6838 results about "User information" patented technology

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

A system for development of an Artificial Intelligence (AI) model while protecting sensitive user information includes: a confidential computing environment in which original prompts to the AI model written by users are collected; a trained synthetic prompt generator to generate synthetic prompts based on the original prompts, wherein the synthetic prompt generator generates anonymized synthetic prompts without sensitive user information identifiable from the original prompts; and a developer computing environment in which the synthetic prompts are submitted to the AI model under development to generate a dataset that includes the synthetic prompts and corresponding AI model output for analysis to determine updates for the AI model while protecting the sensitive user information of actual users.

The embodiment of the invention provides an information generation method and device, electronic equipment and a storage medium. The information generation method comprises the following steps: in response to an obtained question and user information, calling a first agent to determine a target intention based on the question and the user information; a second agent is called to determine a to-be-executed task corresponding to the target intention, the second agent is called to execute the to-be-executed task to generate target information, the target information serves as reply information of the target intention to be sent to the client side, and the first agent and the second agent are responsible for intention confirmation and task planning respectively. According to the technical scheme, the intelligent agent has the learning ability and can make corresponding actions based on the change of the environment, so that the target intention can be determined according to the problem of the user and the experience of similar problems appearing before, the to-be-executed task is further determined, the efficiency of processing the user problem is finally improved, and the user experience is improved.

The invention discloses a mechanical arm control method, system and equipment for realizing a multi-modal universal operation task. The method comprises the following steps: acquiring multi-modal data of text, voice and vision; performing modal alignment on the multi-modal input data, encoding the multi-modal input data into a uniform input representation vector, and splicing the uniform input representation vector into a multi-modal instruction paragraph; retrieving a user information voice database based on voiceprint features of the voice instruction data to obtain task associated voice data; instruction information is input into the strategy large model to obtain an action plan; a post-processing task action planning input controller controls a mechanical arm, and a multi-mode input instruction is updated in real time to perceive the latest task state; and performing closed-loop control based on the initial target instruction information and the environment perception information until the task is completed. The task planning and error correction capacity of the mechanical arm is improved through closed-loop control and voice data retrieval based on the large strategy model, and the dynamic adaptive capacity, the autonomous error correction capacity and the multi-mode interaction capacity of the mechanical arm are improved.

A method for generating personalized responses in a conversation with a user includes generating real-time contexts capturing an environment of the user over time, including generating a particular real-time context based on a first data stream corresponding to a first modality in an environment of the user and a second data stream corresponding to a second modality in the environment of the user, generating historical contexts based on the real-time contexts, in response to receiving a conversational cue provided by the user, generating a current real-time context based on data corresponding to the first modality and the second modality in a current environment of the user, and generating, based on the current real-time context, a personalized response to the conversational cue, including identifying, based on the current real-time context, relevant user information, including identifying one or more relevant historical contexts, and generating the personalized response using the relevant user information.

The invention discloses an intelligent multimedia interactive teaching and examination system and method, and relates to the technical field of education informationization, and the method comprises the steps: obtaining user information and a learning state, and generating a learning portrait; based on the learning portrait, utilizing the generative AI to dynamically generate personalized teaching content, and marking the abnormal state of the user in real time; storing an abnormal state marking result of the user to a dynamic feature field of the learning portrait, and dynamically optimizing a learning path; based on the optimized learning path, a personalized assessment task is dynamically generated, and answer data and learning behavior data of the user are recorded; and performing multi-dimensional evaluation on the performance of the user through a semantic analysis method by utilizing the answer data and the learning behavior data. According to the method, through the comprehensive analysis model and the multi-modal feature vector, multi-dimensional evaluation of the performance of the learner is realized, multiple aspects of knowledge mastery, behavior performance, emotional state and the like are covered, and detailed feedback and improvement suggestions are provided for the learner.

