JP7045531B1 - Plant operation support system, plant operation support method, and plant operation support program - Google Patents

Abstract

The plant operation support system (2) includes an event recognition unit (13), an operation support information creation unit (15), an operation pattern recognition unit (19), an evaluation unit (20), and an update unit (21). The event recognition unit (13) recognizes an event that has occurred in the plant (4) based on the event recognition model (40). The driving support information creation unit (15) displays a screen of driving support information on the display unit (30) based on the driving support information creation model (43,44) and the event recognized by the event recognition unit (13). Let me. The operation pattern recognition unit (19) recognizes the operation pattern of the screen by the operator (U) based on the operation model (45) and the operation log data. The evaluation unit (20) evaluates the event recognition accuracy by the event recognition unit (13) and the operation pattern recognition accuracy by the operation pattern recognition unit (19). The update unit (21) updates the event recognition model (40) based on the evaluation result by the evaluation unit (20).

Description

The present disclosure relates to a plant operation support system, a plant operation support method, and a plant operation support program that support the operation of a plant monitored and controlled by the monitoring and control system.

Conventionally, a monitoring and control system for monitoring and controlling a plant such as a water and sewage plant, a power plant, a chemical plant, or a steel plant is known. Such a monitoring control system collects measurement data output from measurement equipment installed at important points of equipment in a plant, and displays the collected measurement data or process data such as data based on the measurement data on a display unit. The operator determines the state of the plant from the information displayed on the display unit, and inputs data such as set values to the monitoring control system so as to be optimum.

Since it may be difficult to determine the state of the plant, a plant operation support system that supports the operation of the plant may be used. The plant operation support system is equipped with various prediction functions, various simulations, abnormality detection functions, set value guidance functions, and the like, and the plant operation support system supports the judgment of the operator.

Due to the limited number and duration of process data that can be displayed on operation monitoring screens such as the monitoring and control system screen or the plant operation support system screen, it is displayed on the screen in plants that collect measurement data from a large number of measuring instruments. Process data must be switched by the operator according to the events that occur in the plant.

Therefore, in Patent Document 1, the operation of switching the operation monitoring screen when an alarm is generated is recorded, and when a new alarm is generated, the operation of switching the operation monitoring screen performed by the operator in the past regarding the new alarm is extracted and displayed. Therefore, a technique has been proposed that reduces the labor and time required to grasp the state of the plant.

Japanese Unexamined Patent Publication No. 2012-11586

However, the number of events that can occur in a plant, such as water quality abnormalities, quantity abnormalities, equipment failures, inspections, or plant remodeling due to construction work, is enormous, and it is difficult to anticipate all events in advance. In the technique described in Patent Document 1, it is necessary to predefine an event that causes an alarm before the introduction of the plant operation support system, and operation support information for supporting the operation of the plant for an undefined event is provided. Difficult to provide.

The present disclosure has been made in view of the above, and an object of the present invention is to obtain a plant operation support system capable of providing operation support information for supporting the operation of a plant with respect to an event not defined at the time of system introduction. And.

In order to solve the above-mentioned problems and achieve the object, the plant operation support system of the present disclosure is a plant operation support system that supports operation by a plant operator whose monitoring and control is performed by the monitoring control system. It includes a monitoring control data acquisition unit, an event recognition unit, a driving support information creation unit, an operation pattern recognition unit, an evaluation unit, and an update unit. The monitoring control data acquisition unit acquires monitoring control data including time-series measurement data output by the measuring equipment installed in the plant. The event recognition unit recognizes the event that occurred in the plant based on the event recognition model that recognizes the event that occurred in the plant from the monitoring control data and the monitoring control data. The operation support information creation unit creates operation support information based on the operation support information creation model that determines the operation support information, which is information that supports operation from the events that occur in the plant, and the events recognized by the event recognition unit. Then, the screen of the created driving support information is displayed on the display unit. The operation pattern recognition unit recognizes the operation pattern based on the operation model and the operation log data for recognizing the operation pattern of the operator from the operation log data including the operation history of the screen by the operator. The evaluation unit calculated and calculated the conditional entropy for each of the event and the operation pattern based on the frequency of occurrence of each combination of the event recognized by the event recognition unit and the operation pattern recognized by the operation pattern recognition unit. Based on the conditional entropy, the event recognition accuracy by the event recognition unit and the operation pattern recognition accuracy by the operation pattern recognition unit are evaluated. The update unit divides one event into multiple events and integrates multiple events into one event in the event recognition model based on the recognition accuracy of the event by the event recognition unit evaluated by the evaluation unit. Update the event recognition model by doing at least one .

According to the present disclosure, it is possible to provide operation support information for supporting the operation of a plant with respect to an event that is not defined at the time of system introduction.

The figure which shows an example of the structure of the processing system including the monitoring control system and the plant operation support system which concerns on Embodiment 1. The figure which shows an example of the logical expression information included in the event recognition model stored in the event recognition model storage part which concerns on Embodiment 1. The figure which shows an example of the propositional logic definition information included in the event recognition model stored in the event recognition model storage part which concerns on Embodiment 1. The figure which shows an example of the event recognition result by the event recognition part of the plant operation support system which concerns on Embodiment 1. Diagram showing an example of the operation support information creation model for the trend graph stored in the operation support information creation model storage unit of the plant operation support system according to the first embodiment. Diagram showing an example of an operation support information creation model for a scatter diagram stored in the operation support information creation model storage unit of the plant operation support system according to the first embodiment. A diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the first embodiment. A diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the first embodiment and displayed on the display unit. The figure which shows an example of the operation log data definition information stored in the operation log data storage part of the plant operation support system which concerns on Embodiment 1. A diagram schematically showing an example of operation pattern definition information included in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The figure which schematically shows an example of the operation purpose definition information in the operation model stored in the operation model storage part of the plant operation support system which concerns on Embodiment 1. The figure which schematically shows an example of the 1st definition information included in the display signal pattern definition information in the operation model stored in the operation model storage part of the plant operation support system which concerns on Embodiment 1. The figure which schematically shows an example of the 2nd definition information included in the display signal pattern definition information in the operation model stored in the operation model storage part of the plant operation support system which concerns on Embodiment 1. The figure which schematically shows an example of the 3rd definition information included in the display signal pattern definition information in the operation model stored in the operation model storage part of the plant operation support system which concerns on Embodiment 1. The figure which schematically shows an example of the display period pattern definition information in the operation model stored in the operation model storage part of the plant operation support system which concerns on Embodiment 1. The figure which shows typically an example of the operation pattern of the operator recognized by the operation pattern recognition part which concerns on Embodiment 1. The figure which shows an example of the evaluation information used by the evaluation part which concerns on Embodiment 1. An example of the logical formula information included in the event recognition model updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low is schematically shown. figure Schematically, an example of propositional logic definition information included in the event recognition model updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low. Figure to represent A schematic example of an operation support information creation model for a trend graph updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low is schematically shown. figure A schematic example of an operation support information creation model for a scatter plot updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low is schematically shown. figure A flowchart showing an example of processing by the processing unit of the plant operation support system according to the first embodiment. A flowchart showing an example of evaluation update processing by the processing unit of the plant operation support system according to the first embodiment. The figure which shows an example of the hardware configuration of the plant operation support system which concerns on Embodiment 1. The figure which shows an example of the structure of the processing system which concerns on Embodiment 2. A diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the second embodiment and displayed on the display unit. A diagram schematically showing another example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the second embodiment and displayed on the display unit. The figure which schematically shows an example of the event name edit information stored in the event name edit information storage part of the plant operation support system which concerns on Embodiment 2. The figure which shows an example of the structure of the processing system which concerns on Embodiment 3. The figure which shows an example of the structure of the processing system which concerns on Embodiment 4. A diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the fourth embodiment and displayed on the display unit. The figure which shows an example of the structure of the processing system which concerns on Embodiment 5. A diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the fifth embodiment and displayed on the display unit. The figure which shows typically an example of the selection feature amount information stored in the selection feature amount information storage part of the driving support system which concerns on Embodiment 5. A diagram schematically showing an example of a feature amount template stored in the feature amount template storage unit of the driving support system according to the fifth embodiment.

Hereinafter, the plant operation support system, the plant operation support method, and the plant operation support program according to the embodiment will be described in detail based on the drawings.

Embodiment 1.
FIG. 1 is a diagram showing an example of a configuration of a processing system including a monitoring control system and a plant operation support system according to the first embodiment. As shown in FIG. 1, the processing system 100 according to the first embodiment includes a monitoring control system 1, a plant operation support system 2, and an input / output device 3. In the processing system 100, the input / output device 3 may be included in the monitoring control system 1, and may have different input / output devices 3 between the monitoring control system 1 and the plant operation support system 2.

The monitoring and control system 1 monitors and controls the plant 4. Plant 4 is, but is not limited to, a water and sewage plant, a power plant, a chemical plant, or a steel plant. A plurality of measuring instruments are provided in the plant 4, and the measurement data, which is the data measured by these plurality of measuring instruments, is collected by the monitoring control system 1.

For example, if the plant 4 is a sewerage plant, the plant 4 includes equipment such as a sand basin, a pump well, a pump, a first settling basin, a treatment tank, a blower, a final settling basin, a return pump, and a chlorine mixing basin. Is done. The sand basin removes relatively large debris and sand contained in the inflowing sewage. Pump wells store sewage flowing from sand basins. The pump pump first pumps the sewage from the pump well into the settling basin.

The first settling basin settles the relatively small debris and sand contained in the sewage to the bottom and removes the relatively small debris and sand from the sewage. The blower aerates by adding activated sludge to the sewage in the treatment tank and blowing air to remove organic matter in the sewage.

The final settling basin separates the activated sludge mixture flowing from the treatment tank into supernatant water and activated sludge. The return pump is provided in the pipe connecting the final settling basin and the treatment tank, and returns the activated sludge from the final settling basin to the treatment tank. The chlorine mixing basin is sterilized by adding chlorine to the supernatant water flowing from the final settling basin.

When the plant 4 is a sewage treatment plant, the plant 4 is provided with a flow meter, a water level gauge, an air flow meter, various densitometers, and the like as the above-mentioned measuring instruments. The flow meter measures, for example, a flow meter that measures the flow rate of sewage flowing into the pump well, a flow meter that measures the flow rate of the pump, and a flow rate of water containing active sludge returned from the final settling pond to the treatment tank. A flow meter, a flow meter that measures the flow rate of treated water discharged from the plant 4, or the like.

The water level gauge is, for example, a water level gauge that measures the water level of a pump well. The air flow meter is, for example, an air flow meter that measures the aerated air volume of the treatment tank. The densitometer is a dissolved oxygen amount sensor that detects the amount of dissolved oxygen in the treatment tank, an active microorganism concentration sensor that detects the concentration of active microorganisms in the treatment tank, a BOD sensor that detects BOD (Biochemical Oxygen Demand) in the treatment tank, or a plant 4. It is a nitrogen concentration meter that measures the discharged nitrogen concentration, which is the concentration of nitrogen in the water discharged from.

The input / output device 3 includes a display unit 30 and an input unit 31. The display unit 30 is, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display. The input unit 31 includes, for example, a keyboard, a mouse, a keypad, a touch panel, and the like, and is operated by the user of the processing system 100. The user of the processing system 100 is an operator U who manages the operation of the plant 4.

The monitoring control system 1 controls the equipment of the plant 4 or the equipment provided in the equipment based on the control input data output from the input / output device 3. The control input data is, for example, data of a set value of a flow rate of a pump, data of a set value of an aeration air volume of a blower, data of a set value of a flow rate of water containing activated sludge, and the like.

