Information processing device, battery charging method thereof, and battery charging program

Abstract

Automatically select the appropriate battery charging mode based on the user's behavior patterns. [Solution] The information processing device has multiple charging modes, including a first charging mode for full charging and a second charging mode in which the battery charge amount is set to be smaller than that of the first charging mode, and includes a mode setting unit 23 that sets one of the charging modes as the battery charging execution mode, and an activity pattern analysis unit 22 that divides the day into multiple time periods, including a main time period in which the main activity is performed, a sub-time period in which the activity is performed in a different location from the main time period, and a travel time period between the sub-time period and the main time period, based on multiple network ID data that associates network IDs used for communication with time information, and the mode setting unit 23 divides the day into multiple periods based on the multiple time periods and sets the charging execution mode for each divided period.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, a battery charging method thereof, and a battery charging program.

Background Art

[0002] Lithium ion batteries deteriorate when repeatedly charged and discharged due to their chemical properties. As a method of slowing down the deterioration speed, a method of stopping charging at about 80% without fully charging the battery capacity is widely known.

[0003] For example, Patent Document 1 discloses a battery control method having a full charge mode for fully charging a battery and an intermediate charge mode for charging to a voltage lower than the full charge mode, and allowing a user to switch between these charge modes.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The battery control method described in Patent Document 1 requires the user to select a mode. Therefore, the user had to choose whether to prioritize battery usage time or slowing down battery degradation. Consequently, optimal charging control was not always achieved unless the user frequently changed the schedule. For example, if the full charge mode is always selected, battery degradation cannot be suppressed and it does not contribute to extending the battery life. Also, if the intermediate charge mode is always set, the battery life will be shortened when the user is out and about. Furthermore, if the mode is switched according to the user's behavior pattern, the user must always consider their future plans and switch the charging mode themselves, making the operation cumbersome. Furthermore, while there is a known function that automatically switches to intermediate charging when the battery cell power exceeds a certain level, this prevents the full 100% performance time from being utilized when using a new battery, resulting in the same problem as mentioned above: shorter battery life when going out.

[0006] The present invention has been made in view of these circumstances, and aims to provide an information processing device, a battery charging method, and a battery charging program that can automatically set an appropriate battery charging mode according to the user's behavior pattern. [Means for solving the problem]

[0007] One aspect of the present invention is an information processing device comprising: a battery that can be charged from an external power source; a mode setting means for setting one of the charging modes as the battery charging execution mode, which includes a battery that can be charged from an external power source; a first charging mode for full charging; a second charging mode for setting the battery charge amount to a smaller amount than that of the first charging mode; and an activity pattern analysis means for dividing a day into a plurality of time periods, which include a main time period for performing primary activities, a sub-time period for performing activities in a location different from the main time period, and a travel time period between the sub-time period and the main time period, based on a plurality of network ID data that associates network IDs used for communication with time information, wherein the mode setting means divides a day into a plurality of periods based on the plurality of time periods and sets a charging execution mode for each of the divided periods.

[0008] One aspect of the present invention is a battery charging method for an information processing device equipped with a battery that can be charged from an external power source, comprising a plurality of charging modes including a first charging mode for full charging and a second charging mode in which the battery charge amount is set to be smaller than that of the first charging mode, a mode setting step of setting one of the charging modes as the battery charging execution mode, and an activity pattern analysis step in which a computer divides the day into a plurality of time periods, including a main time period in which the main activity is performed, a sub-time period in which the activity is performed in a different location from the main time period, and a travel time period between the sub-time period and the main time period, based on a plurality of network ID data that associates network IDs used for communication with time information, wherein the mode setting step divides the day into a plurality of periods based on the plurality of time periods, and sets a charging execution mode for each of the divided periods.

[0009] One aspect of the present invention is a program for causing a computer to function as the above-mentioned information processing device. [Effects of the Invention]

[0010] According to the present invention, the appropriate battery charging mode can be automatically set according to the user's behavior pattern. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic external view of an information processing device according to one embodiment of the present invention. [Figure 2] This is a schematic diagram showing an example of the hardware configuration of an information processing device according to one embodiment of the present invention. [Figure 3] This is a functional configuration diagram showing an example of the functions provided by an information processing device according to one embodiment of the present invention. [Figure 4] This figure shows an example of network ID data related to one embodiment of the present invention. [Figure 5] This figure shows an example of a user's daily behavior pattern related to one embodiment of the present invention. [Figure 6] This flowchart shows an example of the processing procedure for behavioral pattern prediction processing according to one embodiment of the present invention. [Figure 7] This figure shows an example of the total number of network ID occurrences at each time point used by the behavior pattern prediction unit according to one embodiment of the present invention. [Figure 8] This figure shows the travel time and the number of times it is calculated, which are used by the behavior pattern prediction unit according to one embodiment of the present invention. [Figure 9] This figure shows an example of each time period segmented by the behavior pattern prediction unit according to one embodiment of the present invention. [Figure 10] This diagram illustrates the charging mode set by the mode setting unit according to one embodiment of the present invention. [Figure 11] This flowchart shows an example of the processing procedure for a battery charging method according to one embodiment of the present invention. [Figure 12] This figure shows an example of a notification screen for charging pattern information according to one embodiment of the present invention. [Figure 13] This figure shows an example of a charging pattern adjustment screen in which a user can change the charging pattern information according to one embodiment of the present invention. [Modes for carrying out the invention]

