Mine management system

Abstract

This invention provides a mine management system that can help improve the operating costs or efficiency of dump trucks in mines, even when sufficient operational data for dump trucks in mines has not been accumulated. [Solution] The mine management system 1 comprises a storage device 10 for storing the driving history of a dump truck 101, an information processing device 20, and a terminal device 3. If the driving history of an input route input from the terminal device 3 is not stored in the storage device 10, the information processing device 20 selects a route similar to the input route from among the routes for which the driving history is stored in the storage device 10 as a similar route, and calculates an estimated loading time, which is an estimated value of the loading time of the dump truck 101 on the input route, and an estimated waiting time, which is an estimated value of the waiting time of the dump truck 101 on the input route, based on the driving history of the similar route, and outputs the estimated loading time and estimated waiting time to the terminal device 3.

Description

Technical Field

[0001] The present invention relates to a mine management system for collecting and utilizing operation data of mining machines such as dump trucks and shovels.

Background Art

[0002] In order to increase the productivity of mines, operations are carried out to maintain a high operating rate of shovels and prevent dump trucks from being interrupted at loading sites. Therefore, if the waiting time of dump trucks is not properly managed, the productivity of mines will decrease. For example, when a dump truck loaded from a shovel travels to a predetermined dumping site and then returns to the loading site again, if a large number of preceding dump trucks are waiting, the waiting time will become long, resulting in a decrease in productivity. The amount of productivity decrease is, for example, the fuel cost required for idling during waiting and the lost production volume that should have been obtained by going to other loading sites. Also, productivity will decrease if the speed of the dump truck when traveling between the loading site and the dumping site is not properly managed. For example, if a subsequent dump truck travels at a high speed without considering the time for the shovel to load the preceding dump truck (loading time), not only will the waiting time after arriving at the loading site become long, but the fuel consumed when traveling at an unnecessary speed will also be wasted.

[0003] Regarding these challenges, Patent Document 1 discloses an operating method for suppressing the engine power of dump trucks according to their waiting time and load. This method aims to achieve both fuel conservation and prevention of increased transport time by suppressing engine power in cases where the waiting time is long and the engine load is low. Patent Document 2 discloses a method for calculating an improvement index for travel speed based on travel time and stopping position. This method records the time and location of long-term stops and prevents unnecessary fuel consumption and decreased productivity by reducing the speed of following vehicles traveling through those locations. Patent Document 3 discloses a method for improving productivity by estimating the capacity (carrying capacity) of dump trucks and the capacity (excavation capacity) of excavators and dispatching dump trucks according to the excavator capacity. This method aims to improve productivity by correcting the capacity of dump trucks with the average waiting time and implementing an optimal dump truck deployment. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2017-89505 [Patent Document 2] Japanese Patent Publication No. 2014-238875 [Patent Document 3] WO2015 / 181972 publication [Overview of the project] [Problems that the invention aims to solve]

[0005] However, in the technology described in Patent Document 1, the reduction in fuel consumption due to suppressing the maximum output through power reduction is offset by the increase in fuel consumption due to the longer driving time. In addition, Patent Document 1 does not adequately consider the increase in fuel consumption due to traffic congestion caused by reducing speed.

[0006] Furthermore, while Patent Document 2 mentions reducing speed based on past traffic congestion information, it does not adequately consider the increase in fuel consumption due to new congestion caused by reducing speed.

[0007] Furthermore, the technology described in Patent Document 3 attempts to solve the above-mentioned problems by dispatching dump trucks. However, Patent Document 3 does not adequately consider the case where the transport capacity of the dump trucks far exceeds the excavation capacity of the shovels. Moreover, all of the technologies in Patent Documents 1 to 3 have the problem that when introduced to a new mine, they cannot be effective until sufficient operational data of the dump trucks has been accumulated.

[0008] This invention has been made in view of the above-mentioned problems, and its objective is to provide a mine management system that can help improve the operating costs or operational efficiency of dump trucks in mines. [Means for solving the problem]

[0009] To achieve the above objective, a mine management system for managing the operation of dump trucks that repeatedly travel a route back and forth between a loading area and a discharge area in a mine comprises a storage device for storing the travel history of the dump trucks, an information processing device, and a terminal device capable of communicating with the information processing device. The information processing device, when the travel history of an input route, which is a route input via the terminal device, does not exist in the storage device, selects a route similar to the input route from among the routes whose travel history is stored in the storage device as a similar route, calculates an estimated loading time, which is an estimated value of the loading time of the dump truck on the input route, and an estimated waiting time, which is an estimated value of the waiting time of the dump truck on the input route, based on the travel history of the similar route, and outputs the estimated loading time and the estimated waiting time to the terminal device. [Effects of the Invention]

[0010] According to the present invention, even if sufficient operational data for dump trucks in a mine has not been accumulated, for example, it is possible to support the improvement of the operating costs or operational efficiency of dump trucks in a mine. [Brief explanation of the drawing]

