WO2012026732A2 - 건설기계의 제어 장치 및 제어방법 - Google Patents
건설기계의 제어 장치 및 제어방법 Download PDFInfo
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- WO2012026732A2 WO2012026732A2 PCT/KR2011/006203 KR2011006203W WO2012026732A2 WO 2012026732 A2 WO2012026732 A2 WO 2012026732A2 KR 2011006203 W KR2011006203 W KR 2011006203W WO 2012026732 A2 WO2012026732 A2 WO 2012026732A2
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- Prior art keywords
- job
- work
- pattern
- frequency
- probability model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
- E02F9/26—Indicating devices
- E02F9/264—Sensors and their calibration for indicating the position of the work tool
- E02F9/265—Sensors and their calibration for indicating the position of the work tool with follow-up actions (e.g. control signals sent to actuate the work tool)
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F3/00—Dredgers; Soil-shifting machines
- E02F3/04—Dredgers; Soil-shifting machines mechanically-driven
- E02F3/28—Dredgers; Soil-shifting machines mechanically-driven with digging tools mounted on a dipper- or bucket-arm, i.e. there is either one arm or a pair of arms, e.g. dippers, buckets
- E02F3/36—Component parts
- E02F3/42—Drives for dippers, buckets, dipper-arms or bucket-arms
- E02F3/43—Control of dipper or bucket position; Control of sequence of drive operations
- E02F3/435—Control of dipper or bucket position; Control of sequence of drive operations for dipper-arms, backhoes or the like
- E02F3/438—Memorising movements for repetition, e.g. play-back capability
Definitions
- the present invention relates to a control device and a control method for a construction machine. More specifically, the present invention relates to a control device and a control method for a construction machine that can improve fuel efficiency by controlling each work device of a construction machine such as an excavator according to a work pattern.
- Construction machinery is equipment used for excavation and transportation of soil in construction sites such as civil engineering sites.
- Excavator which is a kind of construction machine, is equipped with a traveling body for moving the equipment, an upper swing body mounted on the traveling body and rotating 360 degrees, and a front work device for work installed in front of the equipment such as boom, arm, bucket. Include.
- Excavators are categorized into crawler crawler excavators and tire wheel excavators according to the driving method of the traveling body. Since crawler excavators are more stable and productive than wheeled excavators, they are widely used on each job site, ranging from 1 tonne of machine weight to over 100 tonnes. On the other hand, the wheel excavator is less stable in operation compared to the crawler excavator due to the tire support method, it is mainly used in workplaces that require frequent work and movement because the road can be moved to the workplace without a transport trailer.
- Such a construction machine such as an excavator performs an excavation work for digging a ground at a civil engineering site, a lifting work for carrying a heavy load, a crushing work for dismantling a building, and a picking work for arranging the ground.
- the construction machine cannot actively cope with each of the various tasks, and thus cannot maintain an optimal working state corresponding to each task having different work characteristics. As a result, the use of unnecessary fuel is increased, resulting in a problem of difficult fuel economy.
- the present invention has been made to solve the above problems, to provide a control device and control method for a construction machine that can be optimized for the characteristics of the work in progress to improve the fuel economy of the construction machine.
- the control device of a construction machine is a work pattern recognition device for analyzing the work performed by the construction machine and the construction machine to match the work pattern analyzed from the work pattern recognition device And a driving control device for controlling each equipment of the work pattern recognition device, wherein the work pattern recognition device receives a driver's operation signal and accumulates a data accumulating unit and a work pattern for determining a current work pattern by analyzing data of the accumulated operation signal. Pattern result for outputting the job determined from the probability model analysis unit and the work pattern analysis unit and the probability analysis model unit to determine the data that is not discriminated from the analysis unit and the operation pattern analysis unit to the drive control device And an output unit.
- the work pattern analyzer may be determined by the first workgroup pattern analyzer and the first workgroup pattern analyzer to determine whether the work pattern corresponds to a pattern of at least one job included in the preset first workgroup. And a second workgroup pattern analyzer configured to determine whether the failing work patterns correspond to at least one work pattern included in the preset second workgroup.
