22 results about "Fcm clustering" patented technology

The invention relates to the field of agricultural fertilization, in particular to a corn straw returning-to-field and fertilization operation path collaborative scheduling optimization method, which comprises the following steps: performing standardization processing on farmland multi-source original data to obtain a field piece unit standardization feature vector; performing imbalance degree analysis on the feature dimension distance component to obtain a feature contribution balance factor; performing activeness evaluation on neighborhood membership distribution of the field units to obtain a spatial membership smoothing factor; obtaining adaptive distance measurement by combining a feature contribution balance factor and a space membership smoothing factor; and performing hardening processing on the final membership matrix to obtain an agricultural machinery path collaborative scheduling instruction set, thereby solving the problems of misalignment of a zoning result, fragmentation of a management boundary and unreasonable agricultural machinery path scheduling in an abnormal straw accumulation region based on a standard FCM clustering algorithm in the prior art.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention relates to the technical field of video content analysis, in particular to a mine surveillance video key frame extraction method, which comprises the following steps: extracting multi-dimensional features of each frame of a candidate frame set, obtaining a fusion value, and obtaining a candidate key frame set; based on an Euclidean distance method, calculating an Euclidean distance between continuous frames of the candidate key frame set, and determining a total clustering number according to a relationship between the Euclidean distance and a preset clustering threshold value; and clustering the frames in the candidate key frame set by using a preset mixed GWO-FCM clustering algorithm and the total clustering number to obtain a key frame set. According to the method, a hybrid clustering algorithm GWO-FCM combining grey wolf optimization and fuzzy C-means is adopted, global search and local optimization capabilities are considered, and the accuracy and representativeness of key frame clustering are ensured. The high-quality extraction of the key frames is realized, the number of redundant frames is obviously reduced, and the compactness and readability of the video abstract are improved.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method combining convolution and mixed attention. The SAR image change detection method comprises the following implementation steps of: firstly, generating a difference chart for two SAR images by using a composite neighborhood intensity difference method; then, a hierarchical FCM clustering algorithm is used for carrying out pre-classification processing on the difference image, a pseudo label matrix is generated, variable and invariable high-probability sample pixels in pseudo label pixels are selected, spatial positions of the pixels are extracted, and on the pixels of the corresponding spatial positions of the two original SAR images, a pseudo label matrix is generated; pixel blocks with the pixel points as the centers are taken as a training set, and pixel blocks with all the pixel points as the centers are extracted from the two original SAR images to serve as a test set; and then training a neural network combining convolution and mixed attention by using the training sample set, and then carrying out change detection analysis on a test set by using the trained network to generate a final change detection result graph. The method has clear advantages in the aspects of SAR speckle noise suppression and change detection precision.

The application discloses a Fl-engine system in a joint learning platform, which comprises a joint learning platform, a local server and an internet of things access end, wherein the joint learning platform is internally provided with an intelligent ecological circle module, a joint learning plan module, a joint learning engine module and a platform support module; the joint learning plan module and the joint learning engine module are electrically connected; and the joint learning engine module and the platform support module are electrically connected. The Fl-engine system in the joint learning platform provided by the application is provided with the joint learning engine module, and incorporates a joint machine learning algorithm, a joint deep learning algorithm, a horizontal joint algorithm and a vertical joint algorithm in the joint learning engine module; through a self-adaptive mechanism, a k-means clustering strategy, a hierarchical clustering strategy, a SOM clustering strategy or a FCM clustering strategy matched with the joint learning engine module is selected from an aggregation strategy module, so that aggregation calculation is realized.

The application discloses a multifunctional radar signal sorting method and system based on a hypergraph, and the method steps are as follows: S1, for RIPS obtained through radar reconnaissance receiver interception, interference pulses in the RIPS are removed; S2, the remaining pulse sequence is subjected to screening rules and FCM clustering, and clustering labels of partial pulse sequences are obtained; S3, feature parameters and data potential energy values of the remaining pulse sequence are used to construct a hypergraph; and S4, the clustering labels of the partial pulse sequences are used to learn the constructed hypergraph, and a final radar signal sorting result is obtained. The application can sort multifunctional radar signals without a marked sample, can effectively alleviate the 'batch increase' problem, and has good sorting performance.

The application discloses an image classification method based on pseudo-label semi-supervised learning. The method firstly improves the label assignment strategy of the FCM clustering algorithm, can generate the membership of each data with a label, and combines the self-training semi-supervised method to propose a new semi-supervised training algorithm SST, and improves the training result of the semi-supervised training. The method is applied to medical image classification processing, and improves the diagnosis efficiency of medical treatment.

