KR101873253B1 - Stochastic Dozer Productivity Estimation Method - Google Patents

Abstract

The present invention relates to a method for evaluating stochastic dozer productivity, the method comprising: a step of acquiring attribute data of a dozer and performance data of an external device; a step of calculating ideal maximum productivity by applying a curve approximation technique to a productivity optimal customized curve; a step of calculating operation environment correction coefficients for productivity variables; a step of calculating direct costs of each production unit and dozer productivity in a deterministic mode and a stochastic mode; and a step of providing a user with stochastically calculated achievement ranges of direct costs of each production unit and dozer productivity. According to the present invention, ideal maximum productivity of a dozer can be calculated and provided as a simple mathematical function.

Description

{Stochastic Dozer Productivity Estimation Method}

More particularly, the present invention relates to a deterministic dozer productivity evaluation format, a stochastic productivity model, and a stochastic dozer using the in situ condition data obtained from actual field case studies, And a method for evaluating productivity.

A dozer is a device with a blade attached to the front of the tractor. This is classified into a crawler and a rubber tire dozer according to the type of the sub-traveling device, and a traction force (a traction force in the case of a crawler dozer and a driving force in the case of a wheel dozer - hereinafter referred to as a traction force) .

The productivity of the dozer is determined by the amount of soil that remains on the blade front over a period of time during which the blade pushes a certain distance in the direction in which the tractor advances. The cycle time and productivity of the dozer are affected by the type of tractor and its specifications, the type of the lower drive, the configuration of the work unit (blade and side plate), the type and condition of the excavated / Variability depends on efficiency, soil transfer distance, equipment aging, weather and climatic conditions (temperature, altitude and rainfall).

The method for evaluating the dozer productivity includes the number of operating cycles, mathematical methods applying empirical dozer blade capacity, evaluation methods based on regression analysis, and stochastic methods using discrete event simulation.

These conventional methods of assessing dozer productivity include, firstly, a number of workplace correction factors (e.g., dozer model, blade type, transmission type, glade, dosing distance, There is a constraint to use deterministic values, without considering the uncertainty and randomness of the dozing operation type, work efficiency and job site visibility). Therefore, it is desirable to measure the ideal optimal productivity measured in an ideal environment by accommodating the stochastic nature of the coefficients.

Second, conventional doser productivity assessment methods do not take into account changes in external environmental conditions that affect dozer productivity. The dozer productivity is certainly volatile unless the doser is operated under certain controlled environmental conditions. The stochastic methods using the existing discrete event simulation also consider the randomness of the production cycle time and calculate the dozer productivity with the probability distribution function. However, there is a limit to the assumption that external environmental conditions remain constant during simulation experiments.

Third, conventional doser productivity assessment methods do not take into account the diversity of work condition correction factors. For example, pilot proficiency is divided into three linguistic expression values: Excellent, Normal, and Lack having a correction factor of 1.00, 0.75, and 0.6, respectively. However, the proficiency of a pilot can not be assigned to a clear value with such confidence. Clearly, there is no reason to declare that the dozer productivity will be reduced to exactly 25% if the pilot's skill level is normal. Due to these issues, it is desirable to perform the Stochastic Dozer Productivity Estimation (SDPE), which takes into account the uncertainties and randomness associated with defining workplace correction factors.

As described above, the format for evaluating the productivity of the conventional doser, that is, the Dozer Productivity Estimating Format (DPEF), provides the graphs of the work environment correction coefficients to be input for the dozer productivity calculation, .

Therefore, analytical techniques that can meet the DPEF and reusability are required by artificially processing uncertainties and randomness of the workplace correction factors.

However, there is no research to date that integrates dozer productivity data by experience into mathematical functions and integrates this data with reusable software code to integrate the advantages of DPEF and reusability for dozer work planning.

As a result, there is a problem in that the construction cost may be exceeded because the construction period can not be matched, and there is a problem in how much the dozer is to be put into the earthwork field and the related plan.

Korean Patent No. 10-0984514 "Real-time construction productivity analysis method using wireless communication based RFID" Korean Patent No. 1379407 "High-rise Building Construction Project Time Estimation System Considering Vertical Variation of Climate Factors"

The present invention computes the ideal maximum productivity of a dozer using a curve approximation technique to the productivity curve of a dozer provided graphically by a conventional dozer productivity evaluation format and provides it as a simple mathematical function, The present invention provides a method for estimating the productivity of a doser and a method for estimating the productivity of a doser and a method for estimating the productivity of a doser by calculating a direct cost per production unit.

