Product guidance information generation method and device and computer equipment

Abstract

The invention relates to a product guidance information generation method and device and computer equipment. The method comprises the steps of obtaining product selling information, product information and feedback information of a plurality of stores, and based on the product selling information, identifying user group types, user preference information and group feature information of suitable user groups of product types; identifying comprehensive feature information of each product type, and generating first product guidance information of each user group type; generating research and development guidance information of a new product based on the first product guidance information and the group feature information of each user group type, and collecting guidance feedback information of each piece of first product guidance information and each piece of product feedback information of the new product; and adjusting to obtain second product guidance information corresponding to each user group type and target research and development guidance information of the new product, and taking the second product guidance information and the target research and development guidance information as target product guidance information. By adopting the method, the corresponding service division adaptability can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data analysis technology, and in particular to a method, device, and computer equipment for generating product guidance information. Background Art

[0002] In the retail industry, smart liquid dispensers, as a type of liquid dispensing device, achieve precise liquid distribution through automation, improving efficiency and user experience. At the same time, big data analysis and intelligent device control technologies have also brought new opportunities and challenges to the retail industry. By collecting and analyzing large amounts of data, we can better understand user needs and market trends, provide users with product guidance, and improve user-product compatibility, thereby enhancing the user experience. Therefore, how to accurately generate product guidance information for different users is one of the main research directions for improving user experience.

[0003] The traditional technical solution is to understand user preferences by collecting and analyzing user purchase data and formulate product guidance information based on this data. However, the data source of this technology is relatively single, the data information is relatively one-sided, and there is less attention paid to new products, resulting in the generated product guidance information having poor guidance accuracy for users. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for generating product guidance information to address the above technical problems.

[0005] In a first aspect, the present application provides a method for generating product guidance information, comprising:

[0006] Obtaining product sales information of multiple stores, product information of each store, and feedback information of each store, and identifying, based on the product sales information, the user group type of the applicable user group for each product type, user preference information corresponding to each applicable user group, and group characteristic information of each applicable user group;

[0007] Based on each piece of feedback information and each piece of product information, identifying comprehensive feature information of each product type, and generating first product guidance information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group;

[0008] Based on the first product guidance information of each user group type and the group characteristic information of each user group type, generating research and development guidance information for new products, and collecting guidance feedback information for each first product guidance information and product feedback information for the new products;

[0009] Based on the guidance feedback information of each first product guidance information and the product feedback information of the new product, adjust the first product guidance information and the product feedback information of the new product to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, and use the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information.

[0010] Optionally, the identifying, based on the product sales information, the user group type of the applicable user group for each product type, the user preference information corresponding to each applicable user group, and the group characteristic information of each applicable user group includes:

[0011] For each product sales information, based on the product sales information, identifying user information of the product selling user, product purchase information of the product selling user, and the type of product purchased by the product selling user, and based on the user information and the product purchase information, identifying user demand characteristics of the product selling user and individual user characteristics of the product selling user;

[0012] For each product type, based on the individual user characteristics of each product selling user corresponding to the product type, query the user group type of the applicable user group of the product type in the user group database;

[0013] Based on the user demand characteristics of each product selling user in the applicable user group, the user preference information corresponding to the applicable user group is identified through a demand characteristic analysis network, and based on the user individual characteristics of each product selling user in the applicable user group, the group characteristic information of the applicable user group is identified through a user characteristic clustering algorithm.

[0014] Optionally, the identifying comprehensive feature information of each product type based on each piece of feedback information and each piece of product information includes:

[0015] Splitting the feedback information into user feedback information and employee feedback information;

[0016] Based on the user feedback information, extracting the usage features of each product type through a feature extraction network, and based on the employee feedback information, extracting the sales features of each product type through the feature extraction network;

[0017] Splitting the product information into product groups of each product type, and extracting product features of each product type through the feature extraction network based on the product groups of each product type;

[0018] The usage characteristics of each product of each product type, the sales characteristics of each product of each product type, and the product characteristics of each product type are used as the comprehensive characteristic information of each product type.

[0019] Optionally, generating first product guidance information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group includes:

[0020] For each product type, based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type, a user preference analysis strategy is used to identify first target feature information of interest to the user group type;

[0021] For each user group type, the product type corresponding to the user group type is used as the primary product type, and the product types other than the primary product type in each product type are used as secondary product types;

[0022] Based on the comprehensive feature information of each product type and the first target feature information of the user group type, the feature adaptation network is used to identify the second target feature information of each secondary product type that the user group type is interested in, and based on the first target feature information of the primary product type, the product guide generation template is used to generate primary product guide information of the primary product type;

[0023] Based on the second target feature information of each secondary product type, the secondary product guidance information of each secondary product type is generated through the product guidance generation template, and the main product guidance information of the main product type and the secondary product guidance information of each secondary product type are used as the first product guidance information of the user group type.

[0024] Optionally, generating new product R&D guidance information based on the first product guidance information of each user group type and the group characteristic information of each user group type includes:

[0025] Obtaining research and development demand information for new products, and based on the research and development demand information, identifying the target user group for the new products;

[0026] Extracting target group characteristic information of the user group target, and screening a target user group type from each of the user group types based on the group characteristic information of each user group type and the target group characteristic information of the user group target;

[0027] Based on the first target feature information concerned by each target user group type and the second target feature information concerned by each target user group type, screening key feature information applicable to the new product through a feature screening strategy;

[0028] Based on each of the key feature information, R&D guidance information for the new product is generated through a R&D guidance generation template.

