Intelligent user classification method and device based on big data analysis and computer equipment

Abstract

The invention relates to a user intelligent classification method and device based on big data analysis and computer equipment. The method comprises the steps of obtaining user transaction information, user interaction information and user basic information of each user, and performing user classification on each user to obtain a user group of each user attribute category; based on the user transaction information and the user interaction information of each user in each user group, through the user evaluation strategy of each user attribute category, respectively evaluating to obtain the user evaluation information of each user, and through a preference identification network, identifying the user preference information of each user; and based on the user evaluation information of each user, identifying the user grade information of each user, and based on the user grade information and the user preference information of each user, adapting the target permission information of each user. By adopting the method, the accuracy of store assessment can be improved.

Description

Technical Field

[0001] The present application relates to the fields of big data analysis and artificial intelligence technology, and in particular to a method, apparatus and computer equipment for intelligent user classification based on big data analysis. Background Art

[0002] In the modern business environment, customer relationship management (CRM) and artificial intelligence (AI) technologies have become critical factors for business success. By collecting, analyzing, and leveraging user data, businesses can better understand user needs, provide personalized services, and improve customer satisfaction and loyalty. Big data analytics technologies provide powerful data processing and analysis capabilities, enabling businesses to extract valuable insights from massive amounts of user data. However, user classification management remains a key research topic in user management. Accurate user classification management can effectively enhance the user experience.

[0003] Traditional user classification methods categorize users into different levels and define the requirements and conditions for each level. However, this method focuses primarily on the amount of investment a user has made in a product and often relies on manual experience. The classification results can be influenced by subjective factors and lack objectivity and accuracy. Furthermore, these methods typically only provide a rough classification of users, failing to provide a more detailed and comprehensive user classification, resulting in low classification accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a user intelligent classification method, device, computer equipment, computer-readable storage medium and computer program product based on big data analysis to address the above technical problems.

[0005] In a first aspect, the present application provides a user intelligent classification method based on big data analysis, comprising:

[0006] Obtaining user transaction information, user interaction information, and basic user information of each user, and classifying each user based on the basic user information to obtain user groups of each user attribute category;

[0007] Based on the user transaction information and user interaction information of each user in each user group, and using the user evaluation strategy of each user attribute category, a quality evaluation process is performed on each user in each user group to obtain user evaluation information of each user, and based on the user transaction information and user interaction information of each user, user preference information of each user is identified through a preference identification network;

[0008] Based on the user evaluation information of each user, identifying the user level information of each user, and adapting the target permission information of each user based on the user level information of each user and the user preference information of each user;

[0009] The user level information corresponding to each of the users and the target authority information of each of the users are used as the target user classification information of each of the users.

[0010] Optionally, the user classification processing is performed on each user based on the basic user information of each user to obtain a user group of each user attribute category, including:

[0011] Split each user's basic information into information data of each information type;

[0012] Based on the information data of each information type of each user, query the user attribute category corresponding to each user in the user database;

[0013] Users of the same user attribute category are divided into the same user group to obtain user groups of each user attribute category.

[0014] Optionally, based on the user transaction information of each user in each user group and the user interaction information of each user in each user group, and using the user evaluation strategy of each user attribute category, quality evaluation processing is performed on each user in each user group to obtain user evaluation information of each user, including:

[0015] In the user evaluation database, query the user evaluation policy corresponding to each user attribute category, and for each user, identify the transaction data of each transaction evaluation type of the user based on the user transaction information of the user;

[0016] Based on the user interaction information of the user, identifying the interaction data of each interaction evaluation type of the user, and based on the transaction data of each transaction evaluation type of the user and the interaction data of each interaction evaluation type of the user, generating a transaction evaluation value for each transaction evaluation type of the user and an interaction evaluation value for each interaction evaluation type of the user by using a user evaluation strategy corresponding to the user attribute category to which the user belongs;

[0017] The transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user are used as the user evaluation information of the user.

[0018] Optionally, the preference identification network includes a transaction preference identification network and an interaction preference identification network, and identifying the user preference information of each user through the preference identification network based on the user transaction information of each user and the user interaction information of each user includes:

[0019] For each user, based on the user's transaction information, identifying the user's transaction data for each product type, and based on the transaction data for each product type, identifying the user's preferred transaction method for each preferred product type through a transaction preference identification network;

[0020] identifying the user's interaction data for each interaction type based on the user interaction information of the user, and identifying the user's preferred interaction mode for each preferred interaction type through an interaction preference identification network based on the interaction data for each interaction type;

[0021] The preferred transaction method for each preferred product type of the user and the preferred interaction method for each preferred interaction type of the user are used as the user preference information of the user.

