JPH0863504A - Work man-hour prediction support system and method for deciding work man-hour prediction approximate expression used in the system - Google Patents

Abstract

PURPOSE: To provide a work man-hour prediction support system which can easily and speedily predict the work man-hour of an object based on design data of the object. CONSTITUTION: At least data on the first kind of noted factor which is known to have correlation with man-hour is extracted among information included in a work completion design data file 112. Results man-hour values being known man-hour which are required for the assembly of respective parts are respectively added to the data. The value of the first kind of the noted factor and the given value of corresponding man-hour are read for the respective kinds of the parts. A regression curve is obtained by using the values and a man-hour prediction approximate expression is defined. Then, it is stored in a man-hour prediction approximate data file 115. The man-hour prediction approximate expression is used for design data of the object which is to be assembled, and man-hour is predicted.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a work man-hour prediction support system for assisting in predicting an assembly man-hour of an object assembled from a plurality of parts based on design data, and a work man-hour used in this system. The present invention relates to a method of determining a prediction approximation formula.

[0002]

2. Description of the Related Art Facilities such as a nuclear power plant, a thermal power plant, and a chemical plant are objects to be assembled as an aggregate of many parts including many kinds of parts, and design data is prepared for the assembly. To be done. That is, the design data gives an instruction as to what part to use, where and how to install it. In the actual assembly, a process plan is made based on the design data, and the work is advanced according to the process plan.

Conventionally, in this type of process plan, the design data is regarded as already given, as shown in, for example, Japanese Unexamined Patent Publication No. 4-321171, "Process Plan Creation Device". Therefore, workability in the assembly process has not been taken into consideration at the time of design.

Recently, it has been considered to create an optimal process plan using a computer. In this method, first, the input data necessary for the process plan, for example, the work man-hours and the constraint conditions are created on a desk in advance, manually input to the computer, and then the order of the processes is changed. ,
This is done by lowering the peak from the heap graph of the entire workforce. In this method, the detailed order of the steps in the process plan and weak constraints (constraints that must be strictly obeyed are called strong constraints, and constraints that should be obeyed rather are called weak constraints. Yes) is a method for achieving optimization.

[0005]

By the way, as described above, the man-hours are prepared on the desk in advance before the process plan is processed. Therefore, no matter how the subsequent process plan is calculated, the total man-hours required for the work remain stored. Therefore, the above process plan has a problem that the amount of work performed by the worker does not change. In this case, in order to reduce the man-hour, it is necessary to return the result of the man-hour assignment to the design department and make a design change to reduce the man-hour. However, in reality, the design data has not been changed for the purpose of reducing man-hours. It is considered that this is due to the following reasons, in addition to the lack of the idea of evaluating design data from the viewpoint of workability.

[0006] Conventionally, when design data is given, an expert has given a man-hour based on the knowledge and experience. However, in the case of an object such as a plant, since there are many parts, this work is very time-consuming. Therefore, after the design data is sent from the design department, the man-hours are set,
Returning the result to the design department, re-designing, and setting the man-hour again is difficult in terms of time because the start time of the assembly is delayed. Therefore, conventionally,
It is probable that the design data was not evaluated from the viewpoint of workability and changed to an appropriate design.

A first object of the present invention is to provide a work man-hour prediction support system which can easily and quickly predict the work man-hours of an object based on the design data of the object. A second object of the present invention is to provide a method for determining a work man-hour prediction approximate expression used for carrying out work man-hour prediction.

[0008]

According to one aspect of the present invention for achieving the above first object, the number of work steps for an object to be assembled as an assembly of a plurality of parts including a plurality of kinds of parts. A work man-hour prediction support system for supporting prediction, comprising: a storage device for storing programs and data; and a central processing unit for executing a program stored in the storage device to perform man-hour prediction support processing. And at least an input device for receiving an instruction input and data input to the central processing unit, and an output device for outputting a result of information processing, and the storage device is an object for which an assembly work is completed. For each of the parts that make up the object, the part identification information that specifies the part, the assembly position information that indicates the installation position of the part, and the size of the part In order to store a work completion design data file that stores design data including at least the information shown below, organized by each item set in advance for each of the above information, and information that defines a man-hour prediction approximation formula for obtaining a predicted man-hour And a man-hour prediction approximate expression file of, and the information processing apparatus reads the work-completion design data from the work-completion design data file, and correlates with the man-hours from the information included in the work-completion design data. It is known that there is at least one type of attention factor extraction means for extracting the data of the first type attention factor, and for each part, the actual man-hour value that is the known man-hour required for assembling the part is given. Means, and for each part, including at least the information for specifying the part, the information on the factor of attention of the first type, and the assigned actual man-hour value, A means for organizing by type and storing it in a storage device as a sub-file, and reading the value of the above-mentioned first-type attention factor and the value of the corresponding man-hours assigned for each component type from the sub-file However, the regression curve is calculated using those values, and the information that defines the regression curve is determined as the approximation formula stored in the above-mentioned man-hour prediction approximation formula file as information that defines the man-hour prediction approximation formula for the component type. Means and design data that should be used to predict the man-hours are read, and for each part, the above noted factors are extracted from the design data included in it,
And, read the information defining the man-hour prediction approximate expression for the part type to which the part belongs from the man-hour prediction approximate expression file, set the man-hour prediction approximate expression, and set the value of the attention factor to the man-hour prediction approximate expression. There is provided a work man-hour prediction support system, characterized in that it comprises means for substituting and calculating a predicted man-hour.

Further, according to another aspect of the present invention, there is provided a work man-hour prediction support system for supporting work man-hour prediction for an object to be assembled as an assembly of a plurality of parts including a plurality of types of parts. A storage device for storing a program and data, a central processing unit for executing a program stored in the storage device to perform a man-hour prediction support process, an instruction input to the central processing unit, and a data At least an input device for accepting an input and an output device for outputting a result of information processing are provided, and the storage device stores information for respectively defining a plurality of man-hour prediction approximation formulas for obtaining a predicted man-hour. Equipped with a man-hour prediction approximate expression file for storing, the man-hour prediction approximate expression stored in the man-hour prediction approximate expression file is the target that was assembled in the past. About the relationship between the installation height and the man-hours of the parts, which is obtained, is a regression line obtained for each part type, and the information processing device reads the design data for which the man-hours should be predicted, and for each part , A means for extracting the attention factor from the design data included therein, and information for defining the man-hour prediction approximate expression for the part type to which the part belongs is read from the man-hour prediction approximate expression file, and the man-hour prediction approximate expression is read. Is set and the value of the factor of interest is substituted into this man-hour prediction approximate expression to obtain a predicted man-hour, and a work man-hour prediction support system is provided.

