DESIGN SUPPORT FACILITY AND DESIGN SUPPORT PROCEDURES

Abstract

Design support facility (2), comprising: a data acquisition unit (11) to acquire a plurality of sets of design candidate data containing a plurality of design parameters as candidates for design data of an electric motor, and to acquire an initial evaluation value from each of the plurality of sets of design candidate data; a data generation unit (13) to select at least one upper set of design candidate data with the first rating value that is relatively high as the first design candidate data from the multitude of sets of design candidate data acquired by the data acquisition unit, and to perform the generation of second design candidate data containing the multitude of design parameters from each set of the first design candidate data; a rating value calculation unit (14) to calculate a second rating value of each set of the first design candidate data based on the multitude of design parameters contained in each set of the first design candidate data, and to calculate the second rating value of each set of the second design candidate data based on the multitude of design parameters contained in each set of the second design candidate data; a design data selection unit (15) for selecting design candidate data to be used as design data of the electric motor from the plurality of sets of first design candidate data and the plurality of sets of second design candidate data on the basis of the second evaluation value calculated by the evaluation value calculation unit; and a preprocessing unit (12) to calculate the first evaluation value of each of the plurality of sets of design candidate data using the plurality of design parameters contained in each of the plurality of sets of design candidate data acquired by the data acquisition unit (11), a desired level of each of the plurality of design parameters and an ideal value of each of the plurality of design parameters, and to output the first evaluation value of each of the plurality of sets of design candidate data to the data acquisition unit (11); the multitude of design parameters includes magnetic flux density, winding temperature, starting current, torque curve, permissible load time, efficiency and power factor; wherein the data acquisition unit (11) comprises a design data conversion unit (11a) configured to convert each design parameter by replacing each design parameter contained in each set of design candidate data into an objective function; and wherein the preprocessing unit (12) is configured to provide the first evaluation value V n the design candidate data x n ' to calculate by using the design parameter g(y m ') after the conversion, the desired level f m asp and the ideal value f m ideal replaced by an evaluation function that is V n = maxm ( g ( ym ' ) − fmaspfmasp − fmideal ) is expressed where max is a mathematical symbol that searches for m with a maximum value in () under m = 1,..., M and expresses the maximum value as V n determines.

Description

AREA OF TECHNOLOGY

[0001] The present disclosure relates to a design support facility and a design support procedure. TECHNICAL BACKGROUND

[0002] The design data for an electric motor encompasses a multitude of design parameters. Examples of these parameters include magnetic flux density, winding temperature, starting current, torque curve, permissible load time, efficiency, and power factor. This multitude of design parameters may include two or more that are in a trade-off relationship or balancing act with one another. Such a balancing act means that the electric motor designer may not be able to easily design or conceptualize the motor based solely on these parameters.

[0003] There is a design support facility (hereinafter referred to as the "conventional design support facility") that assists or helps in the design or construction of an electric motor.

[0004] A conventional design support device comprises a storage unit and a processing unit. The storage unit stores a multitude of sets of design candidates as candidates for the electric motor's design data. Each set of design candidate data contains a multitude of design parameters.

[0005] The processing unit calculates an evaluation value for each set of design candidate data based on the multitude of design parameters contained in each set of design candidate data stored in the memory unit. The processing unit then selects the design candidate data with the highest evaluation value from the multitude of sets of design candidate data stored in the memory unit as the design data for the electric motor.

[0006] Patent literature 1 discloses a technique for determining a valuation function for calculating a valuation value.

[0007] Deb K.: Introduction to evolutionary multiobjective optimization, In: Multiobjective optimization: Interactive and evolutionary approaches 2008 Oct 18 (pp. 59-96), Springer Berlin Heidelberg gives an introduction to evolutionary multiobjective optimization.

[0008] Miettinen K, Ruiz F, Wierzbicki AP: Introduction to multiobjective optimization: interactive approaches, In: Multiobjective optimization: interactive and evolutionary approaches 2008 Oct 18 (pp. 27-57); Book; Springer Berlin Heidelberg also provides an introduction to evolutionary multiobjective optimization.

[0009] US 2019 / 0 325 320 A1 describes an optimization calculation method.

[0010] US 2022 / 0180210 A1 describes an optimization device. REFERENCE LIST PATENT LITERATURE

[0011] Patent Literature 1: JP 2014-74994 A SUMMARY OF THE INVENTIONAL PROBLEM

[0012] The number of design candidates stored in the memory unit of the conventional design support device is finite. Therefore, it is possible that design candidate data with a higher rating than the design candidate data stored in the memory unit (hereinafter referred to as "high-rating design candidate data") may not be stored in the memory unit. In such a case, the problem arises that the computation unit cannot select the high-rating design candidate data as the design data for the electric motor. Even if the conventional design support device determines the rating function for calculating a rating according to the technology disclosed in Patent 1, it is still possible that the high-rating design candidate data will not be stored in the memory unit.

[0013] The present disclosure was made to solve the above problem, and one objective of the present disclosure is to provide a design support device capable of selecting design candidate data with a higher rating than design data of an electric motor in a case where there is design candidate data with a higher rating than pre-prepared design candidate data. SOLUTION TO THE PROBLEM

[0014] This problem is solved by objects having the features according to the independent claims. Advantageous embodiments of the invention are the subject of the figures, the description, and the dependent claims. A design support device according to the present disclosure comprises: a data acquisition unit for acquiring a plurality of sets of design candidate data, which contain a plurality of design parameters as candidates for the design data of an electric motor, and for acquiring a first evaluation value from each of the plurality of sets of design candidate data;a data generation unit to select at least one upper set of design candidate data with a relatively high initial rating as the first design candidate data from the multitude of sets of design candidate data acquired by the data acquisition unit, and to generate second design candidate data containing the multitude of design parameters from each set of the first design candidate data; a rating calculation unit to calculate a second rating for each set of the first design candidate data based on the multitude of design parameters contained in each set of the first design candidate data, and to calculate the second rating for each set of the second design candidate data based on the multitude of design parameters contained in each set of the second design candidate data;and a design data selection unit for selecting design candidate data to be used as design data for the electric motor from the multitude of sets of first design candidate data and the multitude of sets of second design candidate data based on the second evaluation value calculated by the evaluation value calculation unit; and a preprocessing unit for calculating the first evaluation value of each of the multitude of sets of design candidate data using the multitude of design parameters contained in each of the multitude of sets of design candidate data acquired by the data acquisition unit, a desired level of each of the multitude of design parameters, and an ideal value of each of the multitude of design parameters, and outputting the first evaluation value of each of the multitude of sets of design candidate data to the data acquisition unit. ADVANTAGEOUS EFFECTS OF THE INVENTION

