Work content estimating system and work content estimating method

Abstract

An acquiring unit acquires vehicle body information relating to a work machine. An estimating unit estimates the work content of the work machine on the basis of the vehicle body information, and identifies inefficient work in the work content of the work machine on the basis of the vehicle body information.

Description

Work content estimation system and work content estimation method

[0001] This application claims priority to Japanese Patent Application No. 2024-030614, filed on February 29, 2024, the contents of which are incorporated herein by reference.

[0002] Patent Document 1 discloses a technology for estimating the working state of a work machine working at a work site. Patent Document 1 describes estimating whether the work being performed by a dump truck is loading, earth removal, traveling with a load, traveling empty, or stopped.

[0003] International Publication No. 2019 / 017159

[0004] When an operator operates a work machine, they may perform tasks that result in low work efficiency, such as actions to recover from a mistake. Detecting such inefficient work is required in managing a work site. An object of the present disclosure is to provide a work machine work content estimation system and work content estimation method that are capable of detecting inefficient work.

[0005] According to one aspect of the present invention, a work content estimation system for a work machine is a work content estimation system for estimating the work content of the work machine based on body information of the work machine, and comprises an acquisition unit that acquires the body information of the work machine, and an estimation unit that estimates the work content of the work machine based on the body information, and the estimation unit identifies inefficient work in the work content of the work machine based on the body information.

[0006] According to the above aspect, the work content estimation system for a work machine can detect inefficient work.

[0007] FIG. 1 is a diagram showing an example of a work content estimation system according to the first embodiment. FIG. 2 is a diagram showing an example of a work area according to the first embodiment. FIG. 3 is a schematic diagram showing the configuration of a work machine according to the first embodiment. FIG. 4 is a schematic block diagram showing the configuration of a management device according to the first embodiment. FIG. 5 is a flowchart showing data collection processing by the management device according to the first embodiment. FIG. 6 is a flowchart showing analysis processing by the management device according to the first embodiment. FIG. 7 is an example of a histogram showing lost time due to respotting according to the first embodiment.

[0008] First Embodiment Work Content Estimation System 1 Fig. 1 is a diagram showing an example of a work content estimation system according to a first embodiment. The work content estimation system 1 according to the first embodiment analyzes the work content of a dump truck, which is a work machine 20, and displays it to a site supervisor. By checking the work content, the site supervisor can evaluate the skill of the operator of the work machine 20. The work content estimation system 1 includes a management device 10 and a plurality of work machines 20. The work content estimation system 1 is an example of a display control system for the work machines 20.

[0009] The management device 10 analyzes the work details of the work machines 20 based on the vehicle body data of the multiple work machines 20 and displays the results on a display. The work machines 20 transport loads generated at the work site G. Examples of loads transported by the work machines 20 include crushed stone, earth and sand, rocks, coal, etc.

[0010] The work site G according to the first embodiment has a loading site G1 and a soil unloading site G2. Hereinafter, the loading site G1 and the soil unloading site G2 will also be referred to as the work area. The loading site G1 and the soil unloading site G2 are connected by a travel path G3. The travel path G3 includes a general road connecting the loading site G1 and the soil unloading site G2, as well as a transport path prepared within the work area for transporting soil and sand. A dump truck, which is a work machine 20, travels between the loading site G1 and the soil unloading site G2 to transport soil and sand.

[0011] FIG. 2 is a diagram showing an example of a work area according to the first embodiment. Within the work area, work spots SP1 are defined where the work machine 20 will work. For example, a loading spot for loading work is provided at the loading site G1, and an unloading spot for unloading work is provided at the unloading site G2. In the first embodiment, the work machine 20 moves to these work spots SP1 by backing up. Therefore, a turning spot SP2 is provided within the work area for the work machine 20 to turn around. Also, when a work machine 20 arrives at the work area, another work machine 20 may have arrived first. In this case, the work machine 20 must wait at a waiting spot SP3 within the work area for the first-arrived work machine 20 to complete its work.

[0012] Therefore, if there are other work machines 20 present when the work machine 20 enters the work area, it moves to waiting spot SP3 and waits for the other work machines 20 to complete their work. If there are no more other work machines 20 present, the work machine 20 travels to turning spot SP2. The operator of the work machine 20 shifts the gear into reverse at turning spot SP2 and travels in reverse to work spot SP1. After completing the work, the work machine 20 exits the work area.

