Uneven load determination device, program, and uneven load determination method

Abstract

A load unevenness determination device, program, and method are provided that can determine the uneven load on a vehicle based on the temperature inside the tires. [Solution] The uneven load determination device (10) includes an acquisition unit (131) that acquires input data including time-series data of the internal tire temperature and the vehicle speed detected by a detection device (70) mounted on the vehicle (20); a calculation unit (132) that calculates the heat generation coefficient based on the input data using a model in which the heating component of the rate of increase in internal tire temperature is calculated by multiplying the proportional component of the vehicle speed by the heat generation coefficient; and a determination unit (133) that determines the uneven load of the vehicle based on time-series data of the heat generation coefficients of each of the multiple tires mounted on the vehicle.

Description

Technical Field

[0001] The present disclosure relates to an unbalanced load determination device, a program, and an unbalanced load determination method. Background Art

[0002] Conventionally, methods for managing the condition of tires of a moving vehicle have been proposed. Further, methods for determining the loading condition of a vehicle based on tire condition have been proposed. For example, Patent Document 1 discloses a vehicle load detection device that can accurately detect the load of a vehicle based on the dynamic radius of a tire and the internal air pressure of the tire using a preset load determination map. Prior Art Literature Patent Literature

[0003] Patent Document 1 Japanese Patent Laid-Open No. 2010-167865 Summary of the Invention Problem to be Solved by the Invention

[0004] Here, unbalanced load in vehicles such as trucks (the occurrence of uneven load on the vehicle body and tires due to biased loading of cargo, etc.) leads to distortion (deterioration) of the vehicle body or abnormal wear of tires, and therefore needs to be corrected. However, cargo to be delivered is often loaded onto and unloaded from the vehicle by workers under the direction of the shipper, not by the driver or manager of the vehicle, and unbalanced load is often not taken into consideration during loading and unloading of cargo. Therefore, there is a need for technology that enables vehicle managers to grasp the occurrence of unbalanced load.

[0005] An object of the present disclosure, which has been made in view of such circumstances, is to provide an unbalanced load determination device, a program, and an unbalanced load determination method capable of determining unbalanced load of a vehicle based on the internal temperature of a tire. Means for Solving the Problem

[0006] (1) A load bias determination device according to one embodiment of the present disclosure is An acquisition unit acquires input data including time-series data of the tire temperature and the vehicle speed, respectively, as detected by a detection device mounted on the vehicle. A calculation unit that calculates the heat generation coefficient based on the input data, using a model in which the heating component of the rate of increase in the temperature inside the tire is calculated by multiplying the proportional component of the vehicle's speed by the heat generation coefficient, The system includes a determination unit that determines the uneven load on the vehicle based on time-series data of the heat generation coefficient of each of the multiple tires mounted on the vehicle. This configuration allows for the determination of uneven load distribution on the vehicle based on the internal tire temperature.

[0007] (2) As one embodiment of the present disclosure, in (1), The determination unit generates a histogram for a specific period from the time-series data of the heat generation coefficient of each of the plurality of tires, and determines the uneven load of the vehicle by comparing representative values ​​for unloaded or loaded conditions extracted from the histograms of two of the plurality of tires. This configuration makes it easy to determine the left-right or front-to-back deviation of the vehicle.

[0008] (3) In one embodiment of the present disclosure, in (1) or (2), The determination unit determines the left-right imbalance when the vehicle is loaded, based on the time-series data of the heat generation coefficients of the left and right tires when the vehicle is loaded. This configuration allows for the detection of uneven load distribution on the vehicle's left and right sides, enabling corrective actions such as adjusting the loading method based on the detection results.

[0009] (4) In one embodiment of the present disclosure, in any of (1) to (3), The determination unit determines the front-to-rear bias when the vehicle is loaded, based on the time-series data of the heat generation coefficients of the front and rear tires when the vehicle is loaded. This configuration allows for the determination of uneven load distribution between the front and rear of the vehicle, and enables corrective actions such as adjusting the loading method based on the determination results.

[0010] (5) In one embodiment of the present disclosure, in any of (1) to (4), The determination unit determines the left-right bias of the vehicle when it is unloaded, based on the time-series data of the heat generation coefficients of the left and right tires of the vehicle when it is unloaded. This configuration allows for the determination of any left-right imbalance in the vehicle itself, and enables corrective actions such as suspension inspections based on the determination results.

[0011] (6) A program according to one embodiment of the present disclosure is In the load distribution detection device, The system acquires input data including time-series data of the tire temperature detected by a detection device mounted on the vehicle and the vehicle's speed. Using a model in which the heating component of the rate of increase in the temperature inside the tire is calculated by multiplying the proportional component of the vehicle's speed by the heat generation coefficient, the heat generation coefficient is calculated based on the input data, Based on the time-series data of the heat generation coefficient of each of the multiple tires mounted on the vehicle, the system determines the uneven load on the vehicle. This configuration allows for the determination of uneven load distribution on the vehicle based on the internal tire temperature.

[0012] (7) A method for determining uneven load according to one embodiment of the present disclosure is A method for determining uneven loads performed by an uneven load determination device, The system acquires input data including time-series data of the tire temperature detected by a detection device mounted on the vehicle and the vehicle's speed. Using a model in which the heating component of the rate of increase in the temperature inside the tire is calculated by multiplying the proportional component of the vehicle's speed by the heat generation coefficient, the heat generation coefficient is calculated based on the input data, determining an unbalanced load of the vehicle based on time-series data of the heat generation coefficient of each of the plurality of tires mounted on the vehicle. With this configuration, the unbalanced load of the vehicle can be determined based on the internal tire temperature.

