Patent Description:
To maintain and manage enormous numbers of facilities, a person in charge of maintenance work of the facilities executes analytical process of calculating a rank of a degradation level (hereinafter, degradation rank) of each facility from raw data such as the specifications and inspection records of each facility stored in a database.

Standardization of analytical schemes has been in progress for each type of facility. However, a method of determining a value of a parameter such as a correction coefficient of a degradation level in consideration of reliability level of a facility included in a mathematical expression of analytical process has not been established yet. Thus, to determine an analytical scheme, the person in charge of maintenance work is required to adjust the parameter such as the correction coefficient so that analytical process is repeated by changing the parameter little by little without changing raw data.

Since the number of pieces of raw data is enormous, there is a problem in which it takes time if analytical process is re-executed on the whole raw data every time the parameter is changed.

To address this problem, Patent Literature <NUM> discloses a technology in which sets of parameters for which analytical process has been executed in the past and intermediate data as the analytical result are stored in advance and, when analytical process is executed with the parameter after change, if analytical process has been executed in the past with that parameter after change, the intermediate data corresponding to that parameter after change is referred to and analytical process is omitted.

<CIT> discloses an additional learning method where there is created determiner DE1 which has been caused to learn acceleration measurement data which has been obtained by an accelerated aging test and indicates that a facility changes from a normal state to an aged state, and advance label data which is obtained by giving a label to data indicating characteristics of aging in the acceleration measurement data. Measurement data of aging diagnosis is obtained from the facility which is operating, teacher aging degree label data is found from a record of maintenance of the facility, and additional data is obtained from the measurement data and the teacher aging degree label data. When a difference between predicted aging degree label data obtained when the determiner makes determination on all items of learning data including the additional data, and teacher aging degree label data included in the all items of the learning data is greater than a predetermined value, learning data is selected as additional learning data. The additional learning data is learned to update the determiner.

In the conventional technology, unless parameters in a set do not completely match, analytical process is required to be re-executed for the entire raw data. Therefore, in the conventional technology, analytical process not executed in the past with the parameter after change is required to be re-executed. Thus, at this point, there is a problem in which it takes time.

An object of the present disclosure is to provide an apparatus which extracts raw data not requiring re-execution as much as possible with a small amount of calculation also for analytical process not executed in the past with a parameter after change.

A data analysis apparatus according to the present disclosure includes:.

The data analysis apparatus according to the present disclosure uses summary information, and can therefore extract raw data not requiring re-execution as much as possible with a small amount of calculation also for analytical process not executed in the past with a parameter after change.

In the description of embodiments and the drawings, identical or corresponding components are provided with the same reference characters. Description of the components provided with the same reference characters is omitted or simplified as appropriate. In the following embodiments, "unit" may be read as "circuit", "step", "procedure", "process", or "circuitry" as appropriate.

In the following embodiments, an application program is described as an application. Also in the following embodiments, analytical process means degradation rank calculation. In the following, in order to express that analytical process is degradation rank calculation, such an expression as analytical process (degradation rank calculation) may be made.

Embodiment <NUM> is described below with reference to <FIG>.

<FIG> illustrates the block structure of a data analysis apparatus <NUM>. The data analysis apparatus <NUM> includes a definition interpreting unit <NUM>, an analytical processing unit <NUM>, and a data storage unit <NUM>.

The data analysis apparatus <NUM> is as follows. The definition interpreting unit <NUM> receives an analytical process definition <NUM> from an analytical application <NUM> outside of the data analysis apparatus <NUM>, and stores the analytical process definition <NUM> in the data storage unit <NUM>. A change managing unit <NUM> described below receives an analysis instruction <NUM> from the analytical application <NUM>. The analysis instruction <NUM> includes details of a change of a parameter of the analytical process definition <NUM>.

The analytical processing unit <NUM> includes a summary generating unit <NUM>, the change managing unit <NUM>, a group extracting unit <NUM>, and an analysis executing unit <NUM>. The group extracting unit <NUM> is an extracting unit. The analysis executing unit <NUM> is a calculating unit.

When the analysis instruction <NUM> received from the analytical application <NUM> includes an instruction for generating summary information described below, the summary generating unit <NUM> uses the analytical result of the analysis executing unit <NUM> and the information stored in the data storage unit <NUM> to generate summary information 121A of raw data. The summary generating unit <NUM> causes the generated summary information 121A to be stored in the data storage unit <NUM>.

The change managing unit <NUM> receives the analysis instruction <NUM> from the analytical application <NUM>. The analysis instruction <NUM> includes details of change of a parameter.

