Mine management system

Abstract

A mesh set A of unclassified travel routes, which is unclassified travel routes, and a mesh set B of classified travel routes, which is classified travel routes, are provided. A sum set of the mesh set A and the mesh set B, a first difference set of the sum set and the mesh set A, and a second difference set of the sum set and the mesh set B are calculated, and if the size of the first difference set and the size of the second difference set are both equal to or less than a threshold value, the mesh set A and the mesh set B are calculated. When the size of the first difference set and / or the size of the second difference set is greater than the threshold value, the unclassified travel route is classified as the route ID of the classified travel route, and when the size of the first difference set and / or the size of the second difference set is greater than the threshold value, the unclassified travel route is classified as a new route ID. As a result, it is possible to accurately and easily classify the travel route of the mine vehicle.

Description

Technical Field

[0001] The present invention relates to a mine management system. Background Art

[0002] Patent Document 1 discloses a method for determining a mining vehicle's route by comparing the location information of multiple intermediate points along the route with the location information of a pre-set designated route. Patent Document 2 discloses a method for correcting the location data of, for example, dump sites, which serves as the basis for route classification, after the route has been classified.

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent No. 5596661

[0006] Patent Document 2: Japanese Patent No. 5647362 Summary of the Invention

[0007] Problems to be solved by the invention

[0008] The GNSS-measured route of a mining vehicle can sometimes appear to deviate from the designated route it should be classified as, due to errors in GNSS data, when the mining vehicle avoids other vehicles, or when traveling on poor roads. Therefore, the technique of Patent Document 1 uses the location of a comparison point on a designated route, which is used to compare the route with a midway point, to classify the route as a different route from the one it should be classified as. This can result in inadequate productivity evaluation. In contrast, increasing the number of comparison points, as in Patent Document 2, can improve route classification accuracy. However, with more comparison points, setting the distance threshold for each comparison point becomes more complex, and it is also difficult to set the comparison points without interfering with other designated routes.

[0009] Furthermore, in reality, mining sites may also generate routes that differ from the designated route but entirely coincide with a portion of the designated route (inclusive routes), or routes that deviate from the designated route and stop at rest areas (detour routes). Regarding these inclusive and detour routes, the techniques of Patent Documents 1 and 2 require the orientation and travel distance of the road segments connecting the comparison points to be included in the route classification criteria, further complicating the route classification process. Furthermore, if the evaluation target route does not coincide with any designated route, the designated route must be newly established, requiring the dumping site, loading site, and comparison points, and further establishing a separate distance threshold for each location to determine if a route is passable.

[0010] At a mine site, the dumping site and the loading site are frequently moved. Therefore, the techniques of Patent Documents 1 and 2 frequently require frequent updates of the dumping site, the loading site, and the comparison points, and thus require a lot of effort to operate the system.

[0011] An object of the present invention is to provide a mine management system capable of easily and accurately classifying travel routes of mining vehicles.

[0012] Means for solving problems

[0013] In order to achieve the above-mentioned object, the present invention provides a mine management system, which includes: a processing device having a function of generating a grid set represented by an ID, which divides the ground surface into grids at equal intervals, based on the position data and operation data of a mine vehicle, with respect to a travel path starting from a place where the mine vehicle dumps soil and ending at a place where the mine vehicle dumps soil next, and classifying the path ID based on the grid set; a storage device, which associates the grid set and the operation data with respect to the travel path generated by the processing device and stores and accumulates the associated grid set; and a display terminal device, which transmits and receives data to and from the processing device and displays the received data from the processing device, wherein the processing device associates the grid set A of the unclassified travel path as the unclassified travel path and the grid set B of the classified travel path as the classified travel path. A sum set of the logical sum of the grid set A and the grid set B, a first difference set which is the logical difference between the sum set and the grid set A, and a second difference set which is the logical difference between the sum set and the grid set B are calculated; when the sizes of the first difference set and the second difference set are both below respectively preset thresholds, the unclassified driving path is classified as the path ID of the classified driving path, the grid set A and the operation data related to the unclassified driving path are associated with the path ID and recorded in the storage device; when at least one of the sizes of the first difference set and the second difference set is greater than the threshold, a new path ID is newly generated, the grid set A and the operation data related to the unclassified driving path are associated with the new path ID and recorded in the storage device.

[0014] Effects of the Invention

[0015] According to the present invention, the travel routes of mining vehicles can be easily classified with high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram showing the overall image of the mine management system of the present invention.

[0017] Figure 2This is an example of a block diagram of a function for associating and recording productivity index data of a processing device constituting the mine management system according to the first embodiment of the present invention with a path ID.

[0018] Figure 3 This is an explanatory diagram of a grid set representing a travel route in the present invention.

[0019] Figure 4 This is a Venn diagram showing the basic concept of the driving route determination algorithm of the present invention.

[0020] Figure 5 This is a Venn diagram showing examples of types of cases where two travel routes in mesh sets A and B are determined to be the same route and cases where they are determined to be different routes by the processing device constituting the mine management system according to the first embodiment of the present invention.

[0021] Figure 6 This is a flowchart showing an example of a procedure for route classification in the mine management system according to the first embodiment of the present invention.

[0022] Figure 7 This is a comparison table between the mine management system according to the first embodiment of the present invention and a mine management system of the so-called node / link system.

[0023] Figure 8 This is an example of a block diagram of a function of calculating a total value of a productivity index for each route in a processing device constituting the mine management system according to the first embodiment of the present invention.

[0024] Figure 9 This is a flowchart showing an example of a procedure for presenting recommended control parameters based on the total result of productivity indices by a processing device constituting the mine management system according to the first embodiment of the present invention.

[0025] Figure 10A This is a diagram showing an example of a screen that presents candidates for evaluation route IDs.

[0026] Figure 10B This is a diagram showing an example of a screen that visualizes a mesh set associated with an evaluation route ID.

[0027] Figure 11A This is an example of a screen showing the travel frequency of each control parameter in the selected evaluation route ID in a bar graph.

[0028] Figure 11B This is an example of a screen showing productivity comparison based on control parameters in the selected evaluation path ID.

[0029] Figure 11CThis is an example of a screen showing a histogram of the travel frequencies of the standard parameter and the alternative parameter for the same productivity index in the selected evaluation route ID.

[0030] Figure 11D This is an example of a screen showing differences in productivity based on control parameters for a plurality of routes.

[0031] Figure 12 This is a flowchart showing an example of a procedure for determining a deteriorated vehicle in a processing device constituting a mine management system according to a second embodiment of the present invention.

[0032] Figure 13A This is a diagram showing an example of a screen displaying the productivity of each vehicle ID traveling on a selected route ID.

[0033] Figure 13B This is a diagram showing a histogram of predetermined operating data in each cycle in the evaluation path.

[0034] Figure 14 This is a conceptual diagram for explaining an omnidirectional mesh used in a mine management system according to a third embodiment of the present invention.

[0035] Figure 15 This is a schematic diagram for explaining an example of path classification using an omnidirectional grid.

[0036] Figure 16 This is a flowchart showing an example of a procedure for deleting a route ID with a low travel frequency, which is performed by a processing device constituting a mine management system according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0037] Hereinafter, embodiments of the present invention will be described using the accompanying drawings.

