Information processing device, program, and evaluation method

Abstract

To perform an evaluation that appropriately reflects a deterioration state of an evaluation object.SOLUTION: According to an embodiment, an information processing device accepts evaluation time, a state level, manufacturing time, and a new-product price of an evaluation object, calculates an elapsed time from the evaluation time and the manufacturing time, finds a remaining useful life of the evaluation object based on first data, the state level, and the elapsed time, finds a normal useful life based on second data, subtracts the remaining useful life from the normal useful life to calculate an effective time, calculates a residual value rate of the evaluation object based on a function included in third data and the effective time, and calculates an evaluation amount of the evaluation object by multiplying the new-product price and the residual value rate.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present embodiment relates to an information processing device, a program, and an evaluation method for evaluating an evaluation target. [Background technology]

[0002] Patent document 1 (JP Patent Publication No. 2021-2263) discloses an apparatus that calculates an equipment valuation amount for an object of evaluation, such as equipment (facility), taking into account the recovery of remaining useful life through updates (e.g., renovations or additional investments) and in line with the actual condition of the equipment being evaluated. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-2263 Summary of the Invention [Problem to be solved by the invention]

[0004] The present embodiment provides an information processing device, a program, and an evaluation method for performing an evaluation that appropriately reflects the degradation state of an evaluation target. [Means for solving the problem]

[0005] According to this embodiment, an information processing device includes a processing device and a storage device that stores data used by the processing device. The storage device stores first data correlating elapsed time, condition level, and remaining useful life, second data including normal useful life, and third data including a function for calculating a residual value rate based on the effective time. The processing device receives the evaluation time of the evaluation object, the condition level of the evaluation object, the manufacturing time or start time of use of the evaluation object, and the new price of the evaluation object, calculates the elapsed time of the evaluation object from the evaluation time and the manufacturing time or the start time of use, calculates the remaining useful life of the evaluation object based on the first data, the condition level of the evaluation object, and the elapsed time of the evaluation object stored in the storage device, calculates the normal useful life of the evaluation object based on the second data stored in the storage device, calculates the effective time of the evaluation object by subtracting the remaining useful life of the evaluation object from the normal useful life of the evaluation object, calculates the residual value rate of the evaluation object based on the function included in the third data stored in the storage device and the effective time of the evaluation object, and calculates the evaluation amount of the evaluation object by multiplying the new price of the evaluation object by the residual value rate. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an information processing apparatus according to a first embodiment. [Figure 2] FIG. 2 is a diagram showing an example of data used in the information processing apparatus according to the first embodiment. [Figure 3] FIG. 3 is a view showing an example of data processed by an evaluation unit of the information processing apparatus according to the first embodiment. [Figure 4] 6 is a flowchart showing an example of a process for generating remaining service life data and normal service life data according to the first embodiment. [Figure 5] 5 is a flowchart showing an example of processing for generating price function data according to the first embodiment. [Figure 6] 6 is a flowchart showing an example of evaluation processing according to the first embodiment. [Figure 7] A graph showing an example of the distribution of elapsed time and number of assets disposed of. [Figure 8]An example of the distribution of survival rate versus elapsed time. [Figure 9] FIG. 10 is a diagram showing an example of generation of remaining useful life data according to the second embodiment. [Figure 10] FIG. 11 is a diagram showing an example of generating price function data according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, each embodiment will be described with reference to the drawings. In the following description, substantially the same functions and components will be denoted by the same reference numerals and description thereof will be omitted, and duplicated description will be given only when necessary.

[0008] [First embodiment] In the first embodiment, an information processing device that calculates an evaluation amount that reflects the deterioration state of an evaluation object based on an effective time that reflects the state of the evaluation object and a price function will be described.

[0009] In the following, the effective time will be described as an example in the case of effective years, but other units such as effective months, effective days, effective minutes, and effective seconds may be used instead of effective years. The object of evaluation may be a classifiable asset or an asset with an identifiable type. The objects of evaluation belong to one of the asset categories (or types) that are treated statistically, such as metalworking machine tools, construction and mining machine tools, food processing machine tools, agricultural machine tools, textile machine tools, lumbering, woodworking and plywood machine tools, chemical machine tools, printing, bookbinding and paper processing machine tools, motors, transport machines, pumps and compressors, electrical equipment, private automobiles, freight vehicles, laboratory machinery and instruments, analyzers, testing machines, weighing machines and measuring instruments, computers and their accessories, copiers, machinery and equipment, and optical machinery. These asset categories are examples and can be freely changed or set. It is also possible to evaluate objects of evaluation using smaller categories than the above asset categories, such as hydraulic shovels instead of construction and mining machine tools, conveyors instead of transport machines, and lathes instead of metalworking machine tools.

[0010] Fig. 1 is a block diagram showing an example of the configuration of an information processing device 1 according to the first embodiment. The various components illustrated in Fig. 1 may be freely combined or separated as long as they can achieve the same or similar functions and actions.

