KR20140065521A - Method and apparatus for analyzing of construction process statistically - Google Patents

Abstract

The present invention relates to a statistical process analysis method, and a statistical process analysis method according to an embodiment of the present invention is a statistical process analysis method in which, through an RFID reader installed in a moving path of a material on which an RFID tag is mounted, The method of claim 1, further comprising the steps of: acquiring process information; generating an overall process model using the first process information; generating a plurality of process models according to a predetermined classification condition; Generating detailed process information by combining the second process information corresponding to the detailed process model and the productivity information of the detailed process model among the process information and generating detailed process information on the generated detailed process information, And generating standardized information using the standard deviation value and the average value.
Thus, optimal process conditions can be obtained by simulating individual sub-processes prior to process execution to determine the statistical position of cost or productivity according to process conditions.

Description

≪ Desc / Clms Page number 1 > METHOD AND APPARATUS FOR ANALYZING OF CONSTRUCTION PROCESS STATISTICALLY}

TECHNICAL FIELD The present invention relates to a statistical process analysis method, and more particularly, to a technique for statistically analyzing a construction process.

Today, the demand for high-rise construction projects is increasing, and as the height of high-rise buildings increases, the construction methods are becoming more diverse. The curtain wall method, which is used for constructing a skyscraper, is replacing the existing exterior construction method in terms of structure, function, and aesthetic due to various advantages such as its own weight and lightness. For a successful curtain wall construction, thorough and efficient process control is needed. However, the current process management at the construction site depends on the experience of the field manager, and there is a lack of reliable and objective grounds.

Continuous and accurate performance analysis and prediction should be performed to efficiently perform process management. Simulation method is used as a tool to predict construction productivity. However, simulation has great difficulties in application to the field. The simulation method is difficult to reflect the actual situation and requires a skilled engineer to construct and apply it. In addition, there is a problem that it is very difficult to apply changing scene conditions in real time.

The technology to be a background of the present invention is disclosed in Korean Patent Registration No. 10-0984514 (registered on Mar. 19, 201).

The technical problem to be solved by the present invention is to provide a statistical process analysis method and an analysis apparatus for obtaining optimal process conditions by simulating individual detailed processes before execution of a process to determine statistical positions of cost or productivity according to process conditions .

A statistical process analysis method according to an embodiment of the present invention includes the steps of obtaining first process information that the material is used for an entire process through an RFID reader installed in a moving path of a material on which an RFID tag is mounted, A step of generating an overall process model using the process information, a step of generating the entire process model as a plurality of detailed process models according to a predetermined classification condition, Generating second detailed process information by combining the first process information and the second process information with the productivity information of the detailed process model, and generating standardization information by using the standard deviation value and average value of the detailed process information generated for the generated detailed process information, .

The step of generating the standardization information may display the generated standardization information in a predetermined range on a standard normal distribution map for the detailed process information.

The step of generating the standardization information may generate the standardization information Z using the following equation:

Where X is the generated detailed process information, μ is a pre-generated average value of the detailed process information, and σ is a pre-generated standard deviation value of the detailed process information.

The first process information or the second process information may include resource information or utilization time information of the material used in the process.

In addition, the classification condition may include a resource or a position or a movement route of the material used in the process.

The statistical process analyzing apparatus according to another embodiment of the present invention includes a process information acquiring unit that acquires first process information that the material is used in an entire process through an RFID reader installed in a movement path of a material on which an RFID tag is mounted An overall process model generation unit for generating an overall process model using the first process information, a detailed process model generation unit for generating the entire process model in a plurality of detailed process models according to a predetermined classification condition, A detailed process information generating unit for generating detailed process information by combining the second process information corresponding to the detailed process model and the productivity information of the detailed process model among the one process information, And a standardization information generator for generating standardization information using standard deviation values and average values of the detailed process information.

Thus, optimal process conditions can be obtained by simulating individual sub-processes prior to process execution to determine the statistical position of cost or productivity according to process conditions.

