JP2020063920A - Deterioration prediction program, deterioration prediction method, and deterioration prediction device - Google Patents

Abstract

To improve accuracy in predicting deterioration of a battery.SOLUTION: An information processing device 100 acquires time sequential data indicating temporal changes of battery deterioration amounts of a terminal device A and a terminal device B. The information processing device 100 generates models 101, 102 or the like concerning the terminal device A and generates models 103, 104 or the like concerning the terminal device B. The information processing device 100 generates one or more patterns where a use form 1 is associated with a use form 2, for each line segment included in straight lines or polygonal lines indicated by the models 101-104 or the like. The information processing device 100 generates combinations 111-114 or the like by combining the patterns. The information processing device 100 predicts deterioration of a battery for each terminal device based on a dispersion of inclinations of line segments for each use form in the respective combinations such as the combinations 111-114 or the like.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a deterioration prediction program, a deterioration prediction method, and a deterioration prediction device.

  Conventionally, a terminal device operates by power supply from an internal battery or power supply from an external commercial power source. Here, when the battery of the terminal device deteriorates, the time during which the terminal device is operable by the power supply from the battery is shortened. Therefore, it is desirable to perform the deterioration prediction of the battery of the terminal device.

  As a prior art, for example, there is one that learns a deterioration model parameter indicating a deterioration state of a storage battery cell based on usage history data of the storage battery cell and outputs a deterioration speed table as a learning result. In addition, for example, there is a technique of predicting the end of life by monitoring the total operating time in which the terminal device operates with the same battery, the number of times the terminal battery is charged, or the actual operating time in which the terminal device operates with one charge. . In addition, for example, based on the battery information about the remaining amount of each battery to be carried on the day, the power consumption information about the power consumption of the battery for each work type, and the work schedule information on the day There is a technology that determines the order of use of scheduled batteries.

International Publication No. 2015/198663 JP, 2003-256084, A JP, 2013-67047, A

  However, with the conventional technology, it is difficult to predict the deterioration of the battery of the terminal device. For example, when one terminal device is available to outside workers and office workers, the deterioration rate of the battery of the terminal device is not constant, and the accuracy of the battery deterioration prediction decreases.

  In one aspect, the present invention aims to improve the accuracy of battery deterioration prediction.

  According to one embodiment, the time series data indicating the time change of the deterioration degree of the battery of each terminal device is acquired, and the battery of the terminal device of the battery is acquired based on the acquired time series data for each terminal device. In consideration of the break point candidates when the time change of the deterioration degree is approximated by a broken line, one or more models showing a straight line or a broken line approximating the time change of the deterioration degree of the battery of the terminal device are generated, and each terminal device is generated. To generate one or more patterns in which each usage pattern of the plurality of usage patterns of the terminal device is associated with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models. , One of the one or more patterns generated for each of the terminal devices is selected and combined into each of the one or more combinations. That, on the basis of the variation in the inclination of the line segment section of each of the utilization forms, deterioration prediction programs that implement the deterioration prediction of the battery for each of the terminal device, deterioration prediction method, and deterioration prediction system is proposed.

  According to one aspect, it is possible to improve the accuracy of battery deterioration prediction.

FIG. 1 is an explanatory diagram illustrating an example of the deterioration prediction method according to the embodiment. FIG. 2 is an explanatory diagram showing an example of the deterioration prediction system 200. FIG. 3 is an explanatory diagram showing another example of the deterioration prediction system 200. FIG. 4 is a block diagram showing a hardware configuration example of the information processing apparatus 100. FIG. 5 is an explanatory diagram showing an example of the stored contents of the deterioration amount information table 500. FIG. 6 is a block diagram showing a hardware configuration example of the terminal device 201. FIG. 7 is a block diagram showing a functional configuration example of the information processing apparatus 100. FIG. 8 is an explanatory diagram (part 1) showing an operation example of the deterioration prediction system 200. FIG. 9 is an explanatory diagram (part 2) showing an operation example of the deterioration prediction system 200. FIG. 10 is an explanatory diagram (3) showing an operation example of the deterioration prediction system 200. FIG. 11 is an explanatory diagram (4) showing an operation example of the deterioration prediction system 200. FIG. 12 is an explanatory diagram (No. 5) showing an operation example of the deterioration prediction system 200. FIG. 13 is a flowchart (part 1) showing an example of the overall processing procedure. FIG. 14 is a flowchart (part 2) showing an example of the overall processing procedure. FIG. 15 is a flowchart showing an example of the deterioration diagnosis processing procedure.

  Embodiments of a deterioration prediction program, a deterioration prediction method, and a deterioration prediction device according to the present invention will be described below in detail with reference to the drawings.

(One Example of Deterioration Prediction Method According to Embodiment)
FIG. 1 is an explanatory diagram illustrating an example of the deterioration prediction method according to the embodiment. The information processing device 100 is a computer for improving the accuracy of battery deterioration prediction of the terminal device. The battery is, for example, a lithium ion battery.

  The terminal device is included in, for example, a thin client type system. In this system, the terminal device may be shared by a plurality of users. For example, the system includes a terminal device shared by a plurality of outside workers and a terminal device shared by a plurality of inside workers. In addition, this system may be operated so as to replace the terminal device of the outside worker with the terminal device of the inside worker when the battery of the terminal device of the outside worker has deteriorated to a certain degree or more.

  The deterioration of the battery of the terminal device is a decrease in the battery capacity when the battery is fully charged. The deterioration rate of the battery of the terminal device changes according to the usage pattern of the terminal device by the user. Specifically, deterioration of the battery when the terminal device is operating on the battery is called cycle deterioration, deterioration of the battery when the terminal device is operating on commercial power is called neglected deterioration, and deterioration between cycle deterioration and neglected deterioration is called The speed is different. Since the time when the terminal device is operated by the battery and the time when the terminal device is operated by the commercial power supply differ for each user, the deterioration speed of the battery also differs.

  Here, when the battery of the terminal device deteriorates, work efficiency in a system including the terminal device may decrease, and data loss of the terminal device may occur. A replacement battery is prepared. Then, when the amount of deterioration of the battery exceeds the threshold value, it will be replaced with a replacement battery prepared in advance, but if the replacement battery is insufficient, the terminal device becomes unusable and the work efficiency decreases. And the data loss of the terminal device is caused.

  Therefore, it is desired to predict the deterioration of the battery of the terminal device. For example, it is desirable to calculate the deterioration rate of the battery, calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value, and determine how many replacement batteries should be prepared. At this time, specifically, since the deterioration rate of the battery varies depending on the usage pattern of the terminal device by the user, it is desirable to calculate the deterioration speed of the battery according to the usage pattern of the terminal device by the current user. . Therefore, it is desirable to prevent the replacement battery from running out and prevent the terminal device from becoming unusable.

  On the other hand, for example, there is a method of calculating a reference deterioration rate using a test battery and calculating a deterioration required time until the deterioration amount of the battery exceeds a threshold value for each terminal device based on the reference deterioration speed. Conceivable. Further, for example, the average value of the deterioration rate of the battery is calculated for each terminal device from the battery deterioration amount log for each terminal device, and the deterioration amount of the battery exceeds the threshold value based on the average value of the deterioration speed for each terminal device. A possible method is to calculate the required deterioration time. However, since these methods do not calculate the deterioration rate of the battery according to the usage pattern of the terminal device by the current user, it is possible to accurately calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value. I can't.

  In addition, the slope of the last line segment section of the polygonal line obtained by approximating the time change of the deterioration amount of the battery of the terminal device is calculated as the deterioration rate of the battery according to the usage pattern of the terminal device by the current user. A method of doing it is also possible. However, even with this method, it is difficult to accurately calculate the deterioration rate of the battery according to the usage pattern of the terminal device by the current user, and the deterioration required time until the deterioration amount of the battery exceeds the threshold value is accurately calculated. Difficult to do. For example, even if the same user is using the terminal device, the deterioration speed may change as a result of a temporary change in the usage of the terminal device.Therefore, the slope of the last line segment of the polygonal line depends on the current user. The deterioration rate of the battery according to the usage pattern of the terminal device is not always accurately represented.