Effects of the Invention

[0013] According to the present disclosure, it is possible to provide an unbalanced load determination device, a program, and an unbalanced load determination method capable of determining an unbalanced load of a vehicle based on an internal tire temperature.

Brief Description of Drawings

[0014] [Figure 1] FIG. 1 is a diagram showing a configuration example of an unbalanced load determination system including an unbalanced load determination device according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is another diagram showing a configuration example of the unbalanced load determination system of FIG. 1. [Figure 3] FIG. 3 is a diagram for explaining calculation of a heat generation coefficient. [Figure 4] FIG. 4 is a diagram for explaining a first component and a second component of time-series data of a heat generation coefficient. [Figure 5] FIG. 5 is a diagram for explaining a representative value of a heat generation coefficient indicating a loading state of a vehicle. [Figure 6A] FIG. 6A is a diagram for explaining determination of left-right bias when the vehicle is loaded. [Figure 6B] FIG. 6B is a diagram for explaining determination of front-rear bias when the vehicle is loaded. [Figure 6C] FIG. 6C is a diagram for explaining determination of front-rear bias when the vehicle is unloaded. [Figure 7] FIG. 7 is an example of a flowchart showing processing of an unbalanced load determination method according to an embodiment of the present disclosure.

Mode for Carrying Out the Invention

[0015] As used herein, “comprise,” “comprising,” “comprises,” “include,” “including,” “includes,” “have,” “has,” “having,” or variations thereof are open-ended and include one or more described features, integers, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integers, elements, steps, components, functions, or groups thereof.

[0016] Well-known functions or configurations may not be described in detail for the sake of brevity and / or clarity. Where the term “or” is used (e.g., A or B), it is intended to mean “A or B, or both.” When “only A or B, but not both” is intended, the term “only A or B but not both” is used. Thus, the use of the term “or” in this specification is inclusive and not exclusive.

[0017] Hereinafter, an eccentric load determination device 10 (see Figure 1), a program, and an eccentric load determination method according to one embodiment of the present disclosure will be described with reference to the drawings. In each figure, 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] Figures 1 and 2 show examples of the configuration of the uneven load determination system. The uneven load determination system includes an uneven load determination device 10. Figure 1 is a block diagram including an example of the internal configuration of the uneven load determination device 10 and an example of the configuration of the device mounted on the vehicle 20. Figure 2 shows the overall configuration of the uneven load determination system.

[0019] The uneven load determination device 10 determines the uneven load of the vehicle 20. An uneven load is when the cargo loaded on the vehicle 20 is unevenly distributed to the left or right, or front or rear of the vehicle 20, or when an uneven load is generated on the vehicle body and tires 30 due to the effect of the uneven distribution of cargo. Uneven loads can lead to distortion (deterioration) of the vehicle body or abnormal wear of the tires 30, and therefore need to be corrected. However, cargo delivered by the vehicle 20 is often loaded and unloaded by workers under the direction of the shipper, rather than by the driver or manager of the vehicle 20, and uneven loads are often not considered during loading and unloading of cargo. The uneven load determination device 10 according to this embodiment can determine the uneven load of the vehicle 20 based on the internal temperature of the tires 30 (tire internal temperature), which is commonly measured, without using any special measuring devices, by the configuration described below. If the uneven load detection device 10 determines that an uneven load has occurred, it may display a message on the display unit (display) prompting the vehicle manager or other person to change the way the cargo is loaded to eliminate any left-right or front-rear imbalance during loading. Furthermore, if the uneven load detection device 10 determines that an uneven load has occurred when the vehicle is not loaded, it may display a message on the display prompting the vehicle manager or other person to inspect the suspension, along with the result of the determination.

[0020] Here, the vehicle 20 may be, for example, a passenger car, a truck, a bus, or a construction vehicle, and is not limited to a specific type of mobile body as long as it is capable of carrying cargo. In this embodiment, the vehicle 20 is described as a truck that transports cargo. Also, in order to avoid duplication of illustrations in Figures 1 and 2, only one vehicle 20 is shown, but the load distribution determination system may be composed of multiple vehicles 20.

[0021] The uneven load determination device 10 comprises a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 comprises an acquisition unit 131, a calculation unit 132, a determination unit 133, and an output unit 134. The uneven load determination device 10 may be a computer as a hardware configuration. The computer may be a server computer or a portable computer such as a laptop or tablet. Details of the components of the uneven load determination device 10 will be described later. In this embodiment, the uneven load determination device 10 is a computer in a management facility that manages the status of the vehicle 20 (a truck that transports cargo) and the tires 30. The management of the vehicle 20 may include operation management in particular.

[0022] Here, the load bias determination device 10 is not a single device, but may consist of multiple devices located in multiple locations that can send and receive data from each other via the network 40. In other words, multiple devices connected by the network 40 may function as the load bias determination device 10 shown in Figure 1 as a whole. Therefore, for example, the load bias determination device 10 may consist of a single computer as its hardware configuration, or it may consist of multiple computers connected by the network 40. When it consists of multiple computers, the storage unit 12 may be a shared memory that can be accessed by each computer.

[0023] The uneven load determination device 10 may constitute an uneven load determination system together with devices mounted on the vehicle 20 connected via a network 40 (detection device 70 and in-vehicle communication device 80). The network 40 is, for example, the Internet. The network 40 may also be configured to include, for example, a LAN (Local Area Network) in part. Here, the uneven load determination system may further include a terminal device 50 used by the administrator of the vehicle 20. The terminal device 50 is, for example, a general-purpose mobile terminal such as a smartphone or tablet terminal, but is not limited to these. The terminal device 50 may function as a display unit that displays the determination results output from the uneven load determination device 10. The uneven load determination system may also include a storage device 90 (a cloud-based storage device 90) located on the network 40 as viewed from the uneven load determination device 10 and the devices mounted on the vehicle 20. In this embodiment, the storage device 90 includes a database that stores data detected by the detection device 70 mounted on the vehicle 20 as time-series data, linked to the vehicle 20 or tire 30 that is the target of detection. Furthermore, in this embodiment, the uneven load determination device 10 obtains necessary information from the database via the network 40. The database may also store data such as the type of tire 30.