[0012] Next, an information processing apparatus, a battery charging method, and a battery charging program according to an embodiment of the present invention will be described with reference to the drawings. In the following embodiments, a laptop PC (notebook PC) will be described as an example of the information processing apparatus 1, but the present invention is not limited to this example. For example, the information processing apparatus 1 may be an information processing apparatus equipped with a battery that can be charged from an external power source and can be connected to a network. Other examples include smartphones, tablet terminals, game terminals, 2-in-1 PCs, and the like.

[0013] FIG. 1 is a schematic external view of an information processing apparatus 1 according to an embodiment of the present invention. As shown in FIG. 1, the information processing apparatus 1 includes a first housing 2 and a second housing 3. The first housing 2 and the second housing 3 are relatively openably and closably connected by a connecting member 8. An example of the connecting member 8 is a hinge.

[0014] The first housing 2 is substantially rectangular and includes a display 5 provided on a surface facing the second housing 3. The display 5 has a display screen formed of, for example, an LCD (Liquid Crystal Display), an organic EL (Electro Luminescence), or the like. The display 5 may also be a touch panel display with a touch panel superimposed thereon. Instead of the display, an LED or the like that can indicate some operation may also be used.

[0015] The second housing 3 is substantially rectangular, and an input device 4 is provided on a first surface of the second housing 3. The input device 4 is a user interface for a user to perform input operations and voice inputs. Examples of the input device 4 include a keyboard, a touch pad, a track point, and the like. Note that the input device 4 may be a software keyboard or the like. That is, the first surface of the second housing 3 has a touch panel, and a screen such as a keyboard may be displayed on this touch panel to function as the input device 4. Buttons or the like that can be operated may also be used. The input device also includes a microphone and the like.

[0016] Next, the hardware configuration of the information processing apparatus 1 according to the present embodiment will be described with reference to FIG. 2. FIG. 2 is a schematic configuration diagram showing an example of the hardware configuration of the information processing apparatus 1 according to the present embodiment. As shown in FIG. 2, the information processing apparatus 1 includes, in addition to the input device 4 and the display 5 described above, for example, a CPU (Central Processing Unit), a main memory 12, a secondary storage (memory) 13, an external interface 14, a communication interface 15, an audio output device 16, a power supply circuit 17, a battery 6, and an AC adapter 7. These components are directly or indirectly connected to each other via a bus 18 and cooperate with each other to execute various processes.

[0017] The CPU 11 controls the entire information processing apparatus 1 by an OS (Operating System) stored in the secondary storage 13 connected via the bus 18, for example, and executes various processes by executing various programs stored in the secondary storage 13. One or more CPUs 11 may be provided and may cooperate with each other to realize the processes.

[0018] The main memory 12 is composed of a writable memory such as a cache memory or a RAM (Random Access Memory), for example, and is used as a work area for reading the execution program of the CPU 11, writing processing data by the execution program, and the like.

[0019] The secondary storage device 13 is a non-transitory computer-readable storage medium. Examples of secondary storage devices 13 include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, and semiconductor memory. Examples of secondary storage devices 13 include ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), and flash memory. The secondary storage device 13 stores, for example, an OS for controlling the entire information processing device such as Windows®, iOS®, and Android®, a BIOS (Basic Input / Output System), various device drivers for hardware operation of peripheral devices, various application software, and various data and files. The secondary storage device 13 also stores programs for implementing various processes and various data necessary for implementing those processes. Multiple secondary storage devices 13 may be provided, and the aforementioned programs and data may be divided and stored in each secondary storage device 13.

[0020] External interface 14 is an interface for connecting to external devices. Examples of external devices include external monitors, USB memory sticks, external HDDs, and external cameras. Although only one external interface is shown in the example in Figure 2, the system may have multiple external interfaces.

[0021] The communication interface 15 functions as an interface for connecting to a network, communicating with other devices, and sending and receiving information. For example, the communication interface 15 communicates with other devices via wired or wireless connections. Examples of wireless communication include communication via lines such as Bluetooth®, Wi-Fi, mobile communication systems (3G, 4G, 5G, 6G, LTE, etc.), and wireless LAN. An example of wired communication is communication via lines such as a wired LAN (Local Area Network). The audio output device 16 is a device that outputs sound by connecting speakers, earphones, etc.

[0022] The power supply circuit 17 converts power supplied from an external power source (not shown) via an AC adapter 7 or from a battery 6 into a voltage required for the operation of each device constituting the information processing device 1, and supplies power with the converted voltage to the target device. When power is supplied from the AC adapter 7, the power supply circuit 17 supplies any remaining power not supplied to each device to the battery 6, thereby charging the battery 6. Furthermore, when power is not supplied from the AC adapter 7, or when the power supplied from the AC adapter 7 is insufficient, the power supply circuit 17 supplies power from the battery 6 to each device as operating power.

