JP2004279945A - Training system and scenario structuring method of training system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To train even for an unassumable state included neither past experience nor knowledge through a system which allows a trainee to train for an object event while artificially reproducing it. <P>SOLUTION: The training system which artificially reproduces progress of an event to be trained for is equipped with a storage device which stores an event tree 3 having an initial event 2 and a plurality of progress keys K1 to K6 and a simulation means of developing a scenario generated through the event tree 3. Disclosed is a scenario structure method of the training system artificially reproducing the progress of the object event; and the initial event 2 and the plurality of progress keys K are arranged in time-series order to constitute an event tree 3 and a plurality of scenarios are structure along branch paths of the event tree 3. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a training system for performing a training while simulating an event to be trained. It should be noted that the training is not limited to the purpose of coping with actual accidents, disasters, errors, and the like, but also includes, for example, the purpose of enjoying training (improvement of game operation techniques) itself.
[0002]
[Prior art]
2. Description of the Related Art In recent years, a training system that uses a computer graphics (CG) technique such as virtual reality (VR) to perform training while simulating the operation of a plant or the occurrence of a disaster is known. In this type of training system, a plurality of scenarios constructed based on actual accidents and possible accidents are prepared, and one of the scenarios is selected for training or a specific accident is performed. One scenario was prepared assuming the above, and training was conducted based on this scenario.
[0003]
[Patent Document 1]
JP-A-10-293525
[Patent Document 2]
JP-A-2002-366021
[Patent Document 3]
JP 2002-108196 A
[0004]
[Problems to be solved by the invention]
However, in the above-mentioned training system, scenarios are built on the assumption of actual accidents and possible accidents.Therefore, effective training can be performed for unexpected situations that are not available in past experiences and knowledge. It was difficult.
[0005]
SUMMARY OF THE INVENTION An object of the present invention is to provide a training system and a training system scenario construction method capable of performing training even in an unexpected situation that is not found in past experience or knowledge.
[0006]
[Means for Solving the Problems]
In order to achieve the above object, the invention according to claim 1 is a training system for simulating the progress of an event to be trained, wherein the storage device stores an initial event and an event tree having a plurality of progress keys. And a simulation means for expanding a scenario formed by the event tree.
[0007]
Therefore, a scenario corresponding to the branch path of the event tree is formed, and training is performed in accordance with the scenario. In an actual event to be trained, the event may progress in any number of ways due to differences in conditions, factors, and the like. The progress key of the event tree is equivalent to the conditions and factors that make the actual event progress, and by forming a scenario that is developed by branching the progress key, it can occur in the future even if it has not occurred in the past Training can also be conducted on unexpected and unexpected deployments.
[0008]
The training system according to claim 2, further comprising an input device for inputting data designating one scenario to be developed among a plurality of scenarios formed by the event tree, wherein the simulation means includes input data from the input device. Expands the scenario selected by. Therefore, by inputting to the input means, a scenario developed by the simulation means can be selected.
[0009]
Further, the training system according to claim 3 includes a selection unit that randomly selects one scenario from a plurality of scenarios formed by the event tree, and the simulation unit develops the scenario selected by the selection unit. Things.
[0010]
Therefore, the simulation means develops a scenario selected at random. For this reason, the scenario used for training is selected at random, excluding the arbitrariness of the person who performs training, that is, the manager or the trainer.
[0011]
Further, the training system according to claim 4 includes a storage device for storing a branch probability of the progress key of the event tree, and the simulation means forms and selects a scenario to be developed with reference to the branch probability. Therefore, the simulation means can select a scenario with a probability according to an actual event.
[0012]
Further, the invention according to claim 5 is a method for constructing a scenario of a training system for simulating the progress of an event to be trained, wherein an event tree is formed by arranging initial events and a plurality of progress keys in chronological order. , A plurality of scenarios are constructed along a branch path of the event tree.
