882 results about "Trend prediction" patented technology

Systems and methods for data analysis and trend prediction. Multiple networks are combined for analysis to improve the accuracy of the evaluation by broadening the type of criteria considered. Relevant features are extracted from a dataset and at least one network is formed representing various relationships identified among the items contained in the dataset according to heuristics. Statistical analyses are applied to the relationships and the results output to a user via one or more reports to permit a user to evaluate each of the items in the dataset relative to each other. The trend of the relationships may be predicted based on the results of statistical analysis applied to the features over successive discrete time periods.

A system for allocating resources which are anticipated to be available in an on-demand computing at some time in the future, wherein a producer trending agent (PTA) lists resources according to a trend prediction of resource availability in the past, a consumer trending agent (CTA) places bids for listed resources according to previous resource usage trends, and an arbitration system (REAS) matches the bids with the listings to determine if a match exists. If so, a binding contract for consumption of the matched listed resources is established. The PTA also produce tier-based dynamic incentive (TDI) schema which divides potential bidders into tiers, and offers additional resource incentives according to the tiers. Both PTA and CTA dynamically update their available/usage trends, listings and bids, over time.

The invention relates to a large scale crowd video analysis system and a method thereof. The system comprises a crowd density calculation module, a crowd prospect segmentation module, a crowd tracking module, a crowd state analysis module and an event determination module. The crowd density calculation module, the crowd prospect segmentation module and the crowd tracking module process video image data and then the crowd number, a crowd area, a crowd motion direction and a speed are acquired respectively. The crowd state analysis module carries out processing analysis based on the acquired crowd number, the crowd area, the crowd motion direction and the speed and sends an analysis result to the event determination module. The event determination module is used for determining whether a crowd event is abnormal. The crowd event means that the crowd reaching a certain scale carries out one group characteristic behavior in a monitoring area. By using the system and the method, monitoring personnel can complete intelligent trend prediction, characteristic event positioning and other tasks; and effective help is provided for prevention of emergency, suspicious clue tracking and the like.

The system comprises preprocessing board connecting key phase signal to fast signal, key phase board controlling whole period of sampling, high speed collecting board for vibration signal, low speed data collecting board for graded signal. In the method, they are connected to down level server to fulfil data collection, signal analysis, and automatic identification as well as picking up characteristic parameters of operatino of machine set and down level server can make high speed communication with up level server through network card or IP protocol as up level server is equipped with an intelligent reasoning machine integrating professional knowledge rule, fuzzy logic and neural network in one component to carry trend prediction and intelligent diagnosis for operation and failure of machine.

A regional public opinion monitoring and decision-making auxiliary system based on big data comprises an information acquisition and storage module, a data pre-processing module, a big data public opinion analysis module, a public opinion monitoring, early warning and decision-making auxiliary module and a background management module, wherein the information acquisition and storage module is used for carrying out structuralized storage management on acquired public opinion source information to form a regional big data public opinion knowledge base which is updated in real time; the data pre-processing module is used for pre-processing data in the regional big data public opinion knowledge base to form a complete and ordered data set; the big data public opinion analysis module is used for carrying out public opinion analysis and tendency prediction on hot topics with appointed conditions and the like to obtain a public opinion analysis and tendency prediction result; the public opinion monitoring, early warning and decision-making auxiliary module is used for carrying out real-time monitoring and tracking, management and persuasion on customized public opinions dug and analyzed by sensitive word pairs in a pre-defined sensitive word bank, and announcing the customized public opinions to a decision maker in manners of in-station messages, short messages and mails; and the background management module is used for carrying out public opinion information classification management, user and authority management, keyword management, acquisition management, content management, special topic management and analysis report management.

