JPWO2018142507A1 - Simulation method, system and program - Google Patents

Abstract

  A simulation method in a system for performing simulations with different grain sizes, the system using the first data used for the first simulation to generate the second data used for the second simulation Performing a first simulation based on the first data and the model of the first simulation to output a first simulation result, and based on the second data and the model of the second simulation , Executing a second simulation and outputting a second simulation result, and based on the first simulation result and the second simulation result, a model of the first simulation and a model of the second simulation. Step to correct at least one And, including the.

Description

  The present invention relates to a method of correcting a model of simulation.

  In various fields such as transportation, finance, distribution, and industry, with the progress of IoT, analysis and visualization of the current situation by statistical analysis processing or agent simulation using data collected from devices, and future prediction Etc. are done.

  The simulation model, which is simulation definition information, depends on the type and characteristics of data to be acquired. Therefore, it is necessary to correct the model in response to changes in purpose and environment. In particular, in simulations requiring real-time analysis, it is necessary to dynamically correct the simulation model in response to changes in the environment.

  The technique of Patent Document 1 is known as a technique for correcting a simulation model. Patent Document 1 includes “a simulation model storage unit that stores a simulation model, a simulation unit that receives the simulation model and performs a simulation, a real world statistical information storage unit that stores statistical information in the real world, and the simulation A simulation model correction method generation unit that generates a simulation model correction method based on the model and the statistical information; and a simulation model correction unit that corrects the simulation model based on the simulation model correction method and the simulation result; Is described.

U.S. Patent No. 6772363

  The technology described in Patent Document 1 corrects the simulation model so that the statistical information and the simulation result match, and therefore, can not be applied to real-time simulation and simulation considering future prediction.

  The present invention aims to provide a method, system and program for correcting a simulation model based on the current situation or the future prediction.

  The following is a representative example of the invention disclosed in the present application. That is, a simulation method in a system that executes simulations with different granularity, wherein the system comprises at least one computer having a processor, a memory connected to the processor, and a network interface connected to the processor. At least one computer includes model management information for managing a model that is definition information of a first simulation and a second simulation having a larger particle size than the first simulation, and a first for use in the first simulation. History management information for managing the history of data, and the simulation method is used for the second simulation by using the first data acquired by the at least one computer from the history management information Second done Performing the second simulation based on a first step of generating data, and the at least one computer based on the second data and the model of the second simulation acquired from the model management information; A second step of outputting a second simulation result, and the model of the first simulation acquired from the first data acquired from the history management information and the model management information by the at least one computer And a third step of executing the first simulation to output a first simulation result, and the at least one computer based on the first simulation result and the second simulation result. A model of the first simulation and the second simulation Characterized in that it comprises a fourth step of correcting at least one of emissions model, a.

  According to the present invention, it is possible to realize simulation in consideration of real time simulation and future prediction. Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

FIG. 2 is a diagram showing a hardware configuration of a simulation system of Example 1; FIG. 2 is a diagram showing a software configuration of a simulation system of Example 1; FIG. 7 is a diagram showing an operation of a simulation system in correction processing of a simulation model of the first embodiment. 5 is a flowchart illustrating an example of processing executed by the integrated processing server according to the first embodiment. It is a flowchart explaining an example of the process which the micro simulation execution server of Example 1 performs. It is a flowchart explaining an example of the process which the micro simulation execution server of Example 1 performs. 5 is a flowchart illustrating an example of a simulation model correction process performed by the integrated processing server according to the first embodiment. FIG. 18 is a diagram showing an example of history management information of the second embodiment. FIG. 18 is a diagram showing an example of space definition information of the second embodiment. FIG. 7 is a view showing an example of observation data of Example 2; FIG. 7 is a diagram showing an example of statistical data of Example 2; FIG. 16 is a diagram showing an example of prediction data of Example 2. FIG. 16 is a diagram showing an example of prediction data of Example 2. FIG. 18 is a diagram showing an example of statistics calculated from processing results of the micro simulation of the second embodiment. FIG. 18 is a diagram showing an example of statistics calculated from processing results of the micro simulation of the second embodiment. FIG. 18 is a diagram showing an example of internal information output by the macro simulation execution server according to the second embodiment. FIG. 18 is a diagram illustrating an example of space definition information according to the third embodiment.

  Hereinafter, an embodiment according to the present invention will be described with reference to the attached drawings. The same reference numerals are given to the same configuration in each drawing.

