3297 results about "Multi-user" patented technology

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention provides an AI-based multi-user data storage docking and secure transmission method, which comprises the following steps of: acquiring a propagation path of a hot topic according to a multi-modal content association graph, performing fragmentation storage on the propagation path by adopting a clustering algorithm, and generating a fragmentation storage index table; when the relevance of different modal contents is higher than a preset threshold value, determining that the sensitivity grading result is consistent with the multi-modal relevance, performing dynamic rule adjustment by adopting a deep learning model, generating a dynamic auditing rule set, and obtaining boundary judgment parameters of the rule set; and distributing the dynamic auditing rule set and the decision transparency index to each service line by adopting a cross-platform data synchronization protocol according to the auditing decision log, generating a cross-platform consistency auditing standard, and obtaining a synchronization log of standard execution.

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

The invention discloses a virtual-real fusion exhibition display interaction system and a multi-mode perception method, and belongs to the technical field of exhibition display interaction. The system collects audience eyeball fixation points, gesture actions and ambient light data through AR / VR equipment, analyzes coordinates of a region of interest through an eyeball fixation point attention mechanism, a gesture space-time encoder and a multi-modal fusion unit, triggers holographic projection explanation and virtual exhibition stand light and shadow dynamic adjustment (including illumination intensity, color, Gaussian blur and the like) based on a threshold value, and performs real-time display on the virtual exhibition stand. And multi-user collaborative interaction is realized through federal learning. According to the method, reinforcement learning is adopted to optimize an event-driven threshold value, and virtual and real visual splitting is eliminated in combination with ambient light adaptive mapping. The problems of low participation degree, insufficient single-mode interaction information and multi-user cooperation of traditional exhibition are solved, interest analysis accuracy is improved through multi-mode fusion, personalized experience is enhanced through dynamic interaction, the method is suitable for multiple scenes such as museums and science and technology museums, and exhibition intellectualization, immersion and group interaction efficiency are effectively improved.

The invention relates to the technical field of equipment access control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-user access control method and device of equipment, the equipment and a medium, and the method comprises the following steps: receiving a scanning result containing an equipment identity code and operator identification information; generating a verification token based on a scanning result, embedding a time efficiency parameter, distributing the verification token to a user terminal, receiving user characteristic data, establishing a binding relationship with the equipment identity code and the operator identification information, carrying out time efficiency and integrity verification, generating a verification result, and controlling the equipment access authority and generating an operation record according to the verification result. Through a binding mechanism among the equipment identity code, the operator identification information and the user characteristic data, and in combination with the verification token embedded with the aging control parameter, effective management and control of the operation access behavior under the scene that multiple persons use the same equipment at the same time are realized.

The invention belongs to the technical field of mobile communication, and particularly relates to a task unloading and resource allocation method for edge computing. According to the method, a three-layer network structure is established, a distributed decision framework is constructed through reinforcement learning, the task emergency degree is dynamically evaluated, and computing resources are distributed in a differentiated mode; and task unloading and resource allocation are optimized in combination with an edge-cloud collaborative architecture, so that calculation load balancing is realized. According to the method, aiming at a cloud edge-end collaborative edge calculation model, the total cost of a system is defined as a joint optimization problem of task unloading time delay and energy consumption, the problem model is converted into a Markov decision process, and multi-agent and multi-user oriented deep reinforcement learning algorithm agent near-end strategy optimization (MAPPO) is designed; and obtaining an optimal unloading decision through mutual learning among multiple agents. According to the method, the total cost of the system can be effectively reduced, the rationality of edge computing task unloading and resource allocation decision is realized, and meanwhile, the use experience of a user can be improved.