The invention discloses an examiner identification system based on bionic and biometric identification, using synthetically various biometric identification methods based on high-dimension space geometric shape adaptive coverage theory to achieve identification of examiner identity. First of all, through an acquisition equipment, gripping-pen fingerprint is obtained, on-line signature and facial image, and then the data is mapped into high-dimension space observation point after feature extraction, last according to similar sample point continuity in the high-dimension space, through the relation between the observation point and sample set coverage area to obtain different biological characteristic network match degree, then through match degree fusion decision algorithm to identify identity of the examiner, and through automatic addition of new verification data to achieve sample set dynamic updating and trend forecast. Identification of the invention is fast, result is accurate, and the invention is not only suitable for examiner identification in existing examination mode, but also has broader application in the future machine- examination mode.

A method, system and apparatus for estimating an SLA breach value. The method can include processing resource data to identify an acceptable SLA breach value; and, displaying the acceptable SLA breach value through a user interface. The processing step can include identifying a best practices SLA breach value based upon resource data for an aggregation of customers. Alternatively, the processing step can include identifying an average SLA breach value for a specific customer. As a further alternative, the identifying step can include identifying an average SLA breach value for a specific customer for a specific resource. As yet a further alternative, the processing step can include identifying an SLA breach value trend based upon past measured historical systems management data; and, predicting a future SLA breach value based upon the trend. In all cases, the acceptable SLA breach value can be increased by a fixed proportion.

The invention discloses a power equipment current-carrying fault trend prediction method based ona least squares support vector machine. The method provided by the invention comprises the steps of employing historical temperature data to train an LS-SVM regression model, and employing a PSO optimization algorithm to adjust two parameters of the model, namely nucleus width sigma and punishment parameter gamma; employing a PCA algorithm and a K-means clustering algorithm to real-time analyze the temperature of equipment contacts to find contacts with abnormal temperature rising, and using the temperature value asan initial value sequence of prediction;and finally employing the regression model obtained by training to predict the temperature value of the initial value for a long term and for a short term, and analyzing the highest point the contact temperature may reach and the time when the contact temperature reaches the highest point. Through predictive analysis based on PSO-LSSVM, fault development trend of equipment contacts is actively controlled, so the time for timely measures and ensuring the safe operation of power grid is bought. The method provided by the invention can be widely used in the field of power equipment forecast alarm protection.

The present invention discloses a text emotional tendency analysis method applied to the field of financial Web. The method comprises: performing data source screening on Web financial text information and performing data acquisition; according to a Web financial text feature, constructing a financial emotional dictionary; and designing an independent information clearance algorithm and a Web financial text emotional tendency classification algorithm to perform Web text emotional tendency classification. The method is high in classification accuracy and high in classification speed, can effectively analyze whole emotion of a financial market by classifying emotional tendencies of a Web emotional text, and has important help and guidance significances for financial trend prediction.

The system is composed of flow taking transducer, regulation unit of front end signal, regulation unit of back end signal, data collection unit, data analysis unit and trend predictive unit. The present invention has developed software of remote monitoring, trend prediction, data processing, data analysing and circulation detecting for multiple equipment-multiple pollution leakage current by using virtual equipment and graphic programming language LabVIIEW with combining of SQLSERVER2000 databank system.

The invention discloses a wind power quality trend predicting method. The wind power quality trend predicting method includes steps of 1), establishing a power quality trend predicting index system; 2) acquiring data; 3), establishing 'corresponding working condition relation table' of a probability density distribution model of historical monitoring data of power quality trend prediction indexes and wind speed/wind direction data corresponding to the probability density distribution model; 4) matching and analyzing working conditions; and 5) predicting power quality trends of power quality trend prediction indexes of wind power public connecting points in a certain future time and storing prediction results of power quality of various prediction indexes into a database. The wind power quality trend predicting method can predict trend of power quality of key power quality indexes of the wind power public connecting points by means of historical power quality monitoring data.

