US20150134384A1

US20150134384A1 - System and method for allocating resources

Info

Publication number: US20150134384A1
Application number: US14/534,778
Authority: US
Inventors: Tero Heinonen; Ville Koivuranta; Juha Hyyppa; Anttoni Jaakkola
Original assignee: Sharper Shape Ltd
Current assignee: Sharper Shape Ltd
Priority date: 2013-11-08
Filing date: 2014-11-06
Publication date: 2015-05-14
Also published as: US20150130840A1; US9784836B2; US20150131079A1

Abstract

Disclosed is a system for updating probability data of target object property. The system comprises a database for storing probability data, a property object data of a first target object, a property object data of a second target object, a list of actions and a cost of actions. The system also includes a processor having an executable code configured to define a first probability of accuracy of a property object data of a first target object, define a second probability of the first target object having a negative effect on a second target object, calculate a combined probability from the first probability and the second probability and use the combined probability to select to which at least one of the target objects to allocate resources, and update the first probability and the second probability in the database based on the action performed with the allocated resource.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. provisional Patent Application No. 61/901,490, filed on 8 Nov. 2013; and is related to, and claims the benefit of, U.S. Patent Application Ser. No. 61/901,489 filed on 8 Nov. 2013 entitled System for Monitoring Power Lines (Sharpershape001); and U.S. Patent Application Ser. No. 61/901,492, filed on 8 Nov. 2013, entitled System and Method for Reporting Events (Sharpershape003); the disclosures of which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The general area of the disclosure is towards improved resource utilization related to network maintenance, and, more particularly, to a system and a method for allocating resources and executing actions to maintain such network based on probability data of target objects associated with the network and managing such probability data.

BACKGROUND

Infrastructure networks (such as water pipes, oil and gas pipes, electricity lines, etc.) are maintenance intensive, so maintenance budgets are a substantial share of costs for such businesses. Maintenance budgets are high, in part, due to the size of the systems and the number of resources that it takes to properly maintain the system to achieve the appropriate reliability level. Substantial costs are involved in monitoring and identifying of potential threats to objects in the infrastructure networks. Traditionally this has been achieved primarily relying on on-site manual inspection. However, such means have proven expensive, time-consuming and often show inaccurate results over a period of time. Further, independent monitoring and measurement analysis of these extensive networks and keeping the information up to date in a database is time and resource consuming task.
As fundamental economics teach, every organization, commercial, non-profit, or governmental, has limited resources that is, cash flow, capital assets, raw materials, equipment, personnel, etc. These limited resources need to be optimally utilized to best serve the organization's business goals. To comply with regulatory requirements, with limited budgets, it is necessary to find tools and techniques that can be used to optimally allocate resources to maintain infrastructure networks. Such businesses must determine how to allocate available resources for regular maintenance activities. With increasingly competitive markets, the need of optimum resource allocation further intensifies.
Considering an example of power line (PL) networks which usually comprise of conductors, insulators, pylons and other associated structures such as spacer, dead-lines, switch boxes, etc. Generally, the PL networks are very large and are distributed along large geographical area. For example, the power transmission grid of United States consists of about 300,000 km of power lines in total length, which is operated by 500 different power line companies and involving thousands of personnel working to maintain the network.
Such PL networks are often exposed to potential threats, mainly caused by encroaching vegetation, structural changes between “as-built” and “as-is” condition and violating clearance between conductors and assets. FIG. 1 illustrates some examples showing risks due to tree/vegetation growth close to the power lines. As the tree grows it will be eventually so tall that in case it falls down during a storm it would harm the power line. Additionally a branch of a tree might have grown and is for example above the power line, and during the winter time snow load might bend the branch so much to touch the power line leading to some possible damages to the line. In practice as the vegetation grows the situation around the PL network changes and probability of possible damages/failures to the PL network increases.
In case of a disaster such as a major storm a substantial amount of damage may occur to the PL network causing massive disruption to the power distribution and to the whole society dependent on electricity. In all these circumstances, a quick and accurate analysis of the damage is of utmost importance for the electricity transmission and distribution operators, to manage the repair work efficiently. Further a timely regular monitoring of the network features and their spatial relations is required to have a reference to the prior conditions of the power lines in such a network.
According to an estimate, tree growth causes about 20 percent of sustained distribution outages, most of which are of short duration. The percentage of tree growth caused distribution outages is dependent on the portion of the forest of the land mass and the type of forest and can reach 65 percent of all outages in regions with boreal forest such as in Canada, Northern Europe and Russia. Growth-related failures are maintainable and can be effectively controlled through regular tree-trimming. Corrective maintenance refers to repair activities done to restore the system after a fault. Preventive vegetation management is done before a failure actually occurs. The repair field force needs to be sent to the locations where the repairs will have best impact on the number of customers to get their power back, and the number of watts transmitted or distributed.
The efficient allocation of the resources cannot be done without accurate situational data of the target site. Traditionally known methods for allocating resources can be classified as either subjective, accounting, operations research/management science, or the like. All of these methods try to address the same fundamental issue faced by all organizations, which resources to allocate for which purposes, and further how to prioritize and execute actions. Generally, prices and costs are key factors driving such decisions. Technological advancements have provided increased granularity for resource utilization, and thus has enabled the businesses to make more complex allocation decisions.
Therefore, there exists a need to devise a system that solves the problem of measuring and identifying potential risks to the infrastructure networks and provides optimum allocation of resources for infrastructure maintenance, and that overcomes the above-mentioned limitations of existing systems.

