Simulation device, simulation system, and simulation method

Abstract

To provide a technique capable of appropriately updating a model even when measurement data related to an object to be analyzed cannot be obtained during a simulation.SOLUTION: A simulation device updates a governing equation and performs a simulation. The simulation device comprises a processor and a memory that stores a program to be executed by the processor. The processor acquires an event flag that stores events included in a target to be analyzed by the governing equation. The processor generates data in which coefficients of respective terms of the governing equation are stored, for each event stored in the event flag. The processor updates the coefficients of respective terms of the governing equation to coefficients of the corresponding event in the data, on the basis of the event that is a target of the simulation based on the governing equation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a simulation device, a simulation system, and a simulation method. [Background technology]

[0002] Patent Document 1 describes a reduced-order simulation technique that uses data assimilation to determine weighting coefficients for a system to be analyzed so as to minimize the error between measured data and simulation results, as a means for improving the accuracy of reduced-order simulations. Patent Document 1 describes, for example, performing reduced-order modeling (ROM) of the system to be analyzed using snapshot data of velocity and temperature fields obtained by a thermal fluid simulation. Patent Document 1 also describes an example of a simulation program in which the system to be analyzed is represented by a linear sum of basis vectors and weighting coefficients in a number of dimensions fewer than the actual number of dimensions, a reduced-order simulation is performed, and the weighting coefficients are determined by data assimilation so as to minimize the error between the results of the reduced-order simulation being performed and the measured data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-26440 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 does not consider situations where measurement data for the simulation target is not available (cannot be obtained). For example, if the weighting coefficients of the model obtained by ROM are not updated using specific snapshot data, a simulation that assumes a situation that differs from the range of data in ROM (for example, when the temperature, flow velocity, etc. of the analysis target are extremely different) may not produce appropriate results.

[0005] In view of the above-mentioned problems, an object of the present invention is to provide a technology that can appropriately update a model even when measurement data related to an object to be analyzed cannot be obtained during a simulation. [Means for solving the problem]

[0006] According to a first aspect of the present invention, there is provided a simulation device as follows. The simulation device updates a governing equation and performs a simulation. The simulation device includes a processor and a memory for storing a program executed by the processor. The processor acquires an event flag that stores flag data related to an event that is the subject of analysis by the governing equation. The processor generates data that stores a coefficient of each term in the governing equation for each event stored in the event flag. The processor updates the coefficient of each term in the governing equation to the coefficient of the corresponding event in the data, based on the event that is the subject of a simulation based on the governing equation.

[0007] According to a second aspect of the present invention, there is provided a simulation system as follows. The simulation system includes a simulation device that updates a governing equation and performs a simulation, and a terminal device capable of communicating with the simulation device. The simulation device includes a processor, a memory that stores a program executed by the processor, and a communication interface device used for communication. The processor acquires an event flag that stores flag data related to an event that is the subject of analysis by the governing equation. The processor generates data that stores the coefficient of each term in the governing equation for each event stored in the event flag. The processor updates the coefficient of each term in the governing equation to the coefficient of the corresponding event in the data, based on the event that is the subject of a simulation based on the governing equation.

[0008] According to a third aspect of the present invention, there is provided a simulation method as follows: In this method, an event flag that stores flag data for each event is acquired; data that stores the coefficients of each term in a governing equation is generated for each event; an event to be simulated is identified, and the coefficients of each term in the governing equation are updated to the coefficients of the corresponding event in the data; and a simulation is performed using the updated governing equation. [Effects of the Invention]

