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

Abstract

A tire temperature prediction device 10 according to the present disclosure is for predicting the temperature of a tire and comprises a control unit 13. The control unit 13 acquires heat history data indicating a temporal changes in the temperature of a tire installed on a vehicle, and determines, on the basis of the slope of the temperature with respect to time in the acquired heat history data, the timing at which to start temperature prediction processing.

Description

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

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

[0002] Conventionally, there are known techniques for predicting the state of deterioration of tires. For example, Patent Literature 1 discloses a method for predicting the state of deterioration of tires based on the thermal history of the tires, which can improve the accuracy of predicting the state of deterioration of tires that are in use or have been used.

[0003] Meanwhile, technology is also known that uses tire data during operation to predict tire temperatures and tire failures for tires such as mining tires. It is also known that appropriate tire management based on the results of tire failure prediction can lead to more efficient mining operations.

[0004] Japanese Patent Application Laid-Open No. 2017-219477

[0005] However, when the amount of data acquired and analyzed in the tire temperature prediction process increases, while the prediction accuracy improves and the number of services that can be provided to users increases, the load and cost of calculations also increases. In the conventional technology described in Patent Document 1, the tire deterioration state is predicted based on the thermal history of the tire during use, for example, the total amount of heat received. In this case, for example, if the number of tires or vehicles to be predicted is increased, or if missing data is reduced to increase the amount of data in order to improve accuracy, the load on data processing increases, increasing the burden on devices such as servers and costs.

[0006] In view of the above circumstances, an object of the present disclosure is to provide a tire temperature prediction device, a tire temperature prediction method, and a program that reduce the load on tire temperature prediction processing.

[0007] [1] A tire temperature prediction device according to one embodiment of the present disclosure is a tire temperature prediction device that predicts the temperature of a tire, and includes a control unit. The control unit acquires thermal history data indicating the change in temperature of a tire attached to a vehicle over time, and determines the timing to start the temperature prediction process based on the gradient of the temperature over time in the acquired thermal history data.

[0008] [2] A tire temperature prediction device according to one embodiment of the present disclosure is the tire temperature prediction device described in [1] above, wherein the control unit may start the temperature prediction process when it determines that the slope is greater than or equal to a first threshold value.

[0009] [3] A tire temperature prediction device according to one embodiment of the present disclosure is the tire temperature prediction device described in [2] above, wherein the control unit may terminate the temperature prediction process when it determines that the slope has become less than the first threshold value.

[0010] [4] A tire temperature prediction device according to one embodiment of the present disclosure is a tire temperature prediction device described in any one of [1] to [3] above, wherein the control unit may extract a portion of the thermal history data to be used for predicting the temperature from the present based on the slope.

[0011] [5] A tire temperature prediction device according to one embodiment of the present disclosure is the tire temperature prediction device described in [4] above, wherein the control unit may extract data when the slope is greater than or equal to a second threshold value as part of the thermal history data.

[0012] [6] A tire temperature prediction device according to one embodiment of the present disclosure is the tire temperature prediction device described in [5] above, wherein the control unit may optimize the second threshold value so as to reduce the error of the predicted value relative to the actual measured value of the temperature.

[0013] [7] A tire temperature prediction device according to one embodiment of the present disclosure is the tire temperature prediction device described in [6] above, wherein the control unit may determine the second threshold value that minimizes the error when using the data extracted based on each of a plurality of second threshold values, and extract the data based on the determined second threshold value.

[0014] [8] A tire temperature prediction device according to one embodiment of the present disclosure is a tire temperature prediction device described in [6] or [7] above, wherein the error may include a standard deviation of the predicted value relative to the actual measured value over a predetermined period of time.

[0015] [9] A tire temperature prediction device according to one embodiment of the present disclosure is a tire temperature prediction device described in any one of [1] to [8] above, wherein the control unit may generate alert information and notify a user when it determines that the slope exceeds a third threshold value.

[0016]

[10] A tire temperature prediction device according to one embodiment of the present disclosure is the tire temperature prediction device described in any one of [1] to [9] above, wherein the tire may include a mining tire.

[0017]

[11] A tire temperature prediction method according to one embodiment of the present disclosure is a tire temperature prediction method for predicting the temperature of a tire, and includes acquiring thermal history data indicating the change in temperature of a tire attached to a vehicle over time, and determining the timing to start the temperature prediction process based on the gradient of the temperature over time in the acquired thermal history data.

[0018]

[12] A program according to one embodiment of the present disclosure causes a tire temperature prediction device that predicts the temperature of a tire to perform operations including acquiring thermal history data indicating the change in temperature of a tire attached to a vehicle over time, and determining the timing to start the temperature prediction process based on the gradient of the temperature over time in the acquired thermal history data.

[0019] According to the present disclosure, there is provided a tire temperature prediction device, a tire temperature prediction method, and a program that can reduce the load on tire temperature prediction processing.

[0020] 1 is a configuration diagram showing an example of the configuration of a tire temperature prediction system according to an embodiment of the present disclosure; FIG. 2 is a block diagram showing an example of the configuration of the tire temperature prediction device of FIG. 1; FIG. 3 is a block diagram showing an example of the configuration of the measurement device of FIG. 1; FIG. 4 is a block diagram showing an example of the configuration of the terminal device of FIG. 1; FIG. 5 is a sequence diagram showing an example of the operation of the tire temperature prediction system of FIG. 1; and FIG. 6 is a flowchart showing an example of the operation of the tire temperature prediction device of FIG. 1.

[0021] A tire temperature prediction system 1 according to an embodiment of the present disclosure will be described below with reference to the drawings. Common components in the drawings are designated by the same reference numerals.

