CN118212075A

CN118212075A - Product recommendation method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN118212075A
Application number: CN202410381626.5A
Authority: CN
Inventors: 韦笑
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2024-03-29
Filing date: 2024-03-29
Publication date: 2024-06-18

Abstract

The application belongs to the field of artificial intelligence and the field of financial science and technology, and relates to a product recommendation method based on artificial intelligence, which comprises the following steps: preprocessing user data to obtain appointed user data; acquiring an initial product matched with a user; invoking a plurality of product recommendation models; carrying out data analysis on the specified data and the initial product based on the first recommendation model to obtain a corresponding first recommendation product; carrying out data analysis on the appointed data and the initial product based on the second recommendation model to obtain a corresponding second recommendation product; constructing a target recommended product based on the first recommended product and the second recommended product; pushing the target recommended product to the user. The application also provides a product recommendation device, computer equipment and a storage medium based on the artificial intelligence. In addition, the application also relates to a blockchain technology, and the target recommended product can be stored in the blockchain. The method can be applied to the product recommendation scene in the financial field, and effectively improves the accuracy and the intelligence of product recommendation.

Description

Product recommendation method, device, equipment and storage medium based on artificial intelligence

Technical Field

The application relates to the technical field of artificial intelligence development and the technical field of finance, in particular to a product recommendation method, a device, computer equipment and a storage medium based on artificial intelligence.

Background

With the enhancement of people's insurance awareness and the rapid development of the insurance industry, more and more people begin to pay attention to purchasing insurance products. For various insurance companies, some insurance recommendation systems have also been developed to provide users with services for insurance product recommendation. However, at present, these insurance recommendation systems generally recommend insurance products to users based on preset recommendation rules, and because the recommendation rules are fixed in advance, the insurance product recommendation based on the recommendation rules also has a certain fixity, and because the actual demands and preferences of users cannot be fully considered, the accuracy of the insurance product recommendation is low.

Disclosure of Invention

The embodiment of the application aims to provide a product recommendation method, a device, computer equipment and a storage medium based on artificial intelligence, so as to solve the technical problems that an existing insurance recommendation system generally carries out insurance product recommendation on a user based on preset recommendation rules, the recommendation rules are fixed in advance, the insurance product recommendation carried out based on the recommendation rules also has certain fixity, and the actual demands and preferences of the user cannot be fully considered, so that the accuracy of the insurance product recommendation is lower.

In order to solve the technical problems, the embodiment of the application provides a product recommendation method based on artificial intelligence, which adopts the following technical scheme:

Acquiring user data of a user, and preprocessing the user data to obtain corresponding appointed user data; wherein the user data at least comprises health data, demand data and consumption behavior data;

acquiring a user portrait of the user, and acquiring an initial product matched with the user based on the user portrait;

Invoking a plurality of product recommendation models which are built in advance; the product recommendation model at least comprises a first recommendation model and a second recommendation model;

performing data analysis on the specified data and the initial product based on the first recommendation model, and determining a first recommendation product corresponding to the specified data from the initial product;

Performing data analysis on the specified data and the initial product based on the second recommendation model, and determining a second recommendation product corresponding to the specified data from the initial product;

constructing a target recommended product based on the first recommended product and the second recommended product;

pushing the target recommended product to the user.

Further, the step of constructing a target recommended product based on the first recommended product and the second recommended product specifically includes:

Integrating the first recommended product and the second recommended product to obtain a corresponding product set;

carrying out de-duplication treatment on the products contained in the product set to obtain a treated specified product;

And taking the appointed product as the target recommended product.

Further, the step of pushing the target recommended product to the user specifically includes:

Acquiring a held product of the user;

judging whether a first product matched with the held product exists in the target recommended product or not;

If yes, the first product is removed from the target recommended product, and a processed second product is obtained;

pushing the second product to the user.

