WO2022264332A1 - Registration device, registration method, and program - Google Patents

Registration device, registration method, and program Download PDF

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Publication number
WO2022264332A1
WO2022264332A1 PCT/JP2021/022919 JP2021022919W WO2022264332A1 WO 2022264332 A1 WO2022264332 A1 WO 2022264332A1 JP 2021022919 W JP2021022919 W JP 2021022919W WO 2022264332 A1 WO2022264332 A1 WO 2022264332A1
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Prior art keywords
data
similarity
unit
failure
teacher
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PCT/JP2021/022919
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French (fr)
Japanese (ja)
Inventor
俊介 金井
晴久 野末
憲男 山本
文香 浅井
健一 田山
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日本電信電話株式会社
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Application filed by 日本電信電話株式会社 filed Critical 日本電信電話株式会社
Priority to PCT/JP2021/022919 priority Critical patent/WO2022264332A1/en
Priority to JP2023528857A priority patent/JPWO2022264332A1/ja
Publication of WO2022264332A1 publication Critical patent/WO2022264332A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the embodiments relate to a registration device, a registration method, and a program.
  • the failure history information includes, for example, location of failure, cause of failure, and coping method for failure.
  • Embodiments provide a registration device, a registration method, and a program for reliably and easily registering information for creating rules for estimating failures.
  • the determination device of the embodiment includes an acquisition unit, a first calculation unit, a determination unit, a reception unit, a second calculation unit, and a registration unit.
  • the acquisition unit acquires teacher data including information about failures, and acquires operation data including information about operations of one or more devices.
  • the first calculator calculates a first degree of similarity between the teacher data and the operational data for each failure.
  • the determining unit determines, for each piece of operational data, teacher data having a first degree of similarity greater than or equal to a threshold.
  • the reception unit receives correct data based on teacher data that is equal to or greater than a threshold.
  • the second calculation unit calculates a second degree of similarity between the correct data and the teacher data using a plurality of similarity degree calculation methods.
  • the registration unit registers, as new teacher data, correct data corresponding to the highest degree of similarity among the plurality of degrees of second similarity.
  • the embodiment can reliably and easily register information for creating rules for estimating failures.
  • FIG. 1 is a diagram showing the hardware configuration of a failure information registration device according to an embodiment.
  • FIG. 2 is a diagram illustrating functions of the fault information registration device according to the embodiment.
  • FIG. 3 is a diagram showing an example of teacher data included in the teacher DB of FIG.
  • FIG. 4 is a diagram illustrating a part of operational data acquired by the failure information registration device according to the embodiment;
  • FIG. 5 is a diagram showing an example of the degree of similarity between teacher data and operational data.
  • FIG. 6 is a flowchart showing selection of teacher data, which is an example of the operation of the failure information registration device according to the embodiment.
  • FIG. 7 is a flowchart showing candidate calculation of teacher data, which is an example of the operation of the failure information registration device according to the embodiment.
  • FIG. 1 is a diagram showing the hardware configuration of a failure information registration device according to an embodiment.
  • FIG. 2 is a diagram illustrating functions of the fault information registration device according to the embodiment.
  • FIG. 3 is a diagram showing an
  • FIG. 8 is a diagram showing an example of information displayed in step S711 of FIG.
  • FIG. 9 is a flowchart showing determination and learning of correct data, which is an example of the operation of the failure information registration device according to the embodiment.
  • FIG. 10 is a diagram showing an example of a concept for explaining steps S901 and S902 in FIG.
  • FIG. 11 is a diagram showing an example of a concept for explaining step S909 in FIG.
  • FIG. 12 is a flow chart showing a modification of the operation from the start of FIG. 9 to step S904.
  • the fault information registration device 100 of this embodiment includes a processor 101, a ROM 102, a RAM 103, an interface 104, a display 105, and a storage 106.
  • the processor 101 is a processing device that controls the fault information registration device 100 as a whole.
  • the processor 101 is, for example, a CPU (Central Processing Unit).
  • the processor 101 is not limited to a CPU.
  • an ASIC Application Specific IC
  • the number of processors 101 may be two or more instead of one.
  • the ROM 102 is a read-only storage device.
  • the ROM 102 stores firmware and various programs necessary for the operation of the fault information registration device 100 .
  • the RAM 103 is a arbitrarily writable storage device.
  • a RAM 103 is used as a work area for the processor 101 and temporarily stores firmware and the like stored in the ROM 102 .
  • the interface 104 is a device for exchanging information with an external device.
  • the interface 104 receives, for example, operational data, teacher data, and input from the user.
  • the interface 104 may also transmit and receive information to and from an external server or the like.
  • the display 105 is a display device that displays various screens.
  • the display 105 may be a liquid crystal display, an organic EL display, or the like.
  • the display 105 may include a touch panel.
  • the storage 106 is a storage device such as a hard disk.
  • the storage 106 stores, for example, various applications executed by the processor 101, data used as input to the applications, and data obtained by executing the applications.
  • the failure information registration device 100 of the present embodiment includes, as functional blocks, a teacher data acquisition unit 201, a teacher DB (hereinafter, the database is abbreviated as DB) 202, a teacher data selection unit 203, an operation data acquisition unit 204, A similarity calculation method and threshold DB 205, a similarity calculation unit 206, a corresponding data determination unit 207, a corresponding data display unit 208, a correct data reception unit 209, a correct data extraction unit 210, and a teacher data registration unit 211 , including.
  • the teacher data acquisition unit 201, the operational data acquisition unit 204, and the correct data reception unit 209 are realized by the interface 104, for example.
  • the teacher data selection unit 203, the similarity calculation unit 206, the corresponding data determination unit 207, the correct data extraction unit 210, and the teacher data registration unit 211 are implemented by the processor 101, the ROM 102, the RAM 103, and the storage 106, for example.
  • the teacher DB 202 and the similarity calculation method and threshold DB 205 are realized by the storage 106, for example.
  • the corresponding data display unit 208 is realized by the display 105, for example.
  • the teaching data acquisition unit 201 acquires teaching data from an external DB or the like by connecting directly or via a network.
  • the training data includes information for creating rules for estimating the location of failure (also called location of failure).
  • the teacher data includes, for example, at least one of information on one or more failures, information on coping with this failure, or information on recovery from this failure.
  • the location of failure (also referred to as location of failure) indicates a location on the network, and is defined by at least one of a host name and an IP address, for example. The teaching data will be explained later with reference to FIG.
  • the teacher DB 202 stores the teacher data acquired by the teacher data acquisition unit 201.
  • the teacher data selection unit 203 regards the data that can be considered to have a common portion as teacher data. process. On the other hand, if the degree of similarity between the teacher data is less than the threshold, the teacher data selection unit 203 treats each teacher data as teacher data, assuming that there is no common part. For example, when the data part included in the teacher data is common, the teacher data selection unit 203 extracts only the common part as new teacher data, and deletes the data before extraction.
  • the training data selection unit 203 prepares the training data stored in the training DB 202 using the similarity calculation method and the similarity calculation method from the threshold DB 205 . As a result of this maintenance, the failure information registration device 100 can register information for creating rules for accurately estimating the location of failure.
  • the operational data acquisition unit 204 acquires operational data from an external DB or the like via a network or by direct connection.
  • Operational data includes information regarding the operation of one or more devices.
  • Operational data includes, for example, at least one of information on failure of one or more devices, information on dealing with this failure, or information on recovery from this failure.
  • the operation data acquisition unit 204 outputs the acquired operation data to the similarity calculation unit 206 as soon as it is received. Further, the operational data acquisition unit 204 may output the operational data to the similarity calculation unit 206 when a predetermined amount of operational data is stored in a buffer (for example, in the operational data acquisition unit 204). Operational data will be described later with reference to FIG.
  • the similarity calculation method and threshold DB 205 calculates similarity between teacher data, similarity between teacher data and operational data (first similarity), or similarity between correct data and teacher data (second similarity ) and threshold data relating to the threshold for each similarity calculation method are stored.
  • the correct answer data includes correct answer information for resolving the obstacle.
  • These method data and threshold data are preset and stored in the similarity calculation method and threshold DB 205 .
  • the similarity calculation method and threshold DB 205 may store method data and threshold data set based on instructions from the similarity calculation unit 206 .
  • the similarity calculation unit 206 obtains the similarity between the training data obtained from the training DB 202 and the operation data obtained from the operation data obtaining unit 204 from the similarity calculation method and threshold DB 205. Calculated by a similarity calculation method.
  • the similarity calculation unit 206 calculates one or more degrees of similarity between one piece of operational data and one or more teacher data stored in the teacher DB 202 .
  • the similarity calculation unit 206 sets the similarity calculation method and method data or threshold data in the threshold DB 205 based on the instruction from the correct data extraction unit 210 .
  • the similarity calculation unit 206 sets a similarity calculation method that calculates the highest similarity value as the method setting unit, and sets the threshold of this similarity based on the similarity as the threshold setting unit.
  • the threshold is set, for example, to a few percent (eg, 90%) of the highest similarity calculated by the corresponding similarity calculation technique.
  • the similarity calculation unit 206 calculates the similarity between the correct data extracted by the correct data extraction unit 210 and the teacher data by a similarity calculation method corresponding to the correct data and the teacher data. . Since the correct data are determined by the number of similarity calculation methods, the similarity between the correct data and the teacher data is calculated by the number of similarity calculation methods.
  • the corresponding data determining unit 207 determines whether or not the similarity calculated by one or more similarity calculation methods set by the similarity calculation unit 206 is equal to or higher than a set threshold value by combining the training data and the operation data. Decide for each The corresponding data determination unit 207, for example, determines a location where a failure is estimated to occur and is identified from the operational data (for example, the location of the failure device) and locations around the failure location (for example, the location of the failure device). position of the peripheral device) and the similarity between the operational data and one or more teacher data is greater than or equal to a threshold value (see FIG. 8).
  • the corresponding data determination unit 207 also identifies the correct data received by the correct data reception unit 209 and passes it to the correct data extraction unit 210 . Further, the corresponding data determination unit 207 receives the similarity calculated by the similarity calculation method corresponding to the correct data and the teacher data calculated by the similarity calculation unit 206, data). Corresponding data determination unit 207 passes data corresponding to the correct data and the degree of similarity to correct data extraction unit 210 .
  • the corresponding data display unit 208 displays the content determined by the corresponding data determining unit 207.
  • the corresponding data display unit 208 determines whether or not the degree of similarity between one or more operational data and one or more teacher data is equal to or greater than a threshold at the location of the failure-causing device and the location of the peripheral device of the failure-causing device. Shown for each training data.
  • the corresponding data display unit 208 displays, for example, the contents shown in FIG. 8, which will be described later. It should be noted that the corresponding data display unit 208 may not be used as a presentation unit, but may be presented by voice to the user or a device in place of the user. If the information is conveyed to the user or a device acting on behalf of the user, the corresponding data display 208 may convey the information by other means, whether visual or audible.
  • the correct data reception unit 209 receives instruction information in which the user or a device acting on behalf of the user selects teacher data assumed to be most relevant to the correct answer according to the contents displayed by the corresponding data display unit 208 (presence or absence of teacher data and log). accept. Then, the corresponding data display unit 208 receives the correct data determined as corresponding to the teacher data selected by the user or the device representing the user. Correct data is log information corresponding to teacher data. The log information is information output as a log related to each device, and is Syslog information, for example.
  • the correct data extracting unit 210 extracts one or more correct data acquired from the corresponding data determining unit 207, and calculates similarity between the one or more correct data and the teacher data using a plurality of similarity calculation methods.
  • the degree calculation unit 206 is instructed.
  • the correct data extraction unit 210 extracts a set having the maximum similarity from one or more sets of the correct data and the similarity received from the corresponding data determination unit 207 .
  • the correct data extraction unit 210 passes the correct data included in the extracted set having the maximum similarity to the teacher data registration unit 211 .
  • the teacher data registration unit 211 registers the correct data received from the correct data extraction unit 210 in the teacher DB 202 as new teacher data.
  • the teacher data includes at least one of information on failure, information on coping with this failure, or information on recovery from this failure.
  • the teacher data 1 shown in FIG. 3 includes information on failure "failure A” and information on coping with the failure "restart port: ID”.
  • the teacher data 2 shown in FIG. 3 includes information about the failure "failure A” and information about the recovery of the failure "restart-OK”.
  • the teacher data 3 shown in FIG. 3 includes information about a failure called “failure D" and information about how to deal with the failure called "re-insert card: ID”.
  • the teacher data 4 shown in FIG. 3 includes information about the failure "failure D” and information about the recovery of the failure "re-insert-OK”.
  • fault A and “Failure D” are associated with, for example, information specifying the state of the fault.
  • “failure A” indicates information about the state of the failure.
  • restart port: ID indicates that the ID port will be restarted.
  • the ID is a variable, and corresponds to a specific ID number (for example, natural number) in operational data.
  • start-OK indicates that the relevant device (the device corresponding to "failure A” in FIG. 3) has restarted (recovered).
  • re-insertcard:ID indicates to handle by reinserting the ID card.
  • re-insert-OK indicates reinsertion (recovery) into the corresponding device (the device corresponding to "failure A” in FIG. 3).
  • Operational data includes at least one of information about the failure of a certain device or information about the content of the failure.
  • Information about a failure of a device includes, for example, the date and time when the failure occurred, the location of the failure (host name, IP address, etc.), and the degree of urgency to deal with the failure (Emerg, Alert, Notice, Info, etc.).
  • the information about the content of the failure includes, for example, at least one of information about how to deal with this failure and information about recovery from this failure.
  • operational data attention will be focused on the location of the failure, information on how to deal with this failure, and information on recovery from this failure, which are mainly used in this embodiment. Therefore, operational data is described in this embodiment as including at least one of these pieces of information of interest. It should be noted that operational data, in addition to the information of interest, includes the information indicated above even if not specified.
  • Operational data 1 shown in FIG. 4 includes information indicating that a failure has occurred in "equipment A” at location "XX.XX.XX.XX” and information regarding handling of the failure "restart port: 03".