[0009] According to the present invention, a technique is provided that can appropriately update a model even when measurement data regarding an analysis target cannot be obtained during simulation. Note that problems, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiment of the invention. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 2 is a diagram illustrating an example of a configuration of a simulation device and a data flow. [Figure 2] 10 is a flowchart illustrating an example of a process for generating a coefficient update map. [Figure 3]10 is a flowchart illustrating an example of a model update process during a simulation. [Figure 4A] FIG. 10 is a diagram illustrating an example of a map of event flags. [Figure 4B] FIG. 10 is a diagram illustrating an example of a coefficient update map. [Figure 5] FIG. 10 is a diagram illustrating an example of updating model coefficients using a coefficient update map. [Figure 6] FIG. 10 is a diagram for explaining the results of a simulation of indoor temperature caused by an air conditioner. [Figure 7] FIG. 2 illustrates an example of a hardware configuration of a simulation apparatus. [Figure 8] FIG. 1 is a diagram illustrating an example of a system using a simulation device. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiment is an example for explaining the present invention, and for clarity of explanation, appropriate omissions and simplifications have been made. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings. Examples of various types of information may be described using expressions such as "table," "list," and "queue," but the various types of information may also be expressed using data structures other than these. For example, various types of information such as "XX table," "XX list," and "XX queue" may also be expressed as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are interchangeable. When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. When there is no need to distinguish between these multiple components, the subscripts may be omitted. In the embodiments, processing performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., a CPU or a GPU) and performs processing defined by the program using storage resources (e.g., a memory) and interface devices (e.g., a communication port). Therefore, the entity performing the processing by executing the program may be the processor. Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be any computing unit, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device). A program may be installed on a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0012] A simulation device according to an embodiment will be described with reference to Figures 1 to 7. The simulation device is, for example, a device that can update a model using data obtained from equipment, sensors, etc. When a system to be analyzed is expressed as a linear sum of products of basis functions and weighting coefficients (sometimes referred to as a model, governing equation, or governing equation model), the simulation device has a function of appropriately updating weighting coefficients (sometimes referred to as coefficients or model coefficients) according to the simulation target in response to changes in various analysis conditions (events), even when measurement data related to the analysis target is not available during simulation.

[0013] <Configuration and features of the simulation device> FIG. 1 shows an example of the configuration of a simulation device. As shown in FIG. 1, the simulation device includes a coefficient data acquisition unit 1, a memory unit 2, an input unit 3, a coefficient calculation unit 4, a simulation unit 5, and an output unit 6. The coefficient data acquisition unit 1 acquires coefficient data of a model. The memory unit 2 stores data. The input unit 3 inputs data related to, for example, an event flag. The coefficient calculation unit 4 generates a coefficient update map of the model. The simulation unit 5 performs a simulation. The output unit 6 outputs the simulation results, information related to the updated model, and the like. Note that the diagram uses arrows to indicate an example of data flow.

[0014] The coefficient data acquisition unit 1 includes a data acquisition unit 10 capable of acquiring time-series data from sensors or the like, a model input unit 11 that inputs a governing equation model generated by a physical model, ROM (Reduced Order Modeling), or the like, and a state estimation unit 12 that performs state estimation (or data assimilation) using the data acquired from the data acquisition unit 10 and the model input from the model input unit 11. Note that state estimation and data assimilation involve finding statistically optimal values ​​from observed values ​​from sensors or the like and a model before assimilation.

[0015] The state estimation unit 12 estimates the model coefficients in addition to the state quantities from the data by capturing the model coefficients as a self-organizing state space model. This allows the model coefficients to be acquired as time-series data. The model coefficient data acquired in this way is stored in the storage unit 2.

[0016] The input unit 3 also inputs an event flag that stores information (flag data) about the conditions (events) of the time-series data acquired by the data acquisition unit 10. That is, the input unit 3 inputs an event flag that stores flag data about the target event to be analyzed by the governing equation. The event here refers to, for example, the weather, the day of the week, the presence or absence of people, the operating status of equipment, the operating conditions of equipment, etc., and is related to the environment, state, condition, constraint, etc. of the target to be analyzed. The input event flag is then stored in the storage unit 2 in association with the coefficient data. The event flag does not necessarily have to be acquired from the input unit 3, but may be acquired from the data acquisition unit 10 using a sensor, for example. For example, an event flag about an event such as the operating status of equipment or the operating conditions of equipment may be acquired. In addition, the input unit 3 accepts input of an event to be targeted when executing a simulation.

[0017] The coefficient calculation unit 4 generates a coefficient update map based on the event flags and model coefficient data stored in the storage unit 2. The coefficient update map is passed to the simulation unit 5, where it is referenced during simulation execution and used to update the governing equation model used in the simulation. The output unit 6 outputs, for example, the process and / or results of the simulation unit 5.

[0018] In the simulation device, the coefficient data acquisition unit 1, the coefficient calculation unit 4, and the simulation unit 5 are functional components, and can be realized, for example, by using hardware included in the simulation device, which will be described later.

[0019] The data acquisition unit 10 in the coefficient data acquisition unit 1 can be realized, for example, by a processor storing data such as a program in a memory and processing the data. Here, the data acquisition unit 10 may be realized using an interface device that acquires sensor information, etc., and the processor may control this interface device.

[0020] The model input unit 11 in the coefficient data acquisition unit 1 can be realized, for example, by a processor storing data such as a program in a memory and processing the data. Here, the model input unit 11 may be realized using an interface device that acquires the governing equation model, and the processor may control this interface device.

