Work machine parameter selection device and parameter recommendation system

Abstract

The present disclosure provides a work machine parameter selection device that enables a work machine to travel efficiently at a work site where travel routes and slopes change from moment to moment. The parameter selection device includes a parameter selection unit. The parameter selection unit calculates an average load travel distance per cycle of the working machine, a travel ratio for each slope in the load travel, and a virtual travel distance for each slope per cycle (processes P1-P3). In addition, the parameter selection unit calculates a predicted fuel consumption amount for each slope per cycle, a travel time for each slope per cycle, a predicted fuel consumption amount per unit load weight, and a predicted production amount per cycle (processing P4-P8). Then, the parameter selection unit selects recommended parameter settings on the basis of the predicted fuel consumption amount and predicted production amount per cycle for each parameter setting (process P9).

Description

Technical Field

[0001] The present disclosure relates to a parameter selection device and a parameter recommendation system for a work machine.

Background Art

[0002] Conventionally, an invention related to a vehicle energy consumption prediction device for predicting the energy consumption of a vehicle when traveling along a predetermined travel route is known. The conventional device described in Patent Document 1 below includes a fuel conversion gradient data storage means and a consumption calculation means. The fuel conversion gradient data storage means stores the fuel conversion gradient data of each link created by a road gradient data creation device in association with road map data. The consumption calculation means calculates the energy consumption of the vehicle predicted when traveling along the travel route using the fuel conversion gradient data of each link stored in the fuel conversion gradient data storage means (Patent Document 1, Paragraph 0019 and Claim 7).

[0003] The road gradient data creation device described in Patent Document 1 creates data representing the degree of gradient of each link in road map data representing a road as a combination of nodes and links. This device includes a link data input means, an elevation data input means, and a gradient data creation means (Patent Document 1, Paragraph 0008 and Claim 1). The link data input means inputs link data including the start point, end point positions, and road type of each link from a map database recording the road map data.

[0004] The elevation data input means inputs elevation data from an elevation database recording the elevation at each position obtained by dividing a topographic map into meshes at predetermined intervals. The gradient data creation means creates, based on the link data and the elevation data, fuel conversion gradient data obtained by converting and evaluating, as an index indicating how steep the gradient is from the start point to the end point in each link, the data of the gradient into the energy consumption when the vehicle travels along the link.

[0005] Also, an invention related to a driving analysis device for a transport vehicle has been conventionally known. The conventional device described in Patent Document 2 is a driving analysis device for a transport vehicle that repeatedly travels the same route (paragraph 0007 and claim 1 of the same document). This conventional device includes a data storage unit, a data extraction unit, a section extraction unit, and a driving information output unit. The data storage unit acquires, as data for one cycle, the position information during the travel of the transport vehicle and the driving information including the fuel consumption and the load amount, and stores the data in a storage device when the vehicle makes a round trip on the route.

[0006] The data extraction unit extracts reference cycle data and analysis target cycle data from the data for a plurality of cycles stored in the storage device. The section extraction unit extracts an analysis target section from a plurality of sections set by dividing the route using the position information. The driving information output unit outputs the driving information of the reference cycle data and the analysis target cycle data in the analysis target section.

Prior Art Documents

Patent Documents

[0007]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0008] For example, at a work site such as a mine site, the route on which work machines such as dump trucks can travel and the gradient of the route on which the work machines travel change moment by moment as the work progresses. As described above, the conventional device described in Patent Document 1 creates fuel-converted gradient data based on the link data recorded in the map database and the elevation data recorded in the elevation database. Therefore, it is difficult for this conventional device to always update the map database and the elevation database to the latest state at a work site where the driving route and the gradient change moment by moment.

[0009] In addition, the conventional device described in Patent Document 2 targets a transport vehicle that repeatedly travels the same route for analysis. Therefore, it is difficult to analyze the driving of the transport vehicle at a work site where the driving route and the gradient change moment by moment.

[0010] The present disclosure provides a parameter selection device and a parameter recommendation system for a work machine that can efficiently drive a work machine at a work site where the driving route and the slope change moment by moment.

Means for Solving the Problems

[0011] One aspect of the present disclosure is a parameter selection device that selects a recommended parameter setting for the engine based on driving history data including the driving date and time of a work machine, the slope of the driving route, the driving speed, the weight of the load, the parameter setting for engine control, and the fuel consumption per unit time. The work machine loads the load, travels with the load after loading the load, drops the load after traveling with the load, travels empty after dropping the load, and repeats the process until loading the load again as one cycle. Based on the driving history data, the average loaded travel distance per cycle is calculated. Based on the driving history data, the travel ratio per slope, which is the ratio of the cumulative travel distance for each slope to the total travel distance of the work machine during the loaded travel, is calculated. The virtual travel distance per slope per cycle is calculated by multiplying the average loaded travel distance by the travel ratio per slope. Based on the driving history data, for each slope in the loaded travel of the work machine, the average fuel consumption per slope, which is the average value of the fuel consumption, and the average speed per slope, which is the average value of the driving speed, are calculated. The predicted fuel consumption per slope per cycle is calculated by multiplying the virtual travel distance per slope by the average fuel consumption per slope divided by the average speed per slope. The travel time per slope per cycle is calculated by dividing the virtual travel distance per slope by the average speed per slope. Based on the driving history data, for each parameter setting, the predicted fuel consumption per cycle, which is the sum of the predicted fuel consumption per slope, is divided by the specified load weight of the work machine to calculate the predicted fuel consumption per unit load weight. Based on the driving history data, for each parameter setting, the predicted production per cycle is calculated by dividing the specified load weight by the virtual travel time per cycle, which is the sum of the travel times per slope. The parameter selection device is characterized by including a parameter selection unit that selects the recommended parameter setting based on the predicted fuel consumption per cycle and the predicted production per cycle for each parameter setting.

