Tire characteristic estimation device, tire characteristic estimation method, estimation model generation method, estimation model generation device, and program

Abstract

To improve estimation accuracy of tire characteristics.SOLUTION: A tire data group includes a first data group and a second data group each of which includes a plurality of individual tire data. The first data group and the second data group differ in a part of a plurality of structural items. A model generating unit 23 generates a first target estimation model with the plurality of individual tire data included in the first data group as teacher data, and generates a second target estimation model with the plurality of individual tire data included in the second data group as teacher data.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a tire characteristic estimation device, a tire characteristic estimation method, an estimation model generation method, an estimation model generation device, and a program. [Background technology]

[0002] In the design and development of tires, the structure of the tire is designed, including the number, dimensions, and materials of the components (e.g., belts) that make up the tire, and the tire characteristics (e.g., cornering power, rolling resistance, etc.) obtained from the designed structure are predicted. Then, it is evaluated whether the predicted characteristics satisfy the requirements during use. Patent Document 1 listed below discloses an estimation model for estimating tire characteristics (physical values) from multiple rubber materials, fillers, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-149423 Summary of the Invention [Problem to be solved by the invention]

[0004] Tire structures vary greatly depending on the type of tire. For example, the number of belts varies depending on the type of tire. For tires with significantly different structures, estimating tire characteristics using only one estimation model will not provide high estimation accuracy. [Means for solving the problem]

[0005] (1) A tire characteristic estimation device proposed in this disclosure includes a storage means storing a first estimation model and a second estimation model. Each of the first estimation model and the second estimation model is an estimation model for estimating characteristic data representing tire characteristics from specification data including data on multiple structural items that are multiple items representing the tire structure. The estimation device also includes a data acquisition means for acquiring specification data of a tire to be estimated, and an estimation means for selecting one of the first estimation model and the second estimation model based on the multiple structural items for which the acquired specification data is provided, inputting the acquired specification data into the selected estimation model, and outputting characteristic data of the tire to be estimated. This estimation device can improve the accuracy of estimating the characteristics of multiple tires with different structural items.

[0006] (2) In the estimation device of (1), each of the first estimation model and the second estimation model may include a coefficient estimation model that outputs one or more coefficients according to the specification data. The estimation means inputs the acquired specification data into the coefficient estimation model of the selected estimation model, and calculates characteristic data of the tire to be estimated using a relational expression defined by the one or more coefficients output from the coefficient estimation model. This makes it possible to estimate tire characteristics using, for example, an empirically obtained relational expression.

[0007] (3) In the estimation device of (1), each of the first estimation model and the second estimation model may be an estimation model for estimating the characteristic data from the specification data and evaluation condition data indicating evaluation conditions for tire characteristics. The estimation device may include means for acquiring evaluation condition data for the characteristics to be estimated for the estimation target tire. The estimation means may estimate the characteristic data of the estimation target tire using the acquired specification data and the acquired evaluation condition data. This enables estimation taking into account the evaluation conditions.

[0008] (4) In the estimation device of (1), each of the first estimation model and the second estimation model may include a coefficient estimation model that outputs one or more coefficients according to the specification data, and may be an estimation model for estimating the characteristic data from the specification data and evaluation condition data indicating evaluation conditions for tire characteristics. The estimation device may include means for acquiring evaluation condition data for the characteristics to be estimated for the tire to be estimated. The estimation means may input the acquired specification data into the coefficient estimation model of the selected estimation model, and calculate the characteristic data of the tire to be estimated using a relational expression defined by the one or more coefficients output from the selected estimation model and the acquired evaluation condition data. This allows, for example, when a relationship between evaluation conditions and tire characteristics has been empirically obtained, to estimate the tire characteristics using the relational expression.

[0009] (5) In the estimation device of (1), the storage means may store a plurality of first estimation models corresponding to a plurality of evaluation conditions for tire characteristics, and a plurality of second estimation models corresponding to the plurality of evaluation conditions. The estimation means may select one or more estimation models from the plurality of first estimation models and the plurality of second estimation models based on a plurality of structure items for which the acquired specification data is provided. This allows estimation of characteristics according to the evaluation conditions.

[0010] (6) In the estimation device of (1), each of the first estimation model and the second estimation model may be a model generated from a tire data group including a plurality of individual tire data. Each individual tire data may include the specification data and the characteristic data. The tire data group may include a first data group and a second data group, each including the plurality of individual tire data. The individual tire data of the first data group and the individual tire data of the second data group may differ in at least some of the plurality of structural items for which the specification data is provided. The first estimation model may be a model generated using the plurality of individual tire data included in the first data group as training data, and the second estimation model may be a model generated using the plurality of individual tire data included in the second data group as training data. This estimation device can improve the estimation accuracy of the characteristics of a plurality of tires having different structural items.

[0011] (7) The tire characteristic estimation method proposed in this disclosure is an estimation method for estimating tire characteristics using a first estimation model and a second estimation model. Each of the first estimation model and the second estimation model is an estimation model for estimating characteristic data representing tire characteristics from specification data including data on multiple structural items that are multiple items that describe the tire structure. The estimation method includes a data acquisition step of acquiring specification data of a tire to be estimated, and an estimation step of selecting one of the first estimation model and the second estimation model based on the multiple structural items for which the acquired specification data is provided, inputting the acquired specification data into the selected estimation model, and outputting characteristic data of the tire to be estimated. This estimation method can improve the accuracy of estimating the characteristics of multiple tires with different structural items.

[0012] (8) A program proposed in this disclosure is a program that causes a computer to function as an estimation device that estimates tire characteristics using a first estimation model and a second estimation model. Each of the first estimation model and the second estimation model is an estimation model for estimating characteristic data representing tire characteristics from specification data including data on multiple structural items that are multiple items that describe the tire structure. The program causes a computer to function as data acquisition means that acquires specification data of a tire to be estimated, and as estimation means that selects one of the first estimation model and the second estimation model based on the multiple structural items for which the acquired specification data is provided, inputs the acquired specification data into the selected estimation model, and outputs characteristic data of the tire to be estimated.

[0013] (9) A method for generating an estimation model proposed in the present disclosure includes a data acquisition step of acquiring a tire data group including multiple individual tire data. Each individual tire data includes characteristic data representing tire characteristics and specification data including data on multiple structural items representing the tire structure. The generation method includes a model generation step of generating an estimation model using the multiple individual tire data to estimate the characteristic data based on the specification data of a tire to be estimated. The tire data group includes a first data group and a second data group, each including the multiple individual tire data. The first data group and the second data group differ in at least some of the multiple structural items. In the model generation step, a first estimation model is generated using the multiple individual tire data included in the first data group as training data, and a second estimation model is generated using the multiple individual tire data included in the second data group as training data. Using the estimation model generated by this method, it is possible to improve the accuracy of estimating the characteristics of multiple tires with different structural items.

[0014] (10) The generation method of (9) may further include a data extraction step. The first data group and the second data group may include, as part of the plurality of structure items, structure items common to the first data group and the second data group. The data extraction step may extract data including specification data of the common structure items and the characteristic data from the tire data group. The model generation step may include a source model generation step of generating a source model based on the specification data of the common structure items using the extracted data as training data, and a target model generation step of generating the first estimation model using the source model and the plurality of individual tire data in the first data group, and generating the second estimation model using the source model and the plurality of individual tire data in the second data group. This allows for improved estimation accuracy of the estimation model obtained from one of the two data groups, even if the number of individual tire data included in that one data group is small.

