JP7559700B2 - Information processing device, work evaluation method, and work evaluation program - Google Patents

Description

The present invention relates to an information processing device, a task evaluation method, and a task evaluation program.

A variety of efforts are being made across industries and fields to prevent accidents involving workers performing dangerous tasks or monitoring large-scale systems, and to ensure the safe operation of the systems they are working on. For example, technology is known that collects and monitors the brainwave data of workers to detect abnormalities in the workers.

JP 2020-99019 A JP 2011-180873 A JP 2017-200632 A JP 2016-148882 A

However, the above technology cannot accommodate individual differences among workers or the sense of tension specific to plants, making it difficult to properly monitor both the safety aspects of preventing worker accidents and the safety aspects of operating the work targets safely and to support safe operations.

Therefore, in one aspect, the objective is to provide an information processing device, a work evaluation method, and a work evaluation program that can support the safe operation of a plant.

The information processing device according to one aspect has an identification unit that identifies the work to be performed by a worker in relation to plant operation, an acquisition unit that acquires electroencephalogram data of the worker performing the work, and an evaluation unit that evaluates the results of the work based on the work and the electroencephalogram data of the worker.

In one aspect of the work evaluation method, a computer executes a process to identify work to be performed by workers in relation to plant operations, acquire electroencephalogram data of the workers performing the work, and evaluate the results of the work based on the work and the electroencephalogram data of the workers.

In one aspect, the work evaluation program causes a computer to execute a process that identifies work to be performed by workers in relation to plant operations, acquires electroencephalogram data of the workers performing the work, and evaluates the results of the work based on the work and the electroencephalogram data of the workers.

According to one embodiment, it is possible to support safe operation of the plant.

1 is a diagram illustrating an example of the overall configuration of a system according to a first embodiment. 1 is a functional block diagram showing a functional configuration of an information processing device according to a first embodiment. FIG. 13 is a diagram illustrating an example of information stored in a work process DB. FIG. 13 is a diagram illustrating an example of information stored in a correspondence DB. FIG. 13 is a diagram for explaining an example in which a value is evaluated as normal. FIG. 13 is a diagram illustrating an example in which an abnormality is possibly evaluated. 1 is a flowchart showing a flow of an evaluation process performed by the information processing device according to the first embodiment. FIG. 11 is a diagram for explaining an evaluation index according to the second embodiment. FIG. 11 is a diagram for explaining an example of evaluation of a task according to the second embodiment. FIG. 11 is a functional block diagram showing a functional configuration of an information processing device according to a third embodiment. FIG. 13 is a diagram for explaining an example of evaluation of work using the state of a worker according to the third embodiment. FIG. 13 is a diagram illustrating task evaluation based on associations between multiple workers according to the fourth embodiment. FIG. 13 is a diagram illustrating task evaluation based on the relevance of simultaneous tasks according to the fourth embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration.

Below, embodiments of the information processing device, task evaluation method, and task evaluation program disclosed in the present application will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments. In addition, the same elements are given the same reference numerals, duplicated descriptions are omitted as appropriate, and the embodiments can be combined as appropriate within a range that does not cause inconsistencies.

[Embodiment 1]
(Overall configuration example)
Fig. 1 is a diagram showing an example of the overall configuration of a system according to embodiment 1. As shown in Fig. 1, this system supports safe operation of a plant by monitoring the work results of workers who work in a plant 1, and the plant 1, a monitoring room 2, a database 3, and an information processing device 10 are connected via the Internet, a dedicated line, or the like, regardless of whether they are wired or wireless.

Plant 1 is an example of various plants that use petroleum, petrochemicals, chemicals, gas, etc., and includes factories equipped with various facilities for obtaining products. Examples of products are LNG (liquefied natural gas), resins (plastics, nylon, etc.), chemical products, etc. Examples of facilities are factory facilities, machine facilities, production facilities, power generation facilities, storage facilities, facilities at wellheads for extracting petroleum, natural gas, etc., etc.

Plant 1 is constructed using distributed control systems (DCS) and the like. For example, although not shown in the figure, the control system in plant 1 uses process data used in plant 1 to execute various controls on control devices such as field devices installed in the equipment to be controlled and on operation devices corresponding to the equipment to be controlled.

In such a plant 1, workers grasp the operating trends of the plant 1 based on actual plant measurements such as temperature and pressure obtained from various sensors such as temperature sensors and flow meters installed in the plant 1, and operate control devices such as valves and heaters installed in the plant 1, thereby ensuring safe operation of the plant. Here, each worker is equipped with a measuring device that measures and transmits brainwave data.