[0022] (Configuration of Tire Temperature Prediction System 1) An overview of a tire temperature prediction system 1 according to an embodiment of the present disclosure will be described with reference to Fig. 1. Fig. 1 is a configuration diagram showing an example of the configuration of a tire temperature prediction system 1 according to an embodiment of the present disclosure. As shown in Fig. 1, the tire temperature prediction system 1 includes a tire temperature prediction device 10, a measurement device 20, and a terminal device 30. Each of the tire temperature prediction device 10, the measurement device 20, and the terminal device 30 is connected to, for example, a network 40 so as to be able to communicate with each other.

[0023] 1 illustrates only one each of the tire temperature prediction device 10, the measurement device 20, and the terminal device 30 for ease of explanation, but the number of each device included in the tire temperature prediction system 1 is not limited to one. The tire temperature prediction system 1 may include, for example, multiple tire temperature prediction devices 10. The tire temperature prediction system 1 may include, for example, multiple measurement devices 20. The tire temperature prediction system 1 may include, for example, multiple terminal devices 30.

[0024] The tire temperature prediction device 10 includes, for example, one or more server devices that can communicate with each other. As an example, the tire temperature prediction device 10 functions as a server for the terminal device 30. The tire temperature prediction device 10 is configured with one or more computers. In one embodiment, the tire temperature prediction device 10 is described as being configured with one computer. However, the tire temperature prediction device 10 may be configured with multiple computers, such as a cloud computing system. The tire temperature prediction device 10 is not limited to these and may include any general-purpose electronic device such as a PC (Personal Computer), a tablet PC, or a smartphone, or may include other electronic device dedicated to the tire temperature prediction system 1. The tire temperature prediction device 10 predicts the temperature of a tire.

[0025] The measurement device 20 is configured with a computer including one or more sensors. The sensors include, for example, at least a tire pressure monitoring system (TPMS). In addition, the sensors may further include, for example, a digital tachograph, an ECU (Electronic Control Unit), and a car navigation device. The measurement device 20 acquires time-series data regarding tires attached to a vehicle and transmits the data to the tire temperature prediction device 10. For example, the measurement device 20 acquires thermal history data indicating changes in tire temperature over time while the tires attached to the vehicle are in use and transmits the data to the tire temperature prediction device 10. Therefore, the measurement device 20 may be installed on the vehicle or the tires.

[0026] The time-series data related to the tires mounted on the vehicle includes tire measurement values ​​and the dates and times of the measurements. For example, if the measurement device 20 includes a TPMS installed on the tires, the tire measurement values ​​may include tire condition information of the tires, such as tire internal pressure (air pressure), tire cavity temperature, and thermal history. The tire thermal history is the history of heat applied to the tire as the tire is used. The tire thermal history is used to evaluate how much energy has been applied to the tire since it was first used. Generally, the greater the thermal history, the more severe the tire's deterioration. The thermal history may be calculated, for example, by applying the tire cavity temperature to the Arrhenius equation. Furthermore, for example, if the measurement device 20 includes a digital tachograph installed on the vehicle, the tire measurement values ​​may include vehicle driving information, such as the vehicle's driving time, driving distance, speed, acceleration, or tire rotation count.

[0027] The terminal device 30 includes any general-purpose electronic device, including, for example, a PC, a tablet PC, a smartphone, and a wearable device such as a smart watch. Without being limited to these, the terminal device 30 may include other electronic devices dedicated to the tire temperature prediction system 1. As an example, the terminal device 30 functions as a terminal device for the tire temperature prediction device 10 functioning as a server. The terminal device 30 is used by a user who wishes to receive services related to tire temperature prediction from the tire temperature prediction system 1.

[0028] The network 40 is any communication network that allows communication between the tire temperature prediction device 10, the measurement device 20, and the terminal device 30. The network 40 in one embodiment may be, for example, the Internet, a mobile communication network, a local area network (LAN), or a combination thereof.

[0029] The tire temperature prediction system 1 may realize a function of predicting tire temperatures centered on the tire temperature prediction device 10. In this case, the tire temperature prediction system 1 is used to predict the temperatures of one or more tires. In the tire temperature prediction system 1, the tire temperature prediction device 10 acquires time-series data of tire cavity temperatures as thermal history data from, for example, a measurement device 20. The tire temperature prediction device 10 predicts tire temperatures that will change from the present to the future based on the acquired thermal history data.

[0030] In the present disclosure, the term "tire" is not particularly limited, but may include mining tires. For example, the tire may be an OR (Off The Road) tire mounted on mining vehicles such as transport vehicles, construction vehicles, engineering vehicles, and heavy machinery vehicles used at mining sites. However, the tire may be a tire other than an OR tire.

[0031] In the present disclosure, the vehicle may be, for example, a mining vehicle used at a mining site, etc. However, the vehicle is not limited to the above-mentioned mining vehicle, and may be any vehicle capable of being fitted with tires, such as a transport vehicle, a construction vehicle, a work vehicle, a heavy machinery vehicle, a bus, a passenger car, a motorcycle, a bicycle, or an airplane.

[0032] Fig. 2 is a block diagram showing an example of the configuration of the tire temperature prediction device 10 of Fig. 1. The example of the configuration of the tire temperature prediction device 10 will be mainly described with reference to Fig. 2. The tire temperature prediction device 10 has a communication unit 11, a storage unit 12, and a control unit 13. In the tire temperature prediction device 10, the communication unit 11, the storage unit 12, and the control unit 13 are connected to each other so as to be able to communicate with each other via wire or wirelessly.

[0033] The communication unit 11 includes a communication module for connecting to the network 40. The communication module may be a communication module compatible with mobile communication standards such as 4G (4th Generation) and 5G (5th Generation), or may be a communication module compatible with standards such as wired LAN and wireless LAN. The communication module may be a communication module compatible with short-range wireless communication standards such as Wi-Fi (registered trademark), Bluetooth (registered trademark), or infrared communication. In one embodiment, the tire temperature prediction device 10 is communicatively connected to the network 40 via the communication unit 11. This allows the tire temperature prediction device 10 to communicate with the measurement device 20, the terminal device 30, other computers, and the like.