[0038] (summary)

[0039] Figure 1 It is a schematic diagram showing the overall image of the mine management system of the present invention. Figure 1 The illustrated mine management system 1 has the function of receiving operational data and location data from each mining vehicle V operating at a mine site S and evaluating the productivity of the mine site. The mining vehicles V operating at the mine site S include various types, such as mining dump trucks V1, hydraulic excavators V2, and bulldozers V3. Furthermore, even within the mine site S, mining vehicles V of the same type (e.g., mining dump trucks V1) may be mixed with vehicles of different makes, models, and manufacturing periods. The surface on which the mining vehicles V travel varies depending on their path, resulting in differences in fuel consumption, climbing speed, and other factors. These differences can affect the productivity of the mine site S, making it important for the mine management system to manage the productivity of the mine site S.

[0040] Figure 1 The mine management system shown is configured to include a processing device 10 , a storage device 20 , and a display terminal device 30 .

[0041] The processing device 10 is, for example, a computer (e.g., a server) located at or away from the mine site S. The processing device 10 is configured to transmit and receive data with each mining vehicle V, the storage device 20, and the display terminal device 30. For example, the processing device 10 receives inputs such as position data of each mining vehicle V, log data such as operational data, and various request data from the display terminal device 30, received via the network. The position data of each mining vehicle V is the output of a positioning device (e.g., a GNSS mobile station) mounted on each mining vehicle V. Operational data is the output of various sensors (not shown) mounted on the mining vehicle V. For example, in the case of a mining dump truck V1, this includes the output of a weight meter (e.g., a load cell) that measures the load weight and a fuel sensor (e.g., a liquid level gauge) that measures the remaining fuel. It also includes control parameters used for vehicle control (described later). Request data is a signal requesting the display of various data corresponding to operations on the display terminal device 30. Furthermore, the processing device 10 records data to and reads data from the storage device 20.

[0042] In particular, the processing device 10 of the present embodiment has a function of classifying the travel routes of each mining vehicle V in the mine site S by route ID based on the position data and operation data of each mining vehicle V.

[0043] In this application, a "travel route" refers to the path taken from one dumping site to the next. Specifically, a travel route begins at a dumping site where a mining vehicle V unloads its load and ends at the next dumping site (a location where the loaded load is unloaded at a designated loading site after unloading at one dumping site). The unloading of soil by a mining vehicle V can be determined based on changes in the output of a weight meter mounted on the mining vehicle V. For example, if the load, which is greater than a set value m1, decreases to less than a set value m2 (m2 < m1), the load is determined to have been unloaded. The travel route of a mining vehicle V is determined based on position data sequentially output from a positioning device mounted on the mining vehicle V.

[0044] In addition, when referred to as "cycle" below, it refers to the process that the mining vehicle V performs between the starting point and the end point of the driving path, that is, a series of processes of moving from one soil dumping site to the loading site, loading the load, moving to the next soil dumping site, and then dumping the soil.

[0045] In each embodiment, the travel path of a mining vehicle V is defined by a grid set represented by a grid ID, which divides the ground surface into evenly spaced grids, that is, multiple grids representing locations entered during travel. The grid set defining the travel path is generated by the processing device 10 for each mining vehicle V based on data received from each positioning device, and is classified as any of the route IDs specified at the mine site S or a new route.

[0046] The processing device 10 also has functions for calculating data on productivity indicators of the mine based on position data and operation data of each mining vehicle V, and for determining the degradation state of components of each mining vehicle V and the quality of control parameters.

[0047] "Control parameters" include parameters related to the driving control of each mining vehicle V, such as the engine's idle speed, maximum speed, and engine response. For example, the maximum speed is generally related to the engine output. The higher the maximum speed setting, the higher the climbing speed of the mining vehicle V, and the lower the maximum speed setting, the lower the climbing speed. The idle speed is related to the moment when the vehicle speed begins to increase. The higher the idle speed setting, the shorter the time it takes for the mining vehicle V to start accelerating, and the lower the idle speed setting, the longer it takes to increase to the specified engine speed. In addition, the operating feel changes according to the setting of the engine response. The engine response is adjusted, for example, by a program in the ECU (Engine Control Unit).

[0048] In addition to standard parameters, the control parameters of the mining vehicle V and other parameters are pre-recorded as several predetermined values ​​in the storage device 20. The mining vehicle V applies any of the predetermined values ​​to each control parameter while traveling within the mine site S. The control parameters for the mining vehicle V are set, for example, by connecting a predetermined terminal device to the travel control device (controller) of the mining vehicle V and operating the connected terminal device to write and set the predetermined control parameters into the travel control device of the mining vehicle V. If the travel control device of the mining vehicle V has a function for applying received control parameters to itself, for example, the control parameters set by the display terminal device 30 can be recorded in the storage device 20 by the processing device 10 and transmitted to the mining vehicle V, whereupon they are automatically applied to the mining vehicle V. While it is conceivable that control parameters may be set individually for each mining vehicle V operating within the mine site S, it is also possible to set common control parameters for all mining vehicles V operating within the mine site S, or to set different control parameters for each mining vehicle V, for example, for each route ID.

[0049] "Productivity indices" are items used to determine or evaluate the productivity of a mining site S. Specific examples of productivity indices include the load carried per liter of fuel (T / L) of each mining vehicle V, the load carried per unit time (T / h), and the fuel consumption per unit time (L / h). Other examples of productivity indices include travel distance, travel time, and fuel consumption rate. These productivity indices can be calculated, for example, as values ​​for each mining vehicle V operating at the mining site S during a specified period, as a total for all mining vehicles V, as values ​​for each route ID, or as a total for all route IDs. When the same mining vehicle V is traveling on the same route, the value of these productivity indices can also vary depending on the control parameters applied to the mining vehicle V.

[0050] For example, lowering the maximum engine speed reduces the climbing speed of a mining vehicle V. Furthermore, lowering the engine's idle speed slows the acceleration of the mining vehicle V. In this case, the load transported per unit time (T / h) decreases, potentially leading to a decrease in productivity. However, at a mining site S, speed differences are somewhat absorbed by factors such as waiting times and congestion in the loading area. Depending on the travel route, the maximum engine speed setting may have little impact on the load transported per unit time. In contrast, lowering the maximum engine speed and idle speed can suppress fuel consumption, improving the load transported per liter of fuel (T / L) and fuel consumption per unit time (L / h). In large mining sites S with a large number of operating mining vehicles V, the control parameters applied to each mining vehicle V can lead to significant variations in total fuel costs year-to-year. It is desirable to set control parameters that understand the expected changes in the values ​​of each productivity indicator and comprehensively consider these changes.

[0051] The mine management system 1 is a useful system that can estimate how the values ​​of various productivity indicators change based on control parameters and can present the user with changes in the various productivity indicators caused by the control parameters and recommended control parameters. Depending on the circumstances, the mine management system 1 can also be configured to transmit the calculated recommended control parameters to the mining vehicles V and apply them to each mining vehicle V.

[0052] It should be noted that while it is desirable to transmit the operating data and position data of the mining vehicle V to the mine management system 1 in real time, sequential transmission may not be possible depending on the communication conditions and communication costs at the mine site S. The processing device 10 can also be configured to process the input operating data one by one if sequential reception of the operating data is possible. However, if sequential reception of the operating data is not possible, a configuration can be configured to buffer a certain amount of concentrated operating data and process the buffered data. An example of a certain amount of concentrated data is the maximum amount of data from the time of soil dumping to the time of soil dumping along a past travel route.