BRIEF SUMMARY

The present disclosure provides a system and method for allocating resources to maintain a network. More specifically, the present disclosure relates to a system and a method for allocating resources and executing actions to maintain such network based on probability data of target objects associated with the network; and managing such probability data.
In one aspect, embodiments of the present disclosure provide a system for updating probability data of target object property. The system comprises a database for storing probability data, a property object data of a first target object, a property object data of a second target object, a list of actions and a cost of actions. The system also includes a processor having an executable code configured to define a first probability of accuracy of a property object data of a first target object, define a second probability of the first target object having a negative effect on a second target object, calculate a combined probability from the first probability and the second probability and use the combined probability to select to which at least one of the target objects to allocate resources, and update the first probability and the second probability in the database based on the action performed with the allocated resource.
In another aspect, embodiments of the present disclosure provide a method for updating probability data of target object property in a database. The method comprises defining a first probability of accuracy of a property object data of a first target object; defining a second probability of the first target object having a negative effect on a second target object; using the first probability and the second probability to calculate a combined probability; using the combined probability to select to which at least one of the target objects to allocate resources; using the allocated resources to perform an action on the selected at least one of the target objects and updating the first probability and the second probability in the database based on the action.
According to an embodiment, the system and the method enable a user, such as electricity transmission and distribution operators, to order resources in order to execute a mission related to infrastructure maintenance.
Further, the system and the method are configured for generating resource requests which are allocated based on configurable business goals.
Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments.
It will be appreciated that features of the disclosure are susceptible to being combined in various combinations or further improvements without departing from the scope of the disclosure and this provisional application.

DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the disclosure is not limited to specific methods and instrumentalities disclosed herein. Wherever possible, like elements have been indicated by identical numbers.

FIG. 1 illustrates a pictorial representation of exemplary potential threats to an infrastructure network, in accordance to an embodiment of the present disclosure;

FIG. 2 illustrates a high level architecture of a system for managing target object property data to allocate resources and execute actions to maintain an infrastructure network, in accordance with an embodiment of the present disclosure;

FIG. 3 illustrates a block diagram of a system for managing probability data of target objects to allocate resources and execute actions to maintain an infrastructure network, in accordance with an embodiment of the present disclosure;

FIG. 4 illustrates a graphical representation of probability distribution of failure in power lines of various areas in an infrastructure network, in accordance with an exemplary embodiment of the present disclosure; and

