Estimation device

Abstract

To provide an estimation device capable of estimating the time when the tire tread depth decreases to a predetermined value without inputting the prerequisites in advance.SOLUTION: The estimation device includes: an acquisition unit 300 that acquires current and past measurements of tire tread depth of the tires mounted on a vehicle; a calculation unit 310 that calculates the current gradient of the depth reduction amount from the current measured value and the past measured value; and an estimation unit 320 that estimates the timing when the depth decreases to a predetermined value based on the current gradient.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates to an estimation device.

Background Art

[0002] Patent Document 1 discloses a device for estimating the wear state of a tire mounted on a vehicle. This device includes a slope calculation unit that calculates the slope of the slip ratio with respect to the driving force based on a large number of data sets of the slip ratio and the driving force as a regression coefficient representing the linear relationship between the slip ratio calculated based on the sequentially acquired rotational speed of the tire and the driving force of the vehicle. Further, this device includes a slope correction unit that corrects the calculated slope based on an index representing the temperature dependence of the slope and the temperature outside the vehicle at the time of correction, and an estimation unit that estimates the wear state of the tire based on the corrected slope.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the device disclosed in Patent Document 1 estimates the wear state of the tire without using the measured value of the depth of the tire groove, there is a problem that prerequisite conditions such as frictional energy and wear resistance index must be input in advance.

[0005] The present disclosure has been made in consideration of the above facts, and an object thereof is to provide an estimation device that can estimate the timing when the depth of the tire groove decreases to a predetermined value without inputting prerequisite conditions in advance.

Means for Solving the Problems

[0006] The estimation device according to the first aspect is the current measured value of the depth of the groove of a tire mounted on a vehicleIn addition to obtaining the following, among the divided distance segments from when the tire is mounted on the vehicle until the depth reaches the limit value, the vehicle in the target segment associated with the vehicle's current distance traveled An acquisition unit that acquires past measured values, and the current measured value and In the aforementioned target category The aforementioned past measured values ​​and By dividing the difference by the difference between the current mileage and the mileage measured at the time of the past measurement, Current gradient of the decrease in the aforementioned depth Calculated gradient A calculation unit that calculates, From the gradient history database, identify other vehicles ranked from 1st to a predetermined rank based on the difference between the gradient in the target category and the calculated gradient. Among the identified other vehicles, identify the vehicle whose maximum mileage in the target category is closest to the vehicle's current mileage as the specified vehicle. Estimate the gradient of the category after the target category associated with the specified vehicle as the future gradient of the depth reduction. Based on the estimated future gradient, determine the value at which the depth recommends replacing the tires. It includes an estimation unit that estimates the time when it will decrease to a certain level.

[0007] Estimated equipment relating to the first aspect Place Therefore, it is possible to estimate the time when the tire tread depth decreases to a predetermined value without having to input any preconditions beforehand.

[0008] The estimation device according to the second embodiment is an estimation device according to the first embodiment, wherein the estimation unit is If there are multiple specified vehicles, the arithmetic mean, geometric mean, or harmonic mean of the gradients of the multiple specified vehicles in the categories after the target category is estimated as the future gradient. .

[0010] The estimation device relating to the third embodiment is: The first aspect or In the estimation device according to the second embodiment, the estimation unit is: The system estimates the time when the aforementioned depth decreases to a value that recommends tire replacement, and this recommended value is set for each type of tire. .

[0012] The estimation device relating to the fourth aspect is: From the first aspect Third aspect One of the following aspects In the estimation device relating to the above, The aforementioned classification is set such that the gradient decreases as the distance traveled increases. .

[0014] The estimation device relating to the fifth embodiment is an estimation device relating to any one of the first to fourth embodiments, The above classification is set based on a gradient database in which the range of travel distance and the range of gradient are stored for each type of vehicle. . [Effects of the Invention]

[0016] According to this disclosure, it is possible to estimate the time when the depth of the tire groove decreases to a predetermined value without having to input any preconditions beforehand. [Brief explanation of the drawing]

[0017] [Figure 1] This figure shows an example of a schematic configuration of the estimation system according to the embodiment. [Figure 2] This is a block diagram showing an example of the hardware configuration of a central server according to the embodiment. [Figure 3]It is a block diagram showing an example of the functional configuration of the CPU in the center server according to the embodiment. [Figure 4] It is a block diagram showing an example of the configuration of the measurement history database according to the embodiment. [Figure 5] It is a flowchart showing an example of the flow of the estimation process according to the embodiment.