  In addition, the outside workers tend to operate the terminal device with a battery, and the inside workers tend to operate the terminal device with a commercial power source. For this reason, it is possible to refer to the deterioration rate of cycle deterioration to predict the deterioration of the terminal device being used by an outside worker, and to refer to the deterioration speed of neglected deterioration to predict the deterioration of the terminal device being used by an office worker. . However, even with this method, it is difficult to accurately calculate the deterioration rate of the battery, and it is also difficult to accurately calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value. For example, both the outside worker and the inside worker do not always operate the terminal device with only one of the battery and the commercial power source, and the deterioration rate of the battery cannot be calculated accurately.

  In addition, the terminal device does not have a function of accumulating information indicating which user is in use, and may not have information indicating how the usage pattern of the terminal device has changed. . Therefore, it is difficult to calculate the deterioration rate of the battery according to the usage pattern of the terminal device by the current user of the terminal device.

  Therefore, in the present embodiment, the deterioration prediction can be performed for each terminal device based on the property that the variation in the deterioration rate of the battery of the terminal device for each usage mode of the terminal device is reduced by the central limit theorem. The deterioration prediction method will be described.

  In FIG. 1, the information processing device 100 acquires time-series data indicating a temporal change in the degree of deterioration of a battery for each terminal device. The information processing apparatus 100 acquires, for example, time-series data indicating a temporal change in the deterioration amount of the batteries of the terminal devices A and B.

  The information processing apparatus 100 generates, for each terminal device, one or more models that indicate a straight line or a polygonal line that approximates the time change of the deterioration degree of the battery of the terminal device, based on the acquired time series data. The model shows the result assuming a change in the usage pattern of the terminal device. The usage pattern is, for example, a usage pattern of an outside worker and a usage pattern of an office worker. The model showing a straight line indicates that the usage pattern of the terminal device does not change. The model showing the polygonal line indicates that the usage pattern of the terminal device has changed before and after the break point.

  The information processing apparatus 100 generates, for example, one or more models in consideration of the break point candidates when the time-dependent change of the deterioration degree of the battery of the terminal device is approximated by the broken line. The information processing apparatus 100 specifically generates models 101 and 102 for the terminal device A, and models 103 and 104 for the terminal device B. Here, the model for any of the terminal devices may be one of a model showing a straight line and a model showing a broken line.

  The information processing apparatus 100 associates, for each terminal device, any one of a plurality of usage forms of the terminal device with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models. Generate one or more patterns. The pattern indicates the result of assuming what the usage pattern of the terminal device is in each line segment. The information processing apparatus 100 generates, for example, one or more patterns in which the usage patterns 1 and 2 are associated with each line segment section included in a straight line or a polygonal line indicated by the models 101 to 104 and the like.

  The information processing apparatus 100 selects one of the one or more patterns generated for each terminal device to generate one or more combinations. The combination indicates the result of assuming what the usage pattern of each terminal device is in each line segment section. Then, the information processing apparatus 100 performs the battery deterioration prediction for each terminal device based on the variation in the inclination of the line segment for each usage pattern in each of one or more combinations. The slope represents the deterioration rate.

  The information processing apparatus 100 generates, for example, the combinations 111 to 114. Then, the information processing apparatus 100 performs the battery deterioration prediction for each terminal device based on the variation in the inclination of the line segment for each usage pattern in each combination such as the combinations 111 to 114. The variation in the slope of the line segment for each usage pattern is, for example, the variation shown in graphs 121 to 124. The variation in the slope of the line segment corresponds to the variation in the deterioration rate. The deterioration prediction is, for example, calculation of the deterioration speed of the battery or identification of the timing when the deterioration amount exceeds the threshold value.

  Here, due to the central limit theorem, the deterioration rate tends to form a unique statistical distribution for each usage pattern. For example, the deterioration rate tends to vary less depending on the usage pattern. Here, even if the usage patterns are the same, the deterioration rates tend to vary when the users differ, but the deterioration rates tend to vary less for all users who have the same usage patterns.

  Therefore, if the usage pattern of each terminal device in each line segment is correctly estimated, it is considered that the variation of the deterioration rate for each usage pattern becomes small. On the other hand, if the usage pattern of each terminal device in each line segment is erroneously estimated, the degradation rates included in different statistical distributions are correctly treated as the degradation rates included in the same statistical distribution. It is considered that the deterioration rate varies greatly depending on the usage pattern. Therefore, among the combinations generated by the information processing apparatus 100, the combination in which the variation in the deterioration rate for each usage pattern is the smallest corresponds to the result of correctly assuming the usage pattern in each line segment of each terminal device. It is a combination.

  Specifically, the information processing apparatus 100 calculates the variance of the slope of the line segment for each usage pattern for each of the combinations 111 to 114. Here, the information processing apparatus 100 determines that the combination 114 is the correct combination in which the variance of the slope of the line segment for each usage pattern is the smallest and the variation in the deterioration rate for each usage pattern is the smallest. Then, the information processing apparatus 100 calculates the statistical value of the slope of the line segment for each usage pattern in the patterns included in the combination 114 as the deterioration rate for each usage pattern. The statistical value is, for example, an average value, a maximum value, a minimum value, a mode value, a median value, or the like.

  Thereby, the information processing apparatus 100 can accurately calculate the deterioration rate of the battery for each usage pattern of the terminal device, without having information indicating how the usage pattern of the terminal device has changed. . In addition, the information processing apparatus 100 identifies the correct combination corresponding to the result of correctly assuming the usage pattern of each terminal device in each line segment, and therefore, even if the usage of the terminal device is temporarily changed, The deterioration rate can be calculated accurately.

  Then, the information processing apparatus 100 can calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold based on the deterioration speed, and determines how many replacement batteries are preferable. You can enable it.

  Specifically, the information processing apparatus 100 can calculate the deterioration required time by using the deterioration rate of the battery corresponding to the current usage pattern based on the information indicating the current usage pattern of the terminal device. Then, the information processing apparatus 100 can specifically determine how many replacement batteries should be prepared in consideration of the deterioration required time based on the information indicating the current usage pattern. .

  In addition, the information processing apparatus 100 specifically identifies the current usage pattern of the terminal device by referring to the combination determined to be the correct combination, and uses the deterioration rate of the battery corresponding to the current usage pattern to determine the deterioration. The required time may be calculated. Then, the information processing apparatus 100 can output the deterioration required time so that the system administrator can determine how many replacement batteries are preferably prepared.

(Example of deterioration prediction system 200)
Next, an example of the deterioration prediction system 200 to which the information processing device 100 shown in FIG. 1 is applied will be described with reference to FIG.

  FIG. 2 is an explanatory diagram showing an example of the deterioration prediction system 200. In FIG. 2, the deterioration prediction system 200 includes an information processing device 100 and a plurality of terminal devices 201. The deterioration prediction system 200 is used in, for example, a company.

  In the deterioration prediction system 200, the information processing device 100 and the terminal device 201 are connected via a wired or wireless network 210. The network 210 is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet. The information processing device 100 and the terminal device 201 may be directly connected.

  The information processing device 100 is a computer that executes a deterioration prediction of the battery of the terminal device 201. The information processing apparatus 100 receives, from the terminal device 201, time-series data indicating, for example, the time change of the deterioration degree of the battery of the terminal device 201. The time-series data is, for example, data that will be described later with reference to FIG. The information processing device 100 performs battery deterioration prediction for each terminal device 201 based on the received time series data. Specifically, the information processing apparatus 100 may collect time-series data at the beginning of the year and calculate the number of replacement batteries required within the year. The information processing device 100 is used by, for example, the administrator of the deterioration prediction system 200. The information processing device 100 is, for example, a server or a PC (Personal Computer).