[0012] Another aspect of the present disclosure is a parameter recommendation system including the parameter selection device and a control device mounted on the work machine. The control device includes an engine control unit that controls the engine based on the parameter setting, a travel record acquisition unit that acquires the travel history data based on the detection result of a sensor mounted on the work machine, and a travel record transmission unit that transmits the travel history data input from the travel record acquisition unit to the parameter selection device via a communication device mounted on the work machine. The parameter recommendation system is characterized by having these components.

Effects of the Invention

[0013] According to the above aspect of the present disclosure, it is possible to provide a parameter selection device and a parameter recommendation system for a work machine that can efficiently run the work machine at a work site where the travel route and slope change every moment.

Brief Description of the Drawings

[0014]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Modes for Carrying Out the Invention

[0015] Hereinafter, embodiments of a parameter selection device and a parameter recommendation system according to the present disclosure will be described with reference to the drawings.

[0016] In the present disclosure, a dump truck as a transporter used in a mine or the like is assumed as a work machine, and the parameter will be described by taking control parameters (variables) of an engine as a power source as an example. FIG. 1 is a block diagram showing an embodiment of a parameter selection device and a parameter recommendation system according to the present disclosure. The parameter selection device 100 of the present embodiment is, for example, a computer connected to a network NW such as the Internet. The parameter selection device 100 includes, for example, a central processing unit (CPU), a memory, a timer, and an input / output unit.

[0017] The parameter selection device 100 is a device that selects a recommended parameter setting for the engine 201 of the work machine 200 based on the travel history data RD of the work machine 200. Although details will be described later, the travel history data RD of the work machine 200 includes, for example, the travel date and time DAT of the work machine 200, the slope θ [°] of the travel route, the travel speed V [km / h], the weight PLD [t] of the load, the parameter setting PS of the engine 201, and the fuel consumption FCH [l / h] per unit time (see FIG. 2).

[0018] The parameter selection device 100 has, for example, a data input / output unit 101, a travel history recording unit 102, a travel history database 103, a parameter selection unit 104, and an analysis database 105. Each part of the parameter selection device 100 shown in FIG. 1 represents each function of the parameter selection device 100 realized by executing a program stored in the memory of the parameter selection device 100 by the CPU of the parameter selection device 100.

[0019] The parameter recommendation system 500 of this embodiment includes, for example, a parameter selection device 100 and a control device 220 mounted on the work machine 200. In FIG. 1, one work machine 200 and one control device 220 mounted on the work machine 200 are shown, but the parameter recommendation system 500 may include a plurality of control devices 220 mounted on a plurality of work machines 200. Further, the parameter recommendation system 500 may include, for example, a wireless base station 300 connected to the network NW, or a user terminal 400 installed at a user's office or the like and connected to the network NW.

[0020] The work machine 200 is, for example, a dump truck that transports loads such as earth and sand, crushed stone, etc. Further, the work machine 200 is, for example, an extra-large rigid dump truck that transports loads such as minerals in a mine. Note that the work machine 200 is not limited to a dump truck, and may be other machines capable of transporting conveyables, such as a wheel loader or a hydraulic excavator.

[0021] The work machine 200 includes, for example, an engine 201, a generator 202, a motor 203, a communication device 204, a sensor 210, and a control device 220. The engine 201 is, for example, an internal combustion engine that uses light oil or the like as fuel. The output shaft of the engine 201 is connected to, for example, the shaft of the generator 202 to rotate the shaft of the generator 202. The generator 202 generates electricity when the shaft is rotated by the rotation of the output shaft of the engine 201. The rotation speed of the engine 201 is controlled by an accelerator (not shown), and in the case of a manned vehicle, the rotation speed is controlled to increase and decrease by operating the accelerator pedal.

[0022] The motor 203 rotates, for example, by the electric power supplied from the generator 202, and generates power to drive the working machine 200. More specifically, when the working machine 200 is a dump truck, the motor 203 is a traveling motor that rotates the wheels of the dump truck. Note that the motor 203 may rotate by the electric power supplied from a battery of the working machine 200 (not shown). Further, the motor 203 may supply, for example, the regenerative electric power generated by a regenerative brake to the battery.

[0023] The communication device 204 is, for example, a wireless communication device mounted on the working machine 200. The communication device 204 is connected to the wireless base station 300 by wireless communication, for example, and is connected to the network NW via the wireless base station 300. Further, the communication device 204 may be connected to the network NW by satellite communication via, for example, an antenna, a communication satellite, and a gateway.

[0024] The sensor 210 is mounted on the working machine 200, for example, and detects physical quantities related to the working machine 200. The sensor 210 includes, for example, a position sensor 211, a speed sensor 212, an inclination sensor 213, a payload sensor 214, a fuel sensor 215, and an engine sensor 216.