  FIG. 1 is a diagram illustrating a hardware configuration of a simulation system according to a first embodiment.

  The simulation system shown in FIG. 1 includes an integrated processing server 100, a database server 110, a macro simulation execution server 120, a micro simulation execution server 130, and a user terminal 140. The respective devices are connected via a network 190. The type of the network 190 may be WAN (Wide Area Network), LAN (Local Area Network), or the like. Also, the connection method of the network 190 may be either wired or wireless.

  The integrated processing server 100 corrects a simulation model which is definition information of simulation. The simulation model includes parameters characterizing the simulation, variables into which input values are substituted, and the like. The integrated processing server 100 includes a CPU 101, a memory 102, a storage device 103, and a network interface 104 as hardware. Each hardware is connected via an internal bus or the like.

  The CPU 101 executes a program stored in the memory 102. The CPU 101 executes processing according to a program to operate as a module having a predetermined function. In the following description, when processing is described with a module as a subject, it indicates that the CPU 101 is executing a program for realizing the module.

  The memory 102 stores a program executed by the CPU 101 and information necessary for the program. The memory 102 also includes a work area temporarily used by the program.

  The storage device 103 stores data permanently. The storage device 103 may be a storage medium such as a hard disk drive (HDD) or a solid state drive (SSD), a non-volatile memory, or the like. The programs and information stored in the memory 102 may be stored in the storage device 103. In this case, the CPU 101 reads the program and information from the storage device 103, loads the program and information into the memory 102, and executes the program loaded into the memory 102.

  The network interface 104 connects to other devices via a network.

  The database server 110 manages various data. The database server 110 has a CPU 111, a memory 112, a storage device 113, and a network interface 114 as hardware. Each hardware is connected via an internal bus or the like.

  The CPU 111, the memory 112, the storage device 113, and the network interface 114 are hardware similar to the CPU 101, the memory 102, the storage device 103, and the network interface 104.

  The macro simulation execution server 120 executes macro simulation. For example, as a macro simulation on traffic, a simulation that predicts traffic volume, traffic congestion, etc. can be considered. The macro simulation execution server 120 has a CPU 121, a memory 122, a storage device 123, and a network interface 124 as hardware. Each hardware is connected via an internal bus or the like.

  The CPU 121, the memory 122, the storage device 123, and the network interface 124 are hardware similar to the CPU 101, the memory 102, the storage device 103, and the network interface 104.

  The micro simulation execution server 130 executes micro simulation with smaller particle size than macro simulation. For example, as a micro simulation on traffic, a simulation that predicts an optimal route of an individual vehicle can be considered. The micro simulation execution server 130 includes a CPU 131, a memory 132, a storage device 133, and a network interface 134 as hardware. Each hardware is connected via an internal bus or the like.

  The CPU 131, the memory 132, the storage device 133, and the network interface 134 are hardware similar to the CPU 101, the memory 102, the storage device 103, and the network interface 104.

  The user terminal 140 is a terminal used by a user. The user uses the user terminal 140 to instruct simulation execution and correction of the simulation model. The user terminal 140 has a CPU 141, a memory 142, a storage device 143, a network interface 144, an input device 145, and an output device 146 as hardware. Each hardware is connected via an internal bus or the like.

  The CPU 141, the memory 142, the storage device 143, and the network interface 144 are hardware similar to the CPU 101, the memory 102, the storage device 103, and the network interface 104.

  The input device 145 is a device for inputting data and the like, and includes a keyboard, a mouse, a touch panel and the like.

  The output device 146 is a device for outputting data and the like, and includes a display, a touch panel, and the like.

  FIG. 2 is a diagram showing a software configuration of the simulation system of the first embodiment.

  The database server 110 holds simulation model information 211, history management information 212, and space definition information 213.

  The simulation model information 211 is information for managing a simulation model. The simulation model information 211 stores one or more macro simulation simulation models and one or more micro simulation simulation models.

  The history management information 212 stores various data acquired from an agent in micro simulation.

  The space definition information 213 is information for generating data (statistical data) used for macro simulation from data stored in the history management information 212. For example, in micro simulation for analyzing the movement of a moving object, information such as a space in which the moving object moves and a time range of acquired data is stored in the space definition information 213.

  The integrated processing server 100 holds a program that implements the preprocessing module 201, the statistical data generation module 202, and the model correction module 203.

  The preprocessing module 201 generates observation data 301 (see FIG. 3) used for micro simulation from the history management information 212.

