84 results about "Multidimensional space" patented technology

The invention relates to a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method. The method comprises the steps of integrating long-span bridge track vertical deviation three-dimensional historical data added with an environment temperature label; adopting empirical wavelet transform to construct an MRA component four-dimensional data set containing a temperature label and a wavelength label; after mileage dimension resampling, projecting data to an amplitude-wavelength space, drawing an accumulated amplitude growth curve, judging the type of the amplitude-wavelength curve, and determining a boundary wavelength of bridge deformation and track irregularity; projecting the data to an amplitude-temperature space, and judging an amplitude curve and temperature sensitivity; and in combination with the boundary wavelength and the temperature sensitivity, extracting a temperature sensitive component of track irregularity and a construction deviation component in actual bridge deformation. According to the method, synchronous identification and extraction of the temperature sensitive component in the track vertical deviation and the bridge construction deviation component can be realized, and a quantitative basis is provided for accurate maintenance and repair of a long-span railway bridge.

The invention relates to the technical field of power grid data fusion analysis, and discloses a power grid multi-dimensional spatial data fusion analysis method and system, and the method comprises the steps: collecting power grid multi-dimensional spatio-temporal data, and forming a spatio-temporal data standard set; constructing a dynamic weight fusion mechanism according to the spatio-temporal data standard set, obtaining a data quality factor, an aging factor, an importance factor and a correlation factor, and obtaining a fusion weight factor set; constructing a three-dimensional space-time coordinate frame, and mapping each dimension of data in the space-time data standard set to the three-dimensional space-time coordinate frame; fusing the data of each dimension in the three-dimensional space-time coordinate framework based on the fusion weight factor set to obtain a comprehensive association degree value of each space-time coordinate point as a data fusion analysis result; the data fusion analysis result is used for formulating a power grid operation strategy and judging power grid operation abnormity. The method has the outstanding advantages of being high in adaptive capacity, capable of accurately quantifying the comprehensive correlation degree of each coordinate point, capable of being applied to power grids of different scales and different operation scenes and the like.

A method for defining a trajectory of a robot (1) for moving along a path from an initial point (A) to a final point (B) in a multidimensional space, each of whose points has a plurality of space coordinates, which are position and / or orientation coordinates, wherein the trajectory is a continuous map mapping an initial instant in time to the initial point (A), a final instant to the final point (B) and intermediate instants to intermediate points of the path, comprises the steps of defining (S3) a cost function which associates a cost value to a given trajectory, defining (S3) a pluridimensional subspace of said multidimensional space and determining (S4) among trajectories extending along different paths of said subspace, a trajectory which minimizes the cost function.

The invention discloses a robot path planning method and device based on multi-dimensional space modeling and a medium, and the method comprises the steps: carrying out the three-dimensional topology dynamic modeling of building structure data, so as to obtain an initial weighted three-dimensional connected graph; based on the elevator state parameters, weight dynamic updating is conducted on weight nodes of the initial weighted three-dimensional connected graph, and the weighted three-dimensional connected graph is determined; based on the weighted three-dimensional connected graph, through analysis of an improved A * algorithm, a robot global advancing path plan is obtained; according to the robot advancing path planning, robot local advancing path planning is obtained through robot reversing strategy decision analysis; and based on the robot global travel path planning and the robot local travel path planning, through asset counting trajectory analysis, determining a counting scanning line spacing, and performing building asset counting. According to the method, the technical problem that the dynamic constraint condition in the three-dimensional space cannot meet the instant requirement in the existing SLAM technology is solved.

The invention belongs to the field of identification networks of the Internet of Things, and discloses a quick matching and searching method based on an addressing matrix, which comprises the following steps of: mapping each category attribute into a coordinate value of a corresponding dimension in a multi-dimensional space according to a rule, and determining the position of a point in the multi-dimensional space; filling an addressing request in a preset addressing matrix, and sending the addressing request to an edge layer multi-dimensional identification server; the server extracts the attribute content filled in the matrix, and converts the extracted attribute value into a coordinate value of a corresponding dimension in a multi-dimensional space according to a mapping rule; and taking a point obtained by mapping according to the addressing matrix as the position of the addressing request in the multi-dimensional space, carrying out distance calculation with spatial points corresponding to all objects which are mapped and stored in a database, and selecting a communication object corresponding to a nearest point with a distance within a preset threshold range as an optimal communication target. The method solves the problems that an existing addressing mechanism is low in searching efficiency and limits the instant response capability of a network under the condition of large-scale identification data.

