WO2012090718A1 - システムの状況を判断する方法、コンピュータ・プログラム、コンピュータ - Google Patents
システムの状況を判断する方法、コンピュータ・プログラム、コンピュータ Download PDFInfo
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- WO2012090718A1 WO2012090718A1 PCT/JP2011/079069 JP2011079069W WO2012090718A1 WO 2012090718 A1 WO2012090718 A1 WO 2012090718A1 JP 2011079069 W JP2011079069 W JP 2011079069W WO 2012090718 A1 WO2012090718 A1 WO 2012090718A1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B9/00—Safety arrangements
- G05B9/02—Safety arrangements electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0281—Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
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- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/001—Computer implemented control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
- Y02E30/30—Nuclear fission reactors
Definitions
- the present invention relates to a technology for judging or supporting judgment of a system status, and more particularly, to a technology suitable for judging the status of an industrial control system.
- Patent Documents 1 to 3 Conventionally, techniques such as the following Patent Documents 1 to 3 have been proposed for the purpose of detecting an abnormality for a complex system such as a plant.
- the present invention has been made in view of such problems, and one of its purposes is a method, computer program, and method for efficiently determining or supporting the type, location, and cause of an abnormality in a complex system. To provide a computer.
- the present invention is a method applied to a computer that determines the status of a system, the step of receiving measurement data from each of a plurality of measurement objects in the system, and a plurality of attributes corresponding to a plurality of attributes of each measurement object According to the classification, the step of calculating a set of a plurality of abnormal values based on each measurement data and a predetermined calculation algorithm, and based on the set of the plurality of abnormal values and a predetermined determination algorithm, Determining the status of the system.
- the classification may be a hierarchical classification or a non-hierarchical classification.
- Examples of the hierarchical classification include a function / hierarchical structure to be measured, a hierarchical structure on a network, a hierarchical structure of a physical area, and a hierarchical structure by terminal type. Further, different attributes can be adopted as the plurality of attributes.
- the target system is configured by a plurality of subsystems, and each subsystem can be assumed to be configured by various terminals (measurement targets).
- terminals include sensors / actuators / control devices / network devices.
- measurement data each terminal state can be measured in a time-series manner.
- One specific example of the measurement data includes physical measurement values (ICS operating state, physical sensor information), and more specifically, temperature information inside the power generation plant and valve opening / closing events.
- Other specific examples of measurement data include non-physical measurement values (IT system status), and more specifically, network status (access log, latency, packet loss rate), software information (version information, version information, Job record, data transfer record), and hardware information (firmware information).
- a mode for detecting an abnormality from a data set that is not known as normal or abnormal 2.
- ICS time-series physical measurement / event data during normal operation is obtained as learning data
- the latest ICS time-series physical measurement / event data is obtained as application data
- normal / abnormal judgment is obtained as output.
- the degree of abnormality (a numerical value indicating the degree of abnormality) is output.
- Typical anomaly detection methods include HotellingT ⁇ 2 test / One-class SVM / LocalOutlier Factor for numerical data, and non-frequent pattern discovery, naive Bayes, hidden Markov models for event data. Can be mentioned.
- attributes include the function to be measured, network configuration, type, installation location, installation location management organization, and installation location security level.
- functional attributes for example, the entire nuclear power generation system ⁇ large functional elements (steam generator / turbine / generator) ⁇ small functional elements (water supply unit / sodium discharge unit / steam discharge unit) ⁇ each terminal
- the entire network ⁇ each local area ⁇ each network terminal can be mentioned.
- the entire monitoring (measurement) target facility ⁇ each facility ⁇ Each floor-> each room can be listed, and as a hierarchical structure in the terminal type attribute, all terminals-> large classification (sensor / actuator / control / network equipment)-> small classification (temperature sensor / pressure sensor / sound sensor)-> each The terminal type (manufacturer, serial number, version) can be listed.
- the data format and anomaly detection algorithm in each classification / hierarchy are arbitrary, for example, use Hotelling T ⁇ 2 test ⁇ ⁇ ⁇ ⁇ for the physical state time series of the plant, and use the naive Bayes method for access logs of network devices Can do.
- the determining step compares the pattern of the set of the plurality of abnormal values corresponding to each predetermined situation of the system with the set of the plurality of abnormal values calculated, thereby The situation can be judged. Further, the step of determining compares the pattern of the plurality of abnormal value sets corresponding to each predetermined situation of the system with a simplified set of the calculated plurality of abnormal values. Thus, the status of the system can also be determined. Further, the step of determining compares a pattern of the set of the plurality of abnormal values corresponding to each predetermined situation of the system with a simplified version of the set of the plurality of abnormal values calculated. The situation corresponding to the pattern having the highest similarity can be determined as the situation of the system. The step of determining may determine the status of the system based on a change with time of the set of the plurality of abnormal values, or may determine the status of the system regardless of the change with time.
