Processing device

Abstract

This processing device comprises: at least one processor or a plurality of processors; and at least one memory or a plurality of memories connected to the processor. The processors execute processing including: calculating, on the basis of a use history of a vehicle by a user, a moving average, which is an average of actual values of a cruising distance of the user per unit time over the most recent of a fixed period, and a total average, which is an average of the total of the actual values; and predicting a predicted value of the cruising distance of the user per unit time on the basis of the moving average and the total average.

Description

Processing device

[0001] The present invention relates to a processing device.

[0002] As a vehicle usage form, a car-sharing system in which a vehicle is shared by a plurality of users has become widespread. For example, as disclosed in Patent Document 1, according to the car-sharing system, a user can borrow and use a vehicle as needed without owning the vehicle himself / herself.

[0003] Japanese Patent Application Laid-Open No. 2016-143245

[0004] Here, in the car-sharing system, it is conceivable that a vehicle such as an electric vehicle or a hybrid vehicle is used as the vehicle lent to the user. In that case, it is necessary to lend the user a vehicle with a sufficiently charged battery so as to satisfy the cruising range desired by the user. For example, when the cruising range desired by the user is short, a vehicle with a small remaining battery capacity may be lent to the user, but when the cruising range desired by the user is long, it is necessary to lend the user a vehicle with a large remaining battery capacity. Therefore, in a car-sharing system or the like, it is desirable to appropriately predict the cruising range per unit time of the user for each user in order to estimate the cruising range desired by the user.

[0005] Therefore, an object of the present invention is to provide a processing device capable of appropriately predicting the cruising range per unit time of a user for each user.

[0006] In order to solve the above problems, a processing device according to an embodiment of the present invention includes: one or more processors; and one or more memories connected to the processors, and the processor calculates a moving average that is an average of recent fixed periods of actual values of the cruising range per unit time of the user based on the usage history of the vehicle by the user, and a total average that is an average of the total of the actual values, and executes processing including predicting a predicted value of the cruising range per unit time of the user based on the moving average and the total average.

[0007] According to the present invention, it becomes possible to appropriately predict the cruising range per unit time for each user.

[0008] Figure 1 is a schematic diagram showing the general configuration of a car-sharing system according to an embodiment of the present invention. Figure 2 is a block diagram showing an example of the functional configuration of a server according to an embodiment of the present invention. Figure 3 is a flowchart showing an example of the overall processing flow performed by a server according to an embodiment of the present invention. Figure 4 is a flowchart showing an example of the vehicle candidate determination process performed by a server according to an embodiment of the present invention. Figure 5 is a diagram showing an example of a dataset stored in a server according to an embodiment of the present invention. Figure 6 is a graph showing an example of the trends of the moving average and total average according to an embodiment of the present invention.

[0009] Embodiments of the present invention will be described in detail below with reference to the attached drawings. The specific dimensions, materials, numerical values, etc., shown in these embodiments are merely examples to facilitate understanding of the invention and do not limit the present invention unless otherwise specified. In this specification and drawings, elements having substantially the same function and configuration are denoted by the same reference numerals to avoid redundant explanations, and elements not directly related to the present invention are omitted from the illustrations.

[0010] <Configuration of the Car Sharing System> The configuration of the car sharing system 1 according to an embodiment of the present invention will be described with reference to Figures 1 and 2.

[0011] Figure 1 is a schematic diagram showing the general configuration of a car-sharing system 1. As shown in Figure 1, the car-sharing system 1 includes a vehicle 2, a user terminal 3 used by users, and a server 4. The car-sharing system 1 is a system for sharing and using a vehicle 2 among multiple users. The server 4 corresponds to an example of a processing device according to the present invention.

[0012] Note that while Figure 1 shows one vehicle 2 for ease of understanding, in reality, the car-sharing system 1 includes multiple vehicles 2. Also, while Figure 1 shows one user terminal 3 for ease of understanding, in reality, the car-sharing system 1 includes multiple user terminals 3.

