48 results about "Visual behavior" patented technology

The embodiment of the invention provides a form generation method, device and system based on AI enhancement and a storage medium, and relates to the technical field of form generation. The method comprises the steps of generating form structure metadata according to a natural language processing technology, a reinforcement learning algorithm and / or a type security verification rule; according to the form structure metadata, the equipment sensor data and / or the user visual behavior data, the rendering priority of each field is determined; and generating a target form according to the rendering priority and the equipment parameters. By generating the form structure metadata which is reasonable in structure and conforms to the type safety specification, the automation degree and reliability of form design are improved. By fusing form structure metadata, device sensor data and user visual behavior data, a rendering strategy can reflect device characteristics and user attention distribution. The target form is generated by combining the rendering priority with the equipment parameters, so that the form layout is adaptively matched with the terminal equipment, and the loading performance and the interaction experience of the form are improved.

The invention relates to an asymmetric scattering myopia control lens based on human eye visual behaviors, which is applied to the technical field of eye vision optics, and has the design core that an asymmetric visual behavior mode shown by human eyes in an actual visual task is deeply integrated into the optical design of the lens. Therefore, an asymmetric optical structure with scattering intensity distribution highly matched with individual visual habits is constructed. According to the lens, scattering characteristics can be accurately configured in a specific area of the lens according to a preset human eye visual behavior data model, especially in the lower half part of the lens, namely a visual field area corresponding to near-distance tasks such as near-distance reading, writing or electronic equipment use of a wearer; the scattering intensity is higher than that of the upper half part (corresponding to a far task view field area), so that the myopia control efficiency is remarkably improved, and meanwhile, the visual comfort and definition of a wearer are effectively considered.

The invention discloses an intelligent diopter training system and method based on a multi-modal physiological feedback and reinforcement learning algorithm. The intelligent diopter training system comprises a multi-modal physiological data acquisition module, a central processing PC, a user terminal and a cloud data platform. Visual behavior data, ciliary muscle state data and autonomic nerve reaction data of a user are collected in real time through a multi-modal physiological data collection module, multi-modal physiological data fusion and feature extraction are carried out, and a refraction state evaluation model is used for calculating a comprehensive score of a refraction state grade, visual fatigue and adjustment sensitivity of the current user. A personalized training parameter set of the next training period is dynamically decided and generated based on a reinforcement learning algorithm, and the user terminal presents visual training stimulation according to the current personalized training parameter set. The cloud data platform stores the data and generates a visual training effect trend chart, a diopter change prediction report and personalized training suggestions to complete training scheme personalization, training means intellectualization and feedback evaluation comprehensiveness.

The invention relates to the technical field of multi-point defocusing, in particular to an optical stable photochromic multi-point defocusing myopia control lens prescription generation method, which comprises the following steps of: based on eyes of a wearer, scanning cornea by using a non-contact optical device, extracting iris and pupil characteristics, monitoring an adjustment reaction, and performing three-dimensional reconstruction and multi-direction light path tracking analysis to obtain a myopia control lens prescription. And the distribution configuration of the annular belt is optimized, and the overlapping area is adjusted by combining the photochromic requirement, so that the fusion function partition structure is formed. Through collection of multi-source visual parameters and combination of spatial modeling and focusing path analysis, cut-through association of individual visual behaviors and structural attributes in lens customization is realized, functional requirements of different areas and photochromic response are dynamically fused through hierarchical adjustment of the spatial structure of an intervention area, and the visual performance of the lens is improved. And the material and microstructure data are synchronously output to a manufacturing link, so that the collaborative matching capability of the visual area partition is enhanced, and the requirements of a wearer on visual comfort and optical reaction are met.

