Economic Evaluation Simulation Device and Economic Evaluation Simulation Method

Abstract

To enhance accuracy of economic efficiency evaluation for robot business and the like responding to such an unknown environment that a physical property value, a boundary condition and the like cannot be accurately determined beforehand.SOLUTION: An economic efficiency evaluation simulation method of the present invention uses an economic evaluation simulation device comprising: a work simulation execution unit that performs a simulation regarding details of work in business; an economic parameter input unit that inputs an economic parameter; an economic simulation execution unit that uses the economic parameter to perform a simulation regarding economy; an actual operation data acquisition unit that acquires actual operational data relating to the economic parameter and the details of work; and a data assimilation unit that uses the actual operational data and a result obtained by the economic simulation execution unit before the actual operation data acquisition unit acquires the actual operational data, to correct the result by data assimilation processing.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a simulation device and a simulation method for evaluating economy.

Background Art

[0002] Methods for pre-evaluating the economy, profitability, productivity, etc. of robots or machines have been researched and developed.

[0003] For example, Patent Document 1 discloses a production performance evaluation technology for a mechanical system that analyzes production performance in a mechanical system that automatically produces products. Regarding a predetermined part state among a plurality of operations of a mechanical system that performs a plurality of operations in order, a state transition probability between part states before and after each operation and an operation time of each operation are defined. Based on the state transition probability, all transition paths from an initial state to a target state through a plurality of part states are extracted. For each of the transition paths, a path cycle time is calculated from the state transition probability and the operation time. An expected tact time, which is the time required for one part to reach the target state in the mechanical system, is calculated from the path cycle time and the extracted transition paths. A production performance evaluation device for a mechanical system is disclosed.

[0004] Further, Patent Document 2 discloses an evaluation item value calculation process for evaluating a business, a precondition that is an input value for the calculation process, and a user input for receiving the calculation process and the precondition. Based on the precondition and the calculation process, the value of the evaluation item is created in time series, the value of the evaluation item created in time series is displayed, the user is received to input the precondition again, the value of the evaluation item is created again in time series based on the precondition and the calculation process input again, and the value of the evaluation item created again in time series is displayed. A profitability evaluation device is disclosed.

[0005] Patent Document 3 discloses an equipment evaluation system that aims to evaluate the performance degradation of equipment over a long-term period covering the entire life cycle. Based on the measurement data of the target equipment measured up to each of a plurality of times, it estimates the probability density distribution of the parameters representing the performance of the target equipment, obtains the usage pattern of the target equipment, associates the usage pattern with the probability density distribution of the target equipment and stores it, identifies the usage pattern of the target equipment that is similar to the usage pattern of the first equipment different from the target equipment, and uses the group of probability density distributions corresponding to the identified usage pattern to evaluate the future performance degradation of the first equipment. Patent Document 3 also discloses that for parameter estimation, Bayesian estimation can be used, and as methods for obtaining the posterior probability density distribution, Markov chain Monte Carlo methods (MCMC) including the Gibbs method, the Metropolis method, etc., and the particle method which is a type of sequential Monte Carlo method are disclosed.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0007] For example, when predicting in advance the productivity of work by machines or robots in fields such as construction and civil engineering work through simulation, it is impossible to accurately determine in advance physical property values, boundary conditions, etc. that serve as inputs for the simulation. Also, due to the complexity of the work and the environment, it is difficult to accurately simulate the behavior of the robot itself in the simulation model. For example, in civil engineering work, productivity varies greatly depending on the state of the soil at the site (firmness, moisture content, etc.), terrain, weather conditions, etc., making it difficult to accurately predict in advance through simulation.

[0008] Under conditions where it is difficult to predict by prior simulation in this way, it is difficult to directly apply a productivity evaluation method at a production site where the operating environment can be relatively predicted, such as in Patent Document 1.

[0009] Also, in the business evaluation device described in Patent Document 2, in order to evaluate the profitability of a business, a probability distribution is given to items related to business profitability, and the business evaluation is also carried out probabilistically. However, also in this case, mainly facilities such as plants with relatively constant conditions are targeted.

