U.S. patent application number 17/665303 was filed with the patent office on 2022-05-19 for resource allocation and risk modeling for geographically distributed assets.
The applicant listed for this patent is Risk Management Solutions, Inc.. Invention is credited to Jordan Byk, David Carttar, Surender Kumar, Monalisa Samal, Christopher Sams.
Application Number | 20220156664 17/665303 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-19 |
United States Patent
Application |
20220156664 |
Kind Code |
A1 |
Byk; Jordan ; et
al. |
May 19, 2022 |
RESOURCE ALLOCATION AND RISK MODELING FOR GEOGRAPHICALLY
DISTRIBUTED ASSETS
Abstract
A risk exposure model is developed for network or moveable
assets not specific to a single, fixed address or location. An
asset map using a plurality of geographic representation points is
used to identify the physical locations of the asset portions (or
possible physical locations in the case of a moveable asset).
Baseline geographic, geologic, political, and demographic data is
similarly represented using geographic representation points.
Meta-data associated with each geographic representation point is
used to identify details related to the asset or baseline feature
corresponding to the geographic point. Risk exposure values are
then calculated using the geographic representation points specific
to the asset portions that are subject to risks associated with the
location of the asset portion.
Inventors: |
Byk; Jordan; (Morris Plains,
NJ) ; Sams; Christopher; (San Francisco, CA) ;
Carttar; David; (Lawrence, KS) ; Samal; Monalisa;
(Noida, IN) ; Kumar; Surender; (Delhi,
IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Risk Management Solutions, Inc. |
Newark |
CA |
US |
|
|
Appl. No.: |
17/665303 |
Filed: |
February 4, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16570956 |
Sep 13, 2019 |
11244263 |
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17665303 |
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14070843 |
Nov 4, 2013 |
10417592 |
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16570956 |
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61779206 |
Mar 13, 2013 |
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International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A computer-implemented method of managing maps of asset data and
geographic feature data for network or distributed assets,
comprising: generating, by a processor, an asset map of asset
geographic representations corresponding to a plurality of assets,
the asset geographic representations including points, lines,
multi-line segments, or polygons, a first asset of the plurality of
assets having an asset value based on a first set of geographic
locations in an area associated with the first asset, generating,
by the processor, a baseline data map of baseline geographic
representations corresponding to a plurality of geographic features
of the area, each geographic feature of the plurality of geographic
features having risk data; generating a combined map by overlaying
the asset map and the baseline data map, the combined map
comprising a first plurality of intersection points where a
geographic representation in the asset map and a geographic
representation in the baseline data map intersect, determining, for
each intersection point of the first plurality of intersection
points, a first risk exposure value based on an asset value of an
asset corresponding to the intersection point and risk data of a
geographic feature corresponding to the intersection point; causing
rendering the combined map in a graphical user interface, in
association with the first plurality of first risk exposure values
of the first plurality of intersection points, the combined map
displaying a higher density for a first sub-area than for a second
sub-area, the higher density for the first sub-area being based on
one or more larger risk exposure values associated with one or more
of the first plurality of intersection points within the first
sub-area; after some time, updating the asset map to obtain an
updated asset data map, comprising updating information related to
the first set of geographic locations for the first asset.
2. The computer-implemented method of claim 1, further comprising:
updating the combined map to obtain an updated combined map based
at least on the updated asset data map, the updated combined map
comprising a second plurality of intersection points; determining,
for each intersection point of the second plurality of intersection
points, a second risk exposure value; causing a display of the
updated combined map, in association with the second plurality of
second risk exposure values of the second plurality of intersection
points.
3. The computer-implemented method of claim 1, the first asset
having an asset value that changes over time and depends on how
much of a capacity of the first asset is filled.
4. The computer-implemented method of claim 3, the first asset
being a rail switching yard, the asset value of the first asset at
a time being dependent on how many trains are stored by the rail
switching yard at the time.
5. The computer-implemented method of claim 1, the first asset
moving through geographic locations of the first set of geographic
locations at different times.
6. The computer-implemented method of claim 5, the first asset
being a transportation asset, including trailer, a train, a ship,
loading equipment, or repair equipment.
7. The computer-implemented method of claim 5, the first exposure
value being determined for the first asset based on a length of
time taken to move from a first geographic location of the first
set of geographic locations to a second geographic location of the
first set of geographic locations.
8. The computer-implemented method of claim 1, the first asset
occupying all geographic locations of the first set of geographic
locations at once.
9. The computer-implemented method of claim 8, the first asset
being a railroad system.
10. The computer-implemented method of claim 1, a first geographic
feature of the plurality of geographic features being represented
by multiple baseline geographic representations.
11. The computer-implemented method of claim 1, the plurality of
geographic features including public or private infrastructure or
features of natural landscape.
12. The computer-implemented method of claim 1, a second asset of
the plurality of assets being associated with a second set of
geographic locations in the area, the second set of geographic
locations intersecting with the first set of geographic
locations.
13. The computer-implemented method of claim 1, the determining
comprising assigning a larger weight to the asset value of the
first asset when a current geographic location of the first asset
is associated with a later date than another geographic location of
the first set of geographic locations.
14. A non-transitory, computer-readable storage medium storing
computer-executable instructions, which when executed cause a
processor to perform a method of managing maps of asset data and
geographic feature data for network or distributed assets, the
method comprising: generating an asset map of asset geographic
representations corresponding to a plurality of assets, the asset
geographic representations including points, lines, multi-line
segments, or polygons, a first asset of the plurality of assets
having an asset value based on a first set of geographic locations
in an area associated with the first asset, generating a baseline
data map of baseline geographic representations corresponding to a
plurality of geographic features of the area, each geographic
feature of the plurality of geographic features having risk data;
generating a combined map by overlaying the asset map and the
baseline data map, the combined map comprising a first plurality of
intersection points where a geographic representation in the asset
map and a geographic representation in the baseline data map
intersect, determining, for each intersection point of the first
plurality of intersection points, a first risk exposure value based
on an asset value of an asset corresponding to the intersection
point and risk data of a geographic feature corresponding to the
intersection point; causing rendering the combined map in a
graphical user interface, in association with the first plurality
of first risk exposure values of the first plurality of
intersection points, the combined map displaying a higher density
for a first sub-area than for a second sub-area, the higher density
for the first sub-area being based on one or more larger risk
exposure values associated with one or more of the first plurality
of intersection points within the first sub-area; after some time,
updating the asset map to obtain an updated asset data map,
comprising updating information related to the first set of
geographic locations for the first asset.
