16 results about "Functional data analysis" patented technology

The invention relates to the technical field of medical informatization, in particular to an MCI cognitive trajectory and bidirectional outcome dynamic prediction method based on a joint model. The method comprises the following specific steps: firstly, constructing a univariate joint model, then constructing a multivariate joint model, and then constructing a function type multivariate joint model; performing parameter estimation and statistical inference on the model; fitting evaluation is carried out on the function type multivariable joint model based on multi-longitudinal cognition and multi-state outcome; and finally, carrying out individualized dynamic prediction. According to the method, a baseline or longitudinal nerve image is processed by using a function type data analysis technology, and a change track is reconstructed; a plurality of cognitive assessments are used as longitudinal measurement indexes, covariants such as age and gender and functional nerve images are used as predictive factors, two-way outcome of MCI and occurrence time of the two-way outcome are set as survival outcomes, a joint model based on multi-longitudinal cognitive multi-state outcomes is constructed, early screening of high-risk groups is assisted, and the survival outcomes of the high-risk groups are determined. And powerful support is provided for optimal distribution of medical and medical and health resources.

The invention discloses an intelligent power plant digital engineering platform, and relates to the technical field of data engineering.The intelligent power plant digital engineering platform is characterized in that data from different sources are processed through a data integration module, and the data are managed and integrated in a centralized mode; by constructing a data classification type storage structure, classification storage, indexing and management of data of different sources are achieved, and convenient query and retrieval functions are provided; the data analysis module performs deep calculation and analysis processing on the collected data to generate a power plant operation index Dcyzs, and evaluates the operation state of the power plant through the index; the system integration module calculates a performance index Xnzs by extracting performance related data of each functional module, and evaluates and optimizes the system performance according to the performance index Xnzs; through comprehensive system testing and optimization and establishment and continuous adjustment of a user feedback mechanism, continuous improvement of system functions and performance is ensured. Not only is the problem of insufficient data processing capability solved, but also the overall operation efficiency and management level of the power plant are improved.

The invention relates to a territorial space multi-level function data analysis processing method and device, belongs to the technical field of land space distribution data processing, and solves the problems of inconsistent function positioning of different types of territorial spaces and insufficient accuracy of analysis result data in an existing territorial space function analysis method. The invention discloses a territorial space multi-level function data analysis processing method and device. A land parcel is strengthened according to a purpose-function conversion matrix so as to update a function identification unit of the land parcel; quantifying the spatial distribution density of each type of POI of the land parcel according to the kernel density of the POI data; calculating the distribution frequency of different POI types in each function type in the function identification unit; in combination with a kernel density analysis result and a frequency density analysis result, carrying out weighted calculation and standardization processing on the spatial function value of each function identification unit; and determining a data analysis result of each function identification unit in each function hierarchy framework. According to the method, the land space layering is analyzed and processed, so that the accuracy of analysis result data is improved.

In order to explore the effectiveness of the functional data analysis method in the hyperspectral image processing, the application proposes a classification method combining the Gaussian regression mixture model and the MRF hyperspectral function data; first, the polynomial regression is used to fit the hyperspectral image pixel spectrum curve, so as to express the pixel spectrum information in the form of function; then, the neighborhood relationship is introduced to establish the Markov random field model, and the neighborhood Gaussian regression mixture model is established in combination with the Gaussian regression mixture model; finally, according to the maximum posterior probability criterion, the final hyperspectral image classification result is obtained. Since the spatial-spectral information of the hyperspectral image is fully combined, the algorithm has high-precision classification result, and effectively improves the classification performance of the hyperspectral image.

The invention discloses an industrial chain financial risk prediction method based on RGNN-Crossform fusion architecture. The method comprises a data preprocessing module, a relation extraction module, a feature embedding module and a final prediction module. Firstly, aiming at the problems of high noise and serious loss of financial data, a function type data analysis method is used for preprocessing original data, and then an industry chain upstream and downstream enterprise association network is constructed through a data driving method and a text driving method; and then a routing mechanism and a graph neural network are introduced into a feature extraction part of the deep learning prediction model to help the model to learn enterprise risk association structures from different information sources, and finally a financial risk prediction model RGNN-Crossform considering an enterprise association relationship is constructed. Experimental results show that the prediction effect of the constructed RGNN-Crossform model is obviously superior to that of various leading edge financial risk prediction models. Wherein the routing mechanism and the graph neural network module are adopted, so that the model can learn data internal dependence and external correlation characteristics at the same time, and the performance and robustness of the model are improved.

