JP2009235833A - Operation evaluation system and operation evaluation method for construction machine - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an operation evaluation system for evaluating the operational skill of an operator at site using a construction machine which is usually operated by the operator. <P>SOLUTION: This operation evaluation system comprises a transition instruction means 5 for transition to a diagnosis mode, an operation data collecting means 6B for collecting predetermined operation data according the signals from a plurality of detection means 6A, an operation data storage means 6C for storing the predetermined operation data, a diagnosis data generating means 8A for generating diagnosis data according to the predetermined operation data, a diagnosis data storage means 8B for storing the diagnosis data, a reference data storage means 7C for storing reference data for comparing with the diagnosis data, determination means 8C, 8D for determining the result of the operation in the diagnosis mode by comparing the diagnosis data with the reference data, and an output means 9 for outputting the result of the determination by the determination means. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a construction machine operation evaluation system and an operation evaluation method.

  In a construction machine such as a hydraulic excavator, a technique for prompting an operator to perform an appropriate operation for improving fuel efficiency is known (Patent Document 1). In the first prior art, when the fuel consumption is worse than the set value, the operator is warned and an appropriate operation is urged.

As a second conventional technique, an operator is warned when an operation that lowers durability is performed in order to extend the life of a construction machine (Patent Document 2). As another conventional technique, there is also known a technique that enables an operator to be taught an operation procedure of a construction machine (Patent Document 3, Patent Document 4, and Patent Document 5).
Japanese Patent Laid-Open No. 2005-989888 JP 2005-163470 A JP 2004-1987 JP 2004-107925 A JP 2004-132137 A

  In the prior art, attention and warning can be given to the operator to improve fuel consumption and life, but the operator cannot receive a driving course or evaluate the driving skill of the operator. Therefore, in order to improve the driving skill, the operator needs to go to a specialized training school to receive a driving training, or invite an instructor to the work site where the operator is arranged to receive a practical training. For this reason, it takes considerable time to increase the operator's driving skill.

  The present invention has been made paying attention to the above problems, and its purpose is to provide a construction machine that can automatically diagnose the operation result of the operator on the spot using the construction machine that is always in operation. The object is to provide an operation evaluation system and an operation evaluation method. Another object of the present invention is to make it possible to automatically diagnose a driving result more easily and more accurately by repeatedly executing a predetermined driving operation set in advance a predetermined number of times and inputting driving conditions in advance. Another object of the present invention is to provide an operation evaluation system and operation evaluation method for a construction machine. Further objects of the present invention will become clear from the description of the embodiments described later.

  In order to solve the above-described problems, an operation evaluation system for evaluating the operation of a construction machine according to the present invention includes a transition instruction means for shifting to a diagnosis mode, and a construction machine when the transition to the diagnosis mode is performed. Based on signals from a plurality of detection means provided, operation data collection means for collecting predetermined operation data, operation data storage means for storing predetermined operation data collected by the operation data collection means, and operation Diagnostic data generation means for generating diagnostic data based on predetermined operation data stored in the data storage means, and diagnostic data storage means for storing diagnostic data generated by the diagnostic data generation means And by comparing the reference data storage means for storing the reference data for comparison with the diagnostic data, and the diagnostic data and the reference data, Comprising determination means for determining the result of the operation in the cross-sectional mode, and output means for outputting a determination result by the determining means.

  In the diagnosis mode, a predetermined driving operation set in advance is repeatedly executed a predetermined number of times by an operator of the construction machine, and the driving condition when the predetermined driving operation is performed is associated with predetermined driving data. Operating condition setting means for storing in the operating data storage means.

  The determination means can determine overall evaluation for the entire predetermined driving operation, and can also determine individual operation evaluation for each part of the predetermined driving operation.

  The determining means detects a predetermined individual operation evaluation that should be improved most among the individual operation evaluations included in the comprehensive evaluation when the comprehensive evaluation is a predetermined value or less, and outputs the detected predetermined individual operation evaluation. Can also be output via

  Reference data update means for updating reference data stored in the reference data storage means may be provided.

  The reference data update means can update the reference data according to the operation time of the construction machine.

  The diagnostic data can be generated as a feature amount for each of a plurality of preset items.

  An operation evaluation method for evaluating the operation of a construction machine according to the present invention includes a step of displaying a menu for shifting to a diagnosis mode, a step of acquiring an operation condition for performing a predetermined operation, and a diagnosis mode A step of determining whether or not to shift to a diagnosis mode, a step of collecting and storing predetermined operation data from the construction machine when shifting to a diagnosis mode, a step of determining whether or not to end the diagnosis mode, A step of determining a driving result related to a predetermined driving operation by comparing diagnostic data generated based on predetermined driving data with reference data prepared in advance when the diagnostic mode is ended; Execute.

  According to the present invention, the operator can immediately obtain the determination result relating to his / her operation on the spot by simply shifting the construction machine which is normally operated to the diagnosis mode.

  In the diagnosis mode, a predetermined driving operation set in advance can be repeatedly executed a predetermined number of times, and the driving condition in the diagnosis mode can be associated with the driving data. Therefore, the driving operation to be diagnosed can be limited, and the soil quality at the work site can be taken into consideration, so that a relatively accurate determination result can be obtained.

  Since both the comprehensive evaluation and the individual operation evaluation can be obtained, the operator can easily grasp where to improve his / her driving operation.

  Since the reference data can be updated, the accuracy of the operation can be improved by incorporating the operation of an expert who should serve as a model into the reference data. Further, by updating the reference data according to the operation time of the construction machine, it is possible to determine the operation result corresponding to the aging deterioration of the construction machine.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the present embodiment, as described below, the construction skill 2 that the operator normally operates is used as it is, and the driving skill is automatically evaluated on the spot. The operator can obtain an evaluation of his / her driving skill only by repeating a predetermined driving operation 4 set in advance a predetermined number of times. FIG. 1 is a conceptual diagram showing the entirety of the present embodiment. FIG. 1 shows an outline of the present invention for understanding and implementing the present invention, and the scope of the present invention is not limited to the configuration shown in FIG.