[0025] The position sensor 211 includes, for example, a receiver of a global navigation satellite system (GNSS), and detects position information such as the latitude and longitude of the working machine 200. The speed sensor 212 includes, for example, a wheel speed sensor, and detects the speed of the working machine 200. The inclination sensor 213 includes, for example, an inclination sensor, an inertial sensor, or an acceleration sensor, and detects the inclination angle of the working machine 200 with respect to the horizontal plane.

[0026] The load sensor 214 detects the load capacity of the work machine 200 based on, for example, the load acting on the suspension that supports the wheels of the work machine 200. The fuel sensor 215 detects the remaining amount of fuel in the fuel tank based on, for example, the position of the float in the fuel tank. The engine sensor 216 detects, for example, the rotational speed and reaction speed (the rate of change of the engine rotational speed with respect to the amount of depression of the accelerator pedal) of the engine 201 of the work machine 200.

[0027] The control device 220 is, for example, an electronic control unit (ECU) mounted on the work machine 200. The control device 220 can be configured by, for example, one or more microcontrollers including a CPU, a memory, a timer, and an input / output section. The control device 220 has, for example, a travel record acquisition section 221, a travel record transmission section 222, and an engine control section 223. Each part of these control devices 220 represents each function of the control device 220 realized by, for example, executing a program stored in the memory by the CPU.

[0028] The travel record acquisition section 221 acquires travel history data RD based on the detection results of the sensors 210 mounted on the work machine 200. More specifically, the travel record acquisition section 221 acquires, for example, the detection results of the position sensor 211, the speed sensor 212, the gradient sensor 213, the load sensor 214, and the fuel sensor 215. Thereby, the travel record acquisition section 221 acquires, for example, the travel date and time DAT of the work machine 200, the gradient θ of the travel route, the travel speed V [km / h], the weight PLD [t] of the load, and the fuel consumption per unit time FCH [l / h]. Also, the travel record acquisition section 221 acquires, for example, the parameter settings of the engine 201 from the engine control section 223.

[0029] The travel record transmission unit 222 transmits the travel history data RD input from the travel record acquisition unit 221 to the parameter selection device 100 via the communication device 204 mounted on the work machine 200. The engine control unit 223 controls the engine 201 of the work machine 200 in response to an input from an accelerator pedal (not shown) based on, for example, the parameter setting PS for controlling the engine 201 described later and the detection result of the engine sensor 216.

[0030] The parameter selection device 100 receives, for example, the travel history data RD of the work machine 200 transmitted from the travel record transmission unit 222 of the control device 220 mounted on the work machine 200 via the communication device 204 by the data input / output unit 101 connected to the network NW. The travel history recording unit 102 records the travel history data RD received by the data input / output unit 101 in the travel history database 103.

[0031] FIG. 2 is a table showing an example of the travel history data RD recorded in the travel history database 103 of the parameter selection device 100 in FIG. 1. The travel history data RD of the work machine 200 includes, for example, the travel date and time DAT of the work machine 200, the slope θ [°] of the travel route, the travel speed V [km / h], the weight PLD [t] of the load, the parameter setting PS of the engine 201, and the fuel consumption FCH [l / h] per unit time. Further, the travel history data RD may include the position information of the work machine 200. The travel history recording unit 102 of the parameter selection device 100 records, for example, the travel history data RD received from a plurality of work machines 200 in the travel history database 103 together with the identification information of each work machine 200.

[0032] The running date and time DAT of the working machine 200 is, for example, the date and time when the speed sensor 212 detects the speed of the working machine 200. The gradient θ is, for example, the inclination angle of the working machine 200 with respect to the horizontal plane detected by the gradient sensor 213. The running speed V [km / h] is, for example, the speed of the working machine 200 detected by the speed sensor 212. The weight PLD [t] of the load is, for example, the loading capacity of the working machine 200 detected by the loading capacity sensor 214. The fuel consumption FCH [l / h] is, for example, the fuel consumption per unit time calculated based on the remaining amount of fuel detected by the fuel sensor 215. The position information is, for example, information such as the latitude and longitude of the working machine 200 detected by the position sensor 211.

[0033] Figure 3 is a table showing an example of the parameter setting PS of the engine 201 included in the running history data RD of Figure 2. The parameter setting PS of the engine 201 is, for example, a combination of control parameters of the engine 201 preset in the engine control unit 223 of the working machine 200. As shown in the table, the control parameters have data on the number of revolutions NRL [rpm] at low speed, the number of revolutions NRH [rpm] at high speed, and the reaction speed RS, and include a plurality of different settings according to their combinations. Here, the number of revolutions NTL at low speed is the idling speed when the accelerator pedal is not depressed, and the number of revolutions NRH at high speed is the maximum speed when the accelerator pedal is fully depressed (so-called full operation).

[0034] In the example shown in Figure 3, in setting A of the parameter setting PS, the number of revolutions NRL [rpm] of the engine 201 at low speed is set to 1300 [rpm], the number of revolutions NRH [rpm] at high speed is set to 1900 [rpm], and the reaction speed RS is set to "high". Also, in setting C of the parameter setting PS, the number of revolutions NRL [rpm] of the engine 201 at low speed is set to 1200 [rpm], the number of revolutions NRH [rpm] at high speed is set to 1800 [rpm], and the reaction speed RS is set to "medium".