The plant operation support system 2 causes the display unit 30 of the input / output device 3 to display an operation support screen which is a screen of operation support information including process data such as measurement data collected from the plant 4 or data based on the measurement data. Further, the plant operation support system 2 selects the operation support screen, selects the signal to be displayed on the operation support screen, and the period of the signal to be displayed on the operation support screen based on the operation from the operator U to the input unit 31. In response to an operation such as selection, selection of attention time, or input of set value, a process of changing the content of the screen displayed on the display unit 30 is performed.

The plant operation support system 2 has a function of supporting the operation of the plant 4 by the operator U. The plant operation support system 2 includes a monitoring control data acquisition unit 10, a monitoring control data storage unit 11, an event recognition model storage unit 12, an event recognition unit 13, an operation support information creation model storage unit 14, and an operation support. It is provided with an information creation unit 15.

Further, the plant operation support system 2 includes an operation data acquisition unit 16, an operation log data storage unit 17, an operation model storage unit 18, an operation pattern recognition unit 19, an evaluation unit 20, and an update unit 21. The processing unit 22 includes a monitoring control data acquisition unit 10, an event recognition unit 13, an operation support information creation unit 15, an operation data acquisition unit 16, an operation pattern recognition unit 19, an evaluation unit 20, and an update unit 21.

The monitoring control data acquisition unit 10 acquires monitoring control data from the monitoring control system 1 and stores the acquired monitoring control data in the monitoring control data storage unit 11. The monitoring control data includes time-series measurement data output by the measuring equipment installed in the equipment of the plant 4 and control input data which is control data input by the operator U.

The event recognition model storage unit 12 stores an event recognition model 40 that recognizes an event that has occurred in the plant 4 from the monitoring control data. The event recognition model 40 includes logical expression information in which each event is represented by a logical expression of propositional logic, and propositional logic definition information that defines propositional logic. FIG. 2 is a diagram showing an example of logical formula information included in the event recognition model stored in the event recognition model storage unit according to the first embodiment.

The logical formula information 41 shown in FIG. 2 includes an event ID (IDentifier) 411, an event name 412, and a determination formula 413 for each event. The event ID 411 is identification information unique to each event. The event name 412 is information indicating the name of the event. The determination formula 413 is information for determining an event. The "judgment formula" is information indicating the logical formula of the propositional logic judged by the monitoring control data.

In the logical formula information 41 shown in FIG. 2, for example, the event determination formula 413 in which the event ID 411 is “a ₁ ” and the event name 412 is “midnight on a sunny day” is “(p ₁ ∧ p ₄ ∧ p”. ₆ ∧p ₈ ∧p ₁₀ ) ∨ (p ₁ ∧ p ₄ ∧ p ₆ ∧ p ₉ ∧ p ₁₁ ) ”. "Midnight on a sunny day" indicates that the event is midnight on a sunny day.

Further, in the logical formula information 41 shown in FIG. 2, for example, the event determination formula 413 in which the event ID 411 is “a ₂ ” and the event name 412 is the “sunny day rating” is “p ₂ ∧ p ₄ ∧”. p ₆ ∧ p ₈ ∧ p ₁₁ ”. The "sunny day rating" indicates that the event is a sunny day and the operating state of the plant 4 is normal.

Further, in the logical formula information 41 shown in FIG. 2, for example, the event determination formula 413 in which the event ID 411 is “a ₃ ” and the event name 412 is “corresponding to the peak on a sunny day” is “p ₂ ∧ p ₅ ”. ∧p ₇ ∧p ₉ ∧p ₁₂ ”. "Correspondence to peak on a sunny day" indicates that the event is a sunny day and the operating state of the plant 4 is in a peak state.

FIG. 3 is a diagram showing an example of propositional logic definition information included in the event recognition model stored in the event recognition model storage unit according to the first embodiment. The propositional logic definition information 42 shown in FIG. 3 includes a propositional logic ID 421, a target time series data ID 422, a target time series data name 423, a unit 424, and a determination formula 425 for each event.

The propositional logic ID 421 is identification information unique to each propositional logic. The target time-series data ID 422 is identification information unique to each target time-series data, which is time-series measurement data that is the target of propositional logic. The target time-series data name 423 is information indicating the name of the target time-series data. The unit 424 is information indicating a unit of the target time series data. The determination formula 425 is information for determining the propositional logic.

In the propositional logic definition information 42 shown in FIG. 3, for example, in the propositional logic whose propositional logic ID 421 is “p ₁ ”, the target time series data ID 422 is “x ₁ ” and the target time series data name 423 is “pump flow rate”. The unit 424 of the target time-series data is “m ³ / hr”, and the determination formula 425 is “x ₁ ≦ y ₁ ¹ ”. The target time-series data in which the target time-series data ID 422 is "x ₁ " is the time-series data of the measurement data of the measuring device that measures the flow rate of the pump. “Y ₁ ¹ ” is a threshold value, and the propositional logic ID 421 satisfies the propositional logic of “p ₁ ” when the flow rate of the pump pump per hour is “y ₁ ¹ ” or less.

Further, in the propositional logic definition information 42 shown in FIG. 3, for example, in the propositional logic whose propositional logic ID 421 is "p ₂ ", the target time series data ID 422 is "x ₁ " and the target time series data name 423 is "pumping". It is "pump flow rate", the unit 424 of the target time series data is "m ³ / hr", and the determination formula 425 is "y ₁ ¹ <x ₁ ≤ y ₁ ² ". “Y ₁ ¹ ” and “y ₁ ² ” are thresholds, and the propositional logic ID 421 is “p” when the flow rate per hour of the pump is larger than “y ₁ ¹ ” and less than or equal to “y ₁ ² ”. ₂ ”satisfies the propositional logic.

Further, in the propositional logic definition information 42 shown in FIG. 3, for example, in the propositional logic whose propositional logic ID 421 is "p ₃ ", the target time series data ID 422 is "x ₁ " and the target time series data name 423 is "pumping". It is "pump flow rate", the unit 424 of the target time series data is "m ³ / hr", and the determination formula 425 is "y ₁ ² <x ₁ ". “Y ₁ ² ” is a threshold value, and the propositional logic ID 421 satisfies the propositional logic of “p ₃ ” when the flow rate per hour of the pump is larger than that of “y ₁ ² ”.

Further, in the propositional logic definition information 42 shown in FIG. 3, for example, in the propositional logic whose propositional logic ID 421 is "p ₄ ", the target time series data ID 422 is "x ₂ " and the target time series data name 423 is "pump". It is "well water level", the unit 424 of the target time series data is "m", and the determination formula 425 is "x ₂ ≤ y ₂ ¹ ". The target time-series data in which the target time-series data ID is "x ₂ " is the time-series data of the measurement data of the measuring device for measuring the water level of the pump well. “Y ₂ ¹ ” is a threshold value, and when the water level of the pump well is “y ₂ ¹ ” or less, the propositional logic ID 421 satisfies the propositional logic of “p ₄ ”.

Further, in the propositional logic definition information 42 shown in FIG. 3, for example, in the propositional logic whose propositional logic ID 421 is "p ₅ ", the target time series data ID 422 is "x ₂ " and the target time series data name 423 is "pump". It is "well water level", the unit 424 of the target time series data is "m", and the determination formula 425 is "y ₂ ² <x ₂ ". “Y ₂ ² ” is a threshold value, and when the water level of the pump well is higher than “y ₂ ² ”, the propositional logic ID 421 satisfies the propositional logic of “p ₅ ”.

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The event recognition unit 13 of the plant operation support system 2 receives the monitoring control data stored in the monitoring control data storage unit 11 and the event generated in the plant 4 from the event recognition model 40 stored in the event recognition model storage unit 12. recognize.

For example, it is assumed that the logical expression information 41 is the information shown in FIG. 2 and the propositional logical definition information 42 is the state shown in FIG. In this case, the event recognition unit 13 has a determination formula 413 "(p ₁ ∧ p ₄ ∧ p ₆ ∧ p ₈ ∧ p ₁₀ ) ∨ (p) based on the monitoring control data stored in the monitoring control data storage unit 11. If it is determined that ₁ ∧ p ₄ ∧ p ₆ ∧ p ₉ ∧ p ₁₁ ) ”is satisfied, it is recognized that the event that occurred in the plant 4 is“ midnight on a sunny day ”.

Further, the event recognition unit 13 has determined that the determination formula 413 "p ₂ ∧ p ₄ ∧ p ₆ ∧ p ₈ ∧ p ₁₁ " is satisfied based on the monitoring control data stored in the monitoring control data storage unit 11. In this case, it is recognized that the event that occurred in the plant 4 is the “sunny day rating”. Further, the event recognition unit 13 has determined that the determination formula 413 "p ₂ ∧ p ₅ ∧ p ₇ ∧ p ₉ ∧ p ₁₂ " is satisfied based on the monitoring control data stored in the monitoring control data storage unit 11. In this case, it is recognized that the event that occurred in the plant 4 is "corresponding to the peak on a sunny day".

FIG. 4 is a diagram showing an example of an event recognition result by the event recognition unit of the plant operation support system according to the first embodiment. In the example shown in FIG. 4, the event recognition unit 13 recognizes an event whose event ID is "a ₁ ", an event whose event ID is "a ₂ ", and an event whose event ID is "a ₆ " in that order. It is shown that The event recognition unit 13 can also recognize a plurality of events at the same time.

In the above example, the proposition logic definition information 42 is defined with the time-series measurement data included in the monitoring control data as the target data, but the proposition logic definition information 42 contains the time as the data for defining the proposition logic. In addition to the measurement data of the series, the definition of the proposition logic using the control input data included in the monitoring control data may be included. In this case, the event recognition unit 13 recognizes an event based on the time-series measurement data and the control input data.

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The operation support information creation model storage unit 14 of the plant operation support system 2 stores the operation support information creation models 43 and 44 that determine the operation support information which is the information for supporting the operation of the operator U from the event generated in the plant 4. do. The driving support information creation model 43 is a driving support information creation model for a trend graph, and the driving support information creation model 44 is a driving support information creation model for a scatter plot.

FIG. 5 is a diagram showing an example of an operation support information creation model for a trend graph stored in the operation support information creation model storage unit of the plant operation support system according to the first embodiment. As shown in FIG. 5, the driving support information creation model 43 for the trend graph uses information of the event ID 431, the event name 432, the conditional probability 433 in the display time series data unit, and the conditional probability 434 in the display period unit. Included every time.

The event ID 431 is identification information unique to each event, and is the same as the event ID 411 included in the logical expression information 41. The event name 432 is information indicating the name of the event, and is the same as the event name 412 included in the logical expression information 41. The conditional probability 433 of the display time series data unit is information indicating the appropriateness of displaying each time series data on the trend graph when the event is recognized by the event recognition unit 13. The conditional probability 434 for each display period is information indicating the appropriateness of the display period of the time-series data on the trend graph when the event is recognized by the event recognition unit 13.

In the driving support information creation model 43 shown in FIG. 5, when an event whose event ID 431 is “a ₁ ” is recognized by the event recognition unit 13, the time series data whose target time series data ID 422 is “x ₁ ” is generated. The conditional probability indicating the appropriateness of being displayed on the trend graph is "P (x ₁ | a ₁ )", and the conditional probability indicating the appropriateness of the display period being "l ₁ " is "P (". l ₁ | a ₁ ) ”is shown.