[0024] In this embodiment, the vehicle 20 includes a detection device 70 and an in-vehicle communication device 80. The detection device 70 is a device or in-vehicle system equipped with sensors that generates information regarding the state of the tires 30. In this embodiment, the detection device 70 is configured to include a tire pressure monitoring system (TPMS). The detection device 70 may also include a device that detects the ambient temperature in the environment in which the tires 30 are used. The ambient temperature may be detected by the tire pressure monitoring system and another device (e.g., an ambient temperature gauge), or by the tire pressure monitoring system.

[0025] The tire pressure monitoring system monitors the pressure (internal pressure) and temperature (internal tire temperature) of the tire 30 mounted on the vehicle 20. The tire pressure monitoring system may be configured to include, for example, a sensor installed inside the tire 30, a processor that calculates and outputs the pressure of the tire 30 based on the sensor's detected values, and a memory that stores the sensor's detected values. The sensor may include a pressure sensor and a temperature sensor. The temperature sensor may include a sensor installed inside the tire 30 to detect the internal tire temperature, as well as a sensor installed on the outer surface of the tire 30 to detect the ambient temperature.

[0026] Furthermore, the detection device 70 includes a GPS (Global Positioning System) device or a speed sensor. For example, a car navigation system installed in the vehicle 20 may be used as the GPS device or speed sensor.

[0027] The in-vehicle communication device 80 is a device that outputs data detected by the detection device 70. The in-vehicle communication device 80 may be, for example, a dedicated communication device, but it may also be implemented by the communication function of a digital tachograph mounted on the vehicle 20. The information output from the in-vehicle communication device 80 is stored in the database of the storage device 90 on the cloud. In this embodiment, the in-vehicle communication device 80 outputs information on at least the tire temperature, the speed of the vehicle 20, and the ambient temperature, and this information is stored in the database of the storage device 90 on the cloud. Here, the speed of the vehicle 20 may be detected directly by the detection device 70, but it can also be calculated from the location information of the vehicle 20 obtained by the GPS device. In addition, the ambient temperature may be obtained from a weather information service provider via the network 40 instead of being detected by the detection device 70.

[0028] The components of the load bias determination device 10 are described below in detail. The communication unit 11 is composed of one or more communication modules connected to the network 40. The communication unit 11 may include communication modules that support mobile communication standards such as 4G (4th Generation) and 5G (5th Generation). The communication unit 11 may include communication modules that support wired or wireless LAN standards.

[0029] The storage unit 12 is one or more memories. The memories are, for example, semiconductor memories, magnetic memories, or optical memories, but are not limited to these and can be any memory. The storage unit 12 is, for example, built into the load bias determination device 10, but it is also possible to configure it to be accessed externally by the load bias determination device 10 via any interface.

[0030] The storage unit 12 stores various data used in various calculations performed by the control unit 13. The storage unit 12 may also store the results and intermediate data of various calculations performed by the control unit 13.

[0031] In this embodiment, the storage unit 12 may temporarily store various information from the database of the storage device 90 on the cloud, which is acquired via the communication unit 11.

[0032] The control unit 13 is one or more processors. The processors are, for example, general-purpose processors or dedicated processors specialized for specific processing, but are not limited to these and can be any processor. The control unit 13 controls the overall operation of the load bias determination device 10.

[0033] Here, the load bias determination device 10 may have the following software configuration. One or more programs used to control the operation of the load bias determination device 10 are stored in the storage unit 12. When the programs stored in the storage unit 12 are read by the processor of the control unit 13, the control unit 13 is made to function as an acquisition unit 131, a calculation unit 132, a determination unit 133, and an output unit 134.

[0034] The acquisition unit 131 acquires input data including time-series data of the tire temperature and the vehicle speed detected by the detection device 70 mounted on the vehicle 20. In this embodiment, the input data also includes information on the ambient temperature.

[0035] The calculation unit 132 calculates the heat generation coefficient based on the input data, using a model in which the heating component of the rate of increase in tire temperature is calculated by multiplying the proportional component of the vehicle speed 20 by the heat generation coefficient. Here, the model used by the calculation unit 132 calculates the rate of increase in tire temperature (i.e., the change in tire temperature over time) by subtracting the "heat dissipation component" from the "heating component". If the tire temperature is "T" and the time is "t", the rate of increase in tire temperature can be expressed as "dT / dt". That is, a calculation model is used in which (dT / dt) = "heating component" - "heat dissipation component". The heating component is the component that mainly increases the tire temperature. The heat dissipation component is the component that mainly decreases the tire temperature. The units of the rate of increase in tire temperature, the heating component, and the heat dissipation component may be, for example, [℃ / min].