15. The non-transitory, computer-readable storage medium of claim
14, the method further comprising: updating the combined map to
obtain an updated combined map based at least on the updated asset
data map, the updated combined map comprising a second plurality of
intersection points; determining, for each intersection point of
the second plurality of intersection points, a second risk exposure
value; causing a display of the updated combined map, in
association with the second plurality of second risk exposure
values of the second plurality of intersection points.
16. The non-transitory, computer-readable storage medium of claim
14, the first asset having an asset value that changes over time
and depends on how much of a capacity of the first asset is
filled.
17. The non-transitory, computer-readable storage medium of claim
14, the first asset moving through geographic locations of the
first set of geographic locations at different times.
18. The non-transitory, computer-readable storage medium of claim
17, the first exposure value being determined for the first asset
based on a length of time taken to move from a first geographic
location of the first set of geographic locations to a second
geographic location of the first set of geographic locations.
19. The non-transitory, computer-readable storage medium of claim
14, the first asset occupying all geographic locations of the first
set of geographic locations at once.
20. The non-transitory, computer-readable storage medium of claim
14, the determining comprising assigning a larger weight to the
asset value of the first asset when a current geographic location
of the first asset is associated with a later date than another
geographic location of the first set of geographic locations.
Description
BENEFIT CLAIM
[0001] This application claims the benefit under 35 U.S.C. .sctn.
120 as a Continuation of application Ser. No. 16/570,956, filed
Sept. 13, 2019, which is a continuation of application Ser. No.
14/070,843, filed Nov. 4, 2013, U.S. Pat. No. 10,417,592, issued
Sept. 17, 2019 which claims the benefit under 35 U.S.C. .sctn.
119(e) of provisional application 61,779,206, filed Mar. 13, 2013,
the entire contents of which are hereby incorporated by reference
for all purposes as if fully set forth herein. Applicant hereby
rescinds any disclaimer of claim scope in the parent applications
or the prosecution history thereof an advises the USPTO that the
claims in this application may be broader than any claim in the
parent applications.
FIELD OF THE DISCLOSURE
[0002] Embodiments of the present disclosure relate generally to
systems and methods for apportioning resources among geographically
distributed parts of a large facility or asset, such as a railroad
system. More specifically, embodiments of the present disclosure
relate to resource allocation and risk modeling for geographically
distributed assets using geographic representation points for the
asset.
BACKGROUND
[0003] Many municipalities, governmental units, and private
businesses have assets located at a variety of locations, such as
factories located in several cities across the country or around
the world. For various reasons, it may be important to consider
risks to these locations and allocation of resources among such
facilities. Such geographically distributed facilities may be
thought of based on the area that they cover (e.g., the Midwest
region restaurants of a fast food chain) or based on the network
that they define (e.g., the network of an electric power
distribution company).
[0004] As a specific example, a company that supplies ground
transportation provides tractor-trailers to multiple ports around a
country for hauling imports and products from each port to an
inland destination. There are numerous reasons to supply the ports
with a certain number of transportation assets (e.g.,
tractor-trailers, cargo trailers, loading equipment, repair
equipment) which changes over time. The assets supplied to each
port may differ for any of a variety of reasons that may or may not
remain constant. For example, if one set of ports are experiencing
labor difficulties, there may be need to dynamically shift
transportation assets to another port as cargo ships are diverted,
and then shift them back as labor issues are resolved.
[0005] In another example, a utility company may attempt to
anticipate the potential impact of inclement weather and gauge the
appropriate response, limited by finite funds and/or resources.
Because of this limitation, the utility company strives to identify
areas of its network at the greatest risk to a variety of issues,
such as trees felled by wind or snow. In this setting, the utility
company attempts to determine the optimal mix of spare equipment
(e.g., poles, wire, transformers) used to respond to the event, and
appropriately distribute the spare equipment across a number of
staging areas (perhaps 50 locations throughout a geographic service
area). Similarly, the utility company strives to optimize limited
funds for engaging third parties (e.g., tree service contractors)
to perform preventative maintenance along thousands of miles of
roadways within its service area.
[0006] As another example, a fast food restaurant chain may have
several hundred locations around the country. The headquarters of
the company must determine, based on a wide variety of factors, how
much of each food item to supply to each restaurant.
[0007] Such determinations apply in a wide variety of situations.
For instance, aid organizations (e.g., the Red Cross, FEMA)
maintain stocks of various disaster relief items in various
warehouses. When a major weather event such as a hurricane is
forecast, it may be advantageous to move supplies from one
warehouse (e.g., in an area not likely to be impacted) to another
(e.g., closer to the area likely to be impacted).
Counter-intuitively, in some situations it may also be important to
move supplies away from an area likely to be impacted, particularly
if there is a threat that the supplies will be compromised by the
catastrophic event if left at their current location.
[0008] Consider the operations of a railroad or municipal
transportation authority. Knowing where to store operating
equipment and stage spare equipment (rail, railcars, electrical
transformers, and the like) can be critical to reducing downtime in
the event of a catastrophic event, such as the storm surge that
impacted the New York Subway system as a result of Tropical Storm
Sandy in 2012.
[0009] Similar modeling and planning can be used in other
industries as well. The insurance industry may well seek to model
the impact of catastrophic events on various insured properties. In
that industry, multiple layers of insurers have often-overlapping
coverage, all with limits (e.g., caps) and other constraints.
