Tire durability predicting device, program, and tire durability predicting method

Abstract

A tire durability predicting device (10) comprises: an acquiring unit (131) that acquires input data including information relating to the time or travel distance until the tread of a tire is completely worn, and information relating to the destinations and frequencies thereof for a vehicle to which the tire is attached; a generating unit (132) that generates data relating to speed for each destination on the basis of the input data; and a predicting unit (133) that predicts the remaining durability of the tire at the point in time at which the tread of the tire is completely worn, on the basis of the data relating to speed.

Description

Tire durability prediction device, program, and tire durability prediction method

[0001] The present disclosure relates to a tire durability prediction device, a program, and a tire durability prediction method.

[0002] A tire management system that estimates tire deterioration by acquiring data detected by a vehicle while it is running is known. For example, Patent Document 1 discloses a tire management device that includes a deterioration state prediction unit that predicts the deterioration tendency and deterioration state of tires mounted on a vehicle for each mounting position.

[0003] Japanese Patent Application Laid-Open No. 2017-218097

[0004] While the tire management device of Patent Document 1 appropriately manages tires to average the service life of each tire, there is a demand for a method for predicting the remaining durability of tires with sufficient accuracy through simple calculations using less data. For example, the deterioration state of a base tire when the tire tread is completely worn down may be estimated to determine whether retreading is possible and estimate the tire's lifespan. Here, retreading refers to scraping the surface of the tire's tread rubber and applying new rubber to reuse the tire. The base tire is the portion of the tire (the portion to be reused) excluding the tread portion that is replaced during retreading. Furthermore, when making such estimates, actual measurement data from the vehicle on which the target tire is mounted is often not available.

[0005] In view of the above circumstances, an object of the present disclosure is to provide a tire durability prediction device, a program, and a tire durability prediction method that are capable of predicting the remaining durability of a tire even if actual measurement data of a vehicle equipped with the target tire is not available.

[0006] (1) A tire durability prediction device according to one embodiment of the present disclosure includes: an acquisition unit that acquires input data including information on the time or distance traveled until the tire tread is completely worn out and information on the destinations and frequency of vehicles equipped with the tire; a generation unit that generates data on speed for each destination based on the input data; and a prediction unit that predicts the remaining durability of the tire at the time the tire tread is completely worn out based on the data on speed.

[0007] (2) As one embodiment of the present disclosure, in (1), the generation unit generates data regarding load for each vehicle or each destination, and the prediction unit predicts the remaining durability of the tire at the time when the tire tread is completely worn down based on the data regarding the speed and the data regarding the load.

[0008] (3) As one embodiment of the present disclosure, in (2), the input data includes at least one piece of information on the type of cargo, the vehicle model, and the axle load, and the generation unit generates the data on the load based on at least one piece of information on the type of cargo, the vehicle model, and the axle load.

[0009] (4) As an embodiment of the present disclosure, in any one of (1) to (3), the input data includes temperature information, the generation unit calculates an average temperature according to the time of year or area in which the vehicle is traveling, and the prediction unit corrects the predicted remaining durability of the tire based on the average temperature.

[0010] (5) As an embodiment of the present disclosure, in any one of (1) to (4), the prediction unit determines whether or not retreading is possible or the number of times retreading is possible based on the predicted remaining durability of the tire.

[0011] (6) As an embodiment of the present disclosure, in any one of (1) to (5), the prediction unit classifies the tire into one of a plurality of categories based on the predicted remaining durability of the tire.

[0012] (7) As an embodiment of the present disclosure, in any one of (1) to (6), the prediction unit predicts the remaining durability of the tire at the time when the tread of the tire is completely worn using a model that differs depending on the mounting position of the tire.

[0013] (8) A program according to an embodiment of the present disclosure causes a tire durability prediction device to: acquire input data including information on the time or distance traveled until the tire tread is completely worn out and information on the destinations and frequency of vehicles equipped with the tire; generate data on speed for each destination based on the input data; and predict the remaining durability of the tire at the time the tire tread is completely worn out based on the data on speed.

