94 results about "Space perception" patented technology

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

The invention discloses a visual language action large model design method for enhancing spatial perception ability, which comprises the following steps of: acquiring a plurality of frames of task scene images, inputting the images into a two-dimensional image encoder and a visual geometry guidance Transformer VGGT encoder, extracting image features and performing time sequence spatial embedding, encoding a natural language instruction into language embedding representation, and extracting a visual language action large model. Image features and language embedding are fused through a cross attention mechanism, a pre-training large model is input to generate cross-modal representation, the fusion features are input into an action expert module to output an action control sequence in combination with robot body state information, and a robot is driven to execute an operation task. Therefore, damage of additional information to an original pre-training model is avoided, and compared with an original combined structure of a pre-training visual language model and an action expert, utilization of multi-view picture information is enhanced, so that higher understanding ability on space depth is achieved in the task execution process, the task success rate is increased, and the task execution efficiency is improved. And a more efficient and more robust robot sensing and decision-making integrated system is realized.

The invention relates to the field of robot navigation, and particularly discloses a spatial perception observation information quality evaluation method and system, a terminal and a medium, and the method comprises the steps: converting the measurement information of each dimension into a normalization probability according to the measurement target of each dimension; according to the concept of entropy in the information theory, the normalized probability of each dimension is converted into a computable entropy value; a downstream task weight coefficient is introduced for the entropy value of each dimension, the multi-dimensional entropy value after the downstream task weight coefficient is introduced is mapped into a physical interpretable confidence coefficient in a [0, 1] interval through an exponential attenuation function, and the confidence coefficient explicitly represents the uncertainty of observation information of each dimension. According to the method, a space perception quality evaluation system with mathematics interpretability, scene adaptation and real-time calculation is formed, multi-source uncertainty is mapped into executable confidence, and multi-dimensional accurate evaluation of intelligent agent space perception observation information is achieved.

The invention discloses an underground coal mine goaf gap space sensing method based on a multiple detection technology, and the method comprises the steps: obtaining a three-dimensional dielectric constant body through a ground penetrating radar, and determining the space position of a goaf gap; resistivity tomography is carried out through the optimally arranged electrode network, and a three-dimensional resistivity body is inverted; constructing a three-dimensional wave velocity body by adopting sound wave detection, and identifying a velocity low-value region; a dielectric constant body, a resistivity body and a wave velocity body obtained by the three technologies are uniformly registered to a cubic grid with a preset resolution and normalized, a caving zone void probability body is constructed by adopting adaptive weighted fusion based on information entropy and a Bayesian maximum posteriori estimation framework, and finally a three-dimensional void model is generated and the total volume is calculated. Through multi-source data fusion of ground penetrating radar, resistivity tomography and sound wave detection, the limitation of a single technology in goaf space detection is solved, the precision of gap detection is improved, and the method is suitable for goaf space perception and treatment.

The invention discloses a method, a device and a system for identifying personnel under a crane jib and related equipment. Inputting the original image into a segmentation network model to segment personnel and suspension arms, and obtaining personnel suspension arm mask data; inputting the original image into the depth estimation network to obtain depth data of the image; combining the personnel suspension arm mask data and the depth data of the image to obtain personnel suspension arm depth data; according to the depth data of the lifting arm of the person, the overlapping degree of the person and the lifting arm in the horizontal direction, the vertical direction and the depth direction is judged, and the first probability that the person is located below the lifting arm is determined; inputting the position coding information of the depth data of the suspension arm of the person and the model cue word into the large language model to obtain a second probability that the person is located below the suspension arm; and according to the first probability and the second probability, obtaining a comprehensive judgment result of the personnel in the dangerous area below the suspension arm. The personnel under the suspension arm can be accurately identified, the equipment deployment cost is low, the space perception capability is high, and the identification process is more efficient and convenient.