The invention provides a semantic fusion graphical software development management method and system. The method comprises the following steps: acquiring a multi-user interaction behavior data flow containing an operation track sequence and an interface control triggering time sequence; according to operation track sequences of different users and a time-space association mode of an interface control triggering time sequence, constructing a dynamic semantic map; inputting a hierarchical mapping relationship in the dynamic semantic map into a preset edge computing node, and generating a real-time computing power distribution scheme matched with a logic dependency chain in the dynamic semantic map; analyzing a mapping relationship between a control triggering timestamp and a control type in the space-time association mode, and generating an interface control rendering priority queue; the synchronous regulation and control interface control renders the priority queue and an operation instruction response sequence corresponding to the logic dependency chain, and generates a graphical software development process synchronous instruction set; according to the method, the hierarchy and the dependency chain are fused to form the dynamic semantic graph, the hierarchical operation structure and the logic dependency relationship are constructed, and the high efficiency and the consistency of the graphical development process are ensured.

A user equipment and a method for performing a multi-Subscriber Identity Module (multi-SIM) operation are provided. The method includes connecting to a first network associated with a first Universal Subscriber Identity Module (USIM) of the UE; receiving, from the first network, a first configuration indicating that the UE is allowed to transmit an assistance information message for switching to a second network associated with a second USIM of the UE, the first configuration further including a configuration of a timer; transmitting, to the first network, the assistance information message, the assistance information message including information of a preferred Radio Resource Control (RRC) state associated with the first network to indicate that the UE prefers to switch from an RRC_CONNECTED state associated with the first network to the preferred RRC state; and starting the timer upon transmitting the assistance information message.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention provides a multi-party collaborative anti-quantum signature method and system based on homomorphic Hash, and relates to the technical field of digital signatures, the system comprises a plurality of participants and a verification party, and the method is specifically as follows: each participant generates a pair of public and private keys, the public key is composed of a total public key, a total signature matrix and a total Hash matrix of all participants; based on the public and private keys, all participants perform multi-party signature on the target message through a homomorphic hash function, and a generated signature is composed of total hash challenge values of all participants, total response vectors of all participants and commitment value high-order difference values of all participants; according to the signature, the verifier recovers the high order of the total commitment value, further calculates the total hash challenge value, and verifies the validity of the signature through comparison of the total hash challenge value; according to the method, multi-user collaborative signature is realized, and the security of resisting malicious opponents under a quantum computer is obtained.

Systems, methods, and apparatuses are described for enhancing a user experience in event live streaming applications such as video conferencing systems. In an example, first and second virtual meetings are scheduled, each meeting scheduled to include a presentation of the same document. The document is stored in a cloud storage and is associated with both the first meeting and the second meeting. During the first meeting and the second meeting, respective virtual meeting communication data is captured and stored in the cloud storage as metadata associated with the document, and associated with the respective first or second meeting during which it was captured. After the meetings conclude, a user accesses the document and is presented with an option to view the metadata associated with the first or second meeting.

The invention belongs to the technical field of intelligent control, particularly relates to a deep learning-driven intelligent home scene dynamic adaptation method, and aims to solve the problem that an existing intelligent home system is difficult to realize high-precision personalized scene adaptation in a multi-user and multi-device environment due to dependence on a static rule. The method comprises the steps of collecting multi-source heterogeneous user behavior data and performing semantic enhancement preprocessing, constructing a hierarchical time sequence behavior coding model to extract local time sequence dependence and cross-equipment long-range association features, clustering to generate a dynamic scene prototype and mapping the dynamic scene prototype into an executable condition-action rule, after the rules are deployed, a closed-loop optimization mechanism is constructed through explicit and implicit user feedback, and online incremental updating and self-adaptive evolution of the behavior model and the scene rules are achieved. According to the technical scheme, the user complex behavior mode can be deeply understood, the scene adaptation precision is continuously optimized, the individuation level, logic consistency and system robustness of intelligent services are improved, and meanwhile privacy safety and real-time response are guaranteed through edge calculation.