[0013] In the car-sharing system 1, the user sends a reservation request for vehicle 2 to the server 4 using the user terminal 3. The reservation request is a request to reserve vehicle 2 and is sent from the user terminal 3 to the server 4 in response to the user's operation using the user terminal 3. Next, the server 4 processes the reservation of vehicle 2. Details of the processing by the server 4 will be described later. Once the reservation of vehicle 2 is complete, the user can use vehicle 2 for the reserved time. After that, before the reserved time expires, vehicle 2 is returned by the user.

[0014] Vehicle 2, as described above, is the vehicle to be rented to users. Vehicle 2 is capable of wireless communication with Server 4. In particular, Vehicle 2 is an electric or hybrid vehicle that runs using battery power. The car-sharing system 1 includes a charger (not shown), and the battery of Vehicle 2 is charged using the charger.

[0015] User terminal 3 is, for example, a smartphone or personal computer owned by the user. User terminal 3 is capable of wireless communication with server 4.

[0016] The server 4 includes one or more processors 4a and one or more memories 4b connected to the processors 4a. The processors 4a include, for example, a CPU (Central Processing Unit). The memories 4b include, for example, ROM (Read Only Memory) and RAM (Random Access Memory). ROM is a memory element that stores programs and arithmetic parameters used by the CPU. RAM is a memory element that temporarily stores data such as variables and parameters used in processing executed by the CPU.

[0017] Figure 2 is a block diagram showing an example of the functional configuration of server 4. For example, as shown in Figure 2, server 4 has a communication unit 41, a processing unit 42, and a storage unit 43.

[0018] Furthermore, various processes, including those described below, performed by the communication unit 41 or the processing unit 42, can be executed by the processor 4a. In detail, various processes are executed by the processor 4a executing a program stored in the memory 4b. The functions of the storage unit 43 are realized by the memory 4b. However, the functions of the server 4 may be divided among multiple devices, or multiple functions may be realized by a single device.

[0019] The communication unit 41 communicates with the vehicle 2 and the user terminal 3.

[0020] The processing unit 42 performs processing related to the reservation of vehicle 2. Details of this processing will be described later.

[0021] The memory unit 43 stores various types of information. The information stored in the memory unit 43 is used in processing performed by the communication unit 41 or the processing unit 42.

[0022] <Operation of the Car Sharing System> Next, the operation of the car sharing system 1 according to an embodiment of the present invention will be described with reference to Figures 3 to 6.

[0023] As described above, in car-sharing system 1, vehicle 2 is shared by multiple users. In particular, as mentioned above, in car-sharing system 1, electric vehicles or hybrid vehicles are used as vehicles 2 that are rented out to users. However, there are cases where vehicle 2 is returned after use without its battery being charged. Therefore, while vehicle 2 may be returned with a fully charged battery, it may also be returned with a low remaining battery capacity. Consequently, there may be cases where the remaining battery capacity of available vehicles 2 is low. However, since charging a battery takes time, it will not be fully charged immediately if the remaining capacity is low.

[0024] Considering the above circumstances, the car-sharing system 1 needs to lend users vehicles 2 with batteries that are sufficiently charged to satisfy the user's desired driving range. For example, if the user's desired driving range is short, a vehicle 2 with low remaining battery capacity may be lent to the user, but if the user's desired driving range is long, a vehicle 2 with high remaining battery capacity must be lent to the user. Therefore, in the car-sharing system 1, it is desirable to appropriately predict the user's driving range per unit time in order to estimate the user's desired driving range. In this embodiment, by making improvements to the processing related to vehicle 2 reservations performed by the server 4, it is realized that the user's driving range per unit time can be appropriately predicted for each user, and a vehicle 2 suitable for the user can be lent. The details of the processing in this embodiment will be described below.

[0025] Figure 3 is a flowchart showing an example of the overall processing flow performed by server 4. The processing flow shown in Figure 3 begins when server 4 receives a reservation request sent from user terminal 3.

