Interaction method and apparatus based on artificial intelligence, and device and storage medium

Abstract

An interaction method and apparatus based on artificial intelligence, and a device and a storage medium, which relate to the technical field of intelligent interaction. Interaction information of a user is continuously collected, and on the basis of the interaction information, an interaction profile of the user is constructed using a pre-trained model; on the basis of the interaction profile, analysis is performed to generate a recommended interaction interface and a parsing framework; user interaction data is captured in real time, and behaviors are parsed by means of the parsing framework; on the basis of the parsing result, the user profile is further corrected; and on the basis of the updated user profile, the recommended interaction interface is adjusted. A system can provide more personalized user experience by means of real-time updating, and a dynamically adjusted interaction interface makes the interaction between a user and the system more efficient, thereby solving the problem in the prior art of it being difficult for a user who is not familiar with an intelligent terminal product to efficiently use same.

Description

Artificial intelligence-based interaction method, device, equipment and storage medium TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent interaction, and particularly relates to an artificial intelligence-based interaction method, device, equipment and storage medium. BACKGROUND

[0002] With the development of electronic technology, the functions of intelligent terminal products (such as computers, mobile phones, etc.) are also increasingly complex. For some users who are not familiar with the use, many functions of the intelligent terminal product cannot be used smoothly, which leads to the fact that the user purchases a high-priced high-performance intelligent terminal product, but the result is that the high-end function corresponding to the high price of the product is not used.

[0003] At present, some intelligent terminal products are provided with a recommended function to enable the user to quickly familiarize themselves with the operation mode of the intelligent terminal product. However, the recommended function of such products is relatively fixed, and there are a large number of redundant functions for different users, and some small functions that some users want to use are not integrated into the recommended function.

[0004] SUMMARY

[0005] The present application aims to provide an artificial intelligence-based interaction method, device, equipment and storage medium, and aims to solve the problem that users who are not familiar with intelligent terminal products cannot efficiently use intelligent terminal products.

[0006] The present application is implemented in the following manner. In a first aspect, the present application provides an artificial intelligence-based interaction method, comprising:

[0007] continuously collecting interaction information of an interaction object, and constructing an interaction portrait of the interaction object according to the interaction information through a pre-trained user portrait intelligent model;

[0008] based on the interaction portrait, performing multi-dimensional analysis and processing on the interaction object to obtain a recommended interaction interface and an interaction analysis framework corresponding to the interaction object;

[0009] obtaining real-time interaction information of the interaction object on the recommended interaction interface, performing interaction behavior analysis and processing on the real-time interaction information according to the interaction analysis framework, to modify the interaction portrait of the interaction object, and adjusting the recommended interaction interface based on the modified interaction portrait.

[0010] Preferably, the step of continuously collecting interaction information of an interaction object, and constructing an interaction portrait of the interaction object according to the interaction information through a pre-trained user portrait intelligent model comprises:

[0011] collecting an interaction behavior of the interaction object, recording a type label and a time label corresponding to the interaction behavior, and binding the type label and the time label with the interaction behavior to obtain interaction information of the interaction object;

[0012] performing object feature extraction processing on the interaction information according to a pre-trained user portrait intelligent model to obtain a plurality of object features of the interaction object;

[0013] performing object tracing processing on each of the object features to obtain an object vector group of the interaction object pointed to by each of the object features; wherein the object vector group includes a plurality of object image intervals of the object features pointing to the interaction object and an interval confidence corresponding to each of the object image intervals, the object image interval is used to describe an interaction portrait of the interaction object, and the interval confidence is used to describe a possibility degree of the interaction object corresponding to the object image interval;

[0014] performing integration analysis processing on each of the object vector groups to obtain an overall confidence of each of the object image intervals; wherein the overall confidence is a superposition result of the interval confidence of each of the object vector groups in the object image interval;

[0015] performing judgment processing on the overall confidence of each of the object image intervals according to a confidence standard to exclude the object image intervals not meeting the confidence standard, and taking the object image intervals meeting the confidence standard as an interaction portrait of the interaction object.

[0016] Preferably, the pre-training step of the user portrait intelligent model comprises:

[0017] obtaining a plurality of groups of training data; wherein the training data includes interaction information data and object feature data, the interaction information data is used to describe interaction information of an interaction object, and the object feature data is used to describe object features of the interaction object;

[0018] constructing an input layer, a convolution layer, three fully connected layers, and an output layer;

[0019] substituting each group of the training data into the input layer;

[0020] The input layer receives each set of collected training data and transmits each set of training data to the convolutional layer, which is used to collect features of each set of training data to obtain interactive mapping features of each set of training data; wherein the interactive mapping features are used to describe the mapping relationship between the interactive information data and the object feature data in the training data, and the mapping relationship is used to map the interactive information to obtain the object features corresponding to the interactive information;

[0021] The three-layer fully connected layer is used to perform continuous vector flattening processing on various interactive mapping features extracted by the convolutional layer, so as to flatten various interactive mapping features into one-dimensional vector features; the one-dimensional vector features are used to perform basic graphical expression on various interactive mapping features;

[0022] The output layer is used to output the one-dimensional vector features unfolded by the fully connected layer.