[0022] Optionally, identifying user level information of each user based on user evaluation information of each user includes:

[0023] For each user, based on the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user, query the user level adaptation database to identify the user service level corresponding to the user;

[0024] Based on the user service level corresponding to the user, querying the user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user through the user authority database;

[0025] The user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user are used as the user level information of the user.

[0026] Optionally, adapting the target authority information of each user based on the user level information of each user and the user preference information of each user includes:

[0027] For each user, based on the user's corresponding user transaction authority range and the user's preferred transaction method for each preferred product type, adapt the user's target transaction authority information for each preferred product type;

[0028] Adapting target interaction permission information for each preferred interaction type of the user based on the user interaction permission range corresponding to the user and the preferred interaction method for each preferred interaction type of the user;

[0029] The target transaction authority information of each preferred product type of the user and the target interaction authority information of each preferred interaction type of the user are used as the target authority information of the user.

[0030] In a second aspect, the present application also provides a user intelligent classification device based on big data analysis, comprising:

[0031] an acquisition module, configured to acquire user transaction information of each user, user interaction information of each user, and basic user information of each user, and classify each user based on the basic user information to obtain user groups of each user attribute category;

[0032] an identification module configured to perform quality evaluation processing on each user in each user group based on the user transaction information and user interaction information of each user in each user group and using a user evaluation strategy for each user attribute category, thereby obtaining user evaluation information of each user, and to identify user preference information of each user based on the user transaction information and user interaction information of each user through a preference identification network;

[0033] an adaptation module, configured to identify user level information of each user based on user evaluation information of each user, and adapt target authority information of each user based on the user level information of each user and user preference information of each user;

[0034] The determination module is configured to use the user level information corresponding to each user and the target authority information of each user as the target user classification information of each user.

[0035] Optionally, the acquisition module is specifically configured to:

[0036] Split each user's basic information into information data of each information type;

[0037] Based on the information data of each information type of each user, query the user attribute category corresponding to each user in the user database;

[0038] Users of the same user attribute category are divided into the same user group to obtain user groups of each user attribute category.

[0039] Optionally, the identification module is specifically configured to:

[0040] In the user evaluation database, query the user evaluation policy corresponding to each user attribute category, and for each user, identify the transaction data of each transaction evaluation type of the user based on the user transaction information of the user;

[0041] Based on the user interaction information of the user, identifying the interaction data of each interaction evaluation type of the user, and based on the transaction data of each transaction evaluation type of the user and the interaction data of each interaction evaluation type of the user, generating a transaction evaluation value for each transaction evaluation type of the user and an interaction evaluation value for each interaction evaluation type of the user by using a user evaluation strategy corresponding to the user attribute category to which the user belongs;

[0042] The transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user are used as the user evaluation information of the user.

[0043] Optionally, the identification module is specifically configured to:

[0044] For each user, based on the user's transaction information, identifying the user's transaction data for each product type, and based on the transaction data for each product type, identifying the user's preferred transaction method for each preferred product type through a transaction preference identification network;

[0045] identifying the user's interaction data for each interaction type based on the user interaction information of the user, and identifying the user's preferred interaction mode for each preferred interaction type through an interaction preference identification network based on the interaction data for each interaction type;

[0046] The preferred transaction method for each preferred product type of the user and the preferred interaction method for each preferred interaction type of the user are used as the user preference information of the user.

[0047] Optionally, the adaptation module is specifically used to:

[0048] For each user, based on the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user, query the user level adaptation database to identify the user service level corresponding to the user;

[0049] Based on the user service level corresponding to the user, querying the user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user through the user authority database;

[0050] The user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user are used as the user level information of the user.

[0051] Optionally, the adaptation module is specifically used to:

[0052] For each user, based on the user's corresponding user transaction authority range and the user's preferred transaction method for each preferred product type, adapt the user's target transaction authority information for each preferred product type;

[0053] Adapting target interaction permission information for each preferred interaction type of the user based on the user interaction permission range corresponding to the user and the preferred interaction method for each preferred interaction type of the user;