- the pattern of a set of a plurality of abnormal values is a set of abnormal scores calculated for each classification / hierarchy, and it is possible to visualize a classification / hierarchy with a high degree of abnormality and a classification / hierarchy other than that. Further, in a pattern of a set of a plurality of abnormal values, a time-series change can also be considered as a feature (detection order, detection frequency). Moreover, it is desirable not to detect as a pattern when the degree of abnormality is low.
- the step of calculating is based on each measurement data and a predetermined calculation algorithm according to a first classification corresponding to the first attribute of each measurement target.
- Sub-step for calculating a set, and sub-step for calculating a set of second abnormal values based on each measurement data and a predetermined calculation algorithm according to a second classification corresponding to the second attribute of each measurement target The determining step can determine the status of the system based on the first abnormal value set and the second abnormal value set and a predetermined determination algorithm.
- the calculating step calculates a set of network abnormal values based on each measurement data and a predetermined calculation algorithm according to the hierarchical structure of the network corresponding to each network configuration to be measured.
- the determining step may include determining the status of the system based on the set of network outliers and the set of place outliers and a predetermined determination algorithm.
- the step of calculating is based on each measurement data and a predetermined calculation algorithm according to a third classification corresponding to the third attribute of each measurement object.
- the status of the system can be determined.
- the step of calculating includes a sub-step ⁇ ⁇ that calculates a set of type abnormal values based on each measurement data and a predetermined calculation algorithm according to a hierarchical structure corresponding to the type of each measurement target.
- the method may further include a step of displaying a determination result of the system status to the user, and a step of displaying the set of the plurality of abnormal values and the determination result of the system status to the user.
- the system may be an industrial control system or an IT system.
- classification can be simplified. Visualization of abnormal patterns in multiple classifications and hierarchical structures simplifies classification of types, locations, and causes of abnormalities, and can be performed even if there is a lack of knowledge about data trends at the time of various abnormalities. And, if there is prior knowledge about abnormal patterns, it can be used, for example, abnormalities in a certain region & all ICS ⁇ failure due to natural disaster, abnormalities in a certain region & specific ICS ⁇ physical fraud, all regions & It can be judged as abnormal ⁇ network intrusion in specific ICS.
- the block diagram which shows the function of the judgment apparatus 1 Flowchart showing the operation of the determination device 1 Examples of applicable systems Illustration explaining the installation location Diagram explaining network configuration Diagram for explaining the first embodiment Diagram explaining case 1-1 Diagram explaining case 1-2 Diagram explaining case 1-3 Diagram for explaining the second embodiment Diagram explaining case 2-1 Diagram explaining case 2-2 Diagram explaining case 2-3 Hardware configuration of determination apparatus 1
- FIG. 1 is a functional block diagram of a determination apparatus (computer) according to this embodiment.
- the determination device 1 includes an input unit 2, a calculation unit 3, a storage unit 4, a determination unit 5, and an output unit 6.
- the determination device 1 includes an input unit 2, a calculation unit 3, a storage unit 4, a determination unit 5, and an output unit 6.
- an input unit 2 As shown in the figure, the determination device 1 includes an input unit 2, a calculation unit 3, a storage unit 4, a determination unit 5, and an output unit 6.
- the determination device 1 includes an input unit 2, a calculation unit 3, a storage unit 4, a determination unit 5, and an output unit 6.
- FIG. 2 is a flowchart for explaining the basic operation of the determination apparatus 1.
- the determination device 1 receives measurement data from a plurality of measurement objects in the system (S2), and according to a plurality of classifications corresponding to a plurality of attributes of each measurement object, A set of a plurality of abnormal values is calculated based on a predetermined calculation algorithm (S3), and the status of the system is determined based on a set of the plurality of abnormal values and a predetermined determination algorithm (S5). ), And outputs the determination result to the user (S6).
- S3 predetermined calculation algorithm
- S5 predetermined determination algorithm
- FIG. 3 illustrates a system to which the determination apparatus 1 is applied.
- the determination device 1 is applied to an industrial control system, and specific examples thereof include a power generation facility group (FIG. 3A), a factory facility group (FIG. 3B), and a building facility group (FIG. 3 ( c)) Further, although not shown in the figure, a power supply facility group, a water supply facility group, a dredging transportation network facility group, and the like can be given.
- Example 1 Hereinafter, as an example 1, a case where such a determination device 1 is applied to a power generation facility group (FIG. 3A), in particular, a nuclear power generation facility group will be described as an example.
- this explanation makes the significance of the function of each functional block of the determination apparatus 1 shown in FIG. 1 and the operation of each step of the determination apparatus 1 shown in FIG.
- FIG. 4 illustrates the installation location of the nuclear power generation facility group targeted by the determination apparatus 1 in a hierarchical classification structure.