[0023] Preferably, the step of performing multi-dimensional analysis and processing on the interactive objects based on the interactive portraits to obtain a recommended interactive interface and an interactive analysis framework corresponding to the interactive objects comprises:

[0024] Each object image interval of the interactive portrait is analyzed and processed for an object recommendation function to obtain a recommended function corresponding to each object image interval of the interactive portrait and a function attribution label corresponding to each recommended function, and a function priority index of the corresponding recommended function is generated according to the overall confidence of each object image interval;

[0025] Each recommended function is list processed according to the function attribution label and the function priority index of each recommended function to obtain a recommended function list; wherein the recommended function list has a parallel structure and a nested structure, and each recommended function is arranged in the recommended function list in the form of the parallel structure or the nested structure;

[0026] A function link port corresponding to each recommended function is generated; wherein the function link port is used for the interactive object to realize function interaction between the recommended function;

[0027] Each function link port is list processed according to the recommended function list to obtain a recommended interactive interface corresponding to the interactive object;

[0028] The prediction analysis processing is performed based on the interaction with the recommended interaction interface, to obtain several possible interaction behaviors of the interaction object on the recommended interaction interface, and the expansion analysis processing is performed on the interaction portrait according to the possible interaction behaviors, to obtain several portrait modification directions of the interaction portrait; wherein the portrait modification direction is used to describe the modification direction of the interaction portrait of the interaction object.

[0029] The several portrait modification directions of the interaction portrait are collectively used as the interaction analysis framework of the interaction portrait.

[0030] Preferably, the step of obtaining the real-time interaction information of the interaction object on the recommended interaction interface, performing the interaction behavior analysis processing on the real-time interaction information according to the interaction analysis framework, to modify the interaction portrait of the interaction object, and performing the adjustment processing on the recommended interaction interface based on the modified interaction portrait comprises:

[0031] Obtaining the real-time interaction information of the interaction object on the recommended interaction interface;

[0032] Performing the interaction behavior analysis processing on the real-time interaction information according to the interaction analysis framework, to obtain the modification vector of each portrait modification direction corresponding to the real-time interaction information; wherein the modification vector is used to describe the tendency degree of the real-time interaction information on each portrait modification direction;

[0033] Performing the modification processing on the interaction portrait based on the modification vector of each portrait modification direction corresponding to the real-time interaction information;

[0034] Generating several new recommended functions based on the modified interaction portrait, and performing the analysis processing on the list position of each new recommended function based on the recommended function list, to obtain the setting position of each new recommended function in the recommended function list;

[0035] Generating the corresponding new link port according to each new recommended function, and setting each new link port corresponding to each new recommended function in the corresponding position of the recommended interaction interface according to the setting position of each new recommended function in the recommended function list.

[0036] Preferably, the step of performing the interaction behavior analysis processing on the real-time interaction information according to the interaction analysis framework, to obtain the modification vector of each portrait modification direction corresponding to the real-time interaction information comprises:

[0037] According to the respective portrait modification directions of the interaction analysis framework, the real-time interaction information is respectively analyzed in terms of the direction degree to obtain a preliminary vector between the real-time interaction information and each portrait modification direction;

[0038] The preliminary vectors between the real-time interaction information and each portrait modification direction are analyzed in terms of repulsion degree to obtain a repulsion parameter between each preliminary vector; wherein the repulsion parameter is used to describe the mutual repulsion between the direction degrees of each preliminary vector feedback to each portrait modification direction;

[0039] Based on the repulsion parameter between each preliminary vector, the preliminary vectors are adjusted to obtain each modification vector corresponding to the smallest repulsion parameter.

[0040] Preferably, based on the modification vectors of the real-time interaction information corresponding to each portrait modification direction, the step of modifying the interaction portrait comprises:

[0041] Each object image interval feedback by the interaction portrait is taken as a reference interval;

[0042] According to each portrait modification direction, each target interval corresponding to each portrait modification direction is generated based on the reference interval; wherein the target interval is used to describe the object image interval when the modification vector is completely directed to the portrait modification direction;

[0043] According to each modification vector, each target interval is converted to obtain a modified interval corresponding to each modification vector, and the reference interval and each modified interval are analyzed to obtain an intersection interval, and the intersection interval is taken as the modified interaction portrait.

[0044] In a second aspect, the present application provides an interaction device based on artificial intelligence, which is used to realize the interaction method based on artificial intelligence in any one of the first aspect, comprising:

[0045] A portrait construction module is used to continuously collect interaction information of an interaction object, and construct an interaction portrait of the interaction object according to the interaction information through a pre-trained user portrait intelligent model;

[0046] A portrait analysis module is used to analyze and process the interaction object in multiple dimensions based on the interaction portrait to obtain a recommended interaction interface and an interaction analysis framework corresponding to the interaction object;

[0047] An interaction correction module is configured to acquire real-time interaction information of the interaction object on the recommended interaction interface, perform interaction behavior analysis processing on the real-time interaction information according to the interaction analysis framework, correct the interaction portrait of the interaction object, and adjust the recommended interaction interface based on the corrected interaction portrait.

