64 results about "Nearest neighbor graph" patented technology

The invention discloses an open set hyperspectral image instance graph construction method based on a distillation diffusion model, and the method comprises the following steps: extracting the spatial and spectral features of a target domain HSI through a spatial encoder and a spectral encoder, executing forward diffusion in a diffusion classifier by taking a real label as a starting point, and carrying out the condition reverse denoising, and finally, closed set category prediction is obtained. A single sample prediction set is generated through Monte Carlo sampling, a prediction variance is calculated, uncertainty distribution is constructed, an unknown class probability is calculated by using a cumulative distribution function, and finally, a known class probability and an unknown class probability are fused to generate teacher open set prediction through a mapping module. A K neighbor graph structure of a sample is constructed in a feature space, teacher open set prediction and student open set prediction are respectively used as nodes to construct a teacher / student instance graph, a Wasserstein distance of prediction probability distribution of the teacher / student instance graph and the student open set prediction is minimized through a graph isomorphic knowledge distillation module, and knowledge distillation based on prediction manifold alignment is realized.

The invention discloses a multi-modal time sequence hybrid query system and method. The hybrid query method comprises the steps of collecting multi-modal time sequence data, performing normalization, denoising and feature extraction on a numerical time sequence, performing cleaning, conversion and classification feature extraction on text data, and performing enhancement and feature extraction on picture data; the statistical features of the numerical time series are converted into vectors, similarities of different modes are calculated, comprehensive similarity measurement is constructed, and a neighbor graph index and a multi-layer index structure are constructed based on the measurement; the method comprises the following steps: analyzing a mixed query statement input by a user, extracting a multi-modal keyword and a natural language description, and converting the natural language description into a multi-modal feature vector; according to the method, a comprehensive similarity measurement and multi-layer index structure is constructed, so that related data can be quickly positioned and matched in the query process, the problems of low multi-modal search efficiency and poor accuracy are solved, the combination of a time sequence and multi-modal query is realized, and the query efficiency is improved.

The invention discloses a power distribution system state estimation method based on feature map and adaptive multi-channel map attention learning, which comprises the following steps of: firstly, constructing a state recognition model based on feature map and adaptive multi-channel map attention learning, then training the model through acquired data, and in the training process, calculating the state of the power distribution system. According to the method, a k neighbor graph and an inaccurate power distribution network topology are aggregated, a self-adaptive multi-channel graph attention learning network is adopted to implement graph propagation and feature aggregation on an aggregated graph, and finally, a high-precision power distribution system state estimation value is output through a multi-layer perceptron.

The invention discloses a mass spectrum data high-throughput alignment and parallel qualitative method based on deep characterization learning, and relates to the field of chromatography-mass spectrometry data processing and calculation mass spectrography. The method comprises the following steps: performing streaming, micro-batch and single traversal analysis on original mass spectrum data files of a plurality of samples to be detected; extracting metadata and main data by taking the fragmented spectrogram as a unit, inputting the metadata and the main data into a pre-trained depth representation learning model to generate a high-dimensional embedded vector, and incrementally writing the metadata and the main data into a column type storage database; constructing a sparse approximate nearest neighbor graph; executing a clustering algorithm on the graph to generate a consensus spectrogram set, and generating a consensus feature vector for each clustering cluster and a single example cluster; determining the nature of the consensus feature vector; and constructing a sample * feature matrix based on the cluster affiliation relationship and the qualitative result. According to the method, the technical problems of I / O bottleneck, memory overflow, combinatorial explosion, computing power waste and the like in large-scale mass spectrum data analysis are effectively solved.

The invention discloses a novel graph contrast learning framework with mutual information maximization, and relates to the technical field of graph contrast learning, multi-modal learning and mutual information maximization. The framework comprises the following steps: constructing a nearest neighbor graph, spreading similar samples to data of a missing mode, optimizing a loss function, and performing clustering optimization. The existing method has two main problems: (1) the existing method is mostly suitable for complete data and has a poor processing effect on data of a missing mode; and (2) the current palmprint and palm vein fusion recognition technology does not completely capture the complex relationship between the palmprint and palm vein features, resulting in inaccurate clustering result. According to the method, the modal loss in the incomplete palm print and palm vein fusion recognition task is effectively reduced, and the clustering performance is enhanced. According to the method, the mutual information maximization loss function is introduced, the adaptability and robustness of the model to incomplete data are effectively enhanced, and the method has high recognition precision and clustering performance when processing incomplete multi-modal data.