Among the embodiments, the method for estimating the stochastic dozer productivity includes a method for estimating the productivity of the dozer by using a format (Dozer Productivity Estimating Format, DPEF) for estimating the productivity of the dozer and a stochastic dozer productivity evaluation system A) A deterministic mode that estimates the volatility of output results of dosing operations and associated costs risk by applying deterministic mode and field condition data to which dozer productivity evaluation format (DPEF) is applied. ; b) calculating a productivity optimal fit curve by applying a curve approximation technique to predetermined dozer productivity and performance data, calculating a ideal maximum productivity by applying a curve approximation technique to the productivity optimum fit curve and expressing the ideal maximum fit productivity as a mathematical function; c) acquiring the property data of the doser and the performance data of the external equipment and calculating working environment correction coefficients for the productivity variables including the terrain condition, the working condition, and the equipment condition of the earthwork field where the doser is used; d) calculating dozer productivity per hour using the combination of the ideal maximum productivity and the work environment correction factors in the deterministic and stochastic modes, and dividing the doser operating cost per hour into the dozer productivity per hour, Calculating direct costs; And e) comparing the doser productivity per hour obtained in the stochastic mode with the direct cost per production unit based on the calculated doser productivity per hour and the direct cost per production unit calculated in the deterministic mode, And a step of providing the calculated range to the user.

Wherein the attribute data of the doser includes attribute values of a tractor type, an engine type, a drive type, a number of driving wheels, a blade type, and stores the attribute values in a dozer information matrix, And an attribute value of the terrain including the inclination angle and the distance on the work path measured by using the attribute of the terrain, and stores the attribute values of the terrain in the memory.

In step c), the ideal maximum productivity P _max is obtained by obtaining the dozer model D _T , the blade type B _T , the productivity optimum fitting curve F _T , and the working distance D, And calculating the value using the following equation.

[Mathematical Expression]

In the step b), the work environment correction coefficients are calculated by using the correction coefficient (f _RC ) of the working soil, the proficiency coefficient (f ₀ ) of the pilots, the soil correction coefficient f _M), dozer operating mechanism (f _OT), the visibility coefficient (f _V), efficiency factor (f _J), the gear shift coefficient (f _T), the blade correction factor (f _B), the slope correction coefficient (f _G) And in the stochastic mode, it is possible to stochastically change the working environment correction coefficient by applying the probability density function of the productivity variables, assuming that each condition of the earthwork field is an uncertainty changing state .

In the step d), the combination correction coefficient f is calculated using the following equation.

[Mathematical Expression]

)는 시간당 소유 운영 경비(

)와 시간당 운전수 노임(

)을 합산하여 계산하는 것을 특징으로 한다.In step d), the doser operating cost per hour

) Is an operating cost per hour (

) And hourly driving man (

) Are calculated.

In the deterministic mode, the per-hour owned operating expenses and the per-hour driving noyes use a fixed value based on a predetermined value, and in the stochastic mode, the optimistic cost C _min , the most cost C _mod , And one of the evaluation scales of the cost (C _max ) is selected and used.

The evaluation scale in the stochastic mode is calculated by the following equation.

[Mathematical Expression]

The step d) includes the steps of: d-1) collecting the dozer productivity per hour and direct costs per production unit by repeating the predetermined number of simulations; d-2) comparing the simulation execution count with a minimum simulation execution count to confirm the maturity of the simulation result; d-3) if the simulation result does not satisfy the maturity level, increasing the number of simulations to be executed more than the minimum simulation execution count; And d-4) interpreting the per-hour doser productivity and the direct cost per production unit from the values of the normal probability density function and the parameters, the optimal probability density function and the parameters if the simulation result meets the maturity .

In the step (d-4), a confidence interval for the dozer productivity and the direct cost per production unit is obtained using the optimal probability density function and the values of the parameters, and the reliability interval is calculated from the user And provides the user with the probability of the dozer productivity per requested hour and the extent of the direct cost per production unit.

Rating factor (f _O) of the pilot and defined through the earthwork management terminal based on the evaluation carried career and work of the pilot, the earthwork management terminal, including the Years of experience for the pilot, the number of job commitment, construction scale, the accident career And performing evaluation of work performance automatically based on objective indicators.

The proficiency coefficient (f _O ) of the pilot uses either a method of using a predefined value or a method of evaluating a three point ReCurd scale depending on the type of drive device in a deterministic mode, And a triangular probability distribution is generated based on values preliminarily evaluated as lower and higher.

The correction coefficient (f _RC ) of the working gypsum is obtained by collecting experimental and actual data in a deterministic mode to define the actual unit weight (W _L ) of the working soil, , An intermediate value, a maximum value, and a statistical value to calculate an actual unit weight, and then divides the actual unit weight by 2300 1b / 1cy.

[Mathematical Expression]

The soil erosion correction coefficient (f _M ) is defined as a value matching the information given in the work soil matrices by the following equation when the type of the working soil and the type of the driving apparatus are determined.