[0026] When the processing flow shown in Figure 3 is started, first, in step S101, the processing unit 42 performs a process to determine the candidate vehicle, which is a candidate vehicle 2 to be lent to the user.

[0027] Step S101, the process for determining a vehicle candidate, is a process for selecting a vehicle candidate. Through this process, a vehicle candidate is selected from among multiple vehicles 2 registered in the car-sharing system 1. Details of the vehicle candidate determination process in step S101 will be described later.

[0028] Following step S101, in step S102, the communication unit 41 notifies the user of the vehicle candidate determined in step S101.

[0029] In step S102, specifically, the communication unit 41 transmits information indicating the vehicle candidates and information indicating the parking location of the vehicle candidates to the user terminal 3 that sent the reservation request. As a result, the user is notified of the vehicle candidates and their parking locations. The user who has been notified of the vehicle candidates selects the vehicle 2 that they wish to rent from among the vehicle candidates and transmits the selection result to the server 4 via the user terminal 3.

[0030] Following step S102, in step S103, the communication unit 41 receives the user's selection result.

[0031] Following step S103, in step S104, server 4 executes the reservation completion process, and the processing flow shown in Figure 3 ends.

[0032] Step S104, the reservation completion process, involves various processes associated with the completion of the reservation of vehicle 2. For example, in the reservation completion process, the processing unit 42 stores in the storage unit 43 that the user has completed the reservation of vehicle 2. Then, the communication unit 41 transmits to the user terminal 3 that the user has completed the reservation of vehicle 2.

[0033] In detail, during the reservation completion process, the processing unit 42 associates the vehicle 2 selected by the user with the user and stores this information in the storage unit 43. The processing unit 42 also stores the user's reservation start time and end time for vehicle 2 in the storage unit 43 during the reservation completion process. The reservation start time and end time are entered by the user into the user terminal 3 along with the reservation request and transmitted to the server 4. This allows the server 4 to determine the reservation start time and end time. Furthermore, the server 4 can determine the time between the reservation start time and end time as the user's usage time for vehicle 2.

[0034] In this embodiment, by particularly improving the vehicle candidate determination process among the vehicle reservation processes performed by the server 4, it is possible to appropriately predict the user's driving range per unit time for each user, and to provide the user with a vehicle 2 that is suitable for them. The details of the vehicle candidate determination process will be explained below with reference to Figures 4 to 6.

[0035] Figure 4 is a flowchart showing an example of the process flow for determining vehicle candidates performed by Server 4. The processing flow shown in Figure 4 is executed in step S101 of Figure 3.

[0036] When the processing flow shown in Figure 4 is initiated, first, in step S201, the processing unit 42 calculates the moving average of the actual cruising range per unit time for the user who sent the reservation request. The moving average is the average of the above actual values ​​over a recent period.

[0037] Following step S201, in step S202, the processing unit 42 calculates the sum average of the actual values ​​of the cruising range per unit time for the user who sent the reservation request. The sum average is the average of the sum of the above actual values.

[0038] Following step S202, in step S203, the processing unit 42 calculates the maximum value of the actual cruising range per unit time for the user who sent the reservation request.

[0039] In steps S201, S202, and S203, the processing unit 42 calculates a moving average, a total average, and a maximum value, respectively, based on the usage history of the vehicle 2 by the user who sent the reservation request. The usage history mentioned above is, for example, the history of driving range, as will be described later. Here, an example of how to calculate the moving average, total average, and maximum value will be explained with reference to Figure 5.

[0040] Figure 5 shows an example of a dataset 5 stored in the server 4. The dataset 5 is stored in the storage unit 43 of the server 4 for each user registered in the car-sharing system 1, and shows the usage history of each user's vehicle 2.

[0041] As shown in Figure 5, the dataset 5 stores the range, moving average, total range, total time, and total average for each time interval TS as a single row of data. In the example in Figure 5, for example, the unit of distance is [km]. A time interval TS is a temporal interval with a length of one hour. In the example in Figure 5, the time intervals TS become newer as you move downwards. For example, time interval TS1 is the oldest time interval TS. Time interval TS2 is the time interval TS after time interval TS1. Time interval TS3 is the time interval TS after time interval TS2. Time interval TS4 is the time interval TS after time interval TS3. Time interval TS5 is the time interval TS after time interval TS4. And time interval TSn is the newest time interval TS.

