WO2023032907A1

WO2023032907A1 - Information processing device for tires, and program

Info

Publication number: WO2023032907A1
Application number: PCT/JP2022/032385
Authority: WO
Inventors: 忠雄千里内; 貞春米田
Original assignee: Tdk株式会社
Priority date: 2021-08-31
Filing date: 2022-08-29
Publication date: 2023-03-09
Also published as: JP2023034916A

Abstract

An information processing device according to an embodiment of the present invention comprises an information processing unit that acquires evaluation information pertaining to a tire mounted on a vehicle and having a wheel and a rubber part, the evaluation information being acquired on the basis of two or more pieces of tire information that are based on a first detection value detected by a first sensor for detecting the first detection value pertaining to the tire, and respectively correspond to each of two or more different conditions pertaining to a prescribed event.

Description

[Title of invention determined by ISA based on Rule 37.2] Information processing device and program related to tires

The present disclosure relates to an information processing device and a program.

With respect to tires such as automobiles, sensors have been used to monitor tire parameters.
For example, a piezoelectric element that is a strain sensor is attached to a tire, the magnitude of deformation of the tire is detected according to the magnitude of the amplitude of the signal detected by the piezoelectric element, and a load is applied to the tire based on the detection result. estimating the load to be applied.

The technique described in US Pat. No. 6,200,402 uses piezoelectric devices to monitor tire parameters, such as tire rotation counts, obtained based on the strain detected by the piezoelectric devices; An attempt has been made to improve the accuracy of the speed of the tire and the contact surface angle of the tire (see Patent Document 1).

Japanese Patent Publication No. 2016-505438

However, in the conventional technology, due to variations in the characteristics of individual sensors made up of piezoelectric elements or the like, or variations in the degree of adhesion such as the thickness or hardness of the adhesive that bonds the sensor and the tire, the sensor may be damaged. In some cases, variations in sensitivity occur, causing variations in the values detected by the sensors. In this case, it is necessary to calibrate the sensor itself or to calibrate the detected value of the sensor in order to effectively utilize information about the amplitude direction of the detected value output from the sensor.
Also, such calibration often needs to be done after the sensor is mounted on the tire, which can be difficult.

The present disclosure has been made in consideration of such circumstances, and an information processing device and program capable of accurately acquiring evaluation information about tires even if there are individual variations in sensors that detect information about tires. The task is to provide

One aspect of the present disclosure is tire information based on the first detection value detected by a first sensor that detects a first detection value regarding a tire that is provided in a vehicle and has a wheel and a rubber portion, the tire information relating to a predetermined event The information processing device includes an information processing unit that acquires evaluation information about the tire based on two or more pieces of tire information corresponding to two or more different conditions.

One aspect of the present disclosure is tire information based on the first detection value detected by a first sensor that detects a first detection value regarding a tire that is provided in a vehicle and has a wheel and a rubber portion in a computer, A program for executing a step of acquiring evaluation information about the tire based on two or more pieces of tire information corresponding to two or more different conditions relating to a predetermined event.

According to the present disclosure, in the information processing device and the program, it is possible to acquire the evaluation information about the tire with high accuracy even if there are individual variations in the sensors that detect the information about the tire.

It is a figure which shows an example of a schematic structure of the information processing apparatus which concerns on embodiment. It is a figure which shows an example of the attachment position of the sensor which concerns on embodiment. (A) and (B) are diagrams showing other examples of mounting positions of the sensors according to the embodiment. It is a figure which shows an example of the detection value of the strain sensor which concerns on embodiment. FIG. 4 is a diagram showing an example of relative relationship between two distortion signals according to the embodiment; FIG. 10 is a diagram showing another example of the relative relationship between two distortion signals according to the embodiment; It is a figure which shows an example of the characteristic of the value between peaks which concerns on embodiment. It is a figure which shows an example of the characteristic of the change of the value between peaks which concerns on embodiment. It is a figure which shows an example of a state when the grounding time of the tire which concerns on embodiment is long. It is a figure which shows an example of a state when the grounding time of the tire which concerns on embodiment is short. It is a figure which shows an example of the detection value of the strain sensor which concerns on embodiment.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[Information processing device]
FIG. 1 is a diagram showing an example of a schematic configuration of an information processing device 1 according to an embodiment.
For convenience of explanation, the tire 51 and the measurement condition acquisition target 52 are shown in FIG.
In this embodiment, the information processing apparatus 1 has a function of analyzing data regarding the tire 51 based on data regarding the measurement condition acquisition target 52 .
Note that the information processing device 1 may be called an information processing system or the like.

In this embodiment, the tire 51 is one of one or more tires connected to a vehicle such as an automobile. A predetermined number of tires (for example, four in the case of an automobile) are connected to the vehicle.
In this embodiment, a case where the vehicle is an automobile will be described, but the vehicle is not particularly limited, and may be, for example, a motorcycle, a bicycle, or an aircraft. Here, an aircraft can also be regarded as a vehicle when the aircraft is traveling on the ground with its tires.

The tire 51 generally has a wheel and a rubber portion attached around the wheel. The rubber portion may have, for example, a bead portion, a sidewall portion, a shoulder portion, and a tread portion. The tire 51 may be, for example, various tires having a rubber portion, or various tires having a wheel and a rubber portion.
Further, in the present embodiment, the measurement condition acquisition target 52 is the tire 51 or a vehicle provided with the tire 51 (here, a portion other than the tire). Note that the measurement condition acquisition target 52 may be another object.

The information processing device 1 includes a tire characteristic acquisition sensor 11 , a measurement condition acquisition sensor 12 , and a data analysis device 21 .
The data analysis device 21 includes a measurement condition determination section 31 , a comparison data selection section 32 , a data analysis section 33 and a storage section 34 .

The tire characteristic acquisition sensor 11 detects (measures) a predetermined physical quantity value (detection value) relating to the tire 51 . The tire characteristic acquisition sensor 11 outputs the detected value (detection value) to the comparison data selection section 32 of the data analysis device 21 .
Here, the tire characteristic acquisition sensor 11 is attached to the tire 51, for example, but if it can detect a predetermined physical quantity value (detection value) related to the tire 51, it can be provided at a location other than the tire 51. good too. For example, a sensor that is not directly attached to the tire 51 and detects a detection value related to the tire 51 by emitting light and receiving reflected light from the tire 51 may be used as the tire characteristic acquisition sensor 11. good.

The measurement condition acquisition sensor 12 detects (measures) a value (detected value) of a predetermined physical quantity relating to the measurement condition acquisition target 52 . The measurement condition acquisition sensor 12 outputs the detected value (detection value) to the measurement condition determination section 31 of the data analysis device 21 .
Here, the measurement condition acquisition sensor 12 is attached to the measurement condition acquisition target 52, for example, but if it can detect a predetermined physical quantity value (detection value), it can be attached to a location other than the measurement condition acquisition target 52. may be provided.