[0010] The facility evaluation system described in Patent Document 3 also relates to the technical field of remotely providing various services for monitoring, controlling, and diagnosing facilities such as apartment buildings, buildings, and plants. Therefore, environmental conditions, etc. are not as unstable as to require prior prediction. For this reason, it is considered that attention is paid to the probability density distribution after the fact.

[0011] The main object of the present disclosure is to improve the accuracy of economic evaluation simulation for robot operations, etc. corresponding to unknown environments where physical property values, boundary conditions, etc. cannot be accurately determined in advance.

Means for Solving the Problems

[0012] The economic evaluation simulation device of the present disclosure includes a work simulation execution unit that performs a simulation on the content of work in a business, an economic parameter input unit that inputs economic parameters, an economic simulation execution unit that performs a simulation on economy using the economic parameters, an actual operation data acquisition unit that acquires actual operation data related to the content of work and economic parameters, and a data assimilation unit that corrects the result by data assimilation processing using the result obtained by the economic simulation execution unit before the actual operation data acquisition unit acquires the actual operation data and the actual operation data.

Effect of the Invention

[0013] According to the present disclosure, it is possible to improve the accuracy of economic evaluation simulation for robot businesses etc. that respond to unknown environments where physical property values, boundary conditions, etc. cannot be accurately determined in advance.

Brief Description of the Drawings

[0014]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Embodiments for Carrying Out the Invention

[0015] The present disclosure relates to a simulation device and a simulation method for performing economic efficiency evaluation. In particular, it relates to a simulation device and a simulation method for performing economic efficiency evaluation of a robot business. In the following description, the robot business is taken as an example, but the content of the present disclosure is not limited thereto. As will be described later, it is very difficult to evaluate the economic efficiency of a robot business using a working robot or the like in an unknown environment where physical property values, boundary conditions, etc. cannot be accurately determined in advance, and it has not been the subject of conventional simulation techniques. The technology of the present disclosure can also handle businesses having such unknown conditions.

[0016] Hereinafter, the content of the present disclosure will be described using examples.

Examples

[0017] FIG. 1 is a block diagram showing a simulation device for evaluating the economic efficiency of a robot business.

[0018] In this figure, the economic evaluation simulation device 100 for the robot business includes an arithmetic processing unit 101 and an input / output terminal 130. The arithmetic processing unit 101 includes a preprocessing unit 110 and an operation-time processing unit 120. The preprocessing unit 110 has a robot simulation execution unit 111, an economic parameter input unit 112, an economic simulation execution unit 113, and an economic evaluation result display unit 114. The operation-time processing unit 120 has an actual operation data acquisition unit 121, a data assimilation unit 122, and an economic evaluation result display unit 123. The input / output terminal 130 has an input unit 131 and an output unit 132.

[0019] The actual operation data acquisition unit 121 acquires actual operation data during business operation. Here, the actual operation data refers to data including measured values of state variables.

[0020] FIG. 2 is a diagram showing a screen of a specific example of the processing in the robot simulation execution unit 111 of FIG. 1.

[0021] In FIG. 2, as an example, the screen of the output unit 132 (display unit) of the input / output terminal 130 is shown for the work of removing rubble at a construction site or the like.

[0022] In the robot simulation execution unit 111 (FIG. 1), a simulation regarding the work of the robot 202 is performed. The robot 202 (working robot) performs the work of removing the rubble 201 scattered at the work site. The simulation is executed by calculating physical conditions such as the center of gravity and weight of the robot, the friction of the joints, the friction between the ground and the wheels, and the weight of the rubble. Items evaluated by the simulation include those related to work productivity such as work time and work feasibility, the energy consumption that becomes the operation cost of the robot, or items related to maintenance such as damage and deterioration of the parts of the robot, and the amount of materials required for the work. These items may be displayed on the screen like the display item 203.

