Data generation method and device based on artificial intelligence, computer equipment and medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based data generation method, which is applied to the field of financial science and technology, and comprises the following steps: receiving customer information input by a user; analyzing the customer information based on the analysis model to obtain target demand information; screening out a target material from a material content center based on the target demand information; obtaining candidate products matched with the target demand information, and screening out a target product from the candidate products; generating a target product scheme based on the target product; obtaining a target PPT template; and generating a target enterprise drawing based on the customer information, the target product scheme and the target PPT template. The invention further provides a data generation device based on artificial intelligence, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the target enterprise drawing can be stored in a block chain. According to the method and the device, the enterprise drawing can be automatically and accurately generated, the generation efficiency of the enterprise drawing is improved, and the quality of the obtained target enterprise drawing is ensured.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence development technology and financial technology, and in particular to artificial intelligence-based data generation methods, devices, computer equipment and storage media. Background Art

[0002] In the sales practice of the insurance industry, insurance agents play a vital role. They not only need to conduct detailed needs analysis based on the individual conditions of customers, such as professional background, family composition and economic status, but also need to accurately recommend suitable insurance products to customers based on these analyses. This process is highly dependent on the agent's professional knowledge, experience accumulation and market insight. However, in reality, due to the differences in the ability levels of each agent, the accuracy of their analysis and recommended products often varies, making it difficult to ensure that every customer can obtain insurance products that truly meet their needs, thus affecting customer satisfaction and the overall service quality of the insurance industry.

[0003] In the insurance sales preparation stage, making a business plan is an important step for agents to show their professional image, company strength and recommended products to potential customers. In the traditional business plan making process, agents need to manually collect and organize a large amount of information, including but not limited to personal profiles, awards and honors, basic information of the company, product features and advantages, and various materials that stimulate customer interest. Although most of this information is stored in the company's internal system, due to the lack of automated integration tools, agents have to spend a lot of time and energy on manual screening, editing and typesetting, which is not only inefficient, but also very easy to cause information errors or omissions due to human errors, and thus cannot guarantee the quality of the business plan. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a data generation method, device, computer equipment and storage medium based on artificial intelligence to solve the technical problems that the existing method of making business plans is inefficient and cannot guarantee the quality of the business plans.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a data generation method based on artificial intelligence, which adopts the following technical solutions:

[0006] Receive customer information entered by the user through the preset page;

[0007] Analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information;

[0008] Based on the target demand information, select target materials from a preset material content center;

[0009] Acquire candidate products that match the target demand information, and select target products from the candidate products;

[0010] Generate a corresponding target product solution based on the target product;

[0011] Obtain a target PPT template corresponding to the user;

[0012] Generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

[0013] Furthermore, the step of selecting target materials from a preset material content center based on the target demand information specifically includes:

[0014] Call the preset tag library;

[0015] Querying a specified tag matching the target requirement information from the tag library;

[0016] Filtering out the designated material corresponding to the designated tag from the material content center;

[0017] The designated material is used as the target material.

[0018] Furthermore, the step of screening out the target product from the candidate products specifically includes:

[0019] Get the preset rating dimensions;

[0020] Obtaining dimension values ​​of each of the candidate products under the scoring dimension;

[0021] Calculate and process the dimension values ​​based on a preset scoring strategy to obtain a product scoring value for each of the candidate products;

[0022] Filtering out a designated product rating value that meets a preset matching rating condition from all the product rating values;

[0023] A designated product corresponding to the designated product score value is obtained, and the designated product is used as the target product.

[0024] Furthermore, the step of generating a corresponding target product solution based on the target product specifically includes:

[0025] Acquire product information of the target product;

[0026] Get the preset solution generation strategy;

[0027] Perform solution generation processing on the product information based on the solution generation strategy to obtain a corresponding initial solution;

[0028] Adjusting the initial plan to obtain a corresponding adjustment plan;

[0029] The adjustment plan is used as the target product plan.

[0030] Furthermore, the step of generating a corresponding target business plan based on the customer information, the target product solution and the target PPT template specifically includes:

[0031] Call the preset generation engine;

[0032] Parsing the target PPT template based on the generation engine to obtain a corresponding cover page and content page;

[0033] Filling the cover page based on the customer information, and filling the content page based on the target product solution, to obtain a filled target template;

[0034] The target plan is generated based on the target template.