  The statistical data generation module 202 generates statistical data 303 (see FIG. 3) used for micro simulation based on the observation data 301 and the space definition information 213.

  The model correction module 203 corrects the simulation model based on the processing result of the macro simulation and the processing result of the micro simulation.

  The macro simulation execution server 120 holds a program that implements a macro simulation execution module 221 that executes macro simulation.

  The micro simulation execution server 130 holds a program that implements a micro simulation execution module 231 that executes micro simulation.

  The user terminal 140 holds a program for realizing the application 241 for performing operations on the integrated processing server 100 and the like.

  FIG. 3 is a diagram showing the operation of the simulation system in the correction process of the simulation model of the first embodiment.

  The integrated processing server 100 calls the preprocessing module 201 when receiving a correction request for a simulation model from the application 241 of the user terminal 140.

  The preprocessing module 201 accesses the database server 110 to acquire space definition information 213, and acquires data of a predetermined time range from the history management information 212. The preprocessing module 201 executes processing such as data conversion processing and data extraction processing using data acquired from the space definition information 213 and the history management information 212 to generate observation data 301.

  Next, the integrated processing server 100 calls the statistical data generation module 202. At this time, observation data 301 and space definition information 213 are input to the statistical data generation module 202.

  The statistical data generation module 202 generates statistical data 303 using the observation data 301 and the space definition information 213.

  Next, the integrated processing server 100 transmits a macro simulation execution instruction including the statistical data 303 to the macro simulation execution server 120, and transmits a micro simulation execution instruction including the observation data 301 to the micro simulation execution server 130. .

  When the macro simulation execution server 120 receives the execution instruction, the macro simulation execution server 120 calls the macro simulation execution module 221.

  The macro simulation execution module 221 accesses the database server 110 and acquires a simulation model of macro simulation from the simulation model information 211. The macro simulation execution module 221 executes macro simulation using the simulation model and the statistical data 303, and transmits the processing result as the prediction data 304 to the integrated processing server 100.

  The micro simulation execution server 130 calls the micro simulation execution module 231 when receiving the execution instruction.

  The micro simulation execution module 231 accesses the database server 110 and acquires a simulation model of micro simulation from the simulation model information 211. The micro simulation execution module 231 executes micro simulation using the simulation model and the observation data 301, and transmits the processing result as the prediction data 302 to the integrated processing server 100.

  When receiving the predicted data 302 and 304 from the macro simulation execution server 120 and the micro simulation execution server 130, the integrated processing server 100 calls the model correction module 203. At this time, prediction data 302 and 304 are input to the model correction module 203.

  The model correction module 203 accesses the database server 110 and acquires a simulation model of macro simulation and a simulation model of micro simulation from the simulation model information 211. The model correction module 203 corrects the simulation model of micro simulation and / or the simulation model of macro simulation using the prediction data 302 and 304. In this embodiment, it is assumed that a simulation model of micro simulation is corrected. The model correction module 203 also registers the corrected simulation model of micro simulation in the simulation model information 211 as a simulation model of micro simulation to be executed.

  Thus, the system of this embodiment executes micro simulation, performs macro simulation using statistical data 303 generated from data used for micro simulation, and sets simulation models so that each processing result is consistent. to correct.

  In the past, the simulation model was corrected so as to match the history, so that it was not always possible to realize the simulation corresponding to the state change. On the other hand, in the present embodiment, the results of each simulation are compared, and each simulation model is corrected based on the comparison result. Since the simulation is a process of predicting various situation changes, it is possible to perform a simulation corresponding to a state change such as an environmental change by performing the correction as in this embodiment.

  FIG. 4 is a flowchart for explaining an example of processing executed by the integrated processing server 100 according to the first embodiment.

  The integrated processing server 100 receives a simulation model correction request from the user terminal 140 (step S401).

  Next, the integrated processing server 100 acquires space definition information 213, and acquires data of a predetermined time range from the history management information 212 (step S402).

  Specifically, the preprocessing module 201 accesses the database server 110 and acquires data of a predetermined time range from the history management information 212. The time range may be specified by the user or may be set in advance. Also, the preprocessing module 201 may determine the time range with reference to a simulation model of macro simulation.

  Next, the integrated processing server 100 generates observation data 301 (step S403).

  Specifically, the preprocessing module 201 generates observation data 301 using data acquired from the space definition information 213 and the history management information 212.

  Next, the integrated processing server 100 generates statistical data 303 (step S404).