[0013]
Therefore, a scenario corresponding to the branch path of the event tree is constructed. In an actual event to be trained, the event may progress in any number of ways due to differences in conditions, factors, and the like. The progress key in the event tree is equivalent to the conditions and factors that make the actual event progress, and by building a scenario by branching the progress key, it may occur in the future even if it has not occurred in the past Unexpected scenarios can be constructed.
[0014]
According to a sixth aspect of the present invention, a scenario constructing method for a training system associates a branching probability with a progress key of an event tree. Therefore, a scenario can be selected with a probability according to an actual event.
[0015]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, the configuration of the present invention will be described in detail based on the best mode shown in the drawings.
[0016]
1 and 2 show an example of an embodiment of a training system to which the present invention is applied. The training system 1 simulates the progress of an event to be trained in a pseudo manner, and includes a storage device 4 for storing an initial event 2 and an event tree 3 having a plurality of progress keys K, and an event tree 3. A simulation unit 5 for developing the formed scenario is provided.
[0017]
In the present embodiment, the event to be trained is, for example, a self-vehicle fire in a tunnel fire accident in public transportation of radioactive materials. However, the fire is not limited to the fire of the own vehicle, but may be fire of another vehicle or fire from a tunnel facility. In addition, a fire accident other than the tunnel may be used, and a collision accident, a fall accident, a submergence accident, etc. other than the fire accident may be set as the training target events. FIG. 3 shows a main accident scenario assumed during transportation, and FIG. 4 shows a main tunnel fire accident scenario assumed during transportation.
[0018]
FIG. 5 shows the concept of the event tree 3. The event tree 3 includes an initial event 2 and a plurality of progress keys K. For example, assuming the development of an event to be trained, a possible event is defined as a progress key K. In the present embodiment, since the subject vehicle fires in the tunnel, the initial event 2 is "the vehicle fires in the tunnel." Further, the progress key K is, for example, “successful initial fire extinguishing with the digestive organ”, “can communicate wirelessly with the outside”, “can extinguish the fire with a sprinkler”, “no combustible materials around”, “do not leak from the transport container” , "Successful evacuation". However, the progress key K is not limited to these, and for example, “can extinguish with a fire hydrant of tunnel equipment” may be used as the progress key. Further, the number of the progress keys K is not limited to the six described above.
[0019]
The progress keys K are preferably arranged in a time series, for example. By arranging the progress keys K in chronological order, the scenario can be developed according to the actual accident, and the event tree 3 can be easily intuitively grasped when the event tree 3 is shown in the figure. It is also convenient to provide a comment column with a remarks column.
[0020]
A scenario is constructed by the event tree 3 created in this way. In other words, the scenario from the initial event 2 of the event tree 3 to the branch by the last progress key K6 (branch route) is the scenario. That is, the scenario building method of the training system 1 according to the present invention configures the event tree 3 by arranging the initial event 2 and the plurality of progress keys K in chronological order, and forms the plurality of scenarios along the branch path of the event tree 3. To build. Since the event tree 3 of the present embodiment is branched into 22 routes, 22 scenarios are constructed.
[0021]
In event tree 3, if the number of progress keys K is n, then theoretically, 2 ⁿ Scenarios will be created, but the actual number is 2 ⁿ Less than. For example, if the initial response (successful initial fire extinguishing with the digestive organs: first progress key K1) is successful, it is considered that the accident will not progress any further, so it is possible to continue transportation. Yes, scenario development is omitted as shown in FIG. That is, when six progress keys K1 to K6 are set, the number of scenarios is 64 (= 2) in theory. ⁶ ) In the present embodiment, for example, 22. Each scenario is given an identification number, for example.
[0022]
The event tree 3 is stored in the storage device 4. When the event tree 3 is shown in the figure, as shown in FIG. 5, a remarks column (comment column) is provided at the end of the scenario development (the right end in the figure), and a brief description of the accident (comment 13) is provided. ) Is preferably added. This facilitates understanding of each scenario.
