step of constructing relevant processing data. Figure 3 FIG. is a flowchart of a specific embodiment of constructing relevant processing data in the input page generation method of the present application, including the following steps:

[0059] Step 301, obtain the historical order data of the target user from the target big data platform;

[0060] Wherein, the target big data platform includes a financial product purchase service platform, and the historical order data includes financial product historical order completion data; correspondingly, the target big data platform includes a medical and health service platform, and the historical order data includes historical online drug purchase data and doctor's order data;

[0061] Step 302, perform first-class key information identification based on the historical order data to obtain the personal relevant information of the target user;

[0062] Among them, in the financial product selection business, the personal relevant information includes identity card information, family member information, bank card number information, contact information, etc.; correspondingly, in the medical and health service scenario, the personal relevant information includes disease information and age information, where the disease information includes chronic disease information.

[0063] Step 303: Based on the historical order data, perform the identification of the second type of key information to obtain the purchase risk data corresponding to the target user.

[0064] Among them, in the financial product selection business, the purchase risk data includes the user's income situation, whether there is a car loan or mortgage, whether there is a large amount of debt, and the consumption situation at each purchase. Correspondingly, in the medical and health service scenario, the purchase risk data includes the purchase risk data corresponding to chronic diseases.

[0065] Step 304: Construct the portrait data of the target user according to the personal relevant information and the purchase risk data.

[0066] Step 305: Construct a risk control strategy for the target user when purchasing relevant products according to the purchase risk data.

[0067] It should be understood that in the financial product selection business, the risk control strategy includes the small financial products that can be recommended and the large financial products that cannot be recommended set according to the purchase risk data; in the medical and health service scenario, the risk control strategy includes the maximum personal purchase quantity of specific drugs stipulated by the state within a set time period, and also includes the predicted maximum cycle usage of characteristic drugs combined with the user's purchase risk data and the user's purchase cycle. For example: Patient user A has been taking drug B for a long time, and the usage and dosage of drug B are 2 times a day and 3 tablets each time; Patient user A purchases this drug once every two months for a long time. Then, it can be predicted that the usage cycle for patient user A to purchase this drug once is two months. Assuming that the specification of drug B is 30 tablets per box, 12 boxes need to be purchased; this risk control strategy is to set relevant control strategies in advance according to the above 12 boxes to remind the patient that the purchase is seriously excessive.

[0068] In this embodiment, after performing the step of constructing the portrait data of the target user according to the personal relevant information and the purchase risk data, the method further includes: using the unique identification information of the target user as the index field, caching the portrait data of the target user into a preset user portrait cache library, where the unique identification information includes identity identification information or business identification information.

[0069] In this embodiment, after performing the step of constructing a risk control strategy for the target user when purchasing a related product according to the selected purchase risk data, the method further includes: using the unique identification information of the target user as an index field, caching the risk control strategy for the target user when purchasing a related product into a preset risk control information library.

[0070] By using the unique identification information of the target user as an index field, caching the user portrait and the risk control strategy for the user when purchasing a related product into the corresponding cache libraries respectively, so that when the target user purchases a related product subsequently, the user portrait and the risk control strategy for the user when purchasing a related product can be directly retrieved from the target cache library according to the unique identification information.

[0071] Continue to refer to Figure 4 , Figure 4 Yes Figure 2 The flowchart of a specific embodiment of step 201 includes:

[0072] Step 401, obtain the unique identification information of the target user;

[0073] Step 402, based on the unique identification information, retrieve the portrait data corresponding to the target user and the risk control strategy for the target user when purchasing a related product from the target cache;

[0074] Step 403, based on the historical order data, identify the product commonalities when the target user purchases products multiple times;

[0075] Specifically, after identifying the product commonalities when the target user purchases products multiple times based on the historical order data, a financial product purchase prediction is made for the target user according to the product commonalities. Among them, the above product commonalities include the business characteristics commonly possessed by financial business products, for example: the insured are all the same identity object, and they are all financial business products launched for children; furthermore, the common corresponding product price range, for example: the various financial business products launched are all within the set price range in terms of pricing.