- the probability model analyzer analyzes a work frequency of the accumulated data and determines the current work by applying the analyzed work frequency to a probability model according to each work.
- the method of controlling a work pattern includes receiving and accumulating an operation signal of each device from a joystick through a data accumulator, and analyzing a data of the accumulated control signal through a work pattern analyzer to determine a current work pattern. And determining, by using a probability model, data that is not discriminated from the work pattern analyzer through a probability model analyzer, and a job determined from the work pattern analyzer and a probability analysis model unit through a pattern result output unit. It characterized in that it comprises the step of outputting to the drive control device for controlling the.
- the construction machine can be controlled to correspond to the pattern of the work that the construction machine is currently performing. Accordingly, it is possible to reduce the unnecessary consumption of the construction machinery fuel, thereby increasing the fuel economy of the construction machinery.
- the present invention can distinguish the difficult working pattern using a probability model to maximize the fuel economy reduction effect.
- the present invention may provide the driver with information on the current working pattern by outputting the analyzed working pattern on the screen display unit.
- FIG. 1 is a block diagram showing a simplified configuration of a construction machine control apparatus according to an embodiment of the present invention.
- FIG. 2 is a flowchart illustrating a control method of a construction machine according to an embodiment of the present invention.
- FIG. 3 is a flowchart illustrating a control method of a probability model analyzer illustrated in FIG. 1.
- FIG. 4 is a flowchart of determining a work pattern by applying a probability model for an excavation work of a construction machine according to an embodiment of the present invention.
- FIG. 5 is a flow chart for determining a work pattern by applying a probability model for the picking work of a construction machine according to an embodiment of the present invention.
- FIG. 6 is a flowchart of determining a work pattern by applying a probabilistic model for lifting work of a construction machine according to an exemplary embodiment of the present invention.
- Figure 1 shows the configuration of a control device for a construction machine according to one sinking of the present invention.
- the above-mentioned construction machine has various types of equipment, this embodiment will be described by taking an excavator as an example.
- the control device of the construction machine analyzes the work that the excavator is currently performing and controls each work device to match the work pattern.
- the control device includes a work pattern recognition device 110, a drive control device 126, and a screen display unit 124.
- the work pattern control device may be used not only for excavators, but also for construction machines such as forklifts and cranes to control each equipment according to the work patterns.
- the work pattern recognition apparatus analyzes the data accumulation unit 112 that receives and accumulates the operation signal of each work device from the joystick 100 and the work pattern analysis for determining the current work pattern by analyzing the data of the accumulated operation signal.
- Probability model analysis unit 122 for determining the data 114, the work pattern is not determined by the work pattern analysis unit 114 using the probability model, the work pattern analysis unit 114 and the probability analysis model unit 114
- a pattern result output unit 120 for outputting the job determined from the 122 to the drive control device 126 and the screen display unit 124.
- the data accumulator 112 accumulates the operation signal, but the present invention is not limited thereto.
- the data accumulator 112 may be a signal output from the drive control device 126 to each work device, or may be a signal output from a sensor for measuring the actual driving amount of each work device. 112) is possible.
- the work pattern analysis unit 114 analyzes the first work group pattern analysis unit 116 that determines the accumulated operation signal as the first work group, and the second work group pattern analysis that determines the accumulated operation signal as the second work group. Part 118 is included.
- the first work group may comprise a first work, and the first work may be a lifting work for carrying soil.
- the second working group may include second and third operations, the second operation may be an excavation operation for digging the ground, and the third operation may be a selection operation for arranging the ground.
- the first to third operations are not limited to the lifting operation, the excavation operation, and the selection operation, but will be described by way of example.
- the first work group pattern analysis unit 116 analyzes the data of the accumulated operation signal and determines the first work (lifting job).