[0023]
The training system 1 includes, for example, a computer 6, an input device 7 such as a keyboard and a mouse for operating the computer 6, and an output device 8 such as a monitor for displaying CG (computer graphics) created by the computer 6. . The computer 6 includes at least one central processing unit such as a CPU and an MPU, an interface for inputting and outputting data, and a storage device 4 for storing programs and data. The program implements the simulation means 5 and the selection means 9 described later. That is, the central processing unit executes the simulation means based on a control program such as an OS stored in the memory, a program defining a procedure for performing training while displaying the three-dimensional image, data on the three-dimensional image, and other necessary data. 5. The selecting means 9 is realized.
[0024]
The training system 1 includes an input device 7 for inputting data designating one scenario to be developed among a plurality of scenarios formed by the event tree 3. Expand the scenario selected by the data. That is, the manager or the trainee can directly select a scenario for training by operating the input device 7.
[0025]
Further, the training system 1 includes selection means 9 for randomly selecting one scenario from a plurality of scenarios formed by the event tree 3. The simulation means 5 develops the scenario selected by the selection means 9. I do. Since the selecting unit 9 selects a scenario for performing the training, it is possible to automatically select a scenario for performing the training while eliminating the arbitrariness of the manager and the trainee. The selecting means 9 has, for example, a function of generating a random number A, a function of performing four arithmetic operations, and the like.
[0026]
Further, the training system 1 stores the branch probabilities P (1) to P (6) of the progress keys K1 to K6 in the storage device 4, and the simulation means 5 stores the branch probabilities P (1) to P (6). Form and select scenarios for deployment by reference. That is, the branch probabilities P (1) to P (6) are associated with the progress keys K1 to K6 of the event tree 3, and are referred to when a scenario is constructed. By doing so, the scenario selection reflecting the branching probabilities P (1) to P (6) is performed, so that the scenario in which training is performed without the arbitrariness of the manager or the trainer is automatically selected. Can be.
[0027]
If there is existing data, it is used as the branch probabilities P (1) to P (6) of the progress keys K1 to K6. Even if there is no existing data, if there is applicable data, it is used. The data that can be applied mutatis mutandis include, for example, branch probabilities of the same case and similar cases (for example, failure probabilities of the same type of equipment, data described in related documents, and the like). Further, even when there is no existing data or applicable data, the branch probabilities P (1) to P (6) can be obtained using the concept of the fault tree. The fault tree analyzes the cause of each key event retroactively, and is finally decomposed into a "base event" or a "non-expanded intermediate event" that cannot be further expanded for some reason. Furthermore, a questionnaire method for a large number of people (empirically obtaining a probability of an event C of unknown probability based on a result of a questionnaire based on a result of a questionnaire that it is more likely to occur than an event A with a known probability but is also less likely to occur than an event B with a known probability). Can also determine the branch probability.
[0028]
In the present embodiment, the branch probabilities P (1) to P (6) of the progress keys K1 to K6 are obtained as follows. In calculating the branch probabilities P (1) to P (6), it is assumed that the development keys K1 to K6 are independent.
[0029]
The branching probability P (1) of the first progress key “successful initial fire extinguishing by digestive organs” K1 is obtained using, for example, a fault tree concept. FIG. 6 shows a fault tree diagram for obtaining the branch probability P (1). The probability of succeeding in the initial fire extinguishing using the digestive system is indicated by the logical product (and condition) of the probability Pa of carrying the digestive system and the probability Pb of operating the digestive system. That is, the branch probability P (1) of the first progress key K1 is PaPb (for example, 0.855).