  The terminal device 201 is a computer that has a battery and can be operated by the battery. The terminal device 201 can detect the degree of deterioration of the battery, and transmits time-series data indicating the time variation of the degree of deterioration of the battery of the terminal device 201 to the information processing device 100. There are a plurality of usage forms of the terminal device 201. The usage patterns of the terminal device 201 are, for example, a usage pattern by an outside worker and a usage pattern by an inside worker. The plurality of terminal devices 201 are separately used, for example, a terminal device 201 for outside work and a terminal device 201 for inside work. The terminal device 201 for outside work is used by an outside worker. The terminal device 201 for office work is used by office workers.

  In addition, the terminal device 201 can be switched between outside work and inside work. For example, when the battery of the terminal device 201 for outside work deteriorates above a certain level, the terminal device 201 for outside work is changed to inside work, and the terminal device 201 for inside work is changed to outside work. The terminal device 201 may be replaced. The terminal device 201 does not need to know whether the own device is for outside work or inside work, and does not need to store the information indicating whether the own device is outside work or inside work. The terminal device 201 is, for example, a tablet terminal, a smartphone, a wearable terminal, or the like. The terminal device 201 may be diskless.

  In the example of FIG. 2, the deterioration prediction system 200 is used in a company, and an example of a usage pattern of the terminal device 201 is a usage pattern by an outside worker and a usage pattern by an office worker, but the invention is not limited to this. For example, the deterioration prediction system 200 may be used in various places such as schools, restaurants, shopping malls, and hospitals.

  Specifically, the deterioration prediction system 200 may be used in a school, and an example of a usage pattern of the terminal device 201 may be a usage pattern by a student and a usage pattern by a teacher. Further, specifically, the deterioration prediction system 200 may be used in a hospital, and an example of a usage pattern of the terminal device 201 may be a usage pattern by a doctor and a usage pattern by a nurse. Further, specifically, the deterioration prediction system 200 may be used in a shopping mall, and an example of a usage pattern of the terminal device 201 may be a usage pattern by an inventory manager and a usage pattern by a customer service person.

(Another example of the deterioration prediction system 200)
Next, another example of the deterioration prediction system 200 to which the information processing apparatus 100 shown in FIG. 1 is applied will be described with reference to FIG.

  FIG. 3 is an explanatory diagram showing another example of the deterioration prediction system 200. In FIG. 3, the deterioration prediction system 200 includes the information processing device 100 and a plurality of terminal devices 201.

  In the deterioration prediction system 200, the information processing device 100 and the terminal device 201 are connected via a wired or wireless network 210. The network 210 is, for example, a LAN, WAN, the Internet, or the like. The information processing device 100 and the terminal device 201 may be directly connected.

  The information processing device 100 is a computer that executes a deterioration prediction of the battery of the terminal device 201. The information processing apparatus 100 receives, from the terminal device 201, time-series data indicating, for example, the time change of the deterioration degree of the battery of the terminal device 201. The time-series data is, for example, data that will be described later with reference to FIG. The information processing device 100 performs battery deterioration prediction for each terminal device 201 based on the received time series data. Specifically, the information processing apparatus 100 may collect time-series data at the beginning of the year and calculate the number of replacement batteries required within the year. The information processing device 100 is used by, for example, the administrator of the deterioration prediction system 200. The information processing device 100 is, for example, a server or a PC.

  The terminal device 201 is a computer that has a battery and can be operated by the battery. The terminal device 201 can detect the degree of deterioration of the battery, and transmits time-series data indicating the time variation of the degree of deterioration of the battery of the terminal device 201 to the information processing device 100. There are a plurality of usage forms of the terminal device 201. The usage patterns of the terminal device 201 are, for example, a usage pattern by sales, a usage pattern by a sales assistant, a usage pattern by a field SE (System Engineer), and a usage pattern by a development SE. The plurality of terminal devices 201 are separately used as a business terminal device 201, a business assistant terminal device 201, a field SE terminal device 201, and a development SE terminal device 201. The business terminal device 201 is used by a business user. The terminal device 201 for sales assistant is used by the user who is the sales assistant. The terminal device 201 for the field SE is used by the user who is the field SE. The terminal device 201 for the development SE is used by the user who is the development SE.

  In addition, the terminal device 201 can be changed to one for sales, one for sales assistant, one for field SE, and one for development SE. For example, when the battery of the sales terminal device 201 is deteriorated to a certain degree or more, the sales assistant terminal device 201 is changed to sales, the sales terminal device 201 is changed to sales assistant, and the sales assistant and the sales assistant are used. The terminal device 201 may be replaced depending on the purpose. The terminal device 201 does not have to grasp the usage pattern of the own device and does not have to store the information indicating the usage pattern of the own device. The terminal device 201 is, for example, a tablet terminal, a smartphone, a wearable terminal, or the like. The terminal device 201 may be diskless. As described above, the usage pattern of the terminal device 201 is not limited to two and may be three or more.

(Example of hardware configuration of information processing apparatus 100)
Next, a hardware configuration example of the information processing device 100 included in the deterioration prediction system 200 shown in FIG. 2 will be described with reference to FIG.

  FIG. 4 is a block diagram showing a hardware configuration example of the information processing apparatus 100. In FIG. 4, the information processing device 100 includes a CPU (Central Processing Unit) 401, a memory 402, a network I / F (Interface) 403, a recording medium I / F 404, and a recording medium 405. Further, each component is connected by a bus 400.

  Here, the CPU 401 controls the entire information processing apparatus 100. The memory 402 includes, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), and a flash ROM. Specifically, for example, a flash ROM or a ROM stores various programs, and a RAM is used as a work area of the CPU 401. The program stored in the memory 402 is loaded into the CPU 401 to cause the CPU 401 to execute the coded process.

  The network I / F 403 is connected to the network 210 via a communication line and is connected to another computer via the network 210. The network I / F 403 administers an internal interface with the network 210 and controls input / output of data from / to another computer. For the network I / F 403, for example, a modem or LAN adapter can be adopted.

  The recording medium I / F 404 controls reading / writing of data with respect to the recording medium 405 under the control of the CPU 401. The recording medium I / F 404 is, for example, a disk drive, an SSD (Solid State Drive), a USB (Universal Serial Bus) port, or the like. The recording medium 405 is a non-volatile memory that stores data written under the control of the recording medium I / F 404. The recording medium 405 is, for example, a disk, a semiconductor memory, a USB memory, or the like. The recording medium 405 may be removable from the information processing device 100.

  The information processing apparatus 100 may include, for example, a keyboard, a mouse, a display, a printer, a scanner, a microphone, a speaker, and the like, in addition to the above-described components. Further, the information processing apparatus 100 may include a plurality of recording medium I / Fs 404 and recording media 405. Further, the information processing apparatus 100 may not have the recording medium I / F 404 or the recording medium 405.

(Memory contents of deterioration amount information table 500)
Next, an example of the stored contents of the deterioration amount information table 500 will be described with reference to FIG. The deterioration amount information table 500 is realized by, for example, a storage area such as the memory 402 or the recording medium 405 of the information processing apparatus 100 illustrated in FIG.

  FIG. 5 is an explanatory diagram showing an example of the stored contents of the deterioration amount information table 500. As shown in FIG. 5, the deterioration amount information table 500 has fields of PC, date and time, deterioration amount, and current work attribute. The deterioration amount information table 500 stores the deterioration amount information as a record by setting information in each field for each terminal device 201.

  The name of the terminal device 201 is set in the PC field. In the date and time field, the date and time when the deterioration amount of the battery of the terminal device 201 is measured is set. The deterioration amount of the battery of the terminal device 201 is set in the deterioration amount field. The current work attribute field is set with the work attribute of the current user of the terminal device 201, which indicates the current usage pattern of the terminal device 201.

