Environmental Load Prediction Device

Abstract

A technology is provided that can predict the environmental load of a manufacturing process based on the influencing factors of the manufacturing process at the design stage. [Solution] The environmental load prediction device of the present invention stores a prediction formula configured to calculate the environmental load using parameters that represent the characteristics of parts processed in the manufacturing process, and predicts (simulates) the environmental load of the manufacturing process by reading the parameters from process data that describes the design values ​​of the parameters and substituting them into the prediction formula.

Description

[Technical Field]

[0001] The present invention relates to a technique for predicting environmental loads caused by manufacturing processes. [Background technology]

[0002] As global environmental regulations become stronger and the transition to a recycling-oriented society accelerates, companies are being asked to take a proactive approach to the environment. The manufacturing industry, including construction machinery manufacturers, is promoting the improvement of engineering and supply chains using rapidly innovative digital technologies, the development of environmentally friendly products, and the reduction of CO2 emissions through improvements to manufacturing processes. Furthermore, since product performance and manufacturing processes are determined at the design stage, the development of environmentally friendly products requires consideration of not only product performance but also CO2 emissions in the manufacturing process from the design stage.

[0003] Patent Document 1 describes a technology related to CO2 emissions, and the document states that it aims to provide a technology for tracing CO2 emissions in the distribution process of a tangible object. The document also describes a technology in which an information processing device includes an acquisition unit that acquires information on electricity and fuel used in each of a plurality of processes in the distribution process of a tangible object, a calculation unit that calculates the amount of CO2 emissions generated in each of the plurality of processes using the electricity information and the fuel information acquired by the acquisition unit, and a storage control unit that stores the CO2 emissions generated in each of the plurality of processes calculated by the calculation unit in a storage unit in association with the tangible object (see abstract). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-189564 Summary of the Invention [Problem to be solved by the invention]

[0005] The purpose of Patent Document 1 is to trace CO2 emissions in the distribution process. However, CO2 is emitted not only in the distribution process but also in the manufacturing process. Conventional technologies such as Patent Document 1 do not adequately provide a method for predicting CO2 emissions for each step in the manufacturing process.

[0006] The present invention has been made in view of the above problems, and aims to provide a technique that can predict the environmental load of a manufacturing process based on influential factors of the manufacturing process at the design stage. [Means for solving the problem]

[0007] The environmental load prediction device according to the present invention stores a prediction formula configured to calculate the environmental load using parameters that represent the characteristics of parts processed in a manufacturing process, and predicts (simulates) the environmental load of a manufacturing process by reading the parameters from process data that describes design values ​​for the parameters and substituting them into the prediction formula. [Effects of the Invention]

[0008] The environmental load prediction system according to the present invention can predict the environmental load of a manufacturing process at the design stage based on the influencing factors of the manufacturing process. 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]

[0009] [Figure 1] 1 is a configuration diagram of an environmental load prediction device according to a first embodiment. [Figure 2] An example of control data included in the measurement data K21 is shown below. [Figure 3] An example of power data included in the measurement data K21 is shown below. [Figure 4] An example of evaluation result K22 is shown below. [Figure 5] An example of prediction formula K23 is shown below. [Figure 6]An example of process data is shown below. [Figure 7] An example of a flowchart is shown in which the performance calculation unit K25 calculates the work time and power consumption amount for each part using the control data acquired from the control device 102 and the power control data acquired from the sensor 103. [Figure 8] FIG. 7 shows an example of a flowchart illustrating a procedure for determining a prediction formula for the workload and a prediction formula for the environmental load from the stored evaluation results K22. [Figure 9] 11 is a flowchart illustrating a procedure in which a prediction calculation unit 1103 predicts the manufacturing load of each process. [Figure 10] 10 is a graph illustrating the manufacturing loads of two process plans proposed by the comprehensive evaluation unit A35. [Figure 11] An example of a user interface provided by the computer server 110 is shown. [Figure 12] FIG. 1 is a graph depicting power data. [Figure 13] 10 is a graph of frequency characteristics obtained by Fourier transforming power data. [Figure 14] An example of a flowchart will be shown in which the performance calculation unit K25 calculates the work time and power consumption amount for each part using the power data acquired from the sensor 103 in the second embodiment. [Figure 15] 10 is an example of a screen display presenting the results of calculation of the work time for each process. [Figure 16] 10 is an example of a screen display presenting the results of calculation of the amount of power consumption for each process. [Figure 17] 10 is an example of a screen display showing the results of calculating CO2 emissions for each process. [Figure 18] 10 is an example of a screen display for comparing calculation results of manufacturing loads. [Figure 19] 10 is an example of a screen display when the overall evaluation unit A35 selects a combination pattern of processes. [Figure 20] 10 is an example of a screen display presenting details of a process combination pattern. DETAILED DESCRIPTION OF THE INVENTION

