13 results about "Multidimensional scaling" patented technology

The present invention relates to the field of risk identification technology, specifically a big data-driven financial risk identification system, which includes a time data synchronization module, a multidimensional feature mapping module, a financial behavior analysis module, and a risk dynamic identification module. In the present invention, by synchronizing financial data streams in real time and applying multidimensional scaling transformations, the capture and processing of financial data are optimized, and more efficient data stream analysis is achieved. It is possible to accurately capture subtle changes and key forms of time series data in complex financial environments, greatly improving the sensitivity and accuracy of risk identification. Refined similarity calculations and behavior identification help the system discover potential risks earlier, ensuring that financial institutions can respond quickly, thereby effectively preventing and mitigating risk events, and then responding to dynamically changing markets, which can significantly improve the formulation of risk management strategies and the rationality of resource allocation.

The invention provides an MDS dimension reduction-based retrieval enhancement generation semantic matching optimization method and system and a storage medium. The method comprises the following steps of: obtaining a user problem and high-dimensional semantic vectors of a plurality of candidate text segments; constructing a distance matrix based on the two high-dimensional semantic vectors to obtain a high-dimensional semantic similarity; performing dimension reduction on the distance matrix by adopting a multi-dimensional scale analysis (MDS) method, and calculating low-dimensional semantic similarity between the user question and each text fragment based on a distance relationship after dimension reduction; performing weighted fusion on the low-dimensional semantic similarity and the high-dimensional semantic similarity to generate a mixed similarity; and sorting the mixed similarities from high to low, and selecting the text fragments corresponding to the top n of the mixed similarities for generating response output. By reducing the dimension of the high-dimensional retrieval vector to the low-dimensional space, the interference of irrelevant dimensions is reduced, the semantic matching precision and efficiency are improved, and then the quality of the text recalled by the large language model is improved.

The invention discloses a beam moving gantry crane winch fault diagnosis method, and relates to the technical field of fault diagnosis, and the method comprises the steps: carrying out the wavelet decomposition and reconstruction of a gantry crane winch bearing monitoring signal, and generating denoising data; respectively inputting the de-noised data into a first feature extraction path and a second feature extraction path, generating a symbolized embedded vector by the first feature extraction path through standardization, symbolization and phase-space reconstruction processing, and calculating a relative entropy feature value of mode distribution; the second feature extraction path calculates a yoke divergence matrix and generates a dependency feature value by constructing an original embedding vector and a delay embedding vector; constructing two-dimensional feature vectors, and calculating the Euclidean distance of the feature vectors between the samples to generate a distance matrix; performing multi-dimensional scale transformation on the distance matrix to generate low-dimensional space distribution coordinates of the sample; and determining a fault category based on distance matching between the low-dimensional space distribution coordinates and a known fault category space region. And accurate diagnosis of different faults is realized.

The application belongs to the technical field of target positioning, and particularly relates to a cooperative positioning method in a satellite denial environment by fusing land-based navigation and inter-aircraft ranging. The method is mainly applied to cooperative positioning of a UAV group, especially in an environment where satellite signals are blocked or interfered. The scheme of the application is that multiple base stations provide angle of arrival (AOA) and time of arrival (TOA) measurement data, and at the same time, relative distance measurement data between UAVs is combined, a multidimensional scaling (MDS) algorithm and a Procrustes analysis are used to globally optimize the positions of the UAVs. The application not only improves positioning accuracy, but also enhances the robustness and real-time performance of the system, and is particularly suitable for the positioning requirements of a UAV group in a complex environment. The method provides an efficient and reliable solution for high-precision cooperative positioning of a UAV group in a satellite denial environment, and has a wide application prospect.

The invention discloses a spatial motion attitude modeling method and device of a rope-driven flexible arm and a medium, belongs to the technical field of rope-driven flexible arms, and solves the problems of how to eliminate the adverse effects of high-dimensional strong nonlinearity and behavior coupling of the rope-driven flexible arm on modeling and how to improve the modeling accuracy of the rope-driven flexible arm. According to the method, the spatial motion attitude information of the flexible arm and the control information corresponding to the servo steering engine are collected to form an original data set, the spatial motion attitude information is analyzed by using a hybrid multi-dimensional scale transformation and space-time adaptive attention mechanism, and the control information is embedded and coded to obtain the spatial motion attitude information of the flexible arm. According to the modeling method, hidden characteristics in control information are fully extracted, modeling is completed through a multi-task modeling network with a self-adaptive gradient balance mechanism, the flexible arm space motion posture can be analyzed in a self-adaptive mode through the modeling method, the influence caused by high-dimensional strong nonlinearity and behavior coupling is effectively solved, and the modeling accuracy is improved. And the accuracy and generalization ability of network training are improved.

A method is presented for detecting the position, translation, and / or orientation of a remote object relative to an ego object, each of which has a set of sensors arranged on its respective outer surfaces or circumferences. After determining pairwise distances between the sensors of the ego object and the sensors of the remote object, the relative position, translation, and / or orientation are determined using multidimensional scaling and double-centering operations. The method does not require knowledge of the sensor positions of the remote object, and the ego object and the remote object can have different sizes and numbers of sensors.

This application provides a method and apparatus for prototyping visualization based on local singularities. The method includes: acquiring a detrital zircon U-Pb age dataset of a target region; preprocessing the dataset and dividing it into age intervals; performing local singularity analysis on each age interval to calculate the singularity index of each age interval and constructing a local singularity spectrum; performing multidimensional scaling (MDS) analysis based on the singularity index to obtain visualization results in a low-dimensional space; and interpreting the visualization results in conjunction with geological background information to determine prototyping relationships and material migration paths. This application can accurately identify fine prototyping paths missed by traditional methods, significantly improving the precision and reliability of prototyping.