[0013] 3 is a schematic diagram showing the configuration of the work machine 20 according to the first embodiment. The work machine 20 comprises a vehicle body 21, a vessel 22, a lift cylinder 23, a dump operation detection sensor 24, wheels 25, a suspension cylinder 26, a suspension pressure sensor 27, a positioning device 28, and a computer 29.

[0014] The vessel 22 is a loading platform on which a load is carried. The vessel 22 is disposed on top of the vehicle body 21. The vessel 22 is driven by power transmitted from the vehicle body 21. The lift cylinder 23 is a hydraulic cylinder driven by hydraulic oil, and tilts the vessel 22 when driven. The dump operation detection sensor 24 detects whether the vessel 22 is in contact with the vehicle body 21. Note that a work machine 20 according to another embodiment may be provided with a stroke sensor that measures the stroke amount of the lift cylinder 23 instead of the dump operation detection sensor 24, and may determine a dump operation based on a measurement value of the stroke sensor.

[0015] The suspension cylinders 26 are provided between the wheels 25 and the vehicle body 21. The suspension cylinders 26 absorb the impact that the wheels 25 receive from the road surface and suppress vibration of the vehicle body 21. Hydraulic oil is sealed inside the suspension cylinders 26, and the suspension cylinders 26 absorb the impact by expanding and contracting. Note that work machines 20 according to other embodiments may be provided with a suspension device having an air spring or a shock absorber instead of the suspension cylinders 26.

[0016] The suspension pressure sensors 27 detect the load acting on the suspension cylinders 26. The suspension pressure sensors 27 are provided in the suspension cylinders 26 for the left and right front wheels and the left and right rear wheels of the work machine 20.

[0017] The positioning device 28 uses the Global Navigation Satellite System (GNSS) to measure the position of the work machine 20. The position measured by the positioning device 28 is expressed in a global coordinate system.

[0018] The computer 29 collects measurement data from the dump motion detection sensor 24, suspension pressure sensor 27, and positioning device 28, and transmits it to the management device 10 as vehicle body data (vehicle body information). From the measurement data from the dump motion detection sensor 24, it is possible to calculate whether or not the work machine 20 is performing a dumping operation. From the measurement data from the suspension pressure sensor 27, it is possible to calculate the weight of the load on the work machine 20. The computer 29 collects vehicle body data at a predetermined cycle (for example, every second) and transmits it to the management device 10. Transmission to the management device 10 may be performed each time the data is collected, or may be performed by batch processing.

[0019] Configuration of the Management Device 10 FIG. 4 is a schematic block diagram showing the configuration of the management device 10 according to the first embodiment. The management device 10 includes a processor, a memory, an auxiliary storage device, and the like, all connected via a bus. Examples of the processor include a central processing unit (CPU), a graphic processing unit (GPU), and a microprocessor. The management device 10 executes a program to perform calculations to identify the work content of the dump truck. The program may be recorded on a computer-readable recording medium. Examples of the computer-readable recording medium include storage devices such as magnetic disks, magneto-optical disks, optical disks, and semiconductor memories. The program may be received from an external device via a telecommunications line. Note that all or part of the functions of the management device 10 may be implemented using a custom large-scale integrated circuit (LSI), such as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). Examples of PLDs include programmable array logic (PAL), generic array logic (GAL), complex programmable logic devices (CPLD), and field programmable gate arrays (FPGA). Such integrated circuits are also included in the category of processors.

[0020] The management device 10 includes a vehicle body data acquisition unit 11, a data storage unit 12, a state calculation unit 13, an estimation unit 14, a selection unit 15, an evaluation unit 16, and a display control unit 17. That is, the processor of the management device 10 includes the vehicle body data acquisition unit 11, the data storage unit 12, the state calculation unit 13, the estimation unit 14, the selection unit 15, the evaluation unit 16, and the display control unit 17 by executing a program.

[0021] The vehicle body data acquisition unit 11 acquires vehicle body data from the work machine 20 and records the data in association with the acquisition time in the data storage unit 12. The vehicle body data acquisition unit 11 corresponds to the acquisition unit in this disclosure. The vehicle body data of the work machine 20 includes at least measurement data of the suspension pressure and the position of the work machine 20.

[0022] Based on the vehicle body data acquired by the vehicle body data acquisition unit 11, the state calculation unit 13 calculates state data that represents the state of the work machine 20 at the time related to that vehicle body data. Specifically, the state calculation unit 13 calculates the load of the work machine 20 based on measurement data of the suspension pressure of the work machine 20. For example, the state calculation unit 13 calculates the load, traveling speed, and primary category of work content by the following procedure. The primary category of work content indicates one of the values ​​of stopped, loading work, traveling with a load, earth removal work, and traveling without a load. The state calculation unit 13 records the calculated state data in the data storage unit 12 in association with the vehicle body data.