[0034] The storage unit 12 includes, for example, a semiconductor memory, a magnetic memory, an optical memory, etc. The storage unit 12 functions, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 12 stores any information used in the operation of the tire temperature prediction device 10. For example, the storage unit 12 stores a system program, an application program, embedded software, a database, etc. The information stored in the storage unit 12 may be updated with information obtained from the network 40 via the communication unit 11, for example.

[0035] For example, the storage unit 12 may store tire identification information for one or more tires that are the subject of temperature prediction. In the present disclosure, tire identification information is also referred to as a tire ID (Identifier). The tire identification information for a tire is information that can uniquely identify the tire. The tire identification information for a tire is, for example, uniquely issued by the tire temperature prediction device 10, but is not limited to this and may be the tire's serial number or the vehicle number of the vehicle on which the tire is mounted. Furthermore, the storage unit 12 may store information about the tire in association with the tire's tire identification information.

[0036] The tire information includes any information related to the tire. The tire information may include, for example, the above-mentioned tire-related thermal history data, other time-series data, tire damage information, tire configuration information, information on the vehicle on which the tire is mounted, or information on the position of the tire on the vehicle. The tire damage information may be information including, for example, the position, shape, depth, and registration date and time of damage previously sustained to the tire. The tire configuration information includes, for example, the tire type, model number, material properties, tread pattern, belt angle, size, and weight. The information on the vehicle on which the tire is mounted includes, for example, vehicle identification information, type, model number, engine displacement, number of tires mounted, and number of shafts.

[0037] The control unit 13 includes one or more processors. The processor may be, for example, a general-purpose processor such as a CPU (Central Processing Unit) or a dedicated processor specialized for a specific process. The control unit 13 is not limited to a processor and may include one or more dedicated circuits. The dedicated circuits may be, for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The control unit 13 controls each component to realize the functions of the tire temperature prediction device 10, including the functions of the components such as the communication unit 11 and the memory unit 12.

[0038] Fig. 3 is a block diagram showing an example of the configuration of the measurement device 20 in Fig. 1. An example of the configuration of the measurement device 20 will be mainly described with reference to Fig. 3. The measurement device 20 has a communication unit 21, a storage unit 22, an acquisition unit 23, and a control unit 24. In the measurement device 20, the communication unit 21, the storage unit 22, the acquisition unit 23, and the control unit 24 are connected to each other so as to be able to communicate with each other via wire or wirelessly.

[0039] The communication unit 21 includes a communication module for connecting to the network 40. The communication module may be a communication module compatible with mobile communication standards such as 4G and 5G, or may be a communication module compatible with standards such as wired LAN and wireless LAN. The communication module may be a communication module compatible with short-range wireless communication standards such as Wi-Fi (registered trademark), Bluetooth (registered trademark), or infrared communication. In one embodiment, the measurement device 20 is communicatively connected to the network 40 via the communication unit 21. This allows the measurement device 20 to communicate with the tire temperature prediction device 10, the terminal device 30, other computers, and the like.

[0040] The storage unit 22 includes, for example, a semiconductor memory, a magnetic memory, an optical memory, etc. The storage unit 22 functions, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores any information used in the operation of the measuring device 20. For example, the storage unit 22 stores a system program, an application program, embedded software, a database, etc. The information stored in the storage unit 22 may be updatable with information obtained from the network 40 via the communication unit 21, for example.

[0041] The acquisition unit 23 includes one or more sensors. The sensor includes at least a TPMS, for example. In addition, the sensor may further include, for example, a digital tachograph, an ECU, a car navigation device, etc. The acquisition unit 23 acquires time-series data related to tires attached to a vehicle. For example, the acquisition unit 23 acquires thermal history data indicating changes in tire temperature over time while the tires attached to a vehicle are in use. Therefore, the acquisition unit 23 may be installed in the vehicle or the tires.

[0042] The control unit 24 includes one or more processors. The processor may be, for example, a general-purpose processor such as a CPU, or a dedicated processor specialized for a specific process. The control unit 24 is not limited to a processor and may include one or more dedicated circuits. The dedicated circuits may be, for example, an FPGA or an ASIC. The control unit 24 controls each component to realize the functions of the measuring device 20, including the functions of components such as the communication unit 21, the memory unit 22, and the acquisition unit 23.

[0043] Fig. 4 is a block diagram showing an example of the configuration of the terminal device 30 in Fig. 1. An example of the configuration of the terminal device 30 will be mainly described with reference to Fig. 4. The terminal device 30 has a communication unit 31, a storage unit 32, an input unit 33, an output unit 34, and a control unit 35. In the terminal device 30, the communication unit 31, the storage unit 32, the input unit 33, the output unit 34, and the control unit 35 are connected to each other so as to be able to communicate with each other via wire or wirelessly.

[0044] The communication unit 31 includes a communication module for connecting to the network 40. The communication module may be a communication module compatible with mobile communication standards such as 4G and 5G, or a communication module compatible with standards such as wired LAN and wireless LAN. The communication module may be a communication module compatible with short-range wireless communication standards such as Wi-Fi (registered trademark), Bluetooth (registered trademark), or infrared communication. In one embodiment, the terminal device 30 is communicatively connected to the network 40 via the communication unit 31. This allows the terminal device 30 to communicate with the tire temperature prediction device 10, the measurement device 20, other computers, and the like.

[0045] The storage unit 32 includes, for example, a semiconductor memory, a magnetic memory, an optical memory, etc. The storage unit 32 functions, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 32 stores any information used in the operation of the terminal device 30. For example, the storage unit 32 stores system programs, application programs, embedded software, databases, etc. The information stored in the storage unit 32 may be updatable with information obtained from the network 40 via the communication unit 31, for example.