[0010] <First Embodiment> 1 is a configuration diagram of an environmental load prediction apparatus according to a first embodiment of the present invention. The main processing of the environmental load prediction apparatus is executed by a computer server 110. The computer server 110, control device 102, sensor 103, and client computer 120 are connected via a network 200. The network 200 may be wired or wireless.

[0011] The manufacturing site 100 includes equipment 101, a control device 102, and a sensor 103. The control device 102 controls the equipment 101. The sensor 103 measures the amount of electricity consumed by the equipment 101, the amount of fuel gas consumed by the equipment 101, etc. The control device 102 and the computer server 110 can communicate with each other via a network 200.

[0012] The client computer 120 can communicate with the computer server 110 via a network 200. The client computer 120 includes a display device 121 and a processor 122. The client computer 120 and the computer server 110 can also be configured as an integrated unit. In the following, it is assumed that these computers are configured separately, as shown in FIG. 1.

[0013] The computer server 110 functions as an environmental load prediction device that executes the main processing of this embodiment. The computer server 110 includes an input / output unit 1100, a memory unit 1102, a prediction calculation unit 1103, and a performance evaluation unit 1101. The input / output unit 1100 communicates with the client computer 120, the control device 102, and the sensor 103. The memory unit 1102 stores measurement data K21, evaluation results K22, prediction formula K23, etc. The prediction calculation unit 1103 includes a process data processing unit A31, a prediction formula extraction unit A32, an environmental load evaluation unit A33, a workload evaluation unit A34, a comprehensive evaluation unit A35, and a result display execution unit A36. The performance evaluation unit 1101 includes a prediction formula calculation unit K24 and a performance calculation unit K25.

[0014] The measurement data K21 describes the control data acquired from the control device 102 and the power data acquired from the sensor 103. The measurement data K21 also describes the amount of fuel gas (e.g., propane gas) consumed by the equipment 101 in the manufacturing process. The evaluation results K22 are the evaluation results of the environmental load and workload of the target parts in each process calculated by the performance calculation unit K25. The prediction formula K23 is a prediction formula for the environmental load and workload calculated by the prediction formula calculation unit K24.

[0015] The performance calculation unit K25 calculates the workload and environmental load for each part (e.g., the work time and power consumption for each part) from the measurement data K21 and outputs the results as evaluation results K22. The prediction formula calculation unit K24 calculates a prediction formula K23 using the evaluation results K22 calculated by the performance calculation unit K25.

[0016] The process data processing unit A31 processes the measurement data input from the input / output unit 1100 according to the prediction formula K23. The prediction formula extraction unit A32 extracts the prediction formula K23 for each process. The environmental load evaluation unit A33 predicts and evaluates the environmental load of each process using the prediction formula extracted by the prediction formula extraction unit A32. The workload evaluation unit A34 predicts and evaluates the workload using the prediction formula extracted by the prediction formula extraction unit A32. The overall evaluation unit A35 performs an overall evaluation of the process using the environmental load predicted by the environmental load evaluation unit A33 and the workload predicted by the workload evaluation unit A34. The result display execution unit A36 displays the selection result of the process plan or the environmental load corresponding to the evaluation result by the overall evaluation unit A35 on the display device 121.

[0017] 2 shows an example of control data included in the measurement data K21. The control data is data describing the operation of a process carried out by the control device 102 controlling the equipment 101. For example, the control data describes the name of the process, the name and number of items processed in the process, the operation period of the process, the model of the equipment 101 that carried out the process, the weight of the parts processed in the process, and the geometric characteristics of the parts, such as the area / diameter.

[0018] 3 shows an example of power data included in the measurement data K21. The power data is data describing the results of measurements made by the sensor 103 on the power consumed by the equipment 101. The power data describes, for example, a date and time and the power consumed by the equipment 101 on that date and time.

[0019] Figure 4 shows an example of the evaluation result K22. The evaluation result K22 describes the work time, power consumption, workload, environmental load, indirect load, manufacturing load, etc. for each combination of the process described in the control data and the parts that are the target of that process. The procedure for calculating these will be described later.

