Tire durability predicting device, program and tire durability predicting method

Abstract

To provide a tire durability predicting device, a program, and a tire durability predicting method capable of predicting a remaining durability of a target tire even if actual measurement data of a vehicle to which the tire is attached is not available.SOLUTION: A tire durability prediction device (10) comprises: an acquisition unit (131) that acquires input data including information on a time until a tread of a tire is completely worn out or a travel distance, and information on a destination and a frequency of a vehicle to which the tire is attached; a generation unit (132) that generates data on a speed of each destination based on the input data; and a prediction unit (133) that predicts a remaining durability of the tire when the tread of the tire is completely worn out based on the data on the speed.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Conventionally, there is known a system that acquires data detected by a vehicle while it is moving and estimates tire deterioration. For example, Patent Document 1 discloses a tire management device including a deterioration state prediction means that predicts the deterioration tendency and deterioration state of tires mounted on a vehicle for each mounting position. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-218097 A Summary of the Invention [Problem to be solved by the invention]

[0004] Here, the tire management device of Patent Document 1 appropriately manages tires to average the usage period of each tire, but there is a demand for predicting the remaining durability of tires with sufficient accuracy by simple calculations using less data. For example, the deterioration state of a base tire when the tire tread is completely worn down is estimated, and the possibility of retreading is judged, and the life of the tire, etc., may be estimated. Here, retreading refers to scraping the surface of the tread rubber of a tire and attaching new rubber to reuse it. The base tire is the part of the tire (the part to be reused) excluding the tread part that is replaced in the retreading. Furthermore, when making such an estimate, actual measurement data of the vehicle on which the target tire is mounted is often not obtained.

[0005] An object of the present disclosure, made in consideration of the above circumstances, is to provide a tire durability prediction device, program, and 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. [Means for solving the problem]

[0006] (1) A tire durability prediction device according to an embodiment of the present disclosure, An acquisition unit that acquires input data including information on a time or a mileage until the tire tread is completely worn out and information on a destination and a frequency of a vehicle equipped with the tire; A generating unit that generates data related to speed for each destination based on the input data; and a prediction unit for predicting a remaining life of the tire at a time when the tire's tread will be completely worn down based on the speed data. With this configuration, it is possible to predict the remaining durability of a tire even if actual measurement data of the vehicle on which the target tire is mounted is not available.

[0007] (2) As an embodiment of the present disclosure, in (1), The generation unit generates data regarding a load for each of the vehicles or each of the destinations, The prediction unit predicts the remaining durability of the tire at the time when the tire tread is completely worn down based on the speed data and the load data. This configuration allows the remaining durability of the tire to be predicted more accurately, taking the load into account.

[0008] (3) As an embodiment of the present disclosure, in (2), The input data includes at least one piece of information on the type of cargo, the vehicle type, and the axle load; The generation unit generates data regarding the load based on at least one piece of information of the type of cargo, the vehicle model, and the axle load. This configuration allows weighting to be taken into account based on verbal information.

[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 a travel season or a travel area of ​​the vehicle, The prediction unit corrects the predicted remaining durability of the tire based on the average temperature. This configuration allows the remaining durability of a tire to be predicted more accurately, taking into account the 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. With this configuration, it is possible to directly display information that a worker who carries out retreading wants to know, such as whether or not retreading is possible.

[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. With this configuration, the remaining durability of the tire can be clearly indicated to, for example, a worker performing a retreading operation.

[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 away, using different models depending on the mounting position of the tire. With this configuration, the remaining durability of the tire can be predicted more accurately, taking into account the mounting position of the tire.

[0013] (8) A program according to an embodiment of the present disclosure, Tire durability prediction device, Obtaining input data including information on the time or distance traveled until the tread of a tire is completely worn out and information on the destination and frequency of a vehicle equipped with said tire; generating data relating to speed for each of the destinations based on the input data; and predicting the remaining life of the tire at the time the tire's tread is completely worn based on the speed data. With this configuration, it is possible to predict the remaining durability of a tire even if actual measurement data of the vehicle on which the target tire is mounted is not available.