[0035] In the engine control unit 223 of the control device 220 mounted on the working machine 200, for example, one of a plurality of parameter settings PS as shown in FIG. 3 is preselected and defined. The engine control unit 223 controls the engine 201 based on, for example, the defined parameter setting PS and the detection result of the engine sensor 216.

[0036] The wireless base station 300 shown in FIG. 1 is communicably connected to the communication device 204 of the working machine 200 via a wireless communication line and is communicably connected to the parameter selection device 100 via the network NW. Further, the user terminal 400 shown in FIG. 1 is installed, for example, at the user's business office or the like and is communicably connected to the parameter selection device 100 via the network NW. Further, the user terminal 400 has a display unit 401 constituted by, for example, a liquid crystal display device, an organic EL display device, or the like.

[0037] Hereinafter, with reference to FIG. 4, the operation of the parameter selection device 100 of the present embodiment will be described. FIG. 4 is a flowchart showing an example of the operation of the parameter selection device 100 in FIG. 1.

[0038] In the parameter recommendation system 500 of the present embodiment, for example, a user of the working machine 200 who wishes to select a recommended parameter setting for the engine 201 of a specific working machine 200 inputs the identification information of the target working machine 200 to the user terminal 400. The identification information of the working machine 200 input to the user terminal 400 is transmitted from the user terminal 400 to the parameter selection device 100 via the network NW, for example.

[0039] When the parameter selection device 100 receives, for example, the identification information of a specific working machine 200 transmitted from the user terminal 400 by the data input / output unit 101, it starts the processing flow shown in FIG. 4. When the parameter selection device 100 starts the processing flow shown in FIG. 4, first, it executes a process P1 of calculating the average loaded travel distance per cycle based on the travel history data RD of a specific working machine 200 to be analyzed. Here, one cycle is defined as the period from when the working machine 200 loads a load, travels with the load after loading, drops the load after the loaded travel, travels empty after dropping the load, and loads the load again.

[0040] FIG. 5 is a flowchart showing the details of the process P1 of calculating the average loaded travel distance per cycle in FIG. 4. When the parameter selection device 100 starts the process P1 shown in FIG. 5, first, it executes a process P11 of extracting the travel history data RD of a specific working machine 200 to be analyzed from the travel history database 103. More specifically, in this process P11, the parameter selection unit 104, for example, acquires the identification information of a specific working machine 200 to be analyzed from the data input / output unit 101. Further, the parameter selection unit 104 extracts, for example, the travel history data RD of the working machine 200 including the identification information that matches the identification information acquired from the data input / output unit 101 from the travel history database 103.

[0041] Next, the parameter selection device 100 executes a process P12 of totaling the number of cycles based on the extracted travel history data RD. In this process P12, the parameter selection unit 104, for example, compares the threshold value of the load weight PLD[t] for determining the presence or absence of a load on the working machine 200 with the load weight PLD[t] included in the travel history data RD to determine the presence or absence of a load in the time series of the travel history data RD.

[0042] The parameter selection unit 104 totals the number of cycles of the working machine 200 from the travel history data RD based on, for example, the determination result of the presence or absence of a load on the working machine 200 and the position information of the working machine 200. Here, the number of cycles of the working machine 200 is, for example, as described above, one cycle from when the working machine 200 loads a load, travels with the load after loading the load, drops the load after traveling with the load, travels empty after dropping the load, and loads the load again.

[0043] Next, the parameter selection device 100 executes processes P13, P14, and P15 for totaling the loaded travel distance traveled by the working machine 200 while loaded. More specifically, in process P13, the parameter selection unit 104 extracts, for example, the data with the oldest date and time from the time series of the travel history data RD. Further, the parameter selection unit 104 determines whether the working machine 200 is traveling with a load based on a comparison between the weight PLD[t] of the load in the extracted data and a threshold value.

[0044] In process P13, when the parameter selection unit 104 determines that the working machine 200 is traveling with a load (YES), it executes process P14 for totaling the total loaded travel distance. In this process P14, the parameter selection unit 104 calculates the loaded travel distance by multiplying the travel speed V [km / h] of the working machine 200 by the travel time, and totals the total loaded travel distance by adding the calculated loaded travel distance to the total loaded travel distance calculated in the previous process P14. In the first process P14 after starting process P1, for example, the calculated loaded travel distance is added to zero, which is the initial value of the total loaded travel distance.

[0045] Thereafter, the parameter selection unit 104 executes process P15 for determining whether the data extracted from the travel history data RD is the final data. In process P15, when the parameter selection unit 104 determines that the data extracted from the travel history data RD is not the final data (NO), it returns to process P13, extracts the next data from the travel history data RD, and determines whether it is traveling with a load.

[0046] On the other hand, when the parameter selection unit 104 determines that the work machine 200 is performing an empty vehicle run without loading a load in the above-described process P13, that is, when it determines that the work machine 200 is not performing a loaded run (NO), the parameter selection unit 104 executes process P15 without executing process P14. Further, in the above-described process P15, when the parameter selection unit 104 determines that the data extracted from the travel history data RD is the final data (YES), the parameter selection unit 104 executes the next process P16.