In the example of FIG. 1, a line (wired or wireless) for outputting the detection value detected by the tire characteristic acquisition sensor 11 to the measurement condition determining section 31 of the data analysis device 21 is also shown. In this way, the tire characteristic acquisition sensor 11 may output the detected value to the measurement condition determination unit 31 of the data analysis device 21. In this case, the measurement condition determination unit 31 outputs the detection value from the measurement condition acquisition sensor 12. It is possible to use not only the value but also the detected value from the tire characteristic acquisition sensor 11 .
On the other hand, when the measurement condition determination section 31 does not use the detection value of the tire characteristic acquisition sensor 11, the detection value detected by the tire characteristic acquisition sensor 11 is output to the measurement condition determination section 31 of the data analysis device 21. No line (wired or wireless) may be provided.

Here, in the present embodiment, for convenience of explanation, the tire characteristic acquisition sensor 11 and the measurement condition acquisition sensor 12 are different sensors, and the detection value (detection target and detection target) detected by the tire characteristic acquisition sensor 11 is physical quantity) and the detected value (combination of detection target and physical quantity) detected by the measurement condition acquisition sensor 12 are different. Sensor 12 may be a common sensor. In this case, in the example of FIG. 1, roughly speaking, the function of the measurement condition acquisition sensor 12 is shared by the tire characteristic acquisition sensor 11, so the measurement condition acquisition sensor 12 is not provided, and the tire characteristic acquisition sensor 12 is not provided. The detection value of the sensor 11 for measurement is output to the comparison data selection section 32 and the measurement condition determination section 31 of the data analysis device 21 .

In this embodiment, the data analysis device 21 is provided in the tire or the vehicle (here, the portion other than the tire).
The data analysis device 21 may be configured using, for example, a microcomputer. In this case, the data analysis device 21 includes a processor such as a CPU (Central Processing Unit) and a memory (the storage unit 34 in the example of FIG. 1). , to implement various processes.
The storage unit 34 stores various information. The storage unit 34 may store the program.

The measurement condition determination unit 31 acquires information on a predetermined event (also referred to as predetermined event information for convenience of explanation) based on the detection value input from the measurement condition acquisition sensor 12, and based on the acquired predetermined event information to determine at least two conditions (referred to as measurement conditions for convenience of explanation). The measurement condition determination unit 31 outputs information representing each determined measurement condition (for convenience of explanation, also referred to as measurement condition information) to the comparison data selection unit 32 .
Here, in the present embodiment, each measurement condition includes information capable of specifying comparison data (information based on detection values of the tire characteristic acquisition sensor 11) to be selected by the comparison data selection section 32. FIG. The information may be, for example, information indicating the timing of the comparison data to be selected (or the order of arrangement).
Note that the measurement conditions may be called, for example, detection conditions, or simply conditions.

Note that the measurement condition determination unit 31 may further use the detection value detected by the tire characteristic acquisition sensor 11 . In this case, the measurement condition determination unit 31 determines the measurement conditions based on the detection value input from the measurement condition acquisition sensor 12 and the detection value input from the tire characteristic acquisition sensor 11 .

Here, the measurement condition determination unit 31 may have, for example, an analog circuit and an analog-to-digital converter (A/D converter: Analog to Digital Converter). The analog circuit receives a detection value signal (analog signal) output from the tire characteristic acquisition sensor 11 and, if necessary, performs analog processing such as amplification on the signal. The A/D converter converts a detection value signal (analog signal) analog-processed by an analog circuit into a digital signal (detection value data). Then, the measurement condition determining section 31 may process the digital data.

The comparison data selection unit 32 acquires information about the tire 51 (also referred to as tire information for convenience of explanation) based on the detected value input from the tire characteristic acquisition sensor 11 , and also inputs from the measurement condition determination unit 31 . Get the measurement condition information to be used.
Then, the comparison data selection section 32 selects (acquires) tire information corresponding to the measurement conditions represented by the measurement condition information input from the measurement condition determination section 31 . The comparison data selection unit 32 acquires tire information (at least two pieces of tire information) corresponding to at least two measurement conditions, and outputs the tire information to the data analysis unit 33 as comparison data.
In this embodiment, the tire information is information about the characteristics of the tire 51 , and is information that changes according to the degree of deterioration of the tire 51 , for example.

Here, in the present embodiment, detection is always performed by each of the tire characteristic acquisition sensor 11 and the measurement condition acquisition sensor 12 while the vehicle is running, and the data analysis device 21 detects the tire characteristic acquisition sensor 11. A value (or tire information based on the detected value may be stored) in the storage unit 34, and a detected value of the measurement condition acquisition sensor 12 (or predetermined event information based on the detected value may be stored). It is stored in the storage unit 34. Then, the measurement condition determination unit 31 determines measurement conditions based on the information stored in the storage unit 34 (the detection value of the measurement condition acquisition sensor 12 or the predetermined event information based on the detection value). Further, the comparison data selection unit 32 selects tire information corresponding to the measurement conditions based on the information stored in the storage unit 34 (detected values of the tire characteristic acquisition sensor 11 or tire information based on the detected values). is selectively obtained. The constant detection may be, for example, detection at predetermined timings such as constant time intervals.

In this case, in the storage unit 34, simultaneously (or substantially simultaneously) detection values of the tire characteristic acquisition sensor 11 (or tire information based on the detection values may be used) and measurement condition acquisition sensor 12 Detected values (or predetermined event information based on the detected values may be used) are associated with each other and stored. Such correspondence between both pieces of stored information may be performed, for example, by adding detection time information (time stamp) to each piece of stored information. may be done by attachment.

As another example of configuration, the comparison data selection unit 32 receives the detection values that are sequentially output from the tire characteristic acquisition sensor 11 in real time, and the comparison data selection unit 32 selects data based on the measurement condition information input from the measurement condition determination unit 31. The tire information based on the detection value may be selectively acquired at the timing when the measurement condition to be determined is satisfied (or at substantially the same timing as the timing).

That is, in the data analysis device 21, the detected values (or information based on the detected values) of the sensors (the tire characteristic acquisition sensor 11 and the measurement condition acquisition sensor 12) may be stored once and then processed. Alternatively, it may be processed in real time.
Note that the detected value of the sensor or information obtained from the detected value may be called sensing data or the like.

Here, as an example, the predetermined event information acquired by the measurement condition determination unit 31 is information on the detection value of the measurement condition acquisition sensor 12 . In this case, the measurement condition acquisition sensor 12 detects a detection value for a predetermined event.
As another example, the predetermined event information acquired by the measurement condition determination unit 31 may be information of the result of calculation using the detection value of the measurement condition acquisition sensor 12 . In this case, the measurement condition determination unit 31 acquires the predetermined event information by performing a predetermined calculation or the like based on the detection value of the measurement condition acquisition sensor 12 . The calculation or the like may be, for example, calculation using a predetermined calculation formula, or estimation using a predetermined calculation formula (estimation formula) or the like.

Further, as an example, the tire information acquired by the comparison data selection unit 32 is information of the detection value of the tire characteristic acquisition sensor 11 . In this case, the tire characteristic acquisition sensor 11 detects a detection value for predetermined tire information.
As another example, the tire information acquired by the comparison data selection unit 32 may be information obtained as a result of computation using the detection values of the tire characteristic acquisition sensor 11 . In this case, the comparison data selection unit 32 acquires tire information by performing a predetermined calculation or the like based on the detection value of the tire characteristic acquisition sensor 11 . The calculation or the like may be, for example, calculation using a predetermined calculation formula, or may be estimation using a predetermined calculation formula (estimation formula) or the like.