The invention discloses a spatial domain identification method and system based on spatial multi-omics data. The method comprises the following steps: obtaining an expression profile and a spatial position coordinate through spatial multi-omics sequencing, and preprocessing the expression profile and the spatial position coordinate; decomposing and extracting a shared low-dimensional incidence matrix of the multi-omics data by using an anchor concept; constructing an intercellular similarity matrix based on the spatial coordinates, and generating a spatial location map by combining the k-nearest neighbor map and the high-order adjacency matrix; integrating multi-base clustering results by adopting an element-by-element weighting strategy to form an integrated graph; solving an optimal low-dimensional feature through a double-graph regularization objective function (fusing Laplacian matrix constraints of a spatial position graph and an integrated graph); and finally realizing high-precision spatial domain clustering. The method breaks through the limitation that a traditional method ignores spatial dependency and integrated interference, effectively fuses the high-order relation and spatial information of multi-omics data through anchor concept decomposition and a double-graph regularization framework, and remarkably improves cross-scene robustness; the method is suitable for biomedical scenes such as tumor microenvironment analysis and cell heterogeneity analysis.

Disclosed herein are example layouts and layout generation techniques for fault-tolerant quantum computers. Example embodiments comprise methods for performing a layout reduction technique for fault-tolerant quantum computing. In certain embodiments, a layout of an arbitrary quantum circuit is reduced to a layout of exponents of a multiple qubit Pauli matrix and measurements of a multiple qubit Pauli matrix. In certain embodiments, qubits are marked as one of a data, interface, or ancilla qubit for a 2D nearest neighbor graph of qubit connectivity, and an ancilla-path is provided from a respective data qubit to a respective interface qubit

This invention relates to a method and system for mining potential user needs based on hierarchical temporal memory enhancement. First, a dynamic time window hierarchical slicing mechanism is used to decouple the cleaned multimodal user behavior sequence into long-term historical sequences and short-term real-time sequences. A semantically approximate nearest neighbor graph is constructed from the long-term historical sequence, and adaptive community discovery is performed. This is combined with a large language model to generate a high-semantic-density long-term memory abstract representation. Then, a hybrid prompt template integrating system instructions, long-term memory representation, and short-term sequences is constructed to drive the large language model to complete a three-stage thought chain reasoning process: focused denoising, scene instantiation, and function completion, generating a structured reasoning data package. A dual-tower semantic retrieval and scene-constrained filtering architecture is employed to achieve accurate mapping between the reasoning results and the product database, outputting a product list with recommendation reasons. This invention endows the recommendation system with deep logical reasoning and cross-domain association capabilities, improving the accuracy and interpretability of e-commerce recommendations.

The present invention relates to a semi-supervised soft sensor modeling method based on a variable-weighted adaptive local graph construction extreme learning machine. This method comprehensively utilizes the weighted Euclidean distance information in the data input space and the prediction output space to adaptively construct a nearest neighbor graph to accurately approximate the potential structure information of the data. At the same time, considering that different auxiliary variables have different degrees of contribution to the accurate estimation of the dominant variable, different weights are assigned to different auxiliary variables through variable-weighted learning to reduce the adverse effects of redundant variables and noise on graph construction and regression learning. Finally, variable weighting, adaptive graph construction, and extreme learning machine modeling are integrated into a unified learning framework, and the overall optimal solution of the modeling learning is obtained by using alternating iterative optimization. Therefore, the semi-supervised learning framework provided by the present invention can make full use of the supervision information contained in the labeled data, assisted by the structure information contained in the unlabeled data, improve the performance of the extreme learning machine model, and achieve the purpose of enhancing the generalization ability and reliability of the soft sensor model.

This invention discloses a method for quantifying anxiety and depression comorbidities using partial label learning based on brain imaging, belonging to the fields of medical image processing and artificial intelligence. The method is based on a dataset constructed using brain imaging measurements. It selects representative typical anxiety and depression samples with distinctive brain imaging features from clinically diagnosed groups and constructs a sparse nearest neighbor graph by comprehensively considering the relationships between each sample and its nearest ordinary and typical neighbor samples. Partial label learning modeling and optimization are performed through an iterative label propagation mechanism, ultimately outputting label confidence matrices for clinical anxiety patients, depression patients, and patients with comorbid anxiety and depression. This enables a quantitative assessment of the anxiety and depression levels in patients with comorbid anxiety and depression and completes the classification of comorbidity subtypes. This invention improves the accuracy and objectivity of anxiety and depression comorbidity assessment, providing auxiliary support for the clinical diagnosis, pathological mechanism research, and intervention target screening of anxiety and depression comorbidities.

