A method and system for matching a power infrastructure model to measured data

Abstract

A method and system for matching a power infrastructure model to measured data is disclosed. The method comprises receiving a power infrastructure model comprising a plurality of model points indicating estimated positions of entities of the power infrastructure. The method further comprises receiving measured data comprising a plurality of measured points indicating actual positions of entities of the power infrastructure. The method further comprises determining at least one matching pair of points between the plurality of model points and the plurality of measured points. The method further comprises generating a revised model by deforming the power infrastructure model based on the at least one matching pair of points. The system includes one or more processors and a computer readable medium for performing the method. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

Description

A METHOD AND SYSTEM FOR MATCHING A POWER INFRASTRUCTURE MODEL TO MEASURED DATAFIELD

[0001] This disclosure relates to methods and systems for matching a power infrastructure model to measured data. More particularly, the disclosure relates to a method and system for generating a revised model by deforming the power infrastructure model based on at least one matching pair of points, the at least one matching pair of points determined from a plurality of model points and a plurality of measured points. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.BACKGROUND

[0002] There is a general and ongoing need for managing the transmission and distribution of power, in particular electric power, over large networks and areas. To manage such power networks, a model of the power network may be used to represent the positions of entities of the power infrastructure, such as utility poles, transformers, transmission towers, houses, power stations, substations, etc. As well as representing the positions of the entities of the power infrastructure, such models may also include data concerning the characteristics of one or more of the entities, for example voltage values associated with specific power lines, transformers and such like. In this way, infrastructure models play a critical role in enabling the structure, nature, and location of power infrastructure entities to be recorded. However, such models may be inaccurate due to errors in data recordal and / or may become inaccurate overtime, for example as power infrastructure is moved, updated, or replaced over time. Such inaccuracies in the model may, for example, arise due to the construction of new infrastructure nearby (e.g., houses, buildings, and roads), deterioration from age, or may be caused by environmental weathering. An inaccurate representation of the power infrastructure can make it challenging to determine how maintenance is to be carried out, how best to allocate human resources, and how to estimate running, maintenance, and / or repair costs. As such, it would be advantageous to provide methods and systems which address one or more of the above-described problems, for example by improving the accuracy of power infrastructure models in an efficient manner.SUMMARY

[0003] The present disclosure addresses challenges associated with matching a power infrastructure model to measured data.

[0004] According to a first aspect of the present disclosure, there is provided a computer-implemented method for matching a power infrastructure model to measured data. The method comprises receiving a power infrastructure model comprising a plurality of model points indicating estimated positions of entities of the power infrastructure. The method further comprises receiving measured data comprising a plurality of measured points indicating actual positions of entities of the power infrastructure. The method further comprises determining at least one matching pair of points between the plurality of model points and theplurality of measured points. The method further comprises generating a revised model by deforming the power infrastructure model based on the at least one matching pair of points.

[0005] By performing these steps, a power infrastructure model that better reflects the actual positions of entities of the power infrastructure is provided. In this way, accuracy of the power infrastructure model is improved thereby enabling better monitoring, maintenance, and management of the underlying power infrastructure. Specifically, a more accurate power infrastructure model enables better planning, cost estimation, and allocation of human resources, thereby improving efficiency in maintaining the power infrastructure and reducing cost.

[0006] In some implementations, the method comprises determining the at least one matching pair of points based on user input. For example, a selection of one of the plurality of model points and a selection of one of the plurality of measured points may be received, and these points may then be considered to be matching.

[0007] Incorporating user input in determining matching pairs of points enables a human’s innate pattern recognition ability to be utilised to determine matching pairs of points between the model and the real-world data. Furthermore, a trained user, who may have prior experience or knowledge of the power infrastructure network, can determine matching pairs of points more accurately. Using user input to determine the at least one matching pairs of points enables the process of matching the power infrastructure model to the real- world data to be improved.

[0008] In some implementations, in addition or alternatively to relying on user input, the method comprises determining the at least one matching pair of points automatically based on determining a measure of similarity between points of the plurality of model points and points of the plurality of measured points. In some implementations, the measure of similarity is a feature distance between points of the plurality of model points and points of the plurality of measured points. The feature distance may be a distance or an angle between two points. In some implementations, the distance is a Euclidean, Manhattan, cosine, or Procrustes distance. In some implementations, the angle is a Procrustes angle. In some implementations, the feature distance is a combination of one or more types of distances and / or one or more types of angles.

[0009] Determining the matching pairs of points based on feature distance can reduce the time and human resources required for matching points manually, especially when there are a large number of model points and / or measured points to consider. Further, by automating the matching process, the method avoids human error or bias, leading to a more consistent and objective determination of the matching pair of points which is used in the generation of the revised model.

[0010] In some implementations, the method comprises designating a pair of points as being a matching pair based on determining that the feature distance between the pair of points is within a threshold distance. In other words, the method effectively determines that two points are sufficiently similar so as to be determined to be matching points.

[0011] Designating pairs as matching based on a threshold distance provides an efficient way of automatically determining matching points by quickly filtering out unlikely matching pairs (i.e., points that have a low similarity and are therefore unlikely to represent a matching pair). Using a threshold distance also allows the strictness of the determination of matching pairs to be varied because adjusting the threshold distance controls the feature distances for which points are designated as matching pairs. The threshold distance can be used to control the number of matching pairs of points which are determined.

[0012] In some implementations, determining the at least one matching pair of points comprises: identifying a plurality of candidate matching pairs; evaluating each candidate matching pair by: translating and / or rotating, whilst maintaining the relative position between the plurality of model points, the plurality of model points relative to the plurality of measured points such that the points of the candidate matching pair are co-incident; and determining a fitness score of the candidate matching pair based on how closely the remaining model points fit to the remaining measured points; and discarding any candidate matching pairs where the fitness score falls below a threshold value.

