1784 results about "Current user" patented technology

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The invention discloses a distributed trusted virtual human identity authentication system and method based on a block chain, and the system comprises a distributed digital identity system which is used for obtaining user entity identity information registration data, verifying the identity of a user entity, and carrying out the digital identity creation of a virtual human corresponding to a current user entity, generating a distributed digital identity of the target virtual person, and supporting query verification and authorization management of the digital identity of the virtual person; the virtual human digital identity registration system is used for registering a distributed digital identity of a target virtual human corresponding to the current user according to target digital identity registration information obtained when the virtual human digital identity is created; and the digital certificate authentication center is used for responding to the digital identity certificate request of the target virtual person and respectively sending a target digital identity certificate corresponding to the identity of the target virtual person to the distributed digital identity system and the virtual person digital identity registration system, so that the problems of virtual person identity determination and right confirmation are effectively solved.

The invention discloses a database management method and management system, and relates to the technical field of general database management, and the system comprises a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis prediction module, a multi-terminal integration module and a safety guarantee module. In the invention, a role-based access control permission model is constructed in a permission control layer and a module definition, and a field-level data control mechanism is implemented for three roles of a platform administrator, a customer enterprise and an employee, for example, the employee can only check a specific public field in a salary list, and sensitive fields such as a salary calculation formula are dynamically shielded; through a permission inheritance mechanism, the system automatically adds a filtering condition that employee ID = current user ID for a query request of an employee role, data isolation is ensured, a multi-dimensional data relation graph of employee-post-customer-contract is constructed, and a dynamic association path between entities is mapped in real time through a visual view.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intention analysis and strategy generation method, device, equipment and medium, and the method comprises the steps: obtaining interaction data, recognizing an interaction type according to metadata features, selecting a corresponding analysis model to analyze and process data, and generating a text corpus; current user information is extracted from the text corpus, user intention score analysis is executed based on an intention analysis strategy corresponding to the interaction type, and a current user intention score is generated; and generating a demand list according to the user information, the intention score and the interaction type, and generating and outputting an interaction strategy based on the user information and the demand list. Through interaction data processing and automatic intention scoring analysis, the demand list and the interaction strategy of the customer can be quickly and accurately generated, the customer information processing efficiency and accuracy are improved, the workload of manual input and analysis is reduced, and the customer service quality and the response speed are improved.

The invention discloses a virtual historical character dialogue method and system with role knowledge and context awareness, and relates to the technical field of man-machine interaction, and the method comprises the steps: constructing a multi-level role depth model; when a question of a current user is received, identifying information of a virtual scene where the current user is located, analyzing micro-expressions of the face of the user and voice rhythm characteristics of speech of the user, analyzing an emotional state and an interaction intention of the user based on a multi-modal fusion algorithm, and generating a user state vector; executing a dynamic Prompt construction program, extracting related information from the multi-level role depth model and the user state vector, and generating a structured Prompt; and inputting the structured Prompt into a large language model, generating a reply text conforming to role features based on questions of the current user, and driving a virtual character model. The method solves the problem that in the prior art, virtual historical figures cannot provide real immersion and credible interactive experience with emotional connection.

The invention discloses an AI-based intelligent marketing advertisement putting method and system, and the method comprises the steps: collecting and fusing heterogeneous user behavior data streams from a plurality of independent platforms in real time, and generating a user behavior feature map with a unified space-time mark; on the basis of the user behavior characteristic spectrum, a deep auto-encoder and a space-time diagram convolutional network are used for joint modeling, and fine-grained scene-decoupled user interest preference vectors are generated; inputting the user interest preference vector into a pre-trained generative adversarial network, and dynamically generating a personalized advertisement material highly matched with the current user interest preference and scene; and taking the personalized advertisement material as a candidate arm, and dynamically generating an advertisement putting strategy including an advertisement display form, a display opportunity and a display channel in combination with a current user state and historical putting feedback data. By utilizing the embodiment of the invention, the accuracy and the real-time performance of advertisement putting can be improved, and the advertisement conversion rate and the user experience are improved.