- the installation location of the nuclear power generation facility group targeted by the determination device 1 is the global area: A (Japan) as the upper hierarchy, and the sub-area: Aa (Eastern Japan) and the sub-area: Ab ( West Japan) exists, and sub-area: Aa1 (Fukushima) and location: Aa2 (Niigata) as sub-hierarchies below Aa, and sub-area: Ab1 (Fukui) as sub-hierarchies below sub-area: Ab. Location: Ab2 (Saga) exists.
- FIG. 5 illustrates the network configuration of the nuclear power generation facility group targeted by the determination device 1 in a hierarchical classification structure.
- the network configuration of the nuclear power generation facility group targeted by the determination device 1 is root ICS: X (nuclear power generation facility) as an upper hierarchy, and hub ICS: Xa (turbine system) and hub ICS as lower middle hierarchy. : Xb (reactor system) exists, and ICS: Xa1 (generator: solar mark) and: Xa2 (turbine: heart mark) are subordinate to the hub ICS: Xb.
- ICS: Xb1 steam generator: lightning mark
- ICS: Xb2 (reactor: star mark) exist as lower layers.
- FIG. 6 represents the installation location and the network configuration shown in FIGS. 4 and 5 in a hierarchical tree structure, and describes the anomaly score of each component, the installation location, and the anomaly pattern of the network configuration. Is.
- the input unit 2 of the determination device 1 receives measurement data directly or indirectly from a large number of measurement objects in the nuclear power generation facility group (step S2 in FIG. 2).
- the calculation unit 3 of the determination device 1 stores in advance in each measurement data box and the storage unit 4 in accordance with two classifications corresponding to the two attributes (here, the installation location of the nuclear power generation facility group and the network configuration).
- two anomaly patterns each composed of a plurality of anomaly scores are calculated (step S3 in FIG. 2).
- the anomaly score is expressed as a continuous value where the normal state is 0 and the abnormal state is 1.
- the anomaly pattern is expressed here as a (simplified) pattern in which each anomaly score is evaluated in three stages (normal: no shading, low anomaly: light shading, high anomaly: dark shading). .
- the anomaly score of the location: Ab1 (Fukui) is calculated based on the measurement data from the nuclear power generation facility located at the location: Ab1 (Fukui), and the value is 0.1 here.
- the anomaly score values of locations: Aa2 (Niigata), Aa1 (Fukushima), and Ab2 (Saga) were 0.2, 0.0, and 0.2, respectively.
- the anomaly score values of the subarea: Aa (Eastern Japan) and the subarea: Ab (West Japan) are based on the anomaly score values of the lower location groups and the calculation algorithm stored in the storage unit 4 in advance. Were calculated to be 0.2 and 0.1, respectively.
- the value of the anomaly score of the global pass area: A (Japan) is the value of each anomaly score of the subarea: Aa (East Japan) and the subarea: Ab (West Japan) and the calculation stored in the storage unit 4 in advance. Based on the algorithm, it was calculated to be 0.2.
- each anomaly score value is normal (less than 0.5): no shading, low abnormality (0.5 or more, less than 0.8): thin shading, high abnormality (0.8 or more) : Evaluated with dark shading.
- the value of any anomaly score is less than 0.5. Therefore, the anomaly pattern corresponding to the installation location of the nuclear power generation facility group is shown in the shading in the figure. There will be no.
- the anomaly score of ICS: Xb2 (reactor: star mark) is calculated based on the measurement data from the nuclear power generation facility corresponding to ICS: Xb2 (reactor) on the network configuration. 0.8.
- the anomaly score values of Xb1 steam generator: lightning mark
- ICS: Xa2 turbine: heart mark
- Xa1 generator: sun mark
- the anomaly score values of the hub ICS: Xa (turbine system) and the hub ICS: Xb (reactor system) are the anomaly score values of the lower ICS groups and the calculation algorithm stored in the storage unit 4 in advance. And 0.7 and 0.6, respectively.
- the anomaly score value of the route ICS: X is previously stored in the storage unit 4 with the anomaly score value of the hub ICS: Xa (turbine system) and the hub ICS: Xb (reactor system). Based on the stored calculation algorithm, it was calculated as 0.6.
- each anomaly score value is normal (less than 0.5): no shading, low abnormality (0.5 or more, less than 0.8): thin shading, high abnormality (0.8 or more) : Evaluated with dark shading.
- the values of each anomaly score vary, so the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group is the dark shading shown in the figure, which is light. Shading and no shading are mixed.
- HotellingT ⁇ 2 test / One-class SVM / LocalOutlier Factor is given as numerical data
- non-frequent pattern discovery naive Bayes as event data Hidden Markov models can be used.
- the storage unit 4 of the determination device 1 stores the status of a known nuclear power generation facility group (specifically, (1) the type of abnormality that has occurred, (2) the location of the abnormality, (3) the cause of the abnormality) and the situation
- the anomaly pattern corresponding to the installation location of the nuclear power generation facility group below and the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group are stored in association with each other.