[0015] According to the present disclosure, in a case where there are design candidate data with a higher rating than design data of an electric motor that have been prepared in advance, design candidate data with a higher rating than design data of an electric motor can be selected. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a configuration representation which depicts a design support system comprising a design support device 2, according to a first embodiment. Fig. Figure 2 is a hardware configuration representation that depicts the hardware of the design support device 2 according to the first embodiment. Fig. 3 is a hardware configuration representation of a computer in the case that the design support feature 2 is implemented by software, firmware or the like. Fig. Figure 4 is a flowchart illustrating a design support procedure, which is a processing operation carried out in the design support facility 2. Fig. Figure 5 is an explanatory representation that shows an example of an objective function g. Fig. 6A is an explanatory representation of which M design parameters g(y) m ') shows that in each of the N sets of design candidates x1' to x N ' are included. Fig. 6B is an explanatory representation of which M design parameters g(y) m ') shows that the initial draft candidate data z 1,h (h = 1,..., H) are included, which were selected by the data generation unit 13. Fig. Figure 7 is an explanatory representation which shows a generation example (1) for second design candidate data. 2,j shows. Fig. Figure 8 is an explanatory representation which shows a generation example (2) for second design candidate data. 2,j shows. Fig. Figure 9 is an explanatory representation which shows a generation example (3) for second design candidate data. 2,j shows. DESCRIPTION OF THE EXECUTION FORMS

[0016] In order to explain the present revelation in more detail below, a method for carrying out the present revelation will be described with reference to the accompanying drawings. First embodiment.

[0017] Fig. Figure 1 is a configuration representation which depicts a design support system comprising a design support device 2, according to a first embodiment.

[0018] Fig. Figure 2 is a hardware configuration representation that depicts the hardware of the design support device 2 according to the first embodiment.

[0019] In Fig. 1 The design support system comprises a design data storage unit 1, a design support device 2 and a display device 3.

[0020] The design data storage unit 1 is implemented, for example, by a non-volatile or volatile semiconductor memory, such as a random access memory (RAM), a read-only memory (ROM), a flash memory, an erasable programmable read-only memory (EPROM) or an electrically erasable programmable read-only memory (EEPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini-disk or a digital versatile disk (DVD).

[0021] The design data storage unit 1 stores a multitude of sets of design candidate data as candidates for the design data of an electric motor. Each set of design candidate data contains a multitude of design parameters. The design parameters are design specifications of the electric motor.

[0022] Storage unit 1 stores a multitude of design candidates as candidates for the electric motor design data. The design data of electric motors designed in the past cannot be used as is as the design data for the current electric motor design target, because the environmental conditions for installation or the operating conditions differ between the electric motor design target and the electric motor designed in the past.

[0023] Examples of the many design parameters include magnetic flux density, winding temperature, starting current, torque curve, permissible load time, efficiency, and power factor.

[0024] The magnetic flux density, winding temperature, and starting current are each design parameters with a design condition that they must not exceed an upper limit. The torque curve, permissible load time, efficiency, and power factor are each design parameters with a design condition that they must not fall below a lower limit.

[0025] The multitude of design parameters can also include two or more design parameters that are in a balancing relationship with each other. For example, in a case where the performance of one particular design parameter is to be increased by two or more design parameters, while the performance of another design parameter is degraded among the two or more design parameters, the particular design parameter and the other design parameter are in a balancing relationship with each other. Because the particular design parameter and the other design parameter influence each other, the electric motor designer may not be able to design the design data directly. For example, there is a balancing relationship between efficiency and torque curve.

[0026] The design support facility 2 is a facility that supports or assists in the design of an electric motor.

[0027] The design data support facility 2 comprises a data acquisition unit 11, a preprocessing unit 12, a data generation unit 13, a scoring unit 14 and a design data selection unit 15.

[0028] The display unit 3 shows design candidate data, an initial evaluation value, electric motor design data and the like on a display (not shown) according to the display data output by the design support unit 2.

[0029] Data acquisition unit 11, for example, is implemented by a Fig. 2. Data acquisition circuit 21 shown is implemented.

[0030] Data acquisition unit 11 includes a design data conversion unit 11a.

[0031] The data acquisition unit 11 acquires a plurality of sets of design candidate data from the design data storage unit 1, and acquires the first score value of each set of design candidate data from the preprocessing unit 12.

[0032] The design data conversion unit 11a converts each design parameter by replacing each design parameter contained in each set of design candidate data into an objective function.

[0033] The objective function is a function that returns the design parameter after conversion when the design parameter is replaced. Furthermore, the objective function can be, for example, a function with a local minimum value and a function that minimizes the first evaluation value near an upper limit within an upper / lower boundary range or near a lower limit within the upper / lower boundary range.

[0034] The data acquisition unit 11 outputs a multitude of sets of design candidate data, containing a multitude of design parameters, after conversion to the preprocessing unit 12 and the data generation unit 13, and outputs an initial evaluation value for each set of design candidate data to the data generation unit 13.

[0035] In the Fig. The design support facility 2 shown in Figure 1 includes the data acquisition unit 11 and the design data conversion unit 11a. This is merely an example, and the design data conversion unit 11a may be located outside of the data acquisition unit 11.

[0036] The preprocessing unit 12 is, for example, equipped with a Fig. 2. Preprocessing circuit 22 shown is implemented.

[0037] The preprocessing unit 12 includes an interface unit 12a.

[0038] The interface unit 12a, for example, is a human-machine interface in the form of a mouse, a keyboard or a touch panel.

[0039] The interface unit 12a performs a process of receiving a setting of a desired level of each design parameter acquired by the data acquisition unit 11 and a process of receiving the correction of the desired level.