[0054] The target transaction authority information of each preferred product type of the user and the target interaction authority information of each preferred interaction type of the user are used as the target authority information of the user.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] The above-mentioned user intelligent classification method, device and computer equipment based on big data analysis obtains the user transaction information, user interaction information and basic user information of each user, and classifies each user based on the basic user information of each user to obtain user groups of each user attribute category; based on the user transaction information of each user in each user group and the user interaction information of each user in each user group, quality evaluation is performed on each user in each user group through the user evaluation strategy of each user attribute category to obtain user evaluation information of each user, and based on the user transaction information and user interaction information of each user, user preference information of each user is identified through a preference identification network; based on the user evaluation information of each user, user level information of each user is identified, and based on the user level information and user preference information of each user, target authority information of each user is adapted; the level information of the user corresponding to each user and the target authority information of each user are used as target user classification information of each user. This solution combines the basic information of different users to classify the attributes of each user, thereby evaluating users of different user attribute categories separately, improving the comprehensiveness and accuracy of user evaluations of different user attribute categories. This avoids the problem of one-sided user evaluations caused by universal user evaluations. Then, this solution combines the user interaction information and user transaction information of different users to identify the preference information of each user. Then, combined with each user's user evaluation information, each user's user preference information, and each user's user level information, it comprehensively classifies the user's adapted user permission information and user level information. This ensures the accuracy of user classification and can flexibly and intelligently control the user permissions of different user service levels, ensuring the control effect of each user, thereby comprehensively improving the accuracy of user classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] 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.

[0060] Figure 1 1 is a flow chart of a method for intelligent user classification based on big data analysis in one embodiment;

[0061] Figure 2 A flowchart of an example of intelligent user classification based on big data analysis in one embodiment;

[0062] Figure 3 This is a structural block diagram of a user intelligent classification device based on big data analysis in one embodiment;

[0063] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] 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.

[0065] The user intelligent classification method based on big data analysis provided in the embodiment of the present application can be applied to the intelligent control system for user intelligent classification based on big data analysis. The system can be applied to terminals, which can be, but not limited to, various personal computers, laptops, mid-range computers, etc. Among them, the terminal classifies the attributes of each user by combining the basic user information of different users, thereby evaluating users of different user attribute categories separately, improving the comprehensiveness and accuracy of user evaluations of different user attribute categories. To avoid the problem of one-sided user evaluation caused by universal user evaluation, this solution then combines the user interaction information and user transaction information of different users to identify the preference information of each user, and then combines the user evaluation information of each user, the user preference information of each user, and the user level information of each user to comprehensively classify the user's adapted user permission information and user level information, thereby ensuring the accuracy of user classification and the flexible and intelligent management and control of user permissions of different user service levels, ensuring the management and control effect of each user, thereby comprehensively improving the accuracy of user classification.

[0066] In an exemplary embodiment, Figure 1 As shown, a user intelligent classification method based on big data analysis is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S101 to S104. Among them:

[0067] Step S101 , obtaining user transaction information, user interaction information, and basic user information of each user, and classifying each user based on the basic user information to obtain user groups of each user attribute category.

[0068] In this embodiment, the terminal, in response to data transmission operations from the intelligent control system, obtains transaction information for each user at different points in time to obtain user transaction information, as well as user interaction information between each user and the intelligent control system. This transaction information includes transaction details for each product type, including transaction volume, transaction type, transaction method, and additional transaction data. For example, when purchasing tea or beverages, the transaction details include the purchase volume of each product type, whether the purchase was made online or offline, whether the purchase was made to order or immediately made, and additional material requirements for each product type. User interaction information includes, but is not limited to, user activity participation information, user discount usage information, user check-in information, user suggestions and evaluation information, and user feedback information. Then, in response to the user's authorization operation, the terminal obtains basic user information, such as age, gender, work unit, position, and job type. Finally, based on each user's basic information, the terminal classifies each user to obtain user groups for each user attribute category. User attribute categories include, but are not limited to, work-related categories, home-based categories, leisure categories, business categories, tourist categories, and sports enthusiast categories.

[0069] In step S102, based on the user transaction information of each user in each user group and the user interaction information of each user in each user group, a quality evaluation process is performed on each user in each user group through a user evaluation strategy of each user attribute category to obtain user evaluation information of each user, and based on the user transaction information of each user and the user interaction information of each user, the user preference information of each user is identified through a preference identification network.

[0070] In this embodiment, the terminal performs a quality evaluation on each user in each user group based on the user transaction information and user interaction information of each user in each user group, using a user evaluation strategy for each user attribute category. This results in user evaluation information for each user. Furthermore, based on each user's user transaction information and user interaction information, the terminal uses a preference identification network to identify each user's user preference information. Different user attribute types use different user evaluation strategies. These user evaluation strategies are used to categorize, evaluate, and analyze each user's transaction data for each transaction evaluation category and interaction data for each interaction evaluation category. The specific evaluation process will be described in detail later.