[0023] The state calculation unit 13 calculates the load by substituting a value obtained by applying a low-pass filter to the suspension pressure measurement data into a pressure-load conversion function. The state calculation unit 13 calculates the traveling speed of the work machine 20 based on the position data of the work machine 20.

[0024] The state calculation unit 13 calculates the primary category of the work content of the work machine 20 based on the load and traveling speed. Specifically, the state calculation unit 13 sets the primary category of the work content to "stopped" when the traveling speed of the work machine 20 is less than a predetermined value and the change in load is less than a predetermined value. The state calculation unit 13 sets the primary category of the work content to "loading work" when the traveling speed of the work machine 20 is less than a predetermined value and the load is increasing by more than a predetermined change. The state calculation unit 13 sets the primary category of the work content to "earth removal work" when the traveling speed of the work machine 20 is less than a predetermined value and the load is decreasing by more than a predetermined change. The state calculation unit 13 sets the primary category of the work content to "traveling with a load" when the traveling speed of the work machine 20 is more than a predetermined value and the load is more than a predetermined value. The state calculation unit 13 classifies the primary category of the work content as "empty load traveling" when the traveling speed of the work machine 20 is equal to or greater than a predetermined value and the load is less than a predetermined value.

[0025] In other embodiments, the computer 29 of the work machine 20 may calculate the status data and transmit it to the management device 10 together with the vehicle body data.

[0026] The estimation unit 14 estimates the work content within the area (work area) of the work machine 20 based on the time series of data stored in the data storage unit 12. The estimation unit 14 estimates which of the secondary categories, including queuing, pre-spotting, spotting, and re-spotting, the work content within the area of ​​the work machine 20 is. Queuing is a state in which the work machine 20 enters the area and waits at the waiting spot SP3 for other work machines 20 to complete their work. Pre-spotting is a series of driving states in which the work machine 20 moves to the turning spot SP2 in order to turn around. Spotting is a series of driving states in which the work machine 20 moves in reverse to the work spot SP1. Respotting is a series of driving states in which a redo operation is performed to adjust the spotting position of the work machine 20 at the work spot SP1. Respotting is an example of inefficient work. The estimation unit 14 associates the estimated work content with the vehicle data and records it in the data storage unit 12.

[0027] The selection unit 15 accepts the selection of the work site for which a user, such as a site supervisor, wishes to view information. The evaluation unit 16 calculates an evaluation value relating to the operator's driving evaluation based on the data stored in the data storage unit 12. The display control unit 17 generates a signal for displaying the evaluation value calculated by the evaluation unit 16 and outputs it to a display.

[0028] <<Operation of Work Content Estimation System 1>> Figure 5 is a flowchart showing the data collection process by the management device 10 according to the first embodiment. The computer 29 of the work machine 20 collects measurement data from various sensors while the work machine 20 is in operation, and transmits the collected measurement data and the time of measurement to the management device 10 as vehicle body data in a timely manner. When the vehicle body data acquisition unit 11 of the management device 10 receives vehicle body data from the work machine 20 (step S1), it associates the vehicle body data with the ID of the work machine 20 and records it in the data storage unit 12 (step S2). The status calculation unit 13 calculates status data for the work machine 20 based on the vehicle body data received from the work machine 20 and the most recent vehicle body data for that work machine 20 that has already been recorded in the data storage unit 12 (step S3). The status calculation unit 13 associates the calculated status data with the vehicle body data received in step S1 and records it in the data storage unit 12 (step S4). When the vehicle body data acquisition unit 11 acquires vehicle body data relating to the period from time t1 to time tn in step S1, the state calculation unit 13 calculates state data relating to the period from time t1 to time tn in step S3.

[0029] Figure 6 is a flowchart showing analysis processing by the management device 10 according to the first embodiment. The management device 10 performs work analysis of the work machines 20 based on the data stored in the data storage unit 12 at a predetermined timing. The timing for performing the work analysis may be a predetermined time, or may be a timing instructed by the user. The estimation unit 14 of the management device 10 reads vehicle data and status data relating to the target period for the work analysis from the data storage unit 12 (step S11). The vehicle data and status data are associated with the ID and time of the work machine 20, and are therefore treated as time-series data for each work machine 20. The estimation unit 14 selects each work machine 20 one by one (step S12) and performs the following processing.