  The operation evaluation system 1 is provided in a construction machine 2 such as a hydraulic excavator, for example. The driving evaluation system 1 is connected to the management server 3 via, for example, a communication network CN1 such as a satellite communication line or the Internet. The management server 3 centrally manages various construction machines that are distributed in various places. As will be described later, the management server 3 holds reference data generated from the operation of a skilled person.

  First, the hydraulic excavator 2 as a construction machine will be described, and then the configuration of the evaluation system 1 will be described. Note that the present invention is not limited to the hydraulic excavator 2 and can be applied to other construction machines such as a wheel loader, a hydraulic crane, and a bulltozer.

  The excavator 2 includes, for example, a lower traveling body 2A, an upper swing body 2B, and a work device provided at the front of the upper swing body 2B. The lower traveling body 2A includes, for example, tires 2A1 on the front, rear, left and right, respectively. A crawler may be used instead of the tire 2A1. The upper swing body 2B includes a cab 2B1 and the like. An operation device for operating the excavator 2 is provided in the cab 2B1.

  The working device includes a boom 2C, an arm 2D, a bucket 2E, a boom cylinder 2C1, an arm cylinder 2D1, and a bucket cylinder 2E1. The boom 2C is rotatably provided at the front part of the upper swing body 2B, and is rotated by expansion and contraction of the boom cylinder 2C1. The arm 2D is rotatably provided at the tip of the boom 2C and is rotated by the expansion and contraction of the arm cylinder 2D1. The bucket 2E is rotatably provided at the tip of the arm 2D, and is rotated by expansion and contraction of the bucket cylinder 2E1.

  The driving evaluation system 1 includes, for example, a diagnostic button 5, various sensors 6A, an operation data collection unit 6B, an operation data storage unit 6C, an operation condition setting unit 6D, a communication unit 7A, and a reference data update unit 7B. A reference data storage unit 7C, an evaluation determination unit 8, and an evaluation result display unit 9 are provided.

  The diagnostic button 5 as “transition instruction means” is an input device for transitioning to a mode for diagnosing driving operation. The diagnosis button 5 can be composed of a start button and an end button. Alternatively, the diagnosis can be configured to start when the diagnosis button 5 is operated once, and to be ended when operated again. Furthermore, the diagnostic button 5 can also be configured as an input device such as a touch panel.

  Various sensors 6 </ b> A as “detecting means” are provided in each part of the excavator 2 in order to acquire various states of the excavator 2. The various sensors 6A include, for example, a sensor that detects the state of the boom 2C, a sensor that detects the state of the arm 2D, a sensor that detects the state of the bucket 2E, and the relative turning of the upper swing body 2B and the lower traveling body 2A. A sensor for detecting the state of the vehicle, a sensor for detecting the engine state, a sensor for detecting the running state, a sensor for detecting the surrounding environment, and the like can be exemplified.

  For example, the necessary type of sensor 6A is determined according to the type of construction machine, the content of the driving operation 4 to be diagnosed, the state of the work site, and the like. In the embodiments described later, sensors (101 to 108 shown in FIG. 2) for detecting the state of the working device are mainly used.

  The operation data collection unit 6B as “operation data collection means” collects predetermined operation data based on signals output from the various sensors 6A. The predetermined operation data includes, for example, a pressure value when turning left and right, a pressure value when tilting the bucket 2E toward the soil removal side, a pressure value when tilting the bucket 2E toward the excavation side, and rotating the arm 2D and the boom 2C. The pressure value when moving, the pressure value when turning left and right, and the like can be mentioned. The operation data collection unit 6B samples the electrical signals output from the various sensors 6A at a predetermined cycle during the diagnosis mode period from the start of diagnosis to the end of diagnosis.

  The operation data collected by the operation data collection unit 6B is stored in the operation data storage unit 6C as “operation data storage means”. The operation condition setting unit 6D as “operation condition setting means” stores the operation condition at the time of diagnosis in association with the operation data.

  As an operation condition, the information regarding the working environment where the driving operation for diagnosis is performed can be mentioned, for example. More specifically, for example, the type of soil, weather, temperature, accumulated operating time of the aircraft, and the like can be mentioned. In the examples described later, the types of soil and the operation time of the aircraft will be described as examples.

  However, the present invention is not limited thereto, and for example, weather, air temperature, operator age and sex, operator operation experience time, and the like may be added to the operation conditions. For example, the value of a service meter that measures the cumulative operating time of the hydraulic excavator 2 is used as the cumulative operating time of the aircraft.

  The various sensors 6A, the operation data collection unit 6B, the operation data storage unit 6C, and the operation condition setting unit 6D are configured to acquire data from the hydraulic excavator 2. In contrast, the communication unit 7A, the reference data update unit 7B, and the reference data storage unit 7C described below are configured to acquire data from the outside of the hydraulic excavator 2.

  The communication unit 7A is connected to the management server 3 via the communication network CN1, and performs bidirectional communication with the management server 3. The management server 3 is a computer device for centrally managing the states of a plurality of construction machines. The management server 3 collects and manages information (operation time, fuel consumption, etc.) of each construction machine regularly or irregularly.

  The management server 3 of this embodiment further manages reference data. The reference data is data used for diagnosis of driving operation, and is data obtained from the results of driving by a plurality of skilled operators or operators who have performed recommended driving.

  The reference data update unit 7B as “reference data update unit” updates the reference data stored in the reference data storage unit 7C as “reference data storage unit” in a predetermined case. Examples of the predetermined case include a case where an update request is notified from the management server 3 and a case where the cumulative operating time of the excavator 2 exceeds a predetermined value.

  The evaluation determination unit 8 evaluates the driving operation 4 of the operator based on the driving data stored in the driving data storage unit 6C and the reference data stored in the reference data storage unit 7C. The evaluation determination unit 8 includes, for example, a feature amount data generation unit 8A as “diagnosis data generation unit”, a feature amount data storage unit 8B as “diagnosis data storage unit”, a comprehensive evaluation determination unit 8C, and an individual operation And an evaluation determination unit 8D. The comprehensive evaluation determination unit 8C and the individual operation evaluation determination unit 8D correspond to “determination means”.