FIG. 5 illustrates steps of a method for updating probability data of target object property in a database, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to the drawings, particularly by their reference numbers, FIG. 1 illustrates a pictorial representation of exemplary potential threats to an infrastructure network, in accordance to an embodiment of the present disclosure. As an example, FIG. 1 illustrates risks due to tree/vegetation growing close to an infrastructure network, such as power lines (PL).
Referring now to FIG. 2, illustrated is a high level architecture of a system 100 for managing target object property data of target objects constituting an infrastructure network. The term “managing” of the target object property data means acquiring, sending, analyzing and storing of such the target object property data. The system 100 further enables in allocating resources and executing actions to maintain the infrastructure network based on the target object property data. The target object property data is related to the properties of the target objects, explained in detail herein later.
According to an embodiment, the system 100 includes a mission control unit 110. The system 100 also includes a mission target 114, associated with a mission 112. In an example, the mission 112 may be related to mitigating risks to a PL network. The PL network is usually extensive and distributed along a large geographical area. Further, the PL network are often exposed to potential threats, typically caused by encroaching vegetation, and probability of possible failures of the PL network increases due to such threats. Therefore, it is required to timely and regularly monitor the network features (such the target object property data) and spatial relations there-between.
The mission target 114 includes of one or more target objects 118. The target objects 118 are physical world objects of interest, including man-made target objects such as infrastructure (buildings, roads, pipelines, grids including electricity power stations and power lines); natural target objects which are of interest such as ground, trees, river, lake, hills, undergrowth; spatial target objects, such as boundaries, areas, e.g. country, region, municipality and the like. In an example, the mission target 114 includes electricity power lines having pylons and conductors and all trees in a defined area. Alternatively, the mission target 114 may include a vehicle convoy consisting of a defined set of target objects 118 and the like.
Further, each of the target objects 118 includes at least one target object property 119. The target object property 119 includes but not limited any of identifier(s) of target objects 118, spatial information (such as location or pose) of target objects 118, attribute information (such as height, type, or size) of target objects 118, or structural information (such as topological relation) of the target objects 118 with respect to each other. The target object property 119 can be quantified as data relates to the various properties of the target objects 118, accordingly the term “target object property data” and the term “target object property 119” is used interchangeably based on appropriate situations.
As shown in FIG. 2, the mission control unit 110 includes a user terminal 116, one or more surveying modules 120, an analyzing module 130, an action module 160, an action plan optimization means 170 and a record means 180. The mission control unit 110 also includes an action type 164 and one or more action resources 166 for monitoring, guiding, or overriding the mission 112.
The user terminal 116 includes one of a laptop, a personal computer, a desktop computer, a web tablet, wireless devices including, although are not limited to, smart phones, Mobile Internet Devices (MID), wireless-enabled tablet computers, Ultra-Mobile Personal Computers (UMPC), phablets, tablet computers, Personal Digital Assistants (PDA), web pads, smart phones, and iPhone® etc. Further, in an example, the user terminal 116 may be associated with electricity transmission and distribution operators.
The surveying modules 120 of the system 100 are configured to perform utility monitoring task, for example, monitoring of the target object 118, and collecting remote sensing data 122 about the target object 118 and the target object property 119.
Each of the surveying modules 120 includes at least one remote sensing equipment 126. In an embodiment, the remote sensing equipment 126 may include digital remote sensing equipment and instruments such as LiDAR, SAR radar, thermal camera, camera, or video camera, x-ray radar, etc. The remote sensing equipment 126 may be located near by the mission target site or may be located remotely to the target site gathering information by remote communication means. In a preferred embodiment, the remote sensing equipment 126 includes LiDAR systems which have been gaining popularity as a primary information source. LiDAR (also written LIDAR) is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light. The term “LiDAR” comes from combining the words light and radar. This emerging data acquisition tool provides an opportunity to classify a utility corridor scene more reliably and thus generate accurate 3D models of infrastructure features due to LiDAR's ability of make highly dense and accurate data collection as well as and multiple-echo data acquisition, which can also provide information on the internal structure of vegetation.
LiDAR uses ultraviolet, visible, infrared, or near infrared light to image objects and can be used with a wide range of targets, including non-metallic objects, rocks, rain, chemical compounds, aerosols, clouds and even single molecules. LiDAR systems employ a narrow laser beam which can be used to map physical features with very high resolution. Wavelengths from about 10 micrometers to the UV (ca. 250 nm) are used to suit the target. Typically light is reflected via backscattering. Different types of scattering are used for different LiDAR applications; most common are Rayleigh scattering, Mie scattering, Raman scattering, and fluorescence. Based on different kinds of backscattering, the LiDAR can be accordingly called Rayleigh LiDAR, Mie LiDAR, Raman LiDAR, Na/Fe/K Fluorescence LiDAR, and so on. Suitable combinations of wavelengths can allow for remote mapping of atmospheric contents by looking for wavelength dependent changes in the intensity of the returned signal.
In an embodiment, the remote sensing equipment 126 is installed and operated from a mobile platform 128. In an example, the mobile platform 128 includes but not limited to a copter, fixed wing plane, an Unmanned Aerial Vehicle (UAV), Unmanned Aerial System (UAS), satellite, wheel drive terrain vehicle such as a car, forest machine; or on a person backpack, helmet and the like. The surveying modules 120, consisting of mobile platform 128 with sensing equipment 126, is essentially a logical unit in terms of dispatching the mission 112 to the mission target object 118.
The remote sensing equipment 126 is configured to collect remote sensing data 122 and mission prior data 124 about the mission target 114. The mission prior data 124 includes any available data related to the mission target 114 prior to a mission. The mission prior data 124 may be related to useful/relevant information about one or more target object 118. More specifically, the mission prior data 124 essentially contains one or more target object property 119 of the target objects 118.
It may be contemplated by a person ordinarily skilled in the art that the mission prior data 124 can be absolute (as in specific coordinates), relative (to other mission prior data 124), or structural (e.g. topology or proximity between the target objects 118). Further, the mission prior data 124 may be discrete, or probabilistic (in a sense of probability distribution of the target object property 119, or joint probability distribution of several target object properties 119). Moreover, the mission prior data 124 may be in a paper form or, preferably, in an electronic form. The mission prior data 124, in electronic form, may be stored locally or over the Intranet or Internet, and in any suitable storage medium like hard-drives, network drive, servers, discs, tapes, or any combination thereof. Additionally, the collection of the remote sensing data 122 and mission prior data 124 involves regular monitoring of the target object 118, and obtaining data about the target object property 119.