[0030]
The branch probability P (2) of the second progress key "can communicate wirelessly with the outside" K2 is obtained by using, for example, the concept of a fault tree. FIG. 7 shows a fault tree diagram for obtaining the branch probability P (2). The probability of successful wireless communication with the outside is indicated by the logical product of the probability Pc of carrying the wireless device and the probability Pd of operating the wireless device. In addition, the probability Pd that the wireless device operates is obtained based on the probability that the wireless device does not operate (1-Pd), and the probability that the wireless device does not operate (1-Pd) cannot be found by inspection of the wireless device. It is indicated by the logical product of the probability Pe and the probability Pf that the radio device will fail. That is, the branch probability P (2) of the second progress key K2 is PcPd = Pc (1-PePf). The branch probability P (2) is, for example, 0.900.
[0031]
As the branch probability P (3) of the third progress key “fire can be extinguished by the sprinkler” K3, for example, statistical data on indoor sprinklers is applied mutatis mutandis. The branch probability P (3) is, for example, 0.95.
[0032]
The branch probability P (4) of the fourth progress key “No combustible materials around” K4 is calculated based on, for example, the probability of traffic of dangerous goods vehicles. That is, assuming that the traffic probability of the dangerous goods vehicle is Pg, the branch probability P (4) is (1−Pg). The branch probability P (4) is, for example, 0.96.
[0033]
As the branching probability P (5) of the fifth progress key “no leakage from the transport container” K5, for example, the leakage accident rate Ph of general dangerous goods transportation is applied mutatis mutandis. The branch probability P (5) is, for example, 0.99999997.
[0034]
The branch probability P (6) of the sixth progress key “success in evacuation” K6 is obtained by using, for example, the concept of a fault tree. FIG. 8 shows a fault tree diagram for obtaining the branch probability P (6). The probability of successful evacuation (escape from the emergency exit) is indicated by the logical sum (or condition) of the probability Pi of the emergency exit not having a failure and the probability Pj of finding and repairing the failure. The probability Pj of finding and repairing a failure at the emergency exit is indicated by the logical product of the probability Pk of finding a failure at the emergency exit and the probability Pl of failure at the emergency exit. That is, the branch probability P (6) of the sixth progress key K6 is Pi + Pj-PiPj (= Pi + PkPl-PiPkPl). The branch probability P (6) is, for example, 0.99995.
[0035]
The training system 1 of the present embodiment has three training modes, specifically, an arbitrary selection mode 10, an equal probability selection mode 11, and a branch probability selection mode 12, as shown in FIG. The manager or the trainee can operate the input device 7 to select one of the selection modes 10 to 12 according to the purpose. The simulation means 5 of the computer 6 executes the training program of the selected mode.
[0036]
In the optional mode 10, an administrator or a trainee can arbitrarily directly select a scenario for training. For example, when the manager or the trainee directly inputs the identification number of the scenario using the input device 7 (in the case of a keyboard) or clicks the icon corresponding to the scenario (in the case of a mouse), the simulation means 5 receives the input. Predetermined CG data is sequentially read from the storage device 4 according to the scenario corresponding to the identification number (input data) or the scenario corresponding to the scenario identification number (input data) associated with the clicked icon, and the CG is read. Are created and sequentially displayed on the monitor 8. The trainee simulates a virtually reproduced accident or disaster while watching the CG displayed on the monitor 8 and operates the input device 7 to learn how to deal with it. The training data of the trainee is stored in the storage device 4 and can be verified later.
[0037]
In the optional mode 10, since a manager or a trainee can directly specify a scenario relating to an accident or an error that is unexpected or rarely caused, training for an accident or an error that is not expected or rarely occurs is performed. It can be performed efficiently.
