123 results about "Users perceptions" patented technology

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 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 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 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 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 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 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 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 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 network awareness test method and device, electronic equipment and a nonvolatile storage medium. The method comprises: in response to a service test instruction, performing a service test on a network corresponding to a mobile terminal, the service test being used for simulating a real service operation behavior of a user on the mobile terminal; obtaining an index value of a key quality index of the network in a service test process; and determining index scores corresponding to the index values of the multiple types of key quality indexes, and determining a target perception score according to the index scores corresponding to the multiple types of key quality indexes, the target perception score being used for representing the user perception quality degree of the network corresponding to the mobile terminal. According to the method and the device, the technical problem that a perception test mode in the related technology is not comprehensive in network quality evaluation and cannot effectively and accurately reflect the influence of a network on user perception of various services is solved.

Systems and methods include receiving visual content for display on a display device, the visual content including a plurality of layers, the plurality of layers including a background layer and a visual media layer, the plurality of layers being arranged in a presentation layer stack; evaluating the visual content to determine at least one display parameter; determining a desired brightness level for displaying visual content on a display of the display device based on the at least one display parameter; determining an adjustment to a display parameter of the presentation layer stack to achieve a desired brightness level for displaying visual content; generating adjusted visual content based on adjusting the display parameters of the presentation layer stack; and causing the adjusted visual content to be presented on a display device.

The invention relates to the technical field of elderly people literacy training, in particular to a cognitive-driven elderly people AI literacy immersive training system and method. Comprising a user perception and interaction module, a behavior analysis and emotion calculation model, a cognitive modeling module, a self-adaptive teaching content generation module, an immersive learning environment module, an emotion accompanying and feedback module, a learning effect evaluation and progress tracking module and a remote cooperation and support module. The presentation mode, rhythm and difficulty of the teaching content are dynamically adjusted through the cognitive modeling module and the self-adaptive teaching content generation module, a customized learning path is generated for each user, the personalized learning mode is beneficial to improving the learning efficiency, it is ensured that the elderly user can learn according to the rhythm and ability of the elderly user, and the learning efficiency is improved. Therefore, learning motivation and confidence are enhanced.

The invention belongs to the technical field of screen display, and particularly relates to a large-screen display system based on machine learning, which comprises an environment sensing module, a user sensing module, a calculation module, a compensation module and a display control module, the compensation module calculates the distance and the visual angle according to the positioning coordinate parameters, and when the distance and the visual angle exceed a preset distance threshold value and a preset visual angle threshold value, the initial brightness adjustment coefficient is compensated according to the distance, the visual angle and the pupil diameter parameters to generate a corrected brightness adjustment coefficient; according to the correction brightness adjustment coefficient, the distance between the viewer and the large screen and the viewing angle are considered, so that the large-screen display system can dynamically adjust the screen brightness more accurately according to the visual comfort level of the viewer and the influence of ambient light.

Embodiments of the present application disclose a resource scheduling method, a terminal device and a storage medium. The resource scheduling method comprises: obtaining current application scenario information; selecting a corresponding user perception model according to the current application scenario information; the user perception model represents a corresponding relationship between a user perception score and a time delay; determining a pre-configured reference point corresponding to the user perception model; and performing model conversion on the user perception model based on the pre-configured reference point to obtain a performance factor and time delay corresponding relationship; determining a real-time performance factor corresponding to a real-time time delay based on the performance factor and time delay corresponding relationship, so as to adjust the performance capability of a frequency adjustment scheduling mechanism by using the real-time performance factor when the real-time time delay arrives.

This disclosure relates to a user perception optimization method, apparatus, device, medium, and product in the field of edge computing technology. The method includes: constructing a user perception optimization system model with the goal of enhancing the perception of all users through task processing mode selection; transforming the user perception optimization system model into a user perception quality maximization problem based on Markov decision processes; and employing a proximity policy optimization method within a deep reinforcement learning framework to obtain the optimal task scheduling strategy for user perception in the Markov decision process user perception quality maximization problem. This disclosure establishes a dynamic task scheduling model for user perception in edge computing networks, achieving an accurate description of the user perception problem. Based on the Markov decision process user perception quality maximization problem, it uses a proximity policy optimization method within a deep reinforcement learning framework to intelligently derive the optimal task scheduling strategy, ensuring the perception experience of each user throughout a continuous period.

The present application discloses an operating parameter adjustment method, apparatus, electronic device, and storage medium. The operating parameter adjustment method includes: collecting user perception information; wherein the perception information includes at least one of the following: tactile-related information, auditory-related information, and visual-related information; determining a target perception intensity based on the perception information; and determining a target operating parameter of the target component based on a relevant value of the target component corresponding to the target perception intensity, wherein the target operating parameter of the target component includes any of the following: a target frequency of the CPU, a target frequency of the GPU, and a target bandwidth of the memory. Implementing this method is beneficial for optimizing the operating performance and power consumption of the target component.

The application provides an abnormality processing method, device and equipment of a network, performance index data and service index data of each network element are obtained from a performance index database and a service index database respectively, detection of core network abnormal indexes and cause positioning analysis are realized according to the obtained data by using an Isolation Forest algorithm model and an AutoMap algorithm model, the deficiency of manual experience is avoided, real-time online detection is realized, network problems are discovered in time, network healthy operation is ensured, and user perception is improved.