[0048] In a third aspect, the present application provides a computer device, comprising a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the interaction method based on artificial intelligence according to any one of the first aspect when executing the computer program.

[0049] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program makes the processor execute the interaction method based on artificial intelligence according to any one of the first aspect when running on the processor.

[0050] The present application provides an interaction method based on artificial intelligence, which has the following beneficial effects:

[0051] The present application continuously collects interaction information of the user, constructs an interaction portrait of the user according to the interaction information by using a pre-trained model, analyzes based on the interaction portrait, generates a recommended interaction interface and an analysis framework, captures real-time user interaction data, analyzes the behavior by using the analysis framework, further corrects the user portrait according to the analysis result, adjusts the recommended interaction interface according to the updated user portrait, and provides more personalized user experience through real-time updating. The dynamically adjusted interaction interface makes the user and the system interact more efficiently, and solves the problem that users who are not familiar with the intelligent terminal product cannot efficiently use the intelligent terminal product in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0052] Fig. 1 is a step schematic diagram of an interaction method based on artificial intelligence provided by an embodiment of the present application;

[0053] Fig. 2 is a structural schematic diagram of an interaction device based on artificial intelligence provided by an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0055] The implementation of the present application is described in detail below in combination with specific embodiments.

[0056] Referring to Figs. 1 and 2, preferred embodiments of the present application are provided.

[0057] In a first aspect, the present application provides an interactive method based on artificial intelligence, comprising:

[0058] S1: continuously collecting interactive information of an interactive object, and constructing an interactive portrait of the interactive object based on the interactive information through a pre-trained user portrait intelligent model;

[0059] S2: performing multi-dimensional analysis and processing on the interactive object based on the interactive portrait, to obtain a recommended interactive interface and an interactive analysis framework corresponding to the interactive object;

[0060] S3: obtaining real-time interactive information of the interactive object on the recommended interactive interface, performing interactive behavior analysis and processing on the real-time interactive information according to the interactive analysis framework, to modify the interactive portrait of the interactive object, and adjusting the recommended interactive interface based on the modified interactive portrait.

[0061] Specifically, in step S1 of the embodiments provided by the present application, the interactive behavior of the user in the interactive terminal is monitored and recorded in real time, such as clicking, scrolling, browsing, searching, etc., and it should be noted that the monitored items also include the specific interactive content of the user, the number, frequency, order, etc. of various interactive behaviors.

[0062] More specifically, the above-mentioned interactive terminal can be a personal computer, a smart phone, and other smart terminal products that can execute software programs.

[0063] More specifically, data is collected using log systems, event tracking and other monitoring tools, and further useful information can be extracted from the original interactive data as features, which can include user behavior patterns, preferences, etc.

[0064] More specifically, historical data and extracted features are used to train a machine learning model to build a user portrait model, and real-time interactive data is input into the trained user portrait model to generate a real-time user interactive portrait.

[0065] It can be understood that by continuously collecting user interactive information, the intelligent model can more accurately understand the user's behavior and preferences, thereby improving the accuracy of the user portrait. The refinement of the user portrait can help the system to provide more personalized services and content.

[0066] Specifically, in step S2 of the embodiments provided by the present application, the features in the interactive portrait are analyzed from multiple angles, combined with the historical data and real-time interactive data of the user, to predict the user's future possible needs and behaviors.

[0067] More specifically, according to the characteristics in the interaction portrait and the user demand prediction, a personalized interaction interface is designed, and the interaction interface is a port for setting a function link, so that the user can directly use the functions of the interaction terminal through the port, without the need to find the functions through the interface in the original design of the interaction terminal.

[0068] More specifically, a framework is developed for the recommended interaction interface, which can analyze the user's interaction behavior in real time, identify more accurate user portraits from the user feedback from the real-time interaction information in the recommended interaction interface, and continuously optimize the recommended interaction interface and the interaction analysis framework according to the user feedback and interaction data.

[0069] Specifically, in step S3 of the embodiment provided by the present application, the behavior data of the user on the recommended interaction interface is obtained in real time, including the user's browsing and use of the recommended interaction interface.

[0070] More specifically, the real-time data is analyzed by the interaction analysis framework to identify the user's behavior patterns and intentions, and to process and interpret the user's interaction behavior, and the existing interaction portrait is corrected and updated according to the user's real-time interaction information, to ensure that the portrait reflects the latest user preferences and behavior.

[0071] More specifically, according to the corrected interaction portrait, the recommended interaction interface is dynamically adjusted, such as changing the sorting of recommended content, adjusting the layout of UI elements, etc., and at the same time, a user feedback mechanism can be integrated into the interface, such as a rating, a comment or a direct feedback button, so that the user can directly provide opinions on the recommended content or the interface design, and these feedbacks can be used to further refine the user portrait and optimize the interface.

