Information processing device, information processing method, program, and data structure

Abstract

To provide a technology capable of predicting abnormality of a storage battery with high accuracy by using data of the battery measured in periodically inspecting a storage battery system.SOLUTION: A program causes a computer to function as each means of an information processing device for detecting an abnormality sign relating to a plurality of storage batteries constituting an uninterruptible power supply. The information processing device includes acquisition means for acquiring a dataset, for a specific storage battery, to be recorded in each periodical inspection performed at a prescribed time interval and including information on a measurement result to the specific storage battery and information on a use state of the specific storage battery, and determination means for determining the existence / nonexistence of an abnormality sign of the specific storage battery by inputting the dataset for the specific storage battery to a first learning model which has learned in advance. The information on the measurement result to the specific storage battery includes a change degree of an internal resistance value of the specific storage battery in each prescribed time interval, and the information on the use state of the specific storage battery includes the number of use days from a charging date.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, a program, and a data structure. [Background technology]

[0002] Conventionally, a technology for determining deterioration of a storage cell in a system that uses a storage module in which multiple storage cells are connected in series is known (Patent Document 1). In the technology proposed in Patent Document 1, a learning model determines the deterioration of the storage cell and outputs the presence or absence of an environmental abnormality based on the storage element information of the storage cell, actual measurement value time series data, and predicted value time series data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-168451 Summary of the Invention [Problem to be solved by the invention]

[0004] In data centers, for example, storage battery systems are sometimes used as uninterruptible power supplies to ensure that computers installed in the data centers can operate at all times. These storage battery systems as uninterruptible power supplies charge multiple storage batteries and, in the event of a power outage, supply power from the batteries to the equipment in the data center.

[0005] Some storage battery systems have a configuration in which, for example, 100 or more storage batteries are connected in series. In such storage battery systems, there is a risk of losing power supply function if an abnormality occurs in one storage battery causing a disconnection, so it is necessary to detect signs of abnormality in the storage batteries early and perform appropriate maintenance such as replacement.

[0006] On the other hand, in countries where power outages are rare, battery storage systems rarely operate during actual power outages. However, if a battery storage system is configured to constantly monitor the status of each battery by installing a sensor in each battery, the battery storage system configuration becomes complex and expensive. Even if the battery storage system is not configured for constant monitoring by sensors, it may be possible to properly maintain the battery storage system by manually inspecting the batteries at predetermined intervals (e.g., every one to several months).

[0007] Given this background, there is a demand for technology in storage battery systems that can accurately predict signs of abnormalities in storage batteries while utilizing data on storage batteries measured during regular inspections.

[0008] The present invention has been made in consideration of the above-mentioned problems, and its purpose is to realize a technology that can accurately predict abnormalities in storage batteries using data on storage batteries measured during regular inspections of storage battery systems. [Means for solving the problem]

[0009] In order to solve this problem, for example, the program of the present invention has the following configuration: A program that causes a computer to function as each means of an information processing device for detecting an abnormality sign related to a plurality of storage batteries that constitute an uninterruptible power supply, the information processing device comprising: an acquisition means for acquiring a data set for a specific storage battery, the data set including information on measurement results for the specific storage battery and information on a usage status of the specific storage battery, the data being recorded for each periodic inspection performed at a predetermined time interval; pre-learned , pertaining to xgboost a determination means for determining whether or not there is a sign of abnormality in the specific storage battery by inputting a data set for the specific storage battery into a first learning model; the information regarding the measurement result for the specific storage battery includes a degree of change in an internal resistance value of the specific storage battery for each predetermined time interval, and the information regarding the usage status of the specific storage battery includes the number of days of use from the date of charging; The determination means further determines, as a storage battery having a sign of abnormality, a storage battery that is outside a class to which information of the data set of a storage battery determined to be normal among the plurality of storage batteries belongs. , related to clustering or SVM The present invention is characterized in that by inputting the data sets for the plurality of storage batteries into a second learning model, it is possible to determine whether or not there are signs of abnormality in the plurality of storage batteries. [Effects of the Invention]