[0053] In a storage device 20 (e.g., a database), mesh sets, operational data, and the vehicle IDs of mining vehicles V are associated with each other for the travel routes generated by the processing device 10, and are recorded and stored. The storage device 20 stores the positional data and operational data of each mining vehicle V traveling within the same, centrally managed mining site S. The storage device 20 need not be a single storage device; it may be composed of multiple storage devices connected via a network.

[0054] The display terminal device 30 is a computer equipped with a display device, typically a mobile computer such as a laptop computer, tablet computer, or smartphone. It transmits and receives data to and from the processing device 10 and displays the data received from the processing device 10. In response to an operation, the display terminal device 30 transmits a request signal to the processing device 10 requesting a display. It then receives responses from the processing device 10, such as productivity indicators related to the mine site S, the degradation status of each mining vehicle V, and the quality of each mining vehicle V's control parameters, and displays them on the display device. Users of the mine management system 1 can review the data displayed on the display terminal device 30, quickly identify any productivity declines or signs of declines at the mine site S, and implement countermeasures based on the causes of the productivity decline, thereby maintaining and managing the productivity of the mine site S.

[0055] The information displayed on the display terminal device 30 can be displayed in various formats, such as reports and emails. In this embodiment, a dashboard format is used. For example, the operations manager P1 at the mine site S can review the time taken for each cycle using the dashboard information displayed on the display terminal device 30 and replan the operation of each mining vehicle V. The operator instructor P2 can use the dashboard information to identify operators with inexperienced driving skills, such as those who frequently make sudden starts and sudden brakes, and provide driving guidance. The road maintenance personnel P3 can use the dashboard information to quickly identify areas of road surface roughness suspected of reducing productivity and implement repairs. The equipment maintenance personnel P4 can use the dashboard information to identify deterioration and failure of the onboard equipment of the mining vehicle V, quickly perform repairs, and calibrate control parameters to improve the fuel efficiency of the mining vehicle V based on the travel route. Furthermore, the mining manager P5 can verify the dashboard information in conjunction with weather information (history and / or forecast) and mineral prices (history and / or forecast) acquired via the Internet IN, and revise mining and maintenance plans. Furthermore, the mining supervisor P5 can instruct the operations manager P1 , the operator instructor P2 , the road maintenance worker P3 , and the equipment maintenance worker P4 on measures to prevent production reduction based on the confirmation results of the dashboard information and the like.

[0056] (First embodiment)

[0057] -Route classification / productivity index calculation function-

[0058] In the first embodiment, a system for notifying a facility maintenance worker P4 of control parameters for improving productivity at a mine site S will be described.

[0059] Figure 2 This is an example of a block diagram of the processing device 10 that has the function of associating and recording the productivity index with the route ID. Figure 2 In FIG. 1 , the functions of the processing device 10 for path classification and productivity index calculation are represented by blocks 11 - 13 .

[0060] In block 11, the processing device 10 generates a grid set for the travel routes of each mining vehicle V currently traveling within the mining site S based on data such as position and load capacity obtained by positioning devices and weighing scales mounted on the mining vehicles V. As previously described, in this embodiment, a grid set is generated each time a certain amount of data is buffered. The travel routes for which a grid set is generated in block 11 are routes whose route IDs have not yet been classified and are hereinafter referred to as "unclassified travel routes."

[0061] In block 12, the processing device 10 classifies the unclassified driving route by comparing it with the grid set associated with each route ID registered in the database 21 as a route table. The algorithm for classifying the unclassified driving route will be described later.

[0062] Database 21 is a portion of storage device 20 or its storage area. Database 21 stores grid set data for each route ID in a data structure, such as a table. The driving routes associated with the route IDs recorded in database 21 are classified known routes, hereinafter referred to as "classified driving routes."

[0063] In block 12, if the unclassified driving route is determined to match one of the route IDs already recorded in database 21, the route ID of the matching classified route is output. Conversely, in block 12, if the unclassified driving route is determined to not match any of the existing route IDs, a new route ID is output and associated with the grid set of the unclassified driving route and recorded in database 21.

[0064] In block 13, the processing device 10 generates a mine productivity index (e.g., load (T / L), load (T / h)) and a cycle ID based on the mining vehicle V's operating data (fuel consumption, load capacity, travel distance, cycle start time, cycle end time, etc.) associated with the classified travel routes. A "cycle ID" is assigned each time the mining vehicle V executes a cycle. For example, more frequently traveled routes are associated with more cycle IDs, resulting in a richer data set. The productivity index data and cycle ID generated here are associated with the route ID, along with the vehicle ID and control parameters used by the mining vehicle V, and are recorded in the database 22, which serves as a productivity table.

[0065] The database 22 is a portion of the storage device 20 or its storage area. The database 22 stores productivity index data, cycle ID, vehicle ID, and control parameters for each route ID in a data structure, for example, in a table format. Based on this database 22, for example, productivity index data for each mining vehicle V over a predetermined period can be evaluated for each route ID. The quality of the productivity index for the same route ID can be used to determine whether a vehicle with reduced energy efficiency (degraded vehicle) or the need for control parameter adjustment.

[0066] -Driving Path-

[0067] Figure 3 This is an explanatory diagram of a grid set representing a driving path in the present invention. Figure 3In the example, the movement path from the dumping site O as the starting position (starting point) of the cycle to the next dumping site D as the ending position (end point) of the cycle is schematically shown. The shape of each grid is set to be square (for example, quadkey). As long as the entire mine can be covered with the same shape, the shape of the grid can also be, for example, hexagonal (honeycomb) or rectangular (GeoHash). In addition, the size of the grid in the vertical and horizontal directions (latitude and longitude directions) is set to accommodate an entire mining vehicle V and is longer than the movement distance of the mining vehicle V at maximum speed in one sampling cycle of the positioning device. In the case of a square grid, the size of the grid corresponds to a ground surface of 30×30m, for example.

[0068] exist Figure 3 In the figure, the grids that a mining vehicle V passes through on its way from dumping site O to dumping site D are enclosed in bold. The set of grid IDs (m1, m2, ..., mx) of the grids enclosed by this bold line represents the travel path of the mining vehicle V. The travel path of the mining vehicle V is measured, for example, by a positioning device installed in the mining vehicle V, and the grid IDs of the grids it passes through are collected to create a grid set. Using this grid set eliminates the need for complex waypoint settings and judgments using distance thresholds, further simplifying the path classification using set operations, described later.

[0069] -Basic Concepts of Path Determination-

[0070] Figure 4 This is a Venn diagram illustrating the basic concepts of the driving path determination algorithm of the present invention. Consider a case where a grid set A for the first driving path is represented by a set of grid IDs (m1, m2, m3, m4, m5, m6, ..., mx), and a grid set B for the second driving path is represented by a set of grid IDs (m1, m2, m4, m6, ..., my). In this case, we determine a sum set Σ (=A∪B), which is the logical sum of grid sets A and B; a first difference set Δ1 (=Σ-A), which is the logical difference between the sum set Σ and grid set A; and a second difference set Δ2 (=Σ-B), which is the logical difference between the sum set Σ and grid set B. The magnitude of the first difference set Δ1 corresponds to the degree of inconsistency between the second driving path and the first driving path, and the magnitude of the second difference set Δ2 corresponds to the degree of inconsistency between the first driving path and the second driving path.