  The feature amount data generation unit 8A generates feature amounts related to a plurality of preset items based on the operation data stored in the operation data storage unit 6C. For example, as will be described later with reference to FIG. 6, the feature amount data generation unit 8A calculates the feature amount based on the operation data collected during the diagnosis mode period.

  As the feature amount, as will be described later in the embodiment (see FIG. 5), for example, the number and time of operations for raising the boom 2C (hereinafter referred to as boom raising operation) and the operation for lowering the boom 2C (hereinafter referred to as boom) The number and time of the lowering operation), the number and time of the excavation operation of the arm 2D, the number and time of the earthing operation of the arm 2D, the time and number of the earthing operation of the bucket 2E, the number and time of the excavation operation of the bucket 2E, The number and time of the turning operation, the average value of the hydraulic pressure supplied to the boom cylinder 2C1, the average value of the hydraulic pressure supplied to the arm cylinder 2D1, and the like can be mentioned.

  Here, the feature value data generation unit 8A obtains feature values not only for a feature value related to a single operation such as a boom operation or an arm operation, but also for a combination of a plurality of operations. As a combination of a plurality of operations, for example, simultaneous operation of excavation operation by arm 2D and excavation operation by bucket 2E, simultaneous operation of lowering operation of boom 2C and excavation operation by arm 2D, operation of raising boom 2C and arm 2D The simultaneous operation with the excavation operation by, the simultaneous operation with the soil removal operation and the turning operation of the bucket 2E, etc. can be mentioned.

  A skilled operator performs a smooth and efficient operation by simultaneously executing a plurality of lever operations. Therefore, in the present embodiment, as one of the indexes for evaluating the driving operation, a feature amount when operating a plurality of levers at the same time is used.

  The feature amount data storage unit 8B stores feature amount data. The comprehensive evaluation determination unit 8C that can be expressed as "first evaluation means" or "total evaluation means" determines comprehensive evaluation related to the driving operation of the operator based on the feature amount data. The individual operation evaluation determination unit 8D that can be expressed as “second evaluation means” or “individual evaluation means” is based on the feature amount data, for example, whether the arm operation is appropriate or the boom operation is appropriate. Evaluation for each type of operation is determined.

  The evaluation result display unit 9 as “output means” displays a comprehensive evaluation and / or an individual operation evaluation on a display device or the like. Thus, the operator can objectively recognize his skill in driving.

  According to the present embodiment configured as described above, the operator can obtain an objective evaluation of his / her driving skill on the spot using the excavator 2 that is familiar to him / her, without going to a driving school or the like. , Improve usability. Hereinafter, this embodiment will be described in detail.

  Examples of the present invention will be described below. FIG. 2 is an explanatory diagram showing an overall outline of the driving evaluation system 10. First, the correspondence with FIG. 1 will be described. The driving evaluation system 10 is the driving evaluation system 1 in FIG. 1, the data collection controller 110 is the driving data collection unit 6B in FIG. 1, the various sensors 100 are the various sensors 6A in FIG. 1 to the evaluation determination unit 8 in FIG. 1, the input device 160 to the diagnosis button 5 and the operating condition setting unit 6D in FIG. 1, the display device 170 to the evaluation result display unit 9 in FIG. 1, and the communication controller 130 to The communication unit 7A in FIG. 1, the operation data storage unit 140 in the operation data storage unit 6C in FIG. 1, the reference data storage unit 150 in the reference data storage unit 7C in FIG. 1, and the management server 200 in FIG. Respectively corresponding to the management server 3. Further, the feature amount data generation unit 121 is in the feature amount data generation unit 8A in FIG. 1, the feature amount data storage unit 122 is in the feature amount data storage unit 8B in FIG. 1, and the comprehensive evaluation determination unit 123 is in FIG. The overall operation determination unit 8C and the individual operation evaluation determination unit 124 correspond to the individual operation evaluation determination unit 8D in FIG. The reference data update unit 7B shown in FIG. 1 will be described with reference to the flowchart shown in FIG.

  The driving evaluation system 10 includes, for example, a data collection controller 110, a driving evaluation controller 120, and a communication controller 130. Various sensors 100 are connected to the data collection controller 110. The various sensors 100 are a boom raising pressure sensor 101, an arm excavation pressure sensor 102, a bucket excavation pressure sensor 103, a bucket soil pressure sensor 104, a right turn pressure sensor 105, a left turn pressure sensor 106, a boom lowering pressure switch 107, A general term for the arm earth pressure switch 108. Each pressure sensor 101-106 can detect a pressure value from a pressure proportional control valve (PPC valve) for driving each actuator, for example.

  The boom raising pressure sensor 101 is a sensor that detects the pressure value of the boom cylinder 2C1 when raising the boom 2C. The arm excavation pressure sensor 102 is a sensor that detects the pressure value of the arm cylinder 2D1 when excavating the arm 2D. The bucket excavation pressure sensor 103 is a sensor that detects the pressure value of the bucket cylinder 2E1 when excavating the bucket 2E. The bucket discharge pressure sensor 104 is a sensor that detects the pressure value of the bucket cylinder 2E1 when the bucket 2E is discharged.

  The right turning pressure sensor 105 is a sensor that detects the pressure value of the turning motor when the upper turning body 2B is turned in the right direction. The left turning pressure sensor 106 is a sensor that detects the pressure value of the turning motor when the upper turning body 2B is turned leftward. The right direction and the left direction are directions viewed from the operator in the cab 2B1.

  The boom lowering pressure switch 107 is a switch that outputs an on / off signal when the boom 2C is lowered. The arm discharge pressure switch 108 is a switch that outputs an on / off signal when the arm 2D is discharged. The soil removal operation is an operation for discharging a load (such as earth and sand) in the bucket 2E to the outside of the bucket 2E. Excavation operation is operation for accommodating earth and sand etc. in bucket 2E. In addition, the kind of sensor is not restricted to the above-mentioned 101-108. For example, a configuration using an ambient temperature sensor, an oil temperature sensor, an engine speed sensor, a speed sensor, or the like may be used. A sensor that outputs an analog pressure value may be used instead of the pressure switches 107 and 108 that output an on / off signal.

  The data collection controller 110 receives signals from the various sensors 100 and stores these signals in the operation data storage unit 140 as operation data.