Further, the step of pushing the second product to the user specifically includes:

acquiring first user information of the user;

determining the designated push time corresponding to the user based on the first user information;

and pushing the second product to the user based on the specified pushing time.

Further, the product recommendation method based on artificial intelligence further comprises the following steps:

judging whether a data query request triggered by a designated user is received or not; wherein, the data query request carries data identification information;

If yes, obtaining second user information of the appointed user;

Performing authority analysis on the second user information based on a preset authority identification model to obtain a designated authority level corresponding to the designated user;

acquiring a right level interval corresponding to a data query operation;

judging whether the appointed authority level is in the authority level interval or not;

if yes, responding to the data query request based on the data identification information.

Further, the step of responding to the data query request based on the data identification information specifically includes:

acquiring target data corresponding to the data identification information;

determining a target display mode corresponding to the target data;

And displaying the target data based on the target display mode.

Acquiring service data to be stored;

Determining a target data encryption algorithm corresponding to the service data;

encrypting the service data based on the target data encryption algorithm to obtain encrypted target service data;

and storing the target business data.

In order to solve the technical problems, the embodiment of the application also provides a product recommendation device based on artificial intelligence, which adopts the following technical scheme:

The first acquisition module is used for acquiring user data of a user and preprocessing the user data to obtain corresponding appointed user data; wherein the user data at least comprises health data, demand data and consumption behavior data;

The second acquisition module is used for acquiring the user portrait of the user and acquiring an initial product matched with the user based on the user portrait;

the calling module is used for calling a plurality of product recommendation models which are built in advance; the product recommendation model at least comprises a first recommendation model and a second recommendation model;

the first analysis module is used for carrying out data analysis on the specified data and the initial product based on the first recommendation model, and determining a first recommended product corresponding to the specified data from the initial product;

the second analysis module is used for carrying out data analysis on the specified data and the initial product based on the second recommendation model, and determining a second recommended product corresponding to the specified data from the initial product;

the construction module is used for constructing a target recommended product based on the first recommended product and the second recommended product;

and the pushing module is used for pushing the target recommended product to the user.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

pushing the target recommended product to the user.

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

pushing the target recommended product to the user.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

Firstly, acquiring user data of a user, and preprocessing the user data to obtain corresponding appointed user data; then, obtaining a user portrait of the user, and obtaining an initial product matched with the user based on the user portrait; then, a plurality of product recommendation models constructed in advance are called; data analysis is carried out on the specified data and the initial product based on the first recommendation model, and a first recommendation product corresponding to the specified data is determined from the initial product; and performing data analysis on the specified data and the initial product based on the second recommendation model, and determining a second recommendation product corresponding to the specified data from the initial product; subsequently constructing a target recommended product based on the first recommended product and the second recommended product; and finally pushing the target recommended product to the user. According to the application, the initial product matched with the user is obtained according to the user portrait of the user, the data analysis is further carried out on the user data of the user and the initial product based on a plurality of product recommendation models constructed in advance, the final target recommended product is determined from the recommended products respectively output by the plurality of product recommendation models, and then the target recommended product is pushed to the user, so that the target recommended product meeting the requirements corresponding to the user data is recommended to the user based on the use of the plurality of product recommendation models. The accuracy and the intelligence of product recommendation are effectively improved, and the use experience of a user is improved.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of an artificial intelligence based product recommendation method in accordance with the present application;

FIG. 3 is a schematic diagram of one embodiment of an artificial intelligence based product recommendation device in accordance with the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Mov i ng P i cture Experts G roup Aud i o Layer I I I, dynamic video expert compression standard audio plane 3), MP4 (Mov i ng P i ctu re Experts G roup Aud i o Layer I V, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the product recommendation method based on artificial intelligence provided by the embodiment of the application is generally executed by a server/terminal device, and correspondingly, the product recommendation device based on artificial intelligence is generally arranged in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flowchart of one embodiment of an artificial intelligence based product recommendation method in accordance with the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The product recommendation method based on the artificial intelligence provided by the embodiment of the application can be applied to any scene needing to be subjected to product recommendation, and can be applied to products in the scenes, for example, the product recommendation in the field of financial insurance. The product recommendation method based on artificial intelligence comprises the following steps:

