Mine management system

Abstract

This mine management system comprises a storage device that stores travel histories of a dump truck, an information processing device, and a terminal device. If the travel history of an input route input from the terminal device is not stored in the storage device, the information processing device: selects, as a similar route, a route similar to the input route from among routes for which the travel history is stored in the storage device; calculates, on the basis of the travel history of the similar route, an estimated loading time, which is an estimated value of a loading time of the dump truck on the input route, and an estimated waiting time, which is an estimated value of a waiting time of the dump truck on the input route; and outputs the estimated loading time and the estimated waiting time to the terminal device.

Description

Mine management system

[0001] This invention relates to a mining management system that collects and utilizes operational data from mining machinery such as dump trucks and excavators.

[0002] To increase mine productivity, operations are carried out to maintain a high utilization rate of excavators and to ensure a continuous flow of dump trucks at the loading area. Therefore, if the waiting time of dump trucks is not properly managed, mine productivity will decrease. For example, if a dump truck that has received a load from an excavator travels to a designated unloading area and then returns to the loading area after unloading, and there are many preceding dump trucks waiting, the waiting time will be long, reducing productivity. The amount of productivity loss includes, for example, the fuel costs required for idling while waiting, and the lost production that could have been obtained by going to another loading area. Furthermore, productivity will also decrease if the speed at which dump trucks travel between the loading area and the unloading area is not properly managed. For example, if a following dump truck travels at high speed without considering the time it takes for the excavator to load the preceding dump truck (loading time), not only will the waiting time after arriving at the loading area be long, but the fuel consumed by traveling at an excessive speed will also be wasted.

[0003] Regarding these issues, Patent Document 1 discloses an operating method for suppressing the engine power of a dump truck according to its waiting time and load. This operating 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. Here, the time and location of long-term stops are recorded, and the speed of following vehicles traveling through those locations is reduced to prevent unnecessary fuel consumption and decreased productivity. Patent Document 3 discloses a method for improving productivity by estimating the capacity (transporting capacity) of dump trucks and the capacity (excavation capacity) of excavators, and dispatching dump trucks according to the excavator capacity. Here, the capacity of dump trucks is corrected by the average waiting time, and productivity is improved by implementing an optimal dump truck arrangement.

[0004] Japanese Patent Application Laid-Open No. 2017-89505, Japanese Patent Application Laid-Open No. 2014-238875, WO2015 / 181972

[0005] However, in the technology described in Patent Document 1, the decrease in fuel consumption associated with suppressing the maximum output by power suppression is offset by the increase in fuel consumption associated with the increase in driving time. In addition, in Patent Document 1, sufficient consideration has not been given to the increase in fuel consumption associated with traffic jams generated by suppressing the speed.

[0006] Also, in Patent Document 2, although mention is made of reducing the speed based on past traffic jam information, sufficient consideration has not been given to the increase in fuel consumption associated with new traffic jams generated by reducing the speed.

[0007] Further, the technology described in Patent Document 3 aims to solve the above-described problems by dispatching dump trucks. However, in Patent Document 3, sufficient consideration has not been given to the case where the transportation capacity of the dump truck greatly exceeds the excavation capacity of the shovel. Furthermore, any of the technologies of Patent Documents 1 to 3 also has a problem that they cannot exhibit their effects until the operation data of the dump truck is sufficiently accumulated when introduced into a new mine.

[0008] The present invention has been made in view of the above problems, and an object thereof is to provide a mine management system capable of assisting in improving the operation cost or operation efficiency of dump trucks in a mine.

[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.

[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.

[0011] Diagram of the configuration of the mine management system according to the first embodiment. Diagram showing a functional block diagram of the mine management device. Diagram showing an example of a travel history table. Diagram schematically representing the operation data of a dump truck for one cycle from the completion of excavation to the start of the next excavation. Flowchart showing the overall processing of the information processing device. Flowchart showing the details of the process of searching for a similar route ID in the travel history table. Diagram showing an example of mesh information of an input route. Diagram showing an example of mesh information of a comparison route. Diagram showing the z coordinates of the mesh information of the input route arranged in order of travel. Diagram showing the z coordinates of the mesh information of a comparison route arranged in order of travel. Flowchart showing the details of the process of calculating the route ID of the route with the greatest similarity to the mesh information of the input route and its similarity (maximum similarity). Diagram showing an example of a monitoring screen displayed on the terminal device of the operations manager. Flowchart showing the process related to dispatching instructions for dump trucks. Diagram showing an example of a control parameter history table. Diagram showing an example of a management screen displayed on the terminal device of the equipment maintenance worker. Flowchart showing the process of optimizing control parameters related to the drive mode of an engine-driven dump truck. Flowchart showing the process of optimizing control parameters related to fuel consumption of an engine-driven dump truck. Flowchart showing the process of optimizing control parameters related to charging and discharging of a battery-driven dump truck.