The invention provides a pump room scene abnormal state recognition system and method based on video monitoring, and relates to the field of monitoring or monitoring activities, in particular to the field of space perception. The system comprises a synchronous trigger device which is used for synchronously triggering the collection of video data and audio data in a pump room in the same monitoring time interval; and the state identification device is used for intelligently identifying a pump room scene abnormity type by adopting an intelligent scene abnormity identification model on the basis of basic contents obtained by analyzing the video data in the pump room and the audio data in the pump room. According to the invention, in order to solve the technical problem that the recognition precision and efficiency of various specific pump room scene abnormal states cannot be considered at the same time, different intelligent scene abnormal recognition models can be designed for different pump room scenes and multi-modal basic contents screened in a targeted manner are introduced; and various specific pump room scene abnormal state identification is completed, so that the technical problem is solved.

The invention discloses a stroke motion cognition evaluation system and method based on spatial perception nerve enhancement. The stroke motion cognition evaluation system comprises a data acquisition module, a preprocessing module, a feature extraction module, a weight coefficient generation module, a motion cognition evaluation module and a visualization module. Extracting information of different frequency bands of the brain through a feature extraction module, and obtaining power spectral density, a phase locking value and cross-frequency coupling features; meanwhile, a weight coefficient generation module is used for obtaining weight coefficients for performing weight fusion on different features, and the features extracted by a feature extraction module are fused through a motion cognition evaluation module to completely describe complex brain network interaction, so that cognition and spatial perception state evaluation of the subject in the motion imagination process is completed. In addition, threshold adjustment and personalized feedback are performed based on the real-time change of the evaluation result, so that the quality and efficiency of motor imagery training can be remarkably improved.

The invention discloses a robot action generation method integrating multi-layer feature bridging and world knowledge prediction. The method comprises the following steps: extracting multi-layer middle layer visual features and action query hidden variables by utilizing a pre-trained visual language model; an initialization strategy based on robot body sensing state guidance is adopted, real-time pose priori is injected for action query, and traditional all-zero initialization is replaced; a spatial perception vector gating mechanism is introduced into the bridging attention module, and fine-grained selection of specific image region features is achieved; meanwhile, a world knowledge prediction task is integrated, and physical common knowledge is enhanced through explicit modeling environment depth, semantics and a dynamic region; and finally, replacing a traditional L1 regression action head with a diffusion model architecture, and generating an optimal action sequence under multi-modal distribution through an iterative denoising process. According to the method, the operation precision of the robot in a long-range complex task is improved, and the problem of control failure caused by an action'averaging effect 'is solved while the light weight of the model is kept.

The invention discloses a directional floor vibration feedback method and system. The directional floor vibration feedback method comprises the steps of obtaining spatial information and audio information of a virtual object and state information of a target user; obtaining distribution parameters according to the spatial information and the state information, and performing overall frequency scaling on the audio information to obtain vibration waveform data; distributing the vibration waveform data according to the distribution parameters to obtain vibration intensity distribution information; and performing vibration feedback on the target user according to the vibration intensity distribution information. According to the invention, space sound rendering and floor vibration feedback can be driven at the same time, synchronous transmission of sound and vibration is realized, and the space perception and immersion of virtual reality are improved.

A binocular visual field (VF) testing system and a method employing spherical (non-Euclidean) geometric modeling and eye-tracking technology to generate cyclopean visual field maps are disclosed. The system projects dichoptic stimuli to each eye independently at controlled fixation distances and dynamically records vergence responses using eye tracking. A non-Euclidean spatial encoding model based on logarithmic spirals and Jacobian analysis is applied to the resulting binocular data, enabling three-dimensional topographic field mapping. This approach extends traditional monocular field testing by accounting for retinal curvature, binocular integration, and fixation depth effects, resulting in volumetric visual field models more representative of real-world spatial perception. The system includes hardware (BINOSCOPE) and software modules that implement real-time depth encoding, topological mapping, and boundary detection of peripheral visual fields.