The invention discloses a lower limb motion data real-time analysis method and system applied to rehabilitation guidance, and the method comprises the steps: calling a personalized model according to user information, converting real-time motion data into historical motion feature vectors, inputting the historical motion feature vectors into a real-time prediction module, and generating multiple frames of future motion feature vectors through coding and decoding; calculating a deviation value between each future motion feature vector and a preset threshold range to obtain a motion deviation value, calculating a motion risk proportion of each future motion feature vector, performing weighted fusion to obtain a motion risk probability, and generating an equipment parameter adjustment value and pose adjustment information through reverse mapping; and finally, according to risk probability assessment training stage promotion, synchronously displaying correction information and adjusting equipment operation parameters. According to the method, the dynamic model is constructed through pre-training, real-time prediction of future motion features is realized in combination with the lightweight LSTM, and the predictability and personalized adaptation ability of rehabilitation guidance are improved.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

The invention discloses a dynamic power distribution network deduction method based on graph neural network perception and a privacy protection type Transform model. The method comprises the steps of collecting source load storage historical data in a power distribution network and performing standardization processing; performing time sequence segmentation; establishing a dynamic topological structure model of the power distribution network; extracting spatial features of the dynamic topological structure of the power distribution network by using a graph neural network; the spatial features are converted into time sequence data, the time sequence data are combined with time sequence data obtained after time sequence segmentation, and a source network load storage time sequence data set is established; differential privacy processing is carried out; training a Transform model, in the training process, using a differential privacy optimization technology, adding noise to the gradient when the gradient is updated each time, and finally obtaining a trained DP-Transform model; and inputting a specific scene, and deducing the state of the power distribution network by using a DP-Transform model. According to the method, the interpretability of deducing the state of the power distribution network by using the Transform is improved, so that the Transform model can fully receive the graph structure input of the network topology structure of the power distribution network, and privacy protection on user information in the deducing process is considered.

In accordance with the present disclosure, systems and methods are provided for providing personalized education to users. the systems and methods may collect, process, and analyze various user information including data from compatible IoT devices using a composite AI model. The systems and methods may generate a personalized education profile for the user using the composite AI model based on the analyzed user information. The systems and methods may generate and deliver personalized education content aided by the use of the composite AI model. the system may also monitor and receive user feedback in real-time for updating the composite AI model, the personalized education profile, and the personalized education content.

The invention discloses an AI-based cultural tourism content personalized recommendation system, which relates to the technical field of tourism planning and comprises a user information acquisition module, a cultural tourism content database information storage module, a recommendation engine module, an emotion analysis module, a real-time recommendation updating module and a multi-channel display module. According to the method, basic information, tourism preference, historical behaviors, geographic positions and social media data of users are collected, personalized portraits are generated and accurately recommended, sentiment analysis, real-time positions and dynamic behaviors are combined, the recommended content is continuously optimized by the system to adapt to the changing requirements of the users, and the user experience is improved. Through multi-channel display, personalized labels and clustering analysis, the system provides customized tourism suggestions, supports collaborative filtering and content recommendation strategies, real-time feedback and a self-optimization mechanism to improve the precision, adapts to intelligent equipment, recommends tourism contents according to environmental changes, and improves the user experience.

The invention discloses a multi-beam cooperative scheduling method and a multi-beam cooperative scheduling system for low-orbit satellite communication, which mainly solve the problems of poor beam scheduling flexibility and difficulty in adapting to time-varying requirements in a high-dynamic environment in the prior art, and the implementation scheme of the multi-beam cooperative scheduling method comprises the following steps of: collecting online user information by a low-orbit satellite through a wide beam; a hopping beam position is generated by using adaptive clustering under the constraint of channel resources; a hopping beam scheduling algorithm based on deep reinforcement learning outputs an optimal scheduling strategy according to the real-time environment variable; and optimizing a beam hopping time frame structure in each scheduling period, covering accessed users in a beam position through spot beams, and sending service data and control information to complete efficient service transmission. The method can adapt to changes of user distribution and service requirements in low-orbit satellite communication, the utilization rate of spectrum resources is improved, communication delay is reduced, the overall performance and service quality of the system in a high-dynamic environment can be enhanced, and the method can be used for dynamic beam position division, hopping beam scheduling and hopping beam resource allocation in a multi-beam low-orbit satellite communication scene.