[0072] More specifically, according to the monitoring and analysis results, the interaction analysis framework and the recommended interaction interface are iteratively optimized, and a rapid iteration development process is maintained to ensure the sensitivity and adaptability of the system.

[0073] It can be understood that by monitoring and updating the user portrait in real time, the system can more accurately predict user needs and provide a more personalized interaction experience, and the real-time updated user portrait can more accurately reflect the user's current preferences, thereby improving the accuracy of the recommendation system.

[0074] The present application provides an interaction method based on artificial intelligence, which has the following advantages:

[0075] The application collects the interaction information of the user continuously, constructs the interaction portrait of the user according to the interaction information by using the pre-trained model, analyzes based on the interaction portrait, generates the recommended interaction interface and analysis framework, captures the user interaction data in real time, analyzes the behavior through the analysis framework, further corrects the user portrait according to the analysis result, adjusts the recommended interaction interface according to the updated user portrait, and provides more personalized user experience through real-time updating, so that the dynamic adjustment of the interaction interface makes the user and the system interact more efficiently, and solves the problem that the user who is not familiar with the intelligent terminal product cannot use the intelligent terminal product efficiently.

[0076] Preferably, the step of continuously collecting the interaction information of the interaction object and constructing the interaction portrait of the interaction object according to the interaction information by using the pre-trained user portrait intelligent model comprises:

[0077] S11: Collect the interaction behavior of the interaction object, record the type label and time label corresponding to the interaction behavior, and bind the type label and the time label with the interaction behavior to obtain the interaction information of the interaction object;

[0078] S12: Perform object feature extraction processing on the interaction information according to the pre-trained user portrait intelligent model to obtain a plurality of object features of the interaction object;

[0079] S13: Perform object tracing processing on each object feature to obtain an object vector group of the interaction object pointed by each object feature; wherein the object vector group comprises a plurality of object image intervals of the object feature pointing to the interaction object and an interval confidence corresponding to each object image interval, the object image interval is used to describe an interaction portrait of the interaction object, and the interval confidence is used to describe the possibility degree of the interaction object corresponding to the object image interval;

[0080] S14: Perform integration analysis processing on each object vector group to obtain an overall confidence of each object image interval; wherein the overall confidence is the superposition result of the interval confidence of each object vector group in the object image interval;

[0081] S15: According to the confidence standard, judge each object image interval to exclude the object image interval that does not meet the confidence standard, and take the object image interval that meets the confidence standard as the interaction portrait of the interaction object.

[0082] Specifically, the user's behavior, including clicks, browsing, etc., is monitored and recorded, the captured behavior is time-stamped and type-labeled, ensuring that each behavior has a clear time and type context, the time stamp and type label are bound to the corresponding user behavior, forming structured interaction data.

[0083] More specifically, the interaction data is analyzed using a pre-trained user portrait intelligent model, the object features of the user are extracted, which can include the user's interests, preferences, behavior patterns, etc., each feature is traced back to determine its contribution to the user portrait and form an object vector group, each object feature corresponds to an image interval and a confidence, the image interval describes the user's inclination in a certain aspect, and the confidence represents the probability of the inclination.

[0084] More specifically, all object vector groups are integrated to superimpose the confidence of the image interval to form an overall confidence, this step is the key to synthesizing the user interaction portrait, and the purpose is to form a comprehensive and multi-dimensional representation of user features.

[0085] More specifically, according to the set confidence standard, the overall confidence is evaluated, and those image intervals that do not meet the standard are eliminated to ensure that the final user interaction portrait only includes features with high confidence, and all image intervals that meet the confidence standard are integrated into the user's interaction portrait, which represents the user's behavior patterns and preferences, and is used for subsequent personalized recommendation and interaction interface adjustment.

[0086] It can be understood that through detailed behavior tracking and feature extraction, the user portrait constructed is more comprehensive and can more accurately reflect the user's real needs and preferences, and accurate user portraits can significantly improve the relevance of the recommendation system, thereby improving user satisfaction and participation, and the user interaction interface can be adjusted in real time according to the user portrait to provide more personalized and attractive user experience.

[0087] Preferably, the pre-training step of the user portrait intelligent model comprises:

[0088] S121: Obtain a plurality of groups of training data; wherein the training data includes interaction information data and object feature data, the interaction information data is used to describe the interaction information of the interaction object, and the object feature data is used to describe the object features of the interaction object;

[0089] S122: Construct an input layer, a convolution layer, three fully connected layers, and an output layer;

[0090] S123: Substitute each group of training data into the input layer;

[0091] S124: The input layer receives each set of collected training data and transmits each set of training data to the convolutional layer, which is used to collect features of each set of training data to obtain the interaction mapping features of each set of training data; wherein the interaction mapping features are used to describe the mapping relationship between the interaction information data and the object feature data in the training data, and the mapping relationship is used to map the interaction information to obtain the object features corresponding to the interaction information;

[0092] S125: The three-layer fully connected layer is used to perform continuous vector flattening processing on various interaction mapping features extracted by the convolutional layer, so as to flatten various interaction mapping features into one-dimensional vector features; the one-dimensional vector features are used to perform basic graph expression on various interaction mapping features;

[0093] S126: The output layer is used to output the one-dimensional vector features unfolded by the fully connected layer.