[Mathematical Expression]

The dozer operation technique (f _OT ) is defined according to the type of operating technique (OT: 1 = standalone dozing alone, 2 = slot dozing, 3 = side-by-side dozing), and in the deterministic mode, (OT) corresponding to each operation technique, and calculates the sum of the usage ratios of each operation technique per working area in the stochastic mode as 100%.

[Mathematical Expression]

The visibility factor f _V is defined on the basis of data according to weather and sunshine conditions (V: 1 = clearly visible, 2 = dust, rain, snow, fog, or darkness) in a deterministic mode, data for Li curd 3-point scale according to the weather and sunlight condition (impaired, more or less impaired, clear) after the operation in the type of the drive system the value of the visibility selected by the user from a triangular distribution according to the _{(U T)} (V U ) Is generated and is characterized by the following equation.

[Mathematical Expression]

The work efficiency coefficient (f _J ) is defined by dividing the work activity time per hour in deterministic mode by a fixed set value. In the stochastic mode, the work activity time is defined as a minimum value (T _min ), a median value (T _mod ) A random number is generated based on the triangular probability distribution defined by the equation (T _max ), and is obtained using the following equation.

[Mathematical Expression]

The gear shift coefficient (f _T) is characterized in that it represents a 1, using 0.8 When using a direct-drive, and to, depending on the type of the transmission equation (10) When using a power-shift.

[Mathematical Expression]

The blade correction coefficient f _B is defined by the following formula according to the type of the blade (B _T : S = straight, U = universal, A = angle, SU = semi-universal, C = cushion blade).

[Mathematical Expression]

The inclination correction coefficient f _G is defined by utilizing the inclination angle on the dosing path in the deterministic mode, and in the stochastic mode, the inclination correction parameter f _G is defined by the inclination angle parameter (the minimum value θ _min , the median value θ _mod , θ _max )) using an arbitrary value generated by using a triangular probability distribution based on the following equation.

[Mathematical Expression]

The method of evaluating the stochastic dozer productivity of the present invention can calculate the ideal maximum productivity of a dozer by applying a curve approximation technique to a dozer productivity curve provided as an analog value and replace it with a simple mathematical function, By comparing the probability density function of the dozer productivity obtained in the stochastic mode with the direct cost per production unit based on the value of the dozer productivity and the direct cost per unit of production, the dozer productivity and the direct cost per unit of production are calculated stochastically To provide digital values to the user, thereby artificially handling the uncertainty and randomness of the work, thereby meeting the reusability requirements.

Therefore, the present invention can provide more accurate dozer productivity achievement range as probabilistic information in consideration of the variables of the earthwork field, and thus it is possible to support a facility operation plan suitable for the field.

1 is a diagram illustrating a configuration of a stochastic dozer productivity evaluation system according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating a stochastic dozer productivity evaluation method according to an embodiment of the present invention
FIGS. 3A and 3B are block diagrams illustrating detailed algorithms for each step of FIG. 2. FIG.
FIG. 4 is a graph showing the probability of achieving productivity of a catamaran based on a user input value by a stochastic dozer productivity evaluation method according to an exemplary embodiment of the present invention.
FIG. 5 is a graph illustrating a probability of achieving a direct cost per productivity unit based on a user input value by a stochastic dozer productivity evaluation method according to an exemplary embodiment of the present invention.

The description of the present invention is merely an example for structural or functional explanation, and the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, the embodiments are to be construed as being variously embodied and having various forms, so that the scope of the present invention should be understood to include equivalents capable of realizing technical ideas. Also, the purpose or effect of the present invention should not be construed as limiting the scope of the present invention, since it does not mean that a specific embodiment should include all or only such effect.

Meanwhile, the meaning of the terms described in the present invention should be understood as follows.

The terms "first "," second ", and the like are intended to distinguish one element from another, and the scope of the right should not be limited by these terms. For example, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

It is to be understood that when an element is referred to as being "connected" to another element, it may be directly connected to the other element, but there may be other elements in between. On the other hand, when an element is referred to as being "directly connected" to another element, it should be understood that there are no other elements in between. On the other hand, other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

It should be understood that the singular " include "or" have "are to be construed as including a stated feature, number, step, operation, component, It is to be understood that the combination is intended to specify that it does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

In each step, the identification code (e.g., a, b, c, etc.) is used for convenience of explanation, the identification code does not describe the order of each step, Unless otherwise stated, it may occur differently from the stated order. That is, each step may occur in the same order as described, may be performed substantially concurrently, or may be performed in reverse order.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Commonly used predefined terms should be interpreted to be consistent with the meanings in the context of the related art and can not be interpreted as having ideal or overly formal meaning unless explicitly defined in the present invention.

1 is a diagram illustrating a configuration of a stochastic dozer productivity evaluation system according to an embodiment of the present invention.

1, the stochastic dozer productivity assessment system 100 includes a deterministic mode execution module 110, a stochastic mode execution module 120, and a memory 130, but is not so limited, An input module to which the communication module is connected, a communication module supporting wired communication or wireless communication, and the like.