[0047] In process P16, the parameter selection unit 104 divides the total loaded travel distance tabulated in process P14 by the number of cycles tabulated in process P12 to calculate the average loaded travel distance per cycle. Thus, process P1 shown in FIG. 5 ends. Thereafter, the parameter selection device 100 executes process P2 shown in FIG. 4, and calculates a travel ratio per gradient, which is the ratio of the cumulative travel distance for each gradient θ to the total travel distance of the work machine 200 in the loaded run, based on the travel history data RD.

[0048] FIG. 6 is a flowchart showing details of process P2 for calculating the travel ratio per gradient in FIG. 4. When starting process P2 shown in FIG. 6, the parameter selection device 100 executes process P21 of extracting the travel history data RD of the work machine 200 to be analyzed from the travel history database 103. More specifically, in process P21, the parameter selection unit 104 extracts the travel history data RD including the identification information that matches the identification information of the work machine 200 acquired from the data input / output unit 101 from the travel history database 103, in the same manner as in the above-described process P11.

[0049] Next, the parameter selection unit 104 executes a smoothing process P22 for the gradient θ included in the time series of the extracted travel history data RD, for example, to remove noise included in the detection result of the gradient sensor 213. Next, the parameter selection unit 104 executes a process P23 of determining whether or not the work machine 200 is performing a loaded run in the data extracted from the travel history data RD, for example, in the same manner as in the above-described process P13.

[0050] When the parameter selection unit 104 determines in process P23 that the working machine 200 is in a loaded driving state (YES), it executes process P24 to determine whether the weight PLD [t] of the load is Equivalent to the specified loading weight (for example, within ±5% of the specified loading weight ) or not. When the working machine 200 is a rigid dump truck, the specified loading weight of the working machine 200 is, for example, about 300 [t].

[0051] When the parameter selection unit 104 determines in process P24 that the weight PLD [t] of the load is within the range of ±5% of the specified loading weight (YES), it executes process P25 to aggregate the cumulative travel distance for each slope. In this process P25, the parameter selection unit 104 calculates the travel distance at a specific slope θ based on the specific slope θ included in the travel history data RD, the travel speed V [km / h], and the travel time.

[0052] Furthermore, the parameter selection unit 104 adds the calculated travel distance at the specific slope θ to the cumulative travel distance for each slope of that specific slope θ calculated in the previous process P25. When the travel distance is first calculated for the specific slope θ in process P25, the travel distance at that specific slope θ is added to zero, which is the initial value of the cumulative travel distance for each slope of that specific slope θ.

[0053] After that, the parameter selection unit 104 executes process P26. Similar to the aforementioned process P15, when it determines that the data extracted from the travel history data RD is not the final data (NO), it returns to process P23, extracts the next data from the travel history data RD, and determines whether it is a loaded driving state.

[0054] On the other hand, in process P23, when the parameter selection unit 104 determines that the working machine 200 is traveling empty, that is, not carrying a load (NO), it executes process P26 without executing processes P24 and P25. Also, in process P24, when the parameter selection unit 104 determines that it is outside the range of ±5% of the specified loading weight (NO), it also executes process P26 without executing process P25.

[0055] In this way, by executing process P25 after the aforementioned processes P23 and P24, it is possible to extract data with the same load weight PLD[t] from the travel history data RD and aggregate the cumulative travel distance for each slope. Also, in process P26, when the parameter selection unit 104 determines that the data extracted from the travel history data RD is the final data (YES), it executes the next process P27.

[0056] In process P27, the parameter selection unit 104 divides the cumulative travel distance for each slope aggregated in process P25 by the total loaded travel distance aggregated in process P14 to calculate the travel ratio for each slope in loaded travel. By this process P27, it is possible to obtain the ratio of the cumulative travel distance for each slope θ to the total loaded travel distance of the working machine 200. Thus, process P2 shown in FIG. 6 ends.

[0057] Thereafter, the parameter selection device 100 executes process P3 shown in FIG. 4. In this process P3, for example, the parameter selection unit 104 multiplies the average loaded travel distance calculated in process P1 by the travel ratio for each slope calculated in process P2 to calculate the virtual travel distance for each slope per cycle. Further, the parameter selection unit 104 records the calculated virtual travel distance for each slope per cycle in the analysis database 105 shown in FIG. 1.

[0058] FIG. 7 is a table showing an example of a virtual path table recorded in the analysis database 105 as a result of the process P3 for calculating the virtual travel distance per slope per cycle in FIG. 4. The virtual path table includes, for example, for a working machine 200 with specific identification information to be analyzed, for each slope θ, the cumulative travel distance, the travel ratio with respect to the total load travel distance, and the virtual travel distance per cycle, that is, the virtual travel distance per slope. The travel ratio for each slope θ is represented, for example, by a percentage with the total load travel distance set to 100.

[0059] Next, the parameter selection device 100 executes the process P4 shown in FIG. 4. In this process P4, the parameter selection unit 104 extracts data on load travel from the travel history data RD and calculates the average fuel consumption per slope and the average speed per slope, for example, in the same manner as in the above-described processes P21 to P24. Here, the average fuel consumption per slope is the average value of the fuel consumption [l / h] for each slope θ in the load travel of the working machine 200. Also, the average speed per slope is the average value of the travel speed V [km / h] for each slope θ in the load travel of the working machine 200. Note that the numerical values calculated in the processes P4 - P8 shown below are calculated for each setting of the parameter setting PS.

