Mining system

Abstract

A method of mining, including: performing a mining activity on a mine site with a first asset; determining a correlation between position information of the first asset and different operating states thereof; using the correlation to calculate a current operating state of the first asset from position information thereof; and prescribing instructions to the first asset in response to its current operating state.

Description

MINING SYSTEMTECHNICAL FIELD

[0001] The present invention relates to a method and system for mining and more specifically, but not exclusively, to a method and system for calculating an operating state of an asset that is performing a mining activity on a mine site.BACKGROUND

[0002] Materials transport is one of the most important aspects in the mining industry. Because of the scale at which such activities are undertaken, even small improvements in operating efficiency may result in significant savings in operating costs associated with both open-pit and underground mining.

[0003] Within a mining context, materials transportation typically involves the use of assets picking up loads of ore from a pick-up point and transporting said ore to a drop-off point. Such assets include unmanned, autonomous and semi-autonomous vehicles including trucks. The assets are typically controlled by a central controller such as a Fleet Management System (FMS).

[0004] When assigning tasks or activities for vehicles to carry out on the mine site, the central controller is typically aware of the current operating state of each vehicle. For example, the controller may be aware that a truck is currently on-route to a destination and / or waiting or otherwise queuing at said destination. Many existing FMS are reliant on interfacing directly with the vehicle to determine its operating state. Such integration is not without its drawbacks, with communication protocols often being inherently specific to particular types and / or makes of vehicle.

[0005] Against this backdrop, "universal" systems have been developed that utilize strict rules to infer the operating state of vehicles of different type based on instrument and / or sensor values associated with the vehicle. Such systems, however, are prone to misidentifying the operating state of a vehicle due to an over reliance on accurate instrument and / or sensor values, where, for example, the impact of lag on telemetry data may result in the receipt of instrument and / or sensor vales that may arrive out and / or no longer reflect the current operating state of the vehicle.

[0006] The applicant has determined that it would be advantageous to provide an improved system and method of mining, whereby the operating state of an asset can be identified based on position information associated with said asset.

[0007] The present invention has been conceived with the above in mind, and seeks to at least in part alleviate the above-identified problems or to provide the public with a useful choice.SUMMARY

[0008] In a first aspect, the invention provides a method of mining, including: performing a mining activity on a mine site with a first asset; determining a correlation between position information of the first asset and different operating states thereof; using the correlation to calculate a current operating state of the first asset from position information thereof; and prescribing instructions to the first asset in response to its current operating state.

[0009] The current operating state is the calculated current operating state, such that, the method includes the step of prescribing instructions to the first asset in response to its calculated current operating state. The operating state of the first asset may be calculated by comparing probabilities associated with the asset being in each of the operating states at the given time.

[0010] In some embodiments, the method may include receiving position information from other assets on the mine site. The operating state of the first asset may be calculated at least in part by evaluating a proximity of the first asset with respect to other assets on the mine site. The position information of the first and / or other assets on the mine site may comprise instantaneous position data or transient position data. The position information may comprise a sequence of position data. The step of determining a correlation may comprise identifying trends within the sequence.

[0011] The method may include fitting a vehicle awareness module to the first asset, with the vehicle awareness module providing the position information thereof. The method may also include measuring a heading and / or geospatial position of the first asset with the vehicle awareness module, with the position information of a respective asset comprising at least one of the heading and geospatial position thereof.

[0012] In some embodiments, the method may include identifying that the first asset has completed its current mining activity based on a change in the current operating state.

[0013] The method may include reading instrument data from the first asset, with the correlation being determined at least in part by relating the position information of the first asset with instrument data from which the operational state of the first asset can be inferred. The method may also include detecting an error condition of the first asset by comparing the calculated operating state with an actual operational state inferred from the instrument data.

[0014] In some embodiments, the method may include tuning the correlation in substantially real time via machine learning algorithms.