[0094] Specifically, a plurality of sets of interaction information data and object feature data are prepared, which should represent the interaction behavior of the user and the corresponding features.

[0095] More specifically, a neural network model architecture including an input layer, a convolutional layer, three layers of fully connected layers, and an output layer is designed, each layer is designed with a specific function and connection mode, and the training data is input into the input layer, which needs to be preprocessed, such as normalization.

[0096] More specifically, in the convolutional layer, the input data is feature extracted to obtain the interaction mapping features, and the filter of the convolutional layer is used to identify the local features and patterns in the data, and the three-layer fully connected layer further abstracts and integrates the features extracted by the convolutional layer, which maps the high-dimensional features into one-dimensional vectors for the processing of the output layer.

[0097] More specifically, the output layer is responsible for outputting the one-dimensional vector features unfolded by the fully connected layer, and these feature vectors will be used for the next step of interaction portrait construction or other related tasks.

[0098] It can be understood that the convolutional layer can effectively extract local features and patterns in the interaction data, improve the understanding of the model to the user behavior, and the fully connected layer converts high-dimensional features into one-dimensional vectors, so that the features can be comprehensively represented in the final user portrait. Through the training of a large amount of data, the model can be better generalized to unseen data, improving the accuracy of prediction and classification. The model can construct a more detailed and accurate user portrait based on complex data relationships. Compared with the traditional feature engineering method, this automatic feature extraction method can obtain more accurate feature expression more quickly.

[0099] Preferably, the step of performing multi-dimensional analysis on the interactive object based on the interactive portrait to obtain a recommended interactive interface and an interactive analysis framework corresponding to the interactive object comprises:

[0100] S21: performing analysis on each of the object image intervals of the interactive portrait to obtain a recommended function corresponding to each of the object image intervals of the interactive portrait and a function attribution label corresponding to each of the recommended functions, and generating a function priority index of the corresponding recommended function according to the overall confidence of each of the object image intervals;

[0101] S22: performing list processing on each of the recommended functions according to the function attribution label and the function priority index of each of the recommended functions to obtain a recommended function list; wherein the recommended function list has a parallel structure and a nested structure, and each of the recommended functions is arranged in the recommended function list in the form of the parallel structure or the nested structure;

[0102] S23: generating a corresponding function link port according to each of the recommended functions; wherein the function link port is used for the interactive object to realize function interaction between the recommended function;

[0103] S24: performing list processing on each of the function link ports according to the recommended function list to obtain a recommended interactive interface corresponding to the interactive object;

[0104] S25: performing prediction analysis on the interactive behavior based on the recommended interactive interface to obtain a plurality of possible interactive behaviors of the interactive object on the recommended interactive interface, and performing expansion analysis on the interactive portrait according to each of the possible interactive behaviors to obtain a plurality of portrait modification directions of the interactive portrait; wherein the portrait modification direction is used to describe the modification direction of the interactive portrait of the interactive object;

[0105] S26: taking each of the portrait modification directions of the interactive portrait as an interactive analysis framework of the interactive portrait.

[0106] Specifically, different object image intervals in the user portrait are analyzed, a corresponding function is recommended for each interval, a function attribution label is assigned, and a function priority index is generated based on the confidence of the object image interval.

[0107] More specifically, the recommended functions are sorted and listed according to the attribution label and the priority index to form a recommended function list having a parallel structure and a nested structure.

[0108] More specifically, a corresponding function link port is generated for each recommended function, through which the interactive function can be directly realized, avoiding the need for users to find and use these functions according to the inherent design of the interactive terminal in traditional design. According to the recommended function list, the function link port list is listed, thereby designing a recommended interactive interface for the user.

[0109] More specifically, by predicting and analyzing the interaction behavior of the recommended interface, the possible interaction behavior of the user is inferred, and the user portrait is further analyzed and corrected according to these predictions and guidance, the portrait correction is directed to integration, forming an interaction analysis framework to guide the correction direction of the user interaction portrait.

[0110] It can be understood that personalized functions are recommended according to the specific portrait of the user, a customized interactive interface is created for the user portrait, the intuitiveness and ease of use of the interface are improved, and the user experience is enhanced.

[0111] At the same time, the interaction analysis framework corresponding to the recommended interactive interface can further analyze and react to the interaction behavior of the user on the recommended interactive interface, provide a direction for continuous optimization of the user portrait, and dynamically update the user portrait as the user behavior changes.