[0021] The state estimation unit 12 in the coefficient data acquisition unit 1 can be realized, for example, by a processor storing data such as a program in a memory and processing the data.

[0022] The coefficient calculation unit 4 can be realized, for example, by a processor storing data such as a program in a memory and processing the data.

[0023] The simulation unit 5 can be realized, for example, by a processor storing data such as a program in a memory and processing the data.

[0024] In addition, in the simulation device, the storage unit 2, input unit 3, and output unit 6 can be realized, for example, by using hardware included in the simulation device, which will be described later.

[0025] The storage unit 2 can be realized using a storage device, which can be configured using, for example, an auxiliary storage device.

[0026] The input unit 3 can be realized, for example, by a processor storing data such as a program in a memory and processing the data. Here, the input unit 3 may be realized using an interface device, and the processor may control this interface device.

[0027] The output unit 6 can be realized, for example, by a processor storing data such as a program in a memory and processing the data. Here, the output unit 6 may be realized using an interface device, and the processor may control this interface device.

[0028] Next, an example of data processing by the simulation device will be described in more detail with reference to Figures 2 and 3. Figure 2 is a flowchart for explaining an example of the flow from acquisition of coefficient data in the coefficient data acquisition unit 1, storage unit 2, input unit 3, and coefficient calculation unit 4 to generation of a coefficient update map.

[0029] The processor acquires information in the data acquisition unit 10 from sensors attached to the equipment, the equipment, the Internet (website), etc. (step S10). The processor sets a model, initial conditions, etc. required for state estimation in the state estimation unit 12 (step S20). The processor performs state estimation (or data assimilation) in the state estimation unit 12 (step S30). At this time, the processor also outputs, for example, state quantities and model coefficients input in the model input unit 11.

[0030] The processor acquires an event flag related to the data acquired in step S10 (step S40). Here, the processor can arbitrarily determine the type of event flag to acquire. As in the example described below, the processor may determine an event flag including the day of the week and the operating status of the device. The processor associates the model coefficient calculated in step S30 with the event flag acquired in step S40, and stores them as time-series data in the storage unit 2 (step S50).

[0031] The processor determines whether or not to perform a simulation (step S60). If a simulation is not to be performed, the process returns to step S10. If a simulation is to be performed, the process proceeds to step S70.

[0032] The processor classifies the coefficient data stored in the memory unit 2 by repeating steps S10 to S50 in the coefficient calculation unit 4 based on the event flags (step S70). At this time, the classification is not based on time series but only on the event flags (however, the event flags may be related to time).

[0033] The processor calculates update values ​​for the model coefficients in the coefficient calculation unit 4 using the data classified in step S70 (step S80). The processor calculates update values ​​by averaging the classified data group, for example. The processor generates a coefficient update map for each term of the model, which is a polynomial (step S90). The coefficient update map is provided as a matrix based on the event flag. The processor outputs the coefficient update map (step S100). The coefficient update map output by the processor is passed to the simulation unit 5.

[0034] 3 is a flowchart illustrating an example of the flow of model update during a simulation. The processor acquires the coefficient update map output in step S100 (step S110). The processor also receives input of an event to be simulated (step S120).

[0035] The processor extracts the event flag for the next time step (t+dt) during the simulation from the data acquired in step S120 (step S130). Here, t means time, and dt means the time interval in the simulation. The processor checks whether the event flag for the next time step (t+dt) extracted in step S130 matches the event flag for the previous time step (t) (step S140). If the event flags for the next time step (t+dt) and the previous time step (t) match, the processor determines that there is no need to update the model coefficients, and the process proceeds to step S180. If the event flags do not match, the processor determines that the model coefficients need to be updated, and the process proceeds to step S150.

[0036] The processor refers to the coefficient update map acquired in step S110 and acquires update values ​​(step S150). The processor updates the model coefficients of each term in the governing equation model using the update values ​​acquired in step S150 (step S160). The processor outputs the updated model from the output unit 6 and displays it on the display (step S170). The processor may also output a log file related to the updated model from the output unit 6.

[0037] The processor advances the calculation by one step (step S180). The processor checks whether the simulation is to be ended (step S190). If the simulation is not to be ended, the process returns to step S130.

[0038] <Example of coefficient update map> Next, an example of generating a coefficient update map in this embodiment and updating the coefficients of the governing equation model used in simulation will be described using FIGS. 4, 5, and 6, taking an example of an air conditioner.