Here, the comparison data selection unit 32 may have, for example, an analog circuit and an analog-to-digital converter (A/D converter). The analog circuit receives the signal of the detected value output from the tire characteristic acquisition sensor 11, and performs analog processing such as amplification on the signal as necessary. The A/D converter converts a detection value signal (analog signal) analog-processed by an analog circuit into a digital signal (detection value data). Then, the comparison data selection unit 32 may process the digital data.

Note that when the tire characteristic acquisition sensor 11 and the measurement condition acquisition sensor 12 are a common sensor, the tire information and the predetermined event information are respectively acquired based on the values (detected values) detected by the sensors. be done. In this case, for example, the tire information and the predetermined event information are different information based on a common detection value, but as another configuration example, the tire information and the predetermined event information may be common information.
As a mode in which the tire information and the predetermined event information are different information based on a common detection value, for example, the tire information is distortion information (or speed information), and the predetermined event information is speed information (or distortion information) may be used.

The data analysis unit 33 analyzes the comparison data (at least two pieces of tire information) input from the comparison data selection unit 32 .
In this embodiment, the data analysis unit 33 compares at least two pieces of tire information and acquires evaluation information regarding the tire 51 based on the comparison result. Here, the data analysis unit 33 acquires the evaluation information, for example, by performing a predetermined calculation or the like using at least two pieces of tire information. The evaluation information may be information obtained as a result of calculation using at least two pieces of tire information. The calculation or the like may be, for example, calculation using a predetermined calculation formula, or estimation using a predetermined calculation formula (estimation formula) or the like.
Note that the object to be compared with respect to the two pieces of tire information may be arbitrary, for example, one or more of amplitude, level difference between peaks, temporal feature, noise, waveform, dispersion, etc. There may be.

Here, the evaluation information may be, for example, evaluation information regarding the tire 51 itself, or may be information regarding evaluation of an object related to the tire 51, such as a road surface in contact with the tire 51. Alternatively, it may be evaluation information regarding both of them.
Also, the evaluation information may be information on deterioration or information on abnormality of the tire 51, an object related to the tire 51, or both.
In this embodiment, the word "deterioration" is used when the tire 51 is deteriorated to the extent that it can be used, and the word "abnormality" is used when the tire 51 is deteriorated to such an extent that it cannot be used. However, the distinction between the meanings of these terms is an example for convenience of explanation, and the terms are not necessarily limited to the distinction, and any term may be used according to each system or the like.

In the present embodiment, the deterioration of the tire 51 includes, for example, deterioration due to wear of the rubber portion (eg, tread portion) of the tire 51 or due to hardening of the rubber portion of the tire 51 . For example, the deformation of the rubber portion of the tire 51 changes due to abrasion of the rubber portion (for example, the tread portion) of the tire 51, or hardening of the rubber portion of the tire 51. It is possible to grasp the degree of deterioration of the tire 51 .
Further, in the present embodiment, an abnormality of the tire 51 includes burst or puncture of the tire 51, for example.

Here, in the present embodiment, a configuration example in which the information processing device 1 includes the tire characteristic acquisition sensor 11 and the measurement condition acquisition sensor 12 is shown. 11 and/or the sensor 12 for obtaining measurement conditions.
Further, in the present embodiment, for convenience of explanation, each processing unit (measurement condition determination unit 31, comparison data selection unit 32, data analysis unit 33, storage unit 34) of the data analysis device 21 will be described separately. Such division of each processing unit may be arbitrary, and such division of each processing unit may not necessarily exist.

Further, in the present embodiment, each processing unit of the information processing device 1 (each processing unit of the tire characteristic acquisition sensor 11, the measurement condition acquisition sensor 12, and the data analysis device 21) performs each process. (For example, an arithmetic expression or a threshold value), the information may be set in advance in the information processing device 1, or may be automatically set by user (human) operation or machine learning. Specifically, it may be possible to set or change.

Further, when the information processing apparatus 1 is provided with a plurality of divisions of processing units, exchange of information between different processing units may be performed by wire or wirelessly, for example.

<Sensor mounting position>
FIG. 2 is a diagram illustrating an example of mounting positions of sensors according to the embodiment.
FIG. 2 schematically shows how the tire 51 is in contact with the ground 111 .
In FIG. 2, arrows are used to indicate the direction of rotation of the tire 51. As shown in FIG.
Further, in the example of FIG. 2, for convenience of explanation, the rubber portion 121 of the tire 51 is shown, and the illustration of the wheel of the tire 51 is omitted.
In this example, the tire characteristic acquisition sensor 11 may be provided on the inner surface 123 (which may be called an inner wall or the like) of the rubber portion 121 of the tire 51 .

Here, on the inner surface 123 of the rubber portion 121 of the tire 51, the position where the tire characteristic acquisition sensor 11 is provided is, for example, the tire when the tire 51 is viewed in the traveling direction (in the example of FIG. 2, substantially in the horizontal direction). 51 (in the example of FIG. 2, the direction substantially perpendicular to the drawing) may be positioned (for example, the central position).
In the state of the tire 51 shown in FIG. 2, the portion in contact with the ground 111 and its vicinity are deformed. to be done.
Although the example of FIG. 2 shows a configuration example in which the tire characteristic acquisition sensor 11 is adhered to the inner surface 123 of the rubber portion 121, as another example, the tire characteristic acquisition sensor 11 is attached to the inner surface 123 of the rubber portion 121. Embedded configurations may also be used.

The mounting position of the tire characteristic acquisition sensor 11 is not particularly limited, and may be provided on the inner surface (inner wall) of the wheel of the tire 51, for example. Further, a configuration may be used in which an acceleration sensor is provided on the inner surface of the wheel of the tire 51, and the degree of deformation of the tire 51 (for example, the rubber portion 121) is indirectly detected based on the detection value of the acceleration sensor. . Also, FIG. 2 shows an example of the mounting position of the tire characteristic acquisition sensor 11, but the same mounting position may be used for the measurement condition acquisition sensor 12 as well.

FIGS. 3A and 3B are diagrams showing other examples of mounting positions of sensors according to the embodiment.
FIG. 3A shows the tire 51 with the wheel 131 and the rubber portion 132 attached.
In this example, the tire characteristic acquisition sensor 11 is provided between a wheel 131 (for example, a rim portion) and a rubber portion 132 .
FIG. 3B shows the wheel 131 and the tire characteristic acquisition sensor 11 without the rubber portion 132 attached.
Although FIGS. 3(A) and 3(B) show an example of the mounting position of the tire characteristic acquiring sensor 11, the same mounting position may be used for the measurement condition acquiring sensor 12 as well.

<Examples of measurement conditions and examples of types of sensors for acquiring measurement conditions>
An example of measurement conditions based on predetermined event information detected by the measurement condition acquisition sensor 12 is shown.
Examples of types of sensors (in this case, the measurement condition acquisition sensor 12) that detect the detection values (predetermined event information itself may be used) that are the basis of the predetermined event information are also shown.

The measurement condition may be, for example, the speed condition. In this case, the detected value detected by the measurement condition acquisition sensor 12 may be, for example, speed information, or other information that can be used to calculate speed information. There may be. The other information may be, for example, acceleration information or distortion information (for example, distortion occurring in the tire 51 when the tire 51 contacts the ground).