  The driving evaluation controller 120 diagnoses the skill of the driving operation of the operator based on the driving data stored in the driving data storage unit 140 and the reference data stored in the reference data storage unit 150, and outputs the result. .

  The driving evaluation controller 120 includes, for example, a feature amount data generation unit 121, a feature amount data storage unit 122, a comprehensive evaluation determination unit 123, and an individual operation evaluation determination unit 124. Details of the feature data generation and evaluation method will be described later.

  The communication controller 130 is a device for performing bidirectional communication with the management server 200 via the communication network CN1. The communication controller 130 transmits information regarding various states of the excavator 2 to the management server 200. The communication controller 130 stores the reference data received from the management server 200 in the reference data storage unit 150.

  The operation data storage unit 140, the reference data storage unit 150, and the feature amount data storage unit 122 may be configured as separate storage devices, or may be configured to use separate storage areas in one storage device. Good.

  FIG. 3 shows a configuration example of the reference data storage unit 210 provided in the management server 200. A part of the data stored in the reference data storage unit 210 of the management server 200 is transmitted to the excavator 2 and stored in the reference data storage unit 150.

  The management server 200 manages the reference data T10 for each model of each construction machine. The reference data T10 for one model is also distinguished by, for example, whether the cumulative operation time is less than thSMR or more than thSMR. Furthermore, the reference data T10 for each distinction of the cumulative operating time includes data for each soil quality at the site where the excavator 2 performs work.

  That is, even if the construction machines are the same model, the reference data is different if the ranks of the cumulative operation time are different. Further, even if the models are the same and the ranks of the cumulative operation time are the same, the reference data is different if the soil quality at the work site is different.

  In this way, the reference data is prepared in advance for each model, for each rank of each cumulative operation time, and for each soil quality, for example. Each reference data is generated based on a driving operation of an operator who is skilled in driving operation or an operator who has performed recommended driving (hereinafter also referred to as a skilled operator or an advanced operator).

  The reference data storage unit 210 can also include an evaluation management table T20. The evaluation management table T20 manages the operation evaluation of the operator who operates each construction machine for each construction machine under the management of the management server 200.

  The evaluation management table T20 manages, for example, the machine number C20, operator ID (operator identification code) C21, comprehensive evaluation C22, individual operation evaluation C23, and other C24 in association with each other.

  Airframe number C20 is a number for specifying each construction machine. The operator ID is information for identifying each operator. The comprehensive evaluation C22 indicates a comprehensive evaluation value of each operator. The individual operation evaluation C23 indicates an evaluation value of each individual operation of each operator. In addition, in C24, for example, the sex of the operator, the age, the years of operation experience of the construction machine, the number of attendance in the diagnosis mode, and the like can be stored.

  FIG. 4 shows an example of a screen when the diagnosis mode is selected and operating conditions are input. The screen shown in FIG. 4 is displayed on the display device 170. In the example illustrated in FIG. 4, the input device 160 is configured as a touch panel.

  FIG. 4A shows a menu screen 171 for executing the diagnosis mode. The menu screen 171 includes, for example, a display unit 171A for displaying a message explaining the diagnosis mode, a display unit 171B for notifying the content of a predetermined operation (predetermined driving operation) for diagnosing driving, It can comprise including the buttons 161-164.

  The diagnosis start button 161 is a button for starting a diagnosis mode. The diagnosis end button 162 is a button for ending the diagnosis mode. The condition input button 163 is a button for switching to a screen for inputting operation conditions in the diagnosis mode. The cancel button 164 is a button for canceling the display of the diagnostic menu.

  FIG. 4B shows a screen 172 in transition to the diagnostic mode. When the operator operates the diagnosis start button 161, the mode shifts to the diagnosis mode. The operator operates the excavator 2 in accordance with the contents displayed on the display unit 171B.

  The screen 172 being diagnosed includes, for example, a display unit 172A for displaying elapsed time, a display unit 172B for displaying evaluation, a display unit 172C for displaying advice, and buttons 161 to 164. be able to.

  The display unit 172A displays an elapsed time after shifting to the diagnosis mode or / and a scheduled end time of the diagnosis mode. On the display unit 172B, for example, the diagnosis result is displayed in stages such as “excellent, good, acceptable” or numerical values such as “100 points, 80 points, 60 points”. As will be described later, in this embodiment, after the diagnosis mode is completed, comprehensive evaluation and individual operation evaluation are displayed. However, the present invention is not limited to this, and the evaluation may be displayed in real time during execution of the diagnostic mode.

  The display unit 172C displays advice according to the evaluation value (determination result). As a result, the operator can understand what points should be operated with care, which is useful for improving the driving skill.

  Note that the screen 172 during diagnosis shown in FIG. 4B can also be used as a screen after completion of diagnosis.

  FIG. 4C shows a screen 173 for inputting operating conditions. The operating condition input screen 173 includes, for example, a plurality of input units 173A to 173C and a plurality of buttons 165 and 166.

  For example, the operator's name is input to the input unit 173A. The input unit 173B inputs, for example, the soil quality at the site where the diagnosis work is performed. The weather at the time of diagnosis is input to the input unit 173C.

  FIG. 5 shows a state in which feature quantities of a plurality of items set in advance are obtained based on signals output from the various sensors 100 (that is, the sensors 101 to 108). In this embodiment, for example, feature amounts are obtained for 26 items.

  The 26 items are roughly classified into, for example, a group related to each individual operation and a group related to a composite operation. The group of individual operations is further divided into a group related to the boom, a group related to the arm, a group related to the bucket, and a group related to turning.

  The group related to the boom includes, for example, items of an average boom pressure value, a boom raising operation time, a boom raising operation number, a boom lowering operation time, and a boom lowering operation number. The group relating to the arm includes, for example, each item of an arm pressure average value, an arm excavation operation time, an arm excavation operation count, an arm excavation operation count, and an arm excavation operation time. The group related to the bucket includes, for example, items of a bucket discharge pressure average value, a bucket discharge operation time, a bucket discharge operation count, a bucket excavation pressure average value, a bucket excavation operation count, and a bucket excavation operation time. The group related to turning includes, for example, items of the number of turning operations and the turning operation time.