[0014] (9) A tire durability prediction method according to one embodiment of the present disclosure is a tire durability prediction method executed by a tire durability prediction device, which includes the steps of: acquiring input data including information on the time or distance traveled until the tire tread is completely worn out, and information on the destinations and frequency of vehicles equipped with the tire; generating data on speed for each destination based on the input data; and predicting the remaining durability of the tire at the time the tire tread is completely worn out based on the data on speed.

[0015] According to the present disclosure, it is possible to provide a tire durability prediction device, program, and tire durability prediction method that can predict the remaining durability of a tire even if actual measurement data of a vehicle equipped with the target tire is not available.

[0016] Fig. 1 is a diagram showing an example configuration of a tire durability prediction system including a tire durability prediction device according to an embodiment of the present disclosure. Fig. 2 is another diagram showing the example configuration of the tire durability prediction system of Fig. 1. Fig. 3 is a diagram showing an example of a predicted durability index. Fig. 4 is a diagram showing an example of a difference in the predicted durability index depending on conditions. Fig. 5 is an example of a flowchart showing processing of a tire durability prediction method according to an embodiment of the present disclosure.

[0017] A tire durability prediction device 10 (see FIG. 1 ), a program, and a tire durability prediction method according to an embodiment of the present disclosure will be described below with reference to the drawings. In each drawing, identical or corresponding parts are designated by the same reference numerals. In the description of this embodiment, the description of identical or corresponding parts will be omitted or simplified as appropriate.

[0018] 1 and 2 are diagrams showing an example of the configuration of a tire durability prediction system. The tire durability prediction system includes a tire durability prediction device 10. Fig. 1 is a block diagram showing an example of the internal configuration of the tire durability prediction device 10. Fig. 2 shows the overall configuration of the tire durability prediction system.

[0019] The tire durability prediction device 10 according to this embodiment predicts the state of deterioration of a tire 30 mounted on a vehicle 20. The prediction of the state of deterioration is, in other words, a prediction of the remaining durability (remaining durability) of the tire 30 at a certain point in time. The prediction of the state of deterioration of the tire 30 may be used to determine whether a specific task can be performed at a certain point in time, and in this embodiment, it is described as being used to determine whether the tire 30 can be retreaded. As described above, retreading involves scraping the surface of the tread rubber of the tire 30 and applying new rubber to reuse the tire. For example, if the damage, chemical degradation, and mechanical degradation of the base tire are each sufficiently small, it can be determined that sufficient durability remains and that retreading is possible. In this embodiment, the durability of the tire 30 is described as the durability of the base tire.

[0020] As will be described later, the tire durability prediction device 10 can use a durability index to indicate the predicted remaining durability at a given point in time, for example, as a numerical value (score) or a rank. The durability index is a relative index, with a new (first manufactured) tire 30 being rated at 100. Here, the vehicle 20 fitted with the tire 30 to be predicted may be, for example, a passenger car, a truck, a bus, or a construction vehicle, and is not limited to a specific type of moving body. In this embodiment, the vehicle 20 is described as a truck. Also, to avoid redundancy, only one vehicle 20 is shown in FIG. 2 , but the tire durability prediction system accumulates information on multiple vehicles 20. In this embodiment, the multiple vehicles 20 are managed by a single organization (e.g., a company) and are used for work, loading, or unloading at a specific location (e.g., a factory).

[0021] The tire durability prediction device 10 includes a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 includes an acquisition unit 131, a generation unit 132, a prediction unit 133, and an output unit 134. The tire durability prediction device 10 may be, for example, a computer as a hardware configuration. The computer may be a server computer or a portable computer such as a laptop or tablet. Details of the components of the tire durability prediction device 10 will be described later. For example, the tire durability prediction device 10 may be a computer located at a location where a tire 30 is retreaded. Here, the tire durability prediction device 10 may not be a single device, but may be composed of multiple devices located at multiple locations and capable of transmitting and receiving data to and from each other via a network 40. In other words, multiple devices connected via the network 40 may function as a whole as the tire durability prediction device 10 shown in FIG. 1 . Therefore, for example, the tire durability prediction device 10 may be, for example, a single computer as a hardware configuration, or may be composed of multiple computers connected via the network 40. When the system is configured with multiple computers, the storage unit 12 may be a shared memory that can be accessed by each computer.

