JP7708658B2 - Tire wear estimation system and calculation model operation method - Google Patents

Description

The present invention relates to a tire wear estimation system and a calculation model operation method for estimating the wear of tires mounted on a vehicle.

In general, tires wear down depending on the driving conditions and distance traveled. Recently, devices have been commercialized that attach sensors to tires to measure tire pressure and temperature and display the measured pressure and temperature.

Patent Document 1 describes a conventional wear amount estimation system for estimating the amount of tire wear. This wear amount estimation system includes a tire information acquisition unit, a tire severity calculation unit, and a wear amount calculation unit. The tire information acquisition unit acquires air pressure data and temperature data of tires mounted on a vehicle. The tire severity calculation unit calculates tire severity, which indicates the degree of load on the tire, from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit. The wear amount calculation unit has a calculation model that calculates the amount of tire wear based on information on the severity of the tire, and calculates the amount of tire wear using the calculation model by inputting the tire severity.

JP 2021-070341 A

The wear amount estimation system described in Patent Document 1 estimates the amount of tire wear based on a pre-trained computational model. The inventors realized that even when estimating tire wear amount using a computational model for a vehicle or tire for which learning-type computational model training has not been performed, the estimation accuracy is not necessarily good in the initial stage, but it is possible to progress with learning as the vehicle travels, and there is room for improvement in the operation of the learning-type computational model.

The present invention was made in consideration of these circumstances, and its purpose is to provide a tire wear amount estimation system and a calculation model operation method that can select a calculation model during operation and continue to provide wear amount estimation data.

A tire wear amount estimation system according to one embodiment of the present invention includes a data acquisition unit that acquires data on explanatory variables including numerical variables and categorical variables related to a vehicle and tires mounted on the vehicle, a wear amount calculation unit that has a missing value corresponding calculation model and a missing value incompatible calculation model that input the data acquired by the data acquisition unit and calculates the tire wear amount, and a judgment unit that judges whether the data acquired by the data acquisition unit includes an unlearned categorical variable, and the wear amount calculation unit calculates the tire wear amount using one of the missing value corresponding calculation model and the missing value incompatible calculation model based on the judgment result by the judgment unit.

Another aspect of the present invention is a calculation model operation method. The calculation model operation method includes a data acquisition step of acquiring data on explanatory variables including numerical variables and categorical variables related to a vehicle and tires mounted on the vehicle, a wear amount calculation step of calculating a tire wear amount using a missing value corresponding calculation model and a missing value incompatible calculation model to which the data acquired by the data acquisition step is input, and a judgment step of judging whether or not the data acquired by the data acquisition step includes an unlearned categorical variable, and the wear amount calculation step calculates the tire wear amount using one of the missing value corresponding calculation model and the missing value incompatible calculation model based on the judgment result by the judgment step.

According to the present invention, a calculation model can be selected during operation, and wear amount estimation data can be maintained.

1 is a block diagram showing a functional configuration of a tire wear amount estimation system according to an embodiment. FIG. 2 is a block diagram showing a functional configuration of the vehicle-mounted measuring device. FIG. 13 is a schematic diagram for explaining wear amount estimation and learning of a missing value incompatible computation model. 4 is a flowchart showing a procedure for wear amount estimation processing performed by the tire wear amount estimation system.

The present invention will be described below based on a preferred embodiment with reference to Figures 1 to 4. The same or equivalent components and parts shown in each drawing are given the same reference numerals, and duplicated explanations will be omitted as appropriate. Furthermore, the dimensions of the parts in each drawing are enlarged or reduced as appropriate to facilitate understanding. Furthermore, some of the parts that are not important for explaining the embodiment will be omitted in each drawing.

(Embodiment)
1 is a block diagram showing a functional configuration of a tire wear amount estimation system 100 according to an embodiment. The tire wear amount estimation system 100 includes a tire wear amount measurement device 60, an on-vehicle measurement device 70, a weather information server device 80, and a wear amount estimation device 10, and estimates the wear amount of a tire 7. The tire wear amount estimation system 100 can be applied to vehicles and tires for which learning of a learning-type calculation model has not been performed, and can select a calculation model during operation and continue to provide wear amount estimation data.