- the first workgroup pattern analyzer 116 outputs the data determined as the first job (the lifting job) to the pattern result output unit 120, and outputs data that is not determined as the first job (the lifting job) to the second job. Output to the group pattern analysis unit 118.
- the second workgroup pattern analyzer 118 analyzes the data output from the first workgroup pattern analyzer 116 and determines the second workgroup (excavation work) or the third work (selection work).
- the second work group pattern analysis unit 118 outputs the data determined as the second operation (excavation operation) or the third operation (selection operation) to the pattern result output unit 120, and the second or third operation (excavation).
- the data that is not determined by the job, the selection job) is output to the probability model analyzer 122.
- the probability model analyzer 122 determines the data that has not been analyzed by the work pattern analyzer 114 as the first to fourth tasks (the lifting operation, the excavation operation, the selection operation, the trenching operation) using the probability model.
- the pattern result output unit 120 outputs the result.
- the fourth job may be a trenching job in which the second job (excavation job) and the third job (selection job) are mixed to be difficult to distinguish between the second job or the third job.
- the probability model analyzer 122 may determine the probability model according to the third job (selecting job). Apply to determine the work pattern.
- the probabilistic model for each task can be seen in Table 1.
- the pattern result output unit 120 displays the first to fourth jobs (lifting work, excavation work, picking work, and trenching work) determined by the work pattern analyzer 114 and the probability model analyzer 122. 124 and the drive control device 126.
- the drive control device 126 controls each equipment, and includes an engine drive control unit 130, a pump drive control unit 132, and a valve drive control unit 134. In this case, the driving control device 126 controls each device according to the work pattern result received from the pattern result output unit 120.
- FIG. 2 is a flowchart illustrating a work pattern control method according to the present invention.
- the data accumulator 112 receives and accumulates an operation signal of each device from the joystick 100. (S20, S22) In this case, the first workgroup pattern analyzer 116 stores the data accumulator. It is determined whether there is a signal accumulated in 112 (S24).
- the first workgroup pattern analyzer 116 outputs a stop signal to the screen display unit 124 and the drive control device 126. (S42)
- the first workgroup pattern analysis unit 116 analyzes whether the data is all running signals or other signals in addition to the driving signals.
- the first workgroup pattern analysis unit 116 determines whether the immediately preceding job was the first job (lifting job). (S44) If the immediately preceding job was the first job (lifting job), The first workgroup pattern analyzer 116 determines the data as the first workgroup. In the present exemplary embodiment, only the case in which the first work group includes the "lifting job" will be described. Therefore, the first workgroup pattern analyzer 116 determines that the lifting job is the "lifting job," and thus the screen display unit 124 and the driving control device. Output to (126).
- the first workgroup pattern analyzer 116 determines whether the data is the first job (lifting job) (S28).
- the first work group pattern analyzer 116 determines the current job as a "lifting job” and outputs it to the screen display unit 124 and the drive control device 126. Accordingly, the "first job (lifting job)" is displayed on the screen display unit 124 (S36). The driving control device 126 controls each device to fit the lifting job pattern. (S38)
- the first workgroup pattern analyzer 116 outputs the data to the second workgroup pattern analyzer 118. Since the second work group pattern analyzer 118 starts most of the work of the excavator as the arm work and distinguishes the work pattern according to the arm driving pattern, the second work group pattern analyzer 118 cuts and analyzes the data by period based on the arm signal. (S30)
- the second workgroup pattern analyzer 118 determines whether the data is the second workgroup (S32).
- the second workgroup contains tasks that are not included in the first workgroup.
- the case where the second work group includes a second work (excavation work) and a third work (selection work) will be described.
- the second workgroup pattern analyzer 118 determines the current job as an "excavation job” or a "pick job”, and displays the screen display part. 124 and the drive control device 126. (S50) Accordingly, "excavation job” or “selection job” is displayed on the screen display unit 124 (S36), and the drive control device 126 is excavated. Each equipment is controlled to fit the work or the selection work pattern (S38).