The invention relates to the technical field of Web front-end development, and discloses a front-end intelligent menu caching and dynamic authority control method. The method comprises the following steps: firstly, checking whether menu cache data in a local storage of a browser exists and is valid, if so, directly using the cache data, otherwise, obtaining latest menu data from a server and caching according to a preset strategy; the menu data change state of the server side is monitored in a timed polling or WebSocket mode, and the local cache is updated in time when the data change; acquiring role permission information of the current user, matching the role permission information with the menu data, and dynamically generating an accessible menu tree; when routing jump is carried out, the type of a target address is intelligently judged, permission verification is carried out on an internal routing address, and it is ensured that a user can only access pages within the authorization range of the user. According to the application, the server request frequency is reduced, the response speed of the front-end application is improved, and meanwhile, the real-time performance of the menu data and the access security are ensured.

Techniques for determining relevant and personalized supplemental content for a user are described. A system may prompt a language model to generate queries based on an entity (or entities) representing a user's affinity. In some examples, the language model may be prompted to generate an initial query corresponding to the entity and then generate one or more enriched queries based on the initial query. The one or more enriched queries may be used to search one or more content providers for supplemental content results. The supplemental content results may be aggregated, may be validated to ensure correspondence to the enriched query, and may be stored for future output. A system component may request supplemental content for output, at which time the system may select one or more supplemental content from storage, where the selection may be made to optimize relevancy for current user and situational context.

The invention provides a context compression method based on multi-round dialogue intention graph construction, which comprises the following steps: S1, dialogue data acquisition and preprocessing: carrying out natural language processing on each dialogue unit; s2, intention atlas construction is achieved through node design and edge design, and each node comprises original text content and structured semantic information; s3, carrying out context compression and graph structure cutting, and only retaining sub-graphs forming a core semantic link; s4, dynamic context management and topic jump processing: in a multi-topic dialogue, when a user jumps or switches to a new topic, a system records a sub-graph of a current active topic, and contextual nodes of an inactive topic are frozen; s5, context sequence generation and model input: linearizing node contents in the cut sub-graph according to a dependent link sequence to generate a compressed context sequence, and transmitting the context sequence and current user input to a large language model for reasoning; and S6, continuous updating and feedback optimization are carried out.

The invention discloses a network security defense system based on collaborative intrusion detection, which relates to the technical field of network security and comprises a behavior data acquisition module, an anomaly analysis module, a behavior evaluation module, a joint risk evaluation module and a security defense module. The behavior data acquisition module is used for acquiring related access behavior information of a user; the anomaly analysis module is used for analyzing the user login time and the deviation degree of the fingerprint of the current login equipment; the behavior evaluation module is used for analyzing the deviation degree of the user access behavior to judge whether the current user access behavior is abnormal or not, and sending an access risk analysis instruction; the joint risk assessment module is used for analyzing the risk degree of the abnormal internal access behavior of the current user; and the security defense module is used for judging whether the abnormal internal access behavior of the current user has a risk or not so as to obtain and execute a security defense instruction of a corresponding level.

The invention provides a user dialogue generation method and system based on memory fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a user memory knowledge base based on a preset knowledge graph tool; based on the user memory knowledge base, reading current text data input by the current dialogue user and historical text data of the current dialogue user; obtaining short-term memory data, long-term memory data and scene memory data based on the user memory knowledge base, a preset large language model, a preset cue word engineering algorithm, the current text data and the historical text data; and generating a user dialogue based on the preset large language model, the preset cue word engineering algorithm, the short-term memory data, the long-term memory data and the scene memory data. According to the invention, the memory range of the user from recent interaction to long-term important information is fully covered, and the reliability of responding to the user is improved.

The invention discloses a resource mining method based on big data, and the method comprises the steps: receiving a retrieval picture keyword of a current user, updating the historical behavior data and picture resource data of the user in real time, carrying out the clustering of the retrieval keyword of the user, and carrying out the classification of the retrieval keyword. The method comprises the following steps: acquiring a retrieval keyword, adding a corresponding retrieval spatio-temporal index for the retrieval keyword, capturing the dynamic change of the retrieval intention of a user, analyzing the feature preference of the user and the current retrieval intention of the user based on historical behavior data and the retrieval picture keyword, and retrieving a related picture from a picture resource library. The method comprises the following steps: obtaining a picture, carrying out feature extraction and classification on the picture, carrying out intention scoring analysis, carrying out descending order arrangement display on screened picture resources based on an intention scoring result, feeding back a retrieval result based on a user, and optimizing a retrieval model. According to the method, the problems of low user intention matching accuracy and picture resource waste are solved.