The invention relates to the technical field of charging pile management and power dispatching, and particularly discloses a charging pile operation management method and system. The method comprises the following steps: acquiring charging request data and power grid load data, and generating and cleaning a task vector data set; predicting a charging demand based on the data set and generating electricity price gradient information; a multi-objective optimization scheduling function is constructed and solved, and a task allocation scheduling scheme is generated; executing power control according to the scheduling scheme and collecting charging state data; executing dynamic contract charging and incentive generation according to the state data; and finally, performing Hash packaging and privacy processing on an execution result, and performing uplink to generate a credible record. According to the method, the resource allocation efficiency and the charging incentive intelligence level in a multi-user multi-period concurrent charging scene can be improved, and efficient cooperation and credible execution of the whole charging scheduling and settlement process are realized.

The invention discloses a VR teaching experience enhancement system and method, and belongs to the technical field of virtual teaching, and the method specifically comprises the steps: collecting the position data and posture data of a trainee in a VR environment, building a multi-user coordination interaction model based on the position data and posture data of the trainee in the VR environment, and carrying out the interaction of the multi-user coordination interaction model. The method comprises the following steps: acquiring training participants, identifying an interaction relationship and an interaction event among the training participants, generating tactile feedback data corresponding to the interaction event based on the interaction event, performing calibration processing on the tactile feedback data, and synchronously sending the calibrated tactile feedback data to VR equipment of the training participants, the calibration processing comprises adjusting the tactile feedback data based on the spatial propagation difference quantity and the tactile feedback time offset value of the trainees; according to the invention, when multiple trainees cooperatively operate the same virtual object or scene, consistent and real-time tactile response and spatial perception can be obtained, and the teaching experience in a multi-person cooperation scene is improved.

The invention discloses a virtual reality interaction method and system applied to classical famous picture display, and particularly relates to the technical field of virtual reality interaction. Multi-dimensional high-definition image acquisition and composition element three-dimensional modeling are performed on a painting, a cultural context label system is constructed in combination with historical data, a fixation point, an action posture and a staying behavior of a user are perceived in real time, accurate matching of a user behavior and a semantic label is realized, and multi-modal interaction feedback content consistent with the style of the painting is generated, so that the user experience is improved. And typical paths and misunderstanding areas are identified through clustering analysis of multi-user behavior data, and context tags and response logic are dynamically optimized, so that personalized recommendation and semantic guidance are realized, the understanding depth and immersion experience of the users on classical art are effectively improved, and the cultural expression ability and intelligent adaptability of the system are enhanced.

A computing system includes at least one processing circuit having at least one processor and at least one memory device. The processing circuit performs operations including: providing a chat interface to allow a plurality of travelers to initiate a chat session in which the plurality of travelers collaborate to plan a trip; receiving, via the chat session, input from the plurality of travelers related to the trip; and automatically interacting, via a chatbot, with the plurality of travelers within the chat session to plan the trip by: processing, using a machine learning model, a content of the chat session to generate one or more recommendations for the trip, wherein processing the content of the chat session includes generating the one or more recommendations using the input from the plurality of travelers; and providing the generated one or more recommendations to the plurality of travelers within the chat session.

The invention relates to the technical field of real-time rendering, cloud edge collaboration and three-dimensional visualization, in particular to a multi-instance cloud edge collaboration rendering method based on an unreal engine. Through cloud centralized scheduling and edge node plug flow collaborative architecture, on-demand distribution, dynamic start and stop and life cycle management of rendering instances are realized, and the resource utilization efficiency is remarkably improved; the system combines a relay agent mechanism of TURN and Nginx, gets through access restrictions among a public network, a local area network and a plurality of subnetworks, and realizes unified access and high-adaptability pixel flow transmission capability. Through edge-side integrated pixel flow and signaling service, distributed instance management and load balancing scheduling, the concurrent processing capability and horizontal expansibility of the system are enhanced, and the requirements of complex three-dimensional application scenes such as multiple users, real-time interaction and high-performance rendering are met. Through a cloud edge separation architecture, a relay forwarding mechanism and instance dynamic scheduling, efficient and stable pushing of unreal engine pixel streams in a multi-network environment is realized.