- the determination unit 5 of the determination apparatus 1 stores the anomaly pattern corresponding to the installation location of the nuclear power generation facility group and the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group over time and the storage unit 4 in advance.
- the two anomaly patterns are compared, and the situation corresponding to the pattern having the highest similarity is determined as the situation of the nuclear power generation facility group (step S5 in FIG. 2).
- specific examples will be described as (Case 1-1) to (Case 1-3).
- FIG. 7 shows changes over time in the anomaly pattern corresponding to the installation location of the nuclear power generation facility group in Example 1-1 and the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group. is there.
- the anomaly pattern corresponding to the installation location of the nuclear power generation facility group from time T to time T + 1 the value of the anomaly score of only a specific location, location: Aa2 (Niigata) is high.
- the anomaly score value is slightly high as a whole (0.5 to 0.6).
- the determination unit 5 searches the storage unit 4 for both anomaly patterns that are similar to each other, and, for example, (1) the type of abnormality that occurred: natural disaster as the status of the nuclear power generation facility group that corresponds to both anomaly patterns with the highest similarity (2) Location of abnormality: Mainly location: Aa2 (Niigata), (3) Cause of abnormality: It is determined that the earthquake has an impact on ICS.
- FIG. 8 shows changes over time in the anomaly pattern corresponding to the installation location of the nuclear power generation facility group and the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group in Case 1-2. is there.
- the value of the anomaly score of only a specific location, location: Aa2 is high.
- the value of the anomaly score is slightly high only with specific ICS (Xb2 (reactor) and Xa1 (generator)). (0.5 to 0.6).
- the determination unit 5 searches the storage unit 4 for both similar anomaly patterns, and as the status of the nuclear power generation facility group corresponding to the two anomaly patterns having the highest similarity, for example, (1) The type of abnormality that occurred: fraud (2) Location of abnormality: Location: Aa2 (Niigata), (3) Cause of abnormality: Determined as a physical attack on Xb2 (reactor) and Xa1 (generator).
- FIG. 9 shows changes over time in the anomaly pattern corresponding to the installation location of the nuclear power generation facility group and the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group in Case 1-3. is there.
- the value of the anomaly score is slightly high as a whole (0.4 to 0.5).
- the anomaly pattern corresponding to the network configuration of the nuclear power generation facility group the anomaly score value is slightly high (0.5 to 0.5) only with specific ICS (Xb2 (reactor) and Xa1 (generator)). 0.6) I'm concerned.
- the determination unit 5 searches the storage unit 4 for both similar anomaly patterns, and as the status of the nuclear power generation facility group corresponding to both anomaly patterns with the highest similarity, for example, (1) Type of anomaly that occurred: intrusion (2) Location of abnormality: Xb2 (reactor) and Xa1 (generator), (3) Cause of abnormality: Vulnerability of Xb2 (reactor) and dredge Xa1 (generator).
- the determination unit 5 determines that the output unit 6 of the determination apparatus 1 is an anomaly pattern corresponding to the installation location of the nuclear power generation facility group calculated by the calculation unit 3 and an anomaly pattern corresponding to the network configuration of the nuclear power generation facility group.
- the status of the nuclear power generation facility group is displayed to the user (step S6 in FIG. 2).
- Example 2 Hereinafter, as a second embodiment, a case where the determination apparatus 1 is applied to a building facility group (FIG. 3C) will be described as an example.
- FIG. 3C building facility group
- the location of the building facility group targeted by the determination device 1 is the global area: A (the coastal city) as the upper hierarchy, and the sub area: Aa (business building) and the sub area: Ab (the lower hierarchy).
- Ab Ab1 (Residence building low-rise area) and Location: Ab2 (Residence building high-rise area), respectively.
- the management subject (organization) of the business building and the residence building may be the same or different.
- the management subject (organization) may be the same or different between the low-rise area and the high-rise area of each building.
- the device type of the office building facility group targeted by the determination apparatus 1 is the device type: Y (temperature sensor) as the upper layer, and the device type: Ya (manufactured by Y company) as the lower middle layer.
- Device type: Yb manufactured by X company
- Ya2 version 5.0
- Ya1 version 3.0
- device type: Ya Yb
- Yb There are a temperature sensor: Yb2 (version 2.0) and a temperature sensor: Yb1 (version 1.3), respectively, as lower layers below.
- the security level of the office building facility group targeted by the determination device 1 is the security level that anyone can enter, Za (public area), limited people (for example, employees of a specific company)
- Za public area
- limited people for example, employees of a specific company
- Zb entity restricted area
- Zc entity prohibited area
- FIG. 10 represents the location, device type, and security level of the office building facility group targeted by the determination apparatus 1 for each hierarchical tree structure or classification, and the anomaly score of each component, office building Describes the anomaly pattern of facility group location, device type, and security level.