[0023] In addition, although FIG. 2 shows an example of performing a simulation regarding the work of the robot 202, it is generally applicable also when performing a simulation regarding the content of work in a business having unknown conditions. In this case, the robot simulation execution unit 111 functions as a work simulation execution unit.

[0024] FIG. 3 is a diagram showing a screen of a specific example of the process in the economic parameter input unit 112 of FIG. 1.

[0025] In the economic parameter input unit 112 (FIG. 1), parameters (economic parameters) necessary for executing an economic simulation are input. In this case, the input may be performed manually by the user viewing, or may be performed automatically for optimization by the apparatus.

[0026] FIG. 3 shows a screen for input in a state where the prerequisite condition 301, items 302 regarding income, and items 303 regarding expenditure are arranged and displayed.

[0027] Regarding the prerequisite condition 301, parameters such as currency unit, number of project years, operation rate, number of robots, depreciation years, interest rate, selling unit price, electricity cost (unit price) are input. Here, when assuming variation in the parameters, for example, a normal distribution is assumed and the mean and standard deviation are input. Also, for example, when assuming variation year by year, the value for each year is input.

[0028] Regarding the items 302 regarding income, parameters such as sales, amount borrowed, subsidy are input. Here, for example, regarding sales, it is calculated by the following formula using the productivity result calculated by the robot simulation execution unit 111.

[0029] (Sales) = (Selling unit price) × (Production volume per unit) × (Number of units) × (Operation rate) When assuming that unit price and production volume will change in the future, input them over the future period for which the simulation is executed. Also, when assuming variations in each item, for example, assume a normal distribution and input the values of the mean and standard deviation. The processing details when inputting the value of the standard deviation of sales from the results of the robot simulation will be described in Example 3.

[0030] Next, on the input screen for item 303 regarding expenditures, input the values of parameters regarding the initial facility investment amount, power consumption, maintenance cost, labor cost, depreciation cost, corporate tax, insurance premium, etc. Among these, for those calculated using the values calculated by the robot simulation execution unit 111 such as power consumption and maintenance cost. For example, the electricity cost is calculated by the following formula.

[0031] (Electricity cost) = (Electricity price (unit price)) × (Power consumption) When assuming that these will change, input them annually over the future period for which the simulation is executed. Also, when assuming variations in each item, for example, assume a normal distribution and input the values of the mean and standard deviation.

[0032] In the economic simulation execution unit 113 (Fig. 1), the economic viability of the robot business is evaluated based on the above input parameters. As the calculation method, for example, the method described in Patent Document 2 is used to simulate the prediction of the transition of future cash flow (income, expenditure). Specifically, the items shown in Fig. 3 are added together for each period (for example, each year) for calculation.

[0033] Also, as an index indicating the economic viability of the business, for example, IRR (Internal Rate of Return) is calculated from the cash flow by the following formula (1). In other words, IRR is calculated by solving the equation with "r" in the following formula (1) as the unknown.

[0034]

Equation

[0035] In the formula, C nis the cash flow (profit before depreciation) in the nth period, C0 is the initial investment amount, and r (0 < r) is the discount rate.

[0036] The discount rate r that satisfies the above formula (1) is the IRR. IRR is an indicator showing the value of an investment, and the higher the value, the higher the investment value. In addition to IRR, there are various economic evaluation indicators such as net present value and payback period. In this disclosure, IRR will be used for explanation as an example.

[0037] Figure 4 is a graph showing a specific example of the evaluation result by the economic evaluation result display unit 114 in Figure 1. The horizontal axis represents the number of years, and the vertical axis represents the amount of money.

[0038] In this figure, the future income 402 and expenses 403 calculated by the economic simulation execution unit 113 are displayed. The error bar 404 of income and the error bar 405 of expenses indicate the respective variations, for example, displaying the value of the standard deviation. Thereby, the user can confirm the balance of future cash flow and its accuracy. Here, income and expenses are displayed, but for example, only sales among the income or the production volume which is the result of the robot simulation may be displayed. Also, the IRR (406) may be displayed.