[0011] The information processing device 1 operates as an evaluation device that evaluates the asset value of an evaluation target. The information processing device 1 includes, for example, an operation device 2, a display device 3, a storage device 4, and a processing device 5.

[0012] The information processing device 1 receives, from the operating device 2 operated by the user, the asset classification of the object to be evaluated, the deterioration state of the object to be evaluated (e.g., excellent, good, or fair), the evaluation reference time for evaluating the object to be evaluated (e.g., evaluation reference date), the manufacturing time of the object to be evaluated (e.g., manufacturing date) or start time of use (e.g., start date of use), and the new price of the object to be evaluated, all input by the user.

[0013] The information processing device 1 stores remaining durability data A1 to An, normal durability data 8, and value function data 9 in the storage device 4.

[0014] The information processing device 1 calculates, by the processing device 5, an appraisal amount that reflects the deterioration state of the appraisal object based on the above data input by the user and the above data stored in the storage device 4.

[0015] Then, the information processing device 1 causes the display device 3 to display data including the evaluation amount calculated by the processing device 5.

[0016] The operation device 2 accepts an operation from the user and transmits an operation signal indicating the content of the user's operation to the processing device 5. The operation device 2 may be, for example, a keyboard, a touch panel, or a pointing device.

[0017] The display device 3 may be, for example, a liquid crystal display device, an organic EL (Electro Luminescence) device, etc. The display device 3 receives a display signal from the processing device 5 and displays an image corresponding to the display signal.

[0018] The storage device 4 stores various software such as, for example, data input by a user using the operation device 2, data used by the processing device 5, data generated by the processing device 5, and programs 6 executed by the processing device 5. The software stored in the storage device 4 may include, for example, various setting values, pre-collected statistical data, and the like.

[0019] In the first embodiment, the storage device 4 stores, for example, a program 6, input data 7, remaining durability data A1 to An, normal durability data 8, value function data 9, evaluation result data 10, and the like.

[0020] When executed by the processing device 5, the program 6 calculates an evaluation, such as price, that reflects the deterioration state of the evaluation object based on input data 7 entered by the user, remaining service life data A1 to An, normal service life data 8, and price function data 9 stored in the storage device 4, and performs processing to generate and output evaluation result data 10.

[0021] The program 6 causes the processing device 5 to realize the functions of an input control unit 11, a data generation unit 12, an evaluation unit 13, and an output control unit .

[0022] The input data 7 is data input by a user, and includes, for example, the asset classification of the asset to be evaluated, a plurality of condition levels, an evaluation reference time, a manufacturing time, and a new price.

[0023] In the first embodiment, the time of use (operation) may be used instead of the time of manufacture. The condition level may be, for example, one of "excellent," "good," or "fair." The new product price may be the actual new repurchase price or a reasonable estimated price.

[0024] Each of the remaining useful life data A1 to An corresponds to a plurality of asset classes. The remaining useful life data A1 is data that manages the remaining useful life corresponding to each of a plurality of condition levels for each elapsed time for the asset class corresponding to the remaining useful life data A1. For example, the remaining useful life data A1 may correspond to the asset class "construction and mining machinery." For example, the plurality of condition levels may be "excellent," "good," and "fair." The remaining useful life data A2 to An are data that manage the remaining useful life corresponding to each of a plurality of conditions for each elapsed time for asset classes different from the remaining useful life data A1.

[0025] The remaining service life data A1 to An may be data in a table format, for example. The remaining service life data A1 to An do not have to be managed separately. That is, for example, at least two of the remaining service life data A1 to An may be combined and managed as one data.

[0026] The normal life data 8 includes normal life times, such as average life spans, for each of a plurality of asset classes.

[0027] The price function data 9 may be data including an approximation formula with elapsed time as an explanatory variable and residual value rate as an explained variable (objective variable) for each of a plurality of asset classes. The price function data 9 may be data in a table format in which functions and parameters are associated with each of a plurality of asset classes, for example.

[0028] The evaluation result data 10 includes the evaluation amount of the asset to be evaluated calculated by the evaluation unit 13 of the processing device 5.

[0029] Specific examples of the input data 7, remaining durability data A1 to An, normal durability data 8, and price function data 9 will be described later with reference to FIG.

[0030] The processing device 5 may be, for example, a microcomputer or a processor. The processing device 5 executes various processes based on a program 6 and various data stored in the storage device 4. More specifically, the processing device 5 executes the program 6 stored in the storage device 4 to realize the functions of an input control unit 11, a data generation unit 12, an evaluation unit 13, and an output control unit 14.

[0031] The input control unit 11 receives a signal indicating the content input by the user using the operation device 2. The input control unit 11 stores the input data 7 received by the operation device 2 in the storage device 4, for example.

[0032] In addition, the input control unit 11 may receive original data required to generate, for example, remaining service life data A1 to An, normal service life data 8, price function data 9, etc. from the operation device 2, an external information processing device, or an external storage device, and store the same in the storage device 4.