By using the summary information 121A stored in the data storage unit <NUM>, the group extracting unit <NUM> extracts, from the summary information 121A, a group requiring re-execution of analytical process by the analysis executing unit <NUM>. Also, by using the summary information 121A, the group extracting unit <NUM> extracts a group not requiring re-execution of analytical process.

The analysis executing unit <NUM> executes analytical process on raw data of which the analysis executing unit <NUM> is notified by the group extracting unit <NUM> as requiring re-execution, with a parameter after change. The analysis executing unit <NUM> takes the result of analytical process as the final result. As for raw data of which the analysis executing unit <NUM> is not notified as requiring re-execution, the analysis executing unit <NUM> takes the result of calculation of a representative point of a group to which that raw data belongs as the final result.

The data storage unit <NUM> has stored therein (<NUM>) analytical process definition <NUM>, (<NUM>) raw data group <NUM>, (<NUM>) analytical characteristic 110A, and (<NUM>) summary information 121A.

<FIG> illustrates the raw data group <NUM> as a table. The raw data group <NUM> is information indicating the entire raw data. Each row of the raw data group <NUM> is one piece of raw data. The raw data group <NUM> has columns of facility ID, years of aging, and reliability level. Each column of the table indicating the raw data group <NUM> represents a degradation factor for each facility, such as years of aging or reliability level, and these degradation factors are registered as converted into numbers.

In <FIG>, the raw data has data items of two pieces of numerical value data, years of aging x<NUM> and reliability level x<NUM>. However, the raw data may have a data item of one piece of numerical value data or may have data items of a plurality of, three or more, pieces of numerical value data.

The data storage unit <NUM> has the analytical process definition <NUM> stored therein.

<FIG> illustrates the analytical process definition <NUM>. f(g(x<NUM>, x<NUM>, p<NUM>)) in <FIG> is the analytical process definition <NUM>. In this manner, the analytical process definition <NUM> is represented as a mathematical expression. The analytical process definition <NUM> is a definition of analytical process (degradation rank calculation) for finding a degradation rank of a facility from each piece of raw data of the raw data group <NUM>.

As illustrated in <FIG>, the function g is as follows. <MAT> y is a degradation level of a facility. x<NUM> represents years of aging, and x<NUM> represents reliability level. p<NUM> is a parameter indicating a weight of the reliability level x<NUM>. f(g(x<NUM>, x<NUM>, p<NUM>)) indicates a rank of the degradation level of the facility. The degradation level g of the facility monotonically increases with respect to the years of aging x<NUM> and monotonically decreases with respect to the reliability level x<NUM>. The function g is a mathematical expression taking numerical values x<NUM> and x<NUM> of each row of the raw data group <NUM> of <FIG> as inputs. The input x<NUM> and the input x<NUM> of the function g are two data items, the years of aging (xi) and the reliability level (x<NUM>) in <FIG>, and p<NUM> is a parameter indicating a weight of the reliability level x<NUM>. The f(g(x<NUM>, x<NUM>, p<NUM>)) is calculated for each piece of raw data in each row of the raw data group <NUM> of <FIG>. The function f(y) is <MAT> That is, the degradation rank f(y) of the facility is determined stepwise in a manner such that the degradation rank is <NUM> when the degradation level g, as an interim result of analytical process, is smaller than <NUM>, the degradation rank is <NUM> when the degradation level g is <NUM> or larger and smaller than <NUM>, and the degradation rank is <NUM> when the degradation level g is <NUM> or larger.

The data storage unit <NUM> has the analytical characteristic 110A stored therein. The analytical characteristic 110A has registered thereon a list of information about stages (degradation ranks) for determining the final result of analytical process and interim results (degradation levels g) as inputs of the final results (degradation ranks f).

<FIG> illustrates the details of the analytical characteristic 110A corresponding to the analytical process definition <NUM> illustrated in <FIG>. The analytical characteristic 110A is extracted by the definition interpreting unit <NUM> from the analytical process definition <NUM>, as at step S103 described further below. With reference to <FIG>, details of the analytical characteristic 110A corresponding to f(g(x<NUM>, x<NUM>, p<NUM>)), which is the analytical process definition <NUM> illustrated in <FIG>, are described. As information about stages (degradation ranks) for determining the degradation rank f, there are a lower limit (g = <NUM>) and an upper limit (g = <NUM>) of the degradation level g corresponding to the value of the degradation rank f. As monotonicity of the degradation level g, there is a property in which the degradation level g monotonically increases with respect to the years of aging and the degradation level monotonically decreases with respect to the reliability level.

The data storage unit <NUM> has stored therein the summary information 121A described below in <FIG>. The summary information 121A includes the following (<NUM>), (<NUM>), and (<NUM>).

