119 results about "Analysis Dataset" patented technology

The invention belongs to the technical field of industrial big data application, and particularly relates to an industrial big data multidimensional analysis and visualization method based on a JSON document structure. The method comprises the following steps: by taking JSON as a basic carrier of data, constructing an industrial data mart in parallel by utilizing Spark and ElasticSearch through configuring a relational database and a file system data source and defining data conversion and data cleaning operations; configuring an overall process of data analysis in a graphical mode to construct an analysis data set of a multi-dimensional structure, and repeated association operation on massive data being avoided; and for a specific data analysis scene, customizing each dimension calculation index of the data analysis report in a visual dragging mode based on a pre-constructed multi-dimensional analysis data set, and generating an interactive graphic analysis report. According to the method, the JSON document format is used as a carrier of basic data, and the advantages of the JSON document format in storage and analysis are utilized, so that multi-dimensional analysis structure modeling and user-defined interactive analysis are more convenient and efficient.

The invention relates to the technical field of trend analysis and fault diagnosis of industrial equipment, and aims to provide an equipment state trend analysis and fault diagnosis method which comprises the following steps: acquiring historical operation parameters of equipment to be diagnosed; preprocessing the historical operation parameters to obtain an analysis data set; establishing a trend algorithm model according to the analysis data set; obtaining current operation parameters of the equipment to be diagnosed, inputting the current operation parameters into the current trend algorithm model, and comparing the current operation parameters with preset normal state parameters to obtain the degradation degree of the equipment to be diagnosed; and obtaining a prediction trend curve according to the current trend algorithm model, and obtaining a predictive maintenance time window according to the prediction trend curve, the current operation parameters and a preset standard limit value. The equipment state can be monitored in real time, an equipment user can master the accurate operation condition of the equipment conveniently, and meanwhile, an enough time window is provided for follow-up maintenance of the equipment.

The invention discloses a mixed isolated island detection method. On the basis of mixing of artificial intelligence, voltage and frequency positive feedback of a decision tree (DT), the method based on artificial intelligence of the decision tree measures and stores characteristic quantity indexes of default events of selected target positions, information of a data collection is extracted and analyzed to train a DT classification device, and the operation state of DG (Distributed Generation) is judged to be whether network operation or isolated island operation through comparing the former data collection with a data collection in the case of pre-specified events. According to the method, on the basis of the DT method, the inverter control-based positive feedback method is further combined, complementarity and compatibility are strong; through introducing positive feedback disturbance, the characteristic quantity indexes can be obviously distinguished in the case of isolated island operation of the system and the network operation, judging the isolated island by the DT method is facilitated, and through increasing difference of the characteristic variable parameters in the case of isolated island operation or non-isolated island operation, the error rate of isolated island detection is thus reduced, and reliability of isolated island detection is improved.

The invention discloses a financial service management information platform which comprises a foundation platform and an application platform. The financial service management information platform can realize cross-system and trans-department data information sharing on the basis of finishing basic data merging and integration of each legal person organization. Data application marts having different service themes are established through a unified data view, the data application marts comprising a business report data mart, a business index system and business analysis data marts having different topics. Therefore, service level of a rural credit system is improved, and wisdom cooperation financial service is realized under the premise of guaranteeing the system to have good expansibility, safety and stability and high availability.

Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

The invention discloses an MWHTS clear sky observation brightness temperature selection method based on cloud water content inversion, and the method comprises the steps: building the matching data ofan MWHTS observation brightness temperature and a climate data set in time and space, calculating a corresponding MWHTS simulation brightness temperature, and forming an analysis data set and a verification data set; taking the cloud water content 0 in the analysis data set as a strict clear sky threshold, selecting the MWHTS strict clear sky observation brightness temperature, and adjusting thestrict clear sky threshold by taking the calculation precision of the MWHTS simulation brightness temperature under the strict clear sky as a standard to form a corresponding clear sky threshold; based on the analysis data set and the BP neural network, establishing an optimal inversion model of MWHTS observation brightness temperature inversion cloud water content; using the MWHTS observation brightness temperature in the verification data set to invert the cloud water content, and selecting the MWHTS clear sky observation brightness temperature according to the cloud water content inversionvalue and the clear sky threshold. According to the method, the MWHTS observation brightness temperature can be directly utilized to realize effective selection of the MWHTS clear sky observation brightness temperature, the accuracy is high, and the operation is simple and feasible.

The invention discloses an MWHTS simulation brightness temperature calculation method based on a deep neural network. The MWHTS simulation brightness temperature calculation method comprises the steps: establishing a matching data set of an MWHTS observation brightness temperature and a climate data set in space and time; dividing the matching data set into a clear sky data set, a cloud data set and a rain data set according to the cloud water content, and respectively forming a corresponding analysis data set and a corresponding verification data set; training a deep neural network model by utilizing the three analysis data sets, inputting atmospheric parameters in the corresponding verification data sets into the trained deep neural network model, and calculating MWHTS simulation brightness temperature; inputting the atmospheric parameters in the three verification data sets into a radiation transmission model to calculate the MWHTS simulated brightness temperature, comparing the calculation precision with the MWHTS simulated brightness temperature calculation precision based on the deep neural network, and selecting MWHTS channels with higher precision to form an MWHTS simulatedbrightness temperature calculation result. According to the method, the interaction between microwaves and atmospheric molecules is modeled by using the deep neural network, the calculation precisionhigher than that of a business radiation transmission model RTTOV is obtained, and the operation is simple and easy to implement.