Further, in the driving support information creation model 43 shown in FIG. 5, when an event whose event ID 431 is “a ₂ ” is recognized by the event recognition unit 13, time-series data whose target time-series data ID 422 is “x ₁ ”. The conditional probability indicating the appropriateness that is displayed in the trend graph is "P (x ₁ | a ₂ )", and the conditional probability indicating the appropriateness that the display period is "l ₂ " is "P". (L ₂ | a ₂ ) ”is shown.

FIG. 6 is a diagram showing an example of an operation support information creation model for a scatter diagram stored in the operation support information creation model storage unit of the plant operation support system according to the first embodiment. As shown in FIG. 6, the driving support information creation model 44 for the scatter plot has information of the event ID 441, the event name 442, the conditional probability 443 in the combination unit of the display time series data, and the conditional probability 444 in the display period unit. Is included for each event.

The event ID 441 is identification information unique to each event, and is the same as the event ID 411 included in the logical expression information 41. The event name 442 is information indicating the name of the event, and is the same as the event name 412 included in the logical expression information 41. The conditional probability 443 of the combination unit of the display time series data is information indicating the appropriateness of displaying each time series data on the scatter diagram when the event is recognized by the event recognition unit 13. The conditional probability 444 in the display period unit is information indicating the appropriateness of the display period on the scatter diagram of the time series data when the event is recognized by the event recognition unit 13.

In the driving support information creation model 44 shown in FIG. 6, when an event whose event ID 441 is “a ₁ ” is recognized by the event recognition unit 13, the target time series data ID 422 is “x ₁ ” and “x ₂ ”. The conditional probability indicating the appropriateness of displaying two time-series data in the scatter plot is "P (x ₁ , x ₂ | a ₁ )", and the display period is "l ₁ ". It is shown that the conditional probability indicating the above is "P (l ₁ | a ₁ )".

Further, in the driving support information creation model 44 shown in FIG. 6, when the event whose event ID 441 is “a ₂ ” is recognized by the event recognition unit 13, the target time series data ID 422 is “x ₁ ” and “x ₂ ”. The conditional probability indicating the appropriateness of displaying the two time-series data in the scatter plot is "P (x ₁ , x ₂ | a ₂ )", and the display period is "l ₂ ". It is shown that the conditional probability indicating a certain suitability is "P (l ₂ | a ₂ )".

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The operation support information creation unit 15 of the plant operation support system 2 is based on the event recognized by the event recognition unit 13 and the operation support information creation models 43 and 44 stored in the operation support information creation model storage unit 14. The driving support information is created, and the created driving support information is displayed on the display unit 30 of the input / output device 3.

FIG. 7 is a diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the first embodiment. As shown in FIG. 7, the driving support screen 60 created by the driving support information creating unit 15 includes a time-series data selection area 601, a message display area 602, a period selection area 603, and a trend graph display area 604. It includes a scatter plot display area 605 and a set value input area 606. The driving support screen 60 is displayed on the display unit 30 by the driving support information creating unit 15.

The time-series data selection area 601 includes a check box 601a for displaying a trend graph and a check box 601b for displaying a scatter plot. The check box 601a for displaying the trend graph is a condition indicating the appropriateness of displaying the time series data for the event recognized by the event recognition unit 13 according to the number of trend graphs that can be displayed in the trend graph display area 604. Check the time-series data with the highest attachment probability. For example, when there are three trend graphs that can be displayed, the time series data with the highest conditional probabilities for the recognized event is checked.

The check box 601b for displaying the scatter plot checks the two time-series data constituting the time-series data set having the highest conditional probability, which indicates the appropriateness of displaying the time-series data set for the recognized event. Enters. In the message display area 602, the time when the event recognition unit 13 newly recognizes the event and the event name of the newly recognized event are displayed.

In the period selection area 603, the time-series data up to the time before the preset time in the past is displayed for the data in which the check box for displaying the trend graph in the time-series data selection area 601 is checked. The preset time in the past is, for example, a selected period of the specified period. The designated period is a period designated as the target of each of event recognition and operation pattern recognition, and is specified within the overlap period between the acquisition period of monitoring control data and the acquisition period of operation log data described later.

The acquisition period of the monitoring control data is, for example, the period from the time when the oldest monitoring control data is acquired among the monitoring control data stored in the plant operation support system 2 to the time when the latest monitoring control data is acquired. be. Further, the operation log data acquisition period is, for example, a period from the time when the oldest operation data is acquired among the operation data stored in the plant operation support system 2 to the time when the latest operation data is acquired. ..

Further, in the period selection area 603, the display period portion having the largest conditional probability indicating the appropriateness of the display period of the time series data for the event recognized by the event recognition unit 13 is selected retroactively from the current time. The selection period range 603a indicating is displayed.

In the trend graph display area 604, the time-series data of the period indicated by the selection period range 603a in the period selection area 603 is trended with respect to the data in which the check box 601a for displaying the trend graph of the time-series data selection area 601 is checked. Displayed in time series data units according to the graph format.

In the scatter plot display area 605, the time series data of the period indicated by the selection period range 603a in the period selection area 603 is scattered with respect to the data in which the check box 601b for the scatter diagram display of the time series data selection area 601 is checked. Display in the format shown in the figure. In the set value input area 606, a box 606a for selecting the target time-series data for inputting the set value and a box 606b for inputting the set value are displayed.

FIG. 8 is a diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the first embodiment and displayed on the display unit. As shown in FIG. 8, the driving support screen 61 created by the driving support information creating unit 15 and displayed on the display unit 30 includes a time series data selection area 611, a message display area 612, a period selection area 613, and a trend graph. It includes a display area 614, a scatter plot display area 615, and a set value input area 616.

The time-series data selection area 611 includes a check box 611a for displaying a trend graph and a check box 611b for displaying a scatter plot. For example, it is assumed that the time-series data to be checked in the check box 611a is changed by the operation of the operator U to the input unit 31. In this case, the driving support information creation unit 15 displays the driving support screen 61 in which the time-series data displayed in the period selection area 613 and the trend graph display area 614 is changed to the time-series data checked in the check box 611a. Display at 30.

In the example shown in FIG. 8, the trend graph of the time-series data in which the time-series data ID is "x ₁ ", the trend graph of the time-series data in which the time-series data ID is "x ₂ ", and the time-series data ID are A trend graph of time series data of "x ₄ " is shown.

Further, it is assumed that the operator U changes the time-series data to be checked in the check box 611b by operating the input unit 31. In this case, the driving support information creating unit 15 causes the display unit 30 to display the driving support screen 61 in which the time-series data displayed in the scatter diagram display area 615 is changed to the time-series data checked in the check box 611b.

The period selection area 613 includes a selection period range 613a and a period selection bar 613b. The selection period range 613a indicates the period of data to be displayed in each of the trend graph display area 614 and the scatter plot display area 615, and the period selection bar 613b is a bar for changing the selection period range 613a, and is a selection period range. It is located at the center of 613a and can move within the selection period range 613a.

When the operation to the input unit 31 by the operator U is an operation of moving the period selection bar 613b, the driving support information creating unit 15 moves the selection period range 613a with the period selection bar 613b as the center time, and moves the trend graph. The period of the time series data displayed in each of the display area 614 and the scatter plot display area 615 is changed to the period corresponding to the selection period range 613a.

In the trend graph display area 614, the trend graph of the time series data checked by the check box 611a is displayed. Further, the trend graph display area 614 includes the attention time selection bar 614a. The attention time selection bar 614a is a bar for changing the attention time, which is the time at the center of the range of the selection period range 613a, in conjunction with the period selection bar 613b.

When the operation to the input unit 31 by the operator U is an operation to move the attention time selection bar 614a, the driving support information creation unit 15 moves the attention time selection bar 614a together with the period selection bar 613b and the selection period range 613a. ..

The driving support information creating unit 15 displays the value of the time data indicated by the attention time selection bar 614a among the time series data in the trend graph display area 614. In the example shown in FIG. 8, as the data values of the time indicated by the attention time selection bar 614a, the time series data ID is "x ₁ ", the time series data value is "y ₁ ^* ", and the time series data ID is "y 1 *". The value "y ₂ ^* " of the time-series data of "x ₂ " and the value of the time-series data "y ₄ ^* " of which the time-series data ID is "x ₄ " are shown.

Further, the driving support information creation unit 15 highlights the data of the time selected by the attention time selection bar 614a or the period selection bar 613b in the scatter diagram display area 615 as the attention data 615a. Highlighting is performed, for example, by making the color, size, shape, etc. of the attention data 615a plotted in the scatter plot different from other data.

Further, in the scatter diagram display area 615, when the attention data 615a, which is the data that the operator U pays attention to, is selected by the operation of the operator U to the input unit 31, the driving support information creation unit 15 displays the attention data 615a. The attention time selection bar 614a and the period selection bar 613b are moved to the position of the measured time.

Further, the driving support information creating unit 15 displays the value of the attention data 615a in the scatter plot display area 615. In the example shown in FIG. 8, the values of the attention data 615a are the time series data value “y ₂ ^* ” in which the time series data ID is “x ₂ ” and the time series in which the time series data ID is “x ₄ ”. The data value is "y ₄ ^* ".

The set value input area 616 includes a box 616a for selecting the target time-series data for inputting the set value and a box 616b for inputting the set value. The operator U can select the time-series data name of the time-series data to be the target time-series data from the time-series data names of the plurality of time-series data included in the box 616a by operating the input unit 31. Further, the operator U can input the set value to the box 616b by operating the input unit 31.

In the driving support screen 61 shown in FIG. 8, in the box 616a, the target time-series data in which the target time-series data ID 422 is “x ₅ ” is selected, and in the box 616b, “y ₅ ^* ” is selected as the installation value. It is shown to be.

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The operation data acquisition unit 16 of the plant operation support system 2 acquires operation data, which is data indicating an operation by the operator U to the input unit 31, from the input / output device 3, and stores the acquired operation data in the operation log data storage unit 17. To memorize. The operation log data storage unit 17 stores operation log data including a plurality of past time-series operation data.

The operation log data includes a first operation log time series data, a second operation log time series data, a third operation log time series data, and a fourth operation log time series data. The first operation log time-series data has, for example, the type of time-series data selected to be displayed in the trend graph display area 614 by an operation from the driving support information creation unit 15 or the operator U to the input unit 31. It is time series data represented by a series.

The second operation log time-series data has, for example, the type of time-series data selected to be displayed in the scatter plot display area 615 by an operation from the operation support information creation unit 15 or the operator U to the input unit 31. It is time series data represented by a series.

The third operation log time-series data is, for example, data represented by a time series represented by a display period of each trend graph and a display period of each scatter diagram on the driving support screen 61. The fourth operation log time-series data is time-series data in which the attention time selected on the driving support screen 61 is represented in time-series.

In addition to the above-mentioned operation log data, the operation log data definition information is stored in the operation log data storage unit 17. FIG. 9 is a diagram showing an example of operation log data definition information stored in the operation log data storage unit of the plant operation support system according to the first embodiment.

The operation log data definition information 58 shown in FIG. 9 includes an operation log time-series data ID 581 and a time-series data name 582 for each operation log time-series data. The operation log time series data ID 581 is identification information unique to each operation log time series data. The time-series data name 582 is information indicating the name of the operation log time-series data.

In the operation log data definition information 58 shown in FIG. 9, the name of the operation log time series data of the operation log time series data ID 581 "z ₁ " is "selected time series_trend", and the operation log time series data ID 581 "z". The name of the operation log time series data of " ₂ " is "selected time series_scattering diagram".