[0071] Step S103 : identifying the user level information of each user based on the user evaluation information of each user, and adapting the target authority information of each user based on the user level information of each user and the user preference information of each user.

[0072] In this embodiment, the terminal identifies the user level information of each user based on the user evaluation information of each user, and adapts the target authority information of each user based on the user level information of each user and the user preference information of each user. Among them, the user level information of the user is the level content for classifying users, thereby configuring hierarchical authority, and hierarchical management. The user level information includes multiple user service levels, for example, A-level users, B-level users, C-level users, D-level users, E-level users, etc. Among them, the authority of each user service level is, for example, that A-level users enjoy the highest product discounts, priority order sorting, preferred sales methods, priority supply of preferred interactive methods, and optimal reward strength for activity rewards. The authority scope of other levels decreases in turn. Therefore, the target authority information of each user adapted by the terminal is different due to the influence of the user level information and user preference information of different users. The overlapping part of the user level information and user preference information of different users is used as the target authority information of the user.

[0073] Step S104 : Using the user level information corresponding to each user and the target authority information of each user as the target user classification information of each user.

[0074] In this embodiment, the terminal uses the user level information corresponding to each user and the target authority information of each user as the target user classification information of each user.

[0075] Based on the above scheme, by combining the basic information of different users, each user is classified by attributes, so that users of different user attribute categories are evaluated separately, and the comprehensiveness and accuracy of user evaluations of different user attribute categories are improved. To avoid the problem of one-sided user evaluations caused by universal user evaluations, this scheme then combines the user interaction information and user transaction information of different users to identify the preference information of each user. Then, combined with the user evaluation information of each user, the user preference information of each user, and the user level information of each user, the user's adapted user permission information and user level information are comprehensively classified, thereby ensuring the accuracy of user classification and the flexible and intelligent management of user permissions of different user service levels, ensuring the management effect of each user, and thus comprehensively improving the accuracy of user classification.

[0076] Optionally, based on the basic user information of each user, each user is classified to obtain a user group of each user attribute category, including: splitting the basic user information of each user into information data of each information type; based on the information data of each information type of each user, querying the user attribute category corresponding to each user in the user database; dividing users of the same user attribute category into the same user group to obtain user groups of each user attribute category.

[0077] In this embodiment, the terminal divides the basic user information of each user into information data of various information types, wherein the various information types include but are not limited to identity information type, work information type, personality information type, and the like.

[0078] Then, based on the information data of each information type for each user, the terminal searches the user database for the corresponding user attribute category. Finally, the terminal groups users with the same user attribute category into the same user group, thus obtaining user groups for each user attribute category. Each user attribute category in the user database corresponds to an information data range for each information type. Based on the information data range to which the information data for each information type belongs, the terminal selects the user attribute category corresponding to the user.

[0079] Based on the above solution, by splitting and identifying the information data of different users' information types, the user attribute categories corresponding to each user are adapted, thereby improving the accuracy and comprehensiveness of identifying the user attribute categories corresponding to each user.

[0080] Optionally, based on the user transaction information of each user in each user group and the user interaction information of each user in each user group, quality evaluation processing is performed on each user in each user group through the user evaluation strategy of each user attribute category to obtain user evaluation information of each user, including: querying the user evaluation strategy corresponding to each user attribute category in the user evaluation database, and identifying the transaction data of each transaction evaluation type of the user based on the user transaction information of the user; identifying the interaction data of each interaction evaluation type of the user based on the user interaction information of the user, and based on the transaction data of each transaction evaluation type of the user and the interaction data of each interaction evaluation type of the user, generating the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user through the user evaluation strategy corresponding to the user attribute category to which the user belongs; using the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user as the user evaluation information of the user.

[0081] In this embodiment, the terminal searches the user evaluation database for the user evaluation policy corresponding to each user attribute category and, based on the user's transaction information, identifies the user's transaction data for each transaction evaluation type. The transaction evaluation types include, but are not limited to, transaction frequency evaluation types, transaction volume evaluation types, transaction range evaluation types, transaction trend evaluation types, transaction method evaluation types, and transaction type evaluation types.

[0082] Based on the user's user interaction information, the terminal identifies the user's interaction data for each interactive evaluation type. The interactive evaluation types include, but are not limited to, activity evaluation types, discount evaluation types, user feedback evaluation types, product evaluation evaluation types, online attention evaluation types, service support evaluation types (e.g., helping other customers purchase products), and product following evaluation types (following all new products and purchasing most of them).

