58 results about "Long term trend" patented technology

In a hydraulic machine, hydraulic pump failure is detected and the pump lifespan is estimated before the pump failure occurs. The discharge pressure 122, oil temperature 126, and drain filter differential pressure 120 are measured, a correlative relationship 128 between the filter differential pressure and the discharge pressure is determined, and a representative filter differential pressure 130 is calculated from this correlative relationship. Using an oil temperature-differential pressure correlation function, the representative differential pressure value 130 is corrected so that the variable component 132 caused by the oil temperature 126 is eliminated therefrom. The long-term trend and the short-term trend of the increase over time of the corrected differential pressure is calculated. A pump failure is predicted or the pump lifespan is estimated based on the degree of deviation between the long-term trend and the short-term trend.

The invention relates to a sea wave significant wave height long-term trend prediction method based on reanalysis data. The sea wave significant wave height long-term trend prediction method is characterized by comprising the steps that (1) weather forecast data of an ERA-Interim reanalysis data set at each time frequency are collected, (2) coordinates of all lattice points are obtained, (3) SLP anomaly and standard deviation are calculated, (4) principal component analysis of the SLP anomaly is conducted, (5) Box-Cox transformation is conducted on sea area data, (6) a predictive factor of sea wave significant wave height is calculated, (7) the standard deviation of the significant wave height and the predictive factor is calculated, (8) the predictive factor is applied into a prediction model, (9) a significant wave height lagged value is applied into the model, (10) SLP field prediction on the basis of EOF is carried out, (11) predictive factor optimization selection is conducted, (12) the sea wave significant wave height is predicted through the model, (13) the prediction level is evaluated, (14) the sea wave significant wave height long-term trend is calculated, and (15) a significant wave height long-term trend chart is drawn. According to the sea wave significant wave height long-term trend prediction method based on the reanalysis data, the significant wave height long-term trend of multiple time frequencies can be predicted, and accuracy is high.

The invention provides a traffic abnormality detection method. The method comprises the steps of obtaining a target time sequence comprising N elements; according to the target time sequence, obtaining target parameters of the target time sequence, wherein the target parameters comprise a periodic factor and / or jitter density, the periodic factor represents one type of waveform change which is presented in the target time sequence and surrounds the long-term trend, and the jitter density represents the deviation of the actual value and the target value of the target time sequence in the target time; determining a first type to which the target time sequence belongs from a plurality of types according to the target parameter, each type in the plurality of types corresponding to a parameter set, and the target parameter belonging to the parameter set corresponding to the first type; and according to the first type of judgment model corresponding to the first type, the abnormal condition of the target time sequence is detected, and each type in the multiple types corresponds to one type of judgment model. According to the technical scheme, the accuracy of flow abnormity detection can be improved.

CND load balancing in the cloud. Server resources are allocated at an edge data center of a content delivery network to properties that are being serviced by edge data center. Based on near real-time data, properties are sorted by trending traffic at the edge data center. Server resources are allocated for at least one property of the sorted properties at the edge data center. The server resources are allocated based on rules developed from long-term trends. The resource allocation includes calculating server needs for the property in a partition at the edge data center, and allocating the server needs for the property to available servers in the partition.

The invention encompasses a system and method for monitoring a power line. In certain embodiments, a system emits a series of signals that allow for analytic analysis of a power line. For example, by taking multiple signal readings, it is possible to detect an average height reading of a power line and observe long-term trends in the time delay from signal emission to reception of an echo-signal. This allows for accurate measurement of various physical parameters of a power line, for example, the height of the power line above the ground.

The invention discloses a ship operation condition forecasting and early warning system. The system is characterized by comprising a stormy wave observation data module, an atmosphere numerical forecasting module, an atmosphere-sea wave one-way coupling numerical forecasting module and a refined forecasting product module. The ship operation condition forecasting and early warning system providedby the invention can forecast and early warn the operation conditions influencing the ship production, such as the surges, etc., and realizes the short-term accurate forecasting and seven-day long-term trend early warning of the ship operation condition information, such as surges, etc., within 72 hours. The system can real-timely monitor the ocean conditions of the in-port ship operation, carriesout the numerical simulation forecast and scientific evaluation on the ocean conditions, and improves the wharf operation safety and the planned cashing rate.

The invention provides a method and a device for equipment failure early warning, comprising: storing at least two history values corresponding to at least one operation index in advance, wherein, each history value corresponds to one time point; and storing the history values corresponding to at least one operation index; determining at least one target to be detected corresponding to the deviceto be detected; generating, for each of the indexes to be detected, a time series corresponding to the indexes to be detected according to the stored historical value and the time point correspondingto the historical value; at least two long-term trend value being decomposed from the time series; performing linear regression fitting on the decomposed long-term change trend value to obtain a fitting curve; determining a fault warning time point corresponding to the index to be detected according to the fitting curve and the preset warning value; the fault warning time point being alerted priorto the fault warning time point. This scheme can make more reasonable maintenance time.

Method and apparatus for optimized battery life cycle management are described. A battery management system (BMS), comprising a battery, identifies battery-specific factors with associated environmental conditions, and battery history profiles at a current time instant. The BMS measures current, voltage, and / or power of the battery instantaneously. The resulting battery measurements, the battery-specific factors with associated environmental conditions, and the battery history profiles, formed as battery dynamic situations at the current time instant, may be time stamped for estimating an instantaneous battery state of the battery. The time stamped battery dynamic situations may be aggregated for long-term trend analysis for the battery state. The instantaneous battery state estimate is updated by comparing with the long-term trend analysis to manage battery charging or discharging. The battery operating conditions are determined based on the updated battery state estimate. The BMS may manage system power consumptions based on the determined battery conditions.

The present invention discloses a prediction method for the number of freeze-thaw actions in an actual environment. The method comprises: performing statistical analysis on the number of positive / negative transitions of daily maximum temperature and daily minimum temperature in temperature data of an area, to obtain the number of times of freeze-thaw actions in an actual environment of the area; and then establishing a prediction model of the number of freeze-thaw actions based on Mann-Kendall test, Morlet wavelet analysis and an R / S analysis method, wherein Mann-Kendall trend test reflects a long-term trend of the change of the number of freeze-thaw actions over time, the wavelet analysis reveals a periodical change of freeze-thaw actions, and the R / S analysis reflects irregularity of a future trend and provides a basis for prediction of the number of future freeze-thaw actions. By adopting the prediction method for the number of freeze-thaw actions in an actual environment in the research, the trends of the number of freeze-thaw actions in a certain area over time and in the future can be analyzed. Therefore, the prediction method can provide a reference infrastructure construction, service life prediction, maintenance and repairing and so on for civil engineering affected by freeze-thaw actions.

The invention discloses a prediction method and system for hospital outpatient treatment amount, and the method comprises the steps: obtaining the historical business volume data of a to-be-tested hospital in a preset time period, wherein the historical business volume data to comprise the date, the department, the category, and the corresponding treatment amount; supplementing missing values of the historical business volume data according to the prediction time period dimension; constructing a corresponding prediction model according to the prediction time period dimension; and predicting the outpatient treatment amount of the corresponding preset time period dimension by using the prediction model. According to the method, the long-term trend, the annual trend, the monthly trend, the short-term trend, the weekly trend and the Spring Festival factors in the prediction time period are considered, different prediction models are adopted, and high prediction precision can be achieved under the condition that outpatient history emergency data only need to be provided.