  • Operational data 2 shown in FIG. 4 includes information indicating that a failure has occurred in "equipment A” at location "XX.XX.XX.X” and information regarding failure recovery of "restart-OK”.
  • Operational data 3 shown in FIG. 4 includes information indicating that a failure has occurred in "device Z” at location "ZZ.ZZ.XX.YY", and failure handling "re-insert card: 04". contains information about and Operational data 4 shown in FIG. 4 includes information indicating that a failure has occurred in "device Z” at location "ZZ.ZZ.XX.YY” and failure recovery (“re-insert-NG”). (in this case recovery failure).
  • restart port: 03 indicates that a specific number 03 is specified and that the port of 03 will be restarted.
  • re-insert card:04 indicates that a specific number 04 is specified and that the card of 04 will be reinserted.
  • re-insert-NG indicates that re-insertion into the corresponding device (device Z in FIG. 4) did not recover from the failure.
  • the similarity calculation unit 206 calculates the similarity with all the teacher data in the teacher DB 202 for each operational data. It shows that mutual data are similar, so that similarity is large. As shown in FIG. 5, in this embodiment, the similarity is calculated between 0 and 1, for example. The closer the similarity is to 1, the more similar the operation data and the training data are, and the closer the similarity is to 0, the less similar the operation data and the training data are.
  • operational data 1 the degree of similarity with teacher data 1 is the highest at 0.97, and the lowest similarity with teacher data 3 is 0.25. Since the difference between 0.97 and 1 is smaller than the difference between 0.25 and 0, it is reasonable to adopt a similarity of 0.97. Therefore, operational data 1 is determined to be similar to teacher data 1 . That is, according to FIG. 5, failure A occurs in device A based on operational data 1, and failure A based on teacher data 1 occurs. is known to have been carried out.
  • the degree of similarity with teacher data 2 is the highest at 1.00, and the lowest similarity with teacher data 4 is 0.32. Since the difference between 1.00 and 1 is less than the difference between 0.32 and 0, it is reasonable to adopt a similarity of 1.00. Therefore, it is determined that the operational data 2 is similar to the teacher data 2 . That is, according to FIG. 5, a failure A caused by the teacher data 2 occurs in the device A by the operation data 2, and in response to the recovery "restart-OK" by the teacher data 2, "restart-OK" by the operation data 2 is executed. I know it was done.
  • the degree of similarity with teacher data 3 is the highest at 0.97, and the lowest similarity with teacher data 1 is 0.25. Since the difference between 0.97 and 1 is smaller than the difference between 0.25 and 0, it is reasonable to adopt a similarity of 0.97. Therefore, it is determined that the operational data 3 is similar to the teacher data 3 . That is, according to FIG. 5, a failure D caused by the training data 3 occurs in the device Z by the operation data 3, and in response to the countermeasure “re-insert card: ID” by the training data 3, the operation data 3 “re-insert card:04" has been implemented.
  • the degree of similarity with teacher data 3 is the highest at 0.53, and the lowest similarity with teacher data 4 is 0.21. Since the difference between 0.53 and 1 is greater than the difference between 0.21 and 0, it is reasonable to adopt a similarity of 0.21. Therefore, it is determined that the operational data 4 is hardly similar to the teacher data 4 . That is, according to FIG. 5, it is found that a failure D caused by the teacher data 4 occurred in the device Z by the operation data 4, and the recovery "re-insert-OK" by the teacher data 4 was not successful. It turns out that it becomes "re-insert-NG".
  • step S601 the teacher data selection unit 203 reads the teacher data from the teacher DB 202.
  • step S602 the teacher data selection unit 203 determines whether there is teacher data with the same number. Each teacher data is numbered. If the teacher data selection unit 203 determines that there is teacher data with the same number, the process proceeds to step S603, and if it determines that there is no teacher data with the same number, the process proceeds to step S607.
  • the teacher data includes, for example, three numbers: manufacturer number_log number_set number.
  • the teacher data selection unit 203 calculates the degree of similarity between teacher data belonging to the same group determined to have the same number.
  • a similarity calculation method and a certain similarity calculation method from the threshold DB 205 are adopted as a method for calculating this similarity.
  • the training data selection unit 203 may employ, for example, a similarity calculation method different from the similarity calculation method for calculating the similarity between training data and operational data.
  • the teacher data selection unit 203 may employ, for example, a similarity calculation method related to correct data when the teacher data registration unit 211 registers the teacher data.
  • step S604 the teacher data selection unit 203 compares the average similarity calculated in step S603 with a threshold.
  • the threshold of the similarity calculation method used in step S603 is used.
  • the similarity average value is calculated from the similarities calculated for each item of the training data.
  • the training data shown in FIG. 3 has three items, failure, coping, and recovery, and the degree of similarity is calculated for each item.
  • the average value of similarity between teacher data 1 and teacher data 3 is the average value of similarity of failure and similarity of coping.
  • the average value of the similarity between teacher data 1 and teacher data 2 is the same as the similarity of disability because the item to be compared is only disability.
  • step S604 the training data selection unit 203 proceeds to step S605 if the average value of similarities is equal to or greater than the threshold, and proceeds to step S606 if the average value of similarities is smaller than the threshold.
  • the teacher data selection unit 203 sets the common part of the teacher data as teacher data.
  • the teacher data selection unit 203 may add information that the teacher data have a common part.
  • a common part indicates the same information in the same item of teacher data. For example, assume that one teacher data includes “Link Down” and the other teacher data includes “Link is Down” in the same item of teacher data. In this case, the teacher data selection unit 203 determines that the common part is "LinkDown", and sets this item of the teacher data uniformly to "LinkDown". Then, the training data "Link Down" and “Link Down” are regarded as "LinkDown” and similarity calculation is performed.
  • step S605 if there is no common part, the teacher data selection unit 203 performs the same processing as in step S606.
  • the teacher data selection unit 203 sets each teacher data as it is as teacher data without integrating the teacher data.
  • the teacher data selection unit 203 may add information indicating that there is no common part in the teacher data. For example, assume that one teacher data includes "link is broken" and the other teacher data includes “link is down” in the same item of teacher data. In this case, the teacher data selection unit 203 sets "link is broken” and "link is down” as teacher data. In step S606, it can be interpreted that the teacher data selection unit 203 has determined that there is no common part in the teacher data.
  • step S607 the teacher data selection unit 203 determines whether there is teacher data with the same number in another category (for example, another manufacturer). For example, the teacher data selection unit 203 determines whether or not there is teacher data of another manufacturer. If the teacher data selection unit 203 determines that there is teacher data with the same number in another category, it returns to step S601. exit.
  • another category for example, another manufacturer.
  • the teacher data selection unit 203 determines whether or not there is teacher data of another manufacturer. If the teacher data selection unit 203 determines that there is teacher data with the same number in another category, it returns to step S601. exit.
  • step S701 the similarity calculation unit 206 reads the similarity calculation method and the similarity calculation method from the threshold DB 205, and in step S702, the similarity calculation unit 206 reads teacher data from the teacher DB 202.
  • the similarity calculation method read in step S701 if the similarity calculation method has already been determined in step S909 of FIG. 9, this similarity calculation method is read.
  • the default similarity calculation method or the similarity calculation method selected by the user or a device on behalf of the user is loaded. Note that the processing order of steps S701 and S702 may be reversed.
  • step S703 the similarity calculation unit 206 acquires operational data.
  • step S704 the similarity calculation unit 206 determines whether or not the teacher data in the teacher DB 202 have common parts. That is, the similarity calculation unit 206 determines whether the teacher data set in step S605 (there is a common part) or not the teacher data set in step S606 (there is no common part). If the similarity calculation unit 206 determines that there is a common part in the teacher data, the process advances to step S705, and if it determines that there is no common part in the teacher data, the process advances to step S706.
  • step S705 the similarity calculation unit 206 calculates the similarity between one piece of operational data and teacher data having a common part.
  • step S706 the similarity calculation unit 206 calculates the similarity between one piece of operational data and a plurality of teacher data having no common portion.
  • the similarity calculation unit 206 extracts the maximum similarity from these multiple similarities.
  • step S707 the threshold for the similarity calculation method read in step S701 is read from the similarity calculation method and threshold DB 205.
  • the read threshold is read if the threshold has already been determined in step S909 of FIG. 9, or is selected by the default threshold or by the user or a device on behalf of the user if step S909 has not yet been performed.
  • the specified threshold is read.
  • step S708 the corresponding data determination unit 207 determines the location of the faulty device that is estimated to have a fault identified from the operational data and the positions of the peripheral devices of this faulty device.
  • step S709 the corresponding data determination unit 207 determines for each set of teacher data and operational data whether the similarity calculated by the similarity calculation method is equal to or greater than the threshold read in step S707. If the corresponding data determination unit 207 determines that the degree of similarity is equal to or greater than the threshold, the process proceeds to step S710, and if it determines that the degree of similarity is less than the threshold, the process proceeds to step S711.
  • step S710 the corresponding data determination unit 207 determines that the corresponding teacher data "has a log".
  • step S711 the corresponding data determination unit 207 determines that the corresponding teacher data is "no log".
  • step S712 the correspondence data display unit 208 displays whether or not the degree of similarity between the operation data and the teacher data is equal to or greater than a threshold value for each teacher data at the location of the failure device and the location of the peripheral devices of the failure device. 105.
  • Corresponding data display unit 208 displays, for example, the table shown in FIG. 8 on display 105 .
  • step S713 the corresponding data determination unit 207 determines whether there is other operational data.
  • the corresponding data determining unit 207 returns to step S703 if it determines that there is other operational data, and terminates the processing in FIG. 7 if it determines that there is no other operational data.
  • FIG. 8 is an example of a table displayed in step S712.
  • FIG. 8 is an example of a table when the cosine similarity calculation method is read as the similarity calculation method in step S701.
  • teacher data determined to have a log in step S710 is displayed with "Yes" to the right of the data name. It is shown in bold and underlined in FIG.
  • Teacher data determined to be "no log” in step S711 are displayed as "no log" to the right of the data name.
  • the correct data reception unit 209 receives the teacher data selected by the user or a device acting on behalf of the user with reference to the content displayed by the corresponding data display unit 208 for each similarity calculation method.
  • the correct data reception unit 209 receives, for example, information instructing teacher data selected for each similarity calculation method from the teacher data shown in FIG.
  • the user or a device acting on behalf of the user determines the correct data after obtaining information necessary for determining the correct data. In other words, if the network breaks down, the location of the failure is estimated, and correct data is determined and registered after recovery by taking measures. We already know information about the correct answer data.
  • the correct data reception unit 209 determines that the user or a device acting in place of the user is data corresponding to the training data for each similarity calculation method selected in step S901 by the user or a device acting in place of the user as correct data. accept the data. Then, in step S 902 , the corresponding data determining unit 207 identifies the correct data received by the correct data receiving unit 209 and passes the correct data to the correct data extracting unit 210 .
  • the correct data reception unit 209 receives correct data selected for teacher data, as shown in FIG. 10, for example. Note that this teacher data and this correct answer data are data containing the same contents as "teacher data 1", "teacher data 2", “teacher data 3", "teacher data 4", etc. shown in the table of FIG. In addition to this, there is also information on measures included in the operation data in FIG. 4 (eg, "restart port: 03") and information on measures included in the training data in FIG. 3 (eg, "restart port: 03"). It can be correct data.
  • the similarity calculation unit 206 calculates the degree of similarity between the correct data extracted by the correct data extraction unit 210 and the training data by the corresponding similarity calculation method used when receiving the training data in step S901.
  • the similarity calculation unit 206 calculates the similarity between the correct data and the teacher data for each category of the teacher data, calculates the average value from the similarities of all the categories, and uses this average value as the correct data. and the degree of similarity with the training data.
  • This category is, for example, "failure", "countermeasure", and "recovery” described in FIGS.
  • FIG. 5 shows an example of similarity calculated in this way.
  • An example of calculation of the degree of similarity between correct data and teacher data for each category of teacher data is shown in FIG.
  • FIG. 11 shows that the similarity is calculated for each of the categories "failure", "countermeasure” and "recovery”.
  • step S904 the correct data extraction unit 210 extracts correct data for which the similarity calculation unit 200 has not yet calculated similarity among the received correct data based on the teacher data corresponding to the similarity calculation method received in step S901. Determine if data exists. If the correct data extraction unit 210 determines that there is correct data for which the similarity calculation unit 206 has not yet calculated the similarity, the process returns to step S902, and the similarity calculation unit 206 has not yet calculated the similarity. If it is determined that there is no correct data, the process proceeds to step S905.
  • step S905 the corresponding data determination unit 207 obtains information on the similarity calculation method used to calculate the degree of similarity between the correct data calculated by the similarity calculation unit 206 and the teacher data, information on the threshold value of this similarity calculation method, , and pass these data to the correct data extraction unit 210 . There are as many sets of these data as there are similarity calculation methods.
  • step S906 the correct data extraction unit 210 instructs the similarity calculation unit 206, and the similarity calculation unit 206 determines whether the learned similarity calculation method and threshold are stored in the similarity calculation method and threshold DB 205. judge. If the similarity calculation unit 206 determines that the learned similarity calculation method and threshold are stored, the process proceeds to step S907, and if it determines that the learned similarity calculation method and threshold are not stored. to step S908.
  • step S907 the similarity calculation unit 206 stores the average value of the threshold of the learned similarity calculation method and the threshold of the same similarity calculation method as the threshold of this similarity calculation method in the similarity calculation method and threshold DB 205. save. Further, in the case of a similarity calculation method different from a learned similarity calculation method, the similarity calculation unit 206 saves the threshold of the method in the similarity calculation method and threshold DB 205 .
  • step S ⁇ b>908 the similarity calculation unit 206 stores the threshold for each similarity calculation method in the similarity calculation method and threshold DB 205 .
  • step S909 the similarity calculation unit 206 calculates a similarity threshold between the teacher data selected in step S901 and the correct data, using all similarity calculation methods. Then, the similarity calculation unit 206 learns the similarity calculation method with the maximum similarity among the similarities calculated in step S903 and the threshold value corresponding to this similarity calculation method, and uses these as similarity calculation methods. and stored in the threshold DB 205 .