[0033] The data generation unit 12 generates data necessary for evaluation processing, such as remaining service life data A1 to An, normal service life data 8, price function data 9, etc., based on original data received from the operation device 2, an external information processing device, or an external storage device and stored in the storage device 4, and stores the generated data in the storage device 4.

[0034] Specific examples of generating the remaining durability data A1 to An, the normal durability data 8, and the price function data 9 will be described later with reference to FIGS. 4, 5, and 7 to 10. FIG.

[0035] The evaluation unit 13 generates evaluation result data 10 including an evaluation amount reflecting the deterioration state of the evaluation object based on the input data 7, at least one of the remaining durability data A1 to An, normal durability data 8, and price function data 9 stored in the memory device 4, and stores the evaluation result data 10 in the memory device 4.

[0036] The output control unit 14 performs output processing such as displaying various data such as screen data displayed by the program 6 and evaluation result data 10 stored in the storage device 4 on the display device 3, printing it using a printer, or transmitting the various data to other devices.

[0037] Fig. 2 is a diagram showing an example of data used by the information processing device 1 according to the first embodiment. Fig. 2 is an example and can be modified as appropriate.

[0038] Input data 7 includes the asset classification "Construction / Mining Machinery" to which the evaluation object (e.g., an asset such as equipment or facilities) entered by the user belongs, the condition level of the evaluation object "Good", the evaluation reference time "June 30, 2023" which is the reference time for obtaining the evaluation amount, the manufacturing time (or start time of use) of the evaluation object "January 1, 2013", and the new price (or new acquisition price) of the evaluation object "20,000,000".

[0039] The remaining useful life data A1 includes the relationship between the elapsed time (years) "1", "2", etc. and the remaining useful life (lifespan) for each condition level "excellent", "good", and "fair" for the asset classification "construction and mining machinery".

[0040] The remaining useful life data A2...An includes, for asset classifications other than the asset classification "construction and mining machinery", the relationship between the elapsed time "1", "2", etc. and the remaining useful life for each condition level "excellent", "good", and "fair", similar to the remaining useful life data A1.

[0041] Normal service life data 8 includes data relating various asset classifications such as "construction and mining machinery," "accessories," "vehicles," etc., to the average service life (lifespan) of normal service life such as "16.8," "14.2," "12.05," etc.

[0042] The price function data 9 includes data relating the type of price function used to calculate the residual value rate based on the effective time for various asset classifications such as "construction and mining machinery" to the values ​​○ and △ of the parameters a and b of the price function.

[0043] The evaluation unit 13 calculates the evaluated value of the evaluation object belonging to the asset classification "construction and mining machinery" based on input data 7 including the asset classification "construction and mining machinery", remaining useful life data A1 corresponding to the asset classification "construction and mining machinery", the normal useful life of "16.8" for the asset classification "construction and mining machinery" included in normal useful life data 8, the function type "linear" for the asset classification "construction and mining machinery" included in price function data 9, and the values ​​○ and △ of parameters a and b.

[0044] Then, the evaluation unit 13 generates evaluation result data 10 including the calculated evaluation amount.

[0045] 3 is a diagram showing an example of data processed by the evaluation unit 13 of the information processing device 1 according to the first embodiment. The evaluation unit 13 may execute processing while storing various data shown in FIG. 3 in the storage device 4. The evaluation unit 13 may also read out the various data shown in FIG. 3 from the storage device 4 and use it.

[0046] The evaluation unit 13 subtracts the manufacturing time "2013 / 1 / 1" from the evaluation reference time "2023 / 6 / 30" of the input data 7 to calculate the elapsed time "10 years."

[0047] Based on the elapsed time of "10 years" and the condition level of "good" of the input data 7, the evaluation unit 13 refers to the remaining useful life data A1 corresponding to the asset classification "construction / mining machinery" of the input data 7, and obtains the remaining useful life of "11.6 years" corresponding to the elapsed time of "10 years", the condition level of "good", and the asset classification of "construction / mining machinery".

[0048] The evaluation unit 13 refers to the normal life data 8 and obtains the normal life time of "16.8 years" corresponding to the asset classification "construction and mining machinery" of the input data 7.

[0049] The evaluation unit 13 subtracts the remaining useful life of "11.6 years" from the normal useful life of "16.8 years" to calculate the effective life of "5.2 years."

[0050] The evaluation unit 13 refers to the price function data 9 based on the asset classification "construction / mining machinery" of the input data 7, and obtains the type of price function "linear" corresponding to the asset classification "construction / mining machinery" and the values ​​of the parameters a and b (○, △).

[0051] The evaluation unit 13 calculates a residual value rate of "33.1%" based on the effective time of "5.2 years," the type of function of "linear," and the values ​​of the parameters a and b of ○ and △.

[0052] Then, the evaluation unit 13 multiplies the new price "20,000,000 yen" of the input data 7 by the residual value rate "33.1%" to calculate the evaluation amount "6,622,000 yen."