[0020] Figure 5 shows an example of prediction formula K23. Prediction formula K23 is a calculation formula used to calculate the environmental load, indirect load, or production load for each process. Prediction formula K23 defines these calculation formulas for each process. For example, for the cutting process, a formula for calculating the environmental load, a formula for calculating the indirect load, and a formula for calculating the production load are defined (only some of the calculation formulas are shown as examples in Figure 5). The calculation formulas are configured to define factors that affect the load as variables and to calculate the load using these variables. f1(t) is a formula for calculating the environmental load, and f2(p) is a formula for calculating the production load. The indirect load can be defined in a similar way. t is the work time, and p is the power consumption. Other parameters that make up the influencing factors will be explained in Figure 6.

[0021] FIG. 6 shows an example of process data. The process data is data that describes the external characteristics, such as the shape and weight, of parts processed in the manufacturing process for each model of equipment 101. In FIG. 6, these external characteristics are represented by variables d, l, v, m, s, and n. These variables represent the same variables used in prediction formula K23. Each parameter represents the following: d: diameter of part, l: length of part, m: weight of part, s: cross-sectional area of ​​part, n: number of times the process is performed, v: volume of part.

[0022] FIG. 7 shows an example of a flowchart in which the performance calculation unit K25 calculates the work time and power consumption amount for each part using the control data acquired from the control device 102 and the power data acquired from the sensor 103.

[0023] Step S101: The performance evaluation unit 1101 acquires control data from the control device 102 via the input / output unit 1100 in advance, and stores the control data in the storage unit 1102. The performance calculation unit K25 reads the control data from the measurement data K21.

[0024] Step S102: The performance evaluation unit 1101 acquires power data from the sensor 103 via the input / output unit 1100 in advance, and stores the power data in the storage unit 1102. The performance calculation unit K25 reads the power data from the measurement data K21.

[0025] Step S103: The performance calculation unit K25 combines the control data and power data of each process using the operation period field of the control data read in step S101 and the date and time field of the power data read in step S102.

[0026] Step S104: The performance calculation unit K25 calculates the work time per part for each process using the operation period and number of items from the data combined in step S103. For example, the calculation formula is: work time per part = operation period / number of items. The performance calculation unit K25 calculates the work load per part for each process from the work time per part using a standard coefficient for the manufacturing site 100. For example, a standard coefficient is determined in advance for each manufacturing site 100 to convert work time into work load, and the work load per part for each process can be calculated by multiplying the work time per part by the standard coefficient. An example of the standard coefficient is a coefficient that converts work time into the cost of that process.

[0027] Step S105: The performance calculation unit K25 calculates the amount of power consumption per unit time for each component in each process from the power data included in the data combined in step S103. The performance calculation unit K25 totals the power consumption for all components in each process. The performance calculation unit K25 totals the operation period of the equipment for all components in each process. The performance calculation unit K25 calculates the average power consumption per unit time by dividing the total power consumption by the total operation period. The performance calculation unit K25 calculates the power consumption for each component in each process by multiplying the work time for each component calculated in step S104 by the average power consumption.

[0028] Step S105A: This step is carried out when calculating the environmental load due to gases other than CO2. For example, this may be gas consumed by the equipment 101 in the manufacturing process (e.g., fuel gas such as propane gas). The amount of this gas is measured by the sensor 103 and recorded as measurement data K21. The performance calculation unit K25 reads the amount of gas consumed when the equipment 101 manufactures one lot of parts from the measurement data K21. The performance calculation unit K25 calculates the amount of gas consumed per part by dividing the read gas amount by the number of parts processed in one lot.

[0029] Step S106: The actual result calculation unit K25 calculates the CO2 emissions per component resulting from the power consumption of each process by multiplying the power consumption per component calculated in step S105 by a CO2 emission coefficient provided by the electric power company. The actual result calculation unit K25 further calculates the CO2 emissions per component resulting from the gas consumption of each process by multiplying the gas consumption per component calculated in step S105A by a CO2 emission coefficient provided by the gas company. The actual result calculation unit K25 calculates the environmental load per component of each process by adding up these two CO2 emissions. Furthermore, the environmental load may be converted into an emission cost by multiplying by a predetermined coefficient.

[0030] Step S107: The performance calculation unit K25 calculates the sum of the work load calculated in step S104 and the environmental load calculated in step S106 as the manufacturing load for each part in each process.