[0015] (11) In the generation method of (9), each of the first estimation model and the second estimation model may include a coefficient estimation model that outputs one or more coefficients that define a relational expression for calculating the characteristic data. By using the estimation model generated by this method, it is possible to estimate tire characteristics using, for example, an empirically obtained relational expression.

[0016] (12) In the generation method of (9), each of the plurality of individual tire data may include evaluation condition data indicating evaluation conditions for tire characteristics. The first estimation model and the second estimation model may be models for estimating the characteristic data using the specification data and the evaluation condition data. Using the estimation models generated by this method enables estimation taking into account the evaluation conditions.

[0017] (13) In the generation method of (9), each of the plurality of individual tire data may include evaluation condition data indicating evaluation conditions for tire characteristics. The evaluation condition data may be data that can selectively take on a plurality of condition values. Each of the first estimation model and the second estimation model may include a coefficient estimation model that outputs one or more coefficients that define a relational expression. The relational expression may be a relational expression that is defined by the condition value and the one or more coefficients and that calculates the characteristic data. By using the estimation model generated by this method, for example, when the relationship between evaluation conditions and tire characteristics has been empirically obtained, the tire characteristics can be estimated using the relational expression.

[0018] (14) In the generation method of (9), each of the plurality of individual tire data may include evaluation condition data indicating evaluation conditions for tire characteristics. In the model generation step, a plurality of first estimation models corresponding to the plurality of evaluation conditions may be generated, and a plurality of second estimation models corresponding to the plurality of evaluation conditions may be generated. This allows characteristics to be estimated according to the evaluation conditions.

[0019] (15) The estimation model generation device proposed in this disclosure includes a data acquisition means for acquiring a tire data group including a plurality of individual tire data. Each individual tire data includes characteristic data representing tire characteristics and specification data including data on a plurality of structural items representing the tire structure. The generation device includes a model generation means for generating an estimation model using the plurality of individual tire data to estimate the characteristic data based on the tire specification data. The tire data group includes a first data group and a second data group, each including the plurality of individual tire data. The first data group and the second data group differ in at least some of the structural items for which the specification data is provided. The model generation means generates a first estimation model using the plurality of individual tire data included in the first data group as training data, and generates a second estimation model using the plurality of individual tire data included in the second data group as training data. Using the estimation model generated by this device, the accuracy of estimating the characteristics of a plurality of tires with different structural items can be improved.

[0020] (16) A program proposed in this disclosure causes a computer to function as data acquisition means for acquiring a tire data group including multiple individual tire data. Each individual tire data includes characteristic data representing tire characteristics and specification data including data on multiple structural items that represent the tire structure. The program also causes a computer to function as model generation means for generating an estimation model using the multiple individual tire data to estimate characteristic data based on the tire specification data. The tire data group includes a first data group and a second data group, each including the multiple individual tire data. The first data group and the second data group differ in at least some of the multiple structural items for which the specification data is provided. The model generation means generates a first estimation model using the multiple individual tire data included in the first data group as training data, and generates a second estimation model using the multiple individual tire data included in the second data group as training data. Using the estimation model generated by this program can improve the accuracy of estimating the characteristics of multiple tires with different structural items. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a block diagram showing hardware of a tire design support system that functions as a tire characteristic estimation device and an estimation model generation device proposed in the present disclosure. FIG. [Figure 2] FIG. 2 is a block diagram showing functions of the design support system shown in FIG. [Figure 3] FIG. 2 is a diagram showing an example of a tire data group stored in a storage device installed in the design support system. [Figure 4] FIG. 2 is a block diagram showing functions of a learning unit that functions as a generation device. [Figure 5A] FIG. 4 is a diagram showing an example of data from which the tire data group shown in FIG. 3 is extracted. [Figure 5B] FIG. 4 is a diagram showing an example of data from which the tire data group shown in FIG. 3 is extracted. [Figure 6A]FIG. 4 is a diagram showing a modified example of the tire data group shown in FIG. 3. [Figure 6B] FIG. 6B is a diagram showing an example of data extracted from the tire data group of FIG. 6A. [Figure 7] 5 is a diagram illustrating an example of the structure of an estimation model generated by a first generation unit of the source model generation unit and the target model generation unit illustrated in FIG. 4. FIG. [Figure 8A] FIG. 10 is a flowchart illustrating an example of processing executed by a first generation unit. [Figure 8B] 10 is an example of data used in the process executed by the first generation unit. [Figure 9] 5 is a diagram illustrating an example of the structure of an estimation model generated by a second generation unit of the source model generation unit and the target model generation unit illustrated in FIG. 4. FIG. [Figure 10] 5 is a diagram illustrating an example of the structure of an estimation model generated by a third generation unit of the source model generation unit and the target model generation unit illustrated in FIG. 4. FIG. [Figure 11] FIG. 10 is a flowchart illustrating an example of processing executed by a learning unit. [Figure 12] FIG. 10 is a diagram illustrating an example of an estimation model table generated by a learning unit. [Figure 13] 3 is a flowchart showing an example of processing executed by an estimation unit shown in FIG. 2. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0022] The tire characteristic estimation device and tire characteristic estimation model generation device proposed in this disclosure will be described below.

[0023] FIG. 1 is a block diagram showing the hardware of a tire design support system 1 that functions as a tire characteristic estimation device and an estimation model generation device proposed in this disclosure.

[0024] As shown in FIG. 1, the design support system 1 includes a processing device 11, a storage device 12, a display device 13, and an input device .

[0025] The processing device 11 includes, for example, a central processing unit (CPU) and a graphics processing unit (GPU), and operates according to a program stored in the storage device 12. A field programmable gate array (FPGA) may be used as the processing device 11. The storage device 12 includes, for example, a read only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a solid state drive (SSD), and stores programs executed by the processing device 11, tire data for generating an estimation model, and the like. One or more personal computers, server computers, or the like may be used as the processing device 11 and the storage device 12.

[0026] The display device 13 is, for example, a liquid crystal display or an organic EL display, and displays the data stored in the storage device 12, the output results of the estimation model, and the like.

[0027] The input device 14 is a user interface such as a keyboard or a mouse, and receives an operation input from the operator and inputs a signal indicating the content of the operation input to the processing device 11.

[0028] Fig. 2 is a block diagram showing functions of the design support system 1. Fig. 3 is a diagram showing an example of a tire data group stored in the storage device 12. As shown in Fig. 2, the design support system 1 has a learning unit 20, an estimation unit 30, and an estimation model group 40.

[0029] The estimation model group 40 includes a plurality of estimation models for estimating tire characteristics from tire specifications. The tire specifications include, for example, the tire structure and manufacturing method, and the tire structure includes, for example, the types, dimensions, number, and materials of the components that make up the tire, as well as their properties (for example, hardness).

[0030] The learning unit 20 causes the estimation models constituting the estimation model group 40 to learn the tire data group stored in the storage device 12. The estimation unit 30 inputs data on the structure of the tire to be estimated to one or more estimation models constituting the estimation model group 40, and obtains characteristic data of the tire to be estimated as an output from the estimation model.

