Program, information processing method and information processing system

Abstract

To provide a program, an information processing method, an information processing system and others, capable of analyzing material characteristics for a shorter time than before even for high-definition image data.SOLUTION: According to one aspect of the present invention, a program for analyzing material characteristics of a non-homogeneous body from image data of the non-homogeneous body is provided. This program is configured to cause a computer to execute an acquisition step, a conversion step, a construction step, a calculation step and an output step. In the acquisition step, the image data is acquired. In the conversion step, the image data is compressed and converted into compressed data. In the construction step, a discrete numerical model is constructed on the basis of the compressed data and a mathematical model obtained by modelling the material characteristics. In the calculation step, the numerical model is analyzed to calculate a material characteristic value of the non-homogeneous body. In the output step, the material characteristic value is output.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a program, an information processing method, and an information processing system. [Background technology]

[0002] Patent Document 1 discloses a system for analyzing the material properties of polycrystalline solids.

[0003] This material property analysis system inputs numerical data obtained by measuring each crystal grain of the object to be measured using a backscattered electron diffraction device so that each crystal grain includes multiple measurement points, assigns the input numerical data to finite elements, and constructs a finite element model by assigning physical property values expressed as three-dimensional tensors to the finite elements, and then numerically analyzes the numerical data using the finite element model. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-264750 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the material property analysis system disclosed in Patent Document 1 uses the finite element method for analysis, and therefore the analysis time increases polynomially according to the number of nodes in the set mesh.

[0006] In view of the above circumstances, the present invention provides a program, an information processing method, an information processing system, etc., that are capable of analyzing material properties in a shorter time than conventional methods, even when using high-resolution image data. [Means for solving the problem]

[0007] According to one aspect of the present invention, there is provided a program for analyzing material properties of a non-homogeneous body from image data of the non-homogeneous body. The program is configured to cause a computer to execute an acquisition step, a conversion step, a construction step, a calculation step, and an output step. In the acquisition step, image data is acquired. In the conversion step, the image data is compressed and converted into compressed data. In the construction step, a discretized numerical model is constructed based on the compressed data and a mathematical model in which the material properties are modeled. In the calculation step, the numerical model is analyzed to calculate material property values of the non-homogeneous body. In the output step, the material property values are output.

[0008] According to this aspect, even with high-resolution image data, material properties can be analyzed in a shorter time than conventionally possible. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a configuration diagram illustrating an information processing system 100. [Figure 2] FIG. 2 is a block diagram showing the hardware configuration of an information processing device 200. [Figure 3] FIG. 2 is a block diagram showing the hardware configuration of the terminal 300. [Figure 4] FIG. 2 is a block diagram showing functions realized by an information processing device 200 (control unit 210). [Figure 5] 1 is an activity diagram showing the flow of an information processing method executed by information processing device 200. FIG. [Figure 6] 10 is a table showing processing times for image conversion in an example and a comparative example. [Figure 7] 1 is a table showing processing times for image analysis in Examples and Comparative Examples. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0011] Incidentally, the program for realizing the software appearing in this embodiment may be provided as a non-temporary recording medium readable by a computer, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0012] In this embodiment, the term "unit" may also include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In addition, various types of information are handled in this embodiment, and this information may be represented by, for example, physical values of signal values representing voltages and currents, high and low signal values as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations may be performed on a circuit in the broad sense.

[0013] In addition, a circuit in the broad sense is a circuit realized by at least appropriately combining a circuit, circuitry, a processor, a memory, etc. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0014] 1. Hardware Configuration In Section 1, the hardware configuration of this embodiment will be described.

[0015] 1-1. Information Processing System 100 FIG. 1 is a configuration diagram showing an information processing system 100. The information processing system 100 includes an information processing device 200 and a terminal 300, which are connected via a network. These components will be further described. Here, a system exemplified as the information processing system 100 is composed of one or more devices or components. Therefore, for example, even the information processing device 200 alone can become a system exemplified as the information processing system 100.

[0016] 1-2. Information processing device 200 2 is a block diagram showing the hardware configuration of information processing device 200. Information processing device 200 has a control unit 210, a storage unit 220, and a communication unit 250, and these components are electrically connected via a communication bus 260 inside information processing device 200. Each component will be further described.