The present application relates to a kind of hard disk and the near neighbor graph vector retrieval method and device of memory, first according to the distance of each feature vector in data set and constructs near neighbor graph, then it is saved in hard disk and is released in memory according to 4K alignment mode optimization its storage and resource, then according to the structure information of near neighbor graph saved in hard disk, calculate sample set, again construct the near neighbor graph of sample set object as sample near neighbor graph.Using query vector on sample near neighbor graph executes nearest neighbor search to obtain the preliminary more similar query target set, finally with the more similar query target set obtained as the initial solution of near neighbor graph in hard disk, executes nearest neighbor search to obtain the most similar query target.The present application reduces the memory occupation in large-scale data search process under the condition of guaranteeing accuracy, and improves the search efficiency.

The invention discloses a high-dimensional vector clustering algorithm based on GPU acceleration, and the algorithm comprises the steps: obtaining a high-dimensional space vector set, accelerating the construction process of a k-nearest neighbor graph through a GPU, and completing the construction of a global k-nearest neighbor graph; obtaining a high-dimensional space vector set, recursively executing k-means clustering on the high-dimensional space vector set, and completing partition operation of vector objects; independently executing DBSCAN clustering in parallel among different partitions by using a global k-neighbor graph, a partitioning scheme and a GPU thread block to complete partition clustering; vector objects conforming to core point definition are obtained, a core neighbor graph is constructed based on the density direct relation among the vector objects, the core neighbor graph and the partition clustering results are utilized, the partition clustering results are combined, and a global clustering result of the high-dimensional space vector set is obtained. According to the method, the execution time of the DBSCAN algorithm can be remarkably shortened, and the efficiency of large-scale high-dimensional vector clustering is greatly improved on the premise that the clustering precision is guaranteed.

The invention discloses a data acquisition visualization traceability method, which comprises the following steps of: performing real-time acquisition on target data through an equipment sensor, and performing preprocessing operation on the acquired data; generating a unique identifier for each piece of collected data by using a Hash identification method, and storing the identified data into a database; performing normalization processing on the sensor data, converting the data into feature vectors, calculating function association vectors among the data by adopting a gating circulation unit model, and determining the association of the sensor data in combination with a geographic position; the method comprises the following steps: calculating a spatial distance between sensors, constructing a graph model, calculating a function association vector, endowing a weight to an edge in the graph model, adjusting the weight of the edge by adopting a multi-layer dynamic weighted nearest neighbor graph optimization algorithm, screening out a path with the minimum total weight from candidate nodes and the edge, and visually displaying a traceability path in a coordinate system. The weights of the nodes and the edges are distinguished by using different colors and sizes, so that the visual traceability analysis of the data is realized.

This invention proposes a collaborative evolutionary classification method based on cross-view structural attribute dual-domain semantic mining, belonging to the field of multi-view semi-supervised classification. This invention acquires multi-view features from training samples, constructs K-nearest neighbor graphs for each view, and adaptively prunes based on the shared support relationships of the graph structure to obtain a purified adjacency matrix. Subsequently, structural and attribute representations are extracted through a shared graph convolutional network, and the consistency between the two in a unified space is constrained using a structure-attribute consistency loss. Simultaneously, learnable view position encoding and masked self-attention mechanisms are introduced for progressive fusion, and cross-view evolutionary patterns are learned through adjacent view mapping to form an evolutionary loss. Finally, the evolutionary representations of each view are concatenated and mapped to a sample-level fused representation, which is then input into the classifier for prediction. The model is jointly optimized by the classification loss, consistency loss, and evolutionary loss. Compared with other methods, this invention significantly improves the accuracy of classification results with a small amount of labeled data.