The monitoring room 2 is equipped with a computer device that displays the state of the plant 1 in real time, and the operating state of the plant 1 is monitored by an operator or the like. For example, in the monitoring room 2, monitoring is performed based on process data that indicates the operating state of the equipment installed in the plant 1. Here, the process data includes measured values (Process Variables: PV), setting values (Setting Variables: SV), manipulated variables (Manipulated Variables: MV), etc. The process data also includes information on the type of measured value to be output (e.g., pressure, temperature, flow rate, etc.). The monitoring room 2 may be installed near the plant 1, or in a completely different location.

The database 3 stores the evaluation results of the worker's work evaluated by the information processing device 10. The database 3 may be installed as a database server near the plant 1, and is installed in a location accessible to the monitoring room 2 and the information processing device 10.

The information processing device 10 is an example of a computer that evaluates various tasks performed in the plant 1. Specifically, the information processing device 10 identifies tasks performed by workers in relation to the operation of the plant 1, acquires electroencephalogram data of the workers performing the tasks, and evaluates the results of the tasks based on the tasks and the electroencephalogram data of the workers.

For example, the information processing device 10 stores plant-specific tasks related to the operation of the plant 1 in association with electroencephalogram data that is expected to be generated when the tasks are performed normally. Here, the electroencephalogram data stored in advance indicates electroencephalogram data after normal operation, and may be, for example, electroencephalogram data measured in the past when the same task was performed normally, electroencephalogram data measured when the worker's condition was stable, or electroencephalogram data that is theoretically expected to be normal through simulation or the like.

In this state, the information processing device 10 identifies the work to be performed in the plant 1 and the worker who will perform that work from a work schedule or the like, and acquires the electroencephalogram data of the worker after the time for performing the work has elapsed. Then, if the degree of match or similarity between the electroencephalogram data acquired after the work and the electroencephalogram data that serves as the reference for determining whether the work has been performed properly is equal to or greater than an arbitrarily set threshold, the information processing device 10 evaluates that there is a high possibility that the work has been performed properly. The reference electroencephalogram data may be stored in advance, or may be input or set by a manager or the like each time a determination is made. It is to be noted that a "determination of whether or not the work is similar" based on "similarity" using a known method can also be adopted, without being limited to a comparison using thresholds.

On the other hand, if the degree of match or similarity between the electroencephalogram data acquired after the task and the reference electroencephalogram data is less than a threshold, the information processing device 10 evaluates that the task may have been performed abnormally. At this time, for example, the information processing device 10 provides feedback to the worker requesting that the task be reviewed, issues an alert to the monitoring room 2, or stores information in the database 3.

In this way, the information processing device 10 can evaluate the work of the worker based on the electroencephalogram data after the worker has performed the work. Therefore, the information processing device 10 can detect all work errors made by the worker, such as unconscious errors, operational mistakes believed to be normal, and assumptions due to familiarity, and can support the safe operation of the plant. Note that in this embodiment, an example is described in which electroencephalogram data itself is used, but this is not limited to this, and a power spectrum obtained from the analysis of electroencephalogram data can also be used, and electroencephalogram analysis results such as frequency intensity, ratio, time of occurrence of event-related potential, and amplitude can be used as examples of frequency analysis results.

(Functional configuration of information processing device 10)
Next, a description will be given of the functional configuration of the above-mentioned information processing device 10. Fig. 2 is a functional block diagram showing the functional configuration of the information processing device 10 according to the first embodiment. As shown in Fig. 2, the information processing device 10 includes a communication unit 11, a storage unit 12, and a control unit 20.

The communication unit 11 is a processing unit that controls communication with other devices, and is realized, for example, by a communication interface. For example, the communication unit 11 acquires brainwave data from a worker. The communication unit 11 also transmits various information to the worker, the monitoring room 2, the database 3, etc.

The memory unit 12 is an example of a storage device that stores various data and programs executed by the control unit 20, and is realized by, for example, a memory or a hard disk. The memory unit 12 stores the collection DB 13, the work process DB 14, and the correspondence DB 15.

Collection DB13 is a database that stores collected electroencephalogram data of workers. For example, collection DB13 stores the collection date and time and the collected electroencephalogram data for each worker in association with each other.

The work process DB14 is a database that stores work processes performed in the plant 1. FIG. 3 is a diagram showing an example of information stored in the work process DB14. As shown in FIG. 3, the work process DB14 stores "work time", "work process", and "worker" in association with each other. The "work time" stored here indicates the time when the work is performed. The "work process" indicates plant-specific work performed in the plant 1, such as various types of work related to plant operation, such as operating various devices, pressing switches, monitoring alarms, and operating field devices. The "worker" indicates the worker who performs the work (process). The example in FIG. 3 shows that "worker A1" performs the work process "Process A" at "11:00".