The invention provides an intelligent campus security abnormal behavior identification method and system based on machine vision, and belongs to the technical field of campus security protection, and the method comprises the steps: adjusting a sensitivity parameter and a confidence threshold value of a preset visual behavior identification model based on campus schedule context information; inputting the video data of the monitoring area into the visual behavior recognition model to obtain an abnormal behavior recognition result and confidence; judging whether the confidence is smaller than a confidence threshold and whether the abnormal behavior recognition result is a high-risk behavior; if any condition is met, auxiliary data are acquired, and auxiliary judgment information is generated; inputting the abnormal behavior recognition result, the confidence coefficient and the auxiliary judgment information into a preset decision model, and generating a target recognition result and a danger level; if any condition is not met, determining a danger level by querying a preset behavior type danger level mapping table, and taking an abnormal behavior recognition result as a target recognition result; and determining a corresponding target early warning strategy based on the danger level and the campus schedule context information. According to the invention, the accuracy and response efficiency of safety early warning can be improved.

The invention discloses a classroom behavior detection model and method based on parallel state space model and self-attention, and belongs to the technical field of intelligent education. A convolution unit driven by a TeLU activation function is designed, and the problem of subtle behavior feature weakening is solved through nonlinear mapping of a hyperbolic tangent index linear unit. A mixed feature fusion unit module is constructed, and attitude transformation and scene change are adapted through multi-branch structure integration space and channel reconstruction convolution and dynamic feature transformation. A VCAF module is designed, features are decomposed into three types of sub-features capable of being independently processed, and through long-time timing sequence modeling of Mamba and aggregation of a convolution addition self-attention mechanism, behavior semantic confusion with highly similar visual performance is solved, redundancy of long-time static features is removed, and details of short-time dynamic features are reserved. An efficient and reliable behavior detection scheme is provided for classroom scene multi-target behavior recognition, and technical support is provided for intelligent classroom real-time behavior monitoring and teaching quality evaluation.

The invention relates to the technical field of member identity recognition and service recommendation, and discloses a multi-mode member identity recognition and service recommendation system based on a large model. According to the system, visual, behavior and text data of members are acquired through a multi-modal data acquisition module, initial identity feature vectors are extracted by a member identity recognition engine, and prototype matching and feature enhancement are performed by means of a multi-modal prototype memory bank; the incremental interest distillation module dynamically corrects interest characterization based on member follow-up interaction behaviors, and the service recommendation generation module generates a personalized service recommendation list according to refined interest characterization. According to the invention, high-precision and stable recommendation under a new member cold start scene is realized, and the system self-evolution capability is realized.

The invention discloses a real-time detection and intelligent guiding method for an insurance double-recording process and related equipment, and relates to the field of image extraction and semantic analysis. The method comprises the following steps: acquiring a real-time audio stream and a real-time video stream in an insurance double-recording process, determining a current business process node and loading a corresponding multi-modal detection strategy; the strategy defines visual and speech semantic features to be extracted and associated time thresholds. The method comprises the following steps: respectively acquiring audio and video features, and judging whether the audio and video features fall into the same time interval defined by an association time threshold in real time; if yes, judging that time sequence relevance exists and generating a compliance result; and if not, generating first guide prompt information according to the feature missing type. According to the method and the device, the logical association between the visual behaviors and the voice semantics is constructed on the microscopic time slice, so that the problem of audio-visual asynchronous missing detection caused by lack of time sequence collaboration due to discrete verification of audio-visual characteristics in related technologies is solved, and then real-time accurate verification of the consistency of the double-recording intention and the behaviors is realized.

The invention discloses a game testing method and system based on virtual reality. The method comprises the following steps: determining an optimal layout of a plurality of virtual cameras through an adaptive visual angle prediction algorithm and generating a panoramic test view; constructing a multi-dimensional operation data structure to record test operation at high frequency and realize compression storage; collecting performance data, and generating a visual performance distribution thermodynamic diagram through spatial interpolation; natural gesture interaction and multi-user collaborative labeling are realized based on hand feature recognition; and deploying a plurality of AI models to automatically detect vision, behavior and time sequence anomalies. The system comprises six modules including a VR environment construction module, an operation record analysis module, a performance visualization module, a collaborative interaction module, an intelligent detection module and a data storage module which work cooperatively through a standardized interface. A traditional plane game test is converted into an immersive VR test environment, the technical problems that the view field is limited, the operation record is not accurate, the performance analysis is not visual and the cooperation efficiency is low are solved, and an innovative technical means is provided for guaranteeing the game quality.