[0040] Furthermore, the interface unit 12a performs processing of the receipt of a setting of ideal values ​​of the respective design parameters.

[0041] The preprocessing unit 12 calculates an initial evaluation value for each set of design candidate data using a variety of design parameters contained in each set of design candidate data acquired by the data acquisition unit 11, a desired level of each design parameter, and an ideal value of each design parameter.

[0042] The preprocessing unit 12 outputs the first evaluation value of each set of design candidate data to the data acquisition unit 11.

[0043] Furthermore, the preprocessing unit 12 generates display data to display each set of draft candidate data acquired by the data acquisition unit 11 and the first score of each set of draft candidate data, and outputs the display data to the display device 3.

[0044] At the in Fig. The design support unit 2 shown in Figure 1 includes the preprocessing unit 12 and the interface unit 12a. This is merely an example, and the interface unit 12a can also be located outside of the preprocessing unit 12.

[0045] Furthermore, the preprocessing unit 12 generates in the Fig. The design support unit 12 shown here displays display data to show each set of design candidate data, including a variety of design parameters, after conversion. This is only an example, and the preprocessing unit 12 can acquire each set of design candidate data stored in the design data storage unit 1 from the data acquisition unit 11 and generate display data to show each set of design candidate data.

[0046] Furthermore, the preprocessing unit 12 calculates in the Fig. The design support unit 2 shown in Figure 1 calculates the first evaluation value of each set of design candidate data using the multitude of design parameters after conversion contained in each set of design candidate data, the desired level of each design parameter, and the ideal value of each design parameter. This is merely an example, and the preprocessing unit 12 can calculate the first evaluation value of each set of design candidate data by utilizing a multitude of design parameters contained in each set of design candidate data stored in the design data storage unit 1, a desired level of each design parameter, and an ideal value of each design parameter.

[0047] Data generation unit 13, for example, is defined by a Fig. 2. Data generation circuit 23 shown is implemented.

[0048] The data generation unit 13 acquires a multitude of sets of design candidate data, including a multitude of design parameters, after conversion from the data acquisition unit 11.

[0049] The data generation unit 13 selects at least one upper set of design candidate data with a relatively high initial rating as the first design candidate data from the multitude of sets of design candidate data.

[0050] In the Fig. In the design support unit 2 shown, the data generation unit 13 acquires a multitude of sets of design candidate data, including a multitude of design parameters, after conversion from the data acquisition unit 11. This is merely an example, and the data generation unit 13 can acquire a multitude of sets of design candidate data stored in the design data storage unit 1 from the data acquisition unit 11.

[0051] The data generation unit 13 generates second design candidate data from each set of first design candidate data, containing a variety of design parameters.

[0052] In particular, the data generation unit 13 generates each set of the second design candidate data such that a value of any design parameter from the multitude of design parameters contained in each set of the second design candidate data differs from a value of a design parameter corresponding to the arbitrary design parameter contained in the first design candidate data, which is a generation source.

[0053] The data generation unit 13 outputs each set of the first design candidate data and each set of the second design candidate data to the evaluation value calculation unit 14.

[0054] The valuation value calculation unit 14 is, for example, defined by a Fig. The valuation value calculation circuit 24 shown is implemented.

[0055] The evaluation value calculation unit 14 calculates the second evaluation value of each set of first design candidate data based on the multitude of design parameters contained in each set of first design candidate data selected by the data generation unit 13.

[0056] The evaluation value calculation unit 14 calculates the second evaluation value of each set of second design candidate data based on the multitude of design parameters contained in each set of second design candidate data generated by the data generation unit 13.

[0057] The rating value calculation unit 14 outputs each set of first design candidate data, the second rating value of each set of first design candidate data, each set of second design candidate data, and the second rating value of each set of second design candidate data to the design data selection unit 15.

[0058] The design data selection unit 15, for example, is defined by a Fig. The design data selection circuit 25 shown in Figure 2 is implemented. The design data selection unit 15 selects design candidate data to be used as design data for the electric motor from the multitude of sets of first design candidate data and the multitude of sets of second design candidate data based on the second evaluation value calculated by the evaluation value calculation unit 14.

[0059] The design data selection unit 15 outputs the design data of the electric motor, for example, to a design data management unit that is not shown.

[0060] Furthermore, the design data selection unit 15 generates display data for displaying the design data of the electric motor and outputs the display data to the display device 3.

[0061] In Fig. 1 It is assumed that each of the data acquisition unit 11, the preprocessing unit 12, the data generation unit 13, the evaluation value calculation unit 14 and the design data selection unit 15, which are components of the design support facility 2, is implemented by dedicated hardware, as shown in Fig. 2 is shown. That is, it is assumed that the design support facility 2 is implemented by the data acquisition circuit 21, the preprocessing circuit 22, the data generation circuit 23, the evaluation value calculation circuit 24 and the design data selection circuit 25.

[0062] Each of the data acquisition circuit 21, the preprocessing circuit 22, the data generation circuit 23, the evaluation value calculation circuit 24 and the design data selection circuit 25 corresponds, for example, to a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or a combination thereof.

[0063] The components of the Design Support Device 2 are not limited to those implemented by dedicated hardware, and the Design Support Device 2 can be implemented by software, firmware, or a combination of both. The software or firmware is stored as a program in a computer's memory. The computer refers to hardware that executes a program and corresponds, for example, to a central processing unit (CPU), a graphics processing unit (GPU), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a processor, or a digital signal processor (DSP).

[0064] Fig. 3 is a hardware configuration representation of a computer in the case that the design support feature 2 is implemented by software, firmware or the like.

[0065] In a case where the design support device 2 is implemented by software, firmware, or the like, a program that causes a computer to perform each processing operation in the data acquisition unit 11, the preprocessing unit 12, the data generation unit 13, the evaluation value calculation unit 14, and the design data selection unit 15 is stored in a working memory 31. Then, a processor 32 of the computer executes the program stored in the working memory 31.

[0066] Furthermore, it illustrates Fig. 2 an example in which each of the components of the design support facility 2 is implemented by dedicated hardware, and Fig. Figure 3 illustrates an example where the design support device 2 is implemented by software, firmware, or the like. However, this is only an example, and some components of the design support device 2 may be implemented by dedicated hardware, while the remaining components may be implemented by software, firmware, or the like.

