353 results about "User perception" patented technology

The invention relates to the field of knowledge graph construction, in particular to a policy knowledge graph construction method and system based on digital human interaction data analysis. The policy knowledge graph construction system based on digital human interaction data analysis comprises a knowledge graph preliminary construction module, a first graph updating module, a second graph updating module and a digital human interaction module. According to the method, the policy source webpage content change is monitored in real time, and the question information in the digital human interaction data is deeply mined, so that the full-life-cycle dynamic maintenance of the policy knowledge graph is realized; on one hand, a webpage policy updating event is accurately captured, and knowledge injection is automatically completed in combination with a semantic unit matching algorithm; and on the other hand, the conflict characteristics questioned by the user in the interaction data are extracted through dialogue semantic analysis, atlas correction is triggered after confidence assessment and multi-stage verification, it is ensured that the end-to-end timeliness from policy release to user perception is controlled within a reasonable period, and the policy response accuracy of the digital human service is remarkably improved.

A system may include a storage device, configured to store computer-executable instructions. A system may include an ear-bud configured to be positioned within an ear canal of a user, the ear-bud comprising: a speaker, a microphone; and one or more processors in communication with the storage device, wherein the computer-executable instructions, when executed by the one or more processors, cause the one or more processors to: obtain a user hearing profile, obtain an equal-loudness hearing profile, receive audio data from the microphone, and generate a second audio data based on a first sound-pressure level, a second sound-pressure level, a first frequency; and cause the speaker to emit the second audio data within the ear canal of the user, such that the user perceives the audio data as if the user has normal hearing.

The invention relates to a lightweight AI outbound model hot switching framework construction method. Defining a runtime context state unit to form an independent data structure; the AI model is divided into modules responsible for process reasoning execution; the module is in communication with the module responsible for dialogue memory and intention tracking state scheduling through a lightweight intermediate interface; establishing a set of semantic alignment mapping table based on the semantic difference between the models; performing semantic label conversion on a current dialogue state through a mapping table, and injecting a pseudo state vector into the new model to reconstruct an initial reasoning condition; calculating a tension score based on the intention hopping frequency, the slot position switching frequency and the user reply uncertainty; if the controller judges that the switching window is a safe switching window, hot switching logic can be triggered; a prediction module is introduced before switching to perform rehearsal on the reachable intention coverage of the new model, and switching is delayed if the risk is too high. The whole model hot switching process is not perceived by users, dialogue continuity and service continuity are guaranteed, the system resource utilization rate is improved, and model scheduling intelligence is enhanced.

The invention discloses a vehicle control method, a server and a computer readable storage medium. The method comprises the steps that current perception information is acquired, the current perception information comprises user perception information and vehicle perception information, and the user perception information comprises user voice and / or user behaviors; according to the current perception information, determining an intention label, a user intention and candidate function points; and determining a vehicle control instruction according to the intention label, the user intention and the candidate function points. And issuing a vehicle control instruction to the vehicle to complete vehicle control. Thus, by combining the multi-mode current perception information, distinguishing the intention label of the user intention and understanding and reasoning the user intention, the service meeting the user expectation can be actively provided, and the driving experience of the user is improved.

The application discloses a 5G network slice access control method based on service type, which comprises the following steps: 1) in a 5G network supporting multiple network slices, the type of a newly arrived service is judged, and the service is divided into a time delay sensitive service and a non-time delay sensitive service; if the service is the time delay sensitive service, step 2) is executed; if the service is the non-time delay sensitive service, step 3) is executed; 2) a bearing slice of the time delay sensitive service is selected; 3) a bearing slice of the non-time delay sensitive service is selected; 4) the next service needing transmission is waited for, and step 1) is executed. The application can minimize the time delay of the time delay sensitive service, realize the relative fairness of the time delay of various services, minimize the transmission waiting time of the non-time delay sensitive service, and improve user perception.