<FIG> illustrates rectangular division of the raw data group <NUM> in the analytical process definition <NUM> (f(g)) illustrated in <FIG>.

In <FIG>, the horizontal axis represents the years of aging x<NUM> and the vertical axis represents the reliability level x<NUM>. In <FIG>, triangles, stars, and circles represent raw data included in the raw data group <NUM>.

In <FIG>, for each rectangle, raw data as a minimum point of the degradation level g is at an upper-left end point in one rectangle, and raw data as a maximum point of the degradation level g is at a lower-right end point of the rectangle. The reason for this is that, as illustrated in <FIG>, the degradation level g monotonically increases with respect to the years of aging x<NUM> and monotonically decreases with respect to the reliability level x<NUM>.

Description is further specifically made. As illustrated in <FIG>, <MAT> p<NUM> is a positive number. Thus, g decreases with a decrease in the years of aging x<NUM> and an increase in the reliability level x<NUM>. Also, g increases with an increase in the years of aging x<NUM> and a decrease in the reliability level x<NUM>.

In <FIG>, a graph of a lower limit of degradation rank <NUM> (p1 = <NUM>, degradation level g = <NUM>) is a graph of (x<NUM>, x<NUM>) satisfying the following expression.

An upper side in this graph represents a region where the degradation level g is smaller than <NUM>. A graph of an upper limit of degradation rank <NUM> (p1 = <NUM>, degradation level g = <NUM>) is similar to a graph of a lower limit of degradation rank <NUM> (p1 = <NUM>, degradation level g = <NUM>), and is a graph of (x<NUM>, x<NUM>) satisfying <MAT>.

<FIG> illustrates the summary information 121A corresponding to the rectangular division of <FIG>. With reference to <FIG>, the summary information 121A corresponding to rectangular division of <FIG> is described.

The summary information is information about the final result of evaluation of an evaluation target determined stepwise from the interim result of evaluation of the evaluation target calculated by using raw data indicating an attribute of the evaluation target and a parameter.

The summary information is a set of a plurality of interim results with the same final result, and has, as representative data, each piece of raw data which is a source of calculation of two interim results among the plurality of interim results. Here, the evaluation target is a target for which a defined evaluation item is evaluated. In Embodiment <NUM>, an example of the evaluation target is a facility, an example of the defined evaluation item is degradation. Also, an example of the interim result is a degradation level, and an example of the final result is a degradation rank. Specifically, the summary information is as follows.

The summary information 121A is information about a degradation rank of a facility determined from a degradation level, which is calculated by using raw data indicating an attribute of the facility and a parameter, of the facility. The summary information 121A is a set of a plurality of degradation levels with the same degradation rank, and information having, as representative data, each piece of raw data which is a source of calculation of two degradation levels among the plurality of degradation levels. In <FIG>, each of rectangle <NUM>, rectangle <NUM>,. is the summary information 121A. Specifically, description is made as follows. As illustrated in <FIG>, each of groups obtained by division into rectangles is a set of pieces of raw data (each raw in <FIG>) included in the same rectangle.

As illustrated in <FIG>, each group is a set of pieces of raw data included in the same rectangle. The analytical characteristic 110A required for information about a group as one rectangle to be used for determination as to whether degradation rank re-execution is required is a property in which the degradation rank is determined stepwise and the degradation level has monotonicity for each data item (X1, X2) as described further below.

The operation of the data analysis apparatus <NUM> is described. In the drawings of flowcharts described below, what is indicated in parentheses at each step is a subject of operation. The operation procedure of the data analysis apparatus <NUM> is equivalent to a data analysis method. A program achieving the operation of the data analysis apparatus <NUM> is equivalent to a data analysis program <NUM>.

<FIG> is a flowchart illustrating operation of the analytical process definition defining analytical process (degradation rank calculation) of the data analysis apparatus <NUM>. Based on <FIG>, the operation of the analytical process definition by the data analysis apparatus <NUM> is described.

The definition interpreting unit <NUM> receives the analytical process definition <NUM> from the analytical application <NUM>.

The definition interpreting unit <NUM> interprets the received analytical process definition <NUM>.

The definition interpreting unit <NUM> causes the interpreted analytical process definition <NUM> to be stored in the data storage unit <NUM>. Also, the definition interpreting unit <NUM> determines whether the analytical characteristic 110A of the function described in <FIG> is included in the analytical process definition <NUM>. When determining that the analytical characteristic 110A of the function is included, the definition interpreting unit <NUM> causes the included analytical characteristic 110A to be stored in the data storage unit <NUM> as the analytical characteristic 110A.