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 neuromuscular electrical stimulation, and provides a multi-modal feedback self-adaptive adjustment neuromuscular regulation and control treatment system, which comprises wearable working equipment for outputting pulse electric signals to adjust nerves and muscles to form movement or adjustment feeling, electromyographic signals, motion data and user feeling input are collected to conduct feedback adjustment on output parameters of the output pulse electric signals; the handheld control equipment is used for being connected with the wearable working equipment and controlling the output working mode and the working state of the wearable working equipment; and the server-side equipment is used for managing user information and preference of the handheld control equipment, setting function authority of the handheld control equipment, acquiring data acquired by the wearable working equipment, and realizing self-adaptive output of output parameters of the pulse electric signals of the wearable working equipment based on a self-adaptive reinforcement learning algorithm. Adaptive optimization of millisecond-level dynamic electrical stimulation parameters is realized through a multi-modal feedback data fusion technology in combination with a layered reinforcement learning architecture.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a product decision optimization method, device, equipment and medium based on a knowledge graph. And generating a user feature portrait in combination with the user related information, taking the user feature portrait and the information in the knowledge graph as an input state, taking the product information set as an action space, training and generating a decision model based on a preset incentive mechanism, outputting a target item by using the decision model, and updating the knowledge graph and the decision model based on user feedback information. The comprehensiveness of an input state is improved through a fusion modeling mode containing user information, product information and environment information, a dynamic updating mechanism is constructed in combination with a knowledge graph and user feedback, a decision model is driven to be continuously optimized, and then the pertinence of a recommendation result and the self-adaptive capacity of a system are improved.

The invention discloses a virtuality and reality combined display system of a universe museum, and relates to the field of universe technology and museum display technology, the system comprises a data acquisition module, a data analysis and user portrait module, a personalized guide planning module, a virtuality and reality combined display module and a feedback and optimization module; according to the system, user information and cultural relic data are comprehensively and meticulously collected through the data acquisition module, a basis is provided for subsequent personalized display and guide planning, and the data analysis and user portrait module digs potential demands and interests of users, so that displayed contents can accurately match preferences of the users, and the user experience is improved. The virtual-reality combined display module constructs a virtual cultural relic model by using a 3D modeling and rendering technology, realizes seamless joint with an entity exhibit through an AR technology, brings immersive visiting experience to the user, and in addition, due to the existence of the feedback and optimization module, the system can continuously collect user feedback, so that the user experience is improved. And display contents and functions are continuously optimized and perfected.

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 relates to the technical field of task matching, and provides a task matching method based on user information and task characteristics, which comprises the following steps: step 1, collecting and analyzing user information which comprises but is not limited to employment information, skilled direction, experience and price; 2, analyzing task information, wherein the task information comprises but is not limited to basic information and additional information; the basic information collection includes but is not limited to task titles, task description, task categories, budget ranges, time requirements, task importance and geographic position requirements; 3, establishing a multi-dimensional matching model, and mapping the user information and the task features; step 4, optimizing and sequencing matching results by utilizing a machine learning algorithm; and 5, dynamically adjusting the matching weight, and continuously improving the matching precision according to historical data. According to the task matching method based on the user information and the task features, the task allocation efficiency and stability are improved, and personalized task recommendation is provided.