The invention discloses a dynamic credit limit adjustment system and method based on reinforcement learning, and the system comprises a real-time data lake which collects a user behavior flow, obtains a macroeconomic index, and transmits the macroeconomic index to a reinforcement learning decision core; the federated learning engine and each participating mechanism locally deploy an RL agent, upload a model gradient to the coordination server through a security aggregation protocol, quantify the contribution degree of data of each mechanism to the model performance based on an improved Shapley value algorithm for income distribution, and transmit the contribution degree to the reinforcement learning decision core; the learning decision core is reinforced, state space modeling is carried out based on the user behavior flow and the macroeconomic index, and limit adjustment and risk early warning are realized by combining state space modeling output actions; providing an adaptive algorithm; an interpretability engine; the multi-target reward function dynamically balances interest revenue, risk suppression and user life cycle value, gradient limit adjustment avoids user perception mutation, and loss risks are reduced.

The invention relates to a method for dynamically constructing a service quality evaluation system fused with a large language model, which comprises the following steps of: extracting themes, keywords and representative documents from a user evaluation text data set, constructing cue words and inputting the cue words into the large language model to generate candidate primary service dimensions divided by five service dimensions of an SERVQUAL model; the service quality candidate index item list is composed of candidate second-level topics and candidate third-level index items; constructing a multi-channel heterogeneous hypergraph set fusing sliding window co-occurrence and an external knowledge graph; graph structure information is extracted, joint prediction of emotion labels and emotion scores of user evaluation texts is carried out, and topic-level multi-class input factor spaces are constructed to be used for calculating final fusion weights; and performing comprehensive sorting and dynamic screening on the candidate three-level index items to form a multi-level service quality evaluation system index scale. According to the method, accurate modeling of the user perception quality is realized, and the emotional discrimination of the indexes of the service quality evaluation system can be improved.

The invention discloses an AI-based creative thinking auxiliary generation method and system, relates to the technical field of artificial intelligence, and solves the problem of inaccurate sound and picture matching in a traditional method by performing timestamp alignment and feature extraction on audio data and a visual image frame and calculating the correlation between the audio data and the visual image frame by using a cross-modal attention mechanism. According to the method, user eye movement track data is introduced, real attention points of a user are mapped into a visual sequence, and an optimized weight matrix is generated by constructing attention masks and fusing model attention weights, so that a generation result is more in line with perception key points of the user. And meanwhile, a feedback mechanism is established based on the synchronization error score, and when the sound and the picture are detected to be asynchronous, the visual frame timestamp can be dynamically adjusted, so that the self-adaptive correction of the content is realized. On the whole, the method has remarkable advantages in the aspects of improving modal alignment precision, enhancing user perception consistency and optimizing generation result naturalness.

The invention belongs to the technical field of smart travel services, and particularly provides a smart culture guide system based on AR and a knowledge graph. The core of the system comprises an AR-cross-culture interaction module and an AR-dynamic user perception and experience optimization module. The AR-cross-culture interaction module calls a cloud culture knowledge map and three special maps of behaviors, interests and knowledge planes to extract user features by constructing a multi-language semantic mapping library, generates a personalized culture semantic request packet, generates an adaptive explanation text and binds the adaptive explanation text to an AR model for pushing; the AR-dynamic user perception and experience optimization module evaluates the knowledge understanding degree and experience feeling through a multi-dimensional model, and dynamically adjusts the explanation content depth and the AR presentation form. The system realizes multi-language and personalized AR guide in a cross-culture scene, and effectively improves user culture knowledge understanding accuracy and interaction experience stability.

The invention relates to the technical field of Internet of Vehicles and big data processing, and discloses an Internet of Vehicles user perception method and system based on multi-modal data fusion and dynamic portray.The method comprises the following steps that a vehicle terminal collects operation, interaction and environment data and sends the data to a cloud; the cloud access module cleans and vectorizes data; the real-time calculation module generates a fused context feature by using sliding window aggregation and spatial association; the offline module outputs long-term preference and attribute correction characteristics; the dynamic portrait module combines the above characteristics, and constructs a user portrait comprising basic attributes, real-time states and intention labels by using a rule discrimination and deep learning double-track architecture and an adaptive weighting algorithm; and generating and issuing a control instruction according to the matching rule. According to the invention, through spatio-temporal data alignment, static attribute dynamic correction and intention real-time prediction, the problems of portrait lag and prediction delay are solved, and the accuracy and active perception ability of the Internet of Vehicles service are improved.

