210 results about "Real time forecasting" patented technology

A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

A man-machine interface is disclosed. The circle is divided into several angle cells. The required inputted content is placed in the cells, the inputted option direction of motion is detected in real time, the content which the user want to input is forecasted and inputted according to the content in the angle cell directed by the extension line of the direction of motion

Various embodiments of the present invention are directed to a method and apparatus for improving the care of patients (e.g., chronically ill patients). In one example (which example is intended to be illustrative and not restrictive), the invention may be designed to improve such care through real time output (e.g., periodic, such as hourly, daily, weekly or monthly) to deter development of co-morbidities associated with many chronic diseases. Various embodiments of the invention may improve the care of chronically ill patients by: easing data collection, simplifying data transmission, efficiently interpreting chronically ill patient information, providing time series to form the basis of subsequent analysis, expanding the ability of the chronically ill patient or other user of the invention to understand the relationship between the medical practice and the patient's own lifestyle and/or easing the workload of healthcare providers (such as the patient's physician). In this regard, the invention may help in creating dialogue between chronically ill patients and healthcare providers that otherwise would be impossible.

Techniques for monitoring and predicting environmental operating conditions in a data center are provided. In one aspect, a method for real-time, three-dimensional analysis of environmental operating conditions in a data center includes the following steps. High spatial resolution three-dimensional measurements of one or more environmental variables in the data center made at a time t₁ are obtained. Real-time measurements of the environmental variables in the data center made at a time t₂, wherein t₂ is later in time than t₁, are obtained. The high spatial resolution three-dimensional measurements are combined with the real-time measurements to derive a model for the environmental variables in the data center at the time t₂. The model is used to predict three-dimensional distributions of the environmental variables in the data center at the time t₂. A base model can be created and used to derive the model for the data center at the time t₂.

The methods, apparatus, and systems described herein are designed to route customer communications to the best available agent. The methods include predicting a personality type of the customer, determining the occupancy level of agents to provide a list of available agents, providing a routing recommendation based on the predicted personality type of the customer and agent data such as proficiency in handling customers with the predicted personality type, and/or analyzing real-time customer-agent interactions.

The invention provides a system and a method for predicting the coal and gas outburst risk of a mine in real time, which relate to a system and a method for judging mine natural disaster of coal and gas outburst risk degree through combination of artificial intelligence and expert analysis. The system uses a microseismic signal for reflecting the ground stress intensity and the gas outburst quantity for reflecting the gas change as analysis parameters to be combined with the expert experience, and can improve the predicting accuracy of the coal and gas outburst risk to high than 90 percent. The system and the method are characterized in that the system mainly consists of a data collection module, a data transmission module, a real-time data tracking and analysis center and an integrated early warning module, wherein after the data collection module collects underground data, the data is transmitted to the real-time data tracking and analysis center through the data transmission modulefor calculation, analysis and reasoning, and the integrated early warning module gives the early warning when a reasoning result shows that the risk occurs. The invention is applicable to similar mines with the coal and gas outburst risk.

The invention discloses an urban traffic flow prediction method with regional vitality integrated. The method comprises steps: S1, regional division is carried out on an urban road network, and the traffic flow in each region is calculated; S2, a regional vitality model is designed: by using distribution of urban points of interest, holidays and weather information, a 3D convolution neural network(3D CNN) is used to study the dynamic changes of the vitality in each urban region; S3, a flow prediction model is designed: the regional vitality and the traffic flow are integrated, and a convolution long short-term memory network (ConvLSTM) is used for flow prediction; and S4, according to the historical data, the regional vitality model and the flow prediction model are trained at the same time, and the well-trained models are used for predicting the traffic flow in each region in real time. Through integrating the regional vitality and considering influences from a driving force behind crowd activities and external factors, high prediction precision can be acquired.