- the second workgroup pattern analyzer 118 outputs the unidentified data to the probability model analyzer 122.
- the probability model analyzer 122 determines the data that has not been analyzed by the work pattern analyzer 114 as the first to fourth tasks (a lifting operation, an excavation operation, a picking operation, and a trenching operation) using a probability model.
- the determined data is supplied to the screen display unit 124 and the drive control device 126. Accordingly, the screen is displayed on the screen display unit 124 according to the first to fourth work patterns determined by the probability model analyzer 122 (S36), and the driving control device 126 is connected to the first to fourth work patterns.
- Each device is controlled according to the operation S38.
- the fourth operation is a mixture of the second operation (the excavation operation) and the third operation (the selection operation), which makes it difficult to distinguish between the second operation or the third operation. It may be a trenching operation.
- the present invention can use efficient fuel economy by controlling each device according to each work pattern, and can reduce unnecessary fuel consumption.
- FIG. 3 is a flowchart of a probability model analyzer illustrated in FIG. 1.
- the probability model analyzer 122 analyzes a work frequency on data that is not determined by the work pattern analyzer 114 (S50).
- the probability model analyzer 122 determines a work having a high frequency among the data, and applies the probability model according to each work according to the work having the maximum frequency, thereby applying the first to fourth work (lifting work, excavation work, Select and print).
- the probability model analysis unit 122 determines whether the job having the mode is the second job (excavation job). (S60) When the job having the mode is the second job (the excavation job), the probability model analysis unit ( 122 determines data by applying a probability model according to the second operation (excavation operation) (S62).
- the probability model analysis unit 122 determines whether the job having the mode is the third job (selection job). (S70) The job having the mode is the third job. In the case of a job (selection job), the probability model analyzer 122 determines data by applying a probability model according to the third job (selection job) (S72).
- the probability model analyzer 122 determines whether the job having the mode is the first job (the lifting job). (S80) The job having the mode has the first value. In the case of a job (lifting job), the probability model analyzer 122 determines a data by applying a probability model according to the first job (lifting job) (S82).
- FIG. 4 is a flowchart of determining a work pattern by applying data to an excavation probability model when an excavation work has a mode.
- the first reference value refers to a work frequency of 80% or more.
- the probability model analyzer 122 determines the current work as an "excavation work" and outputs it to the screen display unit 124 and the drive control device 126. (S172) Accordingly, The job output on the screen display unit 124 is displayed, and the drive control device 126 controls each device according to the excavation work pattern.
- the probability model analysis unit 122 analyzes the frequency of the lifting work (S164). That is, it is determined whether the frequency of the lifting work is the second reference value or more (S166).
- the second reference value means the number of lifting operations of two or more times.
- the probability model analyzing unit 122 determines that the current job is "not determined” and outputs it to the screen display unit 124 and the drive control device 126.
- the probability model analysis unit 122 determines whether the lifting operation frequency is the third reference value (S168).
- the third reference value means the number of lifting operations at one time.
- the probability model analyzer 122 determines the current task as a fourth task, that is, a "tranging task”, and outputs the same to the screen display unit 124 and the driving control device 126. (S170) Accordingly, the screening unit 124 displays the “ trenching work ", and the drive control device 126 controls each piece of equipment according to the " trenching work ".
- the fourth reference value means a work frequency of 85% or more.
- the probability model analysis unit judges and outputs the current work as the "excavation work”. (S180) If the ratio of the excavating work except the lifting work is 85% or more, the probability model The analysis unit judges the current job as a "transaching job” and outputs it (S178).
- 5 is a flowchart of determining a work pattern by applying data to a pick probability model when the picking operation has a mode.
- the picking job if the job having the maximum frequency is the picking job, it is determined whether the picking job is the fifth reference value. (S180, S182) In this case, the fifth reference value means the work frequency of 100%. do.