<FIG> is a flowchart illustrating operation of execution of analytical process by the data analysis apparatus <NUM>. Based on <FIG>, the operation of execution of analytical process is described.

The change managing unit <NUM> receives, from the analytical application <NUM>, the analysis instruction <NUM> including details of change of a parameter of the analytical process definition <NUM>. The change managing unit <NUM> extracts the details of change of the parameter from the analysis instruction <NUM>, and notifies the summary generating unit <NUM> of it.

When the parameter p1 is changed, by using the summary information 121A, the group extracting unit <NUM> extracts a facility requiring recalculation of the degradation level g from respective facilities corresponding to the plurality of degradation levels g included in the summary information 121A. When a degradation level of the facility is calculated by the analysis executing unit <NUM>, the summary generating unit <NUM> generates new summary information different from the summary information 121A used at step S202. The summary generating unit <NUM> generates summary information for each of the plurality of degradation ranks. By using each summary information generated for each degradation rank, the group extracting unit <NUM> extracts a facility requiring recalculation of a degradation level.

With the use of the summary information 121A stored in the data storage unit <NUM>, the group extracting unit <NUM> extracts a group not requiring re-execution of analytical process (degradation rank calculation) from the summary information 121A, and calculates the analytical result for the representative point of each group with the parameter p<NUM> after change. Details of the present step are described further below.

For the facility extracted by the group extracting unit <NUM>, the analysis executing unit <NUM> recalculates its degradation level by using the raw data of the extracted facility and the parameter after change. Description is specifically made below.

The analysis executing unit <NUM> executes analytical process (degradation rank calculation) on raw data (at least any row in <FIG>) of which the analysis executing unit <NUM> is notified by the group extracting unit <NUM> as requiring re-execution, with the parameter p<NUM> after change included in the analysis instruction <NUM> received at step S201. Then, the analysis executing unit <NUM> takes the result of analytical process as the final result. As for the other pieces of raw data, that is, raw data belonging to a group not requiring re-execution in the determination at step S202, the analysis executing unit <NUM> takes the calculation result of a representative point of a group to which the raw data belongs as the final result.

The summary generating unit <NUM> determines whether an instruction for generating the summary information 121A is included in the analysis instruction <NUM> from the analytical application <NUM>. When the determination result is YES, the summary generating unit <NUM> corrects the summary information 121A used at step S202 by using the analytical result of the analysis executing unit <NUM> and the information stored in the data storage unit <NUM>, and causes the corrected summary information 121A to be stored in a storage unit <NUM>. That is, the summary generating unit <NUM> corrects the summary information 121A so that the summary information 121A corresponds to the parameter p<NUM> after change. The correction of the summary information 121A includes re-generation of the summary information 121A. Details of the present step S204 are described further below in <FIG>.

<FIG> is a flowchart illustrating details of operation of step S202 by the group extracting unit <NUM> extracting, from the raw data group <NUM>, analysis target data which is a target for recalculation of a degradation rank.

The group extracting unit <NUM> refers to the data storage unit <NUM> to determine whether the summary information 121A has been registered. When the determination result is YES, the process proceeds to step S302. When the determination result is NO, the process proceeds to step S304.

The group extracting unit <NUM> determines whether the analytical process definition <NUM> in the data storage unit <NUM> includes the analytical characteristic 110A for the summary information 121A to become usable, with reference to the analytical characteristic 110A and the analytical process definition <NUM>. When the determination result is YES, the process proceeds to step S303. When the determination result is NO, the process proceeds to step S304.

The summary information 121A is taken as a set of one or more interim results (degradation levels). By using representative data and the parameter after change, the group extracting unit <NUM> calculates one or more interim results (degradation levels) of evaluation targets (facilities) having the representative data. Then, the group extracting unit <NUM> determines the final results (degradation ranks) of the calculated one or more interim results (degradation levels), and determines whether the determined one or more final results (degradation ranks) match. When the one or more final results (degradation ranks) match, the group extracting unit <NUM> extracts each of the evaluation targets (facilities) corresponding to the one or more interim results (degradation levels) included in the summary information 121A as an evaluation target (facility) not requiring recalculation of an interim result. Description is specifically made below.