[0013] Determining the at least one matching pair of points in this way enables better determination of matching pairs. In particular, a plurality of candidate matching pairs is identified first, which is a looser requirement enabling more candidate matching pairs to be considered initially. Then, the identified candidate matching pairs are evaluated, which is a stricter requirement, and dissimilar matching pairs are discarded. In this way, these steps avoid inadvertently discarding matching pairs which have been assigned correctly as an incorrect match at an early stage. As a result, a larger number and more accurate set of matching pairs is determined. Moreover, by discarding candidate matching pairs that are outliers where the fitness score falls below a threshold value or based on the largest set of consistent translation and rotation matches, the accuracy of the resulting generated revised power infrastructure model is improved.

[0014] In some implementations, deforming the power infrastructure model comprises: for each matching pair of points, applying a translation to the paired model point such that it is co-incident with its paired measured point.

[0015] By deforming the power infrastructure model in this way, the model point is translated to its actual position, thereby making the model more accurate.

[0016] In some implementations, deforming the power infrastructure model comprises: for each nonpaired model point, applying an equal translation as applied to the nearest paired model point.

[0017] Applying an equal translation to non-paired model points as applied to the nearest paired model point enables nearby model points which do not belong to a matching pair to be translated similarly to their respective nearby matching pair. In this way, non-paired model points are moved to a likely position based on the known position of nearby paired model points of matching pairs. The accuracy of the power infrastructure model is thereby improved because corrections to the model are applied to neighbouring points as well as matched points.

[0018] In some implementations, deforming the power infrastructure model comprises: for each nonpaired model point, applying a proportional translation based on the proximity of the non-paired model point to the nearest paired model point. The proximity can be based on a feature distance, e.g., Euclidean distance or other graph distance measure.

[0019] Applying a proportional translation in this way ensures that the model deformation is sensitive to the spatial distribution of the measured data, which can be particularly beneficial in complex or irregularly shaped infrastructure systems where uniform adjustments may not be appropriate. As a result, improved model accuracy is provided by taking into account how close the non-paired model point is to the paired model point for the translation.

[0020] In some implementations, deforming the power infrastructure model comprises: for each nonpaired model point, applying an aggregated proportional translation based on the proximity of the nonpaired model point to a plurality of paired model points.

[0021] Applying an aggregated proportional translation based on a plurality of paired model points provides a more comprehensive deformation that takes into account the influence of how close the model point is to several paired model points, leading to a more refined and accurate power infrastructure model.

[0022] In some implementations, the method comprises determining a fitness score of the revised model using a combinatorial optimisation algorithm. In some implementations, the combinatorial optimisation algorithm is the Hungarian algorithm or a variant thereof (e.g., Kuhn-Munkres algorithm or Munkres assignment algorithm).

[0023] Using a combinatorial optimisation algorithm enables the determination of how well the model points fits to the measured data following the deformation of the model. Using the Hungarian algorithm is particularly advantageous, as it is efficient, well-established, and can handle unbalanced assignment problems.

[0024] In some implementations, the method comprises iteratively generating an updated revised model, wherein iteratively generating the updated revised model comprises, at each iteration, updating at least one matching pair of points based on user input. In some implementations, the method comprises iteratively generating an updated revised model until the determined fitness score of the revised model falls within a fitness threshold. In a further implementation, the method comprises alternatively or additionally iteratively generating an updated revised model until a user input is received which indicates that the process may be ended.

[0025] By iteratively generating an updated revised model the accuracy of the power infrastructure model is improved by progressively refining the model. Specifically, by updating at least one matching pair of points based on user input, the user can efficiently adjust or correct the generated model at each iteration to improve the fit of the power infrastructure model to the measured data.

[0026] In some implementations, the method further comprises displaying the revised model on a display.

[0027] Displaying the revised model on a display enables the power infrastructure model to be visualised, enabling the power infrastructure model to be interpreted and interacted with more easily by a user.

[0028] According to another aspect of the present disclosure, there is provided a system comprising: one or more processors; and a computer readable medium comprising instructions that, when executed by the one or more processors, cause the system to perform the method of the first aspect.

[0029] According to a further aspect, there is provided a computer readable medium comprising instructions that, when executed by one or more processors, cause the system to perform the method of the first aspect.

[0030] According to yet a further aspect, there is provided a computer program product comprising instructions that, when executed by one or more processors, cause the one or more processors to perform the method of the first aspect.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be provided by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary implementations of the disclosure and are therefore not to be considered to be limiting of its scope, the principles herein are described and explainedwith additional specificity and detail by way of example to illustrate aspects of the disclosure and with reference to the accompanying drawings, in which:

[0032] Figure 1 shows an example computer program for matching an infrastructure model with measured data, according to the present disclosure.

[0033] Figure 2 shows a flowchart of a method for matching a power infrastructure model to measured data, according to the present disclosure.

[0034] Figure 3 shows a plurality of model points of an example power infrastructure model and an example plurality of measured points, according to the present disclosure.

[0035] Figure 4 shows a first example of deforming the power infrastructure model of Figure 3, according to the present disclosure.

[0036] Figure 5 shows a second example of deforming the power infrastructure model of Figure 3, according to the present disclosure.

[0037] Figure 6 shows a third example of deforming the power infrastructure model of Figure 3, according to the present disclosure.

[0038] Figure 7 shows a block diagram of a computer system for performing the method, according to the present disclosure.

[0039] Throughout the description and the drawings, like reference numerals refer to like features.DETAILED DESCRIPTION

[0040] The following is a description of certain embodiments of the invention, given by way of example only and with reference to the drawings.

[0041] Various implementations of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. A reference to an implementation in the present disclosure can be a reference to the same implementation or any other implementation. Such references thus relate to at least one of the implementations herein.

[0042] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various implementations given in this specification.

[0043] Turning to Figure 1 an example computer program for matching an infrastructure model with measured data is shown. Specifically, the computer program shows a top-down view of a powerinfrastructure model comprising a plurality of model points 102, together with satellite imagery of the power infrastructure’s surroundings. The model points 102 represent estimated positions of entities of the power infrastructure. The power infrastructure model can be a 2D and / or 3D representation of the position of entities of the power infrastructure. In addition to model points 102, the model may further include one or more of voltage information, imagery of the area in which the power infrastructure is situated, and information relating to the surrounding environment (e.g., position of nearby trees, forests, terrain, and of the power infrastructure, optionally represented as a point cloud). The voltage information, imagery, and other information relating to the surrounding environment may be overlayed and / or displayed with the plurality of model points 102 on a display. The voltage information may be received from the provider of the power infrastructure, such as a network operator. The imagery may be provided by image or video captured by a camera mounted to a drone or from a third-party satellite imagery database. The computer program comprises a computer interface 106, which can be interacted with by a user to perform the methods discussed herein. In some implementations, a user can interact with the computer interface 106 to toggle between the original and deformed (revised) version of the power infrastructure model to see the changes and assess the quality of the match.