[0076] Step 404, combine the portrait data, the product commonalities, and the risk control strategy to predict the product purchased by the target user this time.

[0077] Specifically, the product purchased by the target user this time, for example: newly launched insurance types, newly launched financial handling services, etc., can be used to predict whether the target user purchases or handles the above newly launched insurance types or services according to the portrait data, the product commonalities, and the risk control strategy.

[0078] Continue to refer to Figure 5 , in some alternative implementation manners, before performing the step 203, the method further includes a step of classifying and caching the front-end input components into a library. Figure 5 FIG. is a flowchart of a specific embodiment of classifying and caching the front-end input components into a library in the input page generation method described in the present application, including the following steps:

[0079] Step 501, obtain the front-end input components selected by a batch of users during historical order processing from a target big data platform;

[0080] Step 502, perform a primary classification process on the front-end input components selected by the batch of users in stages according to preset age group information;

[0081] Specifically, since there are obvious differences in the front-end input operation habits of users in different age groups, considering this difference, perform a primary classification process on the front-end input components selected by the batch of users in stages according to the preset age group information, so as to classify the input components used by users with the same input operation habits together in combination with the age information.

[0082] Step 503, perform a secondary classification process on the result of the primary classification process according to the component type to obtain a classified result of the front-end input components after the secondary classification, where the component type includes a hierarchical input box, a check box, and a cascading selection box;

[0083] Specifically, due to the difference in the content information to be input, it may involve input boxes and selection boxes, and there are also different selection styles for different selection boxes. Therefore, perform a secondary classification process on the result of the primary classification process to obtain a classified result of the front-end input components after the secondary classification, which is convenient for accurately screening the target components in the subsequent process.

[0084] Step 504, cache the classified result of the front-end input components after the secondary classification into a preset front-end input component library in regions, and mark the age group information and component type corresponding to different regions respectively.

[0085] Continue to refer to Figure 6 , in some alternative implementation manners, before performing the step 203, the method further includes a step of pre-generating input restriction conditions for a target user. Figure 6 FIG. is a flowchart of a specific embodiment of pre-generating input restriction conditions for a target user in the input page generation method described in the present application, including the following steps:

[0086] Step 601, obtain the front-end input components selected by the target user during historical order processing from a target big data platform;

[0087] Step 602: Determine the input behavior preference corresponding to the target user according to the front-end input component, where the input behavior preference is jointly characterized by historical input behaviors and historical component screening conditions, and includes the component types habitually selected by the target user.

[0088] Step 603: Based on the input behavior preference corresponding to the target user and the age information of the target user, pre-generate the input restriction conditions.

[0089] Specifically, when pre-generating the input restriction conditions based on the input behavior preference corresponding to the target user and the age information of the target user, age information is introduced to facilitate component screening from the cache partition corresponding to the age information during subsequent front-end input component screening, ensuring rapid screening of target components.

[0090] Continue to refer to Figure 7 , Figure 7 Yes Figure 2 It is a flowchart of a specific embodiment of step 203, including:

[0091] Step 701: Analyze the target information content to analyze all component types used by the target user when inputting the target information content.

[0092] Step 702: Use the input restriction conditions as the limiting parameters of the preset component search engine, and use all component types used by the target user when inputting the target information content as the supplementary parameters of the preset component search engine to jointly screen out the target components from the preset front-end input component library.

[0093] Specifically, based on the limiting parameters, fully considering that the target information content may involve multiple component types, therefore, all component types used by the target user when inputting the target information content are introduced here as the supplementary parameters of the preset component search engine, ensuring more scientific and reasonable screening of target components.

[0094] In this embodiment, the front-end interface includes at least two interface areas.

[0095] Continue to refer to Figure 8 , Figure 8 Yes Figure 2 It is a flowchart of a specific embodiment of step 204, including:

[0096] Step 801: Render the product purchase page in the first interface area, where the product purchase page is displayed using fixed display components.

[0097] Step 802, render the target component in the second interface area, where the target component is used for the target user to enter the target information content when purchasing a recommended product in the first interface area.