[0031] In the following, the learning unit 20 will be described first, and then the estimation unit 30 will be described.

[0032] [Tire data group] As shown in Fig. 3, the learning tire data group includes multiple individual tire data. Each individual tire data is data for one tire. In this figure, each row represents one individual tire data. Each individual tire data includes characteristic data that indicates the tire characteristics, evaluation condition data that is the conditions for evaluating the tire characteristics, and specification data.

[0033] The characteristic data includes, for example, cornering power, noise generated between the road surface and the tire, rolling resistance, friction coefficient, durability, etc. FIG. 3 shows cornering power and friction coefficient as examples. The evaluation condition for cornering power is, for example, the load acting on the tire, and generally, the higher the load, the greater the cornering power. The evaluation condition for friction coefficient is, for example, the road surface condition, such as icy road, gravel road, asphalt, etc. The characteristics recorded in the individual tire data are data measured for actual tires.

[0034] The specification data includes data on structural items that represent the structure of the tire. The structural items include, for example, tire width, tread type (tread shape), tread width, tire height, rubber material, belt material, and belt hardness. FIG. 3 shows, as examples, tire width, tread type, tread width, structural value A, and structural value B. The specification data items may also include the tire manufacturing method. In the individual tire data, not only numerical data such as tire width but also non-numeric data such as rubber material, belt material, and manufacturing method are recorded as numerical values.

[0035] [Study Department] 4 is a block diagram showing the functions of the learning unit 20. As shown in the figure, the learning unit 20 has a data acquisition unit 21, a data extraction unit 22, and a model generation unit .

[0036] The data acquisition unit 21 acquires a tire data group (for example, the data shown in FIG. 3) stored in the storage device 12. If the design support system 1 has a communication device, the data acquisition unit 21 may receive the tire data group from an external device via the communication device.

[0037] [Extraction part] The data extraction unit 22 extracts data to be used for learning from the acquired tire data group. That is, the data extraction unit 22 extracts a plurality of individual tire data corresponding to the characteristics for which an estimation model is to be generated from the tire data group.

[0038] In the example shown in Fig. 3, all of the individual tire data includes cornering power and data on its evaluation conditions, while some of the individual tire data (data surrounded by dashed line area A) does not include friction coefficient, and Null is entered in the cell for the friction coefficient. When generating an estimation model for cornering power, the data extraction unit 22 extracts, from the tire data group, multiple pieces of individual tire data having significant values ​​(non-null values) in the cell for the cornering power. When generating an estimation model for friction coefficient, the data extraction unit 22 extracts, from the tire data group, multiple pieces of individual tire data having significant values ​​(non-null values) in the cell for the friction coefficient.

[0039] The data extraction unit 22 includes a first extraction unit 22A and a second extraction unit 22B. As shown in FIG. 4, the estimation model group 40 includes a source estimation model and a target estimation model. The first extraction unit 22A extracts data for generating the source estimation model from the tire data group, and the second extraction unit 22B extracts data for generating the target estimation model from the tire data group. As will be described later, the target estimation model is generated using the source estimation model as well.

[0040] Also, depending on the tire structure, there may be some structural items for which no data is available. For example, if there are tires with one belt and tires with two belts, all individual tire data will include data on the hardness of the first belt, while data for a tire with only one belt will not include data on the hardness of the second belt. In this case, in the specification data for a tire with only one belt, the cell for the structural item for the second belt will be null.

[0041] In the example shown in FIG. 3, both structural item A (e.g., the hardness of the first belt) and structural item B (e.g., the hardness of the second belt) are items that affect cornering power. The tire data group includes a first data group and a second data group. Each data group is composed of a plurality of individual tire data. Both the individual tire data of the first data group and the individual tire data of the second data group have a significant value for structural item A. In contrast, Null is entered for structural item B in the individual tire data of the first data group, and a significant value is also recorded for structural item B (e.g., the hardness of the second belt) in the individual tire data of the second data group. In this case, as shown in FIG. 5A, the first extraction unit 22A extracts the specification data included in the first data group, the characteristic data to be estimated, and the evaluation condition data thereof from the tire data group. The extracted data is then used to generate a source estimation model in the model generation unit 23. 5B, the second extraction unit 22B extracts the specification data included in the second data group, the characteristic data to be estimated, and the evaluation condition data from the tire data group. The extracted data is used by the model generation unit 23 to generate a target estimation model.

[0042] [Variations of extraction] The processing performed by the extraction units 22A and 22B is not limited to the example described with reference to Figs. 5A and 5B. Fig. 6A is a diagram showing a modified example of a tire data group. In the example shown in Fig. 6A, in the individual tire data of the first data group, a significant value is recorded in structure item A (see dashed line area M1), but Null is recorded in structure item B. Conversely, in the individual tire data of the second data group, a significant value is recorded in structure item B (see dashed line area M2), but Null is recorded in structure item A.

[0043] In the tire data group shown in Fig. 3 described above, all of the structural items included in the data of the first data group are included in the structural items included in the data of the second data group. In contrast, in the tire data group shown in Fig. 6, only some of the structural items included in the data of the first data group are common to some of the structural items included in the data of the second data group. When the tire data group is as shown in Fig. 6, the first extraction unit 22A, which extracts data for a source estimation model, extracts, from the tire data group, specification data, characteristic data (cornering power in Fig. 6B), and evaluation condition data (load in Fig. 6B) of the structural items common to the structural items of the first data group and the second data group, as shown in Fig. 6B.

[0044] 6A and 6B, when a structural item common to the first data group and the second data group is used to generate a source estimation model, a target estimation model (first estimation model in claims) using the individual tire data (data including structural item A) of the first data group and the source estimation model is generated in the model generation unit 23, which will be described later. Also, a target estimation model (second estimation model in claims) using the individual tire data (data including structural item B) of the second data group and the source estimation model is generated in the model generation unit 23, which will be described later.

[0045] On the other hand, as explained with reference to Figures 3, 5A, and 5B, when the data of the first data group is used as data for a source estimation model, this source estimation model also becomes one of multiple target estimation models. That is, the source estimation model generated based on the data of the first data group is used as a target estimation model (first estimation model in the claims) for estimating the characteristics of a tire having the same structural items as the first data group. Also, the model generated based on the data of the second data group is used as a target estimation model (second estimation model in the claims) for estimating the characteristics of a tire having the same structural items as the second data group.

[0046] [Model generation section] The model generation unit 23 generates an estimation model for estimating tire characteristics based on tire specification data, using the data extracted by the model extraction unit 22. As shown in Fig. 4, the model generation unit 23 has a source model generation unit 23A and a target model generation unit 23B. The source model generation unit 23A has a first generation unit 23Aa, a second generation unit 23Ab, and a third generation unit 23Ac. Similarly, the target model generation unit 23B has a first generation unit 23Ba, a second generation unit 23Bb, and a third generation unit 23Bc.