The present invention relates to a multi-robot linkage service control method driven by tourist behavior recognition, including: a first robot acquires a video image sequence of a target tourist, recognizes its visual behavior sequence and determines the service behavior state; when the target tourist meets a preset visual continuation trigger condition, visual continuation information is generated; the visual continuation information includes the visual appearance features, image position, target spatial position, moving direction, visual behavior sequence and service behavior state of the target tourist, and the target spatial position is obtained by coordinate mapping of the image position; the visual continuation information is sent to a second robot, and the second robot determines the corresponding target tourist in the video image it acquires based on this, and continues to recognize the visual behavior sequence and service behavior state of the target tourist; a multi-robot linkage service control instruction is generated according to the service behavior state after continuous recognition, and the robots participating in the linkage are controlled to execute service actions.

This invention relates to the field of data processing, and more particularly to a control method and system for constructing a visual behavior simulation model. The method includes: acquiring real-time business event streams, performing protocol decoding and field parsing to obtain structured behavior data; constructing a behavior dependency graph structure based on the structured behavior data and determining edge weights; extracting the current behavior path based on the behavior dependency graph structure; evaluating the path legality and fit of the current behavior path based on the edge weights to obtain an average path fit; and judging the legality of the current behavior path based on the average path fit, correcting and optimizing invalid current behavior paths to obtain an optimized current behavior path, thus achieving process control. This solves the problem that traditional simulation model construction methods lack automated structure recognition and process rationality judgment mechanisms when facing complex business scenarios.

This application provides a multimodal intelligent perception and interpretation method for abnormal behavior based on visual-linguistic-temporal fusion. The method generates temporal visual behavior segments to be perceived by acquiring real-time behavior monitoring video streams, and semantically embeds and encodes domain expert knowledge text to generate linguistic prior constraint vectors. Based on the above segments and vectors, logical anchor points for the abnormal behavior to be interpreted are generated; causal chain reconstruction is performed to generate a temporal evolution map of abnormal behavior with causal logical constraints; finally, a quantitative evaluation of the evidence chain credibility is performed to generate a perception and interpretation description matching a preset business logic format. This application, through deep fusion and causal reconstruction of visual, linguistic, and temporal features, solves the technical shortcomings of traditional solutions in perceiving complex abnormal behavior with a single dimension and lacking logical explanatory power, thus improving the credibility and business adaptability of the perception results.

The utility model discloses a gradient microstructure optical lens adapted to a simulated outdoor environment, which comprises a substrate and a microstructure layer, the microstructure layer comprises a lower high-density area, a middle transition area and an upper low-density area, the microstructure layer is provided with a plurality of uniformly distributed microstructures, and the microstructures are arranged on the substrate. The density of the microstructures is gradually reduced from the lower part to the upper part along the vertical direction of the lens, and the gradient change range of the density of the microstructures is that 2-5 microstructures are reduced per millimeter. According to the gradient microstructure optical lens adaptive to the simulated outdoor environment, targeted anti-dazzle is achieved, and the dense microstructures on the lower portion effectively restrain ground reflection light which accounts for more than 60% of an outdoor dazzle source; visual field definition optimization: the upper sparse arrangement reduces interference to a long-distance visual field; and ergonomic adaptation: the visual behavior characteristic that the downward watching proportion of human eyes is 70% during outdoor activities is met.

The invention discloses a multi-modal emotion recognition system based on facial expression and eye movement tracking. The multi-modal emotion recognition system comprises a data acquisition module, a data cleaning and quality control module, a visual analysis module, a multi-dimensional feature extraction module and a result feedback module. The data acquisition module is used for acquiring multi-modal original data; the data cleaning and quality control module is used for executing structured processing on the input multi-modal original data; the visual analysis module is used for presenting visual behavior characteristics of the subject during stimulation; the multi-dimensional feature extraction module is used for extracting multi-modal features describing gazing behaviors, physiological reactions and image contents from the eye movement data, the stimulation pictures and the face videos; the result feedback module is used for converting the analysis result of the multi-modal features into visual display content, and generating final emotion classification output according to the eye movement signals, the robustness and generalization ability of emotion classification are greatly improved, and the scene adaptability is high.