The invention provides an integrated watershed management system and relates to the technical field of watershed management. The integrated watershed management system comprises a knowledge management module, a monitoring module, a forecast module, a project evaluation module, and an emergency decision module. The knowledge management module is used for acquiring related knowledge of a watershed and constituting a knowledge base, a model base and a method base of the watershed to be stored in a database. The monitoring module is used for monitoring lake inflow flux and total pollutant amount of the watershed. The forecast module performs statistic analysis and trend prediction to hydrology and water quality of the watershed. The project evaluation module provides a reference scheme for water pollution control of the watershed. The emergency decision module generates corresponding emergency decisions as to water pollution warning and emergency accidents. By the aid of the integrated watershed management system, the monitoring module and the forecast module perform real-time monitoring and warning to the watershed, and corresponding emergency decisions are made through the emergency decision module as to different emergency accidents or warning information, and furthermore, the project evaluation module provides reference for water pollution control scheme planning.

The invention provides an access trend prediction based time series database cache management method. The method comprises: on the basis of providing a data access cache by a conventional database, establishing a time series database dual-cache mechanism; and in addition to a conventional cache, adding a prediction data cache for predicting pre-loading of access data to load the access data into a memory before data access so as to increase the query speed. The method is used for effectively managing the time series database data cache to improve the efficiency of data cache hitting and system data query. A system analyzes recent access data and predicts a data access trend, and the prediction data is loaded to increase the data access speed. The method is suitable for time series database data cache management and is used for improving the efficiency of time series database data query.

The invention relates to an electromechanical device neural network failure trend prediction method, comprising the following steps: (1) obtain a section continuous vibration signal which is sensitive to the failure and is output by a measuring point sensor; (2) respectively carry out exceptional value elimination and missing data filling to the vibration data by a 3 sigma method and an interpolation method; (3) carry out a normalization process to a vibration data sequence; (4) calculate a vibration data sequence which is entropy-weighted according to the sequence which is carried out the normalization process; (5) carry out a time-weighted calculation to the vibration data sequence which is entropy-weighted by utilizing time weight due to the influence of time factor; (6) build a nonlinear dynamic recurrent neural network prediction model by utilizing the data sequence which is obtained by step (5) and determine a hidden layer optimal node number by utilizing a golden section method; (7) carry out normalization process to a trend prediction result and obtain a actual prediction result. A dynamic recurrent neural network model is adopted to carry out prediction in the invention, therefore, the failure prediction reliability is increased. The electromechanical device neural network failure trend prediction method can be widely applied to the failure prediction and analysis of all kinds of electromechanical devices.

A system and method of aggregating and ranking stocks based on the earning capabilities of each stock. The novel system and method use a neighborhood model of pricing trend prediction to aggregate a plurality of “neighboring” or related stocks to predict pricing of one stock within the plurality of related stocks. The system facilitates investors trading stocks by using the novel methodology to rank the stocks and by having an easy-to-use interface.

The invention relates to a multi-condition fault prediction method for complex mechanical equipment. The method comprises the following steps: (1) establishing a multi-PCA (Principal Component Analysis) model specific to a multi-condition process, and calculating corresponding detection indexes, namely, a T2 statistic and an SPE (Square Prediction Error) for each PCA model; (2) optimizing the two detection indexes of T2 statistic and SPE in each of the PCA models, and performing fault detection on the mechanical equipment to obtain fault data of the mechanical equipment in a transition process; (3) performing fault reconstruction on the fault data of the mechanical equipment in the transition process detected by using the two optimized detection indexes of T2 statistic and SPE to obtain an amplitude estimation value fi for minimizing the reconstructed SPE; (4) performing consistent amplitude estimation on the amplitude estimation values fi obtained after reconstruction of the same fault under different conditions in the transition process; (5) performing trend prediction on the fault amplitude value by using a support vector machine prediction model according to an amplitude estimation value fi obtained after the consistent amplitude estimation. The method can be widely applied to fault prediction of electromechanical equipment.

