591 results about "Data normalisation" patented technology

Data collected by a transcranial brain function measuring / stimulating method is accurately projected and displayed onto a brain surface. If there is no three-dimensional head image, data is projected and displayed onto the brain surface of a standard brain. The head surface coordinates are transformed to the brain surface coordinates of the brain surface underlying the head surface by, e.g., a minimum distance search method. The coordinates of a projected point on the brain surface of the head surface and the probability distribution are determined for a standard brain normalized with data on subjects.

The invention relates to a large-scale distributed network safety data acquisition method and system. The method comprises the steps of multimode data acquisition, data analysis and standardization and data distribution and transmission. The system comprises an acquisition agent module, a data acquisition module, a data analysis module and a data distribution and transmission module. With respect to data acquisition, multiple modes such as an active mode, a passive mode and a data stream mirror image mode are adopted, and comprehensive acquisition of various types of data is realized; with respect to data analysis, a data analysis and standardization mechanism based on strategies is adopted, original data are extracted, mapped, replaced, supplemented and the like by means of writing analysis strategies, and therefore quick analysis of a newly added data format and data standardization oriented to multiple application systems are realized; with respect to transmission, the multi-stage connection technology and the multi-path distribution technology are adopted, elastic combination, cascading deployment and multi-path distribution between acquisition systems are realized, and the requirements for vertical and horizontal expansion of a network environment and acquisition of mass data information are met.

A method for providing normalized physiological monitoring data of an individual with a measure of the quality of the normalization, including providing a reference feature based on a physiological attribute of the individual for which an attribute value can be determined; unobtrusively capturing physiological monitoring data for the individual during a series of capture events and determining the capture conditions present during each capture event; detecting the presence of the reference feature in the series of captured physiological monitoring data and determining associated attribute values for each capture event; normalizing the captured physiological monitoring data from each capture event according to differences in the attribute values associated with each event and previously calculated attribute values; and calculating normalization confidence values for the individual at the series of capture events, based on capture conditions, normalization transforms, or semantic data, wherein the confidence values statistically measure the quality of the normalization.

A system, method and program product for normalizing, sanitizing and disambiguating structured data. Structured data includes data stored in a database management system (DBMA), as well labeled files (e.g., XML data). An automated data enhancement processing system is provided, comprising: a system for ingesting data structured in at least one predefined database format; and a set of text analytics processes that treat the ingested data as unstructured, and generate normalized data represented and indexed by consistent, structured metadata.

A system for determining asset prices within a web-based application IS PROVIDED. The system includes a backend server comprising a data collection engine for collecting data from a plurality of data sources, a data standardization engine for standardizing the data collected by the data collection engine, and an Applications Programming Interface (API) engine for carrying out requests as demanded by an end user through a web-based enabled user interface; a database server comprising one or more database tables for storing the data collected from the data collection engine; and a frontend server comprising a web server that interacts with the API engine and communicates with the user interface via a web based application hosted on the web server.

A battery monitoring system may be configured to interface with a first battery monitoring unit and a second battery monitoring unit. The first battery monitoring unit may be configured to output data in a first format. The second battery monitoring unit may be configured to output data in a second format, the second format being different than the first format. The battery monitoring system may include a first polling agent, the first polling agent may be configured to receive data from the first battery monitoring unit and normalize the data into a predetermined data format and store the data in a database. The battery monitoring system may also include a second polling agent. The second polling agent may be configured to receive data from the second battery monitoring unit and normalize the data into the predetermined data format and store the data in a database. Furthermore, the battery monitoring system may be configured to output data associated with the condition of a respective battery unit coupled to at least the first battery monitoring unit and the second battery monitoring unit.

The invention provides an aquaculture dissolved oxygen concentration online forecasting method and an aquaculture dissolved oxygen concentration online forecasting system. The method comprises the following steps of: acquiring water quality index and related meteorological factor data in a scheduled time period to establish an original data set; performing data standardized pre-processing on the original data set by using a normalization method to acquire a training sample data set of a least square support vector regression (LSSVR) machine model; training the LSSVR machine model by using the training sample data set, and optimizing the parameters of the LSSVR machine model to acquire an optimal LSSVR machine model; and acquiring water quality index and related meteorological factor data of an aquaculture ecological environment on line in real time, and inputting the acquired water quality index and related meteorological factor data to the optimal LSSVR machine model to acquire a dissolved oxygen concentration forecasting value. By using the method and the system, accurate and efficient forecasting of the aquaculture dissolved oxygen concentration is realized.