  The feature amount of each item belonging to the group related to the boom is generated based on signals from the sensors 101 and 107 related to the boom. The feature amount of each item belonging to the group related to the arm is generated based on signals from the sensors 102 and 108 related to the arm. The feature amount of each item belonging to the group related to the bucket is generated based on signals from the sensors 103 and 104 related to the bucket. The feature amount of each item belonging to the group related to turning is generated based on signals from the sensors 105 and 106 related to turning.

  The group related to the composite operation includes items obtained by combining a plurality of different operations. For example, the number of simultaneous operations of arm excavation and bucket excavation, the simultaneous operation time of arm excavation and bucket excavation, the number of simultaneous operations of boom raising and arm excavation, the simultaneous operation time of boom raising and arm excavation, The number of simultaneous operations of lowering and arm excavation, the simultaneous operation time of boom lowering and arm excavation, the number of simultaneous operations of bucket soiling and turning, the simultaneous operation time of bucket soiling and turning, and the like can be mentioned.

  That is, items relating to a combination of arm operation and bucket operation, a combination of boom operation and arm operation, and a combination of bucket operation and turning operation are prepared. The skilled operator operates smoothly and rationally by executing a plurality of types of operations simultaneously. In this embodiment, paying attention to the technical viewpoint that a skilled operator performs a complex operation skillfully, items relating to the complex operation are employed.

  FIG. 6 shows a state in which feature quantities are generated based on operation data. For convenience of explanation, FIG. 6 shows only a part of the operation data and the feature amount. The upper side of FIG. 6 shows the operation data, and the lower side of FIG. 6 shows a table T30 for managing the feature amount of each item.

  In the diagnosis mode, a predetermined work (driving operation) determined in advance is repeatedly executed a predetermined number of times. For example, the operator repeats a predetermined operation such as excavating the ground, turning 90 degrees right or left, and discharging the load in the bucket about 10 times. During a period in which the operator repeats a predetermined driving operation a predetermined number of times, the data collection controller 110 acquires signals from the various sensors 100 and stores them in the driving data storage unit 140.

  When the operator finishes executing the predetermined driving operation a predetermined number of times, the operator instructs the driving evaluation controller 120 that the diagnosis mode has ended. The end of the diagnosis mode is notified from the operation evaluation controller 120 to the data collection controller 110. As a result, the data collection controller 110 stops collecting operation data for use in operation diagnosis.

  Therefore, as shown in the upper side of FIG. 6, the operation data storage unit 140 is based on the signals of the sensors 101 to 108 in the period from the time T0 when the diagnosis mode is started to the time Te when the diagnosis mode is ended. Data is stored.

  The feature amount data generation unit 121 calculates a feature amount based on data in the target period from the reference time to the first predetermined time t0. For example, the number of boom operations is detected from the boom raising pressure, and the number is registered in the table T30. The first predetermined time t0 can be set to about 60 seconds, for example.

  The feature amount data generation unit 121 shifts the reference time backward by the second predetermined time t1, and calculates the feature amount based on the data in the new target period from the new reference time to the first predetermined time t0. The feature amount data generation unit 121 stores the calculated feature amount in a location corresponding to the target period in the table T30. The second predetermined time t1 can be set to about 10 seconds, for example.

  Specifically, the target period from the reference time T0 to the time t0, the target period from the reference time T1 (T1 = T0 + t1) until the time t0 elapses, and the time t0 from the reference time T2 (T2 = T1 + t1) elapses. A plurality of target periods are set as shown in FIG.

  The feature amount data generation unit 121 calculates 26 feature amounts based on the operation data within each target period, and stores them in predetermined positions in the table T30. The table T30 manages the feature amount of each item for each target period.

  FIG. 7 is a flowchart showing a driving diagnosis process according to this embodiment. Each flowchart shown below shows an outline of each process to the extent necessary for understanding and implementing the present invention, and may differ from an actual computer program. A person skilled in the art will be able to change the order of the illustrated steps, change or delete some steps, add new steps, and the like.

  The driving evaluation controller 120 causes the display device 170 to display a driving diagnosis menu 171 as shown in FIG. 4A based on an instruction from the operator (S10).

  As described with reference to FIG. 4C, the operator can set the driving conditions in advance when receiving the driving diagnosis. In this embodiment, the system waits until the operator inputs operating conditions (S11). Instead of this, a configuration may be adopted in which a diagnosis can be received for a driving operation without setting driving conditions.

  When the operation condition is input from the operator (S11: YES), the operation evaluation controller 120 waits until the operator instructs the start of diagnosis (S12). When the operator operates the diagnosis start button 161 (S12: YES), the operation evaluation controller 120 instructs the data collection controller 110 to start data collection.

  The data collection controller 110 collects data relating to the 26 measurement target items, and stores them in the operation data storage unit 140 (S13). The driving evaluation controller 120 monitors whether or not the diagnosis end button 162 has been operated (S14). When the operator operates the diagnosis end button 162 (S14: YES), the operation evaluation controller 120 instructs the data collection controller 110 to stop data collection.

  Then, the driving evaluation controller 120 performs a comprehensive evaluation based on the collected driving data (S15), and further executes an individual operation evaluation (S16). The driving evaluation controller 120 can also transmit the comprehensive evaluation and the individual operation evaluation to the management server 200 via the communication controller 130 and store them in the management server 200 (S17).

  FIG. 8 is a flowchart of the comprehensive evaluation process indicated by S15 in FIG. The driving evaluation controller 120 (hereinafter also referred to as the controller 120) acquires the reference time T0 from the data stored in the driving data storage unit 140 (S20).

  The controller 120 calculates each of the 26 feature amounts set in advance using the data included in the target period of the first predetermined time t0 from the reference time T0 (S21). The controller 120 calculates the distance between the pattern (also referred to as an input pattern) obtained from the calculated feature amount and the distribution centroid of the reference data (S22).

  S22 will be described in detail. In the present embodiment, the distribution of feature values of skilled operators in a 26-dimensional space composed of 26 feature values is obtained in advance based on the results of driving operations by a sufficient number of skilled operators. That is, the feature amounts obtained from the model operations of a large number of skilled operators are mapped to the 26-dimensional space, and the feature amount distribution of the skilled operators is obtained. Then, the center of gravity of the feature amount distribution of the skilled operator is calculated, and the distance between the center of gravity and the input pattern is calculated. The input pattern is 26 feature values in a certain target period obtained based on the operation of the operator to be diagnosed.