- the input unit 2 of the determination apparatus 1 receives measurement data directly or indirectly from a large number of measurement objects (devices) in the office building facility group (step S2 in FIG. 2).
- the calculation unit 3 of the determination apparatus 1 includes each measurement data and the storage unit 4 according to two classifications corresponding to the three attributes (here, the location of the office building facility group, the device type, and the security level).
- Two anomaly patterns each composed of a plurality of anomaly scores are calculated based on the calculation algorithm stored in advance (step S3 in FIG. 2).
- the anomaly score is expressed as a continuous value in which the normal state is 0 and the abnormal state is 1, as in the first embodiment.
- each anomaly score was evaluated in three stages (normal: no shading, low abnormality: light shading, high abnormality: dark shading) as in Example 1. ) Expressed with a pattern.
- the anomaly score of the installation location is calculated. The value was 0.1.
- the anomaly score values of installation locations: Aa2 (high-rise area of business building), Aa1 (low-rise area of business building), and Ab2 (high-rise area of residence building) are 0.2, 0.0, 0.2, respectively.
- the anomaly score values of the sub-area: Aa (office building) and the sub-area: Ab (residence building) are calculated in advance in the storage unit 4 and the anomaly score values of the lower installation locations, respectively.
- the anomaly score values of the global area: A (the coastal city) are stored in advance in the storage unit 4 and the anomaly score values of the sub area: Aa (office building) and the sub area: Ab (residence building). was calculated to be 0.2 based on the calculated algorithm.
- Anomaly patterns are calculated from these anomaly scores in the same manner as in the first embodiment. Specifically, each anomaly score value is normal (less than 0.5): no shading, low abnormality (0.5 or more, less than 0.8): thin shading, high abnormality (0.8 or more) : Evaluated with dark shading. In this case, since the value of any anomaly score is less than 0.5, the anomaly pattern corresponding to the location of the device in the office building facility group is It will not be.
- the anomaly score of the device type: Yb1 (version 1.3) was calculated, and here the value was 0.2.
- the anomaly score values of Yb2 (version 2.0), Ya1 (version 3.0), and Ya2 (version 5.0) were 0.0, 0.7, and 0.9, respectively. It was.
- the anomaly score values of the device type: Ya (manufactured by Y company) and the device type: Yb (manufactured by X company) are the anomaly score values of the lower device types and the operations stored in the storage unit 4 in advance.
- the anomaly score value of device type: Y (temperature sensor) is stored in advance in the storage unit 4 and the anomaly score value of device type: Ya (manufactured by Y company) and device type: Yb (manufactured by X company). Based on the stored calculation algorithm, it was calculated as 0.3.
- each anomaly score value is normal (less than 0.5): no shading, low abnormality (0.5 or more, less than 0.8): thin shading, high abnormality (0.8 or more) : Evaluated with dark shading.
- the values of each anomaly score vary, so the anomaly pattern corresponding to the type of device in the office building facility group is dark shading and no shading. Will be mixed.
- each anomaly score value is normal (less than 0.5): no shading, low abnormality (0.5 or more, less than 0.8): thin shading, high abnormality (0.8 or more) : Evaluated with dark shading.
- the values of each anomaly score vary, so the anomaly pattern corresponding to the security level of the devices in the office building facilities group is dark shading and shading. It will be a mix of no hanger.
- the installation location: Aa1 business building low-rise area
- the security level: Zb access restricted area
- the installation location: Aa2 business building high-rise area
- Zc access Forbidden area
- installation location: Ab1 low-rise area of residence building
- Ab2 high-rise area of residence building
- Za public area
- two devices are installed in each installation location, and device types: Yb1 (version 1.3) and Ya2 (version 5.0) are installed in the installation location: Aa1 (business building low-rise area).
- Device type Yb1 (Version 1) for installation location: Ab1 (Lower area of residence building)
- Device type Yb2 (Version 2.0) and Ya1 (Version 3.0) for location: Aa2 (Business building high-rise area) .3) and Ya1 (version 3.0) are installed at Yb2 (version 2.0) and 2 Ya2 (version 5.0) at the installation location: Ab2 (residence building high-rise area).
- the storage unit 4 of the determination device 1 stores the status of a group of known office buildings (specifically, (1) the type of abnormality that has occurred, (2) the location of the abnormality, (3) the cause of the abnormality) and the situation
- the following three types of anomaly patterns three anomaly patterns corresponding to the device installation location, device type, and device security level are stored in association with each other.
- the determination unit 5 of the determination device 1 compares each of the three types of anomaly patterns with time and the three types of anomaly patterns stored in the storage unit 4 in advance, and corresponds to the pattern having the highest similarity.
- the situation to be determined is determined as the situation of the office building facility group (step S5 in FIG. 2). Specific examples will be described as (Case 2-1) to (Case 2-3).