1 is a configuration diagram of a statistical process analyzing apparatus according to an embodiment of the present invention;
FIG. 2 is a flow chart of a statistical process analysis method using the statistical process analyzer according to FIG.
FIG. 3 is an exemplary diagram for explaining acquisition of process information using an RFID tag and a reader in the statistical process analysis method according to FIG. 2;
FIG. 4 is an exemplary diagram for explaining generation of a detailed process model among the statistical process analysis methods according to FIG. 2;
FIG. 5 is an exemplary diagram for explaining generation of standardization information on detailed process information among statistical process analysis methods according to FIG. 2. FIG.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The terms used are terms selected in consideration of the functions in the embodiments, and the meaning of the terms may vary depending on the user, the intention or the precedent of the operator, and the like. Therefore, the meaning of the terms used in the following embodiments is defined according to the definition when specifically defined in this specification, and unless otherwise defined, it should be interpreted in a sense generally recognized by those skilled in the art.

FIG. 1 is a configuration diagram of a statistical process analyzing apparatus according to an embodiment of the present invention, and FIG. 2 is a flowchart of a statistical process analyzing method using the statistical process analyzing apparatus according to FIG.

1 and 2, a statistical process analysis apparatus 100 according to an embodiment of the present invention includes a process information obtaining unit 110, an overall process model generating unit 120, a detailed process model generating unit 130, A detailed process information generation unit 140, and a standardization information generation unit 150.

The process information obtaining unit 110 obtains the first process information that the material is used for the entire process through the RFID reader installed on the movement path of the material on which the RFID tag (Radio Frequency Identification) is mounted (S200). The process information obtaining unit 110 stores the obtained first process information in the process DB 115. [ The term " process " is used to broadly encompass a construction process, a manufacturing process, and the like. Materials used in the process are equipped with RFID tags, and movement information of the materials can be confirmed through a plurality of RFID readers installed on the field. For example, the first process information may include resource information or utilization time information of the material used in the process. Hereinafter, a method for acquiring process information will be described with reference to FIG.

3 is an exemplary diagram for explaining acquisition of process information using an RFID tag and a reader in the statistical process analysis method according to FIG.

Referring to FIG. 3, an RFID tag 310 is attached to each material 300. For example, when the material 300 is a reinforcing structure, the RFID tag 310 may be attached to the outside, and when it is difficult to attach the RFID tag 310 such as sand, gravel, and cement, An RFID tag 310 may be attached. In addition, the RFID tag 310 stores different identification information according to each material 300. It is preferable that a plurality of RFID readers 320 are installed in the field, and in particular, the RFID reader 320 is installed in a path through which the material 300 moves. The RFID reader 320 senses the RFID tag 310 attached to the material 300 in real time, thereby detecting resource information indicating the type of the material 300 or moving time of the material 300 in the field, Time and the like, and transmits it to the process information obtaining unit 110 through the repeater 330.

For example, when the material 300 to which the RFID tag 310 is attached is moved from the position A to the position B, the process information obtaining unit 110 determines that the RFID reader 320 installed at the position A 300 of the material 300 can be measured by calculating the interval between the time of sensing the material 300 and the time of sensing the material 300 by the RFID reader 320 installed at the B position. In this case, the process information obtaining unit 110 can grasp the use information of the material 300 according to the installation place of the RFID reader 320. [ For example, when the process information obtaining unit 110 receives the process information on the material 300 that does not move for a predetermined period of time from the RFID reader 320 installed at a place where the material 300 is temporarily loaded, If it is determined that the material 300 is loaded for temporary storage and process information for the material 300 that does not move for a predetermined period of time is received from the RFID reader 320 installed at a location where the material 300 is actually used, As shown in FIG.

Referring again to FIGS. 1 and 2, the overall process model generation unit 120 generates an overall process model using the first process information stored in the process DB 115 (S210). Here, the whole process model means a simulation model for the whole process. This is generated by combining the respective process elements. When the entire process model is simulated, the productivity information for the process can be obtained. The first process information includes resource information or utilization time information of materials collected through an RFID reader installed in the field. The overall process model generation unit 120 generates the entire process model by using a construction simulation technique or the like for the first process information.

