655 results about "Data decomposition" patented technology

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention relates to the technical field of hard disk security protection, in particular to a security risk protection method and system for a mobile solid state disk. The method comprises the following steps: acquiring newly written data of the mobile solid state disk; performing multi-segment data decomposition on newly written data of the mobile solid state disk, performing independent data encapsulation, and constructing a plurality of data clusters; performing random out-of-order arrangement on the plurality of data clusters, and performing random idle allocation storage to generate data cluster random storage data; generating a random initial encryption key based on newly written data of the mobile solid state disk; based on the random initial encryption key, carrying out distributed storage architecture construction on the data cluster random storage data, and constructing a distributed encryption storage framework; when the access of the mobile solid state disk is identified, acquiring a hard disk work log; and performing access behavior response detection on the hard disk work log, performing abnormal access behavior speculation, and extracting abnormal access behavior data. According to the invention, high-safety and rapid hard disk risk protection is realized.

The invention relates to the technical field of deep learning, and discloses a multi-source ocean data deep learning analysis and prediction platform construction method and device, and the method comprises the steps: collecting historical multi-source ocean environment parameter data, and carrying out the classification preprocessing, and obtaining a sudden change monitoring data set, a periodic monitoring data set, and a statistical monitoring data set; performing data decomposition and mask encryption processing to obtain a local training data set and a distributed training data set; constructing a local prediction model based on the local training data set, and constructing a distributed prediction model based on the distributed training data set; training and iteratively optimizing the local prediction model and the distributed prediction model by adopting a federated learning framework to obtain a target prediction model; and deploying the target prediction model to a real-time calculation unit, performing multi-source marine data analysis on the real-time multi-source marine environment parameter data, and outputting a marine environment parameter prediction result, thereby improving the adaptability and prediction accuracy of the model to marine environment changes.

The invention relates to the technical field of power load prediction, and discloses a power load prediction method and system based on a VMD and MoE system, and the method comprises the steps: inputting original power load data into a trained VMD decomposer, dynamically adjusting a modal number K value and a penalty factor # imgabs0 # value according to a power load fluctuation level, and decomposing the data into K modal components; thirdly, inputting the modal components into the trained MoE system, distributing M groups of expert models and weights for the modal components through a gating network, and adjusting the number of the expert models of the high-frequency and low-frequency modal components according to the fluctuation level; and finally, at least one group of expert models is selected from the M groups of expert models, prediction results are fused through weighted summation based on the selected model and the weight thereof, and a final power load prediction value is obtained. Through the method, the precision and robustness of power load prediction are improved.

The invention discloses a power grid power load intelligent prediction method, system and device and a medium, and relates to the technical field of power distribution network transmission optimization. Power load data is decomposed into a trend component, a periodic component and a random fluctuation component, and then the importance of different components is evaluated by using a feature channel attention layer; the time sequence attention layer captures key moments in each component time sequence and extracts features at the key moments, and then the features extracted based on the importance and the features extracted at the key moments are fused, so that the multi-scale features of the power load are captured; and then inputting the fused features into the dynamic gating residual connection LSTM network for prediction, and in the prediction process, improving the attention degrees of long-term sequences, short-term sequences and fluctuation sequences in different features through a residual modulation function, thereby more accurately capturing the multi-scale features of the power load and obtaining a prediction value of the power load.

The invention discloses a short-term power load prediction method in combination with weather parameters. The method comprises the following steps: acquiring power load data and weather parameters of corresponding time; preprocessing the data; using an EEMD method to decompose the power load data into a high-frequency nonlinear component and a low-frequency linear component; in combination with weather parameters, training a CNN-LSTM model which introduces an attention mechanism and is based on multi-scale feature extraction, and outputting a high-frequency nonlinear component predicted value; for the low-frequency linear component, a time sequence model SARIMA is adopted, and a low-frequency linear component predicted value is output; and adding the high-frequency nonlinear component predicted value and the low-frequency linear component predicted value to obtain a complete load prediction result, carrying out error analysis, and carrying out iterative optimization on the model. According to the method, weather data are introduced, high-frequency nonlinear components are extracted through sequence decomposition, weather parameters are used as input to construct a deep learning prediction model, and the prediction capability of complex load fluctuation and the precision and reliability of short-term load prediction are improved.