[0046] The input unit 33 includes one or more input interfaces that detect user input and acquire input information based on the user's operation. The input interfaces include physical keys, capacitive keys, a touch screen that is integrated with the display of the output unit 34, an imaging module such as a camera, and a microphone that accepts audio input.

[0047] The output unit 34 includes one or more output interfaces that output information to notify the user, such as a display that visually outputs information as an image, a speaker that audibly outputs information as sound, and a vibrator that tactilely outputs information as vibration.

[0048] The control unit 35 includes one or more processors. The processor may be, for example, a general-purpose processor such as a CPU, or a dedicated processor specialized for a specific process. The control unit 35 is not limited to a processor and may include one or more dedicated circuits. The dedicated circuits may be, for example, an FPGA or an ASIC. The control unit 35 controls each component to realize the functions of the terminal device 30, including the functions of the components such as the communication unit 31, the storage unit 32, the input unit 33, and the output unit 34.

[0049] (Operation of Tire Temperature Prediction System 1) An example of the operation of the tire temperature prediction system 1 will be described with reference to Fig. 5. Fig. 5 is a sequence diagram showing an example of the operation of the tire temperature prediction system 1 of Fig. 1. The sequence diagram shown in Fig. 5 shows the operations of the tire temperature prediction device 10, the measurement device 20, and the terminal device 30 included in the tire temperature prediction system 1. Therefore, the description of this operation corresponds to the tire temperature prediction method executed by the tire temperature prediction system 1, and also corresponds to the tire temperature prediction method executed by the tire temperature prediction device 10, the measurement device 20, or the terminal device 30 included in the tire temperature prediction system 1.

[0050] For the purpose of explaining this operation, it is assumed that the control unit 13 of the tire temperature prediction device 10 stores, in the memory unit 12, tire identification information of the tire and information related to the tire that is associated with the tire identification information of the tire.

[0051] In step S101, the control unit 24 of the measuring device 20 acquires time-series data including at least thermal history data related to tires mounted on a vehicle using the acquisition unit 23. The control unit 24 stores the time-series data acquired using the acquisition unit 23 in the storage unit 22.

[0052] In step S102, the control unit 24 of the measurement device 20 provides time-series data including at least the thermal history data acquired in step S101 to the tire temperature prediction device 10. For example, the control unit 24 transmits the time-series data to the tire temperature prediction device 10 via the communication unit 21 and the network 40. As a result, the control unit 13 of the tire temperature prediction device 10 receives the time-series data from the measurement device 20 via the network 40 and the communication unit 11. The control unit 13 acquires the time-series data acquired by the measurement device 20 in step S101 from the measurement device 20. For example, the control unit 13 acquires thermal history data indicating changes over time in the temperature of tires attached to a vehicle. The control unit 13 stores the acquired time-series data in the memory unit 12.

[0053] For example, the control unit 24 of the measurement device 20 acquires time-series data related to a tire at a predetermined timing using a sensor of the acquisition unit 23. The control unit 24 of the measurement device 20 may associate the acquired time-series data related to the tire with tire identification information and transmit the data to the tire temperature prediction device 10. For example, the control unit 24 of the measurement device 20 may transmit the time-series data related to a tire to the tire temperature prediction device 10 every time the control unit 24 acquires the time-series data. Alternatively, the control unit 24 of the measurement device 20 may collectively transmit time-series data measured over a predetermined period to the tire temperature prediction device 10.

[0054] In this operation example, the acquisition unit 23 of the measurement device 20 includes at least a TPMS. Additionally, the acquisition unit 23 may further include, for example, a digital tachograph. Therefore, the time-series data related to the tire may include tire condition information of the tire, such as tire internal pressure (air pressure), tire cavity temperature, and thermal history, as well as vehicle driving information, such as the vehicle's driving time and mileage. However, the time-series data transmitted from the measurement device 20 to the tire temperature prediction device 10 is not limited to the above information, as long as it includes at least thermal history data.

[0055] In step S103, the control unit 13 of the tire temperature prediction device 10 determines the timing to start the tire temperature prediction process based on the temperature gradient with respect to time in the thermal history data acquired in step S102. In the present disclosure, the "temperature gradient with respect to time" refers to, for example, the gradient of the thermal history, i.e., the rate of change of the tire temperature per unit time. The temperature gradient with respect to time corresponds to, for example, the rate of change of the amount of heat received by the tire per unit time.

[0056] For example, the control unit 13 starts the temperature prediction process when it determines that the temperature gradient with respect to time is equal to or greater than a first threshold value. If the gradient is smaller than the first threshold value, the control unit 13 does not execute the calculation process related to tire temperature prediction, but executes the calculation process when the gradient is equal to or greater than the first threshold value.

[0057] In the present disclosure, the "first threshold" is a temperature gradient that is initially set in advance based on empirical rules, for example, by a tire manufacturer that provides users with services related to tire temperature prediction using the tire temperature prediction device 10. The first threshold is, for example, a positive value. As described above, when predicting tire temperatures using the tire temperature prediction device 10, the control unit 13 extracts situations that significantly affect tire damage from the vehicle's driving conditions and performs prediction processing only for those timings.

[0058] In step S104, the control unit 13 of the tire temperature prediction device 10 extracts a portion of the thermal history data to be used for predicting the temperature from the present based on the temperature gradient over time in the thermal history data acquired in step S102.

[0059] For example, the control unit 13 extracts data when the temperature gradient with respect to time is equal to or greater than a second threshold as part of the thermal history data, and removes data when the gradient is smaller than the second threshold in the calculation process for tire temperature prediction, and uses only data equal to or greater than the second threshold in the calculation process.