[0047] The target model generation unit 23B performs so-called transfer learning using the source estimation model generated by the source model generation unit 23A. That is, the first generation unit 23Ba of the target model generation unit 23B generates a target estimation model using the source estimation model generated by the first generation unit 23Aa of the source model generation unit 23A and the data extracted by the second extraction unit 22A. Similarly, the second generation unit 23Bb of the target model generation unit 23B generates a target estimation model using the source estimation model generated by the second generation unit 23Ab of the source model generation unit 23A and the data extracted by the second extraction unit 22A. The third generation unit 23Bc of the target model generation unit 23B generates a target estimation model using the source estimation model generated by the third generation unit 23Ac of the source model generation unit 23A and the data extracted by the second extraction unit 22A.

[0048] The processes performed by the first generation units 23Aa and 23Ba, the second generation units 23Ab and 23Bb, and the third generation units 23Ac and 23Bc will be described in order below.

[0049] [First generation part] FIG. 7 is a diagram showing an example of the structure of an estimation model (source estimation model) generated by the first generation unit 23Aa of the source model generation unit 23A. Among tire characteristics, there are some for which relational expressions for calculating the characteristics have been empirically obtained. For example, as shown in FIG. 7, it is known that cornering power can be calculated from the load, which is an evaluation condition. Specifically, the cornering power can be calculated by the following relational expression: CP=a×W+b×W^2+c×W^3 CP: Cornering power W: Load (evaluation conditions) a, b, and c: coefficients

[0050] In this case, the estimation model includes a coefficient estimation model for estimating the coefficients a, b, and c from the specification data, and a relational expression (empirical regression model). The coefficient estimation model is a machine learning model, such as a neural network. As the coefficient estimation model, a model such as a random forest or a support vector machine that outputs multiple coefficients a, b, and c may be used. Alternatively, as the coefficient estimation model, a regression model (for example, LASSO regression) that outputs multiple coefficients a, b, and c may be used.

[0051] When the estimation model has the structure shown in Fig. 7, the first generation unit 23Aa executes the processing illustrated in Fig. 8A and Fig. 8B. Fig. 8A is a flow diagram showing an example of the processing executed by the first generation unit 23Aa, and Fig. 8B is an example of data used in the processing executed by the first generation unit 23Aa.

[0052] The first generating unit 23Aa acquires a plurality of individual tire data sets having common specification data from the plurality of individual tire data sets extracted by the first extracting unit 22A (S101). That is, the first generating unit 23Aa acquires a plurality of individual tire data sets for tires having the same structure (e.g., one tire). The plurality of individual tire data sets have different evaluation conditions for characteristic data. The plurality of individual tire data sets illustrated in FIG. 8B are for one tire and have the same data (significant values) for all structural items. In contrast, the loads, which are evaluation conditions, are different from one another, and as a result, the characteristic data sets are also different. The first generating unit 23Aa calculates a plurality of coefficients a·b·c using such evaluation conditions (e.g., load) and characteristic data (e.g., cornering power) (S102). The first generating unit 23Aa can calculate the coefficients a·b·c by, for example, using a polynomial regression model. Then, the first generation unit 23Aa trains a coefficient estimation model using the specification data of the multiple individual tire data acquired in S101 and the coefficients a·b·c calculated in S102 as training data (S103). At this time, the coefficients a·b·c are used as correct labels.

[0053] The first generation unit 23Aa acquires a plurality of individual tire data having common specification data, as illustrated in Fig. 8B, from the data extracted by the first extraction unit 22A (data for the source estimation model). Then, the first generation unit 23Aa executes the processes shown in S101 to S103 in Fig. 8A for the plurality of tires. This progresses the learning of the source estimation model.

[0054] The estimation model (target estimation model) generated by the first generation unit 23Ba of the target model generation unit 23B also has the same structure as in Fig. 7. The first generation unit 23Ba of the target model generation unit 23B performs transfer learning using, for example, the source estimation model generated by the first generation unit 23Aa of the source model generation unit 23A and the data extracted by the second extraction unit 22B (for example, the data in Fig. 5B).

[0055] The processing of the first generation unit 23Ba may be similar to the processing of the first generation unit 23Aa of the source model generation unit 23A. Specifically, the second generation unit 23Ba acquires a plurality of individual tire data having common specification data, as illustrated in FIG. 8B, from the data extracted by the second extraction unit 22B (S101 in FIG. 8A). The second generation unit 23Ba then calculates coefficients a·b·c from the data obtained in S101 (S102 in FIG. 8A). Next, the second generation unit 23Ba uses the coefficients a·b·c as correct labels and the specification data of the individual tire data obtained in S101 as explanatory variables to proceed with learning of an estimation model (target estimation model) (S103 in FIG. 8A). In S103, the second generation unit 23Ba may use a source estimation model. For example, the second generation unit 23Ba may adjust (fine-tune) weight parameters of the source estimation model, reuse an intermediate layer of the source estimation model, or add a new layer to the source estimation model.

[0056] [Modification of the first generation unit] The processing performed by the first generators 23Aa-23Ba and the structure of the estimation model are not limited to the examples shown in Figures 7, 8A, and 8B. For example, even if a relational expression for calculating tire characteristic data is known, the estimation model (neural network) generated by the first generators 23Aa-23Ba may directly calculate tire characteristic data. In this case, the difference between the cornering power calculated from this estimation model and the actually measured cornering power (characteristic data included in the individual tire data) may be used as a loss function to train the estimation model.

[0057] The estimation model of the structure shown in FIG. 7 may be used to estimate a characteristic other than cornering power, as long as the characteristic has a relational expression.

[0058] [Second generation part] FIG. 9 is a diagram showing an example of the structure of an estimation model (source estimation model) generated by the second generation unit 23Ab of the source model generation unit 23A. The second generation unit 23Ab generates a plurality of source estimation models corresponding to a plurality of evaluation conditions, respectively. The estimation model illustrated in FIG. 9 is a model for estimating the friction coefficient, which is one of the tire characteristics. As described above, evaluation conditions for the friction coefficient include road surface conditions such as icy roads, gravel roads, and asphalt. In this case, the second generation unit 23Ab generates a model for estimating the friction coefficient on icy roads, a model for estimating the friction coefficient on gravel roads, and a model for estimating the friction coefficient on asphalt.

[0059] The second generation unit 23Ab acquires data with the same evaluation condition from the multiple individual tire data extracted by the first extraction unit 22A (see FIG. 4). For example, the second generation unit 23Ab acquires multiple individual tire data having "icy road" as evaluation condition data. Then, the second generation unit 23Ab trains an estimation model that estimates the friction coefficient on icy roads, using the specification data of the acquired individual tire data as explanatory variables and the friction coefficient on asphalt as a correct answer label. The second generation unit 23Ab performs the same processing as for icy roads for other evaluation conditions (e.g., gravel roads, asphalt, etc.) to generate estimation models for the other evaluation conditions.

[0060] The estimation model (target estimation model) generated by the second generation unit 23Bb of the target model generation unit 23B also has a structure similar to that shown in Fig. 9. The second generation unit 23Bb of the target model generation unit 23B performs transfer learning, for example, using the source estimation model generated by the second generation unit 23Ab of the source model generation unit 23A and the data extracted by the second extraction unit 22B. For example, the second generation unit 23Bb may adjust (fine-tune) the weight parameters of the source estimation model, reuse an intermediate layer of the source estimation model, or add a new layer to the source estimation model.