[0029] Optionally, the guidance feedback information of each first product guidance information and each product feedback information of the new product, adjusting each first product guidance information and the product feedback information of the new product to obtain second product guidance information corresponding to each user group type and target R&D guidance information of the new product, includes:

[0030] For each first product guidance information, based on the guidance feedback information of the first product guidance information, extracting each feedback feature information of the first product guidance information through a feature extraction network, and identifying the feedback type corresponding to each feedback feature information;

[0031] Based on the feedback feature information of each feedback type, and using the feedback adjustment strategy of each feedback type, feedback adjustment processing is performed on the first product guidance information to obtain second product guidance information, and the second product guidance information is used as the second product guidance information for the user group type corresponding to the first product guidance information;

[0032] Based on each of the product feedback information, extracting product feedback features corresponding to each of the product feedback information through the feature extraction network, and identifying the target product feature type corresponding to each product feedback feature;

[0033] Based on the product feedback features of each target product feature type, using a clustering algorithm, filter the key feedback features of each target product feature type, and filter the sub-R&D guidance information corresponding to each target product feature type in the R&D guidance information;

[0034] Based on the key feedback features of each target product feature type, the sub-R&D guidance information corresponding to each target product feature type is adjusted to obtain the target R&D guidance information of the new product.

[0035] In a second aspect, the present application further provides a device for generating product guidance information, comprising:

[0036] an acquisition module configured to acquire product sales information of multiple stores, product information of each store, and feedback information of each store, and identify, based on the product sales information, the user group type of the applicable user group for each product type, user preference information corresponding to each applicable user group, and group characteristic information of each applicable user group;

[0037] a generating module configured to identify comprehensive feature information of each product type based on each feedback information and each product information, and generate first product guide information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group;

[0038] a collection module, configured to generate research and development guidance information for new products based on the first product guidance information for each user group type and the group characteristic information for each user group type, and to collect guidance feedback information for each first product guidance information and product feedback information for each new product;

[0039] An adjustment module is used to adjust each first product guidance information and the product feedback information of the new product based on the guidance feedback information of each first product guidance information and the product feedback information of the new product, to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, and to use the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information.

[0040] Optionally, the acquisition module is specifically configured to:

[0041] For each product sales information, based on the product sales information, identifying user information of the product selling user, product purchase information of the product selling user, and the type of product purchased by the product selling user, and based on the user information and the product purchase information, identifying user demand characteristics of the product selling user and individual user characteristics of the product selling user;

[0042] For each product type, based on the individual user characteristics of each product selling user corresponding to the product type, query the user group type of the applicable user group of the product type in the user group database;

[0043] Based on the user demand characteristics of each product selling user in the applicable user group, the user preference information corresponding to the applicable user group is identified through a demand characteristic analysis network, and based on the user individual characteristics of each product selling user in the applicable user group, the group characteristic information of the applicable user group is identified through a user characteristic clustering algorithm.

[0044] Optionally, the generating module is specifically configured to:

[0045] Splitting the feedback information into user feedback information and employee feedback information;

[0046] Based on the user feedback information, extracting the usage features of each product type through a feature extraction network, and based on the employee feedback information, extracting the sales features of each product type through the feature extraction network;

[0047] Splitting the product information into product groups of each product type, and extracting product features of each product type through the feature extraction network based on the product groups of each product type;

[0048] The usage characteristics of each product of each product type, the sales characteristics of each product of each product type, and the product characteristics of each product type are used as the comprehensive characteristic information of each product type.

[0049] Optionally, the generating module is specifically configured to:

[0050] For each product type, based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type, a user preference analysis strategy is used to identify first target feature information of interest to the user group type;

[0051] For each user group type, the product type corresponding to the user group type is used as the primary product type, and the product types other than the primary product type in each product type are used as secondary product types;

[0052] Based on the comprehensive feature information of each product type and the first target feature information of the user group type, the feature adaptation network is used to identify the second target feature information of each secondary product type that the user group type is interested in, and based on the first target feature information of the primary product type, the product guide generation template is used to generate primary product guide information of the primary product type;

[0053] Based on the second target feature information of each secondary product type, the secondary product guidance information of each secondary product type is generated through the product guidance generation template, and the main product guidance information of the main product type and the secondary product guidance information of each secondary product type are used as the first product guidance information of the user group type.

[0054] Optionally, the acquisition module is specifically used to:

[0055] Obtaining research and development demand information for new products, and based on the research and development demand information, identifying the target user group for the new products;

[0056] Extracting target group characteristic information of the user group target, and screening a target user group type from each of the user group types based on the group characteristic information of each user group type and the target group characteristic information of the user group target;

[0057] Based on the first target feature information concerned by each target user group type and the second target feature information concerned by each target user group type, screening key feature information applicable to the new product through a feature screening strategy;

[0058] Based on each of the key feature information, R&D guidance information for the new product is generated through a R&D guidance generation template.

[0059] Optionally, the adjustment module is specifically configured to:

[0060] For each first product guidance information, based on the guidance feedback information of the first product guidance information, extracting each feedback feature information of the first product guidance information through a feature extraction network, and identifying the feedback type corresponding to each feedback feature information;

[0061] Based on the feedback feature information of each feedback type, and using the feedback adjustment strategy of each feedback type, feedback adjustment processing is performed on the first product guidance information to obtain second product guidance information, and the second product guidance information is used as the second product guidance information for the user group type corresponding to the first product guidance information;

[0062] Based on each of the product feedback information, extracting product feedback features corresponding to each of the product feedback information through the feature extraction network, and identifying the target product feature type corresponding to each product feedback feature;

[0063] Based on the product feedback features of each target product feature type, using a clustering algorithm, filter the key feedback features of each target product feature type, and filter the sub-R&D guidance information corresponding to each target product feature type in the R&D guidance information;

[0064] Based on the key feedback features of each target product feature type, the sub-R&D guidance information corresponding to each target product feature type is adjusted to obtain the target R&D guidance information of the new product.