Step S201, user data of a user are obtained, and the user data are preprocessed to obtain corresponding appointed user data.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the product recommendation method based on artificial intelligence operates may acquire the user data through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connections, wifi connections, bluetooth connections, wimax connections, Z i gbee connections, UWB (u l t ra W i deband) connections, and other now known or later developed wireless connection means. The execution subject of the product recommendation method based on artificial intelligence can be specifically an insurance system with a product recommendation function. Wherein the user data includes at least health data, demand data, and consumption behavior data. In addition, the preprocessing may include at least data cleaning, data interpolation, data normalization, and the like.

Step S202, a user portrait of the user is obtained, and an initial product matched with the user is obtained based on the user portrait.

In this embodiment, a preset portrait system may be queried based on user information of a user to obtain a user portrait of the user, and then a preset product database may be queried based on the user portrait to obtain an initial product matched with the user. Wherein, the portrait system is a pre-constructed system which stores portrait data of each customer. The product database is a database which is built in advance and stores products suitable for product recommendation processing corresponding to different user portraits.

Step S203, calling a plurality of product recommendation models constructed in advance; the product recommendation model at least comprises a first recommendation model and a second recommendation model.

In this embodiment, the preset number of the product recommendation models is not limited, and at least 2 products are included. The product recommendation model at least comprises a first recommendation model and a second recommendation model. Specifically, multiple initial models such as a gradient lifting decision tree model, a random forest model, a XGBoost model, a L i ghtGBM model and the like can be trained through sample data collected in advance, so that various initial models learn the mapping relation between sample user data and recommended products and a trained specified model is obtained. And then screening out the models with the highest model evaluation preset number from all the specified models to be used as the product recommendation models. The sample data comprises sample user data and recommended products.

And step S204, carrying out data analysis on the specified data and the initial product based on the first recommendation model, and determining a first recommendation product corresponding to the specified data from the initial product.

In this embodiment, the specified data and the initial product may be input into the first recommendation model, so that the data analysis may be performed on the specified data and the initial product by the first recommendation model, and a first recommended product corresponding to the specified data in the initial product may be output.

Step S205, performing data analysis on the specified data and the initial product based on the second recommendation model, and determining a second recommended product corresponding to the specified data from the initial product.

In this embodiment, the specified data and the initial product may be input into the second recommendation model, so that the data analysis may be performed on the specified data and the initial product by the second recommendation model, and a second recommended product corresponding to the specified data in the initial product may be output.

Step S206, constructing a target recommended product based on the first recommended product and the second recommended product.

In this embodiment, the above specific implementation process of constructing the target recommended product based on the first recommended product and the second recommended product will be described in further detail in the following specific embodiments, which will not be described herein.

Step S207, pushing the target recommended product to the user.

In this embodiment, the foregoing specific implementation process of pushing the target recommended product to the user will be described in further detail in the following specific embodiments, which will not be described herein. The insurance system also provides contents such as health information, health evaluation and the like, and a user can acquire related information and advice of health management through insurance, namely, the related information and advice of health management matched with the user can be synchronously sent to the user while the target recommended product is pushed to the user.