Mode for Carrying Out the Invention

[0018] As shown in FIG. 1, the estimation system 100 of the present embodiment includes a user terminal 10, a vehicle 12, and a center server 30. The center server 30 is an example of an estimation device. Note that the number of user terminals 10 and vehicles 12 included in the estimation system 100 is not limited to the numbers shown in FIG. 1. The user terminal 10, the vehicle 12, and the center server 30 are mutually connected via a network CN1.

[0019] As shown in FIG. 1, the vehicle 12 of the present embodiment is equipped with tires 14A to 14D. Hereinafter, when the tires 14A to 14D are not distinguished, these are simply referred to as "tires 14". In the present embodiment, a four-wheeled vehicle is applied as the vehicle 12, and the vehicle 12 is equipped with four tires 14. However, it is not limited to this example. A two-wheeled vehicle may be applied as the vehicle 12. In this case, the vehicle 12 is equipped with two tires 14.

[0020] The user terminal 10 is an information processing terminal owned by the user. The user terminal 10 is, for example, a portable information processing terminal such as a smartphone.

[0021] As shown in FIG. 2, the center server 30 is configured to include a CPU (Central Processing Unit) 30A, a ROM (Read Only Memory) 30B, a RAM (Random Access Memory) 30C, a storage 30D, and a communication I / F (Inter Face) 30G. The CPU 30A, the ROM 30B, the RAM 30C, the storage 30D, and the communication I / F 30G are communicably connected to each other via an internal bus 30H.

[0022] The CPU 30A is a central processing unit that executes various programs and controls each part. That is, the CPU 30A reads a program from the ROM 30B or the storage 30D and executes the program using the RAM 30C as a work area. The CPU 30A performs control of each of the above configurations and various arithmetic processes according to the program stored in the ROM 30B or the storage 30D. In the present embodiment, an estimation program is stored in the ROM 30B or the storage 30D.

[0023] The ROM 30B stores various programs and various data. The RAM 30C temporarily stores a program or data as a work area. The storage 30D is composed of an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including an operating system and various data.

[0024] The estimation program is a program for realizing each function of the center server 30.

[0025] The communication I / F 30G is an interface for connecting to the network CN1.

[0026] Figure 3 is a block diagram showing an example of the functional configuration of the CPU 30A. As shown in Figure 3, the CPU 30A has an acquisition unit 300, a calculation unit 310, an estimation unit 320, and an output unit 330. Each functional configuration is realized by the CPU 30A reading an estimation program stored in the ROM 30B or storage 30D and executing it.

[0027] The acquisition unit 300 has the function of acquiring the current measured value of the groove depth of the tire 14 and the past measured value of the groove depth of the tire 14. Hereinafter, the groove depth of the tire 14 will be simply referred to as "depth," and the measured value of the depth will be simply referred to as "measured value." In this embodiment, the acquisition unit 300 acquires the current measured value from the user terminal 10 via the communication I / F 30G.

[0028] Furthermore, the acquisition unit 300 acquires past measured values ​​from the measurement history database stored in the storage 30D. In this embodiment, the acquisition unit 300 acquires past measured values ​​of the vehicle 12 in the target category. The target category is the category to which the current vehicle 12 belongs among the divided travel distances from the time the tire 14 is mounted on the vehicle 12 until its depth reaches a limit value (for example, 1.6 mm). Specifically, the acquisition unit 300 acquires all measured values ​​associated with the target category of the vehicle 12 in the measurement history database. Hereinafter, the divided travel distances from the time the tire 14 is mounted on the vehicle 12 until its depth reaches a limit value will simply be referred to as "category". Details of the measurement history database will be described later.

