ELECTRONIC HUB APPLIANCES USED FOR COLLECTING, STORING, AND PROCESSING POTENTIALLY MASSIVE PERIODIC DATA STREAMS INDICATIVE OF REAL-TIME OR OTHER MEASURING PARAMETERS

• US 20150142991A1
• Filed: 01/08/2015
• Published: 05/21/2015
• Est. Priority Date: 04/21/2011
• Status: Abandoned Application

• Current Assignee: E3 Analytics Corp.
• Original Assignee: Efficiency3 Corp.
• Inventors: ZALOOM, Joseph A.
• Application Number:
  US14/592,813
• Publication Number:
  US 20150142991A1
• Time in Patent Office: Days
• US Class Current: 709/248
• CPC Class Codes: G01D 2204/12 Determination or prediction of behaviour, e.g. likely power consumption or unusual usage patterns
• CPC Class Codes: G01D 2204/14 Displaying of utility usage with respect to time, e.g. for monitoring evolution of usage or with respect to weather conditions
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00001 characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00004 characterised by the power network being locally controlled
• CPC Class Codes: H02J 2310/14 The load or loads being home appliances
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H04L 43/10 Active monitoring, e.g. heartbeat, ping or trace-route
• CPC Class Codes: H04L 65/00 Network arrangements, protocols or services for supporting real-time applications in data packet communication real-time or near real-time messaging, e.g. instant messaging [IM] H04L51/04; selective video distribution H04N21/00
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04N 21/00 Selective content distribution, e.g. interactive television or video on demand [VOD] real-time bi-directional transmission of motion video data H04N7/14 ; broadcast or conference over packet switching networks H04L12/18
• CPC Class Codes: H04N 21/42202 environmental sensors, e.g. for detecting temperature, luminosity, pressure, earthquakes
• CPC Class Codes: H04N 21/436 Interfacing a local distribution network, e.g. communicating with another STB or one or more peripheral devices inside the home
• CPC Class Codes: H04N 21/43615 Interfacing a Home Network, e.g. for connecting the client to a plurality of peripherals home Audio Video Interoperability [HAVI] data switching networks H04L12/2805
• CPC Class Codes: H04N 21/43637 involving a wireless protocol, e.g. Bluetooth, RF or wireless LAN [IEEE 802.11] arrangements for wireless networking or broadcasting of information in indoor or near-field type systems H04B10/114
• CPC Class Codes: H04N 21/442 Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk arrangements for monitoring broadcast services or broadcast-related services H04H60/29; arrangements for identifying or recognising characteristics with a direct linkage to broadcast information H04H60/35; monitoring of user activities for profile generation for accessing a video database G06F16/739; monitoring in wireless networks H04W24/00
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 : Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 60/00 : Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/30 : State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• CPC Class Codes: Y04S 10/40 : Display of information, e.g. of data or controls
• CPC Class Codes: Y04S 20/222 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/242 : Home appliances
• CPC Class Codes: Y04S 20/30 : Smart metering, e.g. specially adapted for remote reading
• CPC Class Codes: Y04S 40/18 : Network protocols supporting networked applications, e.g. including control of end-device applications over a network

Abstract

This technology relates to an electronic hub appliance used for collecting, storing, and processing potentially massive periodic data streams indicative of real-time or other measuring parameters.

Claims

1. A stream processor comprising:

a multichannel data collector having a plurality of channel inputs, the multichannel data collector being configured to collect plural data streams from plural corresponding data stream sources;

a memory coupled to the multichannel data collector, the memory being configured to allocate data storage locations for each of the plural collected data streams;

a clock;

a stream analyzer coupled to the memory and the clock,

the stream analyzer comprising;

a dynamic periods selector structured to dynamically select first and second periods represented by at least one of the plural collected data streams,

a synchronizer structured to synchronize stream data associated with the first period with stream data associated with the second period,

an aggregator coupled to the synchronizer, the aggregator being structured to aggregate the synchronized stream data by a selectable aggregation amount,

a net effect analyzer coupled to the aggregator, the net effect analyzer being structured to determine correlation between the aggregated synchronized stream data, and

a valuator coupled to the net effect analyzer, the valuator being structured to isolate the value of at least one individual action or event the correlated streams represent; and

a control arrangement coupled to the stream analyzer, the control arrangement generating and outputting control signals that remotely trigger automated processes based on predetermined rules and thresholds in response to said isolated value.