An example system may access data of a multidimensional space representing a physical environment and identify interior elements within the multidimensional space using a first machine learning model. The interior element may represent furniture in the physical environment. The system may mask one or more of the interior elements with masks and fill each of the masks with imagery of the physical environment to create an appearance of a defurnished space, the defurnished space having the one or more interior elements appearing as missing from the multidimensional space representing the physical environment. The system may provide all or some of the defurnished space for display.

The present disclosure refers to a method for spatial management of objects. The method comprises providing a hierarchical tree structure with a root node and a plurality of nodes on a plurality of depth levels, wherein a node represents a cell of a spatial subdivision of the multi-dimensional space, encoding coordinates of an object in a multi-dimensional space into a code, the code including a sequence of successive sections, each section including interleaved one or more successive bits of the coordinates from each dimension of the multi-dimensional space, associating the object with a node of the plurality of nodes based on the code, wherein objects located within a cell of the spatial subdivision share a common node at a corresponding depth level in the hierarchical tree structure, and storing the object in an object array of the associated node in the memory. Furthermore, a device and a system are described.

Certain aspects of the present disclosure provide techniques and apparatus for accurately predicting the movement and location of objects in a multidimensional space. An example method generally includes receiving an input defining a location of an object in a three-dimensional space at a first time. The input generally includes a plurality of tokens. Using a transformer neural network, a predicted movement for tokens in the plurality of tokens is generated. Generally, the predicted movement includes a translation and a rotation associated with individual tokens in the plurality of tokens. The predicted movement is translated into an object-wise predicted movement. Based on the object-wise predicted movement and the location of the object in the three-dimensional space, a predicted location of the object at a second time subsequent to the first time is output.

An information processing device includes a control unit. The control unit executes a process including acquiring experimental data including an experimental condition of each of a plurality of times of experiments and an observed value under the experimental condition, and generating graph data indicating a topological structure of a geometric figure representing a correspondence between the experimental condition and the observed value in a multidimensional space with each of the experimental condition and the observed value being a dimension.

The invention discloses a two-dimensional conversion method for a multi-dimensional spatial relationship between GNSS ambiguity vectors, and the method comprises the steps: constructing a GNSS double-difference mathematical model, and obtaining a multi-dimensional ambiguity vector floating solution and a variance-covariance matrix through a least square method; according to the ambiguity vector floating solution and the variance-covariance matrix thereof, obtaining an optimal integer solution and a suboptimal integer solution of the ambiguity vector by means of a least square ambiguity decorrelation adjustment method; by taking the ambiguity vector floating solution, the optimal integer solution and the suboptimal integer solution as end points, calculating the ellipse norm side length of a side formed by any two end points and an A-inner product included angle between any two sides; constructing a two-dimensional Euclidean triangle based on an elliptical norm side length and an A-inner product included angle, and converting a complex and abstract multi-dimensional space relationship among an ambiguity vector floating point solution, an optimal integer solution and a suboptimal integer solution into a simple and visual two-dimensional triangle relationship; the method provides important method support for analyzing the GNSS ambiguity confirmation reliability influence mechanism.

The invention discloses an artificial neural network microwave device wide-range modeling method based on multi-dimensional space division and dynamic adaptive sampling, and relates to the field of microwave device modeling. The invention provides a boundary smooth splicing method based on a Sigmoid function. And overlapping unit samples are shared by the adjacent sub-regions, so that rich data support is provided for the boundary region. And on the basis, a self-adaptive correction function is constructed by utilizing a Sigmoid function: according to the position of the sub-model on each dimension, single-side Sigmoid correction is adopted in a boundary partition, and double-side correction is adopted in a middle partition. Weighted fusion is only carried out in the boundary overlapping region in the correction, so that the outputs of adjacent sub-regions are in smooth transition, and the internal precision of each sub-model is not influenced. And finally, all the corrected sub-models are superposed to form a globally continuous and seamlessly spliced ANN model, so that the problem of boundary discontinuity in multi-dimensional and wide-range modeling is effectively solved.

