AUTOMATICALLY COMMISSIONING LIGHTING CONTROLS USING SENSING PARAMETERS OF THE LIGHTING CONTROLS

• US 20140175990A1
• Filed: 03/01/2014
• Published: 06/26/2014
• Est. Priority Date: 09/10/2008
• Status: Active Grant

• Current Assignee: Building Robotics, Inc. (Siemens AG)
• Original Assignee: Enlighted, Inc. (Siemens AG)
• Inventors: Mohan, Tanuj, Ashar, Premal, Bhatkar, Anup
• Granted Patent: US 9,807,849 B2
• Time in Patent Office: Days
• US Class Current: 315/154
• CPC Class Codes: G01S 11/12 using electromagnetic waves other than radio waves
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: H05B 47/11 by determining the brightness or colour temperature of ambient light
• CPC Class Codes: H05B 47/115 by determining the presence or movement of objects or living beings
• CPC Class Codes: H05B 47/175 by remote control
• CPC Class Codes: Y02B 20/40 Control techniques providing energy savings, e.g. smart controller or presence detection

Abstract

Apparatuses, methods, and systems for automatically commissioning lighting controls using sensing parameters of the light controls are disclosed. One embodiment includes a building control system. The building control system includes a plurality of sensors, a plurality of lights associated with the plurality of sensors, and a controller. The controller is operative to receive sense signals from the plurality of sensors, and generate a graph based on the sensed signals, wherein the graph includes nodes that represent light locations, and edges that represent distances between the lights. For an embodiment, the controller further operative to performing inexact graph matching between a floor plan graph and the sensor graph, thereby automatically commissioning the plurality of lights based on the sensed signals of the plurality of sensors.

Claims

1. A method of locating lights of a structure, comprising:

a plurality of sensor units sensing signal, wherein the plurality of sensor units are associated with a plurality of lights;

generating a sensor graph based on the sensed signals, wherein the graph includes nodes that represent light locations, and edges that represent distances between the lights.

2. The method of claim 1, wherein the sensor units comprises at least one of light sensors, motion sensors, and LQI (link quality indicator) sensors.

3. The method of claim 2, wherein a sensor sub-graph is generated for each of light sensors, motion sensors and LQI sensors.

4. The method of claim 2, wherein a first sensor graph G_Si is generated based on sensed motion, comprising:

identify motions sensors of a plurality of sensor units that sense motion within a predetermined threshold of time, over a plurality of instances of time;

based on the identified motion sensors, construct the graph G_S1, wherein each of the motion sensors is placed on a vertex of the graph G_S1, and an edge between vertices include an edge weight that represents a confidence measure.

5. The method of claim 2, wherein a second sensor graph _Gs2 is generated based on sensed light, comprising:

controlling lighting within an area, wherein a single light within the area is powered to emit light;

sense an intensity of receive light by a plurality of light sensors within the area;

order the light sensors according to the intensity of light received by each of the plurality of light sensors;

construct the second sensor graph G_S2, wherein each sensor is placed on a vertex of the graph G_S2 based on a known location of the single light, and the sensed intensity of light received by each of the plurality of light sensors, and wherein an edge is generated between each vertex that includes a weight that represents a confidence level.

6. The method of claim 2, wherein a third sensor graph G_S3 is generated based on electromagnetic signal LQI, comprising:

associating an electromagnetic signal source with one or more of a plurality of LQI sensors;

sensing an LQI by one or more of the LQI sensors;

constructing the third sensor graph G_S3 based on a value of the LQI at each of the one or more sensors, wherein each LQI sensor is placed on a vertex of the graph G_S3, and wherein an edge is generated between each vertex that includes a weight that represents a confidence level.

7. The method of claim 3, further comprising generating a compiled sensor graph based on synthesis of the sub-graph for each of the light sensors, motion sensors and LQI sensors.

8. The method of claim 7, further comprising generating a first sensor graph G_s1 based on sensed motion signals, a second sensor graph G_s2 based on sensed motion signals, a third sensor graph G_s3 based on sensed LQI and synthesizing the first sensor graph G_s1, the second sensor graph G_s2, and the third sensor graph G_s3 to generate a compiled sensor graph G_s

9. The method of claim 2, further comprising:

activating a single light within an area, and sensing light by a plurality of light sensors;

receiving a sensed light value of light intensity by each of the light sensors;

ordering the sensed light values of the light sensors, wherein the ordering provides a representation of distances between the single light and the light sensor sensing the light.

10. The method of claim 2, wherein motion sensor sense commonly located motion, and motion sensors are correlated with each other based on timing of the sensed motion.