The invention discloses a cross-domain multi-modal dynamic reasoning and generating method and system for space sensing equipment linkage, a terminal and a storage medium, and the method comprises the steps: carrying out the weighted fusion and space-time alignment of multi-source heterogeneous equipment sensing data, and forming a cross-domain space sensing data flow with a unified reference; based on real-time spatial topology and business constraints, dynamically dividing semantic collaboration areas and establishing visual mapping; for each region, fusing spatial semantics, text and visual information, and generating a region cooperative control strategy; rendering the visual content in parallel according to the mapping relation and synchronizing the equipment state; and outputting a result through area-level quality reflection evaluation, if all the data reach the standard, carrying out protocol adaptation and linkage output, otherwise, starting a local regeneration protocol for an area which does not reach the standard, re-calibrating data, updating a strategy and rendering. According to the invention, multi-device collaborative millisecond response, cross-protocol non-stop adaptation and anomaly localization repair are realized.

The application provides a kind of space perception teaching auxiliary method, device, equipment and storage medium, wherein, the method comprises: determining target value for training, and recording the first starting point of first user action;When triggering end instruction, determine the critical point of first user action;The difference between first starting point and critical point is used as measurement value;According to the comparison result between target value and measurement value, determine the guidance information, feedback guidance information in the form that visual impaired person can perceive.The space perception teaching auxiliary method, device, equipment and storage medium provided by the embodiment of the application, the user makes first user action with the help of mobile terminal, realizes the perception of specified length or angle, and does not need teaching aid throughout;And, each time triggering end instruction, mobile terminal will compare the result of measurement value and target value to user in real time, and does not need the feedback of naked eye person.Provide the efficiency of teaching, also reduce economic cost.

The invention discloses an intelligent exhibit guide system and method integrating multi-dimensional space perception and user intention judgment, and belongs to the technical field of indoor positioning and navigation. The method comprises the following steps: acquiring real-time state information of a user; according to the user coordinates, preliminarily screening the plurality of exhibits in a preset candidate radius to obtain a candidate exhibit set; for any candidate exhibit, executing physical visibility constraint verification based on a preset visual range polygon corresponding to the candidate exhibit, so as to update the candidate exhibit set; calculating a comprehensive matching score of each candidate exhibit in the candidate exhibit set based on the user orientation, the user coordinate and the exhibit center coordinate corresponding to the candidate exhibit; and determining a unique target exhibit according to the comprehensive matching score, and pushing guide information associated with the target exhibit. According to the method and the device, the problems of false triggering, confusion of affiliations of adjacent exhibits and dislocation explanation under abnormal positioning caused by only depending on distance judgment in the prior art are solved.

The invention discloses a flight personnel space orientation capability assessment method based on holographic eye movement characteristics, and belongs to the technical field of flight personnel capability assessment. Extracting multi-dimensional eye movement characteristics such as pupil contraction / expansion tracks, iris stability parameters and proportional change rate; aligning and matching the eye movement characteristics with real-time flight attitude and environmental stimulation data; carrying out collaborative analysis on the features, and generating a fusion eye movement feature vector insensitive to ambient light interference; and finally, inputting the spatial orientation capability into a pre-trained spatial orientation capability evaluation model, and outputting quantitative evaluation values of spatial perception acuity, environmental adaptability and stress response level. According to the method, the problems that an existing evaluation method is high in subjectivity, prone to being interfered by the environment and difficult to comprehensively reflect the complex physiological cognitive state of the pilot are solved, and more objective, stable and comprehensive quantitative analysis on the spatial orientation capacity is achieved.

The invention discloses an automatic driving method with cross-domain data fusion and double-space perception enhancement. The automatic driving method comprises the following steps: extracting multi-modal features; constructing a cross-domain data fusion strategy; establishing a double-space perception enhancement module; and performing end-to-end trajectory planning and reasoning. Through dynamic weight distribution based on data set difference and dynamic fine adjustment based on loss feedback, the training weights of conventional and long-tail scenes are balanced, the basic capability of the conventional scene and the generalization capability of the edge scene are cooperatively improved, and the robustness of the system is remarkably improved; according to the invention, a double-space perception enhancement mechanism is provided, and a bird's-eye view global structure and look-around local details are decoupled and fused; according to the method, the characteristics of a long-distance small target are accurately captured by utilizing a self-adaptive cutting technology guided by a multi-modal pre-training model based on comparative learning, so that the problem of information loss caused by insufficient spatial resolution of a single aerial view is solved, and the sensing precision of key targets such as traffic signs is improved.