(Example of hardware configuration of terminal device 201)
Next, a hardware configuration example of the terminal device 201 included in the deterioration prediction system 200 shown in FIG. 2 will be described with reference to FIG.

  FIG. 6 is a block diagram showing a hardware configuration example of the terminal device 201. In FIG. 6, the terminal device 201 includes a CPU 601, a memory 602, and a network I / F 603. Further, each component is connected by a bus 600.

  Here, the CPU 601 controls the entire terminal device 201. The memory 602 includes, for example, ROM, RAM, flash ROM, and the like. Specifically, for example, a flash ROM or a ROM stores various programs, and a RAM is used as a work area of the CPU 601. The program stored in the memory 602 is loaded into the CPU 601 to cause the CPU 601 to execute the coded processing.

  The network I / F 603 is connected to the network 210 via a communication line and is connected to another computer via the network 210. The network I / F 603 administers an internal interface with the network 210 and controls the input / output of data from / to another computer. For the network I / F 603, for example, a modem or a LAN adapter can be adopted.

  The terminal device 201 may include, for example, a keyboard, a mouse, a display, a printer, a scanner, a microphone, a speaker, and the like, in addition to the above-described components. Further, the terminal device 201 may include a recording medium I / F or a recording medium.

(Example of functional configuration of information processing apparatus 100)
Next, a functional configuration example of the information processing device 100 will be described with reference to FIG. 7.

  FIG. 7 is a block diagram showing a functional configuration example of the information processing apparatus 100. The information processing device 100 includes a storage unit 700, an acquisition unit 701, a generation unit 702, a calculation unit 703, a prediction unit 704, and an output unit 705.

  The storage unit 700 is realized by, for example, a storage area such as the memory 402 or the recording medium 405 shown in FIG. The case where the storage unit 700 is included in the information processing device 100 will be described below, but the storage unit 700 is not limited to this. For example, the storage unit 700 may be included in a device different from the information processing device 100, and the storage content of the storage unit 700 may be referred to by the information processing device 100.

  The acquisition unit 701 to the output unit 705 function as an example of a control unit. Specifically, the acquisition unit 701 to the output unit 705 cause, for example, the CPU 401 to execute a program stored in a storage area such as the memory 402 or the recording medium 405 illustrated in FIG. 4, or the network I / F 403. To realize that function. The processing result of each functional unit is stored in a storage area such as the memory 402 or the recording medium 405 shown in FIG. 4, for example.

  The storage unit 700 stores various information that is referred to or updated in the processing of each functional unit. The storage unit 700 stores, for example, time-series data indicating a temporal change in the degree of deterioration of the battery for each terminal device 201. The terminal device 201 is included in, for example, a thin client type system. The battery is, for example, a lithium ion battery. The deterioration of the battery of the terminal device 201 is a decrease in the battery capacity when the battery is fully charged. Specifically, the storage unit 700 stores the time-series data using the deterioration amount information table 500 shown in FIG.

  The storage unit 700 stores, for example, a plurality of usage patterns of the terminal device 201. The plurality of usage patterns include, for example, a usage pattern by an outside worker and a usage pattern by an inside worker. The plurality of usage forms may include usage forms by users engaged in marketing, sales, sales assistant, inside sales, clerical work, development SE, field SE, factory work, and the like. Further, the plurality of usage patterns may include usage patterns by users having different positions engaged in the same work. The plurality of usage patterns may be, for example, a first usage pattern and a second usage pattern that do not indicate a specific usage pattern. Specifically, the storage unit 700 stores outside work and inside work as work attributes indicating the usage pattern of the terminal device 201. Further, the storage unit 700 may specifically store sales, a sales assistant, a field SE, and a development SE as work attributes indicating the usage pattern of the terminal device 201.

  In addition, the storage unit 700 stores, for example, a model indicating a straight line or a polygonal line that approximates the time change of the deterioration degree of the battery of the terminal device 201. The model shows a result assuming a change in the usage pattern of the terminal device 201. The model showing a straight line indicates that the usage pattern of the terminal device 201 does not change. The model indicating the polygonal line indicates that the usage pattern of the terminal device 201 has changed before and after the break point. The model for any of the terminal devices 201 may be either a straight line model or a broken line model.

  Further, the storage unit 700 stores, for example, a pattern in which each line segment section included in the straight line or the polygonal line indicated by the model is associated with any one of the plurality of usage patterns of the terminal device 201. The pattern indicates the result of assuming what the usage pattern of the terminal device 201 is in each line segment. The storage unit 700 also stores, for example, a combination in which patterns are selected and combined for each terminal device 201. The combination indicates the result of assuming what the usage pattern of each terminal device 201 in each line segment is.

  The acquisition unit 701 acquires various types of information used for the processing of each functional unit. The acquisition unit 701 stores the acquired various information in the storage unit 700 or outputs the acquired information to each functional unit. Further, the acquisition unit 701 may output various information stored in the storage unit 700 to each functional unit. The acquisition unit 701 acquires various types of information based on, for example, an operation input by the administrator. The acquisition unit 701 may receive various types of information from a device different from the information processing device 100, for example.

  The acquisition unit 701 acquires time-series data indicating a temporal change in the degree of deterioration of the battery for each terminal device 201. The acquisition unit 701 receives from the terminal device 201, for example, time-series data indicating a temporal change in the degree of deterioration of the battery of the terminal device 201. As a result, the acquisition unit 701 can acquire the time-series data used to predict the deterioration of the battery of the terminal device 201 and start the processing of the generation unit 702.

  The acquisition unit 701 may acquire data indicating the usage pattern at the first time point for each terminal device 201. The first time point is, for example, the current time point. The first time point may be a past time point. The acquisition unit 701 acquires, for example, the current work attribute of the user for each terminal device 201 based on the operation input by the administrator. Accordingly, the acquisition unit 701 can improve the processing efficiency of the generation unit 702.

  The generation unit 702 generates, for each terminal device 201, one or more models that indicate a straight line or a polygonal line that approximates the time change of the deterioration degree of the battery of the terminal device 201, based on the acquired time series data. The generation unit 702 generates, for example, one or more models in consideration of the break point candidates when the time change of the deterioration degree of the battery of the terminal device 201 is approximated by a broken line. Accordingly, the generation unit 702 can assume various conceivable ways of change as the way of change that indicates when the usage pattern of the terminal device 201 has changed.

  The generation unit 702 associates, for each terminal device 201, each usage pattern of a plurality of usage patterns of the terminal device 201 with each line segment section included in a straight line or a polygonal line indicated by each model of the identified one or more models. 1 or more patterns are generated. For example, the generation unit 702 associates, for each terminal device 201, a work attribute of either outside work or inside work with each line segment section included in a straight line or a polygonal line indicated by each model of the identified one or more models. Generate one or more patterns.

  Specifically, the generation unit 702 preferably generates one or more patterns so that the usage patterns associated with the adjacent line segment sections included in the straight lines or polygonal lines indicated by the respective models are different. As a result, the generation unit 702 can assume various conceivable ways of change as the way of change that represents how the usage pattern of the terminal device 201 has changed in each line segment.

  For example, the generation unit 702 may generate one or more patterns for each terminal device 201 based on the acquired data indicating the usage pattern at the first time point. Specifically, the generation unit 702 associates the usage pattern at the first time point with the line segment section corresponding to the first time point among the straight lines or polygonal lines indicated by the respective models, and the adjacent line segment section. , One or more patterns are generated so that different usage patterns are associated with each other. Thereby, the generation unit 702 can improve the processing efficiency and the processing accuracy without generating a pattern in which it is clear that it shows a wrong assumption in consideration of the usage pattern at the first time point. Further, the generation unit 702 can assume various conceivable ways of change as the way of change that represents how the usage pattern of the terminal device 201 has changed in the line segment.