For example, a speed sensor (vehicle speed sensor) that detects speed may be used as the sensor (here, the measurement condition acquisition sensor 12).
Further, as the sensor (here, the measurement condition acquisition sensor 12), a sensor that detects the amount of rotation of the tire 51 (wheel) may be used, and the speed may be calculated based on the amount of rotation. .
Note that when the speed is calculated based on the detection value of the tire characteristic acquisition sensor 11 , the tire characteristic acquisition sensor 11 may be used as the measurement condition acquisition sensor 12 .

As a specific example, a measurement condition such as 20 km/h or 40 km/h may be used as the speed measurement condition. In this case, for example, tire information based on detection values of the tire characteristic acquisition sensor 11 when the vehicle is running at a speed of 20 km/h or 40 km/h is acquired as the tire information corresponding to the measurement conditions.

As another example, an acceleration sensor that detects acceleration may be used as the sensor (here, the measurement condition acquisition sensor 12). In this case, the velocity is detected (eg, calculated) based on the detected acceleration.
As another example, a strain sensor that detects strain may be used as the sensor (here, the measurement condition acquisition sensor 12). In this case, the velocity is detected (calculated, for example) based on the detected strain.

The measurement conditions may be, for example, load conditions. In this case, the detected value detected by the measurement condition acquisition sensor 12 may be, for example, load information, or other information that can be used to calculate load information. There may be.
Here, the load information is information about the force applied to the tire 51 from the vehicle (here, the portion other than the tire). For example, in a vehicle having four tires, the force applied to one tire is approximately 1/4 of the weight of the vehicle, but this is not limitative depending on the structure of the vehicle.

As the sensor (here, the measurement condition acquisition sensor 12), for example, a load sensor that detects a load may be used. The load sensor may be attached to the suspension of the vehicle.
When the load is calculated based on the detected value of the tire characteristic acquisition sensor 11 , the tire characteristic acquisition sensor 11 may be used as the measurement condition acquisition sensor 12 .

As a specific example, as a load measurement condition, even if the condition of the timing when the load of the person riding in the vehicle, the luggage on the vehicle, or the fuel (for example, gasoline) on the vehicle changes. good. In this case, for example, the tire information based on the detection value of the tire characteristic acquisition sensor 11 at that timing is acquired as the tire information corresponding to the measurement conditions.

The measurement conditions may be, for example, air pressure conditions. In this case, the detected value detected by the measurement condition acquisition sensor 12 may be, for example, air pressure information, or other information that can be used to calculate the air pressure information. There may be.

As the sensor (here, the measurement condition acquisition sensor 12), for example, a pressure sensor that detects air pressure may be used. A pressure sensor may be installed in the tire 51, for example.
As a specific example, as the air pressure measurement conditions, a condition before the tire 51 is inflated (for example, immediately before) and a condition after the tire 51 is inflated (for example, immediately after) may be used. For example, timing conditions before and after the tire 51 is inflated may be used.

The measurement condition may be the temperature condition of the tire 51, for example. In this case, the detected value detected by the measurement condition acquisition sensor 12 may be, for example, temperature information, or other information capable of calculating temperature information.

As the sensor (here, the measurement condition acquisition sensor 12), for example, a temperature sensor that detects temperature may be used. The temperature sensor may be installed in the tire 51, for example.
As a specific example, as the measurement conditions, a condition that the tire 51 is heated by running the vehicle and a condition that the tire 51 is heated by running the vehicle may be used. Timing conditions before and after 51 is heated may be used.

For example, the load, the air pressure, or the temperature of the tire 51 are parameters that change while the vehicle is in use. When such parameters are used for measurement conditions, for example, it is possible to obtain tire information when the parameters are desired values while the vehicle is running without preparing a special environment.

<Example of tire information and examples of types of sensors for acquiring tire characteristics>
Information analyzed based on the tire information may be, for example, information on the distortion of the tire 51 . In this case, the tire information based on the detection value detected by the tire characteristic acquisition sensor 11 may be, for example, distortion information, or may be used to calculate distortion information. Other information may be used.

As the sensor (here, the tire characteristic acquisition sensor 11), for example, a strain sensor that detects the strain of the tire 51 may be used. In this case, the strain sensor is installed on the tire 51 in this embodiment. Moreover, in this case, the tire information may be distortion information. As a specific example, the strain sensor can detect the degree of deformation of the tire 51 . When the strain sensor is installed at a predetermined portion of the tire 51, the detected value of the strain sensor changes when the tire 51 rotates and the portion of the tire 51 corresponding to the predetermined portion touches the ground. The predetermined portion of the tire 51 may be the inner surface of the rubber portion 121 of the tire 51, as shown in FIG.

In general, the way the tire 51 deforms changes depending on the wear of the tread portion of the tire 51 and the change in the hardness of the rubber portion of the tire 51. Therefore, the degree of deterioration of the tire 51 can be determined by the difference in the way the tire 51 deforms. It is possible to grasp

As the sensor (here, the tire characteristic acquisition sensor 11), for example, an acceleration sensor may be used. In this case, the acceleration sensor is installed on the tire 51 in this embodiment. Further, in this case, the tire information may be acceleration information.
It is also possible to detect the degree of deformation of the tire 51 based on the detection value of the acceleration sensor.

As the sensor (here, the tire characteristic acquisition sensor 11), for example, a pressure sensor may be used. In this case, the pressure sensor is installed in the tire 51 in this embodiment. Moreover, in this case, the tire information may be pressure information.
As a specific example, a pressure sensor can detect the degree of deformation of the tire 51 . When the pressure sensor is installed at a predetermined location on the tire 51, the detected value of the pressure sensor changes when the portion of the tire 51 corresponding to the predetermined location touches the ground when the tire 51 rotates. The predetermined location on the tire 51 may be a location between the wheel 131 (for example, the rim portion) and the rubber portion 132, as shown in FIGS. 3(A) and 3(B).

Incidentally, generally, a strain sensor, an acceleration sensor, or a pressure sensor may be used in order to grasp the deformation of an object.
In this embodiment, a strain sensor, an acceleration sensor, or a pressure sensor is suitable as a sensor for detecting deformation of the rubber portion of the tire 51, for example.

FIG. 4 is a diagram illustrating an example of a detection value (distortion signal) of the distortion sensor according to the embodiment;
The example of FIG. 4 is an example in which a strain sensor is used as the tire characteristic acquisition sensor 11 .
In the graph shown in FIG. 4, the horizontal axis represents time, and the vertical axis represents the level of the detected value of the strain sensor (for example, amplitude of voltage).
The graph shows an example of a strain signal (strain signal 1011) detected by the strain sensor.

In this example, the strain sensor is installed at a predetermined location on the tire 51 . The strain signal 1011 shown in FIG. 4 indicates that the portion of the tire 51 corresponding to the predetermined location is in contact with the ground at time T1 and time T2 when the tire 51 rotates. .
Here, the time T2 is later than the time T1, and the period in which the tire 51 rotates once (one rotation period 1021) corresponds to the period of (time T2-time T1).