[0061] The estimation model of the structure shown in Fig. 9 may be used to estimate a characteristic other than the friction coefficient. For example, the estimation model of the structure shown in Fig. 9 may be used to estimate cornering power.

[0062] [Third generation part] Fig. 10 is a diagram showing an example of the structure of an estimation model (source estimation model) generated by the third generation unit 23Ac of the source model generation unit 23A. The third generation unit 23Ac generates one source estimation model using specification data and evaluation condition data as explanatory variables. When there are many evaluation conditions, the estimation model illustrated in Fig. 10 may be generated instead of the estimation model illustrated in Fig. 9. For example, when there are many evaluation conditions for evaluating durability, the estimation model illustrated in Fig. 10 may be used as a model for estimating durability.

[0063] The estimation model (target estimation model) generated by the third generation unit 23Bc of the target model generation unit 23B also has a structure similar to that shown in Fig. 10. The third generation unit 23Bc of the target model generation unit 23B performs transfer learning, for example, using the source estimation model generated by the third generation unit 23Ac of the source model generation unit 23A and the data extracted by the second extraction unit 22B. For example, the third generation unit 23Bc may adjust (fine-tune) the weight parameters of the source estimation model, reuse an intermediate layer of the source estimation model, or add a new layer to the source estimation model.

[0064] [Learning process flow] FIG. 11 is a flowchart showing an example of processing executed by the learning unit 20.

[0065] The data acquisition unit 21 acquires a tire data group (FIG. 3 or FIG. 6A) including a plurality of individual tire data (S201). The learning unit 20 acquires the type of characteristic data for which an estimation model is to be generated (S202). For example, the acquired type of characteristic data includes cornering power, friction coefficient, rolling resistance, etc. An operator may input the type of characteristic data for which an estimation model is to be generated to the processing device 11 via, for example, the input device 14.

[0066] The data extraction unit 22 extracts individual tire data including the characteristic data of the type received in S202 from the tire data group acquired in S201 (S203). For example, when an estimation model for estimating cornering power is to be generated, the data extraction unit 22 extracts individual tire data including cornering power as characteristic data. Also, for example, when an estimation model for estimating friction coefficient is to be generated, the data extraction unit 22 extracts individual tire data including friction coefficient as characteristic data.

[0067] Next, the first extraction unit 22A extracts data for generating a source estimation model from the data extracted in S203 (S204). In the above example, the first extraction unit 22A extracts, for example, data having significant values ​​for all structure items (first data group exemplified in FIGS. 3 and 5A). As another example, the first extraction unit 22A extracts individual tire data (data exemplified in FIG. 6B) including specification data, characteristic data, and evaluation condition data for structure items (common structure items) having significant values ​​in all of the multiple data groups.

[0068] The data generating unit 23 determines whether or not a relational expression (empirical regression model) for calculating characteristic data to be estimated has been defined (S205). For example, the data generating unit 23 determines whether or not such a relational expression has been stored in the storage device 12. In the above example, when an estimation model of cornering power is to be generated, the data generating unit 23 determines whether or not a relational expression showing the relationship between cornering power (CP) and evaluation conditions (load at the time of CP measurement) has been stored in the storage device 12.

[0069] If it is determined in S205 that the relational expression is stored in the storage device 12, the source model generation unit 23A (first generation unit 23Aa) generates a source estimation model having the structure shown in FIG. 7, for example (S206).

[0070] Specifically, as described with reference to FIGS. 8A and 8B, the source model generation unit 23A (first generation unit 23Aa) acquires, from the plurality of individual tire data extracted in S204, a plurality of individual tire data pieces, which are data pieces about tires (or one tire) having the same structure but with different evaluation conditions (S101 in FIG. 8A). The source model generation unit 23A calculates a plurality of coefficients a·b·c that define the relational expression using such evaluation conditions (e.g., load) and characteristic data (e.g., cornering power) (S102 in FIG. 8A). Then, the source model generation unit 23A trains a coefficient estimation model using the specification data of the plurality of individual tire data pieces acquired in S101 and the calculated coefficients a·b·c as training data (S103 in FIG. 8A). In S206 in FIG. 11, the source model generation unit 23A repeatedly executes the processes (S101 to S103) illustrated in FIG. 8A to generate a source estimation model.

[0071] Next, the second extraction unit 22B extracts data for generating a target estimation model from the plurality of individual tire data extracted in S203 (S207). Referring to the example described above, the second extraction unit 22B extracts, for example, the individual tire data of the second data group shown in Fig. 5B. Furthermore, as described with reference to Fig. 6A, when only some of the structural items of the first data group and only some of the structural items of the second data group are common, the second extraction unit 22B extracts, for example, each of the individual tire data of the first data group and the individual tire data of the second data group as data for the target estimation model.

[0072] Then, the target model generation unit 23B (first generation unit 23Ba) generates a target estimation model using the source estimation model obtained in S206 and the data obtained in S207 (S208). If multiple data groups (first data group and second data group) are extracted in S207, the target model generation unit 23B generates multiple target estimation models corresponding to the multiple data groups, respectively, in S208. In contrast, if the first data group is extracted as data for a source estimation model, the target model generation unit 23B (first generation unit 23Ba) does not need transfer learning for the first data group, and therefore uses the source estimation model obtained in S206 as one of the multiple target estimation models.

[0073] If it is determined in S205 that a relational expression (empirical regression model) for calculating characteristic data is not defined, the source model generation unit 23A (for example, the second generation unit 23Ab) generates a source estimation model (S209). Referring to the above-mentioned example, if a friction coefficient is designated as the characteristic to be estimated in S202, the source model generation unit 23A (the second generation unit 23Ab) generates a plurality of source estimation models (for example, a model for an icy road, a model for a gravel road, etc.) corresponding to a plurality of evaluation conditions, respectively, as illustrated in Fig. 9.

[0074] Next, the second extraction unit 22B extracts data for generating a target estimation model from the plurality of individual tire data extracted in S203 (S210). The target model generation unit 23B (second generation unit 23Ba) generates a target estimation model using the source estimation model obtained in S209 and the data obtained in S210 (S211).

[0075] For example, when generating an estimation model of a friction coefficient, the target model generation unit 23B (second generation unit 23Bb) generates a target estimation model for a gravel road by using data included in the first data group whose evaluation condition is "gravel road." The target model generation unit 23B (second generation unit 23Bb) also generates a target estimation model for an icy road by using data included in the first data group whose evaluation condition is "icy road." Furthermore, it generates a target estimation model for an asphalt road by using data included in the first data group whose evaluation condition is asphalt.

[0076] The target model generation unit 23B (second generation unit 23Bb) also performs similar processing on the second data group. That is, it generates a target estimation model for gravel roads by using data included in the second data group whose evaluation condition is "gravel road." The target model generation unit 23B (second generation unit 23Bb) also generates a target estimation model for icy roads by using data included in the second data group whose evaluation condition is "icy road." Furthermore, it generates a target estimation model for asphalt by using data included in the second data group whose evaluation condition is asphalt.

[0077] In this way, the target model generation unit 23B (second generation unit 23Ba) generates a plurality of target estimation models corresponding to the plurality of evaluation conditions for each of the first data group and the second data group in S211. As a result, the number of target estimation models generated is equal to "the number of data groups x the number of evaluation conditions."