[0065] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0066] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0067] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0068] The above-mentioned method, device and computer equipment for generating product guide information obtain the product sales information of multiple stores, the product information of each store, and the feedback information of each store, and based on the product sales information, identify the user group type of the applicable user group of each product type, the user preference information corresponding to each applicable user group, and the group characteristic information of each applicable user group; based on each feedback information and each product information, identify the comprehensive characteristic information of each product type, and based on the user group type corresponding to each applicable user group, the comprehensive characteristic information of each product type, and the user preference information corresponding to each applicable user group, generate the first product guide information for each user group type. Guidance information; based on the first product guidance information of each user group type and the group characteristic information of each user group type, generate research and development guidance information for new products, and collect guidance feedback information of each first product guidance information and product feedback information of the new products; based on the guidance feedback information of each first product guidance information and the product feedback information of the new products, adjust each first product guidance information and the product feedback information of the new products to obtain the second product guidance information corresponding to each user group type and the target research and development guidance information of the new products, and use the second product guidance information corresponding to each user group type and the target research and development guidance information of the new products as the target product guidance information. This solution, by combining the actual product sales information, product information, and feedback information collected from different stores, comprehensively analyzes the applicable user groups that prefer each product type, and then analyzes the user preference information, user group type, and group characteristic information of each applicable user group, thereby comprehensively analyzing each user from the perspective of different product types, which not only improves the comprehensiveness of user analysis, but also improves the pertinence of each user analysis related to product type. Then, this solution combines the above analysis results to generate first-level product guidance information for each user group type, thereby improving the guidance accuracy and effectiveness of this first-level product guidance information for that user group type, and also enhancing the user experience of each product type. Furthermore, to increase user group attention to new products, this solution comprehensively generates R&D guidance information for the new product based on different users' preferences for different product types and their attention characteristics. This effectively improves the new product's suitability for applicable users and the user experience of the new product for applicable users. This comprehensively improves the guidance accuracy of the generated product guidance information for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only 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.

[0070] Figure 1 1 is a flow chart of a method for generating product guide information in one embodiment;

[0071] Figure 2 A flowchart of an example of generating product guide information in one embodiment;

[0072] Figure 3 is a structural block diagram of a device for generating product guide information in one embodiment;

[0073] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0075] The method for generating product guidance information provided in the embodiment of the present application can be applied to the application environment of generating product guidance information. Among them, the control module can be a terminal, and the terminal can be but not limited to various personal computers, laptops, mid-range computers, etc. Among them, the terminal comprehensively analyzes the applicable user groups that prefer each product type by combining the sales information, product information, and feedback information of each product actually collected by different stores, and then analyzes the user preference information, user group type, and group characteristic information of each applicable user group for each applicable user group, thereby conducting a comprehensive analysis of each user from the perspective of different product types, which not only improves the comprehensiveness of the user analysis, but also improves the pertinence of the analysis of each user in relation to the product type. Then, this solution generates the first product guidance information for each user group type in combination with the above analysis results, thereby improving the guidance accuracy and guidance effect of the first product guidance information for the user group type, and also improving the user experience effect of the user on each product type. Next, to increase user interest in new products, this solution comprehensively generates R&D guidance information for the new product based on different users' preferences for different product types and their attention characteristics. This effectively improves the new product's suitability for the intended users and the user experience of the new product for those intended users. This comprehensively improves the accuracy of the generated product guidance information for users.

[0076] In an exemplary embodiment, Figure 1 As shown, a method for generating product guide information is provided, which is described by taking the method applied to a terminal as an example, and includes the following steps S101 to S104. Among them:

[0077] Step S101, obtain the product sales information of multiple stores, the product information of each store, and the feedback information of each store, and based on the product sales information, identify the user group type of the applicable user group of each product type, the user preference information corresponding to each applicable user group, and the group characteristic information of each applicable user group.

[0078] In this embodiment, the terminal collects product transaction information of each store and product introduction information of each product sold in each store through official accounts, mini-programs, APPs, and offline collection methods, and obtains user feedback information of each user and employee feedback information of each store employee, thereby obtaining product sales information of each store, product information of each store, and feedback information of each store. Then, based on the product sales information, the terminal identifies the user group type of the applicable user group of each product type, the user preference information corresponding to each applicable user group, and the group characteristic information of each applicable user group. Among them, the product transaction information includes transaction records of each product type (including order volume, order user, order user information), etc. Among them, the order user information records the age, occupation, gender, and user historical order information of the order user, and the user group type is the group type corresponding to the user group. The group type is characterized by user tags, that is, the group type of the user group can correspond to one or more user tags. For example, the user group type of the applicable user group corresponding to product A is the type corresponding to the tags "post-90s", office workers, low sugar lovers, and milk tea lovers. The user preference information is the preference information for the product, including the user's various demand characteristics for the product, and the group characteristic information is the user characteristics mainly included in the individual characteristics of each user in the applicable user group. Among them, the mainly included user characteristics are the individual characteristics of the user obtained by clustering the individual characteristics of each user. The specific processing process will be explained in detail later.

[0079] Step S102: Based on each feedback information and each product information, the comprehensive feature information of each product type is identified, and based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group, the first product guidance information for each user group type is generated.

[0080] In this embodiment, the terminal identifies the comprehensive feature information of each product type based on the feedback information and product information. Furthermore, based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group, the terminal generates first product guidance information for each user group type. This first product guidance information includes product guidance information for each product type for that user group type. The specific generation process will be described in detail later.

[0081] Step S103: Generate R&D guidance information for new products based on the first product guidance information of each user group type and the group characteristic information of each user group type, and collect guidance feedback information of each first product guidance information and product feedback information of each new product.

[0082] In this embodiment, the terminal generates R&D guidance information for new products based on the first product guidance information of each user group type and the group characteristic information of each user group type, and collects guidance feedback information of each first product guidance information and product feedback information of each new product. Among them, the R&D guidance information of the new product is the information of each user group type, and the first product guidance information of the target user group type that the new product is mainly aimed at is used as the basis to generate the R&D guidance information of the new product. The specific generation process will be described in detail later. The guidance feedback information of each first product guidance information is the guidance usage information fed back by the applicable user group of the user group type corresponding to each first product guidance information.

[0083] Step S104: Based on the guidance feedback information of each first product guidance information and the product feedback information of each new product, adjust each first product guidance information and the product feedback information of the new product to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, and use the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information.