The analyzing module 130 is primarily a computing device having standard functional elements, such as, a processor, storage memory, flash memory, input means, output means, a set of programs, etc. In an embodiment, the analyzing module 130 includes a target recognition means 140 and observation rules 150 associated with observations 152.
The analyzing module 130 is configured to extract relevant target object property 119 from the remote sensing data 122 and associating the extracted target object property 119 with the corresponding target object 118. The analyzing module 130 is also configured to update the target objects 118 and the target object properties 119 using the obtained remote sensing data 122 and comparing that with existing mission prior data 124 of the corresponding target objects 118 and target object properties 119. The comparison may be achieved by using the already detected identity of the target objects 118 by the target recognition means 140.
The target recognition means 140 is configured to identify target object 118 of relevance to the mission target 114. In an example, target recognition means 126 may be configured for recognition of the target object property 119, associated with the target object 118, which is of relevance to the mission target 114. The recognition of the target object property 119 is a process of analyzing mission prior data 124 consisting of stored data related to target objects 118 and target object properties 119.
The observation rules 150 are basically in the form of a data structure, or the like. The observation rules 150 are specification of events in terms of target objects 118 and target object properties 119 which are associated with operative, strategic or business goals. For example, in case of PL networks the observation rules 150 may include a tree growing in close proximity to a power line conductor, and in turn may pose a threat to the network.
Further, the observations 152 are occurrences in target object properties 119 which match the observation rules 150. The analyzing module 130 is configured to associate the observations 152 with the observation rules 150 (rule instance, i.e. which rule was triggered), target objects 118 (one, or multiple) and their associated target object properties 119 matching the rule. The analyzing module 130 may be further configured to optionally provide timestamp, classification, priority, likelihood/confidence, etc. for the each observation 152.
According to an embodiment, the action module 160 includes action determination means 162 which is configured to select an action type 164. The action determination means 162 is configured to select an appropriate action type 164 for each observation 152. The action type 164 is a type of strategic, operational or business action which is targeted to manage one or more action resource 166 based on certain observation 152. The action resources 166 are any physical, operational, organizational means to perform the action type 164 with respect to certain target object(s) 118.
It may be contemplated by a person skilled in the art that the action resources 166 may be either identified physical entities (certain person or machine such as a guard, a field Engineer, a contractor, etc.) or logical (a service provided by a subcontractor). Further, in accordance with an embodiment, the action type 164 might be a call to security personnel to the location, an alarm to dispatch the field personnel in a specified area, reminder to initiate procurement of dispatch work for field maintenance, or the like.
In an embodiment, the action module 160 is associated with the action plan optimization means 170. The action plan optimization is the process of assigning one or more action resources 166 to execute an action type 164 with respect to some observation 152. The process may be continuous, one time, or discrete (e.g. hourly, weekly, monthly, yearly). The action plan optimization means 170 provides an action plan 172. The action plan 172 includes assigning action resources 166 for a set or subset of observations 152. In an example, the plan 172 includes selecting the appropriate subcontractor to perform field engineering based on their costs and availability. Further, the action plan 172 may contain additional attributes such as deadline, pricing/cost, supplementary information like images, media, free text, etc.
According to an embodiment of the present disclosure, the action plan optimization means 170 employs simple ranking procedure for preparing action plans 172 which can be used for network-level maintenance scheduling. In one embodiment, the action module 160 may generally rank those in the worst condition as the highest priority without regard to the return on the funds invested. The advantage of this method is its easy-to-use feature. However, the resulting funding allocation is not optimal. In another embodiment, the action module 160 may consider some type of measure of cost-effectiveness in the selection process, if the goal is to provide the best service for the available funds. It may be contemplated by a person skilled in the art that alternate allocation schemes could be found by employing other methods.
The record means 180 of mission control unit 110 is configured for maintaining action results 182 out of a certain action plan 172. The action results 182 are the results as reported by action resources 166 of the execution of the action plan 172 on the target objects 118. The action results 182 may be provided continuously, discretely (e.g., every one min, one month), or one time (after completion of the action plan 172). Further, action results 182 are expressed in terms of target object properties 119, for example, status=cut down (tree), or shape=new shape (of a tree). Moreover, the action results 182 may optionally contain supplementary information such as images, video, media, free text or the like.
The system 100 of the present disclosure is operable to perform the mission 112. The system 100 includes defining the mission target 114, which may be a task dispatched to one or multiple surveying modules 120 to remotely sense mission target 114 and to produce remote sensing data 122. Optionally, the system 100 is operable to process the remote sensing data 122 into target object properties 119. Further, the system 100 is operable to evaluate observation rules 152 for target object properties 119 of the mission target 114 and produce any matching observations 150. Moreover, the system 100 is operable to send the remote sensing data 122, target object properties 119 and/or observations 152 to parties (users such as electricity transmission and distribution operators) needing the data.
According to an embodiment of the present disclosure, the mission 112 could be to mitigate risks to power lines (PL) networks of a target site. In such a situation, the mission 112 includes corridor clearance analysis for the target site. Further, the mission target 114 would be to check current state of all components of the power network including all target objects 118 such as, trees and buildings near the network in the target site. The target object property 119 could be aspects like height, type, species, or size of the trees, buildings, poles for the power lines. Furthermore, the mission prior data 124 may for example be the approximate location of power line and its expected topology (in a sense of a graph). Further, in such instance, the observation rules 150 may include a tree growing in close proximity to a power line conductor, and in turn may pose a threat to the network, and observation 152 can be one or several occurrences needing vegetation management.
Further, in such a case, the action plan 172 could be vegetation management plans such as what to do immediately and what for example next year, taking in consideration the growth. Furthermore, the action type 164 can be undergrowth cutting, tree cutting, maintenance staff field observations, whereas action resources 166 can be for example trimming helicopter, contractors, and/or forest workers. Moreover, the action result 182 can be reported as, for example, results of the tree cutting.