[0083] Based on the user's transaction data for each transaction evaluation type and the user's interaction data for each interaction evaluation type, the terminal generates a transaction evaluation value for each transaction evaluation type and an interaction evaluation value for each interaction evaluation type using the user evaluation policy corresponding to the user's user attribute category. Each user evaluation policy includes transaction evaluation values corresponding to different transaction data ranges for each transaction evaluation type, as well as interaction evaluation values corresponding to different interaction data for each interaction evaluation type. The terminal selects the user evaluation policy corresponding to each user's user attribute category through range adaptation.

[0084] Finally, the terminal uses the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user as the user evaluation information of the user.

[0085] Based on the above solution, by conducting categorized evaluations on each transaction evaluation type and interaction evaluation type, the user's evaluation information can be identified, thereby improving the comprehensiveness and accuracy of the user evaluation analysis.

[0086] Optionally, the preference identification network includes a transaction preference identification network and an interaction preference identification network. Based on the user transaction information of each user and the user interaction information of each user, the preference identification network is used to identify the user preference information of each user, including: for each user, based on the user transaction information of the user, identifying the user's transaction data for each product type, and based on the transaction data of each product type, identifying the user's preferred transaction method for each preferred product type through the transaction preference identification network; based on the user's user interaction information, identifying the user's interaction data for each interaction type, and based on the interaction data of each interaction type, identifying the user's preferred interaction method for each preferred interaction type through the interaction preference identification network; using the user's preferred transaction method for each preferred product type and the user's preferred interaction method for each preferred interaction type as the user's user preference information.

[0087] In this embodiment, the terminal identifies each user's transaction data for each product type based on the user's transaction information. Based on the transaction data for each product type, the terminal uses a transaction preference identification network to identify the user's preferred transaction method for each preferred product type. This transaction preference identification network, as well as the interactive preference identification network described below, is an attention-based multilayer perceptron (MLP) trained using different sample data. Each preferred product type is the user's preferred product type within each product type, along with the corresponding transaction method. These transaction methods include transaction volume preference, transaction product preference, transaction trend preference, and transaction type preference.

[0088] The terminal then identifies the user's interaction data for each interaction type based on the user's user interaction information. Based on the interaction data for each interaction type, the terminal uses an interaction preference identification network to identify the user's preferred interaction method for each preferred interaction type. The preferred interaction methods include activity reward preference type, product discount preference type, experience preference type, service priority preference type, and additional discount preference type.

[0089] Finally, the terminal takes the user's preferred transaction method for each preferred product type and the user's preferred interaction method for each preferred interaction type as the user's user preference information.

[0090] Based on the above solution, by analyzing and identifying the preferred transaction methods of different users' preferred product types and the preferred interaction methods of different users' preferred interaction types, the user's preference information is identified, thereby improving the comprehensiveness and accuracy of the identification of user preference information.

[0091] Optionally, based on the user evaluation information of each user, the user level information of each user is identified, including: for each user, based on the transaction evaluation value of each transaction evaluation type of the user, and the interaction evaluation value of each interaction evaluation type of the user, querying the user level adaptation database to identify the user's corresponding user service level; based on the user's corresponding user service level, querying the user transaction permission range corresponding to the user's corresponding user service level, and the user interaction permission range corresponding to the user's corresponding user service level through the user permission database; using the user transaction permission range corresponding to the user's corresponding user service level, and the user interaction permission range corresponding to the user's corresponding user service level as the user's user level information.

[0092] In this embodiment, for each user, the terminal queries the user level adaptation database based on the user's transaction evaluation value for each transaction evaluation type and the user's interaction evaluation value for each interaction evaluation type, and identifies the user service level corresponding to the user. The user level adaptation database includes the transaction evaluation value ranges for each transaction evaluation type and the user service level corresponding to the interaction evaluation value ranges for each interaction evaluation type. The terminal uses range adaptation to identify the transaction evaluation value range to which the transaction evaluation value for each transaction evaluation type belongs, as well as the interaction evaluation value range to which the interaction evaluation value for each interaction evaluation type belongs, thereby determining the user service level corresponding to each user.

[0093] Based on the user's corresponding user service level, the terminal queries the user rights database for the user transaction rights range and the user interaction rights range corresponding to the user's corresponding user service level. The terminal then uses the user transaction rights range and the user interaction rights range corresponding to the user's corresponding user service level as the user level information of the user. The user rights database includes the user transaction rights range and the user interaction rights range corresponding to each user service level.

[0094] Based on the above solution, by dividing the user's service level, the user transaction permission range corresponding to the user's corresponding user service level and the user interaction permission range corresponding to the user's corresponding user service level are identified, thereby improving the accuracy and comprehensiveness of the identification of user permission ranges.