The group extracting unit <NUM> determines whether re-execution is required for each group included in the summary information 121A (<FIG>). That is, the group extracting unit <NUM> determines whether recalculation of a degradation rank is required for each group of <FIG>. In determination as to whether re-execution is required, the group extracting unit <NUM> first calculates the final result f for two representative points in the group with the parameter p<NUM> after change (step S201). The condition of determination as to whether re-execution of degradation rank calculation is required is whether the degradation ranks f of these two representative points are the same. When the degradation ranks f of the two representative points match, the group extracting unit <NUM> determines that re-execution of degradation rank calculation for the whole raw data belonging to that group is not required. In this case, the group extracting unit <NUM> takes the final result (degradation rank) after change of the parameter p<NUM> of that group as the same final result (degradation rank) for the representative points. When the degradation ranks f of the two representative points do not match, the group extracting unit <NUM> determines that re-execution of degradation rank calculation is required for the whole raw data belonging to that group as one rectangle.

<FIG> schematically illustrates determination as to whether re-execution of degradation rank calculation at step S303 is required. The analytical process definition <NUM> is the function f of <FIG>.

In a graph of a lower limit (degradation level = <NUM> when p1 = <NUM>) of degradation rank <NUM> of <FIG>, p<NUM> becomes <NUM> from <NUM>. A graph of <NUM> = x<NUM>+<NUM>/x<NUM> is on a lower side of <NUM> = x<NUM>+<NUM>/x<NUM>. This is evident if consideration is given by fixing x<NUM>. The same goes for a graph of an upper limit (degradation level = <NUM> when p1 = <NUM>) of degradation rank <NUM>.

In <FIG>, among six rectangles, for a rectangle <NUM>, a rectangle <NUM>, a rectangle <NUM>, and a rectangle <NUM>, the final results (degradation ranks) of two representative points in each rectangle match, with the parameter p<NUM> after change = <NUM>. Thus, for the rectangle <NUM>, the rectangle <NUM>, the rectangle <NUM>, and the rectangle <NUM>, the group extracting unit <NUM> determines that re-execution is not required. For a rectangle <NUM> and a rectangle <NUM>, the final results (degradation ranks) of two representative points in each rectangle do not match. Thus, for the rectangle <NUM> and the rectangle <NUM>, the group extracting unit <NUM> determines that re-execution of degradation rank calculation is required.

The group extracting unit <NUM> determines that re-execution of degradation rank calculation is required for the whole raw data, and step S202 ends.

<FIG> is a flowchart illustrating operation of step S204 of <FIG> by the summary generating unit <NUM> generating the summary information 121A. Generation of the summary information 121A includes re-generation.

With reference to <FIG>, step S204 is described.

Regarding analytical process executed last, the summary generating unit <NUM> organizes a plurality of pieces of raw data with the same degradation rank as the final result into one group.

<FIG> illustrates division regarding the final result obtained by calculating a degradation rank of each piece of raw data in the raw data group <NUM> by using the analytical process definition <NUM> (function f) illustrated in <FIG>. The final result is a degradation rank. In <FIG>, division is illustrated in a case in which degradation ranks of three ranges are present.

The summary generating unit <NUM> divides the group divided at step S401 into a plurality of rectangular regions, and organizes raw data belonging to the inside of the same rectangle into one group and finds a representative point of the group. Details of step S402 are described further below in <FIG>.

For the group of rectangles found at step S402, the summary generating unit <NUM> finds a list of representative points and raw data belonging to that group, and causes the list to be stored in the data storage unit <NUM> as the summary information 121A.

The summary information 121A of <FIG> has been generated in this manner.

<FIG> is a flowchart illustrating operation of step S402 which is a step of division into rectangles. With reference to <FIG>, step S402 is described.

The summary generating unit <NUM> generates the summary information according to either proximity between the interim result of the evaluation target and a lower limit of a stage of determining the final result of evaluation of the evaluation target or proximity between the interim result of the evaluation target and an upper limit of the stage of determining the final result of evaluation of the evaluation target. Specifically, description is made as follows.

The summary generating unit <NUM> selects a plane (straight line) perpendicular to a certain axis so that one group with the same degradation rank f is divided into a small group including a point where the degradation level g of the interim result is in proximity to the lower limit (degradation level g = <NUM>) or the upper limit (degradation level g = <NUM>) of the degradation rank f and a large group of others. Here, as for a lower limit <NUM> or an upper limit <NUM> of the degradation rank f, in the case of the analytical process definition <NUM> illustrated in <FIG>, the degradation rank f as the final result is determined by taking the degradation level g = <NUM> and the degradation level g = <NUM> as boundaries. Thus, the lower limit = <NUM> and the upper limit = <NUM> of the degradation rank f correspond to a curve with the degradation level g = <NUM> and a curve with the degradation level g = <NUM>.

<FIG> illustrates division by a plane (perpendicular to the paper surface). In <FIG>, a certain constant h is selected, and a plane for division is a set of points with X<NUM> = h. A number L of pieces of raw data of the small group is assumed to be constant. In <FIG>, L = <NUM> holds.