The invention relates to the technical field of augmented reality application, and discloses an augmented reality system and method for immersive experience. The method comprises the following steps: collecting dynamic spatial data of a physical environment in real time, and synchronously obtaining an interactive behavior data stream of a user terminal; performing environment semantic modeling on the dynamic spatial data to generate multi-level feature description of a physical environment, and determining a rendering priority sequence of virtual-real superposition content according to the multi-level feature description; extracting behavior pattern characteristics in historical interaction data of the user, performing immersion correlation analysis on the behavior pattern characteristics to obtain environment cognitive preference parameters of the current user, and generating a virtual-real interaction event sequence in combination with the environment cognitive preference parameters and the interaction behavior data flow; and dynamically compensating the virtual-real synchronization error in the immersive experience process according to the rendering priority sequence and the virtual-real interaction event sequence. According to the method, the accuracy and naturalness of virtual-real fusion can be improved, the personalized requirements of the user are met, and the overall immersive experience effect is enhanced.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a search query from a user during a current user session; generating a query embedding for the search query; determining one or more entity embeddings with similarity scores that satisfy a threshold, the one or more entity embeddings including keywords associated with entities; filtering the one or more entity embeddings based on filtering criteria; generating a mapping for the search query based on the one or more entity embeddings, the mapping including an association between keywords in the search query and an entity; and modifying a graphical user interface to display items associated with the entity in an upper portion of the graphical user interface during the current user session. Other embodiments are disclosed herein.

The present application relates to the field of artificial intelligence. Disclosed are a large model inference method and apparatus, and a device and a storage medium. The method comprises: performing permission verification on login information; acquiring an inference request, symmetrically encrypting data to be inferred that is in the inference request, and encrypting a symmetric encryption key and then storing same in a remote authentication service; performing permission verification on the current user by means of an online inference service system, and issuing the inference request to an online inference engine, wherein the online inference engine and the remote authentication service run in a TDX trusted execution environment; and performing legitimacy verification on the inference request by means of the online inference engine, applying for the symmetric encryption key from the remote authentication service, using said data to perform inference, and encrypting an obtained initial inference result. The protection of a model itself is realized on the basis of a TDX technique, such that the model runs in a trusted execution environment, thereby greatly reducing risks such as model stealing or attack, and realizing protection of user-side data.

The invention relates to the technical field of voice semantics, and discloses an information interaction method and device based on multi-agent collaboration, equipment and a medium, and the method comprises the steps: generating a target workflow through a large language model and a current user demand; determining a target AP I according to the target workflow, and generating initial interaction information; and generating current reply evaluation information based on the initial interaction information and the criticizing agent, and determining the initial interaction information as target interaction information under the condition that the current matching degree is greater than or equal to a matching degree threshold value. Through the above mode, the user demand is split through the routing agent and the built-in large language model, the target workflow is generated, and the method adapts to complex and changeable task scenes. According to the method, the target AP I is determined through the induction agent, and the initial interaction information is generated, so that the efficiency and accuracy of calling the AP I are improved, and the performance of an automatic business process system for processing complex interaction information in the fields of financial science and technology, medical treatment, health, old-age care and the like is improved.

The invention discloses a multi-modal news recommendation method and system in combination with clock interests, and relates to the technical field of data mining and recommendation methods.The method comprises the steps that a user behavior sequence, a candidate news set and a related timestamp set are extracted; obtaining the multi-modal coding representation of the historical interaction news through the time perception multi-modal feature coding; performing clock interest modeling based on multi-modal coding representation of historical interactive news, and fusing long and short-term interests through Gaussian weighted aggregation and long-term interest enhancement operation to obtain a user interest vector; and calculating the matching degree of the candidate news and the current user interest vector, applying time period sensitive suppression through time sequence gating, performing click probability prediction on the output of the time sequence gating by adopting a linear network, and performing a dynamic recommendation decision. According to the method, by means of the feature fusion technology of time context perception, the interest continuity of the hour granularity is captured, and the recommended content is more accurately matched with the requirements of the user in different time periods.

A user interface change event is detected. An app identifier of an associated third party app is determined. A dynamic rule data structure associated with the app identifier of the associated third party app is determined. A user interface match state specified by the dynamic rule data structure is determined based on absolute screen positioning or relative screen positioning. A current user interface state of the associated third party app is determined by querying an accessibility API. Upon determining that the user interface match state matches the current user interface state, at least one user interface report field specified by the dynamic rule data structure is determined and at least one report field property of the at least one user interface report field is extracted by querying the accessibility API. An active session data structure associated with the dynamic rule data structure is updated to store the extracted property value.