[0010] According to the present invention, it is possible to accurately predict abnormalities in the storage battery by using data on the storage battery measured during periodic inspection of the storage battery system. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an overview of a storage battery abnormality sign detection system according to an embodiment of the present invention. [Figure 2A] FIG. 1 is a diagram illustrating a configuration example of a storage battery group in a data center according to an embodiment of the present invention. [Figure 2B] FIG. 1 is a diagram illustrating an example of the configuration of a storage battery in a specific storage battery group according to the present embodiment. [Figure 2C] A diagram explaining the relationship between changes in the internal resistance value of a storage battery and abnormalities in the storage battery. [Figure 3] FIG. 10 is a diagram showing an example of learning data according to the present embodiment; [Figure 4] FIG. 10 is a diagram showing another example of learning data according to the embodiment; [Figure 5] FIG. 1 is a diagram showing an example of the functional configuration of an information processing apparatus according to an embodiment of the present invention; [Figure 6] FIG. 2 is a diagram illustrating an example of the functional configuration of a communication device according to the present embodiment. [Figure 7A] FIG. 10 is a diagram showing an example of a data set stored in a measurement data DB according to the present embodiment. [Figure 7B]FIG. 1 is a diagram showing an example of a data set input to a learning model in an estimation stage according to the present embodiment. [Figure 8] FIG. 10 is a diagram showing an example of the relationship between the timing of periodic inspection and discharge time according to the present embodiment; [Figure 9A] A flowchart showing a series of operations in a learning stage by the information processing device according to the present embodiment. [Figure 9B] A flowchart showing a series of operations in an estimation stage performed by the information processing device according to the present embodiment. [Figure 10] FIG. 10 is a diagram showing an example of an abnormality sign notification screen according to the embodiment; [Figure 11] FIG. 10 is a diagram schematically illustrating data distribution of a plurality of storage batteries according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention as claimed, and not all combinations of features described in the embodiments are necessarily essential to the invention. Two or more of the features described in the embodiments may be combined in any desired manner. Furthermore, the same reference numerals are used to designate identical or similar components, and redundant descriptions will be omitted.

[0013] (Overview of the storage battery abnormality detection system) An overview of the storage battery abnormality sign detection system will be described with reference to Figure 1. The storage battery abnormality sign detection system 10 includes an information processing device 100, which is an information processing server, and a communication device 210. The communication device 210 is, for example, a personal computer or a mobile terminal located in a data center 200. The communication device 210 accepts input of measurement results and usage status data related to the storage batteries by a maintenance technician for the storage battery group 230, and transmits the input information related to the measurement results and usage status to the information processing device 100 via a network. The communication device 210 may also accept data input by a maintenance technician for the storage battery group 230 or an administrator of the storage battery abnormality sign detection system 10, and transmit correct answer data (described below) from the learning data to the information processing device 100.

[0014] The information processing device 100 receives and records information relating to measurement results and information relating to usage conditions from the communication devices 210 in each of the multiple data centers, and detects signs of abnormality in the storage battery based on a machine learning model.

[0015] A server group 220 that executes arbitrary processing (for example, processing of a tenant that uses the server resources of the server group) operates in the data center 200. The server group 220 normally operates with power supplied from a power supply (not shown) of the data center 200. The storage battery group 230 includes a plurality of storage batteries, and functions as an uninterruptible power supply to supply power to the server group 220 in emergencies such as power outages.

[0016] The storage battery group 230 in the data center 200 is configured, for example, as shown in FIG. 2A, with a plurality of systems of storage batteries (referred to here as UPS storage battery systems). Each of the plurality of UPS storage battery systems is distinguished, for example, as "UPS No. 1," "UPS No. 2," "UPS No. 3," and "UPS standby," and can independently supply power to the server group 220. The "UPS standby" system operates, for example, when at least one of "UPS No. 1," "UPS No. 2," and "UPS No. 3" fails or is undergoing maintenance, or when the required power cannot be supplied due to an insufficient power supply.

[0017] Each UPS battery system includes a plurality of battery groups 240, such as a battery group A and a battery group B. These battery groups 240 may be connected in series. For example, as shown in FIG. 2B, the battery group 240 is configured to generate the required voltage by connecting a plurality of battery groups 250 in series. If an abnormality such as a disconnection occurs in one battery 250, the UPS battery system may lose its function as a UPS power supply. Therefore, accurately detecting signs of an abnormality in each battery is an important issue for the system to function as a UPS power supply.

[0018] (Detection of signs of abnormalities in storage batteries) Detection of signs of abnormality in a storage battery will be described with reference to FIG. 2C. FIG. 2C shows an example of the transition of the internal resistance values ​​of several individual storage batteries. The horizontal axis of FIG. 2C represents time, and the vertical axis represents the internal resistance value (mΩ). Graph 261 shows the transition of the internal resistance value measured for a specific storage battery at times t-3, . . . , t, and t+1. Times t-3, . . . , t, and t+1 correspond to the timing of measurements during periodic inspections that were carried out in sequence. In other words, graph 261 is a graph of the measurement results for the storage battery recorded during the periodic inspection. Graphs 262 and 263 also represent the measurement results for individual storage batteries.