The present invention relates to urban rainfall runoff pollution prediction in urban water systems, and provides an urban rainfall runoff pollution prediction method based on integrated rolling decomposition method and deep learning algorithm. A rolling decomposition method is firstly used to decompose rainfall runoff sequence data into different sub-sequences; then decomposition is sequentially performed on added data, and future data is excluded, to prevent information leakage; a recurrent neural network is used to model and predict the sub-sequences; and finally, predicted results of the sub-sequences are summed to obtain the predicted result of rainfall runoff pollution.

The invention discloses a double-channel hydrogen production system optimization control method for a multi-power-supply scene, and the method comprises the steps: constructing a double-channel hydrogen production system, and obtaining corresponding operation data; decomposing and reconstructing based on the operation data to obtain the initial input power of the electrolytic cell; formulating a start-stop rule of the electrolytic cell based on the initial input power; correcting the initial input power based on a start-stop rule to obtain actual hydrogen production power; an electrolytic cell mathematical model is constructed, and the unit hydrogen production cost and the energy conversion efficiency are obtained based on the actual hydrogen production power; establishing a target function based on the unit hydrogen production cost and the energy conversion efficiency; and optimizing the target function based on a multi-target particle swarm algorithm to obtain an optimal filtering reconstruction order and an electrolytic cell retention ratio, and completing power distribution and optimal capacity configuration of the double-channel hydrogen production system. Power division and capacity configuration of the alkaline electrolytic bath and the PEM electrolytic bath are achieved, the hydrogen production cost of the wind-solar coupled system is effectively reduced, and the energy conversion efficiency of the electrolytic bath is improved.

The invention relates to an uncertainty online evaluation method based on time sequence data decomposition and a Bayesian principle, and aims to solve the problem that a traditional uncertainty evaluation method is difficult to process online and dynamically update. The method comprises the following steps: firstly, collecting measurement data within a certain time range, constructing a time sequence data set, and decomposing the time sequence data into a trend period item and a random item through an STL decomposition technology; then, on the basis of the data of the random item, parameters of prior distribution and a likelihood function are calculated by using a Bayesian statistical method, joint posterior distribution is further determined, and the uncertainty of a measurement result is calculated on the basis of the posterior distribution; afterwards, the posterior distribution result at the current moment is used as the prior information of uncertainty evaluation at the next moment to be fused and updated with the new observation data, so that the real-time online evaluation of the measurement uncertainty is realized. The method not only improves the accuracy and real-time performance of the evaluation result, but also realizes the continuous updating and optimization of the measurement information, and is suitable for a complex and rapid-change measurement environment.

The invention relates to the technical field of underground cavern surrounding rock displacement prediction, in particular to a high-stress large underground cavern group surrounding rock displacement prediction method. According to the technical scheme, the method comprises the following steps that data collection and arrangement are carried out, and various types of sensors are arranged at different parts of a high-stress large underground cavern group to collect data; the method comprises the following steps: performing preliminary screening, cleaning and normalization processing on collected data, performing feature engineering and data enhancement work, including time-frequency domain feature extraction, wavelet transform-based data decomposition and reconstruction and a data enhancement strategy, and constructing a hybrid neural network model fusing a convolutional neural network and a gating circulation unit. Through multi-source accurate data acquisition and processing, depth feature engineering, hybrid neural network construction training and scientific evaluation and early warning, the accuracy, reliability and timeliness of high-stress large underground cavern group surrounding rock displacement prediction are remarkably improved, powerful support is provided for engineering safety guarantee, and risks and losses are effectively reduced.

The invention discloses a test paper inspection analysis method and system for medical inspection, and relates to the technical field of medical inspection, and the method comprises the following steps: collecting a spectral signal, filtering data, and performing multi-dimensional feature optimization; constructing a depth feature decomposition model and a multi-objective optimization prediction model; and dynamically correcting the test paper data, and generating a visual report according to a detection result. According to the test paper inspection analysis method for the medical inspection, the surface of the test paper is subjected to high-precision scanning to obtain optical characteristic information, the sensitivity of test paper detection to low-concentration target substances is improved, collected multi-dimensional spectral data are decomposed into different characteristic signals, and a dynamic optimization model is combined, so that the accuracy of the test paper inspection analysis is improved. The characteristics of each target substance in the sample are comprehensively analyzed, the adaptability of the detection system is improved, the availability and usability of the detection result are improved by automatically correcting optical deviation and environmental noise in the collection process, and the method has better effects in the aspects of sensitivity, adaptability and availability.