[0039] Figure 5 is a graph for explaining the ensemble Kalman filter method which is an example of the process in the data assimilation unit 122 in Figure 1. In this embodiment, the ensemble Kalman filter method will be explained, but as the data assimilation method, other Kalman filter methods, particle filter methods, adjoint methods, variational methods, etc. other than the ensemble Kalman filter method may be used. The Kalman filter method, especially the ensemble Kalman filter method, has the advantages of less computational complexity and shorter calculation time compared to the particle filter method, etc.

[0040] Here, data assimilation refers to a method of improving the accuracy of simulations by modifying (assimilating) a simulation model using measured values. In the present disclosure, after the start of operation of the robot business, the results of a prior economic simulation are corrected using actual operation data to improve the accuracy of future predicted values.

[0041] In FIG. 5, the state variable 501 on the horizontal axis is a variable of the simulation for which data assimilation is performed. For example, it is the production volume of work by a robot, the amount of energy consumption, or the amount of income, expenditure, etc. The vertical axis shows the probability density.

[0042] In the upper part of this figure, the probability density distribution 502 (solid line) of the prior predicted value by simulation and the probability density distribution 503 (dashed line) of the measured value are shown. Here, for example, a normal distribution is assumed, and the standard deviation in that case is also assumed by the user. The method for estimating the variation of the prior predicted value by simulation when estimating by simulation will be described in Example 2. Here, for example, a normal distribution is assumed. If it is known that there is no error in the probability distribution of the measured value, such as when the state variable is an amount of money, the standard deviation of the normal distribution may be set to an extremely small value.

[0043] The probability density distribution 504 (dotted line) of the simulation after the data assimilation process shown in the lower part of this figure is calculated by Bayes' theorem represented by the following formula (2) using the probability density distribution 502 of the prior predicted value of the simulation and the probability density distribution 503 of the measured value.

[0044]

Equation

[0045] In the formula, p(x|y) represents the probability density distribution 504 of the simulation after the data assimilation process, p(y|x) represents the probability density distribution 503 of the measured value, and p(x) represents the probability density distribution 502 by the prior simulation.

[0046] As described above, the prior simulation results can be corrected by the measurement values through Bayesian estimation, where the prior simulation results are used as the prior probability, the measurement values are used as the likelihood, and the simulation after data assimilation is used as the posterior probability.

[0047] Figure 6 is a graph showing the process of correcting the simulation results by data assimilation. The horizontal axis represents time, and the vertical axis represents the state variable.

[0048] In this figure, the change of the state variable over time is predicted based on the prior simulation results. Then, two data assimilation processes are performed using the measured values of the state variable to correct the prediction of the change of the state variable over time. Note that although all the values represented by the curves and points in the figure contain variations (probability density distributions), for simplicity, their display is omitted here, and representative values such as the mean value and median value obtained from each probability density distribution are shown.

[0049] Curve 601 represents the change of the state variable over time based on the prior simulation results. The part before the first data assimilation 602 is shown as a solid line, and the part after the first data assimilation 602 is shown as a dashed line.

[0050] At the time of the first data assimilation 602, when the measured value 604 is obtained, the predicted value 603 by the prior simulation shown by curve 601 is corrected to the value 605 after data assimilation by the first data assimilation 602. Here, the reason why the value 605 after data assimilation is different from the measured value 604 is that Bayesian estimation is performed on the premise of having variations (probability density distributions) as shown in Figure 5.

[0051] Furthermore, by re - executing the simulation using the value 605 after data assimilation, the simulation results after the first data assimilation 602 are also corrected from curve 601 to curve 606. The part before the second data assimilation 607 is shown as a solid line, and the part after the second data assimilation 607 is shown as a dashed line.

[0052] Next, using the measured values 608 obtained at the time of the second data assimilation 607, the predicted values 609 by simulation are corrected to the values 610 after data assimilation by the second data assimilation 607. Further, by re - executing the simulation using the corrected values, the simulation results after the second data assimilation 607 are corrected from the curve 606 to the curve 611.