[0023] Battery 6 is, for example, a rechargeable battery. A lithium-ion battery is one example of battery 6.

[0024] Figure 3 is a functional configuration diagram showing an example of the functions provided by the information processing device 1. As shown in Figure 3, the information processing device 1 is equipped with a battery charging system 20. The battery charging system 20 includes, for example, a memory unit 21, a behavior pattern analysis unit 22, a mode setting unit 23, and a charging control unit 24.

[0025] A series of processes for realizing the various functions of the battery charging system 20 are stored in the form of a program in a secondary storage device 13, for example. The CPU (processor) 11 reads this program (for example, a power control application) into the main memory 12 and performs information processing and calculations to realize the various functions. The program may be pre-installed in the secondary storage device 13, provided stored in a non-temporary computer-readable storage medium, or distributed via wired or wireless communication. Examples of non-temporary computer-readable storage media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, and semiconductor memory.

[0026] The memory unit 21 stores multiple network ID data, which associate the network ID used for communication with time information. Figure 4 shows an example of network ID data. As shown in Figure 4, the network ID data registers the network ID and its time information acquired at predetermined intervals (30-minute intervals in Figure 4). The time information may include information such as the day of the week and date. Furthermore, the network ID data may include power information indicating whether or not it is connected to an external power source. In Figure 4, "AC" is shown when it is connected to an external power source, in other words, when power is supplied from an external power source, and "DC" is shown when there is no power supply from an external power source, i.e., when it is battery-powered.

[0027] According to the network ID data shown in Figure 4, on Tuesday, October 1, 2024, the device was connected to network ID "A" while connected to an external power source from 7:00 to 8:00, connected to network ID "B" while connected to an external power source from 8:30 to 9:30, connected to network ID "C" while not connected to an external power source (i.e., battery-powered) from 9:30 to 10:00, connected to network ID "C" while not connected to an external power source from 10:00 to 10:30, connected to network ID "B" while battery-powered, and connected to network ID "B" while connected to an external power source from 10:30 to 11:00. The "1" shown in the network ID data indicates the number of occurrences. In the example shown in Figure 4, network ID "A" appeared 2 times, network ID "B" appeared 4 times, and network ID "C" appeared 2 times, for a total of 8 occurrences of network IDs during this period.

[0028] The battery charging system 20 continuously monitors network IDs at predetermined time intervals, resulting in the accumulation of a large amount of network ID data in the storage unit 21.

[0029] The behavior pattern analysis unit 22 predicts the user's behavior pattern by analyzing multiple network ID data stored in the memory unit 21. The behavior pattern analysis unit 22 may, for example, use data from a predetermined period from the present to the past among multiple network ID data stored in the memory unit 21 as the data to be analyzed to predict behavior patterns. This makes it possible to improve the reliability of the estimation because the behavior patterns are predicted using only relatively recent data. Alternatively, when analyzing data from the present to a predetermined past period, data from more recent periods may be given larger weighting coefficients to predict behavioral patterns. This allows for a greater emphasis on relatively recent data, further enhancing the reliability of the estimation.

[0030] Furthermore, human behavior patterns are highly likely to depend on the day of the week. Therefore, the behavior pattern analysis unit 22 may classify the multiple network ID data stored in the memory unit 21 by day of the week, and use the network ID data for each day of the week as the data to be analyzed to predict behavior patterns for each day of the week.

[0031] The behavioral pattern analysis unit 22 divides the day into multiple time periods, including a main time period in which primary activities are performed, a sub-time period in which activities are performed in a different location from the main time period, and a transition time period between the sub-time period and the main time period, as shown in Figure 5, for example. Here, the transition time period includes a first transition time period between the sub-time period and the main time period, and a second transition time period between the main time period and the sub-time period.

[0032] The main time zone is the time spent at one's primary activity location, such as a workplace or school, while the sub-time zone is the time spent at home, primarily for sleeping. Thus, the present invention focuses on the fact that the usage of the information processing device 1 differs between the time spent at home, where human activity is suppressed (such as sleep), and the time spent during periods of active activity, and is characterized by dividing the day into two time zones: the main time zone and the sub-time zone. Furthermore, recognizing that travel time inevitably exists between the main time zone and the sub-time zone, the invention provides a first travel time zone and a second travel time zone between the main time zone and the sub-time zone.

[0033] By dividing the day into a main time period when users are primarily active, a secondary time period when activity is reduced, a first travel time period between the secondary and main time periods, and a second travel time period between the main and secondary time periods, it becomes possible to simplify the user's daily activity pattern. Details of the behavior pattern prediction process performed by the behavior pattern analysis unit 22 will be described later.