Firstly, user data of a user are obtained, and the user data are preprocessed to obtain corresponding appointed user data; then, obtaining a user portrait of the user, and obtaining an initial product matched with the user based on the user portrait; then, a plurality of product recommendation models constructed in advance are called; data analysis is carried out on the specified data and the initial product based on the first recommendation model, and a first recommendation product corresponding to the specified data is determined from the initial product; and performing data analysis on the specified data and the initial product based on the second recommendation model, and determining a second recommendation product corresponding to the specified data from the initial product; subsequently constructing a target recommended product based on the first recommended product and the second recommended product; and finally pushing the target recommended product to the user. According to the application, the initial product matched with the user is obtained according to the user portrait of the user, the data analysis is further carried out on the user data of the user and the initial product based on a plurality of product recommendation models constructed in advance, the final target recommended product is determined from the recommended products respectively output by the plurality of product recommendation models, and then the target recommended product is pushed to the user, so that the target recommended product meeting the requirements corresponding to the user data is recommended to the user based on the use of the plurality of product recommendation models. The accuracy and the intelligence of product recommendation are effectively improved, and the use experience of a user is improved.

In some alternative implementations, step S206 includes the steps of:

and integrating the first recommended product and the second recommended product to obtain a corresponding product set.

In this embodiment, the integration processing refers to combining the first recommended product and the second recommended product to obtain a product set including the first recommended product and the second recommended product.

And carrying out de-duplication treatment on the products contained in the product set to obtain the treated specified products.

In this embodiment, the similarity calculation may be performed on the products included in the product set, and products with similarity greater than a preset similarity threshold may be selected as repeated specific products, so as to perform de-duplication on the specific products included in the product set to obtain a final specified product. The de-duplication process refers to a process mode that only one of the repeated products is retained.

And taking the appointed product as the target recommended product.

The method comprises the steps of integrating the first recommended product and the second recommended product to obtain a corresponding product set; then carrying out de-duplication treatment on the products contained in the product set to obtain a treated specified product; and taking the appointed product as the target recommended product. According to the application, the first recommended product and the second recommended product are subjected to the integration treatment and the de-duplication treatment, so that the final target recommended product is obtained quickly.

In some alternative implementations of the present embodiment, step S207 includes the steps of:

And acquiring the held product of the user.

In this embodiment, the user information of the user may be obtained, and then a preset insurance database may be queried based on the user information, so as to query the insurance product purchase information of the user, and product query may be performed on the insurance product purchase information to obtain the product that has been purchased by the user and is in an effective state as the holding product. The user information may refer to name information of a user, and the insurance database is a database which is built in advance and stores record data of insurance product purchase information of a plurality of clients.

And judging whether a first product matched with the held product exists in the target recommended product.

In this embodiment, the data matching process may be performed on the target recommended product and the held product, so as to determine whether there is a first product matching the held product in the target recommended product.

If yes, the first product is removed from the target recommended product, and a processed second product is obtained.

In this embodiment, if there is a first product matching the held product in the target recommended product, it indicates that the user has purchased the first product.

Pushing the second product to the user.

In this embodiment, the foregoing implementation process of pushing the second product to the user will be described in further detail in the following embodiments, which will not be described herein.

The method and the device acquire the held product of the user; then judging whether a first product matched with the held product exists in the target recommended product or not; if yes, the first product is removed from the target recommended product, and a processed second product is obtained; and subsequently pushing the second product to the user. According to the application, the first product matched with the held product in the target recommended product is intelligently deleted by acquiring the held product of the user, and then the second product subjected to the rejection processing is pushed to the user, so that repeated recommendation of the insurance product to the user can be effectively avoided, and the recommendation intelligence of the insurance product is improved.

In some alternative implementations, the pushing the second product to the user includes the steps of:

And acquiring first user information of the user.

In this embodiment, the first user information may refer to name information of the user or identity information of the user.

And determining the designated push time corresponding to the user based on the first user information.

In this embodiment, the working time and the sleeping time of the user may be queried based on the first user information, so as to remove the working time and the sleeping time from all time periods included in a day, to obtain a removed time period, and the removed time period is used as the specified pushing time.

And pushing the second product to the user based on the specified pushing time.

In this embodiment, after the current time reaches the time node corresponding to the specified pushing time, the second product is pushed to the user.