The invention relates to the technical field of intelligent interactive design, and discloses an intelligent public space interactive design method and system based on spatial awareness, and the method comprises the steps: collecting data in real time through a multi-source sensor in a public space, and carrying out the fusion through employing an LIO-SAM algorithm, and obtaining multi-source fusion data; performing semantic segmentation on the multi-source fusion data by adopting a MaskR-CNN model, and constructing a three-dimensional dynamic space model; a behavior prediction model is constructed on the basis of a Social-STGCNN-Transform network, the future behavior trend of the personnel is predicted on the basis of the multi-source fusion data, and a user behavior analysis result is obtained; generating an intelligent interaction strategy according to the three-dimensional dynamic space model and the user behavior analysis result, and executing the intelligent interaction strategy through interaction equipment in the public space; feedback data is obtained, and the intelligent interaction strategy is evaluated and optimized by adopting a multi-target Bayesian optimization algorithm based on the feedback data; according to the invention, dynamic interaction optimization of the public space is realized.

The application provides a city resident population distribution trend prediction method based on a geographic detector, comprising the following steps: acquiring panoramic street view data of a research area; performing semantic segmentation to obtain visual perception elements and establish visual perception factors; establishing spatial perception factors through the accessibility of different facility types under different travel modes; using the geographic detector to calculate the explanation rate of the visual perception factors and the spatial perception factors on the resident population distribution density; obtaining several high-explanation-rate perception factors, obtaining corresponding weights based on a judgment matrix, establishing a resident intention index, and predicting the city resident population distribution trend. The prediction method is based on the perception of people to the real city environment, takes into account the subjectivity and objectivity of evaluation, and has high reference value for city planning related to city resident population distribution.

The invention belongs to the technical field of natural language processing, and particularly discloses a method for constructing a geographic knowledge graph alignment model, which comprises the following steps of: constructing a geographic knowledge graph structure containing multi-dimensional spatial information; creating a corresponding reverse link for the relation link between each geographic entity by using a reverse link generation strategy; distributing corresponding weights to different neighbor entities of each geographic entity by using a spatial perception attention mechanism, and calculating an output feature of each geographic entity by combining an orthogonal transformation matrix of the relationship and the spatial distance; and generating a negative sample through the adversarial network, training a pre-constructed deep learning network by using the negative sample, performing error calculation by using a combination of vector loss and geographic loss as total loss in the training process, and updating a corresponding weight parameter according to an error result until network convergence reaches an expected target, thereby completing training. According to the method and the device, the accuracy of geographic entity alignment in the geographic knowledge graph can be improved.

The application relates to a blind guiding method and system based on semantic Gaussian mapping and multi-source fusion navigation. In the method, color images, depth images and inertial data are acquired first, and visual-inertial odometer processing is performed to output blind guiding stick pose data; key frames are determined, and target detection frames are obtained through asynchronous semantic estimation; the depth distribution in the detection frame is subjected to double-component probability modeling based on the depth image to generate a three-dimensional Gaussian candidate pair, then the Gaussian candidate pair is updated to a world coordinate system to construct a dense semantic instance map; pose data are fused to generate a cost map containing semantic weights and obstacle inflation information, and then path planning and dynamic obstacle avoidance are performed based on a preset target point, the cost map and the pose data, a navigation vector instruction is output, and blind guiding is completed. Compared with the prior art, the application has the advantages of improving the reliability of space perception and obstacle avoidance, realizing intelligent guiding with space perception and safe obstacle avoidance, and the like.

This invention discloses a method, device, and storage medium for directional device identification and control based on vision and spatial perception, belonging to the field of intelligent control and human-computer interaction technology. The method is applied to portable terminals, achieving "point-and-shoot" by using a coaxially integrated laser and camera module to acquire target images containing laser pointer spots. To address the identification challenges caused by differences in target object size and distance, the method proposes multi-scale adaptive region extraction, generating and matching multiple candidate regions centered on the laser pointer spot. Simultaneously, to eliminate control ambiguities for similar devices in different rooms, the method innovatively utilizes smart home device nodes in known locations within the environment as Bluetooth beacons, achieving zero-deployment-cost collaborative spatial perception and obtaining the terminal's precise spatial location. Finally, by jointly querying and adjudicating the visual recognition results and spatial location information, the target device is uniquely identified and control commands are generated. This invention achieves intuitive interaction with zero learning costs, solving the problems of complexity and confusion in traditional control methods. It has advantages such as accurate identification, fast response, simple deployment, and privacy security, making it particularly suitable for smart home and smart elderly care scenarios.