[0036] First, the heat dissipation component is calculated as (tire internal temperature - ambient temperature) × (heat dissipation coefficient at stop + vehicle speed × heat dissipation increment). (Tire internal temperature - ambient temperature) is the difference between the tire internal temperature and the ambient temperature. The heat dissipation coefficient at stop is a coefficient that indicates the amount of heat dissipated from the tire 30 when the vehicle 20 is stopped. (Vehicle speed × heat dissipation increment) is a coefficient that indicates the amount of heat dissipated from the tire 30 when the vehicle 20 is in motion. As the speed of the vehicle 20 increases, more wind hits the tire 30, resulting in greater heat dissipation. Therefore, the coefficient for when the vehicle 20 is in motion is determined by multiplying by the vehicle speed. Here, the heat dissipation coefficient at stop and the heat dissipation increment may be determined based on past experimental data. In this embodiment, the heat dissipation coefficient at stop and the heat dissipation increment obtained from experimental data are stored in a database. The calculation unit 132 can obtain the heat dissipation coefficient at stop and the heat dissipation increment when calculating the rate of increase of the tire internal temperature via the storage unit 12.

[0037] Furthermore, the heating component is generally related to various factors, and conventionally, complex calculations have been required. In the method disclosed herein, a heat generation coefficient is introduced, and the heating component is calculated as (proportional component of vehicle speed) × (heat generation coefficient). The proportional component of vehicle speed is a value obtained by dividing the speed of vehicle 20 by the reference speed (for example, 80 km / h). When the reference speed is 80 km / h, if the speed of vehicle 20 is 80 km / h, the proportional component of vehicle speed is "1", and if the speed of vehicle 20 is 40 km / h, the proportional component of vehicle speed is "0.5". The calculation unit 132 determines the heat generation coefficient so that the tire internal temperature calculated using the simulation (simulated value or calculated value) is as close as possible to the actually measured tire internal temperature (measured value). Figure 3 shows an example of comparing the simulated value and the measured value by changing the heat generation coefficient. For example, when the simulation is performed with a heat generation coefficient of 0, it deviates significantly from the measured value, but when the heat generation coefficient is set to 10, the simulated value approaches the measured value. The calculation unit 132 changes the heat generation coefficient and takes the value that minimizes the difference between the simulated value and the measured value (8.2 in the example of Figure 3) as the calculated heat generation coefficient (determined heat generation coefficient). Here, the calculation unit 132 determines the heat generation coefficient based on the time-series data of the tire temperature and the vehicle 20's speed, but as in this embodiment, the heat generation coefficient may be determined for each run. A run may be determined from the change in speed, or from the change in position if information on the vehicle 20's position can be obtained from the detection device 70. For example, if the vehicle 20 moves from a parking space to point a, loads cargo at point a and delivers it to point b, loads another load at point b and delivers it to point c, and returns from point c to the parking space, the number of runs is 4, and the heat generation coefficient for each run can be determined. The data obtained by arranging the heat generation coefficients determined for each run in time series will be referred to as "time-series data of heat generation coefficients" below.

[0038] Here, the calculation unit 132 can use known fitting methods as a method for determining the heat generation coefficient. For example, the simulation value may be assigned to the x-axis of a two-dimensional coordinate system and the measured value to the y-axis, and the heat generation coefficient such that y=x can be obtained with the minimum information. Alternatively, assuming a linear change, the heat generation coefficient may be determined by identifying a straight line in the distribution corresponding the simulation value and the measured value, for example, using the least squares method.

[0039] The determination unit 133 determines the uneven load on the vehicle 20 based on the time-series data of the heat generation coefficient calculated by the calculation unit 132. Here, the heat generation coefficient is a coefficient that includes both the heat generation due to the load on the vehicle 20 (load-derived) and the heat generation due to the remaining tread of the tire 30 (tread-derived). The heat generation due to the load corresponds to the increase in the internal pressure of the tire 30 as the vehicle 20 loads cargo, and the increase in the internal temperature of the tire according to Boyle's Law and Charles's Law. The heat generation due to the remaining tread corresponds to the phenomenon in which the heat generation of the tread of the tire 30 decreases as the remaining tread of the tire 30 decreases. Here, the change in the heat generation coefficient due to the load occurs in accordance with the loading and unloading of cargo on the vehicle 20, and for the rear wheels, it may show a change of 2 to 3 times in a few hours. On the other hand, the change in the heat generation coefficient due to the remaining tread shows a change of 1.2 to 1.4 times per year from a new state until the tire 30 is worn, and decreases monotonically unless the tire 30 is replaced. The determination unit 133 may use the differences in these changes in the heat generation coefficient to extract only the changes originating from the load from the time-series data of the heat generation coefficient. Here, regarding the loading of cargo on the vehicle 20, the state in which there is almost no cargo to be loaded is referred to as "empty" or "empty," and the state in which cargo to be delivered is loaded is simply referred to as "loaded" or "loaded."

[0040] Figure 4 shows an example of time-series data of the heat generation coefficient, indicated by each dot. The determination unit 133 may extract a first component, shown by a high-frequency curve originating from the load on the vehicle 20, and a second component, shown by an envelope, originating from the wear of the tires 30, from the time-series data of the heat generation coefficient calculated by the calculation unit 132. The determination unit 133 can further improve the accuracy of determining the uneven load on the vehicle 20 by using only the first component and excluding the influence of the second component originating from the wear of the tires 30. However, the change in the heat generation coefficient originating from the wear of the tires 30 is limited to a change of 1.2 to 1.4 times on an annual basis, as described above. Therefore, when targeting a period of several days to several months (a period sufficiently shorter than one year), the determination unit 133 may determine the uneven load without extracting the first and second components. In this embodiment, when the target period of the histogram described later is one month or several months, the uneven load is determined without extracting the first and second components in particular.

[0041] Here, when tire 30 is mounted as a front wheel, the load on tire 30 is greater than when it is mounted as a rear wheel. This is because the front tires are subjected to a consistently high load regardless of whether the truck vehicle 20 is empty or loaded. For the rear tires, the load differs greatly depending on whether the vehicle 20 is empty or loaded, resulting in a large change in the heat generation coefficient. In particular, when the vehicle 20 is empty, the load on the rear tires is small, so the heat generation coefficient of the rear tires is smaller than that of the front tires. In other words, when the vehicle 20 is empty, the heat generation coefficient of the front tires is larger than that of the rear tires.