The invention provides a virtual machine crashing event processing method and device and a storage medium. According to the method, when a crashing event occurs in a second virtual machine facing a hardware layer of the electronic equipment, a first virtual machine which does not generate the crashing event and bears an operating system temporarily takes over resources occupied by the second virtual machine, the second virtual machine is restarted independently, and after the second virtual machine is restarted, the first virtual machine does not generate the crashing event and bears the operating system. And returning the resources of the second virtual machine taken over by the first virtual machine to the second virtual machine. Therefore, only the second virtual machine with the crashing event is self-recovered, and the electronic equipment is not triggered to restart the whole machine, so that the user perception is reduced, and the user experience is improved.

The invention discloses a vehicle control method, electronic equipment and a computer storage medium, and the method comprises the steps: responding to an operation of starting an anti-carsickness function, and obtaining vehicle information and driving environment information; according to the vehicle information and the driving environment information, power control and vehicle-mounted function control are conducted on the vehicle, and the vehicle-mounted function control is used for adjusting at least one of the view, light, air quality, smell and temperature in the vehicle. According to the technical scheme, after the anti-carsickness function is started, power control and vehicle-mounted function control are conducted on the vehicle according to the vehicle information and the driving environment information, so that at least one of the view, light, air quality, smell and temperature in the vehicle is adjusted, and therefore the driving safety of the vehicle is improved. The vehicle riding feeling is comprehensively optimized from human body perception such as visual field, hearing, smell and touch so as to relieve physiological carsickness pain points, and the perception experience of a user is comprehensively improved.

The invention provides an interest point recommendation method and device for heterogeneous multi-graph representation learning, equipment and a medium, and the method comprises the steps: obtaining a set of users and POIs containing multi-dimensional situation information in a location social network, building a user-POI interaction graph, and extracting feature vector representation of the users and the POIs in an interaction space by adopting a neighborhood similarity measurement method; based on user sign-in time information and text description information, calculating a semantic information similarity distance and a time pattern similarity distance, constructing a user-user perception graph, and learning feature vector representation of the user in a social space; constructing a distance hypergraph by utilizing POI position information, and obtaining feature vector representation of the POI in a position space after dimension reduction clustering processing; and fusing each feature vector representation in the social space and the interaction space as well as the position space and the interaction space, extracting a user representation vector and a POI representation vector, and predicting user preferences by using a neural network model to realize POI recommendation.

The invention relates to the technical field of intelligent household appliances, and discloses an ultrasonic human sensing method and device for an air conditioner, the air conditioner and a storage medium. The method comprises the following steps: receiving an echo signal of a signal transmitted by an ultrasonic sensor so as to extract spatial characteristics and dynamic characteristics in the echo signal; fusing the extracted spatial features and dynamic features; the fused features are input into a lightweight hybrid neural network model to obtain a classification result, and operation parameters of the air conditioner are adjusted according to the classification result; wherein the classification result comprises the perceived user position and the perceived user behavior state. According to the method, the lightweight hybrid neural network model can respond quickly under the condition of ensuring the accuracy of user perception. Therefore, the real-time control requirement of the user is met and the user experience is improved.

The invention discloses a metering instrument equipment data intelligent acquisition management method based on Internet of Things, which belongs to the technical field of Internet of Things data management, comprises data acquisition, data processing, data storage, data analysis and user display, and solves the problems of unified efficient acquisition and intelligent analysis management of heterogeneous metering instrument equipment data. According to the invention, the technical problem of realizing intelligent collaboration and visual application of multi-source data is solved, multiple metering equipment access modes such as wired, wireless, API and Web services are supported, different manufacturers and communication protocols are adapted, local preprocessing and key event reporting are realized, bandwidth and cloud pressure are reduced, and equipment state prediction and accurate alarm are realized. Data security and access compliance are ensured by combining data desensitization encryption, authority control and OAuth2.0 authentication, and user perception and response capabilities are enhanced by cooperating with multi-channel message pushing.