[0019] A known conventional anomaly detection method is to replace or inspect a storage battery when its internal resistance value exceeds a predetermined threshold value 264, which is determined to be a sign of an anomaly. However, as shown in graph 262, if the threshold value is not exceeded when the inspection timing is t, but the internal resistance value of the storage battery increases rapidly between inspection timing t and t+1, there is a risk that the storage battery will fail before inspection timing t+1.

[0020] To address this issue, in this embodiment, in order to detect a storage battery whose internal resistance will exceed a threshold value in the future, signs of abnormality in the storage battery are detected by taking into account not only the internal resistance value but also the trend of change in the internal resistance value. In fact, even if the internal resistance value of a normal storage battery increases over time, the rate of increase decreases. In contrast, the internal resistance value of a storage battery in which an abnormality occurs increases at an accelerated rate (the internal resistance value increases and the rate of increase also increases). This embodiment focuses on the trend of change in the internal resistance value.

[0021] Furthermore, in the detection of signs of abnormality according to this embodiment, a machine learning model is trained using data that can take into account the transition of changes in the internal resistance value as training data, and the presence or absence of signs of abnormality is determined by the machine learning model. For example, in the example of Fig. 2C, data from times t, t-1, and t-2 is input to the (trained) machine learning model, and it is determined whether or not a sign of abnormality is indicated (exceeding threshold 264) by time t+1. It is determined that there are no signs of abnormality for the storage batteries corresponding to graphs 261 and 263, and that there are signs of abnormality for the storage battery corresponding to graph 262.

[0022] 3 shows an example of learning data (also referred to as teacher data) used in this embodiment. This learning data is stored in a learning data DB (described later) of the information processing device 100. The learning data includes, for example, a storage battery identification ID, information on each measurement result corresponding to time t-2 to time t, information on the usage status, and a determination.

[0023] "Judgment" represents correct answer data (also called label), and based on past periodic inspections and the occurrence of abnormalities, "NG" (indicating an abnormality) is assigned to storage batteries that exceeded the threshold 264 between time t and t+1. On the other hand, "OK" (indicating no abnormality) is assigned to storage batteries that did not exceed the threshold 264 between time t and t+1.

[0024] The battery identification ID is an ID for identifying each storage battery. It may be possible to not only simply identify the storage battery, but also, for example, identify which data center the storage battery is located in, which UPS storage battery system the storage battery is located in, and which storage battery in the UPS storage battery system the storage battery is a part of. In this way, when using learning data, it is possible to perform model learning using learning data from a specific data center, or to perform model learning using learning data related to the storage batteries in a specific UPS storage battery system. When storage batteries with different properties (i.e., storage batteries manufactured by different manufacturers or storage batteries of different types with different characteristics) are installed in each data center or each UPS storage battery, it is possible to perform model learning for each storage battery with different properties, and it is possible to realize a learning model suitable for each storage battery with different properties.

[0025] The measurement data corresponding to time t-2 includes the internal resistance value (mΩ) of the target storage battery at time t-2 and the slope of the internal resistance value between time t-1 and time t-2. The slope of the internal resistance value may be, for example, the difference in the internal resistance value between time t-1 and time t-2, or the value obtained by dividing the difference by the number of days between time t-1 and time t-2. Furthermore, the slope may be obtained by multiplying these values ​​(e.g., the value obtained by dividing the difference by the number of days between time t-1 and time t-2) by a predetermined scaling factor.

[0026] The measurement data corresponding to time t-1 includes the internal resistance value (mΩ) of the target storage battery at time t-1 and the slope of the internal resistance value between time t and time t-1. The slope of the internal resistance value may be, for example, the difference in the internal resistance value between time t and time t-1, or may be a value obtained by dividing the difference by the number of days between time t and time t-1. Furthermore, these values ​​may be multiplied by a predetermined scaling factor.

[0027] By adding the gradient of the internal resistance value to each of the measurement data at time t-2 and time t-1, the transition of the degree of change of the internal resistance value can be explicitly given to the machine learning model. That is, unlike when the learning data simply includes the internal resistance value, in this embodiment, the occurrence of an abnormality in the storage battery can be predicted by explicitly taking into account the transition of the degree of change of the internal resistance value (i.e., an accelerated increase in the internal resistance value).

[0028] The measurement data corresponding to time t includes the internal resistance value (mΩ) of the target storage battery at time t. The data indicating the usage status includes information on the usage period, which indicates the number of days elapsed since the storage battery was first charged (number of days of use from the charging date). It has been found that the number of days elapsed since the start of storage battery use correlates with the occurrence of an abnormality.