[0042] The determination unit 133 extracts a representative value of a heat generation coefficient corresponding to an unloaded state and a representative value of a heat generation coefficient corresponding to a loaded state as described below, for use in determining an unbalanced load of the vehicle 20. FIG. 5 is a diagram for explaining the representative values of the heat generation coefficient indicating the loading state of the vehicle 20, in which data of a specific period (one month as an example) extracted from time-series data of the heat generation coefficient is histogrammed with the number of travels as a frequency. In the present embodiment, the determination unit 133 generates a respective histogram for each of the plurality of tires 30 mounted on the vehicle 20. Here, the specific period may be determined based on, for example, the frequency of determination required for an unbalanced load. For example, when determining the presence or absence of an unbalanced load on a daily basis, the specific period may be set to one day, and when determining a rough tendency of an unbalanced load on a weekly or monthly basis, the specific period may be set to one week or one month.

[0043] In the example of FIG. 5, the heat generation coefficient is divided into a plurality of data sections from V0 to V1 (V0 < V1). The determination unit 133 extracts the lower 20% of values from each histogram and uses the extracted values as the representative value for the unloaded state. The determination unit 133 also extracts the upper 20% of values from each histogram and uses the extracted values as the representative value for the loaded state. Here, the reason for extracting the upper 20% and lower 20% of values is to eliminate the influence of values (erroneous data) belonging to the upper or lower range due to the influence of noise. This is also to enable selection of a correct representative value even when the peak position of the heat generation coefficient varies based on differences (for example, differences in type or density) of loaded cargo. Therefore, the extracted values are not limited to the upper 20% and lower 20% as long as the influence of noise or the like can be eliminated.

[0044] The determination unit 133 determines the uneven load on the vehicle 20 based on time-series data of the heat generation coefficients of each of the multiple tires 30 mounted on the vehicle 20. Here, the multiple tires 30 may be all the tires 30 mounted on the vehicle 20, but are not limited to this; any two or more tires 30 are acceptable. For example, the multiple tires 30 may be two tires 30 that will become the left and right tires 30 as described later, or two tires 30 that will become the front and rear tires 30 as described later, or three or four tires 30 that will become the left and right and front and rear tires 30. More specifically regarding the determination of uneven load, the determination unit 133 generates a histogram for a specific period from the time-series data of the heat generation coefficients of each of the multiple tires 30. Then, the determination unit 133 determines the uneven load on the vehicle 20 by comparing representative values ​​for unloaded or loaded conditions extracted from the histograms of two of the multiple tires 30. By comparing representative values ​​for two of the multiple tires 30, the left-right or front-rear bias of the vehicle 20 can be easily determined, as will be explained below.

[0045] Figures 6A to 6C are diagrams illustrating the determination of bias in vehicle 20. In Figures 6A to 6C, the frequency distribution of the histogram is shown schematically. In Figures 6A to 6C, "front" refers to the front wheels, and "rear" refers to the rear wheels. In Figures 6A to 6C, "left" refers to the left side of the tire position, and "right" refers to the left side of the tire position. For example, if the left front wheel is selected as "left," then "right" refers to the right front wheel. For example, if the left rear wheel is selected as "left," then "right" refers to the right rear wheel. Here, the rear wheels may be double wheels (double tires) or mounted on multiple axles. In this case, for example, if the left outer rear wheel is selected as "left," then "right" refers to the right outer rear wheel mounted on the same axle. Also, for example, if the left inner rear wheel is selected as "left," then "right" refers to the right inner rear wheel mounted on the same axle. Furthermore, in this embodiment, the representative value for the unloaded state is the lower 20% of the heat generation coefficient values ​​extracted from the histogram. Also, in this embodiment, the representative value for the loaded state is the upper 20% of the heat generation coefficient values ​​extracted from the histogram.

[0046] Figure 6A is a diagram illustrating the determination of left-right imbalance when loading a vehicle 20. The determination unit 133 extracts representative values ​​for the unloaded and loaded tires 30 of each side. The determination unit 133 determines that there is a left-right imbalance when loading a vehicle 20 if the representative values ​​for the unloaded tires 30 are the same and the difference between the representative values ​​for the loaded tires 30 is greater than the first threshold. Here, the vehicle 20 is generally designed so that the load is applied evenly to both sides. In the example in Figure 6A, the load is biased to the right side, resulting in a greater load on the right tire 30 compared to the left tire 30, a larger rate of increase in the internal temperature of the right tire 30, and a larger heat generation coefficient. Here, the first threshold can be set based on the range of change obtained from prior experiments. For example, in an experiment, an even load (an even way of loading cargo) may be performed, and the difference between the representative values ​​of the load on the left and right tires 30 may be measured to determine the normal range of change, and a first threshold may be set based on the determined normal range of change. In this way, the determination unit 133 can determine the left-right imbalance when loading the vehicle 20 based on the time-series data of the heat generation coefficients of the left and right tires 30 when the vehicle 20 is loaded. By determining the left-right load imbalance of the vehicle 20, it becomes possible to take measures such as correcting the way the cargo is loaded based on the determination result.