[0039] 4A is a diagram for explaining input of an event flag for data acquired by the coefficient data acquisition unit 1. The event flag 13 is data structured to store flag data related to the conditions (events 14) for which the governing equations are to be analyzed. Here, the days of the week and the operation (ON or OFF) of the air conditioner are defined as the events 14 for the air conditioner.

[0040] As an example, the event flags 13 are represented by table data (event flag map 15) as shown in the figure. Here, the days of the week are represented by a to g, and OFF and ON are represented by 0 or 1. For time-series input data, one of the columns in the table will be 1, and the other columns will be 0. In other words, this matrix data (event flag map 15) is linked to the coefficient data obtained by the coefficient data acquisition unit 1.

[0041] FIG. 4B is an example of a generated coefficient update map. Coefficient update map 16 is an example of data storing the coefficients of each term in the governing equation for each event 14. As an example, this coefficient update map 16 is obtained as tabular data as shown in the same figure for each term in the governing equation model. Update values ​​17 of the model coefficients corresponding to the event flags 13 are entered in each column of the table. Here, the days of the week are A to G, OFF and ON are 0 or 1, and the subscripts represent the term numbers. In this way, data is generated that stores the coefficients of each term in the governing equation for each element of event 14 (in this example, days of the week A to G (Sunday to Saturday) and ON and OFF operation of the air conditioner).

[0042] FIG. 5 is a diagram for explaining the updating of model coefficients using the coefficient update map 16. For example, assume that the event flag 13 in the previous time step (t) is D1 (Wednesday, the air conditioner is ON). That is, the model before the update is expressed as in equation (1) using coefficient D1. Note that equation (1) corresponds to the equation of the model before the update shown in the same figure.

[0043] Here, f(t) is the governing equation model and x is the state variable. At the next time step (t+dt), the air conditioner remains on and the day of the week changes to Thursday. At this time, the coefficients of each term in equation (1) are updated to E1, and as a result, the model is updated to equation (2). This equation (2) corresponds to the equation of the updated model shown in the same figure. In other words, the processor refers to the coefficient update map 16, obtains the coefficient that indicates that the air conditioner is on on Thursday, and updates the model coefficients. In this way, the model coefficients are updated based on the event 14 that is the target of the simulation (in this case, the change in conditions from Wednesday to Thursday).

[0044] FIG. 6 shows an example of a 1D simulation in which the time variation of indoor temperature is predicted using a governing equation model generated using temperature data actually acquired from an air conditioner. (a) in the figure shows the case in which the coefficients of the governing equation model are not updated, while (b) in the figure shows the case in which the model coefficients are updated using the method of this embodiment during the simulation. (a) in the figure relates to conventional results. As shown in (b) in the figure, it can be seen that the accuracy of predicting the fluctuation of measured indoor temperature has improved.

[0045] Fig. 7 shows an example of the hardware configuration of a simulation device. As shown in Fig. 7, a simulation device 100 includes, for example, a processor 101, a storage device 102, and an interface device 103. The processor 101 is connected to various pieces of hardware via a bus.

[0046] The processor 101 is a device that executes predetermined processing and can be configured using semiconductor devices. The processor 101 may be configured to include, for example, a CPU (Central Processing Unit). The storage device 102 is a device that stores data and can be configured using a main storage device (memory), an auxiliary storage device, etc. The processor 101 can store data such as programs in the main storage device and execute predetermined processing. As an example, the main storage device is configured using RAM (Random Access Memory). The auxiliary storage device can store data such as programs in a non-volatile manner. The auxiliary storage device can be configured using an HDD (Hard Disk Drive), ROM (Read Only Memory), etc.

[0047] The interface device 103 is a device used for exchanging data with the outside world, and is configured using, for example, an input / output interface device and a communication interface device.

[0048] The input / output interface device is a device that acquires data from an externally connected device and outputs data to an externally connected device. For example, an operation device used by a user for operation may be connected to the input / output interface device, and the user's operation content may be input. Various devices, sensors, etc. may be connected to the input / output interface device, and the processor 101 may acquire data from the various devices, sensors, etc. via the input / output interface device. The simulation device may exchange data with a portable storage medium such as a USB flash drive connected to the input / output interface. A display device configured using a display may be connected to the input / output interface device, and the processor 101 may output data used to display information to the display device via the input / output interface device.

[0049] The communication interface device is a device that is connected to a network and used for communication. The processor 101 may input and output data via the communication interface. For example, the processor 101 may acquire data from various devices, sensors, etc. via communication.