This invention discloses a method and system for precise feeding decision-making in aquaculture based on visual behavior analysis, belonging to the field of image detection technology. The method and system include the following steps: S1, synchronously observing the activity process of fish schools and the action process of feeding equipment within the aquaculture area; S2, dividing the observation into stages around a single feeding process and analyzing the source of current visual changes; S3, identifying the fish school transfer process and interpreting data from the current aquaculture unit; S4, generating feeding control parameters and correcting the complete control process. This invention effectively improves the accuracy and rationality of precise feeding decisions in aquaculture, solving the problem that the feeding action itself can contaminate visual observation, causing the system to misjudge the disturbance caused by feeding as actual increased feeding activity by fish.

The invention discloses a myopia prevention method based on a neural network model, particularly relates to the technical field of visual health monitoring, and is used for solving the problems that the real-time dynamic visual physiological load of a user cannot be perceived and responded due to dependence on a static rule and personalized myopia risk early warning and prevention are difficult to realize in the prior art. Real-time visual behavior data of a user in immersive experience equipment is acquired, a visual adjustment compensation state of the user is evaluated based on a neural network model, and when the state exceeds a preset threshold value, a visual behavior entropy is further calculated and a content semantic focus change rate of a virtual scene is synchronously analyzed so as to analyze a dominant inducement type of state abnormality; matching the fixation point distribution with the scene visual saliency topological graph according to the inducement type to identify specific dominant factors, and dynamically adjusting display parameters of the immersive experience equipment according to the specific dominant factors; accurate evaluation and inducement analysis of the real-time load of the user visual system are achieved, and the myopia prevention effect is achieved while the immersion experience is maintained.

The invention provides an advertisement putting effect monitoring and optimizing method and system, and relates to the technical field of advertisement putting, and the method comprises the steps: obtaining and fusing advertisement vision and user behavior bimodal data to form a cross-modal feature set; triggering a vision-behavior fusion adversarial generative network to dynamically divide a user group and outputting a user-group membership matrix; constructing a causal model containing visual feature gain factors, generating an anti-fact gain matrix, and generating and executing a multi-objective optimization strategy through cooperation of visual optimization and putting strategy agents; the system comprises a bimodal data stream processing module, a distribution drift detection and user group division module, a causal model and gain quantification module, a multi-agent reinforcement learning strategy optimization module and a closed loop iteration optimization module. According to the invention, quantitative monitoring and dynamic optimization of the visual effect of the advertisement are realized, strategy deviation caused by splitting of visual and behavior data is reduced, and the accuracy and adaptive ability of advertisement putting are improved.

The invention discloses a visual impaired person behavior ability assessment method and system, and relates to the technical field of visual rehabilitation, and the method comprises the steps: obtaining a dynamic behavior video of a visual impaired person; analyzing the dynamic behavior video by using a human body posture recognition library and a deep learning algorithm, and determining a head rotation direction and rotation times of the visually impaired person; and comparing and analyzing the head rotation direction and the rotation frequency of the visually impaired person with a pre-established standardized reference table, evaluating the actual use effect of the magnifying visual aid or the visual field expanding visual aid worn by the visually impaired person, and generating a personalized rehabilitation scheme according to the actual use effect. According to the method and the system provided by the invention, the visual behavior capability of a low-vision group is reflected more comprehensively and accurately by evaluating the head rotation frequency index of the visually impaired person in dynamic behaviors such as obstacle avoidance and search, so that powerful support is provided for formulating a personalized rehabilitation scheme.