[0022] The tire durability prediction device 10 may constitute a tire durability prediction system together with devices connected via a network 40. The network 40 may be, for example, the Internet. The network 40 may also include, for example, a local area network (LAN). The tire durability prediction system may further include a terminal device 50 used by a worker performing retreading or the like. The terminal device 50 may be, for example, a general-purpose mobile terminal such as a smartphone or a tablet terminal, but is not limited to these. The terminal device 50 may function as a display unit that displays the durability prediction results or the determination results, such as whether or not retreading is possible, output by the output unit 134 (described later). The tire durability prediction system may also include a storage device 90 (cloud-based storage device 90) on the network 40 as viewed from the tire durability prediction device 10. In this embodiment, the storage device 90 includes a database that stores information about the vehicle 20 or information about the state of the tire 30. Information about the vehicle 20 or information about the condition of the tire 30 may be input using the terminal device 50 by a worker at a location where the tire 30 is retreaded and the vehicle 20 is inspected and repaired (hereinafter simply referred to as the "repair location"). For example, when the vehicle 20 arrives at the repair location for a regular inspection, the worker at the repair location may acquire information about the vehicle 20 or information about the condition of the tire 30 and input it using the terminal device 50, and the input information may be stored in the database. The input information about the condition of the tire 30 is performance data on the tire 30 that has actually been used, and may include information about the tire's lifespan until it becomes a scrap tire and time-series data on actual tread wear values ​​that can be used to generate a model described later. In addition, the worker at the repair location may use the terminal device 50 to input information obtained by interview and store it in the storage device 90. The information obtained by interview is, for example, information heard from the manager of the vehicle 20, and may include information about the destination and frequency of the vehicle 20, which will be described later. In addition, the information obtained by interview may include information about the vehicle 20, such as the maximum load capacity and vehicle weight. In addition, the tire durability prediction device 10 may acquire information from a database, map information, temperature information, and the like via the network 40 .

[0023] The components of the tire durability prediction device 10 will be described in detail below. The communication unit 11 is configured to include one or more communication modules connected to the network 40. The communication unit 11 may include a communication module compatible with mobile communication standards such as 4G (4th Generation) and 5G (5th Generation). The communication unit 11 may include a communication module compatible with a wired or wireless LAN standard, for example.

[0024] The storage unit 12 is one or more memories. The memory may be, for example, a semiconductor memory, a magnetic memory, an optical memory, or the like, but is not limited to these, and may be any memory. The storage unit 12 is, for example, built into the tire durability prediction device 10, but may also be configured to be externally accessed by the tire durability prediction device 10 via any interface.

[0025] The storage unit 12 stores various data used in various calculations performed by the control unit 13. The storage unit 12 may also store results and intermediate data of various calculations performed by the control unit 13.

[0026] In this embodiment, the storage unit 12 may temporarily store various information from the database of the storage device 90 on the cloud, which information is acquired via the communication unit 11 .

[0027] The control unit 13 is one or more processors. The processor may be, for example, a general-purpose processor or a dedicated processor specialized for a specific process, but is not limited to these and may be any processor. The control unit 13 controls the overall operation of the tire durability prediction device 10.

[0028] Here, the tire durability prediction device 10 may have the following software configuration: One or more programs used to control the operation of the tire durability prediction device 10 are stored in the storage unit 12. When the program stored in the storage unit 12 is read by the processor of the control unit 13, it causes the control unit 13 to function as an acquisition unit 131, a generation unit 132, a prediction unit 133, and an output unit 134.