Further, catastrophic events, even if randomly distributed, are
sometimes bunched so that exposure seems unusually high. In
addition, some catastrophic events tend not to be independent but
instead are tied together, e.g., (a) a weather pattern breeds
multiple cyclonic events during a single season; (b) a large
earthquake is accompanied by a tsunami and numerous aftershocks;
(c) a terrorist attack is not isolated but is planned as one of
several coordinated attacks. Continuous geographic distribution of
insured assets such as a rail system complicates planning in
various ways, so interest in modeling is particularly great in the
insurance industry.
[0010] Determining the geospatial locations and how to best to
allocate resources (e.g., electrical wires or train rails) to
geographically diverse assets has traditionally been accomplished
as a combination of geocoding and operations research. Geocoding
conventionally uses location information such as an address or
latitude/longitude coordinates as a representation of each asset
under consideration (e.g., each fast food restaurant). Operations
research takes a number of factors, including the location
information, as a means to optimize the allocation of assets.
[0011] However, not all assets are readily described or optimized
in this manner. Railroads, utility transmission lines, gas and oil
pipelines and the like are continuously distributed throughout
their geographic range, and in any event often do not have
conventional physical addresses corresponding to the locations of
their component parts. Many variables, such as the value of
infrastructure, are not intended to be "optimized," but rather just
allocated.
[0012] The New York City Subway system, for example, has some two
dozen rail yards, in addition to more than 200 miles of track on
its two dozen or so routes. Some of these rail yards have dozens of
tracks, with all of the associated switching devices and controls.
Thus, the amount of spare equipment needed nearby to restore
operation to the yards after a catastrophic event may be orders of
magnitude more for the yards than for the route segments of the
system. However, unlike the food delivery requirements for a group
of fast food restaurants, the distribution of resource needs for
the New York Subway system are based on continuous (rather than
discrete) geographical distributions.
[0013] Consider now an insurance perspective on an asset that has
continuous geographic distribution, such as the New York Subway
system. Using computerized models, underwriters seek to price risk
based on the evaluation of the probability of loss for a particular
location and property type as well as manage portfolios of risks
according to the degree to which losses correlate from one location
to another as part of the same catastrophe event. These
probabilistic (stochastic) catastrophe models include, but are not
limited to, earthquake, fire following earthquake, tropical cyclone
(hurricanes, typhoons, and cyclones), extra-tropical cyclone
(windstorm), storm-surge, river flooding, tornadoes, hailstorms,
terrorism and other types of catastrophe events. These catastrophe
models are built upon detailed geographical databases describing
highly localized variations in hazard characteristics, as well as
databases capturing property and casualty inventory, building
stock, and insurance exposure information.
[0014] Modeling systems using these models allow catastrophe
managers, analysts, underwriters and others in the insurance
markets (and elsewhere) to capture risk exposure data, to analyze
risk for individual accounts or portfolios, to monitor risk
aggregates, and to set business strategy. Typical catastrophe
modeling systems are built around a geographical model comprising
exposure information for individual locations, specific bounded
locations or areas. These locations or areas of interest are
typically defined using for example, a street address, postal code
boundaries, including ZIP codes, city (or other administrative)
boundaries, electoral or census ward boundaries and similar bounded
geopolitical subdivisions.
[0015] A drawback of using these types of mechanisms (e.g., postal
boundaries, cities, municipalities, building IDs, or ZIP codes) to
define locations or areas is that some portions of an asset may not
have an address or representative geopolitical boundaries that can
be used to meaningfully characterize their corresponding risk
exposures. Indeed, some portions of the asset (e.g, train cars,
locomotives, cargo) may themselves be moveable properties without a
fixed location.
[0016] Another drawback of these types of mechanisms to define
locations or areas is that they do not allow the system or user the
flexibility to select different resolutions that would provide the
better geospatial representations of the asset. In addition, it may
be very difficult to identify a single location that characterizes
the risk of the whole geographic area.
[0017] These and other drawbacks exist. For asset portions having a
fixed location but not a corresponding conventional address, use of
a proxy such as ZIP code may result in extremely poor asset
allocations and modeling results. For asset portions that are
moveable, modeling that assumes the asset to be at a single
geographic location again may poorly represent the actual exposure
for any particular catastrophic event.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1A is a schematic representation of a network asset
that is a portion of a rail system which includes asset portions or
sub-assets according to one embodiment.
[0019] FIG. 1B is an illustration of a network asset having
multiple sub-assets that is analyzed using a variable resolution
grid according to one embodiment.
[0020] FIG. 2A is an illustration of a network asset map that has
been overlayed with a baseline map, thereby creating a combined map
showing assets in the context of baseline features, according to
one embodiment.
[0021] FIG. 2B is an illustration of using geographic
representation points to characterize intersections of a network
asset with baseline features according to one embodiment.
[0022] FIG. 3 is a flow diagram of a method for calculating risk
exposure values using a network asset map, a baseline map, and
their associated meta-data, according to one embodiment.
[0023] FIGS. 4A and 4B illustrate a network environment and a
system architecture, respectively, of a system for calculating risk
exposure values according to one embodiment.
[0024] FIG. 5 is a block diagram illustrating components of an
example machine able to read instructions from a machine-readable
medium and execute them in a processor (or controller) according to
one embodiment.
DETAILED DESCRIPTION OF THE DRAWINGS
[0025] Embodiments described herein include methods and systems for
developing risk exposure models for assets that are networks or are
moveable and therefore are not specific to an address or a single
location. The embodiments described herein can also be applied to
resource allocation modeling for supplying or servicing asset
portions of a distributed asset. The models and systems herein use
an asset map to describe the distributed nature of a network or a
moveable asset. An asset map characterizes the asset using a
plurality of geographic representations (points, lines, bounded
areas) to identify the physical locations of portions of the
network asset (or possible physical locations in the case of a
moveable asset). Meta-data is associated with each geographic
representation point reciting details related to the asset portion
at that geographic location. Examples of meta-data include
geographic coordinates (e.g., GPS coordinates, latitude/longitude)
of the asset portion, asset capacity, asset flow or directionality,
asset connectivity within and to other asset portions, asset
portion type (e.g., network, electrical grid substation, bridge,
tunnel, storage, maintenance), and others.