[0017] The control unit 210 processes and controls the overall operations related to the information processing device 200. The control unit 210 is, for example, a central processing unit (CPU) not shown. The control unit 210 realizes various functions related to the information processing device 200 by reading out predetermined programs stored in the storage unit 220. In other words, information processing by software stored in the storage unit 220 is specifically realized by the control unit 210, which is an example of hardware, and can be executed as each functional unit included in the control unit 210. This will be further explained in Section 2. Note that the control unit 210 is not limited to being a single unit, and multiple control units 210 may be provided for each function. A combination of these may also be used.

[0018] The storage unit 220 stores various information necessary for information processing by the information processing device 200. This may be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs related to the information processing device 200 executed by the control unit 210, or as a memory such as a random access memory (RAM) that stores information (arguments, arrays, etc.) temporarily required for program calculations. Alternatively, it may be implemented as a combination of these.

[0019] The communication unit 250 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but may also include wireless LAN network communication, mobile communication such as 5G / LTE / 3G, Bluetooth (registered trademark) communication, etc. as necessary. In other words, it is more preferable to implement it as a collection of multiple communication means. In other words, the information processing device 200 communicates various information with the terminal 300 via the communication unit 250 over a network.

[0020] 1-3. Terminal 300 3 is a block diagram showing the hardware configuration of terminal 300. Terminal 300 has a control unit 310, a storage unit 320, a display unit 330, an input unit 340, and a communication unit 350, and these components are electrically connected via a communication bus 360 inside terminal 300. Descriptions of control unit 310, storage unit 320, and communication unit 350 are omitted because they are substantially the same as the descriptions of control unit 210, storage unit 220, and communication unit 250 in information processing device 200.

[0021] The display unit 330 may be included in the housing of the terminal 300 or may be externally attached. The display unit 330 displays a screen of a graphical user interface (GUI) that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display depending on the type of terminal 300. In the following description, the display unit 330 is assumed to be included in the housing of the terminal 300.

[0022] The input unit 340 may be included in the housing of the terminal 300 or may be externally attached. For example, the input unit 340 may be implemented as a touch panel integrated with the display unit 330. A touch panel allows a user to input tapping, swiping, and the like. Of course, a switch button, a mouse, a QWERT keyboard, or the like may be used instead of a touch panel. That is, the input unit 340 accepts an operation input made by the user. The input is transferred as a command signal to the control unit 310 via the communication bus 360. The control unit 310 can then execute predetermined control and calculation as necessary.

[0023] 2. Functional configuration The functional configuration of this embodiment will be described in Section 2. As described above, information processing by software stored in the storage unit 220 is specifically realized by the control unit 210, which is an example of hardware, and can be executed as each functional unit included in the control unit 210.

[0024] FIG. 4 is a block diagram showing functions realized by the information processing device 200 (control unit 210). As described above, the information processing device 200 (information processing system 100) includes the control unit 210. Specifically, the information processing device 200 (control unit 210) is configured to execute each step of the program of this embodiment. The information processing device 200 (control unit 210) includes an acquisition unit 211, a conversion unit 212, a construction unit 213, a calculation unit 214, and an output unit 215, corresponding to each step of the program of this embodiment. Here, the program of this embodiment is configured to cause a computer such as the information processing device 200 to execute an acquisition step, a conversion step, a construction step, a calculation step, and an output step. The program of this embodiment is a program for analyzing material properties of a heterogeneous body based on image data of the heterogeneous body.

[0025] The acquisition unit 211 is configured to acquire various information. The acquisition unit 211 is configured to execute an acquisition step. For example, the acquisition unit 211 acquires image data of a heterogeneous body from the terminal 300.

[0026] The conversion unit 212 is configured to convert various types of information. The conversion unit 212 is configured to perform a conversion step. For example, the conversion unit 212 compresses the acquired image data and converts it into compressed data.

[0027] The constructor 213 is configured to construct various information. The constructor 213 is configured to execute a construction step. For example, the constructor 213 constructs a discretized numerical model based on the converted compressed data and a mathematical model in which material properties are modeled.

[0028] The calculation unit 214 is configured to calculate various information. The calculation unit 214 is configured to execute a calculation step. For example, the calculation unit 214 analyzes the constructed numerical model to calculate material property values of the heterogeneous body.

[0029] The output unit 215 is configured to output various information. The output unit 215 is configured to execute an output step. For example, the output unit 215 outputs the calculated material property value to the terminal 300.