  For example, the generation unit 702 sets, for each terminal device 201, one of the first usage pattern and the second usage pattern in each line segment section included in a straight line or a polygonal line indicated by each model of the identified one or more models. One or more associated patterns may be generated. As a result, if the number of usage patterns is known, the generation unit 702 can perform the process even if it is unknown what the usage pattern is.

  The generation unit 702 generates one or more combinations in which any one of the one or more patterns generated for each terminal device 201 is selected and combined. As a result, the generation unit 702 assumes various conceivable ways of change as the way of change that represents how the usage pattern of the terminal device 201 has changed in each line segment of each terminal device 201. You can

  The calculation unit 703 calculates the slope of the line segment for each usage pattern in each combination of one or more combinations. The slope represents the deterioration rate. The calculation unit 703 calculates an index value indicating the variation in the slope of the line segment for each usage pattern in each of one or more combinations. The calculation unit 703 calculates the variance of the slope of the line segment section for each usage pattern in each combination of one or more combinations, for example. As a result, the calculation unit 703 is a line for each usage pattern showing the variation of the deterioration rate for each usage pattern, which is an index for determining which combination is the correct combination among the combinations generated by the generation unit 702. The variance of the slope of the minute section can be calculated.

  The prediction unit 704 performs battery deterioration prediction for each terminal device 201 based on the variation in the slope of the line segment section for each usage pattern in each of one or more combinations. The prediction unit 704 specifies, for example, of one or more combinations, a combination in which the total value obtained by adding the variances of the slopes of the line segment for each usage pattern is the smallest. Thereby, the prediction unit 704 can identify the correct combination corresponding to the result of correctly assuming the usage pattern of each terminal device 201 in each line segment section. Then, the prediction unit 704 can accurately perform the battery deterioration prediction for each terminal device 201 based on the correct combination.

  The prediction unit 704 performs battery deterioration prediction for each terminal device 201 based on the statistical value of the slope of the line segment for each usage pattern in the identified combination. The statistical value is, for example, an average value, a maximum value, a minimum value, a mode value, a median value, or the like.

  The prediction unit 704 calculates the deterioration rate of the battery for each terminal device 201, for example, based on the statistical value of the slope of the line segment for each usage pattern in the specified combination. Specifically, the prediction unit 704 calculates the statistical value of the slope of the line segment corresponding to the current usage pattern of the terminal device 201 based on the data indicating the current usage pattern of the terminal device 201. It is calculated as the deterioration rate of the battery.

  Accordingly, the prediction unit 704 can accurately calculate the deterioration rate of the battery for each terminal device 201 without having data indicating how the usage pattern of the terminal device 201 has changed. Then, the prediction unit 704 can calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value based on the deterioration speed, and can determine how many replacement batteries are preferable. Can be

  Specifically, the prediction unit 704 specifies the current usage pattern of the terminal device 201 by referring to the combination determined to be the correct combination without using the data indicating the current usage pattern of the terminal device 201. Good. Then, the prediction unit 704 calculates a statistical value of the slope of the line segment corresponding to the current usage pattern of the terminal device 201 as the deterioration rate of the battery of the terminal device 201.

  Accordingly, the prediction unit 704 can accurately calculate the deterioration rate of the battery for each terminal device 201 without having data indicating how the usage pattern of the terminal device 201 has changed. Then, the prediction unit 704 can calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value based on the deterioration speed, and can determine how many replacement batteries are preferable. Can be

  The prediction unit 704 identifies the timing at which the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value, based on the statistical value of the slope of the line segment for each usage pattern. The prediction unit 704 specifies the timing at which the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value, using the deterioration rate of the battery corresponding to the current usage pattern, for example.

  Specifically, the prediction unit 704 uses the battery deterioration rate calculated for each terminal device 201 to identify the timing at which the battery deterioration amount for each terminal device 201 exceeds the threshold value. Accordingly, the prediction unit 704 can accurately specify the timing when the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value. Then, the prediction unit 704 can determine how many replacement batteries are preferably prepared based on the identified timing.

  The prediction unit 704 preferably refers to the timing at which the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value, based on the data indicating the current usage pattern of the terminal device 201, and preferably prepares how many batteries for replacement. Or calculate. As a result, the predicting unit 704 can make it easier for the administrator to know how many replacement batteries are to be prepared, and can easily prevent a decrease in work efficiency and data loss of the terminal device 201.

  The output unit 705 outputs the execution result of the deterioration prediction. The output format is, for example, display on a display, print output to a printer, transmission to an external device by the network I / F 403, or storage in a storage area such as the memory 402 or the recording medium 405.

  The output unit 705 outputs, for example, the deterioration rate of the battery for each terminal device 201. Thereby, the output unit 705 can allow the administrator to calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value based on the deterioration speed, and how many replacement batteries can be prepared. It is possible to determine whether it is preferable.

  The output unit 705 may output the timing at which the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value, for example. Accordingly, the output unit 705 can allow the administrator to determine how many replacement batteries are preferably prepared.

  The output unit 705 may output, for example, how many replacement batteries are preferably prepared. Accordingly, the output unit 705 can make the administrator know how many replacement batteries are preferably prepared.

(Operation example of deterioration prediction system 200)
Next, an operation example of the deterioration prediction system 200 will be described with reference to FIGS.

  8 to 12 are explanatory diagrams showing an operation example of the deterioration prediction system 200. In the example of FIG. 8, the deterioration prediction system 200 includes the information processing device 100, a terminal device 201 for outside work, and a terminal device 201 for inside work.

  In FIG. 8, the terminal device 201 transmits, to the information processing device 100, time-series data indicating a time change of the deterioration amount of the battery of the terminal device 201. The information processing device 100 receives the time series data indicating the time change of the deterioration amount of the battery of each terminal device 201. Further, the information processing device 100 acquires the current work attribute indicating the current usage pattern of each terminal device 201. The information processing apparatus 100 stores the time series data indicating the time variation of the deterioration amount of the battery of each terminal device 201 and the current work attribute using the deterioration amount information table 500. Next, the description moves to FIG.

  In FIG. 9, the information processing apparatus 100 generates a model showing a straight line or a polygonal line that approximates the time change of the deterioration amount of the battery of each terminal device 201. In the following description, examples of the terminal device 201 for outside work will be referred to as “terminal device A” and “terminal device B”, and an example of the terminal device 201 for inside work will be described as “terminal device a”.

  The information processing apparatus 100, for example, applies a polygonal line regression to time-series data indicating a time change of the deterioration amount of the battery of the terminal device A. Then, the information processing apparatus 100 learns the break points 901 and 902 when the time variation of the deterioration amount of the battery of the terminal device A is approximated by the broken line 900.

  Next, the information processing apparatus 100 considers, for example, whether or not to use the learned break points 901 and 902, and creates a model showing a straight line or a broken line that approximates the time change of the deterioration amount of the battery of the terminal device A. To generate. Specifically, the information processing apparatus 100 generates a model 910 showing a straight line without using the break points 901 and 902. Further, the information processing apparatus 100 specifically uses the break point 901 to generate a model 920 indicating a broken line. Further, the information processing apparatus 100 specifically uses the break point 902 to generate a model 930 indicating a broken line. Further, the information processing apparatus 100 specifically uses the break points 901 and 902 to generate a model 940 indicating a broken line.

  Next, the information processing apparatus 100, for example, in consideration of the fact that the current work attribute of the terminal device A is outside work, the outside work or inside work is performed in each line segment included in the straight line or the polygonal line indicated by the models 910 to 940. Patterns 1 to 4 associated with attributes are generated.

  Pattern 1 is a pattern in which a straight line indicated by the model 910 is associated with outside work. Further, pattern 2 is a pattern in which out-of-work is associated with a line segment section behind the break point 901 in the polygonal line indicated by the model 920, and inside work is associated with a line segment section before the break point 901. Further, pattern 3 is a pattern in which out-of-work is associated with a line segment section behind the break point 902 in the polygonal line indicated by the model 930, and inside work is associated with a line segment section before the break point 902. Further, in the pattern 4, in the polygonal line indicated by the model 940, the line segment section behind the folding point 902 is associated with outside work, and the line segment section between the folding point 901 and the folding point 902 is associated with internal work. This is a pattern in which the line work section in front of the break point 901 is associated with outside work.