[0035] Furthermore, before the step of analyzing and processing the customer information based on a preset analysis model to obtain corresponding target demand information, the method further includes:

[0036] Obtaining pre-collected initial customer data;

[0037] Preprocessing the initial customer data to obtain corresponding customer data;

[0038] Performing labeling processing on the customer data to obtain corresponding sample data;

[0039] Call the preset initial learning model;

[0040] Performing training and evaluation processing on the initial learning model based on the sample data to obtain a designated learning model that meets preset performance conditions;

[0041] The designated learning model is used as the analysis model.

[0042] Furthermore, after the step of generating a corresponding target business plan based on the customer information, the target product solution and the target PPT template, the step further includes:

[0043] Determining whether a preview operation of the target proposal triggered by a user is received;

[0044] If yes, generate a corresponding preview page based on the target plan;

[0045] The preview page is displayed.

[0046] In order to solve the above technical problems, the embodiment of the present application further provides a data generation device based on artificial intelligence, which adopts the following technical solution:

[0047] A receiving module, used to receive customer information entered by the user through a preset page;

[0048] An analysis module, used to analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information;

[0049] A first screening module, configured to screen target materials from a preset material content center based on the target demand information;

[0050] A second screening module is used to obtain candidate products matching the target demand information and screen out target products from the candidate products;

[0051] A first generating module, used to generate a corresponding target product solution based on the target product;

[0052] A first acquisition module, used to acquire a target PPT template corresponding to the user;

[0053] The second generating module is used to generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

[0054] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0055] Receive customer information entered by the user through the preset page;

[0056] Analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information;

[0057] Based on the target demand information, select target materials from a preset material content center;

[0058] Acquire candidate products that match the target demand information, and select target products from the candidate products;

[0059] Generate a corresponding target product solution based on the target product;

[0060] Obtain a target PPT template corresponding to the user;

[0061] Generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

[0062] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0063] Receive customer information entered by the user through the preset page;

[0064] Analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information;

[0065] Based on the target demand information, select target materials from a preset material content center;

[0066] Acquire candidate products that match the target demand information, and select target products from the candidate products;

[0067] Generate a corresponding target product solution based on the target product;

[0068] Obtain a target PPT template corresponding to the user;

[0069] Generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

[0070] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0071] The present application first receives the customer information entered by the user through the preset page; then analyzes and processes the customer information based on the preset analysis model to obtain the corresponding target demand information; then based on the target demand information, the target material is screened out from the preset material content center; subsequently, the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products; and the corresponding target product plan is generated based on the target product; further, the target PPT template corresponding to the user is obtained; finally, the corresponding target plan is generated based on the customer information, the target product plan and the target PPT template. The present application obtains the target demand information by analyzing and processing the customer information based on the use of the analysis model, and then the target material corresponding to the target demand information is screened out based on the use of the material content center, and then the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products, and then the corresponding target product plan is generated based on the target product. Subsequently, based on the customer information, the target product plan and the target PPT template corresponding to the user, the corresponding target plan can be automatically and accurately generated, which effectively reduces the workload of making the plan, improves the efficiency of generating the plan, and ensures the quality of the target plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0073] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0074] Figure 2 A flowchart of an embodiment of the artificial intelligence-based data generation method according to the present application;

[0075] Figure 3 is a structural schematic diagram of an embodiment of an artificial intelligence-based data generation device according to the present application;

[0076] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0077] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0078] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0079] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0080] like Figure 1As 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 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for 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 optical fiber cables, etc.

[0081] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0082] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or 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), a laptop computer, a desktop computer, etc.

[0083] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0084] It should be noted that the artificial intelligence-based data generation method provided in the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the artificial intelligence-based data generation device is generally set in the server / terminal device.

[0085] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0086] Continue to refer Figure 2, showing a flow chart of an embodiment of the data generation method based on artificial intelligence according to the present application. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted. The data generation method based on artificial intelligence provided in the embodiment of the present application can be applied to any scenario where data generation is required, and the data generation method based on artificial intelligence can be applied to products in these scenarios, for example, the generation of business plans in the field of finance and insurance. The data generation method based on artificial intelligence includes the following steps:

[0087] Step S201, receiving customer information entered by the user through a preset page.