[0030] 3. Information Processing Method In Section 3, the flow of the information processing method of the information processing device 200 described above will be explained. This information processing method includes each step of the program of this embodiment. This information processing method includes an acquisition step, a conversion step, a construction step, a calculation step, and an output step. In the acquisition step, image data of a heterogeneous body is acquired. In the conversion step, the image data is compressed and converted into compressed data. In the construction step, a discretized numerical model is constructed based on the compressed data and a mathematical model in which material properties are modeled. In the calculation step, the numerical model is analyzed to calculate material property values of the heterogeneous body. In the output step, the material property values are output.

[0031] 5 is an activity diagram showing the flow of an information processing method executed by information processing device 200. The following will explain each activity in this activity diagram. Here, it is assumed that the image data of the heterogeneous body is created to simulate the material structure of the heterogeneous body and is stored in terminal 300.

[0032] First, the control unit 310 in the terminal 300 transmits image data of the heterogeneous body to the information processing device 200 (activity A110). In activity A110, for example, the following two-stage information processing is executed: (1) The control unit 310 reads out the image data of the heterogeneous body from the storage unit 320. (2) The control unit 310 transmits the image data of the heterogeneous body to the information processing device 200 via the communication unit 350.

[0033] Next, the control unit 210 in the information processing device 200 receives the image data transmitted from the terminal 300 (activity A120). In other words, in the acquisition step, image data of the heterogeneous body is acquired. In activity A120, for example, the following two-stage information processing is executed: (1) The communication unit 250 receives the image data transmitted from the terminal 300. (2) The control unit 210 stores the received image data in the memory unit 220.

[0034] Next, the control unit 210 in the information processing device 200 compresses the image data of the heterogeneous body using singular value decomposition and converts it into compressed data (activity A130). In other words, in the conversion step, the image data of the heterogeneous body is compressed and converted into compressed data using decomposition into matrix multiplication operators. In activity A130, the decomposition into matrix multiplication operators is used as singular value decomposition. In activity A130, for example, the following three-stage information processing is performed: (1) The control unit 210 reads the image data of the heterogeneous body from the storage unit 220. (2) The control unit 210 performs a conversion process and converts the image data of the heterogeneous body into compressed data. (3) The control unit 210 stores the converted compressed data in the storage unit 220. Activity A130 makes it possible to compress image data and construct a numerical model with a lower calculation load than conventional methods.

[0035] Next, the control unit 210 in the information processing device 200 applies a mathematical model, in which the thermal conductivity characteristics of the heterogeneous body are modeled, to the converted compressed data to construct a discretized numerical model (activity A140). In other words, in the construction step, a discretized numerical model is constructed based on the converted compressed data and the mathematical model, in which the thermal conductivity characteristics of the heterogeneous body are modeled. The mathematical model may be prepared in advance corresponding to the type of heterogeneous body. The numerical model may be constructed using decomposition into a matrix multiplication operator, similar to the compressed data. Activity A140, for example, performs the following three-stage information processing: (1) The control unit 210 reads the compressed data and the mathematical model from the storage unit 220; (2) The control unit 210 executes a construction process to construct the numerical model; and (3) The control unit 210 stores the constructed numerical model in the storage unit 220. Activity A140 enables image data compression and construction of a numerical model with a lower computational load than conventional methods.

[0036] Next, the control unit 210 in the information processing device 200 analyzes the constructed numerical model using the homogenization method to calculate the thermal conductivity of the heterogeneous body in the image data (activity A150). In other words, in the calculation step, the numerical model is analyzed using the homogenization method to calculate the thermal conductivity of the heterogeneous body. That is, in the calculation step, the thermal conductivity of the heterogeneous body in the image data is calculated by finding a solution of the discretized numerical model. In activity A150, for example, the following three-stage information processing is performed: (1) The control unit 210 reads the constructed numerical model from the storage unit 220. (2) The control unit 210 performs a calculation process to calculate the thermal conductivity of the heterogeneous body in the image data. (3) The control unit 210 stores the calculated thermal conductivity value in the storage unit 220. Activity A150 allows material properties to be analyzed based on rigorous theory.

[0037] Next, the control unit 210 in the information processing device 200 transmits the calculated value of thermal conductivity to the terminal 300 (activity A160). In other words, in the output step, the calculated value of thermal conductivity is output. In the output step, the value of thermal conductivity may be output to a display. In activity A160, for example, the following two-stage information processing is executed: (1) The control unit 210 reads the calculated value of thermal conductivity from the storage unit 220. (2) The control unit 210 transmits the value of thermal conductivity to the terminal 300 via the communication unit 250.