The invention relates to a fusion platform and a fusion method of heterogeneous multi-source data. The fusion platform includes a data collection unit, a data storage unit, a data standardization unit, a user portrait building unit, a knowledge graph building unit and a visualization unit. The data collection unit is used to collect the heterogeneous multi-source data. The data storage unit is used to cache the heterogeneous multi-source data. The data standardization unit carries out lexical, grammatical and / or semantic analysis on the heterogeneous multi-source data to obtain standardized text data. The user portrait building unit utilizes student tags, which are obtained by quantification, to build user portraits of students. The knowledge graph building unit builds knowledge graphs ofthe students, knowledge graphs of teachers and knowledge graphs of courses, and associates the knowledge graphs of the courses, the knowledge graphs of the students and the knowledge graphs of the teachers to obtain course links, social relationships and teacher-student relationships taking the students as centers. The visualization unit shows the course links, the social relationships and the teacher-student relationships taking the students as the centers.

A data infrastructure for graph-based computing that combines the natural language expressiveness of the Semantic Web and the mathematical rigor of graph theory to discover meaningful associations across multiple sources towards computer-assisted serendipitous insight discovery. The process automatically integrates massive size datasets accessed using Semantic Web standards and technologies and normalizes data in graphs. The process generates a plurality of conditional probability distributions based on type-triple meta-data and triple statistics to model saliency and automatically construct and evaluate a plurality of sub-graphs based on the plurality of conditional probabilities for contextual-saliency. The process then renders a plurality of paths (i.e. sequence of associations) that model meaningful pairwise relations between objects of the normalized integrated data. The pluralities of conditional probabilities reveal and rank previously unknown associations between entities of user-interest in the knowledge graph.

The invention provides an industrial soft gateway based on multiple access and edge computing and an implementation method. The industrial soft gateway comprises a configuration interaction module, adata collection module, a data edge computing module and a data sending control module, wherein the configuration interaction module comprises a connection configuration module and a data standardization module; the data collection module is used for a multiple access collection method; the data edge computing module is used for carrying out real-time computing on the data collected by the data collection module; and the data sending control module is used for carrying out caching and task scheduling and allocation of external forwarding on all data to be sent. According to the industrial softgateway based on multiple access and edge computing and the implementation method, a visual interface is arranged to provide connection configuration and standardized operation for various communication protocols and databases, and meanwhile, a graphical monitoring interface of a data collection state, an edge computing result and a forwarding state is provided. Data standardization can be realized, the data noise can be eliminated and the data features can be extracted through the edge computing module, the network transmission data volume of a data cloud platform is reduced, and the data transmission efficiency is improved.

The invention discloses a heart sound signal classification method based on a convolutional recurrent neural network. The method comprises the following steps: performing noise processing on heart sound data; extracting heart sound characteristics of the heart sound signals; standardizing the data; constructing a convolutional recurrent neural network model; training the constructed neural networkby using the training sample data characteristics, and storing the trained network structure and parameters; and testing the test sample data by using the trained model parameters to obtain a final classification and identification result. According to the invention, the system complexity is reduced; the extracted heart sound characteristics do not need to segment heart sound signals; according to the heart sound signal classification method based on the convolutional neural network, the convolutional neural network and the recurrent neural network are connected in series, a deep learning model with the processing advantages of the convolutional neural network and the recurrent neural network is provided, better expressive force is provided for heart sound signal classification, and an effective and convenient tool is provided for detection of normal and abnormal heart sound signals.

The invention provides a mechanical equipment intelligent fault diagnosis method based on a partial migration convolutional network. The method comprises: collecting operation data of mechanical equipment under different operation working conditions; constituting data sets, taking part of data in the data set X as a source domain training sample set and a target domain test sample set; performingdata standardization on each piece of sample data; training two one-dimensional convolutional neural network models with the same structure and different initialization parameters by using the sourcedomain training sample set, and correcting the two trained convolutional neural network models based on the target domain test sample set to obtain a convolutional neural network mechanical equipmentfault diagnosis model; and performing fault diagnosis on the mechanical equipment based on the real-time operation data by using the fault diagnosis model to output a fault type. The method can be effectively used in more real mechanical fault diagnosis, that is to say, the label-free property of the target domain is considered, so that the trained diagnosis model can better diagnose faults of mechanical equipment.