[0026] The models and systems also include a baseline map that
describes the context in which the network or moveable asset
portions are disposed. For example, the baseline map can include
information typically described in a GIS map such as public
infrastructure (e.g., roads, bridges), private infrastructure
(e.g., electrical grid elements), and features of the natural
landscape (e.g., waterways, flood zones, earthquake faults), as
well as political boundaries, population density, demographics, and
other similar information. As with the assets, these features of
the baseline map can also be described by meta-data that
characterizes the features.
[0027] The asset map and the baseline map are used in cooperation
to identify intersections between asset portions and risks posed by
baseline features (such as geographical features or the physical
surroundings of an asset portion), thereby using the intersection
and the meta-data associated with the intersecting asset and
baseline feature to quantify the risk (also termed a "risk
exposure") to the asset as well as a risk exposure value (i.e., the
potential financial liability that may be incurred by a party or
parties by destruction or damage to the asset from a catastrophe).
These intersections are described as "points" in this
disclosure.
[0028] The terms "geographic representations" and/or "geospatial
representations" are used in this disclosure to generically
describe polygons, single or multi-segment lines, points or other
geometric structures that capture the physical outline of an asset
and/or a baseline feature. The term "map" is used solely for
convenience of explanation. It will be understood that the
generation of a map from data is not necessary, and that the
methods and systems described below can determine risk exposure
values using data in other forms, not merely graphically or
visually represented data.
[0029] The methods and systems described herein can also be used
for supply chain management by forecasting demand and consumption
of discrete assets distributed over a large area (e.g., a set of
restaurants of a chain distributed throughout a geographic region).
Categories of use include, but are not limited to, personnel
assignments, resource and asset (e.g. spare equipment) allocation,
maintenance scheduling and completion, location planning and
business case support (particularly for larger commercial and
industrial facilities where critical infrastructure failures can
interrupt operations), corporate disaster planning and response,
and analysis of other similar scenarios involving allocation of
limited or time-sensitive resources.
Geographic Representation Points of Network and Moveable Assets
[0030] FIG. 1A is a schematic representation a portion of a rail
system 100, which in this example is described as a network asset
because its physical infrastructure is localized (e.g., to rail
beds and/or buildings) but is also distributed over a geographic
area. The rail system includes at least one rail 104, a switching
yard 108, a maintenance facility 112, and a customer loading site
116 (described collectively as "sub-assets" of the rail system
100).
[0031] The various example components of the rail system 100 that
are shown illustrate the diversity of sub-asset types that are
included in the rail system 100 asset as a whole, and also can
illustrate the variation in both time and geographic location of
the value (and therefore the risk exposure value) of the
sub-assets. For example, a value of the switching yard 108, and
similarly a value of the maintenance facility 112 will be much
higher when multiple trains are at one of these locations at the
same time. Correspondingly, the risk exposure value will be higher
in this case because, in the event of a catastrophe, the financial
loss of multiple trains as well as the physical structure of the
switching yard 108 will be higher than the financial loss of the
physical structure of the switching yard alone. Similarly, a value
of the customer loading site 116 is higher when a train is at the
customer loading site and the site itself contains inventory. This
is in contrast to a value of the customer loading site 116 after a
train loaded with inventory has departed, leaving the site as
merely an empty warehouse. As in the example of the switching yard
108, the risk exposure value is higher when the train is at the
customer loading site 116 and the site is filled with
inventory.
[0032] Alternatively, the asset of a train and an asset of the
switching yard 108 can be treated as separate assets using the
methods and systems described herein. That is, because the methods
and systems herein can be applied to moveable assets, a risk
exposure value of a train can be determined and/or calculated as a
function of its location, and also maintained separately from the
switching yard 108 asset.
[0033] Two features of the present disclosure are used to describe
these variations in value and risk as a function of both time and
geographic location: a variable resolution grid and geographic
representation points.
[0034] A variable resolution grid can be used to provide levels of
detail to an asset map and/or a baseline map proportional to the
value of an asset, a density of assets at a particular map
location, and/or a level of risk that is a function of conditions
local to all spans or an area of the asset. In other words, a
variable resolution grid provides a way of focusing specific
concentrations of exposures on a geographical grid to determine
projected loss caused by a catastrophe. Other embodiments of
variable resolution include a user-selectable "uniform resolution
grid," a user-selectable line interval (rather than a grid),
user-selectable regular intervals for use with the geographic
representation points of a distributed asset, and other similar
configurations. FIG. 1B illustrates an application of a variable
resolution grid to the rail system 100 of FIG. 1A. In this case,
the finest resolution of the grid 120 is coincident with the assets
having a highest density of asset value and/or asset risk, in this
case the switching yard 108. Variable resolution grids and their
applications are described in U.S. Pat. No. 8,229,766, which is
incorporated by reference in its entirety.
[0035] Geographic representation points, illustrated as the filled
circles shown in FIG. 2B, are used to represent the intersections
between the geographic representation of an asset (in this case a
rail system) and a baseline feature(s). The benefits of using these
geographic representation points are two-fold. First, the network
nature of the asset can be matched to baseline data (e.g.,
geographic features, public infrastructure, and geologic features)
thereby identifying specific network asset geographic locations to
geographically specific risk factors. This will be described in
more detail in the context of FIGS. 2A, 2B, and 3. Second, each of
the geographic representation points can have associated meta-data
that quantifies the asset (or sub-asset) type, location, and other
characteristics that can be used to quantify characteristics of the
asset or be used with other information to quantify the risk
exposure value associated with a geographical representation point.
Third, the number of geographic representation points and the
interval between them is proportional to the density of
intersections between the asset portions and the risks posed by
baseline features.
Baseline Maps and Risk Value Exposure Determination Method
[0036] FIG. 2A is a schematic illustration of a combined map 200
that includes the portions of the rail system 100 of FIG. 1A that
has been superimposed on a baseline map showing, in this example,
geologic features that can pose risks to the asset and/or
sub-assets. The combined map 200 of this example shows not only the
sub-assets of FIG. 1A, but also a stream 204 and a 20-year flood
zone 208 surrounding the stream.