The deterministic mode execution module 110 applies the existing dozer productivity evaluation format (DPEF), and the stochastic mode execution module 120 calculates the variability of output results (i.e., productivity and total operation cost) Predict associated cost risk,

The stochastic dozer productivity assessment system 100 integrates the existing DPEF and stochastic productivity models and analysis processes from the field condition data obtained from the actual field case studies.

On the other hand, the earthwork management terminal 200 defines the pilots 'proficiency coefficient based on the pilots' career and the performance evaluation of the pilots. Based on the data such as the career indicators of each pilot, the number of work input, the construction scale, Evaluation is carried out.

The earthwork management terminal 200 may be a communication device capable of network connection such as a desktop computer, a laptop computer, a smart phone, a PDA (Personal Digital Assistant), a PMP (Portable Multimedia Player), a navigation terminal, .

FIG. 2 is a block diagram illustrating a stochastic dozer productivity evaluation method according to an embodiment of the present invention, and FIGS. 3A and 3B are block diagrams illustrating detailed algorithms for each step of FIG.

2 and 3, in the stochastic dozer productivity evaluation method, the stochastic dozer productivity evaluation system 100 acquires the variable values of the internal and external systems (S10). That is, the stochastic dozer productivity evaluation system 100 is a tractor and a blade property (that is, tractor type (E _T), the type of engine (E _T: 1 = diesel, 2 = petrol), drives the type (U _T: 1 = tract, 2 = driving wheel), the number of the driving wheel _{(N W: 1 = 2-} wheel drive, 2 = 4-wheel drive, 3 = null), blade type _{(B T: S = straight,} U = universal, SU = semiuniversal, C = cushion blade) (S11). ≪ RTI ID = 0.0 >

The stochastic dozer productivity evaluation system 100 generates a structural query language using these dozer attribute data, writes an external equipment performance chart, searches a dozer model and a blade model, and compares it with a dozer productivity evaluation curve (S20)

For example, if the tractor type, the engine type, the drive type, the number of drive wheels, and the blade type are given as D7G, 1, 1, 3, and U, SDPE performs the structural query language as shown in Equation 1 do.

Stochastic dozer productivity evaluation system 100 includes a tractor and stores the values of the blade properties in road construction information matrix (M _D), and the inclined angle on the terrain properties (that is, the working path measured by the laser rangefinder (θ), operations (Distance D of the path) in the memory 130. [

The optimal productivity curve (F _T ) for the dozer blades can be determined by applying a curve approximation technique to the dozer productivity and performance data presented in the performance specification table of the manufacturer. (S21) The curve fitting tool box is provided in MATLAB It is used for analysis of inquiry data, connection of leading / trailing process data, comparison of candidate models, and removal of singular values for curve fitting.

In addition, assuming that the relationship between dosing distance and productivity is linear, regression analysis of linear models is performed according to the existing case and characteristics of the construction industry, and the quality of alignment is guaranteed using the initial values of the optimization parameters and the curve fitting toolbox can do.

For example, the productivity fit curve (F _T ) of a D7 doser using a straight blade can be expressed as Equation (2).

The stochastic dozer productivity evaluation system 100 obtains a specific dozer model (D _T ), a blade type (B _T ), a productivity optimum fit curve (F _T ), and a work distance (D) The maximum productivity (P _Max ) is calculated (S22)

Thus, the stochastic dozer productivity assessment system 100 can be expressed as a simple mathematical function by calculating the ideal maximum productivity of the dozer expressed by a figure using a curve approximation technique. This analytical method can provide a mathematical calculation of the ideal maximum productivity (P _max ) instead of the existing analytical method that must derive productivity from the figure. At this time, the stochastic dozer productivity evaluation system 100 sets the number of iterative execution of the minimum simulation to 120, the reliability level to 99%, and then the number of iterative execution starts from 0. (S23)

The stochastic dozer productivity evaluation system 100 calculates work environment correction coefficients for productivity variables including topographical conditions, work conditions, and equipment conditions of a soil conditioner in which a doser is used (S30).

In the deterministic mode, the experimental and experimental data are collected and the actual unit weight (W _L ) of the working soil is used. In the stochastic mode, the minimum, medium and maximum values are calculated Applies to unit weight (W _L ).

In deterministic mode, soil is classified into 12 forms: dry mud, wet mud, dry soil, wet soil, soil and gravel, dry gravel, wet gravel, limestone, rock, dry sand, wet sand and shale , And the unit weight of each soil (W _l , unit per cent) is defined as the value used in Peurifoy et al's (2006).