[0042] For example, a vehicle 2 used by a user sends the actual driving range value for a one-hour time interval TS to the server 4 every hour, linked to the user using the vehicle 2. As a result, the server 4 can obtain the actual driving range value for a one-hour time interval TS every hour. Here, the server 4 manages a dataset 5 for each user. Therefore, the server 4 adds and saves the most recently obtained actual driving range value for the one-hour time interval TS to the dataset 5 corresponding to the user using the vehicle 2. As a result, as shown in Figure 5, the actual driving range values ​​for each past time interval TS are registered in the dataset 5 as the user's usage history of the vehicle 2.

[0043] The processing unit 42 then calculates a moving average for each time interval TS based on the history of actual range values. In the example in Figure 5, the moving average is the average of the actual range values ​​for a 1-hour time interval TS over the most recent period. For example, the processing unit 42 calculates the average of the actual range values ​​for the three most recent time intervals TS as the moving average for time interval TS5. For example, the processing unit 42 calculates the average of the actual range values ​​for the three most recent time intervals TS3, TS4, and TS5 as the moving average for time interval TS5. In the example in Figure 5, the processing unit 42 calculates 4 km as the moving average for time interval TS5.

[0044] Further, the processing unit 42 calculates the total sailing distance in each time interval TS based on the history of the actual values of the sailing distances. For example, as the total sailing distance, the processing unit 42 calculates the sum of the actual values of the sailing distances in all past time intervals TS. For example, as the total sailing distance in the time interval TS5, the processing unit 42 calculates the sum of the actual values of the sailing distances in all past time intervals TS, namely, the time intervals TS1, TS2, TS3, TS4, and TS5. In the example of FIG. 5, the processing unit 42 calculates 32 km as the total sailing distance in the time interval TS5.

[0045] Further, the processing unit 42 calculates the total time in the time interval TS. For example, as the total time, the processing unit 42 calculates a value obtained by multiplying the number of all past time intervals TS by 1 hour. For example, as the total time in the time interval TS5, the processing unit 42 calculates 5 hours obtained by multiplying the number of all past time intervals TS, which is 5, by 1 hour.

[0046] Further, the processing unit 42 calculates the total average in each time interval TS based on the history of the total sailing distance and the total time. In the example of FIG. 5, the total average is the average of the sum of the actual values of the sailing distances in the time interval TS of 1 hour. For example, as the total average, the processing unit 42 calculates a value obtained by dividing the total sailing distance by the total time. For example, as the total average in the time interval TS5, the processing unit 42 calculates a value obtained by dividing the total sailing distance in the time interval TS5 by the total time in the time interval TS5, which is 5 hours. In the example of FIG. 5, the processing unit 42 calculates 6.4 km as the total average in the time interval TS5.

[0047] As described above, when creating the dataset 5, the processing unit 42 calculates the moving average, the total sailing distance, the total time, and the total average in each time interval TS based on the sailing distance in each time interval TS.

[0048] Specifically, in step S201 of FIG. 4, the processing unit 42 calculates the latest moving average. The latest moving average corresponds to the moving average in the time interval TSn in the example of FIG. 5. The moving average calculated in step S201 of FIG. 4 corresponds to the moving average of the actual values of the user's voyage distance per hour.

[0049] Also, in step S202 of FIG. 4, the processing unit 42 calculates the latest total average. The latest total average corresponds to the total average in the time interval TSn in the example of FIG. 5. The total average calculated in step S202 of FIG. 4 corresponds to the total average of the actual values of the user's voyage distance per hour.

[0050] Also, in step S203 of FIG. 4, the processing unit 42 calculates the maximum value of the voyage distances in all past time intervals TS. The maximum value calculated in step S203 of FIG. 4 corresponds to the maximum value of the actual values of the user's voyage distance per hour.