The invention provides a method and a system for real-time monitoring and managing urban water resources, and system equipment capable of finishing the method. The method comprises the steps of: real-time monitoring the urban water resources, real-time evaluating the urban water resources, real-time forecasting the urban water resources, real-time managing the urban water resources, real-time scheduling the urban water resources, and real-time early warning the urban water resources safety. By application of the system and method of the invention, an urban water resources system is scientifically classified and comprehensively analyzed so as to obtain the correct construction and correlations of the urban water resources system; the urban water resources are subjected to centralized management; a unified water resources monitoring-center system platform is established; and a novel water resources monitoring terminal is compatible so as to implement a unified and sustainable-development water resources managing system.

The invention relates to a discriminant analysis-based high road real-time traffic accident risk forecasting method. The method comprises the following steps of: building a high road accident risk discrimination model for a detection area; substituting real-time traffic flow characteristic parameters into the high road accident risk discrimination model; and judging whether the risk of traffic accident exists or not. According to the method, traffic accidents can be forecasted in real time by using the real-time traffic flow characteristic parameters acquired by high road traffic detection equipment, the method has relatively high forecasting precision, and technical defects and shortages in the prior art for analyzing traffic safety by using aggregated statistics are overcome. The methodhas practical engineering application value in the aspects of discrimination of the risk of the high road traffic accidents and forecast of the traffic accidents.

The invention discloses a real-time predicting cruise control system based on economical driving. The real-time predicting cruise control system based on economical driving comprises an information collection module, a vehicle dynamics model establishing module and a receding horizon optimal calculation module, wherein the information collection module is used for collecting the driving state information, comprising speed information, the distance between a local vehicle and a front vehicle and the road traffic speed limiting information within the predicted distance, of the local vehicle and the front vehicle; the collected information is transmitted to the vehicle dynamics model establishing module; the vehicle dynamics model establishing module is used for establishing a vehicle dynamics model according to the collected traffic speed limiting information and the driving state information of the front vehicle and the local vehicle, establishing the control problem and determining the optimal objectives and satisfied constraint conditions; and the receding horizon optimal calculation module is used for obtaining the optimal gear sequence and an explicit solution of the optimal engine torque and braking force by optimizing through a method combing a Pontryagin minimum principle with a method of bisection on the basis of the control problem and the constraint conditions proposed by the vehicle dynamics model establishing module, and determining the optimal control rule.

The invention relates to a real-time predicting method for arrival time of a bus. The real-time predicting method comprises the following steps: S1, respectively extracting historical floating bus data and historical running time data of a site section between every two adjacent sites as a training set, screening factors influencing the running time of the bus at one site section according to the training set, and respectively constructing support vector regression models of corresponding site sections according to the screened factors; S2, determining site sections contained in a running route between a current site and a target site of the bus, acquiring respective real-time floating bus data and real-time running time data of the site sections as a predicting set, predicting the running time of the bus at the site section by using the support vector regression model according to the predicting set, and obtaining the arrival time of the bus from the current site to the target site according to the predicted running time of the bus at each site on the running route. According to the method provided by the invention, the prediction accuracy of the arrival time of the bus can be improved.

The invention discloses an urban rainstorm waterlogging assessment and modeling method. The method comprises the steps that an urban earth's surface runoff producing model is established, a time-integration method is adopted to calculate accumulated infiltration and accumulated produced runoff at a moment t, wherein t represents rainfall time; according to the calculated accumulated infiltration and accumulated produced runoff at the moment t, hour-by-hour continuous simulation is conducted on a ponding zone and ponding depth of rainstorm waterlogging by obtaining the rainfall capacity hour by hour and utilizing an equal-volume method, and an urban earth's surface confluence model is established to obtain urban rainstorm waterlogging simulation result at corresponding moment. The time-integration method is adopted to respectively process a water permeable zone and a water non-permeable zone, hour-by-hour continuous simulation can be performed by inputting hour-by-hour rainfall capacities, and real-time forecasting and estimation of the rainstorm waterlogging are achieved through butt joint with rainfall forecasting. According to the method, the whole urban waterlogging and water receding process is considered, and hour-by-hour elaborate simulation of urban waterlogging can be achieved, so that real-time operation influencing the model in forecasting becomes possible.