[0067] The following describes how the in Fig. 1. Design support facility 2 is described.

[0068] Fig. Figure 4 is a flowchart illustrating a design support procedure, which is a processing operation carried out in the design support facility 2.

[0069] Data acquisition unit 11 acquires N sets of draft candidate data x n as candidates for electric motor design data from design data storage unit 1 (step ST1 in Fig. 4). n = 1, ..., N, and N is an integer equal to or greater than 2.

[0070] The design candidate data x n contain a design parameter y m . m = 1,..., M, and M is an integer equal to or greater than 2.

[0071] The design data conversion unit 11a of the data acquisition unit 11, for example, stores an objective function g, as in Fig. 5 shown.

[0072] Fig. Figure 5 is an explanatory representation that shows an example of the objective function g.

[0073] In Fig. 5 represents the horizontal axis as the design parameter y m represents, and the vertical axis represents g(y) m ') represents the value of the objective function g. y m ' is obtained by normalizing the design parameter y m .

[0074] As in Fig. As shown in section 5, the objective function g is a function with a local minimum value. In the example of Fig. 5. A local minimum value exists when the normalized design parameter y m ' is close to 1.0. In this case, the waveform of the in Fig. The objective function g shown in Figure 5 is deformed and does not completely match the actual waveform.

[0075] In the example of Fig. 5 represents the upper / lower limit of the design parameter g(y). m ') after the conversion, which is a value of the objective function g, 0.0 to 1.0. The objective function g is a function that determines the first evaluation value V. n minimized near the upper limit within the upper / lower limit range or near the lower limit within the upper / lower limit range.

[0076] If the design parameter y m , which is in the draft candidate data x n is included, is a design parameter that has a design condition that the design parameter y mthe upper limit y UP,m Since the design data conversion unit 11a must not exceed the design parameter y, it is normalized. m as expressed in the following expression (1). The design parameter with the design condition that the upper limit y UP,m For example, a magnetic flux density, a winding temperature, or a starting current must not be exceeded.

[0077] If the design parameter y m , which is in the design candidate data x n is included, is a design parameter that has a design condition that the design parameter y m the lower limit y LO,m Since the design data conversion unit 11a must not fall below a certain value, it normalizes the design parameter y. m as expressed in the following expression (2). The design parameter that defines the lower limit y LO,mExamples of values ​​that must not be undercut include a torque curve or a permissible load time.

[0078] In the normalization of the design parameter y shown in expression (1) m is the normalized design parameter y m '“1”, if y m = y UP,m is, and the normalized design parameter y m ' is "0" if y m = y LO,m is.

[0079] In the normalization of the design parameter y shown in expression (2). m is the normalized design parameter y m ' "0", if y m = y UP,m is, and the normalized design parameter y m ' is "1" if y m = y LO,m is. ym'=ym−yLO,myUP,m−yLO,m ym'=yUP,m−ymyUP,m−yLO,m

[0080] The design data conversion unit 11a converts the normalized design parameter y m', by using the normalized design parameter y m ', which is in the draft candidate data x n is contained in the objective function g, as expressed in the following expression (3), replaced to represent the design parameter g(y). m ') after the conversion. g(ym')=1−ym'+μ1⋅P1(ym')P1(ym')={0,(ym'≤γm)(ym'−γm)2,(ym'>γm)μ1=1(1−γm)2

[0081] In expression (3) y m a threshold.

[0082] If y LO,m < y m ' < y UP , m is, is 0 < g (y m ') < 1, and if y m ' approximately equal to y UP,m is, is g(y m ') approximately 1.0. If furthermore y m ' > y UP,m , is g(y m ') infinity.

[0083] Data acquisition unit 11 provides the draft candidate data x n ' (n = 1,..., N) including the design parameters g(y1') to g(y M') after conversion by the design data conversion unit 11a to the preprocessing unit 12.

[0084] The preprocessing unit 12 acquires design candidate data x n ' (n = 1,..., N) from the data acquisition unit 11.

[0085] The interface unit 12a of the preprocessing unit 12 receives a setting of a desired level f. m asp of the normalized design parameter y m ' (m = 1,..., M), which is in the design candidate data x n ' is included.

[0086] In particular, if the designer of the electric motor, for example, includes an operation for setting the desired level f m asp of the design parameter y m 'using interface unit 12a, interface unit 12a receives the setting of the desired level f m asp The desired level f m aspindicates a level desired by the designer that is lower than an ideal value f. m ideal The designer of the electric motor can, for example, specify the desired M levels f1. asp up to f M asp Determine using a method for determining a desired level, such as a satisfactory balancing method.

[0087] The interface unit 12a also receives the setting of the ideal value f. m ideal of the normalizing design parameter y m ', which is in the draft candidate data x n ' is included.

[0088] If, in particular, the designer of the electric motor, for example, performs an operation of setting the ideal value f m ideal of the design parameter y m ' when using interface unit 12a, interface unit 12a receives the setting of the ideal value f m ideal ,

[0089] For example, preprocessing unit 12 calculates the first evaluation value V n the design candidate data x n ', by changing the design parameter g(y m ') after the conversion, the desired level f m asp and the ideal value f m ideal replaced by an evaluation function, which is expressed by the following expression (4). Vn=maxm(g(ym')−fmaspfmasp−fmideal)

[0090] In expression (4), max is a mathematical symbol that searches for m with a maximum value in () under m = 1,..., M and expresses the maximum value as V. n determines.

[0091] In the Fig. The design support unit 12 shown calculates the first evaluation value V. n the design candidate data x n'using the evaluation function shown in expression (4). This is only an example, and the preprocessing unit 12 can provide the first evaluation value V n Calculate using a different evaluation function than expression (4). The evaluation function that differs from expression (4) may, for example, include an installation condition or an operating condition of the electric motor.

[0092] For example, preprocessing unit 12 sorts the N design candidates x1' to x N ' in descending order of the first rating V n , generates display data to show the sorted draft candidate data x 1' to x N ' and outputs the display data to display unit 3.

[0093] Display unit 3 shows the sorted design candidate data x1' to x N ' on a display (not shown).

