1377 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 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.

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 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.

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.

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.

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 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.

The invention discloses a user load prediction method and device based on secondary clustering, and relates to the field of power load prediction, and the method comprises the steps: dividing the historical user load data of a to-be-predicted power system into a plurality of groups of first classification data based on a first clustering algorithm; based on a second clustering algorithm, performing secondary division on the multiple groups of first classification data to obtain multiple groups of second classification data; performing iterative training on the initial prediction model according to the multiple groups of second classification data, optimizing model parameters of the initial prediction model during iteration, and completing training until the model parameters meet a preset model parameter threshold value to obtain a load prediction model; wherein the initial prediction model is constructed based on a stacked bidirectional neural network; and inputting current user load data of a to-be-predicted power system into the load prediction model to obtain a load prediction result. According to the invention, the user load prediction accuracy of the power system can be improved.

The invention provides a seat adjusting method and a vehicle, and relates to the technical field of vehicles, the method comprises the steps that information of a current user on a target seat is acquired, and the information of the current user comprises a target body type feature vector and identification information of the current user; obtaining an initial seat adjustment parameter based on the target body type feature vector, and obtaining a seat adjustment parameter of the current user based on the identification information; adjusting the initial seat adjustment parameter based on the seat adjustment parameter to obtain a first seat adjustment parameter of the current user; and adjusting the target seat based on the first seat adjustment parameter. According to the method, the intelligence of seat adjustment can be improved, so that the driving experience of a user is improved.

The invention relates to a user portrait recognition method and a website recommendation method based on semantic analysis, and the user portrait recognition method comprises the steps: dividing a website which is browsed by a current user in a first preset time period and relates to the same category into a plurality of sub-categories, and taking each sub-category as a point of a portrait; and for each sub-category, determining the sub-category as a current sub-category, determining other sub-categories as adjacent sub-categories, constructing edges, pointing to all the adjacent sub-categories, of the current sub-category, and taking a correlation coefficient, pointing to the adjacent sub-categories, of the current sub-category as a weight of the edges to form a user portrait. Consumption habits of consumers and relevance among different commodities are fully considered, on the basis, recommendation weights of websites are corrected layer by layer through browsing information and consumption information of current users in combination with browsing information and consumption information of group users, personalized and high-accuracy website recommendation schemes are provided for the users, and user experience is improved. And the user experience is improved.

The invention discloses a semantic response method and system based on a large model and a knowledge interface, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize reliable answering and analysis in a factual question and answer scene. Storing the standardized description and the vectorized description of the function description of each knowledge interface into a Milvus vector database; matching related knowledge interfaces from the Milvus vector database to serve as candidate interfaces based on user intentions for current user input serving as first questions, and taking knowledge interfaces corresponding to associated historical dialogues as candidate interfaces of the current user input for current user input serving as non-first questions; extracting information corresponding to the required parameters from the current user input, filling the extracted information into an interface document of the candidate interface, and verifying the filled candidate interface; structured data is returned based on the knowledge interface, the structured data is generated into a natural language answer through a large language model, and a data source is attached.

The invention discloses a commodity recommendation method and system based on agent and hotspot information collaboration, and relates to the technical field of intelligent recommendation, and the method comprises the steps: obtaining structured hotspot data, commodity dynamic information and current user data in parallel through a hotspot information collection tool, a commodity information collection tool and a user information collection tool; performing multi-source information fusion based on a large language model, and generating a structured recommendation demand including a target user, a hotspot association direction, a commodity constraint condition, a user preference matching point and a recommendation priority; and matching and screening the commodities meeting the requirements, and performing multi-dimensional priority ranking according to the hotspot association closeness, the user preference matching degree, the commodity cost performance and the inventory sufficiency to generate an optimized commodity recommendation list. According to the method, semantic comprehension and information fusion are carried out based on a large language model through deep collaboration of hotspot information and user requirements, and the accuracy, timeliness and adaptability of commodity recommendation are improved.

The invention relates to the technical field of smart home, and discloses a smart home control method and device for realizing user interaction based on a smart mirror, and the method comprises the steps: confirming a plurality of user registration instructions, obtaining a registration voice stream based on the user registration instructions, carrying out the feature extraction of the registration voice stream, obtaining reference voiceprint features, summarizing the reference voiceprint features, and carrying out the collection of the reference voiceprint features. The method comprises the following steps: obtaining a plurality of reference voiceprint features, confirming to receive a pre-confirmed voice wake-up word, carrying out voice collection based on the voice wake-up word to obtain a user voice stream, obtaining a voiceprint label by using the user voice stream and the plurality of reference voiceprint features, carrying out voice analysis on the user voice stream to obtain a control instruction, and sending the control instruction to a server. And performing permission verification by using the voiceprint tag and the control instruction to obtain a permission verification result, and obtaining response feedback based on the permission verification result. According to the invention, the problem that the personalized intelligent service cannot be provided because the current user identity cannot be distinguished and the control authority cannot be verified according to the user identity in the smart home interaction scene can be solved.

The invention discloses a live broadcast content adjustment method and system based on user interaction behavior analysis, and belongs to the field of image communication, the live broadcast content adjustment method based on user interaction behavior analysis comprises the following steps: processing real-time bullet screens, filtering invalid information by using an NLP technology, obtaining a first processing bullet screen, and combining a domain knowledge base to obtain a second processing bullet screen; performing standardized expression on the first processing bullet screen to obtain a second processing bullet screen, extracting key demand words in the second processing bullet screen, and obtaining a third processing bullet screen; an improved BIRCH clustering algorithm is adopted, bullet screen merging requirements are processed from the third step based on semantic similarity, and the clustering cluster popularity is calculated in real time according to the formula that popularity = occurrence frequency * speaking user value weight; the method has the beneficial effects that the barrage content in the live broadcast process is processed, and the current user requirements are ranked, so that an anchor can intuitively obtain common requirements of most audiences, voting is not needed to be initiated to aggregate user opinions, and the efficiency is high.

