25 results about "Time perception" patented technology

The invention discloses a time enhanced knowledge tracking method based on dual-channel deentanglement, and belongs to the technical field of education data mining and cognitive modeling. According to the technical scheme, the method comprises the steps that time dynamic features and behavior reaction features in a learning interaction sequence are extracted and coded through a time domain encoder and a behavior domain encoder respectively; separating long-term trends and short-term fluctuations in the input features by using a multi-scale decoupling layer based on causal convolution; a time perception dual-channel attention module is adopted to independently decouple time and behavior characteristics after decoupling, and a nonlinear attenuation item based on a real interval is introduced to simulate memory forgetting; and finally, integrating dual-channel information through a gating fusion mechanism and predicting future answering performance of the learner. According to the method, optimization conflicts are effectively relieved, the robustness to a complex learning mode is enhanced, and knowledge state modeling which better accords with a cognitive law is realized.

The invention discloses a retrieval enhancement generation method for a time-sensitive problem, which comprises the following steps of: mixed time perception retrieval: enhancing document retrieval by adding time constraint on the basis of semantic relevance, and guiding by a time card to ensure that the retrieved document not only conforms to the meaning of query, but also conforms to the semantic relevance; the time context is met; the progressive multi-step reflection comprises the following steps of: firstly, acquiring and evaluating an initial document set by applying mixed time perception retrieval; if a document is retrieved, generating a final answer by using a large language model; otherwise, entering a reflection stage, and summarizing useful time information in the retrieved document into a context; and merging document sets accumulated in all iterations to generate a final answer. According to the method, a new framework integrating dynamic knowledge updating and time reasoning into the retrieval and generation process is provided, and accurate and timely response can be made to time-related problems.

A large model memory enhancement method based on time perception consistency feedback and optimization comprises the following steps: 1, data preprocessing: obtaining a historical interaction text generated by each agent in a target scene, and processing the text by using a preprocessing module; 2, attribute mining: extracting agent personal attributes, site attributes, logic attributes and core arguments from each generated content; and 3, keyword management and memory storage: maintaining an independent keyword historical file and a memory library file for each agent, and constructing an evolvable memory which changes along with time. 4, performing memory retrieval and context construction; 5, performing multi-dimensional consistency evaluation, and generating a comprehensive score; and 6, self-adaptive optimization is carried out, and self-feedback and self-evolution of model behaviors are realized. By means of the method, the memory ability and historical consistency of the multi-agent large language model can be effectively enhanced.

The invention discloses a cognitive impairment risk intelligent matching intervention system based on multi-modal data, and belongs to the technical field of cognitive impairment, and the cognitive impairment risk intelligent matching intervention system specifically comprises the following steps: collecting a tactile interaction sequence and a face video data stream when a user executes a cognitive intervention task, and constructing an original multi-modal data set; extracting tactile operation track features and reconstructing a heart rate variability sequence, and generating a synchronous multi-mode feature set; identifying a difference interval of state fluctuation and extracting behavior and physiological features in the interval to form an instant physical and mental load parameter set; in combination with an interaction efficiency index in the task execution record, generating a joint state feature vector, and outputting a personalized adaptation instruction through a pre-trained sub-state-parameter association model; and constructing an adaptive strategy model by using the multi-modal features and an adaptive instruction to realize millisecond-level dynamic adjustment from the real-time multi-modal features to task parameters. According to the method, real-time perception and accurate matching of the cognitive emotion load of the user are realized, and the individuation degree and instantaneity of intervention are improved.

The invention relates to a non-invasive brain stimulation self-adaptive closed-loop regulation and control system. The system comprises a task interaction module which is configured to present a time sensing task to a target object and receive an interaction instruction of the target object; the non-invasive brain stimulation module is used for performing non-invasive brain stimulation; the sensing acquisition module is used for acquiring electroencephalogram data and behavioral data of a target object; the closed-loop regulation and control module comprises at least one block-level regulation and control primary unit, and each block-level regulation and control primary unit comprises a plurality of block-level regulation and control primary units; the trial secondary regulation and control primary unit is used for generating a first feature vector in real time on the basis of the currently collected electroencephalogram data and behavioral data so as to obtain a stimulation parameter of the next trial in the current block; the block-level regulation and control first-level unit is used for processing the currently collected electroencephalogram data and behavioral data to obtain a second feature vector, and based on the second feature vector, adaptive search is carried out under security constraints to obtain stimulation parameters of a next block. By adopting the method, the stability of the stimulation effect can be improved.