[0029] By using such learning data and utilizing the results of past periodic inspections, it becomes possible to predict the deterioration trend of the storage battery, making it possible to implement necessary measures for the storage battery before an abnormality occurs.In addition, it is possible to predict the occurrence of an abnormality in a storage battery where the internal resistance value changes suddenly between time t (the current inspection time) and time t+1 (the next inspection time) by explicitly taking into account the transition in the degree of change in the internal resistance value.

[0030] In this embodiment, the machine learning model will be described using, as an example, xgboost, a gradient boosting method based on a decision tree algorithm. However, the machine learning model may be configured using other machine learning models, for example, a deep neural network. In addition to using a deep neural network with a simple hidden layer, for example, a neural network using an RNN (recurrent neural network) such as LSTM, which can more appropriately take into account the characteristics of temporal changes in time-series data, may also be used. Furthermore, a deep neural network with an attention mechanism (also called a caution mechanism) that can provide output that focuses on characteristic data in time-series data may also be used.

[0031] The learning data may include additional information, as shown in FIG. 4. It may also include other measurement values, such as the voltage (V) of the storage battery at time t-2, time t-1, and time t. Furthermore, the information about the usage status may include connection location information in addition to information about the usage period. The connection location information is the connection location (order from the top) of the target storage battery among a group of storage batteries connected in series. As described above, a group of storage batteries includes multiple storage batteries connected in series. The storage batteries are arranged, for example, in a two-dimensional or three-dimensional configuration in a rack or box. Therefore, when a storage battery discharges during a power outage or a periodic inspection, the temperature change caused by the arrangement within the box affects the deterioration of the storage battery. However, the timing of measurement of the storage battery during a periodic health check may not be during the battery's discharging, as shown in FIG. 8, for example. For example, it is expected that a storage battery will discharge during a very limited time during inspection work to check its operation or when power is needed due to a power outage. Therefore, the timing of measurement and recording during a periodic inspection may not necessarily be such that temperatures that may affect the storage battery can be measured. For this reason, in this embodiment, the connection positions of the storage batteries (or the arrangement of storage batteries arranged in two or three dimensions) are used as input parameters for the learning model. Alternatively, instead of or in addition to the connection positions of the storage batteries, the discharge time may be used. The discharge time is the cumulative time during which the storage battery has discharged. This allows the influence of the operation of the storage battery to be reflected in the detection of signs of deterioration.

[0032] In the present embodiment, an example will be described in which the communication device 210 transmits information related to the measurement results, information related to the usage status, and correct answer data to the information processing device 100, and the information processing device 100 generates training data. However, the communication device 210 may generate training data according to the present embodiment and transmit the generated training data to the information processing device 100. In this case, the information processing device 100 may store the acquired training data in the training data DB 522.

[0033] (Configuration of information processing device) Next, an example of the functional configuration of the information processing device 100 will be described with reference to Fig. 5. Note that each of the functional blocks described with reference to the following figures may be integrated or separated, and the described functions may be realized by different blocks. Also, what is described as hardware may be realized by software, and vice versa.

[0034] The communication unit 501 includes a communication circuit or a communication module that communicates with the communication devices 210 used by the maintenance personnel of the storage battery via a network. The communication unit 501 communicates with the multiple communication devices 210.

[0035] The control unit 502 includes a CPU 510, which is a central processing unit, and a RAM 511. The CPU 510 may be composed of one or more CPUs. The control unit 502 executes a computer program stored in the recording unit 504 to perform a storage battery abnormality sign detection process, which will be described later, and to control the operation of each unit of the information processing device 100. The control unit 502 may further include a computing unit (e.g., a GPU) or dedicated hardware for performing statistical processing such as machine learning at higher speeds.

[0036] The RAM 511 is a volatile storage medium such as a DRAM, and temporarily stores parameters and processing results for the control unit 502 to execute a computer program.

[0037] The power supply unit 503 is a circuit or module for providing power for operating each unit of the information processing device 100. The power supply unit 503 may be configured to further include a battery.

[0038] The recording unit 504 includes a nonvolatile recording medium such as a hard disk or semiconductor memory, and records setting values, calculation results, and the like required for the operation of the information processing device 100. The recording unit 504 also stores information related to the measurement results and information related to the usage status received from the communication device 210 in a measurement data DB 521. The recording unit 504 also stores the above-mentioned learning data in a learning data DB 522.

[0039] The storage battery data receiving unit 512 receives information about the measurement results and information about the usage status input by the storage battery maintenance person from the communication device 210, and records the information in the measurement data DB 521 of the recording unit 504. The information about the measurement results and information about the usage status stored in the measurement data DB 521 is configured, for example, as shown in FIG. 7A.