[0071] In this embodiment, the sizes of the first and second difference sets Δ1 and Δ2 are compared with respectively set thresholds S1 and S2 to determine whether the first and second driving paths are the same or different. In other words, when determining whether the first and second driving paths are identical, the size of the inconsistent areas is evaluated, not the size of the consistent areas, to determine whether the two paths can be considered identical. The degree of inconsistency is not limited to the size of the first and second difference sets Δ1 and Δ2 themselves; for example, it can be evaluated by the ratios R1 and R2 (R1 = Δ1 / Σ, R2 = Δ2 / Σ) of the first and second difference sets Δ1 and Δ2 relative to the sum set Σ.

[0072] It should be noted that the size of a set is, for example, the number of meshes constituting the set, and the sizes of the first difference set Δ1 and the second difference set Δ2 correspond to the number of meshes constituting these difference sets.

[0073] -Path determination type-

[0074] Figure 5 It is about Figure 4 The two driving routes in grid sets A and B described above are shown as a Venn diagram illustrating examples of the types of cases where the processing device 10 determines them to be the same route and different routes. The processing device 10 determines that the two driving routes associated with grid sets A and B are the same route when the magnitudes of the first difference set Δ1 and the second difference set Δ2 (in this example, the ratios R1 and R2) are both less than predetermined thresholds S1 and S2, respectively. Conversely, the processing device 10 determines that the two driving routes associated with grid sets A and B are different routes when at least one of the magnitudes of the first difference set Δ1 and the second difference set Δ2 (the ratios R1 and R2) is greater than the thresholds S1 and S2. While the thresholds S1 and S2 for the ratio R1 and R2 may be different values, in this embodiment, they are set to the same value (e.g., 0.2).

[0075] Through such a judgment algorithm, Figure 5 As shown in type a, the processing device 10 determines that the two driving paths associated with the grid sets A and B are the same path when the ratio R1 is less than the threshold value S1 and the ratio R2 is less than the threshold value S2. In other cases, that is, when at least one of the ratios R1 and R2 is greater than the threshold value, the processing device 10 determines that the two driving paths associated with the grid sets A and B are different paths. As an example, when at least one of the ratios R1 and R2 is greater than the threshold value, Figure 5 Types b, c, and d are exemplified in FIG.

[0076] Figure 5Type b corresponds to the case where the ratio R1 is greater than the threshold S1 and the ratio R2 is greater than the threshold S2. This also applies to cases where the two driving routes are completely different, with some parts of the route not matching each other, or where the two driving routes are partially identical but at least one of the dumping and loading areas is different. The case of completely different routes forms a Venn diagram with Δ1 = B and Δ2 = A.

[0077] Type c occurs when either ratio R1 or R2 is greater than the corresponding threshold S1 or S2, while the other ratio R1 or R2 is less than the corresponding threshold S1 or S2. Type c corresponds to situations where one travel route is somewhat consistent with another, while the other travel route deviates significantly from the first. For example, if two travel routes are scheduled to be the same, with the same starting and ending points, but one travel route causes the mining vehicle V to deviate from the planned route due to a break, etc. (in the case of a detour), grid sets A and B may form a Venn diagram as shown in type c. The travel routes of grid set A, represented by type c, may be used as data for evaluating the productivity of the travel routes associated with grid set B, and it is desirable to distinguish them as exceptions.

[0078] Type d occurs when either ratio R1 or R2 is zero, while the other ratio R1 or R2 is greater than the corresponding threshold. Type d corresponds to situations where the entirety of one route coincides with a portion of another route (one route is contained within the other route). An example of this inclusion is when both dumping sites and the loading area of ​​one route are located on the other route, and at least one of the dumping sites is located midway along the other route. Type c represents the routes of grid sets A and B, which are distinct and therefore require differentiation.

[0079] -Path classification steps-

[0080] Figure 6 This is a flowchart showing an example of a procedure for route classification in the mine management system 1 according to the present embodiment.

[0081] Every time each mining vehicle V travels in the mine site S, the processing device 10 classifies the unclassified travel routes related to the newly acquired position data and operation data of each mining vehicle V and records them in the storage device 20. Figure 4 The grid set A described in the description is set as the grid set of the unclassified driving path, and the grid set B is set as the grid set of the classified driving paths recorded in the storage device 20, and it is determined whether the unclassified driving path is the same path as the classified path or whether it is a new path that does not match any classified path.

[0082] The processing device 10 receives the position data and operation data of each mining vehicle V at any time, and automatically executes the processing of the data when a certain amount of data is buffered. Figure 6 Flowchart processing.

[0083] Step S601

[0084] If a certain number of grid IDs of the passing grids are buffered for the new unclassified driving route, the processing device 10 starts Figure 6 The grid set A of unclassified driving paths is generated.

[0085] Step S602

[0086] After generating mesh set A, processing device 10 calculates the aforementioned sum set Σ, first difference set Δ1, and second difference set Δ2 for mesh set A of unclassified driving routes and mesh set B of classified driving routes (period IDs). In this embodiment, ratios R1 and R2 are further calculated for each mesh set B as values ​​that evaluate the degree of inconsistency between the unclassified driving routes associated with mesh set A and the classified driving routes associated with each mesh set B.

[0087] If there are multiple classified driving routes (cycle IDs), the grid set B for each classified cycle ID is compared with the grid set A, and the sum set Σ, the first difference set Δ1, and the second difference set Δ2 are calculated. However, if there are a large number of classified driving routes (e.g., a predetermined number or more), the processing load on the processing device 10 may be excessively reduced. In this case, a predetermined number (e.g., the predetermined number) or a number obtained by multiplying the total number of classified driving routes by a predetermined coefficient (e.g., 0.2) of classified driving routes may be extracted, and the sum set Σ, the first difference set Δ1, and the second difference set Δ2 may be calculated for each grid set B of the extracted classified driving routes.

[0088] Next, the minimum inconsistency between grid sets A and B is determined. In this case, for example, a grid set B can be determined that minimizes the average (or weighted average) or total of the ratios R1 and R2 (or Δ1 and Δ2). The inconsistency associated with this determined grid set B is determined as the minimum inconsistency. If there is only one classified driving route, the inconsistency of that classified driving route is always the minimum inconsistency.

[0089] Step S603

[0090] After calculating the minimum inconsistency, the processing device 10 determines whether the minimum inconsistency is greater than or less than a preset threshold, and determines whether the unclassified driving path is a different path from or the same path as the classified driving path (cycle ID) with the minimum inconsistency. Specifically, Figure 4 and Figure 5 As described above, if both the first difference set Δ1 and the second difference set Δ2 (in this example, the ratios R1 and R2) are below predetermined thresholds S1 and S2 (e.g., 0.2), the inconsistency is determined to be below the threshold (N), and the unclassified driving path and the classified driving path with the minimum inconsistency are determined to be the same path. In this case, the process proceeds to steps S603 through S606. On the other hand, if at least one of the first difference set Δ1 and the second difference set Δ2 (in this example, the ratios R1 and R2) is greater than the thresholds S1 and S2, the inconsistency is determined to be greater than the threshold (Y), and the unclassified driving path and the classified driving path with the minimum inconsistency are determined to be different paths. In this case, the process proceeds to steps S603 through S604.