[0047] Figure 6B is a diagram illustrating the determination of front-to-rear weight imbalance when loading a vehicle 20. The determination unit 133 extracts representative values ​​for the load of each of the front and rear tires 30. The determination unit 133 determines that there is a front-to-rear weight imbalance in the vehicle 20 when loading if the magnitude of the difference between the representative values ​​of the loads of the front and rear tires 30 is greater than the second threshold. Here, the vehicle 20 is generally designed so that the front wheels are heavier when unloaded, and the load is evenly distributed between the front and rear wheels when loaded. In the example in Figure 6B, the load is biased towards the front, placing a greater load on the front tires compared to the rear tires, resulting in a larger rate of increase in the internal temperature of the front tires and a larger heat generation coefficient. Here, the second threshold can be set based on the range of change obtained from prior experiments. For example, in an experiment, a load without bias may be performed, the difference between the representative values ​​of the loads of the front and rear tires 30 may be measured to determine a normal range of change, and the second threshold may be determined based on the obtained normal range of change. In this way, the determination unit 133 can determine the front-to-rear load imbalance of the vehicle 20 when loaded, based on time-series data of the heat generation coefficients of the front and rear tires 30 of the vehicle 20 when loaded. Once the front-to-rear load imbalance of the vehicle 20 is determined, it becomes possible to take corrective actions such as adjusting the way the cargo is loaded based on the determination result.

[0048] Figure 6C is a diagram illustrating the determination of left-right imbalance in the unloaded vehicle 20. The determination unit 133 extracts representative values ​​for the unloaded left and right tires 30. The determination unit 133 determines that there is a left-right imbalance in the unloaded vehicle 20 if the magnitude of the difference between the representative values ​​of the unloaded left and right tires 30 is greater than the third threshold. Here, the vehicle 20 is generally designed so that the load is applied equally to both sides. Therefore, if there is no problem with the vehicle 20, the representative values ​​of the unloaded left and right tires 30 will be the same, as shown in Figure 6A. In the example in Figure 6C, even though it is unloaded, the right tire 30 is subjected to a larger load compared to the left tire 30, resulting in a larger rate of increase in the internal temperature of the right tire 30 and a larger heat generation coefficient. Here, the third threshold can be set based on the range of change obtained from prior experiments. For example, in an experiment, the difference between representative values ​​of the left and right tires 30 when unloaded is measured in multiple normal vehicles 20 without any loads to determine the normal range of change, and a third threshold can be set based on the determined normal range of change. In this way, the determination unit 133 can determine the left-right bias of the vehicle 20 when unloaded based on time-series data of the heat generation coefficients of the left and right tires 30 of the vehicle 20 when unloaded. By determining the left-right bias of the vehicle 20 itself, it becomes possible to take action such as inspecting the suspension based on the determination result.

[0049] The determination unit 133 may perform all three of the bias determinations described above with reference to Figures 6A to 6C, or it may perform only some of them. Furthermore, for example, the determination of left-right and front-rear biases when the vehicle 20 is loaded may be performed more frequently than the determination of left-right bias when the vehicle 20 is unloaded.

[0050] The output unit 134 may output the judgment results from the judgment unit 133 to a terminal device 50 used by the vehicle manager 20. For example, if the output unit 134 determines that there is a left-right or front-rear imbalance when loading the vehicle 20, it may output a message prompting the vehicle to change how it loads its cargo to eliminate the left-right or front-rear imbalance, along with the judgment result, to the terminal device 50. The manager who sees the outputted information may request the shipper's workers to load and unload cargo in a way that does not cause uneven loading. In addition, if the output unit 134 determines that there is a left-right imbalance when the vehicle 20 is empty, it may output a message prompting the vehicle to inspect its suspension, along with the judgment result, to the terminal device 50. The manager who sees the outputted information may have the vehicle inspect its suspension or plan to do so.

[0051] Figure 7 is an example flowchart showing the processing of the uneven load determination method executed by the uneven load determination device 10 according to this embodiment.

[0052] The acquisition unit 131 acquires input data including time-series data of the tire temperature and the vehicle speed detected by the detection device 70 mounted on the vehicle 20 (step S1). The input data may further include information on the ambient temperature.

[0053] The calculation unit 132 calculates the heat generation coefficient based on the input data, using a model in which the heating component of the rate of increase in tire temperature is calculated by multiplying the proportional component of the vehicle speed 20 by the heat generation coefficient (step S2).

[0054] The determination unit 133 determines the uneven load on the vehicle 20 based on the time-series data of the heat generation coefficient of each of the multiple tires 30 mounted on the vehicle 20 (step S3).

[0055] The output unit 134 outputs the determination result from the determination unit 133 to a terminal device 50 used by the vehicle manager 20 (step S4).

[0056] As described above, the uneven load determination device 10, program, and uneven load determination method according to this embodiment can determine the uneven load of a vehicle 20 based on the tire temperature measured in a typical vehicle 20, with the above configuration.

[0057] In the above descriptions of various embodiments of the concepts of this disclosure, it should be understood that the technical terms used herein are for the sole purpose of describing specific embodiments and are not intended to limit the concepts of this disclosure. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as those generally understood by those skilled in the art to which the concepts of this disclosure belong. Terms as defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of this specification and related art, and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0058] Many variations and modifications can be made to the embodiments without substantially departing from the principles of the concepts of this disclosure. All such variations and modifications are intended to be included herein within the scope of the concepts of this disclosure. Accordingly, the subject matter disclosed above is to be considered illustrative and not restrictive, and the examples of embodiments are intended to encompass all such modifications, extensions, and other embodiments that fall within the spirit and scope of the concepts of this disclosure. Accordingly, to the maximum extent permitted by law, the scope of the concepts of this disclosure should be determined by the broadest acceptable interpretation of this disclosure, including the following claims and their equivalents, and not limited or restricted by the detailed description above.