[0095] Optionally, based on the user level information of each user and the user preference information of each user, the target authority information of each user is adapted, including: for each user, based on the user's corresponding user transaction authority range and the user's preferred transaction method for each preferred product type, adapting the user's target transaction authority information for each preferred product type; based on the user's corresponding user interaction authority range and the user's preferred interaction method for each preferred interaction type, adapting the user's target interaction authority information for each preferred interaction type; using the user's target transaction authority information for each preferred product type and the user's target interaction authority information for each preferred interaction type as the user's target authority information.

[0096] In this embodiment, the terminal adapts the target transaction permission information for each user's preferred product types based on the user's corresponding user transaction permission range and the user's preferred transaction method for each preferred product type. The user transaction permission range includes the required permission information for the different transaction requirements of each preferred product type adapted by the user, such as product sorting priority permission, product flavor customization permission, product reward configuration permission, product discount permission, and product ingredient discount / gift permission.

[0097] The terminal adapts the user's target interaction permission information for each preferred interaction type based on the user's corresponding user interaction permission range and the user's preferred interaction method for each preferred interaction type. The target interaction permission information includes the user's adapted interaction permission range for each product number interaction type, such as the permission range for event participation quotas, the permission range for event reward types, the permission range for product development guidance, and the permission range for interaction priority.

[0098] The above adaptation method involves the terminal selecting the preferred interaction methods within the user's interaction permission range and the preferred interaction methods for each preferred interaction type based on the user's interaction permission range. This selection serves as the first interaction permission information. Within the user's interaction permission range, the terminal removes interaction permission information that is not the first interaction permission information and corresponds to the user's non-preferred interaction types to obtain the second interaction permission information. The first and second interaction permission information are then used as the target interaction permission information for each preferred interaction type. The adaptation method for each transaction permission information is the same as described above, but the data being adapted differs, and this solution does not provide a redundant description.

[0099] Finally, the terminal uses the target transaction authority information of each preferred product type of the user and the target interaction authority information of each preferred interaction type of the user as the target authority information of the user.

[0100] Based on the above solution, by accurately adapting user permissions to different preferred product types and preferred interaction types, the system identifies the target transaction permission information for each user's preferred product type and the target interaction permission information for each user's preferred interaction type, improving the comprehensiveness and accuracy of user permission adaptation. This ensures efficient user management and user experience after user classification.

[0101] The application also provides an example of user intelligent classification based on big data analysis, such as Figure 2 As shown, the specific processing process includes the following steps:

[0102] Step S201 , obtaining user transaction information of each user, user interaction information of each user, and user basic information of each user.

[0103] Step S202: split the basic user information of each user into information data of each information type.

[0104] Step S203: Based on the information data of each information type of each user, the user attribute category corresponding to each user is searched in the user database.

[0105] Step S204 : users of the same user attribute category are divided into the same user group to obtain user groups of different user attribute categories.

[0106] Step S205 : querying the user evaluation policy corresponding to each user attribute category in the user evaluation database, and identifying the transaction data of each transaction evaluation type of each user based on the user's user transaction information.

[0107] Step S206: Based on the user's user interaction information, identify the user's interaction data of each interaction evaluation type, and based on the user's transaction data of each transaction evaluation type and the user's interaction data of each interaction evaluation type, generate the user's transaction evaluation value of each transaction evaluation type and the user's interaction evaluation value of each interaction evaluation type through the user evaluation strategy corresponding to the user attribute category to which the user belongs.

[0108] Step S207 : The transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user are used as the user evaluation information of the user.

[0109] Step S208 , for each user, based on the user's user transaction information, identifying the user's transaction data for each product type, and based on the transaction data for each product type, identifying the user's preferred transaction method for each preferred product type through a transaction preference identification network.

[0110] Step S209 , based on the user interaction information of the user, identifying the user's interaction data for each interaction type, and based on the interaction data for each interaction type, identifying the user's preferred interaction mode for each preferred interaction type through an interaction preference identification network.

[0111] In step S210 , the user's preferred transaction method for each preferred product type and the user's preferred interaction method for each preferred interaction type are used as the user's preference information.

[0112] Step S211 : for each user, based on the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user, query the user level adaptation database to identify the user service level corresponding to the user.

[0113] Step S212 , based on the user service level corresponding to the user, query the user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user through the user authority database.

[0114] In step S213, the user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user are used as the user level information of the user.

[0115] Step S214 , for each user, based on the user transaction authority range corresponding to the user and the user's preferred transaction method for each preferred product type, adapt the user's target transaction authority information for each preferred product type.