  • step S910 the correct data extraction unit 210 determines correct data corresponding to the similarity calculation method determined in step S909, and the teacher data registration unit 211 adds and registers this correct data to the teacher DB 202 as teacher data.
  • teacher data including information about failures is prepared, and based on the teacher data having a high degree of similarity with operation data about the operation of one or more devices, failure information is registered.
  • Correct data containing correct information that eliminates the above is received, the degree of similarity between the correct data and teacher data is calculated by a plurality of similarity calculation methods, and the correct data corresponding to the highest degree of similarity is registered as new teacher data.
  • the fault information registration device of the present embodiment it is possible to reduce resources for inputting many types of data necessary for learning rules including fault causes and fault alarms.
  • this resource is reduced, according to the present embodiment, there is an effect of shortening the time required to create a more accurate database for resolving failures. Therefore, according to this embodiment, the time from failure recovery to learning is shortened.
  • step S1201 the correspondence data determination unit 207 selects teacher data based on the information already possessed by the fault information registration device 100 without receiving information from the user or a device acting on behalf of the user, and selects correct data for the teacher data. can be identified. If the corresponding data determining unit 207 selects the teacher data based on the information already held by the failure information registration device 100 and determines that the correct data for the teacher data can be specified, the process proceeds to step S1202. If it is determined that the teacher data is selected based on the information already possessed by the fault information registration apparatus 100 and the correct data for the teacher data cannot be specified, the process proceeds to step S901.
  • This determination by the corresponding data determination unit 207 is performed when the process has already proceeded to step S910 in the past and there is a similarity calculation method and threshold determined in step S909.
  • the criterion for this determination is determined by the number of similarity calculation methods determined in step S909 and the threshold value of this method.
  • the determination criteria are, for example, that the same similarity calculation method is continuously determined in step S909 by a first value or more, and the threshold values of the similarity calculation methods are all equal to or more than a second value. be. If the determination criteria are satisfied in step S1201, the process proceeds to step S1202. More specifically, for example, the first value of the criterion is 5 and the second value is 0.9. These criteria may be changed appropriately and are not limited to these. For example, the first value of the criterion may be 10 and the second value may be 0.8.
  • step S1202 the corresponding data determination unit 207 selects, for each similarity calculation method, teacher data for which the teacher data was determined to be "logged" in step S710.
  • step S902 the user or a device acting in place of the user selects the teacher data for each similarity calculation method selected by the corresponding data determination unit 207 in step S1202 instead of the teacher data for each similarity calculation method selected in step S901.
  • the correct data reception unit 209 receives data determined as correct data corresponding to this teacher data by a device in place of the user.
  • At least one of the teacher DB 202 and the similarity calculation method and threshold DB 205 may not be included in the failure information registration device 100 and may be outside the failure information registration device 100 .
  • at least one of the teacher DB 202 or the similarity calculation method and threshold DB 205 may be included in an external server or the like.
  • the failure information registration apparatus 100 exchanges information with at least one of the teacher DB 202 and the similarity calculation method and threshold DB 205 via the interface 104 .
  • the device of the embodiment can also be realized by a computer and a program, and the program can be recorded on a recording medium (or storage medium) or provided via a network.
  • each of the above devices and their device parts can be implemented in either a hardware configuration or a combination configuration of hardware resources and software.
  • the combined configuration software is pre-installed in a computer from a network or a computer-readable recording medium (or storage medium), and is executed by the processor of the computer, so that the operation (or function) of each device is controlled by the computer.
  • a program is used to make it happen.
  • the present invention is not limited to the above-described embodiments, and can be variously modified in the implementation stage without departing from the gist of the present invention. Further, each embodiment may be implemented in combination as appropriate, in which case the combined effect can be obtained. Furthermore, various inventions are included in the above embodiments, and various inventions can be extracted by combinations selected from a plurality of disclosed constituent elements. For example, even if some constituent elements are deleted from all the constituent elements shown in the embodiments, if the problem can be solved and effects can be obtained, the configuration with the constituent elements deleted can be extracted as an invention.

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Abstract

A registration device according to an embodiment of the present invention comprises an acquisition unit, a first calculation unit, a determination unit, a reception unit, a second calculation unit, and a registration unit. The acquisition unit acquires training data including failure information, and acquires operation data including information on operations of one or more devices. The first calculation unit calculates, for each failure, a first degree of similarity between the training data and the operation data. The determination unit determines, for each operation data, training data having the first degree of similarity equal to or greater than a threshold value. The reception unit receives correct data on the basis of the training data having the first degree of similarity equal to or greater than a threshold value. The second calculation unit uses a plurality of similarity degree calculation techniques to calculate second degrees of similarity between the correct data and the training data. The registration unit registers, as new training data, correct data corresponding to the largest degree of similarity among the plurality of second degrees of similarity.

Description

登録装置、登録方法、及びプログラムREGISTRATION DEVICE, REGISTRATION METHOD, AND PROGRAM
　実施形態は、登録装置、登録方法、及びプログラムに関する。 The embodiments relate to a registration device, a registration method, and a program.
　ネットワークの障害事例を登録したデータベースから、登録済みの障害事例と重複しないように、障害事例ごとにユニークな障害イベントの組み合わせを抽出し、特徴的な障害イベントとして、障害要因箇所を判定可能なルールを自動で作成及び修正する技術がある。 A rule that can determine the location of a failure as a characteristic failure event by extracting a unique combination of failure events for each failure case from a database that registers network failure examples so as not to overlap with registered failure examples. There is a technology for automatically creating and modifying
　既に運用されているネットワークでは、このルールを生成するために過去の障害履歴情報から障害情報を登録する必要がある。障害履歴情報は、例えば、障害場所、障害原因、障害に対する対処方法を含む。 In a network that is already in operation, it is necessary to register fault information from past fault history information in order to generate this rule. The failure history information includes, for example, location of failure, cause of failure, and coping method for failure.
日本国特開２０１８－０２８７７８号公報Japanese Patent Application Laid-Open No. 2018-028778
　しかし、障害を推定するルールを作るためには、障害への対処が完了した後に障害発生装置に関する情報等を人が手動で登録する必要があり、人の稼働を要する他、登録の遅れや漏れが発生するといった問題がある。 However, in order to create rules for estimating failures, it is necessary for a person to manually register information about the failed device after the failure has been dealt with. occurs.
　実施形態は、障害を推定するルールを作るための情報を確実かつ容易に登録する登録装置、登録方法、及びプログラムを提供する。 Embodiments provide a registration device, a registration method, and a program for reliably and easily registering information for creating rules for estimating failures.
　実施形態の判定装置は、取得部と、第１の計算部と、決定部と、受付部と、第２の計算部と、登録部と、を含む。取得部は、障害に関する情報を含む教師データを取得し、１以上の装置の運用に関する情報を含む運用データを取得する。第１の計算部は、教師データと運用データとの第１の類似度を、障害ごとに計算する。決定部は、第１の類似度が閾値以上である教師データを、運用データごとに決定する。受付部は、閾値以上である教師データに基づいて正解データを受け付ける。第２の計算部は、正解データと教師データとの第２の類似度を複数の類似度算出手法により計算する。登録部は、複数の第２の類似度のうち、最も大きな類似度に対応する正解データを新たな教師データとして登録する。 The determination device of the embodiment includes an acquisition unit, a first calculation unit, a determination unit, a reception unit, a second calculation unit, and a registration unit. The acquisition unit acquires teacher data including information about failures, and acquires operation data including information about operations of one or more devices. The first calculator calculates a first degree of similarity between the teacher data and the operational data for each failure. The determining unit determines, for each piece of operational data, teacher data having a first degree of similarity greater than or equal to a threshold. The reception unit receives correct data based on teacher data that is equal to or greater than a threshold. The second calculation unit calculates a second degree of similarity between the correct data and the teacher data using a plurality of similarity degree calculation methods. The registration unit registers, as new teacher data, correct data corresponding to the highest degree of similarity among the plurality of degrees of second similarity.
　実施形態は、障害を推定するルールを作るための情報を確実かつ容易に登録することができる。 The embodiment can reliably and easily register information for creating rules for estimating failures.
図１は、実施形態に係る障害情報登録装置のハードウェア構成を示す図である。FIG. 1 is a diagram showing the hardware configuration of a failure information registration device according to an embodiment. 図２は、実施形態に係る障害情報登録装置の機能を示す図である。FIG. 2 is a diagram illustrating functions of the fault information registration device according to the embodiment. 図３は、図２の教師ＤＢに含まれる教師データの一例を示す図である。FIG. 3 is a diagram showing an example of teacher data included in the teacher DB of FIG. 図４は、実施形態に係る障害情報登録装置が取得する運用データの一部のデータを示す図である。FIG. 4 is a diagram illustrating a part of operational data acquired by the failure information registration device according to the embodiment; 図５は、教師データと運用データとの類似度の一例を示す図である。FIG. 5 is a diagram showing an example of the degree of similarity between teacher data and operational data. 図６は、実施形態に係る障害情報登録装置の動作の一例である教師データの選定を示すフローチャートである。FIG. 6 is a flowchart showing selection of teacher data, which is an example of the operation of the failure information registration device according to the embodiment. 図７は、実施形態に係る障害情報登録装置の動作の一例である教師データの候補算出を示すフローチャートである。FIG. 7 is a flowchart showing candidate calculation of teacher data, which is an example of the operation of the failure information registration device according to the embodiment. 図８は、図７のステップＳ７１１で表示される情報の一例を示す図である。FIG. 8 is a diagram showing an example of information displayed in step S711 of FIG. 図９は、実施形態に係る障害情報登録装置の動作の一例である正解データの決定、及び学習を示すフローチャートである。FIG. 9 is a flowchart showing determination and learning of correct data, which is an example of the operation of the failure information registration device according to the embodiment. 図１０は、図９のステップＳ９０１、Ｓ９０２を説明するための概念の一例を示す図である。FIG. 10 is a diagram showing an example of a concept for explaining steps S901 and S902 in FIG. 図１１は、図９のステップＳ９０９を説明するための概念の一例を示す図である。FIG. 11 is a diagram showing an example of a concept for explaining step S909 in FIG. 図１２は、図９の開始からステップＳ９０４までの動作の変形例を示すフローチャートである。FIG. 12 is a flow chart showing a modification of the operation from the start of FIG. 9 to step S904.
　以下、実施形態が図面に基づいて説明される。
（ハードウェア構成）
　本実施形態の障害情報登録装置（単に登録装置とも称す）のハードウェア構成の一例が図１を参照して説明される。 Hereinafter, embodiments will be described based on the drawings.
(Hardware configuration)
An example of the hardware configuration of the fault information registration device (simply called the registration device) of this embodiment will be described with reference to FIG.
　本実施形態の障害情報登録装置１００は、プロセッサ１０１と、ＲＯＭ１０２と、ＲＡＭ１０３と、インタフェース１０４と、ディスプレイ１０５と、ストレージ１０６と、を含む。 The fault information registration device 100 of this embodiment includes a processor 101, a ROM 102, a RAM 103, an interface 104, a display 105, and a storage 106.
　プロセッサ１０１は、障害情報登録装置１００の全体を制御する処理装置である。プロセッサ１０１は、例えばＣＰＵ（Central Processing Unit）である。プロセッサ１０１は、ＣＰＵに限るものではない。また、ＣＰＵに代えてＡＳＩＣ（Application Specific IC）等が用いられてもよい。また、プロセッサ１０１は、１つでなく、２つ以上であってもよい。 The processor 101 is a processing device that controls the fault information registration device 100 as a whole. The processor 101 is, for example, a CPU (Central Processing Unit). The processor 101 is not limited to a CPU. Also, an ASIC (Application Specific IC) or the like may be used instead of the CPU. Also, the number of processors 101 may be two or more instead of one.
　ＲＯＭ１０２は、読み出し専用の記憶装置である。ＲＯＭ１０２は、障害情報登録装置１００の動作に必要なファームウェア、各種のプログラムを記憶する。 The ROM 102 is a read-only storage device. The ROM 102 stores firmware and various programs necessary for the operation of the fault information registration device 100 .
　ＲＡＭ１０３は、任意に書き込みできる記憶装置である。ＲＡＭ１０３は、プロセッサ１０１のための作業エリアとして使用され、ＲＯＭ１０２に格納されているファームウェア等を一時的に記憶する。 The RAM 103 is a arbitrarily writable storage device. A RAM 103 is used as a work area for the processor 101 and temporarily stores firmware and the like stored in the ROM 102 .
　インタフェース１０４は、外部の装置との間で情報をやりとりするための装置である。インタフェース１０４は、例えば、運用データ、教師データ、ユーザからの入力を受け付ける。また、インタフェース１０４は、外部のサーバ等との間で情報を送受信してもよい。 The interface 104 is a device for exchanging information with an external device. The interface 104 receives, for example, operational data, teacher data, and input from the user. The interface 104 may also transmit and receive information to and from an external server or the like.
　ディスプレイ１０５は、各種の画面を表示する表示装置である。ディスプレイ１０５は、液晶ディスプレイ、有機ＥＬディスプレイ等であってよい。また、ディスプレイ１０５は、タッチパネルを備えていてもよい。 The display 105 is a display device that displays various screens. The display 105 may be a liquid crystal display, an organic EL display, or the like. Moreover, the display 105 may include a touch panel.
　ストレージ１０６は、ハードディスク等の記憶装置である。ストレージ１０６は、例えばプロセッサ１０１によって実行される各種のアプリケーション、アプリケーションの入力となるデータ、及びアプリケーションの実行によって得られたデータを記憶する。 The storage 106 is a storage device such as a hard disk. The storage 106 stores, for example, various applications executed by the processor 101, data used as input to the applications, and data obtained by executing the applications.
（機能構成）
　次に、本実施形態の障害情報登録装置１００の機能の一例が図２を参照して説明される。 (Function configuration)
Next, an example of the functions of the failure information registration device 100 of this embodiment will be described with reference to FIG.