[0053] Fig. 4 is a flowchart showing an example of a process according to the first embodiment for generating remaining service life data A1 to An and normal service life data 8. In Fig. 4, at least one of S405 and S406 may be executed before or after S402 to S404, or may be executed simultaneously with S402 to S404.

[0054] A specific example of generating the remaining service life data A1 to An and the normal service life data 8 will be described later in the second embodiment.

[0055] In S401, the data generation unit 12 acquires the distribution of elapsed time and number of retired assets (number of failures) for each asset class. The distribution of elapsed time and number of retired assets includes, for example, the relationship between elapsed time (e.g., period of use) and the number of retired assets for each asset class. Note that data compiled and published by a public institution may be used as the distribution of elapsed time and number of retired assets for each asset class.

[0056] In S402, the data generation unit 12 generates a lifespan function for each asset class based on the distribution of elapsed time and number of retired assets for each asset class. For example, the data generation unit 12 may calculate the cumulative retirement rate (cumulative failure rate) for each elapsed time for each asset class, calculate the survival rate = 1 - cumulative retirement rate for each elapsed time, and approximate a graph with the first axis (e.g., horizontal axis) representing elapsed time and the second axis (e.g., vertical axis) representing the survival rate using a function to generate a lifespan function for each asset class. From the lifespan function, it is possible to calculate, for example, the remaining useful life for each elapsed time.

[0057] In S403, the data generating unit 12 adjusts the lifespan function generated in S402 for each asset class, and generates a plurality of lifespan functions corresponding to each of a plurality of state levels.

[0058] For example, the data generating unit 12 sets the lifespan function generated in S402 as the first lifespan function corresponding to the condition level "good" for each asset class.

[0059] For example, the data generating unit 12 may adjust the first lifespan function so that the mean lifespan is "the mean lifespan of the first lifespan function + the standard deviation σ of the first lifespan function," and set the adjusted function as the second lifespan function corresponding to the condition level "excellent." This second lifespan function may be generated by fixing the shape parameters and position parameters included in the first lifespan function so that the shape is similar to that of the first lifespan function, and by changing the scale parameter included in the first lifespan function so that the survival rate decays more slowly with elapsed time than with the first lifespan function.

[0060] For example, the data generating unit 12 may adjust the first lifespan function so that the mean lifespan is "mean lifespan of the first lifespan function - standard deviation σ of the first lifespan function" and set the adjusted function as the third lifespan function corresponding to the "fair" status level. This third lifespan function may be generated by fixing the shape and position parameters included in the first lifespan function so that the shape is similar to that of the first lifespan function, and by changing the scale parameter included in the first lifespan function so that the survival rate decays more rapidly with time than with the first lifespan function.

[0061] In S404, the data generating unit 12 generates remaining useful life data A1 to An for each asset class, which associates elapsed time with remaining useful life for each condition level of "excellent," "good," and "fair," based on the life functions for the multiple condition levels of "excellent," "good," and "fair" generated in S403. Then, the data generating unit 12 stores the remaining useful life data A1 to An for each asset class in the storage device 4.

[0062] In S405, the data generating unit 12 calculates the average lifespan of each asset class based on the distribution of the elapsed time and the number of assets disposed of for each asset class.

[0063] In S406, the data generating unit 12 generates normal durability data 8 that associates the average lifespan with each asset class. Then, the data generating unit 12 stores the normal durability data 8 in the storage device 4.

[0064] 5 is a flowchart showing an example of processing for generating the price function data 9 according to the first embodiment. A specific example of generating the price function data 9 will be described later in the third embodiment.

[0065] In S501, the data generation unit 12 acquires the distribution of elapsed time versus residual value rate for each asset class. The distribution of elapsed time versus residual value rate, for example, correlates the elapsed time (e.g., usage period) and the residual value rate for each asset class. Note that data compiled and published by a public institution may be used as the distribution of elapsed time versus residual value rate for each asset class.

[0066] In S502, the data generation unit 12 generates a price function for each asset class based on the distribution of elapsed time versus residual value rate for each asset class. For example, the data generation unit 12 may generate a price function for each asset class by approximating a graph with a first axis (e.g., horizontal axis) representing elapsed time and a second axis (e.g., vertical axis) representing residual value rate, using a function. For example, the data generation unit 12 may determine the type of function (linear function, exponential function, logarithmic function, power function) and the values ​​of parameters a and b as the price function. The type of function and the values ​​of the parameters may be determined using, for example, the least squares method.

[0067] In S503, the data generation unit 12 generates price function data 9 in which a price function and the values ​​of its parameters a and b are associated with each asset class. Then, the data generation unit 12 stores the price function data 9 in the storage device 4.

[0068] Fig. 6 is a flowchart showing an example of evaluation processing according to the first embodiment. In the explanation of Fig. 6, for ease of understanding, the explanation will be given using an example in which the various data are as shown in Figs. 2 and 3 above.

[0069] In S601, the evaluation unit 13 receives the input data 7 input by the user from the operation device 2.

