Data processing: structural design – modeling – simulation – and em – Simulating electronic device or electrical system – Computer or peripheral device
Reexamination Certificate
1999-02-17
2001-09-11
Teska, Kevin J. (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Simulating electronic device or electrical system
Computer or peripheral device
C703S002000, C703S006000, C345S419000
Reexamination Certificate
active
06289299
ABSTRACT:
FEDERALLY SPONSORED CLAIM
No federally sponsored research and development for this invention.
FIELD OF THE INVENTION
This invention relates generally to systems and methods for visualizing, managing, and controlling information and, more particularly, to systems and methods for providing Interactive Virtual Reality (IVR-3D) three-dimensional displays and other views for use in gaining knowledge from real-time, delayed-time, or fabricated data. The primary focus of the invention is on training, operations, and prediction related to manufacturing and services industries.
BACKGROUND OF THE INVENTION
Today's technology has placed us clearly in the “Information Age” and has become quite proficient in capturing and retaining data. The data or information that is available is diverse both in the sources of the data and in what the data represents. One source of data is from real-time measurements and other observations. A multitude of different sensors, from acceleration and humidity to altitude and pressure, allows data to be captured on virtually any characteristic of our environment. In addition to data that is sensed or measured, other data that we encounter is generated by ourselves, such as with word processors or spread sheets, or it comes from data that has been processed from other sources of data, such as outputs of simulation programs.
One consequence of all of this information is that the information may be simply too great to condense into useful knowledge. One example of “data overload” may be seen in a physical plant. A physical plant may have groups of sensors for monitoring fluid flows, temperatures, pressures, and levels of certain substances. The raw data coming from these sensors are often fed to a control room where the data may be displayed on groups of dials or displays. The human mind, however, is ill equipped to process the amount of data that it is being bombarded every instant with data from many different sources. It is therefore not surprising that an operator may not detect the significance of a particular piece of data, such as a particular reading of a dial. A need therefore exists for a way to monitor the data so that a person can more easily gain knowledge from it and execute the proper controls.
The problem with “data overload” is not limited to operators at a physical plant but is experienced by people in many industries. The financial industry, for example, is also prone to data overload with all of the information it receives concerning financial markets. This information includes data on dividends, stock splits, mergers or acquisitions, strategic partnerships, awards of contracts, interest rates, as well as many other aspects of the market. The medical field is another field in which data overload is prevalent. In the medical field, this information includes information on the human body, information on surgical techniques, data on particular drugs and their side effects and interaction with other substances, and real-time data, such as that captured during a surgery pertaining to a patient or regarding the state of medical devices used in surgery. In process-based manufacturing, such as biotechnology and petrochemicals, real-time data from the production stream must be combined market data about feedstocks and demand for various final products. In telecommunications, real-time data about switching centers, transmission performance, and traffic loads must be combined with market data about provisioning orders and service mixes. Without efficient methods to monitor and control this information, a person becomes overloaded with data and the information loses its essential purpose, namely as a tool to gain knowledge. In addition to the management of raw data, and management of information selected or derived from such raw data, a second problem is the difference between training and operations management environments. Often a trainee must translate the formats and frameworks of information in the training environment into information formats and frameworks of the relevant operations management environment. The closer a training environment is to the relevant operations management environment, the faster a trainee can become productive when assuming an operations management role. For instance, a person trained on several pieces of standalone equipment (not integrated into a production stream) requires substantial additional time to master using the same equipment integrated into a production stream.
A third problem is the difference between operations management and predictive tools. Manufacturing and services businesses manage resources based on predictions of resource pricing, availability, and delivery of raw materials and finished goods and services. Often a person in an operations management role must translate the information output of a predictive tool into formats and frameworks that fit more closely with the formats and frameworks used in operations management. The closer the output of predictive tools is to the relevant operations management environment, the more accurately and quickly a manager can apply the output of the predictive tools. In fully automated cases, the manager may simply need to be notified that the predictive tools are changing one or more variables in operations; in other cases, the manager may have to intervene to implement changes recommended by the predictive tools, such as replacing a type of catalyst.
Extensive operations research has shown that information management through graphics, icons, symbols, and other visualizations on computer driven displays has many advantages for human operators over displays of raw data or information. Graphical tools exist today that take raw data, process the data, and display the data as user-friendlier graphics. The graphical tools, for instance, may generate graphs or charts from which a person can detect a trend in the data. These tools allow a person to more easily view data coming from a small number of sources. With a large number of data sources or when the data is inter-related in a complex manner, a person may still have difficulty deciphering the significance of the information and in making correct operational and management decisions. Problems in deciphering information become even more difficult and expensive during an “alarm avalanche,” that is, when multiple alarms are triggered which may have a root cause or have multiple unrelated causes.
Graphical tools exist today, which take raw data, process the data, and display the data in a user-friendlier manner. The graphical tools, for instance, may generate graphs or charts from which a person can detect a trend in the data. These tools allow a person to more easily view data coming from a small number of sources. With a large number of data sources or when the data are inter-related to each other, a person may still have difficulty deciphering the significance of the information.
A simulation program is another type of tool that has been developed to assist people in understanding a complex system. Simulation programs are especially beneficial when a system is characterized by a large number of variables or when the variables are inter-dependent on each other. A person inputs values for certain variables into the simulation program and the simulation program, based on mathematical relationships between the variables and based on numerical techniques, outputs resulting values of other variables. A person may therefore use a simulation program to determine the optimal values of a set of input parameters so as to maximize the values of other parameters. Simulation programs, for instance, may be used to optimize the lift of an airfoil or to maximize the strength of a steel beam.
Although simulation programs are useful in determining values of certain parameters based on other variables, simulation programs are still at best an approximation of real-life systems and do not provide the same detail of information as a real-time system. As discussed above, simulation programs use mathematical relationships between parame
Daniel, Jr. William E.
Whitney Michael A.
Hardaway/Mann IP Group
Jones Hugh
Teska Kevin J.
Towler, III Oscar A.
Westinghouse Savannah River Company
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