The invention provides a lockage intention recognition method and related equipment thereof, belongs to the field of data processing, and is applied to gate control equipment. The method comprises the following steps: acquiring wireless positioning position data and visual monitoring position data in a target area; performing space-time matching on the wireless positioning position data and the visual monitoring position data, and if matching succeeds, determining that the wireless positioning position data and the visual monitoring position data belong to the same target object; obtaining visual behavior characteristics of the target object; determining a lockage intention prediction value of the target object according to the visual behavior characteristics; and determining and executing a corresponding gate control operation based on the lockage intention prediction value. According to the invention, the accuracy of lockage intention recognition can be improved.

The invention discloses an asymmetric scattering myopia control lens and a manufacturing method thereof, which are applied to the optical field, firstly, the problem that an optical defocus signal is not matched with an actual visual task mode in the prior art is solved by accurately associating optical scattering intensity with a human visual behavior thermodynamic diagram; a stronger myopia out-of-focus signal is provided in a short-distance reading area used by a wearer at high frequency, the myopia control effect is effectively enhanced, high definition is maintained in a long-distance watching area, unnecessary visual interference and comfort reduction are avoided, secondly, waste of optical resources in an existing rotational symmetry design is avoided due to the adoption of the asymmetric design, and the cost is reduced. According to the lens structure, optical energy is distributed according to needs, precise free-form surface machining and micro-nano machining technologies are combined, high-precision manufacturing of an asymmetric microstructure is ensured, smooth transition of scattering intensity between different optical areas is achieved, and finally the innovative lens structure with myopia control efficiency and visual comfort is provided.

The invention discloses a mouse visual behavior and neural signal synchronous recording system based on grating stimulation, and belongs to the field of neuroscience experiment equipment. The system comprises a grating stimulation display, a grating stimulation control software module, a head fixing device, a licking detection module, a reward and punishment module, a neural signal recording module, a synchronous control module and a data acquisition module. Through software and hardware collaborative design, high-precision time synchronization of visual stimulation and neural signals is realized, the time sequence accuracy and repeatability of cross-modal signal acquisition are improved, and the method is suitable for visual processing, learning and memory and neural disease model research.

The invention discloses a self-adaptive learning path recommendation system and a head-mounted device, and belongs to the technical field of intelligent education, the system comprises a multi-modal data acquisition module, a signal feature fusion module, a knowledge graph management module and a self-adaptive path recommendation module, electroencephalogram signals, visual behaviors and learning content data are synchronously collected through a head-mounted device, a fusion feature vector is generated by adopting a signal quality adaptive weighted fusion strategy, the knowledge mastery degree is evaluated based on a deep learning model, and a dynamic knowledge graph is updated in real time. According to the method, the recommendation priority is calculated by integrating the mastery gap, the knowledge importance, the dependence emergency degree, the forgetting risk and the fatigue state, dynamic planning and closed-loop adjustment of a personalized learning path are realized, the mastery evaluation accuracy and the learning efficiency are remarkably improved, and meanwhile, the data privacy security is guaranteed.

The application relates to the technical field of intelligent exhibition halls, and discloses an exhibition hall self-adaptive tour and atmosphere regulation method and system based on sensing. The method collects visual behaviors, physiological signals, interactive behaviors and environmental state data of audiences through a distributed sensor network, adopts a feature extraction and self-adaptive fusion method to obtain a comprehensive state feature vector, adopts a multi-task learning model and a time sequence prediction method to evaluate the interest degree, fatigue degree, emotional state and cognitive load degree of the audiences, adopts a reinforcement learning and multi-objective optimization method to generate a personalized self-adaptive tour strategy, adopts a situation matching and cooperative regulation method to generate an exhibition hall atmosphere regulation strategy, executes services and continuously optimizes through a real-time feedback and dynamic optimization mechanism, and adopts an online learning and model updating method to continuously improve the system performance. The application can realize the intelligentization, personalization and precision of exhibition hall services, and improve the audience visiting experience quality and the exhibition hall service capability.

Provided are a human-factors intelligence user gaze analysis method, apparatus, and system, and an edge computing device, which belong to the technical field of computer vision. The method includes: collecting visual data of a user's field of view through a camera of a head-mounted device, and collecting eye movement data of the user within the field of view through an eye tracker of the head-mounted device; identifying a target object in the visual data; determining target eye movement data associated with gaze on the target object in the eye movement data; and sending the target eye movement data to a target screen to correspondingly present the target eye movement data on the target object displayed on the target screen. Embodiments of the present disclosure improve efficiency of user visual behavior analysis.