[0038]
In the equal probability selection mode 11, the selection means 9 automatically selects a scenario to be trained with equal probability by completely eliminating the arbitrariness of the manager and the trainer. The selection means 9 sets the occurrence probability of each scenario equally by random sampling. For example, as shown in FIG. 9, a random number A (0 to 99) is generated, and this random number A is divided by the number of scenarios (22 in this embodiment) (A / N) to obtain a quotient and a remainder B. By adding 1 to B (B + 1), the values of 1 to 22 can be obtained, and the scenario having the identification number corresponding to this value is selected. The simulation means 5 reads out predetermined CG data one after another in accordance with the scenario selected by the selection means 9, creates CG training images / videos, and sequentially displays them on the monitor 8. The trainee, while watching the CG displayed on the monitor 8, simulates an accident or disaster that is virtually reproduced, operates the input device 7, and learns how to deal with it. The training data of the trainee is stored in the storage device 4 and can be verified later.
[0039]
In the equal probability selection mode 11, by increasing the number of times of training (or the number of trainees) to some extent, considering the entire group of trainers, training can be performed with equal probability for 22 scenarios.
[0040]
In the equal probability selection mode 11, a scenario relating to an accident or an error that is unexpected or rarely occurring can be selected with a probability equal to that of another scenario. It can be performed efficiently.
[0041]
In the branch probability selection mode 12, the arbitrariness of the manager or the trainee is completely eliminated, and a scenario for training is automatically selected with reference to the branch probabilities P (1) to P (6).
[0042]
For example, as shown in FIG. 10, first, the simulation means 5 uses the Monte Carlo calculation method to determine “Yes” (hereinafter, referred to as Y) and “No” (hereinafter, referred to as N) of the six development keys K1 to K6. Find the combination of That is, the branch probabilities P (1) to P (6) are associated with the progress keys K1 to K6 in advance, and whether the progress keys K1 to K6 are Y or N is determined by the branch probability P (1). To P (6). At this time, if the determined combination of Y and N does not match the combination of Y and N of each of the progress keys K1 to K6 in the event tree 3, in other words, if it does not match any of the 22 scenarios, the process is repeated. Find the combination again.
[0043]
Then, the obtained combination of Y and N is collated with 22 scenarios, and training is performed in accordance with the scenario in which the patterns of Y and N match. For example, if the obtained combination is “N, Y, N, Y, N, Y”, that is, the first progress key K1 is N, the second progress key K2 is Y, and the third progress key K3 Is N, the fourth progress key K4 is Y, the fifth progress key K5 is N, and the sixth progress key K6 is Y, as shown in FIG. 5 among the scenarios in which the patterns of Y and N are 22. Since it matches the seventh scenario from the top, training is performed according to the seventh scenario. That is, the simulation means 5 reads out predetermined CG data one after another according to the seventh scenario, creates CG training images / videos, and sequentially displays them on the monitor 8. The trainee simulates a virtually reproduced accident or disaster while watching the CG displayed on the monitor 8 and operates the input device 7 to learn how to deal with it. The training data of the trainee is stored in the storage device 4 and can be verified later.
[0044]
In the branch probability selection mode 12, by increasing the number of times of training (or the number of trainees) to some extent, the scenario selection reflects the branch probability when considering the entire group of trainees. For this reason, in the branch probability selection mode 12, a scenario can be selected in accordance with the actual state of the event to be trained, and the probability of performing daily training for failures and errors is high, and training in a situation closer to the actual state is performed. Can be automatically and efficiently performed.
[0045]
By repeatedly performing each of the selection modes 10 to 12, the training can be repeatedly performed.
[0046]
As described above, the training system 1 has three training modes 10 to 12, and the training modes 10 to 12 can be selected according to the purpose, so that training can be performed efficiently.
[0047]
Further, in the training system 1, since training is performed in accordance with a scenario formed along a branch path of the event tree 3, it is possible to perform training even in an unexpected situation that is not found in past experience or knowledge. . That is, since a plurality of scenarios are constructed along the branch path of the event tree 3, it is possible to construct a scenario even for an unexpected situation which has no past experience or knowledge. It can be performed.
[0048]
The above-described embodiment is an example of a preferred embodiment of the present invention, but is not limited thereto, and various modifications can be made without departing from the spirit of the present invention.