Further, in the operation log data definition information 58, the name of the operation log time series data of the operation log time series data ID 581 "z ₃ " is "display period", and the operation log of the operation log time series data ID 581 "z ₄ " is used. The name of the time series data is "selected time".

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The operation model storage unit 18 of the plant operation support system 2 stores the operation model 45 that recognizes the operation pattern of the operator U from the operation log data stored in the operation log data storage unit 17.

The operation model 45 includes operation pattern definition information that defines an operation pattern, operation purpose definition information that defines an operation purpose, display signal pattern definition information that defines a display signal pattern, and a display period pattern definition that defines a display period pattern. Including information.

FIG. 10 is a diagram schematically showing an example of operation pattern definition information included in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The operation pattern definition information 46 included in the operation model 45 shown in FIG. 10 includes an operation pattern ID 461, an operation purpose ID 462, a display signal pattern ID 463, a display period pattern ID 464, and a conditional probability 465.

The operation pattern ID 461 is identification information unique to each operation pattern. The operation purpose ID 462 is identification information unique to each operation purpose. The display signal pattern ID 463 is identification information unique to each combination of time-series data displayed on the driving support screen 61. The display period pattern ID 464 is identification information unique to each display period of the time-series data selected from the time-series data in the designated period.

The conditional probability 465 represents the probability of recognizing each operation pattern ID 461 when the display signal pattern ID 463 and the display period pattern ID 464 are recognized. In the example shown in FIG. 10, when the operation purpose of the operation purpose ID 462 “q ₁ ”, the display signal pattern of the display signal pattern ID 463 “r ₁ ”, and the display period pattern of the display period pattern ID 464 “s ₁ ” are recognized, The conditional probability 465 indicating the appropriateness of recognizing the operation pattern of the operation pattern ID 461 "b ₁ " is "P (b ₁ | q ₁ , r ₁ , s ₁ )".

Further, for example, when the operation purpose of the operation purpose ID 462 "q ₁ ", the display signal pattern of the display signal pattern ID 463 "r ₂ ", and the display period pattern of the display period pattern ID 464 "s ₂ " are recognized, the operation pattern ID 461 The conditional probability 465 indicating the appropriateness of recognizing the operation pattern of "b ₂ " is "P (b ₂ | q ₁ , r ₂ , s ₂ )".

FIG. 11 is a diagram schematically showing an example of operation purpose definition information in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The operation purpose definition information 47 shown in FIG. 11 includes an operation purpose ID 471, an operation purpose 472, and a determination formula 473 for each operation purpose.

The operation purpose ID 471 is identification information unique to each operation pattern, and is the same as the operation pattern ID 461 shown in FIG. The operation purpose 472 is information indicating the operation purpose, and is the same as the operation purpose ID 462 shown in FIG. The determination formula 473 is information on the determination formula for determining the operation purpose.

In the determination formula 473, "z ₁ ", "z ₂ ", "z ₃ ", and "z ₄ " are operation log data which are time series data of the operation data. Further, in the determination formula 473, "w ₁ ", "w ₂ ", "w ₃ ", "w ₄ ", ... Represent the conditions for determining the operation purpose, and "f ^q (・)". Is a function that calculates an index for determining the operation purpose from the operation log data.

For example, "w ₁ " is a condition that the time series data displayed on the driving support screen 61 has been changed 10 times or more in the past hour, and "w ₂ " is displayed on the driving support screen 61. It is a condition that the selection period range 613a to be changed is changed 30 times or more. Further, "w ₃ " is a condition that the number of times the attention time is changed by operating the attention time selection bar 614a or the period selection bar 613b is 50 times or more, for example.

In the operation purpose definition information 47 shown in FIG. 11, the determination formula 473 of the operation purpose 472 of the “setting value change amount confirmation” in which the operation purpose ID 471 is “q ₁ ” is “f ^q (z ₁ , z ₂ , z ₃ ”. , Z ₄ ) ≡ {w ₁ , w ₂ } ”, and the determination formula 473 of the operation purpose 472 of“ abnormality factor identification ”where the operation purpose ID 471 is“ q ₂ ”is“ f ^q (z ₁ , z ₂ , Z ₃ , z ₄ ) ≡ {w ₃ , w ₄ , w ₅ } ”.

Further, in the operation purpose definition information 47 shown in FIG. 11, the determination formula 473 of the operation purpose 472 of the “effect propagation confirmation” in which the operation purpose ID 471 is “q ₄ ” is “f ^q (z ₁ , z ₂ , z ₃ ”. , Z ₄ ) ≡ {w ₆ } ”, and the determination formula 473 of the operation purpose 472 of the“ increase / decrease tendency confirmation ”where the operation purpose ID 471 is“ q ₅ ”is“ f ^q (z ₁ , z ₂ , z ₃ , Z ₄ ) ≡ {w ₆ , w ₇ } ”.

FIG. 12 is a diagram schematically showing an example of first definition information included in the display signal pattern definition information in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The first definition information 48a shown in FIG. 12 includes a display signal pattern ID 481 and a determination formula 482 for each display signal pattern.

The display signal pattern ID 481 is identification information unique to each combination of time-series data displayed on the driving support screen 61, and is the same as the display signal pattern ID 463 shown in FIG. The determination formula 482 is information of the determination formula for determining the display signal pattern.

In the first definition information 48a shown in FIG. 12, the display signal pattern determination formula 482 in which the display signal pattern ID 481 is “r ₁ ” is “r ₁ ^t ∧ r ₁ ^s ”, and the display signal pattern ID 481 is “r 1”. The determination formula 482 of the display signal pattern of " ₂ " is "r ₁ ^t ∧ r ₂ ^s ".

FIG. 13 is a diagram schematically showing an example of the second definition information included in the display signal pattern definition information in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The second definition information 48b shown in FIG. 13 includes the display signal pattern ID 483 and the determination formula 484 for each display signal pattern of the trend graph.

The display signal pattern ID 483 is information indicating a first display signal pattern which is a pattern of a combination of time series data displayed in the trend graph display area 614 of the driving support screen 61 among the time series data in the designated period. The determination formula 484 is information of the determination formula for determining the first display signal pattern.

In the determination formula 484, "x ₁ ", "x ₂ ", "x ₃ ", ... Are time-series data IDs of time-series data displayed in the trend graph display area 614 of the driving support screen 61. Further, in the determination formula 484, "z ₁ " is the above-mentioned first operation log time series data.

Further, in the determination formula 484, " ^frt (・)" calculates the first display signal pattern which is a combination pattern of the time series data displayed in the trend graph display area 614 from the operation log data “z ₁ ”. It is a function to do.

In the second definition information 48b shown in FIG. 13, for example, the display signal pattern determination formula 484 in which the display signal pattern ID 483 is “r ₁ ^t ” is “f ^rt (z ₁ ) ≡ {x ₁ , x ₂ , x”. ₃ } ”, and the display signal pattern determination formula 484 in which the display signal pattern ID 483 is“ r ₂ ^t ”is“ ^frt (z ₁ ) ≡ {x ₁ , x ₂ , x ₄ } ”.

FIG. 14 is a diagram schematically showing an example of the third definition information included in the display signal pattern definition information in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The third definition information 48c shown in FIG. 14 includes the display signal pattern ID 485 and the determination formula 486 for each display signal pattern in the scatter diagram.

The display signal pattern ID 485 is information indicating a second display signal pattern which is a combination pattern of the time series data displayed in the scatter diagram display area 615 of the driving support screen 61 among the time series data in the designated period. The determination formula 486 is information of the determination formula for determining the second display signal pattern.

In the determination formula 486 shown in FIG. 14, “z ₂ ” is the above-mentioned second operation log time series data. Further, in the determination formula 486 shown in FIG. 14, “ ^frs (・)” is the second display which is a pattern of the combination of time series data displayed in the scatter plot display area 615 from the operation log data “z ₂ ”. It is a function for calculating a signal pattern.

In the third definition information 48c shown in FIG. 14, for example, the display signal pattern determination formula 486 in which the display signal pattern ID 485 is “r ₁ ^s ” is “ ^frs (z ₂ ) ≡ {x ₁ , x ₂ }”. The display signal pattern determination formula 486 in which the display signal pattern ID 485 is “r ₂ ^s ” is “ ^frs (z ₂ ) ≡ {x ₁ , x ₃ }”.

FIG. 15 is a diagram schematically showing an example of display period pattern definition information in the operation model stored in the operation model storage unit of the plant operation support system according to the first embodiment. The display period pattern definition information 49 shown in FIG. 15 includes a display period pattern ID 491 and a determination formula 492 for each display period pattern.

The display period pattern ID 491 is information indicating a combination pattern of time-series data displayed on the driving support screen 61, and is the same as the display period pattern ID 464 shown in FIG. The determination formula 492 is information on the determination formula for determining the display period pattern.

In the determination formula 492 shown in FIG. 15, “z ₃ ” is the above-mentioned third operation log log data, and “l ₁ ”, “l ₂ ”, “l ₃ ”, ... Indicates a display period. Further, in the determination formula 492 shown in FIG. 15, “ ^fs (・)” is selected from the operation log time-series data, which is the time-series operation data whose operation log data ID is “z ₃ ”, during the designated period. It is a function for calculating the display period of the trend graph and the scatter chart that had been used.

In the display period pattern definition information 49 shown in FIG. 15, for example, the display period pattern determination formula 492 in which the display period pattern ID 491 is “ ^s ₁ ” is “fs (z ₃ ) ≡ l ₁ ”, and the display period. The determination formula 492 of the display period pattern in which the pattern ID 491 is “ ^s ₂ ” is “fs (z ₃ ) ≡ l ₂ ”.

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The operation pattern recognition unit 19 of the plant operation support system 2 is an operator based on the operation log time series data stored in the operation log data storage unit 17 and the operation model 45 stored in the operation model storage unit 18. Recognize the operation pattern of U.

Specifically, the operation pattern recognition unit 19 determines the operation purpose based on the operation log data stored in the operation log data storage unit 17 and the operation purpose definition information 47 stored in the operation model storage unit 18. judge. Further, the operation pattern recognition unit 19 determines the display signal pattern based on the operation log data stored in the operation log data storage unit 17 and the display signal pattern definition information stored in the operation model storage unit 18. ..

Further, the operation pattern recognition unit 19 determines the display period pattern based on the operation log data stored in the operation log data storage unit 17 and the display period pattern definition information 49 stored in the operation model storage unit 18. do. Then, the operation pattern recognition unit 19 determines the operation purpose, the display signal pattern, and the display period pattern, and the operation pattern of the operator U based on the operation pattern definition information 46 stored in the operation model storage unit 18. Recognize.

FIG. 16 is a diagram schematically showing an example of an operator's operation pattern recognized by the operation pattern recognition unit according to the first embodiment. In the example shown in FIG. 16, the operation pattern of the operator U recognized by the operation pattern recognition unit 19 is the operation pattern of the operation pattern ID “b ₁ ”, the operation pattern of the operation pattern ID “b ₂ ”, and the operation pattern ID “ It is shown that the order is the operation pattern of "b ₆ " and the operation pattern of the operation pattern ID "b ₇ ". The operation pattern recognition unit 19 can also recognize a plurality of operation patterns at the same time.

In the above example, the operation pattern recognition unit 19 selects the type of time series data included in the trend graph displayed on the driving support screen 61, the trend graph display period, the scatter plot display period, and the driving support screen 61. The operation pattern of the operator U is recognized based on the attention time and the like, but the present invention is not limited to this example.