The tire wear measuring device 60 directly measures the depth of the grooves in the tread of the tire 7, for example, multiple times at each predetermined period during operation when the vehicle is running, and obtains the wear of the tire 7. The predetermined period may be, for example, several weeks to several months, but is not limited to this. The tire wear measuring device 60 transmits the measured wear data of the tire 7 to the wear estimation device 10 via the communication network 9. An operator may measure the depth of each groove using a measuring tool, a camera, or visual inspection, and the tire wear measuring device 60 may store the measurement data input by the operator. The tire wear measuring device 60 may also be a dedicated device that measures the groove depth by a mechanical or optical method and stores the wear amount.

Specifically, for example, if a tire has four grooves, the tire wear measuring device 60 measures at four locations in the width direction, and then measures three locations in the circumferential direction of the same groove, for example at 120° intervals. As a result, uneven wear data in the width direction or circumferential direction of the tire is also stored in the tire wear measuring device 60. Note that the tire wear measuring device 60 may indirectly measure groove depth by calculation from information on the mileage and the tire rotation speed and speed, since tire diameter changes due to wear. In addition, a device that directly measures groove depth may be used in combination with a device that predicts groove depth by calculation from the mileage and the tire rotation speed and speed.

Figure 2 is a block diagram showing the functional configuration of the on-board measurement device 70. The on-board measurement device 70 comprises a vehicle measurement unit 71, a tire measurement unit 72, an information acquisition unit 73, and a communication unit 74. Each unit in the on-board measurement device 70 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The vehicle measurement unit 71 has a speedometer 71a, a GPS receiver 71b, and an acceleration sensor 71c mounted on the vehicle. The speedometer 71a measures the vehicle's speed. The GPS receiver 71b measures the vehicle's current position information (latitude, longitude, and altitude). The acceleration sensor 71c measures the vehicle's acceleration in three axial directions.

The tire measurement unit 72 has a temperature sensor 72a and a pressure sensor 72b. The temperature sensor 72a and the pressure sensor 72b are disposed in the air valve of the tire 7 mounted on the vehicle, or are firmly wrapped around the wheel with a belt or the like and fixed thereto, and measure the temperature and air pressure of the tire 7. The temperature sensor 72a may be disposed in the inner liner of the tire 7, etc.

The information acquisition unit 73 acquires vehicle measurement information (speed, position information, acceleration, etc.) measured by the vehicle measurement unit 71 and tire measurement information (tire temperature, air pressure, etc.) measured by the tire measurement unit 72. The information acquisition unit 73 associates each piece of measurement data included in the vehicle measurement information and tire measurement information with measured time information or acquired time information. The information acquisition unit 73 transmits the vehicle measurement information and tire measurement information together with the time information associated with each piece of measurement data from the communication unit 74 to the wear amount estimation device 10.

If the vehicle is equipped with a device such as a digital tachometer, the information acquisition unit 73 may acquire information such as the vehicle speed, acceleration, and position collected by the device. The communication unit 74 connects to the communication network 9 by wireless communication such as Wi-Fi (registered trademark), and transmits the vehicle measurement information, tire measurement information, and time information acquired by the information acquisition unit 73 to the wear amount estimation device 10 via the communication network 9.

Returning to FIG. 1, the weather information server device 80 provides weather information for various locations. The weather information provided by the weather information server device 80 includes information such as the amount of precipitation, snowfall, temperature, and sunshine hours for various locations. The wear amount estimation device 10 obtains weather information for the location where the vehicle is traveling from the weather information server device 80.

The wear amount estimation device 10 includes a communication unit 11, a data acquisition unit 12, a judgment unit 13, a wear amount calculation unit 14, a memory unit 15, and a learning processing unit 16. Each unit in the wear amount estimation device 10 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The communication unit 11 is connected to the communication network 9 by wireless or wired communication, and communicates with the communication unit 74 of the on-board measurement device 70. The communication unit 11 also communicates with the weather information server device 80 via the communication network 9.

The data acquisition unit 12 acquires vehicle measurement information (speed, position information, acceleration, etc.) and tire measurement information (tire temperature and air pressure, etc.) transmitted from an on-board measurement device 70 mounted on the vehicle. The data acquisition unit 12 calculates and acquires the vehicle's travel distance based on the vehicle measurement information.