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.

In view of the problems that when there are dynamic objects in the scene, a visual SLAM system has a large gap between the positioning result and the true value, and the system robustness is low. Based on the traditional visual odometer, a binocular SLAM front-end optimization algorithm that is robust in a dynamic environment is proposed. The algorithm first dedistorts the binocular image, then extracts and triangulates the image, and pre-screens the dynamic feature points according to the visual geometric constraints combined with the reprojection error chi-square distribution model. On this basis, a multidimensional space directed graph model is constructed by using the information of each feature point itself and the motion consistency of the feature points on the same dynamic object, and an AP clustering algorithm is introduced to accurately screen the feature points, thereby accurately filtering out the point groups that do not meet the static assumption. The overall positioning trajectory of the algorithm is closer to the theoretical value, and can effectively improve the operation accuracy and robustness of the SLAM system in dynamic scenes.

There is provided a method, device and computer program for navigating an agent in a multidimensional space, the method comprising: generating a data field that spatially corresponds to a multidimensional space in which an agent resides, wherein the data field is populated with observation comparison measure data with respect to a stored observation, the stored observation including second sensor data describing an environment of a first position in the multidimensional space, wherein the generating the data field comprises: obtaining a plurality of current observations made by the agent at different respective positions in the multidimensional space, the current observations including first sensor data describing an environment of each of the different respective positions in the multidimensional space; comparing each of the current observations with the stored observation to obtain respective observation comparison measure data for the different respective positions in the multidimensional space; and populating the data field with the observation comparison measure data according to the different respective positions in the multidimensional space; the method further comprising: determining a reliability measure associated with the stored observation by performing a reliability check of the data field; and managing a digital map of the multidimensional space by maintaining, updating, or deleting information in the digital map regarding the stored observation based on the reliability measure.

A method and system for explainable classification of a target point cloud may include receiving a target point cloud comprising N points in a multidimensional space, applying a feature extraction model to extract, for each point, a permutation-invariant feature vector comprising local feature entries, applying a bottleneck function on the feature vectors to produce a global feature vector having F global feature entries, applying a classification model on the global feature vector to classify the target point cloud according to classification criteria, for one or more points, applying an aggregation function over the local feature entries of the respective feature vector, prior to the bottleneck function, to obtain an aggregation value, and indicating importance of the points in the classification based on their aggregation values.

Certain aspects of the present disclosure provide techniques for representing polylines and polygons. A method generally includes obtaining a ordered set of points that represent a polyline or a polygon in a multidimensional space; forming two or more channels from the ordered set of points, each channel has a respective set of coordinate values that corresponds to a respective coordinate direction in the multidimensional space; inputting the two or more channels into a one-dimensional convolutional neural network (1D CNN); and obtaining, as output from the 1D CNN, a feature vector representation of the polyline or polygon.

The invention relates to a Raman spectrum classification and identification method and system, and the method comprises the steps: obtaining Raman spectrum signals of N types of cells, carrying out the preprocessing and dimension reduction processing, and constructing an M-dimensional training data set; calculating a centroid point and a class radius of each class of cells in the M-dimensional space based on the training data set; an inter-class relation graph and an intra-class dispersion graph are constructed based on the centroid points and class radiuses of the N classes of cells, in the inter-class relation graph, the spatial distance between any two classes serves as the inter-class distance, the intra-class dispersion graph takes the centroid points of the classes as the circle center, a data set to be classified is mapped into the inter-class relation graph and the intra-class relation graph after being subjected to preprocessing and dimension reduction processing, and a data set to be classified is obtained; and whether classification is carried out according to the distribution of the data in the inter-class relation graph or the intra-class relation graph is judged based on a preset condition, and if yes, a sorting instruction is sent to corresponding sorting equipment. According to the invention, accurate classification and identification in a multi-dimensional space are realized, and visual application is provided.