[0098] Specifically, the front-end interface includes at least two interface areas. The purchase page of the product and the target component are respectively displayed in different display areas of the front-end interface, so that the target user can enter the target information content when purchasing a recommended product. Compared with the existing purchase system that first clicks on the purchase page, then generates an order, and then uses a front-end component to render and enter, the business recommendation and processing efficiency are improved.

[0099] It should be understood that applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user, such as chronic disease information and age information, as well as historical input behavior habits (historically frequently selected input methods) to screen out the most suitable input component. Finally, the purchase page of the product and the target component are rendered together on the front-end interface of the target user for the target user to enter the target information content when purchasing a recommended product, thereby simplifying the operation process of the user and the rendering process of the page when purchasing financial business products and improving the service efficiency of the financial product purchase prediction service.

[0100] This application predicts the product recommended to the target user based on the portrait data and historical order data of the target user, as well as the preset risk control strategy; based on the product recommended to the target user, predicts the target information content required for the target user to enter when purchasing the recommended product; screens out the front-end input target component according to the target information content and the pre-generated input restriction conditions; renders the purchase page of the product and the target component together on the front-end interface of the target user for the target user to enter the target information content when purchasing a recommended product. Applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user and historical input behavior habits to screen out the most suitable input component. Finally, the purchase page of the product and the target component are displayed together on the front-end interface of the target user for the target user to enter the target information content when purchasing a recommended product, thereby simplifying the operation process of the user and the rendering process of the page when purchasing financial business products and improving the service efficiency of the financial product purchase prediction service.

[0101] Embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, and is a theory, method, technology, and application system that can perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0102] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0103] In embodiments of this application, based on the portrait data and historical order issuance data of the target user, as well as a preset risk control strategy, the products recommended to the target user are predicted; based on the products recommended to the target user, the target information content that the target user needs to enter when purchasing the recommended products is predicted; according to the target information content and pre-generated input restriction conditions, the front-end input target components are screened out; the product purchase page and the target components are rendered together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products. Applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user and the historical input behavior habits to screen out the most suitable input components. Finally, the product purchase page and the target components are displayed together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products, thereby simplifying the operation process of the user and the rendering process of the page when purchasing financial business products, and improving the service efficiency of the financial product purchase prediction service.

[0104] Further referring to Figure 9 As an implementation of the method shown above, this application provides an embodiment of an input page generation device. This device embodiment corresponds to the method embodiment shown in Figure 2 and can be specifically applied to various electronic devices. Figure 2 As shown in

[0105] As Figure 9 shown, the input page generation device 900 described in this embodiment includes: a recommended product prediction module 901, an input information content prediction module 902, a target component screening module 903, and a front-end interface rendering module 904. Among them:

[0106] The recommended product prediction module 901 is used to predict the products recommended to the target user according to the portrait data and historical order data of the target user, as well as the preset risk control strategy;

[0107] The input information content prediction module 902 is used to predict the target information content that the target user needs to input when purchasing the recommended product based on the products recommended to the target user;

[0108] The target component screening module 903 is used to screen out the front-end input target components according to the target information content and the pre-generated input restriction conditions, where the input restriction conditions are jointly generated according to the historical input behavior and historical component screening conditions of the target user;

[0109] The front-end interface rendering module 904 is used to render the purchase page of the product and the target components together to the front-end interface of the target user for the target user to input the target information content when purchasing the recommended product.

[0110] This application predicts the products recommended to the target user according to the portrait data and historical order data of the target user, as well as the preset risk control strategy; based on the products recommended to the target user, predicts the target information content that the target user needs to input when purchasing the recommended product; screens out the front-end input target components according to the target information content and the pre-generated input restriction conditions; renders the purchase page of the product and the target components together to the front-end interface of the target user for the target user to input the target information content when purchasing the recommended product. Applying the input page generation method to the financial product purchase prediction service can fully combine the portrait data of the target user and the historical input behavior habits to screen out the most suitable input components. Finally, the purchase page of the product and the target components are displayed together on the front-end interface of the target user for the target user to input the target information content when purchasing the recommended product, thereby simplifying the operation process of the user and the rendering process of the page when purchasing financial business products, and improving the service efficiency of the financial product purchase prediction service.