The data acquisition unit 12 can calculate and acquire the travel distance based on the position information of the vehicle measurement information. The travel distance of the vehicle may also be calculated based on the speed data in the vehicle measurement information and the time data associated with that data. That is, the travel distance of the vehicle can be calculated by multiplying the speed data arranged in chronological order by the time difference until the next point in time.

If information regarding the vehicle's mileage is provided by the vehicle or an external device for vehicle management, the data acquisition unit 12 does not need to calculate the mileage itself and may acquire information regarding the mileage from the vehicle or the external device.

The data acquisition unit 12 outputs the acquired travel distance to the wear amount calculation unit 14. The data acquisition unit 12 outputs the acquired tire measurement information (tire temperature, air pressure, etc.) to the wear amount calculation unit 14. When the wear amount calculation unit 14 estimates the amount of tire wear based on a calculation model that uses vehicle acceleration as an input element, the data acquisition unit 12 outputs the acceleration data in the vehicle measurement information to the wear amount calculation unit 14.

The data acquisition unit 12 also acquires data from the storage unit 15 that is used to estimate the wear amount of the tire 7, from among the vehicle specification data 15a and tire specification data 15b, and outputs the data to the determination unit 13 and the wear amount calculation unit 14. The storage unit 15 is a storage device that is configured, for example, with an SSD (Solid State Drive), a hard disk, a CD-ROM, a DVD, etc., and stores data provided in advance regarding the specifications of various vehicles and tires 7.

The vehicle specification data 15a includes information on the vehicle's performance, such as the manufacturer, vehicle model, vehicle name, vehicle type, vehicle weight, drive train, overall length, vehicle width, vehicle height, and maximum load capacity. The tire specification data 15b includes information on the performance of the tire 7, such as the manufacturer, product name, tire size, tire width, aspect ratio, wear resistance, tire strength, static stiffness, dynamic stiffness, tire outer diameter, load index, and date of manufacture. The vehicle specification data 15a and tire specification data 15b correspond to the categorical variable portion of the explanatory variables input to each computation model described below.

The determination unit 13 determines whether the vehicle specification data 15a and tire specification data 15b input from the data acquisition unit 12, i.e., the categorical variables, have not been learned in the missing value non-compatible calculation model 14b of the wear amount calculation unit 14. The determination unit 13 outputs the determination result to the wear amount calculation unit 14.

The wear amount calculation unit 14 has a learning type missing value corresponding calculation model 14a and a missing value non-corresponding calculation model 14b, and selects one of the calculation models to calculate the wear amount of the tire 7. The missing value corresponding calculation model 14a is, for example, a decision tree model, and is trained by a method such as gradient boosting. The missing value non-corresponding calculation model 14b is a neural network, and has a multi-layer structure using, for example, convolution calculation such as CNN and fully connected calculation, and determines the coupling coefficient between each layer by learning.

The missing value-compatible calculation model 14a has the property of omitting calculations for the part of the categorical variable in the decision tree when an unlearned categorical variable is input and outputting the result, so it is understood to be a calculation model that can handle missing values.The missing value incompatible calculation model 14b is understood to be a calculation model that does not handle missing values because when an unlearned categorical variable is input, various coefficients in the neural network calculation process have not been learned for the categorical variable, and the output result does not necessarily have good estimation accuracy.

When the determination unit 13 determines that the categorical variable is a learned one, the wear amount calculation unit 14 calculates the tire wear amount using the calculation model with higher wear amount estimation accuracy, either the missing value corresponding calculation model 14a or the missing value non-corresponding calculation model 14b. When the determination unit 13 determines that the categorical variable is a learned one, the wear amount calculation unit 14 may calculate the tire wear amount using the missing value non-corresponding calculation model 14b, assuming that the missing value non-corresponding calculation model 14b has higher tire wear amount estimation accuracy. Furthermore, when the determination unit 13 determines that the categorical variable is a learned one, the wear amount calculation unit 14 may calculate the tire wear amount using an ensemble of the missing value corresponding calculation model 14a and the missing value non-corresponding calculation model 14b.