[0070] In S602, the evaluation unit 14 calculates the elapsed time "10 years" based on the difference between the evaluation reference time "2023 / 6 / 30" included in the input data 7 and the manufacturing time "2013 / 1 / 1".

[0071] In S603, the evaluation unit 14 refers to the residual value useful life data A1 corresponding to the asset type "construction / mining machinery" included in the input data 7, and determines the remaining useful life of "11.6 years" corresponding to the condition level "good" included in the input data 7 and the elapsed time "10 years" calculated in S602. The evaluation unit 14 also refers to the normal useful life data 8, and determines the normal useful life of "16.8 years" corresponding to the asset type "construction / mining machinery" included in the input data 7.

[0072] In S604, the evaluation unit 14 calculates the effective life of "5.2 years" by subtracting the remaining life of "11.6 years" from the normal life of "16.8 years" obtained in S603.

[0073] In S605, the evaluation unit 14 refers to the price function data 9 and calculates the residual value rate of "33.1%" based on the function "f(x)=ax+b" corresponding to the asset type "construction / mining machinery" included in the input data 7, the values ​​○ and △ of the parameters a and b, and the effective time of "5.2 years" calculated in S604.

[0074] In S606, the evaluation unit 14 multiplies the new price "20,000,000" included in the input data 7 by the residual value rate "33.1%" to calculate the evaluation amount "6,622,000 yen."

[0075] In S606, the output control unit 14 displays the evaluation result data 10 including the evaluation amount "6,622,000 yen" on the display device 3, or transmits the evaluation result data 10 to another device.

[0076] The following describes the effects of the information processing device 1 according to the first embodiment.

[0077] As a first comparative example of the evaluation method used in the first embodiment, there is an evaluation method (cost approach) that estimates the remaining useful life from the normal useful life and the effective life to evaluate the evaluation target.

[0078] Here, the effective years are the actual years of use determined from the actual deterioration state of the asset.

[0079] In the first comparative example, the normal useful life - effective years = remaining useful life. Rewriting this equation gives the normal useful life - remaining useful life = effective years.

[0080] In other words, in the first comparative example, if the remaining useful life can be estimated appropriately, the effective life of the object of appraisal can be estimated. However, in the first comparative example, it is difficult to determine an appropriate remaining useful life, for example, by eliminating arbitrariness on the part of the appraiser.

[0081] As a second comparative example, there is a method of classifying the appraisal subject into conditions such as very good (VG), excellent (E), good (G), fair (F), poor (P), scrap (S), etc., and adjusting the appraisal amount. However, in the second comparative example, the adjustment rate must be set arbitrarily based on the appraiser's experience, making it difficult to ensure objectivity.

[0082] A third comparative example is a variation of the market approach (comparable transaction method). In this third comparative example, the relationship between the age or operating hours of the object to be appraised and the market price is plotted to analyze the correlation. However, in the third comparative example, it is difficult to aggregate the market price for each condition of the object to be appraised, making it difficult to obtain an appraisal that appropriately reflects the condition of the object to be appraised.

[0083] The evaluation process executed by the information processing device 1 according to the first embodiment solves the problems of the first to third comparative examples described above.

[0084] The information processing device 1 according to the first embodiment calculates the elapsed time for the evaluation object from the inputted manufacturing time or use start time and the evaluation reference time.

[0085] Next, the information processing device 1 calculates the remaining useful life from the input asset classification, the input condition level (for example, excellent, good, or fair), the remaining useful life data A1 corresponding to the asset classification, and the calculated elapsed time. The information processing device 1 also calculates the normal useful life corresponding to the input asset classification from the input asset classification and normal useful life data 8.

[0086] Next, the information processing device 1 calculates the normal useful life - the remaining useful life to obtain the effective time.

[0087] Next, the information processing device 1 inputs the effective time into the price function corresponding to the input asset classification, and calculates the residual value rate.

[0088] Then, the information processing device 1 calculates the appraisal value by multiplying the input new product price or a new procurement cost such as a reasonable estimate of the new product price by the residual value rate.

[0089] In the information processing device 1 according to the first embodiment that executes the evaluation process described above, by using an effective time and price function (a residual value rate prediction model using effective time) that reflects the state of the object to be evaluated, arbitrariness can be eliminated and evaluation results that reflect the deterioration state of the object to be evaluated can be obtained.

[0090] [Second embodiment] In the second embodiment, a method for generating remaining service life data A1 corresponding to the asset classification "construction and mining machinery" will be described. Note that remaining service life data A2 to An corresponding to asset classifications other than the "construction and mining machinery" can also be generated in the same manner as the remaining service life data A1 corresponding to the asset classification "construction and mining machinery," and therefore descriptions of these will be omitted.

[0091] In the second embodiment, the Weibull distribution is applied to generate the remaining useful life data A1.

[0092] In the Weibull distribution, the probability density function (failure density) is given by the following equation (1).

[0093]

number

[0094] In the above equation (1), t is time. Time t is the variable that forms the horizontal axis of the Weibull distribution graph corresponding to the test load.