The summary generating unit <NUM> divides a small group of raw data with L = <NUM> into a plurality of rectangles. Details of this step S502 is described further below.

The summary generating unit <NUM> determines whether the number of pieces of raw data in a large group is equal to or smaller than L. When the determination result is YES, the process proceeds to step S504. When the determination result is NO, the process proceeds to step S505.

The summary generating unit <NUM> divides a large group with the number of pieces of raw data equal to or smaller than L into rectangles by a method similar to that in step S502. Step S402 ends.

The summary generating unit <NUM> sets a large group as a new group, and returns the process to step S501. With the above, one group belonging to one degradation rank as illustrated in <FIG>, that is, the plurality of raw data, is divided into a plurality of rectangles formed of pieces of raw data the number of which is equal to or smaller than the certain constant L.

<FIG> is a flowchart illustrating operation of step S502. With reference to <FIG>, details of step S502 are described.

The summary generating unit <NUM> selects the smallest rectangle (hereinafter, smallest circumscribed rectangle) so that rectangle includes a small group formed of pieces of raw data the number of which is equal to or smaller than L.

<FIG> illustrates rectangular division of small groups in the analytical process definition <NUM> illustrated in <FIG>. The smallest circumscribed rectangle is illustrated on a left side in <FIG>.

As described in <FIG>, the degradation level g as the interim result exhibits a monotonical increase or monotonical decrease regarding the same data item of the plurality of pieces of raw data. The summary generating unit <NUM> divides the plurality of pieces of raw data into a plurality of regions, and sets numerical value data which causes the interim result to become a minimum value irrespective of the value of the parameter in the region and numerical value data which causes the interim result to become a maximum value irrespective of the value of the parameter in the region as representative data of the region. While an example of the region to be plurally divided by the summary generating unit <NUM> is a rectangle, the region is not restricted to the rectangle. Specifically, description is made as follows.

By using monotonicity of the interim result, the summary generating unit <NUM> acquires a minimum point and a maximum point of the degradation level g in the smallest circumscribed rectangle from end points of the smallest circumscribed rectangle. On the left side in <FIG>, the minimum point of the degradation level g is an upper-left end point <NUM> of the smallest circumscribed rectangle, and the maximum point of the degradation level g is a lower-right end point <NUM> in the smallest circumscribed rectangle.

For the end point <NUM> of the minimum point of the degradation level g and the end point <NUM> of the maximum point of the degradation level g found at step S602, the summary generating unit <NUM> calculates the final results (degradation ranks), and determines whether the final results of the degradation ranks of the minimum point <NUM> and the maximum point <NUM> by using the new parameter p<NUM> match. When the final results of the degradation ranks of the minimum point <NUM> and the maximum point <NUM> match, step S502 ends. When the final results of the degradation ranks of the minimum point <NUM> and the maximum point <NUM> do not match, the process proceeds to step S604. On the left side in <FIG>, the minimum point <NUM> belongs to degradation rank <NUM> and the maximum point belongs to degradation rank <NUM>, and therefore the determination result is NO.

As the right side in <FIG>, as for the small group, the summary generating unit <NUM> takes a point <NUM> in which the interim result (degradation level g) is in most proximity to the lower limit or upper limit of the stage (degradation rank) as one rectangle, and takes a representative point of a rectangle formed of one point <NUM> as its one point. Also, a group formed of points other than the point of that rectangle (rectangle including the point <NUM>) is taken as a new small group, and the process returns to step S601. In the example on the right side in <FIG>, since the upper-right point <NUM> is in most proximity to the lower limit or upper limit of the stage, this point <NUM> is taken as a rectangle formed of one point, and a group formed of other points is taken as a new small group. By the above-described steps S601 to S604, it is possible to narrow down to a rectangle in which the final results (degradation ranks) of the minimum point and the maximum point match.

In Embodiment <NUM>, it is often the case that, for most raw data, the final results do not change even if a parameter is changed. The reason for this is that the final result (degradation rank) is determined stepwise and the interim result (degradation level g) is often changed only slightly even if a parameter is changed. The interim result is often changed only slightly even if a parameter is changed because of the following. In analysis of the degradation rank of a facility, the degradation level has continuity with respect to the parameter, and the change range of the parameter is often small. Continuity is a property in which when the change range of the parameter is small, the change range of the interim result is also small. In addition, since the interim result (degradation level g) has monotonicity, the upper limit and the lower limit of the analytical results (degradation ranks) of the entire raw data in each rectangle can be grasped from the analytical results of the minimum points and the maximum points of the rectangle.