The present invention discloses a method and device for the health management of special vehicles. The method includes: obtaining the acquisition data of components corresponding to various sensors and storing it in a data processing and analysis device, where the acquisition data stored in the data processing and analysis device follows a normal distribution; sequentially selecting the acquisition data with probability values of μ - 2σ, μ - σ, μ, μ + σ, and μ + 2σ in the normal distribution to determine a weighted correction coefficient matrix; correcting the data stored in the data processing and analysis device based on the weighted correction coefficient matrix, and using functional data analysis methods to establish a functional relationship of the corrected data with time; determining the damage conditions of each component of the special vehicle over time based on the functional relationship. This method can correct the data volatility caused by vibration, accurately depict the functional curves of the parameters of each component changing continuously over time, improve the accuracy of the health management of each device, and achieve the health management of each component under the condition of data fluctuations.

The present invention provides a KPI anomaly detection method, system, and medium based on functional data analysis, comprising the following steps: fitting discrete original KPI time series data into a dynamic function curve; extracting features of the dynamic function curve; inputting the extracted features of the dynamic function curve into a classification model, and performing anomaly identification on the features based on the classification model; and obtaining an identification result of whether the original KPI time series data output by the classification model is abnormal. In order to solve the problems of low detection accuracy, low efficiency, and inability to be applied to multi-scenario or multi-dimensional anomaly detection in the prior art KPI anomaly detection methods, the present invention converts the original KPI data into a dynamic function curve and converts the original data into functional data for analysis, which has stronger universality, reduces the workload of operation and maintenance sample data collection and cleaning, and effectively improves detection accuracy and efficiency.

The invention provides an Internet of Vehicles function data acquisition and analysis method and device and a computer program product, and relates to the technical field of industrial information and data processing. The method comprises the following steps: acquiring burying point data of an Internet of Vehicles function of a vehicle from a cloud; according to a target index in the Internet of Vehicles function data analysis index system, determining a to-be-analyzed service of the to-be-analyzed function; determining data entries related to the to-be-analyzed business based on event names in the data entries; according to the vehicle identification codes in the related data entries, executing vehicle number pre-aggregation on the related data entries; according to the vehicle identification code and the event name in the related data entry, executing vehicle dimension detail pre-aggregation on the related data entry; and determining a target index according to the vehicle number pre-polymerization result and / or the vehicle dimension detail pre-polymerization result of the to-be-analyzed service. According to the invention, data acquisition and analysis of corresponding targets can be realized, and product optimization is facilitated.

The invention discloses an individual dynamic metabolic abnormality early warning system based on function type data analysis, which can judge whether the dynamic metabolic condition of an individual to be evaluated is abnormal or not according to the fasting metabolic response data of the individual and a healthy reference population and the metabolic response data after a tolerance test, and carries out early warning if the dynamic metabolic condition exists. The system comprises a data input module, a data analysis module and an early warning module, the data input module is responsible for data input, and input data comprises index data of to-be-evaluated individuals and health reference people at multiple time points before and after a tolerance test; the data analysis module is responsible for processing and analyzing the data and calculating a metabolism score of the individual to be evaluated; the early warning module is responsible for judging whether metabolism abnormity early warning is carried out or not, comparing the metabolism score obtained by the data analysis module with a threshold value, if the metabolism score is lower than the threshold value, early warning is carried out, and otherwise, early warning is not carried out.

A seasoning frying reaction kettle temperature adaptive batch control method, steps include: (1) preprocessing the historical data of multiple batches of kettle temperature, removing abnormal points by Savitzky-Golay filtering, aligning multiple batches of trajectories by dynamic time warping algorithm, and then extracting process common characteristics by functional data analysis; (2) build a double-layer architecture of mechanism model iterative correction layer and real-time dynamic control layer; based on real-time kettle temperature and steam valve opening data, online update mechanism model parameters and real-time parameter setting using adaptive batch PID controller, compensate high-frequency disturbance and saturate the output; (3) fuse the outputs of mechanism model iterative correction layer and real-time dynamic control layer to generate the final steam valve control command of the reaction kettle. This method does not require additional sensors, only relies on conventional measurement data, improves temperature control accuracy, working condition adaptability and multi-batch production stability.