[0050] 8 shows an example of the configuration of a system including a simulation device. This system S (simulation system) includes a simulation device 100a and a terminal device 200. The simulation device 100a is configured as a server, and includes, for example, a processor 101, a storage device 102, and a communication interface device 103a. The system S may be built on a LAN (Local Area Network). For example, in the system S, the simulation device 100a and the terminal device 200 may be installed in the same facility. However, for example, the simulation device 100a may be located in the cloud.

[0051] The communication interface 103a is a device that connects to a network and is used for communication. The processor 101 may input and output data via the communication interface. The processor 101 may acquire data from various devices, sensors, terminal devices 200, etc. via communication.

[0052] The terminal device 200 is used by a user of the simulation device 100a. The terminal device 200 is configured as a computer device capable of communicating with the simulation device 100a, and includes, for example, a processor 201, a storage device 202, a communication device 203, an input device 204, and a display device 205.

[0053] The processor 201 is a device that executes predetermined processing and can be configured using a CPU or the like. The storage device 202 can be configured using a main storage device and an auxiliary storage device. The communication device 203 is an interface device used for communication. The input device 204 is a device used to input user operation content. The user may input information such as starting a simulation or settings, and the simulation device 100a may acquire the content entered by the user and perform processing. The display device 205 is configured using a display and can display information. The simulation device 100a may transmit data used to display information to be provided to the user to the terminal device 200, and the processor 201 of the terminal device 200 may display information on the display device 205 using the data acquired from the simulation device 100a. The input device 204 and the display device 205 may be integrated into one device such as a touch panel display.

[0054] The system S may be configured to include a single terminal device 200. Alternatively, the system S may be configured to include a plurality of terminal devices 200. The configuration of the terminal device 200 is merely an example and may be changed as appropriate. For example, the input device 204 and the display device 205 may be omitted, and the terminal device may be a device that receives and acquires data from the simulation device 100a.

[0055] According to the above description, the following simulation device is provided as an example. This simulation device updates governing equations and performs simulations. The simulation device includes a processor and a memory that stores programs executed by the processor. The processor acquires an event flag that stores flag data related to events that are the subject of analysis by the governing equations. The processor generates data (e.g., a coefficient update map) that stores the coefficients of each term in the governing equations for each event stored in the event flag. The processor updates the coefficients of each term in the governing equations to the coefficients of the corresponding events in the data, based on the events that are the subject of simulations based on the governing equations.

[0056] Here, it is preferable that the coefficients of each term in the governing equation to be stored in the data (for example, a coefficient update map) are calculated based on values ​​acquired by performing state estimation or data assimilation using the data (observed values) acquired from the data acquisition unit 10 and the model input from the model input unit 11. In this case, the accuracy of the simulation can be improved.

[0057] The simulation apparatus can acquire observation values ​​and governing equations via an interface device. Therefore, the simulation apparatus may acquire observation values ​​of sensors and the like and governing equations input by a user via, for example, an input / output interface device. The simulation apparatus may also acquire observation values ​​of sensors and the governing equations via, for example, a communication interface device.

[0058] In order to automate tasks, processes, etc., without human intervention, the simulation device may perform automated processing (automated processing). For example, a program used for the automated processing may be stored in a storage device of the simulation device, and the simulation device may execute the program to acquire observation values ​​of sensors, governing equations, etc., via a communication interface device through the automated processing.

[0059] The simulation apparatus can acquire the event flag via the interface device. Therefore, the simulation apparatus may acquire the event flag input by the user via, for example, an input / output interface device. Also, the simulation apparatus may acquire the event flag input by the user via, for example, a communication interface device.

[0060] In order to achieve automation, the simulation device may perform an automation process. For example, a program used for the automation process may be stored in a storage device of the simulation device, and the simulation device may execute the program to acquire event flags from sensors, devices, the Internet, etc. via a communication interface device through the automation process.

[0061] The simulation device may output data displaying information about the updated governing equations. The simulation device may also output a log file related to the updated governing equations. This allows the user to understand the updated governing equations. The simulation device may also output data related to the updated governing equations at the timing of updating the governing equations.

[0062] Also provided is a system including a simulation apparatus and a terminal device. Here, as an example, the simulation apparatus may transmit data displaying information about the updated governing equations to the terminal device via the communication interface device. The simulation apparatus may transmit a log file related to the updated governing equations to the terminal device via the communication interface device. Note that the simulation apparatus may output data related to the updated governing equations at the timing of updating the governing equations.