The invention discloses a multi-person collaborative human factor risk monitoring method and device based on multi-modal perception, and relates to the technical field of human factor engineering, intelligent perception and group decision behavior modeling. The cognitive state, emotion change and physiological load of participants are sensed in real time by fusing EEG, motion state, positioning track, heart rate, blood pressure and other multi-source information, and the collaborative decision behavior and cognitive consistency of a group in a high-pressure task are evaluated through interactive modeling. The method is suitable for multi-personnel, multi-role and task-intensive cooperation scenes such as emergency management, military drilling, industrial safety, complex traffic guidance and the like.

The invention provides a community susceptible population emotion crisis dynamic monitoring and early warning method based on multi-modal interaction characteristics, and relates to the technical field of intelligent emotion early warning. Comprising the following steps: constructing a multi-modal emotion interaction graph, introducing a self-organizing attention mechanism to realize node-dependent dynamic weighting, and establishing a time-aware migration updating mechanism to capture an emotional energy flow evolution trajectory; aiming at modal deficiency and semantic offset, designing a cross-modal compensation cooperation mechanism to generate deficiency features, constructing an energy propagation model to measure emotional disturbance accumulation and diffusion, and combining reinforcement learning to realize self-learning optimization of an early warning model so as to form a closed-loop dynamic emotional crisis monitoring and early warning system.

The invention discloses a remote sensing image change detection method and system based on double-flow SAM and time sequence perception, and aims to solve the problems of insufficient double-time-phase feature interaction, weak time sequence modeling, poor change region sensitivity and the like in the prior art. According to the method, a cross-time-frame attention type memory mechanism (CPMM) and a time perception two-dimensional space modeling module (Temporal-Aware SS2D) are innovatively introduced on the basis of SAM Encoder, the cross-time-frame attention type memory mechanism (CPMM) enhances alignment of a stable structure and features between time phases through lightweight two-way cross-time-phase modulation, and the time perception two-dimensional space modeling module (SSD) deeply fuses double-time-phase features in a dimension reduction space and fuses global context information. And constructing a framework with local detail retention and global time sequence semantic perception to accurately capture a change trend. According to the method, the adaptability, robustness and calculation efficiency of the model to the time-space characteristics of the remote sensing data are remarkably improved, and the accurate change detection of the high-resolution remote sensing image is realized while the high detection precision is kept.

The invention relates to a smart home dynamic influence maximization method based on time sequence perception, and belongs to the technical field of social network analysis. The method comprises the following steps: inputting a dynamic social network data set into a time sequence snapshot generator to generate a dynamic network sequence; pre-training labeling is carried out based on an influence capacity scoring method of time perception, and dynamic influence capacity scores, static features and dynamic features of nodes in the dynamic network sequence are calculated; the dynamic network sequence passes through a space-time network model to obtain a remodeled space-time feature vector; the remodeled spatio-temporal feature vector passes through a time sequence perception enhancement module to obtain fusion features; inputting the fusion features into a candidate seed node predictor, and predicting candidate seed nodes in each snapshot; and based on the candidate seed nodes in each snapshot, selecting an optimal seed node from the snapshots through an enhanced greedy algorithm to obtain an optimal seed node set. According to the invention, the accuracy of candidate seed prediction can be improved.

The invention discloses a physical information guided wind speed field data downscaling method fusing terrain and time perception. The method comprises the steps of 1, multi-source physical constraint modeling and data construction; step 2, constructing a generative downscaling framework based on a conditional diffusion probability model; step 3, designing a noise prediction network structure fusing terrain and time prior; and 4, model training and rapid sampling reasoning. The method solves the problems that the texture of a wind speed field generated by an existing deep learning downscaling method is excessively smooth, high-frequency turbulence details are lost, and the terrain forcing effect and the wind speed seasonal / daily periodic physical law are ignored, and provides a framework combining explicit physical feature engineering and a conditional diffusion model. Through dual deep fusion of terrain and time information, high fidelity, physical consistency and reasoning efficiency of a generated result are considered.