The work process DB14 can store work processes in any unit, such as work processes for the entire plant 1, work processes for each worker, or work processes for each work group. In addition, the work processes stored in the work process DB14 can be updated periodically by a manager, etc.

The correspondence DB15 is a database that stores electroencephalogram data that is expected to be generated when work is performed normally. Specifically, the correspondence DB15 stores electroencephalogram data that can be used to evaluate whether work has been performed normally for each work process. Figure 4 is a diagram showing an example of information stored in the correspondence DB15. As shown in Figure 4, the correspondence DB15 stores "work processes" and "electroencephalogram data" in association with each other. The "work processes" stored here correspond to each process included in the work process. The "electroencephalogram data" indicates electroencephalogram data that is generated when a process is performed normally. The example in Figure 4 shows that after process A is performed normally, electroencephalogram data AA is measured as the electroencephalogram data of the worker.

The control unit 20 is a processing unit that controls the entire information processing device 10, and is realized by, for example, a processor. This control unit 20 has an identification unit 21, an acquisition unit 22, and an evaluation unit 23. Note that the identification unit 21, the acquisition unit 22, and the evaluation unit 23 can be realized as an example of an electronic circuit such as a processor, or as an example of a process executed by a processor.

The identification unit 21 is a processing unit that identifies work to be performed by workers in relation to plant operation. For example, the identification unit 21 identifies work to be performed on equipment in the plant 1 using a work process chart stored in the work process DB 14. As an example, at "11:00", the identification unit 21 identifies that "Process A" will be performed by "Worker A1" and notifies the acquisition unit 22 of the identified information.

The acquisition unit 22 is a processing unit that acquires the brainwave data of the worker performing the work. Explaining the above example, the acquisition unit 22 acquires the brainwave data from the EEG measuring device of "Worker A1" without delay after "11:00". The acquisition unit 22 stores the acquired brainwave data in the collection DB 13 and outputs it to the evaluation unit 23.

The evaluation unit 23 is a processing unit that evaluates the results of the work execution based on the work and the worker's electroencephalogram data. Specifically, the evaluation unit 23 evaluates whether the work was executed normally based on the correspondence between the work and the worker's electroencephalogram data after the work, and outputs the evaluation result to a display, the monitoring room 2, or the like. For example, the evaluation unit 23 evaluates that the work was executed normally when expected first electroencephalogram data is acquired after the work, and evaluates that the work may have been executed abnormally when the first electroencephalogram data is not acquired after the work.

Figure 5 is a diagram illustrating an example of an evaluation as normal. As shown in Figure 5, the evaluation unit 23 acquires the brainwave data of worker A1 who performed process A at time t1. Then, the evaluation unit 23 compares the "brainwave data AA" associated with "process A" in the correspondence DB 15 with the "brainwave data" of worker A1, and if the degree of match or similarity is equal to or greater than a threshold, evaluates that there is a high possibility that the work (operation) performed by worker A1 was normal. The threshold can be set arbitrarily.

Figure 6 is a diagram illustrating an example of an evaluation that there is a possibility of an abnormality. As shown in Figure 6, the evaluation unit 23 acquires the brainwave data of worker A1 who performed process A at time t1. Then, the evaluation unit 23 compares the "brainwave data AA" corresponding to "process A" stored in the correspondence DB 15 with the "brainwave data" of worker A1, and if the degree of match or similarity is less than a threshold, evaluates that there is a high possibility that the work (operation) performed by worker A1 is abnormal.

At this time, the evaluation unit 23 calculates the degree of deviation between the "EEG data AA" and the "EEG data" of worker A1, and can also change the type of alarm, etc., depending on the degree of deviation. For example, if the degree of deviation is equal to or greater than a threshold or if the similarity is less than a lower limit, the evaluation unit 23 issues an alarm indicating that there is a high possibility of a fault occurring or that the impact of the fault is large. On the other hand, if the degree of deviation is less than a threshold or if the similarity is equal to or greater than a lower limit, the evaluation unit 23 issues an alarm indicating that there is a low possibility of a fault occurring or that the impact of the fault is small.

The lower limit is a threshold value set to determine the type of alarm. Also, the degree of agreement, similarity, deviation, etc. can be calculated using known methods such as wavelength analysis and frequency analysis, or known methods commonly used in statistics and analytics.

(Processing flow)
Fig. 7 is a flowchart showing the flow of evaluation processing by the information processing device 10 according to embodiment 1. As shown in Fig. 7, the identification unit 21 of the information processing device 10 refers to a work process, and when it is time to perform a work (S101: Yes), identifies the performed work (S102).