[0044] In this manner, by providing information relating to the position of entities of the power infrastructure and associated data such as voltage information, the model enables improved monitoring, maintenance, and planning of the power infrastructure. For example, point clouds of the surrounding environment may be used to identify if a tree is close to a power line to determine whether intervention is needed. In another example, voltage information of a power infrastructure entity can be used to determine how it is to be maintained and how maintenance is to be carried out.

[0045] Also shown in Figure 1 is received data comprising a plurality of measured points 104. The measured points 104 represent actual positions of entities of the power infrastructure, as determined for example by a LiDAR scan. As is shown in Figure 1 , due to inaccuracies in the model, the positions of model points 102 do not precisely match the positions of measured points 104. The systems and methods of the present disclosure provide mechanisms to update the model (and thus the position of model points 102) to correct this inaccuracy. These mechanisms will now be described in detail with reference to Figures 2 to 6.

[0046] Turning now to Figure 2, a method 200 for matching a power infrastructure model to measured data is shown. At step 202, the method comprises receiving a power infrastructure model comprising a plurality of model points 102 indicating estimated positions of entities of the power infrastructure. At step 204, the method comprises receiving measured data comprising a plurality of measured points 104 indicating actual positions of entities of the power infrastructure. At step 206, the method comprises determining at least one matching pair of points between the plurality of model points 102 and the plurality of measured points 104. At step 208, the method comprises generating a revised model by deforming the power infrastructure model based on the at least one matching pair of points. In some implementations, at step 210, the method further comprises determining a fitness score of the revised model using a combinatorial optimisation algorithm. In some implementations, at step 212, the method further comprises iteratively generating an updated revised model (e.g., until the determined fitness score of the revised model falls within a fitness threshold), wherein iteratively generating the updated revised model comprises, at each iteration, updating at least one matching pair of points, e.g., based on user input. Each step of method 200 will now be described in more detail.

[0047] The method begins at step 202 with receiving a power infrastructure model comprising a plurality of model points 102 indicating estimated positions of entities of the power infrastructure. In some implementations, the method comprises (directly) receiving the plurality of model points 102 instead of receiving the power infrastructure model. Entities of the power infrastructure may refer to any asset that is part of or connected to the power network. For example, this may include utility poles, transformers, transmission towers, houses, power stations, substations, etc. At this stage, the position of the plurality of model points 102 represents estimated positions of entities of a power infrastructure. For example, these estimated positions may be based on a previous survey of the power infrastructure. As noted above, however, these models may be inaccurate. Power infrastructure may be moved, updated, or replaced over time. For example, the construction of new infrastructure (e.g., houses, buildings, and roads), deterioration due to age or weathering, or natural disasters may necessitate the power infrastructure to be relocated or modified. The remaining steps of method 200 are directed at addressing these potential inaccuracies in the positions of the model points received at step 202.

[0048] In some implementations, instead of “estimated positions” of entities of a power infrastructure, they may be referred to as “model positions” or simply “positions” of entities of a power infrastructure. In some implementations, there may be a one-to-one mapping between the plurality of model points 102 and the entities of the power infrastructure (i.e., there is one particular model point which corresponds to a particular entity, and that particular entity corresponds to that one particular model point).

[0049] From time to time it may be desired to update the model and / or check for inaccuracies. Therefore, data may be obtained indicating the actual position of entities. Accordingly, at step 204, the method comprises receiving measured data comprising a plurality of measured points 104 indicating actual positions of entities of the power infrastructure. The measured data may be obtained using a LiDAR sensor, e.g., mounted to a drone. The plurality of measured points 104 are referred to as actual positions of entities of a power infrastructure, because they indicate the actual measured, i.e., “ground truth”, position of the entities of the power infrastructure in the real world. In some implementations, there may be a many to one mapping between the plurality of measured points 104 and the entities of the power infrastructure (i.e., there is a plurality of measured points 104 which corresponds to a particular entity, and that particular entity corresponds to the plurality of measured points 104). In some implementations, there may be a one-to-one mapping between the plurality of measured points 104 and the entities of the power infrastructure (i.e., there is one particular measured point which corresponds to a particular entity / particular model point, and that particular entity corresponds to that one particular measured point / particular model point).

[0050] Now that data indicating the actual positions of entities of the power infrastructure has been obtained (at step 204), the accuracy of the model (obtained at step 202) can be assessed and improved. More specifically, points in the model can be paired up to their corresponding points in the measured data (at step 206) and the model can be revised or deformed as necessary (at step 208) such that the model position data accurately matches the measured position data. In this context, “deforming” means updating the position of one or more model points 102 of the model relative to other model points 102 of the model.

[0051] With this in mind, at step 206, the method comprises determining at least one matching pair of points between the plurality of model points 102 and the plurality of measured points 104. The matching pair of points (i.e., matching pair) refers to a set of two points, one point from the plurality of model points 102 and one point from the plurality of measured points 104, which have been determined to correspond to the same entity of the power infrastructure. The model point of a matching pair of points may be referredto as a paired model point. Similarly, the measured point of a matching pair of points may be referred to as a paired measured point. Model points 102 and measured points 104 which are not part of a matching pair of points may be respectively referred to as non-paired model points and non-paired measured points.

[0052] In some implementations, the at least one matching pair of points is determined based on user input. For example, a user can select one model point of the plurality of model points 102 and select one measured point of the plurality of measured points 104 and designate it as a matching pair.

[0053] In some implementations, the at least one matching pair of points may be alternatively or additionally determined (automatically) based on determining a measure of similarity, e.g., a feature distance, between points of the plurality of model points 102 and points of the plurality of measured points 104. By considering the feature distance, model points 102 and measured points 104 which are featurewise similar to each other can be (automatically) matched together as they likely correspond to the same entity of the power infrastructure.