[0031] Steps S101 to S107: Supplement: If any process generates an indirect environmental load, such as transportation between processes, operating energy for the entire factory, or mold production, the indirect load can be divided by component in each process. The calculation procedure can be the same as that of S101 to S107. For example, the indirect loads generated in all processes are added up, and the operating periods for all processes and all components are totaled. The indirect load per unit time is calculated by dividing the total indirect load by the total operating period. The indirect load per component in each process can be calculated by multiplying the indirect load per unit time by the operation time calculated in step S104.

[0032] Step S108: The performance calculation unit K25 calculates the production load for all processes as an evaluation result by summing up the production loads for each process calculated in steps S104 to S107. This step corresponds to calculating the "total" in the last line in FIG. 4.

[0033] Step S109: The performance calculation unit K25 saves (a) the component-based workload, component-based power consumption, component-based environmental load, and component-based manufacturing load for each process calculated in steps S104 to S107, and (b) the component-based manufacturing load for all processes calculated in step S108, as evaluation results K22.

[0034] Fig. 8 shows an example of a flowchart for explaining the procedure for determining a prediction formula for the workload and a prediction formula for the environmental load from the evaluation results K22 stored in the flowchart shown in Fig. 7. This flowchart is executed by the prediction formula calculation unit K24.

[0035] Step S201: The prediction formula calculation unit K24 reads the work load per part and the environmental load per part in each process from the evaluation result K22.

[0036] Step S202: The prediction formula calculation unit K24 extracts influential factors in the prediction formulas for the workload and the environmental load by performing a correlation analysis on the evaluation result K22 read in step S201. For example, a correlation coefficient between the workload and each of the characteristic parameters (variables d to n described in the process data in FIG. 6) of the part being cut is calculated. If the correlation coefficient is equal to or greater than a threshold, the variable can be considered to be an influential factor that affects the workload. Influence factors are identified for the environmental load in the same way. If the indirect load was calculated in the flowchart of FIG. 7, influence factors are identified for the indirect load in the same way.

[0037] Step S203: The prediction formula calculation unit K24 performs a regression analysis using the influencing factors extracted in step S202 as explanatory variables and the workload and environmental load read in step S201 as explained functions to determine the following: (a) a prediction formula for the influencing factors and workload; (b) a prediction formula for the influencing factors and environmental load. A formula template (e.g., a polynomial) to be used as the basic form of the prediction formula is prepared in advance. A prediction formula for indirect loads can also be determined by regression analysis in a similar manner.

[0038] Step S204: The prediction formula calculation unit K24 saves the prediction formula determined in step S203 as a prediction formula K23.

[0039] 9 is a flowchart explaining the procedure for predicting the manufacturing load for each process by the prediction calculation unit 1103. During the design stage, the prediction calculation unit 1103 predicts the manufacturing load taking into consideration the manufacturing load (environmental load, work load, and indirect load) for each process in the manufacturing process, according to the following procedure.

[0040] Step S301: The process data processing unit A31 reads the process data of the process plan considered at the design stage. As shown in Fig. 6, the process data describes, for each process, parameters that are candidates for influencing factors and their numerical values.

[0041] Step S302: The prediction formula extraction unit A32 extracts a prediction formula corresponding to the process from the prediction formula K23 using the process name of the process data read in step S301 as a key. If a prediction formula is defined for each combination of process and equipment type, the equipment type may also be used as a key. In this step, the prediction formula for the workload and the prediction formula for the environmental load (and the prediction formula for the indirect load if indirect load is calculated) are obtained.

[0042] Step S303: The environmental load assessment unit A33 calculates the environmental load by substituting the environmental load influence factors from the process data read in step S301 into the environmental load prediction formula read in step S302.

[0043] Step S304: The workload evaluation unit A34 calculates the workload by substituting the influencing factors of the work time from the process data read in step S301 into the workload prediction formula read in step S302.

[0044] Step S305: The prediction calculation unit 1103 calculates the indirect load by substituting the influencing factors of the indirect load from the process data read in step S301 into the prediction formula for the indirect load read in step S302.

[0045] Step S306: The comprehensive evaluation unit A35 calculates an evaluation value for each process based on the predicted values ​​of the environmental load, work load, and indirect load of each process predicted in steps S302 to S305, as well as the numerical values ​​calculated when calculating these, and presents the results. Examples of evaluation values ​​include: (a) work time for each process; (b) power consumption for each process; and (c) CO2 emissions for each process. Examples of these screen displays will be described later.