An example operation may include one or more of implementing a trained artificial intelligence (AI) model to identify a plurality of categories within a multidimensional space using a neural network training capability with patterns of activity risk behavior, features, and model feedback data, receiving a request to determine a category of a profile associated with a computing device, extracting at least one of features and an activity history of the profile from a database executing the trained AI model on the at least one of features and the activity history to map the profile to a point in the multidimensional space, assigning the profile to the category among the plurality of categories based on a location of the point in the multidimensional space with respect to a cluster in the multidimensional space corresponding to the category, and reducing an activity set within a software application when the computing device is accessing the software application based on the category.

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 invention discloses a bidirectional incremental intelligent merging method in distributed storage fault recovery, and relates to the field of distributed data management. The method comprises the following steps: receiving operation sequences of local and remote increments, mapping the operation sequences to a multi-dimensional space-time coordinate system, and generating a multi-dimensional space-time operation point set; based on the multi-dimensional space-time operation point set, constructing a geometric causal cone for each operation point, and constructing a causal interference spectrum by analyzing interference among the geometric causal cones; according to the causal interference atlas and the multi-dimensional space-time operation point set, searching a space-time folding path to avoid or minimize causal conflicts, and obtaining an optimal space-time folding scheme; and projecting a high-dimensional path in the optimal space-time folding scheme into a one-dimensional execution sequence, and executing to achieve a consistent data state. According to the method, conflict analysis and solution are improved to a high-dimensional geometric space, deep semantic understanding and flexible avoidance of operation conflicts are realized, and the accuracy and automation level of data merging are improved.

The utility model relates to the field of machinery. The high-shear homogenizer with the multi-dimensional space three-dimensional shearing mechanism comprises a stator and a rotor, and is characterized in that the rotor comprises a rotating shaft, and four blades which are arranged at equal intervals are arranged on the rotating shaft; the blades are in a block shape, and the upper end faces of the blades are higher than the upper end face of the rotating shaft. An included angle of 14-16 degrees is formed between the blade and the central axis of the rotating shaft, and the included angle is gradually increased from the upper part of the blade to the lower part of the blade. According to the utility model, a unique rotor structure is adopted, so that materials can be efficiently sheared.

The application discloses an unmanned equipment situation awareness system, a construction method, electronic equipment and a storage medium, and belongs to the technical field of intelligent situation awareness of space equipment. The construction method comprises the following steps: mapping physical parameters of a space environment, equipment state parameters and task target parameters to a multidimensional space and a unified time axis; realizing situation awareness through four steps of calculation, extrapolation, statistics and prediction; converting a situation spectrum into a visual dynamic situation graph spectrum according to a result of four-order progressive modeling processing; and intuitively presenting a current situation state and a future situation change trend. The application unifies situation information data in a situation spectrum for management through situation calculation, extrapolation, statistics and prediction, visually expresses a cooperative relationship of a state of the application, a state of a target and an environment state and the like by using a graph structure, makes a relationship network more intuitive and clear, and significantly improves situation awareness capability and decision efficiency of the space unmanned equipment in a complex environment.

The present invention provides a signal consistency comparison method for multi-dimensional space combination, comprising the following steps: Step 1: Based on multi-screen segmentation, multi-channel audio and video signals are spatially combined to form a single-channel spliced ​​signal, and the spatial position of the multi-screen segmentation is obtained and saved; Step 2: The single-channel spliced ​​signal is collected to obtain original signal data, and if the signal is a compressed stream, it is decoded to obtain the decoded original signal data; Step 3: The obtained original signal data is copied to form two identical data, one data is subjected to signal content detection, and the other data is subjected to signal comparison, and an alarm is issued when there is a fault in the signal content or the signal comparison is inconsistent; Step 4: Alarm information is obtained, and arbitration switching is performed according to the alarm information. The present invention greatly reduces the construction cost of the system, reduces the problem of safe broadcast accidents caused by untimely processing after the occurrence of a fault, improves the safety of the entire system, and reduces labor costs.

The application discloses a kind of energy supplement facilities positioning method and system. Obtain multi-modal multi-source vector data and carry out pretreatment;The spatial constraint system of demarcating energy supplement facilities and energy supplement demand distribution boundary;According to the topological structure of road network, select initial candidate site and extract characteristic attribute based on spatial constraint system, generate corresponding multidimensional space characteristic vector;Construct simulation positioning scene to extract the spatial characteristic vector of simulation demand point set, combine heuristic optimization result to generate corresponding construction value score label, obtain labeled dataset;Neural network is trained using labeled dataset, the mapping relationship of space characteristic and construction value is established, and the model parameter trained is stored as offline value evaluator;Using offline value evaluator, the initial candidate site is carried out quickly value retrieval screening, obtain high-value candidate site pool and carry out heuristic optimization, output the final energy supplement facility layout scheme.