[0015] In a second aspect, the invention provides a mining system, comprising: a first asset adapted to perform a mining activity on a mine site; a sensor configured to provide position information of the first asset; and a controller adapted to determine a correlation between the position information of the first asset and different operating states thereof and to calculate a current operating state from position information of the first asset.

[0016] The controller may prescribe instructions to the first asset in response to its current operating state. The current operating state is the calculated current operating state, such that, the method may include the step of prescribing instructions to the first asset in response to its calculated current operating state.

[0017] In some embodiments, the first asset may be one of a plurality of assets of the mine site, with the controller using the position information to evaluate a proximity of the first asset with respect to other assets on the mine site. The position information may comprise a sequence of position data, with the controller being configured to identify or otherwise detect trends within the sequence. In addition, the controller may receive position information from the other assets. The position information may comprise heading and / or geospatial position information.

[0018] The first asset may include at least one instrument configured to provide instrument data to the controller, with the controller using the instrument data to infer an actual operating state of the first asset. The controller may be configured to detect an error condition of the first asset by comparing the calculated current operating state with the actual operational state inferred from the instrument data. The controller may use machine learning algorithms to update the correlation in real time.

[0019] In a third aspect, the invention provides a vehicle awareness module for use with the mining system or method a described herein, with the module comprising a sensor configured to provide position information of an asset to which the module is fitted.

[0020] The sensor may be provided in the form of a high precision GPS unit.

[0021] In a fourth aspect, the invention provides an autonomous mining vehicle having the vehicle awareness module as described herein fitted thereto.

[0022] In a fifth aspect, the invention provides a method of mining, including: performing mining actives on a mine site with a plurality of assets; formulating a set of relationships to correlate asset movements that correlate asset movements and / or interactions with asset operating states; and using the set of relationships to detect an operating state of an asset.BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The invention will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:Figure 1 is a schematic representation of a mining system deployed at a mine site in accordance with an embodiment of the invention;Figure 2 illustrates a vehicle awareness module in accordance with another embodiment of the invention;Figure 3 is a flowsheet illustrating a method of mining in accordance with another embodiment of the invention; andFigure 4 is a flowsheet illustrating a method of mining in accordance with another embodiment of the invention.DETAILED DESCRIPTION

[0024] In the following detailed description, reference is made to accompanying drawings which form a part of the detailed description. It will be readily understood that the aspects of the present disclosure, as generally described herein and illustrated in the drawings may be arranged, substituted, combined, separated and designed in a wide variety of different configurations, all of which are contemplated in this disclosure.

[0025] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, a limited number of the example methods and materials are described herein.

[0026] In the following passages, the invention will be described within a mining context - i.e. on a mine site. It is understood, however, that the invention is not limited to mining per se, and the system and / or method described herein may have application for use in other industries such as, for example, oil and gas extraction and refinement work sites and large scale building and construction work sites.

[0027] The term "mine site" as used herein is understood to include (a) a site where material is mined and thereafter transferred to a stockpile and / or processed and (b) a site where material that has previously been mined and stockpiled is stored and / or transferred to and processed. Example sites include both underground mine sites and above-ground or "open-pit" mine sites.

[0028] The term "mined material" as used herein may refer to both metalliferous and non- metalliferous materials. Iron, gold and copper containing ores are examples of such metalliferous materials. Coal is an example of a non-metalliferous material. The mined material may be material that has been mined in surface (i.e. open cut) or underground operations.

[0029] The term "asset" as used herein is understood to refer to vehicles, equipment, machinery and the like that are used to carry out an activity on a work site. Examples of such equipment include (i) load units such as bulldozers, diggers, loaders and shovels and (ii) materials handling units such as haul trucks and other types of vehicle. Such assets are generally moveable, so as to be capable of being moved between different locations or regions within the mine site. This being said, non-moveable or substantially static machines are also included within the scope of this term. It is understood that such assets may be manned- that is require manual operation and / or be controlled by an operator in direct proximity thereof- and / or autonomous- that is not requiring direct control by an operator.

