68 results about "Space perception" patented technology

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 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 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 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 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 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.

The application discloses a kind of geometric constraint space perception method, device, electronic equipment and storage medium.The method comprises: the image sequence of pipeline environment is carried out spatial perception processing, generates initial space model;Determine local cross-section point set according to the initial space model;Determine first radius based on the local cross-section point set;Second radius is determined according to the first radius;According to the second radius, the image sequence of pipeline environment is carried out spatial perception processing again, generates target space model.The technical scheme, by introducing the constraint of pipeline cross section radius, the space model generated in spatial perception process is constrained and revised, to inhibit scale drift and local geometric distortion, improve the stability and consistency of spatial perception result.

Devices, methods, systems, and media for selectively cancelling sounds at a listener's location are described. Sound sources are identified in the listener's environment using microphones and / or remote devices associated with specific sound sources. A user is presented with information identifying the sound sources, including spatial information, and prompting the user to select one or more of the sound sources. After the user designates a sound source as noise, microphones are used to monitor for sound originating from the designated sound source, and the audio information captured by the microphones is processed to generate an anti-noise signal presented to the listener to cancel sounds originating from the selected sound source. A user may designate multiple sound sources as noise and leave other sound sources unfiltered. The techniques may be used in single-listener and multi-listener applications, and in listening and speaking applications.

The application discloses a space intelligent environment perception and decision method and system based on consistency check, relates to the field of space intelligence, solves the problem of insufficient precision of existing multi-sensor fusion perception, and comprises the following steps: S1, collecting data of a space environment; S2, acquiring local perception data, classifying the local perception data, obtaining a time series data list, and judging data accuracy of different sensors; S3, integrating collected data of each sensor to obtain optimized space data; S4, performing space modeling according to the optimized space data to obtain a navigation world model, constructing a space semantic graph based on the navigation world model; S5, acquiring task information, extracting a route from the space semantic graph to obtain a task route, and simulating the route through the navigation world model; and S6, feeding back the navigation world model according to a simulation result, so that the accuracy of space perception can be effectively improved.

The present application relates to a kind of three-dimensional temperature and salinity field intelligent inversion method based on multi-temporal feature fusion twin network, belong to marine three-dimensional temperature and salinity field information and deep learning technical field.Multiple time series data are input into multiple stacked spatial perception modules together with latitude and longitude information to extract local context information, and the output is classified by month;A spatio-temporal twin visual Transformer encoder containing multiple identical branches and shared weights is constructed, each branch contains a hierarchical image fusion module, a block embedding layer and an L-layer stacked Transformer encoder connected in turn, and the different monthly data obtained are input into different branches for spatio-temporal feature learning;The features learned by each branch are input into a multi-temporal fusion module for fusion enhancement to obtain the inversion result.This method can fully utilize the spatio-temporal and vertical information, while inverting temperature and salinity anomalies at different depths, effectively capturing the vertical correlation, and significantly improving the model training efficiency.

The application discloses a kind of city walking space perception measure method based on embodiment perception, comprising: the environmental elements of the city walking space to be perceived measure are determined;Through the walking of the subject in the city walking space to be perceived measure, perception experimental data are obtained;Based on physiological data and eye movement data, obtain comprehensive perception stimulation index;Based on subjective evaluation data, determine subjective cognitive comprehensive index, by the fusion calculation of the subjective cognitive comprehensive index and comprehensive perception stimulation index, obtain cognitive response intensity index, based on space-time behavior index, determine static behavior index and dynamic behavior index;The static behavior index, dynamic behavior index and cognitive response intensity index are coupled calculation, and behavior response index is obtained;Based on comprehensive perception stimulation index, cognitive response intensity index and behavior response index, the perception measure result of the city walking space to be perceived measure is obtained.

The invention discloses a mixed reality space perception method and system, which is executed under the architecture of cooperative work of mixed reality equipment and a cloud server, and realizes object-level three-dimensional reconstruction and semantic understanding through two-dimensional semantic segmentation, two-dimensional to three-dimensional mapping, multi-modal interaction and visual language model reasoning. And the requirement of the mixed reality application for understanding the accurate and efficient three-dimensional scene is met.

The application discloses a tunnel deformation measurement method, system and device based on echo space perception and acoustic coating and a storage medium; the tunnel three-dimensional curved surface model is established, the monitoring points are generated based on the curvature self-adaption, the special acoustic reflection coating is arranged at the monitoring points, the non-contact echo perception and data fusion are carried out by using the acoustic ranging device, the high-precision and automatic monitoring of the tunnel deformation is realized, the problems of poor applicability, low precision and insufficient efficiency of the traditional method in the complex environment are solved, and the reliability and intelligent level of the tunnel structure health monitoring are remarkably improved.

The invention relates to a three-dimensional temperature and salt field intelligent inversion method based on a multi-time-sequence feature fusion twin network, and belongs to the technical field of ocean three-dimensional temperature and salt field information and deep learning. Inputting the multivariate time sequence data together with latitude and longitude information into a plurality of stacked space sensing modules to extract local context information, and classifying the output monthly; a space-time twinborn vision Transform encoder which comprises a plurality of same branches and shares a weight is constructed, each branch comprises a hierarchical image fusion module, a block embedding layer and an L-layer stacked Transform encoder which are connected in sequence, and obtained data of different months are respectively input into different branches for space-time feature learning; and inputting the learning features of each branch into a multi-time sequence fusion module for fusion enhancement to obtain an inversion result. According to the method, space-time and vertical information can be fully utilized, temperature and salinity anomalies at different depths are inverted at the same time, the vertical incidence relation is effectively captured, and the model training efficiency is remarkably improved.

The invention discloses a service recommendation method and system based on a spatial awareness community, and the method comprises the steps: constructing a user friendship adjacency matrix through the interaction information between users; detecting a local community C of the given user i based on the user friendship adjacency matrix; according to the local community C, calculating the distance between each pair of nodes by using geographical location information of member nodes of the local community C to obtain a community node distance matrix D; according to the distance matrix D, nodes in the local community C are clustered according to the distances of the nodes, a user group close to the geographic position of the given user i is mined on the basis of the local community C, and a local space sensing community CS about the user i is generated; user historical information in a local space sensing community CS is utilized, and service recommendation is carried out in combination with commodity information and user similarity; according to the invention, service items with strong pertinence and high content association degree can be pushed to the user.

The invention provides an AI adaptive ADSR envelope dynamic modulation method and system in a spatial audio scene. The method comprises the following steps: acquiring spatial information; inputting the acquired spatial information parameters into a lightweight AI model so as to predict ADSR envelope parameters; according to the ADSR envelope parameter predicted by the lightweight AI model, dynamically generating an ADSR parameter suitable for the current spatial audio scene, and performing real-time amplitude modulation processing on the audio signal; and continuously optimizing the parameter mapping relation of the lightweight AI model through a reinforcement learning network based on user feedback and the change condition of the space environment so as to adjust the ADSR parameter to the preference requirement required by the user. According to the invention, a system based on spatial perception-AI decision-ADSR dynamic modulation-auditory feedback optimization is constructed, and intelligent cooperation of spatial audio ADSR envelope, spatial position, environmental characteristics and user preference is realized, so that friendly experience and immersion of a user to spatial audio are improved.