[0034] The mode setting unit 23 has multiple charging modes, including a first charging mode for full charging and a second charging mode in which the battery charge amount is set to be lower than that of the first charging mode, and sets one of these charging modes as the charging execution mode for the battery 6. Specifically, the mode setting unit 23 divides a day into multiple periods based on multiple time zones determined by the behavioral pattern analysis unit 22, and sets a charging execution mode for each divided period (see, for example, Figure 10). The charging pattern information, which associates each period with the charging execution mode, is stored in a predetermined storage unit and used by the charging control unit described later. Details of the mode setting process performed by the mode setting unit 23 will be described later.

[0035] The charging control unit 24 performs charging of the battery 6 based on the charging pattern information set by the mode setting unit 23. For example, if an external power supply is connected via an AC adapter during the period when the first charging mode is set as the charging execution mode, the charging control unit 24 sets a first voltage value corresponding to the full charging mode as the target voltage and performs charging until the battery voltage reaches this target voltage. Furthermore, during the period when the second charging mode is set as the charging execution mode, if an external power supply is connected via an AC adapter, the charging control unit 24 will charge the battery until the battery voltage reaches a target voltage of a second voltage value (< first voltage value) corresponding to the second charging mode. For example, the second voltage value is set to 80% of the first voltage value.

[0036] Note that the charging control by the charging control unit 24 is just one example, and known charging control methods can be adopted. For example, instead of charging control based on battery voltage, charging control based on SOC (State of Charge) may be used. In either case, the charge level of the battery 6 in the second charging mode is set lower than the charge level of the battery 6 in the first charging mode.

[0037] Next, we will explain the behavior pattern prediction process performed by the behavior pattern analysis unit 22 described above. Figure 6 is a flowchart showing an example of the processing procedure for the behavior pattern prediction process.

[0038] First, the behavior pattern analysis unit 22 identifies the network ID for the main behavior time period and the network ID for the sub-behavior time period based on multiple data points to be analyzed, and identifies the start time for each main and sub-behavior time period (SA1). The following provides a detailed explanation of this process.

[0039] First, the behavioral pattern analysis unit 22 defines network IDs that appear above a certain level as base IDs based on multiple data points to be analyzed, calculates an expected value from the total number of occurrences of base IDs at each time point, and separates the multiple base IDs into two categories.

[0040] Here, the base ID is the network ID of the place where the user spends the most time during their main activity period and secondary activity period. Since it is self-evident that the time spent sleeping and the time spent primarily active are sufficiently far apart, human behavior can be classified into two time periods by using the expected value, which is the average value of time, as shown in equation (1) below.

[0041] More specifically, the behavioral pattern analysis unit 22 calculates an expected value using, for example, the total number of occurrences of network IDs at each time point, and divides the day into two behavioral time periods based on the expected value. For example, the expected value is calculated by the following equation (1). Note that the total number of occurrences may be calculated by multiplying the data that is closer to the present by a higher weighting coefficient.

[0042] Expected value = ΣP(T) × T (1)

[0043] Here, T is time, and P(T) is the probability of the network ID appearing at time T.

[0044] In the above calculation of expected value, it is recommended to add 24 to the time period from midnight to early morning (for example, from 0:00 to 5:00) (for example, from 24:00 to 29:00). Since the expected value is an average, if we use 2:00, which is midnight, as is, the average value will be small and it may not be considered midnight. Therefore, by treating 2:00 as an extension of the previous day and making it 26:00, we can increase the average value and make it possible to recognize it as midnight.

[0045] Furthermore, the method for separating network IDs into two activity time periods is not limited to the expected value method described above. For example, statistical clustering may be used. Alternatively, human behavior can be considered to follow a daily cycle based on daytime, and the day can be divided into daytime and nighttime periods.

[0046] Next, the behavior pattern analysis unit 22 statistically processes multiple data points for analysis of the two separated base IDs to identify the start time for each of the two separated behavior time periods. Specifically, the behavior pattern analysis unit 22 calculates the total number of occurrences of network IDs at each time point. Figure 7 shows an example of the total number of occurrences of network IDs at each time point. At this point, it has not yet been determined whether the base ID of the two sets is main or sub, but for ease of understanding, a typical daytime is used as the main behavioral image. Alternatively, the probability of occurrence can be calculated statistically from the total number of occurrences and used.

[0047] Next, the behavior pattern analysis unit 22 identifies the first time in each time period A where the total number of occurrences shows an increasing trend that the predetermined threshold is exceeded as the start time. As a result, for example in Figure 7, 9:00 and 20:30 are identified as start times. The reason for determining the start time first is that the beginning of the main behavior time period is when activity starts, so many counts can be obtained, while the end of the sub-behavior start time period is immediately after waking up from sleep, so it is expected that the count will be considerably lower.

[0048] Next, the behavior pattern analysis unit 22 identifies the main and sub-activity time periods based on the total number of occurrences of all network IDs. In other words, it identifies whether the user is active during the day or at night. Furthermore, by utilizing all network IDs, it is possible to check activities including those performed while outside the home. For users who are active during the day, the main activity period is identified as 9:00 to 20:30 in the example in Figure 7, while for users who are active at night, the main activity period is identified as 20:30 to 9:00.