The application discloses a kind of based on lateral light guide and spatial perception neural network's visual haptics deformation reconstruction device and its reconstruction method, reconstruction device includes: micro camera;Sleeve is configured to be covered outside micro camera, multiple mounting holes are formed on the barrel wall of sleeve;Light guide plate has reflecting surface;Elastic component has reflective layer;Multiple optical fibers are located in corresponding mounting hole;The light emitted by optical fiber is reflected to elastic component by reflecting surface, and irradiates reflective layer;When elastic component contacts target, reflective layer is deformed, micro camera is configured to take pictures to reflective layer and obtain haptic image, so as to determine the haptic information of target according to haptic image.Multiple optical fibers emit axial light under the action of light guide plate to form multiple lateral light, that is, annular discrete dot array light source is formed, the uniformity of light in elastic component is improved, so as to facilitate to improve the accuracy of three-dimensional deformation reconstruction.

The invention discloses a multi-modal real-time semantic segmentation intelligent analysis method and device based on a cultural relic model, and relates to the technical field of cultural relic digital analysis. Performing hierarchical labeling on the semantic information of the multi-modal data on the basis of three-dimensional space feature coding and cross-modal projection; based on a parameter fine tuning mode of spatial perception, realizing adaptation of a large language model to multi-modal data, and according to high-dimensional semantic embedding of a deep neural network, performing unified vector space representation on the multi-modal data to realize construction of a multi-modal knowledge base index; and based on a retrieval enhancement generation technology and in combination with the constructed multi-modal knowledge base index, realizing and applying the cultural relic three-dimensional model intelligent question-answering system. According to the invention, a more efficient, accurate and convenient intelligent solution can be provided for historical and cultural heritage research.

The invention discloses a three-dimensional indoor positioning method based on visual sense and 5G sensing fusion, and relates to the technical field of indoor positioning and space perception. The method comprises the following steps: modeling an indoor reflecting surface to obtain a virtual anchor point; a user azimuth vector detected, identified and tracked by a camera target is projected to a scene reflection surface to construct a candidate reflection path and identify a direct incidence path, and the candidate reflection path and the direct incidence path are mapped to a beam codebook of a base station, so that a beam search range is narrowed, and a higher angle resolution is obtained; the user terminal establishes a communication link through the reference signal, and the base station acquires channel state information; in the channel parameter processing stage, angle and time delay estimation is carried out on channel parameters; an estimation result is matched with a direct or virtual anchor point path, and pseudo paths which do not conform to scene constraints are eliminated, so that a direct path and an effective reflection path are distinguished; the position of the user is jointly solved by using multi-angle constraints formed by the direct path and the reflection path, and continuous estimation of the three-dimensional position of the user is realized by combining a filter.

In one embodiment, the method includes rendering, to one or more displays of the VR device, a first output image of the VR environment based on a field of view of the user. The VR environment has a virtual boundary corresponding to the real-world environment. The method further includes determining whether the user is approaching a threshold distance of the virtual boundary, determining a direction of movement and a field of view of the user, accessing one or more images of the real-world environment captured by one or more cameras of the VR device, and rendering, based on the accessed images, a second output image including a portion of the VR environment and a portion of a pass-through view of the real-world environment. The portion of the pass-through view is based on the determined direction of movement and the field of view of the user.