[0060] In the present disclosure, the "second threshold" is a temperature gradient that is initially set in advance based on empirical rules, for example, by a tire manufacturer that provides users with services related to tire temperature prediction using the tire temperature prediction device 10. The second threshold is, for example, a positive value. The second threshold may be the same value as the first threshold, or may be a different value. As described above, the control unit 13 extracts, from the overall thermal history data, thermal history data for a period of time under driving conditions that allow the tire temperature prediction device 10 to maintain its tire temperature prediction accuracy.

[0061] In step S105, the control unit 13 of the tire temperature prediction device 10 predicts the tire temperature with a predetermined temperature prediction algorithm using the thermal history data extracted in step S104. The predetermined temperature prediction algorithm used by the control unit 13 of the tire temperature prediction device 10 may be any conventionally known algorithm.

[0062] For example, in step S104, the control unit 13 of the tire temperature prediction device 10 extracts only the thermal history data when the temperature gradient with respect to time is equal to or greater than the second threshold and the temperature is on an increasing trend, and predicts the temperature change from the present based on the extracted thermal history data. For example, the control unit 13 predicts to what extent the tire temperature will increase from the current value. As a result, the control unit 13 predicts to what extent the tire temperature will increase if the vehicle on which the tire is mounted continues to run under the current driving conditions.

[0063] For example, the control unit 13 of the tire temperature prediction device 10 determines whether the tire temperature will rise to a value that poses a high risk of failure, based on the temperature information predicted in step S105. If the control unit 13 determines that the tire temperature will rise to a value that poses a high risk of failure, the control unit 13 may generate notification information that prompts the user to change the tire usage method, such as driving conditions, so that the tire does not reach such a temperature. Alternatively, the control unit 13 may generate instruction information that indicates specific tire usage methods, such as driving conditions, that will prevent the tire from reaching such a temperature. The control unit 13 may provide the generated information, such as the notification information and instruction information, to the terminal device 30 to provide feedback to the user.

[0064] In step S106, the control unit 13 of the tire temperature prediction device 10 optimizes the second threshold value used in the extraction process in step S104 and changes it from the initial setting value. For example, the control unit 13 optimizes the second threshold value so as to reduce the error of the predicted value calculated in step S105 relative to the actual tire temperature value obtained subsequent to step S105. For example, the control unit 13 determines the second threshold value that minimizes the error when data extracted based on each of multiple second threshold values ​​is used. The control unit 13 extracts data based on the determined second threshold value in the same manner as in step S104. The control unit 13 predicts the tire temperature using the data extracted based on the determined second threshold value with a predetermined temperature prediction algorithm in the same manner as in step S105.

[0065] In the present disclosure, the "error" includes, for example, the standard deviation of the predicted value relative to the actual tire temperature value over a predetermined period. For example, the standard deviation is the non-negative square root of the variance obtained by averaging the squares of the differences between the actual tire temperature values ​​and the predicted tire temperature values ​​over a predetermined period used for the prediction range from the time when the temperature is predicted by the control unit 13 of the tire temperature prediction device 10 in step S105. As described above, the control unit 13 of the tire temperature prediction device 10 may monitor the standard deviation σ of the differences between the actual tire temperature values ​​and the predicted tire temperature values.

[0066] For example, consider a situation where the standard deviation σ is relatively large and the predicted value deviates significantly on the positive side from the actual measured tire temperature value. In this case, if the control unit 13 of the tire temperature prediction device 10 feeds back instruction information to the user based on the predicted value to stop the vehicle early and allow the tires to cool down in order to prevent tire failure, downtime will be excessive and production efficiency will be reduced.

[0067] On the other hand, consider a situation where the standard deviation σ is relatively large and the predicted tire temperature value deviates negatively from the actual tire temperature measurement value. In this case, it is difficult for the control unit 13 of the tire temperature prediction device 10 to accurately determine whether the tire temperature will rise to a value that increases the risk of failure. For example, even if the tire temperature prediction margin increases and the control unit 13 determines that the tire temperature will not rise to a value that increases the risk of failure, the actual tire temperature may reach a temperature that should not be reached. This increases the risk of tire failure.

[0068] In view of the above, it is preferable that the standard deviation σ is relatively small, and may be, for example, ±1.5%, preferably ±1.0%, and more preferably ±0.5%. Here, the standard deviation σ is defined as a relative value, for example, as a percentage of the average value of the actual measured or predicted tire temperature values ​​over a predetermined period used in the prediction range, but is not limited to this. The standard deviation σ may be defined as another relative value, such as a simple ratio rather than a percentage, or as an absolute value, for example, "±1.0°C."

[0069] In step S107, when the control unit 13 of the tire temperature prediction device 10 determines that the gradient of temperature with respect to time in the thermal history data acquired in step S102 exceeds the third threshold, the control unit 13 generates alert information.

[0070] In the present disclosure, the "third threshold" is a temperature gradient that is initially set in advance based on empirical rules, for example, by a tire manufacturer or the like that provides users with services related to tire temperature prediction using the tire temperature prediction device 10. The third threshold is, for example, a positive value. The third threshold may be the same value as at least one of the first threshold and the second threshold, or may be a value different from each of the first threshold and the second threshold. The "alert information" includes, for example, visual information, auditory information, tactile information, etc., for alerting the user that the temperature gradient over time has exceeded the third threshold and the tire temperature is on the rise.

[0071] In step S108, the control unit 13 of the tire temperature prediction device 10 provides the alert information generated in step S107 to the user's terminal device 30. For example, the control unit 13 transmits the alert information to the user's terminal device 30 via the communication unit 11 and the network 40. As a result, the control unit 35 of the terminal device 30 receives the alert information from the tire temperature prediction device 10 via the network 40 and the communication unit 31. The control unit 35 acquires the alert information generated by the tire temperature prediction device 10 in step S107 from the tire temperature prediction device 10.