[0059] Exemplary embodiments are described herein with reference to block diagrams and / or flowchart illustrations of computer implementation methods, apparatus (systems and / or devices), and / or computer program products. It is understood that the blocks in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by computer program instructions executed by one or more computer circuits. These computer program instructions are provided to processor circuits of general-purpose computer circuits, dedicated computer circuits, and / or other programmable data processing circuits, so that instructions executed via the processor of a computer and / or other programmable data processing device can generate a machine that translates and controls transistors, values ​​stored in memory locations, and other hardware components in such circuits to implement the functions / operations specified in the block diagrams and / or flowchart blocks, thereby creating means (functionality) and / or structures for implementing the functions / operations specified in the block diagrams and / or flowchart blocks. The computer program product may be provided as one or more modules of a software product encoded in electrical signals, optical signals, or electromagnetic signals for transmission to a receiver device suitable for execution by a data processing device, for example, downloadable via the Internet.

[0060] It should also be noted that in some alternative implementations, the functions / operations described in a block may be performed in a different order than that shown in the flowchart. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or blocks may sometimes be executed in reverse order depending on the functionality / operations they contain. Furthermore, the functionality of a given block in a flowchart and / or block diagram may be separated into multiple blocks, and / or the functionality of two or more blocks in a flowchart and / or block diagram may be integrated, at least partially. Finally, without departing from the scope of the concepts of this disclosure, other blocks may be added / inserted between illustrated blocks, and / or blocks / operations may be omitted. Furthermore, while some of the diagrams include arrows on the communication path to indicate the main direction of communication, it should be understood that communication may occur in the opposite direction to the depicted arrows.

[0061] Computer program instructions may also be stored in non-temporary, tangible, computer-readable media that can instruct a computer or other programmable data processing device to function in a particular way, causing the instructions stored in the computer-readable media to generate a product containing instructions that implement functions / operations specified in block diagrams and / or flowchart blocks. The term “non-temporary, tangible, computer-readable media” includes semiconductor memory devices, e.g., ROM (read-only memory), EPROM (electrically erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks, and CD-ROM and DVD-ROM disks. The term "non-temporary tangible computer-readable medium" is also intended to encompass types of storage devices that do not necessarily store information permanently, including, for example, RAM (random-access memory), SRAM (static random-access memory), and DRAM (dynamic random-access memory).

[0062] Accordingly, embodiments of the concepts of this disclosure may be embodied in hardware and / or software (including firmware, resident software, microcode, etc.) operating on a processor (also referred to as a controller), such as a digital signal processor, which may collectively be called “circuits,” “modules,” or variations thereof. These terms, as well as terms such as “components,” “engines,” “systems,” “devices,” and “interfaces,” are generally intended to refer to computer-related entities that are hardware (computing device elements), a combination of hardware and software, or software alone. For example, a component may be, but is not limited to, a process, processor, object, executable file, execution thread, program, and / or computer running on a processor. For illustrative purposes, the term “component” may refer to an application running on a controller, or a combination of an application and the controller itself. One or more components may reside within a process and / or execution thread, components may be localized on one computer, and / or distributed across two or more computers. Software may be any programming language.

[0063] Embodiments of the subject matter described herein can be implemented in a computing system having one or more device components in one or more corresponding locations. The device components may include data servers (or other backend components) and may include one or more user devices (e.g., client computers) having a user interface (e.g., a graphical user interface, GUI) that allows a user to interact with the computer system. If the device components of the system are in different locations, they may be interconnected by any form of digital data communication network, and unless otherwise indicated, their collective functionality may be achieved by partitioning the computing process among the device components of the computer system in any way. Examples of wired communication networks include wired local area networks ("LANs") and wired wide area networks ("WANs"), such as the Internet, which do not need to be jointly owned with the rest of the computer system. Examples of wireless communication networks include, for instance, wireless LANs (including Wi-Fi (Wireless Fidelity)), and their communication standards include LTE (Long Term Evolution), 4G (4th generation), or 5G (5th generation). In addition, there are communication standards that enable low-power, wide-area communication, such as LPWA (Low Power Wide Area) and BLE (Bluetooth Low Energy), which includes Bluetooth, a low-power technology suitable for short distances. Client computers may include mobile devices (laptops, tablets, personal digital assistants (PDAs), and mobile phones, etc.).

[0064] In some cases, a computing system can include clients (e.g., client computers) and servers. Clients and servers may be remote from each other and typically interact via a communication network. Clients and servers may run software that defines the client-server relationship.

[0065] A computer system “configured” to perform a specific operation or action means that the system has installed software, firmware, hardware, or a combination thereof that causes the system to perform the operation or action while it is running. One or more computer programs “configured” to perform a specific operation or action means that one or more programs contain instructions that cause the data processing device to perform the operation or action when executed by the device.

[0066] The term "data processing device" refers to data processing hardware and encompasses all types of devices, machines, and equipment for processing data, including, for example, programmable processors, computers, or multiple processors or computers. A device may also be, or further include, a dedicated logic circuit configuration, such as an FPGA (field programmable gate array) or ASIC (application-specific integrated circuit). Optionally, in addition to hardware, a device may include code that constitutes an execution environment for computer programs, such as processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these.

[0067] The processes and logic flows described herein can be implemented by one or more programmable processors that execute one or more computer programs to perform their functions by acting on input data and producing outputs. The processes and logic flows can also be implemented as dedicated logic circuit configurations, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits).

[0068] Computers suitable for running computer programs include, for example, a general-purpose or dedicated microprocessor, or both, or any other type of central processing unit. Generally, the central processing unit receives instructions and data from read-only memory or random-access memory, or both. The elements of a computer are a central processing unit for executing or running instructions, and one or more memory devices for storing instructions and data. Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks, or is operablely coupled to them to receive data from them, transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be incorporated into other devices, for example, a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, such as a Universal Serial Bus (USB) flash drive.