<Example of evaluation information>
An example of evaluation information acquired by the data analysis unit 33 is shown.
The evaluation information includes, for example, the deterioration of the tire 51, the abnormality of the tire 51, the condition of the road surface on which the tire 51 is in contact (for example, the condition of the road surface being wet with rain, the condition of the road surface being frozen, etc.), or may be information relating to one or more of the grip force between the road surface in contact with the tire 51 and the like.

Further, the type of deterioration of the tire 51 is not particularly limited, and may be deterioration due to wear of the tread portion of the tire 51 or deterioration due to a change in hardness of the tire 51, for example.

An example of evaluation performed by the data analysis unit 33 is shown with reference to FIGS. 5 and 6. FIG.
The example of FIG. 5 and the example of FIG. 6 show the case where the distortion signal based on the tire information is evaluated.

FIG. 5 is a diagram illustrating an example of a relative relationship between two distortion signals according to the embodiment;
In the graph shown in FIG. 5, the horizontal axis shows the case where the vehicle speed (vehicle speed) is A (A is a positive value, for example, 20) km per hour, and the case where the vehicle speed (vehicle speed) is the speed per hour. B (B is a positive value greater than A, eg, 40) km is represented, and the vertical axis represents level (eg, amplitude of voltage).
The graph shows a distortion signal 1111 when the speed is A km/h and a distortion signal 1112 when the speed is B km/h.

The data analysis section 33 acquires evaluation information based on the relative value between the two

distortion signals

1111 and 1112 .
The relative value is, for example, the value between the peaks of one distortion signal 1111 (the value corresponding to the magnitude between the positive peak and the negative peak in the example of FIG. 5) and the Even if the difference 1113 (for example, the absolute value and the positive value) between the peak-to-peak value (the value corresponding to the magnitude between the positive peak and the negative peak in the example of FIG. 5) good.

FIG. 6 is a diagram showing another example of the relative relationship between two distortion signals according to the embodiment.
In the graph shown in FIG. 6, the horizontal axis represents the case where the vehicle speed (vehicle speed) is A km/h and the case where the vehicle speed (vehicle speed) is B km/h, and the vertical axis represents the level ( for example, voltage amplitude).
The graph shows a distortion signal 1121 when the speed is A km/h and a distortion signal 1122 when the speed is B km/h.

The data analysis unit 33 acquires evaluation information based on the relative value between the two

distortion signals

1121 and 1122 .
The relative value is, for example, the value between the peaks of one distortion signal 1121 (the value corresponding to the magnitude between the positive peak and the negative peak in the example of FIG. 6) and the value of the other distortion signal 1122. Even if the difference 1123 (e.g., absolute value and positive value) between the peak-to-peak value (the value corresponding to the magnitude between the positive peak and the negative peak in the example of FIG. 6) good.

Here, in the examples of FIGS. 5 and 6, the tire 51 The degree of deterioration of the
For example, the degree of deterioration of the tire 51 is grasped based on the degree of change in the value between peaks due to the difference in measurement conditions.
As the degree to which the value changes, for example, the degree to which the other value is a percentage of one value (the rate of change) may be used, or A measure of how much the value of is increased or decreased (amount of change) may be used.

As the evaluation information, for example, information representing the extent to which such values change may be used, or other information that is calculated based on the extent to which such values change may be used. good.
For example, as in the example of FIG. 5, the tire 51 (for example, a new tire 51) is not degraded when such a value changes to a greater extent, and as in the example of FIG. It is possible to evaluate that the tire 51 having a smaller degree of change in such a value is a deteriorated tire 51 .

FIG. 7 is a diagram illustrating an example of characteristics of a value (Vpp) between peaks according to the embodiment.
In the graph shown in FIG. 7, the horizontal axis represents the speed per hour [km], and the vertical axis represents the difference level between peaks (for example, the difference in voltage amplitude).
The graph shows a peak-to-peak value characteristic 1211 when the deteriorated tire 51 is used and a peak-to-peak value characteristic 1212 when the new tire 51 is used. In the example of FIG. 7, the characteristic 1211 related to the deteriorated tire 51 has a smaller slope, and the characteristic 1212 related to the new tire 51 has a larger slope.

FIG. 8 is a diagram illustrating an example of characteristics of changes in values between peaks according to the embodiment.
In the graph shown in FIG. 8, the horizontal axis represents the degree of deterioration (in the example of FIG. 8, the degree of deterioration increases toward the right), and the vertical axis represents the change [%] in values between peaks.
The graph also shows a characteristic 1311 of changes in values between peaks.

The graph also shows a predetermined threshold value 1321 that serves as a judgment value for changes in values between peaks.
In the example of FIG. 8, the data analysis unit 33 determines that the degree of deterioration of the tire 51 is within the allowable range when the characteristic 1311 exceeds the threshold 1321 (or is equal to or greater than the threshold 1321). On the other hand, when the characteristic 1311 is equal to or less than the threshold value 1321 (or less than the threshold value 1321), the data analysis unit 33 determines that the degree of deterioration of the tire 51 is outside the allowable range (deterioration or abnormal ). As the evaluation information, for example, information indicating whether the degree of deterioration is within the allowable range may be used.

In the example of FIG. 8, [{(peak-to-peak value at speed A km/h)/(peak-to-peak value at speed B km/h)}×100] is used as the change [%] in the value between peaks.
For example, if there is a variation in the detected value of the tire characteristic acquisition sensor 11 that results in a predetermined multiple of the individual variation, the detected value corresponding to each of the two measurement conditions (or the magnification of the detected value is converted while maintaining the magnification) The influence of the variation is canceled by obtaining the ratio of the calculated value).

As another example, when there is a variation in which a predetermined value is added (or subtracted) to the detected value as an individual variation in the tire characteristic acquisition sensor 11, for example, detection corresponding to each of the two measurement conditions The influence of the variation may be canceled by obtaining the difference between the values (or the value converted while retaining the predetermined value included in the detected value).
In addition, even if there are variations in other aspects as individual variations in the tire characteristic acquisition sensor 11, the detection values corresponding to each of the two measurement conditions (or A value converted using the detected value) may be calculated.
In addition, instead of the configuration that cancels the influence of individual variations in the tire characteristic acquisition sensors 11 (the influence of the variations is made zero), a configuration that reduces the effects of individual variations in the tire characteristic acquisition sensors 11 is used. may be

<Example of comparing time-related feature values>
FIGS. 9 to 11 show an example of comparing time-related feature amounts in the data analysis device 21. FIG.
In this example, the object of comparison is a predetermined period of time, specifically the period of time during which the tire 51 touches the ground (contact time).
In this example, the tire characteristic acquisition sensor 11 is a strain sensor. As another example, the tire characteristic acquisition sensor 11 may be an acceleration sensor or a pressure sensor.
In this example, the measurement condition acquisition sensor 12 is a load sensor or an air pressure sensor.