2. The stream processor of claim 1 wherein the memory comprises a non-volatile memory and a random access memory, wherein the stream analyzer copies collected data streams from the non-volatile memory into the random access memory and uses address indexing to analyze the copied streams.

3. The stream processor of claim 1 wherein the wherein the control arrangement generates SMS control notifications.

4. The stream processor of claim 1 further comprising a wireless network interface.

5. The stream processor of claim 1 wherein the data collector is coupled to plural data stream sources via plural sub-metering modules, wherein each of the data streams provided by the sub-metering modules is time-stamped and/or time-encoded.

6. A system comprising:

at least one storage device storing;

(1) energy-consuming equipment operating profiles (metered data), (2) timing of scheduled and unscheduled actions and events, (3) energy pricing templates, (4) control protocols including rules and thresholds, (5) executable program code, (6) equipment specifications and operating parameters, (7) user statistics; and

at least one processor connected to the at least one storage device, the at least one processor executing said stored program code, the stored program code configuring the at least one processor to provide;

a dynamic periods selector that dynamically selects a stream of real-time, recent, or historical energy use data of known operating parameters over time intervals that may encompass a day, week, month, or a year and a baseline period of known operating parameters, from the same stream of data (belonging to the same device, system, or appliance), that may encompass similar time intervals;

an operating profiles synchronizer coupled to the dynamic periods selector that dynamically fetches and synchronizes the energy data over the requested time intervals and sends the data to a coupled energy and weather data aggregator;

an environmental factors synchronizer coupled to the dynamic periods selector that synchronizes the start time of real-time or recent weather data with similar historical weather data as specified by the dynamic periods selector by day, week, month, or year and sends the data to a coupled energy and weather data aggregator;

an energy and weather data aggregator coupled to the operating profiles synchronizer and the environmental factors synchronizer that aggregates the synchronized operating profiles data and environmental factors data in increments ranging from 1 second to 1 hour over the requested time interval (e.g., day, week, month, or year);

a net effect visualizer coupled to the energy and weather data aggregator that visualizes the level of success of scheduled actions and the impact of unscheduled actions, events, and environmental factors by visually superimposing the data synchronized by the operating profiles synchronizer and aggregated by the aggregator in order to give shape, magnitude, and direction to the net effect of a change in operating profile between a selected period and a corresponding baseline “

net effect”

, the net effect visualizer also overlays corresponding environmental factors when such factors influence a device or system'"'"'s operating profile;

a net effect tabulator coupled to the net effect visualizer that tabulates the net effect of the change in operating profile by subtracting the baseline operating profile data from the real-time or recent operating profile data over the selected time intervals in the specified time increments and places the resulting table directly under the net effect visualizer graphs in order to visually connect (or correlate) the shape, magnitude and direction of the net effect of changes in energy operations with their corresponding numeric data;

a net effect analyzer coupled to the net effect tabulator that analyzes the net effect of the change in operating profile against threshold limits and defined rules for real-time and historic fault detection and compares with stored information to form a diagnosis, the net effect analyzer also analyzes the net effect of the change in operating profile for real-time initiation of automated processes when certain conditions between real-time and baseline factors are met;

a net effect monetizer coupled to the net effect tabulator that applies pricing templates to numerically assign and tabulate monetary values to the net effect of the change in operating profiles in specific time increments as derived in the net effect tabulator in order to provide a commonly understood standard for measuring, understanding, and predicting the level of success of implemented energy management actions and placing the resulting table also directly under the net effect visualizer graphs in order to visually connect (or correlate) the shape, magnitude and direction of the net effect of changes in operations to their corresponding changes in costs;

a systems rankings generator/prioritizer coupled to the net effect monetizer that ranks and sorts the order of displayed devices or systems from various energy sources by sorting and stacking the visual graphs and associated tables for each device vertically by cost (as a common denominator) in a computing device, in order to prioritize corrective and energy optimization measures/actions; and

a Diagnostics Center structure coupled to the Dynamic Periods Selector structure dynamically fetches and synchronizes automated systems and appliances generated notifications as well as user generated manual log entries over the requested time intervals and places the resulting table next to the Net Effect Visualizer graphs so that one can quickly diagnose with fidelity and precision the Level of Success of the scheduled energy use actions and events during the period of time that is being analyzed as well as the impact of the unscheduled energy use actions and events that occurred during that same period of time.