In the stochastic mode, a specific value between the minimum value and the maximum value is randomly extracted with a uniform probability, and the actual unit weight (W _L ) is defined as the adopted value. A density of 2,300 1b / 1cy experiment soil because collection productivity value from a productivity estimated curve to the maximum reference value correction factor (f _RC) of the working soil is divided into a 2,300 1b / 1cy from W _L is applied as shown in equation (4) .

The proficiency coefficient (f _O ) of the pilot is defined by the earthwork manager through the earthwork management terminal (200) based on the pilot's career and work performance evaluation. The earthwork management terminal 200 can automatically perform the work performance evaluation based on objective indicators such as the years of career, the number of work inputs, the construction scale, and the accident history for the pilot. The pilot skill level (0) is defined by the earthwork manager as three indicators (O: 1 = poor, 2 = average, 3 = excellent).

Depending on the type of drive, f _O in deterministic mode is either predefined or evaluated as a three point, or average, excellent. These three values are applied differently depending on the type of dozer used: crawler dozer or wheel dozer [0.6, 0.75, 1.0], [0.5, 0.6, 1.0].

On the other hand, in the stochastic mode, the system user extracts the triangular probability distribution based on the value of the pilot's ability, which is selected by subtle and advanced evaluation of the pilot's ability. For example, if the type of drive is 'track' and the pilot's skill level is assumed to be 0.8, then this value is higher than intermediate and lower than advanced. From this value, the system generates the variance by generating a random number (ie, rand ('Triangular', 0.6, 0.8, 1.0)).

The minimum and maximum limit values when the proficiency level is assumed to be 0.8 are set to 0.6 and 1.0. The pilot value of the pilot is selected by the system user in the stochastic mode and can be expressed by Equation (5).

Certain working gravels may not comply with the general characteristics of the gravel measured at the Nebraska Tractor Test Site.

In the case of soil type and condition (ST: 1 = loose stockpile, 2 = hard to cut or frozen with tilt cylinder, 3 = hard to cut or frozen without tilt cylinder, sticky), 5 = rock, ripped or blasted) to search for the soil correction factor (f _M ) proposed by Caterpillar (2010) based on the type of drive device and the type of working soil.

When the type of working soil and the type of driving device are determined, the soil adjustment factor f _M identifies a value that matches the information given in the working soil matrices F _{M in} Equation 6.

The dozer operation technique (f _OT ) is defined according to the type of operating technique (ie OT: 1 = standalone dozing alone, 2 = slot dozing, 3 = side-by-side dozing). Since the dozer operation technique is limited to three types, one of the values representing the corresponding type (OT) in the stochastic mode and the deterministic mode is selected as 1, 2, or 3.

In deterministic mode, f _OT searches and uses predefined values in Caterpillar (2010). This value (f _OT ) is calculated using the total amount of work to be performed by a specific operating technique and the correction coefficient of the operating technique, as shown in Equation (7). In the stochastic mode, f _OT computes the sum of the usage ratios of each operation technique per work area as 100%.

For example, given the workload of standalone dozing (R _S ), slot dozing (R _ST ) and side-by-side dozing (R _SS ) for each operating technique is 70%, 10%, 20% Uniform, 1.15, 1.25), f _OT is calculated as 0.7, 1 + 0.1, 0.8 + 0.2. The variability of f _OT , due to the uncertainty of the side-by-side dozing operating method, and the share of work sharing in this type of work are solved with a stochastic approach.

In deterministic mode, the visibility factor (f _V ) is defined based on data according to weather and sunshine conditions (ie, V: 1 = clearly visible, 2 = dust, rain, snow, fog, or darkness) 2010). In the stochastic mode, this value is recalculated to the LeCurd 3-point scale (ie, impaired, more or less impaired, clear). Depending on the type of driver (U _T ), an arbitrary value from the triangular distribution is generated, which is expressed as Equation 8 with the corresponding value (V _U ) of the visibility selected by the user.

In deterministic mode, the work efficiency coefficient (f _J ) is defined as the work activity time per hour divided by a fixed setpoint of 60. The work activity time can generate a random number based on a triangular probability distribution defined by a minimum value (T _min ), a median value (T _mod ), and a maximum value (T _max ), and in a stochastic mode, Acquire using the same method.

The gear shift coefficient (f _T ) is 1 when power shift is used and 0.8 when a direct drive is used, and according to the type of transmission in Equation (10), it is expressed as follows.

The blade correction coefficient f _B is defined according to the type of the blade (B _T : S = straight, U = universal, A = angle, SU = semiuniversal, C = cushion blade)

In deterministic mode, the slope correction factor (f _G ) is defined using the slope angle on the dosing path. The stochastic mode, as shown in Equation 12, the system user uses a triangular probability distribution as a tilt angle parameter based on (a minimum value (θ _min), median value (θ _mod), the maximum value (θ _max)) input generated Use an arbitrary value.

In the stochastic mode, these three outcomes are optimistic, the most likely, and the pessimistic, respectively.