[0078] In addition, when the first data group is extracted as data for a source estimation model in S204, the target model generation unit 23B (second generation unit 23Bb) does not need to perform transfer learning on the first data group, and therefore the source estimation model obtained in S209 may be used as one of multiple target estimation models.

[0079] In addition, when the number of evaluation conditions is large (for example, when the number of evaluation conditions is 10 or more), in S209, the source model generation unit 23A (the third generation unit 23Ac) may generate one source estimation model (a model having the structure illustrated in FIG. 10) using the specification data and the evaluation condition data as explanatory variables. Then, the target model generation unit 23B (the third generation unit 23Bc) may generate a target estimation model having the structure illustrated in FIG. 10 for each of the plurality of data groups.

[0080] [Estimation Model Table] The learning unit 20 may store in the storage device 12 a table that associates the generated plurality of target estimation models with the structure items of the specification data, the evaluation conditions, and the characteristic data. Hereinafter, this map is referred to as an estimation model table. FIG. 12 is a diagram showing an example of the estimation model table. In this figure, in the leftmost column, the IDs for specifying each target estimation model are recorded. Also, for each ID, the characteristics to be estimated, the evaluation conditions to be applied, and the "present" or "absent" of the structure items are associated.

[0081] In the example shown in this figure, the target estimation models (ID: 1 and ID: 2) are models that output the corner ring power (CP). Also, in this figure, for the target estimation models (ID: 1 and ID: 2), "1 < W < 7" is recorded as an evaluation condition. This indicates that these models can input any value greater than 1 and less than 7 as the load W, which is the evaluation condition. As described above, the corner ring power can be calculated from the following relational expression. Therefore, as the load W, which is the evaluation condition, an operator can input the evaluation condition (load W) for which the operator wants to obtain the characteristic data. CP = a × W + b × W^2 + c × W^3 CP: Corner Ring Power W: Load (Evaluation Condition) a, b, and c: Coefficients

[0082] Furthermore, for the target estimation model (ID: 1), "Yes" is recorded for the structural items "tire width," "tread type," and "structural item A," and "No" is recorded for "structural item B." This indicates that the target estimation model (ID: 1) is applied to tires that do not have structural item B. On the other hand, for the target estimation model (ID: 2), "Yes" is recorded for the structural items "tire width," "tread type," "structural item A," and "structural item B." This indicates that the target estimation model (ID: 2) is applied to tires that have structural item B.

[0083] In the table illustrated in FIG. 12, the target estimation models (ID: 11 and ID: 12) are models for estimating the friction coefficient. In this figure, for the target estimation models (ID: 11 and ID: 12), "frozen road" and "gravel road" are recorded as the road surface conditions, which are evaluation conditions. This indicates that the target estimation model (ID: 11) is a model for estimating the friction coefficient on an icy road, and the target estimation model (ID: 12) is a model for estimating the friction coefficient on a gravel road. For the target estimation models (ID: 11 and ID: 12), "Yes" is recorded for the structural items "tire width," "tread type," and "structural item A," and "No" is recorded for "structural item B." This indicates that the target estimation models (ID: 11 and ID: 12) are applied to tires that do not have structural item B.

[0084] [Estimation part] The following describes the processing executed by the estimation unit 30. FIG.

[0085] The estimation unit 30 acquires specification data of a tire whose characteristics are to be estimated (hereinafter referred to as the estimation target tire) (S301). The specification data of the estimation target tire includes data on multiple structural items such as the dimensions, materials, and positions of tire components, as illustrated in FIG. 3 or FIG. 6A. Depending on the type of estimation target tire, the specification data may not have significant values ​​for some structural items. For example, similar to the data of the first data group illustrated in FIG. 3, the specification data of the estimation target tire may not have significant values ​​for structural item B. The specification data may also include the tire manufacturing method as one of its items.

[0086] The estimation unit 30 acquires the type of characteristic data to be estimated (for example, cornering power, friction coefficient, durability, etc.) (S302). For example, the operator inputs the type of characteristic data to be estimated to the processing device 11 via the input device 14, and the estimation unit 30 accepts this input type.

[0087] Next, the estimation unit 30 acquires evaluation conditions for the characteristics to be estimated. For example, if the operator wishes to estimate the cornering power of the tire to be estimated, the load, which is an evaluation condition for the cornering power, is input to the processing device 11 via the input device 14, and the estimation unit 30 accepts this input load. Also, if the operator wishes to estimate the friction coefficient of the tire to be estimated, the road surface condition, which is an evaluation condition for the friction coefficient (for example, frozen road, gravel road, etc.), is input to the processing device 11 via the input device 14, and the estimation unit 30 accepts this input road surface condition as an evaluation condition.

[0088] Next, the estimation unit 30 selects one of a plurality of target estimation models stored in the storage device 12 based on the structural item for which the specification data acquired in S301 is provided (S304). For example, the estimation unit 30 refers to an estimation model table (see FIG. 12) stored in the storage device 12 and selects a target estimation model corresponding to the characteristic (characteristic to be estimated) received in S302 and the structural item of the specification data acquired in S301.

[0089] For example, if cornering power is specified as the characteristic to be estimated and the specification data acquired in S301 does not have a significant value for structural item B, the estimation unit 30 selects the target estimation model (ID: 1). On the other hand, if cornering power is specified as the characteristic to be estimated and the specification data acquired in S301 has a significant value for structural item B, the estimation unit 30 selects the target estimation model (ID: 2).

[0090] In the above example, a plurality of target estimation models (models exemplified in FIG. 9) corresponding to a plurality of evaluation conditions are generated for the friction coefficient. Therefore, when the friction coefficient is specified in S302, the estimation unit 30 may select a target estimation model based on the structural item for which the specification data acquired in S301 is provided and the evaluation condition (road surface condition) specified in S303.

[0091] The estimation unit 30 inputs the specification data acquired in S301 into the target estimation model selected in S304, and estimates characteristic data (S305). As described with reference to Fig. 7, when the target estimation model has a coefficient estimation model and a relational expression (empirical regression model), the estimation unit 30 estimates the coefficients a·b·c using the coefficient estimation model of the selected target estimation model. Then, the estimation unit 30 inputs the evaluation condition (load W) specified in S303 into the relational expression defined by the coefficients a·b·c, and estimates the cornering power, which is characteristic data.

[0092] Furthermore, as explained with reference to FIG. 10, if the target estimation model selected in S304 is a model that has evaluation conditions as explanatory variables in addition to the specification data, the estimation unit 30 inputs the specification data acquired in S301 and the evaluation conditions specified in S303 into this model, and estimates characteristic data as its output.

[0093] Furthermore, unlike the above example, when a plurality of target estimation models corresponding to a plurality of evaluation conditions are generated (i.e., when the target estimation models have the structure illustrated in FIG. 9), the estimation unit 30 may select these plurality of target estimation models in S304. Then, in S305, the estimation unit 30 may input the specification data acquired in S301 to all of these plurality of target estimation models and output characteristic data.