[0112] Preferably, the step of acquiring real-time interaction information of the interaction object on the recommended interactive interface, performing interaction behavior analysis processing on the real-time interaction information according to the interaction analysis framework, performing correction processing on the interaction portrait of the interaction object, and performing adjustment processing on the recommended interactive interface based on the corrected interaction portrait comprises:

[0113] S31: Acquire real-time interaction information of the interaction object on the recommended interactive interface;

[0114] S32: Perform interaction behavior analysis processing on the real-time interaction information according to the interaction analysis framework to obtain a correction vector corresponding to each portrait correction direction of the real-time interaction information; wherein the correction vector is used to describe the tendency degree of the real-time interaction information on each portrait correction direction;

[0115] S33: Based on the correction vector corresponding to each portrait correction direction of the real-time interaction information, perform correction processing on the interaction portrait;

[0116] S34: Based on the corrected interaction portrait, generate a plurality of new recommended functions, and perform list position analysis processing on each new recommended function based on the recommended function list to obtain the setting position of each new recommended function in the recommended function list;

[0117] S35: generating a corresponding new link port according to each of the new recommended functions, and setting each of the new link ports corresponding to each of the new recommended functions in the recommended interaction interface at a corresponding position according to the setting position of each of the new recommended functions in the recommended function list.

[0118] Specifically, real-time interaction data of users on the recommended interaction interface is collected, and the real-time interaction information is analyzed by using an interaction analysis framework to determine the degree of inclination of the real-time interaction information in each portrait modification direction, i.e., a modification vector.

[0119] More specifically, the interaction portrait of the user is updated and modified in real time according to the modification vector, a new recommended function is generated according to the modified user portrait, the appropriate position of the new recommended function in the existing recommended function list is analyzed and determined, a corresponding new link port is created for the new recommended function, and these ports are arranged in the recommended interaction interface according to the position in the function list.

[0120] It can be understood that through real-time interaction data analysis, the user portrait is dynamically updated to reflect the latest preferences and behaviors of the user, and the recommended interaction interface can be adjusted according to the dynamically updated user portrait to better meet the individual needs of the user. By modifying the user portrait, the system can more accurately predict user needs and thus provide more tailored function recommendations. According to the importance of the new function and user needs, the layout of the recommended interaction interface is optimized to improve the convenience of user operation. Through continuous interaction analysis and interface adjustment, the system aims to provide a more smooth and intuitive user experience.

[0121] Preferably, the step of analyzing the real-time interaction information according to the interaction analysis framework to obtain a modification vector corresponding to each of the portrait modification directions comprises:

[0122] S321: analyzing the real-time interaction information according to each of the portrait modification directions of the interaction analysis framework to obtain a preliminary vector between the real-time interaction information and each of the portrait modification directions;

[0123] S322: analyzing the repulsion degree between the preliminary vectors to obtain repulsion parameters between each of the preliminary vectors; wherein the repulsion parameters are used to describe the mutual repulsion between the direction degrees of each of the preliminary vectors fed back to each of the portrait modification directions;

[0124] S323: based on the repulsion parameters between each of the preliminary vectors, performing vector adjustment processing on each of the preliminary vectors to obtain each of the modification vectors corresponding to the smallest repulsion parameters.

[0125] Specifically, for each portrait modification direction defined in the interaction analysis framework, real-time interaction information is analyzed, and preliminary vectors representing the degree of these directions are generated.

[0126] More specifically, the repulsion between different preliminary vectors, i.e., whether the user behavior tendencies represented by two or more vectors are mutually exclusive or conflicting, is analyzed, and repulsion parameters are calculated, which describe the degree of repulsion between the user behavior tendencies represented by the respective preliminary vectors.

[0127] More specifically, the preliminary vectors are adjusted according to the repulsion parameters to reduce the mutual exclusivity between the vectors, resulting in final modified vectors with minimized repulsion parameters.

[0128] It can be understood that by considering the repulsion between behavior tendencies, more accurate user behavior modification vectors can be generated, improving the accuracy of user portraits, and the analysis of repulsion parameters and the adjustment of vectors can help improve the decision-making efficiency of the recommendation system, reduce false recommendations, and the adjusted modified vectors can better reflect the real preferences of users, thus providing a more personalized user experience.

[0129] Preferably, the step of modifying the interaction portrait based on the modified vectors corresponding to each portrait modification direction according to the real-time interaction information comprises:

[0130] S331: Taking each of the object image intervals fed back by the interaction portrait as a reference interval;

[0131] S332: Generating a corresponding target interval for each portrait modification direction based on the reference interval; wherein the target interval is used to describe the object image interval when the modified vector is completely directed to the portrait modification direction;

[0132] S333: Converting each target interval according to each modified vector to obtain a modified interval corresponding to each modified vector, performing intersection analysis on the reference interval and each modified interval to obtain an intersection interval, and taking the intersection interval as the modified interaction portrait.

[0133] Specifically, the existing object image intervals in the interaction portrait are taken as the reference intervals, which represent the current portrait state of the user.

[0134] More specifically, according to the portrait modification direction, the reference interval is used as the starting point to generate a series of target intervals, which represent the ideal state that the user portrait should reach if the modified vector is completely directed to a certain specific portrait modification direction.

[0135] More specifically, the target interval is converted using the correction vector to obtain a corrected interval, which involves scaling, offsetting or other transformations to reflect the changes in the user portrait under the influence of the correction vector.