Since the existing gradient correction method uses a graphical representation technique, the quality of the analysis is low. SDPE uses a mathematical function, the curve-fitting technique, to compensate for this problem.

Thus, it is possible to deal directly with the variable values of the gradient expressed in the graphical system and to list various calculation results from the functions used in the stochastic mode.

The combination correction coefficient f is calculated by calculating all the obtained task correction coefficients as shown in Equation 13. (S32)

The doser productivity P per hour is calculated by multiplying the ideal maximum productivity P _max by the combination correction factor f and expressed as Equation 14. S41:

Peurifoy et al. (2006), the swell factors by soil type are used as defaults if no specific data is input. If there are available observations available through site surveys, they can be used alternatively. At this stage, the unit of work can be reset according to the volume dimensions set by the earthwork manager.

)와 시간당 운전수노임(

)을 합산하여 시간당 도저 운영비(

)가 계산된다. 결정론적 모드에서 이 두가지 비용은 사전정의된 값을 기반으로 고정값으로 사용하고, 추계론적 모드에서 이 비용은 수학식 15에 나타나 있듯이 세 가지의 평가척도(낙관적인 비용(C_min), 가장 유력한 비용(C_mod), 비관적인 비용(C_max))중 하나를 선택하여 사용한다. 생산유닛당 직접비(C_D)는 수학식 16과 같이

를 시간당 도저 생산성으로 나눔으로써 계산된다.(S42)Operating expenses per hour (

) And hourly driving man (

) To calculate the operating cost per hour (

) Is calculated. In deterministic mode, these two costs are used as fixed values based on the predefined values, and in the stochastic mode, as shown in equation (15), the three rating measures (optimistic cost (C _min ) Cost (C _mod ), and pessimistic cost (C _max )). The direct ratio per production unit (C _D )

By the doser productivity per hour. (S42)

In DPEF calculation, when done by all of the repeating number of executions of the set simulation taking into account the environment correction coefficient described in the previous step, and collecting 120 per dozers productivity (P _S) and the cost per production unit (C _Ds). The minimum number of simulation runs for P _S and C _Ds , 120, is taken from the approach of Ang and Tang (1975) and Lee and Arditi (2006).

The stochastic dozer productivity evaluation system 100 can obtain a threshold value of 1.98 when the simulation execution number is 119, assuming that the confidence interval of the simulation result is 95% or more, and the simulation number is 120 times or more The threshold value is increased by a small amount. Since the threshold value is 1.96 at 95% confidence level of normal distribution, it is judged that the maturity level of the mean interval is achieved because the threshold value is satisfied when the simulation execution time becomes 120 times or more. The stochastic dozer productivity evaluation system 100 checks whether the minimum simulation execution frequency is 120 or less to see if it passes the maturity of the simulation result. That is, it is checked whether more than 120 iterations are required for the simulation (S43)

If the simulation results are matched, we can interpret P _S and C _Ds defined in the previous step from (1) normal probability density function and parameters, (2) optimal probability density and parameter values (S44, S45 )

The probability density function, expressed as f (x; θ), and the random variable X (ie P or C _D ) can be expressed as x ₁ , x ₂ , ... Calculate the maximum likelihood value from the observations (x ₁ , x ₂ , ... x _n ) generated from the θ values, with .x _n sample numbers.

Assuming that the likelihood of a particular sample value x _i is proportional to the value of the probability density function evaluated at x _i , the likelihood of n observations ((x ₁ , x ₂ , ... x _n ) is formulated as in equation .

)는 수학식 17을 최대화하는 값이며, 수학식 17, 즉 L(x₁ , x₂, … , x_n; θ)을 0에 가까운 도함수 θ에 대해 미분하여 수학식 18과 같이 나타낼 수 있고, 이로부터 절대 최대치를 획득한다(Hoel, 1962). 또한, 로그의 가산 성질에 따라 수학식 19와 같이 표현할 수 있다. Maximum likelihood estimate (

) Is a value maximizing Equation (17), and can be expressed as Equation (18) by differentiating Equation 17, i.e., L (x ₁ , x ₂ , ..., x _n ; From this, the absolute maximum is obtained (Hoel, 1962). Also, it can be expressed by the following equation (19) according to the addition property of the log.

)는 수학식 20과 같이 m개의 시뮬레이션 결과로 획득할 수 있다. 이러한 과정을 통해, 각각의 최대 우도 추정치를 지닌 확률분포 모델의 후보군들을 연산할 수 있다. The set of maximum likelihood estimates (

) Can be obtained as m simulation results as shown in Equation (20). Through this process, candidate groups of probability distribution models with maximum likelihood estimates can be computed.

The most probable probability distribution can be identified by measuring the fitness of candidate probability distribution models based on the commonly used chi-square test or negative log-likelihood technique.