Since "the number of rectangles is smaller than the number of pieces of raw data", raw data requiring re-execution with the parameter after change can be extracted with a small amount of calculation. With this, it is possible to obtain an effect of being able to reduce analytical process time even if the changed parameter has not been calculated in the past.

In Embodiment <NUM>, as illustrated in the operation at step S402, by following proximity between the interim result (degradation level g) in the analytical process executed last and the lower limit (g = <NUM>) or upper limit (g = <NUM>) of the stage (degradation rank), the entire raw data is divided into rectangles. This decreases the number of rectangles including a point in which the interim result (degradation level g) is in proximity to the lower limit or upper limit of the stage (degradation rank).

The point in which the interim result (degradation level) is in proximity to the lower limit or upper limit of the stage (degradation rank) has a high possibility that the final result changes with parameter change. Thus, with the operation at step S402, it is possible to obtain an effect of reducing the number of rectangles requiring re-execution at the time of parameter change.

In Embodiment <NUM>, points different from Embodiment <NUM> or points added thereto are mainly described. In the present embodiment, a basic screen processing method of the information search method of the data analysis apparatus <NUM> described in Embodiment <NUM> is described in detail. Functions and structures similar to those of the data analysis apparatus <NUM> of Embodiment <NUM> are provided with the same reference characters and description of these functions and structures are omitted.

In Embodiment <NUM>, the change of the interim result of the same data item of the plurality of pieces of raw data indicates a value equal to or smaller than a constant value when the change of the numerical value data of the same data item of the plurality of pieces of raw data has a value equal to or smaller than a constant value. The summary generating unit <NUM> of Embodiment <NUM> divides the plurality of pieces of raw data into a plurality of regions, and sets a center point of each region as representative data. An example of the region is a rectangle, as with Embodiment <NUM>. As the center point of the region, the barycenter of a figure representing the region can be used. Specifically, description is made as follows.

In Embodiment <NUM>, as the analytical characteristic 110A, in place of monotonicity of the interim result g in Embodiment <NUM>, a property is utilized in which, when the change of raw data is equal to or smaller than a constant value (hereinafter, C value), the change of the interim result g is also equal to or smaller than a constant value (hereinafter, D value).

This property is specifically represented in a mathematical expression as the following <Mathematical Expression <NUM>>. <Mathematical Expression <NUM>>
Certain positive real numbers C and D are present, and for a set of any point (a1, a2,. , aM) and a parameter (q1,. , qN), |g(x1, x2,. , pN)-g(a1, a2,. , qN)| ≤ D
(each of (x1, x2,. , xM) and (p1,. , pN) is any point satisfying max{|xi-ai| |i = <NUM>, <NUM>,. , M} ≤ C, max{|pi-qi| |i = <NUM>, <NUM>,. , M} ≤ C)
In <Mathematical Expression <NUM>>, mathematical expressions <MAT> <MAT> mean that a change of raw data is equal to or smaller than C. a mathematical expression <MAT> means that a change of the interim result g is equal to or smaller than D.

In Embodiment <NUM>, as the analytical characteristic 110A, the above-described property of Mathematical Expression <NUM> regarding the interim result and the above-described C value and D value are stored. In Embodiment <NUM>, as with Embodiment <NUM>, the entire raw data is divided into rectangles, but a width from the center of each rectangle is set to be equal to or smaller than C.

In < Mathematical Expression <NUM>> described above, the rectangle is a set <MAT> and the center of the rectangle is a point (a1, a2,.

Furthermore, as the representative point of the group, the center of the rectangle is stored as one piece of information in the summary information 121A.

In Embodiment <NUM>, when whether re-execution is required for each group is determined, the interim result is calculated with the parameter after change and, by using Mathematical Expression <NUM> described above, it is determined whether the final results of the entire data in the rectangle become the same.

From Embodiment <NUM>, even if the interim result does not have monotonicity, much raw data not requiring re-execution can be extracted with a small amount of calculation.

The hardware structure of the data analysis apparatus <NUM> of Embodiments <NUM> and <NUM> is described with reference to <FIG>.

<FIG> illustrates the hardware structure of the data analysis apparatus <NUM>. The hardware structure of the data analysis apparatus <NUM> is described with reference to <FIG>.

The data analysis apparatus <NUM> is a computer. The data analysis apparatus <NUM> includes a processor <NUM>. The data analysis apparatus <NUM> includes, in addition to the processor <NUM>, other hardware such as a main storage device <NUM>, an auxiliary storage device <NUM>, an input IF <NUM>, an output IF <NUM>, and a communication IF <NUM>. The processor <NUM> is connected via a signal line <NUM> to the other hardware to control the other hardware. IF represents an interface.