[0094] [Search for tire specifications] The design support system 1 may have, as its functions, a search unit that searches for tire specification data that satisfies the characteristics desired by the operator, in addition to the learning unit 20 and the estimation unit 30. This process of the search unit can be executed, for example, as follows.

[0095] The search unit first provides the estimation unit 30 with specification data that is defined in advance or specification data that is set by an operator. The estimation unit 30 selects a target estimation model corresponding to the specification data acquired from the search unit from among a plurality of target estimation models stored in the storage device 12. The estimation unit 30 then inputs the specification data into the selected target estimation model, calculates characteristic data as its output, and provides the calculated characteristic data to the search unit. The search unit may generate new specification data so that the calculated characteristic data approaches the target set by the operator, and provide this to the estimation unit 30. The design support system 1 searches for specification data that achieves characteristics close to the target set by the operator by repeatedly executing the processing of the search unit and the processing of the estimation unit 30.

[0096] The estimation unit 30 may estimate multiple characteristic data based on the specification data acquired from the search unit. That is, the estimation unit 30 may estimate multiple characteristics, such as cornering power, noise generated between the road surface and the tire, rolling resistance, friction coefficient, and durability. The estimation unit 30 may select a target estimation model for estimating these multiple characteristics based on the specification data acquired from the search unit. The estimation unit 30 may then provide the search unit with multiple characteristic data obtained from the multiple target estimation models. The search unit may then generate new specification data so that the entire set of these multiple characteristic data approaches the target set by the operator and provide this to the estimation unit 30. The design support system 1 may search for specification data that achieves values ​​close to the target set by the operator for the multiple characteristics by repeatedly executing the processing of the search unit and the processing of the estimation unit 30. Such a search may be achieved using a so-called genetic algorithm or a gradient method.

[0097] Furthermore, the design support system 1 may display the specification data obtained as a result of processing by the search unit and the estimated characteristic data on the display device 13. In this case, the number of dimensions of the specification data may be reduced, and the specification data with the reduced number of dimensions may be displayed on the display device 13. For example, a method such as a self-organizing map or principal component analysis may be used to reduce the number of dimensions.

[0098] [summary] As described above, in the tire characteristic estimation device (design support system 1) proposed in this disclosure, the estimation unit 30 has data acquisition means (S301 in FIG. 13) that acquires specification data of a tire to be estimated. The estimation unit 30 also selects one of a first target estimation model and a second target estimation model based on a plurality of structural items for which the acquired specification data is provided (S304 in FIG. 13), inputs the acquired specification data into the selected target estimation model, and outputs characteristic data of the tire to be estimated (S305 in FIG. 13). A device including this estimation unit 30 can improve the accuracy of estimating the characteristics of a plurality of tires with different structural items.

[0099] As described above, the tire data group includes a first data group (see FIG. 3) and a second data group (see FIG. 3), each including a plurality of individual tire data. The first data group and the second data group differ in some of the plurality of structural parameters. The model generation unit 23 (target model generation unit 23B) generates a first target estimation model using the plurality of individual tire data included in the first data group as training data, and generates a second target estimation model using the plurality of individual tire data included in the second data group as training data. Using the estimation model generated by this method can improve the accuracy of estimating the characteristics of a plurality of tires with different structural parameters.

[0100] As described above, the first data group and the second data group include, as part of the multiple structural items, structural items common to the first data group and the second data group (see FIGS. 3 and 6A). The data extraction unit 22 (first extraction unit 22A) extracts data including specification data and characteristic data of the common structural items from the tire data group. The model generation unit 23 includes a source model generation unit 23A that generates a source estimation model based on the specification data of the common structural items using the extracted data as training data, and a target model generation unit 23B that generates a first target estimation model using the source estimation model and multiple individual tire data in the first data group, and a second target estimation model using the source estimation model and multiple individual tire data in the second data group. This allows for improved estimation accuracy of the estimation model obtained from one of the two data groups, even if the number of individual tire data included in that one data group is small.

[0101] The tire characteristic estimation device and tire characteristic estimation model generation device proposed in the present disclosure are not limited to the examples described above, and various modifications may be made.

[0102] For example, in the above description, the tire data group including a plurality of individual tire data (e.g., FIG. 3) includes two data groups, a first data group and a second data group, each of which includes data on cornering power. Alternatively, the tire data group may include more data groups. In this case, a plurality of target estimation models may be generated corresponding to the plurality of data groups.

[0103] In the above description, cornering power is exemplified as a characteristic estimated using the estimation model of the structure shown in FIG. 7. However, the estimation model of the structure shown in FIG. 7 may be used to estimate other characteristics. In addition, friction coefficient is exemplified as a characteristic estimated using the estimation model of the structure shown in FIG. 9. However, the estimation model of the structure shown in FIG. 9 may be used to estimate other characteristics. For example, the estimation model of the structure shown in FIG. 9 may be used to estimate cornering power. [Explanation of symbols]

[0104] 11: processing device, 12: storage device, 13: display device, 14: input device, 20: learning unit, 21: data acquisition unit, 22: data extraction unit, 22A: first extraction unit, 22B: second extraction unit, 23: model generation unit, 23A: source model generation unit, 23B: target model generation unit, 23Aa and 23Ba: first generation unit, 23Ab and 23Bb: second generation unit, 23Ac and 23Bc: third generation unit, 30: estimation unit, 40: estimation model group.

Claims

1. a storage means in which a first estimation model and a second estimation model are stored, each of the first estimation model and the second estimation model being an estimation model for estimating characteristic data representing tire characteristics from specification data including data on a plurality of structural items that are a plurality of items representing the structure of the tire; data acquisition means for acquiring specification data of the estimation target tire; a means for acquiring evaluation condition data indicating evaluation conditions for the characteristics to be estimated for the estimation target tire; an estimation means for selecting one of the first estimation model and the second estimation model based on a plurality of structural items for which the acquired specification data is provided, inputting the acquired specification data into the selected estimation model, and outputting characteristic data of the estimation target tire; Including, each of the first estimation model and the second estimation model includes a coefficient estimation model that outputs one or more coefficients according to the specification data, and is an estimation model for estimating the characteristic data from the specification data and the evaluation condition data; The estimation means inputs the acquired specification data into the coefficient estimation model of the selected estimation model, and calculates characteristic data of the estimation target tire using a relational expression defined by the one or more coefficients output from the coefficient estimation model and the acquired evaluation condition data. Tire characteristic estimation device.

2. the storage means stores a plurality of third estimation models corresponding to a plurality of evaluation conditions for tire characteristics, and a plurality of fourth estimation models corresponding to the plurality of evaluation conditions, The estimation means selects one or more estimation models from the first estimation model, the second estimation model, the plurality of third estimation models, and the plurality of fourth estimation models based on the plurality of structural items for which the acquired specification data is provided. The tire characteristic estimation device according to claim 1 .

3. each of the first estimation model and the second estimation model is a model generated from a tire data group including a plurality of individual tire data, each of the individual tire data including the specification data and the characteristic data; the tire data group includes a first data group and a second data group, each of which includes the plurality of individual tire data; the individual tire data of the first data group and the individual tire data of the second data group differ in at least a part of the plurality of structural items for which the specification data is provided, the first estimation model is a model generated using the plurality of individual tire data included in the first data group as training data, The second estimation model is a model generated using a plurality of individual tire data included in the second data group as training data. The tire characteristic estimation device according to claim 1 .

