1193 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 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 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 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 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 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 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.

The invention relates to a power grid dispatching cloud platform access authentication method, device and equipment based on a bastion host, a storage medium and a program product, and relates to the technical field of information security. According to the method, the security risk existing when the power grid dispatching cloud platform is remotely accessed can be reduced, and the operation information is efficiently stored. The method comprises the following steps: sending an access request of a current user to a configured OTP server, so that the OTP server generates a password and a detection token according to the access request; the method comprises the following steps: acquiring a user name of a current user, receiving a password and a detection token returned by an OTP server, packaging and sending the user name, the password and the detection token to a bastion host, verifying the detection token by the bastion host according to the user name, and sending the password to the OTP server by the bastion host under the condition that the verification of the detection token is passed, the OTP server verifies the password password; and logging in the bastion host under the condition that the password verification is passed, and accessing the power grid dispatching cloud platform through the bastion host.

The invention relates to the technical field of personalized health follow-up visit, in particular to a personalized health follow-up visit method, system and device based on a health portrait and a large language model and a medium, and the method comprises the steps: collecting multi-dimensional health data of a user, carrying out the standardization processing and feature extraction of the health data, and generating a plurality of user feature tags; based on the user feature tag, constructing a dynamically updated user health portrait; inputting the user health portrait into a service classification model, and determining a target follow-up visit service line and a follow-up visit mode corresponding to the current user; on the basis of the target follow-up visit service line, the follow-up visit mode and the user health portrait, a prompt word is constructed and input to a large language model, so that personalized follow-up visit content is generated; and based on the follow-up visit content, executing a follow-up visit task on the user through the intelligent call-out system. The method has the beneficial effects that the follow-up visit efficiency and coverage are remarkably improved, and refined, dynamic and humanized health management of each user is really realized.

The embodiments of the disclosure disclose a method, an apparatus, a device and storage medium for message processing, and relate to the technical field of computers. The method includes: displaying a predetermined message identifier in a target interface, wherein at least one of predetermined media content and target resource information is presented in the target interface, and the predetermined message identifier is set to indicate that an unread message currently exists; and in response to a triggering operation of a for the predetermined message identifier, presenting a message interaction area in the target interface, and presenting the unread message sent by at least one sender in the message interaction area, wherein the message interaction area is set for message interaction between the current user and the at least one sender of a unread message.

A big and small model collaborative semantic rewriting system based on complexity sensing comprises a user question receiving module, a context extraction module, a complexity evaluation module, a collaborative strategy module and an output rewriting module, and the user question receiving module is used for receiving questions of a current user and starting a semantic rewriting process. The context extraction module is used for integrating historical dialogue contexts and extracting key entities, the complexity evaluation module is used for evaluating user question semantic complexity and driving strategy decision, the collaborative strategy module is used for dynamically scheduling collaborative modes of small models and large models and optimizing resource allocation, and the output rewriting module is used for generating final rewritten questions. According to the large and small model collaborative semantic rewriting system based on complexity induction, a semantic complexity evaluation algorithm based on machine learning is put forward to evaluate semantic complexity, and a model collaborative strategy algorithm based on a dynamic threshold is put forward to cooperatively process tasks of all levels.

The application discloses a short video code rate self-adaptive method based on meta learning, relates to the technical field of streaming media, and comprises the following steps: S1, offline training, a model is established to represent user characteristics and network prediction information; S2, online learning, according to the characteristics of the current user environment, the model parameters are adjusted and optimized. The short video code rate self-adaptive method based on meta learning is adopted, a new SABR framework based on meta learning is successfully realized, the framework can quickly adapt to different user demands, the practicability and the calculation speed of the system are improved, and the framework has industrial application; the offline training and the online learning technology are successfully combined, the generalization and the stability of the model are enhanced; the idea of action masking is introduced in pre-training, the rationality and the reliability of decision are enhanced, the data amount required by meta learning is effectively reduced, the learning efficiency and the accuracy are improved, and the data demand and the training time in the industrial environment are significantly reduced.

The invention relates to the field of man-machine interaction, and discloses a man-machine interaction method and system based on a large language model, and the method comprises the steps: extracting original semantic features from the current input of a user; obtaining a context representation of the current dialogue; extracting a historical emotional state sequence from the multi-round dialogue historical record; generating a current user emotion representation vector based on the context representation of the current dialogue and the historical emotion state sequence; constructing a hierarchical memory structure based on the current user emotion representation vector and the context representation of the current dialogue; adjusting the attention degree of the historical dialogue content stored in the semantic memory component according to the hierarchical memory structure; and generating a response sequence through a large language model based on the adjusted attention degree, the hierarchical memory structure and the context representation of the current conversation, and outputting the response sequence to the user. According to the technical scheme, the problems of stiff switching and dialogue breakage during emotion turning of a traditional dialogue system are solved.

The invention discloses a user ad-hoc query report realization method and device based on a large language model, and relates to the technical field of natural language processing. The method comprises the following steps: constructing an ad hoc query report system based on the large language model, wherein the ad hoc query report system comprises a semantic analysis module, an analysis engine module and a query statistics module; a natural language query statement input by a user is input into a semantic analysis module to be analyzed through a fine-tuned large language model, a structured query condition set and a structured query result index column set are output and input into an analysis engine module, and according to permission information of the current user, through pre-constructed index metadata and SQL semantic analysis rules, the query result index column set is analyzed, and the query result index column set is analyzed. Generating a query SQL statement; and inputting the query SQL statement into a query statistics module, connecting a target database to execute query operation to obtain a query result set, and performing data formatting processing on the query result set to obtain a final query result. By adopting the method, complex query requirements can be met.