[0046] Step S307: The overall evaluation unit A35 proposes a combination pattern of processes with a low load based on the evaluation value of each process calculated in steps S302 to S306. The result display execution unit A36 outputs the result on the display device 121. An example of the screen display in this step will be described later.

[0047] FIG. 10 is a graph illustrating the production loads of two process plans proposed by the overall evaluation unit A35. The overall evaluation unit A35 selects a combination of equipment types to be used for each process (electric furnace to cutting) and calculates the production load when that combination is used according to the procedures described in FIGS. 7 to 9. The overall evaluation unit A35 creates multiple combinations of equipment types that make up each process (process plans 1 and 2 in FIG. 10) and calculates the production load for each of them in the same way. The result display execution unit A36 displays the results as shown in FIG. 10. The user can decide which process plan to adopt based on the evaluation results. Other examples of evaluation results will be described in the embodiments described below.

[0048] 11 shows an example of a user interface provided by the computer server 110. On this interface, the user can perform the following: (a) input control data; (b) input power data; (c) instruct the computer server 110 to evaluate the manufacturing load per part (implement the flowchart of FIG. 7); (d) instruct the computer server 110 to calculate a prediction formula (implement the flowchart of FIG. 8); and (e) instruct the computer server 110 to evaluate the environmental load / workload / manufacturing load (implement the flowchart of FIG. 9 or some of the steps therein).

[0049] <First embodiment: Summary> The computer server 110 (environmental load prediction device) according to the first embodiment stores a prediction formula configured to calculate the environmental load using parameters that represent the characteristics of parts processed in a manufacturing process, and predicts (simulates) the environmental load of a manufacturing process by reading the parameters from process data describing their design values ​​and substituting them into the prediction formula. The parameters substituted into the prediction formula are values ​​at the design stage of the manufacturing process, so the environmental load of that process can be predicted at the design stage of the manufacturing process. The prediction formula can be created in advance and stored, or it can be determined using the procedure described in FIG. 8.

[0050] The computer server 110 according to the first embodiment extracts influential factors that constitute the variables of the prediction formula by performing correlation analysis on the evaluation result K22 that describes the results of evaluating the manufacturing load of each process, and creates a prediction formula using the influential factors. This makes it possible to construct a highly accurate prediction formula even when the manufacturing process parameters that affect the environmental load are not known in advance.

[0051] <Embodiment 2> In the second embodiment of the present invention, a procedure will be described for calculating the work time and power consumption amount for each part using power data acquired from the sensor 103. The other configurations are the same as those of the first embodiment.

[0052] FIG. 12 is a diagram depicting power data as a graph. This power data describes the change over time in power consumption in a specific piece of equipment 101. FIG. 12 shows that the power consumption in the equipment 101 fluctuates periodically over time. This period is thought to be correlated with the operating time of the equipment 101. Therefore, as will be explained below, the frequency characteristics of the power data are acquired and used to estimate the operating time.

[0053] 13 is a graph of frequency characteristics obtained by Fourier transforming the power data. As will be described below, in the second embodiment, the frequency characteristics of the power data are used to calculate the operation time in the process.

[0054] 14 shows an example of a flowchart in which the performance calculation unit K25 calculates the work time and power consumption amount for each component using the power data acquired from the sensor 103 in the second embodiment. This flowchart can be implemented in place of the flowchart in FIG.

[0055] Step S401: The performance evaluation unit 1101 acquires power data from the sensor 103 via the input / output unit 1100 in advance, and stores the power data in the storage unit 1102. The performance calculation unit K25 reads the power data from the measurement data K21.

[0056] Step S402: The performance evaluation unit 1101 performs noise filtering on the power data read in step S401. Specifically, the performance evaluation unit 1101 removes, from the power data, periods during which the equipment 101 is not operating (power is 0).

[0057] Step S403: The performance evaluation unit 1101 uses the power data that has been noise-filtered in step S402 to calculate the total operating time for all processes and all components, and also calculates the total amount of power consumed over that entire operating time.

[0058] Step S404: The performance evaluation unit 1101 calculates the average power consumption per unit time from the operating time and the power consumption calculated in step S403. For example, the average power consumption per unit time for all processes and all components can be calculated by dividing the total value of the power consumption over the entire operating time by the entire operating time.