  Next, the information processing apparatus 100 calculates, for each of patterns 1 to 4, a regression coefficient of each line segment section in which the outside work is associated and a regression coefficient of each line segment section in which the office work is associated. The regression coefficient is the slope of the line segment and represents the deterioration rate. The information processing apparatus 100 similarly generates a pattern for the terminal device B and the terminal device a. Next, the description moves to FIG.

  In FIG. 10, the information processing apparatus 100 generates one or more combinations by selecting and combining one of the one or more patterns generated for each terminal device 201. Then, the information processing apparatus 100 calculates, for each combination, the variance of the regression coefficient of the line segment in which the office work is associated and the variance of the regression coefficient of the line segment in which the office work is associated, and calculates the sum of the variances. calculate. The information processing apparatus 100 stores the calculation result as shown in Table 1010, for example.

  Here, in the correct combination, as shown in the graph 1000, both the variance of the regression coefficient of the line segment in which the office work is associated and the variance of the regression coefficient of the line segment in which the office work is associated tend to be small. There is. Therefore, the information processing apparatus 100 specifies the combination 22 having the smallest sum of variances as the correct combination. Next, the description moves to FIG. 11.

  In FIG. 11, the information processing apparatus 100 calculates, as the deterioration rate of the battery of the office terminal device 201 for office work, the mode value of the regression coefficient in the line segment section in which the specified combination 22 corresponds to office work. In addition, the information processing apparatus 100 calculates, as the deterioration rate of the battery of the out-of-office terminal device 201, the mode value of the regression coefficient of the line segment section in which the identified combination 22 is associated with out-of-office. The information processing apparatus 100 stores the calculation result as shown in Table 1100, for example.

  In the information processing apparatus 100, since the current work attribute of the terminal device A is out-of-work, the time taken for the battery deterioration amount to exceed the threshold value by using the calculated battery deterioration rate of the out-of-work terminal device 201 is a predetermined time. It is determined to be less than. Further, in the information processing apparatus 100, since the current work attribute of the terminal device a is inside work, the time taken for the battery deterioration amount to exceed the threshold value by using the calculated battery deterioration speed of the terminal device 201 for outside work. It is determined that it is longer than the predetermined time.

  Therefore, the information processing apparatus 100 replaces the batteries of the terminal device A and the terminal device a or replaces the terminal device A and the terminal device a at the timing when the deterioration amount of the battery of the terminal device A exceeds the threshold value. Decide the schedule for switching between outside and inside work. Accordingly, the information processing apparatus 100 can operate the terminal device A and the terminal device a until a predetermined time elapses, without replacing the battery of the terminal device A with a new one.

  Here, for example, a change in the deterioration amount of the battery of the terminal device A becomes a change shown in a graph 1110. Further, for example, a change in the deterioration amount of the battery of the terminal device a becomes a change shown in a graph 1120. Next, the description moves to FIG.

  In the example of FIG. 12, the information processing apparatus 100 has a terminal α being used by the outside worker X at time 50, a terminal β being used by the inside worker Y at time 50, and a terminal β being used by the outside worker Z at time 50. The result of performing the deterioration prediction with the terminal γ is shown.

  As illustrated in graphs 1200 to 1220 of FIG. 12, the information processing apparatus 100 correctly associates the work attribute with each line segment section of the polygonal line that approximates the time change of the deterioration amount of the battery of each terminal device 201, and deteriorates. Predictions can be made. The information processing apparatus 100 can correctly associate the work attributes with each other as shown in the graph 1200 and perform the deterioration prediction. Specifically, as shown in the graph 1200, the information processing apparatus 100 assumes that the usage pattern has changed at time 20, and associates inside work before time 20 and associates outside work after time 20. Then, the information processing apparatus 100 determines whether or not the amount of deterioration of the battery of the terminal device α exceeds the threshold by time 100, for example.

  In addition, the information processing apparatus 100, even if the deterioration speed changes in the portion 1211 after the time 45 in the graph 1210 due to a temporary change in the usage method of the office worker Y, as shown in the graph 1210, the work attribute is displayed. Can be correctly associated and the deterioration prediction can be performed. Specifically, as shown in a graph 1210, the information processing apparatus 100 assumes that the usage pattern has changed at time 18, associates outside work before time 18, and associates inside work after time 18. Then, the information processing apparatus 100 determines whether the deterioration amount of the battery of the terminal device β exceeds the threshold by time 100, for example. In the example of FIG. 12, the information processing apparatus 100 can determine that the deterioration amount of the battery of the terminal device β does not exceed the threshold by the time 100. In the example of FIG. 12, the information processing apparatus 100 can determine that the deterioration amount of the battery of the terminal device α exceeds the threshold by the time 100.

  Here, when the conventional device performs the deterioration prediction, for example, the time change of the deterioration amount of the battery of the terminal device 201 is approximated to a polygonal line, and the portion 1211 after the time 45 is treated as the last line segment section. Tend to end up. Therefore, the conventional device erroneously determines that the deterioration amount of the battery of the terminal device β exceeds the threshold by time 100, using the inclination of the portion 1211 after time 45 as the deterioration speed of the battery. There is a risk that On the other hand, the information processing apparatus 100 can accurately determine whether or not the deterioration amount of the battery of the terminal device β exceeds the threshold by the time 100.

  Similarly, the information processing apparatus 100 corrects the work attributes as shown in the graph 1220 even if the deterioration rate changes in a part of the graph 1220 due to a temporary change in the usage method of the office worker Z. The deterioration prediction can be performed in association with each other. Specifically, the information processing apparatus 100, as shown in the graph 1220, assumes that the usage pattern has changed at time 10, associates outside work before time 10 and associates inside work after time 10. Then, the information processing apparatus 100 determines whether or not the deterioration amount of the battery of the terminal device γ exceeds the threshold by time 100, for example. In the example of FIG. 12, the information processing apparatus 100 can determine that the deterioration amount of the battery of the terminal device γ exceeds the threshold by time 100.

  Accordingly, the information processing apparatus 100 can accurately perform the battery deterioration prediction for each terminal device 201 without having information indicating how the usage pattern of the terminal device 201 has changed. Further, the information processing apparatus 100 accurately predicts the deterioration of the battery of each terminal device 201 even if the usage method of the terminal device 201 changes temporarily while the same user is using the terminal device 201. be able to. Then, the information processing apparatus 100 can make it easier for the administrator to know how many batteries for replacement are preferably prepared, and to prevent a decrease in work efficiency and data loss of the terminal device 201.

(Overall processing procedure)
Next, an example of the overall processing procedure executed by the information processing apparatus 100 will be described with reference to FIGS. 13 and 14. The entire process is realized by, for example, the CPU 401 shown in FIG. 4, a storage area such as the memory 402 and the recording medium 405, and the network I / F 403.

  13 and 14 are flowcharts showing an example of the overall processing procedure. In FIG. 13, the information processing apparatus 100 acquires the deterioration amount time-series data and the work attribute of each terminal device 201 (step S1301). Next, the information processing apparatus 100 sets i = 1 (step S1302). Then, the information processing apparatus 100 transitions to the processing of step S1303.

  In step S1303, the information processing apparatus 100 selects the i-th terminal device 201 (step S1303). Next, the information processing apparatus 100 performs break point estimation on the deterioration amount time-series data of the i-th terminal device 201 and extracts a break point candidate (step S1304). Then, the information processing apparatus 100 generates a regression pattern in which each line segment section that is included in each of the straight line and the polygonal line that approximates the time change of the deterioration amount is associated with the work attribute of outside work or inside work (step S1305).