[0095] m is the shape parameter. The shape parameter m determines the shape of the Weibull distribution.

[0096] η is the scale parameter, which determines the scale of the horizontal axis of a Weibull distribution graph.

[0097] γ is the location parameter. The location parameter γ relates to the location of the peak of the Weibull distribution.

[0098] In the Weibull distribution, the cumulative distribution function (cumulative failure rate) is given by the following equation (2).

[0099]

number

[0100] In the second embodiment, the cumulative retirement rate for each elapsed time of the asset is regarded as the cumulative distribution function of the Weibull distribution, and the survival rate for each elapsed time of the asset is regarded as 1-cumulative distribution function to generate remaining useful life data A1.

[0101] FIG. 7 is a diagram showing an example of the distribution of elapsed time and number of retired assets.

[0102] Figure 7 shows the results of examining the lifespans of multiple assets for multiple asset classifications. In Figure 7, the number of retired assets for multiple elapsed times is tallied for each asset classification. The distribution of elapsed time and number of retired assets may be calculated using, for example, official statistics on asset lifespans, questionnaire surveys, or performance survey results.

[0103] To explain more specifically, for example, for a specific asset class of "homes," the time (e.g., number of years) until assets belonging to this specific asset class of "homes" are disposed of is collected through a questionnaire or the like. The number of valid responses to this questionnaire becomes the population parameter for the specific asset class of "homes." The time until disposal reported is divided into classes according to the elapsed time, such as "0 to less than 5 years," "5 to less than 10 years," "30 to less than 40 years," "40 to less than 50 years," and "50 years or more," and the number of assets disposed of within each elapsed time is tallied.

[0104] For the asset category "Residential," the number of assets disposed of within the elapsed time of "0 years or more but less than 5 years" is "75." For the asset category "Residential," the number of assets disposed of within the elapsed time of "5 years or more but less than 10 years" is "99." The relationship between other asset categories, elapsed time, and number of disposed assets in Figure 7 is similar.

[0105] FIG. 8 is a diagram showing an example of the distribution of elapsed time versus survival rate.

[0106] In Figure 8, survival rates for multiple elapsed times are displayed for multiple asset classes based on the distribution of elapsed time and number of retired assets in Figure 7. Survival rates may also be expressed as reliability.

[0107] For each asset category, the survival rate over time can be calculated by {1 - (cumulative retirement rate over time)}, which is the ratio of the cumulative number of retired assets that have been retired up to that time to the parameter.

[0108] As mentioned above, the parameter for an asset class is the total number of assets whose lifespans have been examined for that asset class.

[0109] A method for calculating the numerical values ​​shown in FIG. 8 from the numerical values ​​shown in FIG. 7 will be specifically described.

[0110] In Figure 7 above, the parameter for the asset class "Housing" is the sum of the number of retired assets for all elapsed times for the asset class "Housing": "0 to less than 5 years," "5 to less than 10 years," "30 to less than 40 years," "40 to less than 50 years," and "50 years or more," totaling 75 + 99 + ... + 503 + 454 + 192. Parameters for other asset classes can be calculated using similar rules.

[0111] In Figure 7 above, for the asset class "Residential," the number of assets disposed of for an elapsed time of "0 to less than 5 years" is "75." In this case, for the asset class "Residential," the cumulative number of disposed assets corresponding to an elapsed time of "0 to less than 5 years" is "75." Therefore, for the asset class "Residential," the survival rate corresponding to an elapsed time of "0 to less than 5 years" is 1 - (75 / parameter for the asset class "Residential") = 0.962.

[0112] In Figure 7 above, for the asset class "Residential," the number of assets disposed of within the elapsed time of "5 to 10 years" is "99." In this case, for the asset class "Residential," the cumulative number of disposed assets corresponding to the elapsed time of "5 to 10 years" is the total number of assets disposed of from "0 to 5 years" to "5 to 10 years," which is "75 + 99." Therefore, for the asset class "Residential," the survival rate corresponding to the elapsed time of "5 to 10 years" is 1 - {(75 + 99) / parameter for the asset class "Residential"} = 0.912.

[0113] In Figure 7 above, for the asset class "residential," the number of assets retired during the elapsed time period "30 to less than 40 years" is "503." In this case, for the asset class "residential," the cumulative number of retired assets corresponding to the elapsed time period "30 to less than 40 years" is the total number of assets retired from "0 to less than 5 years" to "30 to less than 40 years," that is, "75 + 99 + ... + 503." Therefore, for the asset class "residential," the survival rate corresponding to the elapsed time period "30 to less than 40 years" is 1 - {(75 + 99 + ... + 503) / parameter for the asset class "residential"} = 0.912.

[0114] The survival rates corresponding to other asset classes and elapsed times in FIG. 8 are also calculated according to the same rules.

[0115] FIG. 9 is a diagram showing an example of generation of remaining useful data A1 according to the second embodiment.