  Therefore, simply speaking, in S22, it is calculated how close the operation of the operator receiving the diagnosis is to the operation of the skilled operator. In general, it can be determined that the driving skill of the operator is immature as the distance is longer, and the driving skill of the operator is better as the distance is shorter.

  The controller 120 stores the distance calculated in S22 (S23), and increases the reference time by the second predetermined time t1 (S24). That is, as described in FIG. 6, the controller 120 advances the reference time T (starting period) of the target period by the time t1 (T = T + t1).

  The controller 120 determines whether or not the target period in which the reference time is advanced by t1 is within the range where the operation data is recorded (S25). If the reference time at which the target period starts is T, the end time of the target period is T + t0.

  The controller 120 determines whether or not the end time T + t0 exceeds the operation data recording end time Te (S25). That is, the controller 120 determines whether or not all the operation data included in the target period exists (S25).

  When all operation data within the target period exists (S25: NO), the controller 120 obtains a pattern to be input to the 26-dimensional space from each feature amount for the target period (S21), and the input pattern and the expert's A distance from the center of gravity of the feature amount distribution is calculated (S22). The calculated distance is stored (S23), and the target period is shifted by time t1 (S24).

  In this way, for each operation data measured during the diagnosis mode, the distance is calculated and stored while shifting the reference time by t1. When the calculation of the distance is completed for the last target period (the period from time Tn to time Te in FIG. 6) (S25: YES), the controller 120 calculates the average value of each distance for each target period, The fitness is calculated based on the average value (S26). The controller 120 causes the display device 170 to display the calculated fitness (S27). The degree of fitness is an index indicating how much the driving skill of the operator to be diagnosed matches the driving skill of the skilled operator, and the higher the numerical value, the better.

  FIG. 9 shows an example in which the distance between the gravity center of the feature quantity distribution of the skilled operator and the input pattern is obtained as a Mahalanobis' (generalized) distance. As shown in FIG. 9A, in this embodiment, reference data T41 to T44 of skilled operators are prepared for each soil quality such as “soil” and “sand”, for example. Each of the reference data T41 to T44 includes an average value, standard deviation, and correlation matrix of the feature amount pattern that are calculated in advance.

  As shown in FIG. 9B, the pattern x input to the 26-dimensional feature amount space is determined by 26 feature amounts (x1, x2,... X26). As shown in FIG. 9C, by using the average value m and the standard deviation σ, each feature amount is standardized using the formula ui = (xi−mi) / σi (standardization coefficient). Thereby, the standardized input pattern u is obtained.

  The Mahalanobis generalized distance D ^ 2 is obtained using the mathematical formula shown in FIG. The square of D is used in order to obtain the absolute value of the distance in order to simplify subsequent calculations.

  As shown in FIG. 9 (e), the controller 120 includes a fitness determination table T50. The horizontal axis of this table T50 is the distance D ^ 2 calculated in FIG. 9D, and the vertical axis of the table T50 is the fitness (%).

  Two threshold values are set for the distance. The first distance threshold thd1 is a value for discriminating between skilled operators and others. That is, when the distance between the center of gravity of the feature amount distribution of the skilled operator and the input pattern is smaller than the first distance threshold thd1, the fitness level of the operator is high (for example, the fitness level is 100% or higher), Can be considered.

  The second distance threshold thd2 is a value used to detect immature operators who are not diagnosed. In other words, when the distance between the gravity center of the feature amount distribution of the skilled operator and the input pattern is equal to or greater than the second distance threshold thd2, the operator's fitness is low (for example, less than 20%), and the operator is regarded as an immature operator. be able to.

  Two threshold values 100% (also referred to as an upper limit value thH) and 20% (also referred to as a lower limit value thL) are set for the degree of conformity on the vertical axis. If the distance between the center of gravity of the feature amount distribution of the skilled operator and the input pattern exists between the first distance thd1 and the second distance thd2, it is determined that the driving skill of the operator is “good” For example, the screen of the display device 170 displays “Your driving quality is 40%”. The distance calculation method shown in FIG. 9 is one example, and the present invention is not limited to the configuration shown in FIG. The first distance thd1 and the second distance thd2 may be referred to as the first distance threshold thd1 and the second distance threshold thd2.

FIG. 10 is a flowchart of the individual operation evaluation process indicated by S16 in FIG.
The controller 120 determines whether or not the comprehensive evaluation value obtained in S15 of FIG. 7 is equal to or smaller than the second fitness threshold thL (S30). As a result, when the distance is equal to or smaller than the second distance thd2 (S30: YES), the controller 120 identifies the individual operation that has the most adverse effect on the comprehensive evaluation of the operator (S31 to S34).

  S31 to S34 will be described with reference to the explanatory diagram of FIG. First, as shown in the table T60 of FIG. 11A, the controller 120 calculates an average value for each of the 26 feature amounts (S31).

  Next, as shown in a table T61 in FIG. 11B, the controller 120 sets standardization variables of input data (each of the 26 feature values input) using the average value and standard deviation of the skilled operators. Calculate (S32).

  As shown in the table T62 in FIG. 11C, the controller 120 detects a feature quantity whose standardization variable is equal to or larger than a predetermined threshold and whose absolute value is the maximum (S33). In the example illustrated in FIG. 11C, the predetermined threshold is “3”, and in this case, “number of arm operations” corresponds. The controller 120 causes the display device 170 to display the advice corresponding to the sign (either plus or minus) of the standardization variable of the feature amount detected in S33 (S34).

  FIG. 12 shows the message management table T70. The message management table T70 manages the advice displayed on the display device 170 for each feature amount. The table T70 manages, for example, a message number C70, a target item C71, a standardized variable code C72, and a message C73 in association with each other.

  The message number C70 is information for specifying each message. The target item C71 is information for specifying each feature amount. The standardized variable code C72 is information for identifying whether the standardized variable obtained from the feature quantity has a plus sign or a minus sign. The message C73 is data for giving appropriate advice to the operator according to each sign of each feature value. The advice may be displayed as text or may be output as speech by a speech synthesizer.