The invention relates to a hot topic chatting method, system and device based on an intelligent agent and a storage medium. The method comprises the steps of extracting key intention information through a large language model according to current input data and historical dialogue data; constructing a topic vector database and automatically updating; retrieval is performed based on cosine similarity, and hot topics are screened out; the hot topics are integrated into a Prompt template, an auxiliary tool is called in real time to obtain auxiliary information, and a large model is used for generating reply content based on the Prompt template according to the current input data and the auxiliary information; and storing the current input data and reply contents, and updating the historical dialogue data. Personalized emotional appeals of a user are analyzed according to data input by the user through a large language model, key intention information is extracted, a topic vector database is constructed, topics most related to current user input are screened out, and by means of auxiliary tools, the real-time performance of hot topics can be guaranteed, and emotional resonance with the user can be enhanced.

The invention belongs to the field of computer control systems, and provides an interaction control method and system based on smart home equipment, and the method comprises the steps: extracting the context information of a current user when there is missing control parameters, the context information comprising time information, a spatial position, an environment state and a user identity; constructing a control context map of the current user; searching a neighborhood user set similar to the behavior portrait characteristics of the current user, and extracting control parameter records of the neighborhood users in the context similar to the current user; performing weighted fusion on the historical control parameter of the current user and the control parameter of the neighborhood user to generate a target control parameter; forming a complete control command, and issuing the complete control command to the target smart home device to execute a control operation; and updating the control context map based on user feedback or a system monitoring result.

The invention relates to the technical field of information pushing, in particular to a platform user interest recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring user action data and environment perception data, analyzing the user action data and the environment perception data, and constructing a unified space-time semantic network; according to the unified space-time semantic network, analyzing scene features of the user, and determining a multi-dimensional dynamic scene feature set; and analyzing the multi-dimensional dynamic scene feature set, determining composite scene demand information of the current user, and retrieving and pushing real-time demand information of the user according to the composite scene demand information. According to the method and the device, the perception capability of the pushed information to the user demand scene is improved, the multi-dimensional dynamic adaptation of the pushed information to the platform user is realized, the information pushing accuracy for the platform user is improved, and the composite information demand of the user in a complex scene is met.

The invention relates to the technical field of data processing, and discloses an adaptive VAD parameter adjustment method and system based on voiceprint recognition, and the method comprises the steps: obtaining an audio signal through an audio collection device, carrying out the preprocessing of the audio signal, extracting voiceprint features, mapping the voiceprint features to a user voiceprint model, and carrying out the recognition of the voiceprint model; comparing the voiceprint model of the current user with a storage model in a voiceprint library, and judging whether the current user is a registered user or not; determining a VAD parameter according to whether the VAD parameter is a registered user; when it is judged that the user is a non-registered user, VAD parameters are adjusted in real time through feature fluctuation detection of a mute segment and a voice segment; and storing the adjusted VAD parameters in a voiceprint library. The VAD parameter adjusting method can adapt to different user characteristics and environment changes, and shows higher robustness and adaptability in a complex scene.

The invention belongs to the technical field of intelligent recommendation, and relates to a multi-modal knowledge dynamic recommendation method and system based on knowledge enhancement. The method comprises the following steps: acquiring a multi-modal feature tensor of a project; fusing the multi-modal feature tensors in combination with the attention coefficient of the modal to obtain a multi-modal feature fusion vector; generating an entity embedding vector according to the domain knowledge graph and the graph attention network; obtaining a current user interest state according to the multi-modal feature fusion vector and the entity embedding vector, and obtaining interest prediction according to the current user interest state, the multi-modal feature fusion vector and the bias vector in combination with the weight matrix and the nonlinear activation function; if the interest prediction value is greater than or equal to a self-adaptive recommendation threshold value, recommending items to the user, otherwise, not recommending the items; and generating a recommended path explanation according to the recommendation results of the plurality of items. According to the method, through knowledge-guided multi-modal semantic fusion, the accuracy and interpretability of long-tail content recommendation are remarkably improved.