　本実施形態の障害情報登録装置１００は、機能ブロックとして、教師データ取得部２０１と、教師ＤＢ（以下、データベースをＤＢと略す）２０２と、教師データ選定部２０３と、運用データ取得部２０４と、類似度算出手法及び閾値ＤＢ２０５と、類似度計算部２０６と、対応データ決定部２０７と、対応データ表示部２０８と、正解データ受付部２０９と、正解データ抽出部２１０と、教師データ登録部２１１と、を含む。 The failure information registration device 100 of the present embodiment includes, as functional blocks, a teacher data acquisition unit 201, a teacher DB (hereinafter, the database is abbreviated as DB) 202, a teacher data selection unit 203, an operation data acquisition unit 204, A similarity calculation method and threshold DB 205, a similarity calculation unit 206, a corresponding data determination unit 207, a corresponding data display unit 208, a correct data reception unit 209, a correct data extraction unit 210, and a teacher data registration unit 211 ,including.
　また、教師データ取得部２０１、運用データ取得部２０４、及び正解データ受付部２０９は、例えば、インタフェース１０４によって実現される。教師データ選定部２０３、類似度計算部２０６、対応データ決定部２０７、正解データ抽出部２１０、及び教師データ登録部２１１は、例えば、プロセッサ１０１、ＲＯＭ１０２、ＲＡＭ１０３、及びストレージ１０６によって実現される。教師ＤＢ２０２、及び類似度算出手法及び閾値ＤＢ２０５は、例えば、ストレージ１０６によって実現される。対応データ表示部２０８は、例えば、ディスプレイ１０５によって実現される。 Also, the teacher data acquisition unit 201, the operational data acquisition unit 204, and the correct data reception unit 209 are realized by the interface 104, for example. The teacher data selection unit 203, the similarity calculation unit 206, the corresponding data determination unit 207, the correct data extraction unit 210, and the teacher data registration unit 211 are implemented by the processor 101, the ROM 102, the RAM 103, and the storage 106, for example. The teacher DB 202 and the similarity calculation method and threshold DB 205 are realized by the storage 106, for example. The corresponding data display unit 208 is realized by the display 105, for example.
　教師データ取得部２０１は、ネットワークを介してまたは直接に接続して、外部のＤＢ等から教師データを取得する。教師データは、障害箇所（障害位置とも称す）を推定するルールを作るための情報を含んでいる。教師データは、例えば、１以上の障害に関する情報、この障害の対処に関する情報、または、この障害の回復に関する情報の少なくとも１つの情報を含む。なお、障害箇所（障害位置とも称す）は、ネットワーク上の位置を示し、例えば、ホスト名またはＩＰアドレスの少なくともいずれかで規定される。なお、教師データは図３を参照して後に説明される。 The teaching data acquisition unit 201 acquires teaching data from an external DB or the like by connecting directly or via a network. The training data includes information for creating rules for estimating the location of failure (also called location of failure). The teacher data includes, for example, at least one of information on one or more failures, information on coping with this failure, or information on recovery from this failure. Note that the location of failure (also referred to as location of failure) indicates a location on the network, and is defined by at least one of a host name and an IP address, for example. The teaching data will be explained later with reference to FIG.
　教師ＤＢ２０２は、教師データ取得部２０１が取得した教師データを記憶する。 The teacher DB 202 stores the teacher data acquired by the teacher data acquisition unit 201.
　教師データ選定部２０３は、教師ＤＢ２０２に記憶された複数の教師データのうち、教師データ同士の類似度が閾値以上である場合には、共通部分があると見なせるデータは共通部分を教師データと見なす処理を行う。一方、教師データ同士の類似度が閾値未満である場合には、教師データ選定部２０３は共通部分がないとしてそれぞれの教師データを教師データとして扱う。教師データ選定部２０３は、例えば、教師データに含まれるデータ部分が共通している場合は、共通する部分のみを抽出して新たな教師データとし、抽出前のデータは削除する。教師データ選定部２０３は、類似度算出手法及び閾値ＤＢ２０５から類似度算出手法を使用して、教師ＤＢ２０２に記憶される教師データを整備することになる。この整備の結果、障害情報登録装置１００は、障害箇所を精度良く推定するルールを作るための情報を登録することができる。 If the degree of similarity between pieces of teacher data among a plurality of pieces of teacher data stored in the teacher DB 202 is equal to or greater than a threshold, the teacher data selection unit 203 regards the data that can be considered to have a common portion as teacher data. process. On the other hand, if the degree of similarity between the teacher data is less than the threshold, the teacher data selection unit 203 treats each teacher data as teacher data, assuming that there is no common part. For example, when the data part included in the teacher data is common, the teacher data selection unit 203 extracts only the common part as new teacher data, and deletes the data before extraction. The training data selection unit 203 prepares the training data stored in the training DB 202 using the similarity calculation method and the similarity calculation method from the threshold DB 205 . As a result of this maintenance, the failure information registration device 100 can register information for creating rules for accurately estimating the location of failure.
　運用データ取得部２０４は、ネットワークを介してまたは直接接続し、外部のＤＢ等から運用データを取得する。運用データは、１以上の装置の運用に関する情報を含む。運用データは、例えば、１以上の装置の障害に関する情報、この障害の対処に関する情報、または、この障害の回復に関する情報の少なくとも１つの情報を含む。運用データ取得部２０４は、取得した運用データを受け取り次第、類似度計算部２０６に出力する。また運用データ取得部２０４は、運用データが所定量だけ（例えば、運用データ取得部２０４内にある）バッファに記憶されたら、運用データを類似度計算部２０６に出力してもよい。なお、運用データは図４を参照して後に説明される。 The operational data acquisition unit 204 acquires operational data from an external DB or the like via a network or by direct connection. Operational data includes information regarding the operation of one or more devices. Operational data includes, for example, at least one of information on failure of one or more devices, information on dealing with this failure, or information on recovery from this failure. The operation data acquisition unit 204 outputs the acquired operation data to the similarity calculation unit 206 as soon as it is received. Further, the operational data acquisition unit 204 may output the operational data to the similarity calculation unit 206 when a predetermined amount of operational data is stored in a buffer (for example, in the operational data acquisition unit 204). Operational data will be described later with reference to FIG.
　類似度算出手法及び閾値ＤＢ２０５は、教師データ同士の類似度、教師データと運用データとの類似度（第１の類似度）、または、正解データと教師データとの類似度（第２の類似度）を計算するための類似度算出手法（なお、類似度手法とも略す）に関する手法データと、類似度算出手法ごとの閾値に関する閾値データとを記憶する。正解データは障害を解消する正解情報を含む。これらの手法データと閾値データとは予め設定されて類似度算出手法及び閾値ＤＢ２０５に記憶されている。類似度算出手法及び閾値ＤＢ２０５は、類似度計算部２０６からの指示に基づいて設定される手法データと閾値データとを記憶してもよい。 The similarity calculation method and threshold DB 205 calculates similarity between teacher data, similarity between teacher data and operational data (first similarity), or similarity between correct data and teacher data (second similarity ) and threshold data relating to the threshold for each similarity calculation method are stored. The correct answer data includes correct answer information for resolving the obstacle. These method data and threshold data are preset and stored in the similarity calculation method and threshold DB 205 . The similarity calculation method and threshold DB 205 may store method data and threshold data set based on instructions from the similarity calculation unit 206 .
　類似度計算部２０６は、第１の計算部として、教師ＤＢ２０２から取得する教師データと、運用データ取得部２０４から取得した運用データと、の類似度を、類似度算出手法及び閾値ＤＢ２０５から取得した類似度算出手法により計算する。類似度計算部２０６は、１つの運用データと、教師ＤＢ２０２に記憶される１以上の教師データと、の１以上の類似度を計算する。類似度計算部２０６は正解データ抽出部２１０からの指示に基づいて類似度算出手法及び閾値ＤＢ２０５の手法データまたは閾値データを設定する。類似度計算部２０６は、例えば、手法設定部として最も類似度が高い値を算出した類似度算出手法を設定し、閾値設定部としてこの類似度の閾値を類似度に基づいて設定する。閾値は例えば、対応する類似度算出手法により計算された最も高い類似度の数パーセント（例えば、９０％）に設定される。 The similarity calculation unit 206, as a first calculation unit, obtains the similarity between the training data obtained from the training DB 202 and the operation data obtained from the operation data obtaining unit 204 from the similarity calculation method and threshold DB 205. Calculated by a similarity calculation method. The similarity calculation unit 206 calculates one or more degrees of similarity between one piece of operational data and one or more teacher data stored in the teacher DB 202 . The similarity calculation unit 206 sets the similarity calculation method and method data or threshold data in the threshold DB 205 based on the instruction from the correct data extraction unit 210 . For example, the similarity calculation unit 206 sets a similarity calculation method that calculates the highest similarity value as the method setting unit, and sets the threshold of this similarity based on the similarity as the threshold setting unit. The threshold is set, for example, to a few percent (eg, 90%) of the highest similarity calculated by the corresponding similarity calculation technique.
　また類似度計算部２０６は、第２の計算部として、正解データ抽出部２１０が抽出した正解データと教師データとの類似度を、正解データと教師データとに対応する類似度算出手法により計算する。正解データは類似度算出手法の数だけ決定されるので、正解データと教師データとの類似度は、類似度算出手法の数だけ算出される。 The similarity calculation unit 206, as a second calculation unit, calculates the similarity between the correct data extracted by the correct data extraction unit 210 and the teacher data by a similarity calculation method corresponding to the correct data and the teacher data. . Since the correct data are determined by the number of similarity calculation methods, the similarity between the correct data and the teacher data is calculated by the number of similarity calculation methods.
　対応データ決定部２０７は、類似度計算部２０６により設定されている１以上の類似度算出手法が計算した類似度のうち、設定された閾値以上であるかどうかを教師データと運用データとの組ごとに決定する。対応データ決定部２０７は、例えば、運用データから判明する障害が発生していると推定される箇所（例えば、障害発生装置の位置）と、この障害発生箇所の周辺箇所（例えば、障害発生装置の周辺装置の位置）とにおける運用データと１以上の教師データとの類似度が閾値以上であるかどうかを決定する（図８参照）。 The corresponding data determining unit 207 determines whether or not the similarity calculated by one or more similarity calculation methods set by the similarity calculation unit 206 is equal to or higher than a set threshold value by combining the training data and the operation data. Decide for each The corresponding data determination unit 207, for example, determines a location where a failure is estimated to occur and is identified from the operational data (for example, the location of the failure device) and locations around the failure location (for example, the location of the failure device). position of the peripheral device) and the similarity between the operational data and one or more teacher data is greater than or equal to a threshold value (see FIG. 8).
　また対応データ決定部２０７は、正解データ受付部２０９が受け付けた正解データを特定し、正解データ抽出部２１０に渡す。さらに対応データ決定部２０７は、類似度計算部２０６が計算した正解データと教師データとに対応する類似度算出手法による類似度を受け取り、最も大きな類似度の類似度算出手法と教師データ（または正解データ）とを特定する。対応データ決定部２０７は、正解データ抽出部２１０に正解データと類似度との対応するデータを渡す。 The corresponding data determination unit 207 also identifies the correct data received by the correct data reception unit 209 and passes it to the correct data extraction unit 210 . Further, the corresponding data determination unit 207 receives the similarity calculated by the similarity calculation method corresponding to the correct data and the teacher data calculated by the similarity calculation unit 206, data). Corresponding data determination unit 207 passes data corresponding to the correct data and the degree of similarity to correct data extraction unit 210 .
　対応データ表示部２０８は、対応データ決定部２０７が決定した内容を表示する。対応データ表示部２０８は、例えば、障害発生装置の位置と、障害発生装置の周辺装置の位置とにおいて、１以上の運用データと１以上の教師データとの類似度が閾値以上であるかどうかを教師データごとに示す。対応データ表示部２０８は、例えば、後述する図８に示される内容を表示する。なお、対応データ表示部２０８は提示部として表示せず音声によりユーザまたはユーザに代わる装置に提示してもよい。ユーザまたはユーザに代わる装置に情報が伝われば、対応データ表示部２０８は表示または音声に拘らず他の手段により情報を伝達してもよい。 The corresponding data display unit 208 displays the content determined by the corresponding data determining unit 207. The corresponding data display unit 208, for example, determines whether or not the degree of similarity between one or more operational data and one or more teacher data is equal to or greater than a threshold at the location of the failure-causing device and the location of the peripheral device of the failure-causing device. Shown for each training data. The corresponding data display unit 208 displays, for example, the contents shown in FIG. 8, which will be described later. It should be noted that the corresponding data display unit 208 may not be used as a presentation unit, but may be presented by voice to the user or a device in place of the user. If the information is conveyed to the user or a device acting on behalf of the user, the corresponding data display 208 may convey the information by other means, whether visual or audible.
　正解データ受付部２０９は、対応データ表示部２０８が表示した内容（教師データとログの有無）に応じてユーザまたはユーザに代わる装置が、正解に最も関係すると想定される教師データを選択した指示情報を受け付ける。そして、対応データ表示部２０８は、ユーザまたはユーザに代わる装置が選択した教師データに対応するとして決定した正解データを受け付ける。正解データは教師データに対応するログ情報である。ログ情報は、装置ごとに関係するログとして出力される情報であり、例えば、Syslogの情報である。 The correct data reception unit 209 receives instruction information in which the user or a device acting on behalf of the user selects teacher data assumed to be most relevant to the correct answer according to the contents displayed by the corresponding data display unit 208 (presence or absence of teacher data and log). accept. Then, the corresponding data display unit 208 receives the correct data determined as corresponding to the teacher data selected by the user or the device representing the user. Correct data is log information corresponding to teacher data. The log information is information output as a log related to each device, and is Syslog information, for example.