The invention relates to the field of monitoring section water quality prediction in river channel water quality management, in particular to a river channel monitoring section water quality prediction method based on STL decomposition and an attention mechanism, and the method comprises the following steps: 1, decomposing original water quality data into trend terms, periodicity and residual terms with practical significance by using STL decomposition; and secondly, an attention mechanism is introduced to enhance the capturing capability of the deep learning model on spatio-temporal information. According to the method, the accuracy of water quality prediction is improved while it is guaranteed that the trend and the period condition of water quality change can be obtained through all items subjected to data decomposition, and the accumulative error phenomenon in long-time step model prediction is effectively restrained.

The invention relates to the technical field of image processing, solves the problem that in the prior art, video content cannot be accurately identified and analyzed while pictures and materials are accurately associated, and provides a video content analysis method, device and equipment based on multi-modal data processing. The method comprises the following steps: acquiring a to-be-analyzed original video material; performing transcoding processing on the original video material, and determining standard video data in a preset coding format; decomposing the standard video data into a plurality of video clips by using a shot segmentation technology; embedding the blind watermark information into each frame of image in each video clip by using a preset feature processing algorithm; performing compression processing on each video clip, and determining compressed video data; and identifying and analyzing the compressed video data, and outputting text information and target video material information related to video content. According to the method and the device, the accurate association of the video and the material is realized, and the accuracy of identification and analysis of the video content is improved.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

The invention provides a multi-modal sample generation method based on a geographic information conditional diffusion model, and relates to the technical field of remote sensing image generation, and the method comprises the steps: obtaining a remote sensing multi-modal semantic segmentation data set which comprises a multi-source image and a semantic tag corresponding to the multi-source image; inputting the remote sensing multi-modal semantic segmentation data set into a geographic information condition diffusion model for training to obtain a plurality of multi-modal samples; wherein the geographic information condition diffusion model comprises a joint input module and a data embedding module; performing channel splicing processing on a multi-source image and a semantic tag in the remote sensing multi-mode semantic segmentation data set through a joint input module to obtain target image tag data; the method comprises the following steps: performing a noise injection-noise reduction process and data decomposition processing on target image label data through a data embedding module to obtain a plurality of multi-modal samples; according to the method, the problem that a remote sensing image annotation data sample generally lacks text description information is solved, and low-cost and high-efficiency data sample generation is realized.

The invention relates to the technical field of information platform construction, in particular to a construction method and system of a sports information intelligent service platform. The method comprises the following steps: collecting user motion image data and user health basic data; key body node position information in the movement process of the user is extracted; generating a user motion track curve based on the key node position information, and calculating a deviation angle and a deviation distance with a standard track template to obtain motion correctness data; constructing a user fitness risk assessment model according to the action correctness data and the user health basic data, analyzing the difference degree with a preset safety threshold, and generating layered health guidance data; and decomposing the layered health guidance data into quantifiable progress units, and generating progress tracking data. According to the method, the user motion image data and the health basic data are combined, the evaluation model capable of identifying individualized risk factors is constructed, and more accurate safety guidance is realized.

The invention discloses a multi-modal data fused shield tunneling machine cutter state fault prediction system, which comprises a state acquisition module for acquiring multi-modal state data, decomposing the multi-modal state data into time and space related characteristics, and obtaining optimal state data through low-rank matrix decomposition and compression; the anomaly simulation module is used for collecting fault state data, constructing an improved lightweight weight migration network to perform cross-domain feature extraction to obtain a cross-domain common feature vector, and obtaining common features through a feature alignment algorithm based on adversarial training on the basis of the cross-domain common feature vector and the fault state data; and the fault prediction module is used for performing fault prediction through a space-time cross attention dynamic migration network on the basis of the obtained optimal state data and common characteristics, so that long sequence characteristics can be captured when the cutter is abnormal, the calculation amount can be reduced when the cutter is normal, and the real-time performance and the accuracy of fault prediction are improved.

The invention relates to the technical field of data security detection, in particular to a data security detection method for signal transmission software. And obtaining residual time sequence data after decomposition of the plurality of signal-to-noise ratio time sequence data. Because the electromagnetic interference can cause the residual points to present trend characteristics, the value distribution and change trend of the residual points are analyzed, and outliers and trend characteristic values are obtained. The distribution of the outliers subjected to electromagnetic interference is random, so that a local distribution characteristic value is obtained based on the position distribution condition of the outliers. And combining the two indexes to obtain an electromagnetic attribute characteristic value. Furthermore, as the abnormity generated by the network attack is more correlated, the correlation condition of the outliers in the residual time sequence data of the plurality of channels is analyzed and combined with the electromagnetic attribute characteristic value to obtain an electromagnetic interference degree value, and finally, the outliers generated by electromagnetic interference are removed according to the electromagnetic interference degree value to obtain network abnormal points. The detection precision of the network abnormal points is improved, and the safety detection effect is ensured.

