59 results about "Long term trend" patented technology

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.

A flexible system capable of utilizing data from different monitoring techniques and capable of providing assistance to patients with diabetes at several scalable levels, ranging from advice about long-term trends and prognosis to real-time automated closed-loop control (artificial pancreas). These scalable monitoring and treatment strategies are delivered by a unified system called the Diabetes Assistant (DiAs) platform. The system provides a foundation for implementation of various monitoring, advisory, and automated diabetes treatment algorithms or methods. The DiAs recommendations are tailored to the specifics of an individual patient, and to the patient risk assessment at any given moment.

The present invention provides a method for computing traffic between a pair of Points of Presence in an IP network by summing link utilization values measured for each link connecting a pair of Points of Presence and dividing the sum by the number of link utilization values included in the sum. The resulting average link utilization is the average link utilization of any link in the aggregate and may be multiplied by the number of active links connecting a pair of Points of Presence to reflect the total amount of traffic between the two Points of Presence. Future link utilization may be forecast by modeling the observed traffic between a pair of Points of Presence using wavelet multiresolution analysis to create an approximation curve that captures the long-term trend of link utilization and at least one detail curve that captures the short term deviation of link utilization around the long-term trend. A time series model of the approximation curve may then be constructed and used for forecasting. In a similar fashion, deviation of link utilization may be forecast.

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.

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 belongs to the technical field of wireless communication, and particularly relates to a dynamic heterogeneous network flow prediction method based on a deep space-time neural network. Aiming at the problems of small coverage area, low prediction precision, short prediction time and the like of the existing mobile data traffic prediction method, the dynamic heterogeneous network traffic prediction method based on the deep space-time neural network is studied. Considering the characteristics of user mobility, flow data space-time correlation and the like, deeply researching a wide-coverage long-term mobile data flow prediction mathematical model description method in the dynamic heterogeneous network; On the basis, a space-time related convolutional long-short time memory network model is studied to predict the long-term trend of the mobile traffic in the dynamic heterogeneous network; A space-time related three-dimensional convolutional neural network model is studied to capture micro-fluctuation of a mobile flow sequence in the dynamic heterogeneous network; And fusing the long-term trend prediction model and the short-term change model of the mobile traffic, therebyrealizing wide-coverage and high-precision long-term mobile traffic prediction in the dynamic heterogeneous network.

A flexible system capable of utilizing data from different monitoring techniques and capable of providing assistance to patients with diabetes at several scalable levels, ranging from advice about long-term trends and prognosis to real-time automated closed-loop control (artificial pancreas). These scalable monitoring and treatment strategies are delivered by a unified system called the Diabetes Assistant (DiAs) platform. The system provides a foundation for implementation of various monitoring, advisory, and automated diabetes treatment algorithms or methods. The DiAs recommendations are tailored to the specifics of an individual patient, and to the patient risk assessment at any given moment. A central data exchange node or server collects patient data from individual DiAs devices and provides safety assurance, monitoring, telemedicine and database building for the DiAs system.

The invention discloses an autonomous formation flight control method for satellites. Formation flight control is carried out through relative orbit mean elements, and due to the fact that a long-term trend of relative movement between the satellites is accurately reflected through the relative orbit mean elements, long-term changes of the relative movement can be well controlled with the method. The autonomous formation flight control method is characterized in that a mean semi-major axis difference control strategy in an orbital plane is designed, the mode that control targets are set in a partitioned mode is adopted, and it is guaranteed that the drift speeds within control zones are low; when outside the control zones, the control targets can return to the control zones at high speeds. According to the autonomous formation flight control method, due to the mode that small-pulse air injecting is used for orbit control multiple times, and a momentum wheel is used for posture control, influences of posture air injection control on orbits are reduced, and the orbit control execution accuracy is improved.

The invention discloses a combustion online optimizing method of a boiler. The combustion online optimizing method comprises the following steps of: monitoring real-time operation parameters of the boiler through a unit, recording and determining the real-time operation parameters; then comparing the real-time operation parameters, providing an optimal value curve guidance; then establishing a combustion characteristic optimization mathematical model of the boiler by adopting an artificial intelligent neutral network technology; optimizing the combustion characteristic of the boiler by using a genetic algorithm or simulated annealing method; and finally, storing the combustion optimization parameter as a long-term trend data, and carrying out accumulated monitoring and storing on a parameter with an accumulation effect. Through the mode, according to the invention, the economy of the operation of the boiler can be improved, the NOx discharge can be reduced by 20-30 percent after the combustion optimization technology is adopted, the exhaust gas temperature is reduced, the unburned carbon in flue dust is lowered, the efficiency of the boiler is increased, various kinds of loss are reduced, and the efficiency of the whole unit is increased.