The embodiment of the invention provides a mobile terminal service availability inspection method based on system state matrix driving, which belongs to the technical field of physics, and specifically comprises the following steps: constructing a system service state matrix; detecting a structure dimension, a behavior dimension, a signal dimension, an abnormal dimension and a user perception dimension by using a multi-modal probe system, mapping detection results into corresponding discrete state values, and filling and updating a system service state matrix according to the discrete state values; dynamically deciding a subsequent inspection path and a detection priority by combining a real-time environment state and a user behavior mode of the mobile terminal on the basis of each dimension state value filled in the system service state matrix and a predefined association rule; and controlling the multi-mode probe system to execute a corresponding detection action according to the inspection path, and generating an availability evaluation result of the target system service based on the final state of the system service state matrix. Through the scheme disclosed by the invention, the inspection efficiency, accuracy and adaptability are improved.

The present invention discloses a method for optimizing dynamic business acceptance responses through incremental page refresh, comprising the following steps: Step 1: processing the first change information of the target attribute of the target page sent by the front-end device; Step 2: based on the preset suite group request identifier, the front-end device controls the incremental response interaction to determine whether the first change information of the target attribute of the target page is sent; Step 3: based on the preset non-suite group control request identifier, the front-end device controls the incremental response interaction to determine whether the first change information of the target attribute of the target page is sent. The present invention solves the problems that the cloud product acceptance page cannot achieve a fast response, cannot control the number of interactions, and the page response is too long or even stuck due to multiple processing logics. This method significantly reduces the number of dynamic business acceptance responses, significantly improves the system performance of the acceptance page, and significantly improves user perception by controlling the incremental refresh of the page.

This application provides a data processing method, apparatus, device, and storage medium. The data processing method includes: acquiring a target application to be recommended and notification element data of the target application; determining the application recommendation weight of the target application based on the application access duration and application access frequency; determining the notification recommendation weight corresponding to the notification element data based on the element feature weight of the notification category feature corresponding to the notification element data and the application recommendation weight; responding to a notification recommendation request triggered by a target recommendation component, generating and displaying a target recommendation interface corresponding to the current interaction scenario based on the notification element data and the notification recommendation weight. This achieves multi-dimensional determination of the notification weight of each notification content, and then outputs interactive notification element data in a personalized manner based on the notification weight, improving the accuracy and flexibility of notification content recommendation, reducing the efficiency of user perception of notification content, and avoiding the omission of important notification information.

The present invention discloses a method, system, device, and medium for pre-issuing traffic charges, relating to the field of communications. The method comprises: obtaining a user's package selection action instruction, wherein the package selection action instruction includes a package subscription action instruction and a package replacement action instruction; generating account book data for a preset period based on the package selection action instruction, wherein the preset period is determined based on a pre-issuance rate of return; compressing the account book data for the preset period to generate compressed account book data; and after reaching a billing period, decompressing the compressed account book data to activate the account book data. The present invention achieves optimized scheduling of large-scale account book issuance without affecting the accuracy of the bill period and user perception. The present invention is applicable to high-concurrency bill period systems such as operators, virtual operator platforms, and Internet of Things platforms, and has strong engineering practicality and promotion value.

The application provides a point of interest recommendation method and device based on heterogeneous multi-graph representation learning, equipment and medium, the method comprises the following steps: obtaining a user and a POI set containing multi-dimensional context information in a location social network, and establishing a user-POI interaction graph; using a neighborhood similarity measurement method to extract feature vector representations of the user and the POI in the interaction space; based on user check-in time information and text description information, calculating semantic information similarity distance and time pattern similarity distance, constructing a user-user perception graph, and learning feature vector representations of the user in the social space; using POI location information to construct a distance hypergraph, obtaining feature vector representations of the POI in the location space after dimension reduction clustering processing; fusing the feature vector representations in the social space and the interaction space, and the location space and the interaction space, extracting user representation vectors and POI representation vectors, and using a neural network model to predict user preferences to realize POI recommendation.

The invention discloses a method for acquiring and reporting UE (User Equipment) information, which comprises the following steps that: when a terminal accesses a network, a base station inquires the information of the terminal from the terminal in a UE information inquiry way, the terminal reports the UE information to the base station in a signaling way, and the base station updates UE associated information after receiving the UE information and reports the UE information to a core network so as to realize the acquisition and reporting of the UE information. According to the method, the index information of various terminals can be quickly acquired in a mode of actively acquiring the UE information, the terminal information can be quickly acquired for the indexes of which the performance indexes are not excellent, accurate positioning and quick problem closed loop can be performed, the user perception is efficiently improved, and the product competitiveness is improved.

The invention discloses a social perception maximization method based on differentiatable graph optimization, and relates to the technical field of data mining and network analysis, and the method comprises the steps: obtaining social network cascade data, and constructing a weighted bigraph of a user perception event; constructing a social perception forward propagation model according to the weighted bigraph, defining an activation state variable for each user, defining a total activation amount for each event, and introducing a function to evaluate the perception quality of each event; the activation state variables of all the users form an activation vector, the process of searching the optimal activation vector is constructed into a differentiable optimization task, and the optimization target is to maximize the perceived quality sum of all the events; and according to the optimal activation vector, generating a final seed user set as an optimal solution for maximizing social perception. According to the method, the problems of local optimization and model simplification in the prior art are solved, global optimization is realized, and the user influence can be accurately modeled.

The present invention relates to the field of knowledge graph construction, and specifically 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 includes: a knowledge graph preliminary construction module, a first graph update module, a second graph update module and a digital human interaction module. The present invention realizes the dynamic maintenance of the policy knowledge graph throughout its life cycle by real-time monitoring of changes in policy source web page content and deep mining of questioning information in digital human interaction data; on the one hand, it accurately captures web page policy update events and automatically completes knowledge injection in combination with a semantic unit matching algorithm; on the other hand, it uses conversation semantic analysis to extract conflicting features of user questions in interaction data, and triggers graph correction after confidence assessment and multi-level verification, ensuring that the end-to-end timeliness from policy release to user perception is controlled within a reasonable period, significantly improving the policy response accuracy of digital human services.