The invention discloses an application platform system of a tobacco commercial system, which comprises an access layer module, a presentation layer module, a service application layer module, a flow integration layer module, an application integration layer module, a data integration layer module, an application support layer module and a foundation support layer module, wherein the access layer module is used for uniformly providing an access service by utilizing a door component of an application platform; the representation layer module is used for uniformly assembling and representing all functions of each application system of the tobacco commercial system based on the functions of the application door component; the service application layer module is used for splitting application into components to form an application component pond; the flow integration layer module is used for providing an enterprise service bus and a service flow integration engine; the application integration layer module is used for realizing the component-based development of the application system; the data integration layer module is used for realizing the integration of the application on a data layer, uniformly managing basic service data, such as service codes and the like, realizing metadata management and data standardization, improving data quality, normalizing and realizing a data exchange and sharing mechanism, and establishing a database and a data quality management system; the application support layer module comprises a basic application server, a database server and message middleware software; and the foundation support layer module is used for providing server equipment, various infrastructures and matched system monitoring and management system software.

The invention discloses a time-of-use electricity price determining method based on load characteristic classification. The method comprises the following steps that data standardization preprocessing is carried out; data after preprocessing are subjected to clustering analysis through a fuzzy c mean value clustering algorithm, and average power load curves of various power consumers are obtained; the power load curves of all the power consumers are divided according to peak-common-trough periods; the demand response degree is analyzed; the power utilization influence degree is analyzed; and according to the demand response degree and the power utilization influence degree of various power consumers, a time-of-use electricity price adjusting mode is determined, the adjusting principle is that the peak-trough electricity price difference ratio and the demand response degree of various power consumers are in inverse relation, and the peak-trough electricity price difference ratio and the power utilization influence degree is in direct ratio relation. Power load data are subjected to clustering analysis, power consumers are classified according to the load characteristics, time-of-use electricity price based on power consumer load characteristic classification is achieved, and important effect is achieved on traditional time-of-use electricity price adjusting.

The invention relates to an electrocardio-signal emotion recognition method based on deep learning. The electrocardio-signal emotion recognition method comprises the following steps that under variousemotion picture inducing conditions, electrocardio data of a testee under different emotions is acquired through the chest or the wrist, the acquired electrocardio data is segmented into electrocardio signals fixed in length, and corresponding labels are made for the electrocardio data obtained under different emotions; the acquired electrocardio data is preprocessed, baseline drift removal basedon wavelet transformation is firstly performed, and noise removal is performed; data standardization is performed, normalization processing is conducted on noise-removed electrocardio data; a model training stage is performed.

The invention discloses a vehicle following safety automatic assessment method based on machine learning. The vehicle following safety automatic assessment method comprises the steps that data are acquired; data cleaning is performed on the acquired data, the data meeting the requirements are reserved, and the data are standardized and normalized in the same data set D; extraction and modeling of the required feature fields are performed on the cleaned data; modeling data M used for machine learning are extracted from the cleaned and normalized data according to accident records and relevant monitoring data; the M set is randomly extracted and divided into two subsets MT and ME according to the given proportion, MT is used for model training, and ME is used for model performance verification testing; supervised classification and machine learning algorithms are adopted, modeling learning is performed by utilizing training data MT, the obtained model performance is verified by ME data and relevant confusion matrix and model classification accuracy is calculated; the results of each time are recorded and compared, and an optimal model is selected; and all the records in the data set D are automatically assessed by using the optimal model, and the results are appended to the data set D and the results are outputted.

The invention discloses a method for multi-sensor adaptation and real-time data acquisition of a wearable device. The method comprises the following steps that S1 Bluetooth device adaptation, namely a Bluetooth device develops a broadcast packet feature recognition module, a control interface module and a data standardization module; S2 device binding, namely a cloud server establishes a binding relationship between a user ID (Identification) and a device ID in a database; and S3 concurrent real-time acquisition of multi-sensor data, namely a user logs in to the cloud server on a mobile intelligent terminal to obtain a list for the bound devices, the mobile intelligent terminal opens Bluetooth scan and compares the device ID of each Bluetooth device discovered through scanning with the ID of the bound device, a new device thread is created if matching and a connection is established. The method has the beneficial effects that real-time acquisition of the cross-manufacturer multi-sensor data is realized, real-time processing requirements for the multi-sensor data are met, and real-time concurrent acquisition of the multi-sensor data is realized.