[0037] As is shown, the stream 204 flows by the switching yard 108,
indicating the locations of bridges and significant infrastructure
relative to other portions of the assets. The addition of the
20-year flood zone 208 to the combined map 200, which can be
accessed using a publicly available GIS database, indicates
different levels of flooding risk to the sub-assets. That is,
because the switching yard 108 and the maintenance facility 112 are
in the 20-year flood zone 208 surrounding the stream 204 will
reflect a higher flooding risk (and therefore a higher risk
exposure value) compared to the customer loading site 116, which is
outside the flood zone.
[0038] FIG. 2B illustrates a set of geographic representation
points 212 that indicate intersections between the geographic
representations of asset portions and baseline features. As
described above, each point provides a user with meta-data
describing the asset portion and the intersecting baseline feature,
including risks, locations, and the like. As also described above,
the spacing between the geographic representation points of the set
212 varies as a function of the risks posed, the density of assets,
and/or the asset value.
[0039] FIG. 3 shows a method 300 for creating a combined map and
calculating risk exposure values using geographic representation
points, their associated meta-data, and the meta-data associated
with features of a baseline map. As described above in FIG. 1A , a
map of geographic representations (points, polygons, lines,
multi-segment lines, etc.) of a network asset (or data
corresponding to and characterizing the locations, features, etc.
of a network asset) are identified 304. The map includes the
geographic locations and/or coordinates of the various sub-assets
and components of the network asset, reflecting discrete locations
of asset portions. Associated with the geographic representations
of the network map are meta-data that describe, characterize, or
identify a portion of the network at that point.
[0040] A baseline map is identified 308 that describes the various
geographic, geologic, political, and/or other features
characterizing the setting of the asset that can pose risks to the
asset, or portions thereof. As described above, the baseline map
also uses geographic representations (points, polygons, lines,
multi-segment lines, etc.), and associated meta-data to describe
the physical outline of the baseline feature and the risk factors
posed to the asset by the baseline feature. Returning to the
example shown in FIG. 2, a multi-segment line can be used in the
baseline map to trace the path of the stream 204 and one or more
polygons can be used in the baseline map to identify the limits of
the 20-year flood zone 208. Meta-data associated with the lines
representing stream 204 can include, for example, geographic
end-points of line-segments of the stream, its average flow rate,
its flood stage flow rate, its flooding frequency, and other
similar risk factors. Similarly, meta-data associated with the
polygon representing the 20-year flood zone can include flooding
frequency, flooding probability as a function of location within
the flood zone, typical flooding dates, distance from local
emergency services, and other similar information.
[0041] The network map and baseline maps (or non-graphical data)
are overlayed 312 (or otherwise associated with one another for
combined analysis and other use) to form a combined map. The
combined map, showing both asset and baseline features, can then be
used to generate geographic representation points 316 that are
intersections of the geographic representations of the asset (or
portions thereof) and proximate baseline features. It is these
intersections of asset portions and risk for which risk exposure
values are calculated.
[0042] In some examples, multiple variable resolution grids are
used with either or both of the network map or the baseline map.
The variable resolution grid, as described in U.S. Pat. No.
8,229,766 and incorporated by reference herein, provides a method
for providing added detail to assets or baseline features when
warranted. For example, some assets may have discrete
concentrations of value (e.g., the switching yard 108) and some
baseline features may have discrete concentrations of risk (e.g.,
the 20-year flood plain 208). Using a variable resolution grid to
support additional meta-data for these discrete locations is
helpful for creating an accurate risk exposure value. Furthermore,
using a variable resolution grid that lacks such meta-data for
assets or baseline features that do not warrant additional detail
facilitates an efficient use of computational resources.
[0043] The meta-data associated with a geographic representation
point generally describes a location of a point in one or more
coordinate systems, such as a geographic coordinate system (e.g.,
by using a GPS coordinates, latitude and longitude, elevation)
and/or a political coordinate system (e.g., street address). The
meta-data of a geographic representation point also includes
information describing the asset or sub-asset represented by the
point. This information includes, but is not limited, to an owner
and/or operator of the asset and/or sub-asset, a value (which can
also include a value as a function of time, as described in the
example of the switching yard 108), a description or type of asset,
a composition of the asset (e.g., rails, buildings, maintenance
equipment), and other similar data used to quantify an asset value.
In other examples the meta-data does not include asset value, which
is supplied separately.
[0044] Upon generating 316 the geographic representation points of
the intersections, the meta-data associated with both the asset (or
portions thereof) and the baseline feature are retrieved. These
data can be retrieved from a private source (such as an insurance
industry database or a common carrier database) or a public source
(such as a government sponsored GIS database). Regardless of the
source, these data are used to quantify a value of the asset and
establish risk factors due to the asset map and baseline features.
These are then used to calculate 324 a risk exposure value.
[0045] The calculation 324 of a risk exposure value is performed
using conventional methods. For example, in one embodiment a user
assigns a weighing factor representing duration, frequency, time of
year or seasonality, asset type, baseline feature type, risk type
or other similar risk or value factor. These weighing factors are
used to compensate for an absence of meta-data describing either or
both of the asset or feature. These various weights are then
multiplied to calculate a final weighing factor associated with the
geographic representation point. The final weighing factor is then
multiplied by the asset (or asset portion) value to determine the
risk exposure value associated the geographic representation point
of the intersection.
[0046] In this way, the risk exposure value is assigned only to the
portion of the network or distributed asset that is actually
exposed to the risk and not inaccurately distributed to the entire
asset (or to a larger-than-needed portion of the asset). The
benefit of this method is that risk exposure values are more
precisely associated with specific sub-assets or portions of
network assets or assets that are not otherwise assignable to a
single, fixed address. Aspects of the method 300 have already been
provided above in the context of a network asset (rail system 100).
Another particular application is for assets that are moveable,
such as inventory that is transported in a train car or a truck.