[0040] 7A includes a storage battery identification ID 701, information on measurement results for each time period and information on usage status, and information on usage status that is invariant over time 704. The information on measurement results for each time period and information on usage status includes, for example, information on measurement results measured at time tn and information on usage status 702, information on measurement results measured at time tn-1 and information on usage status, and information on measurement results measured at time t and information on usage status 703.

[0041] The storage battery identification ID 701 is the same as that described above in Figures 3 and 4. The internal resistance and voltage values ​​in the information related to the measurement results by time are the same as the internal resistance and voltage values ​​described above in Figure 4. The date is the date on which data such as the internal resistance value was measured. The usage period and discharge time in the information related to the usage status represent the cumulative number of days up to the corresponding time (e.g., tn). The information related to the usage status 704 includes connection position information, which is the same as the connection position information shown in Figure 4.

[0042] Referring again to FIG. 5, the learning data generation unit 513 generates the learning data described above using data on a specific storage battery (e.g., designated by a maintenance technician for the storage battery or an administrator of the anomaly sign detection system 10) among the data stored in the measurement data DB 521. For example, the learning data generation unit 513 calculates the slope of the internal resistance value at time t-1 from the date and internal resistance value at time t-1 and the date and internal resistance value at time t-1 in the measurement data DB 521. Furthermore, the learning data generation unit 513 generates the usage status information shown in FIG. 3 or 4 based on the usage status information at time t and the time-invariant usage status information 704. The specific storage battery is a storage battery that has actually experienced an abnormality or has been confirmed to be operating normally. According to experiments by the inventors, anomaly signs can be detected effectively even when the learning data is composed of data from, for example, three regular inspections (time t-2, time t-1, and time t). However, this is not limiting; data from more regular inspections, for example, five regular inspections, may also be used.

[0043] The learning data generation unit 513 assigns information on the judgment of the learning data (i.e., correct answer data indicating "OK" or "NG") to the data of the storage battery identification ID of the specific storage battery. The learning data generation unit 513 receives correct answer data via the communication device 210 from, for example, a maintenance person for the storage battery or an administrator of the storage battery abnormality sign detection system 10, and adds the correct answer data to the learning data. The learning data generation unit 513 stores the generated learning data in the learning data DB 522.

[0044] The measurement data acquisition unit 514 generates information about measurement data for a specific storage battery and information about its usage status from the measurement data DB 521 in order to execute the abnormality sign detection process in the estimation stage, which will be described later. The storage battery processed by the measurement data acquisition unit 514 is the storage battery to be determined for the presence or absence of an abnormality sign. The measurement data acquisition unit 514 performs processing similar to the processing for generating data other than the "determination" step described for the learning data generation unit 513 to generate a dataset to be input into a learning model for abnormality sign detection. As shown in FIG. 7B, the dataset generated by the measurement data acquisition unit 514 includes the internal resistance values ​​and the slopes of the internal resistance values ​​at time t-2 and time t-1, the internal resistance value at time t, and information about the usage period. Note that the example dataset shown in FIG. 7B has a format corresponding to the learning data shown in FIG. 3. Furthermore, when generating a data set in a format corresponding to the learning data shown in FIG. 4, the generated data set includes the internal resistance value, the slope of the internal resistance value, and the voltage at time t-2 and time t-1, the internal resistance value and voltage at time t, and information on the usage period, connection position, and discharge time.

[0045] The preprocessing unit 515 performs common preprocessing on the training data and the data shown in FIG. 7B before inputting the data into the learning model. This preprocessing reduces the impact of the characteristic variations on the learning and estimation results of the learning model when storage batteries are manufactured by multiple manufacturers and models and the characteristics of storage batteries vary depending on the manufacturer and model. The preprocessing may include, for example, normalizing each parameter in the information related to the measurement results and the information related to the usage status. Normalization may involve scaling each parameter so that it falls within a range of 0 to 1 (e.g., using the maximum value for each manufacturer or model). Alternatively, the preprocessing unit 515 may use a value obtained by dividing the measurement results, such as voltage values ​​and internal resistance values, by the device specification information. For example, the preprocessing unit 515 may divide the voltage value of the measurement results by a standard voltage, which differs for each storage battery product, to calculate the ratio to the standard voltage, and input the calculated ratio to the standard voltage into the learning model. The standard voltage varies depending on the number of series cells in the product. The preprocessing unit 515 may also divide the internal resistance value by a standard internal resistance value, which varies for each product, to calculate a ratio to the standard internal resistance, and use this ratio. The standard internal resistance value varies depending on the number of series cells in the product and the terminal structure. The preprocessing unit 515 may also divide the usage period of the storage battery by an average lifespan, which varies for each product, to calculate a ratio to the average lifespan, and use this ratio. Information such as the maximum value used for normalization, as well as the standard voltage, standard internal resistance, and average lifespan for each manufacturer and model, may be pre-recorded in the recording unit 504.