The application relates to a cluster clustering and positioning method and device based on multi-view fusion, equipment and medium. The method comprises the following steps: acquiring a sparse distance matrix among nodes in a cluster; based on the same, constructing multi-feature views such as an original distance matrix, a Gaussian similarity, node degree centrality and a Laplace feature vector; constructing a self-representation matrix according to the node similarity of each view and stacking the same into a three-order tensor; performing low-rank approximation to construct a fusion affinity matrix, and dividing the cluster into multiple subnets through spectral clustering; extracting a distance submatrix from each subnet, obtaining local relative coordinates through multidimensional scaling transformation; identifying a shared node between subnets as an anchor point, splicing each local coordinate to a global coordinate system through Procrustes analysis, and obtaining a global positioning result. By using the method, the clustering criterion and the positioning geometric configuration requirement can be kept consistent under the condition of non-full-connection sparse distance observation, so that error transmission is inhibited and the global positioning accuracy is improved.

The invention provides a multi-dimensional scale ambiguity-free distance measurement positioning method for measuring incoming wave time difference, and belongs to the technical field of wireless positioning, and the method comprises the steps: measuring the time difference of arriving at different nodes by using a radio signal in an environment, constructing a matrix and a vector for removing rotation and mirror image ambiguity, converting a nonlinear constraint into a solvable model, and carrying out the measurement of the incoming wave time difference. A double fitting error search mechanism is designed, through rotation angle lattice point search, the minimum fitting error of an original image and a mirror image is calculated and compared, the mirror image and rotation blur are judged and eliminated, and therefore the node positioning result after deblurring is determined. According to the scheme, the absolute coordinates of the nodes can be effectively solved without depending on satellite positioning, the positioning error is small, the self-positioning problem of the nodes of the wireless sensor network in the complex shielding environment is remarkably solved, and the self-positioning level of the nodes in the complex environment is improved.

The invention provides a track circuit compensation capacitor feature extraction method based on multi-scale yoke difference correlation, and the method comprises the steps: carrying out the phase-space reconstruction of monitoring signal data of a track circuit compensation capacitor monitoring point, obtaining an embedded vector data set, selecting a sample pair from the embedded vector data set, calculating a yoke difference divergence matrix of the sample pair, and carrying out the extraction of a track circuit compensation capacitor feature. Based on a yoke divergence matrix, calculating a synchronism index, traversing all monitoring signal samples, calculating a synchronism index between each pair of samples, generating a correlation matrix, inputting the correlation matrix to a multi-dimensional scaling algorithm, calculating a corresponding scalar product matrix, performing eigenvalue decomposition on the scalar product matrix, and obtaining a multi-dimensional matrix; and obtaining a spatial distribution matrix of all the monitoring signal samples, and performing comparison and matching based on the spatial distribution matrix of the labeled monitoring data samples and the spatial distribution matrix of the non-labeled monitoring data samples to realize fault identification and diagnosis of the track circuit compensation capacitor. The method has the advantages of high early fault feature extraction identification degree and high fault identification accuracy.

The application provides a multi-dimensional scale ambiguity-free ranging positioning method for measuring the time difference of incoming waves, and belongs to the technical field of wireless positioning. The method utilizes radio signals in the environment, measures the time difference of the radio signals arriving at different nodes, constructs a matrix and a vector for removing rotation and mirror image ambiguity, converts nonlinear constraints into a solvable model, and designs a double fitting error search mechanism. Through rotation angle grid search, the minimum fitting errors of the original image and the mirror image are calculated and compared, the mirror image and the rotation ambiguity are determined and eliminated, and the node positioning result after removing the ambiguity is determined. The scheme of the application can effectively solve the absolute coordinates of the nodes without relying on satellite positioning, has small positioning error, significantly solves the self-positioning problem of the wireless sensor network nodes in a complex shielding environment, and improves the self-positioning level of the nodes in the complex environment.

The invention provides a source analysis visualization method and device based on local singularity, and the method comprises the steps: obtaining a chipping zircon U-Pb age data set of a target region, carrying out the preprocessing of the data set, and dividing the data set into age intervals; performing local singularity analysis on each age interval, calculating to obtain a singularity index of each age interval, and constructing a local singularity spectrum; performing multi-dimensional scale analysis (MDS) based on the singularity index to obtain a visualization result in a low-dimensional space; and in combination with geological background information, interpreting the visualization result to determine a material source relationship and a material migration path. According to the method, a fine object source path omitted by a traditional method can be accurately identified, and the fineness and reliability of object source analysis are greatly improved.

The application discloses a kind of satellite rejection environment under multi-aircraft cooperative autonomous navigation method, comprising: constructing side measurement network adjustment model, utilize inherent distance constraint in cluster topological network to reduce distance estimation error, real-time generation high-precision distance measurement network;Based on distance measurement adjustment theory, cooperative relative positioning is carried out, and distance network under the side measurement network adjustment model is fully utilized to realize cluster global optimal positioning, while improving the positioning accuracy and robustness of cluster system, the implementation range of cooperative positioning in local connection topology is expanded;Based on multidimensional scaling technology and coordinate registration technology, construct two-stage cooperative positioning framework based on TOSA, realize multi-aircraft cluster absolute space coordinate system high-precision positioning.The method effectively utilizes the distance network information composed of paired relative distance measurement to improve the positioning accuracy of cluster.