[0029] The acquisition unit 131 acquires input data including information on the time or mileage until the tread of the tire 30 is completely worn out and information on the destination and frequency of the vehicle 20 on which the tire 30 is mounted. Here, "completely worn out" means worn to a state where the tire 30 is no longer suitable for driving the vehicle 20. A completely worn tire 30 is retreaded and continued to be used if the base tire has durability that allows for retreading, but becomes a scrap tire if the base tire cannot be retreaded. The information on the time or mileage until the tread of the tire 30 is completely worn out is acquired from performance data accumulated in a database. In other words, it can be obtained based on past inspection data of vehicles 20 of the same type as the vehicle 20 on which the tire 30 is mounted (e.g., trucks made by the same company). For example, the acquisition unit 131 can acquire a value of "100,000 km" as the mileage until the tread of the tire 30 is completely worn out based on the performance data. Here, mileage and time can be converted into each other using the average driving speed of the same type of vehicle 20 in the performance data, so the time until the tread of the tire 30 is completely worn out may be acquired. Furthermore, information on the destination and frequency of the vehicle 20 equipped with the tire 30 is data for knowing how the vehicle 20 is used. As described above, in this embodiment, a plurality of vehicles 20 are managed by a single organization (e.g., a company) and move to a specific location (e.g., a factory). For example, information may be obtained for one vehicle 20, such that the destination is Factory A and the frequency is 100%. Furthermore, information may be obtained for a plurality of vehicles 20, such that the destination is Factory A or Factory B, and that 75% of the vehicles 20 go to Factory A and the remaining 25% of the vehicles 20 go to Factory B. Such information may be obtained, for example, by interviewing the manager of the vehicle 20 (as oral information).

[0030] The input data acquired by the acquisition unit 131 may include map information. The input data acquired by the acquisition unit 131 may also include temperature information. The map information and temperature information may be obtained from an information providing device via the network 40. The information providing device is, for example, a computer that provides information services. The input data acquired by the acquisition unit 131 may also include at least one of the following information: the type of cargo of the vehicle 20, the vehicle model, and the axle load. The information on the type of cargo, the vehicle model, and the axle load may be obtained, for example, by a worker at the repair site from the manager of the vehicle 20 and stored in the storage device 90 using the terminal device 50. The type of cargo may be, for example, beverages, food, building materials, etc. The vehicle model may be, for example, a four-wheeled or six-wheeled truck. The axle load information may be, for example, one ton per axle. Instead of the axle load, information on the loading ratio may be obtained.

[0031] The generation unit 132 generates data regarding the speed of the vehicle 20 for each destination based on the input data. The data regarding speed may be, for example, an average speed, but is not limited to this. The generation unit 132 identifies a route from the vehicle 20's starting point (e.g., a company parking lot) to the destination (e.g., factory A or factory B) using, for example, map information. The generation unit 132 may calculate the average speed based on the legal speed limit for the route for each destination. For example, if the route to factory A includes a highway, the average speed will be faster than the route to factory B, which consists only of ordinary roads. In this way, the generation unit 132 generates data regarding the speed of the vehicle 20 for each destination. Here, the speed for each destination may be obtained as oral information through interviews. In this case, the generation unit 132 may use the speed for each destination obtained as input data as is as the data regarding the speed for each destination.

[0032] The generation unit 132 may generate data regarding the load for each vehicle 20 or each destination. The data regarding the load may be, for example, an average load, but is not limited to this. Furthermore, the average load may be expressed as the weight per tire 30, or may be expressed as a ratio to the load (standard load) assumed in the model used for prediction in the prediction unit 133. Here, the generation unit 132 may generate data regarding the load based on at least one of information regarding the type of cargo, vehicle type, and axle load. In this case, it is possible to consider the load based on verbal information rather than actual measurement. For example, the average load may be calculated by dividing the axle load by the number of tires 30 per axle using information regarding the axle load and vehicle type. Furthermore, when the type of cargo is, for example, beverages, transportation is generally performed at the maximum load capacity, so the average load may be calculated based on information regarding the maximum load capacity and vehicle type.

[0033] The generation unit 132 may calculate an average temperature according to the travel season or travel region of the vehicle 20. The generation unit 132 may calculate the average temperature based on the temperature information by averaging the temperatures in the region including the route from the departure point to the destination of the vehicle 20. Furthermore, if the travel season of the vehicle 20 is limited (for example, only in summer), only the temperature information corresponding to the travel season may be used in calculating the average temperature.