[0049] In the above explanation, network IDs that appear above a certain threshold are defined as base IDs, and the main activity period and sub-activity period are separated using the total number of base IDs that appear in each time period. However, this is not the only way. For example, one could use all network IDs, calculate an expected value using the total number of appearances in each time period, and then use the calculated expected value to identify the main activity period and sub-activity period as described above.

[0050] The above method is just one example, and other methods can also be used. For example, the behavioral pattern analysis unit 22 may calculate the cumulative total number of occurrences during the behavioral time period from 9:00 to 20:30 and the behavioral time period from 20:30 to 9:00, and identify the behavioral time period with the largest cumulative total number of occurrences as the main behavioral time period. This makes it easy to identify the time period with the highest activity level as the main behavioral time period.

[0051] Next, the behavior pattern analysis unit 22 calculates the completion time and travel time (SA2). The following explains in detail how the end time and travel time are calculated.

[0052] First, the behavior pattern analysis unit 22 identifies the network ID primarily used during the main activity time period as the main base ID, and identifies the network ID primarily used during the sub-activity time period as the sub-base ID.

[0053] Specifically, the behavior pattern analysis unit 22 extracts multiple network IDs belonging to the main behavior time period from multiple data to be analyzed, and identifies the network ID whose occurrence frequency is above a predetermined value and whose external power connection rate is above a predetermined value as the base ID for the main time period (hereinafter referred to as "main base ID"). Note that the base ID does not necessarily have to be just one; it may be multiple IDs. In this case, any of the IDs that satisfy the conditions will be treated as the same main base ID. For example, this applies when multiple network IDs exist even in the same location and change from day to day. Similarly, the behavior pattern analysis unit 22 extracts multiple network IDs belonging to sub-behavioral time zones from multiple data to be analyzed, and identifies the network IDs among the extracted network IDs whose occurrence frequency is above a predetermined value and whose external power connection rate is above a predetermined value as the base ID for the sub-time zone (hereinafter referred to as "sub-base ID").

[0054] Here, the external power connection rate is calculated as the ratio of the time the network ID is connected to an external power source to the total time the network ID is used. Specifically, it is calculated using equation (2) below.

[0055] External power connection rate = External power connection time / Usage time (2)

[0056] For example, if network ID "A" was used for 4 hours, with 3 hours spent connected to an external power source and 1 hour on battery power, the external power connection rate would be 0.75.

[0057] Next, the behavioral pattern analysis unit 22 calculates the travel time for each day's worth of data to be analyzed, based on the last appearance time of the main base ID and the first appearance time of the sub-base ID. Specifically, it calculates the travel time as the time from the last appearance time of the main base ID to the first appearance time of the sub-base ID. For example, if the last appearance time of the main base ID is 17:30 and the first appearance time of the sub-base ID is 19:30, the travel time will be 2 hours. In this way, the behavioral pattern analysis unit 22 calculates the travel time for each day's worth of data to be analyzed.

[0058] Next, the behavioral pattern analysis unit 22 statistically processes the calculated daily travel time to determine a representative value for the total travel time of the data being analyzed. For example, Figure 8 shows the calculated travel time and the number of times it was calculated. The behavioral pattern analysis unit 22, for example, determines the minimum value (60 minutes in Figure 8) among the multiple calculated travel times as the representative value for travel time. Travel time can be divided into travel time from sub-activity time zones to main activity time zones, and travel time from main activity time zones to sub-activity time zones. However, if the data is insufficient and unreliable, the travel time may be substituted with the travel time for which there is sufficient data. The content of the statistical processing is not particularly limited. For example, the mean, maximum value, median, etc., may be used. The above-mentioned process for calculating travel time can be omitted for simplicity. In this case, the travel time can be a fixed value (for example, 90 minutes) that assumes a typical pre-set travel time.

[0059] Next, calculate the end time. For each time period A where the total number of occurrences shows a decreasing trend, the end time can be defined as the last time that exceeds a predetermined threshold. Also, if the total number of occurrences for the end time is small, the end time may be considered to be the start time of the base time period minus the travel time.

[0060] Next, the behavioral pattern analysis unit 22 uses the representative value of travel time calculated in step SA2 to divide the day into multiple time zones and determine each time zone (SA3).

[0061] First, the behavior pattern analysis unit 22 defines the period from the start time of the main activity period (e.g., 9:00) to the time before travel (e.g., 90 minutes) as the first travel period, and the period from the start time of the sub-activity period (e.g., 20:30) to the time before travel (e.g., 90 minutes) as the second travel period.

[0062] As a result, as shown in Figure 9, a day (24 hours) is divided into four time zones: a sub-time zone (for example, 20:30 to 7:30), the first travel time zone (7:30 to 9:00), the main time zone (9:00 to 19:00), and the second travel time zone (19:00 to 20:30).

[0063] Next, the mode setting process performed by the mode setting unit 23 will be explained in detail with reference to Figure 10. As shown in Figure 10, the mode setting unit 23 divides a day, which is a simplified representation of typical human behavior, into multiple periods based on the time zones categorized by the behavior pattern analysis unit 22, and sets a charging execution mode for each divided period. The mode setting unit 23 divides a day into, for example, a sub-base period, a first travel preparation period, a main base period, and a second travel preparation period.