[0088] In this embodiment, the electronic device (e.g. Figure 1 The server / terminal device shown in the figure) can obtain customer information through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future. The execution subject of this application may be a data generation system, or simply referred to as a system. This application can be applied to the sales scenario of insurance products in the field of financial business, and can assist insurance agents in automatically generating a business plan PPT related to customers. The above-mentioned preset page may be a front-end page developed by the system to assist in the construction and processing of the business plan PPT. The above-mentioned user may be an insurance agent (or simply referred to as an agent) who has the need to generate a business plan PPT. The above-mentioned customer information may include the agent manually entering the customer's name, gender, age and other basic information. And by dropping down the customer group list, the agent selects the customer's customer group type (such as business owners, professional managers, professional technicians, professional investment knowledge, full-time wives, etc.). And through the selection of budget ranges, the agent sets the client's expected total budget (such as 200,000-500,000, 500,000-1 million, 1 million-2 million, 2 million-5 million, 5 million-10 million, 10 million-20 million, and more than 20 million).

[0089] Step S202: Analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information.

[0090] In this embodiment, after the above customer information is input into the analysis model, the sharing model will perform intelligent analysis on the customer information to identify the target demand information of the corresponding customer, including the main demand and the secondary demand. The construction process of the above analysis model will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated here.

[0091] Step S203: based on the target demand information, select target materials from a preset material content center.

[0092] In this embodiment, the specific implementation process of selecting the target material from the preset material content center based on the target demand information will be described in further detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0093] Step S204, obtaining candidate products matching the target demand information, and screening out target products from the candidate products.

[0094] In this embodiment, the above-mentioned target demand information can be matched with the information in a preset insurance product database to screen out the insurance products most relevant to the above-mentioned target demand information and use them as the above-mentioned candidate products. The matching process can be implemented based on keywords, semantic analysis or machine learning algorithms to find out the insurance products most relevant to customer needs. In addition, the system pre-maintains an insurance product database, which contains detailed information on various insurance products, such as product name, coverage, premium, compensation conditions, customer evaluation, etc. In addition. The above-mentioned specific implementation process of screening out the target product from the candidate products will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0095] Step S205: generating a corresponding target product solution based on the target product.

[0096] In this embodiment, the specific implementation process of generating the corresponding target product solution based on the target product will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated in detail here.

[0097] Step S206, obtaining a target PPT template corresponding to the user.

[0098] In this embodiment, multiple sets of pre-built PPT templates can be displayed by using the front-end page. The user selects the corresponding PPT style according to his or her preferences and customer needs. After selecting the style, the front-end interface automatically switches to the corresponding target PPT template.

[0099] Step S207, generating a corresponding target business plan based on the customer information, the target product solution and the target PPT template.

[0100] In this embodiment, the specific implementation process of generating the corresponding target business plan based on the customer information, the target product solution and the target PPT template will be described in further detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0101] The present application first receives the customer information entered by the user through the preset page; then analyzes and processes the customer information based on the preset analysis model to obtain the corresponding target demand information; then based on the target demand information, the target material is screened out from the preset material content center; subsequently, the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products; and the corresponding target product plan is generated based on the target product; further, the target PPT template corresponding to the user is obtained; finally, the corresponding target plan is generated based on the customer information, the target product plan and the target PPT template. The present application obtains the target demand information by analyzing and processing the customer information based on the use of the analysis model, and then the target material corresponding to the target demand information is screened out based on the use of the material content center, and then the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products, and then the corresponding target product plan is generated based on the target product. Subsequently, based on the customer information, the target product plan and the target PPT template corresponding to the user, the corresponding target plan can be automatically and accurately generated, which effectively reduces the workload of making the plan, improves the efficiency of generating the plan, and ensures the quality of the target plan.

[0102] In some optional implementations, step S203 includes the following steps:

[0103] Call the preset tag library.

[0104] In this embodiment, the tag library is a pre-built database that maintains the association between tags and customer needs. This association enables the system to quickly retrieve and match relevant tags according to different customer needs. In the insurance field, the tags in the tag library may include policies, finance, real estate, children, marriage, inheritance, pension, etc.

[0105] A designated tag matching the target requirement information is searched from the tag library.

[0106] In this embodiment, the target requirement information may be used to query the tag library to obtain a designated tag that matches the target requirement information.

[0107] Filter out the designated material corresponding to the designated tag from the material content center.