[0111] In this embodiment, the input page generation device 900 further includes: a historical order data acquisition module, a first type of key information identification module, a second type of key information identification module, a user portrait data construction module, and a risk control strategy construction module. Among them:

[0112] The historical order data acquisition module is used to acquire the historical order data of the target user from the target big data platform;

[0113] The first type of key information identification module is used to perform the first type of key information identification based on the historical order data to obtain the personal relevant information of the target user;

[0114] The second type of key information recognition module is used to recognize the second type of key information based on the historical order issuance data, so as to obtain the purchase risk data corresponding to the target user;

[0115] The user portrait data construction module is used to construct the portrait data of the target user according to the personal relevant information and the purchase risk data;

[0116] The risk control strategy construction module is used to construct a risk control strategy for the target user when purchasing relevant products according to the purchase risk data.

[0117] In this embodiment, the recommended product prediction module 901 includes a user identification information acquisition unit, a prediction input data acquisition unit, a purchase time interval identification unit, and a product prediction unit. Among them:

[0118] The user identification information acquisition unit is used to acquire the unique identification information of the target user;

[0119] The prediction input data acquisition unit is used to pull the portrait data corresponding to the target user and the risk control strategy of the target user when purchasing relevant products from the target cache based on the unique identification information;

[0120] The purchase time interval identification unit is used to identify the product common information when the target user purchases products multiple times based on the historical order issuance data;

[0121] The product prediction unit is used to predict the product purchased by the target user this time by combining the portrait data, the product common information, and the risk control strategy.

[0122] In this embodiment, the input page generation device 900 further includes: a front-end input component batch acquisition module, a front-end input component primary classification module, a front-end input component secondary classification module, and a front-end input component partition cache module. Among them:

[0123] The front-end input component batch acquisition module is used to acquire the front-end input components selected by a batch of users when issuing historical orders from the target big data platform;

[0124] The front-end input component primary classification module is used to perform primary classification processing on the front-end input components selected by the batch of users in stages according to the preset age group information;

[0125] The front-end input component secondary classification module is used to perform secondary classification processing on the primary classification processing result according to the component type to obtain the secondary classification result of the front-end input components, where the component type includes a hierarchical input box, a check box, and a cascading selection box;

[0126] The front-end input component partition cache module is used to cache the classified results of the front-end input components after secondary classification into a preset front-end input component library by region, and mark the age information and component types corresponding to different regions respectively.

[0127] In this embodiment, the input page generation device 900 further includes: a front-end input component specified acquisition module, an input behavior preference determination module, and an input restriction condition pre-generation module. Among them:

[0128] The front-end input component specified acquisition module is used to obtain the front-end input components selected by the target user when making historical orders from the target big data platform;

[0129] The input behavior preference determination module is used to determine the input behavior preference corresponding to the target user according to the front-end input components, where the input behavior preference is jointly characterized by historical input behaviors and historical component screening conditions, and includes the component types habitually selected by the target user;

[0130] The input restriction condition pre-generation module is used to pre-generate the input restriction conditions based on the input behavior preference corresponding to the target user and the age information of the target user.

[0131] In this embodiment, the target component screening module 903 includes a target information content analysis unit and a target component screening unit. Among them:

[0132] The target information content analysis unit is used to analyze the target information content and analyze all component types used by the target user when inputting the target information content;

[0133] The target component screening unit is used to use the input restriction conditions as the limiting parameters of the preset component search engine, and use all component types used by the target user when inputting the target information content as the supplementary parameters of the preset component search engine to jointly screen out the target components from the preset front-end input component library.

[0134] In this embodiment, the front-end interface rendering module 904 includes a first rendering unit and a second rendering unit. Among them:

[0135] The first rendering unit is used to render the product purchase page in the first interface area, where the product purchase page is displayed by fixed display components;

[0136] The second rendering unit is used to render the target components in the second interface area, where the target components are used for the target user to input the target information content when purchasing recommended products in the first interface area.