[0051] Next to step S203 of FIG. 4, in step S204, the processing unit 42 determines whether the moving average at the current time calculated in step S201 is greater than the total average at the current time calculated in step S202.

[0052] As will be described later, based on the comparison result between the moving average and the total average, the processing unit 42 determines a candidate value for the predicted value of the user's voyage distance per hour, and predicts the predicted value based on the candidate value. That is, step S204 is a determination for determining the above candidate value.

[0053] If it is determined that the moving average is greater than the total average (YES in step S204), the process proceeds to step S205. Then, in step S205, the processing unit 42 determines the moving average as the candidate value. On the other hand, if it is determined that the moving average is smaller than the total average (NO in step S204), the process proceeds to step S206. Then, in step S206, the processing unit 42 determines the total average as the candidate value.

[0054] Figure 6 is a graph showing an example of the trends of the moving average and the sum average. In Figure 6, the horizontal axis T represents time, the vertical axis represents distance, the trend of the moving average is shown by a solid line, and the trend of the sum average is shown by a dashed line. In the example in Figure 6, in step S201, the moving average at the current time T0 is calculated as the latest moving average. Also, in step S202, the sum average at the current time T0 is calculated as the latest sum average. Therefore, in the example in Figure 6, it is determined that the moving average is greater than the sum average, and the moving average is selected as the candidate value. On the other hand, at past time T1, the sum average is greater than the moving average. Therefore, if the processing flow in Figure 4 were executed at this time T1, the sum average would be selected as the candidate value.

[0055] The moving average of the user's actual hourly driving range reflects the user's recent usage patterns of Vehicle 2. For example, if in the past Vehicle 2 was frequently used in short-distance situations such as shopping nearby, but recently Vehicle 2 has been used in long-distance situations such as travel, the moving average will be larger as a result of reflecting the user's recent usage patterns of Vehicle 2, as shown in the example in Figure 6.

[0056] On the other hand, the sum of the average of the user's actual driving range per hour reflects the user's usage patterns of Vehicle 2 over the long term. For example, if Vehicle 2 has recently been used in situations where driving range is short, such as shopping nearby, but in the past, Vehicle 2 was often used in situations where driving range is long, such as travel, then the sum of the average will be larger than in the example in Figure 6, as it reflects the user's usage patterns of Vehicle 2 over the long term.

[0057] Therefore, if the moving average is greater than the summation average, determining the moving average as a candidate value allows for a more accurate prediction of the user's hourly driving range, taking into account the user's recent usage patterns of vehicle 2. On the other hand, if the summation average is greater than the moving average, determining the summation average as a candidate value helps prevent the user's hourly driving range from being excessively underestimated as a result of giving more weight to the user's recent usage patterns of vehicle 2.

[0058] Following step S205 or step S206 in Figure 4, in step S207, the processing unit 42 determines whether the candidate value determined in step S205 or step S206 is greater than a threshold.

[0059] As will be described later, the processing unit 42 predicts the user's predicted range per hour based on the comparison result between the candidate value and the maximum value of the actual range per hour. The threshold in step S207 is a value used for comparison between the candidate value and the maximum value, and is a value corresponding to the maximum value. For example, the threshold in step S207 is a value of about 80% of the maximum value.

[0060] If the candidate value is determined to be greater than the threshold (YES in step S207), the process proceeds to step S208. In step S208, the processing unit 42 predicts the candidate value as the predicted value. On the other hand, if the candidate value is determined to be less than the threshold (NO in step S207), the process proceeds to step S209. In step S209, the processing unit 42 predicts the maximum value as the predicted value.

[0061] If the candidate value is smaller than the threshold, it can be determined that the candidate value is excessively small compared to the maximum value. In this case, if the candidate value is used as the predicted value, there is a possibility that the user's predicted driving range per hour will be excessively underestimated. Therefore, in this case, by using the maximum value as the predicted value, it is possible to suppress the excessive underestimation of the user's predicted driving range per hour. On the other hand, if the candidate value is larger than the threshold, it can be determined that the candidate value is about the same as the maximum value, or significantly larger than the maximum value. In this case, by using the candidate value as the predicted value, the user's predicted driving range per hour can be accurately predicted, taking into account the user's usage of vehicle 2.