[0049]
For example, in the above description, the training system 1 has three training modes 10 to 12, but it is not necessary to necessarily have all three training modes.
[0050]
In the branch probability selection mode 12, the obtained branch probabilities P (1) to P (6) may be increased or decreased. That is, the obtained branch probability may be intentionally increased or decreased, and the selection of a scenario may be operated to some extent. For example, the branch probability P of a certain progress key K is expanded and adjusted (for example, the probability value is adjusted to about 0.1) on the N side (a side where a severe accident occurs), so that the Monte Carlo method can be used for automatic calculation. In addition, it is possible to carry out frequent training for experiencing rare accidents. That is, a weighted sampling method may be used.
[0051]
In the branch probability selection mode 12, the branch probability P may be intentionally set to a predetermined value.
[0052]
Further, in the above description, the simulation means 5 displays the CG training image / video on the monitor 8, but instead of the CG image / video, an actually captured image / video is displayed on the monitor 8. It may be displayed.
[0053]
Further, the training system 1 of the present invention may be applied to events other than accidents, for example, training of plant operation or transportation operation, or may be applied to other training. Further, although the actual training is not intended directly, the present invention may be applied to a simulation game such as a driving game, a shooting game, an action game, and the like, which compete in driving technology, steering technology, operation technology, control technology, and the like.
[0054]
【Example】
A training system for truck transport of radioactive waste was built.
[0055]
(Equipment configuration)
The prototype training system mainly consists of the following equipment. That is, calculator: ONYX2, VR (virtual reality) development and execution tool: World Tool Kit UNIX version, display device: CRT, input device: mouse. In the ported version to a personal computer, for example, a personal computer having a CPU: Pentium 4 class, a RAM: 512 MB, and a graphic board: Quadro4 manufactured by NVIDIA was used. In this case, the VR development execution tool: World Tool Kit Windows NT version.
[0056]
(Preparation of target accident scenario)
With the fire of the vehicle in the tunnel as the “initial event”, the progress of the accident was analyzed by performing an event tree analysis using a plurality of progress keys shown in FIG. 5, and a training scenario was created. A logical and systematic training scenario can be constructed by introducing event tree analysis into the study of accident scenarios.
[0057]
(Structure of training program)
The trial training system is an education and training program that uses VR technology to smoothly respond to emergencies such as the transport of radioactive materials or to support decision making. Further, it is configured by the following three programs as conditions for which an administrator can set conditions.
▲ 1 ▼ Training content / condition setting program (for managers)
(2) Emergency response training program (for trainees)
(3) Training result confirmation program (common for managers and trainees)
[0058]
In the training system of the prototype, i) a function that allows the manager to set conditions prior to the training ((1) program above), and ii) a function that the trainee can independently experience and operate (the (2) program) Iii) A function (3) for the trainee to review the results and learn related knowledge under the leadership of the administrator is also realized.
[0059]
(VR scene and setting function)
The VR scenes during the emergency response training program include: 1) inspection scenes during normal operation work, 2) driving scenes, 3) actions such as fire detection, fire extinguishing and communication related to emergency response in case of tunnel fire. Set the scene. In the trial training system, not only the emergency response but also the application to the normal business in the future, the trial work was conducted on a trial basis. Was also incorporated into the training program (i above).
[0060]
A GUI-type training scenario setting screen in which the event tree of FIG. 5 is linked to a trainee's action in a tunnel, which will be described later, is provided. Accordingly, the administrator can set the training scenario by appropriately selecting the screen at the start of the training.
[0061]
(Training program flow and functions)
The flow of the processing in the emergency response training program includes a series of 1) generation of a display interface window, 2) reading of objects such as vehicles and tunnels, 3) generation of a VR space, and 4) setting of action processing. 5) The VR process is repeated in response to various actions of the trainee (fire extinguishing work, viewpoint movement, etc.). Here, the VR processing refers to calculation of the state and position of the object, determination of the action of the trainee, calculation of the trajectory of the trainee, change of the viewpoint, and three-dimensional rendering of the VR space reflecting all of these.