In another exemplary embodiment, the system 100 of the present disclosure provides automatic selection of the mission target 114 based on quality of the mission prior data 124. It may be understood that often the mission prior data 124 is heterogeneous, that is, for some part the data is accurate and recent such that the current state of the target object 118 and target object property 119 can be forecasted; and for some parts the data 124 may be missing or it is likely to have changed. For example, the various possible scenarios could be: 1) the power line corridor has just been surveyed and data is up-to-date, 2) power line corridor was surveyed last year, and the vegetation growth can be forecasted accurately, say over period of one year, 3) part of the power line has never been surveyed, and there are no reliable data, 4) part of the power line is attached to an area where based on satellite imagery/SAR images, all the trees have been recently cut in a large area and which may have caused new or eliminated risks to the power line.
Based on embodiments, the system 100 is configured to analyze quality of the mission prior data 124. In case of finding data elements which need updating or checking, a mission is allocated to perform that update or check. The present system 100 automatically forms missions 112 based on the quality of the mission prior data 124 to perform missions where it is necessary to improve the quality of the data 124. Optionally the analysis is done continuously, and the mission is always selected which will best improve the quality of the mission prior data 124 or which provide the best cost/quality improvement ratio.
The system 100 is configured to estimate the needed time and resources for the task. If there are overlaps with other orders of the same resources, the resource allocation is done by prioritizing the missions. For example, if there is a power line that has been damaged, this could be allocated with higher priority than annual maintenance. Such service may overcome the problems with known project business models which take long time to start the work because of the various steps like plan, tender, select, order, execute, receive data, and accept. The process can take months before any results are received and changing the Mission parameters trigger complex renegotiation and planning process.
According to an embodiment of the present disclosure, the users of the system 100 can set a service level for the resource requests/mission targets. For example, users can set for the mission tag of “no urgency” this can be done when the mission control unit 110 or at least one surveying module 120 or service personnel is free or close by, or “very urgent” where the mission control unit 110 or at least one surveying module 120 or service personnel needs to be immediately dispatched. Users can also allocate funds for each mission. The allocated funds can be used by the system to determine which of the missions is carried out first.
As explained above, the system 100 is associated with managing target object property data to allocate resources and execute actions to maintain an infrastructure network. According to an embodiment of the present disclosure, the system 100, particularly, the analyzing module 130 may be configured for executing performance models, which are used to predict future conditions of the target object 118, and more particularly the target object property 119 associated with the target object 118. The performance models can be classified into two types: deterministic or probabilistic. In deterministic models, the future condition of an infrastructure network predicted as an exact value based on the past information collected about the facility (as explained in conjunction with the FIG. 2). In probabilistic models, the performance of an infrastructure network is predicted by estimating the probability with which the infrastructure network would change to a particular condition state, from a predefined set of possible facility conditions of the random process.
The probabilistic models are usually associated with discretization of the condition states. Moreover, the probabilistic models can also be used to describe the deterioration of the whole infrastructure network. In general, the deterioration process of an infrastructure network is a function of various factors affecting the mechanistic or electric characteristics of the infrastructure network, such as design, environment, materials, construction, age, and the degree of maintenance. This in turn may help to schedule maintenance activities for the considered infrastructure networks. The effectiveness of maintenance planning in infrastructure management depends on the accuracy of the predicted future condition (such as the target object property 119) of the infrastructure network, particularity the target objects 118. If the performance models used in determining the maintenance policies cannot effectively represent the actual deterioration process, the planned maintenance activities might not yield the expected results, which leads to suboptimal use of resources.
Referring now to FIG. 3, illustrated is a block diagram of a system 300 for practicing an embodiment of the present disclosure. According to an embodiment of the present disclosure, the system 300 is configured to be operable on the probabilistic performance model. For example, the system 300 is operable to manage (acquire, send, analyze and store) probability data of target objects (such as the target objects 118) associated with an infrastructure network. Further, the system 300 is operable to update probability data of target object property in a database. Moreover, based on the probability data of the target object property, the system 300 allocates resources and executes actions to maintain such infrastructure network.
The system 300 includes a processor (or server) 310 and a database 320 operatively connected to the server 310. The system 300 could be accessed over a communication network 330. In an example, the communication network 330 includes but not limited to Internet, Intranet, MAN, LAN, and WAN. The system further includes a web-enabled device 340 associated with the user, such as, a power line operator can for using the system 300. The user can use the system 300 via a remote method such as using a web interface with the help of the web-enabled device 340. Alternatively, the user use the system 300 through direct digital integration of the processor 310 and the customer's ERP (Enterprise Resource Planning) or similar system. The system 300 also includes a communication terminal 350 for a survey unit or module (such the surveying module 120) to send and collected data, i.e., the target object property data. The system 300 also includes a communication terminal 360 associated with action resources (such as the action resources 166) i.e. people that update status or data (such as the target object property data) over the communication network 330. In an example, the communication terminal 360 can include but not limited to a smart phone.
Based on embodiments, the system 300 is operated in a manner that information on the missions (such as the mission 112) and available resources (such as the action resources 166) is maintained in the database 320. Further, the database 320 stores probability data, a target object property data (such as the remote sensing data 122 and mission prior data 124 of the target objects) of plurality of target objects (such as a first target object and a second target object), a list of actions (such as the action type 164) and a cost of actions, and the like.
According to an embodiment of the present disclosure, the processor 310 includes an executable code configured to define a first probability of accuracy of a property object data of a first target object; define a second probability of the first target object having a negative effect on a second target object; calculate a combined probability from the first probability and the second probability; use the combined probability to select to which at least one of the target objects to allocate resources; and update the first probability and the second probability in the database, based on the action performed with the allocated resource.
It may be contemplated by a person ordinarily skilled in the art that the processor 310 can be a processor, of the analyzing module 130 of the system 100, containing the executable code (defined above) having intrusions to update the probability data of target object property in the database 320, such as a database of the analyzing module 130.