Next, the acquisition unit 22 acquires the electroencephalogram data of the worker who performed the work (S103). The evaluation unit 23 then compares the electroencephalogram data stored in the correspondence DB 15 with the acquired electroencephalogram data of the worker (S104), and if the degree of agreement is equal to or greater than a threshold (S105: Yes), evaluates that the work was performed normally (S106).

On the other hand, if the degree of agreement is less than the threshold (S105: No), the evaluation unit 23 evaluates that the task may have been abnormally performed (S107). Then, the evaluation unit 23 calculates the degree of discrepancy between the brainwave data stored in the correspondence DB 15 and the acquired brainwave data (S108), and issues an alarm according to the degree of discrepancy (S109).

(effect)
As described above, the information processing device 10 can evaluate the work by indexing the potential consciousness of the worker, and can perform evaluation based on minute changes that are difficult to grasp simply by the presence or absence of the work. As a result, the information processing device 10 can ensure the safe operation of the plant and prevent accidents involving the worker himself.

In addition, by using the post-work EEG data, the information processing device 10 can detect potential discomfort after the worker actually performs the work, making it possible to detect the worker's anxiety at an early stage. As a result, the information processing device 10 can achieve safe operation from the perspectives of both safe operation of the plant 1 and the safety of the workers.

In addition, by storing electroencephalogram data indicating normal work in advance, the information processing device 10 can realize evaluation based on objective indicators, making it possible to perform accurate evaluation without relying on workers.In addition, since the information processing device 10 can identify work from a work process chart, it is possible to prevent overlooking work to be evaluated.

[Embodiment 2]
In the first embodiment, an example in which electroencephalogram data after a task is used has been described, but the present invention is not limited to this example, and evaluation can be performed using electroencephalogram data before and after a task. Therefore, in the second embodiment, an example in which evaluation is performed using electroencephalogram data before and after a task will be described. In addition, an example in which evaluation indices generated from electroencephalogram data are used, rather than the electroencephalogram data itself, will be described.

Figure 8 is a diagram explaining the evaluation index according to the second embodiment. As shown in Figure 8, the information processing device 10 acquires electroencephalogram data of each worker who has successfully executed process A in the past, and generates and stores an evaluation index from the electroencephalogram data.

For example, the information processing device 10 acquires EEG data of a worker who successfully performed the work of process A on January 1, EEG data of a worker who successfully performed the work of process A on January 5, and EEG data of a worker who successfully performed the work of process A on February 1. Next, the information processing device 10 performs EEG analysis of frequency intensity, ratio, spectrum, time of occurrence of event-related potential, amplitude, etc. on each piece of EEG data to generate a characteristic evaluation index α indicating a normal state. Then, the information processing device 10 generates a correspondence DB15 that associates process A with the evaluation index α.

Then, the information processing device 10 performs an evaluation using the generated correspondence DB 15. FIG. 9 is a diagram illustrating an example of evaluation of work according to the second embodiment. As shown in FIG. 9, the information processing device 10 acquires electroencephalogram data before and after the work of worker A1 who performed process A on February 5. Then, the information processing device 10 generates an evaluation index from the electroencephalogram data before and after the work using an analysis method similar to that of FIG. 8. Then, the information processing device 10 compares the generated evaluation index with the evaluation index α described in FIG. 8 to evaluate the work of worker A1.

In this way, the information processing device 10 can evaluate the validity of a task based on the changes in the brainwave data before and after the task. As a result, the information processing device 10 can perform an accurate evaluation that takes into account tasks that continued under stress, tasks that transitioned from a stressful state to a normal state, and the like. Furthermore, the information processing device 10 can perform evaluation using evaluation indices generated from the brainwave data of multiple people, so that evaluations can be performed based on objective indices and appropriate operational support can be provided.

[Embodiment 3]
Incidentally, the information processing device 10 can also use the state of the worker for the evaluation. Thus, in the third embodiment, an example will be described in which the state of the worker is detected based on a time-series change in electroencephalogram data of the worker acquired from the first task to the second task, and the detected state of the worker is further used to evaluate the execution result of the tasks.

Fig. 10 is a functional block diagram showing the functional configuration of the information processing device 10 according to the third embodiment. As shown in Fig. 10, the information processing device 10 according to the third embodiment has a communication unit 11, a storage unit 12, and a control unit 20, similar to Fig. 2 described in the first embodiment. The difference from the first embodiment is that it has a detection unit 24, so the detection unit 24 will be described here.

The detection unit 24 is a processing unit that detects the state of the worker based on time-series changes in the electroencephalogram data of the worker acquired from the first task to the second task. Specifically, the detection unit 24 performs electroencephalogram analysis on the time-series electroencephalogram data acquired from the first task to the second task, and judges the worker's state of tension, poor physical condition, etc., according to the analysis results.