[0116] Step S215 , based on the user interaction permission range corresponding to the user and the user's preferred interaction mode for each preferred interaction type, adapt the target interaction permission information for each preferred interaction type of the user.

[0117] Step S216 , taking the target transaction authority information of each preferred product type of the user and the target interaction authority information of each preferred interaction type of the user as the target authority information of the user.

[0118] 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.

[0119] Based on the same inventive concept, the embodiments of the present application also provide a user intelligent classification device based on big data analysis for implementing the above-mentioned user intelligent classification method based on big data analysis. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more embodiments of the user intelligent classification device based on big data analysis provided below can be referred to the limitations of the user intelligent classification method based on big data analysis above, and will not be repeated here.

[0120] In an exemplary embodiment, Figure 3 As shown, a user intelligent classification device based on big data analysis is provided, including: an acquisition module 310, an identification module 320, an adaptation module 330 and a determination module 340, wherein:

[0121] An acquisition module 310 is configured to acquire user transaction information, user interaction information, and basic user information of each user, and classify each user based on the basic user information to obtain user groups of each user attribute category.

[0122] Identification module 320 is configured to perform quality evaluation processing on each user in each user group based on the user transaction information and user interaction information of each user in each user group and using the user evaluation strategy of each user attribute category to obtain user evaluation information of each user, and identify user preference information of each user based on the user transaction information and user interaction information of each user through a preference identification network;

[0123] The adaptation module 330 is configured to identify the user level information of each user based on the user evaluation information of each user, and adapt the target permission information of each user based on the user level information of each user and the user preference information of each user;

[0124] The determination module 340 is configured to use the user level information corresponding to each user and the target authority information of each user as the target user classification information of each user.

[0125] Optionally, the acquisition module 310 is specifically configured to:

[0126] Split each user's basic information into information data of each information type;

[0127] Based on the information data of each information type of each user, query the user attribute category corresponding to each user in the user database;

[0128] Users of the same user attribute category are divided into the same user group to obtain user groups of each user attribute category.

[0129] Optionally, the identification module 320 is specifically configured to:

[0130] In the user evaluation database, query the user evaluation policy corresponding to each user attribute category, and for each user, identify the transaction data of each transaction evaluation type of the user based on the user transaction information of the user;

[0131] Based on the user interaction information of the user, identifying the interaction data of each interaction evaluation type of the user, and based on the transaction data of each transaction evaluation type of the user and the interaction data of each interaction evaluation type of the user, generating a transaction evaluation value for each transaction evaluation type of the user and an interaction evaluation value for each interaction evaluation type of the user by using a user evaluation strategy corresponding to the user attribute category to which the user belongs;

[0132] The transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user are used as the user evaluation information of the user.

[0133] Optionally, the identification module 320 is specifically configured to:

[0134] For each user, based on the user's transaction information, identifying the user's transaction data for each product type, and based on the transaction data for each product type, identifying the user's preferred transaction method for each preferred product type through a transaction preference identification network;

[0135] identifying the user's interaction data for each interaction type based on the user interaction information of the user, and identifying the user's preferred interaction mode for each preferred interaction type through an interaction preference identification network based on the interaction data for each interaction type;

[0136] The preferred transaction method for each preferred product type of the user and the preferred interaction method for each preferred interaction type of the user are used as the user preference information of the user.

[0137] Optionally, the adaptation module 330 is specifically configured to:

[0138] For each user, based on the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user, query the user level adaptation database to identify the user service level corresponding to the user;

[0139] Based on the user service level corresponding to the user, querying the user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user through the user authority database;

[0140] The user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user are used as the user level information of the user.

[0141] Optionally, the adaptation module 330 is specifically configured to:

[0142] For each user, based on the user's corresponding user transaction authority range and the user's preferred transaction method for each preferred product type, adapt the user's target transaction authority information for each preferred product type;

[0143] Adapting target interaction permission information for each preferred interaction type of the user based on the user interaction permission range corresponding to the user and the preferred interaction method for each preferred interaction type of the user;

[0144] The target transaction authority information of each preferred product type of the user and the target interaction authority information of each preferred interaction type of the user are used as the target authority information of the user.

[0145] Each module in the above-mentioned user intelligent classification device based on big data analysis can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of the processor of the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0146] 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 user intelligent classification method based on big data analysis 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.

[0147] 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.

[0148] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the user intelligent classification method based on big data analysis are implemented.

[0149] 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 user intelligent classification method based on big data analysis are implemented.