[0091] Step S604

[0092] When the minimum inconsistency is greater than the threshold, the processing device 10 adds the unclassified path related to the grid set A generated in step S601 as a new path and records it in the database 21 of the storage device 20 .

[0093] Step S605

[0094] After adding the unclassified driving route as a new route, the processing device 10 generates a new route ID for classifying the added new route, and associates the grid set A related to the unclassified driving route, the operation data obtained from the unclassified driving route, the data of the productivity index calculated based on the operation data, and the vehicle ID of the mining vehicle V traveling on the unclassified driving route with the new route ID and records them in the storage device 20, temporarily ending the process. Figure 6 Wait until the next process starts.

[0095] Step S606

[0096] When the minimum inconsistency is below the threshold, the processing device 10 classifies the unclassified driving route as the same route ID as the classified driving route (cycle ID) with the minimum inconsistency, associates the grid set A, operation data, productivity index and vehicle ID with the classified route ID and records them in the storage device 20, temporarily ending the process. Figure 6 and wait until the next process starts.

[0097] -Comparison with existing methods-

[0098] Figure 7This table compares a mine management system using the so-called node / link method with the mine management system 1 of this embodiment. In the so-called node / link method, the mining vehicle's travel path is represented by nodes and links. If the distance between the mining vehicle's travel position information and a node on the existing route is less than a distance threshold, the mining vehicle is determined to have passed through that node. If the number of nodes determined to have passed through exceeds the consistency threshold, the mining vehicle's travel path is determined to be the same as the existing route. However, this determination requires complex processing, requiring distance calculations based on the position information of at least the number of nodes to be compared. Furthermore, setting the existing route requires numerous parameters, including the location of each node, the number of nodes, the distance threshold, and the consistency threshold. Failure to properly set these parameters can lead to incorrect route determination. Furthermore, while adjustments to the distance threshold can address positioning errors and deviations from the travel path, it is not possible to distinguish between the aforementioned intervening and detour routes, requiring additional criteria for such distinctions.

[0099] In contrast, this embodiment addresses the aforementioned issues of the node / link approach by converting the location data of driving routes into a grid set (a set of grid IDs) and evaluating the degree of inconsistency between the unclassified driving routes and the grid set of classified driving routes. Specifically, the calculations required for route classification are two: the degree of inconsistency (first difference set Δ1 or ratio R1) between the grid set B of the classified driving routes and the sum set Σ, and the degree of inconsistency (second difference set Δ2 or ratio R2) between the grid set A of the unclassified driving routes and the sum set Σ. Furthermore, the only parameters relevant to route classification are the grid size and the thresholds S1 and S2 used to evaluate the inconsistency. Road conditions, variations in driving skills, poor road conditions, evasive driving by other vehicles, and road inconsistencies due to GNSS positioning errors can be easily addressed by adjusting the grid size. Furthermore, by evaluating the degree of inconsistency between routes, the route classification algorithm of this embodiment can also distinguish included and detour routes as distinct from the classified routes, as described above.

[0100] For example, if the unclassified driving route is a route with a detour, even if the starting point and the end point of the unclassified driving route are the same as those of the classified driving route, the unclassified driving route will deviate from the classified route in the middle of the route. Figure 5As shown in the Venn diagram of type c, the second difference set Δ2 (or ratio R2) is greater than threshold S2, and the first difference set Δ1 (or ratio R1) is less than threshold S1. In this case, since the first difference set Δ1 (or ratio R1) is less than threshold S1 and the second difference set Δ2 (or ratio R2) is greater than threshold S2, the two driving routes associated with mesh sets A and B are distinguished as different routes in processing device 10. If necessary, not only can these routes be distinguished as different routes, but the fact that they include a "detour route" can also be recorded as additional information in the cycle ID.

[0101] In addition, when the unclassified driving path is a path "included" in another driving path, the unclassified driving path is entirely consistent with a portion of the classified path. In the case of inclusion, the grid sets A and B of the unclassified driving path and the classified driving path are Figure 5 As shown in the Venn diagram of type d, the first difference set Δ1 (or ratio R1) is zero, and the second difference set Δ2 (or ratio R2) is greater than threshold S2. In this case, since the first difference set Δ1 (or ratio R1) is below threshold S1 and the second difference set Δ2 (or ratio R2) is greater than threshold S2, the two driving routes associated with grid sets A and B are distinguished as different routes in processing device 10. If necessary, not only are these routes distinguished as different, but the fact that they are "included" driving routes can also be recorded as additional information to the cycle ID.

[0102] -Productivity index aggregation function-

[0103] As mentioned above, the control parameters (idle speed of the engine, maximum speed, engine response) applied to the driving control of the mining vehicle V are recorded in the storage device 20 (database 22) according to the driving path (cycle ID). The processing device 10 refers to the data of multiple classified driving paths (cycle IDs) with the same path ID from the storage device 20, generates analysis data of the productivity index calculated based on the operation data of each cycle ID for each control parameter, and outputs the analysis data of each control parameter to the display terminal device 30. At this time, the higher the driving frequency of the path ID, the greater the impact of its productivity on the overall productivity of the mining site S. Therefore, it is preferred that the processing device 10 weights the operating data according to the number of data (driving frequency) of the path ID and performs weighted averaging on the total data to calculate the analysis data. Use Figure 8 An example of this processing will be described.

[0104] Figure 8 This is an example of a block diagram of the processing device 10 that has the function of summing up the productivity index for each path. Figure 8 In FIG. 1 , the aggregation function of the productivity index of the processing device 10 is represented by blocks 14 and 15 .

[0105] In frame 14, the processing device 10, for example, refers to the data of all (or more than the set number) classified driving paths (cycle IDs) accumulated in the database 22 during the evaluation period set by the user on the display terminal device 30, and extracts the path ID whose driving frequency during the evaluation period is higher than a pre-set frequency threshold (that is, the number of data during the evaluation period is more than the specified number) as the evaluation path ID.

[0106] In block 15 , the processing device 10 reads the data of each productivity index for each cycle ID of the extracted evaluation path ID from the database 22 , and totals the data of the productivity index for each evaluation path ID.

[0107] It should be noted that the frequency threshold used in the processing of block 14 is a value set by the user on the display terminal device 30, for example. It is a condition set to reduce variations in the productivity index calculation results caused by factors such as the driving skills of the driver of the mining vehicle V and other factors (e.g., waiting time due to cooperation with other vehicles, excavators, etc.). Therefore, the frequency threshold is preferably set so that even if the extracted route IDs contain several unique cycles (cycles significantly affected by the waiting order and congestion at the loading yard), route IDs with a data count that affects the aggregated results below a certain level are extracted. In particular, by evaluating route IDs with high travel frequency, the overall productivity evaluation trend of the mining site S can be easily determined.

[0108] -Control parameter evaluation steps-

[0109] Figure 9 This is a flowchart showing an example of a procedure for presenting recommended control parameters based on the summary results of productivity indices.

[0110] When the processing device 10 receives a request signal corresponding to a user's operation from the display terminal device 30 regarding the comparison of productivity based on the control parameter, it executes Figure 9 Flowchart processing.

[0111] Step S901

[0112] Upon receiving a request signal from the display terminal device 30, the processing device 10 selects an evaluation route for the evaluation period set by the user and transmits it to the display terminal device 30. For example, the processing device 10 calculates the travel frequency of each route ID during the evaluation period, arranges the route IDs in descending order of travel frequency, and displays these route IDs as candidate evaluation route IDs in a bar graph format along with a frequency threshold on the display terminal device 30. Alternatively, the selected route IDs may be displayed as a route map.