[0011] [Figure 1] Configuration diagram of the mine management system according to the first embodiment. [Figure 2] Functional block diagram of the mine management system [Figure 3] A diagram showing an example of a driving history table. [Figure 4] This diagram schematically represents the operational data of dump trucks for one cycle, from the completion of soil discharge to the start of the next discharge. [Figure 5] A flowchart illustrating the overall processing of an information processing device. [Figure 6] A flowchart detailing the process of searching for similar route IDs in the travel history table. [Figure 7A] A diagram showing an example of mesh information for the input path. [Figure 7B] A diagram showing an example of mesh information for comparison paths. [Figure 7C] This diagram shows the z-coordinates of the mesh information for the input path, arranged in the order of travel. [Figure 7D] A diagram showing the z-coordinates of the mesh information for the comparison route, arranged in the order of travel. [Figure 8] A flowchart detailing the process of calculating the route ID and its similarity (maximum similarity) to the route with the greatest similarity to the input route's mesh information. [Figure 9] This diagram shows an example of a monitoring screen displayed on the terminal device of a train operations manager. [Figure 10] A flowchart illustrating the process involved in dispatching dump trucks. [Figure 11] A diagram showing an example of a control parameter history table. [Figure 12] This diagram shows an example of a management screen displayed on a terminal device used by equipment maintenance personnel. [Figure 13]Flowchart showing a process for optimizing control parameters related to the driving mode of an engine-driven dump truck [Figure 14] Flowchart showing a process for optimizing control parameters related to the fuel consumption of an engine-driven dump truck [Figure 15] Flowchart showing a process for optimizing control parameters related to the charge and discharge of a battery-driven dump truck [[ID=A]]

Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each figure, the same reference numerals are assigned to equivalent elements, and redundant descriptions will be omitted as appropriate.

Examples

[0013] FIG. 1 is a configuration diagram of a mine management system 1 according to a first embodiment of the present invention. The mine management system 1 collectively manages the productivity of a mine (for example, the production cost and production efficiency of mining machinery 百 described later). The mine management system 1 includes a mine management device 2 that performs information processing for collectively managing mining machinery 百 operating within a predetermined mine area, and a terminal device 3 having a display function for presenting various information processed by the mine management device 2 to a user <110>. The mining machinery 百 is composed of a plurality of mining machines such as a dump truck <101> (hereinafter, appropriately referred to as "vehicle"), a shovel <102>, and a dozer <103> operating within a predetermined mine area. Note that the mining machinery 百 managed by the mine management device 2 is not limited to these mining machines <101> to <103>. Further, the mining machines <101> to <103> may be engine-driven or battery-driven.

[0014] It should be noted that there seems to be an error in the original text where "百" is used instead of a specific number. I have translated it as it is in the context, but it might need to be corrected in the original content. Also, the reference numeral "110" in the English translation should be checked in the original to ensure its accuracy.The mine management device 2 collects and stores various data related to mining machinery 100 that are managed collectively in the same mining area, and processes the collected data. The mine management device 2 includes a storage device 10 in which various data of the mining machinery 100 are aggregated and stored, and an information processing device 20 that collects various data of the mining machinery 100 and records it in the storage device 10, as well as processing the various data of the mining machinery 100.

[0015] The storage device 10 stores, for example, operational data and location data collected from mining machines 101 to 103, as a database. The operational data and location data include, for example, the detected values ​​from the sensors of mining machines 101 to 103 and the calculated values ​​from the control devices.

[0016] The information processing device 20 processes various data stored in the storage device 10 and provides productivity information, deterioration / abnormality information, etc., to the terminal device 3. In the mine management system 1, it is desirable that the operation data and location data of the mining machines 101 to 103 be transmitted sequentially, but considering the communication status and communication costs, it is not always possible to transmit them sequentially. Therefore, the information processing device 20 is designed to start processing after accumulating a certain amount of operation data. A certain amount can be determined, for example, by the time equivalent to the longest work cycle in the history of mining machinery 100 from loading to loading, or by the amount of operation data transmitted in the longest work cycle in the history of mining machinery 100.

[0017] Terminal device 3 is, for example, a laptop computer or mobile terminal having a display device 3a and an input device 3b. Terminal device 3 displays productivity information, deterioration / abnormality information, etc., which are processing results of the mine management device 2 (information processing device 20). Terminal device 3 can display various types of information in dashboard format, report format, email format, etc. The various types of information displayed on terminal device 3 include, for example, information related to the productivity of mining machines 101-103, such as route information, waiting time, and optimal number of machines in operation, as described later. Terminal device 3 is used by the user 110 of the mine management system 1.

[0018] Users 110 of the mining management system 1 include, for example, an operations manager 111 who creates or modifies the operation plans for mining machinery 101-103, an operator supervisor 112 who instructs the operators of mining machinery 101-103, a road maintenance worker 113 who maintains and inspects the road surface of the mine routes, etc., an equipment maintenance worker 114 who maintains and inspects mining machinery 101-103 and various other equipment, and a mining and maintenance manager 115 who creates or modifies the mining and maintenance plan for the mine.

[0019] By utilizing the various information displayed on the terminal device 3 (e.g., the dashboard), user 110 can detect a decline in mine productivity early and maintain and manage mine productivity by implementing countermeasures based on the factors causing the decline. For example, the mine operations manager 111 can modify the operation plan for mining machinery 101-103 based on the information displayed on the terminal device 3. Operator instructor 112 can identify operators who need to improve their driving from the information displayed on the terminal device 3 and provide driving guidance. Road maintenance personnel 113 can use the information displayed on the terminal device 3 to identify road surface areas that are causing a decline in productivity early and repair them. Equipment maintenance personnel 114 can identify deterioration or malfunctions of mining machinery 101-103 from the information displayed on the terminal device 3 and perform repairs or calibrations. Equipment maintenance personnel 114 operate the terminal device with an input device to store data of the maintenance work performed in the storage device 10. Furthermore, the mining and maintenance manager 115 can modify the mining and maintenance plan or issue improvement instructions to the operations manager 111, operator supervisor 112, road maintenance personnel 113, and equipment maintenance personnel 114 to prevent a decline in production by combining weather information (history and forecast) and mineral prices (history and forecast) obtained via the internet 120 with the information displayed on the terminal device 3.