For example, the operation pattern recognition unit 19 may add information on various screen operations such as operation guidance, setting value guidance, operation summary, similar pattern search, or addition of abnormality detection to the operator U. The operation pattern can be recognized.

In this case, the operation pattern definition information of the operation model 45 includes the display pattern of the operation guidance, the display pattern of the set value guidance, and the summary of the operation, in addition to the presentation information such as the operation purpose, the display signal pattern, and the display period pattern. It also includes presentation information such as display patterns, search patterns for similar pattern searches, and additional patterns for anomaly detection. The pattern added in this way is defined by the definition information in the operation model 45.

Returning to FIG. 1, the description of the configuration of the plant operation support system 2 will be continued. The evaluation unit 20 of the plant operation support system 2 recognizes the event recognition accuracy and the operation pattern recognition by the event recognition unit 13 based on the event recognized by the event recognition unit 13 and the operation pattern recognized by the operation pattern recognition unit 19. The recognition accuracy of the operation pattern by the unit 19 is evaluated.

For example, the evaluation unit 20 evaluates the event recognition accuracy by the event recognition unit 13 and the operation pattern recognition accuracy by the operation pattern recognition unit 19 by using the evaluation information. FIG. 17 is a diagram showing an example of evaluation information used by the evaluation unit according to the first embodiment.

The evaluation information 50 shown in FIG. 17 includes an event ID 501, an operation pattern ID 502, an event occurrence frequency 503, an operation pattern occurrence frequency 504, and an event and operation pattern occurrence frequency 505. The event ID 501 is identification information unique to each event, and is the same as the event ID 411 shown in FIG. The operation pattern ID 502 is identification information unique to each operation pattern, and is the same as the operation pattern ID 461 shown in FIG.

The event occurrence frequency 503 is information indicating the frequency at which the event occurs, and the evaluation information 50 includes the occurrence frequency 503 of each event. The event occurrence frequency 503 represents the length of time when the event that occurred in the plant 4 is recognized. For example, the frequency at which the event whose event ID is "a ₁ " occurs is "g (a ₁ )". It is expressed as.

The operation pattern occurrence frequency 504 is information indicating the frequency at which the operation pattern occurs, and the evaluation information 50 includes the occurrence frequency 504 of each operation pattern. The frequency of occurrence of the operation pattern 504 represents the length of time when it is recognized that a certain operation pattern has occurred. For example, the frequency of occurrence of the operation pattern whose operation pattern ID is "b ₁ " is "g (". b ₁ ) ”.

The occurrence frequency 505 of the event and the operation pattern is information indicating the frequency at which a certain event and a certain operation pattern occur at the same time, and the evaluation information 50 includes the occurrence frequency 505 for each combination of the event and the operation pattern. Is done. The frequency with which the event ID 501 is "a ₁ " and the operation pattern ID 502 is "b ₁ " at the same time is expressed as "g (a ₁ , b ₁ )".

The evaluation unit 20 calculates the conditional entropy based on the occurrence frequency 505 of the event and the operation pattern, and determines the reliability of the event recognition and the reliability of the operation pattern recognition based on the calculated conditional entropy. .. The reliability of event recognition is an example of the recognition accuracy of an event, and the reliability of operation pattern recognition is an example of the recognition accuracy of an operation pattern.

The evaluation unit 20 calculates the conditional entropy Ha for the event in which the event ID 501 is " _ak ". Here, assuming that the number of events indicated by the evaluation information 50 is "M", "k" is a value from "1" to "M". The conditional entropy Ha is represented by, for example, the following equation (1).

Then, the evaluation unit 20 determines the reliability of the event whose event ID 501 is " _ak " depending on whether the calculated conditional entropy Ha is equal to or less than the threshold value H _TH .

In the above equation ( ₁ ), P (bl | a _k ) is a conditional probability, and is represented by, for example, the following equation (2). In the following equation (2), "M" is the number of events represented by the evaluation information 50, and "N" is the number of operation patterns represented by the evaluation information 50.

When the conditional entropy Ha is equal to or higher than the threshold value _HTH , the evaluation unit 20 has a large uncertainty in the operation pattern even if an event whose event ID 501 is “ _ak ” is determined, and the evaluation unit 20 has a reliability that uniquely determines the operation pattern. Is low, so it is determined that the reliability of recognition of the event whose event ID 501 is " _ak " is low. Further, when the conditional entropy Ha is less than the threshold value _HTH , the evaluation unit 20 determines that the reliability of recognition of the event whose event ID 501 is " _ak " is high.

Further, the evaluation unit 20 calculates the conditional entropy Hb related to the operation pattern in which the operation pattern ID 502 is “ _bl ”. Assuming that the number of events M indicated by the evaluation information 50 and the number of operation patterns indicated by the evaluation information 50 are "N", "l" is a value from "1" to "N".

Then, the evaluation unit 20 determines the reliability of the operation pattern in which the operation pattern ID 502 is " _bl " depending on whether the calculated conditional entropy Hb is equal to or less than the threshold value H _TH . The conditional entropy Hb is represented by, for example, the following equation (3).

In the above equation (3), P ( _ak | _bl ) is a conditional probability, and is represented by, for example, the following equation (4).

When the conditional entropy Hb is equal to or higher than the threshold value _HTH , the evaluation unit 20 has a high degree of uncertainty of the event even if the operation pattern in which the operation pattern ID 502 is " _bl " is determined, and the reliability for uniquely determining the event. Is low, so it is determined that the reliability of recognition of the operation pattern whose operation pattern ID 502 is " _bl " is low. Further, when the conditional entropy Hb is less than the threshold value _HTH , the evaluation unit 20 determines that the recognition reliability of the operation pattern in which the operation pattern ID 502 is " _bl " is high.

The evaluation unit 20 performs the above-mentioned calculation for all events and operation patterns, and determines an event with low recognition reliability and an operation pattern with low recognition reliability.

The update unit 21 determines whether to update at least one of the event recognition model 40 and the operation model 45 based on the evaluation result by the evaluation unit 20. When the update unit 21 determines that the event recognition model 40 is to be updated, the update unit 21 uses the monitoring control data stored in the monitoring control data storage unit 11, for example, to convert an event with low recognition reliability into two or more events. The event recognition model 40 is updated by dividing and creating an event name and a determination formula for two or more divided events.

Here, the update process of the event recognition model 40 and the operation model 45 by the update unit 21 when the evaluation unit 20 determines that the reliability of the recognition of the event whose event ID 501 is “a ₆ ” is low will be described.

FIG. 18 is an example of the logical formula information included in the event recognition model updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low. It is a figure which shows schematically.

When the update unit 21 determines that it is better to divide the event whose event ID 411 is "a ₆ " into two events, the update unit 21 reassigns the event ID 411 of the event after the event ID 411 is "a ₇ ", and the event ID 411 sets the event ID 411. The event that is "a ₆ " is divided into two events. Then, the update unit 21 assigns "a ₆ " as the event ID 411 to one of the two divided events, and assigns "a ₇ " as the event ID 411 to the other event. Further, the update unit 21 assigns the event name 412 to the two divided events, and assigns the determination formula 413 according to the content of the division to the two divided events.

In the logical formula information 41 shown in FIG. 18, the event in which the event ID 411 is “a ₆ ” is divided into an event in which the event ID 411 is “a ₆ ” and an event in which the event ID 411 is “a ₇ ”. Is shown. Further, "rainy day peak correspondence-1" is assigned as the event name 412 to the event whose event ID 411 is "a ₆ ", and "rainy day peak correspondence-1" is assigned as the event name 412 to the event whose event ID 411 is "a ₇ ". Correspondence-2 "is assigned.

Further, in the logical formula information 41 shown in FIG. 18, "p ₃ ∧ p ₅ ∧ p ₇ ∧ p ₈ ∧ p ₁₀ " is assigned as the determination formula 413 to the event whose event ID 411 is "a ₆ ", and the event ID 411 is assigned. "P ₄ ∧ p ₅ ∧ p ₇ ∧ p ₈ ∧ p ₁₀ " is assigned as the judgment formula 413 to the event of "a ₇ ".

FIG. 19 is an example of propositional logic definition information included in the event recognition model updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low. Is a diagram schematically showing.

When the event is divided by making the classification of the pump flow rate finer, the update unit 21 sets, for example, the propositional logic ID 421 of the propositional logic after the propositional logic ID 421 is "p ₄ " in the propositional logic definition information 42. Change to the propositional logic ID 421 after " ₅ ". Further, the update unit 21 has a propositional logic determination formula 425 in which the propositional logic ID 421 is "p ₃ ", a propositional logic determination formula 425 in which the propositional logic ID 421 is "p ₃ ", and a propositional calculus ID 421 "p ₄ ". Replace with the logic judgment formula 425.

In the propositional logic definition information 42 shown in FIG. 19, the newly replaced propositional logic ID 421 “p ₃ ” propositional logic determination formula 425 is “y ₁ ² <x ₁ ≦ y ₁ ³ ” and is newly replaced. The determination formula 425 of the propositional logic of the obtained propositional logic ID 421 "p ₄ " is "y ₁ ³ <x ₁ ".

When updating the logical expression information 41 and the propositional logic definition information 42, the update unit 21 divides the data to be divided by a clustering algorithm such as k-means, and creates a temporary evaluation table based on the new result of the division. It is created and divided and evaluated repeatedly until the conditional entropy is less than the threshold _HTH . When clustering is performed, the updating unit 21 randomly determines an initial value or the like so that the clustering result is different each time.

Further, the updating unit 21 updates the driving support information creation models 43 and 44 when the evaluation unit 20 divides an event having a low recognition reliability into two or more events. FIG. 20 is an example of an operation support information creation model for a trend graph updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low. It is a figure which shows schematically.

As shown in FIG. 20, the update unit 21 has a conditional probability 433 and a conditional probability of the display time series data unit for a new event in which the event ID 431 is “a ₆ ” and a new event in which the event ID 431 is “a ₇ ”. Recalculate the conditional probability 434 for each display period. Such recalculation is performed based on the frequency of occurrence of events, the frequency of display of time series data, and the frequency of display of display periods. The update unit 21 updates the driving support information creation model 43 based on the recalculated conditional probabilities 433 and 434.

FIG. 21 is an example of an operation support information creation model for a scatter plot updated by the update unit when the evaluation unit of the plant operation support system according to the first embodiment determines that the reliability of event recognition is low. It is a figure which shows schematically.

As shown in FIG. 21, the update unit 21 has a conditional probability of a combination unit of display time series data for a new event in which the event ID 431 is “a ₆ ” and a new event in which the event ID 431 is “a ₇ ”. Recalculate 443 and conditional probabilities 444 for display period units. Such recalculation is performed based on the frequency of occurrence of events, the frequency of display of time series data, and the frequency of display of display periods. The update unit 21 updates the driving support information creation model 44 based on the recalculated conditional probabilities 443 and 444.

Further, the update unit 21 divides an operation pattern having a low reliability of the operation pattern into two or more operation patterns, and creates an operation pattern name and a determination formula of the divided two or more operation patterns to create an operation model. Update 45.

Even when the evaluation unit 20 determines that the reliability of the operation pattern is low, the update unit 21 determines that the reliability is low by the same processing as when the evaluation unit 20 determines that the reliability of the event is low. The definition of an appropriate operation pattern is calculated by repeating clustering and evaluation of the clustering result for the data related to the operation pattern.