  Next, the information processing apparatus 100 calculates a regression coefficient for outside work and a regression coefficient for inside work for each of the generated regression patterns (step S1306). Then, the information processing apparatus 100 determines whether all the terminal devices 201 have been selected (step S1307).

  Here, when there is an unselected terminal device 201 (step S1307: No), the information processing apparatus 100 sets i = i + 1 (step S1308). Then, the information processing apparatus 100 returns to the process of step S1303.

  On the other hand, when all the terminal devices 201 have been selected (step S1307: Yes), the information processing apparatus 100 proceeds to the process of step S1401 in FIG.

  In FIG. 14, the information processing apparatus 100 identifies a combination of regression patterns (step S1401). Next, the information processing apparatus 100 calculates, for each combination, the variance of the regression coefficient for outside work and the variance of the regression coefficient for inside work (step S1402). Then, the information processing apparatus 100 identifies a combination in which the total value of the variance relating to outside work and the variance relating to inside work is the smallest (step S1403).

  Next, the information processing apparatus 100 calculates the mode value of the regression coefficient for outside work and the mode value of the regression coefficient for inside work in the specified combination (step S1404). Then, the information processing apparatus 100 executes deterioration diagnosis processing described later in FIG. 15 (step S1405). After that, the information processing apparatus 100 ends the entire process. As a result, the information processing apparatus 100 can accurately predict the deterioration of the battery.

(Degradation diagnosis processing procedure)
Next, an example of the deterioration diagnosis processing procedure executed by the information processing apparatus 100 will be described with reference to FIG. The deterioration diagnosis process is realized by, for example, the CPU 401 shown in FIG. 4, a storage area such as the memory 402 and the recording medium 405, and the network I / F 403.

  FIG. 15 is a flowchart showing an example of the deterioration diagnosis processing procedure. In FIG. 15, the information processing apparatus 100 determines whether or not there is a terminal device 201 for outside work in which the deterioration amount reaches a threshold value within a predetermined period, based on the mode of the regression coefficient regarding outside work (step S1501). .

  Here, when there is no outside office terminal device 201 whose deterioration amount reaches the threshold value within the predetermined period (step S1501: No), the information processing device 100 ends the deterioration diagnosis process. On the other hand, when there is an outside office terminal device 201 whose deterioration amount reaches the threshold value within the predetermined period (step S1501: Yes), the information processing apparatus 100 transitions to the process of step S1502.

  In step S1502, the information processing apparatus 100 identifies the terminal device 201 for out-of-office use where the deterioration amount reaches the threshold value earliest (step S1502). Then, the information processing apparatus 100 determines whether or not there is the inside work terminal device 201 whose deterioration amount is less than the threshold value within the time required until the deterioration amount of the specified outside work terminal device 201 reaches the threshold value. Yes (step S1503).

  Here, when there is the office work terminal device 201 whose deterioration amount is less than the threshold value (step S1503: Yes), the information processing apparatus 100 proceeds to the process of step S1504. On the other hand, when there is no office work terminal device 201 whose deterioration amount is less than the threshold value (step S1503: No), the information processing apparatus 100 proceeds to the process of step S1505.

  In step S1504, the information processing apparatus 100 changes the specified outside work terminal device 201 to inside work, and changes the inside work terminal device 201 with the smallest deterioration amount to outside work (step S1504). Then, the information processing apparatus 100 returns to the process of step S1501.

  In step S1505, the information processing device 100 increments the required number of batteries (step S1505). Then, the information processing apparatus 100 returns to the process of step S1501. Accordingly, the information processing apparatus 100 can calculate the required number of batteries, which indicates the number of replacement batteries that are preferably prepared. Then, the information processing apparatus 100 can notify the user of the required number of batteries and can suppress a decrease in work efficiency.

  As described above, according to the information processing device 100, it is possible to acquire time-series data indicating a temporal change in the degree of deterioration of the battery for each terminal device 201. According to the information processing device 100, for each terminal device 201, based on the acquired time-series data, one or more models showing a straight line or a polygonal line that approximates the time change of the degree of deterioration of the battery of the terminal device 201 are generated. You can According to the information processing apparatus 100, for each terminal device 201, a usage pattern of any of a plurality of usage patterns of the terminal device 201 in each line segment section included in a straight line or a polygonal line indicated by each model of the identified one or more models It is possible to generate one or more patterns that are associated with each other. According to the information processing apparatus 100, it is possible to generate one or more combinations in which any one of the one or more patterns generated for each terminal device 201 is selected and combined. According to the information processing apparatus 100, it is possible to perform the battery deterioration prediction for each terminal device 201 based on the variation in the inclination of the line segment for each usage pattern in each of one or more combinations.

  Accordingly, the information processing apparatus 100 can accurately calculate the deterioration rate of the battery for each usage pattern of the terminal device 201, even if the information processing apparatus 100 does not have information indicating how the usage pattern of the terminal device 201 has changed. You can Further, since the information processing apparatus 100 identifies the correct combination corresponding to the result of correctly assuming the usage pattern of each terminal device 201 in each line segment section, even if the usage of the terminal device 201 is temporarily changed. , The deterioration rate of the battery can be calculated accurately. Then, the information processing apparatus 100 can calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold based on the deterioration speed, and determines how many replacement batteries are preferable. You can enable it. As a result, the information processing apparatus 100 can easily prevent the work efficiency of the user from decreasing and the data loss of the terminal device 201.

  According to the information processing apparatus 100, it is possible to acquire data indicating the usage pattern at the first time point for each terminal device 201. According to the information processing device 100, one or more terminal devices 201 are provided such that the usage patterns associated with adjacent line segment sections included in a straight line or a polygonal line indicated by each model are different based on the acquired data. Pattern can be generated. As a result, the information processing apparatus 100 can improve the processing efficiency and the processing accuracy without generating a pattern in which it is clear that the assumption is wrong when the usage pattern at the first point of time is taken into consideration.

  According to the information processing apparatus 100, a usage pattern by an outside worker and a usage pattern by an office worker can be used as a plurality of usage patterns. As a result, the information processing apparatus 100 can perform the deterioration prediction of the batteries of the terminal device 201 for outside work and the terminal device 201 for inside work.

  According to the information processing device 100, it is possible to identify, from one or more combinations, a combination having the minimum total value obtained by adding the variances of the slopes of the line segment for each usage pattern. According to the information processing device 100, it is possible to perform the deterioration prediction of the battery for each terminal device 201 based on the statistical value of the slope of the line segment for each usage pattern in the specified combination. As a result, the information processing apparatus 100 can specify the correct combination corresponding to the result of correctly assuming the usage pattern of each terminal device 201 in each line segment. For this reason, the information processing apparatus 100 can accurately calculate the deterioration rate of the battery even if the usage of the terminal device 201 temporarily changes even though the same user.

  According to the information processing device 100, the deterioration rate of the battery for each terminal device 201 can be calculated based on the statistical value of the slope of the line segment for each usage pattern. Accordingly, the information processing apparatus 100 can calculate the deterioration required time until the deterioration amount of the battery exceeds the threshold value based on the deterioration speed, and how many replacement batteries should be prepared. Can be made judgmental.

  According to the information processing device 100, it is possible to specify the timing at which the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value, based on the statistical value of the slope of the line segment for each usage pattern. As a result, the information processing apparatus 100 can accurately specify the timing at which the degree of deterioration of the battery for each terminal device 201 exceeds the threshold value. Then, the information processing apparatus 100 can determine how many replacement batteries are preferably prepared based on the identified timing.

  The information processing apparatus 100 can be applied to the terminal device 201 used as a thin client. As a result, the information processing apparatus 100 accurately calculates the deterioration rate of the battery for each usage pattern of the terminal device 201 even when the information processing device 100 does not acquire the information indicating how the usage pattern of the terminal device 201 has changed. can do.

  The deterioration prediction method described in the present embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. The deterioration prediction program described in this embodiment is recorded in a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, or a DVD, and is executed by being read from the recording medium by the computer. The deterioration prediction program described in the present embodiment may be distributed via a network such as the Internet.