[0020] Database 130 is a cloud database that stores operational data collected from other mines. By storing data that can be used at this mine from the data collected and analyzed by the mine management system 1 installed at other mines, and making it accessible from the mine management system 1, even if the operational data for this mine is not complete, productivity improvement instructions can be issued by utilizing the operational data from other mines.

[0021] Figure 2 is a functional block diagram of the mine management device 2. The mine management device 2 comprises an input unit 201, a travel history table 202, a waiting time estimation unit 203, and an optimization unit 204. The operation manager 111, who manages the operation of the dump truck 101, inputs identification information of the route (input route) to instruct the dump truck 101 to load and unload areas via the terminal device 3 into the input unit 201. In addition, the operation data of the dump truck 101 is input into the input unit 201 when the unloading of soil by the dump truck 101 is completed. The input unit 201 calculates the travel history (route identification information, cycle time, loading time, waiting time, etc.) from the operation data of the dump truck 101 and stores it in the travel history table 202.

[0022] The waiting time estimation unit 203 calculates an estimated waiting time for the input route (estimated waiting time) based on the driving history stored in the driving history table 202. The method for calculating the estimated waiting time will be described later.

[0023] The optimization unit 204 calculates the optimal number of dump trucks to travel on the input route (optimal number of dump trucks) or the optimal control parameters to reduce the fuel consumption of dump trucks 101 traveling on the input route, based on the estimated waiting time. The calculated optimal number of dump trucks and control parameters are displayed on the terminal device 3 of, for example, the operations manager 111 or the equipment maintenance worker 114 (shown in Figure 1), and the operations manager 111 issues instructions to change the route to dump trucks 101 or the equipment maintenance worker 114 corrects the control parameters. As a result, the waiting time of dump trucks 101 on the input route is shortened, or the fuel consumption of dump trucks 101 traveling on the input route is reduced. Alternatively, the system may be configured to directly instruct dump trucks 101 to change their route without human intervention, or to directly rewrite the control parameters.

[0024] Figure 3 shows an example of a driving history table 202. The driving history table 202 includes a cycle table 301, a route table 302, and a mesh table 303, and is stored in the storage device 10.

[0025] The cycle table 301 is a table of travel history compiled for each cycle from the start of soil discharge to the start of the next soil discharge. It stores route identification information (route ID) that identifies the route, vehicle identifier (vehicle ID) that identifies the vehicle traveling, cycle start time calculated from operational data, cycle time, waiting time, loading time, load amount, fuel consumption, distance traveled, etc.

[0026] Route table 302 stores a list of mesh IDs that identify the travel route. This list of mesh IDs is arranged in order of travel; for example, in route ID: r2, the first m3 represents the cycle start position, and the last m4 represents the cycle end position. The position information assigned to each of these mesh IDs is stored in mesh table 303, where 3D information such as x, y, and z coordinates is assigned. In addition, to enable comparison with other mines, the mesh size is fixed (e.g., 30m square), and numerical values ​​representing the vertical and horizontal relationship of the mesh (e.g., GPS latitude and longitude) are registered for the x and y coordinates, and numerical values ​​representing the height (e.g., GPS altitude coordinates) are registered for the z coordinate.

[0027] Figure 4 schematically represents the operational data of dump truck 101 for one cycle, from the completion of soil discharge to the start of the next soil discharge. In Figure 4, the time-series changes of vehicle speed, fuel, and load capacity are shown from top to bottom. Here, time T0 is the start time of soil discharge in the previous cycle, time T1 is the end time of soil discharge in the previous cycle (cycle start time), time T2 is the arrival time at the loading area, time T3 is the start time of parking, time T4 is the completion time of parking, time T5 is the completion time of loading, time T6 is the start time of soil discharge, and time T7 is the end time of soil discharge. Calculating the aforementioned travel history from this operational data, the cycle time Tcycle is the elapsed time from time T1 to time T7, the waiting time Tw is the elapsed time from time T2 to time T3, and the loading time Tl is the elapsed time from time T4 to time T5. The route identification information can be either the route ID instructed to the dump truck or a route ID automatically generated from GPS coordinates. Furthermore, in order to evaluate productivity, it is preferable to include the load capacity P, cycle fuel consumption (cumulative fuel value from time T1 to time T7), and mileage (cumulative speed value from time T1 to time T7) in the driving history. This makes it possible to accurately analyze the impact on cycle fuel consumption when waiting time is optimized. Although not mentioned in this embodiment, if the dump truck is equipped with a battery or trolley, calculating and recording the power consumption by accumulating the power received from the trolley or battery also provides useful information for predicting improvements in production costs after optimization.

[0028] Figure 5 is a flowchart showing the overall processing of the information processing device 20. In step S501, the travel history of the route (input route) corresponding to the route identification information input via the terminal device 3 is searched in the travel history table 202. In the following step S502, it is determined whether or not there is a travel history for the input route. If the determination result is YES, the process proceeds to step S505; if the determination result is NO, the process proceeds to step S503.

[0029] In step S503, the system searches the travel history table 202 for route IDs (similar route IDs) of routes similar to the input route (details will be described later).

[0030] In step S504, it is determined whether or not a similar route ID exists. If the result is YES, the process proceeds to step S505; if the result is NO, the process ends.

[0031] In step S505, the driving history of the input route or a similar route is extracted from the cycle table 301.