[0054] In some implementations, determining the at least one matching pair of points based on determining a feature distance comprises determining that the feature distance between the pair of points is within a threshold distance. That is, a pair of points may be designated as being a matching pair based on determining that the feature distance between the pair of points is within a threshold distance. The threshold distance, discussed further below, may also be considered as a threshold similarity or threshold difference. Incorporating a threshold enables matching points to be determined objectively and more efficiently. In particular, in addition to using a threshold distance to determine the at least one matching pair of points, it can also be used to remove outliers. In some implementations, a nearest neighbour approach may be used instead of a threshold, e.g., matching the measured point to a model point that has the closest / smallest feature distance. In some implementations, a combined approach which uses both the threshold and the nearest neighbour approach is used. For example, a nearest neighbour approach may be used first to identify potential matching pairs, then they may be filtered using a threshold to remove matching pairs which have a feature distance that exceeds a threshold feature distance.

[0055] The feature distance discussed herein may be a distance or an angle between two points. In some implementations, the distance is a Euclidean, Manhattan, cosine, or Procrustes distance. The Procrustes distance involves considering a distance value after performing a Procrustes superimposition, which involves optimally translating, rotating, reflecting, and / or uniformly scaling the model points 102 to superimpose it with the plurality of measured points 104. Advantageously, the Procrustes distance takes into account potential inaccuracies between the power infrastructure model and measured data. The Procrustes superimposition for determining the Procrustes distance may be a partial Procrustes superimposition in which the scale is not changed. In some implementations, the model points 102 and measured points 104 can be considered subgraphs of points. In that case, the angle may be a Procrustes angle, i.e., an angle between the two subgraphs (model points 102 and measured points 104) after performing the Procrustes superimposition. The angle between the two subgraphs (model points 102 and measured points 104) is the angle which yields the optimal rotation that minimises the difference between the two subgraphs (while the Procrustes distance reflects the difference in shape of these two subgraphs). In some implementations, a combination of one or more types of distances and / or one or more types of angles may be used. For example, an aggregate distance which takes into account both the Euclidean distance and Procrustes angle can be used, where the distance contributes to 70% of the aggregate distance value (with the angle contributing to the remaining 30%). In some implementations, the featuredistance is additionally or alternatively based on a geodesic distance or a graph distance, e.g., shortest graph distance.

[0056] Referring to Figure 3, a plurality of model points 102 of an example power infrastructure model and an example plurality of measured points 104 is shown. The plurality of model points 102 in this example comprises model points 302, 304, 306, 308, 310, 312, and 314 which indicate the position of entities of the example power infrastructure. The plurality of measured points 104 in this example comprises measured points 402, 404, 406, 408, 410, 412, and 414, which indicate the actual position of entities of the example power infrastructure. In this example, the number of model points and measured points are the same, however, in other implementations there may be more measured points than model points. In some implementations, there may be fewer model points than measured points (e.g., if model points are missing from the client data).

[0057] For the purpose of aiding understanding, matching pairs (the at least one matching pair of points) have been visualised in Figure 3 using lines 502, 504, 506, 508, and 510 drawn between model points 302, 306, 308, and 314 and measured points 402, 406, 408, and 414. Specifically, the following combinations of model points and measured points are matching pairs: [302, 402], [306, 406], [308, 406], [308, 408], [314, 414], As discussed herein, the at least one matching pairs of points may be determined in a number of ways. In this example, matching pair [314, 414] is determined based on user input by a user providing input which indicates that model point 314 and measured point 414 correspond to the same entity of the power infrastructure. Meanwhile, matching pair [302, 402], is determined (automatically) based on determining that the feature distance, in this case Euclidean distance, between the two points are within a threshold distance (not shown). Matching pairs [306, 406], [308, 406], and [308, 408] are identified as a plurality of candidate matching pairs, however after evaluation of these candidate matching pairs, it is determined that the candidate matching pair [308, 406] is an outlier. This is because rotating and translating the model points relative to the measured points (whilst maintaining the relative position between the plurality of model points) such that model point 308 and measured point 406 are co-incident is inconsistent with the translation and rotations from all other matching pairs. As such, matching pair [308, 406] is discarded. This process of evaluating candidate pairs of matching points and discarding outliers will be described in further detail below.

[0058] In some implementations, alternatively or additionally to determining the at least matching pair of points based on user input and / or feature distance, determining the at least one matching pair of points comprises the following steps. First, identifying a plurality of candidate matching pairs. Next, evaluating each candidate matching pair by: translating and / or rotating, whilst maintaining the relative position between the plurality of model points 102, the plurality of model points 102 relative to the plurality of measured points 104 such that the points of the candidate matching pair are co-incident. Evaluating each candidate matching pair (to determine whether the rotation and translation is consistent with the remaining matched pairs translation and rotation) comprises determining a fitness score of the candidate matching pair based on how closely the remaining model points fit to the remaining measured points; and discarding any candidate matching pairs where the fitness score falls below a threshold value. In some implementations, instead or in addition to discarding candidate matching pairs based on fitness score, candidate matching pairs are discarded by selecting the translation and rotation which results in the largest set of matches, and filtering out (removing) the candidate matching pairs that do not match. In some implementations, in addition or alternatively to translating and / or rotating, transformations such as a dilationor reflection are used. In some implementations, the plurality of candidate matching pairs is identified using a threshold distance and / or nearest neighbour approach as previously discussed. The determination of the fitness score is discussed below.

[0059] The fitness score is determined based on how closely the remaining model points (i.e., all model points excluding the paired model point of the candidate matching pair being evaluated) fit to the remaining measured points (i.e., all measured points excluding the paired measured point of the candidate matching pair being evaluated). In some implementations, the remaining model points and the remaining measured points include the paired model point and paired measured point of the candidate matching pair being evaluated. In some implementations, as discussed herein in relation to determining the at least one matching pair, a threshold or a nearest neighbour approach may be used to pair / match the remaining model points to the remaining measured points. In some implementations, the fitness score may be calculated by determining the feature distance between the remaining model points and the remaining measured points (as so paired); and determining an aggregate feature distance or average feature distance. The aggregate feature distance may be determined by summing the feature distance between the remaining model points and the remaining measured points (as so paired), and the average feature distance may be determined by dividing the aggregate feature distance by the number of pairs.