The invention relates to a beam forming design method of an active RIS-assisted ISAC system, and belongs to the field of communication perception integration. A multi-user large-scale multiple-input single-output system model assisted by communication and perception integration and a system model based on an ISAC system assisted by an active RIS are constructed, and a beam forming vector and a reflection coefficient of the active RIS are respectively optimized by adopting an alternating optimization algorithm. According to the method, the propagation blockage problem is solved, the system performance is further improved, the multiplicative fading effect problem of the passive RIS is solved by using the active RIS, the weighted minimum mean square error algorithm, the continuous convex approximation algorithm and the positive semidefinite relaxation algorithm are combined, the system effectiveness is improved to the maximum extent, and the system performance is improved to the maximum extent. The system utility is the sum of the weighted sum rate of the system and the target detection power, the target detection capability is remarkably enhanced while the weighted sum rate of the user is improved, and a new solution is provided for performance optimization of the ISAC system.

The invention relates to an unmanned aerial vehicle emergency communication spectrum scheduling method and system based on dynamic entropy optimization, and the method comprises the steps: 1, constructing an emergency communication system, and calculating the gain of a received signal; 2, constructing a spectrum sensing and user access model; step 3, evaluating the channel quality in the emergency communication system; 4, establishing a state, an action and a reward of deep reinforcement learning according to spectrum sensing, a user access model and channel quality evaluation; 5, constructing a deep reinforcement learning model, and performing training to obtain a trained deep reinforcement learning model; and step 6, obtaining an optimal strategy of unmanned aerial vehicle emergency communication spectrum scheduling in a spectrum environment by using the trained deep reinforcement learning model, and realizing multi-user aggregation spectrum access. According to the invention, through intelligent distributed spectrum management, the problems of limited spectrum resources and energy, multi-user dynamic competition and the like in a disaster rescue scene are solved, and the spectrum utilization efficiency and the communication reliability are remarkably improved.

The invention provides a pelican optimization strategy-based mobile edge computing unloading method, and aims to solve the problems of low computing task unloading efficiency and poor system performance in application scenes such as Internet of Things and smart cities. By introducing chaotic mapping and a flight strategy, a traditional pelican optimization algorithm is improved, and the global search ability and the ability of jumping out of a local optimal solution of the algorithm are enhanced. A calculation unloading model of a multi-user multi-edge server is constructed, and delay and energy consumption in the data transmission and calculation process are comprehensively considered. Experimental results show that the method can effectively reduce the time delay and energy consumption of the system, optimize resource allocation and improve the overall performance of the system. Especially in a multi-user and multi-task complex scene, the method disclosed by the invention is excellent in performance, and a new solution is provided for the calculation unloading problem in the mobile edge calculation.

The invention discloses a video stream concurrent access front-end dynamic scheduling control system and method based on a digital twinborn scene, and relates to the technical field of computer software and network communication, the system comprises three core modules: a self-adaptive request queue module manages UE rendering resources based on a session token pool, distributes tokens through preemptive scheduling, and sends the tokens to a server; in combination with a GPU frame rate feedback dynamic adjustment strategy, queuing through a Promise asynchronous queue when the request fails; the front-end fusing and backoff retry module monitors the connection failure rate, when the connection failure rate reaches a threshold value, fusing is triggered, retry is delayed by adopting an exponential backoff strategy, and a semi-open state tends to recover; and the local cache collaboration module intercepts the request through Service Worker, caches the latest video clip, pushes the local content when the request is delayed, and ensures the freshness through ETag verification. The method and the device are used for efficiently managing loading and interaction of multiple users on real-time rendering pictures at a browser end.

A basketball passing machine providing a multi-user mode and related systems and methods are provided. A controller receives programming information for a basketball practice drill including participant information for multiple users. Based on the programming information received, each pass in the basketball practice drill is associated with a respective one of the multiple users. A launching device passes basketballs to locations at a basketball playing area, and participant information is displayed at the front display for an associated one of the users for a respective one of the passes.