[0137] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0138] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders. Moreover, at least some of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. Their execution order does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0139] To solve the above technical problems, the embodiments of the present application also provide computer devices. For details, please refer to Figure 10 , Figure 10 which is the basic structural block diagram of the computer device in this embodiment.

[0140] The computer device 10 includes a memory 10a, a processor 10b, and a network interface 10c that are communicatively connected to each other through a system bus. It should be noted that Figure 10 only the computer device 10 with components such as a memory 10a, a processor 10b, and a network interface 10c is shown in , but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that a computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0141] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, or a cloud server. The computer device can interact with the user through a keyboard, a mouse, a remote control, a touchpad, or a voice control device, etc.

[0142] The memory 10a includes at least one type of readable storage medium. The readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10a can be an internal storage unit of the computer device 10, such as the hard disk or the memory of the computer device 10. In other embodiments, the memory 10a can also be an external storage device of the computer device 10, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 10. Of course, the memory 10a can also include both the internal storage unit and the external storage device of the computer device 10. In this embodiment, the memory 10a is generally used to store the operating system installed on the computer device 10 and various application software, such as computer-readable instructions of a method for generating an input page. In addition, the memory 10a can also be used to temporarily store various data that have been output or will be output.

[0143] In some embodiments, the processor 10b can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 10b is generally used to control the overall operation of the computer device 10. In this embodiment, the processor 10b is used to run the computer-readable instructions stored in the memory 10a or process data, such as running the computer-readable instructions of the method for generating an input page.

[0144] The network interface 10c can include a wireless network interface or a wired network interface. The network interface 10c is generally used to establish a communication connection between the computer device 10 and other electronic devices.

[0145] The computer device proposed in this embodiment belongs to the field of artificial intelligence technology and is applied to the financial product selection service scenario. This application predicts the products recommended to the target user based on the portrait data and historical order data of the target user, as well as the preset risk control strategy; based on the products recommended to the target user, predicts the target information content that the target user needs to enter when purchasing the recommended products; according to the target information content and the pre-generated input restriction conditions, filters out the front-end input target components; renders the product purchase page and the target components together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products. Applying the input page generation method to the financial product selection prediction service can fully combine the portrait data of the target user and the historical input behavior habits to filter out the most suitable input components. Finally, the product purchase page and the target components are displayed together on the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products, thus simplifying the operation process of the user and the rendering process of the page during the purchase of financial business products and improving the service efficiency of the financial product selection prediction service.

[0146] This application also provides another implementation manner, that is, to provide a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to cause the processor to execute the steps of the input page generation method as described above.

[0147] The computer-readable storage medium proposed in this embodiment belongs to the field of artificial intelligence technology and is applied to the financial product selection service scenario. This application predicts the products recommended to the target user based on the portrait data and historical order data of the target user, as well as the preset risk control strategy; based on the products recommended to the target user, predicts the target information content that the target user needs to enter when purchasing the recommended products; according to the target information content and the pre-generated input restriction conditions, filters out the front-end input target components; renders the product purchase page and the target components together to the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products. Applying the input page generation method to the financial product selection prediction service can fully combine the portrait data of the target user and the historical input behavior habits to filter out the most suitable input components. Finally, the product purchase page and the target components are displayed together on the front-end interface of the target user for the target user to enter the target information content when purchasing the recommended products, thus simplifying the operation process of the user and the rendering process of the page during the purchase of financial business products and improving the service efficiency of the financial product selection prediction service.

[0148] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0149] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields is similarly within the scope of patent protection of the present application. The non-company software tools or components that appear in the embodiments of the present application are only for example and do not represent actual use.

Claims

1. A method for generating an entry page, characterized in that: The steps include: Based on the target user's profile data and historical order data, as well as the preset risk management strategy, predict the products recommended to the target user; Based on the products recommended to the target user, predict the target information content that the target user needs to enter when purchasing the recommended products; Filtering out the front-end input target component according to the target information content and the pre-generated input restriction condition, wherein the input restriction condition is jointly generated according to the historical input behavior of the target user and the historical component screening condition; The product purchase page and the target component are rendered together to the front-end interface of the target user, so that the target user can enter the target information content when purchasing the recommended product.