The invention discloses a high-precision positioning method and device for a wiring error electric leakage user, and a storage medium, and relates to the technical field of electric leakage detection. The method comprises the following steps: collecting residual current of a tested transformer area and time sequence data of load current of each user in the tested transformer area; based on the time sequence data, calculating the time sequence correlation strength between the load current and the residual current of each user; generating a differentiated penalty coefficient for each user based on the time sequence correlation intensity; constructing a regression model taking the residual current as a dependent variable and the load current of each user as an independent variable, and reconstructing a target loss function of the regression model by taking a penalty coefficient as a coefficient of a regularization term; and through minimizing the target loss function, a weight coefficient corresponding to the load current of each user is estimated, and then the user with a wiring error electric leakage fault is determined. According to the method, the problem of misjudgment caused by normal user interference in a traditional electric leakage user identification method is effectively solved.

The present invention discloses a method for intervening in the subjective feeling of motion sickness in virtual reality based on perceptual theory, which relates to the technical field of virtual reality. The method comprises: acquiring proprioception parameter information of a current user, wherein the proprioception parameter information includes but is not limited to head movement data, eye movement data, and physiological data; performing real-time analysis on the currently acquired data using a data analysis algorithm to determine whether the user has early signs of motion sickness; determining, by the data analysis algorithm, that the user has early signs of motion sickness, and immediately initiating a visual perception adjustment step. Furthermore, by real-time capturing and analyzing the user's physiological signals in a virtual reality experience, it is possible to identify and intervene in the early stages of motion sickness, and to formulate an intervention method in a timely manner for users who have subjective feelings of motion sickness, thereby facilitating rapid relief of user discomfort.

The embodiment of the invention relates to the technical field of informatization teaching, in particular to a lifelong learning course recommendation method based on a knowledge graph and a lifelong learning platform. According to the embodiment of the invention, the method comprises the steps: constructing a user portrait and a knowledge graph, extracting a course-knowledge point sub-graph related to a current user from the knowledge graph, processing the course-knowledge point sub-graph through a GNN model to generate a course vector, and carrying out the multi-modal matching according to the similarity between the course vector and the user portrait; and iteratively selecting courses through an MMR algorithm to obtain a candidate course list. According to the method, the user portrait and the knowledge graph are constructed according to the user data and the course data, and the candidate course list is made according to the relationship between the knowledge graph and the user portrait, so that the recommended candidate course list is more matched with the current user, and the course recommendation accuracy and the integrity of a user learning path are improved.

The invention relates to the technical field of directional advertisement, in particular to an advertisement putting method based on a user portrait, which comprises the following steps of: acquiring equipment use time of a target user, recording use types and corresponding use frequencies of software and corresponding advertisement response information, dividing the equipment use time into a plurality of putting characteristic time periods, determining the advertisement putting type of each putting characteristic time period; according to the advertisement feedback attitude of the user in each feature time period, determining an advertisement effective tendency category of the current user in each putting feature time period so as to determine a basic advertisement putting mode; and according to the duration percentage in each time period, determining an advertisement acceptance fluctuation parameter of the user in the current time period so as to judge whether to adjust the advertisement putting content in the current characteristic time period, and according to the advertisement watching duration percentage of each use type of software, determining an adjustment direction. And the overall advertisement putting effect is improved.

The invention relates to a commodity related word recommendation method and device. The method comprises the steps that multiple scene domains inside and outside a commodity platform and targets corresponding to the scene domains are determined; according to the multiple scene domains and the corresponding targets, determining a corresponding recommendation implementation mode; performing word recommendation operation according to the corresponding recommendation implementation mode to obtain a word recommendation result; and according to the word recommendation result, a shopping guide center and a pallet center are adopted to execute personalized recommendation corresponding to the current user for the user portrait and the user growth source. Starting from a search growth scene of shopping guide query recommendation, on the basis of global information multi-dimensional modeling and different-domain information scene transfer modeling, a large model is utilized to optimize word recommendation capability, general basic capability construction is carried out, full-link efficiency improvement of users in different life cycles is continuously optimized, and then search penetration and search growth are improved.

The invention discloses a user permission dynamic adjustment method and system based on multi-dimensional perception, and the method comprises the steps: obtaining multi-dimensional operation data of a to-be-accessed device, multi-dimensional behavior data of a current user in the to-be-accessed device, and multi-dimensional permission change data corresponding to the current user, obtaining a multi-dimensional perception feedback matrix between the current user and the to-be-accessed device; acquiring an elastic permission adjustment factor corresponding to the current user according to the multi-dimensional perception feedback matrix; adjusting a pre-constructed permission adjustment grid model according to the multi-dimensional perception feedback matrix to obtain a multi-dimensional permission adjustment grid model corresponding to the current user; according to the multi-dimensional perception feedback matrix, the elastic permission adjustment factor and the multi-dimensional permission adjustment grid model, the current permission value of the current user is dynamically adjusted, a real-time permission value corresponding to the current user is obtained, and the flexibility and accuracy of permission adjustment are improved.