The application relates to a non-invasive brain stimulation adaptive closed-loop regulation system, which comprises a task interaction module configured to present a time perception task to a target object and receive interactive instructions thereof; a non-invasive brain stimulation module for non-invasive brain stimulation; a sensing and collecting module for collecting electroencephalogram data and behavior data of the target object; and a closed-loop regulation module comprising at least one block-level regulation primary unit, each block-level regulation primary unit comprising a plurality of trial-level regulation primary units; the trial-level regulation primary unit is used for generating a first feature vector in real time based on the currently collected electroencephalogram data and behavior data, so as to obtain stimulation parameters of the next trial in the current block; and the block-level regulation primary unit is used for processing the currently collected electroencephalogram data and behavior data to obtain a second feature vector, performing adaptive search under safety constraints based on the second feature vector, and obtaining stimulation parameters of the next block. The method can improve the stability of the stimulation effect.

The application belongs to the technical field of data processing, and particularly relates to a point of interest recommendation method based on space-time perception; the method comprises the following steps: obtaining user check-in data and inputting the data into an embedding module for processing to obtain a user trajectory embedding matrix and a space-time interval embedding matrix; a trajectory flow graph is constructed, and a user general behavior mode is calculated according to the node features of the graph; a space-time perception attention module is used to process the trajectory embedding matrix and the space-time interval embedding matrix of a user historical trajectory sequence to obtain a user long-term travel preference representation; a space-time perception gated recurrent unit is used to process the trajectory embedding matrix and the space-time interval embedding matrix of a user current trajectory sequence to obtain a user short-term travel preference representation; and a point of interest recommendation result of the user is calculated according to the user general behavior mode, the user long-term travel preference representation and the user short-term travel preference; the application can flexibly and accurately recommend points of interest to users, and has a good application prospect.

The invention relates to an intelligent call routing method and system based on panoramic awareness, and belongs to the field of intelligent routing. The method comprises the following steps: capturing a multi-modal original data stream, performing panoramic perception based on the multi-modal original data stream, and constructing a multi-dimensional emotion field; constructing a field coupling degree model based on the multi-dimensional emotional field and the emotional resonance spectrum, and outputting an optimal matching seat; establishing a user-seat dialogue pair based on the optimal matching seat, and constructing a strategy utility function for outputting an optimal strategy sequence; based on the multi-dimensional emotion field and the optimal strategy sequence, resonance spectrum adjustment amount is obtained through resonance spectrum dynamic tuning, and a seat behavior fine tuning instruction is output; and constructing a strategy scene package and forming a strategy gene pool, and updating the emotional resonance spectrum based on the strategy gene pool. According to the method, real-time perception of user emotions, seat adaptive matching and strategy dynamic optimization are realized through multi-mode emotional field construction, emotional resonance spectrum matching and strategy gene pool evolution.

This invention discloses an online education group question-and-answer matching method based on user style and time awareness, relating to the field of question-and-answer matching using deep learning natural language processing technology. The method involves constructing a BigData dataset; dividing the BigData dataset into training, validation, and test sets; building user style-aware and time-aware question-and-answer matching models; training the models using the training set and obtaining performance metrics using the validation set to find the optimal hyperparameters; and inputting the test set into the final user style-aware and time-aware question-and-answer matching model to obtain the matching results. This invention enhances question extraction by recognizing user style through user style awareness, reducing the impact of noise caused by severe imbalances between the number of questions and other types of dialogue. It also reduces the noise caused by a large number of potential answers to a single question through time awareness. Compared with other traditional question-and-answer matching models, this method improves the model's question-and-answer matching performance and reduces the impact of data noise.