Further, the update unit 21 newly defines an operation pattern based on the operation log data stored in the operation log data storage unit 17, and the first definition information 48a, the second definition information 48b, and the third definition information. Update at least one of 48c.

Further, the update unit 21 calculates the occurrence frequency of the newly defined operation pattern, and updates the conditional probability 465 in the operation pattern definition information 46 included in the operation model 45 based on the calculated operation pattern occurrence frequency. ..

In this way, the plant operation support system 2 updates the event recognition model 40, the operation model 45, and the operation support information creation models 43 and 44 when it is determined that the accuracy of the event recognition and the operation pattern recognition is insufficient. Can be done. As a result, the plant operation support system 2 can display the operation support screen 61 based on appropriate operation support information on the display unit 30 even when an event unexpected in advance occurs.

In the above-mentioned example, the update unit 21 updates the logical expression information 41 and the propositional logical definition information 42 by a method of dividing the event, but the present invention is not limited to this example, and the logic is not limited to the above example. The formula information 41 and the propositional logic definition information 42 can also be updated. Further, the updating unit 21 updates the logical expression information 41 or the propositional logic definition information 42 by changing the propositional logic included in the determination formula 425 for determining an event or shifting the threshold value between a plurality of propositional logics. You can also do it.

Subsequently, the processing by the processing unit 22 of the plant operation support system 2 will be described using a flowchart. FIG. 22 is a flowchart showing an example of processing by the processing unit of the plant operation support system according to the first embodiment.

As shown in FIG. 22, the processing unit 22 of the plant operation support system 2 determines whether or not the monitoring control data has been acquired from the monitoring control system 1 (step S10). When the processing unit 22 determines that the monitoring control data has been acquired (step S10: Yes), the processing unit 22 stores the acquired monitoring control data in the monitoring control data storage unit 11 (step S11).

Next, the processing unit 22 recognizes an event occurring in the plant 4 based on the monitoring control data stored in the monitoring control data storage unit 11 (step S12). Then, the processing unit 22 creates driving support information based on the event recognized in step S12 and the driving support information creation models 43 and 44 (step S13), and the driving support information is based on the created driving support information. The driving support screen 61, which is the screen of the above, is displayed on the display unit 30 (step S14).

When the processing of step S14 is completed, or when it is determined that the monitoring control data has not been acquired (step S10: No), the processing unit 22 determines whether or not the operation data indicating the operation of the operator U has been acquired. Determination (step S15). When the processing unit 22 determines that the operation data has been acquired (step S15: Yes), the processing unit 22 stores the acquired operation data in the operation log data storage unit 17 (step S16).

When the processing of step S16 is completed, or when it is determined that the operation data has not been acquired (step S15: No), the processing unit 22 determines whether or not the evaluation timing has been reached (step S17). The evaluation timing is, for example, a timing that arrives at a preset cycle. When it is determined that the evaluation timing has come (step S17: Yes), the processing unit 22 performs the evaluation update process (step S18). Step S18 is the process of steps S20 to S27 shown in FIG. 23, which will be described in detail later.

When the processing of step S18 is completed, or when it is determined that the evaluation timing has not been reached (step S17: No), the processing unit 22 determines whether or not the operation end timing has been reached (step S19). For example, when the processing unit 22 determines that the power supply (not shown) of the plant operation support system 2 has been turned off, or when it is determined that the operation end operation to the input unit 31 has been performed, it is said that the operation end timing has come. judge.

When the processing unit 22 determines that the operation end timing has not been reached (step S19: No), the process proceeds to step S10, and when it is determined that the operation end timing has been reached (step S19: Yes), the figure. The process shown in 22 is terminated.

FIG. 23 is a flowchart showing an example of the evaluation update process by the processing unit of the plant operation support system according to the first embodiment. As shown in FIG. 23, the processing unit 22 determines the recognition accuracy of the event recognition model 40 (step S20). Further, the processing unit 22 determines the recognition accuracy of the operation model 45 (step S21).

Next, the processing unit 22 determines whether or not the recognition accuracy of the event recognition model 40 is low (step S22). When the processing unit 22 determines that the recognition accuracy of the event recognition model 40 is low (step S22: Yes), the processing unit 22 determines whether or not the recognition accuracy of the operation model 45 is low (step S23).

When the processing unit 22 determines that the recognition accuracy of the operation model 45 is low (step S23: Yes), the processing unit 22 updates the event recognition model 40, the operation model 45, and the driving support information creation models 43 and 44 (step S24). Further, when the processing unit 22 determines that the recognition accuracy of the operation model 45 is not low (step S23: No), the processing unit 22 updates the event recognition model 40 and the driving support information creation models 43 and 44 (step S25).

When the processing unit 22 determines that the recognition accuracy of the event recognition model 40 is not low (step S22: No), the processing unit 22 determines whether or not the recognition accuracy of the operation model 45 is low (step S26). When the processing unit 22 determines that the recognition accuracy of the operation model 45 is low (step S26: Yes), the processing unit 22 updates the operation model 45 (step S27).

When the processing unit 22 determines that the processing of step S24 is completed, the processing of step S25 is completed, the processing of step S27 is completed, or the recognition accuracy of the operation model 45 is not low (step S26: No), the process of FIG. 23 is terminated.

FIG. 24 is a diagram showing an example of the hardware configuration of the plant operation support system according to the first embodiment. As shown in FIG. 24, the plant operation support system 2 includes a computer including a processor 101, a memory 102, a communication device 103, a display device 104, an input device 105, and a bus 106.

The processor 101, the memory 102, the communication device 103, the display device 104, and the input device 105 can send and receive information to and from each other by the bus 106. The monitoring control data storage unit 11, the event recognition model storage unit 12, the driving support information creation model storage unit 14, the operation log data storage unit 17, and the operation model storage unit 18 are realized by the memory 102. The processor 101 executes the function of the processing unit 22 by reading and executing the program stored in the memory 102. The processor 101 is, for example, an example of a processing circuit, and includes one or more of a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and a system LSI (Large Scale Integration).

The memory 102 includes one or more of RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (registered trademark) (Electrically Erasable Programmable Read Only Memory). include. Further, the memory 102 includes a recording medium in which a computer-readable program is recorded. Such recording media include one or more of non-volatile or volatile semiconductor memories, magnetic disks, flexible memories, optical discs, compact discs, and DVDs (Digital Versatile Discs). The processing unit 22 of the plant operation support system 2 may include integrated circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array).

The plant operation support system 2 may be composed of a server device or a client device and a server device. When the plant operation support system 2 is composed of two or more devices, each of the two or more devices has, for example, the hardware configuration shown in FIG. 24. Communication between two or more devices is performed via the communication device 103. Further, the plant operation support system 2 may be composed of two or more server devices. For example, the plant operation support system 2 may be composed of a processing server and a data server.

As described above, the plant operation support system 2 according to the first embodiment is a plant operation support system that supports the operation by the operator U of the plant 4, which is monitored and controlled by the monitoring and control system 1, and is monitored and controlled. It includes a data acquisition unit 10, an event recognition unit 13, a driving support information creation unit 15, an operation pattern recognition unit 19, an evaluation unit 20, and an update unit 21. The monitoring control data acquisition unit 10 acquires monitoring control data including time-series measurement data output by the measuring equipment installed in the plant 4. The event recognition unit 13 recognizes an event that has occurred in the plant 4 based on the event recognition model 40 that recognizes the event that has occurred in the plant from the monitoring control data and the monitoring control data. The operation support information creation unit 15 is based on the operation support information creation models 43 and 44 for determining the operation support information which is the information for supporting the operation from the event generated in the plant 4 and the event recognized by the event recognition unit 13. , The driving support information is created, and the driving support screen 61, which is the screen of the created driving support information, is displayed on the display unit 30. The operation pattern recognition unit 19 recognizes the operation pattern based on the operation model 45 that recognizes the operation pattern of the operator U from the operation log data including the operation history of the operation support screen 61 by the operator U and the operation log data. .. The evaluation unit 20 determines the event recognition accuracy by the event recognition unit 13 and the operation pattern by the operation pattern recognition unit 19 based on the event recognized by the event recognition unit 13 and the operation pattern recognized by the operation pattern recognition unit 19. Evaluate with recognition accuracy. The update unit 21 updates the event recognition model 40 based on the evaluation result by the evaluation unit 20. Thereby, the plant operation support system 2 can provide operation support information for supporting the operation of the plant with respect to an event not defined at the time of system introduction.

Further, the updating unit 21 updates the operation model 45 based on the evaluation result by the evaluation unit 20. As a result, the plant operation support system 2 can more accurately provide operation support information for supporting the operation of the plant with respect to an event that is not defined at the time of system introduction.

Further, the updating unit 21 updates the driving support information creation models 43 and 44 based on the evaluation result by the evaluation unit 20. As a result, the plant operation support system 2 can more accurately provide operation support information for supporting the operation of the plant with respect to an event that is not defined at the time of system introduction.

Embodiment 2.
The processing system according to the second embodiment can display the event name specified by the operator when the operator specifies the event name using the input / output device and recognizes the same event at a subsequent timing. In that respect, it differs from the processing system 100 according to the first embodiment. In the following, the components having the same functions as those of the first embodiment are designated by the same reference numerals and the description thereof will be omitted, and the differences from the processing system 100 of the first embodiment will be mainly described.

FIG. 25 is a diagram showing an example of the configuration of the processing system according to the second embodiment. As shown in FIG. 25, the processing system 100A according to the second embodiment is different from the processing system 100 in that the plant operation support system 2A is provided in place of the plant operation support system 2. The plant operation support system 2A is different from the plant operation support system 2 in that the processing unit 22A is provided in place of the processing unit 22, and further, the event name editing information storage unit 23 is provided.

The processing unit 22A differs from the processing unit 22 in that it includes a driving support information creating unit 15A in place of the driving support information creating unit 15 and further includes an event name editing unit 24. The driving support information creating unit 15A displays the driving support screen 61A, which allows the event name to be specified by the operation of the operator U to the input unit 31, on the display unit 30 instead of the driving support screen 61. It is different from the information creation unit 15.

FIG. 26 is a diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the second embodiment and displayed on the display unit. As shown in FIG. 26, the driving support screen 61A displayed on the display unit 30 by the driving support information creating unit 15A includes a message display area 612A instead of the message display area 612, and the driving support shown in FIG. 8 is included. It is different from the screen 61.

In the message display area 612A, in addition to the event occurrence time and the event ID, the event name display frame 617 on which the event name is displayed is displayed. In the example shown in FIG. 26, the event name of the event whose event ID is "a ₇ " is displayed as "Rainy day peak correspondence-2" in the event name display frame 617. The operator U can edit the event name displayed in the event name display frame 617 by operating the input unit 31.

FIG. 27 is a diagram schematically showing another example of the operation support screen created by the operation support information creation unit of the plant operation support system according to the second embodiment and displayed on the display unit. In the message display area 612A shown in FIG. 27, the event name of the event whose event ID is “a ₇ ” in the event name display frame 617 is changed to “typhoon response” by the operation of the operator U to the input unit 31. ing. The event name designated by the operator U is stored in the event name editing information storage unit 23 shown in FIG.

FIG. 28 is a diagram schematically showing an example of event name editing information stored in the event name editing information storage unit of the plant operation support system according to the second embodiment. The event name editing information 51 shown in FIG. 28 includes an event ID 511, an event name 512, and an event name 513 designated by the operator U.