[0116] For example, the first axis (horizontal axis) represents elapsed time, the second axis (vertical axis) represents survival rate, and the graph obtained by plotting the survival rate for each elapsed time for the asset classification "construction and mining machinery" included in Figure 8 above is estimated as the lifespan function.

[0117] Weibull distributions, such as the Weibull probability density function in equation (1) above and the Weibull cumulative distribution function in equation (2) above, are used as functions that describe the lifespan and failure rate of machines. The Weibull cumulative distribution function represents the cumulative failure rate. The lifespan function is equivalent to (1 - cumulative failure rate function). The lifespan function may also be expressed as a reliability function or a survival rate function.

[0118] By applying the concept of life tables to the life function, it is possible to determine the normal useful life, which is the average lifespan of the asset classification "construction and mining machinery," and generate normal useful life data8.

[0119] In the third embodiment, the estimated life function is associated with the condition level "good" and is set as the first life function.

[0120] The lifespan function (second lifespan function) corresponding to the "excellent" status level is generated by adjusting the first lifespan function. Specifically, the second lifespan function is generated by adjusting the first lifespan function so that (the mean value of the second lifespan function = the mean value of the first lifespan function + the standard deviation σ of the first lifespan function), fixing the shape parameter m and the position parameter γ of the second lifespan function to the shape parameter m and the position parameter γ of the first lifespan function, and adjusting the scale parameter η of the second lifespan function so that the decay of the survival rate with respect to elapsed time is slower than that of the first lifespan function (in other words, so that the deterioration of the evaluation object does not progress). Here, the position parameter γ may be set to zero.

[0121] A lifespan function (third lifespan function) corresponding to the "fair" status level is also generated by adjusting the first lifespan function. Specifically, the third lifespan function is generated by adjusting the first lifespan function so that (mean value of the third lifespan function = mean value of the first lifespan function - standard deviation σ of the first lifespan function), fixing the shape parameter m and the position parameter γ of the third lifespan function to those of the first lifespan function, and adjusting the scale parameter η of the third lifespan function so that the decay of the survival rate with respect to elapsed time is steeper than that of the first lifespan function (in other words, so that the deterioration of the evaluation target progresses). Here, the position parameter γ may be set to zero.

[0122] The scale parameter η of the Weibull distribution cumulative distribution function determines the degree of expansion or contraction of the Weibull distribution cumulative distribution function in the direction of elapsed time (horizontal axis direction). By adjusting the scale parameter η and fixing the shape parameter m and location parameter γ, it is possible to expand or compress the graph of the asset class survival rate in the direction of elapsed time while maintaining a similar shape of the graph of the asset class survival rate.

[0123] The three lifespan functions calculated as above do not result in a survival rate of zero even if the elapsed time is extended indefinitely, so these three lifespan functions are adjusted so that the survival rate becomes zero at a certain elapsed time.

[0124] In this way, by setting life functions corresponding to the condition levels "excellent," "good," and "fair" for the asset classification "construction and mining machinery," it becomes possible to estimate the remaining useful life according to the condition of the object being evaluated, and to calculate the normal useful life - remaining useful life = effective life.

[0125] From the life function for the condition levels "excellent," "good," and "fair" for the asset classification "construction and mining machinery," remaining useful life data A1 is generated that associates elapsed time with remaining useful life for each condition level "excellent," "good," and "fair" for the asset classification "construction and mining machinery."

[0126] [Third embodiment] In the third embodiment, a method for generating price function data 9 including a price function corresponding to the asset class "construction and mining machinery" will be described. Note that price functions corresponding to asset classes other than the "construction and mining machinery" can also be generated in the same manner as the price function corresponding to the asset class "construction and mining machinery," and therefore a description thereof will be omitted.

[0127] FIG. 10 is a diagram showing an example of generating price function data 9 according to the third embodiment.

[0128] In Figure 10, data relating elapsed time and residual value rates for each of a plurality of asset classes is used as the original data. This original data represents the distribution of average residual value rates for each elapsed period for each asset class. For example, official statistics on residual value rates for asset classes, survey results, or performance survey results may be used as this original data.

[0129] For example, based on Figure 10, the price function for the asset class "Construction and Mining Equipment" is: i) Determine the price function f(x) and parameters a and b as an approximate function with elapsed time as the explanatory variable and residual value rate as the objective variable (explained variable). ii) The price function f(x) is determined for each asset class within a range of one year or more and less than the normal useful life. iii) If the effective time is less than one year, round it up to one year. iv) When the remaining useful life becomes zero, the scrap residual value is adjusted to zero or a preset scrap residual value rate. It may be determined as follows.

[0130] When approximating the distribution of the average residual value rate for each period to a price function, the price function can be a linear function (f(x)=ax+b), an exponential function (f(x)=a*exp(b*x)), or a logarithmic function (f(x)=a*log e (b*x)), power function (f(x)=a*x b ) may be selected using, for example, the least squares method, and the parameter values ​​a and b may be determined.