[0094] The electric motor designer, for example, checks the sorted design candidate data x1' to x displayed on the screen. N '.

[0095] If the N sets of design candidates x1' to x N If the components are not sorted as intended, the electric motor designer can perform an operation to correct the desired level f. m asp of the design parameter y m 'perform using interface unit 12a.

[0096] If the interface unit 12a corrects the desired level f m asp Upon receiving the first evaluation value V, the preprocessing unit 12 calculates the first evaluation value V. n the design candidate data x n ' again using the corrected desired level f m asp .

[0097] If the N sets of design candidate data x1' to x N'As sorted as intended by the designer, the preprocessing unit 12 gives the first evaluation value V'. n (n = 1,..., N) of the design candidate data x n ' to data acquisition unit 11.

[0098] Data acquisition unit 11 acquires the first valuation value V n (n = 1,..., N) of the design candidate data x n ' from preprocessing unit 12 (step ST2 in Fig. 4).

[0099] Data acquisition unit 11 provides the draft candidate data x n 'and the first rating value V n to the data generation unit 13.

[0100] Data generation unit 13 acquires the design candidate data x n ' (n = 1,..., N) and the first evaluation value V n from Data Acquisition Unit 11.

[0101] The data generation unit 13 selects from the N sets of design candidate data x1 to x N'the top H sets of design candidate data with the first rating value V n , which is relatively high, as initial candidate design data. 1,h from (step ST3 in Fig. 4). h = 1,..., H. H is an integer equal to or greater than 1.

[0102] Fig. 6A is an explanatory representation showing M design parameters g(y) m ) shows that in each of the N sets of design candidate data x1' to x N ' are included. In the example of Fig. 6A is N = 5 and M = 2.

[0103] Fig. 6B is an explanatory representation of which M design parameters g(y) m ') shows that the initial draft candidate data z 1,h (h = 1,..., H) are included, which were selected by data generation unit 13. In the example of Fig. 6B is H = 3 and M = 2.

[0104] In Fig. 6A and Fig. In 6B, the horizontal axis represents the design parameter g(y1') and the vertical axis represents the design parameter g(y2'). • denotes the design candidate data x n ' (n = 1,..., 5), and ◯ denotes the first design candidate data z 1,h (h = 1, 2 and 3).

[0105] The circled numbers indicate the descending order of the first rating values ​​V n The evaluation value of the circled number = 1 is the evaluation value V1 of the first draft candidate data z. 1,1 and is the highest among the first rating values ​​V1 to V3.

[0106] The rating value of the circled number = 2 is the rating value V2 of the first design candidate data z. 1,2 and is the second highest among the first rating values ​​V1 to V3. The rating value of the circled number = 3 is the rating value V3 of the first draft candidate data z. 1,3and is the third highest among the first rating values ​​V1 to V3.

[0107] The evaluation value of the draft candidate data that is close to the first draft candidate data is... 1,h are present and have a high initial rating value V n their value is often higher than the rating of the draft candidate data, which differs significantly from the initial draft candidate data. 1,h are remotely available.

[0108] Data generation unit 13 generates second design candidate data z. 2,j (j = 1,..., J) including M design parameters from the first design candidate data z 1,h (Step ST4 in Fig. 4) J is an integer equal to or greater than 1.

[0109] In particular, data generation unit 13 generates the second design candidate data z. 2,j such that the value of any design parameter g(y m '') from the M design parameters g(y1'') to g(y M''), which are in the second draft candidate data z 2,j are included, differing from the value of the design parameter g(y) m ') differs, which is found in the first draft candidate data z 1,h is included as a generation source.

[0110] Data generation unit 13 outputs the first design candidate data. 1,h and the second draft candidate data z 2,j to the valuation value calculation unit 14.

[0111] The following is a concrete generation example for the second draft candidates. 2,j described by the data generation unit 13. [Generation example (1)]

[0112] If H = 3, the data generation unit 13 defines a range that is defined by the first design candidate data z1 ,1 , the first draft candidate data z 1,2 and the first draft candidate data z1 ,3 is surrounded, as in Fig. 7 shown.

[0113] Fig. Figure 7 is an explanatory representation that shows a generation example (1) for the second design candidate data. 2,j shows.

[0114] In Fig. Figure 7 represents the horizontal axis as the design parameter g(y1'), which is found in the first design candidate data z 1,h is included, and the design parameter g(y1''), which is contained in the second design candidate data z 2,j The vertical axis represents the design parameter g(y2'), which is included in the first design candidate data z. 1,h is included, and the design parameter g(y2''), which is contained in the second design candidate data z 2,j is included.

[0115] A thick black line indicates a rectangular area defined by the initial design candidate data z1. ,1 , the first draft candidate data z 1,2 and the first draft candidate data z 1,3is surrounded. Here, a thick black line indicates a rectangular area. This is merely an example, and the thick black line could, for instance, indicate a triangular area containing each set of the first draft candidate data z1. ,1 , the first draft candidate data z 1,2 and the first draft candidate data z 1,3 is present at the vertex. Therefore, the data generation unit 13 can define an H-gonal region in which each of the H sets of the first design candidate data z 1,1 up to z 1,H , which were selected by data generation unit 13, are present at the vertex.

[0116] Each Δ that exists in the space is design candidate data including a design parameter g( ym '), which differs from the design parameters g(y m '), which are in the first draft candidate data z 1,hare contained, differs. In the example of Fig. There are 7 and 13 Δ in the area.

[0117] In Fig. 7 contains Δ, which is to the left of the first design candidate data z 1,1 is present, for example the design parameter g(y2''), which has the same value as the design parameter g(y2') that is present in the first design candidate data z 1,1 is included, but does not include the design parameter g(y1''), which has the same value as the design parameter g(y1') that is included in the first design candidate data z 1,1 The Δ contains a design parameter g(y1'') that has a smaller value than the design parameter g(y1'), e.g., by the resolution of the design parameter g(y1').

[0118] For example, Δ contains the data above and alongside the first design candidate data z. 1,2 The design parameter g(y1'') is available, which has the same value as the one in the first design candidate data z.1,2 The design parameter g(y1'') is included, but the design parameter g(y2'') is not included, which has the same value as the one in the first design candidate data z. 1,2 Included design parameters g(y2'). The Δ includes a design parameter g(y2'') that has a larger value than the design parameter g(y2'), e.g., by the resolution of the design parameter g(y2').