The chi-square PDF is performed on a one-tailed basis on a sample size basis, as shown in Equation (21). Also, the threshold value of? Significance is represented by C _{1 ?,? As} shown in Equation 22, v is a degree of freedom constant, _and? (V / 2) represents a gamma function.

The negative log-likelihood technique finds the optimal probability distribution of the observed data. The parametric probability distribution is defined by expressing its probability density function as f (x, θ). Where x is a variable and θ is a parameter. When the variable x is set, PDF is used to calculate the likelihood of θ, denoted by f (θ | x). The joint likelihood of the parameters for the independent arbitrary sample x is calculated as shown in Equation 23 below. The MATLAB statistics toolbox provides a negative negative log-likelihood operation.

As a final step, P and C _D are computed in a deterministic mode, and the normal probability density function and its parameter values are computed by performing a simulation based on a normal probability density function. In addition, the optimal PDF and its parameter values are visualized through the execution of an optimal PDF-based simulation and presented to the system user.

By using optimal PDF and parameters (ρ1, ρ2, ρ3, ρ4 ) values for P and C _D, the user can determine the confidence interval for the hourly productivity and production cathedral direct costs and this for a particular value of P and C _D via It is possible to obtain a probability that can be achieved. (S46, S50)

Table 1 shows the target field conditions, correction factors, and cost information. The attributes (attribute names of major variables), the work conditions (work, site, equipment conditions, etc. including the dozer model name), and the associated correction factors are stochastic dozer productivity Is a variable name used in the evaluation system 100, and the input value represents a user input value directly input to the system.

FIG. 4 is a graph showing the probability of achieving productivity of a catamaran based on a user input value by a stochastic dozer productivity evaluation method according to an exemplary embodiment of the present invention.

In FIG. 4, the X axis represents the productivity per hour, and the Y axis represents the probability of achieving productivity, and the area inside one curve is one.

In the graph of FIG. 4, a curve (normality based simulation) obtained by performing 120 simulations based on a normal distribution, a curve of a result of performing 120 simulations of the optimal probability density function based on Equations 17 to 23, (Best fit PDF based Simulation), deterministic computed in deterministic mode is represented by a deterministic computed as a single value without variability of input values.

On the graph, the shaded area indicated above the critical value means the probability that the productivity of the dozer is above the critical value in the actual work, and it provides the user with this probability to grasp the probability of success.

For example, an area ratio of a cumulative probability distribution area of 113.48 or more, which is a deterministic value, is calculated. If the area ratio is 50%, one doser has a productivity of 50% f, so one more doser can be planned. On the other hand, if the area ratio is 80%, one dozer will have 80% productivity within that period, so that the construction period can be extended without additional doser.

FIG. 5 is a graph illustrating a probability of achieving a direct cost per productivity unit based on a user input value by a stochastic dozer productivity evaluation method according to an exemplary embodiment of the present invention.

In FIG. 5, the X-axis represents the direct cost per production line, and the Y-axis represents the probability of accomplishment.

The graph of FIG. 5 includes a curve of Normality based Simulation, a curve of Best fit PDF based Simulation, and a deterministic value indicated by a vertical line, as in the graph of FIG.

On the graph, the shaded area above the critical value means the probability that the direct cost per production unit in the actual work will be above the critical value, and this probability is provided to the user to grasp the probability of success.

As described above, the stochastic dozer productivity evaluation method of the present invention can provide the probability of actual dozer productivity and failure risk by reflecting experimental data and field condition data provided by the manufacturer. Therefore, it is possible to compensate the productivity of the dozer considering the productivity variable that may occur in the earthwork field, and to support the establishment of the equipment operation plan suitable for the site.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims It can be understood that

100: Probabilistic doser assessment system
110: deterministic mode execution module
120: Stochastic mode execution module
130: memory

Claims (20)