The application provides a traffic cell division method based on resident travel characteristics. Administrative division boundaries and hierarchical road network vector data are acquired and data preprocessing is performed; basic clustering units are divided based on the road network and the administrative division; a clustering index system is established, and resident travel characteristics in the units are acquired through mobile phone signaling in combination with the clustering units; according to the location of the clustering units in the administrative division, a k-means clustering algorithm with spatial characteristics is adopted for preliminary clustering, and clustering results and clustering centers corresponding to each clustering number are obtained; clustering effectiveness indexes of the clustering results of each clustering number are calculated; the clustering effectiveness indexes of each clustering number are compared to determine a proper clustering number; final clustering division is performed through a FCM clustering algorithm with comprehensive spatial characteristics; the clustering results are modified in combination with main hierarchical roads, and traffic cell division is completed. The application improves the robustness of the traffic cell division results and can support the construction of subsequent traffic planning models.

This invention claims protection for a task offloading method for selecting and optimizing energy consumption of trusted edge servers, comprising the following main steps: S1, constructing a system model based on server and device related information data; S2, standardizing task information and MEC cache task classes and performing FCM clustering, followed by encoding matching to obtain the MEC server to be preferentially selected for offloading for each task class; S3, using set pair analysis theory to measure and analyze the reliability relationship in S2; S4, based on the results of S2, dividing the task into subtasks and offloading them in a multi-access point network while calculating energy consumption. Using the WOA algorithm, energy consumption is optimized within the tolerable latency range to obtain the offloading decision for each subtask; S5, based on S4, offloading the subtasks to the corresponding edge nodes or processing them locally. This invention considers issues such as trusted edge server selection and energy consumption optimization during task offloading, meeting user needs and reducing system energy consumption within the tolerable latency range.

The intelligent apple slice fine sorting and drying equipment comprises a conveying mechanism, a height limiting mechanism is mounted at the feeding end of the conveying mechanism, the conveying mechanism comprises a mounting side plate, and a conveying belt is mounted on the inner side of the mounting side plate; a plurality of protruding strip-shaped structures are arranged on the outer surface of the conveying belt at equal intervals. The intelligent selection area comprises a multispectral imaging mechanism, a color grading mechanism and a shape screening mechanism. Multi-spectral fusion detection: RGB + NIR + HDR imaging is combined to improve a YOLOv7 model, the defect detection rate is improved, the error rejection rate is reduced, dynamic color grading, HSV and spectral feature splicing + FCM clustering are adopted, incremental learning is supported, variety differences are adapted, the color sorting accuracy is improved, and the color sorting efficiency is improved. And meanwhile, 3D shape screening, laser triangulation and Poisson curved surface reconstruction are adopted, so that the warping degree detection precision is improved, and the rejection rate of deformed pieces is increased.

This invention relates to the field of agricultural fertilization, and more particularly to a method for optimizing the path coordination scheduling of corn straw return to the field and fertilization operations. The method includes: obtaining standardized feature vectors for field units by standardizing multi-source raw data of farmland; obtaining a feature contribution balancing factor by analyzing the imbalance of distance components in the feature dimensions; obtaining a spatial membership smoothing factor by evaluating the activity of the neighborhood membership distribution of field units; obtaining an adaptive distance metric by combining the feature contribution balancing factor and the spatial membership smoothing factor; and obtaining a set of agricultural machinery path coordination scheduling instructions by hardening the final membership matrix. This method solves the problems of inaccurate partitioning results, fragmented management boundaries, and unreasonable agricultural machinery path scheduling in existing standard FCM clustering algorithms based on abnormal straw accumulation areas.

The invention discloses a combined model photovoltaic output short-term prediction method and model based on self-adaptive FCM clustering, and aims to solve the problems that in a traditional prediction model, the clustering number is difficult to determine autonomously, feature correlation mining is insufficient, parameter optimization is insufficient, prediction precision is limited and the like, and the method comprises the steps that photovoltaic related data such as solar irradiance, temperature and relative humidity are collected; performing z-score standardized preprocessing to remove abnormal values and missing values; high-correlation feature vectors are screened through Pearson's correlation coefficient analysis, and data dimension reduction is achieved; a self-adaptive FCM clustering algorithm is adopted, Markov-included angle cosine comprehensive distance optimization membership calculation is introduced, the optimal clustering number is autonomously determined through a self-adaptive function L (c), and precise clustering is carried out on the data after dimension reduction; and inputting the divided clusters into a CNN-BiLSTM-SA combination model constructed based on a CNN, BiLSTM, an improved Kepler optimization algorithm and a self-attention mechanism, and finally outputting a photovoltaic output short-term prediction result by using key parameters of an IKOA optimization model and important information of an SA dynamic focusing sequence.