[0032] In step S506, the estimated waiting time for the input route is calculated from the extracted travel history. Various methods can be considered for calculating the estimated waiting time, such as using the average value of the actual waiting time accumulated in the travel history, or predicting it from the trend of the actual waiting time. However, here we will explain the method using the queuing model (Equation 1). In Equation 1, W represents the time required for the dump truck to spend at the loading site (waiting time + loading time), and it is shown that this can be calculated from the loading efficiency μ of the shovel 102 and the arrival rate λ of the dump truck.

[0033]

number

[0034] Here, the loading efficiency of the shovel 102 can be calculated from the reciprocal of the loading time Tl in the travel history, 1 / Tl, and the arrival rate λ of the dump truck can be calculated from the reciprocal of the cycle time, 1 / Tcycle. The waiting time can then be calculated by subtracting the loading time Tl from the required time W.

[0035] In step S507, it is determined whether the estimated waiting time is longer than the predetermined target waiting time. If the result is YES, the process proceeds to step S508; otherwise, the process ends.

[0036] Step S508 involves optimizing the operation cost or efficiency of dump truck 101. Here, the method for optimizing the number of dump trucks using a queuing model is explained using equations 2 to 4.

[0037]

number

[0038]

number

[0039]

number

[0040] Equation 2 is a modified version of Equation 1, and calculates the average arrival rate λave of dump trucks using the average loading efficiency μave and average required time Wave calculated from historical data.

[0041] Equation 3 is the result of calculating the current number of dump trucks traveling Ndump based on the average arrival rate λave and cycle time Tcycle, and Equation 4 shows the number of dump trucks traveling Ndt when the required time is shortened by Twt. By presenting these values ​​to the operations manager 111, the operations manager 111 can decide whether to increase or decrease the number of dump trucks traveling on each route. For example, if the loading time Tl of the input route is relatively long and Ndump > Ndt, the operations manager can decide to reduce the number of dump trucks traveling on the input route. With this configuration, even if there is no travel history for the input route, it is possible to optimize the number of dump trucks traveling on the input route by using the travel history of similar routes. Furthermore, even if the excavator 102 and dump truck 101 are automated, it is thought that waiting times will still occur due to variations in transport capacity and excavation capacity due to machine differences and deterioration, or due to environmental disturbances such as the hardness of the road surface and ground, but these waiting times can also be estimated using the above equations.

[0042] Figure 6 is a flowchart detailing the process of searching for similar route IDs in the travel history table 202 (step S503 in Figure 5).

[0043] In step S601, mesh information of the input path (an example is shown in Figure 7A) is extracted.

[0044] In step S602, the similarity between the mesh information of the input route and the mesh information of other routes in the travel history table 202 is calculated, and the route ID and its similarity (maximum similarity) of the route with the greatest similarity are calculated (details will be described later).

[0045] In step S603, it is determined whether the maximum similarity calculated in step S602 is greater than a predetermined value. If the result is YES, the process proceeds to step S604; if the result is NO, the process proceeds to step S605.

[0046] In step S604, it is determined that a similar route exists, the route ID with the highest similarity is set as the similar route ID, and the flow is terminated.

[0047] In step S605, it is determined that there are no similar routes, and a route ID that does not exist in the travel history table 202 (in this case, Null) is set as the similar route ID, and the flow is terminated.

[0048] Figure 8 is a flowchart detailing the process (step S602 in Figure 6) for calculating the route ID and its similarity (maximum similarity) of the route with the greatest similarity to the mesh information of the input route.

[0049] In step S801, a list of mesh IDs corresponding to the input route IDs is extracted from the route table 302, and the reference mesh count is calculated. For simplicity of explanation, the length of the mesh ID list is used as the reference mesh count here, but it is also acceptable to count only different mesh IDs and not duplicate mesh IDs. Then, route IDs whose mesh count falls within the range of reference mesh count ± a predetermined number (for example, 10% of the reference mesh count) are extracted from the route table 302, and a comparison route ID list is generated.

[0050] In step S802, altitude information for the input route's mesh ID list is extracted from the mesh table 303, and the reference altitude is calculated. Here, the value obtained by subtracting the minimum z coordinate from the maximum z coordinate is used as the reference altitude, but the reference altitude is not limited to this; the average or median of the z coordinates may also be used as the reference altitude. Then, the altitude (comparison altitude) is extracted in the same way from the mesh ID list of route IDs included in the aforementioned comparison route ID list, and only route IDs whose comparison altitude is within the range of reference altitude ± a predetermined value (for example, 10% of the reference altitude) remain in the comparison route ID list, and the other route IDs are deleted from the comparison route ID list. This makes it possible to exclude a large number of route IDs that are unlikely to be similar route IDs before comparing the mesh information, thereby reducing the computational load associated with comparing the mesh information.

[0051] In step S803, it is determined whether the comparison route ID list is empty or not. If the result is YES, the flow ends; if the result is NO, the process proceeds to step S804.

[0052] In step S804, the comparison route ID at the beginning of the comparison route ID list is taken from the list, and mesh information corresponding to the comparison route ID (an example is shown in Figure 7B) is extracted from the route table 302 and the mesh table 303.

[0053] In step S805, the similarity between the mesh information of the input path and the mesh information of the comparison path is calculated. Specifically, as shown in Figure 7A, the set of z coordinates of the rectangular area (number of meshes in the x-axis direction = w, number of meshes in the y-axis direction = h) in the mesh information of the input path is used as template T(i,j), and template matching is performed on the mesh information of the comparison path shown in Figure 7B. Here, using normalized cross-correlation (Equation 5), the set of z coordinates of the rectangular area with the same shape as template T(i,j) in the mesh information of the comparison path is used as comparison part I(a+i,b+j), and the correlation coefficient R(a,b) with template T(i,j) is calculated for all combinations of a and b, and the maximum value of the correlation coefficient R(a,b) is used as the similarity. This makes it possible to calculate similarity that takes into account both the horizontal shape and elevation of the path.