The application belongs to the technical field of databases, and specifically discloses an efficient GPU near neighbor graph index construction method based on data locality, which comprises the following steps: dividing index original data into multiple subsets, and performing data processing on the multiple subsets to obtain rearranged data; constructing subgraphs corresponding to each subset in the rearranged data, and constructing a graph index based on the subgraphs; and refining the structure of the graph index to obtain a final graph index. The method can effectively alleviate the problems of insufficient memory resources and excessive calculation cost in the subgraph merging stage, significantly reduce the construction cost of the graph index, and effectively extend to large-scale data sets to improve scalability.

This invention relates to the field of voice interaction technology, and more particularly to a voice dialogue system based on a large model, comprising: a feature extraction module for acquiring samples containing multi-turn dialogue information and extracting multi-dimensional features; an alignment and processing module for achieving fine alignment through cross-modal alignment matrices and path search, and calculating a contextual consistency score based on role transition states; a stability calculation module for determining sample stability scores based on a teacher-student model mechanism; an anomaly assessment module for constructing a sample nearest neighbor graph based on semantic embedding vectors, calculating structural anomaly scores and sample retention values, and rescuing correctable samples by supplementing with nearest neighbor information; and a training module for selecting voice dialogue training samples according to sample retention values ​​and training the model. This invention provides high-quality samples for model training through a multi-dimensional quality assessment and data filtering mechanism, improving the accuracy and fluency of voice dialogue.

The invention discloses a federated graph network fault diagnosis method, device and equipment fusing nearest neighbor graph composition and adaptive clustering, and aims to solve the problems of poor data privacy, weak sample relevance and insufficient model generalization ability in the prior art. On the premise of strictly protecting data privacy, relation information of graph structure data is effectively utilized, an incidence relation between samples is constructed through a nearest neighbor graph composition technology based on a federated learning framework, structured features are extracted through a graph neural network, and then the problem of non-independent identically distributed data is solved through a self-adaptive clustering technology; and finally, high-precision and high-generalization-ability fault diagnosis is realized.

The present invention discloses a method, system and medium for node classification of a homogeneous and heterogeneous matching network based on macro-micro message passing for graph contrastive learning, including: First, construct a k-nearest neighbor graph based on node feature similarity using the node features of the original graph, and randomly remove connected edges from the original graph and the k-nearest neighbor graph respectively to generate corresponding structural views and feature views. Then, define view roles for the structural views and feature views according to the homogeneous and heterogeneous matching types of the network to determine the priority view and the auxiliary view; Next, perform alignment encoding on the priority view and the auxiliary view to obtain node representations under different views; Furthermore, construct an InfoNCE contrastive loss function to optimize and train the node embedding representation of the model; Finally, use the optimized node embedding representation for the node classification task for training and evaluation. The present invention can effectively improve the quality of node representations, thereby improving the accuracy of node classification in homogeneous and heterogeneous matching networks.

The application discloses a communication product configuration method based on multi-modal analysis and vector matching, and particularly relates to the technical field of intelligent configuration of communication products, and is used for solving the problem that the efficiency of manually reading documents and manually selecting and configuring is low, errors are easy to occur, and it is difficult to trace back when the customer demand is proposed in the form of multi-source and multi-format documents, the field caliber is complex, and the rule constraints are numerous in the group and government and enterprise customer scenarios of operators. By taking the field dictionary and structured constraints as the basis, the multi-source and multi-format communication product demand document is sequentially converted into demand parameters, demand configuration graphs and multi-channel feature vectors, and vector matching of rule perception such as field mandatory, mutual exclusion of rights and interests, region, contract period and cost is performed on the hierarchical near neighbor graph index in combination with the template graph, so that the communication product configuration scheme meeting the business constraints is automatically output under the unified caliber, the workload of manually reading documents and selecting by experience is significantly reduced, and the accuracy and consistency of the configuration result in the complex scenario are improved.

Disclosed are a method and apparatus for learning equivariant and invariant representations for rotation of an image based on a graph convolutional network. The method of learning an equivariant and invariant representation for rotation of an image based on a graph convolutional network performed by a computer device includes learning an equivariant representation for rotation of an image by using a self-weighted nearest neighbors graph convolutional network (SWN-GCN); and finally obtaining the equivariant representation for the rotation of the image obtained from the self-weighted nearest neighbors graph convolutional network as an invariant representation of the rotation of the image by using permutation invariance of global average pooling (GAP).