A system and method for compression performs analysis of incoming audio or video data, and selects a manifold based on the analysis of the data. A deep learning model is then trained for the manifold. The data is broken down into components and entropy maximization algorithms are utilized for each component before compression commences. Finally, the system translates the compressed data into a standard file format.

The invention relates to the technical field of water conservancy data processing, in particular to a GIS-based water conservancy project management system, which comprises a non-stationary data decomposition module, a nested autoregression modeling module, a space-time dynamic reconstruction module, a risk quantitative mapping module and a risk decision optimization module. According to the method, long-term changes and short-term fluctuations are quantified and synthesized term by term through hydrological time series data in trend fitting and fluctuation amplitude extraction, the precision and timeliness of time series prediction are improved, dynamic integration of data of multiple monitoring points is achieved in combination with geographic information matching and spatial difference value distribution analysis, and the accuracy and timeliness of time series prediction are improved. According to the method, the spatial distribution of water resources is accurately predicted, the correlation degree between regions is quantified, risk index quantification is combined with a spatial interpolation technology, so that risk region division is more detailed, the pertinence of emergency management is optimized, and the scientificity and execution efficiency of risk decision are further improved based on scheduling data analysis and influence factor quantification.

The invention discloses a multi-modal information interface complexity evaluation method and system, and the method comprises the steps: collecting multi-modal interface elements which comprise visual data, auditory data and interaction data; decomposing the interface complexity of the multi-modal interface, and dividing the interface complexity into information complexity, visual complexity, auditory complexity and cognitive complexity; respectively calculating an index of information complexity, an index of visual complexity, an index of auditory complexity and an index of cognitive complexity; respectively carrying out normalization processing on all the calculated indexes; allocating a weight to each index; and comprehensive score calculation: calculating a comprehensive score through the normalized index value and the weight corresponding to each index. According to the method, the multi-modal complexity is decomposed into four dimensions of information, vision, auditory sense and cognition, comprehensive analysis is carried out on multi-modal information fusion, a dynamic optimization process is embedded, the evaluation efficiency and the interface availability are remarkably improved, and the practicability is better.

The invention relates to the field of wind power multi-step prediction, and discloses a wind power multi-step prediction system based on an SVMD algorithm and a DIATE loss function, comprising: (1) missing value interpolation: interpolating missing values in an original wind power data set by using an artificial neural network; (2) data decomposition: adaptively decomposing original wind power data based on an SVMD algorithm; (3) constructing a prediction model: training an LSTM prediction model by using a DISATE loss function supporting shape and time change detection; and (4) model optimization: optimizing trainable parameters of the LSTM-DIATE prediction model through an Adam optimization algorithm. According to the method, the SVMD algorithm is utilized to realize self-adaptive decomposition of the original wind power data, so that the negative effect of subjectively setting the number of modes on the prediction result is avoided, and meanwhile, the DIATE loss function covering the shape and the time change detection standard is taken as the training target of the prediction model; the method improves the capability of a prediction model to timely and accurately capture sharp changes in wind power data, and has a high practical application value.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention provides a wind power prediction method, which comprises the following steps: acquiring an actual wind power data set, and then obtaining a processed data set; carrying out complete empirical mode decomposition of adaptive noise on the processed data set, and decomposing the data into a plurality of intrinsic mode function components; performing global search on the hyper-parameter of the physical constraint-based Mama model by using a Bayesian optimization algorithm to obtain an optimal hyper-parameter; taking the intrinsic mode function component as an input feature of a physical constraint-based Mama model, combining the optimal hyper-parameter, and integrating the intrinsic mode function component into the physical constraint-based Mama model for training to obtain a trained physical constraint-based Mama model; and utilizing the trained Mama model based on the physical constraint to carry out wind power prediction. According to the method, the precision and stability of wind power prediction can be improved, and the problem of frequent adjustment of the MPPT system caused by rapid change of the wind speed is further reduced.

A system and method for compression performs analysis of incoming audio or video data, and selects a manifold based on the analysis of the data. A deep learning model is then trained for the manifold. The data is broken down into components and entropy maximization algorithms are utilized for each component before compression commences. Finally, the system translates the compressed data into a standard file format.