For example, when alpha waves are detected, the detection unit 24 determines that the subject is in a relaxed state, and when beta waves are detected, the detection unit 24 determines that the subject is in a tense state. Note that the detection targets are not limited to alpha waves and beta waves. For example, the settings can be changed as desired by acquiring, through analysis, feature quantities that characterize the state of the detection target, such as characteristic waveforms that indicate abnormal physical condition, or power spectra that occur with a particularly high probability in a certain state.

The evaluation unit 23 then evaluates the results of the work execution using the detected worker status. FIG. 11 is a diagram illustrating an example of work evaluation using the worker status according to the third embodiment. In FIG. 11, an example is described in which worker A1 executes process A at time t1 and then executes process B at time t2.

As shown in FIG. 11, the evaluation unit 23 evaluates the work of process A based on the electroencephalogram data of worker A1 acquired after the work of process A, while re-evaluating the work of process A based on the time-series changes in the electroencephalogram data from process A to process B.

For example, if the evaluation unit 23 evaluates that the work was performed normally based on the electroencephalogram data after process A and alpha waves are also detected, it determines that it is unlikely that worker A is anxious about his or her work, and evaluates that it is highly likely that the work of process A was performed normally.

On the other hand, even if the electroencephalogram data after process A is evaluated as normal execution, if beta waves are also detected, the evaluation unit 23 determines that there is a high possibility that worker A is anxious about his or her work, and evaluates that there is a possibility that the work of process A was performed abnormally.

In this way, the information processing device 10 can detect psychological changes in workers using time-series changes in brainwave data and use them for evaluation. As a result, the information processing device 10 can detect even small dangers as much as possible in a system that requires safe operation, such as the plant 1, which has a large impact on the surrounding area if a failure occurs, and can effectively provide support for safe operation.

[Embodiment 4]
Incidentally, the state of the plant 1 is generally monitored in real time in a monitoring room 2 or the like. Therefore, the state of the plant 1 after a worker has performed work is also monitored in the monitoring room 2 or the like. Therefore, by monitoring the linkage between the electroencephalogram data of the worker who actually performs work in the plant 1 and the electroencephalogram data of the monitor who performs real-time monitoring in the monitoring room, monitoring from both the aspects of work and monitoring can be realized.

Therefore, in the fourth embodiment, an example is described in which multiple tasks that are related to each other are identified, EEG data is acquired from each worker performing the multiple tasks, and the multiple tasks are evaluated based on the interrelationship of the EEG data from each worker.

Figure 12 is a diagram explaining the work evaluation based on the correlation between multiple workers according to the fourth embodiment. Here, process A indicates work in plant 1, and process B indicates work of monitoring an alarm in plant 1 after process A is performed, and both processes are related to each other. Furthermore, the information processing device 10 stores the correspondence between process A and the brainwave data, and the correspondence between process B and the brainwave data in the correspondence DB 15.

In this state, as shown in FIG. 12, the information processing device 10 acquires electroencephalogram data of worker A1 who performed process A at time T1, and performs an evaluation by comparing this electroencephalogram data with the electroencephalogram data stored in the correspondence DB 15.

Similarly, after the execution of process A, the information processing device 10 acquires electroencephalogram data of worker B1 performing process B, and performs an evaluation by comparing this electroencephalogram data with the electroencephalogram data stored in the correspondence DB 15.

Then, when the EEG data of worker A1 is normal and the EEG data of worker B1 is normal, the information processing device 10 evaluates that process A was performed normally. When the EEG data of worker A1 is normal and the EEG data of worker B1 is abnormal, the information processing device 10 evaluates that although process A may have been performed abnormally, the risk of a large-scale failure or the like is small (low risk). When the EEG data of worker A1 is abnormal and the EEG data of worker B1 is normal, the information processing device 10 evaluates that process A may have been performed abnormally and the risk of a large-scale failure or the like is relatively high (medium risk). When the EEG data of worker A1 is abnormal and the EEG data of worker B1 is normal, the information processing device 10 evaluates that there is a high possibility that process A was performed abnormally and the risk of a large-scale failure or the like is also high (high risk).

In this way, the information processing device 10 can evaluate the work using not only the brainwave data of the worker A1 who actually performs the work, but also the brainwave data of the worker B1 who monitors the results of the work. As a result, the information processing device 10 can realize early detection of abnormalities by monitoring related tasks collectively, rather than a single task.

In the above embodiment, an example was described in which process A was given more importance, but this is not limited to the above and can be changed as desired depending on the work content and the capabilities of the workers. For example, if the monitoring worker B1 is a more experienced worker than worker A1 or is a good worker who makes fewer mistakes, the information processing device 10 can prioritize the evaluation of worker B1. For example, if the electroencephalogram data of worker B1 is evaluated as normal, the information processing device 10 evaluates the data as normal or low risk.