　正解データ抽出部２１０は、対応データ決定部２０７から取得した１以上の正解データを抽出し、この１以上の正解データと教師データとの類似度を複数の類似度算出手法により計算するように類似度計算部２０６に指示する。また正解データ抽出部２１０は、対応データ決定部２０７から受け取った正解データと類似度との１以上の組から、最大の類似度を有する組を抽出する。そして正解データ抽出部２１０は、抽出した最大の類似度を有する組に含まれる正解データを教師データ登録部２１１に渡す。 The correct data extracting unit 210 extracts one or more correct data acquired from the corresponding data determining unit 207, and calculates similarity between the one or more correct data and the teacher data using a plurality of similarity calculation methods. The degree calculation unit 206 is instructed. Further, the correct data extraction unit 210 extracts a set having the maximum similarity from one or more sets of the correct data and the similarity received from the corresponding data determination unit 207 . Then, the correct data extraction unit 210 passes the correct data included in the extracted set having the maximum similarity to the teacher data registration unit 211 .
　教師データ登録部２１１は、正解データ抽出部２１０から受け取った正解データを教師ＤＢ２０２に新たな教師データとして登録する。 The teacher data registration unit 211 registers the correct data received from the correct data extraction unit 210 in the teacher DB 202 as new teacher data.
（教師データと運用データ）
　教師ＤＢ２０２が記憶している教師データの一例が図３を参照して説明される。 (teaching data and operational data)
An example of teacher data stored in the teacher DB 202 will be described with reference to FIG.
　教師データは、例えば、障害に関する情報、この障害の対処に関する情報、または、この障害の回復に関する情報の少なくともいずれかを含む。図３に示される教師データ１は、「障害Ａ」という障害に関する情報と、「restart port:ID」という障害の対処に関する情報を含む。図３に示される教師データ２は、「障害Ａ」という障害に関する情報と、「restart-OK」という障害の回復に関する情報を含む。図３に示される教師データ３は、「障害Ｄ」という障害に関する情報と、「re-insert card:ID」という障害の対処に関する情報と、を含んでいる。図３に示される教師データ４は、「障害Ｄ」という障害に関する情報と、「re-insert-OK」という障害の回復に関する情報と、を含んでいる。 The teacher data, for example, includes at least one of information on failure, information on coping with this failure, or information on recovery from this failure. The teacher data 1 shown in FIG. 3 includes information on failure "failure A" and information on coping with the failure "restart port: ID". The teacher data 2 shown in FIG. 3 includes information about the failure "failure A" and information about the recovery of the failure "restart-OK". The teacher data 3 shown in FIG. 3 includes information about a failure called "failure D" and information about how to deal with the failure called "re-insert card: ID". The teacher data 4 shown in FIG. 3 includes information about the failure "failure D" and information about the recovery of the failure "re-insert-OK".
　「障害Ａ」「障害Ｄ」は、例えば、障害の状態を特定する情報に対応づけてある。例えば、「障害Ａ」は障害がどのような状態であるかの情報を示す。 "Fault A" and "Failure D" are associated with, for example, information specifying the state of the fault. For example, "failure A" indicates information about the state of the failure.
　「restart port:ID」は、IDのポートを再開することにより対処することを示す。IDは変数であり、運用データでは特定のID番号（例えば、自然数）が対応する。「restart-OK」は、該当装置（図３では「障害Ａ」に対応する装置）が再開したこと（回復）を示す。「re-insert card:ID」は、IDのカードを再度挿入することにより対処することを示す。「re-insert-OK」は、該当装置（図３では「障害Ａ」に対応する装置）に再度挿入されたこと（回復）を示す。 "restart port: ID" indicates that the ID port will be restarted. The ID is a variable, and corresponds to a specific ID number (for example, natural number) in operational data. "restart-OK" indicates that the relevant device (the device corresponding to "failure A" in FIG. 3) has restarted (recovered). "re-insertcard:ID" indicates to handle by reinserting the ID card. "re-insert-OK" indicates reinsertion (recovery) into the corresponding device (the device corresponding to "failure A" in FIG. 3).
　運用データ取得部２０４が取得する運用データの一例が図４を参照して説明される。 An example of operational data acquired by the operational data acquisition unit 204 will be described with reference to FIG.
　運用データは、ある装置の障害に関する情報、または、障害に関する内容に関する情報の少なくともいずれかを含む。ある装置の障害に関する情報は、例えば、障害が発生した日時、障害の発生箇所（ホスト名、ＩＰアドレス等）、障害への対処すべき緊急度（Emerg, Alert, Notice, Info等）を含む。障害に関する内容に関する情報は、例えば、この障害の対処に関する情報、または、この障害の回復に関する情報の少なくともいずれかを含む。以下では、運用データについては、本実施形態で主に利用される、障害の発生箇所、この障害の対処に関する情報、及び、この障害の回復に関する情報に注目する。従って本実施形態では、運用データはこれらの注目される情報のうちの少なくともいずれかを含むものとして説明される。なお、運用データは、注目される情報以外に、明記しなくとも上記に示した情報を含んでいることに注意する。 Operational data includes at least one of information about the failure of a certain device or information about the content of the failure. Information about a failure of a device includes, for example, the date and time when the failure occurred, the location of the failure (host name, IP address, etc.), and the degree of urgency to deal with the failure (Emerg, Alert, Notice, Info, etc.). The information about the content of the failure includes, for example, at least one of information about how to deal with this failure and information about recovery from this failure. In the following, with regard to operational data, attention will be focused on the location of the failure, information on how to deal with this failure, and information on recovery from this failure, which are mainly used in this embodiment. Therefore, operational data is described in this embodiment as including at least one of these pieces of information of interest. It should be noted that operational data, in addition to the information of interest, includes the information indicated above even if not specified.
　図４に示される運用データ１は、「装置Ａ」に位置「XX.XX.XX.XX」で障害が発生していることを示す情報と、「restart port:03」という障害の対処に関する情報を含む。図４に示される運用データ２は、「装置Ａ」に位置「XX.XX.XX.XX」で障害が発生していることを示す情報と、「restart-OK」という障害の回復に関する情報を含む。図４に示される運用データ３は、「装置Ｚ」に位置「ZZ.ZZ.XX.YY」で障害が発生していることを示す情報と、「re-insert card:04」という障害の対処に関する情報と、を含んでいる。図４に示される運用データ４は、「装置Ｚ」に位置「ZZ.ZZ.XX.YY」で障害が発生していることを示す情報と、「re-insert-NG」という障害の回復（この場合は回復の失敗）に関する情報と、を含んでいる。 Operational data 1 shown in FIG. 4 includes information indicating that a failure has occurred in "equipment A" at location "XX.XX.XX.XX" and information regarding handling of the failure "restart port: 03". including. Operational data 2 shown in FIG. 4 includes information indicating that a failure has occurred in "equipment A" at location "XX.XX.XX.XX" and information regarding failure recovery of "restart-OK". include. Operational data 3 shown in FIG. 4 includes information indicating that a failure has occurred in "device Z" at location "ZZ.ZZ.XX.YY", and failure handling "re-insert card: 04". contains information about and Operational data 4 shown in FIG. 4 includes information indicating that a failure has occurred in "device Z" at location "ZZ.ZZ.XX.YY" and failure recovery ("re-insert-NG"). (in this case recovery failure).
　「restart port:03」は、具体的な番号である03が指定され、この03のポートを再開することにより対処することを示す。「re-insert card:04」は、具体的な番号である04が指定され、この04のカードを再度挿入することにより対処することを示す。「re-insert-NG」は、該当装置（図４では装置Ｚ）に再度挿入しても障害は回復されなかったことを示す。 "restart port: 03" indicates that a specific number 03 is specified and that the port of 03 will be restarted. "re-insert card:04" indicates that a specific number 04 is specified and that the card of 04 will be reinserted. "re-insert-NG" indicates that re-insertion into the corresponding device (device Z in FIG. 4) did not recover from the failure.
（類似度）
　教師データと運用データとの類似度の一例が図５を参照して説明される。なお、図５に示される教師データ１から４と、運用データ１から４とはそれぞれ図３及び図４の教師データ１から４と、運用データ１から４と同一である。ここでは類似度の意味について説明する。 (Degree of similarity)
An example of the degree of similarity between teacher data and operational data will be described with reference to FIG. 5 are the same as the teacher data 1 to 4 and the operational data 1 to 4 shown in FIGS. 3 and 4, respectively. Here, the meaning of the degree of similarity will be explained.
　類似度計算部２０６が、ある運用データごとに教師ＤＢ２０２にある全ての教師データとの類似度を計算する。類似度が大きいほど互いのデータが類似していることを示す。図５に示されるように、本実施形態では例えば、類似度が０から１の間になるように計算される。類似度が１に近いほど運用データと教師データとがよく類似していることを示し、逆に類似度が０に近いほど運用データと教師データとがほとんど類似していないことを示す。 The similarity calculation unit 206 calculates the similarity with all the teacher data in the teacher DB 202 for each operational data. It shows that mutual data are similar, so that similarity is large. As shown in FIG. 5, in this embodiment, the similarity is calculated between 0 and 1, for example. The closer the similarity is to 1, the more similar the operation data and the training data are, and the closer the similarity is to 0, the less similar the operation data and the training data are.
　運用データ１に関しては、教師データ１との類似度が最も高く０．９７であり、最も低い類似度は教師データ３との０．２５である。０．９７と１との差は０．２５と０との差よりも小さいので、類似度０．９７が採用されることが妥当である。従って、運用データ１は教師データ１と類似していると判定される。すなわち、図５によれば、運用データ１による装置Ａにおいて教師データ１による障害Ａが発生し、教師データ１による対処「restart port:ID」に対応して運用データ１による「restart port:03」が実施されたことがわかる。 Regarding operational data 1, the degree of similarity with teacher data 1 is the highest at 0.97, and the lowest similarity with teacher data 3 is 0.25. Since the difference between 0.97 and 1 is smaller than the difference between 0.25 and 0, it is reasonable to adopt a similarity of 0.97. Therefore, operational data 1 is determined to be similar to teacher data 1 . That is, according to FIG. 5, failure A occurs in device A based on operational data 1, and failure A based on teacher data 1 occurs. is known to have been carried out.
　運用データ２に関しては、教師データ２との類似度が最も高く１．００であり、最も低い類似度は教師データ４との０．３２である。１．００と１との差は０．３２と０との差よりも小さいので、類似度１．００が採用されることが妥当である。従って、運用データ２は教師データ２と類似していると判定される。すなわち、図５によれば、運用データ２による装置Ａにおいて教師データ２による障害Ａが発生し、教師データ２による回復「restart-OK」に対応して運用データ２による「restart-OK」が実施されたことがわかる。 Regarding operational data 2, the degree of similarity with teacher data 2 is the highest at 1.00, and the lowest similarity with teacher data 4 is 0.32. Since the difference between 1.00 and 1 is less than the difference between 0.32 and 0, it is reasonable to adopt a similarity of 1.00. Therefore, it is determined that the operational data 2 is similar to the teacher data 2 . That is, according to FIG. 5, a failure A caused by the teacher data 2 occurs in the device A by the operation data 2, and in response to the recovery "restart-OK" by the teacher data 2, "restart-OK" by the operation data 2 is executed. I know it was done.
　この結果、装置Ａについては、障害Ａが発生し、対処「restart port:03」が実施され回復「restart-OK」になったことがわかる。 As a result, it can be seen that for device A, failure A occurred, and the response "restart port: 03" was implemented, resulting in recovery "restart-OK".
　また運用データ３に関しては、教師データ３との類似度が最も高く０．９７であり、最も低い類似度は教師データ１との０．２５である。０．９７と１との差は０．２５と０との差よりも小さいので、類似度０．９７が採用されることが妥当である。従って、運用データ３は教師データ３と類似していると判定される。すなわち、図５によれば、運用データ３による装置Ｚにおいて教師データ３による障害Ｄが発生し、教師データ３による対処「re-insert card:ID」に対応して運用データ３による「re-insert card:04」が実施されたことがわかる。 Regarding operational data 3, the degree of similarity with teacher data 3 is the highest at 0.97, and the lowest similarity with teacher data 1 is 0.25. Since the difference between 0.97 and 1 is smaller than the difference between 0.25 and 0, it is reasonable to adopt a similarity of 0.97. Therefore, it is determined that the operational data 3 is similar to the teacher data 3 . That is, according to FIG. 5, a failure D caused by the training data 3 occurs in the device Z by the operation data 3, and in response to the countermeasure “re-insert card: ID” by the training data 3, the operation data 3 “re-insert card:04" has been implemented.
　運用データ４に関しては、教師データ３との類似度が最も高く０．５３であり、最も低い類似度は教師データ４との０．２１である。０．５３と１との差は０．２１と０との差よりも大きいので、類似度０．２１が採用されることが妥当である。従って、運用データ４は教師データ４にほとんど類似していないと判定される。すなわち、図５によれば、運用データ４による装置Ｚにおいて教師データ４による障害Ｄが発生し、教師データ４による回復「re-insert-OK」は成功しなかったことがわかり、運用データ４による「re-insert-NG」となることがわかる。 Regarding operational data 4, the degree of similarity with teacher data 3 is the highest at 0.53, and the lowest similarity with teacher data 4 is 0.21. Since the difference between 0.53 and 1 is greater than the difference between 0.21 and 0, it is reasonable to adopt a similarity of 0.21. Therefore, it is determined that the operational data 4 is hardly similar to the teacher data 4 . That is, according to FIG. 5, it is found that a failure D caused by the teacher data 4 occurred in the device Z by the operation data 4, and the recovery "re-insert-OK" by the teacher data 4 was not successful. It turns out that it becomes "re-insert-NG".
　この結果、装置Ｚについては、障害Ｄが発生し、対処「re-insert card:04」が実施されたが、回復「re-insert-OK」は成功せず「re-insert-NG」となったことがわかる。 As a result, failure D occurred in device Z, and countermeasure "re-insert card: 04" was implemented, but recovery "re-insert-OK" did not succeed, resulting in "re-insert-NG". I understand that.
（教師データ選定処理）
　次に、障害情報登録装置１００の教師データ選定部２０３の動作の一例が図６を参照して説明される。 (Training data selection processing)
Next, an example of the operation of the teacher data selection unit 203 of the failure information registration device 100 will be described with reference to FIG.