The invention discloses a treated sewage quality prediction method based on combination of support vector classification and a GRU neural network, and belongs to the technical field of sewage treatment. Missing value processing, abnormal value elimination and data standardization are carried out on the collected sewage historical data, a PCA principal component analysis method is adopted to carryout dimension reduction on the data, and the selected auxiliary variable is used as an input variable of a sewage quality prediction model; a sewage effluent key prediction model is established by adopting a GRU neural network suitable for processing time series data, a support vector machine model is firstly introduced to classify sewage quality data, and then the classified data is respectivelymodeled through the GRU neural network algorithm to predict effluent quality. When the SVM model is trained, a grid search method and a cross validation method are used for optimizing model parameters, the prediction precision of the obtained joint prediction model is more accurate, the model effect is better, the network performance can meet the actual application requirements, and accurate prediction of the effluent quality of the sewage treatment system can be realized.

The invention discloses a network traffic abnormality detection method based on an SVM (Support Vector Machine), which comprises the steps of reading historical network traffic data; extracting network traffic features of the historical network traffic data; carrying out data standardization on the network traffic features; carrying out reduction on the network traffic features to obtain simplified and optimized feature subsets; and training the optimal feature subset by utilizing the SVM to obtain an SVM classifier; adding processed online test network traffic data into the SVM classifier, carrying out calculation by the SVM classifier to obtain a final classification result, and determining whether the processed online test network traffic data is abnormal network traffic data. Compared with the prior art, according to the network traffic abnormality detection method disclosed by the invention, network traffic feature data is subjected to feature reduction and dimensionality reduction by a PCA-TS (Principal Component Analysis-Tabu Search) method, and the optimal feature subset is selected. The problems of long classification detection time, low efficiency and occupation for a larger storage space, which are brought by the curse of dimensionality, are avoided; and moreover, processing time is reduced for subsequent processing, and classification accuracy of the classifier is improved.

The present application relates to a data standardization processing method and device based on data resources, a computer apparatus and a storage medium. The method comprises the following steps of:obtaining an initial table, wherein the initial table includes initial data; extracting key fields of the initial data from an initial table; obtaining the mapping relationship between the initial table and the standard table, wherein standard fields are included in the standard table; according to the mapping relation, converting the key fields to the standard fields; generating a standardized table corresponding to the initial table is generated using the converted standard fields. The method can realize the standardization of data in many regions, and provides convenience for data updatingand sorting in different regions.

The invention discloses a micro-service-based real-time calculation system for a traffic environment and a realization method thereof. A presentation layer receives a user request operation and sendsa request to an API gateway layer; the API gateway layer performs service discovery and matching by calling a micro-service management layer and calls a micro-service layer for service; a micro-service activates a data collector to collect traffic social data in real time; a data standardization engine standardizes the collected data and transmits the collected data to a distributed message engine; the distributed message engine transmits the standardized data to a flow processing engine, and the flow processing engine sends an accident object result to the distributed message processing engine; and the distributed message engine feeds back an output result to the micro-service layer, and the presentation layer obtains the result from the micro-service layer by calling a corresponding API.A model is abstracted to form an independent layer; the advantage of a domain-driven design method in coping with complex business expansion can be applied; and the running efficiency of a software system can be improved.

A method of identifying a network of actors within a data set, the method comprising: —importing data from one or more data sources; —normalising the data in one or more fields to create a consolidated data set; —identifying one or more networks based on identical or similar instances of one or more pieces of data in the consolidated data set; and —calculating a measure of influence of one or more of the actors in an identified network.

The invention belongs to the technical field of lung adenocarcinoma prediction, and specifically relates to an identification of early diagnosis markers of lung adenocarcinoma based on co-expression similarity, and a constructing method of a risk prediction model. The constructing method includes the steps of: data remodeling and grouping, data standardization, phase specific gene extraction, geneco-expression correlation analysis, unsupervised cluster analysis, specific and non-specific co-expression network analysis, functional pathway gathering, significant variation pathway identification, screening of early screening marker genes by using an REE algorithm, establishment of a classification model based on early screening risk genes, survival analysis verification, and the like. The identification of early diagnosis markers of lung adenocarcinoma based on co-expression similarity, and the constructing method of a risk prediction model can realize the early diagnosis of lung cancer,and can identify gene markers which change significantly with the progress of lung cancer at the same time.