Further, according to one aspect of the present invention for achieving the second object of the present invention, the work man-hours for an object to be assembled as an assembly of a plurality of parts including a plurality of kinds of parts are predicted. A method for determining an approximate expression of work man-hours for performing the assembly work, and for an object for which assembly work has been completed, for each part that constitutes the object, part identification information that identifies the part, and installation of the part. Assembling position information indicating the power position and design data including at least information indicating the size of the relevant part are taken in, and from the information included in the work completion design data, the attachment of the component is a first-type attention factor. Using the information indicating the height as the second type of attention factor, the information indicating the size of each component is extracted, and for each component, the actual man-hour value which is the known man-hour required for assembling the component is calculated. For at least one of the extracted second type attention factors, a representative value is determined by dividing the value of the attention factor into a certain range, and design data is determined for each component type. Each value is divided into classes, and the approximation formula determining means obtains a regression curve for each class of each component type, and information defining the regression curve is calculated by a man-hour prediction approximation formula for the class of the component type. A work manpower prediction approximate expression determination method is provided, which is characterized in that it is obtained as information defining

[0011]

In the system of the present invention, in order to predict the man-hours,
Based on the design data of the object that has already been assembled, the man-hour prediction approximation formula is obtained in advance. Then, the man-hours of the design data of the object to be assembled are predicted by using this man-hour prediction approximate expression. Further, in the present invention,
The man-hour prediction approximation formula used for prediction is determined as follows.

These processes are performed by using the information processing device and the storage device.

For a target object for which assembly work has been completed, the storage device stores, for each part that constitutes the target object, part identification information that specifies the part, assembly position information that indicates the installation position of the part, and A work completion design data file that stores design data including at least information indicating size, arranged and stored for each item set in advance for each of the above information, and information that defines a man-hour prediction approximate expression for obtaining a predicted man-hour. And a man-hour prediction approximate expression file for storing.

In the information processing apparatus, various means realized thereby support the determination of the approximate expression and the man-hour prediction using the approximate expression.

First, the factor of interest extraction means reads out the work completion design data from the work completion design data file,
From the information included in the work completion design data, data of at least one first-type attention factor that is known to have a correlation with the man-hours is extracted. Further, the means for assigning the actual manhour values respectively imparts to each part, the actual manhour values which are known manhours required for assembling the parts. This assignment can be performed, for example, by receiving a man-hour value given by an expert via an input device. Moreover, you may input a measured engineering value.

The assigned manhour value is stored together with other information as a subfile in the storage device. In the subfile, for each part,
At least the information for identifying the part, the information of the first-type attention factor, and the assigned actual man-hour value are included, and these are sorted and stored according to the part type. The approximate expression determining means reads out the value of the above-mentioned factor of interest of the first type and the value of the corresponding man-hours given above for each component type from the sub-file, and uses the values to calculate the regression curve. And the information defining the regression curve is stored in the man-hour prediction approximate expression file as information defining the man-hour prediction approximate expression for the component type. This gives an approximate expression.

The means for obtaining the estimated man-hours reads the design data for which the man-hours should be predicted, extracts the above noted factors from the design data contained in each part,
And, read the information defining the man-hour prediction approximate expression for the part type to which the part belongs from the man-hour prediction approximate expression file, set the man-hour prediction approximate expression, and set the value of the attention factor to the man-hour prediction approximate expression. Substituting to obtain the estimated man-hours.

As described above, in the present invention, the man-hour prediction approximation formula is determined by using the design data of the object assembled in the past separately from the design data of the object for which the man-hour should be predicted. , When the design data of the object for which the man-hours should be predicted is completed, by using the man-hour prediction approximation formula,
The man-hours can be easily predicted in a short time. Therefore, it is possible to quickly return the man-hour prediction result to the design department and redo the design. Also, even in the middle of design,
Since the man-hours can be predicted for the part where the design is fixed, the work man-hours can be taken into consideration at the design stage. Also, when estimating the construction cost, if there is design data,
It will be possible in a short time.

Since the past design data and the new design data are generally different from each other, it is not always possible to predict the man-hours for all parts. But,
Even if it is an object with many types and many parts,
There are some parts that are commonly used.
It is considered that objects of similar format have a high degree of commonality of parts and a high degree of commonality of design. Therefore, if there is a man-hour prediction approximation formula for an object close to the target object, man-hour prediction can be performed for many parts. Then, for a part for which a corresponding approximate expression is not prepared, a man-hour may be input by an expert as in the conventional case. Even if such a manual input is made, it is only a part, and the time required for it is much shorter than that of the case where the whole is done manually.

According to the present invention, even if the manual input by an expert is included in part, the man-hour prediction for most of the parts is performed by a prepared man-hour prediction approximation formula. Therefore, the man-hours can be predicted quickly and easily.

[0021]

Embodiments of the present invention will be described below with reference to the drawings. It should be noted that the present embodiment is an example of a system that can be applied to design support of a plant having piping, equipment, and the like. In this example, more specifically, a nuclear plant,
Prediction used to evaluate design data used when constructing plants such as power plants and chemical plants where pipes and equipment are installed in buildings from the viewpoint of work such as installation and assembly. An example of calculating the man-hour will be described. Of course, the present invention is not limited to the following examples.

FIG. 1 shows an example of the configuration of a hardware system used in the embodiment of the present invention. In FIG. 1, the hardware system used in this embodiment is
The information processing device main body 100, the external storage device 110, the input device 120, the output device 130, the flexible disk device 140, and the communication control device 150 are provided.

The information processing apparatus main body 100 is a computer including a central processing unit (CPU) 101 and a main memory 102 as main elements. An external storage device 110, an input device 120, and
Output device 130 and flexible disk device 140
And the communication control device 150 are connected.