[0084] In this embodiment, the terminal adjusts each first product guidance information and the product feedback information of each new product based on the guidance feedback information of each first product guidance information and the product feedback information of each new product to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product. The terminal then uses the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information. The specific adjustment process will be described in detail later.

[0085] Based on the above solution, by combining the actual sales information, product information, and feedback information collected from different stores, a comprehensive analysis is conducted on the applicable user groups that prefer each product type. Then, for each applicable user group, the user preference information, user group type, and group characteristic information of each applicable user group are analyzed. This allows for a comprehensive analysis of each user from the perspective of different product types, improving not only the comprehensiveness of the user analysis but also the specificity of the analysis of each user in relation to the product type. Then, based on the above analysis results, this solution generates first product guidance information for each user group type, thereby improving the guidance accuracy and effectiveness of this first product guidance information for that user group type and also improving the user experience of each product type. Furthermore, to enhance user group attention to new products, this solution comprehensively generates R&D guidance information for the new product based on the user preferences and user attention characteristics of different users for different product types, thereby effectively improving the adaptability of the new product to the applicable users and the user experience of the new product for the applicable users. This comprehensively improves the guidance accuracy of the generated product guidance information for users.

[0086] Optionally, based on the sales information of each product, identify the user group type of the applicable user group for each product type, the user preference information corresponding to each applicable user group, and the group characteristic information of each applicable user group, including: for each product sales information, based on the product sales information, identify the user information of the product selling user, the product purchase information of the product selling user, and the type of product purchased by the product selling user, and based on the user information and the product purchase information, identify the user demand characteristics of the product selling user and the user individual characteristics of the product selling user; for each product type, based on the user individual characteristics of each product selling user corresponding to the product type, query the user group type of the applicable user group for the product type in the user group database; based on the user demand characteristics of each product selling user of the applicable user group, identify the user preference information corresponding to the applicable user group through a demand characteristic analysis network, and based on the user individual characteristics of each product selling user of the applicable user group, identify the group characteristic information of the applicable user group through a user characteristic clustering algorithm.

[0087] In this embodiment, for each product sales information, the terminal identifies the user information of the product sales user, the product purchase information of the product sales user, and the type of product purchased by the product sales user based on the product sales information, and identifies the user demand characteristics of the product sales user and the user individual characteristics of the product sales user based on the user information and the product purchase information. The product sales information includes the user identity information of the product sales user (hereinafter referred to as the user) of the purchased product. The terminal then uses the user identity information of the user as the user information of the user, and based on the user identity information of the user, queries the purchase record database to obtain the user's historical purchase record information and the user's product purchase information. The terminal then queries the product database for the product type corresponding to the product purchased by the user based on the product purchased by the user.

[0088] Based on the user's product purchase information, the terminal queries the product database for the key features of each product purchased by the user, and clusters the key features of each product to obtain the key features of the target product. Finally, the terminal uses the key features of the target product as the user's user demand features. The terminal presets a feature extraction strategy, and based on the user information of each user, uses the feature extraction strategy to extract the feature data of each user feature type of each user to obtain the individual user features of each user. The feature extraction strategy includes the user feature types to be extracted, and the correspondence between each user feature type and the information tags in the user information. Then, based on the correspondence, the terminal filters the user information content corresponding to each user feature type to obtain the feature data of each user feature type.

[0089] For each product type, the terminal performs clustering processing based on the individual user characteristics of each product selling user corresponding to the product type to obtain the target user individual characteristics of the user group. Then, the terminal searches the user group database for user tags corresponding to each target user individual characteristic, and uses all user tags as the user group type of the applicable user group for the product type.

[0090] Based on the user demand characteristics of each product sales user in the applicable user group, the terminal uses a demand characteristic analysis network to identify user preference information corresponding to the applicable user group. Furthermore, based on the individual user characteristics of each product sales user in the applicable user group, the terminal uses a user characteristic clustering algorithm to identify group characteristic information for the applicable user group. The demand analysis network is a large language model based on natural language processing technology. Each user demand characteristic must first be converted into text-formatted feature content, and then semantically analyzed by the demand characteristic analysis network to obtain user preference information corresponding to the applicable user group.

[0091] Based on the above solution, by splitting each user's individual characteristics and user information for fine-grained analysis, the user group type, user preference information, and group characteristic information of each applicable user group can be identified, thereby improving the accuracy and comprehensiveness of identification.

[0092] Optionally, based on each feedback information and each product information, comprehensive feature information of each product type is identified, including: splitting each feedback information into each user feedback information and each employee feedback information; extracting each product usage feature of each product type through a feature extraction network based on each user feedback information, and extracting each product sales feature of each product type through a feature extraction network based on each employee feedback information; splitting each product information into product groups of each product type, and extracting each product feature of each product type through a feature extraction network based on the product groups of each product type; using each product usage feature of each product type, each product sales feature of each product type, and each product feature of each product type as comprehensive feature information of each product type.

[0093] In this embodiment, the terminal divides the feedback information into user feedback information and employee feedback information. User feedback information includes feedback on the user experience, product experience, and product recommendations, while employee feedback information includes feedback on the sales status, sales trends, and sales stability of each product. Before feature extraction, each feedback information can be converted into text content.

[0094] Based on user feedback, the terminal uses a feature extraction network to extract usage features for each product type. Based on employee feedback, the terminal also uses a feature extraction network to extract sales features for each product type. The terminal then divides product information into product groups within each product type and, based on these product groups, uses a feature extraction network to extract product features for each product type. Both of these feature extraction networks are large language models based on natural language processing technology.

[0095] Finally, the terminal uses the usage characteristics of each product type, the sales characteristics of each product type, and the product characteristics of each product type as comprehensive characteristic information of each product type.

[0096] Based on the above solution, by comprehensively analyzing the product usage characteristics, product sales characteristics, and product characteristics of each product type, the comprehensive feature information of each product type is determined, thereby improving the comprehensiveness and accuracy of the analysis of each product type.