The first probability of accuracy of the property object data of the first target object includes a probability value i.e. correctness of available property object data with respect to the first target object. In an example, if we consider the first target object to be a tree and a property object data to be a height of the tree, in that case the accuracy of the property object data would depend on when the height of the tree was monitored last time. Therefore, the first probability of accuracy of the property object data of the first target object would be more if the property object data is new (i.e. the first target object is monitored recently, for example, with the help of the surveying module 120) as compared to old property object data. Further, the first probability of accuracy of the property object data of the first target object could be as high as 1 (if the property object data is very recent), otherwise the first probability of accuracy could be low as 0.5 (if the property object data is one year or two year old data) or even 0 (if the property object data is very old). According to an embodiment, the property object data of the first target object is selected from a group consisting of an identifier, spatial information, attribute information and structural information.
The second probability of the first target object having a negative effect on a second target object includes a probable value i.e. a threat level associated with the second target object that the first target object would have a negative effect on the second target. According to an embodiment, the negative effect includes the first target object causing damage or failure to the second target object. In an example, if we consider the second target object to be a power line (or pylori), in that case negative effect can be falling of the tree on the power line to break such power line.
In an example, the second probability of the first target object (such as the tree) having the negative effect on the second target object (such as the power line) would be more if the tree is located near to the power line (or about to touch the power line very soon) as compared to when the tree is located far. Further, the second probability of the first target object having the negative effect on the second target object could be as high as 1 (if the tree is located very near to the power line), otherwise the second probability could be low as 0 (if the tree is located far or located at a safer distance from the power line).
The system 300, as mentioned above, is operable to calculate a combined probability from the first probability and the second probability. According to embodiment of the present disclosure, the combined probability is calculated by multiplying the first probability with the second probability. According to embodiment of the present disclosure, the combined probability distribution is calculated by multiplying the first probability distribution with the second probability distribution.
The system 300, as mentioned above, is also operable to use the combined probability to select to which at least one of the target objects to allocate resources. It may be contemplated by a person ordinarily skilled in the art that the system 300 may be employed or executed with respect to a power line network having a plurality of first target objects (such as trees) and a plurality of second target objects (such as power lines or pylons). Therefore, the at least one of the target objects (to be allocated with resources) are those target objects (such as trees) for which the combined probability is high. For example, based on a high combined probability, trees belonging to a specific zone of a corridor of the power line network may need immediate attention as compared to other trees belonging to other zones and having low combined probability.
The system 300, as mentioned above, is further operable to update the first probability and the second probability in the database 320, based on the action performed with the allocated resource. According to an embodiment, the action is selected from the group consisting of measuring the property object data of the first target object, measuring the property object data of the second target object, performing action on the first target object, performing action on the second target object and allocating equipment for performing actions. As mention herein, the action of measuring the property object data of the first and second target objects, can include collecting property object data of the first and second target objects (for example, with the help of the surveying module 120). Further, performing action on the first and second target objects can relate to any maintenance or correction work performed to correct the functional and/or structural aspects of the target objects (for example, cutting the trees having high combined probability).
Once the action is performed on target objects with the allocated resource, the property object data of such target objects changes with such actions. Accordingly, the first and second probability of the target object changes with such actions. For example, when a first target object (such as the tree) is having a high probability of falling into a second target object (such as the power line) is cut recently, for example, last week. In such instance, the first probability of accuracy of the property object data of the first target object can be as high as 1 (since it is done recently). Further, the second probability of the first target object having the negative effect on the second target object becomes as low as 0. Therefore, such changes in the first probability and the second probability are updated in the database 320 with respect to the target objects.
In an embodiment, the system 300 is operable to allocate and handle continuous operations as a Mission Priority Queue (MPQ). As per the MPQ, users, such as power line operators can add their missions via the web-enabled device 340 based on the priority of the mission. The MPQ is made accessible over the communication network 330 directly to the users (for the part of their own Missions, authentication and identification of the customer is needed for the access). The MPQ is operated in the server 310 and dispatched to surveying module 120; or the MPQ may be distributed (over telecommunication) between central server 310 and all active surveying modules 120, where surveying modules 120 independently choose their missions from the shared MPQ.
Further, based on the MPQ the action plan optimization means 170 can help to optimize a mission (such as the mission 112) based on some predefined criteria, such as, cost of execution (proximity, logistics), value of the mission, cost of the mission, and opportunity cost combined. Further, optimization can include a set up for an auction of survey resources, say by, the customers willing to pay the highest price gets the service now; those looking at a lower price need to wait till capacity is available.
The benefits of such a system 300, as taught by the present disclosure includes fast response in case of value based prioritization criteria as the user such as a network infrastructure operator can get guaranteed immediate service by bidding the highest price to their mission. Further, the system 300 provides flexibility in changes, as continuous optimization process takes all changes into account immediately as they become available in the system, such as new, changed, or cancelled missions. Moreover, the system 300 provides improved asset utilization by employing schemes such as when there is no high priority tasks, low priority tasks could be taken at lower costs which would otherwise not realize due to preventive costs, and therefore results in better utilization of assets by survey operator.
In another exemplary embodiment, the system 300 of the present disclosure provides optimization of mission by setting priority queues. FIG. 4 shows a graphical representation of probability distribution of failure in power lines of various areas in an infrastructure network. Further, FIG. 4 illustrates an exemplary probability distribution of failure in power lines for a mission 112 required to measure all power lines in areas A, B and C (constituting an infrastructure network). Therefore, the various priority queues (sequence of attending to the areas A, B and C) can include sequences such as ABC, ACB, BAC, BCA, CAB and CBA.