[0046] The model processing unit 516 inputs the learning data shown in FIG. 3 or 4 to perform a process of training an internal machine learning model, and also inputs the input data shown in FIG. 7B using the trained machine learning model to perform an abnormality sign detection process for a storage battery. The machine learning model used by the model processing unit 516 in this embodiment functions as a classifier that outputs a prediction result as a binary value (OK or NG). As described above, in this embodiment, the learning model uses, for example, xgboost, a gradient boosting method based on a decision tree algorithm. However, the machine learning model may also be configured using, for example, a deep neural network. The anomaly information providing unit 517 transmits the result of the abnormality sign detection process performed by the model processing unit 516 in the estimation stage to the communication device 210. The anomaly information providing unit 517 transmits the result of the abnormality sign detection process to the communication device 210, for example, as information on an abnormality sign notification screen, which will be described later with reference to FIG. 10.

[0047] (Configuration of communication device) Next, an example of the functional configuration of the communication device 210 will be described with reference to FIG. 6. In this embodiment, a personal computer will be used as an example of the communication device, but the communication device may be other electronic devices such as a smartphone or a tablet terminal. Note that each of the functional blocks described with reference to the following figures may be integrated or separated, and the described functions may be realized by different blocks. Also, what is described as hardware may be realized by software, and vice versa.

[0048] The communication unit 601 includes a communication circuit and the like, and communicates with the information processing device 100 by connecting to the Internet via wireless communication such as a wireless LAN, or to a network via wireless communication for mobile phones, for example.

[0049] The control unit 602 includes a CPU 610 and a RAM 611, and controls the operation of each unit in the communication device 210 by the CPU 610 executing a computer program recorded in the recording unit 607, for example.

[0050] The operation unit 603 includes at least one of a keyboard, a mouse, or a touch panel, and can perform operations on GUIs for various operations displayed on the display unit 606 (for example, inputting information related to measurement results and usage conditions, and providing correct answer data). The power supply unit 604 provides power to each unit of the communication device 210. The imaging device 305 is, for example, a camera mechanism including an imaging element, and takes pictures according to instructions from the control unit 602.

[0051] The display unit 606 includes a display device such as an LCD, an OLED, etc. In response to an instruction from the control unit 602, the display unit 606 displays a GUI for inputting information related to measurement results and information related to usage conditions and for providing correct data, a GUI for an abnormality sign notification screen, etc.

[0052] The recording unit 607 includes a nonvolatile memory such as a semiconductor memory, and stores programs and setting values ​​executed by the control unit 602. The computer programs stored in the recording unit 607 include an operating system and various applications for realizing the various functions of the communication device 210.

[0053] (A series of operations for anomaly detection processing in the learning stage) Next, a series of operations of the abnormality sign detection process in the learning stage, which is executed in the information processing device 100, will be described with reference to FIG. 9A. This process is realized by the CPU 510 of the control unit 502 executing a computer program recorded in the recording unit 504. In the following description, for ease of explanation, the processing entity of each step will be collectively described as the control unit 502, but each unit functioning within the control unit 502 executes the corresponding process depending on the processing content. This process is a process for training a machine learning model in the model processing unit 516, and the learning of the machine learning model is not yet complete.

[0054] In S401, the control unit 502 receives information about the measurement result and information about the usage status from the communication device 210. As described above, the control unit 502 stores the information about the measurement result and information about the usage status in the measurement data DB 521 in the format shown in FIG.

[0055] In S402, the control unit 502 acquires information necessary for generating learning data from the communication device 210. The information necessary for generating learning data is the supervised data shown in FIG.

[0056] 3 or 4 based on the data (information related to the measurement results and information related to the usage status) shown in Fig. 7A stored in the measurement data DB 521 and the correct answer data acquired in S402. The generation of the learning data is performed as described above for the learning data generation unit 513.

[0057] In S405, the control unit 502 executes learning for the learning model. The control unit 502 trains the machine learning model using the generated learning data. Since well-known learning methods can be used for learning the machine learning model, such as decision trees and deep neural networks, detailed explanations will be omitted. When the control unit 502 finishes learning the machine learning model using the learning data, it ends the series of operations of the anomaly sign detection process in the learning stage.