- the probability model analyzer 122 determines the current job as the “choice job” and outputs the current job to the screen display unit 124 and the drive control device 126. Accordingly, the "selection operation” is displayed on the screen display unit 124, and the drive control device 126 controls each device according to the selection operation pattern (S196).
- probability model analysis unit 122 determines whether the previous job was a trenching job (S184).
- the probability model analyzer 122 determines whether the lifting job frequency is greater than or equal to the sixth reference value. (S198, S200) In this case, the sixth reference value means the number of lifting jobs more than two times. .
- the probability model analyzing unit 122 determines that the current job is "not determined” and outputs it to the screen display unit 124 and the drive control device 126.
- the probability model analysis unit 122 determines whether the lifting operation frequency is a seventh reference value.
- the seventh reference value means the number of one lifting operations.
- the probabilistic model analysis unit 122 determines the current job as a "transaching job” and outputs it to the screen display unit 124 and the driving control device 126 (S208). Accordingly, the screen display unit 124 displays a "trenching operation", and the drive control device 126 controls each piece of equipment according to the "trenching operation”.
- the probability model analyzer 122 determines whether all data except the lifting tasks are the picking tasks. (S204) When all the data except the lifting tasks are the picking tasks, the probability model analyzing unit ( In operation S212, when all data except the lifting operation is not the selection operation, the probability model analysis unit 122 determines that the data is a “tranching operation” and outputs the determination. When the previous operation is not a trenching operation (S184), the probability model analyzer 122 determines whether the lifting operation frequency is equal to or greater than an eighth reference value. (S186, S188) At this time, the eighth reference value is lifted two or more times. It means the number of tasks.
- the probability model analysis unit 122 determines that the current job is "not determined” and outputs it to the screen display unit 124 and the drive control device 126.
- the probability model analyzer 122 determines whether the lifting operation frequency is a ninth reference value.
- the ninth reference value means the number of one lifting operation.
- the probabilistic model analysis unit 122 determines whether the ratio of the picking operation is greater than or equal to the tenth reference value. At this time, the tenth reference value is 85%. If the proportion of the selection task is 85% or more, the probability model analyzer 122 determines the current task as the "selection task" and outputs it. (S222) If the ratio of the selection task is not 85% or more, the probability model The analyzer 122 determines and outputs the current job as a "transaching job" (S194).
- the probability model analyzer 122 determines whether all data except the lifting task is a picking operation. (S216) If all data except the lifting task is a picking task, the probability model analyzing unit 122 ) Determines and outputs the current job as a "selecting job”. (S218) If all data except the lifting job is not a picking job, the probability model analyzing unit 122 determines the current job as a "tranching job” and outputs it. (S220)
- FIG. 6 is a flowchart of determining a working pattern by applying data to a lifting probability model when the lifting operation has a mode.
- the eleventh reference value is the number of lifting tasks of six or more times. Means.
- the probability model analyzer 122 determines the current task as a "lifting task” and outputs it to the screen display unit 124 and the driving control device 126. (S234) Accordingly, The job output on the screen display unit 124 is displayed, and the drive control device 126 controls each device according to the lifting job pattern.
- the probability model analyzing unit 122 determines that the current job is "not determined” and outputs it to the screen display unit 124 and the drive control device 126 (S236).
- the construction machine control method according to the invention can improve the fuel economy by controlling each work device of the construction machine such as an excavator according to the work pattern.