FIG. 9 is a diagram showing an example of a state in which the contact time of the tire according to the embodiment is long. FIG. 9 shows how the tire 211 is in contact with the ground 111 and is rotating. In addition, in FIG. 9, the direction of rotation of the tire 211 is indicated using an arrow.
Here, the tire 211 is an example of the tire 51 .
In the example of FIG. 9, the ground contact point Z1 of the tire 211 represents a new contact point with the ground 111, and the ground contact point Z2 of the tire 211 represents a point leaving the ground 111 from now on. In other words, the example of FIG. 9 shows the moment when the contact point Z1 of the tire 211 touches the ground 111 and the contact point Z2 separates from the ground 111. FIG.
The distance 221 between the contact point Z1 and the contact point Z2 depends on the time (contact time) during which the same point of the tire 211 (for example, the contact point Z1 or the contact point Z2) is in contact with the ground 111. The size is.

FIG. 10 is a diagram showing an example of a situation in which the contact time of the tire according to the embodiment is short. FIG. 10 shows how the tire 212 is in contact with the ground 111 and is rotating. 10 also shows the direction of rotation of the tire 212 using an arrow.
Here, the tire 212 is an example of the tire 51 .
In the example of FIG. 10, the ground contact point Z11 of the tire 212 represents a new contact point with the ground 111, and the ground contact point Z12 of the tire 212 represents a point leaving the ground 111 from now on. In other words, the example of FIG. 10 shows the moment when the contact point Z11 of the tire 212 touches the ground 111 and the contact point Z12 separates from the ground 111. FIG.
The distance 222 between the contact point Z11 and the contact point Z12 depends on the time (contact time) during which the same point of the tire 212 (for example, the contact point Z11 or the contact point Z12) is in contact with the ground 111. The size is.

Here, the example of FIG. 9 shows a case where the load applied to the tire 211 (for example, the tire 51) is larger or the air pressure of the tire 211 (for example, the tire 51) is lower than the example shown in FIG. Yes, and in these cases the distance 221 and contact time will be longer.
On the other hand, the example of FIG. 10 shows a case where the load applied to the tire 212 (eg, the tire 51) is smaller than the example of FIG. 9, or the case where the air pressure of the tire 212 (eg, the tire 51) is high. , in these cases the distance 222 and ground contact time are reduced.

FIG. 11 is a diagram illustrating an example of a detection value (distortion signal) of the distortion sensor according to the embodiment; In the graph shown in FIG. 11, the horizontal axis represents time [msec], and the vertical axis represents the output (detected value) [V] of the strain sensor.
The graph shows an example of the strain signal 1411 detected for a tire with a large load (for example, the tire in the state shown in FIG. 9) and a tire with a small load (for example, the tire in the state shown in FIG. 10). An example of a detected distortion signal 1412 is shown.

Here, in the example of FIG. 11, the time T11 at which the positive peak occurs in the distortion signal 1411, the time T12 at which the positive peak occurs in the distortion signal 1412, the time T13 at which the negative peak occurs in the distortion signal 1412, the distortion signal The time T14 at which the negative peak occurs at 1411 is shown.
The moment when the positive peak occurs in the strain signal 1411 and the strain signal 1412 represents the moment (or the vicinity thereof) when a predetermined portion of the tire touches the ground. Moreover, the moment when the negative peak occurs in the strain signal 1411 and the strain signal 1412 represents the moment (or the vicinity thereof) when a predetermined portion of the tire leaves the ground.
In general, when the vehicle is running, the strain sensor detects a value (output from the strain sensor) at the moment when a predetermined portion of the tire corresponding to the position of the strain sensor touches the ground and at the moment when the predetermined portion is separated from the ground. change greatly.

The contact time 1421 estimated from the detected strain signal 1411 for the heavily loaded tire is greater than the contact time 1422 estimated from the detected strain signal 1412 for the lightly loaded tire.
In the example of FIG. 11, the contact time 1421 estimated from the distortion signal 1411 corresponds to the period between time T11 and time T14. Further, the contact time 1422 estimated from the distortion signal 1412 corresponds to the period between time T12 and time T13.
The contact start time T11 in the contact time 1421 of the tire with a large load is earlier than the contact start time T12 in the contact time 1422 of the tire with a small load. In addition, the contact end time T14 in the contact time 1421 of the tire with a large load is later than the contact end time T13 in the contact time 1422 of the tire with a small load.

Here, in the example of FIG. 11, the strain signal 1411 detected for a tire with a large load (for example, the tire in the state shown in FIG. 9) and a tire with a small load (for example, the tire in the state shown in FIG. 10) Although the trends in the detected strain signal 1412 for , and the strain signals detected for low-inflated tires and high-inflated tires have similar trends.

In general, when the tire 51 deteriorates, the rubber portion of the tire 51 hardens. Therefore, when the tire 51 deteriorates, the rubber portion becomes difficult to deform, and it is predicted that the contact time will be shortened.
Thus, in general, a new tire has a longer contact time than an old tire because the rubber is softer. In other words, the more the tire deteriorates, the smaller the amount of deformation of the tire.

In this example, in the analysis performed by the data analysis device 21, when the load or air pressure, which is the information of the measurement conditions, changes, the contact time obtained from the strain signal is greater for the new tire than for the old tire. becomes larger. In this embodiment, evaluation information may be acquired using such a tendency.
In this example, the degree of deterioration of the tire 51 is evaluated based on the amount of change in contact time of the tire 51 .

Here, for example, the degree of deterioration of the tire is evaluated based on the magnitude of the detected value (e.g., amplitude) of the strain sensor attached to the inner surface of the tire, or the change in the magnitude of the detected value under certain measurement conditions. If this is the case, the magnitude of the strain sensor detection value (e.g., amplitude) or changes in the magnitude may vary depending on the sensitivity of the sensor, which may reduce the accuracy of the evaluation.
On the other hand, in the present embodiment, by extracting (acquiring) information that does not change depending on the sensitivity of the sensor (comparison results of tire information under different measurement conditions) and evaluating the degree of tire deterioration based on the information, , it is possible to perform highly accurate evaluations that are not affected by individual variations in sensors.

<Example of comparing noise>
An example of noise comparison in the data analysis device 21 is shown.
In this example, the object of comparison is noise included in the detection value of the tire characteristic acquisition sensor 11, for example, noise of high-frequency components.
In this example, the tire characteristic acquisition sensor 11 is a strain sensor. As another example, the tire characteristic acquisition sensor 11 may be an acceleration sensor or a pressure sensor.
In this example, the measurement condition acquisition sensor 12 is a speed sensor.

Here, in general, when a tire is used and deteriorated, the rubber of the tire becomes harder and the cushioning property is reduced compared to a new tire. Therefore, as the tire deteriorates due to use, the noise (in this example, high-frequency components) included in the detection values detected by the tire characteristic acquisition sensor 11 during high-speed driving is greater than that of a new tire. It is considered to be.

In this example, in the analysis performed by the data analysis device 21, when the speed (vehicle speed) that is the measurement condition changes, the old tire is detected by the tire characteristic acquisition sensor 11 more than the new tire. The amount of change in noise (in this example, high frequency components) included in the detected value increases. In this embodiment, evaluation information may be acquired using such a tendency.

As described above, in the present embodiment, information that does not change depending on the sensitivity of the sensor (comparison results of tire information under different measurement conditions) is extracted, and the degree of deterioration of the tire is evaluated based on the information, so that individual sensors can be measured. Accurate evaluation is possible without being affected by variations in

In the present embodiment, an example of comparing tire information under two different measurement conditions is shown for the sake of simplification of explanation, but it is possible to compare tire information under three or more different measurement conditions. may be broken.