The method comprises the steps of obtaining first user information of the user; then determining the designated push time corresponding to the user based on the first user information; and pushing the second product to the user based on the designated pushing time. According to the application, the specified pushing time corresponding to the user is determined based on the first user information, and the second product is pushed to the user based on the specified pushing time, so that the user is not disturbed in a working or sleeping time period, but the insurance product is pushed to the user in an idle time period, the pushing intelligence of the insurance product is effectively improved, and the use experience of the user is improved.

In some alternative implementations, the electronic device may further perform the steps of:

Judging whether a data query request triggered by a designated user is received or not; wherein the data query request carries data identification information.

In this embodiment, the insurance recommendation system further provides data query, data statistics and analysis functions, and the agent can check its sales performance, policy renewal condition, etc. at any time. Through visual presentation of data, an agent can better know own business conditions and make corresponding decisions and adjustments. Under the application scene that the user uses the insurance recommendation system to perform the data query operation, the user can trigger the data query request corresponding to the data identification information by clicking a data query button preset in the insurance recommendation system and inputting the data identification information. The data identification information may refer to corresponding description information of data that a user needs to query, for example, information including sales performance, warranty duration, and the like.

If yes, second user information of the appointed user is obtained.

In this embodiment, the second user information may refer to name information of the user or identification information of the user.

And carrying out authority analysis on the second user information based on a preset authority identification model to obtain a designated authority level corresponding to the designated user.

In this embodiment, the authority identification model is a model previously constructed and stored with a mapping relationship between client information and authority levels of each client. And the second user information is input into the authority identification model, and the authority analysis is carried out on the second user information through the authority identification model, so that the authority level corresponding to the appointed client information which is the same as the second user information is obtained, and the appointed authority level is obtained.

And acquiring a right level interval corresponding to the data query operation.

In this embodiment, the preset permission level table may be queried to query a permission level interval corresponding to the data query operation from the permission level table. The authority level table is a data table which is constructed in advance and stores authority levels corresponding to various business operations one by one.

And judging whether the appointed authority level is in the authority level interval or not.

In this embodiment, the specified authority level may be compared with the authority level interval by a numerical comparison, so as to determine whether the specified authority level is within the authority level interval according to the obtained numerical comparison result.

In this embodiment, if the specified permission level is within the permission level interval, it indicates that the user has permission to perform a data query on the insurance system. And if the designated authority level is not in the authority level interval, indicating that the user does not have the authority for carrying out data query on the insurance system. The foregoing specific implementation process of the response processing to the data query request based on the data identification information will be described in further detail in the following specific embodiments, which are not described herein.

Judging whether a data query request triggered by a designated user is received or not through a request; if yes, obtaining second user information of the appointed user; then carrying out authority analysis on the second user information based on a preset authority identification model to obtain a designated authority level corresponding to the designated user; then acquiring a right level interval corresponding to the data query operation; subsequently judging whether the appointed authority level is in the authority level interval or not; if yes, responding to the data query request based on the data identification information. After receiving a data query request triggered by a designated user, the method intelligently uses the permission identification model to conduct permission analysis on second user information of the designated user, and only when the designated permission level of the designated user is determined to be within the permission level interval, the data identification information is used for responding to the data query request, so that unauthorized access and data leakage are effectively prevented, the processing standardization of the data query request is improved, and the safety of privacy data and rights data is ensured.

In some optional implementations of this embodiment, the responding to the data query request based on the data identification information includes the following steps:

and acquiring target data corresponding to the data identification information.

In this embodiment, the data identification information may be queried in the security system to obtain the target data corresponding to the data identification information. In an application scenario in the insurance finance field, the target data may include insurance information of the customer, for example, including policy status, insurance amount, insurance duration, etc., or may further include processing information of an agent, and zero profit includes sales performance, policy duration, etc.

And determining a target display mode corresponding to the target data.