[0034] The prediction unit 133 predicts the remaining durability of the tire 30 at the time when the tread of the tire 30 is completely worn down, based on the data related to the speed. As described above, the input data acquired by the acquisition unit 131 does not include actual measurement data of the vehicle 20. Furthermore, the data related to the speed is generated by the generation unit 132 based on the input data. Therefore, the tire durability prediction device 10 can predict the remaining durability of the tire 30 even if actual measurement data of the vehicle 20 on which the target tire 30 is mounted is not available.

[0035] FIG. 3 is a diagram illustrating an example of a durability index predicted by the prediction unit 133. In this embodiment, the prediction unit 133 uses the durability index as the durability of the tire 30. In the example of FIG. 3, a characteristic curve C1 is shown as a prediction of the durability index (vertical axis) of the casing tire versus the mileage (horizontal axis). As shown by the characteristic curve C1 in FIG. 3, the durability index of the tire 30 decreases as the mileage of the vehicle 20 increases, i.e., as time passes. The characteristic curve C1 is obtained by setting conditions that affect the durability index in a model that indicates changes in the durability index of the tire 30. The conditions that affect the durability index include at least the speed of the vehicle 20, and in this embodiment, correspond to the speed-related data (average speed) generated by the generation unit 132. When the average speed of the vehicle 20 is high, the durability index indicated by the characteristic curve C1 decreases significantly. On the other hand, when the average speed of the vehicle 20 is low, the decrease in the durability index indicated by the characteristic curve C1 becomes gradual. Here, the characteristic curve C1 may be calculated using a model generated by a known method. For example, the model may be generated by machine learning using performance data stored in a database as learning data, or may be a theoretical formula (mathematical model) whose coefficients are determined using performance data.

[0036] In the example of Figure 3, the mileage D1 corresponds to the mileage (e.g., 100,000 km) until the tread of the tire 30 is completely worn out. D2 corresponds to the mileage (e.g., 200,000 km) until the tread is completely worn out after the first retreading of the tire 30. D3 corresponds to the mileage (e.g., 300,000 km) until the tread is completely worn out after the second retreading of the tire 30. The remaining durability of the tire 30 (value of the durability index of the casing tire) predicted at times such as D1, D2, and D3 is used in the determination described below. In other words, the determination described below is efficiently performed using the mileage or time until the tread of the tire 30 is completely worn out as a unit (e.g., in units of 100,000 km).

[0037] Here, another condition that affects the durability index is the load. When the generation unit 132 generates data related to the load (average load), the prediction unit 133 may calculate a characteristic curve C1 corresponding to the average load. That is, the prediction unit 133 may predict the remaining durability of the tire 30 at the time when the tread of the tire 30 is completely worn out, based on data related to the speed and data related to the load. At this time, taking the load into consideration allows for a more accurate prediction of the remaining durability of the tire 30. When the average load is heavy, the durability index indicated by the characteristic curve C1 decreases significantly. On the other hand, when the average load is light, the decrease in the durability index indicated by the characteristic curve C1 becomes gradual.

[0038] Another condition that affects the durability index is temperature. When the generation unit 132 generates the average temperature, the prediction unit 133 may calculate a characteristic curve C1 corresponding to the average temperature. In this embodiment, the prediction unit 133 corrects the predicted remaining durability of the tire 30 based on the average air temperature. For example, when the average air temperature is significantly higher than the air temperature assumed in the model, the prediction unit 133 may weight the characteristic curve C1 so as to increase the slope (see characteristic curve C2 in FIG. 4 ). In this case, taking the air temperature into consideration allows for a more accurate prediction of the remaining durability of the tire 30.