The invention provides a communication method and device and a computer readable storage medium, relates to the field of communication, and aims to obtain a personalized package so as to effectively improve the service experience of a user. The method comprises the following steps: a network data analysis function sends multi-dimensional perception data to an operation agent, wherein the multi-dimensional perception data comprises data obtained by carrying out at least one of the following processing on data reported by a plurality of network function network elements by the network data analysis function: network perception processing, user perception processing or service perception processing; correspondingly, the operation agent receives the perception data and generates a personalized package based on the multi-dimensional perception data.

The invention discloses a mobile communication network problem analysis method and apparatus, and an electronic device. The method comprises the following steps: firstly, acquiring complaint data, signaling data and network performance index data in a mobile network management system; the complaint data carries a mobile communication network problem, and the signaling data, the network performance index data and the complaint data are associated through a user identifier; determining a resident area of the user according to the signaling data; and determining a cell with poor user perception quality according to the complaint data, determining a target cell according to the resident area and the cell with poor user perception quality; screening out performance index data of the target cell from the network performance index data; identifying abnormal data from the performance index data of the target cell based on a preset threshold value; and finally, based on the abnormal data, performing root cause positioning on the mobile communication network problem so as to complete the analysis of the mobile communication network problem. According to the method, the mobile communication network problem and the root cause thereof can be accurately identified.

The invention provides a service hierarchical scheduling method and device based on service types, applied to a base station of an operator, and the method comprises the following steps: obtaining a traffic data packet sent by a terminal; extracting a plurality of service-related features of the traffic data packet, and processing the plurality of service-related features of the traffic data packet by using a pre-trained service model to obtain a service to which the traffic data packet belongs; based on the service to which the traffic data packet belongs, determining the service type to which the traffic data packet belongs; determining a guarantee resource configuration parameter of the traffic data packet based on a preset guarantee strategy and the service type to which the traffic data packet belongs; and scheduling the traffic data packet based on the guarantee resource configuration parameter. According to the invention, differential guarantee and scheduling can be carried out on flow data packets of different service types, and the user perception of a wireless network is improved.

The invention relates to the technical field of wireless communication perception, in particular to a user perception method based on a multi-layer intelligent omnidirectional metasurface, and proposes to realize accurate perception of reflection and refraction user orientations by utilizing heterogeneous characteristics of propagation paths on two sides of a multi-layer intelligent omnidirectional metasurface structure. Based on the architecture, a user reflection and refraction type judgment algorithm based on uplink pilot frequency information is provided, and the user reflection and refraction types are determined based on the heterogeneous characteristics of user channels on the two sides of the multi-layer intelligent omni-directional metasurface and based on least square optimization problem solving and target function size comparison. The propagation path angle estimation algorithm specifically comprises the following steps: firstly, based on uplink pilot frequency information, designing a channel estimation algorithm comprising alternating optimization, constant-rank manifold optimization, convex relaxation and other methods to estimate and obtain a channel matrix by utilizing channel constant-rank and angular domain sparse characteristics; and finally, based on a user type determination result, obtaining user angle information based on each channel matrix by using an orthogonal matching pursuit algorithm.

The invention relates to the technical field of three-dimensional model image data processing, and discloses a three-dimensional model particularization splitting deduction and multi-mode feedback method, which comprises the following steps: carrying out lightweight three-dimensional model reconstruction according to an equipment process structure diagram, and generating a glTF format data packet with a process information embedding characteristic; establishing a virtual three-dimensional scene through a three.js engine, and instantiating a three-dimensional model object in a glTF format; non-uniform granulation processing is carried out on the equipment model by adopting a splitting and deducting mechanism, discrete motion tracks of parts are solved through a response algorithm, and an equipment dynamic decomposition path display effect is constructed; a calibration sensing link framework is established, an interaction response mechanism based on user behaviors is constructed, and a multi-mode visual feedback system of a large screen is achieved; according to the method, the technical bottlenecks of slow model loading, high interaction delay and the like in traditional industrial large screen display are effectively solved, and the dynamic interaction display effect and user perception of an industrial equipment structure are improved.