In this embodiment, the target data type corresponding to the target data may be obtained, so as to obtain a specified display mode corresponding to the target data type and serve as a target display mode corresponding to the target data. And presetting display modes corresponding to the data of different data types for the data of different data types. For text type data, a report form display mode is adopted; for video type data, a display mode of voice and video playing is adopted, and the like.

And displaying the target data based on the target display mode.

The application obtains the target data corresponding to the data identification information; then determining a target display mode corresponding to the target data; and then carrying out display processing on the target data based on the target display mode. According to the application, only after whether the designated authority level is in the authority level interval is detected, the target data corresponding to the data identification information is acquired later, and the target data is intelligently displayed according to the target display mode corresponding to the target data, so that the intelligent and normalized response processing of the data query request is improved, and the use experience of the designated user is improved.

In some optional implementations of this embodiment, the electronic device may further perform the following steps:

And acquiring service data to be stored.

In this embodiment, the service data is data that needs to be stored. In the application scenario of financial insurance, the service data may include customer information of a customer and insurance data, and the insurance data may include data such as policy status, insurance amount, insurance duration, and the like. The business data may alternatively include business statistics of the insurance agent, such as sales performance, policy renewal, etc.

And determining a target data encryption algorithm corresponding to the service data.

In the present embodiment, for service data of different service types, data encryption algorithms corresponding to the service data of different service types are preset so that normalized data encryption processing can be performed on the corresponding service data using the data encryption algorithms. For data with the service type being the policy state type, an AES data encryption algorithm is adopted; for data with the business type of insurance amount, adopting triple DES data encryption algorithm; for data with service type of insurance period type, adopting RSA data encryption algorithm; for the data with the business type of sales performance type, adopting a Bl owf i sh data encryption algorithm; and adopting a Twof i sh data encryption algorithm for the data with the service type of the policy renewal condition type.

And encrypting the service data based on the target data encryption algorithm to obtain encrypted target service data.

In this embodiment, the encrypted target service data may be obtained by performing encryption processing on the service data using encryption logic of the target data encryption algorithm.

And storing the target business data.

In this embodiment, a specified storage area named by the data name of the target service data may be set in the insurance system, and then the target service data may be stored in the specified storage area, so as to complete normalized storage of the target service data.

The application obtains the business data to be stored; then determining a target data encryption algorithm corresponding to the service data; then, encrypting the service data based on the target data encryption algorithm to obtain encrypted target service data; and storing the target business data later. According to the application, the target data encryption algorithm with the corresponding relation with the service data is used for encrypting the service data, and then the target service data obtained after the encryption is stored, so that the intelligent storage processing of the service data is realized, and the service data can be effectively ensured to be safely protected.

In some optional implementations of this embodiment, the insurance system further includes the following functions:

1. integration of WeChat platform: the insurance system combines the agent management tool with the WeChat platform, thereby realizing information concentration and convenient communication. By taking WeChat as a main operation platform, the agent can conveniently communicate with the client, input policy information, inquire the policy state and the like, and no extra complicated steps are needed. The integration innovation greatly improves the operation convenience and the working efficiency of users.

2. Customer information management optimization: the insurance system is optimized for agent's customer information management. Through the tool, the agent can easily add, search and manage the client information, and meanwhile, the system also provides intelligent searching and sorting functions, so that the agent can quickly locate target clients and policy information. In addition, the real-time display of the tracking and payment conditions of the policy is enhanced, and the agent can know the state and payment conditions of the policy at any time and make corresponding processing in time. The agent can also check the insurance information of the client at any time, including the insurance policy state, insurance amount, insurance period, etc., thereby ensuring the agent to grasp the client information in real time

3. Policy manages one-stop services: the insurance system provides one-stop policy management functions. The agent can communicate with the client through WeChat, know the policy requirement of the client and enter the policy. The agent can know the state and payment condition of the policy in real time, and is convenient to follow up and process in time. The novel policy management mode not only improves the working efficiency of agents, but also optimizes the service experience of clients, and the clients can conveniently view and manage the policy information of the clients on WeChat.