[0150] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of a user intelligent classification method based on big data analysis.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 user intelligent classification method based on big data analysis, characterized in that: The method comprises: Obtaining user transaction information, user interaction information, and basic user information of each user, and classifying each user based on the basic user information to obtain user groups of each user attribute category; Based on the user transaction information and user interaction information of each user in each user group, and using the user evaluation strategy of each user attribute category, a quality evaluation process is performed on each user in each user group to obtain user evaluation information of each user, and based on the user transaction information and user interaction information of each user, user preference information of each user is identified through a preference identification network; Based on the user evaluation information of each user, identifying the user level information of each user, and adapting the target permission information of each user based on the user level information of each user and the user preference information of each user; The user level information corresponding to each of the users and the target authority information of each of the users are used as the target user classification information of each of the users.

2. The method according to claim 1, characterized in that The user classification processing is performed on each user based on the basic user information of each user to obtain a user group of each user attribute category, including: Split each user's basic information into information data of each information type; Based on the information data of each information type of each user, query the user attribute category corresponding to each user in the user database; Users of the same user attribute category are divided into the same user group to obtain user groups of each user attribute category.

3. The method according to claim 1, characterized in that The user evaluation information of each user in each user group is obtained by performing a quality evaluation process on each user in each user group based on the user transaction information and the user interaction information of each user in each user group and by using the user evaluation strategy of each user attribute category, including: In the user evaluation database, query the user evaluation policy corresponding to each user attribute category, and for each user, identify the transaction data of each transaction evaluation type of the user based on the user transaction information of the user; Based on the user interaction information of the user, identifying the interaction data of each interaction evaluation type of the user, and based on the transaction data of each transaction evaluation type of the user and the interaction data of each interaction evaluation type of the user, generating a transaction evaluation value for each transaction evaluation type of the user and an interaction evaluation value for each interaction evaluation type of the user by using a user evaluation strategy corresponding to the user attribute category to which the user belongs; The transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user are used as the user evaluation information of the user.

4. The method according to claim 1, wherein The preference identification network includes a transaction preference identification network and an interaction preference identification network. The identification of the user preference information of each user based on the user transaction information of each user and the user interaction information of each user through the preference identification network includes: For each user, based on the user's transaction information, identifying the user's transaction data for each product type, and based on the transaction data for each product type, identifying the user's preferred transaction method for each preferred product type through a transaction preference identification network; identifying the user's interaction data for each interaction type based on the user interaction information of the user, and identifying the user's preferred interaction mode for each preferred interaction type through an interaction preference identification network based on the interaction data for each interaction type; The preferred transaction method for each preferred product type of the user and the preferred interaction method for each preferred interaction type of the user are used as the user preference information of the user.

5. The method according to claim 3, characterized in that The identifying of user level information of each user based on the user evaluation information of each user includes: For each user, based on the transaction evaluation value of each transaction evaluation type of the user and the interaction evaluation value of each interaction evaluation type of the user, query the user level adaptation database to identify the user service level corresponding to the user; Based on the user service level corresponding to the user, querying the user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user through the user authority database; The user transaction authority range corresponding to the user service level corresponding to the user and the user interaction authority range corresponding to the user service level corresponding to the user are used as the user level information of the user.

6. The method according to claim 5, characterized in that Adapting the target authority information of each user based on the user level information of each user and the user preference information of each user includes: For each user, based on the user's corresponding user transaction authority range and the user's preferred transaction method for each preferred product type, adapt the user's target transaction authority information for each preferred product type; Adapting target interaction permission information for each preferred interaction type of the user based on the user interaction permission range corresponding to the user and the preferred interaction method for each preferred interaction type of the user; The target transaction authority information of each preferred product type of the user and the target interaction authority information of each preferred interaction type of the user are used as the target authority information of the user.

7. A user intelligent classification device based on big data analysis, characterized in that: The device comprises: an acquisition module, configured to acquire user transaction information of each user, user interaction information of each user, and basic user information of each user, and classify each user based on the basic user information to obtain user groups of each user attribute category; an identification module configured to perform quality evaluation processing on each user in each user group based on the user transaction information and user interaction information of each user in each user group and using a user evaluation strategy for each user attribute category, thereby obtaining user evaluation information of each user, and to identify user preference information of each user based on the user transaction information and user interaction information of each user through a preference identification network; an adaptation module, configured to identify user level information of each user based on user evaluation information of each user, and adapt target authority information of each user based on the user level information of each user and user preference information of each user; The determination module is configured to use the user level information corresponding to each user and the target authority information of each user as the target user classification information of each user.

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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