The risk exposure to the inventory can change as a function of the
location of the transporting vehicle, the time of year, route,
duration of the trip, etc. For example, the risk exposure to
inventory being transported by vessel during hurricane season in a
coastal area could be greater than the same inventory being
transported on inland rivers or lakes during the same time of
year.
Risk Value Determination System and System Environment
[0047] FIG. 4A illustrates an example of a system environment 400
used for performing the method 300. The system environment 400
includes a risk exposure system 404, described in detail in FIG.
4B, a network 408, a baseline database 412 and an asset database
416.
[0048] The network 408 is configured to permit communication
between the risk exposure system 404 and other information sources,
such as the baseline database 412 and the asset database 416. The
network 408 may comprise any combination of local area and/or wide
area networks, using both wired and wireless communication systems.
In one embodiment, the network 408 uses standard communications
technologies and/or protocols. Thus, the network 408 may include
links using technologies such as Ethernet, 802.11, worldwide
interoperability for microwave access (WiMAX), 3G, 4G, CDMA,
digital subscriber line (DSL), etc. Similarly, the networking
protocols used on the network 408 may include multiprotocol label
switching (MPLS), transmission control protocol/Internet protocol
(TCP/IP), User Datagram Protocol (UDP), hypertext transport
protocol (HTTP), simple mail transfer protocol (SMTP) and file
transfer protocol (FTP). Data exchanged over the network 408 may be
represented using technologies and/or formats including hypertext
markup language (HTML) or extensible markup language (XML). In
addition, all or some of links can be encrypted using conventional
encryption technologies such as secure sockets layer (SSL),
transport layer security (TLS), and Internet Protocol security
(IPsec).
[0049] The baseline database 412 includes geographic, geologic,
political, and demographic data collected by public and/or private
sources to describe and characterize environments in which assets
are disposed. The baseline database 412 and the information therein
can be accessed by the risk exposure system 404 for information
regarding baseline features discussed above.
[0050] In one example, the baseline database 412 is a GIS database
that can provide data renderable into a graphic format (i.e., a
map) and also provide meta-data that can be further used by the
risk exposure system 404 to quantify risks posed by baseline
features. For example, some meta-data includes seasonal
fluctuations in water levels (such as in stream 204), traffic
patterns (for example, used to quantify the risk exposure to
perishable freight transported by truck), crime rates, and data
that can otherwise influence risk exposure. The number of meta-data
elements can include size or position of an asset or a feature of
an asset in multiple dimensions (height, width, depth, diameter,
circumference), construction/composition (concrete, metal, plastic,
optical fibers), operating capacities by season, minimum or maximum
operational limits associated with the asset (e.g., volume,
pressure, frequency, flow rate), fragility, alternative routes,
age, useful life, accuracy of location, directionality of flow
(e.g., one way or bidirectional), ownership status (partially or
wholly owned), commodity or cargo type, identifying names,
geopolitical references, and other information.
[0051] The asset database 416 stores data used by the risk exposure
system 404 to quantify the risk to an asset, or portion thereof.
The data stored by the asset database 416 includes the meta-data
described above that includes, but is not limited to, GPS
coordinates, latitude/longitude of the asset, asset type (e.g.,
moveable, network, electrical grid substation, train car, inventory
shipment), and other characteristics.
[0052] FIG. 4B illustrates a system architecture of the risk
exposure system 404 used for calculating a risk exposure value of
an asset portion, as described above. The system architecture of
the risk exposure system 404 includes an asset database 420, a
local baseline database 424, a query engine 428, a combined map
generator 432, a risk factor database 436, and a risk exposure
value calculator 440.
[0053] The asset database 420 stores meta-data associated with
network assets, moveable assets, or other assets that are
distributed and not otherwise associated with a single, fixed,
street address. As described above, the meta-data describing the
asset and stored in the asset database 420 includes the geospatial
representations of the various asset portions or sub-assets, the
relationship or connection between the various portions of the
assets to each other and the asset as a whole, types, and other
similar characteristics or data used for the calculation of a risk
exposure value. As with the other databases described below, the
asset database 420 can be a relational database or other type of
data storage system used to store and retrieve data.
[0054] Similar to the asset database 420, the local baseline
database 424 stores data characterizing baseline features and the
risks that the features pose to the asset portions. Where the
baseline database 412 described above is an external database
operated by a third party, such as a government GIS database
documenting the geographic limits of 20-year flood zones, the local
baseline database 424 permits the risk exposure system 404 to
record and access information regarding baseline risks identified
by or recorded in the system 404 separately from the baseline
database 412. For example, referring again to FIG. 2, if the rail
switching yard 108 is known to flood more frequently than would be
indicated by data related to the stream 204 and stored in baseline
database 412, this data can be stored at the local baseline
database 424 for use in the risk exposure value calculation.
[0055] Not only can private observations that enhance the
understanding of publicly known risks be stored in the local
baseline database 424, but also risks known privately to the
operator of the risk exposure system 404 can also be recorded in
the local baseline database. For example, risks specific to the
asset itself (e.g., chemical spill, explosion, theft, arson) can be
entered into the baseline database 424 and used in the calculation
of a risk exposure value. Similarly, the proximate location of
risks that compounds the risk exposure value of other risks can be
identified. That is, the presence of hazardous waste, chemicals, or
other volatile hazards has inherent risk, but also increases the
risk exposure value of separate, but related, risks. For example, a
train carrying hazardous waste that derails causes more damage than
a train carrying plywood.
[0056] The query engine 428 is configured to communicate with data
sources external to the risk exposure system 404, such as the
baseline database 412 and the asset database 416. In one example,
the query engine 428 is an application programming interface
("API") that provides functionality for exchanging data between the
risk exposure system 404 and, for example, the baseline database
412 and the asset database 416.