[0060] In some implementations, determining the fitness score alternatively or additionally uses a combinatorial optimisation algorithm. That is, the remaining model points may be paired / matched with the remaining measured points using a combinatorial optimisation algorithm. A combinatorial optimisation algorithm enables the remaining model points to match with the remaining measured points efficiently by using the algorithm to determine a match which maximises or minimises a cost or cost function. In some implementations, the combinatorial optimisation algorithm is optimised to minimise a feature distance, as discussed herein. The feature distance which is minimised by the combinational optimisation algorithm may be an aggregate feature distance or an average feature distance (as previously mentioned). In some implementations, the combinatorial optimisation algorithm used is the Hungarian algorithm (or a variant of the Hungarian algorithm), i.e., Kuhn-Munkres algorithm or Munkres assignment algorithm.

[0061] In some implementations, instead of identifying a plurality of candidate matching pairs and evaluating each candidate matching pair, a plurality of candidate sets each comprising two matching pairs are identified and evaluated. That is, the determining may instead involve evaluating each candidate set (which comprises two matching pairs) by translating, rotating, dilating, and / or reflecting while maintaining the relative position between the plurality of model points 102, the plurality of model points 102 relative to the plurality of measured points 104 such that the points of the candidate set (which comprises two matching pairs) are respectively co-incident. Additionally, the evaluating may instead comprise determining a fitness score of the candidate set based on how closely the remaining model points fit to the remaining measured points. Finally, the determining may instead comprise discarding any candidate sets where the fitness score falls below a threshold value. Using two matching pairs enables the evaluation to be more efficient, as translation, rotation, dilation, and / or reflection of the plurality of model points 102 is more restrictive given that two matching pairs need to be co-incident rather than just one matching pair. In this way, less transformations and therefore fitness scores need to be considered, thereby reducing time and processing resources requirements to determine the at least one matching pair of points. The complexity of the algorithm is polynomial in the number of model points 102 and measured points 104. Additionally, the accuracy of the matching between the model points and the measured points is improved. In someimplementations, a candidate set of more than two matching pairs is considered, which can further decrease processing time and computational requirements. The plurality of candidate sets may be identified using a threshold distance and / or using nearest neighbour approach as previously discussed.

[0062] As discussed, in some implementations, there may be a many to one mapping between the plurality of measured points 104 and the plurality of model points 102. In some implementations, the paired measured point of a first matching pair may also be the paired measured point in another matching pair. In some implementations, the paired measured point of a first matching pair is not the paired measured point of another matching pair.

[0063] In some implementations, to determine the at least one matching pair of points, instead of considering matching pairs from all of the plurality of model points 102 and all of the plurality of measured points 104, a subset of model points and / or subset of measured points are considered. Reducing the number of points being considered, especially when there are a large number of points, reduces the processing time and computational requirements.

[0064] Now that certain points have been designated as matching points, a mechanism for further improving the accuracy of the model based on this determination will be outlined. In particular, and turning back to the method of Figure 2, at step 208, the method comprises generating a revised model by deforming the power infrastructure model based on the at least one matching pair of points. This can involve adjusting the position, e.g., translating, the paired model points 302, 306, 308, 314 and / or the non-paired model points 304, 310, 312. In some implementations, deforming the power infrastructure model comprises: for each matching pair of points, applying a translation to the paired model point 302, 306, 308, 314 such that it is co-incident with its paired measured point 402, 406, 408, 414. By applying a translation to the paired model point 302, 306, 308, 314 such that it overlaps with its paired measured point 402, 406, 408, 414, the position of the model point, which corresponds to an entity of the power infrastructure model is corrected to be the position of the measured point, which corresponds to the actual position of the entity of the power infrastructure model. This deformation is described in more detail in relation to Figure 4 below.

[0065] Referring to Figure 4, a first example of deforming the power infrastructure model of Figure 3 is shown. In this example, for each matching pair of points [302, 402], [306, 406], [308, 408], [314, 414], a translation is applied to the paired model point 302, 306, 308, 314 such that it is co-incident with its paired measured point 402, 406, 408, 414. As shown in Figure 4, the model points 302, 306, 308, and 314 of the matching pairs of points [302, 402], [306, 406], [308, 408], [314, 414] are respectively translated to be coincident with their corresponding measured points 402, 406, 408, and 414. These translations are represented in Figure 4 as translation vectors, 602, 604, 606, and 608, in which the direction of the arrow shows the direction of the translation, and the length of the arrow shows the magnitude / distance of the translation.

[0066] In some implementations, deforming the power infrastructure model based on the at least one matching pair of points comprises adjusting (e.g., translating) one or more non-paired model points 304, 310, 312 based on the deformation made to the nearest paired model point 302, 306, 308, 314 (of a matching pair). By deforming the non-paired model points 304, 310, 312 in this way, the model points (and therefore the overall power infrastructure model) may be matched more accurately with the measured points by also translating nearby neighbouring points according to the closest matching pair. In some implementations, deforming the power infrastructure model based on the nearest paired model point 302, 306, 308, 314 comprises: for each non-paired model point, applying an equal translation as applied to thenearest paired model point. That is, the same translation is applied to the non-paired model point 304, 310, 312 as the translation which translates the nearest paired model point 302, 306, 308, 314 of a matching pair to its corresponding paired measured point 402, 406, 408, 414. This deformation of non-paired model points 304, 310, 312 is described in more detail in relation to Figure 5 below. In some implementations, instead of applying an equal translation as applied to the paired model point, the translation is proportional to the proximity of the non-paired model point 304, 310, 312 to the nearest paired model point 302, 306, 308, 314. That is, the translation may be scaled depending on how close the non-paired model point 304, 310, 312 is to the nearest paired model point 302, 306, 308, 314. This proportional deformation is described in more detail in relation to Figure 5 below. In some implementations, a feature distance as previously discussed is used to determine how close the non-paired model point 304, 310, 312 is to the nearest paired model point.