[0069] To provide user interaction, embodiments of the subject matter described herein can be implemented on a computer having a display device for displaying information to the user, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, and a keyboard and pointing device, such as a mouse or trackball, on which the user can provide input to the computer. Other types of devices can also be used to provide user interaction; for example, the feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, voice, or tactile input. Furthermore, the computer can interact with the user by sending documents to and receiving documents from devices used by the user, for example, by sending a web page to a web browser on the user's client device in response to a request received from a web browser. Input may also be provided by a connection from an external input device instead of an input device such as a keyboard. Any connection method can be used, such as USB (Universal Serial Bus), HDMI (High-Definition Multimedia Interface), or Bluetooth. The embodiments may also include input / output or I / O devices or user interfaces not included herein, which may be connected directly or indirectly to the system.

[0070] The term "machine learning" refers to a process (an automated process, meaning a process with virtually no human intervention except for their arbitrary initiation) in which an algorithm, defined by multiple variable numerical parameters (e.g., thousands, millions, or billions of numerical parameters), is trained to perform a desired function by iteratively modifying the numerical parameters to reduce the discrepancy (e.g., as measured by the loss function) between multiple training input datasets to the algorithm and the corresponding desired output of the algorithm, for example, by a process such as backpropagation.

[0071] In one example, the algorithm is a "neural network" typically defined by a sequence of one or more processing layers, where the output of each processing layer (except the last processing layer) is the input to the next processing layer in the sequence. This is called a "feedforward" network. Each processing layer consists of one or more "neurons" (or "neural units") that receive all or a subset of the input to the corresponding processing layer. Each neuron performs a function of the received data, specifically a function defined by a corresponding subset of numerical parameters (e.g., a nonlinear function) (e.g., a nonlinear function of a weighted sum of the input values ​​of a subset of the input, where the weights are defined by a corresponding subset of numerical parameters). The outputs of the neurons in a given layer collectively constitute the output of the layer. Many types of neural network layers are known, such as transformer layers, recursive layers, and convolutional layers.

[0072] Several algorithms trained by machine learning process data collected from the real world by sensors (e.g., cameras (still or video cameras), microphones, temperature sensors, pressure sensors, strain sensors, accelerometers, etc.) and / or generate data to encode images, audio signals, or control data for electromechanical devices such as robots, vehicles, or vehicle components. For example, a trained algorithm performs a classification task, in which received data (e.g., images or audio signals) is assigned to one of a predetermined set of categories; for example, in the case of images, each category could be an image depicting (a part of) a tire. Contribution to the United Nations-led Sustainable Development Goals (SDGs)

[0073] The SDGs have been proposed to realize a sustainable society. One embodiment of this disclosure is considered to be a technology that can contribute to "No. 9 Industry, Innovation and Infrastructure" and other goals. [Explanation of Symbols]

[0074] 10 Unbalanced load determination device 11 Communications Department 12 Storage section 13 Control Unit 20 vehicles 30 tires 40 Networks 50 Terminal devices 70 Detection device 80 In-vehicle communication device 90 Storage device 131 Acquisition Department 132 Arithmetic section 133 Judgment section 134 Output section

Claims

1. An acquisition unit acquires input data including time-series data of the tire temperature and the vehicle speed, respectively, as detected by a detection device mounted on the vehicle. A calculation unit that calculates the heat generation coefficient based on the input data, using a model in which the heating component of the rate of increase in the temperature inside the tire is calculated by multiplying the proportional component of the vehicle's speed by the heat generation coefficient, A load bias determination device comprising: a determination unit that determines the load bias of a vehicle based on time-series data of the heat generation coefficient of each of the multiple tires mounted on the vehicle.

2. The uneven load determination device according to claim 1, wherein the determination unit generates a histogram for a specific period from time-series data of the heat generation coefficient of each of the plurality of tires, and determines the uneven load of the vehicle by comparing representative values ​​for unloaded or loaded conditions extracted from the histograms of two of the plurality of tires.

3. The load bias determination device according to claim 1 or 2, wherein the determination unit determines the left-right bias when the vehicle is loaded based on time-series data of the heat generation coefficients of the left and right tires when the vehicle is loaded.

4. The load bias determination device according to claim 1 or 2, wherein the determination unit determines the front-to-rear bias when the vehicle is loaded, based on time-series data of the heat generation coefficients of the front and rear tires when the vehicle is loaded.

5. The load bias determination device according to claim 1 or 2, wherein the determination unit determines the left-right bias of the vehicle when it is unloaded based on time-series data of the heat generation coefficients of the left and right tires of the vehicle when it is unloaded.

6. In the load distribution detection device, The system acquires input data including time-series data of the tire temperature detected by a detection device mounted on the vehicle and the vehicle's speed, Using a model in which the heating component of the rate of increase in the temperature inside the tire is calculated by multiplying the proportional component of the vehicle's speed by the heat generation coefficient, the heat generation coefficient is calculated based on the input data, A program that determines the uneven load on a vehicle based on time-series data of the heat generation coefficient of each of the multiple tires mounted on the vehicle.

7. A method for determining uneven loads performed by an uneven load determination device, The system acquires input data including time-series data of the tire temperature detected by a detection device mounted on the vehicle and the vehicle's speed, Using a model in which the heating component of the rate of increase in the temperature inside the tire is calculated by multiplying the proportional component of the vehicle's speed by the heat generation coefficient, the heat generation coefficient is calculated based on the input data, A method for determining uneven load on a vehicle, comprising determining the uneven load on a vehicle based on time-series data of the heat generation coefficient of each of the multiple tires mounted on the vehicle.

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