[0119] Data generation unit 13 generates all of the 13 Δ that are present in the area as the second design candidate data z. 2,j (j = 1,..., J).

[0120] Here, the data generation unit selects 13 in a case where the upper limit of the second design candidate data is z. 2,j is determined, Δ with a relatively high first valuation value V n , which corresponds to the upper limit, from the 13 Δ and generates each of the selected Δ that corresponds to the upper limit as the second design candidate data z.2,j (j = 1,..., J). In this case, J is the upper limit of the second design candidate data z. 2,j .

[0121] The first rating value V n Each Δ is calculated, for example, by the data generation unit 13 using expression (4) similarly to the preprocessing unit 12. In this case, the data generation unit 13 must achieve the desired level f. m asp and the ideal value f m ideal acquired from pre-processing unit 12. [Generation example (2)]

[0122] As in Fig. As shown in Figure 8, the data generation unit 13 defines an area that includes each set of the first design candidate data. 1,h surrounds (h = 1,..., H).

[0123] Fig. Figure 8 is an explanatory representation which shows a generation example (2) for second design candidate data. 2,j shows.

[0124] In Fig. Figure 8 represents the horizontal axis as the design parameter g(y1'), which is found in the first design candidate data z 1,h is included, and the design parameter g(y1''), which is contained in the second design candidate data z 2,j The vertical axis represents the design parameter g(y2'), which is included in the first design candidate data z. 1,h is included, and the design parameter g(y2''), which is contained in the second design candidate data z 2,j is included.

[0125] Area (1) is an area containing the first design candidate data. 1,1 surrounds, and area (2) is an area containing the first design candidate data. 1,2 surrounds. Furthermore, area (3) is an area that contains the data of the first design candidate. 1,3 surrounds.

[0126] In the example of Fig. 8 the sizes of the regions (1) to (3) are equal, and each of the regions (1) to (3) comprises eight Δ.

[0127] However, this is only one example, and the sizes of regions (1) to (3) can differ from each other. Furthermore, the shape of each of the regions (1) to (3) is not limited to a quadrilateral and can also be a polygon.

[0128] The data generation unit 13 generates all of the eight Δ that are present in each of the areas (1) to (3) as the second design candidate data z. 2,j (j = 1,..., J).

[0129] However, in one case where the upper limit of the second draft candidate data is z 2,j The data generation unit 13 Δ is determined, selecting a relatively high initial rating V. n , which corresponds to the upper limit, from 23 (= 8 × 3 - 1) Δ, and generates each of the selected Δ that corresponds to the upper limit as the second design candidate data z. 2,j(j = 1,..., J). Δ, which is present at the upper left vertex of region (3), overlaps with Δ at the lower right vertex of region (1). Therefore, Δ with a relatively high first evaluation value V is used here. n The upper limit is selected from 23 (= 8 × 3 - 1) Δ instead of 24 (= 8 × 3) Δ. In this case as well, the data generation unit 13 calculates, for example, the first evaluation value V. n each Δ with expression (4) similar to the preprocessing unit 12.

[0130] Furthermore, the data generation unit 13 calculates in a case where the upper limit of the second design candidate data z 2,j The number of Sel(n) of the second design candidate data that can be selected from each of the areas (1) to (3) is determined, based on the first evaluation value V. n (n = 1, 2, 3).

[0131] For example, if the upper limit is L and 0 ≤ V nIf ≤ 1,0, the number Sel(n) is calculated as the following expression (5). Sel(n)=L×Vn / 1.0

[0132] For example, if L = 10, V1 = 0.5, V2 = 0.3, and V2 = 0.2, the data generation unit 13 calculates "5" as the number of Sel(1) of the second design candidate data that can be selected from range (1). Furthermore, the data generation unit 13 calculates "3" as the number of Sel(2) of the second design candidate data that can be selected from range (2), and calculates "2" as the number of Sel(3) of the second design candidate data that can be selected from range (3).

[0133] In this example, the data generation unit 13 generates each of the Δ of Sel(1) (= 5) with a relatively high first evaluation value V. n among the eight Δ that are present in area (1), the second design candidate data z 2,j .

[0134] Furthermore, the data generation unit 13 generates each of the Δ of Sel(2) (= 3) with a relatively high first evaluation value V n among the eight Δ that are present in area (2), as the second design candidate data z 2,j .

[0135] Furthermore, the data generation unit 13 generates each of the Δ of Sel(3) (= 2) with a relatively high first evaluation value V n among the eight Δ that are present in area (3), as the second design candidate data z 2,j . [Generation example (3)]

[0136] In generation example (2), the data generation unit 13 defines a range in which each set of the first design candidate data z 1,h (h = 1 ,..., H) is present in the middle.

[0137] This is just one example, and the data generation unit 13 can adjust ranges (1) to (3) such that each set of the first design candidate data z 1,h(h = 1,..., H) is present near the boundary of the area, as in Fig. Figure 9 shows the generation example (3). Generation example (3) is similar to generation example (2), except for the setting of the range.

[0138] Fig. Figure 9 is an explanatory representation which shows a generation example (3) for second design candidate data. 2,j shows.

[0139] In Fig. Figure 9 represents the horizontal axis as the design parameter g(y1'), which is found in the first design candidate data z. 1,h is included, and the design parameter g(y1''), which is contained in the second design candidate data z 2,j The vertical axis represents the design parameter g(y2'), which is included in the first design candidate data z. 1,h is included, and the design parameter g(y2''), which is contained in the second design candidate data z 2,j is included.

[0140] The valuation value calculation unit 14 acquires the first design candidate data z. 1,h (h = 1,..., H) and the second design candidate data z 2,j (j = 1,..., J) from data generation unit 13.

[0141] The valuation value calculation unit 14 calculates the second valuation value E 1,h the first draft candidate data z 1,h based on the normalized design parameters y m ' (m = 1,..., M), which are in the first design candidate data z 1,h are included, as expressed in the following expression (6) (step ST5 in Fig. 4). E1,h=summ(g(ym')−fmaspfmasp−fmideal)

[0142] In expression (6), sum is a mathematical symbol in which the total value of the values ​​in () in each of m = 1,..., ME is represented. 1,h is.