[0053] By repeating the above - mentioned process at the timing when measured data is obtained, the simulation can be corrected so as to approach the measured values. As described above, when it is considered that there is no error in the measured data, by setting the variation of the measured data to an extremely small value, the values after the data assimilation process will match the measured values.

[0054] The economic - evaluation result display unit 123 displays the simulation results after the data assimilation process. The display format is the same as the display method shown in FIG. 4. However, since the operation has already started here, for the sake of showing the measured values up to the current time, the values obtained by simulation, and the values after the data assimilation process, they may be displayed separately by revenue, expenditure, and IRR. Also, when the state variables after data assimilation are the final economic - evaluation parameters such as revenue and expenditure, the results of data assimilation can be directly displayed. However, for parameters such as production volume and energy consumption, by re - executing the economic - simulation execution unit 113 using the values after the data assimilation process, the final economic - evaluation parameters can be obtained.

[0055] FIG. 7 is a configuration diagram showing an economic - evaluation simulation system for a robot business.

[0056] As shown in this figure, the economic - evaluation simulation system for a robot business is configured by connecting a computer 700 corresponding to the economic - evaluation simulation device 100 (FIG. 1) for a robot business and an input / output device 716.

[0057] Computer 700 includes a CPU (701), a RAM (702), a ROM (703), an HDD (704), a communication I / F (705), an input / output I / F (706), and a media I / F (707). Here, CPU is the abbreviation of Central Processing Unit, RAM is the abbreviation of Random Access Memory, ROM is the abbreviation of Read Only Memory, HDD is the abbreviation of Hard Disk Drive, and I / F is the abbreviation of Interface.

[0058] The communication I / F (705) is connected to an external communication device 715. The input / output I / F (706) is connected to an input / output device 716. The media I / F (707) reads and writes data from / to a recording medium 717. Further, the CPU (701) controls each processing unit by executing a program (also referred to as an "application" and also abbreviated as an "app") loaded into the RAM (702). This program can be provided via a communication line or recorded on a recording medium 717 such as a CD-ROM and distributed.

Example

[0059] In Example 2, a method for estimating the variation of pre-predicted values by simulation will be described. Note that the description of the content common to Example 1 will be omitted. When estimating the variation of pre-predicted values by simulation, variations are given to the parameters of the analysis conditions for the robot simulation execution unit 111 (FIG. 1), and multiple simulations are executed using random numbers generated by the Monte Carlo method.

[0060] FIG. 8 is a flowchart showing the process of probability distribution evaluation by simulation.

[0061] In this figure, first, the probability distribution of the input parameters, which are the analysis conditions of the simulation, is input (step S801). For example, the degree of variation assumed by parameters such as the weight and shape of the object grasped by the robot is input. Next, probability distribution estimation (step S802) is performed. Here, first, random number generation (step S803) by the Monte Carlo method is executed for the probability distribution input in step S801. Next, the robot simulation is executed for the number of generated random numbers (step S804), and probability distribution estimation is performed using the obtained analysis results (step S805). Finally, the estimated probability distribution is output (step S806). The output probability distribution is used as the probability density distribution 502 (Figure 5) of the prior prediction value by the simulation at the time of data assimilation.

[0062] Figure 9 is a diagram for explaining the Monte Carlo method.

[0063] In this figure, on the left side, a graph of a curve 901 showing the probability density for the input parameters is shown. On the other hand, on the right side, a set of the input parameter 902 and the output parameter 903 obtained by the simulation is shown in a table. Here, the weight of the grasped object is shown as an example of the input parameter.

[0064] Specifically, random numbers are generated for a specified number of times (for example, 1000 times) according to the probability density input in the probability distribution input of the input parameters (step S801). That is, random numbers are generated so that the generation density becomes higher at locations where the value of the vertical axis of the curve 901 is larger.