[0030] The estimation unit 14 divides the time series data related to the work machine 20 selected in step S12 into partial time series having the same value for the primary division of the work content, and calculates aggregated data representing each feature amount (step S13). For each divided partial time series, the estimation unit 14 calculates aggregated data including the time period, the primary division of the work content, the forward travel distance, and the reverse travel distance. The travel distance may be calculated by, for example, partial integration of the travel speed. Next, the estimation unit 14 selects, one by one, aggregated data from the divided partial time series having the value of the primary division "loading work" or "earth removal work" as first aggregated data (step S14).

[0031] The estimation unit 14 extracts, as second aggregated data representing travel to the area, aggregated data relating to a time earlier than the first aggregated data selected in step S14, in which the forward travel distance exceeds a predetermined value and which corresponds to a time period closest to the first aggregated data (step S15).The estimation unit 14 extracts, between the first aggregated data and the second aggregated data, aggregated data in which the backward travel distance exceeds a predetermined distance, as third aggregated data representing spotting work (step S16).

[0032] The estimation unit 14 determines whether the forward travel distance related to the third aggregated data exceeds a predetermined distance (e.g., 1 / 2 the body length of the work machine 20) (step S17). If the forward travel distance related to the third aggregated data exceeds the predetermined distance (step S17: YES), the estimation unit 14 determines that the third aggregated data includes respotting work. The estimation unit 14 identifies the portion of the time period related to the third aggregated data before the time when the vehicle first switched from reverse travel to forward travel as a time period related to spotting work in the secondary category, and identifies the portion after that time as a time period related to respotting work in the secondary category (step S18). If the forward travel distance related to the third aggregated data does not exceed the predetermined distance (step S17: YES), the estimation unit 14 determines that the third aggregated data does not include respotting work. In other words, the estimation unit 14 determines that the value of the secondary category of the work content for the time period related to the third aggregated data is spotting.

[0033] The estimation unit 14 extracts aggregated data between the second and third aggregated data whose first category is "stop" as fourth aggregated data representing queuing work (step S19). That is, the estimation unit 14 determines the value of the second category of the work content for the time period related to the fourth aggregated data to be "queuing." Note that if there is no aggregated data between the second and third aggregated data whose first category is "stop," the estimation unit 14 may estimate that no queuing work occurred.

[0034] The estimation unit 14 extracts the aggregated data with the longest forward distance between the third aggregated data and the fourth aggregated data as the fifth aggregated data representing pre-spotting (step S20). That is, the estimation unit 14 determines the value of the secondary category of the work content for the time period related to the fifth aggregated data to be pre-spotting.

[0035] Furthermore, if the work selected in step S14 is earth unloading work (step S21: earth unloading work), the estimation unit 14 estimates the value of the secondary section from the start of the first aggregated data (i.e., the end of spotting) to the time when a part of the vessel 22 separates from the vehicle body 21 as pre-dumping (step S22). Furthermore, the estimation unit 14 estimates the value of the secondary section from the end of pre-dumping to the time when the traveling speed becomes greater than a predetermined value (e.g., zero) as earth unloading work (step S23). Next, the estimation unit 14 identifies the value of the secondary section for the period from immediately after the earth unloading work until the entire vessel 22 comes into contact with the vehicle body 21 as post-dumping (step S24). On the other hand, if the work selected in step S14 is loading work (step S21: loading work), the estimation unit 14 identifies the value of the secondary section from the start of the first aggregated data to the time when the traveling speed becomes greater than a predetermined value (e.g., zero) as loading work (step S25).

[0036] The estimation unit 14 records the secondary division values ​​estimated in steps S16 to S25 in the data storage unit 12 in association with the work data (step S26).

[0037] When a user evaluates work at a work site, the user operates the management device 10 to input an instruction to display evaluation information.

[0038] The selection unit 15 accepts from the user a selection of one of a plurality of work sites for which the user wishes to check the evaluation results. The evaluation unit 16 reads out data on the work machines 20 deployed at the selected work site G from the data storage unit 12. The evaluation unit 16 calculates an evaluation value for each work site based on the read-out vehicle body data, condition data, and secondary category values ​​of the work of the work machines 20. The display control unit 17 displays the calculated evaluation values ​​on the display. The display control unit 17 may also display information related to the secondary category values ​​for each predetermined period (e.g., one day), each operator, or each group of operators side by side.