[0029] Furthermore, in this embodiment, the acquisition unit 300 acquires a category associated with the current mileage from the gradient database stored in the ROM 30B or storage 30D as the target category. The gradient database is a database in which the range of mileage and the range of the gradient of the decrease in depth (hereinafter simply referred to as "gradient") are stored for each category. For example, in the gradient database, the gradient is stored so that it decreases as the mileage increases. In this way, in this embodiment, by estimating the characteristics of the tire 14 using the gradients of multiple categories (for example, 3 categories), the tire characteristics, including nonlinear characteristics, can be estimated with high accuracy. Furthermore, the gradient database may store the range of travel distance and the range of gradient for each category, for each type of vehicle.

[0030] Furthermore, the acquisition unit 300 acquires the driving history of the vehicle 12 when the current measured value was measured, and the driving history of the vehicle 12 when past measured values ​​were measured. In this embodiment, mileage is used as the driving history. Specifically, the acquisition unit 300 acquires the mileage of the vehicle 12 from when the tire 14 is mounted on the vehicle 12 until the current measured value is measured (hereinafter referred to as "current mileage") from the vehicle 12. The acquisition unit 300 also acquires the mileage of the vehicle 12 from when the tire 14 is mounted on the vehicle 12 until past measured values ​​are measured (hereinafter referred to as "past mileage") from the measurement history database. However, this is not the only example. For example, instead of the current mileage, the acquisition unit 300 may acquire the period from when the tire 14 is mounted on the vehicle 12 until the current measured value is measured. Alternatively, instead of past mileage, the acquisition unit 300 may acquire the period from when the tire 14 is mounted on the vehicle 12 until past measured values ​​are measured.

[0031] As shown in Figure 4, the measurement history database according to this embodiment stores, for each vehicle ID (Identification), the measurement date, the measured value measured on that measurement date, and the mileage of the vehicle 12 at the time the measured value was taken. The vehicle ID is information that identifies each of the vehicles 12 included in the estimation system 100. The measurement history database also stores, for each category, the measurement date, the measured value measured on that measurement date, and the mileage of the vehicle 12 at the time the measured value was taken.

[0032] For example, in the example shown in Figure 4, the categories are divided into initial, intermediate, and late categories. The initial category is the first category, the category one step after the initial category is the intermediate category, and the category one step after the intermediate category is the late category. In the example shown in Figure 4, the vehicle with vehicle ID "0001" has a measured value of 7mm and a mileage of 3000km in the initial category on April 8, 2022, and a measured value of 6mm and a mileage of 5000km on May 3, 2023. Furthermore, the vehicle with vehicle ID "0001" does not have depth measurements taken in the intermediate and late categories. In the example shown in Figure 4, "-" indicates that no data is stored.

[0033] Furthermore, in the example shown in Figure 4, the vehicle with vehicle ID "0002" had a measured value of 7.5 mm and a mileage of 4000 km on August 5, 2020, and a measured value of 6.3 mm and a mileage of 5032 km on December 15, 2020, during the initial period. Then, during the intermediate period, the measured value was 5.8 mm and a mileage of 27200 km on September 24, 2021, and a measured value of 4.2 mm and a mileage of 32000 km on March 7, 2022. Finally, during the late period, the measured value was 3.6 mm and a mileage of 48059 km on November 3, 2022, and a measured value of 2.5 mm and a mileage of 60000 km on April 5, 2023.

[0034] Returning to Figure 3, the calculation unit 310 has the function of calculating the calculated gradient, which is the current gradient of the depth decrease, from the current measured value and the past measured value. Specifically, the calculation unit 310 calculates the calculated gradient by dividing the difference between the current measured value and the past measured value by the difference between the current mileage and the past mileage. If the acquisition unit 300 acquires a period instead of mileage, the calculation unit 310 calculates the calculated gradient by dividing the difference between the current measured value and the past measured value by the difference between the period until the current measured value was measured and the period until the past measured value was measured.

[0035] The calculation unit 310 then stores the calculated gradient in the gradient history database stored in the storage 30D. The gradient history database is a database in which the calculated gradient for each vehicle ID and each category is stored.

[0036] The estimation unit 320 has the function of estimating the time when the depth decreases to a predetermined value based on the calculation gradient calculated by the calculation unit 310.