[0143] The valuation value calculation unit 14 calculates the second valuation value E 2,jthe second draft candidate data z 2,j based on the design parameter y m '' (m = 1,..., M), which is in the second design candidate data z 2,j is contained as expressed in the following expression (7) (step ST5 in Fig. 4). E2,j=summ(g(ym'')−fmaspfmasp−fmideal)

[0144] The evaluation value calculation unit 14 provides the first design candidate data. 1,h (h = 1,..., H), the second valuation value E 1,h the first draft candidate data z 1,h , the second draft candidate data z 2,j (j = 1,..., J) and the second rating value E 2,j the second draft candidate data z 2,j to the design data selection unit 15.

[0145] The design data selection unit 15 acquires the first design candidate data z. 1,h (h = 1,..., H), the second valuation value E 1,h the first draft candidate data z 1,h, the second draft candidate data z 2,j (j = 1,..., J), and the second rating value E 2,j the second draft candidate data z 2,j from the valuation value calculation unit 14.

[0146] The design data selection unit 15 selects design candidate data, which are to be used as design data for the electric motor, from the first design candidate data. 1,1 up to z 1,H and the second draft candidate data z 2,1 up to z 2,J based on the second valuation value E 1,h (h = 1,..., H) and the second evaluation value E 2,j (j = 1,..., J) from (step ST6 in Fig. 4).

[0147] Specifically, the design data selection unit 15 specifies the highest second rating value by specifying the second rating values ​​E 1,1 to E 1,H , E 2,1 to E 2,J compares them.

[0148] Then, the design data selection unit 15 selects as the design data of the electric motor the design candidate data that corresponds to the highest second evaluation value from the first design candidate data z. 1,1 up to z 1,H and the second draft candidate data z 2,1 up to z 2,J out of.

[0149] The design data selection unit 15 outputs the design data of the electric motor, for example, to a design data management unit that is not shown.

[0150] Furthermore, the design data selection unit 15 generates display data for displaying the design data of the electric motor and outputs the display data to the display device 3.

[0151] In the first embodiment described above, the design support device 2 is configured to include the data acquisition unit 11 for acquiring a plurality of sets of design candidate data containing a plurality of design parameters as candidates for design data of an electric motor, and for acquiring a first evaluation value for each set of design candidate data, and the data generation unit 13 for selecting at least one upper set of design candidate data with the first evaluation value being relatively high, as first design candidate data from the plurality of sets of design candidate data acquired by the data acquisition unit 11, and for generating second design candidate data containing the plurality of design parameters from each set of first design candidate data.Furthermore, the design support unit 2 comprises the evaluation value calculation unit 14 to calculate a second evaluation value of each set of the first design candidate data based on a variety of design parameters contained in each set of the first design candidate data, and to calculate a second evaluation value of each set of the second design candidate data based on a variety of design parameters contained in each set of the second design candidate data, and the design data selection unit 15 to select design candidate data to be used as design data of the electric motor from the variety of sets of the first design candidate data and the variety of sets of the second design candidate data based on the second evaluation value calculated by the evaluation value calculation unit 14.Therefore, in a case where there are design candidate data with a higher rating than the pre-prepared design candidate data, the design support facility 2 can select design candidate data with a higher rating than the electric motor design data.

[0152] In the first embodiment, the design support device 2 is further configured to include the preprocessing unit 12 for calculating a first evaluation value of each set of design candidate data using a plurality of design parameters contained in each set of design candidate data acquired by the data acquisition unit 11, a desired level of each of the design parameters, and an ideal value of each of the design parameters, and outputting the first evaluation value of each set of design candidate data to the data acquisition unit 11. Therefore, the design support device 2 increases the probability that the at least one upper set of design candidate data selected by the data generation unit 13 consists of design candidate data containing a design parameter that is highly likely to achieve the performance desired by the designer.Furthermore, the probability is increased that the second design candidate data generated by data generation unit 13 are design candidate data that contain a design parameter which is highly likely to achieve the performance desired by the designer.

[0153] In the first embodiment, the design support device 2 is configured such that the preprocessing unit 12 includes the interface unit 12a to receive a setting of a desired level for each of the design parameters acquired by the data acquisition unit 11. Therefore, the designer can set a desired level using the design support device 2.

[0154] Furthermore, in the first embodiment, the preprocessing unit 12 generates display data to show each set of design candidate data acquired by the data acquisition unit 11 and the first evaluation value of each set of design candidate data, and outputs the display data to the display device 3. The design support device 2 is then configured such that the interface unit 12a receives a correction of the desired level for each design parameter acquired by the data acquisition unit 11. Therefore, in the design support device 2, the designer can, for example, select desired design candidate data as the first design candidate data.

[0155] In the Fig. The design support unit 2 shown in Figure 1 converts the design data conversion unit 11a to the normalized design parameter y. m ' using the objective function g, which determines the first valuation value V nminimized near the upper limit within the upper / lower limit range or near the lower limit within the upper / lower limit range.

[0156] This is just one example, and in a case where the normalized design parameter y m ' is a design parameter that is better because its value is higher if the normalized design parameter y m If the value is greater than the lower limit, the design data conversion unit 11a can use the normalized design parameter y. m 'Convert using the objective function g, as expressed in expression (8) below. A better design parameter is, for example, the efficiency or power factor, the higher its value. g(ym')=ym'+μ2⋅P2(ym')P2(ym')={0,(ym'≤γm)(ym'−γm)2,(ym'>γm)μ2=1(10−10)2

[0157] In expression (8), if y m ' > y LO,m , g(y m ') <1.

[0158] In the Fig. 1. The design support device shown in section 2 calculates the assessment value calculation unit 14, the second assessment value E. 1,h the first draft candidate data z 1,h according to expression (6), and calculates the second valuation value E 2,j the second draft candidate data z 2,j according to expression (7).