  The closer the standardization variable is to 0, the closer the driving skill of the operator who received the diagnosis is to that of the skilled operator, and it can be determined that the operator is an excellent operator. As the standardization variable increases in the positive direction, the characteristic amount is larger than that of the skilled operator. Conversely, as the standardization variable increases in the negative direction, the characteristic amount is smaller than that of the skilled operator.

  Accordingly, in the example shown in FIG. 11C, since the value of the standardized variable for the number of arm operations is “−5.7”, the number of arm operations for the operator may be less than the number of arm operations for the skilled operator. Recognize. Therefore, the controller 120 can advise to intentionally increase the number of arm operations. Thereby, the operator can grasp the problem of his / her driving skill and can improve the driving skill by subsequent practice.

  FIG. 13 is a flowchart showing a process for updating the reference data stored in the reference data storage unit 150. The controller 120 determines whether or not the communication controller 130 has received an update notification from the management server 200 (S40). The management server 200 transmits an update notification to each construction machine, for example, when updating the reference data stored in each construction machine.

  When the update notification is received from the management server 200 (S40: YES), the controller 120 receives the reference data from the management server 200 and stores the received reference data in the reference data storage unit 150 (S41). When the update notification from the management server 200 has not been received (S40: NO), S41 is skipped and the process proceeds to S42.

  The controller 120 determines whether or not the cumulative operating time SMR of the excavator 2 has reached a preset threshold thSMR (S42). When the cumulative operating time SMR of the hydraulic excavator has reached the threshold value thSMR (S42: YES), the controller 120 changes the reference data used for driving evaluation to reference data equal to or greater than the threshold value thSMR (S43).

  As described with reference to FIG. 3, in this embodiment, the cumulative operating time of the construction machine is divided into a plurality of stages of less than thSMR and more than thSMR, and reference data is prepared for each stage. This is because when the operation time is lengthened, the secular change or the like may affect the driving operation.

  Therefore, the controller 120 updates the reference data stored in the reference data storage unit 150 to reference data for thSMR or more (S43). The controller 120 acquires reference data for thSMR or more from the management server 200 and stores it in the reference data storage unit 150 (S43).

  Alternatively, the reference data storage unit 150 may store both the reference data for less than thSMR and the reference data for more than thSMR from the beginning, and switch according to the cumulative operating time of the excavator 2. However, in this case, it is necessary to increase the storage capacity of the reference data storage unit 150.

  According to this embodiment configured as described above, the operator can use his or her familiar hydraulic excavator 2 on the spot without going to a driving school or inviting an instructor to the work site. Objective evaluation of skills can be obtained. Thus, the operator can easily diagnose his / her own driving skill.

  In this embodiment, for example, an operator who prepares reference data according to a work environment such as soil quality and receives a diagnosis can set the work environment when the diagnosis mode is executed as an operation condition. Therefore, driving can be diagnosed according to different working environments for each work site, and more appropriate evaluation can be obtained.

  In the present embodiment, since a predetermined driving operation set in advance is executed in the diagnosis mode, it can be compared with the driving operation of a skilled operator, limited to the predetermined driving operation. Accordingly, the operation of the operator can be evaluated relatively easily.

  In this embodiment, first, comprehensive evaluation is performed, and then individual operations are evaluated. Therefore, the operator can know the skill of each operation.

  In the present embodiment, when the comprehensive evaluation is less than or equal to a predetermined value, the individual operation that has the greatest influence on the comprehensive evaluation is specified, and advice for improving the individual operation is given to the operator. Therefore, the operator can improve the driving skill by practicing while being aware of the specified individual operation.

  In the present embodiment, the reference data stored in the reference data storage unit 150 can be updated. Therefore, the operation of the operator can be evaluated using the latest reference data, and the diagnostic accuracy can be improved. The more the data relating to the driving operations of more skilled operators is accumulated, the more accurately the characteristic amount distribution of the skilled operators can be obtained.

  In this embodiment, since the reference data is switched and used according to the accumulated operation time, it is possible to perform an appropriate operation evaluation corresponding to the secular change of the hydraulic excavator 2.

  A second embodiment will be described with reference to FIG. In the present embodiment, the management server 200 collects data related to driving operations of skilled operators from each work site, and can automatically update reference data in the management server 200.

  FIG. 14 is a flowchart showing the reference data registration process. The operator displays a diagnostic menu (S50). An operator can input an operator ID or the like via the input device 160. Based on the entered operator ID or the like, it is determined whether or not the operator is a skilled operator who has been previously certified (S51).

  For example, the management server 200 manages operator IDs of skilled operators. The operator ID input at each work site is transmitted to the management server 200 via the communication controller 130 and the communication network CN1. The management server 200 compares the received operator ID with the operator ID of the skilled operator, and determines whether or not the operator is an expert operator (S51).

  If the operator is not an expert operator (S51: NO), the normal driving diagnosis process described in FIG. 7 is executed (S52). Although omitted in FIG. 14, when the operator ID is input, operating conditions (work environment conditions) such as soil quality are input.

  When the diagnosis start button 161 is operated (S53: YES), the data collection controller 110 collects data from the various sensors 100 and stores the collected data in the operation data storage unit 140 (S54).

  When the diagnosis end button 162 is operated (S55: YES), the driving evaluation controller 120 performs the comprehensive evaluation described above (S56). When the comprehensive evaluation value is equal to or greater than a predetermined value set in advance (S57: YES), the operation data accumulated in S54, the feature amount data calculated in S56, and the like are transmitted to the management server 200 (S58). The management server 200 updates the reference data stored in the reference data storage unit 210 using the received data.

  Configuring this embodiment like this also achieves the same effects as the first embodiment. Furthermore, in the present embodiment, by having a skilled operator at each work site execute the diagnostic mode, the reference data managed centrally by the management server 200 is automatically updated, thereby improving the accuracy of the reference data. be able to.