[0039] The prediction unit 133 may determine whether retreading is possible or the number of retreads possible based on the predicted remaining durability of the tire 30. The prediction unit 133 may determine whether retreading is possible or the number of retreads possible using a predetermined lower limit of the durability index at which retreading is possible. In this case, information that the retreading worker wants to know, such as whether retreading is possible, can be directly displayed. For example, the lower limit of the durability index may be 20. In the example of FIG. 3 , after the second retreading is performed, it is predicted that the durability index will fall below 20 at time D3 when the tread is completely worn. Therefore, the prediction unit 133 determines that a third retreading is not possible. In other words, the prediction unit 133 determines that the number of retreads possible is two. Here, for more careful determination, variance in the prediction (characteristic curve C1) may be taken into consideration. For example, an upper limit characteristic curve C1U and a lower limit characteristic curve C1L according to a set probability may be calculated using a standard deviation or the like. In this case, when determining whether or not retreading is possible, an additional condition may be added, such as that the durability index of the lower limit characteristic curve C1L does not fall below 0. For example, in the example of Figure 3, the durability index of the lower limit characteristic curve C1L is 0 or greater at time D3, so the prediction unit 133 determines that two retreads are possible. However, if the durability index of the lower limit characteristic curve C1L is below 0 at time D3, the prediction unit 133 determines that a second retread is not possible and that the number of possible retreads is one.

[0040] Here, the prediction unit 133 may classify the tire 30 into one of a plurality of categories based on the predicted remaining durability of the tire 30. The plurality of categories may be, for example, Rank A if the remaining durability is 80 or more, Rank B if it is 50 or more but less than 80, Rank C if it is 20 or more but less than 50, and Rank D if it is less than 20. In this case, the remaining durability of the tire 30 can be clearly shown to, for example, a worker performing a retread. For example, Rank A indicates that there is almost no damage to the base tire. Therefore, with information such as "Rank A at time D1," a worker at the repair site can understand that the tire 30 is predicted to be in good condition at time D1, even without obtaining a specific remaining durability value.

[0041] Here, the prediction unit 133 may use different models depending on the mounting position of the tire 30 to predict the remaining durability of the tire 30 at the time when the tread of the tire 30 is completely worn. Taking the mounting position of the tire 30 into consideration, the remaining durability of the tire 30 can be predicted more accurately. For example, FIG. 4 illustrates a characteristic curve C3 based on a different model. Generally, the load on the tire 30 differs between the front wheel, the inner rear wheel, and the outer rear wheel. Here, the rear wheel has a dual tire configuration, and the outer wheel refers to the side of the vehicle 20. For example, when the tire 30 is mounted on the outer rear wheel, the load on the tire 30 is smaller than that on the front wheel and the inner rear wheel. Therefore, the characteristic curve C3 calculated using a model corresponding to the tire 30 mounted on the outer rear wheel has a more gradual decrease in durability index than the characteristic curve C1 corresponding to the front wheel.

[0042] The output unit 134 may output the prediction result or determination result of the prediction unit 133 (e.g., whether or not retreading is possible) to a terminal device 50 or the like used by the worker performing the retreading. The prediction result may be, for example, a change (graph) in the durability index as shown in FIG. 3 or FIG. 4. The worker may create a retreading work plan or the like based on the displayed prediction result or determination result. The worker may also estimate the lifespan of the tires 30 and create a procurement plan to secure the required number of tires 30. The worker may also plan a rotation to change the mounting position of the tires 30. The worker may also create a plan to have tires 30 used by one business operator used by another business operator in order to effectively utilize tires 30 with sufficient durability.

[0043] 5 is an example of a flowchart showing the processing of the tire durability prediction method executed by the tire durability prediction device 10. Here, the program stored in the storage unit 12 may be read by the processor of the control unit 13, and the processor functioning as the acquisition unit 131, the generation unit 132, the prediction unit 133, and the output unit 134 may execute each of the processing steps in FIG.

[0044] The acquisition unit 131 acquires input data including at least information on the time or distance traveled until the tread of the tire 30 is completely worn out, and information on the destination and frequency of the vehicle 20 on which the tire 30 is mounted (step S1).

[0045] The generating unit 132 generates data relating to speed for each destination based on the input data (step S2).

[0046] The prediction unit 133 predicts the remaining durability of the tire 30 at the time when the tread of the tire 30 is completely worn down, based on the data related to the speed (step S3).