[0038] Next, the control unit 310 in the terminal 300 receives the thermal conductivity value transmitted from the information processing device 200 (activity A170). In activity A170, for example, the following two-stage information processing is executed: (1) The communication unit 350 receives the thermal conductivity value transmitted from the information processing device 200. (2) The control unit 310 stores the received thermal conductivity value in the memory unit 320.

[0039] Next, the control unit 310 in the terminal 300 causes the display unit 330 to display the received thermal conductivity value as the thermal conductivity of the heterogeneous body in the image data (activity A180). In activity A180, for example, the following two-stage information processing is performed: (1) The control unit 310 reads out the thermal conductivity value from the storage unit 320. (2) The display unit 330 displays the thermal conductivity value as the thermal conductivity of the heterogeneous body in the image data.

[0040] 4. Experimental Example In Section 4, an experimental example of this embodiment will be described.

[0041] FIG. 6 is a table showing the processing time for image conversion in the examples and comparative examples. FIG. 7 is a table showing the processing time for image analysis in the examples and comparative examples. In both the examples and comparative examples, image data simulating the material structure of a non-homogeneous body was created, and the thermal conductivity characteristics were analyzed. Five image resolutions were used: 64 x 64 pixels, 128 x 128 pixels, 256 x 256 pixels, 512 x 512 pixels, and 1024 x 1024 pixels.

[0042] 4-1.Example The heat conduction characteristics of a non-homogeneous body were analyzed using the information processing of this embodiment. As an example, a case where the image resolution is 256×256 pixels will be described.

[0043] First, the acquisition step will be explained. Image data created to simulate the material structure of a non-homogeneous body is stored in a 256 x 256 (2 8 ×2 8 =2 16 ) as a two-dimensional array.

[0044] Next, the conversion step will be explained. 15 Then, the maximum singular value calculated by the singular value decomposition is multiplied by 10. -6 The following singular values were truncated. Here, the remaining dimension that was not truncated is a. Next, the resulting a×2 15 The array of 2a×2 14 Then, the maximum singular value calculated by the singular value decomposition is multiplied by 10. -6 The following singular values were discarded: The above operation was repeated until singular value decomposition could no longer be applied (15 times), and the image data was converted into a matrix multiplication format.

[0045] Next, the construction steps will be explained. A partial differential equation (mathematical model) that describes the heat conduction characteristics in the microscopic structure of a non-homogeneous body is prepared. Next, the differential operator of the partial differential equation is expressed in matrix multiplication format. Next, a discretized numerical model is constructed by applying the matrix multiplication of the singular value decomposed image data to the matrix multiplication of the differential operator.

[0046] Next, the calculation step will be explained. The solution of the constructed numerical model (discretized partial differential equation) was analyzed, and the homogenized thermal conductivity value was calculated.

[0047] Next, the output step will be described. The calculated thermal conductivity value was displayed on a display.

[0048] 4-2.Comparative Example The thermal conductivity of a non-homogeneous body was analyzed using the finite element method. As in the example, the case where the image resolution is 256 x 256 pixels will be described.

[0049] First, the first step will be explained. Image data created to simulate the material structure of a non-homogeneous body is stored in a 256 x 256 (2 8 ×2 8 =2 16 ) as a two-dimensional array.

[0050] Next, the second step will be described. The second step corresponds to the conversion step in the embodiment. One element was assigned to each pixel of the image data, and an element stiffness matrix was constructed based on the pixel values of the image data. Next, the constructed element stiffness matrices were superimposed to construct an overall stiffness matrix.

[0051] Next, the third step will be described. The third step corresponds to the calculation step in the example. The solution of the constructed global stiffness matrix was analyzed, and the value of the homogenized thermal conductivity was calculated.

[0052] Next, the fourth step will be explained. The calculated thermal conductivity value was displayed on the display.

[0053] 4-3. Experimental result 1 Experimental result 1 is a result of comparing the processing time in the conversion step of the embodiment with the processing time in the second step of the comparative example. Experimental result 1 will be explained based on FIG.

[0054] The processing time for the conversion step in the example varied depending on the target image resolution as follows: 0.003 seconds for 64x64 pixels, 0.005 seconds for 128x128 pixels, 0.02 seconds for 256x256 pixels, 0.07 seconds for 512x512 pixels, and 0.24 seconds for 1024x1024 pixels.