[0054]

number

[0055] Furthermore, a simpler approach is to arrange the z-coordinates of the meshes constituting the input route in the order of travel, as shown in Figure 7C, and the z-coordinates of the meshes constituting the comparison route in the order of travel, as shown in Figure 7D. By calculating the correlation coefficient R using Equation 6, the similarity that takes into account the altitude difference and distance of the route that has the greatest impact on fuel efficiency can be calculated. Note that the z-coordinates of the input route are denoted as Z (uppercase) to distinguish them from the z-coordinates of the comparison route.

[0056]

number

[0057] In step S806, the maximum similarity value calculated so far (maximum similarity) is compared with the similarity value calculated this time. If the currently calculated similarity value is greater, the currently calculated similarity value is saved as the maximum similarity value, and the comparison route ID is saved as the similar route ID. By repeating this process from steps S804 to S806 until the comparison route ID list is empty, the route ID of the route most similar to the input route can be calculated as the similar route ID.

[0058] Figure 9 shows an example of the monitoring screen 900 displayed on the terminal device 3 of the operations manager 111. The monitoring screen 900 displays the operating status of dump trucks 101 and excavators 102, for example, in order of the most frequently traveled routes. By referring to the monitoring screen 900 and making appropriate dispatches, the operations manager 111 can improve the operating cost or efficiency of dump trucks 101. The estimated loading time and estimated waiting time displayed here are the values ​​from the latest cycle extracted from the travel history table 202, or a weighted average where the weight increases as the cycle becomes more recent. For route ID:r1, the estimated loading time is 5 minutes and the estimated waiting time is 10 minutes, indicating that on average, nearly two vehicles (RD001 and RD003 in this case) are waiting to load. In such a situation, the operations manager 111 assigns a vehicle to a route where there is no waiting, such as route ID:r3 (in this case, RD004 is assigned to route ID:r3). Furthermore, by setting the target waiting time to approximately 50% of the estimated loading time (or within the range of 25% to 75%), calculating the optimal number of dump trucks using Equation 4, and comparing it with the estimated number of dump trucks calculated by Equation 3, it becomes easy to determine whether the number of dump trucks is appropriate.

[0059] Next, we will describe the case in which the information processing device 20 issues dispatch instructions for the dump truck 101 without going through the judgment of the operations manager 111.

[0060] Figure 10 is a flowchart showing the process related to dispatching instructions for dump truck 101. By prioritizing this process for routes with high travel frequency, a high level of productivity improvement can be maintained.

[0061] In step S1001, the estimated waiting time for the input path is calculated using the method described above.

[0062] In step S1002, it is determined whether the estimated waiting time is longer than the predetermined target waiting time. If the result is YES, the process proceeds to step S1003; if the result is NO, the process ends.

[0063] In step S1003, the trend of the most recent estimated waiting time extracted from the driving history table 202 is analyzed to determine whether the estimated waiting time is increasing or not. Various methods can be applied to the trend analysis, but for example, the estimated waiting time for the past few times can be extracted from the driving history table 202 and it can be determined whether the trend is increasing or not using linear approximation. If the result of the determination in step S1003 is YES, proceed to step S1004; if the result is NO, terminate the flow. This determination is not mandatory, but adding this determination can prevent excessive vehicle dispatch changes.

[0064] In step S1004, it is determined whether the dump trucks currently traveling on the input route can be rerouted to another route. Here, as mentioned above, it is determined whether there is a route with a smaller estimated number of dump trucks than the optimal number of dump trucks. If the result is YES, the process proceeds to step S1006; if the result is NO, the process proceeds to step S1005.

[0065] In step S1006, dump trucks that have finished unloading soil along the input route are assigned to the route with the largest gap between the estimated number of dump trucks and the optimal number of dump trucks, and the flow ends.

[0066] In step S1005, it is determined whether there are any dump trucks traveling along the input route that can be refueled or serviced. For example, a vehicle with less than half a fuel tank remaining is considered a vehicle that can be refueled, and a vehicle that has a scheduled parts replacement or periodic inspection and is ready for it is considered a vehicle that can be serviced. If the result of the determination in step S1005 is YES, the process proceeds to step S1007; if the result is NO, the process proceeds to step S1008.

[0067] In step S1007, the vehicle with the lowest fuel level among the vehicles selected in step S1005 that are eligible for refueling is instructed to go to a refueling station, or the vehicle that is eligible for maintenance is instructed to go to a maintenance station, and the flow ends.

[0068] In step S1008, the vehicle with the lowest fuel level is instructed to go to the waiting area, and the flow is terminated. By automating this series of processes, it is possible to reduce unnecessary waiting time and fuel consumption, and to improve production efficiency by utilizing the otherwise wasted waiting time for refueling and maintenance.

[0069] (summary) In the first embodiment, a mine management system 1 for managing the operation of a dump truck 101 that repeatedly travels a route back and forth between a loading area and a discharge area in a mine includes a storage device 10 for storing the travel history of the dump truck 101, an information processing device 20, and a terminal device 3 that can communicate with the information processing device 20. When the travel history of an input route, which is a route input via the terminal device 3, is not stored in the storage device 10, the information processing device 20 selects a route similar to the input route from among the routes for which the travel history is stored in the storage device 10 as a similar route. Based on the travel history of the similar route, it calculates an estimated loading time, which is an estimated value of the loading time of the dump truck 101 on the input route, and an estimated waiting time, which is an estimated value of the waiting time of the dump truck 101 on the input route, and outputs the estimated loading time and the estimated waiting time to the terminal device 3.