According to an embodiment of the present disclosure, the processor 310 is operable to determine the priority order (or priority queues) in a manner that the combined cost of probable failure of the plurality of target objects with respect to the priority order is minimized.
In an example, the database 320 stores information pertaining to probability of failure of the target objects and cost of failure of the target objects. The processor 310 is operable to calculate a combined cost of probable failure of the plurality of target objects as a function of at least one of: the probability of failure, the cost of failure, and/or a time required by the resources to perform an action. The processor 310 is thereafter operable to select, from the plurality of target objects, at least one target object to which to allocate the resources, based on the combined cost of probable failure.
Further, the processor 310 is operable to calculate the combined cost of probable failure by summing individual costs of probable failure of the plurality of target objects as a function of time. According to an embodiment, the processor 310 is operable to calculate an individual cost of probable failure for a given target object by using following equation.
C(1−P̂T)
wherein, C is the cost of failure of the given target object;
P is a probability of non-failure of the given target object in a time unit; and
T is the time (in time units) required by the resources to perform an action on the given target object.
In an example, assuming number of infrastructure segments (particularly target objects associated with the segments) in the area A as 1, the area B as 2 and in the area C as 3. Further, measurement time/verification time per segment is assumed to be one week per segment. Therefore, total time required to check (or to perform action) the entire network (constituted by the areas A, B and C) is 1+2+3=6 weeks. Further, defining, for example, cost of failure per segment in area A is 10 units, in area B is 20 units and in area C is 5 units. Moreover, defining, for example, probabilities of failure in a week in segments in area A, B and C are 1%, 3% and 10%, respectively. Therefore, the probabilities of non-failure would be 0.99, 0.97 and 0.9 with respect to the probabilities of respective failure 1%, 3% and 10%. Example of a segment is a power line between points X and Y i.e. if there is a failure in the said segment between X and Y electricity can not be distributed thru the segment (i.e the electricity network or grid can be considered to consist of multiple connected segments).
Based on conventional methodology, a mission would be implemented in round robin manner i.e., area A would be measured first, followed by area B and further followed by area C. In that case, based on the above equation probable cost failure associated with the areas A, B and C can be calculated as:
Cost A=10×(1−0.99̂1)=0.1 units
Cost B=20×(1−0.97̂3)=1.74 units
Cost C=5×(1−0.9̂6)=2.34 units
Therefore, the combined cost of probable failure=4.2 units
However, based on mission optimization, as employed by the present system 300, the order of work will be optimized, for example, by changing order of maintenance work to be area B, area A and then area C. In that case, based on the above equation probable cost failure associated with the areas A, B and C can be calculated as:
Cost A=10×(1−0.99̂3)=0.58 units
Cost B=20×(1−0.97̂2)=0.29 units
Cost C=5×(1−0.9̂6)=2.34 units
Therefore, the combined cost of probable failure=3.8 units, which is lower than the first case where no optimization of mission was employed.
The system 300 of the present disclosure enables to set cost of failure for each of the segments and also possible profits for each of the segments. Said information can be used to select which of the segments will be measured/surveyed first in the order of the minimum total cost. Further, in case there are observations 152, the system 300 provides which segment needs to be managed through which action type 164. In an example, the action type 164 includes the tree cutting crew to be sent to the site to cater to an immediate threat to the PL, when the observations become available, without the need to wait for all results. The said cost model can be also used to determine pricing for the resource allocation of mobile platforms 128 or other mission related resources.
In another aspect, the system 300 of the present disclosure is further configured for selecting which of missions should be performed or should be allocated resources. For example the system can be configured to determine purchase price (from network owners respective) or selling price (from perspective of vendor performing missions) related to purchasing/selling of resources to missions. The determination of the purchase and/or selling price can be based on quality of mission data and can be optimized based on the probability of failure in each of the segments. The probability of failure is initially assumed to have arbitrary value such as 1%. The probability is adjusted each time the missions are performed. For example, based on embodiments, probability is decreased if corridor clearing analysis demonstrates that likelihood of having trees interfering with the power line is low, otherwise the probability is increased.
Further, if there are areas where last check has been made a long time ago, the probability of failure can be set to increase as function of time since the vegetation grows and environment can change. Further, the parameters of this function can be based on prior measurements gathered from the same area i.e. empirical growth model in the locality. The probability of failure is construed as a function of time from last measurement of the probability where the probability increases over time. Therefore, the present system 300 enables to provide improved model to define the probability of failure based on the mission data.
Referring now to FIG. 5, illustrated is a method 500 for updating probability data of target object property in a database, in accordance with an embodiment of the present disclosure.
At step 502, a first probability of accuracy of a property object data of a first target object is defined. In an example, the property object data of the first target object is selected from a group consisting of an identifier, spatial information, attribute information and structural information.
At step 504, a second probability of the first target object having a negative effect on a second target object is defined. In an example, the negative effect includes the first target object causing damage or failure to the second target object.
At step 506, a combined probability is calculated using the first probability and the second probability. In an example, the combined probability is calculated by multiplying the first probability with the second probability.
At step 508, the combined probability is used to select to which at least one of the target objects is to allocate resources.
At step 510, the allocated resources is used to perform an action on the selected at least one of the target objects. In an example, the action is selected from the group consisting of measuring the property object data of the first target object, measuring the property object data of the second target object, performing action on the first target object, performing action on the second target object, and allocating equipment for performing actions. In an embodiment, the action is prioritized by using combined probability and cost of non-action.
At step 512, based on the action the first probability and the second probability are updated in the database.
The steps 502 to 512 are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.
It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting of the scope of the disclosure. Expressions such as “including”, “comprising”, “incorporating”, “consisting of”, “have”, “is” used to describe the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural.
While certain exemplary embodiments have been described and shown in the accompanying drawings, it is to be understood that such embodiments are merely illustrative of and not restrictive on the broad present disclosure, and that this present disclosure is not limited to the specific constructions and arrangements shown and described, since various other modifications and/or adaptations may occur to those of ordinary skill in the art. It is to be understood that individual features shown or described for one embodiment may be combined with individual features shown or described for another embodiment.