[0057] The combined map generator 432 receives data from the asset
database 420 regarding a particular asset, and also receives data
from the local baseline database 424 for a range of baseline
features proximate to the asset geolocation. The combined map
generator 432 may also receive data from the query engine 428 that
is relevant to the asset and the baseline feature but is stored
externally to the system 404. The combined map generator 432
identifies intersections of asset portions or sub-assets and
baseline features, thereby associating a risk factor from a
specific baseline feature relevant to a specific asset portion or
sub-asset. As described above, the benefit of this is that risks
specific to an asset portion are associated with the portion and
not generic to the asset as a whole. The combined map generator can
optionally produce a graphic depiction of the asset portion and the
baseline feature, as well as their intersection, on a geographic,
geologic, demographic, or political map. Furthermore, the combined
map generator 432 can generate geographic representation points
without using a variable resolution grid or baseline map, instead
creating a geographic representation point of the asset at regular
intervals between a starting point and an endpoint.
[0058] The risk factor database 436 is used in connection with the
combined map generator 432 to quantify the risk factor to the asset
that is posed by the baseline feature by providing a weight to the
associated risk. These weights are used to differentiate the
geographic representation points using the meta-data associated
with each point. That is, the weights are used to determine, in
part, the risk exposure value allocated to each point.
[0059] Depending on the implementation environment, various
weighting schemes are used in various embodiments. The weights can
be applied to various geographic representation points using rules,
asset values, or conditions provided by a user and/or automatically
inferring the weights from the meta-data. For example, a geographic
representation point associated with a train rail crossing a river
can have a higher weight than a rail crossing an infrequently used
or geographically remote road. In another example, risks of an
interruption to the operations of a business caused by a delivery
delay can be weighted based on the location of the delay relative
to the delivery point, the downstream business impacts that
compound upon a delay, and other factors. Also, because of the
variability in quality and quantity of meta-data, weights can be
used as proxies for missing meta-data, or as an override for
existing meta-data as applied to asset values instead of a
geographic representation point. As described above, multiple
weights associated with a geographic representation point can be
multiplied to provide a single weight for a point.
[0060] The risk exposure value calculator 440 then calculates a
risk exposure value using the meta-data stored in the asset
database 420, the various weighing factors, the local baseline
database 424, and the risk factor database 436. The calculation
involves two sets of weights, one from the combined map generator
432 and one from the risk factor database 436, and a value of an
asset from the asset database 420. In one implementation, weights
from 436 are applied against to the asset value first, causing an
interim asset value allocation across a group or a type of
geographic representation points. Weights for individual points
within the group or type are then applied to the interim asset
values, thus generating a risk exposure value for each point. In
some embodiments, weights are provided relative to some normalized
level, such as a dynamically determined normalization point
allowing good dynamic range of weights above and below the
normalization point. Depending on application, weighting factors
may be provided as input to the system in multiple forms (currency,
percents, time, etc.); those skilled in the art will recognize that
conversion to common forms may be required in such situations. In
other environments, multiple forms may be supported directly (e.g.,
normalization points may be specified in both miles and kilometers
to avoid the need to convert individual measurements that may be
supplied in either format).
Supply Chain Management Example
[0061] The described systems and methods can also be applied in
other contexts in which portions or elements of a network have
varying inputs and/or outputs. For example, discrete restaurant
locations can have raw material needs that vary as a function of
season, geography, customer demographics, location, or other
factors, including dependencies on public/commercial infrastructure
and access to and/or from suppliers and customers. The systems and
methods described above can be used to record meta-data describing
the particular needs and/or patterns of the discrete locations and
provide the locations with supplies appropriately.
[0062] Continuing with the example of a set of discrete restaurants
in a geographically distributed network of restaurants, a specific
restaurant may be situated in a climactically warm area near a
controlled-access highway access point. Because of its location
near the controlled-access highway, the restaurant may consume some
supplies at rates different from those of other restaurants in the
same network but located in town centers. Each will also have
different opportunity for re-supply because of different access to
transportation infrastructure. For example, the restaurant near the
controlled-access highway may consume more materials used for
drive-through delivery of food than a restaurant in a town center
that hosts more dine-in customers. The restaurant near the
controlled-access highway is much more subject to variances in
business given construction on the highway than a restaurant in the
town center. Similarly, the restaurant in a town center is much
more likely to have power restored more quickly following an
outage, given its proximity to population, than the restaurant near
the controlled-access highway.
[0063] Using the system and methods described above, meta-data
describing the consumption patterns of various supplies by the
different restaurants can be stored in the asset database 420. For
example, the part numbers of the various supplies, their
consumption rates, the variation of the consumption rate as a
function of time of year, local supplies, and other similar
information can be stored in the asset database 420. Similarly,
risks to the supply, average delivery times, common carrier costs
and non-delivery rates, power outage impacts can be stored in the
local baseline database 424.
[0064] The other elements of the system then function in
substantially the same way as described above to determine the risk
exposure value of a missed shipment as well as the risk exposure
value of over-supplying the restaurant or carrying excess inventory
at the restaurant. These competing factors can then be balanced in
the order-fulfillment and shipment process and other business
planning processes such as on-site power generation.
Computing Device
[0065] FIG. 5 is a block diagram illustrating components of an
example machine able to read instructions from a machine-readable
medium and execute them in a processor (or controller).
Specifically, FIG. 5 shows a diagrammatic representation of a
machine in the example form of a computer system 500 within which
instructions 524 (e.g., software) for causing the machine to
perform any one or more of the methodologies discussed herein may
be executed. In alternative embodiments, the machine operates as a
standalone device or may be connected (e.g., networked) to other
machines. In a networked deployment, the machine may operate in the
capacity of a server machine or a client machine in a server-client
network environment, or as a peer machine in a peer-to-peer (or
distributed) network environment.
[0066] The machine may be a server computer, a client computer, a
personal computer (PC), a tablet PC, a set-top box (STB), a
personal digital assistant (PDA), a cellular telephone, a
smartphone, a web appliance, a network router, switch or bridge, or
any machine capable of executing instructions 524 (sequential or
otherwise) that specify actions to be taken by that machine.
Further, while only a single machine is illustrated, the term
"machine" shall also be taken to include any collection of machines
that individually or jointly execute instructions 524 to perform
any one or more of the methodologies discussed herein.