[0113] Step S902

[0114] When one or more evaluation route IDs are selected from the candidates displayed on the display terminal device 30, the processing device 10 aggregates the productivity index data for the evaluation route IDs sent from the display terminal device 30 by control parameter, and further performs a weighted average based on the travel frequency of the route ID to generate analysis data. For example, analysis data for the standard parameters and other alternative parameters (parameters α, β, etc.) are calculated for each evaluation route ID, such as the maximum engine speed, idle speed, and engine response. If multiple evaluation route IDs are specified, analysis data is calculated using the same method for each evaluation route ID.

[0115] Step S903

[0116] After calculating the analysis data of the productivity index for each control parameter for the evaluation path ID, the processing device 10 compares the productivity index for each control parameter and determines the control parameter with the optimal productivity index. In addition to comparing the productivity indexes specified by the display terminal device 30, other methods can be used, such as pre-setting a priority for the productivity indexes and comparing the highest-priority productivity indexes. If the result indicates that an alternative parameter with improved productivity compared to the standard parameter exists (Y), the processing device 10 proceeds to steps S903 and S904. If not (N), the processing device 10 proceeds to steps S903 and S905.

[0117] Step S904

[0118] When there is an alternative parameter that improves productivity compared to the standard parameter, the processing device 10 indicates that the alternative parameter with the highest productivity (e.g., parameter α) is the recommended parameter, and sends the comparative data of the productivity index of each parameter to the display terminal device 30 and displays it as dashboard information, and ends. Figure 9 Flowchart processing.

[0119] Step S905

[0120] When there is no alternative parameter that improves productivity compared to the standard parameter, the processing device 10 indicates that the standard parameter is the recommended parameter, sends the comparative data of the productivity index of each parameter to the display terminal device 30 and displays it as dashboard information, and ends. Figure 9 Flowchart processing.

[0121] It should be noted that in Figure 9In the example, the user (e.g., equipment maintenance personnel P4) is prompted with recommended parameters. When the control parameters are changed, it is assumed that the user enters each mining vehicle V to set the recommended parameters. If the control parameters of the mining vehicle V can be changed remotely, the processing device 10 can also send the recommended parameter data to each mining vehicle V, and the control parameters of the mining vehicle V can be changed without manual operation by the user. In addition, instead of setting the control parameters of all vehicles operating at the mining site S to be the same, for example, for each route ID, the control parameters can be set by Figure 9 The recommended parameters are determined by the processing and different control parameters are set in the mining vehicle V for each path ID.

[0122] -Display example-

[0123] Figure 10A and Figure 10B An example of an evaluation route selection screen displayed by the processing device 10 on the display terminal device 30 is shown.

[0124] Figure 10A This is a diagram showing an example of a screen that presents candidates for evaluation route IDs. Figure 10A The screen is as follows Figure 9 The step S901 is displayed on the display terminal device 30. Figure 10A In FIG, an example is shown in which a bar graph is arranged from the left to the right according to the travel frequency during the evaluation period, together with a frequency threshold. Figure 10A A bar graph is shown as an example, but it can also be displayed as a road map.

[0125] By displaying it in this way, the user can understand at a glance how many appropriate route IDs there are as candidates for evaluable route IDs. Figure 10A In the example, it can be seen that 4 path IDs exceed the frequency threshold and have sufficient data. In addition, considering the margin relative to the frequency threshold, even if some abnormal values ​​are mixed in, it can be estimated whether the impact on productivity evaluation is small. Figure 10A In the example, the leftmost path ID among the four path IDs exceeding the frequency threshold has a larger margin relative to the frequency threshold than the other three path IDs. If the number of outliers is the same, the influence is estimated to be the smallest.

[0126] Figure 10B : is a diagram showing an example of a screen that visualizes a mesh set associated with an evaluation path ID. Figure 10B The screen displays a grid, for example Figure 10AAlthough not specifically shown in the figure, by overlaying it with a map or satellite photo of the mine site S, the user can understand at a glance where the vehicle is traveling within the mine site S. This is also convenient for use as a reference when studying control parameters for other closely located route IDs (for example, route IDs with different loading areas but the same dumping area).

[0127] Figures 11A-11D An example of a display screen of analysis data displayed on the display terminal device 30 by the processing device 10 is shown.

[0128] Figure 11A This is an example of a screen showing the travel frequency of each control parameter in the selected evaluation route ID in a bar graph. Figure 11A In the figure, the data of each cycle ID based on the selected evaluation path ID is shown, and a bar chart is arranged from the left to the right according to the driving frequency, that is, the number of cycle IDs during the evaluation period, and the frequency threshold (which can also be Figure 10A By displaying it in this way, the user can grasp the control parameters with sufficient data and more meaningful at a glance.

[0129] Figure 11B An example of a screen showing comparison of productivity based on control parameters in the selected evaluation path ID is shown. Figure 11B The values ​​of the control parameters shown are representative values ​​(e.g., average values, median values) of the data of the total cycle ID. For example, if Figure 11B If the productivity indicator is expressed as the amount of load carried per liter of fuel (T / L), then by setting alternative parameters (particularly parameter α) as control parameters compared to the standard parameters for the evaluation route ID, a larger load can be carried with the same fuel consumption. This display is not limited to the amount of load carried per liter of fuel (T / L) but can also be expressed as the amount carried per unit time (T / h). Furthermore, for the same evaluation route ID, control parameters based on multiple productivity indicators can be compared and displayed side by side on a single screen for easy overview.

[0130] exist Figure 11B In the case of the example, it can be seen that if the carrying capacity per liter of fuel is (T / L), the parameter α is recommended. By confirming other productivity indicators, it can be understood whether the parameter α is recommended regardless of which productivity indicator in the evaluation path ID or whether the control parameters recommended according to the productivity indicator are different.

[0131] Figure 11CThis screen shows an example of a histogram of the travel frequency (number of cycle IDs) for the standard parameter and parameter α for the same productivity indicator for the selected evaluation route ID. The dotted histogram represents the standard parameter data, while the solid histogram represents the parameter α data. Figure 11C The horizontal axis is a specified productivity index, such as the amount of load carried per liter of fuel (T / L). Figure 11C In the example, the data of the standard parameters and the data of the replacement parameters are close to normal distribution, the estimated deviation is small, and the influence of outliers is small. Figure 11C If a certain amount of data showing very poor transport capacity (T / L) is found in the standard parameters, even if Figure 11B Even if the standard parameters seem unoptimal, it can be understood that the reason may be caused by irregular data. This can be taken into consideration to more appropriately determine the control parameters.

[0132] Figure 11D This is an example of a screen showing the difference in productivity based on control parameters for multiple paths. When multiple evaluation path IDs are selected, it is possible to display at a glance which control parameter is applied to each evaluation path ID (in Figure 11D In the example, which of the standard parameters and the parameter α is applied to improve productivity, such as the amount of load carried per liter of fuel (T / L). Figure 11D In the example, the dotted line histogram represents the data of the standard parameter, and the solid line histogram represents the data of the parameter α. Figure 11D In the example, the four route IDs show differences in productivity based on control parameters. However, by selecting control parameters that increase productivity for the most frequently traveled route IDs, productivity can be improved across the entire mining site S. Furthermore, by making this selection, it is also possible to understand how productivity may change for other route IDs.