An apparatus for calculating an optimized functional signature, comprising at least a processor; and a memory containing instructions configuring the at least a processor to receive a vitality profile relating to a user, wherein the vitality profile comprises a plurality of function data; analyze the plurality of function data to link a temporal element to each of the plurality of function data; calculate a functional signature as a function of the analyzed plurality of function data linked to a temporal element; generate an optimized functional signature as a function of the functional signature, wherein generating the optimized functional signature further comprises: identifying at least one constraint associated with the function signature using a vitality machine learning model; determining a resource allocation as a function of the optimized functional signature; and present the optimized functional signature and the resource allocation using a display device.

The invention relates to the technical field of ecological environment monitoring methods, in particular to an ecological environment monitoring method and system based on ecological function data analysis. According to the method, an upstream engineering data set and a downstream monitoring data set are endowed with initial credibility weights, weight values are dynamically adjusted based on the deviation degree of an actual ecological response index and an expected effect in an engineering influence range, and a checking task list is generated when the deviation of an accounting result exceeds a threshold value. And updating a weight adjustment rule set by executing a verification task, generating an optimized data packet containing a calibration data table, an abnormal list and a rule set configuration file, and realizing dynamic tracking and data comparison of ecological protection measure effects. The system comprises a memory and a processor, and realizes data processing and analysis through a distributed architecture. According to the method, the problems of data authenticity verification and causal association proof in ecological compensation are effectively solved, and a scientific decision basis is provided for drainage basin ecological management.

The invention discloses a heterogeneous data packet scheduling method and system based on time-varying features and priorities, and relates to the field of data processing, and the method comprises the steps: collecting multivariate heterogeneous data, and converting the multivariate heterogeneous data into carbon emission data in a uniform format by adopting a functional data analysis method; performing time-varying feature extraction and data grouping on the carbon emission data by using a CNN-LSTM model based on an attention mechanism; according to a data scheduling demand estimation model, respectively calculating a demand value of each data packet; and calculating the similarity between each data packet and the scheduling link according to the demand value, and scheduling the data packets. The mass heterogeneous data can be effectively and orderly managed and utilized by performing packet scheduling on the mass heterogeneous data, and the intelligent level of a dual-carbon digital monitoring center is improved.

The present invention relates to the field of transaction management technology, and in particular to a transaction management method and system based on the interaction of multiple transaction objects. The present invention analyzes the transaction needs of users through transaction demand data; analyzes the combined transaction matching degree of multiple transaction objects in the user's shopping cart through the characteristic data and function data of multiple transaction objects, obtains multiple transaction object combinations according to the combined transaction matching degree analysis results, and obtains the combined attribute data of the multiple transaction object combinations; analyzes the transaction priority of multiple transaction object combinations in the user's shopping cart according to the transaction demand analysis results of the user and the combined attribute data of the multiple transaction object combinations; and recommends the optimal transaction object combination to the user according to the transaction priority analysis results. The present invention can solve the technical problems of single commodity recommendation, lack of accurate analysis of user needs, lack of basis for combined recommendation, and unclear priority sorting in the prior art, and improve the shopping experience and satisfaction of users.

This invention discloses an individual dynamic metabolic abnormality early warning system based on functional data analysis. It can determine whether there are abnormalities in the dynamic metabolic status of the individual being evaluated based on fasting and metabolic response data after a tolerance test, and issue an early warning if abnormalities are found. The system includes a data input module, a data analysis module, and an early warning module. The data input module is responsible for inputting data, including indicator data from the individual being evaluated and the healthy reference group at multiple time points before and after the tolerance test. The data analysis module is responsible for processing and analyzing the data and calculating the metabolic score of the individual being evaluated. The early warning module is responsible for determining whether to issue a metabolic abnormality warning by comparing the metabolic score obtained from the data analysis module with a threshold. If the metabolic score is lower than the threshold, an early warning is issued; otherwise, no warning is issued.

The invention discloses a visualization system and method for low-code index arrangement and chart reuse of a linkage label system and a medium, and the method comprises the following steps: realizing accurate group screening through data definition and label integration. Collaborative data filtering of a data set and a label system is carried out to realize rapid group screening; based on a defined data set, a user can arrange a data applet or a data canvas through a self-service pattern component supporting and pulling mode, the complex index calculation requirement is met by means of flexible data operation support, the component reuse efficiency is improved by means of a data applet reuse mechanism, and the user experience is improved. A convenient canvas arrangement and output scheme is combined to adapt to diversified office scenes, meanwhile, non-homologous configuration of a data text component is innovated to break through the limitation of multi-source data integration, and a set of efficient, flexible and functional data analysis and visualization solution is integrally constructed.