The event ID 511 is identification information unique to each event, and is the same as the event ID 411 included in the logical expression information 41. The event name 512 is information indicating the name of the event, and is the same as the event name 412 included in the logical expression information 41. The event name 513 is information indicating the event name designated by the operator U.

In the driving support information creation models 43 and 44, the event name editing unit 24 shown in FIG. 25 changes the event name 432 and 442 of the event having the event name 513 specified by the operator U in the event name editing information 51. For example, when the driving support information creation models 43 and 44 are in the states shown in FIGS. 20 and 21, the event name of the event in which the event IDs 431 and 441 are "a ₇ " is designated by the operator U as "typhoon response". If so, the event name editorial unit 24 changes the event name of the event in which the event IDs 431 and 441 in the driving support information creation models 43 and 44 are "a ₇ " from "rainy day peak response-2" to "typhoon response". change.

As a result, when the operator U specifies the event name using the input / output device 3, the operation support information creation unit 15A displays the event name specified by the operator U when the same event is recognized at a subsequent timing. can do. Therefore, the plant operation support system 2A recognizes a new event and names the event according to a predetermined rule even if the event name is different from the event name preferred by the operator U. The event name preferred by the operator U can be displayed on the display unit 30.

The hardware configuration example of the plant operation support system 2A according to the second embodiment is the same as the hardware configuration of the plant operation support system 2 shown in FIG. 24. The processor 101 can execute the function of the processing unit 22A by reading and executing the program stored in the memory 102.

As described above, the plant operation support system 2A according to the second embodiment includes an event name editing unit 24 that changes the event name of the event recognized by the event recognition unit 13 to the event name designated by the operator U. As a result, the plant operation support system 2A can display the event name preferred by the operator U on the display unit 30.

Embodiment 3.
The processing system according to the third embodiment is different from the processing system 100 according to the first embodiment in that the evaluation criteria regarding the fineness of event recognition can be changed. In the following, the components having the same functions as those of the first embodiment are designated by the same reference numerals and the description thereof will be omitted, and the differences from the processing system 100 of the first embodiment will be mainly described.

FIG. 29 is a diagram showing an example of the configuration of the processing system according to the third embodiment. As shown in FIG. 29, the processing system 100B according to the third embodiment is different from the processing system 100 in that the plant operation support system 2B is provided in place of the plant operation support system 2. The plant operation support system 2B differs from the plant operation support system 2 in that the processing unit 22B is provided in place of the processing unit 22.

The processing unit 22B is different from the processing unit 22 in that the evaluation standard changing unit 25 is further provided. When the evaluation standard changing unit 25 receives a request for changing the threshold value _HTH , which is an evaluation standard for the fineness of event recognition by the evaluation unit 20 by the operation of the operator U to the input unit 31, the value included in the change request. The threshold value _HTH of the evaluation unit 20 is changed to.

When the threshold value _HTH is fixed, it is conceivable that the determined event may be classified too coarsely or finely as compared with the event recognition requested by the operator U, but in the plant operation support system 2B, it may be classified too finely. , The threshold value _HTH , which is an evaluation criterion for the fineness of event recognition, can be changed by the operator U. Therefore, the plant operation support system 2B can make the event classification finer according to the event recognition requested by the operator U.

The request for changing the threshold value H _TH is not limited to the one including the threshold value H _TH itself, and may include information indicating a method for changing the threshold value H _TH . The request for changing the threshold value H _TH may include information that can specify whether to increase the threshold value H _TH or decrease the threshold value H _TH .

The hardware configuration example of the plant operation support system 2B according to the third embodiment is the same as the hardware configuration of the plant operation support system 2 shown in FIG. 24. The processor 101 can execute the function of the processing unit 22B by reading and executing the program stored in the memory 102.

Similar to the plant operation support system 2A, the plant operation support system 2B further includes an event name editing information storage unit 23 and an event name editing unit 24, and replaces the operation support information creation unit 15 with the operation support information creation unit 15A. It may be configured to include.

As described above, the plant operation support system 2B according to the third embodiment includes the evaluation standard changing unit 25 that changes the evaluation standard used for the evaluation of the recognition accuracy in the evaluation unit 20 based on the request by the operator U. As a result, the plant operation support system 2B can make the event classification finer according to the event recognition requested by the operator U.

Embodiment 4.
The processing system according to the fourth embodiment recognizes an operation pattern by using the operation data excluding the operation data of the set period among the operation data of the operation support screen by the operator as the operation log data. It is different from the processing system 100 according to 1. In the following, the components having the same functions as those of the first embodiment are designated by the same reference numerals and the description thereof will be omitted, and the differences from the processing system 100 of the first embodiment will be mainly described.

FIG. 30 is a diagram showing an example of the configuration of the processing system according to the fourth embodiment. As shown in FIG. 30, the processing system 100C according to the fourth embodiment is different from the processing system 100 in that the plant operation support system 2C is provided in place of the plant operation support system 2. The plant operation support system 2C differs from the plant operation support system 2 in that the processing unit 22C is provided in place of the processing unit 22.

The processing unit 22C differs from the processing unit 22 in that the processing unit 22C includes a driving support information creating unit 15C and an operation data acquisition unit 16C in place of the driving support information creating unit 15 and the operation data acquisition unit 16. The driving support information creating unit 15C replaces the driving support screen 61 in which the operation log data storage unit 17 can be set not to store the operation log data by operating the input unit 31 of the operator U. It differs from the driving support information creating unit 15 in that it is displayed on the display unit 30.

FIG. 31 is a diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the fourth embodiment and displayed on the display unit. As shown in FIG. 31, the driving support screen 61C displayed on the display unit 30 by the driving support information creating unit 15C is different from the driving support screen 61 shown in FIG. 8 in that it further has a learning mode selection area 618.

The learning mode selection area 618 is an area for selecting whether to turn the learning mode on or off, and the operator U operates the box shown in the learning mode selection area 618 by operating the input unit 31. This allows you to choose whether to turn the learning mode on or off.

The operation data acquisition unit 16C is data indicating an operation on the input unit 31 by the operator U from the time when the operator U selects to turn on the learning mode to the time when the operator U selects to turn off the learning mode. The operation data is acquired from the input / output device 3, and the acquired operation data is stored in the operation log data storage unit 17.

Further, the operation data acquisition unit 16C operates the input unit 31 by the operator U from the time when the operator U selects to turn off the learning mode to the time when the operator U selects to turn on the learning mode. The operation data, which is the indicated data, is not acquired from the input / output device 3.

In this way, the operation data acquisition unit 16C stores the operation data excluding the operation data for the period set by the operation of the operator U as the operation log data. Therefore, the operation pattern recognition unit 19 can recognize the operation pattern based on the operation log data excluding the operation data for the period set by the operation of the operator U.

Therefore, for example, when the operator U does not want to use the operation log data for a certain period for determining the accuracy of event recognition, the operator U does not use the operation log data for a certain period and causes the plant operation support system 2C to recognize the event. The accuracy can be determined. If you do not want to use the operation log data for a certain period to judge the accuracy of event recognition, for example, ask a new operator U who does not understand the plant 4 to understand the plant 4. This is a case where the plant operation support system 2C is temporarily operated for the purpose of education. In this case, the operation pattern may be different from the efficient operation pattern performed by the skilled operator U.

The hardware configuration example of the plant operation support system 2C according to the fourth embodiment is the same as the hardware configuration of the plant operation support system 2 shown in FIG. 24. The processor 101 can execute the function of the processing unit 22C by reading and executing the program stored in the memory 102.

Similar to the plant operation support system 2A, the plant operation support system 2C further includes an event name editing information storage unit 23 and an event name editing unit 24, and replaces the operation support information creation unit 15 with the operation support information creation unit 15A. It may be configured to include. Further, the plant operation support system 2C may be configured to include the evaluation standard changing unit 25, similarly to the plant operation support system 2B.

As described above, the plant operation support system 2C according to the fourth embodiment uses the operation history of the operation history of the operation support screen 61C by the operator U excluding the operation history of the set period as the operation log data. An operation data acquisition unit 16C to be stored in the data storage unit 17 is provided. The operation pattern recognition unit 19 recognizes an operation pattern based on the operation log data stored in the operation log data storage unit 17 and the operation model 45. Thereby, the plant operation support system 2C can recognize the operation pattern by using the operation log data excluding the operation history of the period that the operator U does not want to use for determining the accuracy of the event recognition.

Embodiment 5.
The processing system according to the fifth embodiment calculates the feature amount of the time-series measurement data specified by the operator, classifies the feature amount reflecting the calculated feature amount, and updates the event recognition model. It is different from the processing system 100 according to the first embodiment. In the following, the components having the same functions as those of the first embodiment are designated by the same reference numerals and the description thereof will be omitted, and the differences from the processing system 100 of the first embodiment will be mainly described.

FIG. 32 is a diagram showing an example of the configuration of the processing system according to the fifth embodiment. As shown in FIG. 32, the processing system 100D according to the fifth embodiment is different from the processing system 100 in that the plant operation support system 2D is provided in place of the plant operation support system 2. The plant operation support system 2D differs from the plant operation support system 2 in that the processing unit 22D is provided in place of the processing unit 22, and the selected feature amount information storage unit 26 and the feature amount template storage unit 27 are further provided.

The processing unit 22D differs from the processing unit 22 in that it includes a driving support information creating unit 15D and an updating unit 21D in place of the driving support information creating unit 15 and the updating unit 21, and further has a feature amount calculation unit 28. .. The driving support information creating unit 15D replaces the driving support screen 61 with a display unit 30 that can specify the operation log time-series data and its feature amount by operating the input unit 31 of the operator U. It is different from the driving support information creation unit 15 in that it is displayed on the screen.

FIG. 33 is a diagram schematically showing an example of an operation support screen created by the operation support information creation unit of the plant operation support system according to the fifth embodiment and displayed on the display unit. As shown in FIG. 33, the driving support screen 61D displayed on the display unit 30 by the driving support information creating unit 15D is different from the driving support screen 61 shown in FIG. 8 in that it further has a feature amount selection area 619.

The feature amount selection area 619 is an area for selecting a feature amount of time-series data which is time-series measurement data used for event classification processing by the update unit 21D, and is a selection box 619a for selecting time-series data. And a selection box 619b for selecting a feature amount.

The operator U can select the time series data in the selection box 619a by operating the input unit 31, and select the feature amount in the selection box 619b. In the selection box 619a, the time-series data selected in the time-series data selection area 611 is displayed so as to be selectable as a selection candidate, and the operator U is selected from the time-series data selected in the time-series data selection area 611. Select the feature amount Select the time series data.

The feature amount information of the time series data selected by the operator U is stored in the selected feature amount information storage unit 26. FIG. 34 is a diagram schematically showing an example of the selected feature amount information stored in the selected feature amount information storage unit of the driving support system according to the fifth embodiment. The selected feature amount information 52 shown in FIG. 34 includes the target time-series data ID 521, the target time-series data name 522, the unit 523, and the selected feature amount 524 for each target time-series data.

The target time-series data ID 521 is identification information unique to each target time-series data, which is time-series measurement data to be selected as a feature amount. The target time-series data name 522 is information indicating the name of the target time-series data. The unit 523 is information indicating a unit of the target time series data. The selected feature amount 524 is a feature amount selected by the operator U.

In the example shown in FIG. 34, the feature amount selected by the operator U as the feature amount of the target time-series data in which the target time-series data ID 521 is “x ₅ ” is “integral [1 day]”. "Integral [1 day]" is a feature quantity obtained by integration in units of 1 hour. When different feature quantities are selected for the same target time-series data, the selected feature quantity 1, the selected feature quantity 2, ... Are added in this order in the selected feature quantity information 52.