[0067] The example computer system 500 includes a processor 502
(e.g., a central processing unit (CPU), a graphics processing unit
(GPU), a digital signal processor (DSP), one or more application
specific integrated circuits (ASICs), one or more radio-frequency
integrated circuits (RFICs), or any combination of these), a main
memory 504, and a static memory 506, which are configured to
communicate with each other via a bus 508. The computer system 500
may further include graphics display unit 510 (e.g., a plasma
display panel (PDP), a liquid crystal display (LCD), a projector,
or a cathode ray tube (CRT)). The computer system 500 may also
include alphanumeric input device 512 (e.g., a keyboard), a cursor
control device 514 (e.g., a mouse, a trackball, a joystick, a
motion sensor, or other pointing instrument), a storage unit 516, a
signal generation device 518 (e.g., a speaker), and a network
interface device 820, which also are configured to communicate via
the bus 508.
[0068] The storage unit 516 includes a machine-readable medium 522
on which is stored instructions 524 (e.g., software) embodying any
one or more of the methodologies or functions described herein. The
instructions 524 (e.g., software) may also reside, completely or at
least partially, within the main memory 504 or within the processor
502 (e.g., within a processor's cache memory) during execution
thereof by the computer system 500, the main memory 504 and the
processor 502 also constituting machine-readable media. The
instructions 524 (e.g., software) may be transmitted or received
over a network 526 via the network interface device 520.
[0069] While machine-readable medium 522 is shown in an example
embodiment to be a single medium, the term "machine-readable
medium" should be taken to include a single medium or multiple
media (e.g., a centralized or distributed database, or associated
caches and servers) able to store instructions (e.g., instructions
524). The term "machine-readable medium" shall also be taken to
include any medium that is capable of storing instructions (e.g.,
instructions 524) for execution by the machine and that cause the
machine to perform any one or more of the methodologies disclosed
herein. The term "machine-readable medium" includes, but not be
limited to, data repositories in the form of solid-state memories,
optical media, and magnetic media.
[0070] Risk exposure system 404, as well as its constituent
components asset database 420, local baseline database 424, query
engine 428, combined map generator 432, risk factor database 436
and risk exposure value calculator 440 are, in various embodiments,
implemented using one or more computers configured such as computer
500 discussed above. Those of skill in the art will recognize that
based on processing requirements, several various components may be
implemented on a common one of such computers, or several of such
computers can operate in a collaborative fashion to implement one
or more of such components.
Other Considerations
[0071] While particular embodiments are described, it is to be
understood that modifications will be apparent to those skilled in
the art without departing from the spirit of the invention
described herein. The scope of the invention is not limited to the
specific embodiments described herein. Other embodiments, uses and
advantages of the invention will be apparent to those skilled in
art from consideration of the specification and practice of the
embodiments disclosed herein.
[0072] The embodiments herein have been described in particular
detail with respect to several possible embodiments. Those of skill
in the art will appreciate that the invention may be practiced in
other embodiments. The particular naming of the components,
capitalization of terms, the attributes, data structures, or any
other programming or structural aspect is not mandatory or
significant, and the mechanisms that implement various embodiments
may have different names, formats, or protocols. Further, the
system may be implemented via a combination of hardware and
software, as described, or entirely in hardware elements. Also, the
particular division of functionality between the various system
components described herein is merely exemplary, and not mandatory;
functions performed by a single system component may instead be
performed by multiple components, and functions performed by
multiple components may instead performed by a single
component.
[0073] Some portions of above description present the features of
the embodiments in terms of algorithms and symbolic representations
of operations on information. These algorithmic descriptions and
representations are the means used by those skilled in the data
processing arts to most effectively convey the substance of their
work to others skilled in the art. These operations, while
described functionally or logically, are understood to be
implemented by computer programs. Furthermore, it has also proven
convenient at times, to refer to these arrangements of operations
as modules or by functional names, without loss of generality.
[0074] Unless specifically stated otherwise as apparent from the
above discussion, it is appreciated that throughout the
description, discussions utilizing terms such as "determining" or
the like, refer to the action and processes of a computer system,
or similar electronic computing device, that manipulates and
transforms data represented as physical (electronic) quantities
within the computer system memories or registers or other such
information storage, transmission or display devices.
[0075] Certain aspects of the described embodiments include process
steps and instructions described herein in the form of an
algorithm. It should be noted that various of the process steps and
instructions disclosed herein could be embodied in software,
firmware or hardware, and when embodied in software, could be
downloaded to reside on and be operated from different platforms
used by real time network operating systems.
[0076] The described embodiments also relate to an apparatus for
performing the operations herein. This apparatus may be specially
constructed for the various purposes, or it may comprise a
general-purpose computer selectively activated or reconfigured by a
computer program stored on a computer readable medium that can be
accessed by the computer and run by a computer processor. Such a
computer program may be stored in a computer readable storage
medium, such as, but is not limited to, any type of disk including
floppy disks, optical disks, CD-ROMs, magnetic-optical disks,
read-only memories (ROMs), random access memories (RAMs), EPROMs,
EEPROMs, magnetic or optical cards, application specific integrated
circuits (ASICs), or any type of media suitable for storing
electronic instructions, and each coupled to a computer system bus.
Furthermore, the computers referred to in the specification may
include a single processor or may be architectures employing
multiple processor designs for increased computing capability.
[0077] In addition, the described embodiments are not described
with reference to any particular programming language. It is
appreciated that a variety of programming languages may be used to
implement the teachings as described herein.
[0078] The described embodiments are well suited to a wide variety
of computer network systems over numerous topologies. Within this
field, the configuration and management of large networks comprise
storage devices and computers that are communicatively coupled to
dissimilar computers and storage devices over a network, such as
the Internet.
[0079] Finally, it should be noted that the language used in the
specification has been principally selected for readability and
instructional purposes and may not have been selected to delineate
or circumscribe the inventive subject matter. Accordingly, the
disclosure is intended to be illustrative, but not limiting, of the
scope of the invention.
* * * * *