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

A user analysis method and system based on big data, a product and a medium relate to the field of commodity data processing, and the method comprises the following steps: obtaining a specific operation sequence of a current user in a current full-decreasing sales promotion scene, extracting an order-on decision behavior feature in the specific operation sequence according to a preset user behavior analysis rule, and obtaining a decision-on decision behavior feature of the order-on decision behavior feature; the order-on decision behavior feature comprises an abnormal shopping behavior; on the basis of the make-on decision behavior characteristics, calculating a make-on possibility score for each commodity purchased by the user, determining a recommendation weight of each commodity according to the make-on possibility score, and when the make-on possibility score of a target commodity exceeds a preset score threshold value, determining that the target commodity is a recommended commodity; and reducing the recommendation weight of the target commodity in the current full-reduction sales promotion scene to a preset weight. By implementing the method, the accuracy of the e-commerce platform to push commodities to the user can be improved.

The invention provides a data access method and device based on a cloud control platform and electronic equipment, and relates to the technical field of data security and privacy protection.The method comprises the steps that to-be-accessed data is obtained, a current user role, a current operation mode and a current data object type are input to a preset rule function, and a first desensitization algorithm is obtained, processing the to-be-accessed data by using a first desensitization algorithm to obtain a first desensitization result, and sending the first desensitization result to the current user role; and under the condition that the first desensitization algorithm cannot be output by utilizing the preset rule function, determining a target weight, determining a second desensitization algorithm according to a feature interval where the target weight is located in the preset rule table, processing the to-be-accessed data by utilizing the second desensitization algorithm to obtain a second desensitization result, and sending the second desensitization result to the current user role. Dynamic desensitization is carried out on the sensitive data, so that the data protection requirements in different scenes are met, and the data security of the third-party application in the cloud control platform is ensured.

Embodiments described herein are directed to systems and methods for modifying issue groups for a graphical user interface of an issue tracking platform. An issue dashboard graphical user interface can include multiple sets of issue tickets, where each set of issue tickets is grouped in accordance with a queue definition. A prompt can be generated and include predetermined prompt text including a recommendation criteria, data extracted from the multiple sets of issue tickets including an issue title and assignee, and data associated with a current user account authenticated with respect to the frontend application. A group modification interface can be displayed and include at least a portion of a generative response. In response to a user input, a new issue ticket group can be created by performing a structured query on issues managed by the issue tracking platform and saving the structured query as a new queue definition.

The embodiment of the invention discloses a data replication method and device for a PostgreSQL master-slave database and a medium, relates to the technical field of database management and is used for solving the problems that existing data replication is poor in flexibility and low in efficiency. The method comprises the steps of receiving a WAL log record sent by a PostgreSQL main library through a preset data preprocessing layer, judging a WAL log version of the WAL log record, analyzing the WAL log record according to a log format corresponding to the WAL log version, and converting analyzed data into a corresponding SQL statement; obtaining a to-be-copied database and a to-be-copied data table specified by a current user, and matching the SQL statements one by one based on a corresponding filtering rule to filter the SQL statements; and re-packaging the filtered SQL statement to obtain a reconstructed WAL log stream, and sending the reconstructed WAL log stream to the PostgreSQL slave library.

The invention discloses a user portrait construction method, system and device based on cross-modal alignment, and the method comprises the steps: carrying out the feature extraction of a plurality of modal data of the same user, and obtaining a plurality of feature vector data corresponding to the modal data; sequentially mapping each piece of feature vector data into the constructed shared semantic space to obtain an embedded vector matched with each piece of feature vector data; all the embedded vectors are processed through a cross-modal attention mechanism, processing results are fused to obtain cross-modal fusion data of each user, and the processing process comprises the steps that the weight of each embedded vector of each user is updated through a comparison loss function, and based on the entity relation extracted from the cross-modal fusion data, the cross-modal fusion data of each user are obtained; constructing a knowledge graph corresponding to the current user; and inputting the knowledge graph into the constructed user portrait generation model to obtain the user portrait of the current user. According to the method provided by the embodiment of the invention, the accurate user portrait can be constructed.

The embodiment of the invention discloses a scene map navigation method and device based on a knowledge base and a medium, and the method comprises the steps: receiving a task instruction of a current user based on a preset language large model, so as to judge whether the task instruction is associated with an existing instruction of a preset knowledge base; if not, acquiring the current location of the robot corresponding to the task instruction, and performing target search on the scene map according to the current location to determine the room to which the target object belongs; if yes, based on the current positioning of the robot corresponding to the existing instruction and the task instruction, performing target search to determine the room to which the target object belongs; and receiving the current location of the robot, the scene map and the task instruction to perform task planning to generate an action sequence corresponding to the task instruction, so as to execute navigation of each target position point in the action sequence based on a preset function packet in the scene map.