The invention discloses a bus bunching prediction method and system based on a time-aware network. The method comprises the following steps: preprocessing bus arrival data, fusing multi-source spatial-temporal characteristics, introducing time period codes, and processing abnormal and missing values; targeted denoising is performed on data with different characteristics based on Fourier analysis, so that the data quality is improved; a time perception neural network is established, local features and long-term dependence are captured through a CNN-LSTM architecture, a time decay factor mechanism is introduced, dynamic modeling of a time sequence information importance decay process is realized, and a long-term rule of data and short-term disturbance after time calibration are effectively integrated; and a dual attention mechanism of time dimension and feature dimension is fused, key features are extracted and integrated, and multi-step prediction of bus bunching is realized. According to the method, the non-linear relation and the space-time dynamic rule of the bus data can be fully captured, and the problem of unequal intervals in a bunching time sequence is effectively solved.

The application provides a network hot review confrontation performance self-adaptive evaluation method and system based on space-time perception and a terminal device, and is suitable for the technical field of network security. The method comprises the following steps: inputting multi-source network data into a multi-scale space-time data fusion model to generate multi-scale space-time features; generating a fusion feature vector through a feature depth fusion processing model based on the multi-scale space-time features; training a three-party strategy network model to be trained based on the fusion feature vector, determining a three-party game equilibrium strategy and an evaluation score corresponding to the three-party game equilibrium strategy; adjusting evaluation indexes through an adaptive evaluation model based on environmental state features to obtain new evaluation index weights; and determining an adaptive evaluation score based on the new evaluation index weights and the evaluation score.

A dynamic scene navigation method based on a two-stage optimization structure and a space-time perception mechanism comprises the steps that four frames of time sequence depth images are obtained through depth estimation and a future frame prediction network, current and future space changes are represented, time sequence depth features and a target state are subjected to joint modeling through a ViT structure with the minimum change, and the time sequence depth images are obtained; global attention association between an image area and a target is realized, and target-oriented spatial-temporal features are extracted; in the strategy learning stage, basic obstacle avoidance and target driving capabilities are learned in a low-dynamic environment by imitation learning, and then two-stage optimization is performed in a high-dynamic environment by combining reinforcement learning with an evaluation result of an imitation strategy, so that the strategy convergence speed and the environmental adaptability are improved. According to the method, the robust navigation performance in a high-dynamic narrow environment can still be realized under the condition that a global map is not needed.

The invention relates to the technical field of neural activity detection, in particular to a meditation sleep-aiding method and system based on brainwave feedback, and the method comprises the following steps: obtaining meditation frequency band electroencephalogram original data, extracting an amplitude extreme value and identifying a wave crest trend, adjusting a detection interval boundary, extracting a micro-amplitude change and judging a micro-change structure. And analyzing dominant frequency drift trend identification linkage characteristics, judging sleep-aiding transition states, adjusting ear side signals, and monitoring amplitude trend to complete environment mode switching. According to the method, meditation depth change is identified through the linkage relation between the dominant frequency and meditation degree, targeted neural feedback intervention is effectively realized through real-time perception of hemisphere activity difference and linkage regulation and control of ear side sound signal parameters, and meanwhile, multi-dimensional adjustment of sleep-aiding environmental elements is driven in a stable state, so that the meditation degree is improved, and the meditation degree is improved. The precision and the feedback effect of brain state adjustment are enhanced, and the synchronous adaptability and the dynamic response capability of meditation guiding and sleep-aiding intervention are effectively improved.

The invention is applicable to the technical field of knowledge maps, and provides a space-time knowledge map construction method based on space-time perception entity influence enhancement, which comprises the following steps: constructing a space-time knowledge map according to entities, relationships and space-time information; obtaining a relation weight between the space-related entity and the target entity by using the space position information and the space-time tetrad information; adjusting the relation weight in combination with a time factor to obtain a dynamic relation weight; the total influence of the entity in specific time and space is obtained by combining the dynamic relation weight and a self-influence coefficient obtained by relation weight normalization; and performing relation and entity attribute enhancement according to the dynamic relation weight and the total influence to obtain an enhanced space-time knowledge graph. According to the method, the strength of the entity relationship can be dynamically quantified, and the influence state of the entity along with the time change can be described, so that the space-time dependency relationship between the entities can be dynamically captured.