The linkage between related tasks is not limited to two, nor is it limited to evaluation using EEG data at a certain moment. For example, when multiple tasks are performed simultaneously, each task can be evaluated using the EEG data of each worker. Note that "simultaneous" includes both the same time, such as time T, and a certain time span, such as between time T1 and time T2.

Here, an example of evaluating multiple tasks that are related to each other and are performed between time T1 and time T2 will be described. FIG. 13 is a diagram illustrating task evaluation based on the correlation of simultaneous tasks according to embodiment 4. As shown in FIG. 13, between time T1 and time T2, process A is performed by worker A1, process B of alarm monitoring of process A by monitor B1, and related process C using the results of process A by worker C1 are performed. Furthermore, it is assumed that the information processing device 10 stores the correspondence between process A and the electroencephalogram data, process B and the electroencephalogram data, and related process C and the electroencephalogram data in the correspondence DB 15.

In this state, as shown in FIG. 13, the information processing device 10 acquires, between time T1 and time T2, the brainwave data of worker A1 who performed process A, the brainwave data of supervisor B1 who performed process B, and the brainwave data of worker C1 who performed related process C. Then, when each of the brainwave data is normal, the information processing device 10 evaluates that each process was performed normally.

As described above, the information processing device 10 can evaluate a series of tasks as a whole, rather than evaluating multiple related tasks separately. As a result, the information processing device 10 can provide not only the evaluation results, but also detailed information for examining which tasks are problematic, which tasks are affecting other tasks, and so on.

[Embodiment 5]
Although the embodiment of the present invention has been described above, the present invention may be embodied in various different forms other than the above-described embodiment.

[Numbers, etc.]
The thresholds, the number of computers, the number of workers, the processes, the process names, and the like used in the above embodiment are merely examples and can be changed as desired. The electroencephalogram data stored in the correspondence DB 15 may be electroencephalogram data indicating an abnormality, may be numerical values, or may be information indicating a predetermined change for identifying an abnormality or normality. In the above embodiment, an example using electroencephalogram data after work has been described, but this is not limited thereto, and for example, evaluation can be performed by the same process using electroencephalogram data before, during, and after work.

[Time series evaluation]
The information processing device 10 described above can also evaluate the work based on the change in the electroencephalogram data before and after the work. Specifically, the information processing device 10 performs an evaluation based on the electroencephalogram data of the worker A1 after the work of the process A, and then monitors the time-series change in the electroencephalogram data of the worker A1, performs a further evaluation based on the monitoring result, and displays the evaluation result on a display or in the monitoring room 2.

For example, if the information processing device 10 detects no abnormality in the time series changes in the EEG data of worker A1 within a certain time after the execution is evaluated as normal, the information processing device 10 evaluates that the possibility of a malfunction is extremely small and displays the result. Also, if the information processing device 10 detects an abnormality in the time series changes in the EEG data of worker A1 within a certain time after the execution is evaluated as normal, the information processing device 10 evaluates that although there is a possibility of a malfunction, the magnitude of the malfunction is small and displays the result.

Furthermore, if no abnormality is detected in the time series changes in the EEG data of worker A1 within a certain time period after the execution is evaluated as abnormal, the information processing device 10 evaluates that there is a possibility of a malfunction, but that the magnitude of the malfunction is small, and displays the result. Furthermore, if a further abnormality is detected in the time series changes in the EEG data of worker A1 within a certain time period after the execution is evaluated as abnormal, the information processing device 10 evaluates that there is a possibility of a major malfunction, and displays the result.

The abnormality can be determined by a known method, such as determining whether pre-stored EEG data indicating an abnormal state has been detected. In this way, the information processing device 10 can estimate and notify the severity of the disability by further evaluating the task based on the change in EEG data before and after the task.

The information processing device 10 can also acquire electroencephalogram data for each worker performing each process in the plant 1 while performing each process, and evaluate each task based on the time series changes in the electroencephalogram data of each worker. For example, the information processing device 10 can monitor the time series changes in the electroencephalogram data of workers for each task performed during plant operations, and identify and collect tasks, work locations, work positions, and task contents in which abnormalities have been detected, thereby generating a risk map that shows locations where work errors are frequent.

[system]
The information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified.

In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure. In other words, all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

Furthermore, each processing function performed by each device may be realized, in whole or in part, by a CPU (Central Processing Unit) and a program analyzed and executed by the CPU, or may be realized as hardware using wired logic.