[0047] The output unit 134 outputs the prediction result of the prediction unit 133 to the terminal device 50 or the like used by the worker who will perform the retreading (step S4). The output unit 134 may output the determination result of the prediction unit 133 (for example, whether or not retreading is possible).

[0048] As described above, the tire durability prediction device 10, program, and tire durability prediction method according to the present embodiment, by virtue of the above-described configuration, can predict the remaining durability of a tire 30 even if actual measurement data of the vehicle 20 on which the target tire 30 is mounted is not available. In particular, at locations where retreading is performed, it is possible to predict with sufficient accuracy the state of deterioration of a base tire when the tread is completely worn based on performance data of the same type of vehicle 20 or verbal information obtained through interviews, and determine whether or not retreading is appropriate. Furthermore, because actual measurement data of each individual vehicle 20 is not required, it is possible to predict the remaining durability of tires 30 collectively for multiple vehicles 20 managed by a single company, for example.

[0049] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art would easily be able to make various modifications or alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included within the scope of the present disclosure. For example, the functions included in each component or step can be rearranged so as not to cause logical inconsistencies, and multiple components or steps can be combined or divided into one. The embodiments of the present disclosure can also be realized as a storage medium on which a program executed by a processor included in an apparatus is recorded. It should be understood that these are also included within the scope of the present disclosure.

[0050] REFERENCE SIGNS LIST 10 Tire durability prediction device 11 Communication unit 12 Storage unit 13 Control unit 20 Vehicle 30 Tire 40 Network 50 Terminal device 90 Storage device 131 Acquisition unit 132 Generation unit 133 Prediction unit 134 Output unit

Claims

1. A tire durability prediction device comprising: an acquisition unit that acquires input data including information on the time or distance traveled until the tire tread is completely worn out and information on the destinations and frequency of vehicles equipped with the tire; a generation unit that generates data regarding the speed for each destination based on the input data; and a prediction unit that predicts the remaining durability of the tire at the time the tire tread is completely worn out based on the data regarding the speed.

2. A tire durability prediction device as described in claim 1, wherein the generation unit generates data regarding the load for each vehicle or each destination, and the prediction unit predicts the remaining durability of the tire at the time when the tire tread is completely worn down based on the data regarding the speed and the data regarding the load.

3. A tire durability prediction device as described in claim 2, wherein the input data includes at least one of information on the type of cargo, vehicle model, and axle load, and the generation unit generates data regarding the load based on at least one of information on the type of cargo, vehicle model, and axle load.

4. A tire durability prediction device as described in any one of claims 1 to 3, wherein the input data includes temperature information, the generation unit calculates an average temperature according to the time or area in which the vehicle is driven, and the prediction unit corrects the predicted remaining durability of the tire based on the average temperature.

5. A tire durability prediction device as described in any one of claims 1 to 4, wherein the prediction unit determines whether or not retreading is possible or the number of times retreading is possible based on the predicted remaining durability of the tire.

6. A tire durability prediction device according to any one of claims 1 to 5, wherein the prediction unit classifies the tire into one of a plurality of categories based on the predicted remaining durability of the tire.

7. A tire durability prediction device as claimed in any one of claims 1 to 6, wherein the prediction unit predicts the remaining durability of the tire at the time when the tread of the tire is completely worn away using a different model depending on the mounting position of the tire.

8. A program that causes a tire durability prediction device to perform the following operations: acquire input data including information on the time or distance traveled until the tire tread is completely worn out, and information on the destinations and frequency of the vehicle on which the tire is mounted; generate data regarding the speed for each destination based on the input data; and predict the remaining durability of the tire at the time the tire tread is completely worn out based on the data regarding the speed.

9. A tire durability prediction method executed by a tire durability prediction device, comprising: acquiring input data including information on the time or distance traveled until the tire tread is completely worn out, and information on the destinations and frequency of a vehicle equipped with the tire; generating data regarding the speed for each destination based on the input data; and predicting the remaining durability of the tire at the time the tire tread will be completely worn out based on the data regarding the speed.

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