[0108] In this embodiment, the material content center is a pre-built content center that stores various stimulus materials and stores the corresponding relationships between various stimulus materials and tags. The system can automatically filter out the designated stimulus materials that match the customer's needs (designated standard configuration) from the material content center according to the corresponding relationship between tags and stimulus materials, and use them as the corresponding target materials.

[0109] The designated material is used as the target material.

[0110] In this embodiment, the system will regularly optimize the tags in the tag library, including deleting redundant tags, merging similar tags, adding new tags, etc. This optimization helps to improve the accuracy and practicality of the tags, so that the system can better meet customer needs.

[0111] The present application calls a preset tag library; then queries the specified tag that matches the target demand information from the tag library; then filters out the specified material corresponding to the specified tag from the material content center; and subsequently uses the specified material as the target material. The present application queries the specified tag that matches the target demand information based on the use of the tag library, and then based on the use of the material content center, it can quickly and accurately filter out the target material corresponding to the specified tag, thereby improving the screening efficiency of the target material and ensuring the accuracy and matching of the obtained target material.

[0112] In some optional implementations of this embodiment, step S204 includes the following steps:

[0113] Get the preset scoring dimensions.

[0114] In this embodiment, the above-mentioned scoring dimensions may specifically include the matching degree of the coverage, the cost-effectiveness of the premium (the ratio of premium to coverage), the average score or satisfaction of customer evaluation, etc. Each scoring dimension may be assigned a different weight to reflect its importance to the customer's product selection.

[0115] Obtain the dimension value of each of the candidate products under the scoring dimension.

[0116] In this embodiment, a dimension value quantification strategy for products is set according to actual business needs, and dimension values ​​of candidate products under various scoring dimensions are generated according to the quantification processing steps of the dimension values ​​in the dimension value quantification strategy.

[0117] The dimension values ​​are calculated and processed based on a preset scoring strategy to obtain a product scoring value for each of the candidate products.

[0118] In this embodiment, the above scoring strategy may specifically adopt a weighted sum strategy.

[0119] Filter out the designated product rating values ​​that meet the preset matching rating conditions from all the product rating values.

[0120] In this embodiment, the matching score condition refers to the condition that the product score value is greater than a preset score threshold. There is no specific limitation on the value of the score threshold, which can be set according to actual business needs. The higher the product score value, the higher the recommendation of the corresponding product.

[0121] A designated product corresponding to the designated product score value is obtained, and the designated product is used as the target product.

[0122] In this embodiment, the designated product refers to a product whose product score value is greater than a score threshold among all candidate products.

[0123] This application obtains a preset scoring dimension; then obtains the dimension value of each candidate product under the scoring dimension; then calculates and processes the dimension value based on a preset scoring strategy to obtain the product scoring value of each candidate product; subsequently, the designated product scoring value that meets the preset matching scoring conditions is screened out from all the product scoring values; finally, the designated product corresponding to the designated product scoring value is obtained, and the designated product is used as the target product. This application obtains a preset scoring dimension, and obtains the dimension value of each candidate product under the scoring dimension; then calculates and processes the dimension value based on the use of a scoring strategy to obtain the product scoring value of each candidate product; then, the designated product scoring value that meets the matching scoring conditions is screened out from all the product scoring values, and the designated product corresponding to the designated product scoring value is obtained as the final target product, so that the screening process of the candidate products can be completed intelligently and accurately, and the optimal target product can be automatically recommended, thereby improving the accuracy of the obtained target product.

[0124] In some optional implementations, step S205 includes the following steps:

[0125] Acquire product information of the target product.

[0126] In this embodiment, the product information may refer to the product name or product code of the target product.

[0127] Get the preset solution generation strategy.

[0128] In this embodiment, the strategy content of the above-mentioned solution generation strategy includes: generating a product solution including all important terms of the product, coverage, premium calculation method, compensation process, etc.

[0129] Based on the solution generation strategy, solution generation processing is performed on the product information to obtain a corresponding initial solution.

[0130] In this embodiment, the solution generation process for the product information may be performed according to the policy content of the solution generation policy, thereby obtaining a corresponding initial solution.

[0131] The initial plan is adjusted to obtain a corresponding adjustment plan.

[0132] In this embodiment, the generated initial plan is personalized according to the specific needs of the customer (such as age, health status, occupation, etc.). For example, for older customers, the system may recommend insurance products that include pension protection; for customers in poor health, the system may recommend insurance products that include specific disease protection, thereby obtaining corresponding adjustment plans.