[0067] Referring to Figure 5, a second example of deforming the power infrastructure model of Figure 3 is shown comprising adjusting one or more non-paired model points 304, 310, 312 based on the nearest paired model point 302, 306, 308, 314 of a matching pair. Specifically, non-paired model points 304 and 310 are translated by applying an equal translation as applied to the nearest paired model point. In this case, non-paired model point 304 is translated by applying the same translation vector 602 as applied to paired model point 302 as non-paired model point 304 is closest to paired model point 302. Whereas, nonpaired model point 310 is translated by translation vector 606, as non-paired model point 310 is closest to paired model point 308. Meanwhile, for non-paired model point 312, instead of applying an equal translation as applied to nearest paired model point 314, the translation is proportional to the proximity of non-paired model point 312 to nearest paired model point 314. Specifically, the translation is scaled such that the magnitude of the translation decreases the further non-paired model point 312 is to nearest paired model point 314. As shown in Figure 5, non-paired model point 312 is translated by translation vector 610 which is scaled down compared to translation vector 608 which is applied to nearest paired model point 314, due to the distance between non-paired model point 312 and nearest paired model point 314. It is noted that translation vector 610, while having a smaller magnitude, has the same direction as translation vector 608.

[0068] In some implementations, alternatively or in addition to the aforementioned methods of deforming the power infrastructure model, deforming the power infrastructure model comprises adjusting (e.g., translating) one or more non-paired model points 304, 310, 312 based on a nearest plurality of paired model points 302, 306, 308, 314 (of a plurality of matching pairs). Specifically, in some implementations, deforming the power infrastructure model comprises: for each non-paired model point, applying an aggregated proportional translation based on the proximity of the non-paired model point 304, 310, 312 to a plurality of paired model points 302, 306, 308, 314. By applying an aggregated proportional translation to the nonpaired model point, the accuracy of the adjustment of the non-paired model point 304, 310, 312 is further improved as it takes into account both a plurality matching pairs and the distance to the plurality of matching pairs. In some implementations, the non-paired model point 304, 310, 312 is translated and scaled (as previously described) based on its feature distance to each paired model point of the nearest plurality of paired model points 302, 306, 308, 314. In some implementations, to determine the translation of the nonpaired model point 304, 310, 312 based on the nearest plurality of paired model points 302, 306, 308, 314, an overall translation is determined which takes the average of the translation applied to each respective paired model point 302, 306, 308, 314 (which makes it co-incident with its respective paired measured point 402, 406, 408, 414), as well as accounts for the feature distance of the non-paired model point 304, 310,312 to each of the nearest plurality of paired model points 302, 306, 308, 314. The nearest plurality of paired model points 302, 306, 308, 314 can be determined using a threshold or a nearest neighbour approach as previously described. This aggregate proportional deformation is described in more detail in relation to Figure 6 below.

[0069] Referring to Figure 6, a third example of deforming the power infrastructure model of Figure 3 is shown by adjusting one or more non-paired model points 304, 310, 312 based on a nearest plurality of paired model points 302, 306, 308, 314 of a plurality of matching pairs. Non-paired model point 312 is translated in the same way as in Figure 5, as the only nearby paired model point is paired model point 314. However, for non-paired model point 304 an overall translation vector 612 is determined by taking the average of the translation vectors 602 and 604 (which correspond to paired model points 302 and 306 respectively). This is because non-paired point 304 is nearby to paired model points 302 and 306. Specifically, to arrive at translation vector 612, the direction is determined by adding translation vectors 602 and 604 together via vector addition, and the magnitude determined by taking the average of the lengths of translation vectors 602 and 604. A similar approach is taken for non-paired model point 310, as it is close to both paired model points 306 and 308. It is noted that, in this example, the determination of whether the non-paired points 304, 310, 312 is nearby to paired model points 302, 306, 308, 314 is based on the position of the paired model points 302, 306, 308, 314 after they have been translated to be co-incident with their corresponding paired measured point 402, 406, 408, 414 (i.e., the position of paired model points 302, 306, 308, 314 after performing translations 602, 604, 606, and 608 in Figure 4).

[0070] That is, determining the proximity of the non-paired model point to the paired model point in any of the methods discussed herein may be based on the original position of the paired model point. However, in some implementations, it may be based on its translated position, e.g., after the paired model point 302, 306, 308, 314 has been translated from its original position to be co-incident with its corresponding paired measured point 402, 406, 408, 414.

[0071] It is also noted that not all non-paired model points need to be adjusted based on the paired model point(s). In some implementations, non-paired model points of the plurality of model points 102 which are not close to the paired model points can be left in their original position.

[0072] As has just been described, the disclosed methods and systems provide a mechanism for matching model data to measured data so as to improve the accuracy of the model. Optionally, further steps may be performed to further increase the accuracy of the model, as will now be described. In particular, and returning to Figure 2, in some implementations the method further comprises, at step 210, determining a fitness score of the revised model using a combinatorial optimisation algorithm. This provides a measure of how well the revised (or deformed) model now matches the measured data, providing an objective measure of how successful the initial deformation has been.

[0073] The fitness score can be determined in the same way as how the fitness score is determined for a candidate matching pair (or candidate sets comprising two or more matching pairs) when determining the at least one matching pair of points, described above. That is, the fitness score in step 210 may be determined for the revised model by matching all model points to all measured points (after the power infrastructure is deformed in step 208) using a combinatorial optimisation algorithm. In some implementations, this may involve matching only the remaining non-paired model points to the remaining non-paired measured points (as the matching pairs of points are already matched). In some implementations, the combinatorial optimisation algorithm is optimised to minimise a feature distance, thefeature distance as discussed herein. The feature distance which is minimised may be an aggregate feature distance or average feature distance of the matches, as previously discussed. In some implementations, a metric which incorporates both the Euclidean distance and feature distance is considered, which minimises the feature distance for the matching pairs of points, and minimises the Euclidean distance between the remaining non-paired model points and the remaining non-paired measured points. In some implementations, the combinatorial optimisation algorithm used is the Hungarian algorithm (or a variant of the Hungarian algorithm), i.e., Kuhn-Munkres algorithm or Munkres assignment algorithm. By determining the fitness score of the revised model, an overall score for how well the model points and the measured points match with each other after applying the deformation to the power infrastructure model (specifically to the plurality of model points 102) can be evaluated.