A method for evaluating a stochastic dozer productivity performance by a stochastic dozer productivity evaluation system that integrates a format for evaluating dozer productivity (Dozer Productivity Estimating Format, DPEF) and a stochastic productivity modeling and analysis process,
comprising the steps of: a) obtaining a parameter of the system including predetermined doser productivity, performance data and attribute data of the doser;
b) calculating a combination correction coefficient by combining ideal maximum productivity and working environment correction coefficients in a deterministic mode and a stochastic mode, respectively;
c) calculating dozer productivity per hour based on said calculated ideal maximum productivity and combination correction factor in deterministic mode and stochastic mode, respectively, and calculating direct cost per production unit based on said calculated doser productivity per hour and doser operating cost per hour Calculating;
d) calculating a probability of the dozer productivity achievement by comparing the dozer productivity per second calculated in the stochastic mode on the basis of the first dozer productivity calculated in the deterministic mode, Comparing the direct costs per second production unit calculated in the stochastic mode on the basis of the direct costs per first production unit to calculate a probability for the achievement range of direct costs per production unit,
Wherein the deterministic mode is applied to a dozer productivity evaluation format (DPEF), the stochastic mode estimates the volatility of output results of the dosing operation and the associated cost risk by applying field condition data,
The step b)
Obtaining a productivity optimal fit curve by applying a curve approximation method that estimates approximate points between discrete values on the basis of the predetermined doser productivity and performance data to calculate the ideal maximum fit curve based on the obtained productivity optimum fit curve, Productivity is obtained and workplace correction factors for productivity variables including terrain condition, working condition, and equipment condition of the earthwork are used,
The step d)
Wherein the deterministic mode calculates the first doser productivity and the direct cost per the first production unit as a single value and the stochastic mode calculates the direct cost per second production unit and the direct production cost per second production unit, Calculating an area of a probability distribution curve of the doser productivity per second over a single value of the doser productivity per the first time as a probability for the dozer productivity achievement range, Wherein the area of the probability distribution curve of the direct cost per second production unit that is at least a single value of the production cost per unit of production is calculated as a probability with respect to the achievement range of the direct cost per production unit.
The method according to claim 1,
The step a)
Wherein the attribute data of the doser includes attribute values of a tractor type, an engine type, a drive type, a number of driving wheels, a blade type, and stores the attribute values in a dozer information matrix,
Wherein the attribute value of the terrain including the inclination angle and the distance on the work path measured by using the laser measuring instrument is stored in the memory and attribute values of the terrain are stored in the memory.
The method according to claim 1,
In the step b), the ideal maximum productivity P _max is obtained by obtaining the dozer model D _T , the blade type B _T , the productivity optimum fitting curve F _T , and the working distance D, Obtained by using
[Mathematical Expression]

A method for evaluating a stochastic dozer productivity.
The method according to claim 1,
The work environment correction coefficients in the step b)
In the deterministic mode, the correction coefficient (f _RC ), the pilot's skill factor (f _O ), the soil correction factor (f _M ), the dozer operation technique (f _OT ) , The visibility coefficient (f _V ), the working efficiency coefficient (f _J ), the gear shift coefficient (f _T ), the blade correction coefficient (f _B ) and the inclination correction coefficient (f _G )
Wherein a stochastic model is used to stochastically change a working environment correction factor by applying a probability density function of productivity variables assuming that each condition of the earthwork field is an uncertain change state in the stochastic mode.
5. The method of claim 4,
In the step b), the combination correction coefficient f is calculated by using the following mathematical expression
[Mathematical Expression]

A method for evaluating a stochastic dozer productivity.
The method according to claim 1,
In step c), the doser operating cost per hour

) Is an operating cost per hour (

) And hourly driving man (

) Is calculated by summing up the estimated doser productivity.
The method according to claim 6,
In the deterministic mode, the operating cost per hour and the driver's driving per hour use a fixed value based on a preset value,
Wherein one of the evaluation scales of the optimistic cost (C _min ), the most probable cost (C _mod ), and the pessimistic cost (C _max ) is selected and used in the stochastic mode.
delete The method according to claim 1,
The step c)
c-1) collecting the dozer productivity per hour and direct costs per production unit by repeating a predetermined number of times of simulation execution;
c-2) comparing the simulation execution count with a minimum simulation execution count to check the maturity of the simulation result;
c-3) increasing the number of simulations to be executed more than the minimum simulation execution time if the simulation result does not satisfy the maturity; And
c-4) calculating the per-hour doser productivity and the direct cost per production unit from the values of the normal probability density function and the parameters, the optimal probability density function and the parameters if the simulation result meets the maturity Wherein the method comprises the steps of:
10. The method of claim 9,
In the step c-4)
Using the values of the optimal probability density function and the parameters thereof to determine a confidence interval for the doser productivity per hour and the direct cost per production unit and using the determined confidence interval to calculate the required doser productivity per hour from the user, And providing a probability to the user of the achievement range of the direct cost per user.
5. The method of claim 4,
The skill factor coefficient (f ₀ ) of the pilot is defined through a mission management terminal based on the pilot's career and the performance evaluation of the pilot. The mission management terminal includes the pilot's career years, the number of work inputs, And evaluating the performance of the work based on the objective indicators automatically.
5. The method of claim 4,
The proficiency coefficient (f _O ) of the pilot uses either a method of using a predefined value or a method of evaluating the three point ReCurd scale depending on the type of drive device in a deterministic mode, And a triangular probability distribution is generated and extracted based on values preliminarily evaluated as lower and higher.
delete delete delete delete delete delete 5. The method of claim 4,
The blade correction coefficient f _B is defined by the following formula according to the type of blade (B _T : S = straight, U = universal, A = angle, SU = semiuniversal, C = cushion blade)
[Mathematical Expression]

A method for evaluating a stochastic dozer productivity.
delete

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