The invention relates to the field of household appliances, and provides an intelligent air conditioner control method, system and device based on federal learning and a medium. According to the environment data and the time information corresponding to the environment data, an air conditioner prediction operation mode is obtained through a global preference model; wherein the global preference model is obtained and issued by the aggregation server by utilizing federal learning on the basis of a local training model which participates in uploading of the client; according to the environment data, the time information corresponding to the environment data and the air conditioner prediction operation mode, an air flow distribution prediction result is obtained through an indoor simulation model created on the basis of computational fluid dynamics; and according to the airflow distribution prediction result, the flexible air guide mechanism is controlled to adjust the angle. The problems that a traditional air conditioner has the risk of user privacy leakage and is poor in multi-user adaptability are solved, and collaborative optimization of high efficiency, energy conservation, accurate airflow regulation and control and user privacy protection is achieved.

The invention discloses a digital employee collaborative screening method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: receiving multi-user concurrent requests, carrying out semantic isolation, generating an adjustment factor in combination with user portrait features, generating an initial semantic vector through two-channel semantic coding, and carrying out semantic conflict detection, thereby effectively avoiding semantic cross and confusion, and improving the user experience. The intention analysis of multi-user concurrent requests is realized; acquiring request features in real time, calculating computing power demand levels of different requests, sequencing the requests with the same computing power demand level, and performing resource allocation; a user adjustment instruction is obtained through a front end, a demand difference is identified, an incremental calculation scheme is generated, and state synchronization control is carried out; according to the method, digital employees are divided into analytic, computational and feedback intelligent agents, collaborative screening is carried out, feedback verification is carried out, it is ensured that a screening result better meets user requirements, and real-time capability iteration of a digital employee cluster is achieved.

The invention discloses a method for sharing a host based on multiple input devices, and belongs to the technical field of computers, and the method comprises the following steps: after a management user logs in a multi-seat configuration tool, creating second seats with the same number as new input devices according to a Udev rule, and enabling the management user to be a user operating the input device corresponding to a first seat of the host; according to a binding instruction sent by the management user, the second seats and the new input devices are bound one by one, and operation users of the input devices corresponding to the second seats are common users; according to a permission configuration instruction sent by the management user, configuring an application window permission for each common user; and sending the binding relationship between the second seat and the new input device and the application window authority to a window manager, so that the window manager manages the application window according to the binding relationship and the application window authority. According to the invention, the problems of disordered management and complex configuration when multiple users share one host window are solved, and more flexible and efficient equipment management experience is provided for the users.

The invention discloses an unloading strategy and resource scheduling combined method based on the Internet of Vehicles. The method comprises the following steps: constructing a multi-server and multi-user vehicle edge computing system; constructing a communication model according to a vehicle edge computing system; constructing a calculation model; establishing a joint optimization problem model by taking balance of time delay and energy consumption and maximization of task success rate as targets; and solving the joint optimization problem model by using a grey wolf optimization algorithm based on chaotic mapping and differential mutation and a resource allocation algorithm based on delay balance to obtain a task unloading strategy set and a resource allocation strategy set. According to the unloading strategy and resource scheduling combined method based on the Internet of Vehicles, the problems that in the prior art, the task success rate is low due to uneven resource allocation in a multi-server environment, and single target optimization in a dynamic environment is difficult to meet the real-time performance and energy efficiency requirements at the same time are solved.

The invention discloses an intelligent scalp detection system and a detection method thereof, relates to the technical field of intelligent scalp detection, and aims at obtaining shooting conditions and definition evaluation through real-time environment perception and quality detection, solving hair occlusion and image noise by using deep learning segmentation and directional enhancement, showing a scalp deep structure by means of multi-modal fusion and splicing, and improving the detection accuracy. A diagnosis and optimization strategy is output in comprehensive analysis and self-adaptive closed-loop feedback; through software algorithm level expansion, online training and multi-label pathological expansion can be performed on multi-user long-period data, and adaptive evolution and group statistical analysis are realized; according to the method, the image acquisition stability and segmentation accuracy can be effectively improved, the adaptability of detection to complex illumination and multi-band information is enhanced, the risk of pathological misjudgment such as grease, inflammation and fungi is reduced, and personalized nursing and wide health management application are supported.