[0133] -Effect-

[0134] As described above, in this embodiment, the location data of the driving route is converted into a grid set, and the degree of inconsistency between the unclassified driving route and the grid set of the classified driving route is evaluated. Consequently, the calculations required for route classification are merely two inconsistency calculations, and the parameters suitable for route classification are merely the grid size and the thresholds S1 and S2 for evaluating the degree of inconsistency. GNSS positioning errors and deviations from the driving path of the mining vehicle V can be addressed by adjusting the grid size, eliminating the need to define specific locations for route determination or distance thresholds for each location. Furthermore, as previously mentioned, routes associated with "included" and "detour routes" can also be classified as different from the classified routes. Since there is no need to define specific locations for route determination for each classified route, even if the dump or loading area moves, there is no need to reconfigure the locations for route determination or the distance thresholds. Therefore, according to this embodiment, the driving routes of the mining vehicle V can be classified with high accuracy and ease.

[0135] Furthermore, since travel routes can be classified with high precision, the data count for each route ID (number of cycle IDs) can also be accurately aggregated, enabling appropriate ranking of productivity index values. Furthermore, since the productivity difference based on control parameters can be calculated and displayed for each route ID, this facilitates optimal control parameter setting and user judgment for each route ID. The ability to accurately aggregate the data count for each route ID makes it possible to identify route IDs with truly high travel frequency and select control parameters to improve productivity for these frequently traveled routes, thereby easily improving overall productivity at the mining site S.

[0136] (Second embodiment)

[0137] use Figure 12 13 and 13 illustrate a second embodiment of the present invention.

[0138] This embodiment differs from the first embodiment in the analysis performed after route classification: productivity comparisons are performed for mining vehicles V traveling the same route. Specifically, the processing device 10 references data on multiple travel routes with the same route ID from the storage device 20 (database 22), generates analysis data by comparing productivity indicators calculated based on operational data for each vehicle ID, and outputs this analysis data to the display terminal device 30. This allows for the extraction of degraded vehicles, for example. The route classification process is the same as in the first embodiment, and its description is omitted.

[0139] Figure 12 This is a flowchart showing an example of a procedure for determining a deteriorated vehicle.

[0140] When the processing device 10 receives a request signal corresponding to a user's operation from the display terminal device 30 regarding the comparison of productivity based on the control parameter, it executes Figure 12 Flowchart processing.

[0141] Step S1201

[0142] Step S1201 is Figure 9 The same processing as step S901 is performed. That is, when a request signal is received from the display terminal device 30, the processing device 10 selects the evaluation path during the evaluation period set by the user and sends it to the display terminal device 30. For example, the processing device 10 calculates the travel frequency of each path ID during the evaluation period, arranges the path IDs in order of travel frequency from high to low, and uses these path IDs as candidates for evaluation path IDs, and displays them on the display terminal device 30 in the form of a bar graph together with the frequency threshold. The display is not limited to the bar graph form, and other display forms can also be used. In addition, the selected evaluation path can also be displayed in a Pareto chart. The display of the evaluation path ID is, for example, displayed on a map representing the area where the mining vehicle operates in a manner that allows the route to be determined (as a straight line, for example). In addition, when multiple evaluation path IDs are displayed, they can also be displayed in a color such as red or blue, or in the thickness of the line, in a manner that allows the routes with high travel frequency and routes with low travel frequency to be distinguished.

[0143] Step S1202

[0144] After selecting one or more evaluation route IDs and the productivity index to be evaluated from the candidates displayed on the display terminal device 30, the processing device 10 calculates data for the selected productivity index for each vehicle ID traveling along the evaluation route ID transmitted from the display terminal device 30. The calculated productivity index value can be set as a weighted average value corresponding to the travel frequency of the route ID, similar to the first embodiment. For example, by selecting the load transported per liter of fuel (T / L) and the fuel consumption rate (L / km) as productivity indicators, the degradation state of the powertrain (engine, generator, inverter, motor, etc.) of the mining vehicle V can be estimated. If multiple (at least two or more) evaluation route IDs are selected, the comparison results of each productivity index can also be displayed.

[0145] Step S1203

[0146] After calculating the productivity index for each vehicle ID, the processing device 10 compares the selected productivity index data with a productivity threshold for each vehicle ID to determine whether the productivity exceeds the productivity threshold. The productivity threshold is calculated based on the variance σ of the productivity indexes for multiple vehicle IDs over multiple past periods (e.g., 2-3σ).

[0147] Step S1204

[0148] As a result of the comparison with the productivity threshold, the processing device 10 determines that the vehicle ID whose productivity is lower than the productivity threshold is a degraded vehicle, displays the analysis data in a format that allows comparison of the productivity index in the evaluation route ID with other vehicle IDs on the display terminal device 30, and presents it to the user (e.g., equipment maintenance personnel P4), and ends. Figure 12 When comparing analysis data of degraded vehicles, the order in which the productivity index values ​​are displayed can be arbitrarily changed based on a designated index among a plurality of indexes included in the productivity index.

[0149] Step S1205

[0150] As a result of the comparison with the productivity threshold, the processing device 10 determines that the vehicle ID with a productivity exceeding the productivity threshold is a normal vehicle, displays the analysis data in a format that allows the productivity index in the evaluation path ID to be compared with other vehicle IDs on the display terminal device 30, and presents it to the user (e.g., equipment maintenance personnel P4), and ends. Figure 12 When comparing the analysis data of a normal vehicle, the order in which the productivity index values ​​are displayed can be arbitrarily changed based on a designated index among a plurality of indexes included in the productivity index.

[0151] Figure 13A and Figure 13B 2 shows an example of a screen showing analysis data on the degradation state of the vehicle, which is displayed on the display terminal device 30 by the processing device 10 .

[0152] Figure 13A : is a diagram showing an example of a screen displaying the productivity of each vehicle ID traveling on the selected route ID. Figure 13A In this example, the productivity index representative value (e.g., average value) for each vehicle ID within a single evaluation route ID is displayed along with the productivity threshold, with the vehicle IDs arranged from left to right in descending productivity order. This display shows that mining vehicles V with vehicle IDs (v1-v3) are normal vehicles with productivity exceeding the productivity threshold, while mining vehicle V with vehicle ID (v4) is a degraded vehicle with productivity below the productivity threshold.

[0153] Figure 13BThis is a diagram showing a histogram of the specified operating data for each cycle in the evaluation route. The specified operating data is selected by the user on the display terminal device 30, for example, to determine the cause of productivity reduction. For example, it is one or more items of operating data such as fuel consumption, load carrying amount, and required time for each cycle of the evaluation route ID. When multiple operating data are selected, it is preferred to select Figure 13B The display arrangement shows multiple. Figure 13B In the figure, the histogram represented by the dotted line is the data of the normal vehicle (v1, v2, v3), and the histogram represented by the solid line is the data of the degraded vehicle (v4). Figure 13B If the fuel consumption per cycle is , then it can be seen that the degraded vehicle (v4) consumes more fuel than the normal vehicles (v1, v2, v3) as a whole. In this case, for example, it is suspected that the powertrain of the degraded vehicle (v4) is abnormal.