There is disclosed method of and system for performing the following steps: obtaining a data set comprising a plurality of datum; reviewing the data set to determine the presence of at least one of a set of monitoring traits which includes one or more monitoring traits; analyzing the data set to extract a secondary data set which includes data related to each of the datum having one or more of the monitoring traits; adjusting the contents of the set of monitoring traits based on consideration of the data from the secondary data set; generating a set of vectors, wherein each of the vectors comprises data indicative of the source of each datum; and, creating a key, wherein the key comprises a selected set of the vectors.

The invention discloses a user data classification method and device based on Internet of Things data, computer equipment and a storage medium, is applied to the field of smart medical treatment of smart cities, and relates to data processing and block chains. The method comprises the following steps: after receiving current user medical data uploaded by an Internet of Things medical terminal, a the cloud server forms a to-be-analyzed data set according to the medical data of the current user and other key data corresponding to the current user and retrieved in the cloud server; and by takinga convolutional neural network stored in a cloud server as an operation model of input data composed of the current user medical data and the key data, a classification result is obtained and an optimal treatment path is determined. According to the method, rapid acquisition of the user medical data based on the Internet of Things equipment is realized, the optimal treatment scheme data is rapidlyand accurately recommended based on the historical medical data, and the cost of acquiring the optimal treatment scheme data is reduced.

The invention provides a malicious URL detection method and system. The malicious URL detection method comprises the steps of obtaining a URL to-be-analyzed data set, and obtaining a malicious URL training sample set; utilizing the malicious URL training sample set to train an SVM support vector machine to classify the URL to-be-analyzed data set to obtain a malicious URL data set and a to-be-labeled URL data set; clustering the to-be-labeled URL data set by adopting a clustering algorithm to obtain a to-be-labeled URL sample set; marking the to-be-marked URL sample set according to a malicious judgment result so as to divide the to-be-marked URL sample set into a marked malicious URL sample set and an unmarked URL sample set; combining the marked malicious URL sample set and the maliciousURL training sample set in a set union solving mode to obtain an updated malicious URL training sample set; and subtracting the marked malicious URL sample set from the to-be-marked URL data set to obtain an updated URL test data set. The malicious URL detection method and system are novel in design and high in practicability.

The invention discloses a construction method and prediction method of an industry electric quantity demand prediction model. The construction method of the industry electric quantity demand prediction model comprises the steps that: the first leading factor of industry electric quantity increase is obtained according to a set industry electric quantity correlation analysis program, wherein the industry electric quantity correlation analysis program is a combination of a correlation rule mining program and a principal component analysis program; according to a pre-established historical industry electric quantity correlation analysis data set, the second dominant factor of the industry electric quantity increase is extracted; and according to the first dominant factor and the second dominant factor, an ARIMA model and the multiple regression model are corrected and coupled, so that the industry electric quantity demand prediction model is obtained. Compared with a traditional method, the method has the advantages of greatly improved prediction precision, high applicability and feasibility, can be popularized to other industries.

The invention discloses a chart document panel analysis and understanding method, which comprises the following steps of: collecting an initial chart document, and analyzing the initial chart document to obtain a chart analysis data set; constructing a key point segmentation model, and obtaining the position coordinates of the scale points based on the chart analysis data set and the key point segmentation model; constructing a multi-target detection model, carrying out legend position detection based on the multi-target detection model, and obtaining legend positions; and analyzing the position coordinate of the scale point and the legend position based on a matching rule of the scale point and the scale value of the rectangular expansion and a matching rule of the legend and the label of the maximum intersection-to-union ratio to obtain an analysis result of the chart document panel. According to the method provided by the invention, the problem of panel understanding of the chart document is efficiently and accurately solved, and the method plays an important positive role in realizing automatic data extraction and content understanding of the chart document by a machine.

Disclosed are a method and system for propagating data changes in a hierarchy of dataset models in which each dataset model comprises an analytic and one or more parent datasets, including a primordial dataset. The analytic is executed to instantiate a first instance of the data model. After a change in a primordial dataset, each instance of a dataset model that descends from the primordial dataset is invalidated, and the analytic is re-executed to create a second instance of the data model. Analytical results may be displayed. The first dataset model may include a metric in which the definition of the metric comprises metadata of the dataset model. Metric values may be stored in a first cache, re-computed on a new instance of the dataset model, and stored in a second cache.

The invention provides a medication decision-making method and system based on deep learning. The method comprises the following steps: collecting patient-related information, medication scheme-related information and actual curative effect evaluation information of a medication scheme on an actual curative effect of a patient to obtain an analysis data set; analyzing and processing the analysis data set through a constructed curative effect prediction model so as to predict predicted curative effect evaluation information of each medication scheme for different patient treatment effects; andrecommending the medication scheme suitable for the personalized requirements of the patient to the patient according to the predicted curative effect evaluation information. The medication decision-making method and system have high accuracy and stability during medication scheme recommendation, and are suitable for medical clinical application.

The invention belongs to the technical field of temperature estimation, and discloses a high-temporal-spatial-resolution near-surface air temperature reconstruction method and system, and equipment, and the construction method of a high-temporal-spatial-resolution near-surface air temperature model comprises the steps: dividing the weather of 1979-2018 into weather under a sunny day condition and weather under a non-sunny day condition, and through an existing reanalysis data set; establishing daily maximum and minimum temperature models in different weather states to estimate maximum and minimum temperature values by utilizing meteorological station data and combining with hour temperature data of a Chinese surface meteorological element driven meteorological station and MODIS daily Ts, and obtaining a daily average temperature data set through summation and averaging; and after linear regression correction, finally outputting a Chinese daily value near-surface air temperature data set from 1979 to 2018, wherein the spatial resolution is 0.1 degree. The data set constructed by the method can better estimate the daily maximum temperature, minimum temperature and average temperature, and is convenient for further analyzing seasonal and periodic change conditions of the temperature in the Chinese region.