[0055] On the other hand, the processing time for the second step in the comparative example varied depending on the resolution of the target image as follows: 0.04 seconds for 64 x 64 pixels, 0.19 seconds for 128 x 128 pixels, 0.79 seconds for 256 x 256 pixels, 4.11 seconds for 512 x 512 pixels, and 21.10 seconds for 1024 x 1024 pixels.

[0056] Experimental result 1 shows that the time required for image conversion is shorter in the Example than in the Comparative Example, and the difference becomes larger as the resolution increases. The reason for this is that in the Comparative Example, the number of processes for constructing a stiffness matrix in the finite element method increases polynomially with respect to the resolution, whereas in the Example, the number of singular value decompositions applied to image data increases logarithmically with respect to the resolution.

[0057] 4-4. Experimental result 2 Experimental result 2 is a result of comparing the processing time in the calculation step of the example with the processing time in the third step of the comparative example. Experimental result 2 will be explained based on FIG.

[0058] The processing time for the calculation step in the example varied depending on the target image resolution as follows: 5.76 seconds for 64x64 pixels, 9.89 seconds for 128x128 pixels, 14.98 seconds for 256x256 pixels, 24.71 seconds for 512x512 pixels, and 32.69 seconds for 1024x1024 pixels.

[0059] On the other hand, the processing time for the third step in the comparative example varied depending on the resolution of the target image as follows: 0.11 seconds for 64 x 64 pixels, 0.47 seconds for 128 x 128 pixels, 3.40 seconds for 256 x 256 pixels, 36.21 seconds for 512 x 512 pixels, and 214.42 seconds for 1024 x 1024 pixels.

[0060] According to Experimental Result 2, the time required for image analysis is shorter in the Comparative Example than in the Example when the resolution is small, but the increase in processing time relative to the resolution is more gradual in the Example, and it is shorter in the Example at resolutions of 512 x 512 pixels or more. The reason for this is that the processing time in the Comparative Example increases polynomially with respect to the resolution, whereas the processing time in the Example increases logarithmically with respect to the resolution.

[0061] According to aspects of the present embodiment, material properties can be analyzed in a shorter time than conventional methods, even for high-resolution image data. In addition, due to the simple configuration, the saved resources can be used for other core functions.

[0062] Although the embodiment of the present invention has been described above, the present invention is not limited to this and can be modified as appropriate within the scope of the technical idea of the invention.

[0063] 5. Variations In Section 5, a modification of this embodiment will be described.

[0064] The control unit 210 performs write processing (storage processing) and read processing of various data and information to the memory unit 220, but this is not limited to this, and for example, the information processing of each activity may be performed using a register or cache memory within the control unit 210.

[0065] In the present embodiment, the image data is compressed using decomposition into matrix multiplication operators in the transformation step, but this is not limiting. The image data may also be compressed using tensor decomposition in the transformation step. Specific examples of tensor decomposition include CP decomposition, Tucker decomposition, and tensor train decomposition. According to this aspect, image data of a non-homogeneous body can be compressed with a lower computational load than conventional methods.

[0066] In the present embodiment, the material properties of the non-homogeneous body to be analyzed are described as heat conduction properties, but the present invention is not limited to this. For example, the material properties of the non-homogeneous body may be elastic properties. According to this embodiment, it is possible to analyze the main material properties of the non-homogeneous body.

[0067] In the present embodiment, the construction step illustrates applying the compressed data decomposed into a matrix multiplication operator to a mathematical model in which the thermal conductivity characteristics of a heterogeneous body are modeled to construct a numerical model decomposed into a matrix multiplication operator, but the construction step is not limited to this. In the construction step, a tensor-decomposed numerical model may be constructed based on the tensor-decomposed compressed data and the mathematical model. Specific examples of tensor decomposition include CP decomposition, Tucker decomposition, and tensor train decomposition. According to this aspect, the material properties of a heterogeneous body can be analyzed based on tensor decomposition, which has a lower computational load than conventional methods.

[0068] 6.Other It may be provided in the following manner.

[0069] (1) A program for analyzing material properties of a non-homogeneous body from image data of the non-homogeneous body, the program being configured to cause a computer to execute an acquisition step, a conversion step, a construction step, a calculation step, and an output step, wherein the acquisition step acquires the image data, the conversion step compresses the image data and converts it into compressed data, the construction step constructs a discretized numerical model based on the compressed data and a mathematical model in which the material properties are modeled, the calculation step analyzes the numerical model to calculate material property values of the non-homogeneous body, and the output step outputs the material property values.