The application discloses a feature-level deep explainable air conditioner fault diagnosis method based on a neural network model, belongs to the technical field of computer systems based on specific calculation models, and comprises the following steps: S1, constructing fault training data; S2, converting the fault training data into graph data based on a node classification k-nearest neighbor graph construction strategy, and obtaining graph structure data; S3, obtaining high-order node representation, updating network parameters by using a loss function based on the high-order node representation, and completing a final fault diagnosis task; S4, analyzing a diagnosis process and a result of the graph neural network by using three feature-level explainable methods, and obtaining a quantitative influence law of the number of graph convolution layers, the number of channels and the learning rate on the performance of the graph convolution fault diagnosis network; and S5, obtaining explanation effects of the three feature-level explainable methods under a cold water unit fault experiment. The application solves the problem that high diagnosis precision and deep explainability of a model cannot be considered in the prior art.

This application relates to the field of new pollutant source tracing technology in water bodies, and provides a method and related equipment for predicting new pollutant sources. The method includes: acquiring mass spectrometry data from multiple locations in a target water body area; constructing a material screening matrix for each location based on all mass spectrometry data; constructing a heterogeneous graph structure based on the material screening matrix, and constructing a nearest neighbor graph for the target water body locations; calculating the prior knowledge view embedding corresponding to the target water body area based on the heterogeneous graph structure, and calculating the data similarity view embedding corresponding to the target water body area based on the nearest neighbor graph; and performing a fusion prediction on the target water body area based on the prior knowledge view embedding and the data similarity view embedding to obtain the new pollutant source tracing prediction result for the target water body area. The method of this application can improve the accuracy and interpretability of new pollutant source tracing prediction in water bodies.

The invention provides an intelligent identification method and system for abnormal auditing data of a hospital. The intelligent identification method comprises the steps of obtaining a multi-dimensional data set of the hospital, calculating a local outlier factor LOF of each data record, distributing a differential privacy budget and generating a privacy data set; constructing a weighted K nearest neighbor graph based on the privacy data set and the LOF value, and performing community division on the graph; calculating weighted intermediary centrality, community connection strength and average path length of each node, fusing the weighted intermediary centrality, the community connection strength and the average path length into a structural anomaly score, and screening data records of which the scores are higher than a first threshold value as first-level candidate anomaly data; for each piece of first-level candidate abnormal data, mapping the data structure abnormal score into a time window, extracting a time sequence track, and calculating an average time sequence track of the community as a prototype track; outputting a time sequence similarity distance; constructing a confirmation threshold value, wherein the threshold value is adjusted based on community structure characteristics, data differential privacy budget and a structure anomaly score; and when the time sequence similarity distance is greater than the threshold value, identifying the data as abnormal data.

The present application discloses a vector retrieval method and device based on a proximity graph, which relates to the field of vector retrieval. The method includes determining the centrality degree of each vector in the entire vector dataset according to the vector dataset by using the integral function of the non-central chi-square distribution; obtaining a centrality degree set according to the centrality degrees of all vectors; constructing a proximity graph according to the similarity distances between the vectors in the vector dataset and by using the method of incrementally adding vectors; performing a traversal calculation on the vector centrality degrees; based on the centrality degree set, performing an optimization and edge trimming operation on the proximity graph to obtain a final proximity graph; using the feature vector of the query object as a query vector to search on the final proximity graph to obtain a vector retrieval result. The present application can improve the accuracy and efficiency of vector retrieval based on a proximity graph.

The invention provides a trajectory clustering method based on a graph convolutional network, and the method comprises the steps: extracting feature vectors of all trajectories at different visual angles from an original event log, and obtaining multi-visual-angle trajectory features; using a K-nearest neighbor algorithm to construct a K-nearest neighbor graph for each view angle according to the multi-view-angle track features to obtain a multi-view-angle graph; performing convolution operation on the multi-view image by using a multi-view image encoder based on an image convolution network to obtain an initial public representation; inputting the multi-view image into a neural network for fusion, and optimizing the structure of a fusion image through rank constraint of a Laplacian matrix; using the initial common representation and the fusion image to learn the common representation, using a multi-view image decoder to reconstruct a multi-view image, and optimizing the common representation by minimizing the difference between the reconstructed multi-view image and the original multi-view image to obtain a final common representation; and clustering the tracks in the original event log based on the final common representation. By the adoption of the scheme, track clustering of the event logs is achieved.