[0159] This is just one example, and the valuation value calculation unit 14 can be the second valuation value E 1,h the first draft candidate data z 1,h calculate according to the following expression (9), and the second valuation value E 2,j the second draft candidate data z 2,j calculate according to the following expression (10). E1,h=maxm(g(ym')−fmaspfmasp−fmideal) E2,j=maxm(g(ym'')−fmaspfmasp−fmideal)

[0160] It should be noted that in the present disclosure any component of the embodiment can be modified or any component of the embodiment can be omitted. COMMERCIAL APPLICABILITY

[0161] The present disclosure relates to a design support facility and a design support procedure. REFERENCE MARK LIST

[0162] 1: Design data storage unit, 2: Design support unit, 3: Display unit, 11: Data acquisition unit, 11a: Design data conversion unit, 12: Preprocessing unit, 12a: Interface unit, 13: Data generation unit, 14: Evaluation value calculation unit, 15: Design data selection unit, 21: Data acquisition circuit, 22: Preprocessing circuit, 23: Data generation circuit, 24: Evaluation value calculation circuit, 25: Design data selection circuit, 31: Main memory, 32: Processor

Claims

[1] Design support facility (2), comprising: a data acquisition unit (11) to acquire a plurality of sets of design candidate data containing a plurality of design parameters as candidates for design data of an electric motor, and to acquire an initial evaluation value from each of the plurality of sets of design candidate data; a data generation unit (13) to select at least one upper set of design candidate data with the first rating value that is relatively high as the first design candidate data from the multitude of sets of design candidate data acquired by the data acquisition unit, and to perform the generation of second design candidate data containing the multitude of design parameters from each set of the first design candidate data; a rating value calculation unit (14) to calculate a second rating value of each set of the first design candidate data based on the multitude of design parameters contained in each set of the first design candidate data, and to calculate the second rating value of each set of the second design candidate data based on the multitude of design parameters contained in each set of the second design candidate data; a design data selection unit (15) for selecting design candidate data to be used as design data of the electric motor from the plurality of sets of first design candidate data and the plurality of sets of second design candidate data on the basis of the second evaluation value calculated by the evaluation value calculation unit; and a preprocessing unit (12) to calculate the first evaluation value of each of the plurality of sets of design candidate data using the plurality of design parameters contained in each of the plurality of sets of design candidate data acquired by the data acquisition unit (11), a desired level of each of the plurality of design parameters and an ideal value of each of the plurality of design parameters, and to output the first evaluation value of each of the plurality of sets of design candidate data to the data acquisition unit (11); the multitude of design parameters includes magnetic flux density, winding temperature, starting current, torque curve, permissible load time, efficiency and power factor; wherein the data acquisition unit (11) comprises a design data conversion unit (11a) configured to convert each design parameter by replacing each design parameter contained in each set of design candidate data into an objective function; and wherein the preprocessing unit (12) is configured to provide the first evaluation value V n the design candidate data x n ' to calculate by using the design parameter g(y m ') after the conversion, the desired level f m asp and the ideal value f m ideal replaced by an evaluation function that is Vn=maxm(g(ym')−fmaspfmasp−fmideal) is expressed where max is a mathematical symbol that searches for m with a maximum value in () under m = 1,..., M and expresses the maximum value as V n determines. [2] Design support device according to claim 1, wherein the preprocessing unit (12) has an interface unit (12a) to receive a setting of a desired level of each of the plurality of design parameters acquired by the data acquisition unit (11). [3] Design support device according to claim 2, wherein the preprocessing unit (12) generates display data to display each of the plurality of sets of design candidate data acquired from the data acquisition unit (11) and the first evaluation value of each of the plurality of sets of design candidate data, and outputs the display data to a display device (3), and the interface unit (12a) receives correction of the desired level of each of the plurality of design parameters acquired from the data acquisition unit (11). [4] Design assistance device according to claim 1, wherein the data generation unit generates each set of the second design candidate data such that a value of one of the plurality of design parameters contained in each set of the second design candidate data differs from a value of a design parameter corresponding to one of the plurality of design parameters contained in the first design candidate data which is a generation source for the generation. [5] Design support procedures, including: Acquire, through a data acquisition unit (11), a plurality of sets of design candidate data containing a plurality of design parameters as candidates of design data of an electric motor, and acquire, through a data acquisition unit, a first evaluation value from each of the plurality of sets of design candidate data; Selecting, by a data generation unit (13), at least one upper set of design candidate data with the first rating that is relatively high, as the first design candidate data from the multitude of sets of design candidate data acquired by the data acquisition unit, and performing, by a data generation unit, the generation of second design candidate data, which contain the multitude of design parameters from each set of the first design candidate data; Calculate, by means of an evaluation value calculation unit (14), a second evaluation value of each set of the first design candidate data based on the multitude of design parameters contained in each set of the first design candidate data, and calculate, by means of an evaluation value calculation unit, the second evaluation value of each set of the second design candidate data based on the multitude of design parameters contained in each set of the second design candidate data; Selecting, by a design data selection unit (15), design candidate data to be used as design data of the electric motor from the plurality of sets of first design candidate data and the plurality of sets of second design candidate data on the basis of the second evaluation value calculated by the evaluation value calculation unit; and Calculate, by a preprocessing unit (12), the first evaluation value of each of the plurality of sets of design candidate data using the plurality of design parameters contained in each of the plurality of sets of design candidate data acquired by the data acquisition unit (11), a desired level of each of the plurality of design parameters and an ideal value of each of the plurality of design parameters, and output, by a preprocessing unit (12), the first evaluation value of each of the plurality of sets of design candidate data to the data acquisition unit (11); the multitude of design parameters includes magnetic flux density, winding temperature, starting current, torque curve, permissible load time, efficiency and power factor; wherein a design data conversion unit (11a) of the data acquisition unit (11) converts each design parameter by replacing each design parameter contained in each set of design candidate data into an objective function; and wherein the preprocessing unit (12) provides the first evaluation value V n the design candidate data x n ' calculated by using the design parameter g(y) m ') after the conversion, the desired level f m asp and the ideal value f m ideal replaced by an evaluation function that is Vn=maxm(g(ym')−fmaspfmasp−fmideal) is expressed where max is a mathematical symbol that searches for m with a maximum value in () under m = 1,..., M and expresses the maximum value as V n determines.

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