When the determination unit 13 determines that the categorical variable is an unlearned one, the wear amount calculation unit 14 calculates the tire wear amount using the missing value corresponding calculation model 14a, and causes the missing value non-compatible calculation model 14b to be trained by the learning processing unit 16. In this case, after the missing value non-compatible calculation model 14b has been trained for a predetermined period (e.g., several months), the wear amount calculation unit 14 switches from the missing value corresponding calculation model 14a to the missing value non-compatible calculation model 14b to calculate the tire wear amount.

In addition, when the learning of the missing value incompatible calculation model 14b progresses and the tire wear amount estimation result by the missing value incompatible calculation model 14b becomes better than the tire wear amount estimation result by the missing value compatible calculation model 14a, the wear amount calculation unit 14 switches from the missing value compatible calculation model 14a to the missing value incompatible calculation model 14b to calculate the tire wear amount.

The learning processing unit 16 acquires the wear amount of the tire 7 from the tire wear amount measuring device 60 via the communication unit 11. FIG. 3 is a schematic diagram for explaining the wear amount estimation and learning of the missing value incompatible calculation model 14b. In the learning process of the missing value incompatible calculation model 14b, the tire wear amount as output data is estimated by the missing value incompatible calculation model 14b based on the input data, and compared with the teacher data. The teacher data uses the wear amount of the tire 7 measured by the tire wear amount measuring device 60.

The learning processing unit 16 performs learning by comparing the amount of wear of the tire 7 estimated by the missing value incompatible calculation model 14b with the teacher data, setting new coefficients in the calculation process such as weighting, and repeatedly updating the calculation model. The input data to the missing value incompatible calculation model 14b is roughly classified into vehicle measurement information, tire measurement information, and other information.

The input data related to vehicle measurement information and tire measurement information corresponds to the numerical variable portion of the explanatory variables input to the calculation model. The input data related to vehicle measurement information includes the vehicle acceleration and mileage. The mileage is acquired by the data acquisition unit 12 as described above. The input data related to tire measurement information includes the temperature and air pressure of the tire 7.

The input data from other information includes road surface conditions estimated based on weather information, vehicle specification data 15a, and tire specification data 15b. As described above, vehicle specification data 15a and tire specification data 15b correspond to the categorical variable portion of the explanatory variables input to the calculation model. Of the vehicle specification data 15a, the vehicle name and drive train are categorical variables input to the calculation model. Also, of the tire specification data 15b, the product name and tire size are categorical variables input to the calculation model.

Next, the operation of the tire wear amount estimation system 100 will be described. FIG. 4 is a flowchart showing the procedure of the wear amount estimation process by the tire wear amount estimation system 100. The data acquisition unit 12 of the wear amount estimation device 10 acquires data on numerical variables and categorical variables related to a vehicle and tires mounted on the vehicle (S1). The determination unit 13 determines whether the data on categorical variables acquired by the data acquisition unit 12 includes an unlearned categorical variable (S2).

If it is determined in step S2 that an unlearned categorical variable is included (S2: YES), the wear amount calculation unit 14 calculates the tire wear amount using the missing value corresponding calculation model 14a and sets it as an estimated result (S3). The wear amount calculation unit 14 also starts learning the missing value non-corresponding calculation model 14b (S4) and ends the process.

If it is determined in step S2 that no unlearned categorical variables are included (S2: NO), the wear amount calculation unit 14 calculates the tire wear amount using the missing value non-compatible calculation model 14b, sets it as an estimated result (S5), and ends the process.

The missing value incompatible calculation model 14b started in step S4 is trained, for example, for a set predetermined period of time. After the missing value incompatible calculation model 14b is trained, the determination unit 13 determines that the categorical variables that were determined to be untrained have been trained. Furthermore, the missing value incompatible calculation model 14b may be considered to have been trained when the tire wear amount estimation accuracy becomes better than the tire wear amount estimation accuracy using the missing value compatible calculation model 14a.

The tire wear amount estimation system 100 uses the determination unit 13 to determine whether or not the categorical variable has not been learned, and estimates the tire wear amount using one of the missing value corresponding calculation model 14a and the missing value non-corresponding calculation model 14b based on the determination result. This allows the tire wear amount estimation system 100 to select a calculation model during operation even when the categorical variable has not been learned, and maintain the provision of a function to notify the user of the estimated data of the tire wear amount. The tire wear amount estimation system 100 uses the missing value corresponding calculation model 14a to estimate the tire wear amount even when the categorical variable has not been learned, and maintains the provision of a function to notify the user by displaying the wear amount for each groove of the tire on a display screen such as a display.