[0039] For example, the evaluation unit 16 calculates the incidence of respotting, which is an inefficient task. Specifically, the evaluation unit 16 calculates the ratio of the number of aggregated data items related to respotting to the number of aggregated data items related to spotting in the time series of the secondary task segments read from the data storage unit 12. The evaluation unit 16 may also calculate the ratio of the total time related to respotting to the total time related to spotting in the time series of the secondary task segments read from the data storage unit 12. By the management device 10 displaying the incidence of respotting, the site supervisor can recognize the skill level of the operators at the work site and can also consider revising the location of work spots at the work site. The evaluation unit 16 may calculate the average forward time, average backward time, average forward distance, and average backward distance for tasks in which respotting did not occur, and the average forward time, average backward time, average forward distance, and average backward distance for tasks in which respotting occurred, and the display control unit 17 may display these values ​​side by side for comparison. This allows the site supervisor to recognize the extent to which respotting reduces work efficiency.

[0040] For example, the evaluation unit 16 calculates the lost time due to respotting. Specifically, the evaluation unit 16 extracts tasks for which respotting did not occur for each cycle from the time series of secondary task segments read from the data storage unit 12, and calculates the time required for each cycle. The evaluation unit 16 also extracts tasks for which respotting occurred for each cycle from the time series of secondary task segments read from the data storage unit 12, and calculates the time required for each cycle. The display control unit 17 displays the time for tasks for which respotting did not occur and the time for tasks for which respotting occurred so that they can be compared. At this time, the evaluation unit 16 calculates the difference between the average time for tasks for which respotting did not occur and the time for each task for which respotting occurred as the lost time. FIG. 7 is an example of a histogram representing the lost time due to respotting according to the first embodiment. For example, as shown in FIG. 7, the display control unit 17 displays the time for tasks for which respotting did not occur and the time for tasks for which respotting occurred as a histogram. This allows the site supervisor to recognize the extent to which work efficiency is reduced due to respotting.

[0041] For example, the evaluation unit 16 according to another embodiment may calculate the lost time as the difference between the standard time for the spotting task and the time actually required for the spotting task. In this case, the evaluation unit 16 calculates the time required for spotting, including respotting, for each task from the time series of the secondary task segments read from the data storage unit 12. The evaluation unit 16 displays a predetermined standard time for the spotting task and the time required for spotting so that they can be compared. In this case, the evaluation unit 16 calculates the difference between the time required for spotting and the standard time as the lost time.

[0042] For example, the evaluation unit 16 calculates the amount of production loss due to respotting. Specifically, the evaluation unit 16 extracts work cycles in which respotting did not occur from the time series of secondary divisions of work read from the data storage unit 12, and calculates the average value of the processing load per unit time. The evaluation unit 16 also extracts work cycles in which respotting occurred from the time series of secondary divisions of work read from the data storage unit 12, and calculates the average value of the processing load per unit time. This allows the site supervisor to recognize the extent of loss caused by respotting.

[0043] For example, the evaluation unit 16 calculates the occurrence rate of respotting depending on whether queuing occurs or not. Specifically, the evaluation unit 16 extracts tasks in which queuing did not occur for each cycle from the time series of the secondary divisions of tasks read from the data storage unit 12, and calculates the proportion of the extracted cycles in which respotting occurs. The evaluation unit 16 also extracts tasks in which queuing occurred for each cycle from the time series of the secondary divisions of tasks read from the data storage unit 12, and calculates the proportion of the extracted cycles in which respotting occurs. The display control unit 17 displays the probability of respotting occurring when queuing occurs and the probability of respotting occurring when queuing does not occur side by side. This allows the site supervisor to consider the causal relationship between queuing and respotting.

[0044] For example, the evaluation unit 16 generates KPIs (Key Performance Indicators) based on the values ​​related to the secondary categories. Examples of KPIs include the number of queuings, the average number of queuings per cycle, the queuing frequency, the average queuing time, the amount of fuel consumed for queuing, the average time for pre-spotting, the average distance for pre-spotting, the amount of fuel consumed for pre-spotting, the average time for spotting, the average distance for spotting, the amount of fuel consumed for spotting, the spotting improvement rate, the number of re-spottings, the probability of re-spotting, the loading time, the number of loadings, the pre-dumping time, the unloading time, and the post-dumping time.

[0045] For example, the evaluation unit 16 identifies a KPI that has a correlation with the time required for spotting from among multiple indicators related to the operation of the work machine 20. Specifically, the evaluation unit 16 extracts time periods in which the secondary category of the work read from the data storage unit 12 is spotting, and analyzes the correlation between the KPI value for each time period and the time required for spotting. The display control unit 17 displays KPIs with a large absolute value of correlation with the time required for spotting.