[0064] The mode setting unit 23 sets the first charging mode during the first travel preparation period, which is the period from the start of the first travel time period until a predetermined time before. This makes it possible to fully charge the battery 6 in preparation for battery operation during travel in the first travel time period.

[0065] Furthermore, the mode setting unit 23 sets the second charging mode during the sub-base period, which is the sub-time period excluding the first travel preparation period. Here, the mode setting unit 23 may also set the sub-base period to include the second travel time period. In this way, by setting the second charging mode during the sub-time period, which is mainly spent at home, it is possible to suppress battery degradation.

[0066] Furthermore, the mode setting unit 23 sets the charging execution mode during the second travel preparation period, which is from the start of the second travel period until a predetermined time before, according to the rate of going out during the sub-time period. Here, the sub-time zone departure rate is a parameter relating to the percentage of time spent using the network in a location other than the primary activity location for that sub-time zone (e.g., home).

[0067] For example, the mode setting unit 23 calculates the outing rate for a sub-time period using the total number of occurrences of network IDs other than sub-base IDs (hereinafter referred to as "sub-outing IDs") in the sub-time period. More specifically, the outing rate is calculated by dividing the total number of occurrences of sub-outing IDs by the total number of occurrences of all network IDs (hereinafter referred to as "overall IDs") in a day. Specifically, it can be calculated using the following equation (3).

[0068] Sub-time zone outing rate = Total number of sub-outing IDs / Total number of overall IDs (3)

[0069] The mode setting unit 23 sets the first charging mode as the charging execution mode if the rate of going out during the sub-time period is above a predetermined threshold, and the second charging mode as the charging execution mode if the rate of going out during the sub-time period is below the predetermined threshold. This makes it possible to fully charge the battery during the second travel preparation period, for example, if the rate of going out during the sub-time period is above a threshold, in other words, if the time spent in a place other than home is relatively long during the sub-time period and there is a high possibility of using the information processing device 1 on battery power, then sufficient power can be charged to the battery in preparation for going out. As for the end time, it is usually unnecessary to set the end time for the main time period because the count is large. However, when using a method to determine the travel time from the next start time due to simplification, etc., it is necessary to consider adding the period in which the sub-outing ID appears during the travel time.

[0070] Furthermore, if the rate of going out during the sub-duration period is below a threshold, in other words, if the time spent at home during the sub-duration period is relatively long, there is less need to store a large amount of power in the battery 6. Therefore, by setting the second charging mode during the second travel preparation period, battery degradation can be suppressed and the battery life can be extended.

[0071] The mode setting unit 23 sets the charging execution mode for the main base period, which is the main time period minus the second travel preparation period, according to the rate of going out during the main time period. Here, the mode setting unit 23 may also include the first travel time period in the main base period.

[0072] The "outing rate during peak hours" is a parameter that relates to the percentage of time, for example, when a user uses the network in a location other than their primary activity location (e.g., their workplace) during peak hours.

[0073] For example, the mode setting unit 23 calculates the outing rate during the main time period using the total number of occurrences of network IDs other than the main base ID (hereinafter referred to as "main outing IDs") during the main time period. More specifically, the outing rate during the main time period is calculated by dividing the total number of occurrences of main outing IDs by the total number of occurrences of all IDs. Specifically, it can be calculated using the following equation (4).

[0074] Main time zone outing rate = Total number of main outing IDs appearing / Total number of overall IDs appearing (4)

[0075] The mode setting unit 23 sets the full charge mode as the charging execution mode when the rate of going out during the main time period is above a predetermined threshold, and the second charge mode as the charging execution mode when the rate of going out during the main time period is below the predetermined threshold. This allows, for example, when the user spends a relatively long time in a location other than their main activity location during the main time period and there is a high probability of using the information processing device 1 on battery power, setting the first charge mode makes it possible to charge the battery 6 with sufficient power. On the other hand, when the user spends a long time in their main activity location during the main time period, there is a low probability of using the information processing device 1 on battery power, so setting the second charge mode suppresses the degradation of the battery 6 and extends its lifespan.

[0076] The mode setting unit 23 may also have a mode that always sets the second charging mode, separate from the pattern in Figure 10, to consider cases where the main base period and the sub-base period have the same network ID, or where only one of them exists. In this case, there is no need to fully charge the device because it is stationary and not moved.

[0077] Next, a battery charging method performed by the battery charging system 20 provided in the information processing device 1 according to this embodiment will be described with reference to Figure 11. Figure 11 is a flowchart showing an example of the processing procedure for the battery charging method. The battery charging method described below is performed repeatedly, for example, at predetermined time intervals (for example, every week, every month, etc.).

[0078] First, the battery charging system 20 extracts network ID data from the present to a predetermined past period stored in the memory unit 21 as data to be analyzed (SB1).