4. Intelligent reminding and pushing functions: the insurance system has intelligent reminding and pushing functions. The agent can set the reminding time of the policy, and the system can automatically send reminding information to the client through WeChat to remind the client of the renewal of the fee or the expiration of the policy. Therefore, the agent does not need to manually follow each client, so that the workload is greatly reduced, and the satisfaction degree and the policy renewal rate of the clients are improved.

5. Data statistics and analysis: the insurance system also provides data statistics and analysis functions, and agents can check sales performance, policy renewal conditions and the like at any time. Through visual presentation of data, an agent can better know own business conditions and make corresponding decisions and adjustments.

6. The reinforcement agent supports: insurance systems focus not only on user experience, but also on agent support and services. The agent can obtain more comprehensive insurance product information, sales skills, training resources and the like through the insurance system. The insurance system also provides community functions of agent communication and interaction, and the agent can share experience and communication problems with other peers, so that the service level is improved together.

In summary, the insurance system provides a brand-new management experience and improvement of working efficiency for agents by integrating innovation in multiple aspects such as WeChat platform, optimizing customer information management, providing one-stop policy management service, intelligent reminding and pushing functions, data statistics and analysis functions and the like. The innovative functions and features make the insurance system an important tool in the insurance industry, which is helpful for optimizing the service quality of insurance agents and improving customer satisfaction.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

It should be emphasized that, to further ensure the privacy and security of the target recommended product, the target recommended product may also be stored in a node of a blockchain.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (B l ockcha i n), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (ART I F I C I A L I NTE L L I GENCE, A I) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-On-y Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence-based product recommendation device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device is particularly applicable to various electronic devices.

As shown in fig. 3, the product recommendation device 300 based on artificial intelligence according to the present embodiment includes: a first acquisition module 301, a second acquisition module 302, a calling module 303, a first analysis module 304, a second analysis module 305, a construction module 306, and a pushing module 307.

Wherein:

the first obtaining module 301 is configured to obtain user data of a user, and pre-process the user data to obtain corresponding specified user data; wherein the user data at least comprises health data, demand data and consumption behavior data;

a second obtaining module 302, configured to obtain a user portrait of the user, and obtain an initial product matched with the user based on the user portrait;

a calling module 303, configured to call a plurality of product recommendation models that are built in advance; the product recommendation model at least comprises a first recommendation model and a second recommendation model;

the first analysis module 304 is configured to perform data analysis on the specified data and the initial product based on the first recommendation model, and determine a first recommended product corresponding to the specified data from the initial product;

A second analysis module 305, configured to perform data analysis on the specified data and the initial product based on the second recommendation model, and determine a second recommended product corresponding to the specified data from the initial product;

A construction module 306, configured to construct a target recommended product based on the first recommended product and the second recommended product;

and the pushing module 307 is configured to push the target recommended product to the user.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method based on artificial intelligence in the foregoing embodiment one by one, which is not described herein again.

In some alternative implementations of the present embodiment, the building block 306 includes:

The first processing sub-module is used for integrating the first recommended product and the second recommended product to obtain a corresponding product set;

The second processing submodule is used for carrying out de-duplication processing on the products contained in the product set to obtain the processed appointed product;

and the first determining submodule is used for taking the specified product as the target recommended product.

In some optional implementations of this embodiment, the pushing module 307 includes:

the first acquisition submodule is used for acquiring the held product of the user;

The judging submodule is used for judging whether a first product matched with the held product exists in the target recommended product or not;

The third processing submodule is used for eliminating the first product from the target recommended product if yes, so as to obtain a processed second product;

And the pushing sub-module is used for pushing the second product to the user.

In some optional implementations of this embodiment, the pushing submodule includes:

An acquisition unit configured to acquire first user information of the user;

A determining unit, configured to determine the specified push time corresponding to the user based on the first user information;

and the pushing unit is used for pushing the second product to the user based on the specified pushing time.