[0070] According to the first embodiment configured as described above, if the driving history of the input route input via the terminal device 3 is not stored in the storage device 10, the estimated loading time and estimated waiting time of the dump truck 101, calculated based on the driving history of the most similar route to the input route, are output to the terminal device 3 held by the operations manager 111, etc. This makes it possible to support the improvement of the operating costs or operational efficiency of the dump truck 101 in the mine, even if sufficient operational data of the dump truck 101 in the mine has not been accumulated.

[0071] Furthermore, in the first embodiment, the storage device 10 stores the location information of the route traveled by the dump truck 101 as mesh information, and the information processing device 20 calculates the similarity between the mesh information of the input route and the mesh information of one or more routes stored in the storage device 10, and selects the route with the highest similarity and a similarity above a predetermined value from among the mesh information of one or more routes as the similar route. This makes it possible to select a similar route that is similar to the input route in a simple and highly accurate manner.

[0072] Furthermore, the information processing device 20 in the first embodiment calculates the estimated number of dump trucks, which is the estimated number of dump trucks 101 traveling on the input route, based on the travel history of similar routes, and outputs the estimated number of dump trucks to the terminal device 3. This allows the operations manager 111 to adjust the number of dump trucks traveling on the input route by referring to the estimated number of dump trucks displayed on the terminal device 3.

[0073] Furthermore, the information processing device 20 in the first embodiment sets a target waiting time, which is a target value for the waiting time of the dump truck 101 on the input route, based on the travel history of the similar route, and outputs the target waiting time to the terminal device 3. This allows the mine's operations manager 111 to adjust the number of dump trucks traveling on the input route by referring to the target waiting time of the input route displayed on the terminal device 3.

[0074] Furthermore, the information processing device 20 in the first embodiment calculates the optimal number of dump trucks to bring the estimated waiting time closer to the target waiting time based on the travel history of similar routes, and outputs the optimal number of dump trucks to the terminal device 3. This allows the operations manager 111 to adjust the number of dump trucks traveling on the input route by referring to the optimal number of dump trucks for the input route displayed on the terminal device 3.

[0075] Furthermore, in the first embodiment, if the estimated waiting time of the input path is longer than the target waiting time, the information processing device 20 instructs the dump truck 101, which is traveling along the input path and is refuelable or serviceable, to suspend operations. This makes it possible to shorten the waiting time of the input path without reducing the productivity of the mine.

[0076] Furthermore, the information processing device 20 in the first embodiment sets the target waiting time to fall within the range of 25% to 75% of the estimated loading time. This makes it possible to set a target waiting time that can stably improve the operating cost or operational efficiency of the dump truck 101, regardless of variations in the loading time or cycle time of the dump truck 101. [Examples]

[0077] A second embodiment of the present invention will be described, focusing on the differences from the first embodiment. In the second embodiment, the optimization unit 204 (shown in Figure 2) improves the operating cost or operating efficiency of the dump truck 101 by optimizing the control parameters of the dump truck 101. Note that the configuration other than the optimization unit 204 is the same as in the first embodiment, so a description will be omitted.

[0078] Figure 11 shows an example of the control parameter history table 1100. The control parameter history table 1100 is included in the driving history table 202 and stores the control parameter settings for each vehicle cycle in chronological order. The control parameter history table 1100 is linked to the cycle table 301 of the driving history table 202 stored in the storage device 10, and stores the control parameter settings for each vehicle for the route ID, vehicle ID, and cycle start time of the cycle table 301. The format of the control parameter history table 1100 can be anything as long as it shows the control parameter settings for each cycle. The control parameter here switches the drive mode of the dump truck 101 and is set to one of the following: normal mode, which is the standard drive mode; eco mode, which consumes less fuel than normal mode; or power mode, which drives with more power than normal mode. The control parameter setting is switched by an equipment maintenance worker 114 or operator operating a switch (not shown) provided on the dump truck 101.

[0079] Figure 12 shows an example of the management screen 1200 displayed on the terminal device 3 of the equipment maintenance worker 114. The management screen 1200 displays the waiting time for each route (actual waiting time or estimated waiting time calculated using the method described above), arranged from left to right in order of the most frequently traveled route. The target waiting time, shown by the dashed line in the figure, is set in advance based on the loading time to prevent the excavator 102 from waiting for dump trucks (at least one truck will be on standby) and to prevent the waiting time for dump trucks from becoming too long (so that there are no more than two vehicles on standby). If, on the management screen 1200, all vehicles are set to normal mode and there are many routes where the waiting time is longer than the target waiting time, the equipment maintenance worker 114 can determine in advance that there is a good chance that fuel consumption can be reduced by changing the settings of the control parameters.

[0080] Figure 13 is a flowchart showing the process for optimizing control parameters related to the drive mode of an engine-driven dump truck 101.

[0081] In step S1301, the estimated waiting time for the route is calculated using the method described above, based on the data in the cycle table 301.

[0082] In step S1302, it is determined whether or not there are no dump trucks waiting. If the result is YES, the process proceeds to step S1303; if the result is NO, the process proceeds to step S1308. The state of having no dump trucks waiting is defined, for example, as a state in which the estimated number of dump trucks, as explained in Figure 9, is one or more less than the optimal number of dump trucks.