The invention provides a document-level sentiment analysis method based on specific domain sentiment words. The method is implemented by the following steps of collecting a document data set, traininga set of prototype words by using a Skip-gram word vector model to obtain a word vector corresponding to each prototype word, recombining the word vectors by utilizing an attention mechanism, and capturing a relation between non-continuous words in the word vectors; synthesizing the words and sentences by using an asymmetric convolutional neural network and a bidirectional gate recurrent neural network based on the attention mechanism respectively, thereby forming document vector characteristics; generating sentiment eigenvectors by utilizing a domain sentiment dictionary of the Skip-gram word vector model; and finally, combining the document vector characteristics and the sentiment eigenvectors by utilizing a linear combination layer to form document characteristics beneficial to document classification. The sentiment analysis is widely applied to the product analysis, the commodity recommendation, the stock price trend prediction and the like; and the method provided by the invention can accurately and efficiently carry out sentiment analysis on documents, and has great commercial values.

The invention discloses a time sequence analysis based optical transmission network trend prediction method, which specifically comprises the steps that 1) network management performance parameters are screened, and network management performance parameters capable of reflecting the network running state are selected; 2) network management performance data is acquired, and specified network management performance data is acquired through a northbound interface of the optical transmission network for network management; 3) a time sequence is formed, the network management performance data is acquired in an uninterrupted manner in a sampling period, and the network management performance data is arranged into a time sequence of a certain performance parameter feature value according to a certain time interval; 4) the time sequence is decomposed, and a trend term, a periodic term and a stochastic term in the time sequence are decomposed through analyzing a time sequence sample; 5) predicted values of the decomposed terms are calculated, and the predicted values are estimated according to respective prediction models in allusion to the three different types of decomposed terms; and 6) a final predicted value is calculated, and the final predicted value is calculated according to a time sequence addition model and performs cross validation with an actual value.

The invention provides an offshore crane gearbox fault diagnosis device and method based on multivariate data. The device comprises a temperature sensor, an acceleration sensor, an embedded monitoring unit and a remote monitoring and maintenance center. Temperature and acceleration sensors are arranged. A GPRS module transmits collected data to an upper computer. Bearing temperature trend prediction based on a gray model and support vector regression model residual compensation is carried out according to a collected temperature signal. Vibration fault feature extraction is carried out on a vibration signal by using the combination of empirical mode decomposition and envelope spectrum analysis. Gearbox lubricating oil samples are regularly extracted for convention physical and chemical property analyzing and emission spectrographic analyzing. Wear trend analysis and fault early warning are carried out according to the content of metal wear in oil samples. Fusion comparing is carried out on three analysis results to give a gearbox fault diagnosis result. According to the invention, the fault diagnosis accuracy of an offshore crane gearbox can be effectively improved, and the diagnosis result is accurate and reliable.

The invention relates to a blast furnace molten iron silicon content four-classification trend prediction model establishing method and application and belongs to the technical field of automatic detection. The method comprises acquiring historical data; performing data pre-processing; determining model input variables and output variables; clustering molten iron silicon content samples through a fuzzy mean clustering method effectively to obtain model output variable four-classification trend change block division standards; establishing the four-classification trend prediction model through an extreme learning machine. By means of the model, the silicon content change trend can be predicted, the trend change amplitude can be obtained, that is, four-classification trend change conditions of sharp rise, small rise, sharp decrease and small decrease of the molten iron silicon content of the next furnace can be predicted. Guidance for blast furnace operators to determine furnace conditions in advance, take measures of small adjustment, early adjustment and the like and prevent rapid changes of the furnace conditions.

The invention relates to a GNSS-based real-time deformation monitoring system, including a data monitoring, calibration, storage, and distribution processing center, a deformation monitoring data processing module, a real-time dynamic filtering centimeter-level deformation early warning module, and a near real-time two-way filtering/overall adjustment sub- Centimeter-level deformation early warning module, precise post-processing + time series analysis millimeter-level deformation analysis and prediction module. Real-time, near-real-time, post-processing, deformation warning and prediction with step-by-step improvement in accuracy, proposed a fusion scheme of various baseline filtering and adjustment methods, so that the accuracy is improved step by step, real-time warning of centimeter-level deformation, and millimeter-level Time-lapse update of displacement and prediction of deformation trend.