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Abstract
Description
Max = A | A>=0.8 | A | ||
A<0.8 | C>2 | N.D | ||
C=1 | A(C제외)>=0.85 | A | ||
A(C제외)<0.85 | D | |||
C=0 | D | |||
Max = B | B=all | B | ||
B!=all | 직전=D | C>2 | N.D | |
C=1 | B(C제외)=1 | B | ||
B(C제외)!=1 | D | |||
C=0 | D | |||
직전!=D | C>2 | N.D | ||
C=1 | B(C제외)=1 | B | ||
B(C제외)!=1 | D | |||
C=0 | B>=0.85 | B | ||
B<0.85 | D | |||
Max = C | C>=6 | C | ||
C<6 | 보류 |
Claims (12)
- 건설 기계가 수행하고 있는 작업을 분석하는 작업 패턴 인식 장치와상기 작업 패턴 인식 장치로부터 분석된 작업 패턴에 맞도록 상기 건설 기계의 각 작업 장치의 구동을 제어하는 구동 제어 장치를 포함하며,상기 작업 패턴 인식 장치는운전자의 조작 신호 및 상기 건설기계의 각 장비의 구동 데이터 중 어느 하나를 입력받아 축적하는 데이터 축적부와상기 축적된 조작 신호의 데이터를 분석하여 현재 작업 패턴을 판별하는 작업 패턴 분석부와상기 작업 패턴 분석부로부터 판별되지 못한 데이터를 확률 모델을 이용하여 판별하는 확률 모델 분석부와상기 작업 패턴 분석부 및 확률 분석 모델부로부터 판별된 작업을 상기 구동 제어 장치로 출력하는 패턴 결과 출력부를 포함하는 것을 특징으로 하는 건설기계의 제어 장치.
- 상기 제1항에 있어서,상기 작업 패턴 분석부는,상기 작업 패턴이 기 설정된 제1 작업 그룹에 포함된 적어도 하나의 작업의 패턴에 패턴에 해당되는지 여부를 판단하는 제 1 작업 그룹 패턴 분석부 및상기 제 1 작업 그룹 패턴 분석부에서 판별되지 못한 상기 작업 패턴들이 기 설정된 제2 작업 그룹에 포함된 적어도 하나의 작업의 패턴에 해당되는 지 여부를 판단하는 제 2 작업 그룹 패턴 분석부를 포함하는 것을 특징으로 하는 건설기계의 제어 장치.
- 제 1 항에 있어서,상기 확률 모델 분석부는 상기 축적된 데이터에 대한 작업 빈도를 분석하여 작업 빈도가 많은 작업을 추출하고, 각 작업별로 준비된 확률 모델들 중 상기 작업 빈도가 많은 작업에 대응되는 확률 모델을 적용하여 현재 작업의 패턴을 판별하는 것을 특징으로 하는 건설기계의 제어 장치.
- 제 2 항에 있어서,상기 확률 모델 분석부는 상기 축적된 데이터에 대한 작업 빈도를 분석하여 작업 빈도가 많은 작업을 추출하고, 각 작업별로 준비된 확률 모델들 중 상기 작업 빈도가 많은 작업에 대응되는 확률 모델을 적용하여 현재 작업의 패턴을 판별하는 것을 특징으로 하는 건설기계의 제어 장치.
- 제 4 항에 있어서,상기 작업 빈도가 많은 작업이 제 1 작업일 경우, 상기 확률 모델 분석부는 제 1 확률 모델을 이용하여 분석하며,상기 제 1 확률 모델은,상기 제 1 작업이 기 설정된 기준값 보다 빈도가 높은 경우, 상기 작업 패턴이 제 1 작업인 것으로 결정하고, 상기 기준값 보다 빈도가 낮은 경우 상기 작업 패턴이 제 1 작업이 아닌 것으로 결정하는 것을 특징으로 하는 건설기계의 제어 장치.
- 제 4 항에 있어서,상기 작업 빈도가 많은 작업이 제 2 작업인 경우, 상기 확률 모델 분석부는 제 2 확률 모델을 이용하여 분석하며,상기 제 2 확률 모델은,상기 제 2 작업이 제 1 기준값 보다 빈도가 높거나 같은 경우, 상기 작업 패턴이 제 2 작업인 것으로 결정하고,상기 제 2 작업이 상기 제 1 기준값 보다 빈도가 낮고 상기 제 1 작업의 빈도가 제 2 기준값 보다 높은 경우 상기 작업 패턴이 상기 제 2 작업이 아닌 것으로 결정하고,상기 제 2 작업이 상기 제 1 기준값 보다 빈도가 낮고 상기 제1 작업의 빈도가 제3 기준값인 경우, 제 1 작업과 다른 제 2 작업의 빈도가 제4 기준값 이상이면 상기 작업 패턴이 제2 작업인 것으로, 제4 기준값 보다 낮으면 상기 작업 패턴이 제4 작업인 것으로 결정하고,상기 제 2 작업이 상기 제1 기준값 보다 빈도가 낮고 상기 제 1 작업의 빈도가 제3 기준값 보다 낮은 경우, 상기 작업 패턴이 제4 작업인 것으로 결정하는 것을 특징으로 하는 건설기계의 제어 장치.