11. The method of claim 10, further comprising selecting a sampling rate of the sensed motion

12. The method of claim 2, wherein the LQI sensors sense electromagnetic signals associated with the plurality of sensor units of the plurality of lights.

13. The method of claim 1, further comprising inexact matching of the sensor graph with a floor plan graph of a floor plan of the structure.

14. The method of claim 13, wherein the floor plan graph includes exact locations of the sensors within the structure, and matching includes placing each of the sensors based on the exact location of the sensors of the floor plan graph and information about the sensors of the sensor graph, wherein the information comprises the vertices and estimated distances between the vertices.

15. The method of claim 14, wherein first-degree neighbors of the vertices of the floor plan graph are known, wherein the first-degree neighbors of the vertices of the sensor graph are estimated based on sensed signals of the sensor, wherein both the floor plan graph and the sensor graph include a same number of vertices.

16. The method of claim 15, further comprising matching vertices of the sensor graph with vertices of the floor plan graph that include a similar edge structure, wherein the sensor graph and the floor plan graph are planar and include vertices having a bounded degree.

17. The method of claim 16, wherein an edge structure is determined based on a number of first degree neighboring vertices, and the similar edge structure is determined as vertices having a same number of first degree neighboring vertices.

18. The method of claim 15, further comprising identifying vertices of the floor plan graph that include unique characteristics, and matching vertices of the sensor graph that includes the unique characteristics.

19. The method of claim 15, further comprising first matching vertices of the sensor graph that include confidence levels above a threshold, and subsequently matching vertices of the sensor graph that include confidence levels below the threshold.

20. The method of claim 15, further comprising scoring a quality of the matching based on at least one of:

a. a difference in a cardinality of an edge set of the floor plan graph and the sensor graph;

b. a cardinality of a vertex set of the floor plan graph and the sensor graph;

c. a measure of a dis-similarity in an edge structure of the floor plan graph and the sensor graph.

21. The method of claim 15, further comprising:

selecting a vertex of the floor plan graph to match a vertex of the sensor graph, wherein a degrees of the vertex of the floor plan graph and a degrees of the vertex of the sensor graph are a maximum, and a difference between the degrees of the vertex of the floor plan graph and the degrees of the vertex of the sensor graph are a minimum, and the vertex of the floor plan graph and the vertex of the sensor graph have not been previously matched, wherein the degrees reflects a number of neighboring vertices.

22. The method of claim 16, wherein the inexact matching includes:

a. matching a vertex from the sensor graph to a vertex of the floor plan graph, wherein the vertices are determined to have similar topological or structural properties;

b. computing a cost of the matching of the vertex from the sensor graph to the vertex of the floor plan graph as a measure of a difference in topology or structure of the vertex from the sensor graph and the vertex of the floor plan graph;

c. match one-degree neighboring graphs of the matched vertices based on a topology or structural;

d. matching a next vertex pair, wherein the next vertex pair belongs to the one-degree neighboring graphs which have similar topological or structural properties; and

e. determining a match to be acceptable if a cost of matching is below a threshold value.

23. The method of claim 22, wherein the vertices are determined to have similar topological or structural properties based on a determination of a difference in degree of vertices.

24. The method of claim 22, wherein computing the cost of the matching of the vertex from the sensor graph to the vertex of the floor plan graph comprises computing a sum of difference in degree of vertices, and a matching of one-degree neighboring graphs.

25. The method of claim 22, wherein one-degree neighboring graphs are matched, ensuring that a sum of degree difference between matched vertices is below a value.

26. The method of claim 25, wherein, the value is based on a sum of difference of edge count and vertex count, and further based on an allowable deviation of a square root vertex count.

27. The method of claim 22, wherein the cost of matching the sensor graph with the floor plan graph is based on a sum of cost of matched vertices.

28. The method of claim 22, wherein the acceptable cost of matching the sensor graph with the floor plan graph is based on a difference in edge count and further based on an allowed deviation of a square root vertex count.

29. The method of claim 22, further comprising first manually matching corner vertices and one or more central vertices, thereby improving efficiency with respect to time and correctness of the inexact matching.

30. A building control system, comprising:

a plurality of sensors;

a plurality of lights associated with the plurality of sensors;

a controller, the controller operative to;

receive sense signals from the plurality of sensors;

generate a sensor graph based on the sensed signals, wherein the graph includes nodes that represent light locations, and edges that represent distances between the lights;

perform inexact graph matching between a floor graph and the sensor graph to provide automatic commissioning of the plurality of lights based on the sensed signals of the plurality of sensors.