The invention discloses a system for real-time monitoring the abnormal change of satellite telemetry parameters, comprising a data receiving module, a data preprocessing module, a parametric variation detection module, an out-of-range abnormality judging module, an amplitude variation abnormality judging module, a long periodic variation abnormality judging module, an alarming filtering module and a data recording module. By adopting the invention, the abnormal variations of the trend of the telemetry parameters can be detected, wherein the variations include the short-term trend abnormal variation and the long-term trend abnormal variation, the periodic variations of the satellite telemetry parameters can be continuously and circularly monitored, the exact monitoring and alarming for various abnormal circumstances of the satellite telemetry variations can be realized, and plenty of field testing information is provided for analyzers. The system solves the problems of real-time monitoring, judging, alarming, and the like of many telemetry parameters and has higher sensitivity and real-time property, thereby reducing the labor intensity of a tester and improving the test efficiency.

Methods and systems for detecting internal hemorrhaging in a person are provided. In an exemplary embodiment, one method includes the steps of measuring physiological conditions associated with the person and processing the measured physiological conditions using a probabilistic network to determine if the person has internal hemorrhaging. The method also includes the steps of determining the severity of any internal hemorrhaging by determining the amount of blood lost by the person and classifying this loss as non-specific, mild, moderate, and severe. The physiological measurements include an electrocardiogram, a photoplethysmogram, and oxygen saturation, respiratory, skin temperature, and blood pressure measurements. The probabilistic network included with one system determines whether there is internal hemorrhaging based on a number of factors including a physiological model, medical personnel inputs, transfer function, statistical, and spectral information, short and long term trends, and previous hemorrhage decisions.

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.

Systems and methods are disclosed herein for detecting an anomaly in a discrete signal, where a long-term trend of the discrete signal is identified. Samples in the signal correspond to a number of data packets arriving at a location in a network within a time interval. The long-term trend is subtracted from the discrete signal to obtain a detrended signal. A cyclic pattern is identified in the detrended signal and is subtracted from the detrended signal to obtain a residual signal. Anomaly detection is performed on the residual signal.

The invention discloses a standardized automatic analysis method for a neonatal brain function, which comprises the following seven continuous standard steps: S1, neonatal medical history data are acquired; S2, signal connection and signal quality are confirmed; S3, upper and lower boundaries of an amplitude integrated electroencephalogram are determined, background pattern classification is carried out, and burst inhibition detection is carried out; S4, a sleep awakening cycle is recognized; S5, suspicious areas in the electroencephalogram are recognized; S6, the symmetry of the amplitude integrated electroencephalogram is evaluated; and S7, the long-term trend of the amplitude integrated electroencephalogram is described. By adopting seven continuous steps, standardized integration is carried out on the neonatal brain function analysis methods, automatic analysis is realized at the same time, the output result provides reliable medical data for doctors, and the efficiency and the accuracy are improved.

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.

A system for integrating industrial doors into a total system for monitoring and control of a building, warehouse or group of buildings. Controllers for each door motor have absolute position shaft encoders that provide the exact shaft position to a controller system that allows real-time control of torque and current. Each motor controller can communicate bidirectionally over a wireless (or wired) network with other controllers and / or with one or more designated locations. Each controller can provide data over the network from any sensor or data collection device at the controller, motor or door. A program executing on a PC, laptop, tablet, smartphone can communicate with any or all of the controllers, integrate control of multiple doors, perform statistical analysis on collected data from each door and provide real-time security functions as well as long term trend data, energy usage and loss and predictive analytics.

The invention discloses a method for monitoring displacement of an anchor structure by utilizing a pressure difference. The method includes: step one, fixedly arranging a datum-point liquid-pressure sensor outside an anchor room, arranging a survey-point liquid-pressure sensor to a surface of an anchorage zone, and arranging a water tank above the datum-point liquid-pressure sensor; step two, filling monitoring ports of the datum-point liquid-pressure sensor and the survey-point liquid-pressure sensor with water through a valve and a water pipe, and removing the air; and step three, calculating the displacement of the anchorage zone in a forced direction according to changes of the pressure difference measured by the survey-point liquid-pressure sensor, and obtaining data such as wriggle frequency, range and long-term trend of the anchorage zone. According to the method, the pressure difference is used for monitoring displacement of the anchor structure so as to obtain relevant data such as the wriggle frequency, the range and the long-term trend of the anchorage zone, the monitoring accuracy and speed are high, a network can be accessed directly, requirements of internet of things are satisfied, and the detecting cost is low.