[0133] The adjustment plan is used as the target product plan.

[0134] The present application obtains the product information of the target product; then obtains a preset solution generation strategy, and performs solution generation processing on the product information based on the solution generation strategy to obtain a corresponding initial solution; then adjusts the initial solution to obtain a corresponding adjustment solution; and subsequently uses the adjustment solution as the target product solution. The present application obtains the product information of the target product, and then performs solution generation processing on the product information based on the use of a solution generation strategy to obtain an initial solution, and adjusts the initial solution to automatically and intelligently generate the final target product solution, thereby improving the generation efficiency of the target product solution. In addition, the generated target product solution not only contains the basic information and terms of the target product, but also intelligently performs personalized adjustments based on the specific needs of the customer, which helps to meet the individual needs of the customer and improve customer satisfaction.

[0135] In some optional implementations, step S207 includes the following steps:

[0136] Invokes the preset generation engine.

[0137] In this embodiment, the generation engine is a pre-built AI generation engine that has the function of automatically generating preliminary content of a PPT based on input information.

[0138] The target PPT template is parsed based on the generation engine to obtain the corresponding cover page and content page.

[0139] In this embodiment, by using the generation engine to parse the target PPT template, the template structure of the target PPT template can be determined, which may include at least a cover page and a content page, and may also include a directory page. The content page may specifically include a personal honor page, a family structure page, a demand confirmation page, a motivational material page, a suggestion page, a product solution page, a product two-point page, an exclusive rights and interests page, a corporate strength page, a warm reminder page, etc.

[0140] The cover page is filled based on the customer information, and the content page is filled based on the target product solution to obtain a filled target template.

[0141] In this embodiment, the target template is obtained by filling the cover page in the target PPT template with customer information, and filling the content page in the target PPT template with the target product solution, and ensuring the accuracy and logic of the content filling.

[0142] The target plan is generated based on the target template.

[0143] In this embodiment, PPT quality inspection standards can also be formulated, including content integrity, format standardization, text accuracy, etc. After the target plan is generated, the system calls the quality inspection module to automatically inspect the generated target plan, and marks or prompts the content that does not meet the standards found during the automatic inspection.

[0144] This application calls a preset generation engine; then parses the target PPT template based on the generation engine to obtain the corresponding cover page and content page; then fills the cover page based on the customer information, and fills the content page based on the target product solution to obtain the filled target template; subsequently generates the target plan based on the target template. This application parses the target PPT template based on the use of a generation engine to obtain the cover page and content page, and then fills the cover page based on the customer information, and fills the content page based on the target product solution, so that the corresponding target plan can be automatically and accurately generated, thereby improving the generation efficiency of the target plan and ensuring the data accuracy and standardization of the obtained target plan.

[0145] In some optional implementations of this embodiment, before step S202, the electronic device may further perform the following steps:

[0146] Obtain pre-collected initial customer data.

[0147] In this embodiment, the initial customer data is valuable data for building a sharing model. In demand mining, the initial customer data usually includes basic information of customers (such as name, gender, age, etc.), customer group classification (such as business owners, professional managers, etc.) and expected total budget, etc. Among them, the initial customer data related to demand mining can be collected from various channels (such as databases, API interfaces, manual input, etc.).

[0148] The initial customer data is preprocessed to obtain corresponding customer data.

[0149] In this embodiment, the above-mentioned preprocessing includes data cleaning and feature engineering. Among them, data cleaning includes removing invalid characters, processing missing values, correcting erroneous data and other processing to ensure data quality. Feature engineering processing includes: selecting the most useful features for model prediction from the original data. In demand mining, this may include age, gender, occupation, income, budget, etc. Then use statistical methods or machine learning algorithms to extract new features from the original data. For example, features such as purchase frequency and purchase amount can be extracted from the customer's purchase history. The features are subsequently standardized or normalized to ensure that they have the same importance in the model.

[0150] The customer data is labeled to obtain corresponding sample data.

[0151] In this embodiment, the customer data is labeled by manual labeling or machine labeling, that is, the real needs or preferences of the customers are marked, so as to obtain labeled customer data as the sample data.

[0152] Call the preset initial learning model.

[0153] In this embodiment, there is no specific limitation on the selection of the above initial learning model, and a suitable machine learning or deep learning model can be selected according to the nature of the problem and the characteristics of the data. In demand mining, a classification model (such as logistic regression, support vector machine, decision tree, etc.) or a regression model (such as linear regression, random forest regression, etc.) can be selected as the above initial learning model.