[0060] Also, in this process P4, the parameter selection unit 104 performs smoothing of, for example, the calculated average fuel consumption per slope and the average speed per slope. As a result, the smoothed average fuel consumption per slope and the average speed per slope are obtained for each parameter setting PS. Here, the parameter setting PS included in the travel history data RD includes a plurality of settings as described above. Therefore, the average fuel consumption per slope and the average speed per slope are calculated for each setting of the parameter setting PS.

[0061] Next, the parameter selection device 100 executes process P5 shown in FIG. 4. In this process P5, for example, the parameter selection unit 104 calculates the average fuel consumption per slope [l / km] by dividing the average fuel consumption per slope [l / h] obtained in the previous process P4 by the average speed per slope [km / h] also obtained in the previous process P4. Further, the parameter selection unit 104 multiplies the calculated average fuel consumption per slope [l / km] by the virtual travel distance per slope [km] calculated in process P3 to calculate the predicted fuel consumption per slope per cycle [l]. The predicted fuel consumption per slope [l] is calculated for each setting included in the parameter setting PS.

[0062] Next, the parameter selection device 100 executes process P6 shown in FIG. 4. In this process P6, for example, the parameter selection unit 104 divides the virtual travel distance per slope [km] calculated in process P3 by the average speed per slope [km / h] calculated in process P4 to calculate the travel time per slope per cycle [h]. The travel time per slope [h] is calculated for each setting included in the parameter setting PS.

[0063] Next, the parameter selection device 100 executes process P7 shown in FIG. 4. In this process P7, for example, based on the travel history data RD, for each parameter setting PS, the predicted fuel consumption per cycle [l], which is the sum of the predicted fuel consumption per slope per cycle [l], is divided by the specified loading weight [t] of the work machine 200 to calculate the predicted fuel consumption per unit loading weight per cycle [l / t]. The predicted fuel consumption [l / t] is calculated for each setting included in the parameter setting PS.

[0064] Next, the parameter selection device 100 executes process P8 shown in FIG. 4. In this process P8, for example, based on the travel history data RD, for each parameter setting PS, the specified loading weight [t] of the work machine 200 is divided by the virtual travel time per cycle "h", which is the sum of the travel times per slope, to calculate the predicted production amount per cycle [t / h]. The predicted production amount [t / h] is calculated for each setting included in the parameter setting PS.

[0065] Next, the parameter selection device 100 executes the process P9 shown in FIG. 4. In this process P9, the parameter selection unit 104 calculates, for example, one cycle for each parameter setting PS calculated in processes P7 and P8 unit loading weight in Based on the predicted fuel consumption [l / t] and the predicted production amount [t / h] per cycle, the recommended parameter setting is selected.

[0066] FIG. 8 is an image diagram showing an example of the output result of the recommended parameter setting selected in the process P9 of FIG. 4. In the example shown in FIG. 8, the current parameter setting PS of the engine 201 of the work machine 200 to be analyzed is setting B. In setting B, the rotation speed of the low rotation of the engine 201 is 1200 [rpm], the rotation speed of the high rotation is 1800 [rpm], and the reaction speed is high. In the setting B of this parameter setting PS, the predicted fuel consumption is 0.234 [l / t], and the predicted production amount is 633 [t / h].

[0067] On the other hand, if the parameter setting PS of the engine 201 of the work machine 200 is changed from the current setting B to setting C, the reaction speed of the engine 201 is changed from high to medium. As a result, it becomes possible to reduce the predicted fuel consumption from 0.234 [l / t] to 0.233 [l / t] without reducing the predicted production amount per cycle. Therefore, in the above-described process P9, the parameter selection device 100 selects, for example, setting C with the lowest predicted fuel consumption as the recommended parameter setting from among settings A, B, and C, which are the parameter settings PS with the highest predicted production amount. In process P9, the method for selecting the recommended parameter setting based on the predicted fuel consumption and the predicted production amount is not particularly limited, and the user can appropriately set it in consideration of the balance between fuel efficiency and production amount.

[0068] Finally, the parameter selection device 100 executes, for example, a process P10 of recording and outputting recommended parameter settings. In this process P10, the parameter selection unit 104 records, for example, the recommended parameter settings of a specific working machine 200 to be analyzed in the analysis database 105. Further, the parameter selection unit 104 outputs, for example, the recommended parameter settings of a specific working machine 200 to be analyzed to at least one of the working machine 200 and the user terminal 400 via the data input / output unit 101.

[0069] That is, the parameter selection device 100 transmits, for example, the recommended parameter settings selected by the parameter selection unit 104 in process P9 to the user terminal 400 via the data input / output unit 101. In this case, the user terminal 400 can display the recommended parameter settings received from the data input / output unit 101 on the display unit 401 as shown in FIG. 8.

[0070] Further, the parameter selection device 100 transmits, for example, the recommended parameter settings selected by the parameter selection unit 104 in process P9 to the communication device 204 of the working machine 200 via the data input / output unit 101. In this case, the engine control unit 223 controls the engine 201 based on the recommended parameter settings received from the data input / output unit 101 via the communication device 204. After the end of the process P10 of recording and outputting the recommended parameter settings, the parameter selection device 100 ends the process flow shown in FIG. 4.