[0062] Following step S208 or step S209, in step S210, the processing unit 42 determines a candidate vehicle based on the user's predicted hourly driving range and the user's usage time of vehicle 2.

[0063] In step S210, for example, the processing unit 42 calculates the user's desired driving range by multiplying the predicted value predicted in step S208 or step S209 by the usage time. Then, the processing unit 42 selects a vehicle candidate from among the multiple vehicles 2 registered in the car-sharing system 1, which has a battery that is sufficiently charged to satisfy the user's desired driving range. The processing unit 42 can also calculate the user's usage time of vehicle 2 based on the reservation start time and reservation end time of vehicle 2 that are sent to the server 4 along with the reservation request.

[0064] As explained above, based on the user's usage history of the vehicle 2, the server 4 calculates a moving average, which is the average of the actual values ​​of the user's driving range per unit time over a recent period, and a total average, which is the average of the sum of the actual values. Based on the moving average and the total average, the server 4 predicts the user's driving range per unit time. This allows the server 4 to appropriately predict the user's driving range per unit time for each user, taking into account both the user's recent usage patterns of the vehicle 2 and the user's long-term usage patterns of the vehicle 2.

[0065] Therefore, for example, in car-sharing system 1, it is possible to lend out a vehicle 2 that is suitable for the user. Thus, users can more easily reserve vehicle 2 at the time they want to use it, while at the same time, the loss of opportunities to provide vehicle 2 to users can be suppressed. In particular, since the number of chargers installed in car-sharing system 1 is limited, being able to lend out a vehicle 2 that is not fully charged can also suppress the increase in the number of vehicles 2 waiting to be charged.

[0066] The above examples illustrate the processes performed by Server 4 with reference to Figures 3 to 6. However, the processes performed by Server 4 are not limited to the examples above.

[0067] For example, the above example illustrates a case where the user's predicted range per unit time is calculated based on their predicted range per hour. However, the above unit time does not have to be one hour. For example, the length of the time interval TSn in Figure 5 does not have to be one hour. In this case, the user's predicted range per unit time will be calculated based on a range per hour different from that of one hour.

[0068] Furthermore, for example, after step S102 in Figure 3, it is conceivable that the user who received notification of vehicle candidates may determine that there is no vehicle 2 among the vehicle candidates that they wish to borrow. In this case, information indicating that there is no vehicle 2 among the vehicle candidates that the user wishes to borrow is sent to the server 4, and the process returns to step S101. The processing unit 42 then expands the range of locations of vehicles available for the user to borrow, for example, and executes the vehicle candidate determination process again. After that, the processes from step S102 onward are performed again.

[0069] Furthermore, for example, if a user who has received notification of vehicle candidates determines that there is no vehicle 2 among the candidates that they wish to rent, the server 4 may reduce the threshold in step S207 of Figure 4. This makes it easier for the user's predicted driving range per unit time to decrease, and as a result, the number of vehicles 2 selected as vehicle candidates can be increased.

[0070] Furthermore, for example, if the number of vehicles 2 determined as vehicle candidates exceeds the standard number, the server 4 may change the threshold in step S207 of Figure 4. For example, the threshold in step S207 of Figure 4 may be increased. This can prevent an increase in the effort required for the user to make a selection due to an excessively large number of vehicles 2 determined as vehicle candidates.

[0071] Furthermore, for example, server 4 may perform the process of charging the batteries of vehicles 2 registered in the car-sharing system 1 that have not been reserved by any user. This increases the number of vehicles 2 that can be selected as candidate vehicles when a reservation request is sent to server 4.

[0072] <Effects of the Car Sharing System> Next, the effects of the car sharing system 1 according to the embodiment of the present invention will be described.