The invention relates to a large data visualization analysis and processing system for enterprise operation data analysis, which is characterized by comprising a data source judgment module, a data collection module, a data storage module, a data cleaning module, a data standardization processing module, and a background task scheduling module, wherein the data source judgment module is used for judging a data source for adopting a corresponding data collection mode; the data collection module collects data according to the corresponding data collection mode; the data storage module stores the data collected by the data collection module into to-be-cleaned data; the data cleaning module carries out data cleaning on the to-be-cleaned data and generates already-cleaned data; the data standardization processing module carries out standardization processing on the already-cleaned data, and a public navigation field is generated and stored to a data pool; and the background task scheduling module calls a built analysis model to analyze the data in the data pool and generates a visualization analysis result report. Compared with the prior art, the system of the invention has the advantages that the working efficiency is improved, cross-system data analysis is supported, secrecy is high and the like.

The invention discloses an intelligent monitoring and early warning system for converter station equipment. The system comprises a data acquisition module, a data preprocessing module, an equipment fault prediction module and an equipment early warning module. The data acquisition module is used for acquiring the state data of the converter station equipment; the data preprocessing module is usedfor carrying out intelligent trend classification, missing value intelligent filling, abnormal value processing and data standardization processing on the data of an equipment state sensor; the equipment fault prediction module is used for carrying out data feature selection on the standardized data obtained by the data preprocessing module, and training, predicting and evaluating equipment parameter monitoring data through an LSTM network according to data features; and the equipment intelligent early warning module is used for comparing a fault threshold expert knowledge base with an outputequipment state prediction result and carrying out intelligent early warning on the equipment. According to the method, the relation between historical monitoring data and different monitoring data ofthe converter station equipment is effectively utilized, an intelligent monitoring and early warning system of the converter station equipment is established, and timely early warning of equipment state parameters is achieved.

The invention provides a method and device for data standardization processing of medical big data and relates to the technical field of medical entity identification. The method comprises the steps that a first group of candidate entities of a to-be-processed statement is determined according to an entity mark sequence of the to-be-processed statement; word extraction is conducted to the to-be-processed statement according to a preset medical noumenon term word extraction strategy, so a second group of candidate entities can be determined; entities in the to-be-processed statement are determined from the first group of candidate entities and the second group of candidate entities; screening is conducted according to preset syntactic analysis and screening rules, so a candidate standardization term in the to-be-be-processed statement can be determined; if the candidate standardization term in the to-be-processed statement can match a preset medical noumenon term library, the candidate standardization term in the to-be-processed statement can be determined as the standardization term; and if the matching fails, a matching failure problem report will be generated, or fuzzy matching will be conducted to the candidate standardization term which is not matched and belongs to a disease term type, so the standardization term can be determined.

A distributed ecological intelligent internet pension service system comprises a government-level multi-department unified command and supervision layer, a region-level central management and control layer, a community-level service guarantee layer and a user terminal function layer. The user terminal function layer acquires user health basic data and sends the data to the region-level central management and control layer; the region-level central management and control layer receives the data and community comprehensive service guarantee service configuration information, builds a user database according to the information, generates region user group reports and distributes the reports to corresponding organizations; the government-level multi-department unified command and supervision layer sends related decisions and instructions to the region-level central management and control layer. The distributed ecological intelligent internet pension service system is an intelligent service system based on the internet of things, the internet and pension, and has the advantages of data standardization, structure networking, system self-evolution, operation disintermediation, high information security degree and the like.

The invention provides a pseudo-acoustic curve construction method based on nonsubsampled Contourlet transformation. The method comprises the following steps: reading logging data; carrying out pretreatment on read logging data curves, wherein the pretreatment comprises curve correction, data standardization and normalization processing; carrying out multistage nonsubsampled Contourlet transformation on the logging data curves respectively, carrying out weight fusion according to a coupling degree between the logging data curves so as to generate a high frequency fusion curve and a low frequency fusion curve; carrying out nonsubsampled Contourlet inverse transformation on the high frequency fusion curve and the low frequency fusion curve so as to generate a pseudo-acoustic curve.

The invention discloses a medical big data multi-center integration platform and method. Data of each data branch center server are accessed to a data center server and subjected to quality assessment, and a next step is entered if the quality assessment passes; if the quality assessment fails, the data center server feeds back the conclusion of failure to each corresponding data branch center server; the quality assessment includes the assessment of data integrity, data repetition rate, data bias and data size; the data center server establishes and maintains standard variables and a standarddictionary, meanwhile, preprocesses the data according to the standard variables and the standard dictionary; the data standardization processing includes standardization of the variables and standardization of data values; a one-to-one mapping relationship between data variables of the data branch center servers and the standard variables of the data center server is established by means of a similarity matching algorithm and manual auditing; and the data standardized by the data center server are utilized.