[0072] In step S109, the control unit 35 of the terminal device 30 outputs the alert information acquired from the tire temperature prediction device 10 in step S108 using the output unit 34. For example, the control unit 35 may display the alert information as visual information on a display of the output unit 34. For example, the control unit 35 may play the alert information as auditory information through a speaker of the output unit 34. For example, the control unit 35 may vibrate a vibrator of the output unit 34 in accordance with the alert information as tactile information.

[0073] As a result of the above-described processing of steps S108 and S109 in the tire temperature prediction system 1, the control unit 13 of the tire temperature prediction device 10 notifies the user of the alert information generated in step S107.

[0074] (Operation of tire temperature prediction device 10) An example of the operation of the tire temperature prediction device 10 will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the operation of the tire temperature prediction device 10 of Fig. 1. The flowchart of Fig. 6 shows in more detail the flow of the processing from step S103 to step S105 executed by the tire temperature prediction device 10 in the sequence diagram shown in Fig. 5.

[0075] In step S201, the control unit 13 of the tire temperature prediction device 10 determines whether the gradient of the temperature with respect to time is equal to or greater than a first threshold. If the control unit 13 determines that the gradient of the temperature with respect to time is equal to or greater than the first threshold, it executes the process of step S202. If the control unit 13 determines that the gradient of the temperature with respect to time is not equal to or greater than the first threshold, i.e., is less than the first threshold, it executes the process again from step S201.

[0076] In step S202, if the control unit 13 of the tire temperature prediction device 10 determines in step S201 that the gradient of the temperature with respect to time is equal to or greater than the first threshold value, the control unit 13 starts the tire temperature prediction process.

[0077] In step S203, the control unit 13 of the tire temperature prediction device 10 extracts a portion of the thermal history data to be used for predicting the temperature from the present, based on the gradient of temperature with respect to time in the thermal history data acquired in step S102 of Fig. 5. The explanation for step S203 is the same as the content described above for step S104 of Fig. 5.

[0078] In step S204, the control unit 13 of the tire temperature prediction device 10 predicts the tire temperature using a predetermined temperature prediction algorithm, using the thermal history data extracted in step S203. The explanation for step S204 is the same as that for step S105 in FIG. 5 .

[0079] In step S205, the control unit 13 of the tire temperature prediction device 10 determines whether the gradient of the temperature with respect to time is less than a first threshold. If the control unit 13 determines that the gradient of the temperature with respect to time is less than the first threshold, it executes the process of step S206. If the control unit 13 determines that the gradient of the temperature with respect to time is not less than the first threshold, i.e., is equal to or greater than the first threshold, it executes the process again from step S204.

[0080] In step S206, if the control unit 13 of the tire temperature prediction device 10 determines in step S205 that the gradient of the temperature with respect to time has become less than the first threshold value, the control unit 13 ends the tire temperature prediction process.

[0081] (Effects) The tire temperature prediction device 10 according to the embodiment described above determines the timing to start the temperature prediction process based on the temperature gradient with respect to time in the acquired thermal history data. Such a tire temperature prediction device 10 can reduce the load on the tire temperature prediction process. For example, the tire temperature prediction device 10 can extract situations that have a significant impact on tire damage from the vehicle's driving conditions and perform prediction processing only at those times. The tire temperature prediction device 10 can also perform calculation processing with the minimum necessary, limiting it to important situations where it is desirable to predict tire temperatures.

[0082] The tire temperature prediction device 10 starts the temperature prediction process when it determines that the slope is equal to or greater than the first threshold. This allows the tire temperature prediction device 10 to perform the tire temperature prediction process only in situations where the tire temperature is on an upward trend and it is desirable to check whether the tire temperature will rise to a value that poses a high risk of failure. Unlike conventional technology, the tire temperature prediction device 10 does not perform the prediction process continuously over the entire period of the thermal history data, but instead performs the prediction process only in specific situations, thereby reducing the load on the tire temperature prediction process.

[0083] The tire temperature prediction device 10 terminates the temperature prediction process when it determines that the slope is less than the first threshold. As a result, the tire temperature prediction device 10 can avoid executing the tire temperature prediction process in situations where the tire temperature rising trend has eased and there is no need to check whether the tire temperature will rise to a value that would increase the risk of failure. The tire temperature prediction device 10 can avoid executing the prediction process more than necessary, thereby reducing the load on the tire temperature prediction process.

[0084] The tire temperature prediction device 10 extracts a portion of the thermal history data to be used for predicting the tire temperature from the present based on the gradient of temperature over time in the acquired thermal history data. Such a tire temperature prediction device 10 can improve the accuracy of tire temperature predictions. The inventors have found, through their research, that including thermal history data from periods such as when loading and unloading cargo on a vehicle in the calculation process for tire temperature prediction tends to reduce prediction accuracy. Therefore, the tire temperature prediction device 10 can also improve the accuracy of tire temperature predictions by, for example, excluding data acquired during such periods from the acquired thermal history data. The tire temperature prediction device 10 can exclude data from periods in which data reliability decreases in the thermal history data based on the gradient.

[0085] The tire temperature prediction device 10 extracts data when the temperature gradient with respect to time is equal to or greater than a second threshold as part of the thermal history data. This allows the tire temperature prediction device 10 to exclude data from periods when, for example, the vehicle is stopped to unload cargo and tire temperatures tend to remain constant rather than rising. Therefore, the tire temperature prediction device 10 can accurately predict tire temperatures using only data from periods when tire temperatures tend to rise and the accuracy of the temperature prediction can be maintained. Unlike conventional technology, the tire temperature prediction device 10 does not perform prediction processing based on all past data after acquiring thermal history data over an entire period. Instead, it can predict tire temperatures from the present to the future in real time if, for example, a trend toward an increasing gradient is detected in the thermal history data.