[0154] The initial learning model is trained and evaluated based on the sample data to obtain a designated learning model that meets preset performance conditions.

[0155] In this embodiment, the initial learning model can be trained using labeled sample data so that the initial learning model can learn the association between features and requirements. The performance of the model is then evaluated through cross-validation, accuracy, recall, F1 score and other indicators until a specified learning model that meets the performance conditions is obtained. Among them, the performance conditions may refer to indicators such as accuracy, recall, F1 score, etc. that are greater than the preset indicator threshold, and there is no specific limitation on the value of the indicator threshold, which can be set according to actual business needs.

[0156] The designated learning model is used as the analysis model.

[0157] In this embodiment, the trained model can be integrated into the system so that it can process new customer information and mine needs in real time. In addition, the hyperparameters of the analysis model can be adjusted using grid search, random search and other methods to improve the performance of the analysis model.

[0158] The present application obtains the initial customer data collected in advance; pre-processes the initial customer data to obtain the corresponding customer data; then annotates the customer data to obtain the corresponding sample data; then calls the preset initial learning model; subsequently trains and evaluates the initial learning model based on the sample data to obtain a specified learning model that meets the preset performance conditions; and finally uses the specified learning model as the analysis model. The present application obtains the customer data by pre-processing the initial customer data collected in advance, annotates the customer data to obtain sample data, and then trains and evaluates the preset initial learning model based on the sample data, thereby efficiently and accurately constructing an analysis model that meets the preset performance conditions, improving the construction efficiency of the analysis model, and ensuring the model effect of the obtained analysis model.

[0159] In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:

[0160] It is determined whether a preview operation of the target proposal triggered by a user is received.

[0161] In this embodiment, an obvious "preview" button is set at an appropriate position of the front-end interface of the system (such as a page or a toolbar after the PPT is generated). The user can click the "preview" button to trigger the preview operation of the target plan.

[0162] If so, a corresponding preview page is generated based on the target plan.

[0163] In this embodiment, the preview page is a pre-built web page containing the content of the target plan. The preview interface provides basic control functions such as page turning, zooming, full screen, etc., so that the agent can better view the PPT effect.

[0164] The preview page is displayed.

[0165] In this embodiment, in the preview interface, the user can see the PPT effect of the generated target plan, including page layout, content presentation, animation effect, etc. In addition, a "Save" button can be set in the preview interface. After confirming that the PPT effect is satisfactory, the user can click the button to save the target plan.

[0166] The present application determines whether a preview operation of the target plan triggered by the user is received; if so, a corresponding preview page is generated based on the target plan; and the preview page is subsequently displayed. After receiving a preview operation of the target plan triggered by the user, the present application will automatically and intelligently generate a corresponding preview page based on the target plan, and display the preview page, so that the user can conveniently preview the generated PPT effect of the target plan, thereby improving the user experience and ensuring that the production process of the plan PPT is both efficient and flexible.

[0167] In some optional implementations, the user information obtained is subject to the user's consent and complies with relevant laws and policies.

[0168] In addition, the non-Company software tools or components that appear in the embodiments of the present application are merely examples and do not represent actual use.

[0169] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0170] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned target plan, the above-mentioned target plan can also be stored in a node of a blockchain.

[0171] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0172] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0173] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0174] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the 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-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0175] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0176] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a data generation device based on artificial intelligence, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0177] like Figure 3As shown, the artificial intelligence-based data generation device 300 described in this embodiment includes: a receiving module 301, an analyzing module 302, a first screening module 303, a second screening module 304, a first generating module 305, a first acquiring module 306 and a second generating module 307. Among them:

[0178] Receiving module 301, used to receive customer information entered by the user through a preset page;

[0179] The analysis module 302 is used to analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information;

[0180] A first screening module 303 is used to screen target materials from a preset material content center based on the target demand information;

[0181] The second screening module 304 is used to obtain candidate products matching the target demand information and screen the target product from the candidate products;

[0182] A first generating module 305, configured to generate a corresponding target product solution based on the target product;

[0183] A first acquisition module 306 is used to acquire a target PPT template corresponding to the user;

[0184] The second generating module 307 is used to generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

[0185] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0186] In some optional implementations of this embodiment, the first screening module 303 includes:

[0187] The first calling submodule is used to call the preset tag library;

[0188] A query submodule, used to query the specified tag matching the target demand information from the tag library;

[0189] A first screening submodule, configured to screen out designated materials corresponding to the designated tag from the material content center;

[0190] The first determining submodule is used to use the designated material as the target material.