[0030] The term "mining activity" refers, generally, to processes and activities carried out by an asset that result in or at least contribute to the movement of mined material from a source to a drop-off location or stockpile. Such activities include, for example excavating, digging, and transporting mined material. With respect to the transport of mined material, it is understood thateach such activity involves the movement of material from (i) a source location or region on the mine site (typically near or proximate a load unit) and (ii) a drop off point to which mined material is provided. The term "drop-off as used herein encompasses any machine or processing site which may process and / or store mined ore. Example drop-off points include crushers, waste dumps and stockpiles.

[0031] In general terms, an embodiment of the invention as shown in the Figures relates to a system 10 that is adapted to be deployed or otherwise installed at a mine site 12. The system comprises a plurality of assets 14 that are configured to perform mining activities on the mine site 12. The plurality of assets 14 includes at least a first asset 16 and a second asset 18. The first asset 16 includes a sensor 20. The sensor 20 is configured to send position information 22 of the first asset 16 to a controller 24. The controller 24 uses historical position information 22 received from the sensor 20 over time to determine a correlation between the position of the first asset 16 within the mine site 12 and different operating states of the first asset 16. Upon receipt of new position information 22 form the first asset 16, the controller 24 is configured to calculate or otherwise deduce a current operating state of the first asset 16. Optionally, the controller 24 may then prescribe instructions to the first asset 16 in an instructing message or telegram 26, in response to the current operating state thereof.

[0032] A problem with conventional rules based evaluation is that it is rigid in that it is reliant on evaluating instrument values based on a set of hard coded rules that are manually configured. For example, typical FMS systems may determine an operating state of an asset using the following, sequential, rule set: if < the first asset 16 is within 10 meters of a second asset 18> and <the second asset 18 status is loading> then <the first asset 16 is being loaded by the second asset 18> is true. It can become problematic when the instrument values from the first and second assets 16, 18 out of sync or arrive at the controller 24 at different times. In addition, the often harsh and unforgiving operating environments of a mine site can lead to unreliability of the instruments of the first and / or second assets, leading to failure of the evaluation. For example, tip switches may fail to be detected sporadically, whilst payload, wheel speed and steering data may be delayed in arriving at the controller 24. Furthermore, the hard-coded values used to assess the instrument values may be difficult to determine or, at times, may not a good choice to capture all instances. A FMS is reliant on accurate state detection of the assets under its authority - each time it makes the wrong decision based on faulty state data, an operator will have to monitor and change an instruction / asset assignment manually, significantly impacting productivity. To this end, use ofexisting FMS by the applicant suggest that up to 10% of an FMS operator's working day may be spent addressing such state detection errors.

[0033] Advantageously, the mining system 10 of the proposed invention uses a machine learning based approach evaluation rather than a rules based evaluation to calculate the "most likely" operating state of the first asset 16. The difference with machine learning is that the controller 24 uses probabilistic results and is less reliant on multiple sources of instrument data. For example, the controller 24 may calculate an operating state of the first asset 16 using the following process: Given that <first asset 16 is travelling toward the second asset 18> and <first asset 16 has stopped at a distance X from the second asset 18> then the operating state of the first asset is most probably 'queueing'. The expectation is that by being less 'hard-coded' and fixed in the evaluation (as opposed to the aforementioned rules-based approach), the system 10 will be more robust to bad data (at least in part because it relies on less data sources in total) and can be self-tuned rather than manually tuned.

[0034] Referring now to Figure 1, which shows an embodiment of the mining system 10 deployed at a representative mine site 12.

[0035] As shown, a plurality of assets 14 are deployed at mine site 12. The plurality of assets 14 include first asset 16. In the example shown, the first asset is provided in the form of a materials handling unit, or, more particularly, a haul truck 16a that is adapted to transport payloads of mined material within the mine site 12. The plurality of assets 14 also includes second asset 18. In the example shown, the second asset is provided in the form of a load unit, or, more particularly, a digger 18a that is adapted to excavate or otherwise mine material. It is understood that the plurality of assets 14, including first asset 16 and second asset 18, may include other forms of asset adapted to perform different mining activities, for example other forms of materials handling units such as trains and conveyors, other forms of load units such as excavators and buckets, and processing equipment such crushers, sorters and the like.