[0097] Optionally, based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group, first product guidance information for each user group type is generated, including: for each product type, based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type, first target feature information of the user group type is identified through a user preference analysis strategy; for each user group type, the product type corresponding to the user group type is used as the main product type, and the product types other than the main product type in each product type are used as secondary product types; based on the comprehensive feature information of each product type and the first target feature information of the user group type, second target feature information of each secondary product type that the user group type is concerned about is identified through a feature adaptation network, and based on the first target feature information of the main product type, main product guidance information of the main product type is generated through a product guidance generation template; based on the second target feature information of each secondary product type, secondary product guidance information of each secondary product type is generated through a product guidance generation template, and the main product guidance information of the main product type and the secondary product guidance information of each secondary product type are used as the first product guidance information of the user group type.

[0098] In this embodiment, for each product type, the terminal uses a user preference analysis strategy based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type to identify the first target feature information of the user group type. The user preference analysis strategy may be an analysis strategy based on a Kissmetrics analysis program to perform preference analysis.

[0099] For each user group type, the product type corresponding to the user group type is used as the main product type, and the product types other than the main product type in each product type are used as secondary product types.

[0100] Then, based on the comprehensive feature information of each product type and the first target feature information of the user group type, the terminal uses a feature adaptation network to identify the second target feature information of each secondary product type of interest to the user group type. The feature adaptation network is an artificial neural network based on a feature similarity algorithm, which is used to calculate the similarity between each first target feature information and each feature content in each comprehensive feature information. Based on the similarity, the terminal selects the adapted feature content between each first target feature information and each feature content in each comprehensive feature information as the second target feature information.

[0101] Then, based on the first target feature information of the main product type, the terminal generates main product guide information for the main product type using a product guide generation template. The product guide generation template includes a generation strategy for different guide methods for each product type. The terminal generates product guide information for each product type using the target feature information of each product type and the generation strategy for each guide method using a text generation model based on artificial intelligence technology.

[0102] Finally, the terminal generates secondary product guidance information for each secondary product type through a product guidance generation template based on the second target feature information of each secondary product type, and uses the main product guidance information of the main product type and the secondary product guidance information of each secondary product type as the first product guidance information of the user group type.

[0103] Based on the above solution, by analyzing the characteristics that each user group type is adapted to each product type, product guidance information for each product type of different user group types is generated respectively, thereby improving the adaptability of the product guidance information to the user group type and the user experience effect of the user group type on the product guidance information.

[0104] Optionally, based on the first product guidance information of each user group type and the group characteristic information of each user group type, R&D guidance information for new products is generated, including: obtaining R&D demand information of new products, and identifying the target user group for the new products based on the R&D demand information; extracting the target group characteristic information of the user group targets, and screening the target user group types in each user group type based on the group characteristic information of each user group type and the target group characteristic information of the user group targets; screening each key feature information applicable to the new product through a feature screening strategy based on the first target feature information concerned by each target user group type and the second target feature information concerned by each target user group type; generating R&D guidance information for the new product through an R&D guidance generation template based on each key feature information.

[0105] In this embodiment, the terminal obtains R&D demand information for a new product and, based on this R&D demand information, identifies the target user group for the new product. This R&D demand information includes R&D-related information (holidays, events, seasons, corporate plans) and the target user group. The user group can be a company's classification of user groups, and the applicable users of each user group can intersect, overlap, and include each other. For example, the target user group for a new product may be young women who enjoy experiencing new things and have a preference for certain types of flowers or fruits (such as jasmine and lemon).

[0106] The terminal extracts the target group characteristic information of the user group target and, based on the group characteristic information of each user group type and the target group characteristic information of the user group target, selects a target user group type from each user group type. Specifically, the terminal uses a cosine similarity algorithm to calculate the similarity between the group characteristic information of each user group type and the target group characteristic information of the user group target, and selects the user group type with a similarity greater than a threshold as the target user group type.

[0107] Then, based on the first target feature information that each target user group type is concerned about, and the second target feature information that each target user group type is concerned about, the terminal uses a feature screening strategy to screen the key feature information applicable to the new product. Finally, based on each key feature information, the terminal generates R&D guidance information for the new product through an R&D guidance generation template. The feature screening strategy is to cluster each first target feature information and each second target feature information, and the feature information obtained is used as the key feature information. The R&D guidance template includes a guidance generation strategy for the product feature type corresponding to each key feature information. Based on each key feature information, the terminal generates R&D guidance information for the new product through a text generation model using artificial intelligence technology in accordance with the guidance generation strategy for the product feature type corresponding to different key feature information.

[0108] Based on the above solution, we start with the R&D needs of new products, screen target user groups, and conduct feature analysis to determine R&D guidance information for new products. This improves the pertinence and accuracy of R&D guidance information.

[0109] Optionally, the guidance feedback information of each first product guidance information and the product feedback information of each new product are adjusted to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, including: for each first product guidance information, based on the guidance feedback information of the first product guidance information, through the feature extraction network, extracting each feedback feature information of the first product guidance information, and identifying the feedback type corresponding to each feedback feature information; based on the feedback feature information of each feedback type, through the feedback adjustment strategy of each feedback type, feedback adjustment processing is performed on the first product guidance information to obtain the second product guidance information, and The second product guidance information is used as the second product guidance information of the user group type corresponding to the first product guidance information; based on the feedback information of each product, the product feedback features corresponding to each product feedback information are extracted through the feature extraction network, and the target product feature type corresponding to each product feedback feature is identified; based on the product feedback features of each target product feature type, the key feedback features of each target product feature type are screened through the clustering algorithm, and the sub-R&D guidance information corresponding to each target product feature type is screened in the R&D guidance information; based on the key feedback features of each target product feature type, the sub-R&D guidance information corresponding to each target product feature type is adjusted to obtain the target R&D guidance information of the new product.