[0131] The price function f(x) and parameters a and b for the asset class "construction and mining machinery" determined by such means are stored in price function data 9. The price functions f(x) and parameters a and b for other asset classes are determined in a similar manner and stored in price function data 9.

[0132] The embodiments of the present invention are presented as examples and are not intended to limit the scope of the invention. The present embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. The present embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the inventions described in the claims and their equivalents. [Explanation of symbols]

[0133] 1...information processing device, 2...operation device, 3...display device, 4...storage device, 5...processing device, 6...program, 7...input data, A1 to An...remaining durability data, 8...normal durability data, 9...price function data, 10...evaluation result data, 11...input control unit, 12...data generation unit, 13...evaluation unit, 14...output control unit

Claims

1. a processing device; a storage device for storing data used by the processing device; Equipped with The storage device includes: First data relating elapsed time, a condition level, and a remaining useful life; Second data including a normal service life; Third data including a function for calculating a residual value rate based on an effective time; Remember, The processing device includes: receiving an evaluation time of the evaluation object, a condition level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new price of the evaluation object; calculating an elapsed time of the evaluation object from the evaluation time and the manufacturing time or the start time of use; calculating a remaining useful life of the evaluation object based on the first data stored in the storage device, the condition level of the evaluation object, and the elapsed time of the evaluation object, and calculating a normal useful life of the evaluation object based on the second data stored in the storage device; Calculating the effective life of the evaluation object by subtracting the remaining useful life of the evaluation object from the normal useful life of the evaluation object; Calculating a residual value rate of the object to be evaluated based on the function included in the third data stored in the storage device and the effective time of the object to be evaluated; calculating an appraised value of the object to be appraised by multiplying the new price of the object to be appraised by the residual value rate of the object to be appraised; Information processing device.

2. the state level is one of a first level, a second level that is less deteriorated than the first level, and a third level that is more deteriorated than the first level, The change in the first level of remaining useful life corresponds to a first lifespan function based on the aggregated survival rate for the evaluation subject; a change in the remaining useful life of the second level corresponds to a second life function obtained by adjusting the first life function so that deterioration of the evaluation object does not progress beyond the survival rate; a change in the remaining useful life at the third level corresponds to a third life function obtained by adjusting the first life function so that deterioration of the evaluation object progresses more rapidly than the survival rate; the second life function is obtained by adjusting the first life function so that an average value of the second life function is a value obtained by adding an average value of the first life function and a standard deviation of the first life function; the third life function is obtained by adjusting the first life function so that an average value of the third life function is a value obtained by subtracting the standard deviation of the first life function from an average value of the first life function. The information processing device according to claim 1.

3. 3. The information processing device of claim 2, wherein the second lifetime function and the third lifetime function are obtained by fixing a shape parameter and a position parameter included in a cumulative failure rate function on which the first lifetime function is based, and changing a scale parameter.

4. 2. The information processing device of claim 1, wherein the function included in the third data is generated by approximating the relationship between the elapsed time and the residual value rate aggregated for the evaluation object to one of a linear function, an exponential function, a logarithmic function, and a power function.

5. On the computer, a function of receiving an evaluation time of the evaluation object, a condition level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new price of the evaluation object; a function of calculating an elapsed time of the evaluation object from the evaluation time and the manufacturing time or the use start time; a function of calculating a remaining useful life of the evaluation object based on first data stored in a storage device and correlating elapsed time, a status level, and a remaining useful life, the status level of the evaluation object, and the elapsed time of the evaluation object, and also calculating a normal useful life of the evaluation object based on second data stored in the storage device and including a normal useful life; a function of calculating an effective time of the evaluation object by subtracting a remaining useful time of the evaluation object from a normal useful time of the evaluation object; Third data stored in the storage device and including a function for calculating a residual value rate based on an effective time; and a function for calculating a residual value rate of the evaluation object based on the effective time of the evaluation object; a function of multiplying the new price of the object to be evaluated by the residual value rate of the object to be evaluated to calculate the evaluated amount of the object to be evaluated; A program to achieve this.

6. receiving, by a processing device, an evaluation time of the evaluation object, a condition level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new product price of the evaluation object; calculating, by the processing device, an elapsed time of the evaluation object from the evaluation time and the manufacturing time or the use start time; The processing device calculates the remaining useful life of the evaluation object based on first data stored in a storage device and correlating elapsed time, status level, and remaining useful life, the status level of the evaluation object, and the elapsed time of the evaluation object, and calculates the normal useful life of the evaluation object based on second data stored in the storage device and including the normal useful life; calculating an effective life of the evaluation object by subtracting a remaining useful life of the evaluation object from a normal useful life of the evaluation object by the processing device; Calculating, by the processing device, a residual value rate of the evaluation object based on third data stored in the storage device and including a function for calculating a residual value rate based on an effective time, and the effective time of the evaluation object; calculating an appraised value of the object to be appraised by multiplying the new price of the object to be appraised by the residual value rate of the object to be appraised by the processing device; An evaluation method comprising:

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