[0083] In step S1308, the operator or equipment maintenance worker 114 is notified to set the drive mode of the dump truck 101 to power mode, and the flow is terminated. This improves acceleration and increases the maximum speed, shortening the cycle time and suppressing the decrease in production efficiency due to waiting for the dump truck.

[0084] In step S1303, it is determined whether the estimated waiting time is greater than the target waiting time. If the result is YES, the process proceeds to step S1304; if the result is NO, the process proceeds to step S1307.

[0085] In step S1304, the cycle table 301 and the control parameter history table 1100 are merged, and it is determined whether the difference between the fuel consumption in normal mode and the fuel consumption in eco mode is greater than a predetermined value. If the result of the determination in step S1304 is YES, the process proceeds to step S1305; if the result is NO, the process proceeds to step S1307.

[0086] In step S1305, the estimated waiting time when the drive mode of the dump truck 101 is set to eco mode is calculated, and it is determined whether the estimated waiting time is less than a predetermined value (for example, +10% of the target waiting time). The estimated waiting time when set to eco mode is calculated using historical data of waiting time, cycle time, and loading time when eco mode is set.

[0087] In step S1307, the operator or equipment maintenance worker 114 is notified to set the drive mode of the dump truck 101 to normal mode, and the flow is terminated. This prevents frequent notification of setting changes to the operator or equipment maintenance worker 114 by maintaining normal mode when the fuel improvement effect is not significant.

[0088] In step S1306, the operator or equipment maintenance worker 114 is notified to set the drive mode of the dump truck 101 to eco mode, and the flow is terminated. This configuration allows for the setting of control parameters that are optimal for each route and the current situation. Although this explanation assumes that the operator or equipment maintenance worker 114 intervenes in changing the control parameter settings, the control parameter settings may be changed automatically.

[0089] Figure 14 is a flowchart illustrating the process for optimizing control parameters related to fuel consumption in an engine-driven dump truck 101. Here, we will explain a method that automatically adjusts the values ​​of each control parameter, rather than switching between predetermined settings as described previously.

[0090] Since the processes in steps S1401 to S1403 are the same as those in steps S1301 to S1303 in Figure 13, their explanation will be omitted.

[0091] Steps S1408 to S1410 are processes that adjust control parameters to shorten the cycle time when it is determined in step S1402 that there is no dump truck waiting. Specifically, in step S1408, the amount of fuel injected at idle is increased from the normal setting. In the following step S1409, the amount of fuel injected at maximum output is increased to the upper limit. In the following step S1410, the fuel injection response, which had been reduced for fuel-efficient operation, is increased to the upper limit. Through this series of processes, the vehicle's acceleration and driving speed are improved, and a decrease in production efficiency can be prevented.

[0092] On the other hand, if it is determined in step S1403 that the waiting time is longer than the target waiting time, the process proceeds to step S1404. In step S1404, the idle fuel injection amount is reduced. Reducing the idle fuel injection amount during the cycle is particularly effective when the downhill driving time is long and has almost no impact on the cycle time, so it should be configured to be implemented with the highest priority. In the following step S1405, it is determined whether the estimated waiting time is longer than a predetermined time (for example, twice the loading time). If the result of the determination in step S1405 is YES, the process proceeds to step S1406, and if the result is NO, the process proceeds to step S1412.

[0093] In step S1406, the amount of fuel injected at maximum output when the accelerator is fully open is reduced by a certain percentage compared to the normal setting. In the following step S1407, the fuel injection response to accelerator input is further reduced compared to the normal setting in order to further decrease acceleration. By adjusting the control parameters in steps S1406 and S1407, although the maximum speed and acceleration speed are reduced, the predetermined time in step S1405 is adjusted so that there is no waiting for the shovel 102 dump truck (production efficiency is not reduced), thereby reducing fuel costs without lowering production efficiency.

[0094] If the waiting time is determined to be less than or equal to the target waiting time in step S1403, the idle fuel injection amount is set to the normal setting in step S1411, and the fuel injection amount and fuel injection response at maximum output are set to the normal setting in the following step S1412, and the flow is terminated.

[0095] Figure 15 is a flowchart showing the process for optimizing the control parameters related to charging and discharging of a battery-powered dump truck 101. Here, the optimization is performed to reduce the life cycle cost of the dump truck 101 by extending the replacement cycle of the expensive battery. Steps S1501 and S1502 are the same as steps S1301 and S1302 in Figure 13, so their explanation is omitted.

[0096] If it is determined in step S1502 that there is no dump truck waiting, in order to prevent the excavator 102 from waiting for a dump truck, the maximum charging current is increased in step S1508 above the normal setting, the maximum discharge current is increased in the following step S1509 above the normal setting, and the charge / discharge response is increased in the following step S1510 above the normal setting, and the flow is terminated. Executing steps S1508 to S1510 causes the battery temperature to rise, shortening the battery life and worsening the life cycle cost of the dump truck 101. However, the improved dump truck driving force (dump truck carrying capacity) reduces the waiting time for the excavator 102 to load, thereby reducing the loss of production opportunities in the mine and preventing a deterioration in overall production costs for the mine.

[0097] If it is determined in step S1502 that there are dump trucks waiting, then in step S1503 it is determined whether the estimated waiting time is longer than a predetermined time. The predetermined time is set to, for example, twice the loading time. In this case, if there are two or more dump trucks waiting, the result of the determination in step S1503 is YES. If the result of the determination in step S1503 is NO, proceed to step S1504; if the result is YES, proceed to step S1504.

[0098] In step S1504, the maximum charging current, maximum discharging current, and charge / discharge response are set to normal settings, and the flow is terminated.