[0014] (9) A method for predicting tire durability according to an embodiment of the present disclosure, A tire durability prediction method executed by a tire durability prediction device, comprising: The tire durability prediction device, Obtaining input data including information on the time or distance traveled until the tread of a tire is completely worn out and information on the destination and frequency of a vehicle equipped with said tire; generating data relating to speed for each of the destinations based on the input data; and predicting the remaining life of the tire at the time the tire's tread is completely worn based on the speed data. With this configuration, it is possible to predict the remaining durability of a tire even if actual measurement data of the vehicle on which the target tire is mounted is not available. Effect of the Invention

[0015] According to the present disclosure, it is possible 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 on which the target tire is mounted is not available. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a tire durability prediction system including a tire durability prediction device according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is another diagram showing an example of the configuration of the tire durability prediction system of FIG. [Diagram 3] FIG. 3 is a diagram illustrating a predicted durability index. [Figure 4] FIG. 4 is a diagram illustrating the difference in predicted durability index depending on conditions. [Diagram 5] FIG. 5 is an example of a flowchart showing the process of a tire durability prediction method according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, 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 with reference to the drawings. In each drawing, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same 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 including 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 the present embodiment predicts the deterioration state of a tire 30 mounted on a vehicle 20. The prediction of the deterioration state 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 deterioration state of the tire 30 may be used to determine whether a specific task or the like can be performed at a certain point in time, and in the present embodiment, the prediction is described as being used to determine whether the tire 30 can be retreaded. As described above, retreading refers to scraping the surface of the tread rubber of the tire 30 and attaching new rubber to reuse the tire. For example, when the damage, chemical deterioration, and mechanical deterioration of the base tire are each sufficiently small, it can be determined that sufficient durability remains and that retreading is possible. In the present embodiment, the durability of the tire 30 is described as the durability of the base tire.

[0020] The tire durability prediction device 10 can use a durability index as described later to indicate the durability predicted to remain at a certain point in time, for example, as a numerical value (score) or a rank. The durability index is a relative index with a new tire 30 (at the time of initial manufacture) being 100. Here, the vehicle 20 equipped 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, in order to avoid redundancy in the illustration in FIG. 2, only one vehicle 20 is shown, but the tire durability prediction system accumulates information on a plurality of vehicles 20. In this embodiment, the plurality of vehicles 20 are managed by one organization (for example, a company) and are used to carry out work, loading, or unloading at a specific location (for example, 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 a 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 at a location where the tire 30 is retreaded. Here, the tire durability prediction device 10 may not be a single device, but may be configured by multiple devices that are arranged at multiple locations and can transmit and receive data to each other via a network 40. In other words, the multiple devices connected by the network 40 may function as the tire durability prediction device 10 shown in FIG. 1 as a whole. Therefore, for example, the tire durability prediction device 10 may be configured by one computer as a hardware configuration, or may be configured by multiple computers connected by 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 be configured to include, for example, a local area network (LAN) in part. Here, the tire durability prediction system may further include a terminal device 50 used by an operator who performs retreading or the like. The terminal device 50 is, 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 prediction result of the durability or the determination result such as whether or not retreading is possible, which is output by the output unit 134 described later. The tire durability prediction system may also be configured to include a storage device 90 (storage device 90 on the cloud) 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. The information about the vehicle 20 or the information about the state of the tire 30 may be inputted using the terminal device 50 by a worker at a place where the tire 30 is retreaded and the vehicle 20 is inspected and repaired (hereinafter simply referred to as the "repair place"). For example, when the vehicle 20 comes to the repair place for a regular inspection, the worker at the repair place may obtain the information about the vehicle 20 or the information about the state of the tire 30 and input it using the terminal device 50, and the inputted information may be stored in the database. The inputted information about the state of the tire 30 is actual data of the tire 30 that has actually been used, and may include information about the life until the tire becomes a waste tire and time-series data of the actual measured value of the wear of the tread that can be used for generating a model described later. In addition, the worker at the repair place may input information obtained by hearing using the terminal device 50 and store it in the storage device 90. The information obtained by hearing 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 described later. In addition, the information obtained by hearing may include information about the vehicle 20, such as the maximum load capacity and the weight of the vehicle body. In addition, the tire durability prediction device 10 may obtain 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 includes 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.