[0154] According to this embodiment, users (e.g., equipment maintenance personnel P4) can easily identify degraded vehicles with reduced productivity. Furthermore, by classifying vehicles into normal and degraded categories and comparing the trends of operating data for degraded vehicles with those for normal vehicles, the cause of the reduced productivity of degraded vehicles can be easily estimated, allowing, for example, equipment maintenance personnel P4 to improve the efficiency of maintenance work on degraded vehicles.

[0155] (Third embodiment)

[0156] use Figures 14 to 16 A third embodiment of the present invention will be described. In this embodiment, a method for further improving the accuracy of path classification and suppressing the computation load will be described.

[0157] Figure 14 This is a conceptual diagram used to illustrate the omnidirectional grid. Figure 14 In the embodiment, for simplicity, the grid set B of the classified travel path is represented by one grid. In this embodiment, for the grid set B of the classified travel path passed by the mining vehicle V, the set of grids adjacent to the grid set B and surrounding the grid set B in the vertical, horizontal, and diagonal directions ( Figure 14 The grids B1-B8) that is, the omnidirectional grid B' is added to the grid set B of the classified driving path, thereby performing path classification more robustly.

[0158] Figure 15This is a schematic diagram illustrating an example of route classification using omnidirectional meshes. As in the first and second embodiments, B represents the mesh set of the classified route, and A represents the period set. In this embodiment, an omnidirectional mesh set B' of mesh set B is newly recorded in database 22 of storage device 20. Furthermore, during the route classification process, the ratio R2' (R2' = (Σ' - (B∪B') / Σ') of the difference set Δ2' (Δ2' = Σ' - (B∪B')) obtained by subtracting B∪B' from the sum set Σ' (= B∪B'∪A) of the mesh sets A and B and the omnidirectional mesh set B' is calculated. This difference set Δ2' or the ratio R2' indicates the degree of disparity between the mesh set A of the unclassified route and the sum of the mesh set B of the classified route and the omnidirectional mesh set B'. In this embodiment, Δ2' or R2' is used instead of Δ2 or R2 in the first and second embodiments to determine whether Δ2' or R2' is less than a threshold value S2.

[0159] This effectively prevents routes that should be classified as having the same route ID from being identified as different routes, preventing the number of route IDs from increasing beyond a certain limit during the evaluation period while also increasing the amount of data for the same route ID. This allows for more data-based control parameters and vehicle degradation assessments.

[0160] (Fourth embodiment)

[0161] Figure 16 This flowchart illustrates an example of a procedure for deleting low-travel route IDs, performed by a processing device constituting a mine management system according to a fourth embodiment of the present invention. In this embodiment, the processing device 10 deletes data from the storage device 20 (database 22) for route IDs whose number of recorded data during a predetermined evaluation period falls below a predetermined set number. Other than this, the processing performed in this embodiment is the same as in any of the first through third embodiments.

[0162] The processing device 10 is, for example, Figure 16 Executed when the previous execution of the process has passed the preset setting period Figure 16 However, Figure 16 The processing may be executed sequentially or when the route IDs of the classified travel routes reach a preset number.

[0163] Step S1601

[0164] when Figure 16 When the processing starts, the processing device 10 first reads a predetermined evaluation period (for example, the past three months) from the storage device 20 in order to calculate the travel frequency of each route ID.

[0165] Step S1602

[0166] After reading the evaluation period, the processing device 10 calculates the travel frequency for each route ID based on the number of cycle IDs accumulated during the evaluation period.

[0167] Step S1603

[0168] After calculating the travel frequency of each route ID, the processing device 10 determines whether the travel frequency is lower than a preset value (threshold). For route IDs whose travel frequency is higher than the set value, the process ends here. Figure 16 Regarding the route ID whose travel frequency is lower than the set value, the process proceeds to step S1604.

[0169] Step S1604

[0170] For the route ID whose travel frequency is lower than the set value, the processing device 10 deletes the route ID from the storage device 20 (database 22), and ends the process. Figure 16 The deletion process of the route ID is not limited to the deletion of data. For example, the content that cannot be used for route classification can be added to the data of the route ID with the specified unusable flag. The route ID with the unusable flag is deleted in the route classification process ( Figure 6 ) is excluded from the utilization data.

[0171] According to the present embodiment, in the route classification process, travel routes with low travel frequencies are excluded from classification candidates, and the computational load of the processing device 10 can be suppressed in accordance with the reduction in travel routes requiring inconsistency determination.

[0172] Description of Reference Numerals

[0173] 1…mine management system, 10…processing device, 20…storage device, 30…display terminal device, A, B…grid set, D…dumping site (end point), O…dumping site (starting point), S1, S2…threshold value, V…mine vehicle, Δ1…first difference set, Δ2…second difference set, Σ…sum set.

Claims

1. A mine management system comprising: a processing device having a function of generating a grid set represented by IDs, each of grids that divide the ground surface into equally spaced grids, based on the position data and operation data of the mining vehicle, for a travel route starting from a location where the mining vehicle deposited soil and ending at a location where the mining vehicle deposited soil next, and classifying the route IDs based on the grid set; a storage device for associating the grid set and the operation data with respect to the driving route generated by the processing device and storing and accumulating the data; as well as a display terminal device that transmits and receives data to and from the processing device and displays the data received from the processing device; The mine management system is characterized in that: The processing device operates, with respect to a grid set A that is an unclassified driving path and a grid set B that is a classified driving path, on a sum set that is a logical sum of the grid set A and the grid set B, a first difference set that is a logical difference between the sum set and the grid set A, and a second difference set that is a logical difference between the sum set and the grid set B. When the sizes of the first difference set and the second difference set are both below predetermined thresholds, the unclassified driving route is classified as the route ID of the classified driving route, and the grid set A and the operation data related to the unclassified driving route are associated with the route ID and recorded in the storage device. When at least one of the sizes of the first difference set and the second difference set is greater than the threshold, a new route ID is newly generated, and the mesh set A and the operation data related to the unclassified driving route are associated with the new route ID and recorded in the storage device.

2. The mine management system according to claim 1, characterized in that: When the size of the second difference set is larger than the threshold and the size of the first difference set is smaller than the threshold, the processing device makes a determination distinguishing the difference from the classified paths.

3. The mine management system according to claim 1, characterized in that: When the size of the first difference set is zero and the size of the second difference set is larger than the threshold, the processing device makes a judgment distinguishing the difference from the classified path.

4. The mine management system according to claim 1, characterized in that: The storage device records control parameters applied to the driving control of the mining vehicle for each of the driving paths. The processing device refers to the data of multiple driving routes with the same route ID from the storage device, aggregates the data of the productivity index calculated based on the operation data for each of the control parameters, generates analysis data, and outputs the analysis data for each of the control parameters to the display terminal device.

5. The mine management system according to claim 4, characterized in that: The processing device calculates the analysis data by performing a weighted average on the total value of the productivity index according to the number of data of the route ID.

6. The mine management system according to claim 1, characterized in that: The processing device refers to a plurality of data on the travel routes with the same route ID from the storage device, generates analysis data by comparing productivity index data calculated based on the operation data for each mining vehicle, and outputs the analysis data to the display terminal device.

7. The mine management system according to claim 6, characterized in that: The route IDs are output in a display order based on arbitrarily designated productivity indicators among the productivity indicators included in the analysis data.

8. The mine management system according to claim 1, characterized in that: The processing device deletes data of a path ID whose number of data recorded in a predetermined evaluation period is less than a predetermined set number from the storage device.

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