[0073] In the car-sharing system 1 according to this embodiment, the server 4 of the processor 4a performs the following processes based on the user's usage history of the vehicle 2: calculating a moving average, which is the average of the actual values ​​of the user's driving range per unit time over a recent period, and a total average, which is the average of the sum of the actual values; and predicting the user's predicted driving range per unit time based on the moving average and the total average. This makes it possible to appropriately predict the user's driving range per unit time for each user by taking into account both the user's recent usage patterns of the vehicle 2 and the user's usage patterns of the vehicle 2 over the long term.

[0074] Therefore, for example, in car-sharing system 1, it is possible to lend out a vehicle 2 that is suitable for the user. Thus, users can more easily reserve vehicle 2 at the time they want to use it, while at the same time, the loss of opportunities to provide vehicle 2 to users can be suppressed. In particular, since the number of chargers installed in car-sharing system 1 is limited, being able to lend out a vehicle 2 that is not fully charged can also suppress the increase in the number of vehicles 2 waiting to be charged.

[0075] Furthermore, in the server 4 of the car-sharing system 1 according to this embodiment, the processor 4a performs a process that includes determining candidate values ​​for the predicted value based on the comparison result of the moving average and the summation average, and predicting the predicted value based on the candidate values. This appropriately predicts the driving range per unit time for each user, taking into account both the user's recent usage patterns of the vehicle 2 and the user's usage patterns of the vehicle 2 over the long term.

[0076] However, processor 4a may predict the predicted value without basing it on the comparison result of the moving average and the sum average. For example, processor 4a may determine the average of the moving average and the sum average as a candidate value for the predicted value, and predict the predicted value based on that candidate value.

[0077] Furthermore, in the server 4 of the car-sharing system 1 according to this embodiment, the processor 4a performs a process that includes determining the larger of the moving average and the summation average as a candidate value. This allows for a more appropriate prediction of the driving range per unit time for each user, taking into account both the user's recent usage patterns of the vehicle 2 and the user's usage patterns of the vehicle 2 over the long term.

[0078] However, when processor 4a determines candidate values ​​for the predicted value based on the comparison result of the moving average and the summary average, it may determine a value other than the larger of the moving average and the summary average as the candidate value. For example, processor 4a may determine a value obtained by adding or multiplying a predetermined value to the larger of the moving average and the summary average as the candidate value. In other words, processor 4a may determine the candidate value based on the larger of the moving average and the summary average.

[0079] Furthermore, in the server 4 of the car-sharing system 1 according to this embodiment, the processor 4a performs processing that includes predicting predicted values ​​based on the maximum value of actual values ​​in addition to candidate values. This makes it possible to appropriately predict the driving range per unit time for each user by taking into account the user's recent usage patterns of the vehicle 2, the user's usage patterns of the vehicle 2 in the long term, and the above-mentioned maximum value. For example, it is possible to prevent the driving range per unit time for each user from being excessively underestimated.

[0080] However, processor 4a may predict the predicted value without taking the above maximum value into account. For example, processor 4a may determine candidate values ​​for the predicted value based on the comparison result of the moving average and the summation average, and predict those candidate values ​​themselves as the predicted value.

[0081] Furthermore, in the server 4 of the car-sharing system 1 according to this embodiment, the processor 4a performs processing that includes predicting a predicted value based on the comparison result between a candidate value and the maximum value. This makes it possible to more appropriately predict the driving range per unit time for each user by taking into account the user's recent usage patterns of the vehicle 2, the user's usage patterns of the vehicle 2 in the long term, and the above-mentioned maximum value. For example, it is possible to appropriately prevent the driving range per unit time for each user from being excessively underestimated.

[0082] However, processor 4a may predict the predicted value without basing it on a comparison between the candidate value and the maximum value. For example, processor 4a may predict the average of the candidate value and the maximum value as the predicted value.