Next, the detailed process model generation unit 130 generates a plurality of detailed process models according to a predetermined classification condition (S220). A sub-process is an individual process unit that makes up the entire process. Several unit processes can be combined according to the classification conditions, including the resource or location of the material used in the process, or the movement path. And a detailed process model is a simulation model in which such a detailed process is constructed using, for example, a construction simulation technique. Hereinafter, the detailed process model will be described with reference to FIG.

FIG. 4 is an exemplary diagram for explaining generation of a detailed process model among the statistical process analysis methods according to FIG. 2. FIG.

Referring to FIG. 4, the entire process model 400 can block the entire process into several unit processes based on the first process information. In this case, the entire process model 400 can be subdivided into a plurality of sub-process models 410. For example, the number of detailed process models 410 may be set differently according to the classification conditions, including the resource or location or travel path of the material used in the process. For example, if the condition for classifying the detailed process model 410 is a resource, the entire process model 400 can be subdivided according to the process in which the concrete block is used and the process in which the rebar is used. In addition, when the sorting condition is the installation position of the material, the entire process model 400 can be subdivided into processes in the zones in advance. In this case, each detailed process model 410 can obtain information about productivity in each detail process through simulation individually.

Referring to FIGS. 1 and 2 again, the detailed process information generator 140 combines the second process information corresponding to the detailed process model among the first process information and the productivity information of the detailed process model to obtain detailed process information (S230). The second process information may include resource information or utilization time information of the material used in the detailed process subdivided according to criteria for classifying the detailed process model among the first process information obtained through the process information obtaining unit 110 . The productivity information of detailed process model shows the result of simulating detailed process model using construction simulation technique. The detailed process information generation unit 140 stores the detailed process information, which is the combination of the second process information and the productivity information of the detailed process model, in the process DB 115.

Next, the standardization information generator 150 generates standardization information using the standard deviation value and the average value of the generated detailed process information with respect to the generated detailed process information. For example, the normalization information generator 150 may generate the standardization information Z by using the following equation (1).

In Equation (1), X represents the generated detailed process information, μ represents the pre-generated average value of the detailed process information, and? Represents the pre-generated standard deviation value of the detailed process information. In this case, a standard deviation value and an average value can be obtained for a plurality of detailed process information stored in the process DB 115. [

In addition, the standardization information generator 150 may display the generated standardization information in a predetermined range on the standard normal distribution map for the detailed process information. For example, the numerical values of productivity and unit process costs among the detailed process information are different from each other, so objective comparison is impossible. However, when this is displayed on the standard normal distribution map, data having different units can be compared. A detailed description thereof will be described with reference to FIG.

FIG. 5 is an exemplary diagram for explaining generation of standardization information on detailed process information among statistical process analysis methods according to FIG. 2. FIG.

Referring to FIG. 5, productivity and cost required to complete the unit process, which are the main object of interest, can be represented by a standard normal distribution diagram. The standard data for productivity can be represented as shown in Table 1 below, and the standard data for the unit process cost can be shown in Table 2 below. In this case, the unit process cost can be obtained using (labor cost + equipment cost (won / hour)) / productivity (unit / hour) + material cost (won / unit), but is not limited thereto.

Scene WINCH Upper worker Lower worker Productivity (unit / hour) A One One One 4.182 B One One One 3.660 C One One One 4.656 A 2 2 2 8.664 B 2 2 2 8.016 C 2 2 2 9.552

Scene WINCH Upper worker Lower worker Cost (won / unit) A One One One 1515898.13 B One One One 1518165.57 C One One One 1514279.64 A 2 2 2 1515347.64 B 2 2 2 1516588.32 C 2 2 2 1513920.85

Table 1 and Table 2 show that the productivity and cost data are different depending on the equipment or manpower used in the same field, and the productivity data and the cost data are different from each other, so that the relative comparison becomes difficult. When the detailed process information of Table 1 and Table 2 are standardized, they can be represented as Tables 3 and 4 respectively.