2. The method for generating an entry page according to claim 1, characterized in that: Before executing the step of predicting the product recommended to the target user based on the target user's portrait data and historical order data, and the preset risk management strategy, the method further includes: Obtain the historical order data of the target user from the target big data platform; Based on the historical order data, identify the first type of key information to obtain personal information related to the target user; Based on the historical order data, the second type of key information is identified to obtain the purchase risk data corresponding to the target user; Constructing the target user's profile data based on the personal related information and the purchase risk data; Based on the purchase risk data, a risk management strategy is constructed for the target user when purchasing related products.

3. The method for generating an entry page according to claim 2, characterized in that: After executing the step of constructing the target user's portrait data based on the personal related information and the purchase risk data, the method further includes: Using the unique identification information of the target user as the index field, cache the portrait data of the target user into a preset user portrait cache library, wherein the unique identification information includes identity identification information or business identification information; After executing the step of constructing a risk control strategy for the target user when purchasing related products according to the purchase risk data, the method further includes: The unique identification information of the target user is used as an index field to cache the risk management and control strategy of the target user when purchasing related products in a preset risk management and control information library.

4. The method for generating an entry page according to claim 1 or 2, characterized in that: The step of predicting the product recommended to the target user based on the target user's portrait data and historical order data, as well as the preset risk management strategy, specifically includes: Obtain the unique identification information of the target user; Based on the unique identification information, pull the portrait data corresponding to the target user and the risk management strategy of the target user when purchasing related products from the target cache; Based on the historical order data, identifying common product information when the target user purchases the product multiple times; The product purchased by the target user this time is predicted by combining the portrait data, the common product information and the risk management strategy.

5. The method for generating an entry page according to claim 1, characterized in that: Before executing the step of screening out the front-end entry target component according to the target information content and the pre-generated entry restriction condition, the method further includes: Obtain the front-end input components selected by batch users when placing historical orders from the target big data platform; Performing initial classification processing on the front-end input components selected by the batch of users in stages according to the preset age group information; Performing secondary classification processing on the primary classification processing result according to the component type to obtain the front-end input component classification result after secondary classification, wherein the component type includes a hierarchical input box, a check-type selection box, and a cascade selection box; The classification results of the front-end input components after the secondary classification are cached in a preset front-end input component library by region, and the age group information and component types corresponding to different regions are marked.

6. The method for generating an entry page according to claim 2, characterized in that: Before executing the step of screening out the front-end entry target component according to the target information content and the pre-generated entry restriction condition, the method further includes: Obtaining from the target big data platform the front-end input component selected by the target user when placing historical orders; According to the front-end input component, determining the input behavior preference corresponding to the target user, wherein the input behavior preference is jointly characterized by historical input behavior and historical component screening conditions, including the component type habitually selected by the target user; The entry restriction condition is pre-generated based on the entry behavior preference corresponding to the target user and the age information of the target user.

7. The method for generating an entry page according to claim 6, characterized in that: The step of screening out the front-end input target component according to the target information content and the pre-generated input restriction condition specifically includes: Analyze the target information content to find out all component types used by the target user when entering the target information content; The input restriction condition is used as the limiting parameter of the preset component search engine, and all component types used by the target user when entering the target information content are used as the supplementary parameters of the preset component search engine to jointly filter out the target component from the preset front-end input component library.

8. A device for generating an input page, characterized in that: include: The recommended product prediction module is used to predict the products recommended to the target users based on their profile data and historical order data, as well as the preset risk management strategy; An input information content prediction module is used to predict the target information content that the target user needs to enter when purchasing the recommended product based on the product recommended to the target user; A target component screening module, used to screen out the front-end entry target components according to the target information content and pre-generated entry restriction conditions, wherein the entry restriction conditions are jointly generated according to the historical entry behavior of the target user and the historical component screening conditions; The front-end interface rendering module is used to render the product purchase page and the target component to the front-end interface of the target user, so that the target user can enter the target information content when purchasing the recommended product.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the input page generation method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the input page generation method according to any one of claims 1 to 7 are implemented.

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