The external storage device 110 is composed of, for example, a hard disk device. The number of hard disk devices is not limited to one, but a plurality of hard disk devices can be used. However, the external storage device 110 is not limited to the hard disk device. A storage device using another storage medium can be used. For example, a large-capacity optical disk device, a RAM disk device, or the like can be used instead of or together with the hard disk device.

In the external storage device 110, a program file 111 in which programs such as an operating system and the support system of this embodiment are stored, and already installed,
The construction completion design data file 112 in which the design data of the object for which construction such as assembly is completed is stored, the prediction target design data file 113 in which the design data of the object for which the man-hours are to be predicted are stored, and this system are used. A man-hour prediction approximate expression file 114 for storing information for defining a man-hour prediction approximate expression for predicting man-hours,
Predicted man-hour data file 115 for storing the predicted man-hour data, work file 116 for temporarily storing various sub-files created when the system is operating, data used for making a process plan, and the data And a process plan data file 117 for storing the process plan data established by the above.

In this embodiment, each file is managed by the operating system. When performing data processing, the support system of the present embodiment moves all or part of the data stored in those files to the main memory 102 for processing. When new data that does not exist in the main memory 102 is needed, the data is transferred from the corresponding file to the main memory 102 by the operating system. That is, the support system of the present embodiment can process the data of the file as if they were all in the main memory 102.

In the construction completion design data file 112,
Design data for the completed construction object is stored. This data is converted to digital data. In this embodiment, an object is a plant, and an example in which design data therefor is stored is shown. The design data dealt with in this embodiment is data for constructing a plant by installing and assembling pipes, equipment, etc. in a building that already exists. FIG. 2 schematically shows a part thereof. That is, FIG. 2 shows a work area A _ij that constitutes a basic unit for performing an assembly work in a space where a plant as an object is installed. In FIG. 2, the work area A _ij has a large diameter pipe 210 and a small diameter pipe 220.
And the device 230 are arranged. Of course, this is a simple configuration for the sake of explanation, and in reality, there are many pipes of various diameters, and a wide variety of devices regardless of size are arranged. There is.

The work area is a basic unit of construction work, and is a concept born from an area construction method devised in order to efficiently carry out work on a predetermined work area for each work group. Specifically, since two or three rooms on the same floor are used as a unit to perform work, this unit is called a work area.

In this construction completion design data file, design data of various objects are stored for each object. Therefore, regarding the design data of each target,
With the name (and / or symbol) of that object,
Data indicating the characteristics of the design specifications are added (none is shown). Examples of the data indicating this feature include a model name of the plant, information indicating the environment in which the plant is installed (for example, information indicating the structure of the building), information indicating the scale of the plant, and the like.

When the man-hour prediction approximate expression is generated, the design data of the object close to the design specification of the target plant is selected and used. In addition, when there are a plurality of design data of objects having similar design specifications, all or part of them can be used.

FIG. 3 shows a part of the design data stored in the construction completion design data file. This design data is composed of records having a plurality of predetermined items. One part is filed as one record. Items for recording data for each part include an area name that is information indicating a work area in which the part is to be installed, a part type that is information indicating the type of each part, and information for specifying each part. Is a product code, a position (coordinates) that is information indicating the mounting position of each component, a size that is information indicating the size of the component, and a weight that is information indicating the weight of the component. Although not shown in FIG. 3, information about various processes when assembling the parts is stored. For example, there is information on loading, installation, fixing methods, etc., information on pretreatment (for example, cleaning), painting, posttreatment, etc. These pieces of information are arranged as shown in FIG. 4, for example. The method of installing the component includes, for example, a supporting method and a mounting method. More specifically, a ceiling hanging method, a wall supporting method, a floor supporting method, a bolting method,
Designation of mounting method such as belt wrapping, welding, etc.
Information about the content of those methods, such as welding methods, is included.

In this embodiment, a component type subfile (see FIG. 5) is created by classifying the design data shown in FIG. 3 into component types for each area and rearranging them. It is used as a substantial master file instead of the construction completion design data file. FIG. 6 shows an example of a data storage format in this case.

The prediction target design data file 113 stores design data of a target object for which the number of man-hours should be predicted. The design data file is basically the same as the construction completion design data file 112 described above. That is, it has the configuration shown in FIGS. Further, in the prediction target design data file 113, the same parts code is attached to the same parts as the parts used in the construction completion design data file 112.

The man-hour prediction approximate expression file 114 stores the man-hour prediction approximate expression generated by the procedure described later. FIG. 9 schematically shows the structure of this file. As shown in FIG. 9, in this file, the representative aperture, the constants a ₁ and a _{3 of} the approximate expression, and the coefficients a ₂ and a ₄ are stored, respectively.

The estimated man-hour data file 115 stores the man-hour data predicted for the assembly of parts that appear in each work area and are included in the design data of the object whose design is to be evaluated. The predicted man-hour data file 115 stores each data of component type, part code, size, installation height and man-hour. This is schematically shown in FIG. Further, FIG. 8 shows an example of the storage format of the data. In addition, this estimated man-hour data file 11
5, the same parts code is attached to the same parts as the parts used in the construction completion design data file 112.

In addition to these files, it is possible to provide an actual man-hour storage file in which the actual man-hour data obtained for each part of the construction completion object is stored. The value of the man-hour actually measured in assembling the target object is used as the measured man-hour. This file is basically the estimated man-hour data file 1 shown in FIGS. 7 and 8.
It has the same configuration as 15.

This embodiment further includes a flexible disk device 140 as another type of external storage device. The flexible disk device 140 can be used for inputting programs, data, etc. to the system, storing a part of the data, and transferring it to the device.

The information processing apparatus 100 has a CPU 101.
Has various means realized by executing a program. As typical examples thereof, for example, a factor of interest extracting means, a means for assigning actual manpower values, a means for organizing the actual manpower values into parts and storing them in a storage device, a representative value determining means, an approximate expression determining means, and a prediction. There is a means for obtaining the man-hours.