[0065] Next, perform robot simulations for the number of generated random numbers, and record the pairs of input parameter 902 and output parameter 903. The output parameter 903 is an item to be evaluated in the robot simulation, and corresponds to, for example, the working time or the consumed energy. In this embodiment, both the input parameter 902 and the output parameter 903 are described in one case, but one or both of them may be plural. Also, in this embodiment, the simulation is executed for all of the specified number of times of the Monte Carlo method. However, when the execution time of the simulation is long, execute it for a small number of times in advance, database the results, and estimate the value of the output parameter 903 from the value of the input parameter 902 using machine learning or the like.

[0066] Next, the probability density distribution estimation will be described.

[0067] FIG. 10 is a diagram for explaining the probability density distribution estimation.

[0068] In this figure, the left graph 1001 shows the output parameter 903 (FIG. 9) of the robot simulation in a histogram format. On the right side, a smooth probability density distribution curve 1002 estimated by the probability density distribution estimation from the histogram of the graph 1001 is shown. Here, for example, a method such as KDE (Kernel Density Estimation) is used. The probability density distribution curve 1002 obtained here is output in step S806 (FIG. 8) and used as the probability density distribution 502 (FIG. 5) of the prior prediction value by the simulation at the time of data assimilation.

Example

[0069] FIG. 11 is a configuration diagram showing a robot business economic evaluation simulation device when implementing a re - plan of the operation of the robot business using the simulation results with improved accuracy by data assimilation processing. Note that the description of the content common to the first embodiment is omitted.

[0070] In this figure, an operation replanning unit 1101 is added to the operation processing unit 120 in FIG. 1. The operation replanning unit 1101 performs replanning of the operation based on the future economic evaluation according to the result of the data assimilation process obtained by the data assimilation unit 122. For example, if the simulation result shows that the maintenance cost of the robot is high, replanning such as replacing the robot, reviewing the number and arrangement of robots, etc. can be considered.

[0071] Also, in this case, the replanning may be performed manually by the user viewing what is shown on the economic evaluation result display unit 123, or the system may perform optimization automatically.

[0072] When the user performs it manually, for example, there is a method of changing the value of the operation conditions (prerequisites) on the economic parameter input screen in FIG. 3, re-executing the economic simulation, and examining it by looking at the values of IRR before and after the change.

[0073] When the system performs it automatically, the items input in the economic parameter input unit 112 are set as design variables, and optimization is performed to maximize or minimize the objective variable using the difference between income and expenditure evaluated in the economic simulation and IRR as the objective variables. Optimization methods in this case include the gradient method and GA (genetic algorithm).

[0074] Hereinafter, the desirable embodiments according to the present disclosure will be collectively described.

[0075] The economic evaluation simulation device preferably further includes an economic evaluation result display unit that displays at least one of the result obtained by the economic simulation execution unit and the correction result which is the data corrected by the data assimilation process.

[0076] The work simulation execution unit preferably evaluates at least one of work productivity, energy consumption, items related to maintenance, and the amount of materials required for the work.

[0077] The economic evaluation simulation device further has an operation replanning unit. It is desirable that the data assimilation unit performs future economic evaluation, and the operation replanning unit performs operation replanning based on the future economic evaluation.

[0078] It is desirable that the data assimilation unit uses the Kalman filter method, particle filter method, adjoint method, or variational method.

[0079] The economic parameter preferably includes at least one of sales, borrowing amount, subsidy, initial investment amount, labor cost, insurance premium, electricity cost, corporate tax, maintenance cost, depreciation cost, and repair and management cost.

[0080] The operation simulation execution unit performs a process of adding variations by the Monte Carlo method to the input data of the simulation performed before starting actual operation, and it is desirable that the data assimilation unit uses the input data with variations.

[0081] Note that the present disclosure is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present disclosure, and are not necessarily limited to those having all the configurations described.

[0082] Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can also be added to the configuration of one embodiment.

[0083] Also, for a part of the configuration of each embodiment, addition, deletion, or replacement with other configurations is possible. Also, the above-described respective configurations, functions, processing units, processing means, etc. may be realized in hardware, for example, by designing a part or all of them with an integrated circuit.