The invention discloses a load prediction method and system for a regional integrated energy system, and the method comprises the steps: collecting a real-time load data sequence of the regional integrated energy system, carrying out the data decomposition of the real-time load data sequence, and obtaining a plurality of real-time load data subsequences; inputting each real-time load data sub-sequence into a predetermined load prediction model for load prediction to obtain a real-time load prediction result of the real-time load data sequence, and optimizing parameters of the hybrid model of the time neural network-bidirectional gating circulation unit by adopting a sparrow search algorithm to obtain a load prediction model, a dynamic model of load prediction can be quickly and effectively established, and the precision and efficiency of load prediction can be improved by combining a data decomposition algorithm in a load prediction process.

The invention discloses a dance movement digital acquisition method based on motion capture and a teaching system, and solves the problems of subjective evaluation of traditional teaching, neglect of posture difference in the prior art and the like. The motion capture module collects posture and motion data of a dance actor, decomposes the data into basic motions and extracts features to form a standard sequence, generates a personalized teaching evaluation sequence in combination with a student posture feature difference, collects student motions in real time and compares the student motions with the standard, and generates a targeted teaching strategy according to the difference. The system comprises a cloud data server, a motion capture module and the like, and can store standard data, analyze motion characteristics and perform interactive feedback. According to the invention, through accurate capture, personalized evaluation and real-time feedback, the teaching accuracy and efficiency are improved; and data support is provided for dance innovation.

The invention discloses a multi-scale space-time fusion water quality prediction and anti-counterfeiting method based on a dynamic graph neural network. Comprising the following steps: 1) collecting water quality index hour data of a plurality of monitoring stations in a drainage basin; 2) decomposing the data into a plurality of intrinsic mode functions through variational mode decomposition; 3) constructing a dynamic graph neural network spatial feature extraction module, and generating a discrete dynamic graph structure; 4) constructing a multi-scale time feature extraction module, and synchronously capturing short-term fluctuation and long-term trend; 5) designing a residual fusion mechanism to integrate the spatial-temporal characteristics, and outputting a water quality prediction result through a full connection layer; and 6) calculating a path distance between the input data and a prediction result through a dynamic time warping algorithm, and comparing residual distribution by combining K-S to realize authenticity discrimination of the input data. The method can fully excavate the spatial and temporal characteristics of the basin water quality under the condition that the geographical spatial distribution of the sites is unknown, improves the prediction precision, carries out the authenticity recognition of the water quality data of an unknown source, and prevents the data from being tampered.

The invention discloses a regional power grid wind power generation power prediction optimization method which comprises the following specific steps: firstly, collecting data such as wind speed and wind direction through meteorological sensors arranged in a wind power plant and around the wind power plant, and carrying out cleaning and de-noising preprocessing; secondly, decomposing the meteorological data into intrinsic mode function components with different frequencies by adopting variational mode decomposition, constructing a convolutional neural network to perform feature extraction on each component, and adaptively adjusting the size of a convolution kernel according to the periodic characteristics of the data; then, a bidirectional long-short-term memory network based on an attention mechanism is used for fusing features, time sequence information is captured, power prediction is achieved through a full-connection neural network, and the weight of the FCNN is initialized through a random forest regression model to improve the initial performance of the model; according to the method, the prediction precision is improved through multi-level characteristic decomposition and a self-adaptive mechanism, the real-time response capability is good, and the requirements of the regional power grid for the precision and timeliness of wind power generation power prediction are met.

The invention provides an air target trajectory prediction system based on a time sequence decomposition and reconstruction model. The system comprises a sensor, a database and a trajectory prediction unit. The sensor is used for collecting track data of an aerial target in real time and transmitting the track data to the database for storage and management, and the integrity and real-time performance of the data are ensured. The trajectory prediction unit is composed of a data preprocessing module, an online decomposition module, a component prediction module and a data reconstruction module. The data preprocessing module performs cleaning, abnormal value detection and normalization processing on trajectory data, the online decomposition module decomposes the data into a trend item, a season item and a residual item, the component prediction module predicts the decomposed data through a deep learning model, and the data reconstruction module performs weighted combination on each component to generate a final prediction result. High-precision and real-time prediction of the future trajectory of the air target is realized by cleaning, decomposing, predicting and reconstructing the trajectory data of the air target. The method has the advantages of high prediction precision, high real-time processing capability and the like, and is suitable for trajectory prediction in a complex aerial environment.