[0058] (A series of operations for anomaly detection processing in the estimation stage) Next, a series of operations of the abnormality sign detection process in the estimation stage, which is executed in the information processing device 100, will be described with reference to FIG. 9B . This process is realized by the CPU 510 of the control unit 502 executing a computer program recorded in the recording unit 504. In the following description, for ease of explanation, the processing entity of each step will be collectively described as the control unit 502, but each unit functioning within the control unit 502 executes the corresponding process depending on the processing content. This process is a process for determining the presence or absence of an abnormality sign from newly input storage battery data using a trained machine learning model in the model processing unit 516. While the process shown in FIG. 9B is described using an example in which processing is performed on one specific storage battery, abnormality sign detection may be performed on multiple storage batteries by repeating the processes of S921 and S922.

[0059] In S921, the control unit 502 generates the data shown in Fig. 7B for a specific storage battery that is the target of the abnormality sign detection process from the data shown in Fig. 7A stored in the measurement data DB 521. After generating the data shown in Fig. 7B, the control unit 502 may apply the preprocessing by the preprocessing unit 515 described above to the generated data.

[0060] In S922, the control unit 502 executes an abnormality sign detection process. The control unit 502 uses a trained machine learning model to determine whether or not there is an abnormality sign for the specific storage battery being processed. The control unit 502 inputs the data generated in S921 into the trained machine learning model and obtains an output from the machine learning model (i.e., two values: OK or NG).

[0061] In S923, the control unit 502 transmits information about the storage battery in which a sign of abnormality has been detected (for example, the storage battery identification ID and the transition of the degree of change in the internal resistance value) to the communication device based on the determination result obtained from the machine learning model. After transmitting the storage battery information to the communication device 210, the control unit 502 ends this series of processes.

[0062] The processes of S921 and S922 may be repeatedly executed for multiple storage batteries, and abnormality sign information (also referred to as an abnormality sign notification screen) for multiple storage batteries as shown in FIG. 10 may be transmitted to the communication device 210. The abnormality sign notification screen 1000 shown in FIG. 10 displays the storage battery identification IDs of one or more storage batteries in which an abnormality sign has been detected, and storage battery information 1002 corresponding to the storage battery identification IDs. The storage battery information 1002 may display at least one of the characteristics of the storage battery indicating the abnormality sign, such as a change in the slope of the internal resistance value 1003, a usage period 1004, and a connection position 1005. In this way, it is possible to simultaneously identify storage batteries that are estimated to have an abnormality sign at the current time (time t), and to obtain an overview of the characteristics of each storage battery, thereby easily confirming the validity of the estimation result.

[0063] As described above, in this embodiment, a data set for a specific storage battery, including information about measurement results for the specific storage battery and information about usage status, recorded at each regular inspection performed at a predetermined time interval, is acquired and input into a trained machine learning model to determine whether or not there is a sign of abnormality in the specific storage battery. The information about the measurement results for the specific storage battery includes the degree of change in the internal resistance value of the specific storage battery at each predetermined time interval, and the machine learning model is explicitly provided with a trend in the degree of change in the internally provided value. This makes it possible to accurately predict signs of abnormality in the storage battery using data on the storage battery measured during regular inspections of the storage battery system. According to this embodiment, even with a small amount of data from, say, three regular inspections, the trend in the degree of change in the internally provided value can accurately predict signs of abnormality in the storage battery, thereby simplifying the configuration of the storage battery system (which does not require a sensor that constantly monitors the characteristics of the storage battery).

[0064] In the above embodiment, the internal resistance value, the slope of the internal resistance value, and the voltage are given as examples of information related to the measurement results of the storage battery, but the information related to the measurement results may further include other information, such as at least one of the temperature, the number of discharges, the discharge time, and the discharge capacity of the storage battery obtained during periodic inspection.

[0065] In the above-described embodiment, the abnormality sign detection process determines whether or not there is an abnormality sign for each individual storage battery. However, in addition to the abnormality sign determination for each individual storage battery described above, the model processing unit 516 may use a machine learning algorithm such as clustering or SVM (support vector machine) to determine, among the multiple storage batteries, a storage battery that does not fall into the class of storage batteries determined to be normal as a storage battery showing an abnormality sign.

[0066] For example, as shown in Fig. 11, when considering a space in which information related to the measurement results of each storage battery and information related to the usage status are vector components, data on multiple storage batteries is distributed at various positions within the space (e.g., 1103 and 1104). By applying a machine learning algorithm such as clustering or SVM to the data on these storage batteries, a storage battery 1103 included in class 1102 of normal storage batteries is distinguished from other storage batteries 1104. The distinguished storage battery 1104 is then determined to be a storage battery with signs of abnormality.