[0059] Step S405: The power data read in step S401 is set as a function f(t) of time t. The performance evaluation unit 1101 calculates the frequency characteristic F(ω) of the power data using the following (Equation 1).

[0060]

number

[0061] Step S406: The performance evaluation unit 1101 extracts the frequencies of peak values ​​in the frequency characteristic F(ω) generated in step S405. The performance evaluation unit 1101 further excludes frequencies of peak values ​​near zero. The performance evaluation unit 1101 determines the smallest frequency among the peak values ​​extracted above as the processing frequency. More specifically, the performance evaluation unit 1101 calculates the moving average of the frequency characteristic F(ω) and removes peaks near a frequency of 0 from the moving average data. Among the peaks in the moving average data after removal, the one with the smallest frequency is identified, and its inverted value is determined as the work time per part.

[0062] Step S407: The performance evaluation unit 1101 calculates the inverted value of the machining frequency extracted in step S406. The performance evaluation unit 1101 sets the calculated inverted value as the work time per part.

[0063] Step S408: The performance evaluation unit 1101 calculates the power consumption per component by multiplying the average power consumption calculated in step S404 by the operation time calculated in step S407. The performance evaluation unit 1101 further calculates the CO2 emission amount per component by multiplying the power consumption per component by a CO2 emission coefficient provided by the power company.

[0064] Step S409: The performance evaluation unit 1101 stores the component-based work time, component-based power consumption, and component-based environmental load (CO2 emissions) for each process calculated in steps S407 and S408 as evaluation results K22.

[0065] <Third Embodiment> In the third embodiment of the present invention, an example of a screen display of the evaluation results presented by the comprehensive evaluation unit A 35 will be described. The configuration of the computer server 110 is the same as in the first and second embodiments.

[0066] Fig. 15 is an example of a screen display that presents the results of calculating the work time for each process. In S104, the performance calculation unit K25 calculates the work time for each part in each process. The overall evaluation unit A35 can calculate the work time for each process by aggregating the work times for all parts in that process. The result display execution unit A36 outputs the results on the display device 121. Fig. 15 is an example of this screen display, which can be presented in S306, for example.

[0067] Fig. 16 is an example of a screen display that presents the results of calculating the amount of power consumption for each process. In S105, the performance calculation unit K25 calculates the amount of power consumption for each component in each process. The overall evaluation unit A35 can calculate the amount of power consumption for each process by aggregating the amounts of power consumption for all components in that process. The result display execution unit A36 outputs the results on the display device 121. Fig. 16 is an example of such a screen display, which can be presented in S306, for example.

[0068] Fig. 17 is an example of a screen display that presents the results of calculating the CO2 emissions for each process. In S106, the performance calculation unit K25 calculates the CO2 emissions for each part in each process. The overall evaluation unit A35 can calculate the CO2 emissions for each process by aggregating the CO2 emissions for all parts in that process. The result display execution unit A36 outputs the results on the display device 121. Fig. 17 is an example of such a screen display, which can be presented in S306, for example.

[0069] FIG. 18 is an example of a screen display for comparing the calculation results of the manufacturing load. In S307, the overall evaluation unit A35 aggregates the predicted values ​​of the manufacturing load for each combination of processes. For example, combination pattern A (process A in FIG. 18) is composed of the combinations of the processes shown in FIGS. 15 to 17, and the power consumption, CO2 emissions, and work time are shown in the graph for process A in FIG. 18. Similarly, the power consumption, CO2 emissions, and work time are aggregated for process combination patterns B and C (process B and process C in FIG. 18), respectively. The combination pattern may be selected randomly from the processes required to manufacture the product, while satisfying constraints such as the order of the processes and the relationships between the processes, or multiple combination patterns may be selected using an arbitrary search algorithm. The user can select the combination pattern (A to C in this example) that results in the smallest total manufacturing load.

[0070] FIG. 19 is an example of a screen display when the overall evaluation unit A35 selects a process combination pattern. In FIG. 18, the user selects one of the process combination patterns, but instead, the overall evaluation unit A35 may select it. For example, the overall evaluation unit A35 calculates an overall evaluation value for the process combination pattern by aggregating the power consumption, CO2 emissions, and work time for each process combination pattern. The graph on the right side of FIG. 19 shows the results. When aggregating, the power consumption, CO2 emissions, and work time may each be converted into costs and then the total may be calculated. The graphs on the left and center of FIG. 19 show the values ​​before aggregation (for convenience of illustration, only CO2 emissions and work time are shown in FIG. 19). The overall evaluation unit A35 selects the process combination pattern with the lowest overall evaluation (i.e., the aggregated value of the manufacturing load). In this example, process combination pattern C has the lowest aggregated value, so the overall evaluation unit A35 selects process combination pattern C.