[0110] In this embodiment, for each first product guidance information, the terminal extracts feedback feature information of the first product guidance information based on the guidance feedback information of the first product guidance information through a feature extraction network, and identifies the feedback type corresponding to each feedback feature information. The feature extraction network is the same as the feature extraction network described above, both of which are large language models based on natural language processing technology. The feedback types include positive feedback and negative feedback.

[0111] Based on the feedback feature information of each feedback type, the terminal performs feedback adjustment processing on the first product guidance information using the feedback adjustment strategy for each feedback type to obtain second product guidance information, and uses the second product guidance information as the second product guidance information for the user group type corresponding to the first product guidance information. The feedback adjustment strategy for each feedback type includes: a positive feedback adjustment strategy that identifies overlapping feature information between the feedback feature information and target feature information in the first product guidance information that matches the feedback feature information, performs feature extraction, and regenerates the product guidance information using a product guidance generation template based on the overlapping feature information to obtain the second product guidance information. A negative feedback adjustment strategy that identifies overlapping feature information between the feedback feature information and target feature information in the first product guidance information that matches the feedback feature information, filters non-overlapping feature information from the target feature information, and regenerates the product guidance information using a product guidance generation template based on the non-overlapping feature information to obtain the second product guidance information.

[0112] Based on the feedback information of each product, the terminal extracts the product feedback features corresponding to each product feedback information through a feature extraction network, and identifies the target product feature type corresponding to each product feedback feature.

[0113] Then, based on the feedback features of each product of each target product feature type, the terminal uses a clustering algorithm to screen the key feedback features of each target product feature type and selects the sub-R&D guidance information corresponding to each target product feature type from the R&D guidance information. Finally, based on the key feedback features of each target product feature type, the terminal adjusts the sub-R&D guidance information corresponding to each target product feature type to obtain the target R&D guidance information for the new product.

[0114] Based on the above solution, guidance adjustments are made through feedback information, which improves the flexibility of guidance information and the efficiency of immediate response to user feedback, thereby comprehensively improving the user experience effect and R&D guidance adaptability of the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product.

[0115] The application also provides an example of generating product guidance information, such as Figure 2 As shown, the specific processing process includes the following steps:

[0116] Step S201: Acquire product sales information of multiple stores, product information of each store, and feedback information of each store.

[0117] Step S202: For each product sales information, based on the product sales information, identify the user information of the product selling user, the product purchase information of the product selling user, and the type of product purchased by the product selling user; and based on the user information and the product purchase information, identify the user demand characteristics of the product selling user and the individual user characteristics of the product selling user.

[0118] Step S203 , for each product type, based on the individual user characteristics of each product selling user corresponding to the product type, the user group type of the applicable user group of the product type is searched in the user group database.

[0119] In step S204, based on the user demand characteristics of each product selling user in the applicable user group, the user preference information corresponding to the applicable user group is identified through the demand characteristic analysis network, and based on the user individual characteristics of each product selling user in the applicable user group, the group characteristic information of the applicable user group is identified through the user characteristic clustering algorithm.

[0120] Step S205, for each product sales information, based on the product sales information, identify the user information of the product selling user, the product purchase information of the product selling user, and the type of product purchased by the product selling user, and based on the user information and product purchase information, identify the user demand characteristics of the product selling user and the individual user characteristics of the product selling user.

[0121] Step S206 , for each product type, based on the individual user characteristics of each product selling user corresponding to the product type, query the user group type of the applicable user group of the product type in the user group database.

[0122] Step S207: Based on the user demand characteristics of each product selling user in the applicable user group, the user preference information corresponding to the applicable user group is identified through the demand characteristic analysis network, and based on the user individual characteristics of each product selling user in the applicable user group, the group characteristic information of the applicable user group is identified through the user characteristic clustering algorithm.

[0123] Step S208 , for each product type, based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type, a user preference analysis strategy is used to identify the first target feature information that the user group type is concerned about.

[0124] Step S209 : for each user group type, the product type corresponding to the user group type is used as the primary product type, and the product types other than the primary product type in each product type are used as secondary product types.

[0125] Step S210, based on the comprehensive feature information of each product type and the first target feature information of the user group type, the feature adaptation network is used to identify the second target feature information of each secondary product type that the user group type is concerned about, and based on the first target feature information of the main product type, the product guide generation template is used to generate the main product guide information of the main product type.

[0126] Step S211, based on the second target feature information of each secondary product type, generates secondary product guidance information for each secondary product type through a product guidance generation template, and uses the main product guidance information of the main product type and the secondary product guidance information of each secondary product type as the first product guidance information of the user group type.

[0127] Step S212: Acquire the R&D demand information of the new product, and identify the target user group for the new product based on the R&D demand information.

[0128] Step S213 , extracting target group characteristic information of the user group target, and filtering the target user group type in each user group type based on the group characteristic information of each user group type and the target group characteristic information of the user group target.

[0129] Step S214 , based on the first target feature information concerned by each target user group type and the second target feature information concerned by each target user group type, key feature information applicable to the new product is screened through a feature screening strategy.

[0130] Step S215 : Based on each key feature information, R&D guidance information of the new product is generated by generating a R&D guidance template.

[0131] Step S216: collecting guidance feedback information of each first product guidance information and product feedback information of each new product.

[0132] Step S217 : for each first product guidance information, based on the guidance feedback information of the first product guidance information, extract each feedback feature information of the first product guidance information through a feature extraction network, and identify the feedback type corresponding to each feedback feature information.

[0133] Step S218: Based on the feedback feature information of each feedback type, feedback adjustment processing is performed on the first product guidance information through the feedback adjustment strategy of each feedback type to obtain second product guidance information, and the second product guidance information is used as the second product guidance information for the user group type corresponding to the first product guidance information.

[0134] Step S219 : Based on the product feedback information, the product feedback features corresponding to the product feedback information are extracted through a feature extraction network, and the target product feature type corresponding to each product feedback feature is identified.