[0136] More specifically, the intersection of the reference interval and all correction intervals is analyzed to determine the common part of these intervals, i.e. the intersection interval, which represents the user portrait after considering all correction factors. The intersection interval obtained by analysis is taken as the corrected interaction portrait, which more accurately reflects the user's current interaction situation and preferences.

[0137] It can be understood that through this process, the user's interaction portrait can be continuously updated to reflect the user's latest behavior and preferences in real time. A more accurate user portrait can improve the matching degree of the recommendation system, thereby improving the relevance of recommended content and user satisfaction.

[0138] Referring to FIG. 2, in a second aspect, the present application provides an interaction device based on artificial intelligence, which is used to implement the interaction method based on artificial intelligence of any one of the first aspect, comprising:

[0139] The portrait construction module is configured to continuously collect interaction information of the interaction object, and construct an interaction portrait of the interaction object based on the interaction information through a pre-trained user portrait intelligent model.

[0140] The portrait analysis module is configured to analyze and process the interaction object based on the interaction portrait in multiple dimensions to obtain a recommended interaction interface and an interaction analysis framework corresponding to the interaction object.

[0141] The interaction correction module is configured to obtain real-time interaction information of the interaction object on the recommended interaction interface, analyze and process the real-time interaction information based on the interaction analysis framework to correct the interaction portrait of the interaction object, and adjust the recommended interaction interface based on the corrected interaction portrait.

[0142] In this embodiment, the specific implementation of each module in the above device embodiment is described above in the method embodiment, and will not be described here.

[0143] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor implements the interaction method based on artificial intelligence of any one of the first aspect when executing the computer program.

[0144] In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, causes the processor to perform the interaction method based on artificial intelligence according to any one of the first aspect.

[0145] The above only shows the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An artificial intelligence-based interaction method, characterized in that, include: Continuously collect interaction information of interactive objects, and construct an interaction profile of the interactive object based on the interaction information using a pre-trained user profile intelligent model; Based on the interaction profile, the interaction object is analyzed and processed in multiple dimensions to obtain the recommended interaction interface and interaction parsing framework corresponding to the interaction object. The system acquires real-time interaction information of the interactive object on the recommended interactive interface, performs interactive behavior analysis processing on the real-time interaction information according to the interaction parsing framework, corrects the interaction profile of the interactive object, and adjusts the recommended interactive interface based on the corrected interaction profile.

2. The artificial intelligence-based interaction method as described in claim 1, characterized in that, The steps of continuously collecting interaction information of interactive objects and constructing an interaction profile of the interactive objects based on the interaction information using a pre-trained user profile intelligent model include: Collect the interaction behavior of the interactive object, and record the type tag and time tag corresponding to the interaction behavior. Bind the type tag and the time tag to the interaction behavior to obtain the interaction information of the interactive object. Based on a pre-trained user profile intelligent model, the interaction information is processed to extract the object features of the interaction object, so as to obtain several object features of the interaction object. Each of the object features is subjected to object tracing processing to obtain an object vector group of the interactive object pointed to by each object feature; wherein, the object vector group includes several object image intervals of the interactive object pointed to by the object features and interval confidence of each object image interval, the object image interval is used to describe an interactive profile of the interactive object, and the interval confidence is used to describe the probability of the interactive object corresponding to the object image interval. The object vector groups are integrated and analyzed to obtain the overall confidence level of each object image interval; wherein the overall confidence level is the result of the superposition of the interval confidence levels of each object vector group in the object image interval; The overall confidence level of each object image interval is judged according to the confidence criteria to exclude object image intervals that do not meet the confidence criteria, and the object image intervals that meet the confidence criteria are collectively used as the interactive profile of the interactive object.

3. The artificial intelligence-based interaction method as described in claim 2, characterized in that, The pre-training steps of the user profile intelligent model include: Acquire several sets of training data; wherein, the training data includes interaction information data and object feature data, the interaction information data is used to describe the interaction information of the interactive object, and the object feature data is used to describe the object features of the interactive object; Construct an input layer, a convolutional layer, three fully connected layers, and an output layer; Substitute the training data from each group into the input layer; The input layer receives the collected training data from each group and transmits the training data to the convolutional layer. The convolutional layer is used to collect features from each group of training data to obtain the interaction mapping features of each group of training data. The interaction mapping features are used to describe the mapping relationship between the interaction information data and the object feature data in the training data. The mapping relationship is used to perform mapping processing on the interaction information to obtain the object features corresponding to the interaction information. The three fully connected layers are used to perform continuous vector flattening on the various interaction mapping features extracted by the convolutional layers, so as to flatten the various interaction mapping features into one-dimensional vector features; the one-dimensional vector features are used to perform basic graphical representation of the various interaction mapping features. The output layer is used to output the one-dimensional vector features expanded from the fully connected layer.