[0191] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0192] In some optional implementations of this embodiment, the second screening module 304 includes:

[0193] The first acquisition submodule is used to acquire a preset scoring dimension;

[0194] The second acquisition submodule is used to obtain the dimension value of each candidate product under the scoring dimension;

[0195] A calculation submodule, used to calculate and process the dimension values ​​based on a preset scoring strategy to obtain a product scoring value for each of the candidate products;

[0196] A second screening submodule is used to screen out a designated product rating value that meets a preset matching rating condition from all the product rating values;

[0197] The second determination submodule is used to obtain a designated product corresponding to the designated product score value, and use the designated product as the target product.

[0198] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0199] In some optional implementations of this embodiment, the first generating module 305 includes:

[0200] A third acquisition submodule is used to acquire product information of the target product;

[0201] The fourth acquisition submodule is used to acquire a preset solution generation strategy;

[0202] A first generating submodule, configured to perform solution generating processing on the product information based on the solution generating strategy to obtain a corresponding initial solution;

[0203] An adjustment submodule, used to adjust the initial solution to obtain a corresponding adjustment solution;

[0204] The third determination submodule is used to use the adjustment solution as the target product solution.

[0205] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0206] In some optional implementations of this embodiment, the second generating module 307 includes:

[0207] The second calling submodule is used to call the preset generation engine;

[0208] A parsing submodule, used for parsing the target PPT template based on the generation engine to obtain a corresponding cover page and content page;

[0209] A filling submodule, used to fill the cover page based on the customer information, and fill the content page based on the target product solution, to obtain a filled target template;

[0210] The second generating submodule is used to generate the target plan based on the target template.

[0211] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0212] In some optional implementations of this embodiment, the artificial intelligence-based data generation device further includes:

[0213] A second acquisition module is used to acquire the pre-collected initial customer data;

[0214] A preprocessing module, used for preprocessing the initial customer data to obtain corresponding customer data;

[0215] A labeling module, used to label the customer data to obtain corresponding sample data;

[0216] A calling module is used to call a preset initial learning model;

[0217] A processing module, used for training and evaluating the initial learning model based on the sample data to obtain a specified learning model that meets preset performance conditions;

[0218] A determination module is used to use the specified learning model as the analysis model.

[0219] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0220] In some optional implementations of this embodiment, the artificial intelligence-based data generation device further includes:

[0221] A judgment module, used to judge whether a preview operation of the target plan triggered by a user is received;

[0222] A third generating module, for generating a corresponding preview page based on the target plan if yes;

[0223] The display module is used to display the preview page.

[0224] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data generation method in the aforementioned implementation mode, and will not be repeated here.

[0225] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0226] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the 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 microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0227] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0228] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, 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, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of a data generation method based on artificial intelligence, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0229] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the artificial intelligence-based data generation method.

[0230] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0231] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0232] In the embodiment of the present application, firstly, the customer information entered by the user through the preset page is received; then, the customer information is analyzed and processed based on the preset analysis model to obtain the corresponding target demand information; then, based on the target demand information, the target material is screened out from the preset material content center; subsequently, the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products; and the corresponding target product scheme is generated based on the target product; further, the target PPT template corresponding to the user is obtained; finally, the corresponding target plan is generated based on the customer information, the target product scheme and the target PPT template. The present application obtains the target demand information by analyzing and processing the customer information based on the use of the analysis model, and then the target material corresponding to the target demand information is screened out based on the use of the material content center, and then the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products, and then the corresponding target product scheme is generated based on the target product, and subsequently, based on the customer information, the target product scheme and the target PPT template corresponding to the user, the corresponding target plan can be automatically and accurately generated, which effectively reduces the workload of making the plan, improves the efficiency of generating the plan, and ensures the quality of the target plan.

[0233] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the artificial intelligence-based data generation method as described above.