[0062]
In the prototype training system, the history (log) of each action of the trainee in the inspection scene and the training scene corresponding to the tunnel is recorded in real time and in the background, and can be reproduced and confirmed at the time of briefing after the end of the training. ing. Specifically, the training conditions, the trainee's actions in the VR space, and the results (fire state) are represented as a file (referred to as a dat file). Also, a file (referred to as an act file) describing the coordinate positions and execution order of the trainee's actions (fire extinguishing work, wireless communication, etc.) in the VR space and a file (referred to as a path file) of a locus of movement in the VR space are stored. Is done.
[0063]
(Train suspend and resume function)
In order to cope with the need to interrupt the program for any reason during training, an interrupt function and a function to resume from the interrupted point have been added. These histories are also recorded.
[0064]
(Building a data collection for decision support)
During the training process, transportation routes, inspection items, packages (loads), emergency response manuals, nuclear-related information, and methods for assessing risks associated with transportation are provided to assist in decision-making training or as a reference for learning the entire transportation business. A data collection on the results was constructed. These pieces of information can be called up on the web browser screen by making selections on the menu.
[0065]
(Training flow)
When the training scenario is set by the administrator, the emergency response training program starts, and the training proceeds according to the following flow.
[0066]
In the inspection scene, we will inspect the vehicle parts such as tires, fuel tanks, brakes, steering wheels, etc. and inspect the transported goods and the gantry (support device) at both ends while walking through the vehicle periphery, and click the mouse on the inspection points The color of the object is set to change to red. In addition, in order to obtain carry-on items (digestive organs, wireless devices, radiation detectors), walk through and enter the warehouse behind the vehicle, and then perform the acquisition by mouse operation.
[0067]
Subsequently, when the check is completed and the start button on the driver's seat is pressed, the scene is automatically switched to a scene in which the section of the expressway is moved. In addition to the own vehicle, vehicles traveling in the overtaking lane and vehicles traveling in the oncoming lane move by the automatic path, and thus there is no scene for the trainee to operate. About 30 seconds after the entrance of the tunnel, a signboard for notifying the occurrence of a fire is displayed on the driver's seat.
[0068]
When the trainee clicks on the signboard to confirm, the scene automatically switches to the emergency response scene in the tunnel. Here, the trainee goes out of the driver's seat to check the status of the fire, and performs an initial fire extinguishing operation using the digestive organs carried by the trainee. However, according to the set scenario, necessary objects such as other vehicles loaded with combustibles are read and drawn. The trainee continues to operate the sprinkler, and regardless of whether or not the fire is finally extinguished, performs the communication work by radio and the measurement work by the radiation detector, and escapes from the vehicle or the emergency exit. When the above actions are performed, the training ends automatically.
[0069]
(Briefing after training)
After the training, a training result confirmation program was created in order to brief the results (confirmation of the training status) between the manager and the trainee. This program allows you to check the history of the training results (scenario setting, whether or not to acquire inspections and personal belongings, response procedures, digestion status) conducted in the emergency response training program on the screen. . Further, it has a function of reproducing a training situation as a VR scene. That is, the dat file and the path file saved by the emergency response training program are read, and the setting status of the training scenario, the locations where inspections are performed / not performed in the pre-driving inspection scene, the acquisition status of portable equipment, and the emergency situation in the event of a tunnel fire A summary of the training results of the response status and the like (for example, the order of using portable equipment) is displayed.
[0070]
(A collection of related information data for support)
In the trial training system, a collection of relevant information data on the following items related to transportation was created for decision-making support and learning during training, and was referred to by a web browser when the above-described training result confirmation program was executed. The items included in the data collection are vehicle information, transportation information, transportation routes, work inspection items, equipment to be carried, traffic statistics, assumed accident scenarios, emergency response manuals, and technical data on transportation safety.