The factor of interest extracting means reads the construction completion design data from the construction completion design data file 112,
From the information included in the construction completion design data, at least one first factor data of interest, which is known to have a correlation with the man-hours, and at least 1 which affects the calculation of the man-hours. And the data of the factor of interest of the second type of are extracted. The means for assigning actual man-hours is
The actual man-hour numerical values, which are known man-hours required for assembling the parts, are given. The means for organizing the actual man-hours values into the component types and storing the result in the storage device includes at least information for identifying the parts, information on the first-class attention factor, and the assigned actual man-hours values. The parts are sorted into types and stored in the storage device as subfiles. The approximate expression determining means reads out the value of the above-mentioned factor of interest of the first type and the value of the corresponding man-hours given above for each component type from the sub-file, and uses the values to calculate the regression curve. , The information defining the regression curve, as the information defining the man-hour prediction approximation formula for the part type,
The man-hour prediction approximate expression file 114 is stored.

The means for obtaining the estimated man-hours reads the design data for which the man-hours should be estimated from the prediction target design data file 113, and extracts the above noted factors from the design data contained in each design data. Then, information that defines the man-hour prediction approximate expression for the component type to which the part belongs is read from the man-hour prediction approximate expression file 114, the man-hour prediction approximate expression is set, and the value of the factor of interest is set in the man-hour prediction approximate expression. By substituting for the estimated man-hours.

The representative value deciding means is
For at least one of the extracted second-type attention factors, the representative value is determined by dividing the value of the attention factor into a certain range, and each component type is classified into each representative value. In addition, the representative value determination means determines a representative value for each of the at least one of the first-type attention factors in a certain range for each of at least one of the first-type attention factors. The process of classifying the parts according to the representative value is further performed. Incidentally, the approximate expression determination means, when the parts are classified by class, for each class of each part type, obtain a regression curve, information defining the regression curve, for the class of the part type concerned. The man-hour prediction approximate expression data file 114 is stored as information defining the man-hour prediction approximate expression.

The input device 120 is, for example, a keyboard,
Among commonly used input devices such as a mouse and a touch panel, this embodiment includes a keyboard 121 and a mouse 122 as shown in FIG. The keyboard 121 can be used to input various instructions and data, for example. The mouse 122 is used, for example, for inputting instructions and selections.

As the output device 130, in this embodiment,
A display device is connected. In addition to this, a printer may be connected. The display device of the present embodiment, as shown in FIG.
A CRT display 131 is used. Of course, it is not limited to this. For example, other displays such as a liquid crystal display and a plasma display can also be used.

The communication control device 150 is for transmitting and receiving data to and from other systems via communication means.
For example, by connecting to a system that performs design, receiving design data, receiving man-hour data from a management computer at an assembly site, and sending predicted man-hour data obtained by this system to another system. Can be used in some cases.

Next, the support operation for creating the estimated man-hours for the design data by the system of this embodiment will be described.

FIG. 11 is a flow chart showing the operation procedure of the system of this embodiment. The CPU 101 executes the program created according to this flowchart,
The functions of the above-described means are realized.

The CPU 101 reads the design data from the construction completed design data file 112, classifies it into data for each work area, and classifies each work area into parts according to the component type. A component type subfile as shown is created (step 1001). At this time, first, data representing the characteristics of the stored design data for the plant is read from the construction completed design data file 112, and is displayed on the output device 130 so that the operator can use any of the design data. Ask them to choose. This selection allows the selection of design data for more than one plant. Therefore, design data regarding a plurality of plants may be selected.
The CPU 101 reads out the designated construction completion design data from the file 112. As mentioned above, this step is
If the data is held from the beginning, the process of this step may be omitted and the construction completion design data file may simply be copied to create the component type subfile.

The component type sub-file is stored in the work file 116. Further, other sub-files described later are also stored in the work file 116 in the same manner.

Next, based on this part type subfile,
The designation of a factor (a factor of interest) that has a large influence on the manhour setting is accepted (step 1002). This designation is made by input device 1
It starts from 20. The attention factor is specified by the nature of the object,
Various settings are made depending on the structure, the form of parts, and the like. For example, in a plant, large-sized equipment that is generally difficult to install is often placed on the floor. On the other hand, as the piping, a large number of piping having various diameters are installed. Piping is not limited to being placed on the floor, but can be hung from the ceiling, supported on wall surfaces, etc.
There are various mounting modes, and as a result, the mounting position has various heights from the floor to the ceiling. In some cases, it may be installed in a groove provided on the floor or embedded under the floor. Even if a machine such as a crane is used for such piping work, it is ultimately necessary for the worker to directly perform the assembly work. Therefore, the workability of the worker greatly affects the work man-hour. Therefore, it can be said that the factors that affect the workability in such a pipe correspond to the diameter indicating the size of the pipe and the installation height. Therefore,
In this embodiment, as the factors of interest, attention is paid to the pipe diameter and installation height, and these are designated.
Since the weight has almost no difference in the workability when a crane is used, it is not specified as a factor of interest here.

Here, it was revealed by the study of the present inventor that the installation height has a correlation with the number of steps. That is, the installation height can be treated as a quantity known to have a correlation with the number of steps. Moreover, the installation height rarely directly affects the performance of the piping in the plant. Therefore, it is an amount that allows design changes for reasons unrelated to performance. In the present specification, such data is regarded as the first type of factor of interest. Not only the installation height but also data of other items, data having similar properties can be treated as this type 1 attention factor.

Further, the larger the diameter, the more time and effort it takes to install the data, so this is data that affects the calculation of man-hours. However, since the diameter directly affects the performance of an object such as a plant, it is often difficult to change the design. Therefore, the design change of the aperture is generally not allowed unless the performance is changed. This type of data is referred to herein as the second type of factor of interest. Not only the installation height but also data of other items, data having the same property can be treated as the second type attention factor. An example of this is the weight of parts when assembled without using a crane for work.

In addition to the above, even for equipment that is not placed on the floor, the designation of the factor of interest is accepted, as with piping. In this case, the attention factor may be, for example, the capacity and / or weight (second type attention factor), which is the information indicating the size, and the installation height (first type attention factor). In addition,
In the present embodiment, for convenience of explanation, only an example of piping is shown, but in practice, processing is performed on equipment other than piping in the same procedure as the data for piping.