[0037] In this embodiment, a predetermined value is applied that recommends replacing the tire 14 (hereinafter referred to as the "recommended replacement value"). In other words, the estimation unit 320 has the function of estimating the time when the depth will decrease to the recommended replacement value. However, this is not the only example. Any value may be applied as the predetermined value. Also, in this embodiment, the recommended replacement value is predetermined by the administrator of the center server 30 or a user, etc. However, this is not the only example. The recommended replacement value may be set for each type of tire 14. For example, the recommended replacement value may be set to 4 mm if the tire 14 is a summer tire, and to 50% of the depth when new if it is a studless tire.

[0038] Furthermore, in this embodiment, the estimation unit 320 estimates the future gradient of the depth reduction (hereinafter simply referred to as "future gradient") based on the calculated gradient. Specifically, the estimation unit 320 estimates the future gradient based on the calculated gradient, the distance traveled as the travel history, and the measured values ​​of other vehicles other than vehicle 12 (hereinafter simply referred to as "other vehicles"). However, it is not limited to this example. The estimation unit 320 may estimate the future gradient based only on the calculated gradient without using the distance traveled or the measured values ​​of other vehicles.

[0039] In this embodiment, the estimation unit 320 estimates the gradient of a specific vehicle in a later category than the target category as the future gradient of vehicle 12. A specific vehicle is one of the other vehicles whose difference between the gradient in the target category and the calculated gradient ranks from 1st to a predetermined rank (e.g., 4th), and whose maximum mileage in the target category is closest to the current mileage of vehicle 12. However, this is not the only example. Any other vehicle may be used as the specific vehicle. For example, as the specific vehicle, one may be one of the other vehicles whose difference between the maximum mileage in the target category and the current mileage of vehicle 12 ranks from 1st to a predetermined rank, and whose gradient in the target category is closest to the calculated gradient. Alternatively, a vehicle of the same type as vehicle 12 may be used as the specific vehicle. In summary, a specific vehicle is another vehicle whose gradient is expected to be similar to that of vehicle 12. Note that there may be multiple specific vehicles. In this case, the estimation unit 320 estimates the arithmetic mean, geometric mean, or harmonic mean of the gradients of a specific vehicle in a category after the target category as the future gradient of the vehicle 12.

[0040] Specifically, the estimation unit 320 identifies other vehicles from the gradient history database whose difference between the gradient in the target category and the calculated gradient ranks from 1st to a predetermined rank. Then, the estimation unit 320 identifies the vehicle whose maximum mileage in the target category is closest to the current mileage of vehicle 12 as the specified vehicle. The estimation unit 320 then estimates the gradient of a later category associated with the specified vehicle in the gradient history database as the future gradient of vehicle 12. For example, if the target category is a medium-term category, the estimation unit 320 estimates the gradient of a later category associated with the specified vehicle as the future gradient of vehicle 12.

[0041] The estimation unit 320 then estimates the time when the depth will decrease to a predetermined replacement recommendation value based on the estimated future gradient. Specifically, the estimation unit 320 estimates the distance traveled to reach the replacement recommendation value by adding the distance calculated by dividing the difference between the current measured value and the replacement recommendation value by the estimated future gradient to the current mileage. The estimation unit 320 then estimates the time when the depth will decrease to the replacement recommendation value by adding the number of days calculated by dividing the estimated mileage by the daily mileage of the vehicle 12 to the measurement date of the current measured value.

[0042] The output unit 330 outputs the time when the depth decreases to the recommended replacement value, as estimated by the estimation unit 320. For example, the output unit 330 outputs the time when the depth decreases to the recommended replacement value to the user terminal 10. Alternatively, the output unit 330 may output the mileage at which the depth decreases to the recommended replacement value, as estimated by the estimation unit 320.

[0043] Next, the flow of the estimation process will be explained using Figure 5. The estimation process is performed when the CPU 30A reads the estimation program from the ROM 30B or storage 30D, loads it into the RAM 30C, and executes it.

[0044] In step S100 of Figure 5, the CPU 30A waits until it obtains the current measured value from the user terminal 10. Once the CPU 30A obtains the current measured value (step S100: YES), it proceeds to step S102.

[0045] In step S102, the CPU 30A obtains the current mileage from the vehicle 12.

[0046] In step S104, CPU 30A obtains the target category based on the current mileage obtained in step S102. Specifically, CPU 30A obtains the category associated with the current mileage obtained in step S102 from the gradient database as the target category.