[0086] The tire temperature prediction device 10 optimizes the second threshold value so as to reduce the error of the predicted value relative to the actual measured temperature value. As a result, the tire temperature prediction device 10 can optimize the extraction criteria when extracting a portion of the thermal history data used to predict the tire temperature from the present, thereby enabling the tire temperature prediction device 10 to predict the tire temperature with high accuracy.

[0087] The tire temperature prediction device 10 determines a second threshold that minimizes the error when using data extracted based on each of a plurality of second thresholds. This enables the tire temperature prediction device 10 to accurately perform an optimization process for the second threshold and obtain a more appropriate second threshold. The tire temperature prediction device 10 extracts data based on the determined second threshold. This enables the tire temperature prediction device 10 to optimize the extraction criteria used when extracting a portion of the thermal history data used to predict the temperature from the present, thereby enabling the tire temperature to be predicted with high accuracy.

[0088] The error includes the standard deviation of the predicted value relative to the actual measured value over a predetermined period of time. This allows the tire temperature prediction device 10 to obtain a more appropriate monitoring item, namely the standard deviation, in the process of optimizing the second threshold value.

[0089] When the tire temperature prediction device 10 determines that the slope has exceeded the third threshold, it generates alert information and notifies the user. This allows the tire temperature prediction device 10 to warn the user that the slope of the temperature over time has exceeded the third threshold and that the tire temperature is on the verge of rising rapidly. By checking the alert information from the tire temperature prediction device 10 using the terminal device 30, the user can be motivated to voluntarily change the way the tire is used, such as the vehicle driving conditions, so that the tire temperature does not rise to a value that increases the risk of failure.

[0090] The tires include mine tires. This allows the tire temperature prediction device 10 to reduce the load on the process of predicting the temperatures of mine tires and improve the accuracy of temperature prediction.

[0091] The tire temperature prediction device 10 can reduce unexpected tire failures by providing the generated information, such as the notification information and instruction information, to the terminal device 30 and providing feedback to the user. The tire temperature prediction device 10 can also reduce unexpected tire failures in mining vehicles used at mining sites, for example, and prevent a decrease in productivity due to work such as transporting mining vehicles and changing tires. In addition, the tire temperature prediction device 10 can eliminate the need for workers to periodically inspect tires to prevent unexpected failures, thereby reducing costs.

[0092] (Modifications) While the present disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art could make various modifications and 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 configurations and functions included in each embodiment can be rearranged so as not to cause logical inconsistencies. Furthermore, the configurations and functions included in each embodiment can be used in combination with other embodiments, and multiple configurations and functions can be combined into one, divided, or partially omitted.

[0093] For example, an embodiment is possible in which a general-purpose computer functions as the tire temperature prediction device 10 according to the above-described embodiment. Specifically, a program describing the processing content for realizing each function of the tire temperature prediction device 10 according to the above-described embodiment is stored in the memory of the general-purpose computer, and the program is read and executed by a processor. Therefore, the present disclosure can also be realized as a program executable by a processor or a non-transitory computer-readable medium storing the program. Examples of non-transitory computer-readable media include a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory.

[0094] In the above embodiment, at least a part of the processing operations executed by the tire temperature prediction device 10 may be executed by the measurement device 20 or the terminal device 30. For example, instead of the tire temperature prediction device 10, the terminal device 30 may execute a series of processes related to the tire temperature prediction method. In other words, the terminal device 30 may be the "tire temperature prediction device" described in the claims.

[0095] In the above embodiment, at least a part of the processing operations executed by the measurement device 20 or the terminal device 30 may be executed by the tire temperature prediction device 10 .

[0096] In the above embodiment, the tire temperature prediction system 1 has been described as including the tire temperature prediction device 10, the measurement device 20, and the terminal device 30, but is not limited to this. The tire temperature prediction system 1 does not need to include at least one of the measurement device 20 and the terminal device 30. For example, the tire temperature prediction system 1 may be configured only with the tire temperature prediction device 10, without including both the measurement device 20 and the terminal device 30.

[0097] In the above embodiment, the tire temperature prediction device 10 starts the tire temperature prediction process when it determines that the temperature gradient with respect to time is equal to or greater than the first threshold value. However, this is not limited to this. The tire temperature prediction device 10 may start the tire temperature prediction process when the temperature gradient with respect to time is less than the first threshold value, as long as it is possible to perform the minimum necessary calculation process only in important situations where it is desirable to predict the tire temperature in the thermal history data.

[0098] For example, the first threshold value may be a negative value. For example, the tire temperature prediction device 10 may start the tire temperature prediction process when the temperature gradient with respect to time is negative and the temperature is on a decreasing trend, and predict the temperature change from the present. For example, the tire temperature prediction device 10 may predict to what extent the tire temperature will decrease from the current value. This allows the tire temperature prediction device 10 to predict to what extent the tire temperature will decrease if the vehicle on which the tire is mounted continues to run under the current driving conditions. Such a temperature decrease prediction process is effective, for example, when it is desired to know to what extent the tire will cool while the vehicle is running in the rain.

[0099] In the above embodiment, the tire temperature prediction device 10 has been described as terminating the tire temperature prediction process when it determines that the temperature gradient with respect to time is less than the first threshold value, but this is not limiting. The tire temperature prediction device 10 may not terminate the tire temperature prediction process even when it determines that the temperature gradient with respect to time is less than the first threshold value.

[0100] In the above embodiment, the tire temperature prediction device 10 extracts a portion of the thermal history data to be used for predicting the temperature from the present based on the gradient of the temperature over time. However, this is not limited to this. The tire temperature prediction device 10 does not have to execute the process of extracting a portion of the thermal history data. Instead, the tire temperature prediction device 10 may execute the tire temperature prediction process using all of the acquired thermal history data.