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.

An air delivery system includes a controllable flow generator operable to generate a supply of pressurized breathable gas to be provided to a patient for treatment and a pulse oximeter configured to determine a measure of patient effort during a treatment period and provide a patient effort signal for input to control operation of the flow generator. An existing problem for known devices includes discriminating between obstructive sleep apnea (OSA) and central sleep apnea (CSA). OSA is indicative of upper airway collapse and can be used as an input to auto-titration algorithms for the CPAP pressure applied or the end-expiratory pressure (EEP) used in a bi-level device. If these parameters were available in real-time in a flow generator, they could be used to (a) contribute to auto-titration algorithms and (b) be recorded with respiratory specific parameters to allow physicians to observe long-term trends and have a richer data set to determine the long term management of the patient.

The invention relates to a stock medium and long term trend prediction method based on a Bayes classifier. The method comprises the steps of selecting stock data, and determining all starting points and an interval length dj; dividing a compartment, and calculating the interval slope of historical data; learning the interval slope of the historical data and predicting confidence coefficient judgment compartments, so as to obtain the average price of stocks of a plurality of trading days by taking the confidence coefficient judgment compartment as the starting points; calculating confidence, and comparing the confidence and a preset threshold value; predicting a future compartment slope, and converting the future compartment slope to obtain the average price of the stock of the plurality of trading days by taking the prediction interval starting points as the starting points; normalizing the ups and downs of the average price of the stock of the plurality of trading days by taking the prediction interval starting points as the starting points; and building a stock tank. According to the method provided by the invention, the accumulative errors can be prevented, the trend change of the stocks in the prediction intervals can be displayed, the fluctuating change tendency of a stock market can be caught better, and the transaction exposure can be effectively estimated.

The invention discloses a meteorological sensitive electric quantity mining method capable of considering multi-region difference. The method comprehensively considers the non-linear relationship between the electric quantities of a plurality of mined regions and various meteorological factors such as temperature, humidity and rainfall; the long-term trend component, namely, amount of natural increase of the electric quantities can be finely stripped by technologies such as X-12-ARIMA, so that the change rule of the electric quantities along with weather conditions and day types is effectively acquired, and precise mining of daily meteorological sensitive electric quantities at the specific regions can be realized; the meteorological sensitive electric quantities can be used for better carrying out deep analysis on the root cause of electric quantity change, so that short-term electric quantity forecasting is guided.

The present invention provides a method for computing traffic between a pair of Points of Presence in an IP network by summing link utilization values measured for each link connecting a pair of Points of Presence and dividing the sum by the number of link utilization values included in the sum. The resulting average link utilization is the average link utilization of any link in the aggregate and may be multiplied by the number of active links connecting a pair of Points of Presence to reflect the total amount of traffic between the two Points of Presence. Future link utilization may be forecast by modeling the observed traffic between a pair of Points of Presence using wavelet multiresolution analysis to create an approximation curve that captures the long-term trend of link utilization and at least one detail curve that captures the short term deviation of link utilization around the long-term trend. A time series model of the approximation curve may then be constructed and used for forecasting. In a similar fashion, deviation of link utilization may be forecast.

The invention discloses a method for predicting medicine sales trends on the basis of medical big data. The method includes steps of (1), acquiring stationary time series data on the basis of the medical big data; (2), building autoregressive moving average models on the basis of the medical big data; (3), verifying residual series of time series models to determine whether the residual series arewhite noise series checking models or not; (4), building long-term trend prediction models and then computing periodicity indexes of medicines; (5), carrying out estimation by the aid of least squareprocesses to obtain periodicity index prediction models; (6), computing weights of proportions of the long-term trend prediction models and the periodicity index prediction models by the aid of standard deviation and acquiring combined prediction models; (7), analyzing, comparing and presenting prediction results. The method has the advantages that ARIMA (autoregressive integrated moving average)models, periodicity index models and the combined prediction models are built by the aid of the medical sales big data, accordingly, non-linear time-varying time series data can be accurately predicted, and the method has functions of predicting medicine sales.

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.