[0135] Step S220, based on the product feedback features of each target product feature type, the key feedback features of each target product feature type are screened by a clustering algorithm, and the sub-R&D guidance information corresponding to each target product feature type is screened in the R&D guidance information.

[0136] Step S221 : Based on the key feedback features of each target product feature type, the sub-R&D guidance information corresponding to each target product feature type is adjusted to obtain target R&D guidance information for the new product.

[0137] Step S222: The second product guidance information corresponding to each user group type and the target R&D guidance information of the new product are used as target product guidance information.

[0138] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of 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 these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0139] Based on the same inventive concept, embodiments of the present application also provide a device for generating product guidance information for implementing the aforementioned method for generating product guidance information. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for generating product guidance information provided below can be found in the aforementioned limitations of the method for generating product guidance information, and will not be further elaborated here.

[0140] In an exemplary embodiment, Figure 3 As shown, a device for generating product guide information is provided, including: an acquisition module 310, a generation module 320, a collection module 330, and an adjustment module 340, wherein:

[0141] The acquisition module 310 is configured to acquire product sales information of multiple stores, product information of each store, and feedback information of each store, and identify the user group type of the applicable user group for each product type, user preference information corresponding to each applicable user group, and group characteristic information of each applicable user group based on the product sales information;

[0142] a generating module 320 for identifying comprehensive feature information of each product type based on each feedback information and each product information, and generating first product guide information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group;

[0143] A collection module 330 is configured to generate R&D guidance information for new products based on the first product guidance information for each user group type and the group characteristic information for each user group type, and to collect guidance feedback information for each first product guidance information and product feedback information for each new product;

[0144] The adjustment module 340 is used to adjust each first product guidance information and the product feedback information of the new product based on the guidance feedback information of each first product guidance information and the product feedback information of the new product, to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, and use the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information.

[0145] Optionally, the acquisition module 310 is specifically configured to:

[0146] For each product sales information, based on the product sales information, identifying user information of the product selling user, product purchase information of the product selling user, and the type of product purchased by the product selling user, and based on the user information and the product purchase information, identifying user demand characteristics of the product selling user and individual user characteristics of the product selling user;

[0147] For each product type, based on the individual user characteristics of each product selling user corresponding to the product type, query the user group type of the applicable user group of the product type in the user group database;

[0148] Based on the user demand characteristics of each product selling user in the applicable user group, the user preference information corresponding to the applicable user group is identified through a demand characteristic analysis network, and based on the user individual characteristics of each product selling user in the applicable user group, the group characteristic information of the applicable user group is identified through a user characteristic clustering algorithm.

[0149] Optionally, the generating module 320 is specifically configured to:

[0150] Splitting the feedback information into user feedback information and employee feedback information;

[0151] Based on the user feedback information, extracting the usage features of each product type through a feature extraction network, and based on the employee feedback information, extracting the sales features of each product type through the feature extraction network;

[0152] Splitting the product information into product groups of each product type, and extracting product features of each product type through the feature extraction network based on the product groups of each product type;

[0153] The usage characteristics of each product of each product type, the sales characteristics of each product of each product type, and the product characteristics of each product type are used as the comprehensive characteristic information of each product type.

[0154] Optionally, the generating module 320 is specifically configured to:

[0155] For each product type, based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type, a user preference analysis strategy is used to identify first target feature information of interest to the user group type;

[0156] For each user group type, the product type corresponding to the user group type is used as the primary product type, and the product types other than the primary product type in each product type are used as secondary product types;

[0157] Based on the comprehensive feature information of each product type and the first target feature information of the user group type, the feature adaptation network is used to identify the second target feature information of each secondary product type that the user group type is interested in, and based on the first target feature information of the primary product type, the product guide generation template is used to generate primary product guide information of the primary product type;

[0158] Based on the second target feature information of each secondary product type, the secondary product guidance information of each secondary product type is generated through the product guidance generation template, and the main product guidance information of the main product type and the secondary product guidance information of each secondary product type are used as the first product guidance information of the user group type.

[0159] Optionally, the acquisition module 330 is specifically configured to:

[0160] Obtaining research and development demand information for new products, and based on the research and development demand information, identifying the target user group for the new products;

[0161] Extracting target group characteristic information of the user group target, and screening a target user group type from each of the user group types based on the group characteristic information of each user group type and the target group characteristic information of the user group target;

[0162] Based on the first target feature information concerned by each target user group type and the second target feature information concerned by each target user group type, screening key feature information applicable to the new product through a feature screening strategy;

[0163] Based on each of the key feature information, R&D guidance information for the new product is generated through a R&D guidance generation template.

[0164] Optionally, the adjustment module 340 is specifically configured to:

[0165] For each first product guidance information, based on the guidance feedback information of the first product guidance information, extracting each feedback feature information of the first product guidance information through a feature extraction network, and identifying the feedback type corresponding to each feedback feature information;

[0166] Based on the feedback feature information of each feedback type, and using the feedback adjustment strategy of each feedback type, feedback adjustment processing is performed on the first product guidance information to obtain second product guidance information, and the second product guidance information is used as the second product guidance information for the user group type corresponding to the first product guidance information;

[0167] Based on each of the product feedback information, extracting product feedback features corresponding to each of the product feedback information through the feature extraction network, and identifying the target product feature type corresponding to each product feedback feature;

[0168] Based on the product feedback features of each target product feature type, using a clustering algorithm, filter the key feedback features of each target product feature type, and filter the sub-R&D guidance information corresponding to each target product feature type in the R&D guidance information;

[0169] Based on the key feedback features of each target product feature type, the sub-R&D guidance information corresponding to each target product feature type is adjusted to obtain the target R&D guidance information of the new product.

[0170] Each module in the aforementioned product guide information generation device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0171] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for generating product guidance information is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0172] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0173] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method for generating product guide information when executing the computer program.

[0174] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for generating product guide information are implemented.