As described above, in the information processing apparatus 1 according to the present embodiment, even if there is individual variation in the sensor (in the present embodiment, the tire characteristic acquisition sensor 11) that detects information (detection value) regarding the tire 51, Evaluation information regarding the tire 51 can be obtained with high accuracy.

In the information processing apparatus 1 according to this embodiment, by comparing tire information under a plurality of different measurement conditions, the sensor (in this embodiment, the tire characteristic acquisition sensor 11) is not affected by individual variations ( or reduced) to obtain the evaluation information. Accordingly, in the information processing apparatus 1 according to this embodiment, it is possible to eliminate the need for calibration for calibrating individual variations in the sensors (in this embodiment, the tire characteristic acquisition sensor 11).

For example, a sensor detects a waveform (detection value) representing tire characteristics under the same measurement conditions (for example, a condition at a speed of 20 km/h), and the level difference (absolute value) between the positive and negative peaks of the waveform is used. A method for determining the degree of tire deterioration by using the sensor (hereinafter referred to as method A1 for convenience of explanation) is also conceivable. The accuracy of determination of the degree is lowered.
On the other hand, in the present embodiment, a plurality of tire information obtained under a plurality of different conditions (for example, 20 km/h and 40 km/h) are compared, and the evaluation reflects the relative relationship between the plurality of tire information. Acquiring the information can improve the accuracy of the evaluation even if the sensor has individual variations and is not calibrated.
Note that the information processing apparatus 1 according to the present embodiment acquires evaluation information based on tire information under a plurality of different measurement conditions as described above. condition) based on the tire information (determination of method A1 described above) may be used together. Moreover, in the information processing apparatus 1 according to the present embodiment, any other evaluation method may be used together.

Here, in this embodiment, the case where one tire characteristic acquisition sensor 11 is used is shown, but as another configuration example, a plurality of tire characteristic acquisition sensors 11 may be used. In this case, the plurality of tire characteristic acquisition sensors 11 may be, for example, sensors that detect detection values of different physical quantities. In addition, the data analysis device 21 may perform analysis based on the detection value of the tire characteristic acquisition sensor 11 for each tire characteristic acquisition sensor 11, or may perform analysis based on the detection values of two or more tire characteristic acquisition sensors 11. Analysis based on detected values may be performed.

For example, when a plurality of tire characteristic acquisition sensors 11 that detect information (detected values) about one tire (common tire) are provided, the data analysis device 21 detects the tire characteristic acquisition sensors 11 Alternatively, evaluation information may be acquired for a combination of these detection values using the detection values of two or more tire characteristic acquisition sensors 11. .

Here, as a mode of using the detection values of two or more tire characteristic acquisition sensors 11 that detect the same physical quantity detection value, for example, the detection values of these two or more tire characteristic acquisition sensors 11 (or Information based on detected values) may be used, and one of the detected values (or information based on the detected values) of these two or more tire characteristic acquisition sensors 11 (for example, , median, etc.) may be selected and adopted.
Further, as a mode using the detection values of two or more tire characteristic acquisition sensors 11 that detect detection values of different physical quantities, for example, the detection values of these two or more tire characteristic acquisition sensors 11 (or the detection Information based on values) may be used to calculate one piece of evaluation information (common evaluation information).

When one vehicle is equipped with a plurality of tires, for example, for each tire, the data analysis device 21 evaluates based on the detection value of the tire characteristic acquisition sensor 11 installed on the tire. Get information. That is, the characteristics of each of the plurality of tires are usually independent.
However, when one vehicle is equipped with a plurality of tires, there is a configuration in which the data analysis device 21 acquires evaluation information based on the detection values of the tire characteristic acquisition sensors 11 installed on two or more tires. may be used. For example, a configuration may be used in which the data analysis device 21 acquires evaluation information regarding the entire vehicle based on the detection values of the tire characteristic acquisition sensors 11 installed on two or more tires.

Also, in this embodiment, a case where one measurement condition acquisition sensor 12 is used has been shown, but as another configuration example, a plurality of measurement condition acquisition sensors 12 may be used.
In this case, the plurality of measurement condition acquisition sensors 12 may be, for example, sensors that detect different physical quantities. The data analysis device 21 may use measurement conditions based on the detection values of the measurement condition acquisition sensors 12 for each measurement condition acquisition sensor 12, or may use two or more measurement condition acquisition sensors. With 12 detection values, measurement conditions based on combinations of these detection values may be used.

Here, as a mode of using the detection values of two or more measurement condition acquisition sensors 12 that detect the detection value of the same physical quantity, for example, the detection values of these two or more measurement condition acquisition sensors 12 (or Information based on detected values) may be used, and one of the detected values (or information based on the detected values) of these two or more measurement condition acquisition sensors 12 (for example, , median, etc.) may be selected and adopted.
Further, as an embodiment using the detection values of two or more measurement condition acquisition sensors 12 that detect detection values of different physical quantities, for example, the detection values of these two or more measurement condition acquisition sensors 12 (or the detection Information based on values) may be used to calculate a piece of measurement condition information (common measurement condition information).

[Configuration example of information processing device]
The information processing device 1 includes, for example, a first device having a tire characteristic acquisition sensor 11, a second device having a measurement condition acquisition sensor 12, and a third device having a data analysis device 21 as separate devices. may be configured.
As one configuration example, the first device includes a tire characteristic acquisition sensor 11, a first data acquisition section, and a first transmission section. The second device includes a measurement condition acquisition sensor 12, a second data acquisition section, and a second transmission section. The third device includes a first data receiver, a second data receiver, and an information processor.
The information processing section has the functions of the data analysis device 21 shown in FIG. has the function of

In this configuration example, in the first device, the tire characteristic acquisition sensor 11 detects a detection value, the first data acquisition unit acquires the data of the detection value, and the first transmission unit transmits the data wirelessly or by wire. Send. In the second device, the measurement condition acquisition sensor 12 detects the detection value, the second data acquisition section acquires the data of the detection value, and the second transmission section transmits the data wirelessly or by wire. In the third device, the first data receiving section receives data transmitted from the first device, the second data receiving section receives data transmitted from the second device, and the information processing section receives the first data receiving section. and the data received by the second data receiving unit.

In addition, in the third device, the first data receiving section and the second data receiving section may be shared.
Further, when the tire characteristic acquisition sensor 11 and the measurement condition acquisition sensor 12 are shared, the first device and the second device are shared. In this case, in the third device, the first data receiving section and the second data receiving section are shared.

Here, for example, the third device may be provided in a place other than the tire 51 and the vehicle (here, a portion other than the tire). For example, the third device is a server device connected to a network such as the Internet. may be provided for. In this configuration, it is possible to transmit information about the respective detected values from the first device and the second device to the server device (the third device), and perform various information processing in the server device.