The invention discloses a food sensory evaluation device and method capable of synchronously collecting vision and mouse operation, and belongs to the technical field of food sensory digital evaluation. The device comprises a telescopic experiment table, a display screen, a visual behavior capture instrument, a food heat preservation pad, a mouse, a synchronous control unit and the like, and can synchronously collect eye movement tracks and mouse operation data of a testee under the condition of constant temperature. The method comprises the steps of constant-temperature control, eye movement collection, mouse operation, synchronous collection, data association verification and result output, and the data validity is verified by calculating the matching degree of pupil staying time and mouse operation duration. According to the method, temperature fluctuation and subjective deviation are effectively overcome, objectivity, accuracy and repeatability of food sensory evaluation are improved, and the method is suitable for multiple application scenes such as food research and development, quality control and market research.

This application discloses a human-centric intelligent user gaze analysis method, apparatus, system, and edge computing device, belonging to the field of computer vision technology. The method includes: acquiring visual data of the user's field of vision via a camera of a head-mounted device, and acquiring eye movement data of the user within the field of vision via an eye tracker of the head-mounted device; identifying target objects in the visual data; determining target eye movement data associated with gaze on the target object within the eye movement data; and sending the target eye movement data to a target screen so that the target eye movement data is correspondingly displayed on the target object shown on the target screen. The embodiments of this application improve the efficiency of user visual behavior analysis.

The invention discloses a learner computational thinking path planning method based on multi-modal data reasoning. The method comprises the steps of obtaining context information, visual behavior data, thinking process data and action execution data of a learner target task; determining cognitive state information based on the visual behavior data, the thinking process data, and the action execution data; extracting a first knowledge entity of the target task; matching the first knowledge entity with a second knowledge entity in a preset computational thinking hypergraph to determine a target knowledge entity, and based on the cognitive state information and the target knowledge entity, performing reasoning in the preset computational thinking hypergraph to generate a knowledge node path sequence; and fusing the knowledge node path sequence, the context information and the cognitive state information to generate a teaching action sequence. According to the method, the optimal learning path accurately matched with the real-time cognitive demand and the ability state of the learner can be planned, personalized cultivation of dynamic adaptation is realized, and the method can be widely applied to the technical field of computers.

The invention provides a digital health information cognition error recognition method and system based on an eye movement signal, and particularly relates to the technical field of digital health and medical safety, and the scheme comprises the steps: collecting the eye movement signal and personal information of a target user in the process of processing digital health information; and processing the eye movement signal and the personal information by using the target cognitive error recognition model to obtain a recognition result of the digital health information cognitive error. According to the scheme, by capturing attention distribution and visual behaviors of the user in real time, cognitive deviation of an individual in the digital health information understanding process can be effectively recognized, and the accuracy and reliability of recognizing cognitive errors generated when the user interprets the digital health information are improved.

The invention relates to the technical field of multi-target tracking, in particular to a motion perception multi-target tracking method based on query transfer, which comprises the following steps: receiving visual target query and multi-source data stream; a combined expert decoder is adopted to process the visual query, and visual behavior characterization capable of adapting to complex motion of the target is generated; performing time sequence coding on the multi-source data stream to generate a data sequence; a time sequence consistency reward value is generated by calculating the overlapping degree of the visual residence time sequence and the data active time sequence, and reinforcement learning training is carried out on the association strategy model according to the time sequence consistency reward value; inputting the fused state space into the model to generate an association decision, and constructing a global identity trajectory; and when the target is reproduced, rebinding is executed through a multi-dimensional matching mechanism fusing appearance, motion and space prior. Through fusion of a visual trajectory and multi-source Internet of Things data and introduction of a reinforcement learning optimization long-term association strategy, accurate and long-term cross-modal target tracking in a complex farm interaction scene is realized.