When the CPU 101 receives the designation of the attention factor, the CPU 101 extracts the designated attention factor from the component type sub-file, and selects the second of the attention factors.
Using one of the factors of interest in the key as a key, the record array is arranged and a factor extraction subfile is created (step 1).
003). That is, from the part type sub-file,
The relevant parts are extracted using the installation height as the first type of attention factor and the caliber as the second type of attention factor. The diameter is selected from the data that represents the size of the pipe. On the other hand, the installation height is selected from the coordinate data indicating the mounting position.
Then, the records are sorted using the factors of interest as keys, and as shown in FIG. 12, the area name, the part type, the part code,
Create an attention factor extraction sub-file that has the diameter and installation height as items. FIG. 13 shows an example of the format of how the data of this sub-file is held.

Next, a subfile in which the actual man-hours are input is created based on the noted factor extraction subfile shown in FIG. 12 (step 1004). First, a sub file for inputting actual man-hours is created based on the sub file shown in FIG. The structure of this subfile is, for example,
It is represented as shown in FIG. To input the actual man-hours, first, a table as shown in FIG. 14 is displayed on the screen of the display device 131. At this time, the man-hour column is still blank.
In this state, the input device 120 waits for input of man-hour data. At the time of inputting, a cursor (not shown) is moved to the man-hour column to be input with the mouse 122, and input is performed from the keyboard 121. The value of the man-hour uses the value determined by the expert. It is preferable to input a value independently determined by a plurality of experts. In that case, a plurality of man-hour columns are provided. When a plurality of man-hours is input for one part, an average value of these is obtained, and finally the average value is adopted as the man-hour. Therefore, the actual manhour input subfile is
4, a file having a structure as shown in FIG. 15 is obtained.

When the man-hours of the object used are actually measured, the man-hours may be input using the actual measured man-hour file as a result.

Next, the CPU 101 rearranges the records in the order of the size of the caliber, for example, in the order of the smallest caliber, in the actual man-hour input file in which the man-hours are input (step 1005). Specifically, CPU1
01 causes the display device 131 to display guidance for requesting the designation of the factor of interest. Then, when the attention factor is designated, the CPU 101 sorts out the actual man-hour input file using the designated attention factor as a key. In this embodiment,
Caliber and installation height shall be specified as factors of interest. FIG. 16 shows an example of an attention factor-specific subfile that stores the organized results. FIG. 17 shows an example of how the data is held in this attention factor-specific subfile.

Next, processing for grouping pipes of various diameters is performed (step 1006). First, the range for determining the representative value is designated. To specify the range, a standard value is set in advance in the system, and this standard value is used if there is no particular change. The standard value is set for each component type. In this embodiment, regarding the piping,
“10 cm” is the standard value indicating the range. The designation of this range may be input from the outside.
In that case, the guidance for requesting the range designation is displayed on the screen of the display device 131, and the range designation from the input device 120 is accepted.

The CPU 101 determines the representative value on the basis of the attention factor subfile shown in FIG. In the case of the present embodiment, the median value of the range is selected as the representative value. That is, the CPU 101 determines the central value of the range as the representative aperture for every 10 cm, rounds off the actual aperture, and groups all by the representative aperture. FIG. 18 schematically shows a configuration example of the first representative value subfile created by grouping. FIG. 19 shows an example of the storage format of the data.

The determination of the representative value is not limited to the above method, including the case of determining the second representative value described later. For example, the numerical values within the designated range may be averaged and the average value may be used as the representative value.

Next, the CPU 101 sorts the first representative value sub-files for each representative value using the first type attention factor as the second key in a predetermined order (step 1007). Here, it is assumed that the installation height is designated as the first type attention factor. Therefore, with respect to the first representative value subfile, the installation heights are sorted in ascending order for each representative value. The results are shown in FIG. 20 and FIG.
Create the installation height order subfile shown in 1. Figure 21
As shown in FIG. 6, the installation height order subfile records the number of pipes belonging to each representative diameter as the number of target spools.

Next, the CPU 101 determines a representative value for the representative factor of the first type for each representative value for the installation height order subfile (step 1008). Here again, the standard value preset in the system is used as the range. Of course, it may be specified from the outside. The standard value is 10 cm in this embodiment.
The CPU 101 rounds off the installation height in units of 10 cm to determine a representative installation height. Then, the records of the installation height order subfile are classified into classes by the representative installation height to create a second representative value subfile. FIG. 22 schematically shows a part of this second representative value subfile. In FIG. 22, for example, the caliber 150 ±
A data group for pipes belonging to the class of 5 cm, from an installation height of 10 cm, is not shown, for example, 3
Up to 10 cm, every 10 cm is divided into classes, and the work man-hours for each are shown. Also, FIG.
Shows an example of the data storage format. In this file, as shown in FIG. 23, the number of target spools is recorded for each representative installation height. Here, for the work man-hours that become the representative value in each class, a statistically processed value such as a median value or an average value in the class is selected.

Next, using the second representative value subfile,
The relationship between the work man-hour (Y) and the installation height (X) is applied to the following linear prediction approximation formulas (1) and (2) to obtain constants a ₁ and a ₃
And processing for obtaining coefficients a ₂ and a ₄ (step 1
009). These approximation formulas are derived by the inventors based on actual measurement data on plant construction.

Y = a ₁ + a ₂ X ··· (1) Y = a ₃ + a ₄ X ··· (2) Here, the constant a ₁ is a preparation for work in a low place work without using a scaffold. Is the time required for. The coefficient a ₂ is a proportional coefficient to the installation height (X) in the work at a low place without using a scaffold. The constant a ₃ is the time required to prepare for work in high-place work using a scaffold. The coefficient a ₄ is a proportional coefficient with respect to the installation height (X) in high-altitude work using a scaffold.

The reason why the two types of predictive approximation formulas are used is that the work of the present inventors has revealed that there is a difference in workability between the work using the scaffold and the work not using the scaffold. That is, when a scaffold is required, it is a case where an operator cannot work on the floor, and the installation height of the product is usually more than 1.5 m. In this case, man-hours for assembling and dismantling the scaffold are added. Moreover, this man-hour depends on the height of the scaffold to be assembled. Also,
The work on the scaffold is poor in workability because the range of movement is limited, danger is avoided, and more careful movement is required. In addition, the time required for hoisting when using a crane is increased. Therefore, we decided to predict the man-hours by using two types of approximate expressions.