[0063] The terminal device may also receive data transmitted from the simulation device that displays information about the updated governing equations. The terminal device may then perform a display based on the received data. The terminal device may also receive data transmitted from the simulation device that is a log file related to the updated governing equations.

[0064] Although the embodiments have been described above, the present invention is not limited to the above-described embodiments and includes various modifications and equivalent configurations within the spirit of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, for example, other configurations may be added, deleted, or replaced with part of the configuration of the embodiment.

[0065] In the embodiment, an example of application to an air conditioner is shown as a specific example, but the technology shown in the embodiment is not limited to a specific application, and by appropriately setting event flags, it can be applied to various analysis targets (plant facilities, industrial equipment, medical equipment, etc.). In addition, this technology can also be applied to cyber-physical systems, operation optimization, real-time control, and other applications where 1D simulation is expected to be used. Therefore, it has good versatility. [Explanation of symbols]

[0066] 1 Coefficient data acquisition section 2 Storage section 3 Input section 4 Coefficient calculation section 5 Simulation Section 6 Output section 10 Data Acquisition Section 11 Model input section 12 State estimation unit 13 Event Flags 14 Events 15 Event Flag Map 16 Coefficient update map 17 Update Value

Claims

1. A simulation device that updates a governing equation and performs a simulation, a processor; a memory for storing a program executed by the processor; Equipped with The processor: acquiring an event flag that stores flag data related to an event that is the subject of analysis by the governing equation; generating data storing coefficients of each term of the governing equation for each event stored in the event flag; updating a coefficient of each term of the governing equation to a coefficient of the corresponding event in the data, based on an event that is a target of a simulation based on the governing equation; A simulation device characterized by:

2. The simulation device according to claim 1, The processor: Calculating coefficients of each term of the governing equation to be stored in the data based on observed values ​​and values ​​obtained by performing state estimation using the governing equation or data assimilation; A simulation device characterized by:

3. 3. The simulation device according to claim 2, The processor: generating data storing an average value of the coefficient of each term of the calculated governing equation for each event stored in the event flag; A simulation device characterized by:

4. 3. The simulation device according to claim 2, a communication interface device for acquiring data from an external device through communication; The processor: acquiring the observed values ​​and the governing equation via the communication interface device; A simulation device characterized by:

5. The simulation device according to claim 4, The processor: obtaining the observations and the governing equations through an automated process; A simulation device characterized by:

6. The simulation device according to claim 1, a communication interface device for acquiring data from an external device through communication; The processor: acquiring the event flag via the communication interface device; A simulation device characterized by:

7. 7. The simulation device according to claim 6, The processor: acquiring the event flag through an automated process; A simulation device characterized by:

8. The simulation device according to claim 1, an interface device for outputting data to the outside, The processor: outputting, via the interface device, data indicative of information relating to the updated governing equations; A simulation device characterized by:

9. The simulation device according to claim 1, an interface device for outputting data to the outside, The processor: outputting a log file relating to the updated governing equations via the interface device; A simulation device characterized by:

10. a simulation device that updates the governing equations and performs a simulation; a terminal device capable of communicating with the simulation device; Equipped with The simulation device includes: a processor; a memory for storing a program executed by the processor; a communication interface device used for communication; Equipped with The processor: acquiring an event flag that stores flag data related to an event that is the subject of analysis by the governing equation; generating data storing coefficients of each term of the governing equation for each event stored in the event flag; updating a coefficient of each term of the governing equation to a coefficient of the corresponding event in the data, based on an event that is a target of a simulation based on the governing equation; A simulation system comprising:

11. The simulation system according to claim 10, The processor: transmitting data indicating information about the updated governing equations to the terminal device via the communication interface device; A simulation system comprising:

12. The simulation system according to claim 10, The processor: transmitting a log file relating to the updated governing equations to the terminal device via the communication interface device; A simulation system comprising:

13. The simulation system according to claim 10, The terminal device receiving data transmitted from the simulation device, the data indicating information regarding the updated governing equations; A simulation system comprising:

14. The simulation system according to claim 10, The terminal device receiving a log file relating to the updated governing equations, the log file being data transmitted from the simulation device; A simulation system comprising:

15. Gets the event flag that stores the flag data for each event. generating data storing coefficients of each term in a governing equation for each of the events; Identify the events to be simulated, updating the coefficient of each term in the governing equation to the coefficient of the corresponding event in the data; performing a simulation using the updated governing equations; A simulation method comprising:

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