The system obtains, from a database, multiple videos and multiple metadata corresponding to the multiple videos. The metadata includes tags generated by a large language model to describe a video. The system converts the metadata into a first vector in a multidimensional space. The first vector encodes information included in the tags as a numerical representation in the multidimensional space. A distance between the first vector and a second vector indicates similarity between the metadata represented by the first vector and a second metadata represented by the second vector. The system obtains a natural language query (NLQ) associated with the database, and converts the NLQ into a third vector in the multidimensional space. The system determines whether the first vector satisfies a distance threshold to the third vector. Upon determining that the first vector satisfies the distance threshold, the system presents the video as a result to the NLQ.

The invention discloses a vegetation evolution three-dimensional dynamic feature extraction and visualization method based on remote sensing data, and belongs to the technical field of remote sensing information processing and ecological environment monitoring, and the method comprises the following steps: 1, obtaining and processing basic data; step 2, calculating horizontal gradient vegetation coverage space centroid migration; 3, identifying vertical gradient vegetation evolution characteristics; and step 4, vegetation evolution three-dimensional dynamic feature extraction and visualization. According to the method, based on multi-dimensional space fusion analysis of remote sensing vegetation indexes and digital elevation model data, horizontal migration characteristics and vertical gradient evolution characteristics of vegetation coverage are identified, and a vegetation three-dimensional curved surface model is constructed by integrating horizontal and vertical vegetation dynamic change characteristics; according to the method, efficient and accurate extraction and visualization of the spatial structure and the dynamic change trend of large-range vegetation are realized, and high-precision spatial analysis technical support is provided for ecological environment monitoring, surface vegetation dynamic research and ecological restoration evaluation.

This invention discloses an online uniformity monitoring method and system for TMR mixing process based on dynamic particle morphology recognition, relating to the field of intelligent monitoring technology in feed processing. The method includes: during material mixing, real-time acquisition of dynamic images of the material within the mixing chamber; identification of material particles in the dynamic images; extraction of the characteristic length of each particle; and calculation of skewness and kurtosis based on the characteristic length; division of the effective observation area of ​​the dynamic images into multiple sub-regions based on the centroid coordinates of all material particles in the dynamic images; extraction of particle characteristic statistics within each sub-region; and construction of a multidimensional spatial uniformity index set to characterize the mixing state of the material; input of the skewness, kurtosis, and multidimensional spatial uniformity index set into a pre-trained evaluation model, outputting a comprehensive uniformity index and corresponding quality level.

The present invention discloses a method for labeling a multi-category target domain dataset based on zero labeling, comprising: obtaining target domain foreground images of different categories; performing quantitative analysis of multidimensional spatial features based on the target domain foreground images of different categories and constructing a cross-category common description model based on the analyzed multidimensional spatial features; obtaining the optimal source domain of the target based on the cross-category common description model; converting the image of the obtained optimal source domain; constructing a target domain synthetic dataset based on the converted image; generating target domain labels based on the target domain synthetic dataset, and training a detection model based on the target domain synthetic dataset and the target domain labels; and labeling the target based on the target domain label-trained detection model to obtain a labeled target domain dataset. The present invention also discloses a system, an electronic device, and a computer-readable storage medium, which can realize deep learning model training without manual labeling.

Certain aspects of the present disclosure provide techniques and apparatus for accurately predicting the movement and location of objects in a multidimensional space. An example method generally includes receiving an input defining a location of an object in a three-dimensional space at a first time. The input generally includes a plurality of tokens. Using a transformer neural network, a predicted movement for tokens in the plurality of tokens is generated. Generally, the predicted movement includes a translation and a rotation associated with individual tokens in the plurality of tokens. The predicted movement is translated into an object-wise predicted movement. Based on the object-wise predicted movement and the location of the object in the three-dimensional space, a predicted location of the object at a second time subsequent to the first time is output.