- 제 5 에 있어서,상기 작업 빈도가 많은 작업이 제 3 작업인 경우, 상기 확률 모델 분석부는 제 3 확률 모델을 이용하여 분석하며,상기 제 3 확률 모델은,상기 제 3 작업의 빈도가 제 4 기준값 이상인 경우, 상기 작업 패턴이 제 3 작업인 것으로 결정하고,상기 제 3 작업의 빈도가 상기 제 4 기준값 미만인 경우, 현재 작업 패턴 직전의 작업 패턴이 제 4 작업인지 확인하고, 이후 제1 작업의 빈도와 제1 작업을 제외한 제3 작업의 빈도를 기 설정된 각각의 기준 작업의 빈도를 다시 확인하여 상기 작업 패턴이 상기 제 3 작업인지 여부를 결정하는 것을 특징으로 하는 건설기계의 제어 장치.
- 데이터 축적부를 통해 운전자의 조작 신호 및 상기 건설기계의 각 장비의 구동 데이터 중 어느 하나를 입력받아 축적하는 단계와작업 패턴 분석부를 통해 상기 데이터 축적부에 축적된 데이터를 분석하여 현재 작업 패턴을 판별하는 단계와상기 작업 패턴 분석부에 의해 작업 패턴이 판별되지 못한 경우, 확률 모델 분석부를 통해 확률 모델을 이용하여 판별하는 단계와패턴 결과 출력부를 통해 상기 작업 패턴 분석부 및 확률 분석 모델부로부터 판별된 작업 패턴에 대응되게 각 장비를 제어하는 구동 제어 장치로 출력하는 단계를 포함하는 것을 특징으로 하는 건설기계의 제어 방법.
- 제 8 항에 있어서,상기 확률 모델 분석부는 상기 축적된 데이터에 대한 작업 빈도를 분석하여 작업 빈도가 많은 작업을 추출하고, 각 작업별로 준비된 확률 모델들 중 상기 작업 빈도가 많은 작업에 대응되는 확률 모델을 적용하여 현재 작업의 패턴을 판별하는 것을 특징으로 하는 건설기계의 제어방법.
- 제 8 항에 있어서,상기 작업 빈도가 많은 작업이 제 1 작업일 경우, 상기 확률 모델 분석부는 제 1 확률 모델을 이용하여 분석하며,상기 제 1 확률 모델은,상기 제 1 작업이 기 설정된 제11 기준값 보다 빈도가 높은 경우, 상기 작업 패턴이 제 1 작업인 것으로 결정하고, 상기 제11 기준값 보다 빈도가 낮은 경우 상기 작업 패턴이 제 1 작업이 아닌 것으로 결정하는 것을 특징으로 하는 건설기계의 제어 방법.