[0071] FIG. 20 is an example of a screen display that presents details of a process combination pattern. The overall evaluation value in FIG. 19 is calculated by aggregating power consumption, CO2 emissions, and work time. A breakdown of these may be displayed on the screen as shown in FIG. 20. Furthermore, the design values ​​of each process that makes up the process combination pattern (each parameter described in the process data) may also be displayed. By presenting these, the user can numerically grasp the basis of the overall evaluation.

[0072] <Modifications of the present invention> The above-described embodiments are merely examples for explaining the present invention, and appropriate omissions and simplifications have been made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings. When there are multiple components having the same or similar functions, they may be described using the same reference numeral with different subscripts. Furthermore, when it is not necessary to distinguish between these multiple components, the subscripts may be omitted.

[0073] In the above embodiments, the components of the computer server 110 and the functions and processes of the components may be partially or entirely implemented by hardware (for example, by designing the logic that executes each function as an integrated circuit). These components may also be implemented as programs (software) that are read and executed by a processing unit (for example, a CPU) to implement each function. Information related to the programs may be stored in, for example, semiconductor memory (flash memory, SSD, etc.), magnetic storage devices (hard disk drives, etc.), and recording media (magnetic disks, optical disks, etc.).

[0074] An arithmetic device executes a 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 that executes the program and performs the processing may be the processor. Similarly, the entity that executes the program and performs the processing may be a controller, device, system, computer, or node that has a processor. The entity that executes the program and performs the processing may be any arithmetic device, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit is, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0075] 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.

[0076] In addition, in the above description of each embodiment, the control lines and information lines are those that are considered necessary for the description of the embodiment, but they do not necessarily represent all the control lines and information lines related to the product. In reality, it can be considered that almost all components are interconnected.

[0077] In the above embodiment, it has been described that the prediction formula calculation unit K24 calculates the prediction formula by performing correlation analysis such as regression analysis on the evaluation results K22. As the correlation analysis performed on the evaluation results K22, other appropriate methods such as machine learning may also be used.

[0078] In the above embodiments, examples of environmental loads include the amount of CO2 emitted in the manufacturing process and the amount of gas used in the manufacturing process converted into CO2 emissions. Other amounts of loads generated in the manufacturing process that can be converted into CO2 emissions can also be used as environmental loads in the above embodiments. For example, if the amount of other consumed substances that can be measured in the equipment 101 can be converted into CO2 emissions, the amount of the consumed substances can be treated as environmental loads. [Explanation of symbols]

[0079] 101: Equipment 102: Control device 103: Sensor 110: Computer server 1100: Input / output section 1102: Storage section 1103: Prediction calculation unit 1104: Actual performance calculation unit 200: Network 120: Client computer 121:Display device 122: Processor

Claims

1. An environmental load prediction device for predicting an environmental load caused by a manufacturing process, a prediction calculation unit that simulates the environmental load of the manufacturing process; a storage unit for storing a prediction formula used by the prediction calculation unit to simulate the environmental load; Equipped with the prediction formula is configured to calculate the environmental load by substituting into the prediction formula a parameter representing at least one of a shape feature of a part to be processed in the manufacturing process or a weight of the part; The prediction calculation unit acquires process data describing design values ​​of the parameters, The prediction calculation unit simulates the environmental load using the parameters described in the process data and the prediction formula. An environmental load prediction device characterized by:

2. the environmental load prediction device further includes a prediction formula calculation unit that calculates the prediction formula, the environmental load prediction device further includes a performance calculation unit that calculates the environmental load for each of the parts in the manufacturing process, the prediction formula calculation unit extracts influential factors constituting variables of the prediction formula from the parameters described in the process data by performing a correlation analysis on the environmental load for each of the parts; The prediction formula calculation unit uses the extracted influencing factors to create the prediction formula for simulating the environmental load.

2. The environmental load prediction device according to claim 1.

3. The prediction formula calculation unit creates the prediction formula by performing a regression analysis in which the extracted influencing factors are used as explanatory variables and the environmental load for each part is used as an explained variable.