- FIG. 11 shows changes over time of the three types of anomaly patterns corresponding to the installation location, device type, and device security level of the office building facility group in Example 2-1. It is. As shown in the figure, in the anomaly pattern corresponding to the device installation location from time T to time T + 1, the anomaly score value of only a specific installation location: Aa2 (business ridge high-rise area) is high ( 0.9) On the other hand, the anomaly score values of other installation locations including the adjacent installation locations: Aa1 (business ridge low-rise area) are not so high.
- the temperature sensor Yb2 (version 2.0) and the temperature sensor: Ya1 (version 3.0) (both installed at Aa2 (business building high-rise area))
- the installed anomaly score is slightly high (0.5 to 0.6).
- the anomaly score value of security level: Zc (no entry area) is high (0.8).
- the security judgment unit 5 searches the storage unit 4 for both similar anomaly patterns, and, for example, (1) the type of anomaly that has occurred as the situation of the office building facility group corresponding to both anomaly patterns with the highest similarity: (2) Abnormal location: Aa2 (high-rise area of business building), (3) Cause of abnormality: Internal crime (Aa1 (low-rise area of business building) adjacent to Aa2 (business high-rise area) ), Aa (the whole business building) has a low anomaly score).
- FIG. 12 shows changes over time of the three types of anomaly patterns corresponding to the installation location, device type, and device security level of the office building facility group in Example 2-2. It is. As shown in the figure, in the anomaly pattern corresponding to the device installation location from time T to time T + 1, two geographically separated installation locations: Aa1 (business building low-rise area) and Ab2 (residence building) High anomaly score (only in high-rise area) (0.8 to 0.9), while other installation locations including Aa2 (business building high-rise area) and Ab1 (residence building low-rise area) The value of anomaly score is not so high.
- the security judging unit 5 searches the storage unit 4 for similar anomaly patterns that are similar to each other, and, for example, (1) the type of abnormality that has occurred as the situation of the office building facility group corresponding to both anomaly patterns with the highest similarity: Specific sensor failure (2) Abnormal location: Ya2 (version 5.0) Aa2 (business ridge high-rise area) and Ab1 (residential ridge low-rise area), (3) Cause of abnormality: Ya2 (version Judged as bug 5.0).
- FIG. 13 shows changes over time of the three types of anomaly patterns corresponding to the installation location, device type, and device security level of the office building facility group in Example 2-2. It is. As shown in the figure, in the anomaly pattern corresponding to the device installation location from time T to time T + 1, two geographically separated installation locations: Aa2 (business building high-rise area) and Ab2 (residence building) High anomaly score (high-rise area) only (0.8 to 0.9), while adjacent installation locations: Aa1 (business building low-rise area), Ab1 (residence building low-rise area) other installation locations The value of anomaly score is not so high.
- the anomaly score of the temperature sensor Yb2 (version 2.0) is high (1.0), and the anomaly score corresponding to the temperature sensor manufactured by Y Co. Slightly higher (0.5 to 0.6).
- the anomaly score value of the security level: Zc (no entry area) is high (0.8), and the security level: Za (public area). The anomaly score is slightly higher (0.5).
- the security judging unit 5 searches the storage unit 4 for similar anomaly patterns that are similar to each other, and, for example, (1) the type of abnormality that has occurred as the situation of the office building facility group corresponding to both anomaly patterns with the highest similarity: Compatibility problems between sensors, (2) Location of abnormality: Aa2 (Business Building High-rise Area) and Ab2 (Residence Building High-rise Area) where version 2.0 manufactured by Company X and products manufactured by Company Y coexist, (3) Cause: It is determined that a problem has occurred due to a mixture of version 2.0 manufactured by company X and a product manufactured by company Y.
- the output unit 6 of the determination apparatus 1 displays the anomaly pattern corresponding to the installation location, type, and security level of the device calculated by the calculation unit 3 and the status of the office building facility group determined by the determination unit 5. (Step S6 in FIG. 2).
- FIG. 14 is a functional block diagram illustrating a hardware configuration of the determination apparatus 1.
- the hardware configuration of the determination device 1 includes a (low-speed and high-speed) bus 40, a CPU (arithmetic control device) 41 connected to the bus, a RAM (random access memory: storage device) 42, a ROM (read-only).
- a memory (storage device) 43, an HDD (hard disk drive: storage device) 44, a communication interface 45, and an input / output force interface 46 are provided.
- a mouse (pointing device) 47, a flat panel display (display device) 48, a keyboard 49, and the like connected to the input / output interface 46 are provided.
- the determination apparatus 1 has been described as adopting a general personal computer architecture, for example, the CPU 41, the HDD 44, and the like can be multiplexed in order to obtain higher data processing capability and availability.
- various types of computer systems such as a laptop type or tablet type personal computer, a PDA (Personal Digital Assistant), or a smartphone can be employed.