- 제 8 항에 있어서,상기 작업 빈도가 많은 작업이 제 2 작업인 경우, 상기 확률 모델 분석부는 제 2 확률 모델을 이용하여 분석하며,상기 제 2 확률 모델은,상기 제 2 작업이 제 1 기준값 보다 빈도가 높거나 같은 경우, 상기 작업 패턴이 제 2 작업인 것으로 결정하고,상기 제 2 작업이 상기 제 1 기준값 보다 빈도가 낮고 상기 제 1 작업의 빈도가 제 2 기준값 보다 높은 경우 상기 작업 패턴이 상기 제 2 작업이 아닌 것으로 결정하고,상기 제 2 작업이 상기 제 1 기준값 보다 빈도가 낮고 상기 제1 작업의 빈도가 제3 기준값인 경우, 제 1 작업과 다른 제 2 작업의 빈도가 제4 기준값 이상이면 상기 작업 패턴이 제2 작업인 것으로, 제4 기준값 보다 낮으면 상기 작업 패턴이 제4 작업인 것으로 결정하고,상기 제 2 작업이 상기 제1 기준값 보다 빈도가 낮고 상기 제 1 작업의 빈도가 제3 기준값 보다 낮은 경우, 상기 작업 패턴이 제4 작업인 것으로 결정하는 것을 특징으로 하는 건설기계의 제어 방법.
- 제 8 에 있어서,상기 작업 빈도가 많은 작업이 제 3 작업인 경우, 상기 확률 모델 분석부는 제 3 확률 모델을 이용하여 분석하며,상기 제 3 확률 모델은,상기 제 3 작업의 빈도가 제 4 기준값 이상인 경우, 상기 작업 패턴이 제 3 작업인 것으로 결정하고,상기 제 3 작업의 빈도가 상기 제 4 기준값 미만인 경우, 현재 작업 패턴 직전의 작업 패턴이 제 4 작업인지 확인하고, 이후 제1 작업의 빈도와 제1 작업을 제외한 제3 작업의 빈도를 기 설정된 각각의 기준으로 다시 확인하여 상기 작업 패턴이 상기 제 3 작업인지 여부를 결정하는 것을 특징으로 하는 건설기계의 제어방법.
Priority Applications (3)
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EP11820159.9A EP2610407A2 (en) | 2010-08-23 | 2011-08-23 | Apparatus and method for controlling construction machinery |
CN201180041004.9A CN103069083B (zh) | 2010-08-23 | 2011-08-23 | 工程机械的控制装置及控制方法 |
US13/818,621 US20130158679A1 (en) | 2010-08-23 | 2011-08-23 | Apparatus and method for controlling construction machinery |
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KR10-2010-0081222 | 2010-08-23 | ||
KR1020100081222A KR101799101B1 (ko) | 2010-08-23 | 2010-08-23 | 건설기계의 작업 패턴 인식 장치 및 작업 패턴 인식 방법 |
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US (1) | US20130158679A1 (ko) |
EP (1) | EP2610407A2 (ko) |
KR (1) | KR101799101B1 (ko) |
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EP2765240A4 (en) * | 2011-10-05 | 2015-10-28 | Volvo Constr Equip Ab | SYSTEM FOR CONTROLLING LEVELING WORK WITH A BAGGER |
KR102425743B1 (ko) * | 2015-08-21 | 2022-07-28 | 현대두산인프라코어(주) | 건설기계 및 건설기계의 제어 방법 |
CN112004975A (zh) * | 2018-03-30 | 2020-11-27 | 住友重机械工业株式会社 | 施工机械的驾驶支援系统、施工机械 |
US11697917B2 (en) * | 2019-07-26 | 2023-07-11 | Deere & Company | Anticipatory modification of machine settings based on predicted operational state transition |
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- 2011-08-23 US US13/818,621 patent/US20130158679A1/en not_active Abandoned
- 2011-08-23 EP EP11820159.9A patent/EP2610407A2/en not_active Withdrawn
- 2011-08-23 WO PCT/KR2011/006203 patent/WO2012026732A2/ko active Application Filing
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CN103069083A (zh) | 2013-04-24 |
EP2610407A2 (en) | 2013-07-03 |
KR20120021920A (ko) | 2012-03-09 |
KR101799101B1 (ko) | 2017-11-20 |
CN103069083B (zh) | 2016-11-02 |
WO2012026732A3 (ko) | 2012-05-18 |
US20130158679A1 (en) | 2013-06-20 |
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