[0024] The storage unit 12 is one or more memories. The memory is, 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 accessed from the outside 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 obtained from the database of the storage device 90 on the cloud 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 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. Here, completely worn out means worn out to a state that is not suitable for the vehicle 20 to travel. If the base tire has durability that allows retreading, the completely worn tire 30 is retreaded and continues to be used, but if the base tire cannot be retreaded, the tire becomes a waste tire. Information on the time or distance traveled 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 the same type of vehicle 20 (e.g., trucks of the same company) as the vehicle 20 on which the tire 30 to be predicted is mounted. For example, the acquisition unit 131 can obtain a value of "100,000 km" as the distance traveled until the tread of the tire 30 is completely worn out based on the performance data. Here, since the distance traveled and the time can be converted into each other using the average traveling speed of the same type of vehicle 20 in the performance data, the time until the tread of the tire 30 is completely worn out may be acquired. Moreover, the 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, the multiple vehicles 20 are managed by one organization (e.g., a company) and move to a specific location (e.g., a factory). For example, for one vehicle 20, information may be obtained that the destination is Factory A and the frequency is 100%. Also, for multiple vehicles 20, information may be obtained that the destination is Factory A or Factory B, and 75% of the vehicles 20 go to Factory A and the remaining 25% of the vehicles 20 go to Factory B in terms of frequency. Such information is obtained, for example, by hearing from 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 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 a service of providing information. The input data acquired by the acquisition unit 131 may include at least one of the types of cargo, vehicle model, and axle load of the vehicle 20. The information on the types of cargo, vehicle model, and axle load may be, for example, information obtained by a worker at a repair site from a manager of the vehicle 20 and stored in the storage device 90 using the terminal device 50. The types of cargo are, for example, beverages, food, building materials, etc. The vehicle model is, for example, a four-wheeled or six-wheeled truck. The information on the axle load is, for example, 1 ton per axle. Instead of the axle load, information on the loading rate may be obtained.

[0031] The generating unit 132 generates data on the speed of the vehicle 20 for each destination based on the input data. The data on the speed is, for example, an average speed, but is not limited thereto. The generating unit 132 identifies a route from the starting point of the vehicle 20 (for example, a company parking lot) to the destination (for example, Factory A or Factory B) using, for example, map information. The generating unit 132 may calculate the average speed based on the legal speed on the route for each destination. For example, when a highway is included in the route to Factory A, the average speed is faster than the route to Factory B that is only an ordinary road. In this way, the generating unit 132 generates data on the speed of the vehicle 20 for each destination. Here, the speed of each destination may be obtained as oral information by hearing. In this case, the generating unit 132 may use the speed of each destination obtained as input data as it is as the data on the speed of each destination.

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

[0033] The generation unit 132 may calculate an average temperature according to the traveling season or traveling area of ​​the vehicle 20. The generation unit 132 may calculate the average temperature by averaging the temperatures of the area including the route from the departure point to the destination of the vehicle 20 based on the temperature information. Furthermore, when the traveling season of the vehicle 20 is limited (for example, only in summer), only the temperature information corresponding to the traveling 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. In addition, 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 obtained.

[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 base tire with respect to the traveling distance (horizontal axis). The durability index of the tire 30 decreases with an increase in the traveling distance of the vehicle 20, that is, with the passage of time, as shown by the characteristic curve C1 of FIG. 3. The characteristic curve C1 is obtained by setting a condition that affects the durability index in a model that indicates a change in the durability index of the tire 30. The condition that affects the durability index includes at least the speed of the vehicle 20, and corresponds to the data on the speed (average speed) generated by the generation unit 132 in this embodiment. 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 FIG. 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 (value of the durability index of the casing tire) of the tire 30 predicted at the time points D1, D2, D3, etc. is used in the determination described below. That is, 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 the characteristic curve C1 according 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 down, based on the data related to the speed and the data related to the load. At this time, the remaining durability of the tire 30 can be predicted more accurately by taking the load into consideration. 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 above average temperature is generated by the generation unit 132, the prediction unit 133 may calculate a characteristic curve C1 according 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). At this time, the remaining durability of the tire 30 can be predicted more accurately by taking the air temperature into consideration.

[0039] The prediction unit 133 may determine whether or not retreading is possible or the number of times retreading is possible based on the predicted remaining durability of the tire 30. The prediction unit 133 may determine whether or not retreading is possible or the number of times retreading is possible using a predetermined lower limit of the durability index at which retreading is possible. At this time, it is possible to directly indicate whether or not retreading is possible, which is information that the worker who performs retreading wants to know. For example, the lower limit of the durability index may be 20. In the example of FIG. 3, it is predicted that the durability index will fall below 20 at the time D3 when the second retreading is performed and the tread is completely worn out. Therefore, the prediction unit 133 determines that the third retreading is not possible. In other words, the prediction unit 133 determines that the number of times retreading is possible is two. Here, for more careful determination, the variation of 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 the set probability may be calculated using a standard deviation or the like. In this case, in determining whether or not retreading is possible, a further determination 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 Fig. 3, since the durability index of the lower limit characteristic curve C1L is 0 or greater at time D3, 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 the possible number of 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 and less than 80, rank C if it is 20 or more and 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 who performs retreading. For example, rank A indicates that there is almost no damage to the base tire. Therefore, with information such as "rank A at the time of D1", a worker at the repair site can understand that the tire 30 is predicted to be in good condition at the time of D1, even without obtaining a specific remaining durability value.