　ステップＳ６０１において、教師データ選定部２０３が教師ＤＢ２０２から教師データを読み込む。 In step S601, the teacher data selection unit 203 reads the teacher data from the teacher DB 202.
　ステップＳ６０２において、教師データ選定部２０３が同一の番号が付された教師データがあるかどうかを判定する。教師データは、それぞれ番号が付されている。教師データ選定部２０３が、同一番号の教師データがあると判定した場合はステップＳ６０３に進み、同一番号の教師データがないと判定した場合はステップＳ６０７に進む。教師データは例えば、メーカ番号_ログ番号_セット番号の３つの番号を含む。 In step S602, the teacher data selection unit 203 determines whether there is teacher data with the same number. Each teacher data is numbered. If the teacher data selection unit 203 determines that there is teacher data with the same number, the process proceeds to step S603, and if it determines that there is no teacher data with the same number, the process proceeds to step S607. The teacher data includes, for example, three numbers: manufacturer number_log number_set number.
　ステップＳ６０３において、教師データ選定部２０３は同一番号であると判定された同じグループに属する教師データ同士で類似度を計算する。この類似度を計算する手法は、類似度算出手法及び閾値ＤＢ２０５からある類似度算出手法を採用する。教師データ選定部２０３は、例えば、教師データと運用データとの類似度を計算する類似度算出手法とは異なる類似度算出手法を採用してもよい。これとは異なり、教師データ選定部２０３は、例えば、教師データ登録部２１１が教師データを登録した際の正解データに関連する類似度算出手法を採用してもよい。 In step S603, the teacher data selection unit 203 calculates the degree of similarity between teacher data belonging to the same group determined to have the same number. A similarity calculation method and a certain similarity calculation method from the threshold DB 205 are adopted as a method for calculating this similarity. The training data selection unit 203 may employ, for example, a similarity calculation method different from the similarity calculation method for calculating the similarity between training data and operational data. Alternatively, the teacher data selection unit 203 may employ, for example, a similarity calculation method related to correct data when the teacher data registration unit 211 registers the teacher data.
　ステップＳ６０４において、教師データ選定部２０３は、ステップＳ６０３で計算された類似度の平均値を閾値と比較する。この閾値はステップＳ６０３で使用した類似度算出手法の閾値を使用する。類似度の平均値は、教師データの項目ごとに類似度が計算され、これら類似度から算出される。例えば、図３に示される教師データは障害、対処、回復の３つの項目があり、それぞれの項目に関して類似度が計算される。例えば、教師データ１と教師データ３との類似度の平均値は、障害の類似度と、対処の類似度との平均値である。また、教師データ１と教師データ２との類似度の平均値は、比較される項目が障害のみなので障害の類似度と同一になる。 In step S604, the teacher data selection unit 203 compares the average similarity calculated in step S603 with a threshold. As this threshold, the threshold of the similarity calculation method used in step S603 is used. The similarity average value is calculated from the similarities calculated for each item of the training data. For example, the training data shown in FIG. 3 has three items, failure, coping, and recovery, and the degree of similarity is calculated for each item. For example, the average value of similarity between teacher data 1 and teacher data 3 is the average value of similarity of failure and similarity of coping. Also, the average value of the similarity between teacher data 1 and teacher data 2 is the same as the similarity of disability because the item to be compared is only disability.
　ステップＳ６０４において、教師データ選定部２０３は、類似度の平均値が閾値以上である場合にはステップＳ６０５に進み、類似度の平均値が閾値よりも小さい場合にはステップＳ６０６に進む。 In step S604, the training data selection unit 203 proceeds to step S605 if the average value of similarities is equal to or greater than the threshold, and proceeds to step S606 if the average value of similarities is smaller than the threshold.
　ステップＳ６０５において、教師データ選定部２０３は、教師データの共通部分を教師データと設定する。この場合に教師データ選定部２０３は、教師データに共通部分があるとの情報を付加してもよい。共通部分とは、教師データの同一項目にある同一の情報を示す。例えば、教師データの同一項目に、一方の教師データが「Link Down」を含み、他方の教師データが「LinkがDownした」を含んでいたとする。この場合には、教師データ選定部２０３は、共通部分が「LinkDown」であると判定し、教師データのこの項目は「LinkDown」に統一して設定する。そして、教師データ「Link Down」と「LinkがDownした」とはそれぞれ「LinkDown」であると見なして類似度計算がなされる。 In step S605, the teacher data selection unit 203 sets the common part of the teacher data as teacher data. In this case, the teacher data selection unit 203 may add information that the teacher data have a common part. A common part indicates the same information in the same item of teacher data. For example, assume that one teacher data includes "Link Down" and the other teacher data includes "Link is Down" in the same item of teacher data. In this case, the teacher data selection unit 203 determines that the common part is "LinkDown", and sets this item of the teacher data uniformly to "LinkDown". Then, the training data "Link Down" and "Link Down" are regarded as "LinkDown" and similarity calculation is performed.
　なお、ステップＳ６０５において、この共通部分がない場合には教師データ選定部２０３はステップＳ６０６と同様の処理を行う。 It should be noted that in step S605, if there is no common part, the teacher data selection unit 203 performs the same processing as in step S606.
　ステップＳ６０６において、教師データ選定部２０３は、教師データを統合せずそれぞれの教師データをそのまま教師データとして設定する。この場合に教師データ選定部２０３は、教師データに共通部分がないとの情報を付加してもよい。例えば、教師データの同一項目に、一方の教師データが「リンクが切れました」を含み、他方の教師データが「LinkがDownした」を含んでいたとする。この場合には、教師データ選定部２０３は「リンクが切れました」と「LinkがDownした」とを教師データとして設定する。ステップＳ６０６では教師データ選定部２０３が教師データに共通部分がないと判定したとも解釈できる。 In step S606, the teacher data selection unit 203 sets each teacher data as it is as teacher data without integrating the teacher data. In this case, the teacher data selection unit 203 may add information indicating that there is no common part in the teacher data. For example, assume that one teacher data includes "link is broken" and the other teacher data includes "link is down" in the same item of teacher data. In this case, the teacher data selection unit 203 sets "link is broken" and "link is down" as teacher data. In step S606, it can be interpreted that the teacher data selection unit 203 has determined that there is no common part in the teacher data.
　ステップＳ６０７において、教師データ選定部２０３は他のカテゴリ（例えば、他のメーカ）で同一番号の教師データがあるかどうかを判定する。教師データ選定部２０３は、例えば、他のメーカの教師データがあるかどうかを判定する。教師データ選定部２０３は、他のカテゴリで同一番号の教師データがあると判定した場合にはステップＳ６０１に戻り、他のカテゴリで同一番号の教師データがないと判定した場合には教師データ選定処理を終了する。 In step S607, the teacher data selection unit 203 determines whether there is teacher data with the same number in another category (for example, another manufacturer). For example, the teacher data selection unit 203 determines whether or not there is teacher data of another manufacturer. If the teacher data selection unit 203 determines that there is teacher data with the same number in another category, it returns to step S601. exit.
（教師データ候補算出処理）
　次に、障害情報登録装置１００が教師データの候補を算出する処理の動作の一例が図７を参照して説明される。 (Teaching Data Candidate Calculation Processing)
Next, an example of the operation of the process of calculating training data candidates by the failure information registration device 100 will be described with reference to FIG.
　ステップＳ７０１において、類似度計算部２０６が類似度算出手法及び閾値ＤＢ２０５から類似度算出手法を読み込み、ステップＳ７０２において、類似度計算部２０６が教師データを教師ＤＢ２０２から読み込む。ステップＳ７０１で読み込まれる類似度算出手法は、既に図９のステップＳ９０９により類似度算出手法が決定されている場合にはこの類似度算出手法が読み込まれ、まだステップＳ９０９を経験していない場合にはデフォルトの類似度算出手法もしくはユーザまたはユーザに代わる装置に選択された類似度算出手法が読み込まれる。なお、ステップＳ７０１とステップＳ７０２は処理順序が逆でも構わない。 In step S701, the similarity calculation unit 206 reads the similarity calculation method and the similarity calculation method from the threshold DB 205, and in step S702, the similarity calculation unit 206 reads teacher data from the teacher DB 202. As for the similarity calculation method read in step S701, if the similarity calculation method has already been determined in step S909 of FIG. 9, this similarity calculation method is read. The default similarity calculation method or the similarity calculation method selected by the user or a device on behalf of the user is loaded. Note that the processing order of steps S701 and S702 may be reversed.
　ステップＳ７０３において、類似度計算部２０６が運用データを取得する。 In step S703, the similarity calculation unit 206 acquires operational data.
　ステップＳ７０４において、類似度計算部２０６が教師ＤＢ２０２の教師データに共通部分があるかどうかを判定する。すなわち、類似度計算部２０６は、ステップＳ６０５で設定された教師データである（共通部分がある）かどうか、ステップＳ６０６で設定された教師データである（共通部分がない）かどうかを判定する。類似度計算部２０６が教師データに共通部分があると判定した場合にはステップＳ７０５に進み、教師データに共通部分がないと判定した場合にはステップＳ７０６に進む。 In step S704, the similarity calculation unit 206 determines whether or not the teacher data in the teacher DB 202 have common parts. That is, the similarity calculation unit 206 determines whether the teacher data set in step S605 (there is a common part) or not the teacher data set in step S606 (there is no common part). If the similarity calculation unit 206 determines that there is a common part in the teacher data, the process advances to step S705, and if it determines that there is no common part in the teacher data, the process advances to step S706.
　ステップＳ７０５において、類似度計算部２０６が１つの運用データに対して共通部分がある教師データとの類似度を算出する。 In step S705, the similarity calculation unit 206 calculates the similarity between one piece of operational data and teacher data having a common part.
　ステップＳ７０６において、類似度計算部２０６が１つの運用データに対して共通部分がない複数の教師データとの類似度を算出する。類似度計算部２０６は、これらの複数の類似度の中から最大の類似度を抽出する。 In step S706, the similarity calculation unit 206 calculates the similarity between one piece of operational data and a plurality of teacher data having no common portion. The similarity calculation unit 206 extracts the maximum similarity from these multiple similarities.
　ステップＳ７０７において、ステップＳ７０１で読み込まれた類似度算出手法の閾値を類似度算出手法及び閾値ＤＢ２０５から読み込む。読み込まれる閾値は、既に図９のステップＳ９０９により閾値が決定されている場合にはこの閾値が読み込まれ、まだステップＳ９０９を経験していない場合にはデフォルトの閾値もしくはユーザまたはユーザに代わる装置に選択された閾値が読み込まれる。 In step S707, the threshold for the similarity calculation method read in step S701 is read from the similarity calculation method and threshold DB 205. The read threshold is read if the threshold has already been determined in step S909 of FIG. 9, or is selected by the default threshold or by the user or a device on behalf of the user if step S909 has not yet been performed. The specified threshold is read.
　ステップＳ７０８において、対応データ決定部２０７が、運用データから判明する障害が発生していると推定される障害発生装置の位置と、この障害発生装置の周辺装置の位置と、を決定する。 In step S708, the corresponding data determination unit 207 determines the location of the faulty device that is estimated to have a fault identified from the operational data and the positions of the peripheral devices of this faulty device.
　ステップＳ７０９において、対応データ決定部２０７が、類似度算出手法が計算した類似度のうち、ステップＳ７０７で読み込まれた閾値以上であるかどうかを教師データと運用データとの組ごとに決定する。そして、対応データ決定部２０７が、類似度が閾値以上であると判定した場合にはステップＳ７１０に進み、類似度が閾値未満であると判定した場合にはステップＳ７１１に進む。 In step S709, the corresponding data determination unit 207 determines for each set of teacher data and operational data whether the similarity calculated by the similarity calculation method is equal to or greater than the threshold read in step S707. If the corresponding data determination unit 207 determines that the degree of similarity is equal to or greater than the threshold, the process proceeds to step S710, and if it determines that the degree of similarity is less than the threshold, the process proceeds to step S711.
　ステップＳ７１０において、対応データ決定部２０７が、対応する教師データは「ログ有り」であると判定する。 In step S710, the corresponding data determination unit 207 determines that the corresponding teacher data "has a log".
　ステップＳ７１１において、対応データ決定部２０７が、対応する教師データは「ログ無し」であると判定する。 In step S711, the corresponding data determination unit 207 determines that the corresponding teacher data is "no log".
　ステップＳ７１２において、対応データ表示部２０８が、障害発生装置の位置と、障害発生装置の周辺装置の位置とにおいて、運用データと教師データとの類似度が閾値以上であるかどうかを教師データごとディスプレイ１０５に示す。対応データ表示部２０８は例えば、図８に示される表をディスプレイ１０５に表示する。 In step S712, the correspondence data display unit 208 displays whether or not the degree of similarity between the operation data and the teacher data is equal to or greater than a threshold value for each teacher data at the location of the failure device and the location of the peripheral devices of the failure device. 105. Corresponding data display unit 208 displays, for example, the table shown in FIG. 8 on display 105 .
　ステップＳ７１３において、対応データ決定部２０７が、他に運用データがあるかどうかを判定する。対応データ決定部２０７は、他に運用データがあると判定した場合にはステップＳ７０３に戻り、他に運用データがないと判定した場合には図７の処理を終了する。 In step S713, the corresponding data determination unit 207 determines whether there is other operational data. The corresponding data determining unit 207 returns to step S703 if it determines that there is other operational data, and terminates the processing in FIG. 7 if it determines that there is no other operational data.
　図８は、ステップＳ７１２において表示される表の一例である。 FIG. 8 is an example of a table displayed in step S712.
　図８の例では、ステップＳ７０１において類似度算出手法としてコサイン類似度算出手法が読み込まれた場合の表の一例である。図８では、ステップＳ７１０において「ログ有り」と判定された教師データは、データ名の右横に「有」と表示される。図８では太文字かつ下線で示されている。ステップＳ７１１において「ログ無し」と判定された教師データは、データ名の右横に「無」と表示される。 The example of FIG. 8 is an example of a table when the cosine similarity calculation method is read as the similarity calculation method in step S701. In FIG. 8, teacher data determined to have a log in step S710 is displayed with "Yes" to the right of the data name. It is shown in bold and underlined in FIG. Teacher data determined to be "no log" in step S711 are displayed as "no log" to the right of the data name.