[0070] According to this aspect, it is possible to analyze material properties in a shorter time than conventional methods, even when using high-resolution image data. Furthermore, due to the simple configuration, the saved resources can be used for other core functions.

[0071] (2) The program according to (1) above, wherein the converting step compresses the image data using tensor decomposition.

[0072] According to this aspect, image data of a non-homogeneous body can be compressed with a lower calculation load than conventional methods.

[0073] (3) A program according to (2) above, wherein the construction step constructs the tensor-decomposed numerical model based on the tensor-decomposed compressed data and the mathematical model.

[0074] According to this embodiment, it is possible to analyze the material properties of a non-homogeneous body by using tensor decomposition, which has a lower calculation load than conventional methods.

[0075] (4) In the program described in (2) above, the tensor decomposition is a decomposition into matrix multiplication operators.

[0076] According to this aspect, it is possible to compress image data and construct a numerical model with a lower calculation load than conventional methods.

[0077] (5) The program according to (3) above, wherein the tensor decomposition is a decomposition into matrix multiplication operators.

[0078] According to this aspect, it is possible to compress image data and construct a numerical model with a lower calculation load than conventional methods.

[0079] (6) The program according to any one of (1) to (5) above, wherein the calculation step calculates the material characteristic value using a homogenization method.

[0080] According to this embodiment, the material characteristics can be analyzed based on a rigorous theory.

[0081] (7) The program according to any one of (1) to (6) above, wherein the material properties are thermal conductivity properties.

[0082] According to this embodiment, it is possible to analyze the main material properties of a non-homogeneous body.

[0083] (8) The program according to any one of (1) to (6) above, wherein the material properties are elastic properties.

[0084] According to this embodiment, it is possible to analyze the main material properties of a non-homogeneous body.

[0085] (9) An information processing method comprising the steps of the program described in any one of (1) to (8) above.

[0086] According to this aspect, it is possible to analyze material properties in a shorter time than conventional methods, even when using high-resolution image data. Furthermore, due to the simple configuration, the saved resources can be used for other core functions.

[0087] (10) An information processing system comprising a control unit, the control unit being configured to execute each step of the program described in any one of (1) to (8) above.

[0088] According to this aspect, it is possible to analyze material properties in a shorter time than conventional methods, even when using high-resolution image data. Furthermore, due to the simple configuration, the saved resources can be used for other core functions. Of course, this is not the case. [Explanation of symbols]

[0089] 100: Information Processing Systems 200: Information processing device 210: Control unit 211: Acquisition Department 212: Conversion section 213: Construction Department 214: Calculation section 215: Output section 220: Storage section 250: Communications Department 260: Communication bus 300: Terminal 310: Control unit 320: Storage section 330: Display section 340: Input section 350: Communications Department 360: Communication bus

Claims

1. A program for analyzing material properties of a non-homogeneous body from image data of the non-homogeneous body, configured to cause a computer to perform the steps of obtaining, converting, constructing, calculating, and outputting; In the acquiring step, the image data is acquired, In the converting step, the image data is compressed and converted into compressed data, In the construction step, a discretized numerical model is constructed based on the compressed data and a mathematical model in which the material properties are modeled; In the calculation step, the numerical model is analyzed to calculate a material property value of the non-homogeneous body; In the output step, the material characteristic value is output. program.

2. 2. The program according to claim 1, The transforming step compresses the image data using tensor decomposition. program.

3. 3. The program according to claim 2, In the construction step, the tensor-decomposed numerical model is constructed based on the tensor-decomposed compressed data and the mathematical model. program.

4. 3. The program according to claim 2, The tensor decomposition is a decomposition into matrix multiplication operators: program.

5. 4. The program according to claim 3, The tensor decomposition is a decomposition into matrix multiplication operators: program.

6. 2. The program according to claim 1, In the calculation step, the material characteristic value is calculated using a homogenization method. program.

7. 2. The program according to claim 1, The material property is a thermal conductivity property. program.

8. 2. The program according to claim 1, The material property is an elastic property. program.

9. An information processing method, comprising: The program comprises the steps of any one of claims 1 to 8. Information processing methods.

10. An information processing system, A control unit is provided, The control unit is configured to execute the steps of the program according to any one of claims 1 to 8. Information processing system.

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