[Hardware]
Next, a hardware configuration example of the information processing device 10 will be described. Note that other devices may have a similar hardware configuration. Fig. 14 is a diagram for explaining a hardware configuration example. As shown in Fig. 14, the information processing device 10 has a communication device 10a, a HDD (Hard Disk Drive) 10b, a memory 10c, and a processor 10d. In addition, each unit shown in Fig. 14 is connected to each other by a bus or the like.

The communication device 10a is a network interface card or the like, and communicates with other servers. The HDD 10b stores the programs and DBs that operate the functions shown in FIG. 2.

The processor 10d reads out a program that executes the same processes as the processing units shown in FIG. 2 from the HDD 10b, etc., and expands it in the memory 10c, thereby operating a process that executes each function described in FIG. 2, etc. For example, this process executes a function similar to that of each processing unit possessed by the information processing device 10. Specifically, the processor 10d reads out a program having functions similar to those of the identification unit 21, the acquisition unit 22, the evaluation unit 23, etc., from the HDD 10b, etc. Then, the processor 10d executes a process that executes the same processes as the identification unit 21, the acquisition unit 22, the evaluation unit 23, etc.

In this way, the information processing device 10 operates as an information processing device that executes a task evaluation method by reading and executing a program. The information processing device 10 can also realize functions similar to those of the above-described embodiment by reading the program from a recording medium using a media reading device and executing the read program. Note that the program in these other embodiments is not limited to being executed by the information processing device 10. For example, the present invention can be similarly applied to cases where another computer or server executes a program, or where these cooperate to execute a program.

This program can be distributed via a network such as the Internet. In addition, this program can be recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO (Magneto-Optical disk), or a DVD (Digital Versatile Disc), and can be executed by being read from the recording medium by a computer.

10 Information processing device 11 Communication unit 12 Storage unit 13 Collection DB
14 Work process DB
15 Correspondence DB
20 Control unit 21 Identification unit 22 Acquisition unit 23 Evaluation unit 24 Detection unit

Claims (17)