Claims

1. A method for updating probability data of target object property in a database, comprising the steps of

defining a first probability of accuracy of a property object data of a first target object;

defining a second probability of the first target object having a negative effect on a second target object;

using the first probability and the second probability to calculate a combined probability;

using the combined probability to select to which at least one of the target objects to allocate resources;

using the allocated resources to perform an action on the selected at least one of the target objects; and

updating the first probability and the second probability in the database, based on the action.

2. A method of claim 1 wherein the property object data of the first target object is selected from a group consisting of an identifier, spatial information, attribute information and structural information.

3. A method of claim 1 wherein the negative effect is the first target object causing damage or failure to the second target object.

4. A method of claim 1 wherein the combined probability is calculated by multiplying the first probability with the second probability.

5. A method of claim 1 wherein the action is selected from the group consisting of measuring the property object data of the first target object, measuring the property object data of the second target object, performing action on the first target object, performing action on the second target object, and allocating equipment for performing actions.

6. A method of claim 5 where the action is prioritized by using combined probability and cost of non-action.

7. A system for updating probability data of target object property, the system comprising

a database for storing probability data, a property object data of a first target object, a property object data of a second target object, a list of actions and a cost of actions; and

a processor having an executable code configured to

define a first probability of accuracy of a property object data of a first target object;

define a second probability of the first target object having a negative effect on a second target object;

calculate a combined probability from the first probability and the second probability;

use the combined probability to select to which at least one of the target objects to allocate resources; and

update the first probability and the second probability in the database, based on the action performed with the allocated resource.

8. A system of claim 7 wherein the property object data of the first target object is selected from a group consisting of an identifier, spatial information, attribute information and structural information.

9. A system of claim 7 wherein the negative effect is the first target object causing damage or failure to the second target object.

10. A system of claim 7 wherein the combined probability is calculated by multiplying the first probability with the second probability.

11. A system of claim 7 wherein the action is selected from the group consisting of measuring the property object data of the first target object, measuring the property object data of the second target object, performing action on the first target object, performing action on the second target object, and allocating equipment for performing actions.