[0083] Furthermore, in the server 4 of the car-sharing system 1 according to this embodiment, the processor 4a performs a process that includes predicting a candidate value as a predicted value when the candidate value is greater than the threshold corresponding to the maximum value, and predicting the maximum value as a predicted value when the candidate value is less than the threshold. As a result, the driving range per unit time for each user can be predicted more appropriately for each user by taking into account the above-mentioned maximum value in addition to the user's recent usage patterns of the vehicle 2 and the user's usage patterns of the vehicle 2 in the long term. For example, it is possible to more appropriately prevent the driving range per unit time for each user from being excessively underestimated.

[0084] However, when the processor 4a determines a candidate value for the predicted value based on the comparison result between the candidate value and the maximum value, it may predict the predicted value using a method other than those described above. For example, if the candidate value is greater than the maximum value and the corresponding threshold, the processor 4a may predict a value obtained by adding or multiplying a predetermined value to the candidate value, and if the candidate value is less than the threshold, it may predict a value obtained by adding or multiplying a predetermined value to the maximum value. In other words, the processor 4a may predict the predicted value based on the larger of the candidate value and the threshold.

[0085] Furthermore, in the server 4 of the car-sharing system 1 according to this embodiment, the processor 4a performs processing that includes determining candidate vehicles 2 to lend to the user based on the prediction results of the predicted values ​​and the usage time of the vehicle 2 by the user. This makes it possible to appropriately predict the driving range per unit time for each user and lend a vehicle 2 that is suitable for the user.

[0086] However, the method of using the predicted values ​​is not limited to the examples above. For example, the processing unit equipped with the above-described processor 4a may be applied to systems other than the car-sharing system 1. In that case, for example, the processor 4a may predict the power consumption per unit time based on the above-described predicted values. Alternatively, for example, the processor 4a may predict the time until the battery is depleted based on the predicted power consumption per unit time and the remaining battery capacity.

[0087] Preferred embodiments of the present invention have been described above with reference to the attached drawings. However, it goes without saying that the present invention is not limited to the embodiments described above, and that various modifications or alterations within the scope of the claims also fall within the technical scope of the present invention.

[0088] For example, the processes described using flowcharts in this specification do not necessarily have to be performed in the order shown in the flowcharts. Additional processing steps may be adopted, and some processing steps may be omitted.

[0089] Furthermore, for example, the series of processes performed by server 4 as described above may be implemented using software, hardware, or a combination of software and hardware. The programs constituting the software are pre-stored in a storage medium provided inside or outside the information processing device, for example.

[0090] 1. Car-sharing system 2. Vehicle 3. User terminal 4. Server (processing device) 4a. Processor 4b. Memory 5. Dataset 41. Communication unit 42. Processing unit 43. Storage unit

Claims

1. A processing device comprising one or more processors and one or more memories connected to the processors, wherein the processor performs the following processes: calculating a moving average, which is the average of the actual driving range per unit time of the user over a recent period of time, and a total average, which is the average of the sum of the actual values, based on the user's vehicle usage history; and predicting a predicted value for the user's driving range per unit time based on the moving average and the total average.

2. The processing apparatus according to claim 1, wherein the processor performs a process that includes: determining candidate values ​​for the predicted value based on the comparison result of the moving average and the summation average; and predicting the predicted value based on the candidate values.

3. The processing apparatus according to claim 2, wherein the processor performs a process that includes determining the larger of the moving average and the summation average as the candidate value.

4. The processing apparatus according to claim 2, wherein the processor performs a process that includes predicting the predicted value based on the maximum value of the actual values ​​in addition to the candidate value.

5. The processing apparatus according to claim 4, wherein the processor performs a process that includes predicting the predicted value based on the result of comparing the candidate value with the maximum value.

6. The processing apparatus according to claim 5, wherein the processor performs a process that includes predicting the candidate value as the predicted value when the candidate value is greater than a threshold corresponding to the maximum value, and predicting the maximum value as the predicted value when the candidate value is less than the threshold.

7. The processing apparatus according to any one of claims 1 to 6, wherein the processor performs a process that includes determining a candidate vehicle to lend to the user based on the prediction result of the predicted value and the user's vehicle usage time.

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