The present invention discloses a VR teaching experience enhancement system and method, which belongs to the field of virtual teaching technology. The method specifically includes: collecting position data and posture data of trainees in the VR environment, constructing a multi-user coordinated interaction model based on the position data and posture data of the trainees in the VR environment, identifying the interaction relationship and interaction events between the trainees, generating tactile feedback data corresponding to the interaction events based on the interaction events, performing calibration processing on the tactile feedback data, and synchronously sending the calibrated tactile feedback data to the trainee's VR device, the calibration processing including adjusting the tactile feedback data based on the spatial propagation difference amount and tactile feedback time offset value of the trainee; the present application enables multiple trainees to obtain consistent, real-time tactile response and spatial perception when collaboratively operating the same virtual object or scene, thereby improving the teaching experience in multi-person collaborative scenarios.

The application discloses a mountainous city park CES space probability mapping method considering population differences, and steps include: constructing a space feature classification system; dividing population categories according to population difference characteristics and obtaining space perception data; generating CES value space vector data and frequency data through data cleaning; generating environment layer grid data set and park boundary; performing space density interpolation to output CES value density distribution map; training and verifying using a space probability prediction model, performing probability prediction taking population categories as grouping variables, converting and standardizing mapping probability data and maximum value index to generate a CES space distribution map considering population differences; and statistically analyzing to generate a frequency feature map, a correlation diagram and a flow direction diagram. The technical scheme realizes precise mapping of CES space distribution of population differences, reveals the correlation mechanism of landscape elements and CES value, and provides scientific support for mountainous park renewal design.

This invention discloses a novel multimodal semantic-spatial representation method for embodied perception and spatial reasoning, belonging to the field of embodied perception. Addressing the two major pain points of existing methods—insufficient spatial coherence in semantic reasoning and poor relation consistency and interpretability—this invention designs an optimization mechanism: on the one hand, it designs a transitive relation learning mechanism to correct relation conflicts and missing relations in reasoning under weak supervision, improving the completeness and logical consistency of spatial representation; on the other hand, relying solely on RGB images and depth maps, it integrates monocular depth estimation as a spatial modality into embodied perception, injecting implicit three-dimensional spatial attributes into the semantic structure, constructing a unified multimodal semantic-spatial representation, and enhancing the spatial coherence of semantic reasoning. This invention improves the performance of structured scene modeling and spatial grounding description generation through advanced spatial representation, and can be widely used in embodied spatial perception scenarios.

The invention relates to the technical field of electric digital data processing, in particular to a coordinated reasoning system based on a multi-space intelligent machine. The system comprises at least one first central machine, wherein any first central machine is connected with at least two space intelligent machines; when a certain first central machine receives a question queried by a user, the system executes the following steps: the first central machine decomposes the received question to obtain a plurality of sub-queries, and sends the sub-queries to corresponding space intelligent machines respectively; the space intelligent machine processes the received sub-query and returns a query result and related memory data to the first central machine; and the first central machine receives the query result returned by the space intelligent machine and the memory data related to the received sub-query, performs reasoning in combination with the memory data related to the question and stored in the context storage library, generates a final answer corresponding to the question, and updates the context storage library. According to the method, the comprehensiveness of spatial perception and the accuracy of decision reasoning can be improved.

The invention provides an intelligent construction and optimization method and system for a text travel illumination scene based on self cognition, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining space structure data of a target text travel space and tourist behavior track data; carrying out joint analysis on the data based on an own cognitive model, extracting motion dynamics characteristics from a tourist behavior track, calculating a cognitive load level of the tourist in combination with spatial structure data, and obtaining a cognitive characteristic representation result containing a spatial perception response mode and cognitive load level distribution; dividing the target travel space into a plurality of illumination response areas according to a spatial perception response mode, and establishing an illumination demand descriptor associated with the cognitive load level for each area to obtain an illumination demand mapping relationship; and generating a lighting parameter control instruction according to the mapping relation and the constraint condition to realize intelligent lighting control, and iteratively optimizing the model based on feedback data.

The invention discloses a space perception method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the positioning and map construction based on an image sequence, and obtaining a three-dimensional point cloud map; detecting and labeling objects in the image sequence to obtain semantic label information; determining a three-dimensional semantic map based on the three-dimensional point cloud map and the semantic tag information; and performing semantic analysis and spatial reasoning based on the three-dimensional semantic map to obtain a target spatial perception result.