[0234] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0235] In the embodiment of the present application, firstly, the customer information entered by the user through the preset page is received; then, the customer information is analyzed and processed based on the preset analysis model to obtain the corresponding target demand information; then, based on the target demand information, the target material is screened out from the preset material content center; subsequently, the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products; and the corresponding target product scheme is generated based on the target product; further, the target PPT template corresponding to the user is obtained; finally, the corresponding target plan is generated based on the customer information, the target product scheme and the target PPT template. The present application obtains the target demand information by analyzing and processing the customer information based on the use of the analysis model, and then the target material corresponding to the target demand information is screened out based on the use of the material content center, and then the candidate products matching the target demand information are obtained, and the target product is screened out from the candidate products, and then the corresponding target product scheme is generated based on the target product, and subsequently, based on the customer information, the target product scheme and the target PPT template corresponding to the user, the corresponding target plan can be automatically and accurately generated, which effectively reduces the workload of making the plan, improves the efficiency of generating the plan, and ensures the quality of the target plan.

[0236] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course 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, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0237] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they 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 disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A data generation method based on artificial intelligence, characterized in that: The steps include: Receive customer information entered by the user through the preset page; Analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information; Based on the target demand information, select target materials from a preset material content center; Acquire candidate products that match the target demand information, and select target products from the candidate products; Generate a corresponding target product solution based on the target product; Obtain a target PPT template corresponding to the user; Generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

2. The artificial intelligence-based data generation method according to claim 1, characterized in that: The step of selecting target materials from a preset material content center based on the target demand information specifically includes: Call the preset tag library; Querying a specified tag matching the target requirement information from the tag library; Filtering out the designated material corresponding to the designated tag from the material content center; The designated material is used as the target material.

3. The artificial intelligence-based data generation method according to claim 1, characterized in that: The step of screening out the target product from the candidate products specifically includes: Get the preset rating dimensions; Obtaining dimension values ​​of each of the candidate products under the scoring dimension; Calculate and process the dimension values ​​based on a preset scoring strategy to obtain a product scoring value for each of the candidate products; Filtering out a designated product rating value that meets a preset matching rating condition from all the product rating values; A designated product corresponding to the designated product score value is obtained, and the designated product is used as the target product.

4. The artificial intelligence-based data generation method according to claim 1, characterized in that: The step of generating a corresponding target product solution based on the target product specifically includes: Acquire product information of the target product; Get the preset solution generation strategy; Perform solution generation processing on the product information based on the solution generation strategy to obtain a corresponding initial solution; Adjusting the initial plan to obtain a corresponding adjustment plan; The adjustment plan is used as the target product plan.

5. The artificial intelligence-based data generation method according to claim 1, characterized in that: The step of generating a corresponding target business plan based on the customer information, the target product solution and the target PPT template specifically includes: Call the preset generation engine; Parsing the target PPT template based on the generation engine to obtain a corresponding cover page and content page; Filling the cover page based on the customer information, and filling the content page based on the target product solution, to obtain a filled target template; The target plan is generated based on the target template.

6. The artificial intelligence-based data generation method according to claim 1, characterized in that: Before the step of analyzing and processing the customer information based on the preset analysis model to obtain the corresponding target demand information, the method further includes: Obtaining pre-collected initial customer data; Preprocessing the initial customer data to obtain corresponding customer data; Performing labeling processing on the customer data to obtain corresponding sample data; Call the preset initial learning model; Performing training and evaluation processing on the initial learning model based on the sample data to obtain a designated learning model that meets preset performance conditions; The designated learning model is used as the analysis model.

7. The artificial intelligence-based data generation method according to claim 1, characterized in that: After the step of generating a corresponding target business plan based on the customer information, the target product solution and the target PPT template, the method further includes: Determining whether a preview operation of the target proposal triggered by a user is received; If yes, generate a corresponding preview page based on the target plan; The preview page is displayed.

8. A data generation device based on artificial intelligence, characterized in that: include: A receiving module, used to receive customer information entered by the user through a preset page; An analysis module, used to analyze and process the customer information based on a preset analysis model to obtain corresponding target demand information; A first screening module, configured to screen target materials from a preset material content center based on the target demand information; A second screening module is used to obtain candidate products matching the target demand information and screen out target products from the candidate products; A first generating module, used to generate a corresponding target product solution based on the target product; A first acquisition module, used to acquire a target PPT template corresponding to the user; The second generating module is used to generate a corresponding target plan based on the customer information, the target product solution and the target PPT template.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the artificial intelligence-based data generation method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the artificial intelligence-based data generation method according to any one of claims 1 to 7.

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