[0047] In step S106, the CPU 30A determines whether it is possible to obtain past measured values ​​for the target category. Specifically, the CPU 30A determines whether past measured values ​​for vehicle 12 in the target category are stored in the measurement history database. If the CPU 30A determines that it is possible to obtain past measured values ​​for vehicle 12 in the target category (step S106: YES), it proceeds to step S110. On the other hand, if the CPU 30A determines that it is not possible to obtain past measured values ​​for vehicle 12 in the target category (step S106: NO), it proceeds to step S108.

[0048] In step S108, the CPU 30A stores the acquired current measured value, the acquired current mileage, and the measurement date (specifically, the execution date of step S108) in the measurement history database, associating them with the vehicle ID that identifies the vehicle 12, and then terminates this estimation process.

[0049] In step S110, the CPU 30A obtains past measured values ​​and past mileage for the vehicle 12 in the target category from the measurement history database.

[0050] In step S112, the CPU 30A calculates the gradient. Specifically, the calculation unit 310 calculates the gradient by dividing the difference between the current measured value and the past measured value by the difference between the current mileage and the past mileage.

[0051] In step S114, the CPU 30A determines whether the category associated with the calculated gradient calculated in step S112 in the gradient database is the same as the target category obtained in step S104. If the category associated with the calculated gradient is the same as the obtained target category (step S114: YES), the CPU 30A proceeds to step S118. On the other hand, if the category associated with the calculated gradient is different from the identified target category (step S114: NO), the CPU 30A proceeds to step S116.

[0052] In step S116, CPU 30A changes the target category in the gradient database to the category associated with the calculated gradient calculated in step S112, and proceeds to step S118.

[0053] In step S118, the CPU 30A identifies a specific vehicle from the gradient history database. Specifically, the CPU 30A identifies other vehicles from the gradient history database whose difference between the gradient and the calculated gradient in the target category is ranked from 1st to a predetermined rank. Then, among the identified other vehicles, the CPU 30A identifies the vehicle whose maximum mileage in the target category is closest to the current mileage of vehicle 12 as the specific vehicle.

[0054] In step S120, the CPU 30A estimates the future gradient of vehicle 12 based on the gradient history database, using the gradients of the sections after the target section for the specific vehicle.

[0055] In step S122, the CPU 30A estimates the time when the depth will decrease to the recommended replacement value based on the estimated future gradient. Specifically, the estimation unit 320 estimates the distance traveled to reach the recommended replacement value by adding the distance calculated by dividing the difference between the current measured value and the recommended replacement value by the estimated future gradient to the current mileage. Then, the estimation unit 320 estimates the time when the depth will decrease to the recommended replacement value by adding the number of days calculated by dividing the estimated mileage by the daily mileage of the vehicle 12 to the measurement date of the current measured value.

[0056] In step S124, the CPU 30A outputs the time when the depth estimated in step S122 decreases to the recommended replacement value, and terminates this estimation process.

[0057] [remarks] In the above embodiment, a center server 30, configured separately from the user terminal 10, was used as the estimation device. However, this is not the only example. A device built into the user terminal 10 may be used as the estimation device. Alternatively, a device built into the vehicle 12 may be used as the estimation device.

[0058] Furthermore, in the above embodiment, the calculation unit 310 calculated the calculated gradient from the current measured value and the past measured value. However, it is not limited to this example. For example, the calculation unit 310 may estimate how the vehicle 12 is used (for example, how often the brakes are used and the types of roads it travels on relatively often) from the maintenance record of the vehicle 12, and calculate the calculated gradient based on how the vehicle 12 is used. This utilizes the fact that when the vehicle 12 uses the brakes relatively often, or travels relatively often on gravel roads, the wear of the tires 14 progresses relatively rapidly, resulting in a steeper gradient. Conversely, when the vehicle 12 uses the brakes relatively infrequently, or travels relatively often on flat roads, the wear of the tires 14 progresses relatively slowly, resulting in a gentler gradient.

[0059] Furthermore, the maintenance records mentioned above may include not only measured values ​​but also records of tire repair or replacement, etc.