This invention discloses a method and apparatus for fault location of a metering master station based on accelerated fuzzy c-means clustering. The method includes: calling the accelerated fuzzy c-means clustering algorithm to process the abnormal dataset, iterating continuously until the cluster set of the abnormal dataset is less than a preset threshold or the number of iterations reaches a set upper limit, obtaining the cluster set and membership set after clustering; obtaining the classification result corresponding to the fault type and fault characteristics based on the cluster set and the membership set; comparing the classification result with the normal data in the log to locate the fault area. This invention uses a KCN network to accelerate the iteration speed of the FCM clustering network, ensuring both accuracy and speed in data classification and fault location.

The invention discloses an EMD-BiGRU photovoltaic power generation power prediction method based on FCM clustering and SSA optimization. Photovoltaic related data is obtained and preprocessed, the photovoltaic related data is divided into three types of weather data sets through FCM clustering, EMD decomposition parameters and BiGRU hyper-parameters are optimized through SSA, decomposed subsequences and meteorological characteristics are input into optimized BiGRU prediction, and a time sequence is superposed to obtain a result. According to the method, hyper-parameter empirical configuration is replaced, the data complexity is reduced, the photovoltaic power frequency characteristics are accurately captured, the prediction decision coefficient is higher than 0.96, and the method is suitable for short-term photovoltaic power generation power prediction.

The method for identifying abnormal water level data of the main stream of the Yangtze River based on meta learning comprises the following steps: step 1: identifying abnormal water level data based on an FCM abnormal value detection model; step 2: based on a meta learning MAML model, the meta learning task is divided into a meta training task and a meta testing task; step 3: in the meta training task stage, a plurality of task training FCM abnormal value detection models are designed to obtain FCM abnormal value detection model initialization parameters θ; and step 4: in the meta testing task stage, the FCM clustering model initialization parameters θ are fine-tuned through support set data to predict query set node categories and detect whether the nodes are abnormal. The method for identifying abnormal water level data of the main stream of the Yangtze River based on meta learning can train an abnormal identification model applicable to water level data of other stations based on water level data of a few stations, and can achieve good identification effect and strong generalization ability of the model.

The invention discloses a power transmission line icing thickness prediction method based on similar day selection and CNN-BiLSTM. The method comprises the following steps: obtaining the icing thickness and meteorological data of a power transmission line; meteorological data with high correlation with the icing thickness is screened out to serve as input data of a data clustering and prediction model; fCM clustering processing is carried out on the meteorological data according to the characteristics of the meteorological data, and a historical data set is divided into different clusters with respective similar characteristics; calculating membership degrees of the to-be-tested day under different clusters by using a grey correlation analysis method, and taking the cluster with the highest membership degree as a selected similar day set for training and prediction; a CNN is added on the basis of the BiLSTM model, and a CNN-BiLSTM model is obtained; inputting key meteorological data and icing thickness data of a similar day set to train a CNN-BiLSTM model; using the trained CNN-BiLSTM model to predict the icing thickness of the to-be-measured day, and outputting an icing thickness prediction result. According to the method, the prediction performance is more stable, the prediction precision is higher, and the problems that the model is easily interfered by irrelevant modes and the prediction precision of the icing thickness of a single model is low are effectively solved.

The invention discloses a thermal power plant equipment performance degradation monitoring method and system based on multi-source heterogeneous data. The method comprises the following steps: collecting historical operation data of each piece of equipment of a turboset and selecting parameters; some data such as non-unit start-stop data in the collected historical data need to be subjected to steady-state screening and working condition division; an FCM clustering algorithm is adopted to perform synchronous clustering on the energy efficiency state indexes, so that the steam turbine set determines the energy efficiency reference state of the steam turbine set; establishing a multivariate state evaluation model combined with the correction information entropy weight through the obtained sample data; through the established model, the output value of the model can be obtained, the deviation degree of the equipment can be obtained by comparing and analyzing the output value of the model and the monitoring value of the unit equipment, and through analysis, the degradation degree of the equipment performance is evaluated and faults are eliminated. According to the method, real-time equipment performance evaluation of the unit is realized, appropriate diagnosis measures of the unit are taken in time according to evaluation, and the aim of reducing energy consumption of the unit is fulfilled.