  Specifically, the statistical data generation module 202 generates statistical data 303 using the space definition information 213 and the observation data 301.

  Next, the integrated processing server 100 transmits a macro simulation execution instruction including the statistical data 303 to the macro simulation execution server 120 (step S405).

  Next, the integrated processing server 100 receives the prediction data 304 from the macro simulation execution server 120 (step S406). At this time, the integrated processing server 100 sets the received prediction data 304 as an index for determining whether the correction of the simulation model is necessary.

  Next, the integrated processing server 100 transmits, to the micro simulation execution server 130, a micro simulation execution instruction including the observation data 301 (step S407). The integrated processing server 100 may transmit a micro simulation execution instruction before receiving the prediction data 304.

  Next, the integrated processing server 100 executes simulation model correction processing (step S408). Details of the correction process of the simulation model will be described with reference to FIG.

  In addition, a present Example is not limited to the processing content of macro simulation and micro simulation.

  5A and 5B are flowcharts illustrating an example of processing executed by the micro simulation execution server 130 according to the first embodiment.

  FIG. 5A shows processing in the case where there is one simulation model of micro simulation stored in the simulation model information 211.

  When the micro simulation execution module 231 receives the execution instruction, the micro simulation execution module 231 accesses the database server 110 and acquires a simulation model from the simulation model information 211 (step S501).

  The micro simulation execution module 231 executes micro simulation using the acquired simulation model and the observation data 301 included in the execution instruction (step S502).

  The micro simulation execution module 231 transmits the prediction data 302 which is the processing result of the micro simulation to the integrated processing server 100 (step S503), and transmits a completion notification (step S504).

  FIG. 5B shows processing in the case where there are two or more micro simulation simulation models stored in the simulation model information 211.

  When the micro simulation execution module 231 receives the execution instruction, the micro simulation execution module 231 accesses the database server 110 and acquires simulation models of all micro simulations from the simulation model information 211 (step S511).

  The micro simulation execution module 231 selects one target simulation model from among a plurality of simulation models (step S512).

  The micro simulation execution module 231 executes micro simulation using the target simulation model and the observation data 301 included in the execution instruction (step S513).

  The micro simulation execution module 231 transmits the prediction data 302 to the integrated processing server 100 (step S514). At this time, the micro simulation execution module 231 transmits the prediction data 302 together with identification information of the target simulation model.

  The micro simulation execution module 231 determines whether the processing has been completed for all the acquired simulation models (step S515).

  If it is determined that the process has not been completed for all the acquired simulation models, the micro simulation execution module 231 returns to step S512 and executes the same process.

  If it is determined that the processing has been completed for all the acquired simulation models, the micro simulation execution module 231 transmits a completion notification to the integrated processing server 100 (step S516).

  The micro simulation execution module 231 may present internal information indicating the state of the agent after simulation in response to a request from the integrated processing server 100 or the user terminal 140.

  FIG. 6 is a flowchart for explaining an example of a simulation model correction process performed by the integrated processing server 100 according to the first embodiment.

  When receiving the response to the execution instruction from the micro simulation execution server 130, the integrated processing server 100 calls the model correction module 203.

  The model correction module 203 determines whether the received response is a completion notification (step S601).

  If it is determined that the received response is the prediction data 302, the model correction module 203 stores the received prediction data 302 in the work area of the memory 102 (step S602). Thereafter, the model correction module 203 shifts to the waiting state, and returns to step S601 if a response is received.

  When the prediction data 302 and the identification information of the simulation model are included, the model correction module 203 associates the prediction data 302 and the identification information of the simulation model, and stores them in the work area of the memory 102.

  If it is determined that the received response is a completion notification, the model correction module 203 determines whether the prediction data 302 has been received multiple times (step S603).

  Specifically, the model correction module 203 refers to the work area of the memory 102 to determine whether two or more pieces of prediction data 302 are stored.

  If it is determined that the prediction data 302 has been received a plurality of times, the model correction module 203 calculates statistics of each simulation model (step S604).

  Specifically, the model correction module 203 selects one prediction data 302, and uses the prediction data 302 to calculate a statistic that can be compared with the processing result of the macro simulation. It is assumed that the method of calculating the statistic is set in advance. In addition, the calculation method of a statistic may be matched and managed with a simulation model. The model correction module 203 executes the same process on all the prediction data 302 stored in the work area of the memory 102.