[0175] In one embodiment, a computer program product is provided, including a computer program, which implements the steps of the method for generating product guide information when executed by a processor.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0177] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0178] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for generating product guidance information, characterized in that: The method comprises: Obtaining product sales information of multiple stores, product information of each store, and feedback information of each store, and identifying, based on the product sales information, the user group type of the applicable user group for each product type, user preference information corresponding to each applicable user group, and group characteristic information of each applicable user group; Based on each piece of feedback information and each piece of product information, identifying comprehensive feature information of each product type, and generating first product guidance information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group; Based on the first product guidance information of each user group type and the group characteristic information of each user group type, generating research and development guidance information for new products, and collecting guidance feedback information for each first product guidance information and product feedback information for the new products; Based on the guidance feedback information of each first product guidance information and the product feedback information of the new product, adjust the first product guidance information and the product feedback information of the new product to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, and use the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information.

2. The method according to claim 1, characterized in that The identifying, based on the product sales information, the user group type of the applicable user group for each product type, the user preference information corresponding to each applicable user group, and the group characteristic information of each applicable user group includes: For each product sales information, based on the product sales information, identifying user information of the product selling user, product purchase information of the product selling user, and the type of product purchased by the product selling user, and based on the user information and the product purchase information, identifying user demand characteristics of the product selling user and individual user characteristics of the product selling user; For each product type, based on the individual user characteristics of each product selling user corresponding to the product type, query the user group type of the applicable user group of the product type in the user group database; Based on the user demand characteristics of each product selling user in the applicable user group, the user preference information corresponding to the applicable user group is identified through a demand characteristic analysis network, and based on the user individual characteristics of each product selling user in the applicable user group, the group characteristic information of the applicable user group is identified through a user characteristic clustering algorithm.

3. The method according to claim 1, characterized in that The identifying comprehensive feature information of each product type based on each feedback information and each product information includes: Splitting the feedback information into user feedback information and employee feedback information; Based on the user feedback information, extracting the usage features of each product type through a feature extraction network, and based on the employee feedback information, extracting the sales features of each product type through the feature extraction network; Splitting the product information into product groups of each product type, and extracting product features of each product type through the feature extraction network based on the product groups of each product type; The usage characteristics of each product of each product type, the sales characteristics of each product of each product type, and the product characteristics of each product type are used as the comprehensive characteristic information of each product type.

4. The method according to claim 1, wherein The generating of first product guidance information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group includes: For each product type, based on the user preference information of the user group type corresponding to the product type and the comprehensive feature information of the product type, a user preference analysis strategy is used to identify first target feature information of interest to the user group type; For each user group type, the product type corresponding to the user group type is used as the primary product type, and the product types other than the primary product type in each product type are used as secondary product types; Based on the comprehensive feature information of each product type and the first target feature information of the user group type, the feature adaptation network is used to identify the second target feature information of each secondary product type that the user group type is interested in, and based on the first target feature information of the primary product type, the product guide generation template is used to generate primary product guide information of the primary product type; Based on the second target feature information of each secondary product type, the secondary product guidance information of each secondary product type is generated through the product guidance generation template, and the main product guidance information of the main product type and the secondary product guidance information of each secondary product type are used as the first product guidance information of the user group type.

5. The method according to claim 4, characterized in that The generating of new product R&D guidance information based on the first product guidance information of each user group type and the group characteristic information of each user group type includes: Obtaining research and development demand information for new products, and based on the research and development demand information, identifying the target user group for the new products; Extracting target group characteristic information of the user group target, and screening a target user group type from each of the user group types based on the group characteristic information of each user group type and the target group characteristic information of the user group target; Based on the first target feature information concerned by each target user group type and the second target feature information concerned by each target user group type, screening key feature information applicable to the new product through a feature screening strategy; Based on each of the key feature information, R&D guidance information for the new product is generated through a R&D guidance generation template.

6. The method according to claim 4, characterized in that The guidance feedback information of each first product guidance information and each product feedback information of the new product are adjusted to obtain second product guidance information corresponding to each user group type and target R&D guidance information of the new product, including: For each first product guidance information, based on the guidance feedback information of the first product guidance information, extracting each feedback feature information of the first product guidance information through a feature extraction network, and identifying the feedback type corresponding to each feedback feature information; Based on the feedback feature information of each feedback type, and using the feedback adjustment strategy of each feedback type, feedback adjustment processing is performed on the first product guidance information to obtain second product guidance information, and the second product guidance information is used as the second product guidance information for the user group type corresponding to the first product guidance information; Based on each of the product feedback information, extracting product feedback features corresponding to each of the product feedback information through the feature extraction network, and identifying the target product feature type corresponding to each product feedback feature; Based on the product feedback features of each target product feature type, using a clustering algorithm, filter the key feedback features of each target product feature type, and filter the sub-R&D guidance information corresponding to each target product feature type in the R&D guidance information; Based on the key feedback features of each target product feature type, the sub-R&D guidance information corresponding to each target product feature type is adjusted to obtain the target R&D guidance information of the new product.

7. A device for generating product guidance information, characterized in that: The device comprises: an acquisition module configured to acquire product sales information of multiple stores, product information of each store, and feedback information of each store, and identify, based on the product sales information, the user group type of the applicable user group for each product type, user preference information corresponding to each applicable user group, and group characteristic information of each applicable user group; a generating module configured to identify comprehensive feature information of each product type based on each feedback information and each product information, and generate first product guide information for each user group type based on the user group type corresponding to each applicable user group, the comprehensive feature information of each product type, and the user preference information corresponding to each applicable user group; a collection module, configured to generate research and development guidance information for new products based on the first product guidance information for each user group type and the group characteristic information for each user group type, and to collect guidance feedback information for each first product guidance information and product feedback information for each new product; An adjustment module is used to adjust each first product guidance information and the product feedback information of the new product based on the guidance feedback information of each first product guidance information and the product feedback information of the new product, to obtain the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product, and to use the second product guidance information corresponding to each user group type and the target R&D guidance information of the new product as the target product guidance information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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