[0036] Each of the assets 14 is in communication with a controller 24. The controller 24 is a fleet management system that is configured to provide instructions and / or assignments to the respective assets 14. For example, the controller 24 may "assign" a number of materials handling vehicles such as haul truck 16a to serve a respective load unit such as digger 18a. Once assigned to a respective load unit, the materials handling vehicle may transport material mined or otherwise sourced by the load unit to a destination, such as a material stockpile or processing equipment suchas a crusher. As shown, the first asset (i.e. haul truck 16a) 16 is assigned to the second asset (i.e. digger 18a) 18. The assets 14 are in wireless communication with the controller 24. What is meant by this is that each of the assets 14 sends position information 22 to the controller 24, via a message telegram. The position information 22 comprises location data of the asset and / or heading data. For example, the position information 22 may provide a geospatial location data of the respective asset on the mine site using the global positioning system GPS. Alternatively or additionally, the position information 22 may provide proximity data that describes a relative distance between a respective assets. Alternatively or additionally, the position information 22 may provide heading data of a respective asset, for example a direction that a haul truck is currently facing. The position information 22 may be sent to and received by the controller 24 in substantially "real time" or at otherwise set intervals. Alternatively, triggers may be used to define when position information 22 is sent from a respective asset - for example upon completion of a current assignment or mining activity. Notably, the position information 22 may comprise instantaneous data - such as, for example, the location of the assets 14 at a particular moment in time - and / or transient position data - such as, for example, a path or sequence of moves taken by the assets 14 within a recent time period.

[0037] The controller 24 is configured to receive the position information 22 from the respective assets 14. In operation, the controller 24 correlates the position information 22 against a known or actual operating state of the respective assets 14 to develop a correlation therebetween, such that the "most likely" operating state of an asset may be determined in the future from position information only. The known or actual operating state of a respective asset 14 may be determined based on interrogation of instrument data 28 received from values of one or more instrument(s) 30 of the asset. For example, the controller 24 may interrogate a "tip switch sensor" 30a of the haul truck 16a to determine when the haul truck 16a is in a "dumping" or "loading" state, and / or use changes in measured payload values (measured, for example, via suspension loads of the vehicle or other onboard load sensors) to determine such operating states.

[0038] Over time, the controller 24 is able to map a relationship between the actual operating state of an asset (inferred from the instrument data 28) and the position information 22. For example, with reference to the embodiment shown in the Figure, the controller 24 may determine that there is a strong likelihood that haul truck 16a is being loaded by digger 18a if its location data indicates that it is within a certain radius or proximity of the digger 18a (a location of which may be determined from position information 22'), or if its location data indicates that the truck 16a has performed a sequence of moves within the vicinity of the digger 18a over a recent time period.Accordingly, in the absence of or in preference to instrument data of the first asset 16, the controller 24 may use the position information 22 to calculate the operating state thereof. For example, the controller 24 may assess that there is a 90% probability that the haul truck 16a is being loaded by digger 18a when it is within a ten meter proximity of the digger 18a and having a westerly heading, with only a 10% probability that the truck 16a is merely travelling past or otherwise bypassing the digger 18a. Over time, the correlation between the position information and the operating state of the respective assets can be tuned automatically, such that the controller 24 is able to "learn", for example, what relative proximity between assets best indicates an interaction therebetween. This correlation between the position information of an asset and its operating state is inherent to the performance of the invention. Put differently, it is understood that the controller 24 treats the position information 22 and instrument data 28 as a sequence of related data, rather than as multiple, discrete, separate instances in time. Such sequence based interrogation reveals more about what the asset 14 is doing at a given moment in time, and is a more reliable indicator of actual operating state than an alignment of conditions at an instance in time. The controller 24 uses machine learning algorithms to identify or otherwise detect trends in the sequence of data to determine the correlation between the position information 22 and instrument data 28, and thereby infer the most likely operating state of the asset 14 at that given time.