  Next, the model correction module 203 calculates the difference between the statistics of each simulation model and the determination index, and selects a simulation model to be actually used based on the difference (step S605). At this time, the model correction module 203 gives the selected simulation model a flag indicating that it is a simulation model to be used. Thereafter, the model correction module 203 ends the correction process of the simulation model.

  The micro simulation execution module 231 executes the micro simulation using the simulation model to which the flag is attached, when performing the micro simulation for the purpose of normal analysis.

  Specifically, the model correction module 203 calculates the difference between the statistic and the determination index, and selects a simulation model corresponding to the statistic with the smallest difference.

  If it is determined in step S603 that the prediction data 302 has not been received a plurality of times, the model correction module 203 calculates statistics of the simulation model (step S606). The method of calculating the statistic is the same as step S604.

  Next, the model correction module 203 determines whether the difference between the statistical value of the simulation model and the determination index is equal to or less than the threshold value m (step S 607).

  If it is determined that the difference is equal to or less than the threshold value m, the model correction module 203 ends the correction process of the simulation model.

  If it is determined that the difference is larger than the threshold m, the model correction module 203 corrects the simulation model (step S608), and registers the corrected simulation model in the simulation model information 211. For example, the model correction module 203 may consider a method of correcting parameters included in a simulation model.

  The micro simulation execution module 231 executes the micro simulation using the corrected simulation model when performing the micro simulation for the purpose of ordinary analysis.

  Next, the model correction module 203 transmits a micro simulation execution instruction to the micro simulation execution server 130 (step S609). Thereafter, the model correction module 203 ends the correction process of the simulation model. Therefore, the correction of the simulation model is performed until the difference between the statistics of the simulation model and the determination index becomes equal to or less than the threshold value m.

  As described above, in the first embodiment, the simulation model of the micro simulation is corrected to match the processing result of the macro simulation.

  For example, by generating statistical data using data between a current time and a predetermined time before, it is possible to correct a simulation model reflecting a global state change or the like. In addition, it is not necessary to prepare in advance statistical data to be used for macro simulation.

  In addition, although the example which correct | amends the simulation model of micro simulation in Example 1 was shown, the simulation model of macro simulation may be correct | amended.

  In the present embodiment, each server is realized using a physical computer, but each server may be realized using a virtualization technology. Also, the functions possessed by each server may be integrated into one computer. For example, the integrated processing server 100, the macro simulation execution server 120, and the micro simulation execution server 130 may be integrated into one computer. Further, the integrated processing server 100 and the database server 110 may be integrated into one computer.

  In the second embodiment, the simulation system of the first embodiment will be described using specific values. The simulation system of the second embodiment simulates the operation of a ship. The system configuration and the software configuration are omitted in the first embodiment because they are described. The agent of micro simulation of Example 2 is a ship.

  FIG. 7 is a diagram of an example of the history management information 212 according to the second embodiment.

  The history management information 212 stores ship state information. Specifically, the history management information 212 includes an entry composed of a time 701, a mobile unit ID 702, a longitude 703, a latitude 704, and a destination 705.

  Time 701 is the time when the state information is acquired. The mobile body ID 702 is identification information that uniquely identifies a ship. The longitude 703 and the latitude 704 are information indicating the detailed position of the ship. The destination 705 is information indicating a destination of the ship.

  FIG. 8 is a diagram showing an example of space definition information 213 of the second embodiment.

  The space definition information 213 includes information 800 defining a space (XY plane) in which the ship moves, and information 810 defining a time of history.

  Information 800 includes an entry composed of space dimension 801 and space mesh 802. The space dimension 801 is identification information of coordinates that define a space. The space mesh 802 is a division unit of space in the coordinate direction.

  Information 810 includes an entry comprised of time dimension 811 and time step 812. The time dimension 811 is information indicating a unit time for acquiring a history. The time step 812 is information indicating a time unit for collecting state information.

  FIG. 9 is a view showing an example of observation data 301 of the second embodiment.

  Observation data 301 of the second embodiment is data used by the micro simulation. Specifically, the input data is an entry composed of time 901, mobile ID 902, position (X) 903, position (Y) 904, speed (X) 905, speed (Y) 906, and destination 907. Including.

  The time 901 is the same as the time 701. The mobile ID 902 is the same as the mobile ID 702. Position (X) 903 indicates the position of the X axis of the ship in space. Position (Y) 904 indicates the position of the Y axis of the ship in space. The velocity (X) 905 indicates the velocity in the X axis direction of the ship. The velocity (Y) 906 indicates the velocity in the Y-axis direction of the ship. The destination 907 is the same as the destination 705.