[0071] As described above, the parameter selection device 100 of the present embodiment is a device that selects the recommended parameter settings of the engine 201 of the work machine 200 based on the travel history data RD of the work machine 200, and includes a parameter selection unit 104. As shown in FIG. 2, the travel history data RD includes the travel date and time DAT of the work machine 200, the slope θ of the travel route, the travel speed V [km / h], the weight PLD [t] of the load, the parameter setting PS of the engine 201, and the fuel consumption per unit time FCH [l / h]. As shown in FIG. 4, the parameter selection unit 104 calculates the average loaded travel distance per cycle based on the travel history data RD (process P1). Here, one cycle of the work machine 200 is defined as the process of loading a load, traveling with the load after loading, dropping the load after traveling with the load, traveling empty after dropping the load, and loading the load again. Also, the parameter selection unit 104 calculates the travel ratio per slope, which is the ratio of the cumulative travel distance for each slope θ to the total travel distance of the work machine 200 during loaded travel, based on the travel history data RD (process P2). Further, the parameter selection unit 104 multiplies the average loaded travel distance by the travel ratio per slope to calculate the virtual travel distance per slope per cycle (process P3). Also, the parameter selection unit 104 calculates the average fuel consumption per slope, which is the average value of the fuel consumption FCH [l / h], and the average speed per slope, which is the average value of the travel speed V [km / h], for each slope θ in the loaded travel of the work machine 200 based on the travel history data RD (process P4). Also, the parameter selection unit 104 multiplies the average fuel consumption per slope by the virtual travel distance per slope per cycle, which is obtained by dividing the average fuel consumption per slope by the average speed per slope, to calculate the predicted fuel consumption per slope per cycle (process P5). Also, the parameter selection unit 104 divides the virtual travel distance per slope by the average speed per slope to calculate the travel time per slope per cycle (process P6). Also, the parameter selection unit 104 divides the predicted fuel consumption per cycle, which is the sum of the predicted fuel consumption per slope, for each parameter setting PS based on the travel history data RD, by the specified load weight of the work machine 200, When the weight PLD[t] of the load is equivalent to the specified load capacity of the working machine 200, calculate the cumulative travel distance for each slope θ, and calculates the travel ratio per slope, which is the ratio of the cumulative travel distance for each slope θ to the total travel distance of the work machine 200 during loaded travel. (process P2). Further, the parameter selection unit 104 multiplies the average loaded travel distance by the travel ratio per slope to calculate the virtual travel distance per slope per cycle (process P3). Also, the parameter selection unit 104 calculates the average fuel consumption per slope, which is the average value of the fuel consumption FCH [l / h], and the average speed per slope, which is the average value of the travel speed V [km / h], for each slope θ in the loaded travel of the work machine 200 based on the travel history data RD (process P4). Also, the parameter selection unit 104 multiplies the average fuel consumption per slope by the virtual travel distance per slope per cycle, which is obtained by dividing the average fuel consumption per slope by the average speed per slope, to calculate the predicted fuel consumption per slope per cycle (process P5). Also, the parameter selection unit 104 divides the virtual travel distance per slope by the average speed per slope to calculate the travel time per slope per cycle (process P6). Also, the parameter selection unit 104 divides the predicted fuel consumption per cycle, which is the sum of the predicted fuel consumption per slope, for each parameter setting PS based on the travel history data RD, by the specified load weight of the work machine 200, In one cycle Calculate the predicted fuel consumption per unit payload (Process P7). Further, based on the driving history data RD, the parameter selection unit 104 divides the specified payload by the virtual driving time per cycle, which is the sum of the driving times for each slope, for each parameter setting PS to calculate the predicted production per cycle (Process P8). Then, as shown in FIG. 8, the parameter selection unit 104 selects a recommended parameter setting based on the predicted fuel consumption and predicted production per cycle for each parameter setting PS (Process P9). unit loading weight in Based on the predicted fuel consumption and predicted production per cycle for each parameter setting PS, select a recommended parameter setting (Process P9).

[0072] With such a configuration, according to the parameter selection device 100 of the present embodiment, the driving history data RD of the work machine 200 is aggregated to calculate the virtual driving distance for each slope of the virtual driving route in which the work machine 200 performs a load-carrying drive in one cycle. Furthermore, using this virtual driving distance for each slope, for each parameter setting PS of the engine 201, the predicted fuel consumption per unit payload and the predicted production in one cycle of the work machine 200 can be calculated. As a result, at a work site such as a mine site where the route that the work machine 200 can travel and the gradient of the route that the work machine 200 travels change moment by moment as the work progresses, under uniform conditions, from among a plurality of parameter settings PS, a recommended parameter setting considering the balance between the predicted fuel consumption and the predicted production can be selected. Therefore, according to the present embodiment, it is possible to provide a parameter selection device 100 that can efficiently drive the work machine 200 at a work site where the driving route and the slope θ change moment by moment.