The data analysis apparatus <NUM> includes the definition interpreting unit <NUM> and the analytical processing unit <NUM> as functional components. The analytical processing unit <NUM> includes the summary generating unit <NUM>, the change managing unit <NUM>, the group extracting unit <NUM>, and the analysis executing unit <NUM>. The functions of the definition interpreting unit <NUM> and the analytical processing unit <NUM> are implemented by the data analysis program <NUM>.

The processor <NUM> is a device which executes the data analysis program <NUM>. The data analysis program <NUM> is a program which achieves the functions of the definition interpreting unit <NUM> and the analytical processing unit <NUM>. The processor <NUM> is an IC (Integrated Circuit) which performs arithmetic processing. Specific examples of the processor <NUM> are a CPU (Central Processing Unit), DSP (Digital Signal Processor), and GPU (Graphics Processing Unit). The processor <NUM> is included in circuitry.

The main storage device <NUM> is a storage device. Specific examples of the main storage device <NUM> are an SRAM (Static Random Access Memory) and DRAM (Dynamic Random Access Memory). The main storage device <NUM> retains the arithmetic operation result of the processor <NUM>. The data storage unit <NUM> is implemented by the main storage device <NUM>.

The auxiliary storage device <NUM> is a storage device which nonvoluntarily stores data. A specific example of the auxiliary storage device <NUM> is an HDD (Hard Disk Drive). Also, the auxiliary storage device <NUM> may be a portable recording medium such as an SD (registered trademark) (Secure Digital) memory card, NAND flash, flexible disk, optical disk, compact disk, Blu-ray (registered trademark) disc, or DVD (Digital Versatile Disk). The data storage unit <NUM> is implemented by the auxiliary storage device <NUM>. The auxiliary storage device <NUM> has the data analysis program <NUM> stored therein.

The input IF <NUM> is a port to which data is inputted from each device. The output IF <NUM> is a port to which various devices are connected and from which data is outputted by the processor <NUM> to various devices. The communication IF <NUM> is a communication port for the processor <NUM> to communicate with another device. To the communication IF <NUM>, the analytical application <NUM> is connected.

The processor <NUM> loads the data analysis program <NUM> from the auxiliary storage device <NUM> into the main storage device <NUM>, and reads and executes the data analysis program <NUM> from the main storage device <NUM>. In the main storage device <NUM>, not only the data analysis program <NUM> but also an OS (Operating System) are stored. While executing the OS, the processor <NUM> executes the data analysis program <NUM>. The data analysis apparatus <NUM> may include a plurality of processors replacing the processor <NUM>. The plurality of these processors share execution of the data analysis program <NUM>. Each of the processors is, as with the processor <NUM>, a device which executes the data analysis program <NUM>. Data, information, signal values, and variable values to be used, processed, or outputted by the data analysis program <NUM> are stored in the main storage device <NUM>, the auxiliary storage device <NUM>, or a register or cache memory in the processor <NUM>.

The data analysis program <NUM> is a program which causes a computer to execute each process, each procedure, or each step obtained by reading "unit" of the definition interpreting unit <NUM> and the analytical processing unit <NUM> as "process", "procedure", or "step".

Also, the data analysis method is a method to be performed by the data analysis apparatus <NUM> as a computer executing the data analysis program <NUM>. The data analysis program <NUM> may be provided as stored in a computer-readable recording medium or may be provided as a program product.

Claim 1:
A data analysis apparatus (<NUM>) comprising:
an extracting unit (<NUM>), by using summary information, which is information about a degradation rank of a facility as a final result of evaluation of the facility as an evaluation target determined stepwise from a degradation level as an interim result of evaluation of the evaluation target calculated by using raw data indicating an attribute of the evaluation target and a parameter, is a set of a plurality of interim results with same said final result, the raw data being organized into groups based on the final result, and is information having, as representative data, each piece of raw data as a source of calculation of two interim results among the plurality of interim results, to extract, when the parameter is changed, an evaluation target requiring recalculation of the interim result from respective evaluation targets corresponding to the plurality of interim results included in the summary information; and
a calculating unit (<NUM>) to recalculate an interim result of the evaluation target extracted by the extracting unit (<NUM>) by using raw data of the extracted evaluation target and the parameter after change, characterized in that
the summary information is a set of one or more said interim results, and
the extracting unit (<NUM>), by using the representative data and the parameter after change, calculates one or more interim results of said evaluation targets having the representative data for each group, determines final results of the one or more interim results calculated and, when the determined one or more final results match for a specific group, extracts each evaluation target corresponding to the one or more interim results included in the specific group of the summary information as an evaluation target not requiring recalculation of the interim result.