67 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.

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.

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.

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.

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.

An information processing device (100) comprises a model (110) that, in response to input data representing the structure of a molecule of interest, generates output data representing energy relating to the molecule of interest and force relating to the molecule of interest. The information processing device (100) acquires, for individual pieces of structure data (101), a plurality of first feature amounts relating to the structure of the molecule of interest in accordance with the result of inputting the structure data (101) to the model (110). The information processing device (100) calculates, for each piece of structure data (101), a second feature amount relating to the distance between atoms in the structure of the molecule of interest represented by the structure data (101). The information processing device (100) selects, from among the plurality of pieces of structure data (101), structure data (101) to be used when updating the model (110), the structure data (101) being selected on the basis of the distribution of coordinates in a multidimensional space (120) representing combinations of the plurality of acquired first feature amounts and the calculated second feature amounts.

The embodiment of the application provides a kind of vector control method and system based on multidimensional space, it is related to the technical field of motor technology.The method comprises: obtaining the real-time current error signal of the current loop of motor controller;Based on the real-time current error signal, selective harmonic analysis is performed on the preset key frequency point to construct the multi-dimensional vibration vector representing the vibration state of the system, wherein the key frequency point covers the audible frequency range of human ear;The multi-dimensional vibration vector is first processed to calculate the perceived vibration energy value;According to the deviation between the perceived vibration energy value and the preset target energy value, generate proportional-integral parameter adjustment amount;Based on the proportional-integral parameter adjustment amount, dynamically update the proportional-integral controller parameters of the current loop.Through the application, the problem of low motor control sensing accuracy is solved, and the sensing accuracy is improved.

The application discloses a two-dimensional conversion method for multi-dimensional space relation among GNSS ambiguity vectors, comprising the following steps: constructing a GNSS double difference mathematical model, and obtaining a multi-dimensional ambiguity vector floating point solution and a variance-covariance matrix thereof by using a least square method; obtaining an optimal integer solution and a suboptimal integer solution of the ambiguity vector by means of a least square ambiguity decorrelation adjustment method according to the ambiguity vector floating point solution and the variance-covariance matrix thereof; taking the ambiguity vector floating point solution, the optimal integer solution and the suboptimal integer solution as end points, calculating an elliptic norm side length of an edge formed by any two end points and an A-inner product included angle between any two edges; and constructing a two-dimensional Euclidean triangle based on the elliptic norm side length and the A-inner product included angle, so as to convert a complex and abstract multi-dimensional space relation among the ambiguity vector floating point solution, the optimal integer solution and the suboptimal integer solution into a simple and direct two-dimensional triangle relation; and the application provides important method support for analyzing a GNSS ambiguity confirmation reliability influence mechanism.

The present application relates to a long-span bridge track vertical deviation temperature-sensitive component extraction method in multi-dimensional space, comprising: integrating the long-span bridge track vertical deviation three-dimensional historical data with additional environmental temperature labels; using empirical wavelet transform to construct a four-dimensional data set containing temperature labels and wavelength labels; after resampling in the mileage dimension, projecting the data to the amplitude-wavelength space, drawing the accumulated amplitude growth curve, identifying the amplitude-wavelength curve type and determining the boundary wavelength of the bridge deformation and track irregularity; then projecting the data to the amplitude-temperature space to identify the amplitude curve and temperature sensitivity; combining the boundary wavelength and temperature sensitivity to extract the temperature-sensitive component of the track irregularity and the construction deviation component in the actual bridge deformation. The method of the present application can realize the synchronous identification and extraction of the temperature-sensitive component in the track vertical deviation and the bridge construction deviation component, and provide quantitative basis for the accurate maintenance and repair of long-span railway bridges.

The invention relates to the technical field of mathematical modeling, in particular to a mathematical modeling system utilizing a computer multi-dimensional space, and the system firstly carries out unified analysis and feature vectorization processing on data from different sources and different structures, and automatically constructs a dynamically adjustable multi-dimensional mathematical space structure based on the importance and correlation of features. A modeling task is executed in the space, model parameters are established by analyzing collaborative changes between dimensions, and a preliminary model result is output; the system further utilizes an error feedback mechanism to carry out dynamic correction on the space structure and model parameters, and continuous optimization and self-adaptive adjustment of the model are achieved. And finally, the system outputs the corrected model result in a structured manner, and generates a visual space projection drawing for an external system to call and a user to analyze. The method can effectively solve the problems that unified modeling of multi-source heterogeneous data is difficult, the space structure lacks self-adaptive ability and the model stability is insufficient.