[0012] Embodiments of the present invention will be described below with reference to the drawings. In each figure, equivalent elements are denoted by the same reference numerals, and redundant explanations will be omitted as appropriate.

[0013] Figure 1 is a diagram showing the configuration of a mine management system 1 according to a first embodiment of the present invention. The mine management system 1 centrally manages the productivity of a mine (for example, the production cost and production efficiency of mining machinery 100, which will be described later). The mine management system 1 includes a mine management device 2 that performs information processing for the collective management of mining machinery 100 operating within a predetermined mining area, and a terminal device 3 that has a display function for presenting various information processed by the mine management device 2 to a user 110. The mining machinery 100 consists of, for example, a dump truck 101 (hereinafter referred to as "vehicle" as appropriate), a shovel 102, a bulldozer 103, and other mining machines that operate within a predetermined mining area. The mining machinery 100 managed by the mine management device 2 is not limited to these mining machines 101 to 103. Also, the mining machines 101 to 103 may be engine-driven or battery-driven.

[0014] 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 sensors of mining machines 101 to 103 and the calculated values ​​from 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 mining 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 the 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 to 103, such as route information, waiting time, and optimal number of machines in operation, which will be described later. Terminal device 3 is used by the user 110 of the mine management system 1.

[0018] Users 110 of the mine management system 1 include, for example, an operations manager 111 who creates or modifies the operation plans for mining machinery 101 to 103, an operator instructor 112 who instructs the operators of mining machinery 101 to 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 the mining machinery 101 to 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 (for example, 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, which has an input device, to store the 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 using 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 the 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 the 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 driving, 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 three-dimensional 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., 30 m 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. When the aforementioned travel history is calculated 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. In addition, the route identification information may be the route ID that the dump truck was instructed to use, or a route ID that is 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 a 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]

[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 a predetermined target waiting time. If the result is YES, the process proceeds to step S508; if the result is NO, the process ends.

[0036] In step S508, optimization is performed to improve the operating cost or operational efficiency of the 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]

[0038]

[0039]

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

[0041] Equation 3 is the result of calculating the current number of dump truck trips Ndump based on this average arrival rate λave and the cycle time Tcycle, and Equation 4 indicates the number of dump truck trips Ndt when the required time is shortened by Twt. By presenting these values to the operation manager 111, the operation manager 111 can make a decision to increase or decrease the number of dump truck trips on each route. For example, when the loading time Tl of the input route is relatively long and Ndump > Ndt, a decision can be made to reduce the number of dump truck trips on the input route. With this configuration, even when there is no driving history of the input route, it is possible to optimize the number of dump truck trips on the input route using the driving history of similar routes. Also, even if the automation of the excavator 102 and the dump truck 101 progresses, it is considered that waiting time still occurs due to variations in transportation and excavation capabilities due to differences and deterioration, or environmental disturbances such as the hardness of the road surface and the ground. However, the waiting time in this case can also be estimated using the above equations.

[0042] Figure 6 is a flowchart showing the details of the process of searching for the similar route ID in the driving history table 202 (step S503 in FIG. 5).

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

[0044] In step S602, the similarity between the mesh information of the input route and the mesh information of other routes existing in the driving history table 202 is calculated, and the route ID of the route with the highest similarity and its similarity (maximum 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 determination result is YES, the process proceeds to step S604, and if the determination result is NO, the process proceeds to step S605.

[0046] In step S604, it is determined that there is a similar route, the route ID that obtained the maximum similarity is set as the similar route ID, and the flow ends.

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

[0048] FIG. 8 is a flowchart showing details of a process (step S602 in FIG. 6) for calculating the route ID of the route having the highest similarity with the mesh information of the input route and its similarity (maximum similarity).