The invention relates to a pollution flashover index forecasting method for a regional power grid, belonging to the technical field of transmission and distribution monitoring. The method of the invention comprises the following steps: (1) forecasting the current value of the equivalent salt deposit density in real time by an insulator surface equivalent salt deposit density forecasting model; (2) forecasting pollution flashover critical voltage by an insulator pollution flashover critical voltage forecasting model; (3) forecasting the insulator pollution flashover index of the power grid by a pollution flashover graded forecasting and prewarning model; and (4) judging the state. The invention has the advantages of providing a multivariable equivalent salt deposit density time sequence forecasting model based on phase space reconstruction, solving by a supporting vector machine model, solving the forecasting problem under the conditions of small sample of equivalent salt deposit density data and noise, and improving the forecasting prediction.

A short-term traffic flow forecasting method based on road network space relation constraint Lasso is characterized in that road network constraint is realized by the least absolute shrinkage and selection operator Lasso, road network short-term traffic flow is forecast in three steps of road network analysis, model training and real-time forecasting. The invention provides the short-term traffic flow forecasting method based on the road network space relation constraint Lasso, actually measured expressway traffic flow data are combined, an Lasso algorithm is utilized to forecast the short-term traffic flow, a road network correlation analysis and variable selection method is given, and the expressway short-term traffic flow can be forecast relatively, accurately and rapidly.

The invention discloses an output water quality online monitoring and real-time forecasting system of a water reclamation plant and a control method of the system. The system comprises a data monitoring and acquiring system, a data centre and a master control computer, wherein the data monitoring and acquiring system acquires the water quality physicochemical index data of the inlet water and outlet water under a disinfection technology, and transmits the data to the data centre; the data centre stores the water quality physicochemical index data, and displays the variation of the quality of the outlet water on real time; the master control computer reads the water quality physicochemical index data of the inlet water, and forecasts the quality of the outlet water, and updates the forecasting results on real time based on the variation of the quality of the inlet water, and displays the forecasting results. By adopting the system and control method disclosed by the invention, the quality of the outlet water of the water reclamation plant can be monitored and forecasted on real time, and the least physicochemical index monitoring results of the outlet water is read, and therefore, the master control computer can dynamically display the variation of the quality of the outlet water on real time; the real-time forecasting condition of the quality of the outlet water can be applied for guiding to solve the risk due to the quality of the outlet water, so that, the ability and efficiency of the water reclamation plant to handle the variation of the quality of the source water can be improved, the safety of the quality of the outlet water of the water reclamation is ensured, and the safety is improved.

The present invention relates to a real-time forecasting system of ocean floating body motion based on ocean wave image remote sensing and artificial intelligence. Firstly, marine environment big datais acquired by a marine environment sensor module, at the same time, floating body power response big data is obtained by a floating body power response sensor module, then the two types of data aretransmitted to a data memory and then are stored into a database, data stored in the data memory is used to train an artificial intelligence learning module, an artificial intelligence forecasting model which reflects floating body motion based on marine image input is established, finally a floating body motion response real-time forecasting module is established by the trained artificial intelligence forecasting model based on the marine environment big data and the floating body power response big data, and results are all transmitted to an auxiliary decision-making module and a computer. According to the system, a safe operation window and auxiliary decision-making information are provided for offshore operations affected by floating body motion, and a problem of how to operate safelyand efficiently in the event of a swaying floating body is solved.

A traffic parameter short-time prediction method based on empirical mode decomposition (EMD) and classification combination prediction in an abnormal state relates to the technical field of traffic information. The prediction method includes being combined with the data sequence process method of the EMD, solving unstable data sequence of traffic parameters in an abnormal state into a stable intrinsic mode function (IMF) with multi-scale features; constructing a filter bank based on EMD filtering characteristics, reorganizing the IMF into high-frequency filtering, medium-frequency filtering, and low-frequency filtering; according to different characteristics of the IMF of each group, performing predictions by using the grey theory, kalman filtering and auto regressive moving average (ARMA) model respectively; accumulating results of all the groups to generate real-time predicting results of the traffic parameters of next time interval; and according to the real-time predicting results of the traffic parameters and historical data in the abnormal state, and performing multistep prediction so as to obtain a final predicting result of the traffic parameters and a future development tendency. The traffic parameter short-time prediction method based on the EMD and the classification combination prediction in the abnormal state has a better predicting capacity on the traffic parameters in the abnormal state and a future variation tendency.