[0074] A further optional step of the method may involve iteratively updating the model, so as to further improve its accuracy. In particular, and referring again to Figure 2, in some implementations the method further comprises iteratively generating, at step 212, an updated revised model (e.g., until the determined fitness score of the revised model falls within a fitness threshold), wherein iteratively generating the updated revised model comprises, at each iteration, updating at least one matching pair of points based on user input. Specifically, a user may identify one or more incorrect matches at each iteration and provide as input the correct match as a matching pair of the at least one matching pair of points. That is, an incorrect matching pair of points may be modified or deleted in response to receiving user input of the correct matching pair. The correct matching pair may be added to the at least one matching pair of points or replace the entire set of the at least one matching pair of points previously identified (e.g., in the previous iteration). Replacing the entire set of the at least matching pair points with the matching pair of points identified by user input in the present iteration enables computational and processing requirements to be reduced. This is because there are fewer matching pairs of points to consider for the deformation in step 208. By iteratively generating an updated revised model in this way, the fit of the model points to the measured points can be improved and refined. In some implementations, instead or in addition to receiving user input to update the at least one matching pair of points, the revised model in the previous iteration may be input as the power infrastructure model in the next iteration. In this way, steps 202 - 210 may be carried out again on the revised power infrastructure model to further refine the fit between the power infrastructure model and the measured data. The fitness threshold may be set such that iteration stops when the model points closely matches with the measured points. In some implementations, the fitness threshold additionally or alternatively stops when a number of iteration cycles are performed.

[0075] The above detailed description describes a variety of exemplary mechanisms for improving the accuracy of a power infrastructure model. However, the described arrangements and methods are merely exemplary, and it will be appreciated by a person skilled in the art that various modifications can be made without departing from the scope of the appended claims. Some of these modifications will now be briefly described, however this list of modifications is not to be considered as exhaustive, and other modifications will be apparent to a person skilled in the art.

[0076] In the above examples, the power infrastructure model is matched to the measured data based on points representing entities of the power infrastructure. Alternatively or additionally, in some implementations the power infrastructure model is matched to the measured data using bays. A bay is an electrical line / wire between two entities of the power infrastructure. Bays are matched if the two points that are connected by the bays are matched. Information for how the entities may be connected to each other,i.e., bay data, can be provided from an information source such as the provider of the power infrastructure. The bay data may be part of the power infrastructure model that is received in step 202 of Figure 2. The way in which the at least one matching pair of points may be determined, as discussed in relation to step 206 of Figure 2, may equally apply when matching the power infrastructure model to the measured data using bays. In particular, instead of matching a plurality of model points 102 to a plurality of measured points, a plurality of bays (a set of two model points connected according to bay data) may be mapped to a plurality of measured points. That is, determining the at least one matching pair of points would instead involve determining the at least one matching pair of bays. When determining the at least one matching pair of bays based on a feature distance, using a threshold or nearest neighbour approach (as discussed herein), a feature distance between either or both model points of the bay may be considered when being compared to the plurality of measured points. When determining the at least one matching pair of points comprises identifying a plurality of candidate matching pairs, evaluating each candidate matching pair, and discarding any candidate matching pairs where the fitness score falls below a threshold value, this may be determined for bays in the same way as candidate sets comprising two matching pairs, as discussed herein. The steps 208, 210, and, 212 of Figure 2 may be performed in the same way as described herein by considering each model point of the bay as an individual (paired) model point. Using bay data enables improved matching as it takes into account the connected nature of two points at a time (which form a bay) in the matching process.

[0077] In some implementations, the power infrastructure model is matched to the measured data using spans. A span is a group of bays. Spans may group bays based on voltage level, but can be alternatively defined by the client in other grouping arrangements. Spans are matched if the entire group of bays (i.e., electrical lines) is matched. Information for how bays may be grouped together as spans, i.e., span data, can be provided from an information source such as the provider of the power infrastructure. In some implementations, span data comprises bay data. The span data may be part of the power infrastructure model that is received in step 202. The way in which the at least one matching pair of points may be determined, as discussed in relation to step 206, may equally apply when matching the power infrastructure model to the measured data using spans. In particular, instead of matching a plurality of model points 102 to a plurality of measured points, a plurality of spans may be mapped to a plurality of measured points. That is, determining the at least one matching pair of points would instead involve determining the at least one matching pair of spans. When determining the at least one matching pair of spans based on a feature distance, using a threshold or nearest neighbour approach (as discussed herein), a feature distance between any or all model points of the span may be considered when being compared to the plurality of measured points. When determining the at least one matching pair of points comprises identifying a plurality of candidate matching pairs, evaluating each candidate matching pair, and discarding any candidate matching pairs where the fitness score falls below a threshold value, this may be determined for spans in the same way as candidate sets comprising two or more matching pairs, as discussed herein. The steps 208, 210, and, 212 of Figure 2 may be performed in the same way as described herein by considering each model point of the span as an individual (paired) model point. Using span data enables further improved matching as it takes into account the connected nature of bays, which are lines between two points, for the matching process.

[0078] The described methods may be computer implemented, i.e. may be implemented using computer executable instructions. In particular, a computer apparatus may implement the method of Figure 2 byexecuting instructions stored on a computer readable medium. A computer interface can be used to perform the methods discussed herein, such as computer interface 106 of Figure 1. Figure 7 shows a block diagram of one implementation of a computing device 700 within which a set of instructions, for causing the computing device to perform any one or more of the methodologies discussed herein, may be executed. In alternative implementations, the computing device may be connected (e.g., networked) to other machines in a Local Area Network (LAN), an intranet, an extranet, or the Internet. The computing device may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The computing device may be a personal computer (PC), a tablet computer, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0079] Further, while only a single computing device is illustrated, the term “computing device” shall also be taken to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Each computing device may have the structure shown in Fig. 7. Alternatively, a plurality of processors within a single computing device, such as computing device 700, can perform the independent computations.