  Regarding the above-described embodiment, the following supplementary notes are further disclosed.

(Supplementary note 1) For a computer,
Obtaining time-series data showing the time change of the deterioration degree of the battery for each terminal device,
For each of the terminal devices, based on the acquired time-series data, considering the break point candidates when the time change of the deterioration degree of the battery of the terminal device is approximated by a broken line, the deterioration degree of the battery of the terminal device Generate one or more models showing straight lines or polygonal lines approximating time changes,
For each of the terminal devices, one or more that associates one of the usage patterns of the terminal device with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models Generate a pattern,
Based on the variation in the slope of the line segment for each usage pattern in each of the one or more combinations in which any one of the one or more patterns generated for each terminal device is selected and combined, Performs battery deterioration prediction for each terminal device,
A deterioration prediction program that executes processing.

(Supplementary note 2) In the computer,
Acquiring data indicating the usage pattern at the first time point for each terminal device, executing processing,
In the process of generating the one or more patterns, the usage patterns associated with the adjacent line segment sections included in the straight lines or polygonal lines indicated by the respective models are different for each terminal device based on the acquired data. Thus, the deterioration prediction program according to appendix 1, wherein the one or more patterns are generated.

(Supplementary note 3) The deterioration prediction program according to Supplementary note 1 or 2, wherein the plurality of usage patterns include a usage pattern by an outside worker and a usage pattern by an inside worker.

(Supplementary Note 4) The processing to be performed is a line segment section for each of the usage patterns in a combination of the one or more combinations in which the total value obtained by adding the variances of the slopes of the line segment sections for each of the usage patterns is minimum The deterioration prediction program according to any one of appendices 1 to 3, wherein the deterioration prediction of the battery is performed for each terminal device based on the statistical value of the slope.

(Supplementary Note 5) In the processing to be performed, the deterioration rate of the battery for each terminal device is calculated based on the statistical value of the slope of the line segment for each usage pattern. The deterioration prediction program described in any one.

(Supplementary Note 6) The processing to be performed is characterized in that the timing at which the degree of deterioration of the battery for each of the terminal devices exceeds a threshold value is specified based on the statistical value of the slope of the line segment for each of the usage patterns. The deterioration prediction program according to any one of appendices 1 to 5.

(Supplementary note 7) The deterioration prediction program according to any one of supplementary notes 1 to 6, wherein the terminal device is a device used for a thin client.

(Supplementary note 8) The computer
Obtaining time-series data showing the time change of the deterioration degree of the battery for each terminal device,
For each of the terminal devices, based on the acquired time-series data, considering the break point candidates when the time change of the deterioration degree of the battery of the terminal device is approximated by a broken line, the deterioration degree of the battery of the terminal device Generate one or more models showing straight lines or polygonal lines approximating time changes,
One or more, in which each usage pattern of the plurality of usage modes of the terminal device is associated with each line segment section included in a straight line or a polygonal line indicated by each model of the one or more identified models for each terminal device. Generate a pattern,
Based on the variation in the slope of the line segment for each usage pattern in each of the one or more combinations in which any one of the one or more patterns generated for each terminal device is selected and combined, Performs battery deterioration prediction for each terminal device,
A deterioration prediction method characterized by executing processing.

(Supplementary Note 9) Time-series data indicating the time change of the deterioration degree of the battery for each terminal device is acquired,
For each of the terminal devices, based on the acquired time-series data, considering the break point candidates when the time change of the deterioration degree of the battery of the terminal device is approximated by a broken line, the deterioration degree of the battery of the terminal device Generate one or more models showing straight lines or polygonal lines approximating time changes,
For each of the terminal devices, one or more that associates one of the usage patterns of the terminal device with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models Generate a pattern,
Based on the variation in the inclination of the line segment for each usage pattern in each of the one or more combinations in which any one of the one or more patterns generated for each terminal device is selected and combined, Performs battery deterioration prediction for each terminal device,
A deterioration prediction device having a control unit.

100 information processing device 101-104,910-940 model 111-114 combination 121-124,1000,1110,1120,1200-1220 graph 200 deterioration prediction system 201 terminal device 210 network 400,600 bus 401,601 CPU
402, 602 Memory 403, 603 Network I / F
404 recording medium I / F
405 recording medium 700 storage unit 701 acquisition unit 702 generation unit 703 calculation unit 704 prediction unit 705 output unit 900 broken line 901,902 broken point 1010,1100 table 1211 part

Claims (8)

On the computer,
Obtaining time-series data showing the time change of the deterioration degree of the battery for each terminal device,
For each of the terminal devices, based on the acquired time-series data, considering the break point candidates when the time change of the deterioration degree of the battery of the terminal device is approximated by a broken line, the deterioration degree of the battery of the terminal device Generate one or more models showing straight lines or polygonal lines approximating time changes,
For each of the terminal devices, one or more that associates one of the usage patterns of the terminal device with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models Generate a pattern,
Based on the variation in the inclination of the line segment for each usage pattern in each of the one or more combinations in which any one of the one or more patterns generated for each terminal device is selected and combined, Performs battery deterioration prediction for each terminal device,
A deterioration prediction program that executes processing. On the computer,
Acquiring data indicating the usage pattern at the first time point for each terminal device, executing processing,
In the process of generating the one or more patterns, the usage patterns associated with the adjacent line segment sections included in the straight lines or polygonal lines indicated by the respective models are different for each terminal device based on the acquired data. The deterioration prediction program according to claim 1, wherein the one or more patterns are generated as described above.   The deterioration prediction program according to claim 1 or 2, wherein the plurality of usage patterns include a usage pattern by an outside worker and a usage pattern by an inside worker.   The processing to be performed is a statistic of the slope of the line segment for each usage pattern in the combination of the one or more combinations that has the smallest total value of the variances of the slopes of the line segment for each usage pattern. The deterioration prediction program according to any one of claims 1 to 3, wherein deterioration prediction of a battery for each of the terminal devices is performed based on a value.   The processing to be performed calculates a deterioration rate of a battery for each terminal device based on a statistical value of a slope of a line segment for each usage pattern. Deterioration prediction program described in.   The processing to be performed specifies the timing at which the degree of deterioration of the battery for each terminal device exceeds a threshold value, based on the statistical value of the slope of the line segment for each of the usage patterns. 5. The deterioration prediction program according to any one of 5. Computer
Obtaining time-series data showing the time change of the deterioration degree of the battery for each terminal device,
For each of the terminal devices, based on the acquired time-series data, considering the break point candidates when the time change of the deterioration degree of the battery of the terminal device is approximated by a broken line, the deterioration degree of the battery of the terminal device Generate one or more models showing straight lines or polygonal lines approximating time changes,
For each of the terminal devices, one or more that associates one of the usage patterns of the terminal device with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models Generate a pattern,
Based on the variation in the inclination of the line segment for each usage pattern in each of the one or more combinations in which any one of the one or more patterns generated for each terminal device is selected and combined, Performs battery deterioration prediction for each terminal device,
A deterioration prediction method characterized by executing processing. Obtaining time-series data showing the time change of the deterioration degree of the battery for each terminal device,
For each of the terminal devices, based on the acquired time-series data, considering the break point candidates when the time change of the deterioration degree of the battery of the terminal device is approximated by a broken line, the deterioration degree of the battery of the terminal device Generate one or more models showing straight lines or polygonal lines approximating time changes,
For each of the terminal devices, one or more that associates one of the usage patterns of the terminal device with each line segment section included in a straight line or a polygonal line indicated by each model of the specified one or more models Generate a pattern,
Based on the variation in the inclination of the line segment for each usage pattern in each of the one or more combinations in which any one of the one or more patterns generated for each terminal device is selected and combined, Performs battery deterioration prediction for each terminal device,
A deterioration prediction device having a control unit.

Images