[0060] Furthermore, in the above embodiment, the estimation unit 320 estimated the gradient of a specific vehicle in a later category than the target category as the future gradient of vehicle 12. However, it is not limited to this example. For example, the estimation unit 320 may estimate the future gradient of vehicle 12 by multiplying the calculated gradient of vehicle 12 by a coefficient for each category calculated from the specific vehicle. In this case, the coefficient for each category is calculated, for example, by dividing the gradients of the specific vehicle in the mid-term and late-term categories by the gradient of the specific vehicle in the initial category, respectively.

[0061] Specifically, when the acquisition unit 300 acquires an initial category as the target category, the estimation unit 320 estimates the gradients of the intermediate and late categories of the vehicle 12 by multiplying the calculated gradient of the vehicle 12 (i.e., the initial gradient of the vehicle 12) by a coefficient for each category calculated from a specific vehicle.

[0062] Furthermore, the estimation unit 320 may estimate the future gradient of the vehicle 12 using not only information about a specific vehicle but also tire information related to the tire 14 (for example, the type of tire 14). For example, the acquisition unit 300 acquires tire information from user information. The calculation unit 310 then calculates a coefficient for each category from the tire information. The estimation unit 320 then estimates the future gradient of the vehicle 12 by multiplying the calculated gradient by the coefficient for each category.

[0063] Furthermore, the processing that the CPU reads and executes in the above embodiment may be executed by various processors other than the CPU. Examples of such processors include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacturing, and dedicated electrical circuits such as ASICs (Application Specific Integrated Circuits) which have a circuit configuration specifically designed to execute a particular process. The above processing may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (for example, multiple FPGAs, and a combination of a CPU and an FPGA). More specifically, the hardware structure of these various processors is an electrical circuit that combines circuit elements such as semiconductor elements.

[0064] Furthermore, although the above embodiments describe a configuration in which each program is pre-stored (installed) in ROM or storage, the invention is not limited to this. Programs may be provided in the form of recordings on recording media such as CD-ROM (Compact Disc Read Only Memory), DVD-ROM (Digital Versatile Disc Read Only Memory), and USB (Universal Serial Bus) memory. Programs may also be provided in the form of downloads from external devices via a network.

[0065] The processing flow described in the above embodiment is just one example, and unnecessary steps may be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0066] Furthermore, the configurations of the user terminal 10, the vehicle 12, and the center server 30 described in the above embodiment are examples and may be modified as needed without departing from the main purpose. [Explanation of Symbols]

[0067] 12 vehicles 14 tires 30. Center Server (Estimated Device) 300 Acquisition Department 310 Calculation Unit 320 Estimation Department

Claims

1. An acquisition unit acquires the current measured value of the groove depth of the tires mounted on the vehicle, and also acquires the past measured value of the vehicle in a target category associated with the vehicle's current mileage, from among the divided mileage categories from when the tires were mounted on the vehicle until the depth reached the limit value. A calculation unit calculates the current gradient of the depth reduction by dividing the difference between the current measured value and the past measured value in the target category by the difference between the current mileage and the mileage when the past measured value was measured, An estimation unit identifies other vehicles from a gradient history database whose difference in gradient between the target category and the calculated gradient ranks from 1st to a predetermined rank, identifies the vehicle whose maximum mileage in the target category is closest to the vehicle's current mileage among the identified vehicles as a specified vehicle, estimates the gradient of the category after the target category associated with the specified vehicle as the future gradient of the depth decrease, and estimates the time when the depth will decrease to a value that recommends replacing the tires based on the estimated future gradient. An estimation device equipped with the following features.

2. The estimation device according to claim 1, wherein, if there are multiple specified vehicles, the estimation unit estimates the arithmetic mean, geometric mean, or harmonic mean of the gradients of the multiple specified vehicles in the classifications after the target classification as the future gradient.

3. The estimation device according to claim 1, wherein the estimation unit estimates the time when the depth decreases to a value that recommends tire replacement, and the value that recommends replacement is set for each type of tire.

4. The estimation device according to claim 1, wherein the section is set such that the gradient decreases as the travel distance increases.

5. The estimation device according to claim 1, wherein the range of travel distance and the range of gradient are set based on a gradient database stored for each type of vehicle.

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