  The present embodiment is, for example, “of the reference data storage means (210) provided in the management device by transmitting and storing the operation data or / and the diagnosis data to the management device (200). The construction machine operation evaluation system further includes reference data registration means (S50 to S58) for updating the stored content. "

  In addition, this invention is not limited to the Example mentioned above. A person skilled in the art can make various additions and changes within the scope of the present invention. For example, although the soil environment has been mainly described as the work environment, the present invention is not limited to this, and for example, a configuration in which environmental conditions such as ambient temperature and oil temperature are considered may be used. Alternatively, the reference data may be updated by storing the reference data in a storage medium and connecting the storage medium to the operation evaluation controller. Furthermore, the result of the operator's operation evaluation may be stored in a storage medium, and the storage medium may be configured to be portable by the operator. In this case, the operator can make efforts to improve the driving skill using the past diagnosis result even at the work site of the transfer destination.

Explanatory drawing which shows the whole outline | summary of a present Example. Explanatory drawing which shows the whole structure of a driving | operation evaluation system. Explanatory drawing which shows the reference | standard data storage part provided in a management server. Explanatory drawing which shows screen examples, such as diagnostic mode. Explanatory drawing which shows the relationship between the signal of each sensor, and each feature-value. Explanatory drawing which shows a mode that each feature-value is calculated | required based on driving | operation data. The flowchart of a driving | operation diagnosis process. The flowchart of comprehensive evaluation processing. Explanatory drawing which shows the method for calculating | requiring a correlation with the data of a skilled operator. The flowchart of an individual operation evaluation process. Explanatory drawing which shows the mode for detecting the individual operation which should be improved most. Explanatory drawing of a message management table. The flowchart of a reference | standard data update process. The flowchart of the reference | standard data registration process which concerns on 2nd Example.

Explanation of symbols

  1: driving evaluation system, 2: hydraulic excavator, 2A: lower traveling body, 2A1: tire, 2B: upper turning body, 2B1: cab, 2C: boom, 2C1: boom cylinder, 2D: arm, 2D1: arm cylinder, 2E: Bucket, 2E1: Bucket cylinder, 3 management server, 4: Predetermined operation, 5: Diagnosis button, 6A: Various sensors, 6B: Operation data collection unit, 6C: Operation data storage unit, 6D: Operation condition setting unit 7A: communication unit, 7B: reference data update unit, 7C: reference data storage unit, 8: evaluation determination unit, 8A: feature amount data generation unit, 8B: feature amount data storage unit, 8C: comprehensive evaluation determination unit, 8D : Individual operation evaluation determination unit, 9: evaluation result display unit, 10: driving evaluation system, 100: various sensors, 101: boom raising pressure sensor, 102: arm excavation pressure sensor, 03: Bucket excavation pressure sensor, 104: Bucket discharge pressure sensor, 105: Right turn pressure sensor, 106: Left turn pressure sensor, 107: Boom lowering pressure switch, 108: Arm discharge pressure switch, 110: For data collection Controller: 120: Controller for driving evaluation, 121: Feature amount data generating unit, 122: Feature amount data storage unit, 123: Comprehensive evaluation determining unit, 124: Individual operation evaluation determining unit, 130: Controller for communication, 140: Driving data Storage unit, 150: reference data storage unit, 160: input device, 161: diagnosis start button, 162: diagnosis end button, 163: condition input button, 164: cancel button, 170 display device, 171: diagnosis menu screen, 172: Screen during or after diagnosis, 173: Operation condition input screen, 200: Management support Ba, 210: reference data storage unit.

Claims (8)

An operation evaluation system for evaluating the operation of the construction machine (2),
A transition instruction means (5, 160) for shifting to the diagnosis mode;
Operation data collection means (6B, 110) for collecting predetermined operation data based on signals from a plurality of detection means (6A, 100) provided in the construction machine when the diagnosis mode is entered;
Operation data storage means (6C, 140) for storing the predetermined operation data collected by the operation data collection means;
Diagnostic data generation means (8A, 121) for generating diagnostic data based on the predetermined operation data stored in the operation data storage means;
Diagnostic data storage means (8B, 122) for storing the diagnostic data generated by the diagnostic data generation means;
Reference data storage means (7C, 150) for storing reference data for comparison with the diagnostic data;
Determination means (8C, 8D, 123, 124) for determining the result of operation in the diagnostic mode by comparing the diagnostic data and the reference data;
Output means (9, 170) for outputting the determination result by the determination means;
A construction machine operation evaluation system comprising: In the diagnostic mode, a predetermined driving operation set in advance by the operator of the construction machine is repeatedly executed a predetermined number of times.
The operation condition setting means (6D, 163) for causing the operation data storage means to store an operation condition when the predetermined operation is performed in association with the predetermined operation data. Construction machine operation evaluation system.   The operation evaluation of the construction machine according to claim 2, wherein the determination unit determines an overall evaluation for the whole of the predetermined driving operation, and can also determine an individual operation evaluation regarding each part of the predetermined driving operation. system.   The determination unit detects a predetermined individual operation evaluation that should be improved most among the individual operation evaluations included in the comprehensive evaluation when the comprehensive evaluation is equal to or less than a predetermined value, and the detected predetermined individual operation is detected. The construction machine operation evaluation system according to claim 3, wherein the evaluation is output via the output means.   The operation evaluation system for a construction machine according to any one of claims 1 to 4, further comprising reference data update means (7B, S40 to S43) for updating reference data stored in the reference data storage means.   6. The construction machine operation evaluation system according to claim 5, wherein the reference data update means updates the reference data in accordance with an operation time of the construction machine.   The operation evaluation system for a construction machine according to any one of claims 1 to 6, wherein the diagnostic data is generated as a feature amount related to each of a plurality of preset items. An operation evaluation method for evaluating the operation of the construction machine (2),
Displaying a menu for shifting to the diagnostic mode (S10);
A step (S11) of acquiring operating conditions for performing a predetermined driving operation;
A step (S12) of determining whether or not to shift to the diagnosis mode;
If the diagnostic mode is entered, a step (S13) of collecting and storing predetermined operation data from the construction machine;
Determining whether to end the diagnostic mode (S14);
When the diagnosis mode ends, a step of determining a driving result related to the predetermined driving operation by comparing diagnostic data generated based on the predetermined driving data with reference data prepared in advance ( S15, S16)
A construction machine operation evaluation method that executes each of the above.

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