The invention discloses a user information management method and system based on application information, and relates to the technical field of user information management, and the method comprises the steps: obtaining user information through an application program, generating a unique distributed identity label, and carrying out the multi-stage verification of the user identity through an edge calculation node and a distributed node network; user behaviors are analyzed in real time through a fuzzy logic algorithm, the priority of user data is adjusted according to an analysis result, and the data access permission is dynamically allocated. According to the method, the unique distributed identity label is generated, multi-level verification is carried out in combination with the edge computing node and the distributed node network, the identity verification efficiency and safety are remarkably improved, user behaviors are analyzed in real time through the fuzzy logic algorithm, the data priority is dynamically adjusted, the access authority is allocated, and the user experience is improved. The efficient management of resources is realized, and the flexibility of authority management and the user experience are improved.

An embodiment includes a method to increase the efficiency of security checkpoint operations. A security checkpoint kiosk serves as a Relying Party System (RPS). The RPS establishes a secure local connection between the RPS and a User Mobile-Identification-Credential Device (UMD). The RPS sends a user information request to the UMD, via the secure local connection, seeking release of user information associated with a Mobile Identification Credential (MIC). The RPS obtains authentication of the user information received in response to the user information request. The RPS retrieves user travel information based on the user information. The RPS determines that the user travel information matches the user information. When the user travel information matches the user information, the RPS approves the user to proceed past the security checkpoint kiosk.

The invention discloses a network information security verification management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence data management, and the method comprises the following steps: collecting network flow data and user behavior data, constructing a user behavior mode model through employing a machine learning algorithm, and calculating the abnormal degree of a user network behavior; calculating a safety coefficient through the abnormal degree quantized value of the user network behavior, comparing the safety coefficient with a preset threshold range, and judging a risk level of the user network behavior; marking user information with security threats, and counting network traffic fluctuation amplitude and user abnormal operation frequency; by means of the network information security verification management method and system, network information security verification management is achieved.

The invention relates to the technical field of television image communication, in particular to a user information input method and system in an intelligent television all-in-one machine. The method comprises the following steps: collecting a user image, carrying out white balance correction and focusing processing, generating a moving object focusing image, carrying out identification and attention tendency analysis on a character in the focusing image, obtaining character tendency data, analyzing a historical interaction image, identifying an interaction mode and constructing a user intention prediction model, and obtaining a user intention prediction result. And when the tendency data shows that the user preferences the television content, intention drilling and auxiliary adjustment are performed on the current image based on the prediction model, predicted user input information is generated, logic conflicts in the predicted information are detected, feedback optimization is performed, and final user input information is output. According to the invention, a more accurate and more flexible user information input method is realized.

A system uses a large language model (LLM) to implement a controlled artificial intelligence chat environment. The system may control interaction with the LLM using prompt templates that may be selected, customized, and / or modified based on information known about the user with whom the LLM will be interacting. Further, the system may evaluate output of the LLM to make changes to the LLM, the prompt templates, and so on. In some implementations, the system may use evaluation training data to adapt and fine-tune the LLM and / or another language model to evaluate output of the LLM in order to evaluate the efficacy of the chronic condition and / or disease management coaching path(s), and make improvements to the online or offline implementation of the language model in the future.

The invention relates to the technical field of computers, and provides a multi-modal learning resource intelligent recommendation method and system based on an AI large model, and the method comprises the steps: carrying out the intention analysis of interaction behavior data and user information based on the AI large model, and obtaining a user feature vector; obtaining multi-modal resource data based on the user feature vector, and integrating the multi-modal resource data to obtain a resource representation vector; mining implicit association between resources and knowledge points and a mapping relation between user requirements and target skills based on a knowledge graph in combination with user feature vectors and resource representation vectors to obtain a knowledge network; generating candidate recommended learning resources based on the context awareness information and the knowledge network; and performing sorting optimization on the candidate recommendation learning resources based on the user feature vector and the resource representation vector in combination with the current stage index of the multi-modal resource data, and generating an initial resource recommendation list. According to the embodiment of the invention, the content features of the learning resources are comprehensively captured, and the resource recommendation accuracy is improved.