[0084] Also, the above-described respective configurations, functions, etc. may be realized in software by a processor interpreting and executing a program for realizing each function.

[0085] Information such as programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card (where IC is an abbreviation for Integrated Circuit), an SD card, or a DVD (Digital Versatile Disc). Additionally, cloud computing can also be utilized.

[0086] Also, the control lines and information lines shown are those considered necessary for explanation, and not all control lines and information lines are necessarily shown on the product. In reality, it can be considered that almost all components are interconnected.

[0087] Furthermore, the communication means connecting each device is not limited to a wireless LAN and may be changed to a wired LAN or other communication means.

Explanation of Signs

[0088] 100: Economic evaluation simulation device for robot business, 101: Arithmetic processing unit, 110: Preprocessing unit, 111: Robot simulation execution unit, 112: Input unit for economic parameters, 113: Economic simulation execution unit, 114, 123: Economic evaluation result display unit, 120: Processing unit during operation, 121: Acquisition unit for actual operation data, 122: Data assimilation unit, 130: Input / output terminal, 131: Input unit, 132: Output unit.

Claims

A work simulation execution unit that performs a simulation on the content of work in a business with unknown conditions, An economic parameter input unit that inputs the economic parameters of the business, An economic simulation execution unit that performs a simulation on the economic performance of the business using the first result of the evaluation by the work simulation execution unit and the economic parameters, and evaluates the economic performance of the business, An actual operation data acquisition unit that acquires actual operation data related to the content of the work and the economic parameters, A data assimilation unit that corrects the second result by data assimilation processing using the second result obtained by the economic simulation execution unit before the actual operation data acquisition unit acquires the actual operation data and the actual operation data, The work simulation execution unit calculates physical conditions and evaluates at least one of work productivity, energy consumption, items related to maintenance, and the amount of materials required for the work, The economic parameter includes at least one of a precondition, an item related to income, and an item related to expenditure, and is an economic evaluation simulation device.

2. The economic evaluation simulation device according to claim 1, further comprising an economic evaluation result display unit that displays at least one of the second result obtained by the economic simulation execution unit and the corrected result that is the data corrected by the data assimilation processing.

3. Further comprising an operation replanning unit, The data assimilation unit performs a future economic evaluation, The operation replanning unit replans the operation of the business based on the future economic evaluation, and is an economic evaluation simulation device according to claim 1.

4. The data assimilation unit uses a Kalman filter method, a particle filter method, an adjoint method, or a variational method, and is an economic evaluation simulation device according to claim 1.

5. The economic parameter includes at least one of sales, borrowing amount, subsidy, initial investment amount, labor cost, insurance premium, electricity cost, corporate tax, maintenance cost, depreciation cost, and repair and management cost, and is an economic evaluation simulation device according to claim 1.

6. The work simulation execution unit performs a process of giving variations by the Monte Carlo method to the input data of the simulation performed before starting the actual operation, The economic evaluation simulation device according to claim 1, wherein the data assimilation unit uses the input data having the variation.

7. A method executed by an economic evaluation simulation device having a work simulation execution unit, an economic parameter input unit, an economic simulation execution unit, an actual operation data acquisition unit, and a data assimilation unit, comprising: the work simulation execution unit performs a simulation on the content of work in a business having unknown conditions; the economic parameter input unit inputs economic parameters of the business; the economic simulation execution unit performs a simulation on the economic performance of the business using the first result of the evaluation by the work simulation execution unit and the economic parameters, and evaluates the economic performance of the business; the actual operation data acquisition unit acquires actual operation data related to the content of the work and the economic parameters; the data assimilation unit corrects the second result by data assimilation processing using the second result obtained by the economic simulation execution unit before the actual operation data acquisition unit acquires the actual operation data and the actual operation data; the work simulation execution unit calculates physical conditions and evaluates at least one of work productivity, energy consumption, items related to maintenance, and the amount of materials required for the work; The economic parameter includes at least one of a precondition, an item related to income, and an item related to expenditure, and is an economic evaluation simulation method.

Images