3. The environmental load prediction device according to claim 2.

4. the environmental load prediction device further includes a performance calculation unit that calculates the environmental load for each of the parts in the manufacturing process, the performance calculation unit acquires power data describing the power consumption of equipment operating in the manufacturing process; the performance calculation unit calculates the amount of power consumption of the equipment for each of the components based on the power data; The performance calculation unit calculates the environmental load for each component by estimating the amount of CO2 emissions for each component based on the amount of power consumption for each component.

3. The environmental load prediction device according to claim 2.

5. the performance calculation unit calculates the amount of power consumption of the equipment within a unit time for each of the parts in the manufacturing process based on the power data; the performance calculation unit calculates a total amount of power consumption by adding up the amount of power consumption within the unit time for all of the components; the performance calculation unit calculates a total operating period by summing up the total power consumption amount and the operating period of the equipment in the manufacturing process for all of the components; the performance calculation unit calculates an average amount of power consumption of the equipment within a unit time by dividing the total amount of power consumption by the total operating period; the performance calculation unit calculates the amount of power consumption of the equipment for each of the parts by multiplying the average amount of power consumption by the work time for each of the parts in the manufacturing process; The performance calculation unit calculates the environmental load for each component by multiplying the power consumption amount for each component by a CO2 emission coefficient.

5. The environmental load prediction device according to claim 4.

6. the prediction formula is configured to calculate a workload caused by the manufacturing process by substituting the parameters into the prediction formula; The performance calculation unit acquires control data describing an operation period of equipment operating in the manufacturing process, the performance calculation unit calculates the workload for each part in the manufacturing process based on the operation period described in the control data; the prediction formula calculation unit extracts influential factors constituting variables of the prediction formula from the parameters described in the process data by performing a correlation analysis on the workload for each of the parts; the prediction formula calculation unit creates the prediction formula for simulating the workload using the extracted influential factors, The prediction calculation unit simulates the workload using the parameters described in the process data and the prediction formula.

3. The environmental load prediction device according to claim 2.

7. the performance calculation unit calculates the operation time for each part in the manufacturing process by dividing the operation period described in the control data by the number of parts processed in the manufacturing process; The performance calculation unit converts the work time into the workload by multiplying the work time by a standard coefficient.

7. The environmental load prediction device according to claim 6.

8. the prediction formula is configured to calculate an indirect load caused by the manufacturing process by substituting the parameters into the prediction formula; The prediction calculation unit simulates the indirect load using the parameters described in the process data and the prediction formula.

2. The environmental load prediction device according to claim 1.

9. the environmental load prediction device further includes a comprehensive evaluation unit that evaluates the manufacturing process based on the predicted environmental load; the prediction calculation unit predicts the environmental load for each combination of the parameters described in the process data; The comprehensive evaluation unit presents the combination with the smallest environmental impact as a candidate for implementation of the manufacturing process.

2. The environmental load prediction device according to claim 1.

10. the performance calculation unit acquires power data describing the power consumption of equipment operating in the manufacturing process; the performance calculation unit creates frequency data describing a frequency characteristic of the power data, the performance calculation unit calculates the work time for each of the parts in the manufacturing process based on the frequency data, the performance calculation unit calculates the amount of power consumption for each of the parts by multiplying the work time for each of the parts by an average power consumption of the equipment in the manufacturing process; The performance calculation unit calculates the environmental load for each component by estimating the amount of CO2 emissions for each component based on the amount of power consumption for each component.

3. The environmental load prediction device according to claim 2.

11. the performance calculation unit identifies a minimum frequency that is the smallest among the frequencies corresponding to peak values ​​included in the frequency data, The performance calculation unit calculates an inverted value of the minimum frequency as the operation time for each part.

11. The environmental load prediction device according to claim 10.

12. the performance calculation unit calculates a moving average of the frequency data, The performance calculation unit removes the peak value having a frequency closest to 0 from among the peak values ​​of the moving average, and then identifies the peak value having the smallest frequency from among the remaining peak values ​​as the minimum frequency.

12. The environmental load prediction device according to claim 11.

13. The environmental load prediction device further includes a result display execution unit that outputs a user interface that presents the predicted environmental load.

2. The environmental load prediction device according to claim 1.

14. The environmental load prediction device further includes a result display execution unit that outputs a user interface that presents the implementation candidates.

10. The environmental load prediction device according to claim 9.

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