An identification unit that identifies work to be performed by workers regarding the operation of the plant;
an acquisition unit for acquiring electroencephalogram data of the worker performing the work;
an evaluation unit that evaluates a result of execution of the work based on the work and electroencephalogram data of the worker;
a storage unit that stores work performed by a worker in relation to the operation of the plant in association with electroencephalogram data that serves as a reference for determining whether the work has been performed normally;
The identification unit identifies an operation to be performed on a device in the plant,
The acquisition unit acquires electroencephalogram data of the worker after the work,
The evaluation unit evaluates whether the task was performed normally based on the similarity between the reference electroencephalogram data stored in the memory unit in correspondence with the identified task and the electroencephalogram data of the worker after the task . An identification unit that identifies work to be performed by workers regarding the operation of the plant;
an acquisition unit for acquiring electroencephalogram data of the worker performing the work;
and an evaluation unit that evaluates a result of the work based on the work and electroencephalogram data of the worker,
The evaluation unit evaluates that the task has been performed normally when first electroencephalogram data is acquired after the task, and evaluates that the task may have been performed abnormally when first electroencephalogram data is not acquired after the task. An identification unit that identifies work to be performed by workers regarding the operation of the plant;
an acquisition unit for acquiring electroencephalogram data of the worker performing the work;
and an evaluation unit that evaluates a result of the work based on the work and electroencephalogram data of the worker,
The information processing device wherein the evaluation unit visualizes the possibility of an injury occurring due to the task or identifies the extent of the impact of the injury based on changes in electroencephalogram data acquired before and after the task. An identification unit that identifies work to be performed by workers regarding the operation of the plant;
an acquisition unit for acquiring electroencephalogram data of the worker performing the work;
an evaluation unit that evaluates a result of execution of the work based on the work and electroencephalogram data of the worker;
a detection unit that detects a state of the worker based on a time series change in electroencephalogram data of the worker acquired from a first task to a second task,
The information processing device, wherein the evaluation unit further uses the detected state of the worker to evaluate a result of execution of the first task. An identification unit that identifies work to be performed by workers regarding the operation of the plant;
an acquisition unit that acquires electroencephalogram data of the worker performing the work, and acquires time-series electroencephalogram data of each of a plurality of workers performing a work process in the plant, the work process including a plurality of tasks, while the worker is performing the work process;
an evaluation unit that evaluates a result of the work based on the work and the electroencephalogram data of the workers, and evaluates the work process based on the time-series electroencephalogram data of each of the plurality of workers,
The identification unit identifies a plurality of tasks related to each other,
The acquisition unit acquires electroencephalogram data of each worker performing the plurality of tasks,
The information processing device, wherein the evaluation unit evaluates the plurality of tasks based on the results of comparing each of the electroencephalogram data acquired for each of the plurality of tasks with each of the reference electroencephalogram data for determining whether each of the plurality of tasks has been performed normally. the acquisition unit acquires electroencephalogram data of a first worker who performs the work at a site of the plant, and acquires electroencephalogram data of a second worker who monitors the plant where the work is performed;
The information processing apparatus according to claim 5 , wherein the evaluation unit evaluates the plurality of tasks based on a link between the electroencephalogram data of the first worker and the electroencephalogram data of the second worker. 7. The information processing device according to claim 6, wherein the evaluation unit evaluates that there is a possibility that the work has been performed abnormally when a predetermined change occurs in either the electroencephalogram data of the first worker or the electroencephalogram data of the second worker. The computer
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
Execute the process ,
The process of identifying includes identifying an operation to be performed on equipment in the plant,
The acquiring process acquires electroencephalogram data of the worker after the work,
The evaluation process is a work evaluation method in which the evaluation process evaluates whether the work performed by a worker in relation to the operation of the plant is performed based on the similarity between the reference electroencephalogram data associated with an identified work and the electroencephalogram data of the worker after the work, the reference electroencephalogram data being stored in a memory unit that associates and stores the work performed by the worker in relation to the operation of the plant with the reference electroencephalogram data for determining whether the work was performed properly . On the computer,
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
Execute the process ,
The process of identifying includes identifying an operation to be performed on equipment in the plant,
The acquiring process acquires electroencephalogram data of the worker after the work,
The evaluation process is a work evaluation program that evaluates whether the work was performed normally based on the similarity between the reference electroencephalogram data associated with an identified work and the electroencephalogram data of the worker after the work, the reference electroencephalogram data being stored in a memory unit that associates and stores the work performed by a worker in connection with the operation of the plant with the reference electroencephalogram data for determining whether the work was performed normally . The computer
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
Executing a process of evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
The evaluation process evaluates that the task has been performed normally if first electroencephalogram data is acquired after the task, and evaluates that the task may have been performed abnormally if first electroencephalogram data is not acquired after the task, in this task evaluation method. On the computer,
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
Executing a process for evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
The evaluation process evaluates that the task has been performed normally if first electroencephalogram data is acquired after the task, and evaluates that the task may have been performed abnormally if first electroencephalogram data is not acquired after the task. The computer
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
Executing a process of evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
The evaluation process is a task evaluation method in which the possibility of an injury occurring due to the task is visualized or the extent of the impact of the injury is determined based on changes in electroencephalogram data obtained before and after the task. On the computer,
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
Executing a process for evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
The evaluation process is a task evaluation program that visualizes the possibility of an injury occurring due to the task or identifies the extent of the impact of the injury based on changes in electroencephalogram data obtained before and after the task. The computer
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
Evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
Executing a process of detecting a state of the worker based on a time series change in electroencephalogram data of the worker acquired from a first task to a second task;
The task evaluation method, wherein the evaluating process further uses the detected state of the worker to evaluate a result of execution of the first task. On the computer,
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work;
Evaluating a result of the execution of the work based on the work and the electroencephalogram data of the worker;
Executing a process of detecting a state of the worker based on a time series change in electroencephalogram data of the worker acquired from a first task to a second task;
The evaluating process further uses the detected state of the worker to evaluate a result of execution of the first task. The computer
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work, and acquire time-series electroencephalogram data for each of a plurality of workers performing a work process in the plant including a plurality of tasks while performing the work process;
Executing a process of evaluating an execution result of the work based on the work and the electroencephalogram data of the workers, and evaluating the work process based on the time-series electroencephalogram data of each of the plurality of workers;
The process of identifying includes identifying a plurality of tasks related to each other,
The acquiring process acquires electroencephalogram data of each worker performing the plurality of tasks,
A task evaluation method in which the evaluation process evaluates the multiple tasks based on the results of comparing each of the electroencephalogram data acquired for each of the multiple tasks with each of the reference electroencephalogram data for determining whether each of the multiple tasks has been performed normally. On the computer,
Identifying the tasks that workers will perform in relation to operating the plant;
Acquire electroencephalogram data of the worker performing the work, and acquire time-series electroencephalogram data for each of a plurality of workers performing a work process in the plant including a plurality of tasks while performing the work process;
executing a process of evaluating an execution result of the work based on the work and the electroencephalogram data of the workers, and evaluating the work process based on the time-series electroencephalogram data of each of the plurality of workers;
The process of identifying includes identifying a plurality of tasks related to each other,
The acquiring process acquires electroencephalogram data of each worker performing the plurality of tasks,
The evaluation process is a task evaluation program that evaluates the multiple tasks based on the results of comparing each of the electroencephalogram data acquired for each of the multiple tasks with each of the reference electroencephalogram data for determining whether each of the multiple tasks has been performed normally.

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