4. The artificial intelligence-based interaction method as described in claim 2, characterized in that, The steps of performing multi-dimensional analysis and processing on the interactive object based on the interactive profile to obtain the recommended interactive interface and interactive parsing framework corresponding to the interactive object include: The object recommendation function is analyzed and processed for each object image interval of the interactive profile to obtain the recommendation function and the function belonging tag of each recommendation function for each object image interval of the interactive profile. The function priority index of the corresponding recommendation function is generated according to the overall confidence level of each object image interval. Based on the function affiliation tags and function priority indices of each of the recommended functions, each of the recommended functions is listed to obtain a list of recommended functions; wherein, the list of recommended functions has a parallel structure and a nested structure, and each of the recommended functions is set in the list of recommended functions in the form of the parallel structure or the nested structure; A corresponding function link port is generated based on each of the recommended functions; wherein, the function link port is used to enable the interactive object to perform functional interaction with the recommended functions; Based on the recommended function list, each function link port is listed to obtain the recommended interactive interface for the corresponding interactive object; Based on the recommended interactive interface, predictive analysis of interactive behavior is performed to obtain several possible interactive behaviors of the interactive object on the recommended interactive interface. Based on the various possible interactive behaviors, the interactive profile is further analyzed to obtain several profile correction directions of the interactive profile. The profile correction direction is used to describe the correction direction of the interactive profile of the interactive object. The various modification points of the interactive profile are collectively used as the interactive parsing framework of the interactive profile.

5. The artificial intelligence-based interaction method as described in claim 4, characterized in that, The steps of obtaining real-time interaction information of the interactive object on the recommended interactive interface, performing interaction behavior analysis processing on the real-time interaction information according to the interaction parsing framework to correct the interaction profile of the interactive object, and adjusting the recommended interactive interface based on the corrected interaction profile include: Obtain the real-time interaction information of the interactive object on the recommended interactive interface; The real-time interaction information is processed by the interaction parsing framework to obtain the correction vectors for each of the portrait correction directions corresponding to the real-time interaction information; wherein, the correction vectors are used to describe the degree of tendency of the real-time interaction information in each of the portrait correction directions. Based on the correction vectors corresponding to the real-time interaction information for each of the portrait correction directions, the interactive portrait is corrected. Based on the corrected interactive profile, several new recommendation functions are generated, and the list position of each new recommendation function is analyzed based on the recommendation function list to obtain the setting position of each new recommendation function in the recommendation function list. Based on each of the newly added recommendation functions, corresponding new link ports are generated, and based on the setting position of each of the newly added recommendation functions in the recommendation function list, each of the newly added link ports corresponding to each of the newly added recommendation functions is set in the corresponding position in the recommendation interaction interface.

6. The artificial intelligence-based interaction method as described in claim 5, characterized in that, The steps of performing interactive behavior parsing processing on the real-time interactive information according to the interactive parsing framework to obtain the correction vectors corresponding to each of the portrait correction points for the real-time interactive information include: Based on each of the portrait correction directions in the interaction parsing framework, the real-time interaction information is analyzed and processed to determine the degree of directionality, so as to obtain a preliminary vector between the real-time interaction information and each of the portrait correction directions. The repulsion degree between the real-time interactive information and the preliminary vectors of each of the portrait correction directions is analyzed to obtain the repulsion parameter between each of the preliminary vectors; wherein, the repulsion parameter is used to describe the mutual exclusivity between the degree of direction of each of the preliminary vectors toward each of the portrait correction directions. Based on the repulsion parameter between the initial vectors, vector adjustment processing is performed on each initial vector to obtain each corrected vector corresponding to the smallest repulsion parameter.

7. The artificial intelligence-based interaction method as described in claim 5, characterized in that, The steps for correcting the interactive profile based on the correction vectors corresponding to each profile correction point in the real-time interaction information include: The various object image ranges fed back by the interactive profile are used as the baseline ranges; Based on the baseline interval, corresponding target intervals are generated according to each of the image correction directions; wherein, the target interval is used to describe the object image interval when the correction vector completely points to the image correction direction; Each of the target intervals is transformed according to each of the aforementioned correction vectors to obtain the corresponding... The correction intervals of the correction vectors are analyzed by performing intersection analysis on the base interval and each of the correction intervals to obtain the intersection interval, and the intersection interval is used as the interaction profile after correction processing.

8. An interactive device based on artificial intelligence, characterized in that, A method for implementing an AI-based interaction method according to any one of claims 1-7, comprising: The profile building module is used to continuously collect interaction information of interactive objects and build an interaction profile of the interactive objects based on the interaction information through a pre-trained user profile intelligent model. The profile analysis module is used to perform multi-dimensional analysis and processing on the interactive object based on the interactive profile, and obtain the recommended interactive interface and interactive analysis framework corresponding to the interactive object. The interaction correction module is used to obtain the real-time interaction information of the interactive object on the recommended interaction interface, perform interaction behavior analysis processing on the real-time interaction information according to the interaction parsing framework, correct the interaction profile of the interactive object, and adjust the recommended interaction interface based on the corrected interaction profile.

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements an artificial intelligence-based interaction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when run by a processor, causes the processor to execute an artificial intelligence-based interaction method as described in any one of claims 1-7.

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