[0067] The control unit 502 may execute multiple machine learning algorithms using the model processing unit 516, and when a storage battery in which an abnormality sign has been detected by the processing of S922 matches a storage battery 1104 that does not belong to the class of normal storage batteries, ultimately detect the storage battery as a storage battery with an abnormality sign. In this way, maintenance can be performed on storage batteries that are determined to have a high probability of having an abnormality sign by the multiple machine learning algorithms. On the other hand, the control unit 502 may execute multiple machine learning algorithms using the model processing unit 516, and when it is determined that there is an abnormality sign by any one of the machine learning algorithms, ultimately detect the storage battery as a storage battery with an abnormality sign. In this case, maintenance can be performed widely on storage batteries that may have an abnormality sign.

[0068] The invention is not limited to the above-described embodiment, and various modifications and variations are possible within the scope of the gist of the invention. [Explanation of symbols]

[0069] 100...information processing device, 210...communication device, 513...learning data generation unit, 514...measurement data acquisition unit, 515...preprocessing unit, 516...model processing unit

Claims

1. A program that causes a computer to function as each means of an information processing device for detecting an abnormality sign related to a plurality of storage batteries that constitute an uninterruptible power supply, the information processing device comprising: an acquisition means for acquiring a data set for a specific storage battery, the data set including information on measurement results for the specific storage battery and information on a usage status of the specific storage battery, the data being recorded for each periodic inspection performed at a predetermined time interval; a determination means for determining whether or not there is a sign of abnormality in the specific storage battery by inputting a data set for the specific storage battery into a first learning model related to xgboost that has been trained in advance, the information regarding the measurement result for the specific storage battery includes a degree of change in an internal resistance value of the specific storage battery for each predetermined time interval, and the information regarding the usage status of the specific storage battery includes the number of days of use from the date of charging; The program is characterized in that the determination means is further capable of determining whether or not the plurality of storage batteries have signs of abnormality by inputting the data sets for the plurality of storage batteries into a second learning model related to clustering or SVM that has been pre-trained and that determines that a storage battery that is determined to be normal among the plurality of storage batteries and that does not fall within the class to which the information in the data set belongs is a storage battery that has signs of abnormality.

2. 2. The program according to claim 1, wherein the information regarding the usage status of the specific storage battery further includes a connection position of the specific storage battery among the plurality of storage batteries connected in series.

3. The program according to claim 1 or 2, characterized in that the information regarding the usage status of the specific storage battery further includes the arrangement of the specific storage battery among the plurality of storage batteries arranged in a two-dimensional or three-dimensional configuration.

4. 4. The program according to claim 1, wherein the information about the measurement results for the specific storage battery further includes at least one of the number of times discharged, the discharge time, and the discharge capacity.

5. The program described in claim 1, further comprising a detection means for detecting at least one of the specific storage battery determined to have signs of abnormality by the first learning model and the storage battery determined to have signs of abnormality by the second learning model as a storage battery with signs of abnormality.

6. 6. The program according to claim 1, wherein the information about the measurement results is based on measurement results measured when the plurality of storage batteries are not discharging.

7. the plurality of storage batteries include storage batteries from different manufacturers, 7. The program according to claim 1, wherein the information about the measurement results uses values ​​that are normalized in advance for different storage batteries manufactured by different manufacturers.

8. An information processing method executed by an information processing device for detecting an abnormality sign related to a plurality of storage batteries constituting an uninterruptible power supply, comprising: an acquisition step of acquiring a data set for a specific storage battery, the data set including information on measurement results for the specific storage battery and information on a usage status of the specific storage battery, the data being recorded for each periodic inspection performed at predetermined time intervals; a determination step of determining whether or not there is a sign of abnormality in the specific storage battery by inputting a dataset for the specific storage battery into a first learning model related to xgboost that has been trained in advance, the information regarding the measurement result for the specific storage battery includes a degree of change in an internal resistance value of the specific storage battery for each predetermined time interval, and the information regarding the usage status of the specific storage battery includes the number of days of use from the date of charging; The information processing method is characterized in that the judgment step further comprises inputting the data set for the plurality of storage batteries into a second learning model related to clustering or SVM that has been pre-trained and that judges a storage battery that is determined to be normal among the plurality of storage batteries and that falls outside the class to which the information in the data set belongs as a storage battery that is showing signs of abnormality, thereby making it possible to determine whether or not the plurality of storage batteries have signs of abnormality.

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