An explainable sequence recommendation method fusing time perception and path reasoning, comprising the following steps: 1) constructing a time sequence cooperation knowledge graph; 2) performing node representation learning on the time sequence cooperation knowledge graph through TransE; 3) designing a time perception system, updating the current time item vector through the path and item attention mechanism, and then combining the user vector to generate an item prediction vector; 4) designing a path reasoning system, using a reinforcement learning framework to perform associated path reasoning, and obtaining a multi-path vector through path representation learning; 5) model training, training the time perception system and the path reasoning system to obtain an item prediction result. The application introduces a double system to simulate the human reasoning process, and improves the accuracy and explainability of the recommendation result by learning the time dependence characteristics of the user behavior in the time sequence cooperation knowledge graph.

The application discloses a traffic flow prediction method and system based on auxiliary node enhanced space-time perception, and the method comprises the following steps: acquiring the positions and traffic flow sequences of nodes; selecting a target node, calculating the distances between the target node and other nodes, and selecting several nodes with distances less than a preset distance threshold or the first several nodes with the smallest distances as auxiliary nodes; embedding the traffic flow sequence of the target node point by point to extract local features; embedding the traffic flow sequences of the target node and the auxiliary nodes to extract global features; fusing the local features and the global features of the target node by using a time attention mechanism to obtain multi-scale features of the target node, and fusing the multi-scale features of the target node and the global features of the auxiliary nodes by using a space interaction attention mechanism to obtain fused features; and predicting the traffic flow of the target node at the next moment based on the fused features. The application improves the accuracy of flow prediction.

The application discloses an industrial process remaining time prediction method and device and a storage medium, and belongs to the field of data processing. The method comprises the following steps: acquiring a track prefix sequence and inputting the track prefix sequence into an activity completion model to output a complete track prefix sequence after activity completion; extracting a semantic feature vector and a time interval feature vector of each activity in the complete track prefix sequence, splicing the semantic feature vector and the time interval feature vector to generate a fusion feature vector, and arranging the fusion feature vector in an activity order to form a feature sequence; inputting the feature sequence into a remaining time prediction model to output a predicted remaining time of an industrial process instance; wherein the activity completion model is obtained based on a BERT architecture and combined with contrast learning training, and the remaining time prediction model is based on a Transformer architecture and introduces a time perception attention mechanism. The effective repair of missing industrial process activities, the deep fusion of multi-dimensional features and the accurate modeling of time dynamic characteristics are realized, and the accuracy of the predicted remaining time is improved.

The invention relates to the technical field of human body motion analysis, in particular to a 3D human body posture estimation and multi-key-point time sequence analysis method. Comprising the following steps of multi-modal video access, human body key point space-time perception and extraction, 3D posture reconstruction and credibility modeling, key point time sequence feature quantification, time sequence stage perception and logic judgment, quantitative evaluation and intelligent diagnosis and result output, namely, analysis results are output in the forms of scores, texts, graphs or voices. The method is used for guiding a user to perform action correction, and solves the problems of insufficient time sequence semantic understanding, poor individual difference adaptability and lack of interpretability and targeted guidance of an analysis result in human body posture estimation and action analysis in the prior art.

The invention discloses an auditing manuscript verification optimization method and system based on space-time perception and a pseudo regulation generation strategy, and the method comprises the steps: firstly constructing an auditing knowledge base containing multi-level regulations and systems, and then carrying out the preprocessing of an auditing manuscript sample through the auditing knowledge base, and obtaining a preprocessed auditing manuscript; extracting timeline features in the preprocessed audit manuscript; according to the method, the functions of accurately restoring a legal environment in a specific time-space and introducing a pseudo-regulation generation strategy to fill a business and a legal semantic gap are realized, and a multi-level system can be fully covered through a multi-source bursting retrieval mechanism; meanwhile, a composite mode of space-time perception, reverse generation and bursting retrieval is adopted, so that the defects of law and regulation reference failure, high retrieval noise and unclear system application range in the existing auditing qualification can be fully eliminated, the auditing work is more compliant, intelligent and efficient, the accuracy and reliability of an auditing conclusion are guaranteed, and the auditing efficiency is improved. And the device is suitable for wide popularization and use.