  FIG. 10 is a diagram of an example of statistical data 303 of the second embodiment.

  The statistical data 303 of Example 2 indicates the density of ships in space at a certain time. Specifically, statistical data 303 includes an entry composed of time 1001, point ID 1002, and density 1003.

  A time 1001 indicates a time or the like which is a starting point of generation of statistical data. Point ID 1002 is identification information of a certain point (area) in the space. The density 1003 is the density of the ship at the point corresponding to the point ID 1002.

  FIG. 11 is a diagram of an example of the prediction data 304 of the second embodiment.

  The prediction data 304 of the second embodiment has the same data structure as that of the statistical data 303. The prediction data 304 stores information indicating the density of each point after the macro simulation is performed. The time after macro simulation is set as time 1101.

  In the second embodiment, the density of each point is set as the determination index.

  FIG. 12 is a diagram illustrating an example of the prediction data 302 of the second embodiment.

  The prediction data 302 of the second embodiment stores information indicating the state of each ship after the micro simulation has been performed. Specifically, the prediction data 302 includes an entry including a time 1201, a mobile unit ID 1202, a position (X) 1203, a position (Y) 1204, and a destination 1205.

  Time 1201 is a time after micro simulation. The mobile object ID 1202 and the destination 1205 are the same as the mobile object ID 902 and the destination 907. Position (X) 1203 and position (Y) 1204 are positions of the X-axis and Y-axis of the ship after micro simulation.

  13A and 13B are diagrams showing an example of statistics calculated from the processing result of the micro simulation of the second embodiment.

  In the second embodiment, based on the prediction data 302, the density of the ship at each point is calculated as a statistic. The statistic includes an entry composed of a time 1301, a point ID 1302, a density 1303, a determination index 1304, and a difference 1305.

  The time 1301, the point 1302, and the density 1303 are the same as the time 1001, the point ID 1002, and the density 1003. The determination index 1304 is the same as the density 1103. The difference 1305 is the size of the difference between the density 1303 and the determination index 1304.

  FIG. 14 is a diagram illustrating an example of internal information output by the macro simulation execution server 120 according to the second embodiment.

  The internal information stores information indicating the state of the ship after micro simulation. Specifically, the internal information includes an entry composed of time 1401, mobile unit ID 1402, speed 1403, and traveling direction 1404.

  Time 1401 is a time after simulation. The mobile ID 1402 is the same as the mobile ID 702. The velocity 1403 is an absolute value of the velocity of the ship. The traveling direction 1404 is the traveling direction of the ship.

  Here, the flow of the process of the second embodiment will be described with reference to FIG.

  The preprocessing module 201 generates observation data 301 shown in FIG. 9 using the history management information 212 shown in FIG. 7 and the space definition information 213 shown in FIG.

  The statistical data generation module 202 generates statistical data 303 shown in FIG. 10 using the space definition information 213 shown in FIG. 8 and the observation data 301 shown in FIG.

  The macro simulation execution module 221 executes macro simulation using the statistical data 303 shown in FIG. 10 to calculate the density of each point after 5 minutes. Specifically, prediction data 304 shown in FIG. 11 is output.

  The micro simulation execution module 231 executes micro simulation using the observation data 301 shown in FIG. 9 to calculate the position of each ship after 5 minutes. Specifically, prediction data 302 shown in FIG. 12 is output.

  Here, it is assumed that the micro simulation shown in FIG. 5A is performed.

  The model correction module 203 sets the prediction data 304 as the determination index of each point, and calculates statistics using the prediction data 302. The model correction module 203 calculates the difference between the determination index and the statistic for each point. As the above processing results, results as shown in FIGS. 13A and 13B are output.

  The model correction module 203 determines whether the total value of the differences at each point is equal to or less than the threshold value m. If it is determined that the sum of the differences at each point is greater than the threshold m, the simulation model is corrected.

  As described above, by correcting the simulation model of the micro simulation so that the processing result of the macro simulation and the processing result of the micro simulation match, it is possible to perform a simulation corresponding to a future state change.

  In the third embodiment, the simulation system of the first embodiment will be described using specific values. The simulation system of the third embodiment simulates sales and the like of products. The system configuration and the software configuration are omitted in the first embodiment because they are described. The agent of the micro simulation of the third embodiment is a customer. Therefore, the history management information 212 stores the purchase history of the product of the customer in chronological order.