[0049] In step S801, a mesh ID list corresponding to the route ID of the input route is extracted from the route table 302, and a reference mesh number is calculated. Here, for simplicity of explanation, the length of the mesh ID list is used as the reference mesh number, but it may also be the number obtained by counting only different mesh IDs without counting the same mesh ID as the reference mesh number. Then, route IDs having a mesh number within the range of the reference mesh number ± a predetermined number (for example, 10% of the reference mesh number) are extracted from the route table 302 to generate a comparison route ID list.

[0050] In step S802, altitude information of the mesh ID list of the input route is extracted from the mesh table 303, and a reference altitude is calculated. Here, the value obtained by subtracting the minimum z coordinate from the maximum z coordinate is calculated as the reference altitude, but the reference altitude is not limited to this, and the average value or median value of the z coordinates may also be used as the reference altitude. Then, the altitude (comparison altitude) is extracted from the mesh ID list of the route ID included in the above-described comparison route ID list in the same method, and only the route ID for which the comparison altitude is within the range of the reference altitude ± a predetermined value (for example, 10% of the reference altitude) is left in the comparison route ID list, and the other route IDs are deleted from the comparison route ID list. As a result, a large number of route IDs with a low possibility of becoming similar route IDs can be excluded before comparing the mesh information, so that the computational load associated with comparing the mesh information can be suppressed.

[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 a rectangular area (number of meshes in the x-axis direction = w, number of meshes in the y-axis direction = h) in which z coordinates exist from 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 a 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 the similarity considering both the horizontal shape and elevation of the path.

[0054]

[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, which have 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]

[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 a 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 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 the 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 value 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 (in this case, RD001 and RD003) 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 or not.

[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 trucks 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 a 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, the process proceeds to step S1004; if the result is NO, the flow ends. 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 truck 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 an estimated number of dump trucks that is less 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 production efficiency can be increased 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 comprises 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 so that it falls 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.

[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 its description will be omitted.

[0078] Figure 11 shows an example of a 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 a 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 by 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 of 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 makes it possible to set 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 showing the process for optimizing control parameters related to fuel consumption of an engine-driven dump truck 101. Here, instead of switching between predetermined settings as described previously, we will explain a method for automatically adjusting the values ​​of each control parameter.

[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 acceleration and speed of the vehicle 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 since it has almost no impact on the cycle time, it is 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 the accelerator input is further reduced compared to the normal setting in order to reduce acceleration even more. 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 dump truck of the shovel 102 (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 ends.

[0095] Figure 15 is a flowchart showing the process of 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 above, 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 discharging 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, the result of the determination in step S1503 is YES if there are two or more dump trucks waiting. 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. As a result, although the dump truck's carrying capacity is reduced, the rise in battery temperature is suppressed, improving battery life and 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.

[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. A mine management system for managing the operation of dump trucks traveling along a route between a loading area and a discharge area in a mine, comprising: a storage device for storing the travel history of the dump trucks within the mine; an information processing device; and a terminal device capable of communicating with the information processing device and inputting the travel route, wherein the information processing device, when the travel history of an input route, which is a travel route input via the terminal device, does not exist in the storage device, selects another travel route similar to the input route from among the travel 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.

2. A mine management system according to claim 1, wherein the storage device stores location information of the route traveled by the dump truck as mesh information, and the information processing device 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, and selects the route with the highest similarity and a similarity higher than a predetermined value from among the mesh information of one or more routes as the similar route.

3. A mine management system according to claim 1, wherein the information processing device calculates an estimated number of dump trucks, which is the estimated number of dump trucks 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.

4. A mine management system according to claim 1, wherein the information processing device sets a target waiting time, which is a target value for the waiting time of the dump truck on the input route, based on the travel history of the similar route, and outputs the target waiting time to the terminal device.

5. A mine management system according to claim 4, wherein the information processing device 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.

6. A mine management system according to claim 4, wherein the information processing device instructs a dump truck that is traveling along the input route and is refuelable or maintainable to suspend operations when the estimated waiting time of the input route is longer than the target waiting time.

7. A mine management system according to claim 4, wherein the information processing device instructs the engine-driven dump truck traveling along the input route to reduce the amount of fuel injected at idle when the estimated waiting time is longer than the target waiting time, and instructs the engine-driven dump truck traveling along the input route to reduce the amount of fuel injected at maximum output when the estimated waiting time is longer than a predetermined time set to be longer than the target waiting time.

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

9. A mine management system according to claim 4, characterized in that the information processing device sets the target waiting time to fall within a range of 25% to 75% of the estimated loading time.

Images