[0039] It is contemplated that should there be a mismatch between the calculated operating state of a respective asset (as determined by evaluating the position information 22 against the correlation) and the actual operating state inferred from the instrument data 28, then the controller 24 may identify an error condition of that asset 14. For example, should the position information 22 suggest that haul truck 16a is currently being loaded by digger 18a yet a tip switch sensor 30a of the haul truck 16a be reporting that a load tray is not in a lowered position, then the controller 24 may identify that the tip switch sensor 30a has failed and instruct the haul truck 16a be taken offline once the current task is completed.

[0040] Once the controller 24 has calculated an operating state of a respective asset 14, it is then able to prescribe instructions thereto (and / or to other assets 14) by an instructing message telegram 26. With reference to the embodiment shown, the controller 24 may instruct digger 18a to begin loading haul truck 16a upon calculating that the haul truck is waiting at the digger 18a for loading. Likewise, upon calculating that the haul truck 16a has been loaded by the digger 18a and is currently waiting, the controller 24 may, for example, instruct the haul truck 16a to travel to the nearest available stockpile to dump the payload of material.

[0041] Turning now to Figure 2. It is contemplated that the position information 22 of a respective asset 14 is provided by an awareness module 32. The awareness module 32 may be a "retrofit" module 32 that is installed or otherwise fitted to an asset. In this way, it is understood that the module 32 may be a universal module 32 that is agnostic to particular asset type and OEMs - allowing additional assets to easily be brought online and added into the system 10 without the need for bespoke coding and the like. As a result, the need to integrate OEM instrument communication protocols with the controller 24 is greatly obviated, as such instruments would not be needed to detect asset operating state in future (i.e. once the correlation has been mapped or otherwise determined). In this manner, deployment of existing or known types of asset at a new mine site 12 would be simpler and faster.

[0042] The awareness module 32 comprises sensor 20. Preferably, sensor 20 is provided in the form of a high precision GPS unit. The high precision GPS unit provides high resolution location and heading data of the respective asset 14 to which it is fitted. The awareness module 32 may also include a user interface 34. The user interface 34 may, for example, be a tablet that is fitted or otherwise carried by the asset 14. The user interface 34 may provide current and / or upcoming task information to an operator of the respective asset 14, as well as provide an indication of the "calculated" operating state thereof. In this respect, an operator of the respective asset 14 may be able to assist in tuning the algorithms that are used in determining the correlation between the position information and the operating state of the asset - by, for example, identifying when the calculated operating state (as indicated on interface 34) does not match the current, actual operating state of the asset (either as determined by the operator or as inferred from instrument data 28 from on-board instruments 30) and logging or otherwise labelling such occurrences via the interface 34. It is also contemplated, that in other embodiments, the sensor 20 may be an "OEM" or otherwise existing sensor of the asset, said sensor having analogous functions to the high precision GPS unit described above.

[0043] A method 100 of operating a mine site 12 using the system 10 will now be described with reference to Figure 3.

[0044] In an initial or performing step 110, mining activities are performed by at least one of the assets 14 on the mine site 12. Examples of such mining activities may, for example, include the generation or extraction of an ore product by load unit 18a, and transport of said ore product to a material stockpile or crusher by haul truck 16a.

[0045] As the mining activities 110 are being performed, each of the assets 14 is in communication with controller 24 of the mine site 12, sending position information 22 of the respective asset to the controller 24. Accordingly, it is understood that in a receiving step 120, the controller receives position information 22 from the respective assets 14. The position information is provided by sensor 20. The sensor 20 may form part of respective awareness modules 32 that are retrofitted or otherwise installed to the respective assets 14 in a fitting step 122. In a measuring step 124, the sensor 20 measures a geospatial position and / or heading of the respective asset to which it is fitted or otherwise part of, with the measured data then being sent to controller 24 in a sending step 126.