  Here, the flow of the process of the second embodiment will be described with reference to FIG.

  The preprocessing module 201 uses the history management information 212 and the space definition information 213 to make observation data including an entry including a group of fields indicating identification information of purchased goods, number of purchases, purchase amount, presence or absence of advertisement recognition, etc. Generate 301.

  Here, the space definition information 213 of the third embodiment will be described. FIG. 15 is a diagram illustrating an example of space definition information 213 according to the third embodiment.

  The space definition information 213 of the third embodiment has the same data structure as the space definition information 213 of the second embodiment. In the third embodiment, not the physical space but the space of the product is assumed. The space is composed of an axis indicating a product category and an axis indicating a product grade. The space mesh 802 stores information indicating the division of axes of the product category, and information indicating the division of axes of the product grade.

  Further, as indicated by the information 810, the history management information 212 stores a purchase history on a date basis, and indicates that statistical data is generated by aggregating the purchase history on a weekly basis.

  It returns to the explanation of FIG.

  The statistical data generation module 202 generates statistical data 303 using the observation data 301 and the space definition information 213 shown in FIG. The statistical data includes an entry composed of fields indicating sales after one week of each product category, the number of sales, and the advertisement recognition rate.

  The macro simulation execution module 221 executes macro simulation using the statistical data 303 and outputs prediction data 304. The prediction data 304 includes an entry made up of fields indicating sales of each product category one week later, the number of sales, and the advertisement recognition rate.

  The micro simulation execution module 231 executes micro simulation using the observation data 301 and outputs prediction data 302. The prediction data 302 includes an entry made up of a group of fields indicating the purchase item, the number of purchases, the purchase amount, and the advertisement recognition status of each customer one week later.

  Here, it is assumed that the micro simulation shown in FIG. 5A is performed.

  The model correction module 203 sets the forecast data 304 to sales of each product category as a determination index, and uses the forecast data 302 to calculate a statistic indicating the sales of each product category. The model correction module 203 calculates the difference between the determination index and the statistic for each product category.

  The model correction module 203 determines whether the total value of the differences of each product category is equal to or less than the threshold value m. If it is determined that the sum of the differences of each product category is larger than the threshold m, the simulation model is corrected.

  As described above, by correcting the simulation model of the micro simulation so that the processing result of the macro simulation and the processing result of the micro simulation match, it is possible to perform a simulation corresponding to a future state change.

  The present invention is not limited to the embodiments described above, but includes various modifications. Further, for example, the above-described embodiments are described in detail in order to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to those having all the described configurations. In addition, part of the configuration of the embodiment can be added to, deleted from, or replaced with another configuration.

  Further, each of the configurations, functions, processing units, processing means, etc. described above may be realized by hardware, for example, by designing part or all of them with an integrated circuit. The present invention can also be realized by a program code of software that realizes the functions of the embodiment. In this case, a storage medium storing the program code is provided to the computer, and a CPU provided in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium implements the functions of the above-described embodiments, and the program code itself and the storage medium storing the same constitute the present invention. As a storage medium for supplying such a program code, for example, a flexible disk, a CD-ROM, a DVD-ROM, a hard disk, an SSD (Solid State Drive), an optical disk, a magneto-optical disk, a CD-R, a magnetic tape, A non-volatile memory card, ROM or the like is used.

  Further, program code for realizing the functions described in the present embodiment can be implemented by a wide range of programs or script languages such as assembler, C / C ++, perl, shell, PHP, Java, and the like.

  Furthermore, by distributing the program code of the software for realizing the functions of the embodiment through a network, the program code is stored in a storage means such as a hard disk or a memory of a computer or a storage medium such as a CD-RW or CD-R. The CPU included in the computer may read out and execute the program code stored in the storage unit or the storage medium.

  In the above-described embodiment, the control lines and the information lines indicate what is considered necessary for the description, and not all the control lines and the information lines in the product are necessarily shown. All configurations may be connected to each other.