[0099] In step S1505, the maximum battery charging current is reduced compared to the normal setting, in the following step S1506, the maximum discharge current is reduced compared to the normal setting, and in the following step S1507, the charge / discharge response is reduced compared to the normal setting, ending the flow. Although this reduces the dump truck's carrying capacity, it improves battery life by suppressing the rise in battery temperature, thereby reducing the life cycle cost of the dump truck 101.

[0100] (summary) In the second embodiment, the information processing device 20 instructs the engine-driven dump truck 101 traveling along the input path to reduce the amount of fuel injected at idle if the estimated waiting time in the input path is longer than the target waiting time, and instructs the engine-driven dump truck 101 traveling along the input path to reduce the amount of fuel injected at maximum output if the estimated waiting time is longer than a predetermined time set to be longer than the target waiting time.

[0101] According to the second embodiment configured as described above, it is possible to reduce the fuel cost of the dump truck 101 without causing the shovel 102 to wait for a dump truck (reducing production efficiency).

[0102] Furthermore, in the second embodiment, if the estimated waiting time is longer than a predetermined time set to be longer than the estimated loading time, the information processing device 20 instructs the battery-powered dump truck 101, which is traveling along the input path, to reduce the maximum charging current and the maximum discharging current. This suppresses the rise in battery temperature, improving battery life and thus reducing the life cycle cost of the dump truck 101.

[0103] Although embodiments of the present invention have been described in detail above, the present invention is not limited to the embodiments described above and includes various modifications. For example, the embodiments described above are described in detail in order to explain the present invention in an easy-to-understand manner and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to add parts of the configuration of one embodiment to the configuration of another embodiment, and it is also possible to delete parts of the configuration of one embodiment or replace parts of parts of another embodiment. [Explanation of Symbols]

[0104] 1...Mine management system, 1...Reciprocal, 2...Mine management device, 3...Terminal device, 3a...Display device, 3b...Input device, 10...Storage device, 20...Information processing device, 100...Mining machinery, 101...Dump truck (mining machinery), 102...Shovel (mining machinery), 103...Dozer (mining machinery), 110...User, 111...Operations manager, 112...Operator instructor, 113...Road maintenance worker, 114...Equipment maintenance worker, 115...Mining / maintenance manager, 120...Internet, 130...Database, 201...Input unit, 202...Driving history table, 203...Waiting time estimation unit, 204...Optimization unit, 301...Cycle table, 302...Route table, 303...Mesh table, 900...Monitoring screen, 1100...Control parameter history table, 1200...Management screen.

Claims

1. In a mine management system that manages the operation of dump trucks traveling along a route between a loading area and a discharge area in a mine, A storage device for storing the driving history of the dump truck within the mine, Information processing equipment and The system comprises a terminal device that can communicate with the aforementioned information processing device and can input the aforementioned travel route, The aforementioned information processing device is If the driving history of the input route, which is a driving route entered via the terminal device, does not exist in the storage device, then another driving route similar to the input route is selected as a similar route from among the driving routes whose driving history is stored in the storage device. Based on the driving history of the aforementioned similar routes, the estimated loading time, which is an estimated value of the loading time of the dump truck on the input route, and the estimated waiting time, which is an estimated value of the waiting time of the dump truck on the input route, are calculated. The estimated loading time and the estimated waiting time are output to the terminal device. A mine management system characterized by the following features.

2. In the mine management system according to claim 1, The storage device stores the location information of the route traveled by the dump truck as mesh information. The aforementioned information processing device is The similarity between the mesh information of the input path and the mesh information of one or more paths stored in the storage device is calculated. Among the mesh information of one or more paths, the path with the highest similarity and whose similarity is greater than a predetermined value is selected as the similar path. A mine management system characterized by the following features.

3. In the mine management system according to claim 1, The aforementioned information processing device is Based on the travel history of similar routes, the estimated number of dump trucks traveling along the input route is calculated. The estimated number of dump trucks is output to the terminal device. A mine management system characterized by the following features.

4. In the mine management system according to claim 1, The aforementioned information processing device is Based on the driving history of the aforementioned similar routes, a target waiting time is set, which is the target value for the waiting time of the dump truck on the input route. The target waiting time is output to the terminal device. A mine management system characterized by the following features.

5. In the mine management system according to claim 4, The aforementioned information processing device is Based on the travel history of similar routes, the optimal number of dump trucks is calculated to bring the estimated waiting time closer to the target waiting time. The optimal number of dump trucks is output to the terminal device. A mine management system characterized by the following features.

6. In the mine management system according to claim 4, The information processing device, when the estimated waiting time of the input path is longer than the target waiting time, instructs the dump truck that is traveling along the input path and is refuelable or maintainable to cease operations. A mine management system characterized by the following features.

7. In the mine management system according to claim 4, The aforementioned information processing device is If the estimated waiting time is longer than the target waiting time, the engine-driven dump truck traveling along the input path is instructed to reduce the amount of fuel injected at idle. If the estimated waiting time is longer than a predetermined time set to be longer than the target waiting time, the engine-driven dump truck traveling along the input path is instructed to reduce the amount of fuel injected at maximum output. A mine management system characterized by the following features.

8. In the mine management system according to claim 4, The information processing device, when the estimated waiting time is longer than a predetermined time set to be longer than the estimated loading time, instructs the battery-powered dump truck traveling along the input path to reduce the maximum charging current and the maximum discharging current. A mine management system characterized by the following features.

9. In the mine management system according to claim 4, The information processing device sets the target waiting time so that it falls within the range of 25% to 75% of the estimated loading time. A mine management system characterized by the following features.

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