The invention discloses a digital identity verification method and device based on a zero-trust architecture, terminal equipment and a storage medium, and belongs to the field of network security, and the method comprises the steps: obtaining an identity verification result according to user information and multi-factor identity verification information; according to the identity verification result, generating a session token and creating a session; collecting behavior data, equipment fingerprint data and geographic position data of the user during the session in real time, and dynamically adjusting the access authority stored by the session token according to the behavior data, the equipment fingerprint data and the geographic position data; each time a request sent by a client is received, the zero-trust architecture gateway determines the current session state of the server by verifying the current session token. Therefore, through the implementation of the invention, various identity authentication technologies can be organically integrated, continuous identity authentication under a zero-trust architecture is realized, and the security of digital identity authentication is improved.

According to various embodiments, a system and method for verifying a user's identity using both document-based and biometric data is disclosed. The system may prompt a user to upload an image of a government-issued identification document and extract user information from the image using optical character recognition (OCR). The system may also extract the embedded face image from the ID and prompt the user to take a real-time selfie while performing one or more randomized actions or poses. A facial recognition engine may compare the extracted ID image to the live selfie to determine a similarity score. Based on this match, along with optional document authenticity checks, the system may confirm the user's identity in real-time for use cases such as account access, onboarding, and regulatory KYC compliance.

A method and device for UE-to-Network (U2N) Relay discovery are disclosed. In one embodiment, a second relay UE receives a first U2N Relay discovery message from a first relay UE, wherein the first U2N Relay discovery message includes a User Info Identity (ID) of the first relay UE, a Layer-2 ID of a third relay UE, and an ID of a serving cell of the third relay UE. Furthermore, the second relay UE transmits a second U2N Relay discovery message to a remote UE, wherein the second U2N Relay discovery message includes a User Info ID of the second relay UE, the Layer-2 ID, and the ID of the serving cell.

The invention discloses an automatic feasibility research report generation method fusing a knowledge graph and a large model, and relates to the technical field of leather inlaying, and the method comprises the following steps: extracting and cleaning information from multi-source heterogeneous data, and carrying out standardization processing on terminologies; constructing a knowledge graph, and converting the knowledge into embedded vectors which can be processed by a computer; according to user information, related sub-graph information is accurately retrieved from the knowledge graph, and cue words containing professional knowledge are dynamically combined; the large model generates a first draft according to cue words, a system automatically checks, and when conflicts are found, the model is required to be regenerated; and integrating the content which is verified to be correct with related standard terms, automatically adding a data reference source, and generating a document which is standard in format and clear in risk point. According to the method, real-time and automatic professional knowledge conformity checking and self-correction can be carried out on technical parameters and logic assertions output by the model in the generation process.

The invention provides an attack defense method and device for an e-commerce intelligent customer service large model, and relates to the technical field of artificial intelligence security, and the method comprises the steps: 1, monitoring an interaction data flow between a user and an intelligent customer service in real time, recognizing an abnormal interaction behavior, and activating a multi-layer defense strategy when the abnormal behavior is detected; 2, based on a multi-layer defense strategy, sensitive words, special character sequences and potential injection marks in an input text are recognized and processed through the dynamically updated multi-field sensitive word detection rule base and the special symbol matching rule set, and a preliminarily filtered text is generated; according to the method, various malicious attacks aiming at the e-commerce intelligent customer service large model are effectively resisted, the user information security is maintained, and the customer service quality and the e-commerce operation reliability are improved.

The invention relates to the technical field of user data processing, in particular to a user portrait enhancement method based on a dynamic tag, which is applied to a system method for designing data acquisition, data processing, data analysis and data classification of a user information and user portrait data use platform. By analyzing the user behavior in real time and dynamically adjusting the label in the user portrait, the more accurate and comprehensive user portrait can be constructed, and the real-time interest and long-term preference of the user can be captured. Based on the dynamically updated user portraits, the system can provide highly personalized recommendation services, commodity recommendation, content recommendation or service recommendation can be closer to the real demands of the users, and the satisfaction degree and participation degree of the users are improved.