The CPU 101 uses the work man-hours (Y) and the installation height (X) of the second representative value sub-file to determine the constants a ₁ and a ₃ and the coefficients a ₂ and a ₄ to the floor work limit. Determine based on the installation height. The method for obtaining is, for example, regression analysis, specifically, from the installation height (X) and the work man-hour (Y),
A constant a such that the difference between the work man-hour obtained from the approximate expression and the work man-hour (Y) by the least-squares method becomes the overall minimum.
Determine ₁ and coefficient a ₂ , and constant a ₃ and coefficient a ₄ . FIG. 9 shows an example of the determined constant a ₁ and coefficient a ₂ , and constant a ₃ and coefficient a ₄ .

When determining the constants and coefficients,
A calculation for obtaining the above approximate expression is performed using the data of the installation height and the work man-hour for each of the same diameters across the frame of the work area. Further, when the installation height and the work man-hour are performed for a plurality of construction completion design data, these can be used together. For this reason, if the second representative value sub-file is further arranged by the representative aperture for each product type beyond the work area and the frame of the design data before the calculation, the calculation becomes easy.

FIG. 24 shows an example of the man-hour prediction approximate expression obtained in this way. In the graph of FIG. 24, the horizontal axis represents the installation height and the vertical axis represents the number of work lines. According to the graph of FIG. 24, the work man-hour increases with the image of the installation height, and as a whole, it becomes a straight line that gradually rises to the right. And
At a certain height, the degree of rising to the right becomes steeper. That is, in the present embodiment, the point is the installation height of 1.5 m.

The constants a ₁ and a ₃ thus obtained
And the coefficients a ₂ and a ₄ and the man-hour prediction approximate expression are stored in the man-hour prediction approximate expression file 114. Note that after the target man-hour prediction approximation formula for the required component type is obtained, various sub-files created up to that point are deleted or a part thereof is stored in the work file 116. deep.

Next, the man-hours are predicted for the design data of which the man-hours should be predicted (step 1010).
The procedure is as follows.

The CPU 101 first reads the design data for which the man-hour prediction should be performed from the prediction target design data file 113 for each work area. The prediction target design data does not directly show its own structure, but has the same structure and items as the construction completion design data file shown in FIG. Then, the CPU 101 extracts information regarding the size and the installation height of the read work area for each product. That is, in the case of the above-mentioned piping, the size information and the installation position coordinate information are read out. Then, the aperture is extracted from the size information, and the installation height is extracted from the position coordinates.

Next, the CPU 101 detects the representative caliber to which the caliber belongs from the man-hour prediction approximate expression file 114 (see FIGS. 1 and 9). Further, the CPU 101 determines whether the extracted installation height is 1.5 m or less,
If it is 1.5 m or less, the man-hour prediction approximate expression file 114
From the constant a ₁
And each value of the coefficient a ₂ is read out. Then, these values are applied to the constant a ₁ and the coefficient a ₂ of the above formula (1), the value of the extracted installation height is substituted into (X), and the work man-hour (Y)
Ask for.

On the other hand, when the CPU 101 determines that the extracted installation height exceeds 1.5 m, the constant a ₃ and the coefficient a ₄ obtained for the representative aperture from the man-hour prediction approximate expression file 114 are determined. Read each value of. Then, these values are applied to the constant a ₃ and the coefficient a ₄ in the above equation (2), and the extracted installation height value is substituted for (X) to obtain the work man-hour (Y).

These processes are carried out for parts such as pipes included in the work area. The same processing is performed for all work areas. The estimated man-hour data file 115 shown in FIG. 7 is created from the work man-hour data thus obtained and the corresponding design data. The estimated man-hour data file 115 is finally stored in the external storage device 110.

Table 1 shows an example of the relationship between the estimated man-hours thus obtained and the actually-measured man-hours in actual work. This table shows the working man-hours of pipes having a diameter in the range of 285 mm to 299 mm belonging to the representative diameter of 290 mm. As is clear from comparison between the estimated work man-hours shown in the table and the actual work work man-hours, it can be seen that the man-hours can be predicted with a very small error. That is, according to this embodiment, it is supported that the man-hour prediction can be performed with high accuracy.

[0075]

[Table 1]

The work man-hours thus obtained can be used, for example, for preparing a process plan. For example, for the entire large-diameter pipe in the work area, take the sum of those man-hours,
Used as input data for process planning.

The process planning is performed by creating a process bar for each part in each work area. Prior to that, the process plan creator needs to store the start date and end date data in the information processing device in advance. Specifically, the process plan data file 117 is created in the external storage device 110, and the start date and the end date are stored in the process plan data file 117. The width of the process bar is the work period, and the height is the work staff. The work man-hour is
This is the area of the process bar. The process plan creation device applies the process from the startable date of the process to the end date, and determines the work personnel using the work man-hour evaluation result.

By using the result of calculating the man-hours considering the workability of the worker, a more realistic process plan can be obtained, and the number of workers required at a certain time can be uniquely obtained. In the overall process plan, the period that requires the most work personnel is extracted and the process belonging to that period is selected. The work corresponding to the process can be found by reversing the data. When it is desired to reduce the peak of work personnel, design support corresponding to the work can be extracted and displayed to the user to support the design.

Since the man-hours can be predicted based on the design data, the design data can be evaluated from the viewpoint of workability of assembly. That is, it is possible to realize a work that requires too many man-hours, a study of suitability of a mounting position of a component, and the like by simulation. For example, a more appropriate design can be performed by appropriately changing the layout of components on the design data and predicting the number of man-hours corresponding thereto. Such an operation can be extremely easily performed in the present system by using a line number prediction approximation formula for the target object, if the formula is obtained in advance.

This also means that the man-hour estimation or the like associated with the design change can be performed quickly and easily. The modification of the man-hour estimate due to this design modification can be performed in the same manner as the modification of the design data described above. The present system can also support changes in the process plan accompanying this.

Although one embodiment of the work man-hour prediction support system of the present invention has been described above, it goes without saying that the present invention is not limited to this. Various modifications are possible. For example, the following modified examples can be given.