[0046] In a determining or correlating step 130, the controller 24 determines a correlation between historical (i.e. previously received) position information 22 of a respective one of the assets 14 and the operating state thereof. During this step, the controller 24 may, for example, read instrument data 28 from an instrument 30 of the respective asset 14 in a reading step 132 in order to infer an "actual" operating state of the asset 14. In a following or relating step 134, the correlation may be determined, at least in part, by relating or otherwise mapping inferred "actual" operating states of the asset 14 inferred from the historical instrument data 28 against the corresponding historical position information thereof. A parametrization step 136 may follow, in which proximity boundaries around assets are formulated to capture interactions therebetween - for example by formulating a boundary of a known radius around a respective asset in order to capture or otherwise parameterize that a second asset is interacting therewith when said second asset enters is located within said boundary. Such boundaries effectively define relationships between interacting assets 14. It is noted that the correlation between position information and operating state is not constant, and that the method 100 sees the controller 24 update or otherwise automatically "tune" the correlation over time using machine learning algorithms. For example, the controller 24 may continuously monitor the instrument data 28 (when available) in a tuning step 138, and therefore use the "actual" operating state (as inferred from the instrument data) to update the correlation based on the position information and / or to refine the boundaries set in the parametrization step 136.

[0047] In a calculating step 140, an operating state of a respective asset is calculated or otherwise detected using the correlation. In particular, position information of a respective asset is evaluated against the correlation, to determine or otherwise classify a "most likely" operating state thereof. The calculating step 140 includes a comparing step 142, in which the operating state of a respective asset is calculated by comparing probabilities associated with the asset being ineach of its known operating states at the given time (i.e. as set or otherwise reflected by its position information).

[0048] In a prescribing step 150, message telegrams 26 are sent or otherwise broadcast to respective assets 14 on the mine site 12 to provide updated instructions and / or activity assignment information. During this step, the controller 24 may carry out a task completion identification step 152, identifying that a respective asset has completed its mining activity based on a change in its calculated operating state. For example, the controller 24 may determine that the haul truck 16a has completed a given transport cycle upon calculating that the haul truck 16a has dumped a payload at a stockpile, before prescribing a new activity to the haul truck 16a. Similarly, should the controller 24 determine that the current activity of a respective asset 14 is not yet completed, it may not send a message telegram 26 to that asset - thereby reducing the number of telegrams 26 that are sent and reducing bandwidth requirements.

[0049] A further method 200 of operating a mine site 12 using the system 10 will now be described with reference to Figure 4.

[0050] In an initial or performing step 210, mining activities are performed by at least one of the assets 14 on the mine site 12. Examples of such mining activities may, for example, include the generation or extraction of an ore product by load unit 18a, and transport of said ore product to a material stockpile or crusher by haul truck 16a.

[0051] In an engineering or formulating step 220, the controller 24 may create or otherwise formulate a set of relationships that are used to correlate asset movements and / or interactions with asset operating states. For example, the set of relationships may define a plurality of boundaries or "interaction zones" around respective assets 14, with assets that are located within said zones being said to be interacting with one another - for example, one such boundary may define a specified radius around load unit 18a, with haul truck 16a being said to be interacting with said load unit 18a when it is located within that zone.

[0052] In a detecting or calculating step 230, position information 22 of a respective asset 14 is evaluated against the set of relationships defined in step 220, to automatically detect an operating state of that asset - effectively based on its current / recent movements and interactions with other assets on the mine site 12.