[0101] In the above embodiment, the tire temperature prediction device 10 extracts data when the temperature gradient with respect to time is equal to or greater than the second threshold as part of the tire thermal history data. However, this is not limited to this. The tire temperature prediction device 10 may extract data when the temperature gradient with respect to time is less than the second threshold as part of the tire thermal history data, as long as it is possible to exclude data from a period in which the reliability of the thermal history data decreases based on the gradient. The tire temperature prediction device 10 may predict a temperature change from the present based on the extracted thermal history data.

[0102] For example, the second threshold value may be a negative value. For example, the tire temperature prediction device 10 may extract only thermal history data when the temperature gradient with respect to time is negative and the temperature is on a decreasing trend, and predict a temperature change from the present based on the extracted thermal history data. For example, the tire temperature prediction device 10 may predict to what extent the tire temperature will decrease from the current value. This allows the tire temperature prediction device 10 to predict to what extent the tire temperature will decrease if the vehicle on which the tire is mounted continues to travel under the current driving conditions. Such a temperature decrease prediction process is effective, for example, when it is desired to determine to what extent the tire will cool while the vehicle is traveling in the rain.

[0103] In the above embodiment, the tire temperature prediction device 10 has been described as optimizing the second threshold value so as to reduce the error of the predicted value relative to the actual temperature measurement value. However, this is not limited to this. The tire temperature prediction device 10 is not limited to a configuration in which the tire temperature prediction device 10 itself automatically optimizes the second threshold value, and may notify the user via the terminal device 30 of the second threshold value calculated as the optimal value. In this way, the tire temperature prediction device 10 may prompt the user to change the setting of the second threshold value from the initial setting. The tire temperature prediction device 10 may change the setting of the second threshold value from the initial setting based on an input operation by the user who received the notification using the input unit 33 of the terminal device 30.

[0104] Alternatively, the tire temperature prediction device 10 may not even need to perform the process of optimizing the second threshold value so as to reduce the error of the predicted value relative to the actual measured temperature value. The tire temperature prediction device 10 may maintain the value of the second threshold value as the initial setting without changing it.

[0105] In the above embodiment, the tire temperature prediction device 10 determines the second threshold value that minimizes the error when using data extracted based on each of the plurality of second threshold values. However, the tire temperature prediction device 10 is not limited to this. The tire temperature prediction device 10 may determine the second threshold value other than the minimum value, or may determine the second threshold value using any other different method.

[0106] In the above embodiment, the error has been described as including the standard deviation of the predicted value relative to the actual measured value over a predetermined period, but is not limited to this. The error may include, for example, a simple difference between the actual measured value and the predicted value. The difference may be, for example, the maximum value over a predetermined period or the latest value over a predetermined period. Alternatively, the error may include, for example, the difference between the average value of the actual measured value over a predetermined period and the average value of the predicted value over a predetermined period.

[0107] In the above embodiment, the tire temperature prediction device 10 generates alert information and notifies the user when it determines that the temperature gradient with respect to time exceeds the third threshold value. However, this is not limited to this. The tire temperature prediction device 10 does not have to execute the process of generating and notifying the alert information.

[0108] In the above embodiment, the tires are described as including mining tires, but are not limited to this. The tires may include any other tires instead of or in addition to mining tires.

[0109] According to the present disclosure, it is possible to provide a tire temperature prediction device, a tire temperature prediction method, and a program that can reduce the load on tire temperature prediction processing.

[0110] REFERENCE SIGNS LIST 1 Tire temperature prediction system 10 Tire temperature prediction device 11 Communication unit 12 Memory unit 13 Control unit 20 Measuring device 21 Communication unit 22 Memory unit 23 Acquisition unit 24 Control unit 30 Terminal device 31 Communication unit 32 Memory unit 33 Input unit 34 Output unit 35 Control unit 40 Network

Claims

1. A tire temperature prediction device that predicts the temperature of a tire, comprising a control unit, wherein the control unit acquires thermal history data that indicates changes in the temperature of tires attached to a vehicle over time, and determines the timing to start the temperature prediction process based on the gradient of the temperature over time in the acquired thermal history data.

2. A tire temperature prediction device according to claim 1, wherein the control unit starts the temperature prediction process when it determines that the slope is equal to or greater than a first threshold value.

3. A tire temperature prediction device according to claim 2, wherein the control unit terminates the temperature prediction process when it determines that the slope is less than the first threshold value.

4. A tire temperature prediction device according to any one of claims 1 to 3, wherein the control unit extracts a portion of the thermal history data to be used for predicting the temperature from the present based on the gradient.

5. A tire temperature prediction device according to claim 4, wherein the control unit extracts data when the slope is equal to or greater than a second threshold value as part of the thermal history data.

6. A tire temperature prediction device according to claim 5, wherein the control unit optimizes the second threshold value so as to reduce an error between the predicted value and the actual measured value of the temperature.

7. A tire temperature prediction device according to claim 6, wherein the control unit determines the second threshold value that minimizes the error when using the data extracted based on each of a plurality of second threshold values, and extracts the data based on the determined second threshold value.

8. A tire temperature prediction device according to claim 6, wherein the error includes a standard deviation of the predicted value relative to the actual measured value over a predetermined period of time.

9. A tire temperature prediction device according to any one of claims 1 to 3, wherein, when the control unit determines that the slope exceeds a third threshold, the control unit generates alert information and notifies a user.

10. A tire temperature prediction device according to any one of claims 1 to 3, wherein the tire includes a mine tire.

11. A tire temperature prediction method for predicting the temperature of a tire, comprising: acquiring thermal history data indicating changes in the temperature of a tire mounted on a vehicle over time; and determining the timing to start the temperature prediction process based on the gradient of the temperature over time in the acquired thermal history data.

12. A program that causes a tire temperature prediction device that predicts tire temperature to perform operations including: acquiring thermal history data that indicates changes in the temperature of tires attached to a vehicle over time; and determining the timing to start the temperature prediction process based on the gradient of the temperature over time in the acquired thermal history data.

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