[0079] Next, the battery charging system 20 uses the extracted data to predict the user's daily activity pattern (SB2). As a result, as shown in Figure 10, the day (24 hours) is divided into sub-time zones, first travel time zones, main time zones, and second travel time zones. The detailed processing is as described above.

[0080] Next, the battery charging system 20 divides the day into multiple periods based on the designated sub-time periods, first travel time periods, main time periods, and second travel time periods (SB3). As a result, for example, as shown in Figure 10, the day is divided into a sub-base period, a first travel preparation period, a main base period, and a second travel preparation period. In special cases where there is no travel, the day may be set as a single unit without division.

[0081] Next, the battery charging system 20 sets either the first charging mode or the second charging mode as the charging execution mode during the sub-base period, the first mobile preparation period, the main base period, and the second mobile preparation period (SB4).

[0082] Next, the battery charging system 20 stores charging pattern information, which associates the sub-base period, the first movement preparation period, the main base period, the second movement preparation period, and the charging execution mode, in a predetermined storage unit (not shown) (SB5), and then terminates this process.

[0083] As described above, according to the information processing device 1, the battery charging method for the information processing device, and the battery charging program of this embodiment, the behavior pattern analysis unit 22 divides the day into multiple time periods, including a main time period in which the main activity takes place, a sub-time period in which activities are performed in a different location from the main time period, and a travel time period between the sub-time period and the main time period. In this way, the user's daily behavior pattern can be simplified by dividing it into a main time period corresponding to the activity time, a sub-time period spent at home, and a travel time period when moving between home and the main activity location. This eliminates the need for large amounts of data and computationally intensive processing such as machine learning.

[0084] Furthermore, the mode setting unit 23 divides the day into multiple periods based on the time zones defined by the behavior pattern analysis unit 22, and sets a charging execution mode for each divided period. This makes it possible to automatically set a battery charging mode according to the user's daily behavior pattern. By shortening the period during which a full charge is performed according to the user's daily behavior pattern and increasing the battery charging time in the second charging mode, which suppresses battery degradation, it is possible to extend the lifespan of the battery 6 while avoiding power shortages during battery operation as much as possible.

[0085] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. Various modifications or improvements can be made to the above embodiments without departing from the spirit of the present invention, and such modified or improved forms are also included in the technical scope of the present invention.

[0086] Furthermore, the processing procedure described in the above embodiment is merely an example, and unnecessary steps may be deleted, new steps added, or the processing order rearranged without departing from the spirit of the present invention.

[0087] For example, the battery charging system 20 may set the charging execution mode to a first charging mode if the current network ID does not match the main base ID or sub base ID. This makes it possible to set the first charging mode when the user is likely to be away from home. For example, when away from home, there are not many opportunities to connect to an external power source and charge the battery. Therefore, by setting the first charging mode, it is possible to prioritize fully charging the battery 6 and avoid power shortages while on the go as much as possible.

[0088] Furthermore, the battery charging system 20 may work in conjunction with the calendar function to set the first charging mode in the time before any scheduled events that are likely to require battery operation, such as going out or attending meetings. This makes it possible to avoid power shortages during battery operation as much as possible.

[0089] Furthermore, the battery charging system 20 may include a notification unit that notifies the user of charging pattern information, which associates each period (sub-base period, first movement preparation period, main base period, second movement preparation period) with the charging execution mode. In addition, the battery charging system 20 may include a pattern setting unit for the user to change the charging pattern information. For example, the battery charging system 20 may be configured to display a UI screen on the display 5, as shown in Figure 12, allowing the user to check detailed charging pattern information and change settings.

[0090] For example, the UI screen shown in Figure 12 displays buttons that allow the user to select between "Automatic Mode" and "Fixed Mode." Here, "Automatic Mode" is a mode in which the battery charging system 20 automatically sets the charging execution mode based on the user's behavior pattern. "Fixed Mode" is a mode in which, for example, the second charging mode is always set. Furthermore, if the user selects "automatic mode," the system may, for example, present the user with charging pattern information and allow them to change the charging pattern information. Figure 13 shows an example of a charging pattern adjustment screen in which the user can change the charging pattern information. [Explanation of Symbols]

[0091] 1: Information Processing Device 2: First cabinet 3: Second cabinet 4: Input devices 5: Display 6: Battery 7: AC adapter 8: Connecting member 11: CPU 12: Main memory 13:Secondary storage device 14: External Interface 15: Communication Interface 16: Audio output device 17: Power supply circuit 18: Bus 20: Battery charging system 21: Storage section 22: Behavioral Pattern Analysis Department 23: Mode setting section 24: Charging Control Unit

Claims

1. A battery that can be charged from an external power source, A mode setting means for setting one of the charging modes as the battery charging execution mode, which includes a first charging mode for full charging and a second charging mode in which the battery charge amount is set to be less than that of the first charging mode. A behavioral pattern analysis means that divides a day into multiple time periods, including a main time period in which primary activities are performed, a sub-time period in which activities are performed in a location different from the main time period, and a travel time period between the sub-time period and the main time period, based on multiple network ID data that associate network IDs used for communication with time information. Equipped with, The mode setting means is an information processing device that divides a day into multiple periods based on a plurality of time periods and sets a charging execution mode for each of the divided periods.