The invention relates to a model correction based wind power forecasting system and a method. The model correction based wind power forecasting system comprises a numerical weather forecasting server, a fan, an anemometer tower, a wind power forecasting server, a database server and a wind power forecasting system client. The model correction based wind power forecasting method comprises the following steps: firstly using a numerical weather forecasting model provided by the meteorological department to forecast the weather situation of a wind power plant, and then building a real-time forecasting model, and converting the forecasting value of the numerical weather forecasting model to power output of the wind power plant. The system and the method are adopted to reduce various model errors and ensure the accuracy rate of forecasting results.

The invention discloses an event-driven-based remaining parking berth number real-time prediction method and an event-driven-based remaining parking berth number real-time prediction system. The event-driven-based remaining parking berth number real-time prediction method comprises the following steps: acquiring historical parking information and performing continuous repairing and smoothing process on the historical parking information, wherein the historical parking information comprises historical vehicle arriving time, historical vehicle leaving time, driving event occurrence time and historical remaining parking berth number; and obtaining arriving probability and leaving probability of parking vehicles from the current time to the next time based on the driving event occurrence time and according to the historical vehicle parking information, supposing that the parking vehicle arriving probability is subjected to normal distribution; obtaining a remaining parking berth number prediction model according to a difference value between the remaining parking berth number acquired actually at the present time and the sum of the arriving probability and the leaving probability of the parking vehicles from the current time point to the next time point; and inputting both the current time point and the next time point into the remaining parking berth number prediction model, and outputting a remaining parking berth number prediction value in real time.

The invention provides a real-time wind power prediction and error analysis method based on mixture Gaussian distribution.The real-time wind power prediction and error analysis method is characterized by including the steps of data acquisition and processing, mixed Gaussian distribution model establishment, calculation of mixed distribution function parameters and model evaluation based on an expectation maximization algorithm and the like, linear combination is conducted on multiple Gaussian distributions to obtain a 'multi-weight mixed Gaussian distribution model', model parameters are estimated by adopting the expectation maximization algorithm, and the computational complexity of the maximum likelihood estimation is reduced; modeling is performed by directly relying on the objective law of error distributions themselves, man-made subjectivity is avoided, the shortcoming that a single distribution model is inflexible in shape and poor in universality is overcome, and more accurate description to real-time wind power prediction errors is achieved.The real-time wind power prediction and error analysis method has the advantage of being scientific, reasonable, good in applicability, high in prediction accuracy and the like.

A computer-based system for characterizing in real time the nutritional components of one of more ingredients for a ruminant feed ration, including dry matter, NDF, NDFd, lignified NDF ratio, peNDF, percent starch, IVSD, and particle size for a forage material; and IVSD and particle size for a grain material. The system utilizes proprietary NIRS equations based upon prior samplings of a variety of crop species like dual-purpose corn silage, leafy corn silage, brown midrib (“BMR”) corn silage, grass (silage/dry), alfalfa (silage/dry), BMR forage sorghum, normal dent starch grain, floury endosperm starch grain, vitreous endosperm grain, and steam-flaked corn grain, and applies those equations to current samplings of a corresponding crop to predict in real time the characteristics of such forage or grain material. The real-time characterization system may also utilize the predicted data to calculate a “total ration fermentation index” value that takes into account the total NDFd and IVSD characteristics (including RAS and RBS) of the forage and starch ingredients to be used in a feed ration to ensure that the ration will not contribute too much or too little digestibility to the cow. A “flash fermentation index” identifies feed formulations, based upon the real-time characterizations of the ingredients, that are too “hot” to feed to the cows without incurring the risk of lost production and adverse health issues.