5609 results about "User information" patented technology

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 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 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.

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.

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.

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 discloses an intelligent question-answering system training method based on machine learning, and the method comprises the steps: optimizing a knowledge learning sample to obtain optimized knowledge, determining a knowledge weight, constructing an industry knowledge base, fusing multi-modal question content to obtain fused question content, and carrying out the training of the intelligent question-answering system. Obtaining industry description information and a user portrait according to the fused question content and the user information, and inputting the fused question content, the industry description information and the user portrait into a question and answer reinforcement learning model to obtain answer content, and obtaining a first reward signal, a second reward signal and a dynamic reward weight according to the answer content and the user question response information, adjusting the question and answer reinforcement learning model, inputting the multi-modal question content into the adjusted question and answer reinforcement learning model to obtain optimized answer content, and updating the industry knowledge base by adopting a user interaction log and the knowledge weight. According to the method, intelligent question and answer system training can be accurately and efficiently carried out, and meanwhile powerful support is provided for intelligent question and answer services of all industries.

The invention belongs to the technical field of catering informatization service, and provides an automatic matching catering menu pushing system, which comprises a user data acquisition module, a catering menu pushing module and a catering menu pushing module, the menu data management module stores and manages menu classification, labels and states, and obtains content feature data; the user portrait construction module generates a multi-dimensional portrait according to the multi-aspect information of the user; the menu matching module combines collaborative filtering and a content recommendation algorithm, and generates a recommendation menu based on the user portrait; the intelligent pushing module pushes dishes of nearby merchants according to the portraits, and the pushing effect is evaluated through the click rate and the conversion rate; according to the method, personalized and accurate catering menu recommendation is realized, and real-time updating and optimization are carried out in combination with geographic positions and time period factors, so that personalized pushing which better fits user preferences and dining environments is provided.

The invention relates to a distributed data security storage and encryption system based on an AI technology. The system comprises a data acquisition and preprocessing layer used for acquiring original data and preprocessing the original data to obtain an identifiable data object; the AI identification and strategy decision layer is used for identifying data features of identifiable data objects based on an interpretable AI model to obtain an encrypted instruction packet; the encryption execution and distributed storage layer is used for performing encryption storage processing on the original data content according to an encryption instruction packet to obtain data access interface information; and the dynamic access control layer is used for obtaining user information in response to a data access request instruction of a user, recombining and decrypting the data by using the data distribution mapping index based on the access control strategy table, and returning a plaintext data fragment. By adopting the system, a high-strength, differentiated and multi-level security mechanism can be realized through the AI, intelligent encryption strategy decision is carried out, and the security requirements of multiple scenes and multiple types of data are met.

The invention discloses a credit loan fraud data identification method and system based on artificial intelligence and a graph algorithm, and relates to the technical field of credit loan risk control. User information and behavior data are collected from multiple sources, a relation network graph is constructed after cleaning, dense communities are identified, cross-community intermediary nodes are positioned, community portraits are generated, behavior patterns are quantified, and similar groups are clustered. According to the method, a complex fraud mode is accurately recognized, hidden association is mined through a graph algorithm, the recognition accuracy is improved in combination with community portraits and behavior clustering, the dynamic adaptability is high, real-time feedback and incremental learning ensure that novel fraud is rapidly coped with, and the method is suitable for popularization and application. A high-interpretability report meets compliance requirements, user trust is improved, large-scale data is efficiently processed, modular design adapts to different credit and loan scenes, cost is reduced through automatic decision, the omission ratio is reduced, fraud loss is directly reduced, and the rate of return on investment is high.

A mobile consumer device with a display, processor(s), and memory: identifies a merchant device in proximity to the consumer device based on broadcasted information transmitted by the first merchant device, the broadcasted information including a first identifier corresponding to the first merchant device; transmits the first identifier to a server and receives from the server an electronic communication including identification and transaction information associated with the merchant; displays the identification information, receives user selection of the merchant identification information; and in response, displays the merchant transaction information, receives supplemental user information, and transmits the supplemental transaction information to the server for completion of the transaction.