<Configuration example according to the above embodiment>
As one configuration example, an information processing device (information processing device 1 in the example of FIG. 1) is provided in a vehicle and includes a wheel (wheel 131 in the example of FIG. 3A) and a rubber portion (FIG. 3A). In the example of , the first detection value (the value detected by the tire characteristic acquisition sensor 11 in the example of FIG. 1) related to the tire (tire 51 in the example of FIG. 1) having the rubber portion 132) is detected. Tire information based on the first detection value detected by the sensor (in the example of FIG. 1, the tire characteristic acquisition sensor 11), two or more different conditions related to the predetermined event (in the example of FIG. 1, the measurement condition determination unit 31) based on two or more pieces of tire information corresponding to each of the measurement conditions determined by the information processing unit (in the example of FIG. 1, for example, the measurement condition determination unit 31, comparison data selection 32, a data analysis unit 33, and a storage unit 34).

As one configuration example, in the information processing device, the predetermined event is one or more of tire rotation speed, tire load, tire air pressure, and tire temperature.
As one configuration example, in the information processing device, the first detection value detected by the first sensor is one or more of strain, acceleration, and pressure.
As one configuration example, in the information processing device, the evaluation information is information about tire deterioration, information about tire abnormality, information about the condition of the road surface that the tire contacts, or information about the grip force between the tire and the road surface that the tire contacts. Contains one or more of information.
As one configuration example, in the information processing device, the information on tire deterioration includes one or more of information representing changes in tire hardness and information representing the degree of wear of the tread portion of the tire.

As one configuration example, in the information processing device, the condition regarding the predetermined event is the second sensor (value detected by the measurement condition acquisition sensor 12 in the example of FIG. 1) that detects the second detection value regarding the tire or the vehicle. In the example of FIG. 1, the condition is based on predetermined event information, which is information about a predetermined event based on the second detection value detected by the measurement condition acquisition sensor 12).
As one configuration example, an information processing apparatus includes one or both of a first sensor and a second sensor.
As one configuration example, in an information processing device, the first sensor and the second sensor are common sensors, the first detection value and the second detection value are common detection values, and tire information and predetermined event information are are different information based on common detection values.

It is also possible to provide a program that realizes the processing executed by the information processing device.
As one configuration example, the program is provided in a computer (in the example of FIG. 1, the computer constituting the information processing device 1), a first sensor that detects a first detection value related to a tire that is provided in a vehicle and has a wheel and a rubber portion for executing a step of acquiring evaluation information about a tire based on two or more pieces of tire information based on the first detection value detected by and corresponding to two or more different conditions relating to a predetermined event. program.

It should be noted that a program for realizing the functions of any component in any device (for example, the information processing device 1) described above is recorded on a computer-readable recording medium, and the program is read into a computer system. You can also set it to execute. The term "computer system" as used herein includes an operating system and hardware such as peripheral devices. In addition, "computer-readable recording medium" means portable media such as flexible discs, magneto-optical discs, ROM, CD (Compact Disc)-ROM (Read Only Memory), and storage such as hard disks built into computer systems. It refers to equipment. In addition, "computer-readable recording medium" means a certain amount of memory, such as volatile memory inside a computer system that acts as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. It also includes those holding time programs. The volatile memory may be, for example, RAM (Random Access Memory). The recording medium may be, for example, a non-transitory recording medium.

Moreover, the above program may be transmitted from a computer system storing this program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in a transmission medium. Here, the "transmission medium" for transmitting the program means a medium having a function of transmitting information, such as a network such as the Internet or a communication line such as a telephone line.
Also, the above program may be for realizing part of the functions described above. Furthermore, the above program may be a so-called difference file, which can realize the functions described above in combination with a program already recorded in the computer system. A difference file may be referred to as a difference program.

Also, the function of any component in any device (for example, the information processing device 1, etc.) described above may be implemented by a processor. For example, each process in the embodiment may be implemented by a processor that operates based on information such as a program and a computer-readable recording medium that stores information such as the program. Here, for the processor, for example, the function of each section may be implemented by separate hardware, or the function of each section may be implemented by integrated hardware. For example, a processor includes hardware, which may include at least one of circuitry that processes digital signals and circuitry that processes analog signals. For example, a processor may be configured using one or more circuit devices and/or one or more circuit elements mounted on a circuit board. An IC (Integrated Circuit) or the like may be used as the circuit device, and a resistor, capacitor, or the like may be used as the circuit element.

Here, the processor may be, for example, a CPU. However, the processor is not limited to the CPU, and various processors such as GPU (Graphics Processing Unit) or DSP (Digital Signal Processor) may be used. Also, the processor may be, for example, a hardware circuit based on ASIC (Application Specific Integrated Circuit). Also, the processor may be composed of, for example, a plurality of CPUs, or may be composed of a plurality of ASIC hardware circuits. Also, the processor may be configured by, for example, a combination of multiple CPUs and multiple ASIC hardware circuits. The processor may also include one or more of, for example, amplifier circuits or filter circuits that process analog signals.

Although the embodiment of this disclosure has been described in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and includes design etc. that do not deviate from the gist of this disclosure.

1 information processing device 11 tire characteristic acquisition sensor 12 measurement condition acquisition sensor 21 data analysis device 31 measurement condition determination unit 32 comparison data selection unit 33 data analysis unit 34

storage unit

51, 211, 212 tire 52 measurement condition acquisition target 111

ground

121, 132 Rubber portion 123 Inner surface 131

Wheels

221, 222

Distances

1011, 1111, 1112, 1121, 1122, 1411, 1412 Distortion signal 1021 One

rotation period

1113, 1123

Differences

1211, 1212, 1311 Characteristics 1321

Thresholds

1421, 1422 Contact time Z1 , Z2, Z11, Z12 Ground point

Claims

Tire information based on a first detection value detected by a first sensor provided in a vehicle and detecting a first detection value regarding a tire having a wheel and a rubber portion, each of two or more different conditions relating to a predetermined event. An information processing unit that acquires evaluation information about the tire based on the two or more pieces of tire information corresponding to
Information processing equipment.
The predetermined event is one or more of the speed of rotation of the tire, the load applied to the tire, the air pressure of the tire, or the temperature of the tire.
The information processing device according to claim 1 .
The first detection value detected by the first sensor is one or more of strain, acceleration, or pressure.
The information processing apparatus according to claim 1 or 2.
The evaluation information is information on deterioration of the tire, information on abnormality of the tire, information on the condition of the road surface in contact with the tire, or information on the grip force between the road surface on which the tire contacts and the tire. including one or more
The information processing apparatus according to any one of claims 1 to 3.
The information on the deterioration of the tire includes one or more of information representing changes in the hardness of the tire, or information representing the degree of wear of the tread portion of the tire,
The information processing apparatus according to claim 4.
The condition regarding the predetermined event is a condition based on predetermined event information, which is information regarding the predetermined event based on the second detection value detected by a second sensor that detects a second detection value regarding the tire or the vehicle. ,
The information processing apparatus according to any one of claims 1 to 5.
one or both of the first sensor and the second sensor;
The information processing device according to claim 6 .
The first sensor and the second sensor are common sensors,
The first detection value and the second detection value are common detection values,
The tire information and the predetermined event information are different information based on the common detection value,
The information processing apparatus according to claim 6 or 7.
to the computer,
Tire information based on a first detection value detected by a first sensor provided in a vehicle and detecting a first detection value regarding a tire having a wheel and a rubber portion, each of two or more different conditions relating to a predetermined event. A program for executing a step of acquiring evaluation information about the tire based on two or more pieces of tire information corresponding to .