- the software configuration of the determination device 1 includes an operating system (OS) that provides basic functions, application software that uses the functions of the OS, and driver software for input / output devices. Each of these software is loaded on the RAM 42 and executed by the CPU 41 or the like, and the determination device 1 as a whole has an input unit 2, an operation unit 3, a storage unit 4, a determination unit 5 and an output shown in FIG. The function as the unit 6 is achieved and the operation shown in FIG.
- OS operating system
- Judgment device 1 ... Judgment device, 2 ... input part, 3 ... arithmetic unit, 4 ... Storage unit 5 ... Judgment unit 6 ... Output unit 4 ... Personal computer (computer system), 41 ... CPU (arithmetic control device), 42 ... RAM (random access memory: storage device), 43 ... ROM (read-only memory: storage device), 44. HDD (hard disk drive: storage device), 47. Mouse (pointing device), 48 ... Flat panel display,
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Abstract
Description
図1は、本実施態様に係る判断装置(コンピュータ)の機能ブロック図である。同図に示すように、この判断装置1は、入力部2と、演算部3と、記憶部4 と、判断部5と、出力部6とを備える。なお、判断装置1のより具体的なハードウェア構成については、図14を参照して、後述する。
以下、実施例1として、このような判断装置1が発電施設群(図3(a))、特に原子力発電施設群に適用される場合を例に挙げて説明する。また、かかる説明により、図1に示した判断装置1の各機能ブロックの機能、図2に示した判断装置1の各ステップの動作の意義をより明確にする。
以下、実施例2として、判断装置1がビル施設群(図3(c))に適用される場合を例に挙げて説明する。また、かかる説明により、上述の実施例1とともに、図1に示した判断装置1の各機能ブロックの機能、図2に示した判断装置1の各ステップの動作の意義をより明確にする。
同様に、これらのアノマリー・スコアからアノマリー・パターンが演算される。具体的には、各アノマリー・スコアの値を平常(0.5未満):網掛けなし、低い異常(0.5以上、0.8未満):薄い網掛け、高い異常(0.8以上):濃い網掛けで評価し、この場合は、各アノマリー・スコアの値がそれぞればらつい ているので、オフィスビル施設群中のデバイスのセキュリティ・レベルに対応するアノマリー・パターンとしては、濃い網掛けと網掛けなしが混在したものとなる。
2…入力部、
3…演算部、
4…記憶部
5…判断部
6…出力部
4…パーソナル・コンピュータ(コンピュータ・システム)、
41…CPU(演算制御装置)、
42…RAM(ランダム・アクセス・メモリ:記憶装置)、
43…ROM(リード・オンリ・メモリ:記憶装置)、
44…HDD(ハード・ディスク・ドライブ:記憶装置)、
47…マウス(ポインティング装置)、
48…フラット・パネル・ディスプレイ、
Claims (20)
- システムの状況を判断するコンピュータに適用される方法であり、
前記システム中の複数の計測対象からそれぞれ計測データを受け取るステップと、
各計測対象の複数の属性に対応する複数の分類に従い、各計測データと予め定められた演算アルゴリズムとに基づいて複数の異常値の集合を演算するステップと、
前記複数の異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断するステップと
を含む方法。 - 前記複数の分類の少なくとも一つは、階層的分類である請求項1に記載の方法。
- 前記判断するステップは、予め定められた前記システムの各状況に対応した前記複数の異常値の集合のパターンと演算された前記複数の異常値の集合とを比較することで、前記システムの状況を判断する請求項1又は2に記載の方法。
- 前記判断するステップは、予め定められた前記システムの各状況に対応した前記複数の異常値の集合のパターンと演算された前記複数の異常値の集合を単純化したものとを比較することで、前記システムの状況を判断する請求項1又は2に記載の方法。
- 前記判断するステップは、予め定められた前記システムの各状況に対応した前記複数の異常値の集合のパターンと演算された前記複数の異常値の集合を単純化したものとを比較し、その類似度の最も高いパターンに対応する状況を前記システムの状況と判断する請求項1又は2に記載の方法。
- 前記判断するステップは、前記複数の異常値の集合の経時変化に基づいて、前記システムの状況を判断する請求項1乃至5のいずれかに記載の方法。
- 前記複数の属性の少なくとも一つは、前記計測対象の機能、ネットワーク構成、種類、設置場所、設置場所の管理組織、設置場所のセキュリティ・レベルのいずれかである請求項1乃至6のいずれかに記載の方法。
- 前記演算するステップは、各計測対象の第一属性に対応する第一分類に従い、各計測データと予め定められた演算アルゴリズムとに基づいて第一異常値の集合を演算するサブ・ステップと、各計測対象の第二属性に対応する第二分類に従い、各計測データと予め定められた演算アルゴリズムとに基づいて第二異常値の集 合を演算するサブ・ステップとを含み、