[0073] In addition, the parameter recommendation system 500 of the present embodiment includes the aforementioned parameter selection device 100 and a control device 220 mounted on the work machine 200. The control device 220 includes an engine control unit 223, a travel record acquisition unit 221, and a travel record transmission unit 222. The engine control unit 223 controls the engine 201 based on the parameter setting PS. The travel record acquisition unit 221 acquires travel history data RD based on the detection results of the sensors 210 mounted on the work machine 200. The travel record transmission unit 222 transmits the travel history data RD input from the travel record acquisition unit 221 to the parameter selection device 100 via the communication device 204 mounted on the work machine 200.

[0074] With such a configuration, according to the parameter recommendation system 500 of the present embodiment, it becomes possible to acquire the travel history data RD of the work machine 200 by the control device 220 mounted on the work machine 200 and transmit it to the parameter selection device 100 via the communication device 204 of the work machine 200.

[0075] Furthermore, the parameter recommendation system 500 of the present embodiment further includes a user terminal 400 that is communicably connected to the parameter selection device 100. Then, the parameter selection device 100 transmits the recommended parameter setting of the engine 201 of the work machine 200 to the user terminal 400. And the user terminal 400 has a display unit 401 that displays the recommended parameter setting received from the parameter selection device 100.

[0076] With such a configuration, according to the parameter recommendation system 500 of the present embodiment, the user of the work machine 200 can refer to the recommended parameter setting of the engine 201 displayed on the display unit 401 of the user terminal 400 and make a setting change to the work machine 200.

[0077] Also, in the parameter recommendation system 500 of the present embodiment, the parameter selection device 100 transmits the recommended parameter settings of the engine 201 to the work machine 200. Further, the engine control unit 223 of the control device 220 mounted on the work machine 200 controls the engine 201 based on the recommended parameter settings received from the data input / output unit 101 via the communication device 204.

[0078] With such a configuration, according to the present embodiment, it is possible to provide a parameter recommendation system 500 that can efficiently run the work machine 200 at a work site where the travel route and the slope θ change moment by moment.

[0079] As described above, the embodiments of the parameter selection device and the parameter recommendation system according to the present disclosure have been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and design changes and the like within the scope not departing from the gist of the present disclosure are also included in the present disclosure.

Explanation of Reference Numerals

[0080] 100 Parameter selection device 104 Parameter selection unit 200 Work machine 201 Engine 204 Communication device 210 Sensor 220 Control device 221 Travel record acquisition unit 222 Travel record transmission unit 223 Engine control unit 400 User terminal 401 Display unit DAT Travel date and time FCH Fuel consumption per unit time PLD Weight of load PS Parameter setting RD Travel history data V Travel speed θ Slope

Claims

1. A parameter selection device that selects a recommended parameter setting for the engine based on driving history data including the driving date and time of the work machine, the slope of the driving route, the driving speed, the weight of the load, the parameter setting for engine control, and the fuel consumption per unit time, wherein the work machine loads the load, travels with the load after loading the load, drops the load after traveling with the load, travels empty after dropping the load, and repeats the process until loading the load again as one cycle, calculates the average loaded travel distance per cycle based on the driving history data, calculates the cumulative travel distance for each slope when the weight of the load is equal to the specified load capacity of the work machine based on the driving history data, and calculates the travel ratio for each slope, which is the ratio of the cumulative travel distance for each slope to the total travel distance of the work machine during the loaded travel, calculates the virtual travel distance for each slope per cycle by multiplying the average loaded travel distance by the travel ratio for each slope, calculates the average fuel consumption for each slope, which is the average value of the fuel consumption, and the average speed for each slope, which is the average value of the driving speed, for each slope in the loaded travel of the work machine based on the driving history data, calculates the predicted fuel consumption for each slope per cycle by multiplying the average fuel consumption per slope by the virtual travel distance for each slope per cycle divided by the average speed for each slope, calculates the travel time for each slope per cycle by dividing the virtual travel distance for each slope by the average speed for each slope, calculates the predicted fuel consumption per unit load weight in one cycle by dividing the predicted fuel consumption per cycle, which is the sum of the predicted fuel consumption for each slope, by the specified load weight of the work machine for each parameter setting based on the driving history data, calculates the predicted production per cycle by dividing the specified load weight by the virtual travel time per cycle, which is the sum of the travel times for each slope, for each parameter setting based on the driving history data, selects the recommended parameter setting based on the predicted fuel consumption per unit load weight and the predicted production in one cycle for each parameter setting, characterized by comprising a parameter selection unit.

2. A parameter recommendation system comprising the parameter selection device according to claim 1 and a control device mounted on the work machine, wherein the control device, an engine control unit that controls the engine based on the parameter setting, a travel record acquisition unit that acquires the travel history data based on the detection result of a sensor mounted on the work machine, a travel record transmission unit that transmits the travel history data input from the travel record acquisition unit to the parameter selection device via a communication device mounted on the work machine, and is characterized by having the above.

3. The parameter recommendation system further comprises a user terminal communicably connected to the parameter selection device, wherein the parameter selection device transmits the recommended parameter setting to the user terminal, and the user terminal has a display unit that displays the recommended parameter setting received from the parameter selection device. The parameter recommendation system according to claim 2 is characterized by this.

4. The parameter selection device transmits the recommended parameter setting to the work machine, and the engine control unit controls the engine based on the recommended parameter setting received from the parameter selection device via the communication device. The parameter recommendation system according to claim 2 is characterized by this.

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