A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and / or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and / or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

The invention discloses a traffic charge pre-sending method, system, device and medium, and relates to the communication field, the method comprises the following steps: obtaining a package selection action instruction of a user, the package selection action instruction comprising a package ordering action instruction and a package replacement action instruction; account book data of a preset period is generated based on the package selection action instruction, and the preset period is determined based on a pre-sending return rate; compressing the account book data of the preset period to generate compressed account book data; and after reaching a charging period, decompressing the compressed account book data to activate the account book data. On the premise that the account period accuracy and the user perception are not affected, optimization scheduling of large-scale account book issuing behaviors is achieved, the method is suitable for high-concurrency account period systems such as an operator, a virtual business platform and an Internet of Things platform, and the method has high engineering practicability and popularization value.

The invention discloses an active and passive combined video monitoring system fault positioning method, which comprises the following steps of: S1, acquiring a real-time monitoring video stream, and acquiring video frame data, a video frame statistical index and video stream session information from the real-time monitoring video stream; s2, analyzing the video frame data to obtain a video fault index; s3, detecting a video server and a network path condition to obtain a network degradation condition of the network node; and S4, carrying out association analysis by taking the video fault index, the network path and the network degradation condition as data sources, and positioning the fault. According to the invention, from user perception, accurate positioning of video faults is realized, network camera faults, network transmission node faults and server faults can be positioned, an accurate monitoring means is provided for rapid repair of the video faults, and the video fault inspection and repair cost is greatly reduced.

Systems and methods for interacting with virtual objects in a three-dimensional space using a wearable system are disclosed. The wearable system can be programmed to permit user interaction with interactable objects in a field of regard (FOR) of a user. The FOR includes a portion of the environment around the user that is capable of being perceived by the user via the AR system. The system can determine a group of interactable objects in the FOR of the user and determine a pose of the user. The system can update, based on a change in the pose or a field of view (FOV) of the user, a subgroup of the interactable objects that are located in the FOV of the user and receive a selection of a target interactable object from the subgroup of interactable objects. The system can initiate a selection event on the target interactable object.

The invention discloses an analysis method for 5G network user perception improvement based on wireless intelligent arrangement, and the method specifically comprises the following steps: 1, the perception of an online user terminal is evaluated in real time, and the perception evaluation comprises the multi-dimensional evaluation of the data transmission congestion state of the user terminal and the wireless channel quality; and 2, when the perceptibility evaluation result indicates that the user experience is degraded, a wireless intelligent arrangement decision is triggered, and the decision is based on the service requirement, the terminal capability and the space-time position of the user terminal. According to the analysis method for improving 5G network user perception based on wireless intelligent arrangement, through AI-driven dynamic resource scheduling and in combination with a predictive network self-optimization function, the end-to-end time delay of low-time-delay services such as high-definition video calls, cloud games and VR / AR is guaranteed to be lower than 20 milliseconds, the lagging phenomenon is remarkably eliminated, and the user perception is improved. Seamless service continuity guarantee is achieved, and measurement-free switching is achieved through a wireless fingerprint grid knowledge base.

The invention relates to the technical field of keyboard error correction, and discloses an AI-based keyboard error automatic correction method and system, and the method comprises the steps: collecting the contact pressure distribution, sliding track and stay time of keyboard input by a user, and constructing an input intention probability matrix; performing error detection and preliminary correction through the first local lightweight model to obtain a first correction result, and transmitting the first correction result to the cloud large model for deep semantic analysis to obtain a second correction result; performing recursive knowledge distillation on the first local lightweight model according to the second correction result to obtain a second local lightweight model; and collecting behavior feedback data of accepting or rejecting the second correction result by the user, performing task allocation dynamic adjustment on the second local lightweight model and the cloud large model, and outputting a double-layer asynchronous collaborative error correction strategy, thereby improving the error correction quality while ensuring the response speed, effectively reducing the user perception delay, and improving the user experience. And smoother input experience is provided.