[0046] <Other Embodiments> One embodiment has been described in detail above with reference to the drawings, but the specific configuration is not limited to that described above, and various design changes and the like are possible. That is, in other embodiments, the order of the above-described processes may be changed as appropriate. Furthermore, some of the processes may be executed in parallel. The management device 10 according to the above-described embodiment may be configured by a single computer, or the configuration of the management device 10 may be divided among multiple computers, and the multiple computers may function as the management device 10 by cooperating with each other. In this case, some of the computers configuring the management device 10 may be installed in the computer 29 of the work machine 20. For example, in other embodiments, the computer 29 of the work machine 20 may estimate the primary classification of the work content.

[0047] The management device 10 according to the embodiment described above estimates that a portion of spotting work in which the work machine 20 moves forward by more than half the vehicle body length as respotting work, but this is not limited to this. For example, a management device 10 according to another embodiment may estimate that an action is respotting if even a small amount of forward movement occurs during spotting work.

[0048] The work machine 20 according to the embodiment described above is a dump truck, but is not limited to this. For example, the work machine 20 according to other embodiments may be another type of transport machine.

[0049]

[0043] The work machine 20 in the embodiment described above moves to the work spot SP1 by traveling in reverse, but this is not limited to this. For example, at a work site G according to another embodiment, the work site G may move to the work spot SP1 by traveling forward. In this case, the work site G may not have a turning spot SP2. Also, in this case, pre-spotting may not occur in the series of work tasks. In the embodiment described above, the work machine work content estimation system 1 that estimates the work content of the work machine 20 based on the vehicle body information of the work machine 20 comprises a vehicle body data acquisition unit 11 that acquires the vehicle body information of the work machine 20, and an estimation unit 14 that estimates the work content of the work machine 20 based on the vehicle body information. The estimation unit 14 identifies inefficient tasks in the work content of the work machine 20 based on the vehicle body information. The estimation unit 14 may identify inefficient tasks based on a predetermined combination of vehicle body information. The work content may include spotting work, which is a series of trips within the loading site G1 toward a loading spot, or a series of trips within the unloading site G2 toward a unloading spot. The estimation unit 14 may identify, based on the vehicle body information, a portion where the work machine 20 has traveled a predetermined distance or more after changing its direction of travel during spotting work as inefficient work. The work machine 20 may be a dump truck. A work content estimation method for estimating the work content of the work machine 20 based on the vehicle body information of the work machine 20 comprises the steps of acquiring vehicle body information of the work machine 20, estimating the work content of the work machine 20 based on the vehicle body information, and identifying inefficient work in the work content of the work machine 20 based on the vehicle body information.

[0050] 1...Work content estimation system 10...Management device 11...Vehicle body data acquisition unit (acquisition unit) 12...Data storage unit 13...State calculation unit 14...Estimation unit 15...Selection unit 16...Evaluation unit 17...Display control unit 20...Work machine 21...Vehicle body 22...Vessel 23...Lift cylinder 24...Dump operation detection sensor 25...Wheels 26...Suspension cylinder 27...Suspension pressure sensor 28...Positioning device 29...Computer G...Work site G1...Loading area G2...Soil discharge area G3...Travel path SP1...Work spot SP2...Turning spot SP3...Waiting spot

Claims

1. A work content estimation system for a work machine that estimates the work content of a work machine based on body information of the work machine, comprising: an acquisition unit that acquires body information of the work machine; and an estimation unit that estimates the work content of the work machine based on the body information, wherein the estimation unit identifies inefficient work within the work content of the work machine based on the body information.

2. The work content estimation system for a work machine according to claim 1, wherein the estimation unit identifies the inefficient work based on a predetermined combination of the vehicle information.

3. A work content estimation system for a work machine as described in claim 2, wherein the work content includes spotting work, which is a series of driving within a loading yard towards a loading spot, or a series of driving within a dump yard towards a dump spot, and the estimation unit identifies, based on the vehicle information, a portion of the spotting work where the work machine has driven a predetermined distance or more after changing its direction of travel, as inefficient work.

4. The work content estimation system according to claim 1, wherein the work machine is a dump truck.

5. A work content estimation method for estimating the work content of a work machine based on body information of the work machine, comprising the steps of: acquiring body information of the work machine; estimating the work content of the work machine based on the body information; and identifying inefficient work within the work content of the work machine based on the body information.

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