The embodiment of the invention discloses an offline intelligent wearable device NFC service processing scheme. Comprising the following steps: controlling an NFC card reader on the offline intelligent wearable equipment to be in a closed state; receiving an NFC service triggering instruction; entering a specified service application, and automatically switching the NFC card reader from a closed state to an open state through the specified service application; the target information is read from the target NFC machine tool through the NFC card reader in the open state, and the user information of the offline intelligent wearable device is written back to the target NFC machine tool, so that the target NFC machine tool interacts with the server side of the specified service application according to the user information and executes the corresponding target service; according to the target information, short-distance wireless communication connection with the target NFC machine tool is established, an execution result of the target service returned by the target NFC machine tool is received, and the maximum communication distance corresponding to the short-distance wireless communication connection is larger than the NFC communication distance of the target NFC machine tool.

The invention discloses an intelligent parking method and device based on multi-source data fusion and a medium, and relates to the technical field of big data technologies. The method comprises the following steps: collecting multi-source parking data, and analyzing the multi-source parking data to generate a parking lot label library; generating a user tag based on historical search records and residence time preferences of the user, and constructing a user information database; when it is detected that a user triggers a parking request, performing multi-dimensional matching calculation on a user tag and a parking lot tag library to obtain a recommended parking space sequence; and optimizing the recommended parking space sequence based on the real-time traffic state and the user credit score to generate a final navigation path. According to the method, a dynamic parking recommendation mechanism is constructed by collecting and analyzing the multi-source parking data and the user preference data.

The invention discloses an intelligent rehabilitation training system based on multi-parameter detection and a control method, and relates to the technical field of intelligent rehabilitation training, the system comprises a user information acquisition unit, a multi-source parameter acquisition unit, a rehabilitation training plan customization unit, a feedback and control unit and a quantitative evaluation unit; multi-source detection parameters corresponding to movement, physiology and environment of a target user are collected in real time through a preset sensor, the health-related data and the multi-source detection parameters are fused, a rehabilitation training domain knowledge graph is accessed to construct a training scheme recommendation model, and a personalized rehabilitation training plan of the target user is output; according to the achievable degree of the target user for each rehabilitation training action, real-time feedback is provided through a visual interface and voice prompt, and the target user is guided to adjust the training action; a brand new solution is provided for rehabilitation training based on multi-parameter detection, the training state of the user can be monitored in real time, a personalized training scheme is generated, and the rehabilitation effect and efficiency are remarkably improved.

The invention discloses a transaction data management method based on a block chain and electronic equipment, and relates to the technical field of block chains and the field of financial science and technology. The method comprises the steps of obtaining first data, second data, third data and fourth data in a block chain network through a block chain node server of a financial institution; and verifying the first data, the second data, the third data and the fourth data through a block chain node server of the financial institution, and sending all the data to an application server of the financial institution after the verification is passed. The technical problem that the privacy of user information and the transparency of the transaction process cannot be considered in the existing transaction process is solved.

The invention discloses a precision marketing recommendation system based on an AI large model, relates to the technical field of information recommendation, is used for improving the problem of product information recommendation efficiency reduction caused by tag recommendation delay, and comprises the steps of collecting user data, analyzing user information frequency modulation potentials by using a K nearest neighbor algorithm, performing classification, screening out users with high frequency modulation potentials, and performing recommendation on the users with high frequency modulation potentials. The method comprises the following steps: setting detection time to collect recommendation information data of a user, screening key tags from the recommendation information data, calculating a tag demand coefficient, detecting a user emptying behavior, carrying out secondary screening on the key tags to obtain evaluation tags, and judging whether demand down-regulation processing is carried out on the evaluation tags by utilizing a corrected geometric average method. And marking the evaluation labels requiring down-regulation processing, obtaining label attributes of the marked labels, calculating down-regulation coefficients of different marked labels according to the label attributes of the marked labels and the label demand coefficients, and setting skipping time for the marked labels according to the down-regulation coefficients.

A system and method for an intelligent hair drying / styling apparatus with user information transmission and storage capabilities is herein provided. The hair drying / styling apparatus houses a control circuit board and an infrared or temperature sensor (or camera) in order detect an individual's hair condition moisture level to determine a user specific, customizable dryer setting. The information detected by the sensor is stored locally, on a proximal Internet-enabled device, or on a remote or cloud-based server and accessed by the hair drying / styling apparatus through a wireless local area network connectivity function. This innovation will enable any number of hair professionals and / or end consumers to improve styling and dry time. According to the present invention, energy usage is reduced to enable a more efficient design.