[0053] The applicant has determined that embodiments of the present invention as described herein provide an approach to substantially autonomous mining that is not based on detecting asset status at a particular instant in time and is not reliant on multiple conditions being true at such instant in time, but rather observes the behavior of the assets over time to collectively determine whether such behaviors indicate a state change. Mining methods and systems that follow this approach may provide at least the following advantages over existing "rules-based" approaches to determining the operating state of assets under the control of an FMS:• The method is not dependent on manually set-up components, and therefore avoids the introduced errors from such approaches.• The accuracy of detecting the operating state of an asset correctly is improved.• Reporting and / or instructions that are dependent on asset state will be more accurate.• More states and activities can be detected, including, with respect to materials handlings units such as haul trucks, stopped while travelling empty / full, bunching, and other delays not previously automatically detected.

[0054] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavor to which this specification relates.

[0055] Throughout this specification and the claims which follow, unless the context requires otherwise, the word ‘comprise’, and variations such as ‘comprises’ and ‘comprising’, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.LEGEND

Claims

CLAIMS1. A method of mining, including: performing a mining activity on a mine site with a first asset; determining a correlation between position information of the first asset and different operating states thereof; using the correlation to calculate a current operating state of the first asset from position information thereof; and prescribing instructions to the first asset in response to its current operating state.

2. The method of claim 1, wherein the operating state of the first asset is calculated by comparing probabilities associated with the asset being in each of the operating states at the given time.

3. The method of claim 1 or claim 2, including receiving position information from the other assets on the mine site.

4. The method of claim 3, wherein the operating state of the first asset is calculated at least in part by evaluating a proximity of the first asset with respect to other assets on the mine site.

5. The method of any one of claims 1 to 4, including fitting a vehicle awareness module to the first asset, with the vehicle awareness module providing the position information thereof.

6. The method of claim 5, including measuring a heading and / or geospatial position of the first asset with the vehicle awareness module, with the position information of a respective asset comprising at least one of the heading and geospatial position thereof.

7. The method of any one of claims 1 to 6, wherein the position information comprises a sequence of position data, with the step of determining a correlation comprising identifying trends within the sequence.

8. The method of any one of claims 1 to 7, including reading instrument data from the first asset, with the correlation being determined at least in part by relating the position information ofthe first asset with instrument data from which the operational state of the first asset can be inferred.

9. The method of claim 8, including detecting an error condition of the first asset by comparing the calculated operating state with an actual operational state inferred from the instrument data.

10. The method of any one of claims 1 to 9, including tuning the correlation in substantially real time via machine learning algorithms.

11. A mining system, comprising: a first asset adapted to perform a mining activity on a mine site; a sensor configured to provide position information of the first asset; and a controller adapted to determine a correlation between the position information of the first asset and different operating states thereof and to calculate a current operating state from position information of the first asset; wherein the controller prescribes instructions to the first asset in response to its current operating state.

12. The mining system of claim 11, wherein the first asset is one of a plurality of assets of the mine site, with the controller using the position information to evaluate a proximity of the first asset with respect to other assets on the mine site.

13. The mining system of claim 11 or claim 12, wherein the position information comprises a sequence of position data, with the controller being configured to identify or otherwise detect trends within the sequence.

14. The mining system of any one of claims 11 to 13, wherein the position information comprises heading and / or geospatial position information.

15. The mining system of any one of claims 11 to 14, wherein the first asset includes at least one instrument configured to provide instrument data to the controller, with the controller using the instrument data to infer an actual operating state of the first asset.

16. The mining system of claim 15, wherein the controller is configured to detect an error condition of the first asset by comparing the calculated current operating state with the actual operational state inferred from the instrument data.

17. The mining system of claim 16, wherein the controller uses machine learning algorithms to update the correlation in real time.

18. A vehicle awareness module for use with the mining system of any one of claims 11 to 17, the module comprising a sensor configured to provide position information of an asset to which the module is fitted.

19. An autonomous mining vehicle having the vehicle awareness module of claim 18 fitted thereto.

20. A method of mining, including: performing mining activities on a mine site with a plurality of assets; formulating a set of relationships that correlate asset movements and / or interactions with asset operating states; and using the set of relationships to detect an operating state of an asset.

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