The application provides an optimal dosing amount prediction method based on an FCM-ANFIS model, which comprises the following steps: S1, using the whole-year raw water quality sample data in historical operation as a data set; S2, pre-processing the data set; S3, analyzing the water quality data to describe the correlation between the water quality data, and performing clustering analysis on the pre-processed sample data, and iteratively solving to obtain a clustering center and a membership matrix; S4, establishing an ANFIS prediction model; S5, learning and training the parameters of the ANFIS model, and establishing an FCM-ANFIS fuzzy reasoning system for dosing amount prediction; S6, dividing the pre-processed data set into N classes after clustering through an FCM clustering algorithm, and inputting into the ANFIS fuzzy reasoning system to perform training and learning to obtain an optimal fuzzy reasoning system; and S7, taking the raw water quality as sample data, and performing prediction through the fuzzy reasoning system; the application can effectively realize online prediction of the optimal dosing amount of a coagulant in a drinking water treatment plant.

The present application belongs to the technical field of asphalt paving segregation detection, and particularly relates to an asphalt temperature segregation detection method, system and device. The method comprises the following steps: S1, acquiring an asphalt infrared image; S2, performing superpixel processing on the asphalt infrared image by using a multi-scale morphological watershed algorithm to obtain a superpixel image; S3, performing clustering processing on the superpixel image by using a K-Means clustering algorithm to obtain a plurality of segmentation regions; S4, processing each segmentation region by using an FCM clustering algorithm to generate a fuzzy label, and merging the fuzzy label into the superpixel image as a segmentation result; and S5, extracting the temperatures of different points in the partition, and judging whether there is segregation in the corresponding partition according to the temperatures. The present application can improve positioning accuracy, thereby meeting the requirements of asphalt temperature detection during the paving process.

The invention discloses an expressway traffic state monitoring method and system based on full-trajectory tracking data, and the method comprises the steps: obtaining standardized data through data preprocessing and time-space consistency conversion, carrying out the refined preprocessing of full-trajectory data, and completing the key feature screening through employing a PCC and MI combined two-stage strategy. Extracting multi-dimensional candidate features through a sliding window, rejecting linear redundant features by adopting a Pearson's correlation coefficient method, dividing traffic states through fuzzy C-means clustering based on the screened features, generating state labels, and realizing traffic state fuzzy division conforming to physical characteristics by utilizing FCM clustering; according to the method, the pain point of data imbalance is solved through the auto-encoder, the finally constructed recognition model has excellent performance in various indexes, and reliable technical support can be provided for accurate monitoring and active management and control of expressway traffic.

The application discloses a kind of based on coupling feature matrix time sequence segment analysis comprehensive load short-term prediction method, comprising:1, original data are collected and normalized, and load data is arranged into the form of load coupling feature matrix, and the time sequence segment sample of load coupling feature matrix and external parameter is constructed by sliding time window reorganization;2, sample time sequence feature and load form coupling feature are extracted simultaneously by 3DCNN network, and clustering analysis is carried out using FCM clustering;3, based on multi-task learning framework combines LSTM network, constructs LSTM-MTL model to realize comprehensive load prediction, and membership degree search mechanism is used to iteratively reconstruct training set and verification set, to improve the final prediction performance.The application is aimed at the problem that energy coupling form is complex and influences comprehensive load prediction performance, by clustering analysis to the time sequence segment sample of coupling feature matrix, the load fluctuation feature extraction capability is enhanced, so as to effectively improve the prediction accuracy of comprehensive load.

This invention discloses a bridge health monitoring method based on fuzzy clustering envelope generative adversarial network (GAN). The method uses FCEGAN to augment imbalanced samples in bridge structural health monitoring. FCEGAN converts time-series data of different modes from sensor data acquired in the bridge health monitoring system into time-frequency image data. Then, based on the time-frequency image data, a multi-level clustering consensus mechanism is employed to jointly optimize multi-level FCM clustering, sample distribution consistency, and dimensionality, generating data samples with better separability and diversity, i.e., envelope samples. Next, the envelope samples are integrated into a GAN for training. Finally, the trained envelope GAN is used to generate new data samples to balance the different mode categories in structural health monitoring. The method of this invention can generate highly separable and diverse samples, thereby improving the ability to identify and classify anomalies.