[0080] The example computing device 700 includes a processor 702, a main memory 704 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 706 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., a data storage device 718), which communicate with each other via a bus 730.

[0081] Processor 702 represents one or more general-purpose processors such as a microprocessor, central processing unit, or the like. More particularly, the processor 702 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 702 may also be one or more special-purpose processors such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. Processor 702 is configured to execute the processing logic (instructions 722) for performing the operations and steps discussed herein.

[0082] The computing device 700 may further include a network interface device 708. The computing device 700 also may include a video display unit 710 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 712 (e.g., a keyboard or touchscreen), a cursor control device 714 (e.g., a mouse or touchscreen), and an audio device 716 (e.g., a speaker). The infrastructure model can be displayed on a display, e.g., video display unit 710.

[0083] It will be apparent that some features of computer device 700 shown in Fig. 7 may be absent. For example, one or more computing devices 700 may have no need for display device 710 (or any associated adapters). This may be the case, for example, for particular server-side computer apparatuses 700 which are used only for their processing capabilities and do not need to display information to users. Similarly, user input device 712 may not be required. In its simplest form, computing device 700 comprises processor 702 and memory 704.

[0084] The data storage device 718 may include one or more machine-readable storage media (or more specifically one or more non-transitory computer-readable storage media) 728 on which is stored one ormore sets of instructions 722 embodying any one or more of the methodologies or functions described herein. The instructions 722 may also reside, completely or at least partially, within the main memory 704 and / or within the processor 702 during execution thereof by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable storage media.

[0085] The various methods described above may be implemented by a computer program. The computer program may include computer code arranged to instruct a computer to perform the functions of one or more of the various methods described above. The computer program and / or the code for performing such methods may be provided to an apparatus, such as a computer, on one or more computer readable media or, more generally, a computer program product. The computer readable media may be transitory or non- transitory. The one or more computer readable media could be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or a propagation medium for data transmission, for example for downloading the code over the Internet. Alternatively, the one or more computer readable media could take the form of one or more physical computer readable media such as semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disc, and an optical disk, such as a CD-ROM, CD-R / W or DVD.

[0086] In an implementation, the modules, components and other features described herein can be implemented as discrete components or integrated in the functionality of hardware components such as ASICS, FPGAs, DSPs or similar devices.

[0087] A “hardware component” is a tangible (e.g., non-transitory) physical component (e.g., a set of one or more processors) capable of performing certain operations and may be configured or arranged in a certain physical manner. A hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be or include a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations.

[0088] Accordingly, the phrase “hardware component” should be understood to encompass a tangible entity that may be physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein.

[0089] In addition, the modules and components can be implemented as firmware or functional circuitry within hardware devices. Further, the modules and components can be implemented in any combination of hardware devices and software components, or only in software (e.g., code stored or otherwise embodied in a machine-readable medium or in a transmission medium).

[0090] It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other implementations will be apparent to those of skill in the art upon reading and understanding the above description. Although the present disclosure has been described with reference to specific example implementations, it will be recognized that the disclosure is not limited to the implementations described but can be practiced with modification and alteration within the spirit and scope of the appended claims. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than a restrictive sense. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0091] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist, only some of which have been mentioned above. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.

Claims

CLAIMS1. A computer-implemented method for matching a power infrastructure model to measured data, comprising: receiving a power infrastructure model comprising a plurality of model points indicating estimated positions of entities of the power infrastructure ; receiving measured data comprising a plurality of measured points indicating actual positions of entities of the power infrastructure; determining at least one matching pair of points between the plurality of model points and the plurality of measured points; and generating a revised model by deforming the power infrastructure model based on the at least one matching pair of points.

2. The computer-implemented method of claim 1 , wherein the at least one matching pair of points is determined based on user input.

3. The computer-implemented method of claim 1 , wherein the at least one matching pair of points is determined automatically based on determining a feature distance between points of the plurality of model points and points of the plurality of measured points.

4. The computer-implemented method of claim 3, wherein a pair of points is designated as being a matching pair based on determining that the feature distance between the pair of points is within a threshold distance.

5. The computer-implemented method of any of claims 1 to 4, wherein determining the at least one matching pair of points comprises: identifying a plurality of candidate matching pairs; evaluating each candidate matching pair by: translating and / or rotating, whilst maintaining the relative position between the plurality of model points, the plurality of model points relative to the plurality of measured points such that the points of the candidate matching pair are co-incident; and determining a fitness score of the candidate matching pair based on how closely the remaining model points fit to the remaining measured points; and discarding any candidate matching pairs where the fitness score falls below a threshold value.

6. The computer-implemented method of claims 1 to 5, wherein deforming the power infra-structure model comprises: for each matching pair of points, applying a translation to the paired model point such that it is coincident with its paired measured point.

7. The computer-implemented method of claim 6, wherein deforming the power infrastructure model comprises:for each non-paired model point, applying an equal translation as applied to the nearest paired model point.

8. The computer-implemented method of claim 6, wherein deforming the power infrastructure model comprises: for each non-paired model point, applying a proportional translation based on the proximity of the non-paired model point to the nearest paired model point.

9. The computer-implemented method of claim 6 or claim 8, wherein deforming the power infrastructure model comprises: for each non-paired model point, applying an aggregated proportional translation based on the proximity of the non-paired model point to a plurality of paired model points.

10. The computer-implemented method of any of claims 1 to 9, wherein the method further comprises determining a fitness score of the revised model using a combinatorial optimisation algorithm.11 . The computer-implemented method of claim 10, wherein the combinatorial optimisation algorithm is the Hungarian algorithm.

12. The computer-implemented method of claim 10 or claim 11 , wherein the method further comprises: iteratively generating an updated revised model, wherein iteratively generating the updated revised model comprises, at each iteration, updating at least one matching pair of points based on user input.

13. The computer-implemented method of any of claims 1 to 12, wherein the method further comprises displaying the revised model on a display.

14. A system comprising: one or more processors; a computer readable medium comprising instructions that, when executed by the one or more processors, cause the system to perform the method of any of claims 1 to 13.

15. A computer readable medium comprising instructions that, when executed by one or more processors, cause the system to perform the method of any of claims 1 to 13.

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