In some optional implementations of the present embodiments, the artificial intelligence based product recommendation device further includes:

The first judging module is used for judging whether a data query request triggered by a designated user is received or not; wherein, the data query request carries data identification information;

the third acquisition module is used for acquiring second user information of the appointed user if yes;

The third analysis module is used for carrying out authority analysis on the second user information based on a preset authority identification model to obtain a designated authority level corresponding to the designated user;

the fourth acquisition module is used for acquiring a right level interval corresponding to the data query operation;

The second judging module is used for judging whether the appointed authority level is in the authority level interval or not;

and the response module is used for responding to the data query request based on the data identification information if yes.

In some optional implementations of this embodiment, the response module includes:

The second acquisition sub-module is used for acquiring target data corresponding to the data identification information;

the second determining submodule is used for determining a target display mode corresponding to the target data;

and the display sub-module is used for displaying and processing the target data based on the target display mode.

a fifth acquisition module, configured to acquire service data to be stored;

the determining module is used for determining a target data encryption algorithm corresponding to the service data;

The encryption module is used for carrying out encryption processing on the service data based on the target data encryption algorithm to obtain encrypted target service data;

And the storage module is used for storing the target business data.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes, but is not limited to, a microprocessor, an application specific integrated circuit (APP L I CAT I on SPEC I F I C I NTEGRATED C I rcu I t, AS IC), a programmable gate array (Fie l d-Programmab L E GATE AR RAY, FPGA), a digital Processor (D I G I TA L S I GNA L Processor, DSP), an embedded device, and the like.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (SMART MED I A CARD, SMC), a secure digital (Secu RE D I G I TA L, SD) card, a flash memory card (F L ASH CARD) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of an artificial intelligence based product recommendation method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Cent ra l Process i ng Un i t, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as executing computer readable instructions of the artificial intelligence based product recommendation method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

According to the embodiment of the application, the initial product matched with the user is obtained according to the user portrait of the user, the data analysis is further carried out on the user data of the user and the initial product based on a plurality of product recommendation models constructed in advance, the final target recommended product is determined from the recommended products respectively output by the plurality of product recommendation models, and then the target recommended product is pushed to the user, so that the target recommended product meeting the requirements corresponding to the user data is recommended to the user based on the use of a plurality of product recommendation models. The accuracy and the intelligence of product recommendation are effectively improved, and the use experience of a user is improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence-based product recommendation method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. The product recommending method based on artificial intelligence is characterized by comprising the following steps of:

pushing the target recommended product to the user.

2. The method for recommending products based on artificial intelligence according to claim 1, wherein the step of constructing the target recommended product based on the first recommended product and the second recommended product comprises:

And taking the appointed product as the target recommended product.

3. The method for recommending products based on artificial intelligence according to claim 1, wherein the step of pushing the target recommended product to the user comprises the steps of:

Acquiring a held product of the user;

pushing the second product to the user.

4. The artificial intelligence based product recommendation method according to claim 3, wherein said step of pushing said second product to said user comprises:

acquiring first user information of the user;

and pushing the second product to the user based on the specified pushing time.

5. The artificial intelligence based product recommendation method of claim 1, further comprising:

If yes, obtaining second user information of the appointed user;

acquiring a right level interval corresponding to a data query operation;

6. The method for recommending products based on artificial intelligence according to claim 5, wherein the step of responding to the data query request based on the data identification information comprises the steps of:

acquiring target data corresponding to the data identification information;

determining a target display mode corresponding to the target data;

And displaying the target data based on the target display mode.

7. The artificial intelligence based product recommendation method of claim 1, further comprising:

Acquiring service data to be stored;

and storing the target business data.

8. An artificial intelligence based product recommendation device, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the artificial intelligence based product recommendation method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the artificial intelligence based product recommendation method according to any of claims 1 to 7.