Firstly, in the above-described embodiment, the example in which the process plan is executed in the same hardware system is shown, but it may be executed in different systems. In that case, the predicted man-hour data stored in the predicted man-hour data file 115 is transferred to the system that executes the process plan via the communication control device 150. It is also possible to receive the changed design data from the system that executes the process plan, predict the man-hours of the changed design data, and send it to the system that executes the process plan.

Secondly, in the above embodiment, the size (the diameter of the pipe) and the installation height are selected as factors of interest, but the present invention is not limited to this. Other factors may be used as long as they are factors that affect the calculation of work man-hours, that is, factors that have a correlation with man-hours. For example, in the case where manual work is mainly performed, the weight of parts has a great influence on the calculation of man-hours and will be selected as a factor of interest. Further, the regression curve may be obtained by including various types of factors in addition to the above two types of factors.

Thirdly, in the above embodiment, the representative value of each of the factors of interest is determined and classified into classes, but the present invention is not limited to this. The representative values may be determined only for some of the factors of interest, and the classification may be performed. For example, in the above embodiment, if the amount of data is small, the representative value may not be determined, and thus the man-hour prediction approximate expression may be obtained without classifying.

Fourthly, in the above embodiment, a linear expression is used as the man-hour prediction approximate expression, but the present invention is not limited to this.
For example, a higher-order polynomial of second order or higher may be used.

Fifthly, in the above-mentioned embodiment, it has been described that there is data of the corresponding bore diameter and installation height for the parts to be predicted, but between the construction completion design data and the prediction target design data, For example, there may be cases where there is no data for a certain aperture. In such a case, the approximate expression may be obtained using the data about the representative values before and after that.

[0087]

According to the present invention, man-hours can be predicted based on design data. In addition, the prediction can be performed in consideration of the workability, including the difference in the shape of parts and the mounting position. Therefore, the design data can be evaluated from the viewpoint of assembly, and the design for easy assembly can be performed. Further, even when the design is changed, the number of man-hours can be predicted quickly and easily. Therefore, it is possible to easily change the process plan accordingly.

[Brief description of drawings]

FIG. 1 is a block diagram showing an example of the configuration of a hardware system used in an embodiment of the present invention.

FIG. 2 is an explanatory diagram showing a work area A _ij that constitutes a basic unit for performing an assembly work in a space where a plant, which is an object, is installed, and a configuration inside thereof.

FIG. 3 is an explanatory diagram showing a part of design data stored in a construction completion design data file.

FIG. 4 is an explanatory view schematically showing an example of a storage format of a part of the design data stored in the construction completion design data file on a storage device.

FIG. 5 is an explanatory diagram schematically showing a part of a design data master file configured by rearranging design data by classifying and classifying parts types for each area.

FIG. 6 is an explanatory diagram showing an example of a data storage format of a design data master file.

FIG. 7 is an explanatory view schematically showing a part of a predicted man-hour data file.

FIG. 8 is an explanatory diagram showing an example of a data storage format of a predicted man-hour file.

FIG. 9 is an explanatory view schematically showing a part of a man-hour prediction approximate expression file.

FIG. 10 is a front view showing an example of an input device used in the present embodiment and a display device which is an output device.

FIG. 11 is a flowchart showing an operation procedure of the system of this embodiment.

FIG. 12 is an explanatory diagram schematically showing a part of a target factor extraction subfile.

FIG. 13 is an explanatory diagram showing an example of a data storage format of the noted factor extraction subfile.

FIG. 14 is an explanatory view schematically showing a part of an actual manhour input sub-file used for actual manhour input.

FIG. 15 is an explanatory diagram showing an example of a data storage format of the actual manhour input subfile.

FIG. 16 is an explanatory diagram schematically showing a part of a subfile for each factor of interest.

FIG. 17 is an explanatory diagram showing an example of a data storage format of the attention factor-specific subfile.

FIG. 18 is an explanatory diagram showing an example of a first representative value subfile in which a first representative value is determined for the size of parts and arranged.

FIG. 19 is an explanatory diagram showing an example of a data storage format of the first representative value subfile.

FIG. 20 is an explanatory diagram showing an example of an installation height order subfile in which the first representative value subfile is arranged for each area for each installation height.

FIG. 21 is an explanatory diagram showing an example of a data storage format of the installation height order subfile.

FIG. 22 is an explanatory diagram showing an example of a second representative value subfile in which the installation height is set as the second representative value and arranged.

FIG. 23 is an explanatory diagram showing an example of a data storage format of the second representative value subfile.

FIG. 24 is a graph showing the relationship between the installation height and the number of man-hours.

[Explanation of symbols]

100 ... Information processing apparatus main body, 101 ... CPU, 102 ...
Main memory, 110 ... External storage device, 111 ... Program file, 112 ... Construction completion design data file, 113
... prediction target design data file, 114 ... man-hour prediction approximate expression file, 115 ... predicted man-hour data file, 116
... work file, 117 ... process plan data file,
120 ... Input device, 130 ... Output device, 140 ... Flexible disk device, 150 ... Communication control device.

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification number Reference number within the agency FI Technical indication location G06F 15/21 R 9191-5H 15/60 608 A (72) Inventor Masanori Takamoto Hitachi City, Ibaraki Prefecture 7-2-1 Omika-cho, Ltd. Energy Research Laboratory, Hitachi, Ltd. (72) Inventor Shigeru Ogoshi 3-1-1, Yukimachi, Hitachi City, Ibaraki Hitachi Ltd. Hitachi Works

Claims (14)