[0041] Here, 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 using different models depending on the mounting position of the tire 30. At this time, the mounting position of the tire 30 can be taken into consideration to more accurately predict the remaining durability of the tire 30. For example, FIG. 4 illustrates a characteristic curve C3 based on a different model. In general, the load on the tire 30 is different between the front wheel, the inner rear wheel, and the outer rear wheel. Here, the rear wheel has a double tire configuration, and the outer side 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 of 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 the determination result (e.g., whether or not retreading is possible) of the prediction unit 133 to a terminal device 50 or the like used by a worker who performs 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 life of the tire 30 and create a procurement plan or the like to secure the required number of tires 30. The worker may also plan a rotation to change the mounting position of the tire 30. The worker may also create a plan to have a tire 30 used by one business operator used by another business operator in order to effectively utilize tires 30 having sufficient durability.

[0043] Fig. 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 processing in Fig. 5.

[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 away, 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 on 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 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 due to the above configuration. In particular, in places where retreading is performed, it becomes possible to predict the deterioration state of the base tire at the time when the tread is completely worn down with sufficient accuracy based on performance data of the same type of vehicle 20 or verbal information obtained by interview, and to determine whether or not retreading is possible. In addition, since actual measurement data of each vehicle 20 is not required, it is possible to predict the remaining durability of tires 30 collectively for multiple vehicles 20 managed by one 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 a person 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 or alterations are included in 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 into one or divided. The embodiments of the present disclosure can also be realized as a storage medium having a program executed by a processor provided in an apparatus recorded thereon. It should be understood that these are also included in the scope of the present disclosure. Contributing to the United Nations-led Sustainable Development Goals (SDGs)

[0050] The SDGs have been proposed to realize a sustainable society. One embodiment of the present disclosure is considered to be a technology that can contribute to "No. 9 Build infrastructure for industry and technological innovation." [Explanation of symbols]

[0051] 10. Tire durability prediction device 11 Communications Department 12 Storage section 13 Control section 20 Vehicles 30 Tires 40 Network 50 Terminal Equipment 90 Storage device 131 Acquisition Department 132 Generation part 133 Prediction Department 134 Output section

Claims

1. An acquisition unit that acquires input data including information on a time or a mileage until the tire tread is completely worn out and information on a destination and a frequency of a vehicle equipped with the tire; A generating unit that generates data related to speed for each destination based on the input data; a prediction unit that predicts a remaining durability of the tire at a time when the tread of the tire is completely worn down based on the data regarding the speed.

2. The generation unit generates data regarding a load for each of the vehicles or each of the destinations, The tire durability prediction device according to claim 1 , wherein the prediction unit predicts a remaining durability of the tire at a time when a tread of the tire is completely worn down, based on the data related to the speed and the data related to the load.

3. The input data includes at least one of information on a type of cargo, a vehicle type, and an axle load; The tire durability prediction device according to claim 2 , wherein the generation unit generates the data regarding the load based on at least one piece of information of the type of the load, the vehicle model, and the axle load.

4. The input data includes temperature information; The generation unit calculates an average temperature according to a travel season or a travel area of ​​the vehicle, The tire durability prediction device according to claim 1 , wherein the prediction unit corrects the predicted remaining durability of the tire based on the average air temperature.

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

7. 4. The tire durability prediction device according to claim 1, wherein the prediction unit predicts the remaining durability of the tire at the time when a tread of the tire is completely worn away by using a model that differs depending on a mounting position of the tire.

8. Tire durability prediction device, Obtaining input data including information on the time or distance traveled until the tread of a tire is completely worn out and information on the destination and frequency of a vehicle equipped with said tire; generating data relating to speed for each of the destinations based on the input data; and predicting the remaining life of the tire at a time when the tire's tread will be completely worn based on the speed data.

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

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