The feature amount calculation unit 28 acquires the time-series data selected by the operator U from the monitoring control data storage unit 11, and the feature amount of the time-series data selected by the operator U from the selected feature amount information storage unit 26. Get information. The feature amount calculation unit 28 calculates the feature amount selected by the operator U based on the acquired time-series data and the feature amount information. The driving support information creation unit 15D displays the time-series data selected by the operator U with the feature amount calculated by the feature amount calculation unit 28 in the period selection area 613, the trend graph display area 614, and the scatter plot display area 615. The displayed driving support screen 61D is displayed on the display unit 30.

The feature amount calculation unit 28 calculates the feature amount of the time series data by using the feature amount template stored in the feature amount template storage unit 27. FIG. 35 is a diagram schematically showing an example of a feature amount template stored in the feature amount template storage unit of the driving support system according to the fifth embodiment.

The feature amount template 53 shown in FIG. 35 includes a feature amount name 531 and a calculation formula 532 for each feature amount. The feature amount name 531 is information indicating the name of the feature amount. The calculation formula 532 is information on the calculation formula for calculating the feature amount. In the example shown in FIG. 35, the calculation formula 532 is a calculation formula when the measuring instrument provided in the plant 4 outputs measurement data in 1-minute units, and is a 1-minute unit difference calculation formula and 10-minute unit. Information such as a difference calculation formula, a 1-hour unit difference calculation formula, a 10-minute integration calculation formula, a 1-hour unit integration calculation formula, or a daily integration calculation formula.

When the update unit 21D updates the event recognition model 40, when the feature amount is calculated by the feature amount calculation unit 28 among the time-series data which is the time-series measurement data stored in the monitoring control data storage unit 11. For the series data, the classification criteria are recalculated using the feature amount calculated by the feature amount calculation unit 28 instead of the time-series measurement data stored in the monitoring control data storage unit 11, and the event recognition model. Update 40.

The operator U may recognize the event by using the feature amount of the time series data such as the slope, the maximum value, or the minimum value of the graph when the time series data is displayed on the driving support screen 61D. In such a case, the recalculation of the classification standard using only the time-series data stored in the monitoring control data storage unit 11 may not be the classification considering the feature amount taken into consideration by the operator U. be.

In the plant operation support system 2D according to the fifth embodiment, the operator U can select a feature amount as a way of viewing the time-series data, and the time-series data processed into the selected feature amount is used for recalculation of the classification standard. Therefore, it is possible to perform classification that reflects the feature amount that the operator U takes into consideration when confirming the time series data.

The example of the hardware configuration of the plant operation support system 2D according to the fifth embodiment is the same as the hardware configuration of the plant operation support system 2 shown in FIG. 24. The processor 101 can execute the function of the processing unit 22D by reading and executing the program stored in the memory 102.

Similar to the plant operation support system 2A, the plant operation support system 2D further includes an event name editing information storage unit 23 and an event name editing unit 24, and replaces the operation support information creation unit 15 with the operation support information creation unit 15A. It may be configured to include. Further, the plant operation support system 2D may be configured to include the evaluation standard changing unit 25, similarly to the plant operation support system 2B. Further, the plant operation support system 2D may be configured to include an operation data acquisition unit 16C instead of the operation data acquisition unit 16 as in the plant operation support system 2C.

As described above, the plant operation support system 2D according to the fifth embodiment includes a feature amount template storage unit 27, a selected feature amount information storage unit 26, and a feature amount calculation unit 28. The feature amount template storage unit 27 stores a feature amount template 53 including a plurality of calculation formulas for calculating different feature amounts from the time-series measurement data. The selected feature amount information storage unit 26 contains information on the time-series measurement data selected by the operator U among the time-series measurement data included in the monitoring control data, and the time-series measurement data selected by the operator U. The information of the feature amount selected by the operator U is stored as the feature amount of. The feature amount calculation unit 28 calculates the feature amount selected by the operator U in the time-series measurement data selected by the operator U using the calculation formula included in the feature amount template 53, and updates the calculated result. Output to unit 21D. As a result, the plant operation support system 2D can perform classification that reflects the feature amount that the operator U takes into consideration when confirming the time series data.

The configuration shown in the above embodiments is an example, and can be combined with another known technique, can be combined with each other, and does not deviate from the gist. It is also possible to omit or change a part of the configuration.

1 monitoring control system, 2,2A, 2B, 2C, 2D plant operation support system, 3 input / output device, 4 plants, 10 monitoring control data acquisition unit, 11 monitoring control data storage unit, 12 event recognition model storage unit, 13 event Recognition unit, 14 operation support information creation model storage unit, 15, 15A, 15C, 15D operation support information creation unit, 16, 16C operation data acquisition unit, 17 operation log data storage unit, 18 operation model storage unit, 19 operation pattern recognition Unit, 20 Evaluation unit, 21,21D update unit, 22, 22A, 22B, 22C, 22D processing unit, 23 event name editing information storage unit, 24 event name editing unit, 25 evaluation standard change unit, 26 selected feature quantity information storage Unit, 27 Feature quantity template storage unit, 28 Feature quantity calculation unit, 30 Display unit, 31 Input unit, 40 Event recognition model, 41 Logical expression information, 42 Proposition logic definition information, 43, 44 Driving support information creation model, 45 Operation Model, 46 operation pattern definition information, 47 operation purpose definition information, 48a first definition information, 48b second definition information, 48c third definition information, 49 display period pattern definition information, 50 evaluation information, 51 event name editing information, 52 Selected feature amount information, 53 Feature amount template, 58 Operation log data definition information, 60, 61, 61A, 61C, 61D Operation support screen, 100, 100A, 100B, 100C, 100D processing system.

Claims (9)

A plant operation support system that supports the operation of plant operators who are monitored and controlled by the monitoring and control system.
A monitoring control data acquisition unit that acquires monitoring control data including time-series measurement data output by the measuring equipment installed in the plant, and a monitoring control data acquisition unit.
An event recognition unit that recognizes an event that occurred in the plant based on the event recognition model that recognizes an event that occurred in the plant from the monitoring control data and the monitoring control data.
The operation support information is created based on the operation support information creation model for determining the operation support information which is the information for supporting the operation from the event generated in the plant and the event recognized by the event recognition unit. The driving support information creation unit that displays the created screen of the driving support information on the display unit,
An operation pattern recognition unit that recognizes the operation pattern based on the operation model that recognizes the operation pattern of the operator from the operation log data including the operation history of the screen by the operator and the operation log data.
Conditional entropy for each of the event and the operation pattern was calculated and calculated based on the frequency of occurrence for each combination of the event recognized by the event recognition unit and the operation pattern recognized by the operation pattern recognition unit. An evaluation unit that evaluates the event recognition accuracy by the event recognition unit and the recognition accuracy of the operation pattern by the operation pattern recognition unit based on the conditional entropy .
Based on the event recognition accuracy by the event recognition unit evaluated by the evaluation unit , the division of one event into a plurality of events and the integration of a plurality of events into one event in the event recognition model. A plant operation support system including an update unit that updates the event recognition model by performing at least one of them . The update part
The plant operation support system according to claim 1, wherein the operation model is updated based on the evaluation result by the evaluation unit. The update part
The plant operation support system according to claim 1 or 2, wherein the operation support information creation model is updated based on the evaluation result by the evaluation unit. The plant according to any one of claims 1 to 3, further comprising an event name editing unit that changes the event name of the event recognized by the event recognition unit to the event name designated by the operator. Driving support system. The plant according to any one of claims 1 to 4, wherein the evaluation unit includes an evaluation standard changing unit that changes the evaluation standard used for evaluating the recognition accuracy based on a request by the operator. Driving support system. It is provided with an operation data acquisition unit that stores the operation history of the operation history of the screen by the operator excluding the operation history of the set period as the operation log data in the operation log data storage unit.
The operation pattern recognition unit is
The plant operation support according to any one of claims 1 to 5, wherein the operation pattern is recognized based on the operation log data stored in the operation log data storage unit and the operation model. system. A feature template storage unit that stores feature templates containing multiple formulas that calculate different features from time-series measurement data, and a feature template storage unit.
Among the time-series measurement data included in the monitoring control data, the operator as the feature amount of the time-series measurement data information selected by the operator and the time-series measurement data selected by the operator. A selected feature amount information storage unit that stores information on the feature amount selected by
A feature that calculates the feature amount selected by the operator in the time-series measurement data selected by the operator using the calculation formula included in the feature amount template, and outputs the calculated result to the update unit. The plant operation support system according to any one of claims 1 to 6, further comprising a quantity calculation unit. It is a plant operation support method that supports the operation by the operator of the plant that is monitored and controlled by the monitoring and control system.
A monitoring control data acquisition step for acquiring monitoring control data including time-series measurement data output by the measuring equipment installed in the plant, and
An event recognition step for recognizing an event occurring in the plant based on the event recognition model for recognizing an event occurring in the plant from the monitoring control data and the monitoring control data, and an event recognition step for recognizing the event occurring in the plant.
The operation support information is created based on the operation support information creation model for determining the operation support information which is the information for supporting the operation from the event generated in the plant and the event recognized by the event recognition step. A driving support information creation step for displaying the created driving support information screen on the display unit, and
An operation pattern recognition step for recognizing the operation pattern based on the operation model for recognizing the operation pattern of the operator from the operation log data including the operation history of the screen by the operator and the operation log data.
Conditional entropy for each of the event and the operation pattern was calculated and calculated based on the frequency of occurrence of each combination of the event recognized by the event recognition step and the operation pattern recognized by the operation pattern recognition step. An evaluation step for evaluating the event recognition accuracy by the event recognition step and the recognition accuracy of the operation pattern by the operation pattern recognition step based on the conditional entropy, and an evaluation step.
Division of one event into multiple events and integration of multiple events into one event in the event recognition model based on the recognition accuracy of the event by the event recognition step evaluated by the evaluation step. A plant operation support method comprising an update step of updating the event recognition model by performing at least one of them . A monitoring control data acquisition step for acquiring monitoring control data including time-series measurement data output by measuring equipment installed in a plant that is monitored and controlled by the monitoring control system, and
An event recognition step for recognizing an event occurring in the plant based on the event recognition model for recognizing an event occurring in the plant from the monitoring control data and the monitoring control data, and an event recognition step for recognizing the event occurring in the plant.
The operation support is based on the operation support information creation model that determines the operation support information that is the information that supports the operation by the operator of the plant from the event that occurred in the plant, and the event recognized by the event recognition step. The driving support information creation step of creating information and displaying the created screen of the driving support information on the display unit, and
An operation pattern recognition step for recognizing the operation pattern based on the operation model for recognizing the operation pattern of the operator from the operation log data including the operation history of the screen by the operator and the operation log data.
Conditional entropy for each of the event and the operation pattern was calculated and calculated based on the frequency of occurrence of each combination of the event recognized by the event recognition step and the operation pattern recognized by the operation pattern recognition step. An evaluation step for evaluating the event recognition accuracy by the event recognition step and the recognition accuracy of the operation pattern by the operation pattern recognition step based on the conditional entropy, and an evaluation step.
Division of one event into multiple events and integration of multiple events into one event in the event recognition model based on the recognition accuracy of the event by the event recognition step evaluated by the evaluation step. A plant operation support program characterized by having a computer execute an update step of updating the event recognition model by performing at least one of them .

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