　また、今回の教師データ候補算出処理において類似度算出手法として読み込まれていないレーベンシュタイン類似度算出手法に関しては、全ての教師データについて類似度は計算していないので「ログ無し」を意味する「無」が表示される。 In addition, regarding the Levenshtein similarity calculation method that is not read as a similarity calculation method in this training data candidate calculation process, the similarity is not calculated for all training data, so "no log" means "no log". ” is displayed.
（正解データ決定処理、学習処理）
　次に、障害情報登録装置１００が正解データの候補を算出し、正解データを決定する処理の動作の一例と、類似度算出手法とその閾値の最適なものを学習する処理の動作の一例とが図９を参照して説明される。 (Correct data decision processing, learning processing)
Next, an example of the operation of the process of calculating candidates for correct data and determining the correct data by the failure information registration device 100, and an example of the operation of the process of learning the similarity calculation method and its optimum threshold value will be described. It will be described with reference to FIG.
　ステップＳ９０１において、正解データ受付部２０９は、対応データ表示部２０８が表示した内容をユーザまたはユーザに代わる装置が参照して選択した教師データを類似度算出手法ごとに受け付ける。正解データ受付部２０９は、例えば、図８に示される教師データから類似度算出手法ごとに選択された教師データを指示する情報を受け付ける。なお、ユーザまたはユーザに代わる装置は、正解データを決定する場合、正解データの決定に必要な情報を知り得た上で正解データを決定している。換言すると、ネットワークが故障して、障害箇所を推定し、対処を実施して回復したのち正解データを決定及び登録するため、ユーザまたはユーザに代わる装置は、一連の処理において対処を実施して回復した正解データに関する情報を既に知っている。 In step S901, the correct data reception unit 209 receives the teacher data selected by the user or a device acting on behalf of the user with reference to the content displayed by the corresponding data display unit 208 for each similarity calculation method. The correct data reception unit 209 receives, for example, information instructing teacher data selected for each similarity calculation method from the teacher data shown in FIG. When determining correct data, the user or a device acting on behalf of the user determines the correct data after obtaining information necessary for determining the correct data. In other words, if the network breaks down, the location of the failure is estimated, and correct data is determined and registered after recovery by taking measures. We already know information about the correct answer data.
　ステップＳ９０２において、正解データ受付部２０９は、ユーザまたはユーザに代わる装置がステップＳ９０１で選択した類似度算出手法ごとの教師データに対応するデータであって、ユーザまたはユーザに代わる装置が正解データとして決定したデータを受け付ける。そしてステップＳ９０２において、対応データ決定部２０７が、正解データ受付部２０９が受け付けた正解データを特定し、正解データ抽出部２１０に正解データを渡す。正解データ受付部２０９は、例えば、図１０に示されように、教師データに対して選択された正解データを受け付ける。なお、この教師データとこの正解データは、それぞれ図３のテーブルに示される「教師データ１」「教師データ２」「教師データ３」「教師データ４」等と同様の内容を含むデータである。また、この他にも図４の運用データに含まれる対処に関する情報（例えば、「restart port:03」）、図３の教師データに含まれる対処に関する情報（例えば、「restart port:03」）も正解データになり得る。 In step S902, the correct data reception unit 209 determines that the user or a device acting in place of the user is data corresponding to the training data for each similarity calculation method selected in step S901 by the user or a device acting in place of the user as correct data. accept the data. Then, in step S 902 , the corresponding data determining unit 207 identifies the correct data received by the correct data receiving unit 209 and passes the correct data to the correct data extracting unit 210 . The correct data reception unit 209 receives correct data selected for teacher data, as shown in FIG. 10, for example. Note that this teacher data and this correct answer data are data containing the same contents as "teacher data 1", "teacher data 2", "teacher data 3", "teacher data 4", etc. shown in the table of FIG. In addition to this, there is also information on measures included in the operation data in FIG. 4 (eg, "restart port: 03") and information on measures included in the training data in FIG. 3 (eg, "restart port: 03"). It can be correct data.
　ステップＳ９０３において、類似度計算部２０６は、正解データ抽出部２１０が抽出した正解データと教師データとの類似度を、ステップＳ９０１で教師データを受け付ける際の対応する類似度算出手法により計算する。類似度計算部２０６は、例えば、教師データのカテゴリごとに正解データと教師データとの類似度を計算し、全てのカテゴリの類似度からその平均値を計算してこの平均値を、この正解データと教師データとの類似度とする。このカテゴリは、例えば、図３及び図４に記載される「障害」「対処」「回復」である。図５はこのようにして計算された類似度の一例を示す。教師データのカテゴリごとに正解データと教師データとの類似度の計算の一例は、図１１に示されている。図１１ではカテゴリである「障害」「対処」「回復」ごとに類似度が計算されていることを示している。 In step S903, the similarity calculation unit 206 calculates the degree of similarity between the correct data extracted by the correct data extraction unit 210 and the training data by the corresponding similarity calculation method used when receiving the training data in step S901. For example, the similarity calculation unit 206 calculates the similarity between the correct data and the teacher data for each category of the teacher data, calculates the average value from the similarities of all the categories, and uses this average value as the correct data. and the degree of similarity with the training data. This category is, for example, "failure", "countermeasure", and "recovery" described in FIGS. FIG. 5 shows an example of similarity calculated in this way. An example of calculation of the degree of similarity between correct data and teacher data for each category of teacher data is shown in FIG. FIG. 11 shows that the similarity is calculated for each of the categories "failure", "countermeasure" and "recovery".
　ステップＳ９０４において、正解データ抽出部２１０は、ステップＳ９０１で受け付けた類似度算出手法に対応する教師データに基づいて受け付けた正解データのうち、類似度計算部２０６がまだ類似度を計算していない正解データがあるかどうかを判定する。正解データ抽出部２１０が、類似度計算部２０６がまだ類似度を計算していない正解データがあると判定した場合にはステップＳ９０２に戻り、類似度計算部２０６がまだ類似度を計算していない正解データがないと判定した場合にはステップＳ９０５に進む。 In step S904, the correct data extraction unit 210 extracts correct data for which the similarity calculation unit 200 has not yet calculated similarity among the received correct data based on the teacher data corresponding to the similarity calculation method received in step S901. Determine if data exists. If the correct data extraction unit 210 determines that there is correct data for which the similarity calculation unit 206 has not yet calculated the similarity, the process returns to step S902, and the similarity calculation unit 206 has not yet calculated the similarity. If it is determined that there is no correct data, the process proceeds to step S905.
　ステップＳ９０５において、対応データ決定部２０７は、類似度計算部２０６が計算した正解データと教師データとの類似度を計算した類似度算出手法の情報と、この類似度算出手法の閾値の情報と、教師データと、を受け取り、これらのデータを正解データ抽出部２１０に渡す。これらのデータの組は、類似度算出手法の数だけある。 In step S905, the corresponding data determination unit 207 obtains information on the similarity calculation method used to calculate the degree of similarity between the correct data calculated by the similarity calculation unit 206 and the teacher data, information on the threshold value of this similarity calculation method, , and pass these data to the correct data extraction unit 210 . There are as many sets of these data as there are similarity calculation methods.
　ステップＳ９０６において、正解データ抽出部２１０は類似度計算部２０６に指示し、類似度計算部２０６が、学習済みの類似度算出手法及び閾値が類似度算出手法及び閾値ＤＢ２０５に記憶されているかどうかを判定する。類似度計算部２０６が、学習済みの類似度算出手法及び閾値が記憶されていると判定した場合にはステップＳ９０７に進み、学習済みの類似度算出手法及び閾値が記憶されていないと判定した場合にはステップＳ９０８に進む。 In step S906, the correct data extraction unit 210 instructs the similarity calculation unit 206, and the similarity calculation unit 206 determines whether the learned similarity calculation method and threshold are stored in the similarity calculation method and threshold DB 205. judge. If the similarity calculation unit 206 determines that the learned similarity calculation method and threshold are stored, the process proceeds to step S907, and if it determines that the learned similarity calculation method and threshold are not stored. to step S908.
　ステップＳ９０７において、類似度計算部２０６は、学習済みの類似度算出手法の閾値と同一の類似度算出手法の閾値との平均値をこの類似度算出手法の閾値として類似度算出手法及び閾値ＤＢ２０５に保存する。また類似度計算部２０６は、学習済みの類似度算出手法と異なる類似度算出手法の場合には、その手法の閾値を類似度算出手法及び閾値ＤＢ２０５に保存する。 In step S907, the similarity calculation unit 206 stores the average value of the threshold of the learned similarity calculation method and the threshold of the same similarity calculation method as the threshold of this similarity calculation method in the similarity calculation method and threshold DB 205. save. Further, in the case of a similarity calculation method different from a learned similarity calculation method, the similarity calculation unit 206 saves the threshold of the method in the similarity calculation method and threshold DB 205 .
　ステップＳ９０８において、類似度計算部２０６は、類似度算出手法ごとの閾値を類似度算出手法及び閾値ＤＢ２０５に保存する。 In step S<b>908 , the similarity calculation unit 206 stores the threshold for each similarity calculation method in the similarity calculation method and threshold DB 205 .
　ステップＳ９０９において、類似度計算部２０６は、全ての類似度算出手法により、対応するステップＳ９０１で選択された教師データと正解データとの類似度の閾値を計算する。そして、類似度計算部２０６は、ステップＳ９０３で計算された類似度のうち最大の類似度となった類似度算出手法とこの類似度算出手法に対応する閾値を学習し、これらを類似度算出手法及び閾値ＤＢ２０５に記憶する。 In step S909, the similarity calculation unit 206 calculates a similarity threshold between the teacher data selected in step S901 and the correct data, using all similarity calculation methods. Then, the similarity calculation unit 206 learns the similarity calculation method with the maximum similarity among the similarities calculated in step S903 and the threshold value corresponding to this similarity calculation method, and uses these as similarity calculation methods. and stored in the threshold DB 205 .
　ステップＳ９１０において、正解データ抽出部２１０がステップＳ９０９で決定された類似度算出手法に対応する正解データを決定し、教師データ登録部２１１がこの正解データを教師データとして教師ＤＢ２０２に追加し登録する。 In step S910, the correct data extraction unit 210 determines correct data corresponding to the similarity calculation method determined in step S909, and the teacher data registration unit 211 adds and registers this correct data to the teacher DB 202 as teacher data.
　以上に説明された実施形態に係る障害情報登録装置よれば、障害に関する情報を含む教師データを用意して、１以上の装置の運用に関する運用データとの類似度が高い教師データに基づいて、障害を解消する正解情報を含む正解データを受け付け、正解データと教師データとの類似度を複数の類似度算出手法により計算して最も大きな類似度に対応する正解データを新たな教師データとして登録することにより、障害を解消するためのより精度の高い教師データを教師ＤＢに記憶させることが可能になる。この結果、ネットワークにおける障害箇所を精度よく推定することができる。また、本実施形態の障害情報登録装置によれば、障害原因と障害アラームとを含むルールを学習させるために必要な多くの種類のデータを入力するためのリソースを削減することができる。またこのリソースが削減されるため、本実施形態によれば、障害を解消するためのより精度の高いデータベースを作成する時間も短くなる効果を奏する。従って本実施形態によれば、障害回復から学習までの時間が短縮される。 According to the failure information registration device according to the embodiment described above, teacher data including information about failures is prepared, and based on the teacher data having a high degree of similarity with operation data about the operation of one or more devices, failure information is registered. Correct data containing correct information that eliminates the above is received, the degree of similarity between the correct data and teacher data is calculated by a plurality of similarity calculation methods, and the correct data corresponding to the highest degree of similarity is registered as new teacher data. Thus, it is possible to store more accurate teacher data in the teacher DB for resolving the failure. As a result, it is possible to accurately estimate the fault location in the network. Further, according to the fault information registration device of the present embodiment, it is possible to reduce resources for inputting many types of data necessary for learning rules including fault causes and fault alarms. In addition, since this resource is reduced, according to the present embodiment, there is an effect of shortening the time required to create a more accurate database for resolving failures. Therefore, according to this embodiment, the time from failure recovery to learning is shortened.
（変形例）
＜正解データ決定処理＞
　図９のステップＳ９０５よりも前の正解データを求めるステップは、さらに図１２に示すステップが追加されてもよい。図９のステップに追加されるステップによる処理が図１２を参照して説明される。 (Modification)
<Correct data determination processing>
A step shown in FIG. 12 may be added to the step of obtaining correct data prior to step S905 in FIG. Processing by steps added to the steps of FIG. 9 will be described with reference to FIG.
　ステップＳ１２０１において、対応データ決定部２０７が、ユーザまたはユーザに代わる装置から情報を受け付けることなく、障害情報登録装置１００が既に有している情報に基づいて教師データを選択しこの教師データに対する正解データを特定することができるかどうかを判定する。対応データ決定部２０７が、障害情報登録装置１００が既に有している情報に基づいて教師データを選択し、この教師データに対する正解データを特定することができると判定した場合にはステップＳ１２０２に進み、障害情報登録装置１００が既に有している情報に基づいて教師データを選択し、この教師データに対する正解データを特定することができないと判定した場合にはステップＳ９０１に進む。 In step S1201, the correspondence data determination unit 207 selects teacher data based on the information already possessed by the fault information registration device 100 without receiving information from the user or a device acting on behalf of the user, and selects correct data for the teacher data. can be identified. If the corresponding data determining unit 207 selects the teacher data based on the information already held by the failure information registration device 100 and determines that the correct data for the teacher data can be specified, the process proceeds to step S1202. If it is determined that the teacher data is selected based on the information already possessed by the fault information registration apparatus 100 and the correct data for the teacher data cannot be specified, the process proceeds to step S901.