The invention relates to the technical field of heating systems, and particularly discloses a seat cushion heating integrated intelligent control heating system which comprises a user sensing module, a temperature control module, an energy consumption management module, a safety protection module, a communication interaction module and a self-checking diagnosis module. The user sensing module collects comprehensive data through a multi-source sensor group and generates a user habit feature set, and the temperature control module dynamically adjusts the working mode of a heating area according to the data and real-time environment parameters, so that personalized heating is realized; for example, according to sitting postures and physiological characteristics of different users, appropriate temperatures are provided for different temperature zones, the comfort level of the users is improved, a safety protection module compares an energy consumption evaluation index with a preset safety threshold value, a multi-stage protection strategy is implemented, and when the energy consumption evaluation index is too high, measures such as power reduction, partition power failure and forced heat dissipation are taken, so that the safety of the users is improved. The temperature of the seat cushion is effectively prevented from being too high, and safety accidents such as user scalding and fire disasters are avoided.

The invention provides a beam forming optimization method for an ISAC system based on a reconfigurable antenna structure, and the method comprises the steps: firstly, designing an energy-saving DFBS based on a TTD reconfigurable antenna architecture based on a downlink terahertz large-scale MIMO ISAC system; the DFBS is switched between the modular MIMO and the uniform MIMO by dynamically controlling the antenna sub-arrays; in the sensing stage, modular MIMO is deployed, and a larger array aperture is realized by using fewer antennas; the beam dispersion effect at the main lobe and the grating lobe is utilized to realize rapid user perception; in the communication stage, uniform MIMO is adopted, and a TTD network is activated to reduce the near-field beam dispersion effect; then, based on the channel state information obtained in the sensing stage, proposing a joint optimization problem of analog / digital hybrid beam forming and TTD network delay so as to maximize a system sum rate; finally, analog beam forming and time delay are designed through a serial time delayer method, and then alternating iteration optimization solution is carried out on digital beam forming.

The invention discloses an FTTR-based equipment control method and system, equipment, a medium and a program product, and belongs to the technical field of smart home. The method comprises the following steps: acquiring an initial equipment control model; the initial equipment control model is trained through a first loss function and at least one second loss function, a target equipment control model is obtained, the first loss function is used for representing the matching degree of the probability value of executing the multiple candidate actions by the equipment and the real action label, and the second loss function is used for representing the matching degree of the probability value of executing the multiple candidate actions by the equipment; the at least one second loss function is determined according to a user preference index, and the user preference index comprises at least one of an equipment operation energy consumption index, an equipment operation safety index and a user perception feedback index; and sending the target equipment control model to the equipment. Through the mode, the problem that the response of a related equipment control system to the fuzzy request has deviation can be effectively solved, and the accurate regulation and control capability of the equipment control system is improved.

The invention discloses a fixed network user network quality control method and device and electronic equipment. The method comprises the following steps of: firstly, obtaining problem data in network operation of a fixed network user; the problem data comprises perception problem data fed back by a user and abnormal performance data monitored by a network side; inputting the perception problem data into a user perception poor quality model to obtain a first quality reason of a user perception level; and inputting the abnormal performance data into a network poor quality early warning model to obtain a second quality reason of a network side performance level, fusing the first quality reason and the second quality reason to obtain a poor-quality root reason of the fixed network user network; and finally, according to the poor-quality root cause, poor-quality order dispatching and problem repair are implemented, so that the network quality of the fixed network user is managed and controlled. According to the method, the user perception and the network performance in the network quality of the fixed network user can be considered, so that the user experience is improved, and the network reliability and service level are enhanced.