前記判断するステップは、前記第一異常値の集合及び第二異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断する
請求項1乃至7のいずれかに記載の方法。 - 前記演算するステップは、各計測対象のネットワーク構成に対応するネットワークの階層構造に従い、各計測データと予め定められた演算アルゴリズムとに基づいてネットワーク異常値の集合を演算するサブ・ステップと、各計測対象の設置場所に対応する場所の階層構造に従い、各計測データと予め定められた演算ア ルゴリズムとに基づいて場所異常値の集合を演算するサブ・ステップとを含み、
前記判断するステップは、前記ネットワーク異常値の集合及び前記場所異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断する
請求項8に記載の方法。 - 前記演算するステップは、各計測対象の第三属性に対応する第三分類に従い、各計測データと予め定められた演算アルゴリズムとに基づいて第三異常値の集合を演算するサブ・ステップを更に含み、
前記判断するステップは、前記第一異常値の集合、第二異常値の集合、及び第三異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断する 請求項8に記載の方法。 - 前記演算するステップは、各計測対象の種別に対応する階層構造に従い、各計測データと予め定められた演算アルゴリズムとに基づいて種別異常値の集合を演算するサブ・ステップと、各計測対象の設置場所に対応する階層構造に従い、各計測データと予め定められた演算アルゴリズムとに基づいて場所異常値の集合を演 算するサブ・ステップと、各計測対象の設置場所のセキュリティ・レベルに対応する構造に従い、各計測データと予め定められた演算アルゴリズムとに基づいてセキュリティ異常値の集合を演算するサブ・ステップとを含み、
前記判断するステップは、前記種別異常値の集合、前記場所異常値の集合及び前記セキュリティ異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断する請求項10に記載の方法。 - 前記システムの状況の判断結果をユーザに表示するステップを更に備える請求項1乃至11のいずれかに記載の方法。
- 前記複数の異常値の集合と前記システムの状況の判断結果とをユーザに表示するステップを更に備える請求項1乃至12のいずれかに記載の方法。
- 前記システムは、インダストリアル・コントロール・システムである請求項1乃至13のいずれかに記載の方法。
- コンピュータに、
前記システム中の複数の計測対象からそれぞれ計測データを受け取るステップと、
各計測対象の複数の属性に対応する複数の分類に従い、各計測データと予め定められた演算アルゴリズムとに基づいて複数の異常値の集合を演算するステップと、
前記複数の異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断するステップと
を実行させるコンピュータ・プログラム。 - システムの状況を判断するコンピュータであり、
前記システム中の複数の計測対象からそれぞれ計測データを受け取る入力部と、
各計測対象の複数の属性に対応する複数の分類に従い、各計測データと予め定められた演算アルゴリズムとに基づいて複数の異常値の集合を演算する演算部と、
前記複数の異常値の集合と予め定められた判断アルゴリズムとに基づいて、前記システムの状況を判断する判断部と
を含むコンピュータ。 - 前記演算アルゴリズム及び前記判断アルゴリズムを記憶する記憶部を更に備える請求項16に記載のコンピュータ。
- 前記記憶部は、複数の異常値の集合とシステムの状況とを対応させて記憶しており、
前記判断部は、演算された複数の異常値の集合と記憶された複数の異常値の集合とを比較することで、前記システムの状況を判断する請求項17に記載のコンピュータ。 - 前記記憶部は、複数の異常値の集合を単純化したパターンとシステムの状況とを対応させて記憶しており、
前記判断部は、演算された複数の異常値の集合と記憶されたパターンとを比較することで、前記システムの状況を判断する請求項17又は18に記載のコンピュータ。 - 前記システムの状況の判断結果をユーザに表示する出力部をさらに備える請求項16乃至19のいずれかに記載のコンピュータ。
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US9753801B2 (en) | 2015-10-05 | 2017-09-05 | Fujitsu Limited | Detection method and information processing device |
US10754719B2 (en) | 2015-12-09 | 2020-08-25 | Nec Corporation | Diagnosis device, diagnosis method, and non-volatile recording medium |
CN117608255A (zh) * | 2024-01-19 | 2024-02-27 | 新立讯科技股份有限公司 | 新能源智慧工厂ba自控系统的远程监控管理系统及方法 |
CN117608255B (zh) * | 2024-01-19 | 2024-04-05 | 新立讯科技股份有限公司 | 新能源智慧工厂ba自控系统的远程监控管理系统及方法 |
Also Published As
Publication number | Publication date |
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EP2660675A1 (en) | 2013-11-06 |
JP5501481B2 (ja) | 2014-05-21 |
CN103261988A (zh) | 2013-08-21 |
US9857775B2 (en) | 2018-01-02 |
US20130274899A1 (en) | 2013-10-17 |
EP2660675A4 (en) | 2017-04-05 |
CN103261988B (zh) | 2016-01-20 |
JPWO2012090718A1 (ja) | 2014-06-05 |
TWI515522B (zh) | 2016-01-01 |
TW201239562A (en) | 2012-10-01 |
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