Scene WINCH Upper worker Lower worker Standardization score location A One One One -1.00754 15-20% B One One One -1.12204 10 to 15% C One One One -1.90357 15-20% A 2 2 2 -1.96411 15-20% B 2 2 2 -1.01543 15-20% C 2 2 2 -1.87856 15-20%

Scene WINCH Upper worker Lower worker Standardization score location A One One One -0.89953 15-20% B One One One -0.71401 20-25% C One One One -1.03196 15-20% A 2 2 2 0.096836 50 to 55% B 2 2 2 0.226487 55 to 60% C 2 2 2 -0.09098 45 to 50%

In Tables 3 and 4, comparison between the two indices is possible using the standardized scores, which are objective comparative figures for productivity and cost, as shown in Tables 1 and 2. In FIG. 5, the standardization score, which is the standardization information about the productivity and the cost, which is detailed process information, can be displayed on the standard normal distribution map.

For example, if the number of equipment in a particular process is 2 and the number of workers is 4, the productivity of the process is about 55 ~ 60% of the productivity of the whole process, and the cost is 10 To 15%. In this case, the number of equipment or the number of workers employed by the user setting can be changed, and the standard normal distribution of productivity and cost under the changed conditions also changes. Accordingly, the user can determine the optimum process productivity and cost while viewing the standard normal distribution map according to the standardization information while changing the process conditions.

As described above, according to the embodiment of the present invention, optimal process conditions can be obtained by simulating individual sub-processes before the process is executed to determine statistical positions of costs or productivity according to process conditions.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, Therefore, the present invention should be construed as a description of the claims which are intended to cover obvious variations that can be derived from the described embodiments.

100: Optimization process analyzer
110: Process information acquisition unit
115: Process DB
120: overall process model generation unit
130: detailed process model generation unit
140: detailed process information generating unit
150: Standardized information generation unit
300: Material
310: RFID tag
320: RFID reader
330: Repeater
400: Full process model
410: Detailed process model

Claims (10)

A statistical process analysis method using a statistical process analyzer,
Acquiring first process information that the material is used for an entire process through an RFID reader installed in a movement path of a material to which the RFID tag is attached;
Generating an overall process model using the first process information;
Generating the entire process model as a plurality of detailed process models according to predetermined classification conditions;
Generating detailed process information by combining second process information corresponding to the detailed process model and productivity information of the detailed process model among the first process information; And
And generating standardization information using the standard deviation value and the average value of the detailed process information generated for the generated detailed process information. The method according to claim 1,
Wherein the step of generating the standardization information comprises:
And displaying the generated standardization information in a predetermined range on a standard normal distribution map for the detailed process information. The method according to claim 1,
Wherein the step of generating the standardization information comprises:
A statistical process analysis method for generating the standardization information (Z) using the following equation:

Where X is the generated detailed process information, μ is a pre-generated average value of the detailed process information, and σ is a pre-generated standard deviation value of the detailed process information. 2. The method according to claim 1, wherein the first process information or the second process information includes:
And resource information or utilization time information of the material used in the process. 2. The method according to claim 1,
And a resource or location or movement path of the material used in the process. A process information acquiring unit for acquiring first process information that the material is used for an entire process through an RFID reader installed in a movement path of a material on which the RFID tag is mounted;
An overall process model generation unit for generating an overall process model using the first process information;
A detailed process model generation unit for generating the entire process model as a plurality of detailed process models according to predetermined classification conditions;
A detailed process information generation unit for generating detailed process information by combining the second process information corresponding to the detailed process model and the productivity information of the detailed process model among the first process information; And
And a standardization information generator for generating standardization information using the standard deviation values and average values of the detailed process information generated for the generated detailed process information. The method according to claim 6,
Wherein the standardization information generation unit comprises:
And displays the generated standardization information in a predetermined range on a standard normal distribution map for the detailed process information. The method according to claim 6,
Wherein the standardization information generation unit comprises:
A statistical process analyzing apparatus for generating the standardization information (Z) using the following equation:

Where X is the generated detailed process information, μ is a pre-generated average value of the detailed process information, and σ is a pre-generated standard deviation value of the detailed process information. 7. The method of claim 6, wherein the first process information or the second process information includes:
And resource information or utilization time information of the material used in the process. 7. The method according to claim 6,
And a resource or position or movement path of the material used in the process.

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