[Claims] 1. A work man-hour prediction support system for supporting work man-hour prediction for an object to be assembled as an assembly of a plurality of parts including a plurality of kinds of parts, for storing a program and data. Storage device,
A central processing unit for executing a program stored in a storage device to perform a man-hour prediction support process, an input device for receiving an instruction input and data input to the central processing unit, and a result of information processing. At least an output device for outputting is provided, and the storage device indicates, for an object for which assembly work has been completed, for each part that constitutes the object, part specifying information for specifying the part and an installation position of the part. A work completion design data file that stores the assembly position information and design data including at least the information indicating the size of the part, arranged and stored for each item set in advance for each of the above information, and for calculating the estimated man-hours. And a man-hour prediction approximate expression file for storing information defining the man-hour prediction approximate expression. The work completion design data is read from the file, and the data of at least one first-type factor of interest, which is known to have a correlation with the man-hours, is read from the information included in the work completion design data. Attention factor extraction means for extracting, means for assigning, to each part, the actual manhour value that is a known man-hour required for assembling the part, information for identifying the part for each part, attention of the first type A means for including at least factor information and assigned actual man-hours values, organizing these by component type and storing them in a storage device as a subfile, and from the subfile, for each component type Value and the value of the corresponding man-hours given above are obtained, a regression curve is obtained using those values, and the information defining the regression curve is used as the man-hour forecast for the part type. As the information that defines the measurement approximation formula, the approximation formula determination means stored in the above-mentioned man-hour prediction approximation formula file and the design data for which the man-hours should be predicted are read, and the above-mentioned attention from the design data included in each component is noted. Extract the factors and read the information defining the man-hour prediction approximation formula for the part type to which the part belongs from the man-hour prediction approximation formula file, set the man-hour prediction approximation formula, and set the man-hour prediction approximation formula A work man-hour prediction support system comprising means for substituting the value of a factor of interest to obtain a predicted man-hour. 2. The target factor extraction means according to claim 1,
A work man-hour prediction support system which further extracts data of at least one type 2 attention factor that influences man-hour calculation. 3. The information processing apparatus according to claim 2,
For at least one of the extracted second type attention factors, the representative value is determined by dividing the value of the attention factor into certain ranges, and the representative value is determined by classifying each component type into the respective representative values. Further, the approximate expression determination means obtains a regression curve for each class of each component type, and defines information for defining the regression curve, and defines a man-hour prediction approximate expression for the class of the component type. A work man-hour prediction support system, characterized in that the information is stored in the man-hour prediction approximate expression file. 4. The representative value determining means according to claim 3, wherein, for each of at least one of the first type attention factors, the representative factor determination means sets the value of the attention factor for each predetermined range. A work man-hour prediction support system characterized in that a representative value is determined, and a process of classifying each component into classes is performed according to the representative value. 5. The means for obtaining a predicted man-hour according to claim 4, wherein for each part, the first type and second type attention factors are extracted from the design data contained therein, and
From the above-mentioned man-hour prediction approximation formula file, the information that defines the man-hour prediction approximation formula for the class of the second factor attention factor of the part type to which the part belongs is set, and the man-hour prediction approximation formula is set. A work man-hour prediction support system, characterized in that a predicted man-hour is obtained by substituting a value of a first-type attention factor of the part into an expression. 6. The item of interest of the first type according to claim 5,
It is a design item for which design changes are acceptable,
The second factor of attention is the work man-hour prediction support system, which is a design item whose design value is difficult to change, although it affects man-hour calculation. 7. The factor of interest of the first type according to claim 6,
The height of the mounting position of the part, and the second type of factor of attention is the work man-hour prediction support system that is the size of the part. 8. The approximation formula determining means according to claim 7, wherein the height of the mounting position of the component is a low place work at a height equal to or less than a predetermined value, and the height exceeds the value. The work man-hour prediction support system that determines the man-hour prediction approximate expression for each of the high-place work of. 9. The display device according to claim 1, 2, 3, 4, 5, 6, 7 or 8, wherein at least a display device is provided as the output device, and means for giving a performance man-hour value is provided in each of the display devices. A work man-hour prediction support system that displays a message prompting the user to enter a man-hour value for each part and accepts a numerical value input from an input device as a man-hour value. 10. A method according to claim 2, 3, 4, 5, 6, 7 or 8.
In the above, the work man-hour prediction support system is characterized in that the object is a plant having piping as a component, and the size of the factor of interest of the second type is the diameter of the piping. 11. A method for determining a work man-hour prediction approximate expression for predicting a work man-hour for an object to be assembled as an assembly of a plurality of parts including a plurality of kinds of parts, wherein the assembling work is completed. For each object, at least including, for each part that constitutes the object, part identification information that specifies the part, assembly position information that indicates the position where the part should be installed, and information that indicates the size of the part. Import the design data and select the first from the information included in the work completion design data.
The information indicating the mounting height of the part is extracted as the factor of attention of the species, and the information indicating the size of the component is extracted as the factor of the second type of attention, and the known man-hours required for assembling the component are extracted. Each of the actual man-hours values is given, and for at least one of the extracted second-class attention factors, the representative value is determined by dividing the value of the attention factor into a certain range, and the design data is divided into parts. The representative value of the product type is divided into classes, and the approximate expression determining unit obtains a regression curve for each class of each component type, and provides information defining the regression curve for the class of the component type. A method for determining a work manpower prediction approximate expression, characterized by being obtained as information defining a manpower prediction approximating expression. 12. The method according to claim 11, wherein, for each of at least one of the first-type factors of interest classified among the above-mentioned classes, a representative value of the factors of interest is determined in a certain range, A work man-hour prediction approximate expression determination method characterized by further performing a process of classifying each component into respective representative values. 13. A work man-hour prediction support system for supporting work man-hour prediction for an object assembled as an assembly of a plurality of parts including a plurality of kinds of parts, for storing a program and data. Storage device,
A central processing unit for executing a program stored in a storage device to perform a man-hour prediction support process, an input device for receiving an instruction input and data input to the central processing unit, and a result of information processing. The storage device includes at least an output device for outputting, and the storage device includes a man-hour prediction approximate expression file for storing information defining each of a plurality of man-hour prediction approximate expressions for obtaining the predicted man-hour, and the man-hour prediction The man-hour prediction approximation formula stored in the approximation formula file is a regression line obtained for each part type, regarding the relationship between the installation height and the man-hours of the parts obtained for the object assembled in the past, The information processing apparatus reads the design data for which the man-hours should be predicted, and extracts the attention factor from the design data included in each design data. And the information for defining the man-hour prediction approximation formula for the part type to which the part belongs from the above man-hour prediction approximation formula file, setting the man-hour prediction approximation formula, and setting the man-hour prediction approximation formula to the target factor of interest. A work manpower prediction support system comprising means for substituting a value to obtain a predicted manpower. 14. The manpower-prediction-approximation-expression formula according to claim 13, wherein each of the plurality of man-hour-prediction-approximation formulas is obtained for each class by classifying each component type by its size. A certain work manpower prediction support system.