The invention provides a federated learning-based neighbor graph indexing method and system and electronic equipment. The method comprises the following steps: each client participating in federated learning trains a local performance prediction model used for predicting the retrieval performance of the neighbor graph based on a local sample data set of the client to obtain a corresponding local model parameter; the federation server participating in federation learning obtains local model parameters and test data sets of all the clients, a global test data set is obtained based on the test data sets of all the clients, and contribution quantized values corresponding to all the clients are calculated through the global test data set; and the federation server side aggregates all the local model parameters based on the contribution quantized value to obtain global model parameters, so that each client side updates a local performance prediction model based on the global model parameters, and constructs a corresponding neighbor graph index for the to-be-queried data set by using the updated local performance prediction model. According to the technical scheme, neighbor graph intelligent indexing supporting distributed data can be realized.

The present invention relates to the technical field of oil and gas development, and particularly relates to a multi-view clustering well selection method for oil well fracturing based on multi-order nearest neighbor cycle graph constraints. The method includes obtaining dynamic and static parameters affecting the fracturing effect of oil wells and normalizing the dynamic and static parameters; dividing the multi-view data set according to the sources of the dynamic and static parameters; adding low-level feature extractors to each view; extracting high-level semantic features of each view by using the self-attention mechanism, and calculating the embedded features with information interaction in a weighted average manner; the decoder reconstructs all data, including the missing views; constructing a multi-order fusion nearest neighbor graph; calculating the loss function and updating the network parameters by using the stochastic gradient descent algorithm; using the embedded features finally obtained by the model to perform K-means clustering to complete the well selection for oil well fracturing measures. The method provided by the present invention applies deep multi-view clustering to the field of well selection for oil well fracturing measures, providing an important reference for well selection for oil well fracturing measures.

The invention provides a multi-label feature set generation method and system, and the method comprises the steps: generating a corresponding training sample in real time according to a multi-label data set, and detecting a label set corresponding to the multi-label data set in real time; calculating a mark weight corresponding to each original mark in real time according to a preset rule, and constructing a corresponding constrained nearest neighbor graph in real time according to the training sample and the mark weights in real time; generating a target feature sequence corresponding to the multi-label data set in real time through an importance evaluation framework; generating a corresponding training feature set according to the target feature sequence, and correspondingly inputting the mark set and the training feature set into a preset network to correspondingly train a feature set output model; and acquiring actual feature data input by the user in real time, and outputting a target feature set corresponding to the actual feature data in real time through the feature set output model. According to the method, the feature set can be quickly and effectively generated, and the working efficiency is improved.

The invention discloses a video clip content similarity assessment method and system, electronic equipment and a medium, and the method comprises the steps: obtaining a to-be-assessed video, carrying out the preprocessing of the to-be-assessed video, and obtaining the preprocessed video data; according to the preprocessed video data, performing video segmentation on the to-be-evaluated video to obtain a plurality of short video clips; extracting a key frame in each short video clip to obtain a key frame corresponding to each short video clip; constructing a nearest neighbor graph corresponding to each key frame in each short video clip according to the key frame corresponding to each short video clip; constructing a bipartite graph corresponding to each short video clip according to the nearest neighbor graph corresponding to each key frame in each short video clip; calculating the similarity between the two short video clips based on the bipartite graph; and evaluating the similarity of the two short video clips according to the similarity. According to the method, the accuracy of evaluating the similarity of the video clip contents can be improved, and the interpretability of the whole method is improved.

The application discloses a cell space polarity quantification analysis method based on optimal transmission theory, and the steps include: 1) performing slice quality filtering, feature screening, total amount standardization and logarithmic conversion on an original space omics count matrix to obtain a standardized expression matrix; 2) performing principal component analysis on RNA and protein data respectively to construct a low-dimensional embedding for similarity calculation; 3) calculating the difference between the actual distribution of each molecule and the uniform distribution based on the first Wasserstein distance to realize continuous gradient quantification along the plant-animal axis; 4) constructing a k-nearest neighbor graph based on the distance between molecules to identify molecular communities with common spatial positioning patterns; and 5) calculating the matching significance of the observed distribution and the ideal pattern, and then controlling the false discovery rate through the Benjamini-Hochberg procedure to screen out molecules with significant gradient distribution characteristics. The application significantly improves the spatial distribution pattern recognition and quantification ability.