4. An estimation method for estimating a tire characteristic by using a first estimation model and a second estimation model, each of the first estimation model and the second estimation model is an estimation model for estimating characteristic data representing tire characteristics from specification data including data on a plurality of structural items that are a plurality of items representing a tire structure; a data acquisition step of acquiring specification data of the estimation target tire; acquiring evaluation condition data indicating evaluation conditions for the characteristics to be estimated for the estimation target tire; an estimation step of selecting one of the first estimation model and the second estimation model based on a plurality of structural items for which the acquired specification data is provided, inputting the acquired specification data into the selected estimation model, and outputting characteristic data of the estimation target tire; Including, each of the first estimation model and the second estimation model includes a coefficient estimation model that outputs one or more coefficients according to the specification data, and is an estimation model for estimating the characteristic data from the specification data and the evaluation condition data; In the estimation step, the acquired specification data is input to the coefficient estimation model of the selected estimation model, and characteristic data of the estimation target tire is calculated using a relational expression defined by the one or more coefficients output from the coefficient estimation model and the acquired evaluation condition data. Methods for estimating tire characteristics.

5. A program that causes a computer to function as an estimation device that estimates tire characteristics by utilizing a first estimation model and a second estimation model, each of the first estimation model and the second estimation model is an estimation model for estimating characteristic data representing tire characteristics from specification data including data on a plurality of structural items that are a plurality of items representing a tire structure; data acquisition means for acquiring specification data of the estimation target tire; a means for acquiring evaluation condition data indicating evaluation conditions for the characteristics to be estimated for the estimation target tire; and an estimation means for selecting one of the first estimation model and the second estimation model based on a plurality of structural items for which the acquired specification data is provided, inputting the acquired specification data into the selected estimation model, and outputting characteristic data of the tire to be estimated; Make the computer function as each of the first estimation model and the second estimation model includes a coefficient estimation model that outputs one or more coefficients according to the specification data, and is an estimation model for estimating the characteristic data from the specification data and the evaluation condition data; The estimation means inputs the acquired specification data into the coefficient estimation model of the selected estimation model, and calculates characteristic data of the estimation target tire using a relational expression defined by the one or more coefficients output from the coefficient estimation model and the acquired evaluation condition data. A program that makes a computer function like this.

6. a data acquisition step of acquiring a tire data group including a plurality of individual tire data, each of the individual tire data including characteristic data representing the characteristics of the tire, specification data including data on a plurality of structure items representing the structure of the tire, and evaluation condition data being condition values ​​indicating evaluation conditions for the characteristics of the tire; a model generation step of generating an estimation model for estimating the characteristic data based on the specification data of the estimation target tire by using the plurality of individual tire data; Including, the tire data group includes a first data group and a second data group, each of which includes the plurality of individual tire data; the first data group and the second data group differ in at least a part of the plurality of structural items, In the model generation step, a first estimation model is generated using the plurality of individual tire data included in the first data group as training data, and a second estimation model is generated using the plurality of individual tire data included in the second data group as training data, each of the first estimation model and the second estimation model includes a coefficient estimation model that outputs one or more coefficients that define a relational expression for calculating the characteristic data; In the model generation step, calculating the one or more coefficients based on the characteristic data of the plurality of individual tire data included in the first data group and the evaluation condition data, and generating the coefficient estimation model of the first estimation model using the calculated one or more coefficients and the specification data as training data; Calculating the one or more coefficients based on the characteristic data and the evaluation condition data of the plurality of individual tire data included in the second data group, and generating the coefficient estimation model of the second estimation model using the calculated one or more coefficients and the specification data as training data. A method for generating an estimation model of tire characteristics, comprising:

7. further comprising a data extraction step; the first data group and the second data group include, as part of the plurality of structure items, a structure item common to the first data group and the second data group; In the data extraction step, data including specification data of the common structural items and the characteristic data is extracted from the tire data group; The model generation step includes: a source model generating step of generating a source model based on specification data of the common structural items using the extracted data as training data; a target model generating step of generating the first estimation model using the source model and the plurality of individual tire data of the first data group, and generating the second estimation model using the source model and the plurality of individual tire data of the second data group. The method for generating an estimation model of tire characteristics according to claim 6.

8. In the model generation step, a plurality of third estimation models corresponding to a plurality of evaluation conditions are generated, and a plurality of fourth estimation models corresponding to a plurality of evaluation conditions are generated. The method for generating an estimation model of tire characteristics according to claim 6.

9. a data acquisition means for acquiring a tire data group including a plurality of individual tire data, wherein each individual tire data includes characteristic data representing tire characteristics, specification data including data on a plurality of structure items representing a tire structure, and evaluation condition data which is a condition value indicating an evaluation condition for the tire characteristics; a model generating means for generating an estimation model for estimating characteristic data based on tire specification data by using the plurality of individual tire data; Including, the tire data group includes a first data group and a second data group, each of which includes the plurality of individual tire data; the first data group and the second data group differ in at least some of the plurality of structure items for which the specification data is provided, the model generation means generates a first estimation model using the plurality of individual tire data included in the first data group as training data, and generates a second estimation model using the plurality of individual tire data included in the second data group as training data; each of the first estimation model and the second estimation model includes a coefficient estimation model that outputs one or more coefficients that define a relational expression for calculating the characteristic data; The model generation means calculating the one or more coefficients based on the characteristic data of the plurality of individual tire data included in the first data group and the evaluation condition data, and generating the coefficient estimation model of the first estimation model using the calculated one or more coefficients and the specification data as training data; Calculating the one or more coefficients based on the characteristic data and the evaluation condition data of the plurality of individual tire data included in the second data group, and generating the coefficient estimation model of the second estimation model using the calculated one or more coefficients and the specification data as training data. A device for generating an estimation model of tire characteristics.

10. a data acquisition means for acquiring a tire data group including a plurality of individual tire data, wherein each individual tire data includes characteristic data representing tire characteristics, specification data including data on a plurality of structure items which are a plurality of items representing the tire structure, and evaluation condition data which is a condition value indicating an evaluation condition for the tire characteristics; a model generating means for generating an estimation model for estimating characteristic data based on tire specification data by using the plurality of individual tire data; Make the computer function as the tire data group includes a first data group and a second data group, each of which includes the plurality of individual tire data; the first data group and the second data group differ in at least some of the plurality of structure items for which the specification data is provided, the model generation means generates a first estimation model using the plurality of individual tire data included in the first data group as training data, and generates a second estimation model using the plurality of individual tire data included in the second data group as training data; each of the first estimation model and the second estimation model includes a coefficient estimation model that outputs one or more coefficients that define a relational expression for calculating the characteristic data; The model generation means calculating the one or more coefficients based on the characteristic data of the plurality of individual tire data included in the first data group and the evaluation condition data, and generating the coefficient estimation model of the first estimation model using the calculated one or more coefficients and the specification data as training data; Calculating the one or more coefficients based on the characteristic data and the evaluation condition data of the plurality of individual tire data included in the second data group, and generating the coefficient estimation model of the second estimation model using the calculated one or more coefficients and the specification data as training data. A program that makes a computer function like this.

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