Claims (12)

A simulation method in a system that executes simulations with different particle sizes,
The system comprises at least one computer having a processor, a memory connected to the processor, and a network interface connected to the processor.
The at least one computer includes model management information for managing a model that is definition information of a first simulation and a second simulation having a larger particle size than the first simulation, and a first used for the first simulation. Hold history management information to manage the history of data, and
The simulation method is
A first step of generating the second data to be used for the second simulation by using the first data acquired from the history management information by the at least one computer;
The at least one computer executes the second simulation based on the second data and the model of the second simulation acquired from the model management information, and outputs a second simulation result. 2 steps,
The at least one computer executes the first simulation on the basis of the first data acquired from the history management information and the model of the first simulation acquired from the model management information. The third step of outputting the simulation results of
A fourth step in which the at least one computer corrects at least one of the model of the first simulation and the model of the second simulation based on the first simulation result and the second simulation result And a simulation method characterized by including. The simulation method according to claim 1, wherein
The fourth step is
A fifth step of calculating a statistic that is comparable to the second simulation result using the first simulation result;
A sixth step of correcting a model of the first simulation based on a comparison result of the statistic and the second simulation result. The simulation method according to claim 2, wherein
The sixth step is
Calculating a difference between the statistic and the second simulation result;
Correcting the model of the first simulation so as to reduce the difference. The simulation method according to claim 2, wherein
The model management information stores models of a plurality of first simulations,
In the third step, the first simulation result is calculated for each of the plurality of first simulation models;
In the fifth step, the statistic is calculated for each of the plurality of first simulation results;
The sixth step is
Calculating a difference between each of the plurality of statistics and the second simulation result;
Determining a model of a first simulation corresponding to the statistic that minimizes the difference as a model to be used for the first simulation. A system that performs simulations with different particle sizes, and
The system comprises at least one computer having a processor, a memory, and a network interface,
A model management unit that manages a model that is definition information of a first simulation and a second simulation having a larger particle size than the first simulation;
A history management unit that manages a history of first data used for the first simulation;
A generation unit that generates second data used for the second simulation based on the first data managed by the history management unit;
A first simulation execution unit that executes the first simulation based on the first data acquired from the history management unit and the model of the first simulation, and outputs a first simulation result;
A second simulation execution unit that executes the second simulation based on the second data generated by the generation unit and the model of the second simulation, and outputs a second simulation result;
A system comprising: a model correction unit configured to correct a model of the first simulation and a model of the second simulation based on the first simulation result and the second simulation result. The system according to claim 5, wherein
The model correction unit
Calculating a statistic that is comparable to the second simulation result using the first simulation result;
A system, comprising: correcting a model of the first simulation based on a comparison result of the statistic and the second simulation result. The system according to claim 6, wherein
The model correction unit
Calculating the difference between the statistic and the second simulation result;
A system comprising: correcting a model of the first simulation such that the difference is reduced. The system according to claim 6, wherein
The model management unit manages a plurality of first simulation models;
The first simulation execution unit calculates the first simulation result for each of the plurality of first simulation models,
The model correction unit
Calculating the statistic for each of the plurality of first simulation results;
Calculating a difference between each of the plurality of statistics and the second simulation result;
A system characterized in that a model of a first simulation corresponding to a statistic that minimizes the difference is determined as a model used for the first simulation. A program executed by a computer that executes simulations with different particle sizes,
The computer is
A processor, a memory connected to the processor, and at least one computer having a network interface connected to the processor;
Managing a history of first data used in the first simulation and model management information for managing a model which is definition information of a second simulation having a larger particle size than the first simulation and the first simulation; History management information, and the program holds
A first procedure of generating second data used for the second simulation using the first data acquired from the history management information;
A second procedure for executing the second simulation based on the second data and the model of the second simulation acquired from the model management information, and outputting a second simulation result;
The first simulation is executed based on the first data acquired from the history management information and the model of the first simulation acquired from the model management information, and a first simulation result is output. 3 steps,
Performing, on the computer, a fourth procedure of correcting at least one of the model of the first simulation and the model of the second simulation based on the first simulation result and the second simulation result A program that is characterized by The program according to claim 9, wherein
The fourth procedure is
A fifth procedure of calculating a statistic that is comparable to the second simulation result using the first simulation result;
And a sixth procedure for correcting the model of the first simulation based on the comparison result of the statistic and the second simulation result. The program according to claim 10, wherein
The sixth procedure is
A procedure for calculating the difference between the statistic and the second simulation result;
Correcting the model of the first simulation so as to reduce the difference. The program according to claim 10, wherein
The model management information stores models of a plurality of first simulations,
The third procedure calculates the first simulation result for each of the plurality of first simulation models,
In the fifth procedure, the statistic is calculated for each of the plurality of first simulation results,
The sixth procedure is
Calculating the difference between each of the plurality of statistics and the second simulation result;
And determining a model of a first simulation corresponding to the statistic with the smallest difference as the model used for the first simulation.

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