[0071]
(Summary)
Through the design, development, and evaluation of this training system, we gained the prospect of using VR as a training system for various tasks in the industrial field, including emergency response.
[0072]
【The invention's effect】
As described above, the training system according to claim 1 includes the storage device that stores the event tree having the initial events and the plurality of progress keys, and the simulation unit that expands the scenario formed by the event tree. Training can also be performed on unexpected developments that may occur in the future even if they have not occurred in the past.
[0073]
Further, the training system according to the second aspect includes an input device for inputting data designating one scenario to be developed among a plurality of scenarios formed by the event tree, and the simulation means includes: Since the scenario selected by the input data is developed, the scenario used for training can be arbitrarily selected.
[0074]
Further, the training system according to claim 3 includes a selection unit for randomly selecting one scenario from a plurality of scenarios formed by the event tree, and the simulation unit expands the scenario selected by the selection unit. Therefore, training can be performed by selecting a scenario at random.
[0075]
Further, the training system according to claim 4 is provided with a storage device for storing a branch probability of the progress key of the event tree, and the simulation means forms and selects a scenario to be developed with reference to the branch probability. Training can be done by selecting a scenario that matches the event.
[0076]
Furthermore, in the scenario building method of the training system according to claim 5, an initial event and a plurality of progress keys are arranged in chronological order to form an event tree, and a plurality of scenarios are built along a branch path of the event tree. Scenarios can also be built for unexpected deployments that may not occur in the past but may occur in the future.
[0077]
In the training system scenario construction method according to the sixth aspect, the branching probability is associated with the progress key of the event tree, so that the constructed scenario is selected with the probability according to the actual event.
[Brief description of the drawings]
FIG. 1 is a block diagram conceptually showing an example of an embodiment of a training system to which the present invention is applied.
FIG. 2 is a flowchart showing selection of a scenario of the training system.
FIG. 3 is a diagram showing a main accident scenario assumed during transportation.
FIG. 4 is a diagram showing a main tunnel fire accident scenario assumed during transportation.
FIG. 5 is a conceptual diagram showing an event tree.
FIG. 6 is a conceptual diagram for calculating a branch probability of a first progress key.
FIG. 7 is a conceptual diagram for calculating a branch probability of a second progress key.
FIG. 8 is a conceptual diagram for calculating a branch probability of a sixth progress key.
FIG. 9 is a conceptual diagram of scenario selection in an equal probability selection mode.
FIG. 10 is a conceptual diagram of scenario selection in a branch probability selection mode.
[Explanation of symbols]
1 Training system
2 Initial events
3 Event tree
4 Storage device
5 Simulation means
7 Input device
9 Selection means
K progress key
P branch probability

Claims (6)

A training system for simulating the progress of an event to be trained, comprising: a storage device for storing an event tree having an initial event and a plurality of progress keys; and a simulation means for expanding a scenario formed by the event tree. A training system comprising: An input device for inputting data designating one scenario to be developed among the plurality of scenarios formed by the event tree, wherein the simulation unit develops the scenario selected by the input data from the input device The training system according to claim 1, wherein: 2. The apparatus according to claim 1, further comprising: selecting means for randomly selecting one scenario from a plurality of scenarios formed by the event tree, wherein the simulation means expands the scenario selected by the selecting means. Or the training system according to 2. 4. The storage device according to claim 1, further comprising a storage device configured to store a branch probability of the progress key of the event tree, wherein the simulation unit forms and selects a scenario to be developed with reference to the branch probability. The training system according to 1. A scenario building method of a training system that simulates the evolution of an event to be trained in a pseudo manner. A method for constructing a scenario of a training system, comprising constructing a plurality of scenarios. The method according to claim 5, wherein a branch probability is associated with a progress key of the event tree.

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