When the determination unit 13 determines that an unlearned categorical variable is included, the tire wear amount estimation system 100 trains the missing value incompatible calculation model 14b using the learning processing unit 16. As a result, after the missing value incompatible calculation model 14b has been trained, the tire wear amount estimation system 100 can estimate and provide the tire wear amount using the missing value incompatible calculation model 14b.

The tire wear amount estimation system 100 performs learning of the missing value incompatible calculation model 14b by the learning processing unit 16 for a predetermined period (e.g., several months), and thereafter, the tire wear amount can be estimated and provided using the missing value incompatible calculation model 14b.

The tire wear amount estimation system 100 may also perform learning of the missing value incompatible calculation model 14b until the tire wear amount estimation result is better than the estimation result by the missing value compatible calculation model 14a. In this case, the tire wear amount estimation system 100 can provide a tire wear amount estimation result with higher estimation accuracy using the missing value incompatible calculation model 14b with a good estimation result. The tire wear amount estimation accuracy in each calculation model can be verified, for example, by calculating the RMSE (root mean square error) between the measured value and the estimated value of the tire wear amount obtained multiple times.

The wear amount calculation unit 14 uses, for example, a decision tree model as the missing value corresponding calculation model 14a, and a neural network model as the missing value incompatible calculation model 14b. This allows the tire wear amount estimation system 100 to easily construct each calculation model in accordance with a known calculation model construction method.

The tire wear amount estimation system 100 described in this embodiment uses, for example, a missing value corresponding calculation model 14a and a missing value non-corresponding calculation model 14b trained on one vehicle having a certain vehicle name and the tires fitted to the vehicle. When this tire wear amount estimation system 100 is diverted to another vehicle having the same vehicle name and equipped with tires of the same specifications (same product name and tire size, etc.), the categorical variables of the missing value corresponding calculation model 14a and the missing value non-corresponding calculation model 14b become trained.

On the other hand, if the tire wear estimation system 100 is diverted to another vehicle of the same vehicle name or a different vehicle name that is equipped with tires of different specifications, the categorical variables of the missing value corresponding calculation model 14a and the missing value non-corresponding calculation model 14b will include unlearned items. Also, if the tire wear estimation system 100 is diverted to another vehicle of a different vehicle name that is equipped with tires of the same specifications, the categorical variables of the missing value corresponding calculation model 14a and the missing value non-corresponding calculation model 14b will include unlearned items. As described above, the tire wear estimation system 100 can continue to provide tire wear estimation data by the missing value corresponding calculation model 14a even when unlearned items are included in the categorical variables.

Next, features of the tire wear amount estimation system 100 and the calculation model operating method according to the embodiment and the modified example will be described.
The tire wear amount estimation system 100 includes a data acquisition unit 12, a wear amount calculation unit 14, and a judgment unit 13. The data acquisition unit 12 acquires data of explanatory variables including numerical variables and categorical variables related to a vehicle and a tire 7 mounted on the vehicle. The wear amount calculation unit 14 has a missing value corresponding calculation model 14a and a missing value non-corresponding calculation model 14b to which the data acquired by the data acquisition unit 12 is input, and calculates the tire wear amount. The judgment unit 13 judges whether or not the data acquired by the data acquisition unit 12 includes an unlearned categorical variable. The wear amount calculation unit 14 calculates the tire wear amount using one of the missing value corresponding calculation model 14a and the missing value non-corresponding calculation model 14b based on the judgment result by the judgment unit 13. As a result, the tire wear amount estimation system 100 can select a calculation model during operation and continue to provide estimated data of the tire wear amount even when the categorical variable has not been learned.

The system further includes a learning processing unit 16 that learns the missing value incompatible calculation model 14b when the determination unit 13 determines that an unlearned categorical variable is included. As a result, the tire wear amount estimation system 100 can estimate and provide the tire wear amount using the missing value incompatible calculation model 14b after learning the missing value incompatible calculation model 14b.