　対応データ決定部２０７のこの判定は、過去に既にステップＳ９１０まで進んでいてステップＳ９０９で決定された類似度算出手法と閾値がある場合に行われる。この判定の基準（判定基準）は、ステップＳ９０９で決定された類似度算出手法の回数とこの手法の閾値の値により決定される。判定基準は、例えば、ステップＳ９０９において同一の類似度算出手法が第１の値以上連続して決定されていて、かつ、その類似度算出手法の閾値がいずれも第２の値以上であることである。ステップＳ１２０１で判定基準を満たせばステップＳ１２０２に進む。より具体的には、例えば、判定基準の第１の値は５であり、第２の値は０．９である。これらの判定基準は適切に変更されてもよく、これらに限定されない。例えば、判定基準の第１の値は１０であり、第２の値は０．８である場合があってもよい。 This determination by the corresponding data determination unit 207 is performed when the process has already proceeded to step S910 in the past and there is a similarity calculation method and threshold determined in step S909. The criterion for this determination (determination criterion) is determined by the number of similarity calculation methods determined in step S909 and the threshold value of this method. The determination criteria are, for example, that the same similarity calculation method is continuously determined in step S909 by a first value or more, and the threshold values of the similarity calculation methods are all equal to or more than a second value. be. If the determination criteria are satisfied in step S1201, the process proceeds to step S1202. More specifically, for example, the first value of the criterion is 5 and the second value is 0.9. These criteria may be changed appropriately and are not limited to these. For example, the first value of the criterion may be 10 and the second value may be 0.8.
　ステップＳ１２０２において、対応データ決定部２０７が、ステップＳ７１０で教師データが「ログ有り」であると判定された教師データを類似度算出手法ごとに選択する。そしてステップＳ１２０２の処理後は、ステップＳ９０２に進む。なお、ステップＳ９０２においては、ユーザまたはユーザに代わる装置がステップＳ９０１で選択した類似度算出手法ごとの教師データではなくＳ１２０２で対応データ決定部２０７が選択した類似度算出手法ごとの教師データから、ユーザまたはユーザに代わる装置がこの教師データに対応するデータであって正解データとして決定したデータを正解データ受付部２０９が受け付ける。
＜教師ＤＢ２０２、類似度算出手法及び閾値ＤＢ２０５＞
　教師ＤＢ２０２または類似度算出手法及び閾値ＤＢ２０５の少なくともいずれかは、障害情報登録装置１００に含まれず、障害情報登録装置１００の外部にあってもよい。例えば、教師ＤＢ２０２または類似度算出手法及び閾値ＤＢ２０５の少なくともいずれかは、外部のサーバ等に含まれてもよい。この場合、障害情報登録装置１００は、インタフェース１０４を介して教師ＤＢ２０２または類似度算出手法及び閾値ＤＢ２０５の少なくともいずれかと情報のやりとりを行う。 In step S1202, the corresponding data determination unit 207 selects, for each similarity calculation method, teacher data for which the teacher data was determined to be "logged" in step S710. After the process of step S1202, the process proceeds to step S902. Note that in step S902, the user or a device acting in place of the user selects the teacher data for each similarity calculation method selected by the corresponding data determination unit 207 in step S1202 instead of the teacher data for each similarity calculation method selected in step S901. Alternatively, the correct data reception unit 209 receives data determined as correct data corresponding to this teacher data by a device in place of the user.
<Teacher DB 202, Similarity Calculation Method and Threshold DB 205>
At least one of the teacher DB 202 and the similarity calculation method and threshold DB 205 may not be included in the failure information registration device 100 and may be outside the failure information registration device 100 . For example, at least one of the teacher DB 202 or the similarity calculation method and threshold DB 205 may be included in an external server or the like. In this case, the failure information registration apparatus 100 exchanges information with at least one of the teacher DB 202 and the similarity calculation method and threshold DB 205 via the interface 104 .
　実施形態の装置は、コンピュータとプログラムによっても実現でき、プログラムを記録媒体（または記憶媒体）に記録することも、ネットワークを介して提供することも可能である。 The device of the embodiment can also be realized by a computer and a program, and the program can be recorded on a recording medium (or storage medium) or provided via a network.
　また、以上の各装置及びそれらの装置部分は、それぞれハードウェア構成、またはハードウェア資源とソフトウェアとの組み合わせの構成のいずれでも実施可能となっている。組み合わせの構成のソフトウェアとしては、予めネットワークまたはコンピュータ読み取り可能な記録媒体（または記憶媒体）からコンピュータにインストールされ、当該コンピュータのプロセッサに実行されることにより、各装置の動作（または機能）を当該コンピュータに実現させるためのプログラムが用いられる。 In addition, each of the above devices and their device parts can be implemented in either a hardware configuration or a combination configuration of hardware resources and software. The combined configuration software is pre-installed in a computer from a network or a computer-readable recording medium (or storage medium), and is executed by the processor of the computer, so that the operation (or function) of each device is controlled by the computer. A program is used to make it happen.
　なお、本発明は、上記実施形態に限定されるものではなく、実施段階ではその要旨を逸脱しない範囲で種々に変形することが可能である。また、各実施形態は適宜組み合わせて実施してもよく、その場合組み合わせた効果が得られる。さらに、上記実施形態には種々の発明が含まれており、開示される複数の構成要件から選択された組み合わせにより種々の発明が抽出され得る。例えば、実施形態に示される全構成要件からいくつかの構成要件が削除されても、課題が解決でき、効果が得られる場合には、この構成要件が削除された構成が発明として抽出され得る。 It should be noted that the present invention is not limited to the above-described embodiments, and can be variously modified in the implementation stage without departing from the gist of the present invention. Further, each embodiment may be implemented in combination as appropriate, in which case the combined effect can be obtained. Furthermore, various inventions are included in the above embodiments, and various inventions can be extracted by combinations selected from a plurality of disclosed constituent elements. For example, even if some constituent elements are deleted from all the constituent elements shown in the embodiments, if the problem can be solved and effects can be obtained, the configuration with the constituent elements deleted can be extracted as an invention.
１００…障害情報登録装置
１０１…プロセッサ
１０２…ＲＯＭ
１０３…ＲＡＭ
１０４…インタフェース
１０５…ディスプレイ
１０６…ストレージ
２０１…教師データ取得部
２０２…教師ＤＢ
２０３…教師データ選定部
２０４…運用データ取得部
２０５…閾値ＤＢ
２０６…類似度計算部
２０７…対応データ決定部
２０８…対応データ表示部
２０９…正解データ受付部
２１０…正解データ抽出部
２１１…教師データ登録部

  100... Failure information registration device 101... Processor 102... ROM
103 RAM
104... Interface 105... Display 106... Storage 201... Teacher data acquisition unit 202... Teacher DB
203: Teacher data selection unit 204: Operation data acquisition unit 205: Threshold value DB
206... Similarity calculation unit 207... Corresponding data determination unit 208... Corresponding data display unit 209... Correct data reception unit 210 Correct data extraction unit 211... Teacher data registration unit

Claims (8)

  1. 　障害に関する情報を含む教師データを取得し、１以上の装置の運用に関する情報を含む運用データを取得する取得部と、
    　前記教師データと前記運用データとの第１の類似度を、前記障害ごとに計算する第１の計算部と、
    　前記第１の類似度が閾値以上である教師データを、前記運用データごとに決定する決定部と、
    　前記閾値以上である教師データに基づいて正解データを受け付ける受付部と、
    　前記正解データと前記教師データとの第２の類似度を複数の類似度算出手法により計算する第２の計算部と、
    　複数の第２の類似度のうち、最も大きな類似度に対応する正解データを新たな教師データとして登録する登録部と、
    　を備える登録装置。     an acquisition unit that acquires teacher data including information about failures and acquires operation data including information about operation of one or more devices;
    a first calculation unit that calculates a first degree of similarity between the training data and the operational data for each fault;
    a determination unit that determines, for each operational data, teacher data whose first degree of similarity is equal to or greater than a threshold;
    a reception unit that receives correct data based on teacher data that is equal to or greater than the threshold;
    a second calculation unit that calculates a second degree of similarity between the correct data and the teacher data using a plurality of similarity degree calculation methods;
    a registration unit that registers, as new teacher data, correct data corresponding to the highest degree of similarity among the plurality of second degrees of similarity;
    A registration device comprising:
  2. 　前記最も大きな類似度を算出した類似度算出手法を前記第１の類似度を計算する手法に設定する手法設定部と、
    　前記設定された類似度算出手法により計算された前記第２の類似度に基づいて前記閾値を設定する閾値設定部と、
    　をさらに備え、
    　前記取得部が運用データをさらに取得する場合に、前記第１の計算部が前記設定された手法を使用して第１の類似度を計算し、かつ、前記決定部が前記設定された閾値を使用して前記教師データを決定する、請求項１に記載の登録装置。     a method setting unit that sets a similarity calculation method that calculates the highest similarity to a method for calculating the first similarity;
    a threshold setting unit that sets the threshold based on the second similarity calculated by the set similarity calculation method;
    further comprising
    When the acquisition unit further acquires operational data, the first calculation unit calculates a first similarity using the set method, and the determination unit determines the set threshold 2. The registration device of claim 1, wherein the training data is determined using:
  3. 　前記第１の類似度が閾値以上である１以上の教師データと、この教師データとの類似度の計算に使用された運用データと、を提示する提示部をさらに備え、
    　前記受付部は、提示された教師データ及び運用データに基づいて決定された正解データを受け付ける、請求項１または２に記載の登録装置。     further comprising a presentation unit that presents one or more training data whose first degree of similarity is equal to or greater than a threshold, and operational data used to calculate the degree of similarity with the training data;
    3. The registration device according to claim 1, wherein said reception unit receives correct data determined based on presented teacher data and operational data.
  4. 　前記取得部は、運用時に障害が発生した装置、前記装置の位置、及び前記装置の障害の状態に関する情報を含む運用データをさらに取得する、請求項１乃至３のいずれか１項に記載の登録装置。 4. The registration according to any one of claims 1 to 3, wherein said acquisition unit further acquires operation data including information on a device in which a failure occurred during operation, a location of said device, and a failure state of said device. Device.
  5. 　前記取得部は、前記教師データとして、前記障害、前記障害の対処、または前記障害の回復の少なくとも１つに関する情報を取得し、前記運用データとして、１以上の装置の障害、前記障害の対処、または前記障害の回復の少なくとも１つの運用に関する情報を取得し、
    　前記決定部は、前記類似度が前記閾値以上である教師データを、前記障害、前記対処、または前記回復の少なくとも１つに対応する前記運用データごとに決定する、請求項１乃至４のいずれか１項に記載の登録装置。     The acquisition unit acquires, as the training data, information on at least one of the failure, the handling of the failure, or recovery from the failure, and the operation data, the failure of one or more devices, the handling of the failure, or obtaining information about at least one operation of said failure recovery;
    5. The determination unit according to any one of claims 1 to 4, wherein the determination unit determines teacher data whose similarity is equal to or greater than the threshold for each operational data corresponding to at least one of the failure, the countermeasure, and the recovery. The registration device according to item 1.
  6. 　前記閾値がある値以上であり、かつ、前記最も大きな類似度を算出した類似度算出手法がある回数以上同一である場合には、前記決定部は、既に装置が有している情報から前記受付部で使用される前記閾値以上である教師データを選択し、
    　前記受付部は、前記選択された教師データに対する正解データを受け付ける、請求項１乃至５のいずれか１項に記載の登録装置。     If the threshold is equal to or greater than a certain value, and if the similarity calculation method used to calculate the highest similarity is the same for a certain number of times or more, the determining unit selects the receiving Select teacher data that is equal to or greater than the threshold used in the section,
    6. The registration device according to any one of claims 1 to 5, wherein said reception unit receives correct data for said selected teacher data.
  7. 　取得部が、障害に関する情報を含む教師データを取得し、１以上の装置の運用に関する情報を含む運用データを取得し、
    　第１の計算部が、前記教師データと前記運用データとの第１の類似度を、前記障害ごとに計算し、
    　決定部が、前記第１の類似度が閾値以上である教師データを、前記運用データごとに決定し、
    　受付部が、前記閾値以上である教師データに基づいて正解データを受け付け、
    　第２の計算部が、前記正解データと前記教師データとの第２の類似度を複数の類似度算出手法により計算し、
    　登録部が、複数の第２の類似度のうち、最も大きな類似度に対応する正解データを新たな教師データとして登録すること、
    　を備える登録方法。     an acquisition unit acquires teacher data including information about a failure, acquires operation data including information about operation of one or more devices,
    a first calculation unit calculating a first degree of similarity between the teacher data and the operational data for each failure;
    a determining unit determining, for each operational data, teacher data whose first degree of similarity is equal to or greater than a threshold;
    A reception unit receives correct data based on teacher data that is equal to or greater than the threshold;
    A second calculation unit calculates a second degree of similarity between the correct data and the teacher data by a plurality of similarity calculation methods,
    registering the correct data corresponding to the highest degree of similarity among the plurality of degrees of second similarity as new teacher data;
    A registration method comprising:
  8. 　コンピュータを、請求項１乃至６のいずれか１つに記載の登録装置の各部として機能させるためのプログラム。
     

          A program for causing a computer to function as each part of the registration device according to any one of claims 1 to 6.
PCT/JP2021/022919 2021-06-16 2021-06-16 Registration device, registration method, and program WO2022264332A1 (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013025367A (en) * 2011-07-15 2013-02-04 Wakayama Univ Facility state monitoring method and device of the same
JP2020091561A (en) * 2018-12-04 2020-06-11 日立グローバルライフソリューションズ株式会社 Abnormality diagnosis device and abnormality diagnosis method
WO2021033274A1 (en) * 2019-08-20 2021-02-25 日本電信電話株式会社 Pattern extraction and rule generation device, method, and program

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013025367A (en) * 2011-07-15 2013-02-04 Wakayama Univ Facility state monitoring method and device of the same
JP2020091561A (en) * 2018-12-04 2020-06-11 日立グローバルライフソリューションズ株式会社 Abnormality diagnosis device and abnormality diagnosis method
WO2021033274A1 (en) * 2019-08-20 2021-02-25 日本電信電話株式会社 Pattern extraction and rule generation device, method, and program

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