2. The aforementioned behavioral pattern analysis means is Based on the multiple network ID data, the total number of network ID occurrences at each time point is calculated, and by statistically processing the calculated total number of network ID occurrences at each time point, the start time of the main time zone and the start time of the sub-time zone are identified. The period from the start time of the identified main time zone until the predetermined travel time is defined as the first travel time zone. The information processing apparatus according to claim 1, wherein the second travel time period is defined as the period from the start time of a specified sub-time period until a predetermined travel time before that time.

3. The information processing device according to claim 2, wherein the behavior pattern analysis means calculates the total number of occurrences of network IDs at each time using a plurality of network ID data, identifies the first time in each of two time periods where the total number of occurrences shows an increasing trend that exceeds a predetermined threshold, and identifies one of the identified times as the start time of the main time period and the other time as the start time of the sub-time period.

4. The aforementioned network ID data includes power information indicating whether or not it is connected to the external power supply. The aforementioned behavioral pattern analysis means is Using the start time of the main time period and the start time of the sub-time period, the day is divided into a main activity period and a sub-activity period. Based on the number of occurrences of each network ID belonging to the aforementioned main activity time period and the power supply information, the base network ID is identified as the main base ID. Based on the number of occurrences of each network ID belonging to the aforementioned sub-activity time period and the power supply information, the base network ID is identified as the sub-base ID. The travel time is calculated using the last occurrence time of the main base ID and the first occurrence time of the sub base ID in a given day. The period from the start of the aforementioned main time period until before the aforementioned travel time shall be defined as the first travel time period. The information processing apparatus according to claim 2, wherein the second travel time period is defined as the period from the start of the sub-time period until before the travel time.

5. The aforementioned behavioral pattern analysis means is The multiple network ID data are classified by day of the week, The information processing device according to claim 1, which divides one day into multiple time zones for each day of the week using the network ID data for each day of the week.

6. The aforementioned travel time period includes a first travel time period between the sub-time period and the main time period, The information processing apparatus according to claim 1, wherein the mode setting means sets a first charging mode during a first travel preparation period from the start of the first travel time period until a predetermined time before.

7. The information processing apparatus according to claim 6, wherein the mode setting means sets a second charging mode in a sub-base period obtained by excluding the first movement preparation period from the sub-time period.

8. The aforementioned travel time period includes a second travel time period between the main time period and the sub-time period, The information processing apparatus according to claim 6, wherein the mode setting means sets a second charging mode in the sub-base period obtained by subtracting the first travel preparation period from the second travel time period and the sub-time period.

9. The multiple time periods include a second transition time period between the main time period and the sub-time period. The mode setting means is Using the total number of occurrences of network IDs other than the subbase ID during the sub-time period, the sub-time period outing rate, which is a parameter relating to the proportion of network use in locations other than the main activity location during the sub-time period, is calculated. The information processing device according to claim 4, which sets a charging execution mode during the second travel preparation period from the start of the second travel period until a predetermined time before, based on the rate of going out during the sub-time period.

10. The mode setting means is Using the total number of occurrences of network IDs other than the main base ID during the main time period, the "outing rate during the main time period," which is a parameter relating to the proportion of network use in locations other than the main activity location during the main time period, is calculated. The information processing device according to claim 9, which sets a charging execution mode in the main base period, which is the main period minus the second travel preparation period, based on the rate of going out during the main period.

11. The aforementioned travel time period includes a first travel time period between the sub-time period and the main time period, The information processing apparatus according to claim 10, wherein the mode setting means sets a charging execution mode in the first travel period and the main base period obtained by subtracting the second travel preparation period from the main period, based on the rate of going out during the main time period.

12. The information processing apparatus according to claim 4, wherein the mode setting means sets the first charging mode as the charging execution mode when the current network ID does not match the main base ID or the sub base ID.

13. The information processing apparatus according to claim 1, further comprising a notification means for notifying a user of charging pattern information associated with each of the aforementioned periods and charging execution modes.

14. The information processing apparatus according to claim 13, further comprising a pattern setting means for a user to change the charging pattern information.

15. A method for charging the battery of an information processing device equipped with a battery that can be charged from an external power source, The system has multiple charging modes, including a first charging mode for full charging and a second charging mode in which the battery charge amount is set to be less than that of the first charging mode, and a mode setting step of setting one of these charging modes as the charging execution mode for the battery, A behavioral pattern analysis process that divides a day into multiple time periods, including a main time period in which primary activities are performed, a sub-time period in which activities are performed in a different location from the main time period, and a travel time period between the sub-time period and the main time period, based on multiple network ID data that associate network IDs used for communication with time information. The computer executes this, The mode setting step is a battery charging method for an information processing device, which divides a day into multiple periods based on a plurality of time zones and sets a charging execution mode for each of the divided periods.

16. A battery charging program for causing a computer to function as an information processing device according to any one of claims 1 to 14.

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