In addition, the wear amount calculation unit 14 calculates the tire wear amount using the missing value incompatible calculation model 14b after a predetermined period of learning by the learning processing unit 16 has elapsed. As a result, the tire wear amount estimation system 100 performs learning of the missing value incompatible calculation model 14b by the learning processing unit 16 for a predetermined period (e.g., several months), and thereafter, can estimate and provide the tire wear amount using the missing value incompatible calculation model 14b.

In addition, after the estimation result from the missing value incompatible calculation model 14b becomes better than the estimation result from the missing value compatible calculation model 14a, the wear amount calculation unit 14 calculates the tire wear amount using the missing value incompatible calculation model 14b. This allows the tire wear amount estimation system 100 to provide a tire wear amount estimation result with higher estimation accuracy using the missing value incompatible calculation model 14b that has a better estimation result.

The missing value-compatible calculation model 14a is a decision tree model, and the missing value non-compatible calculation model 14b is a neural network model. This allows the tire wear estimation system 100 to easily construct each calculation model in accordance with known calculation model construction methods.

The calculation model operation method includes a data acquisition step, a wear amount estimation step, and a judgment step. The data acquisition step acquires data on explanatory variables including numerical variables and categorical variables related to a vehicle and a tire 7 mounted on the vehicle. The wear amount calculation step calculates the tire wear amount using the missing value corresponding calculation model 14a and the missing value incompatible calculation model 14b to which the data acquired in the data acquisition step is input. The judgment step judges whether or not the data acquired in the data acquisition step includes an unlearned categorical variable. The wear amount calculation step calculates the tire wear amount using one of the missing value corresponding calculation model 14a and the missing value incompatible calculation model 14b based on the judgment result in the judgment step. According to this calculation model operation method, even if the categorical variable has not been learned, a calculation model can be selected during operation to continue providing estimated data on the tire wear amount.

The above describes the embodiments of the present invention. These embodiments are illustrative, and it will be understood by those skilled in the art that various modifications and changes are possible within the scope of the claims of the present invention, and that such modifications and changes are also within the scope of the claims of the present invention. Therefore, the descriptions and drawings in this specification should be treated as illustrative rather than restrictive.

7 Tire, 12 Data acquisition unit, 13 Determination unit, 14 Wear amount calculation unit,
14a missing value corresponding calculation model, 14b missing value non-corresponding calculation model,
16 learning processing unit, 100 tire wear amount estimation system.

Claims (6)

a data acquisition unit for acquiring data on explanatory variables including numerical variables and categorical variables relating to a vehicle and tires mounted on the vehicle;
a wear amount calculation unit having a missing value corresponding calculation model and a missing value incompatible calculation model that input the data acquired by the data acquisition unit, and calculating a tire wear amount;
a determination unit that determines whether or not an unlearned categorical variable is included in the data acquired by the data acquisition unit,
The tire wear amount estimation system is characterized in that the wear amount calculation unit calculates the tire wear amount using one of the missing value corresponding calculation model and the missing value incompatible calculation model based on the judgment result by the judgment unit. The tire wear amount estimation system according to claim 1, further comprising a learning processing unit that learns the missing value incompatible calculation model when the determination unit determines that an unlearned categorical variable is included. The tire wear amount estimation system according to claim 2, characterized in that the wear amount calculation unit calculates the tire wear amount using the missing value incompatible calculation model after a predetermined period of learning by the learning processing unit has elapsed. The tire wear amount estimation system according to claim 2, characterized in that the wear amount calculation unit calculates the tire wear amount using the missing value incompatible calculation model after the estimation result using the missing value incompatible calculation model becomes better than the estimation result using the missing value compatible calculation model. The tire wear amount estimation system according to any one of claims 1 to 4, characterized in that the missing value corresponding calculation model is a decision tree model, and the missing value incompatible calculation model is a neural network model. A data acquisition step for acquiring data on explanatory variables including numerical variables and categorical variables relating to a vehicle and tires mounted on the vehicle;
a wear amount calculation step of calculating a tire wear amount using a missing value corresponding calculation model and a missing value incompatible calculation model to which the data acquired by the data acquisition step is input;
a determination step of determining whether or not an unlearned categorical variable is included in the data acquired by the data acquisition step,
The calculation method for calculating a tire wear amount, comprising the steps of: calculating a tire wear amount using one of the missing value corresponding calculation model and the missing value incompatible calculation model based on a determination result by the determination step;