Apparatus, method, and program

Abstract

The present invention provides a device equipped with a processor, wherein the processor acquires the margin of the equipment's production performance, determines the trial amount for controlling the equipment according to the margin, generates control data for controlling the equipment according to the trial amount, and outputs the control data for the equipment. [Solution] The processor may acquire control data from past equipment control operations and determine the number of trials such that the smaller the margin, the closer the resulting control data will be to the aforementioned past control data.

Description

Technical Field

[0001] The present invention relates to an apparatus, a method, and a program.

Background Art

[0002] Patent Document 1 describes a plant control system. [Prior Art Document] [Patent Document] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2022-95082

Summary of the Invention

[0003] (1) In a first aspect of the present disclosure, there is provided an apparatus including a processor, the processor acquiring a margin of production results of equipment, determining a trial amount in the control of the equipment according to the margin, generating control data for controlling the equipment according to the trial amount, and outputting the control data of the equipment.

[0004] (2) In the apparatus of (1) above, the processor may acquire control data of the results of controlling the equipment, and determine the trial amount so that the control data is closer to the control data of the results as the margin is smaller.

[0005] (3) In the apparatus of (2) above, the processor may acquire at least one of past control data used for controlling the equipment, the distribution of the past control data, or the range of the past control data as the control data of the results.

[0006] (4) In any of the apparatuses of (1) to (3) above, the processor may determine a larger trial amount as the margin is larger.

[0007] (5) In any of the apparatuses of (1) to (4) above, the processor may acquire state data of the equipment, and acquire the margin of production results of the equipment based on the acquired state data.

[0008] (6) In the apparatus described in (5) above, the processor may obtain the production volume of the production process in the equipment from the acquired state data, and calculate a larger margin as the production volume increases.

[0009] (7) In the apparatus described in (6) above, the processor may obtain the amount of storage in the storage section between the multiple production processes in the equipment from the acquired state data, calculate that the larger the amount of storage, the greater the margin of the production process upstream of the storage section, and determine the trial amount for each production process according to the corresponding margin.

[0010] (8) In any of the devices described in (1) to (7) above, the processor may perform an improvement evaluation of the operation of the equipment, and the higher the improvement evaluation, the larger the margin may be calculated.

[0011] (9) A second aspect of the present disclosure provides a method performed by a processor for obtaining a margin of production performance of equipment, determining a trial amount for controlling the equipment according to the margin, generating control data for controlling the equipment according to the trial amount, and outputting the control data for the equipment.

[0012] (10) In a third aspect of the present disclosure, a program is provided which, when executed by a processor, causes the processor to acquire a margin of production performance of the equipment, to determine a trial amount for controlling the equipment according to the margin, to generate control data for controlling the equipment according to the trial amount, and to output the control data for the equipment.

[0013] It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. Furthermore, subcombinations of these features may also constitute an invention. [Brief explanation of the drawing]

[0014] [Figure 1] An example configuration of the control support device 10 of this embodiment is shown together with the equipment 20. [Figure 2] An example of the operation of the control support device 10 of this embodiment is shown. [Figure 3] This diagram illustrates the multiple production processes in equipment 20. [Figure 4] This diagram illustrates the process of modifying control data based on actual control data. [Figure 5] This diagram illustrates the generation of control data based on the trial quantity. [Figure 6] Examples of a computer 2200 in which multiple aspects of the present invention may be embodied in whole or in part are shown. [Modes for carrying out the invention]

[0015] The present invention will be described below through embodiments of the invention, but these embodiments are not intended to limit the invention as defined in the claims. Furthermore, not all combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0016] Figure 1 shows an example configuration of the control support device 10 of this embodiment together with the equipment 20. The control support device 10 is connected to the equipment 20. The control support device 10 supplies control data to the equipment 20 for operating the equipment 20 according to status data indicating the state of the equipment 20. The control support device 10 collects operational data from the equipment 20 in response to the supply of control data. The control support device 10 may be a distributed control system (DCS) as an example. Alternatively, the control support device 10 may supply control data to the equipment 20 via at least one of a PIMS (Plant Information Management System) or a DCS. The control support device 10 may be located inside or outside the equipment 20.

[0017] Equipment 20 is installed in a plant, etc. Such a plant may be, for example, an industrial plant such as a chemical or metal plant, a plant that manages and controls wellheads and surrounding areas of gas or oil fields, a plant that manages and controls power generation such as hydroelectric, thermal, or nuclear power, a plant that manages and controls environmental power generation such as solar or wind power, a plant that manages and controls water supply and sewage systems or dams, etc. Equipment 20 may also be installed in a building or transportation facility, etc. Such equipment 20 may have one or more process devices, one or more power generation devices, and one or more other devices.

[0018] Equipment 20 may have one or more field devices. Field devices may include, for example, sensor devices such as pressure gauges, flow meters, and temperature sensors; valve devices such as flow control valves and on-off valves; actuator devices such as fans and motors; imaging devices such as cameras or video cameras for photographing the conditions or objects of the plant; acoustic devices such as microphones or speakers for collecting abnormal noises from the plant or emitting alarm sounds; position detection devices for outputting position information of Equipment 20; or other devices.

[0019] Here, the status data indicates the state of the equipment 20. The status data may indicate physical quantities that can be varied by the control of the equipment 20, or it may indicate the output value of the equipment 20. For example, the status data may be measurement data such as temperature, pressure, or flow rate measured by measuring instruments such as sensors installed in the equipment 20, the energy efficiency or cost of the equipment 20 calculated from said measurement data, or the property values ​​of the products manufactured by the equipment 20. The status data may also include information regarding the production performance of the equipment 20.

[0020] The control data indicates the control content for equipment 20. The control data may indicate the output value of equipment 20 or the set value of the manipulated variable for equipment 20. As an example, the control data may indicate the raw material flow rate, temperature set value, actuator operating amount, or valve opening degree in equipment 20. The state data may change by operating equipment 20 in accordance with the control data.

[0021] The control support device 10 includes a data acquisition unit 100, a margin acquisition unit 110, a determination unit 120, a generation unit 130, and an output unit 140.

[0022] The data acquisition unit 100 is connected to the facility 20 by wire or wirelessly. The data acquisition unit 100 acquires information regarding the facility 20 for generating control data. The data acquisition unit 100 may acquire the state data of the facility 20 from the facility 20. The data acquisition unit 100 may acquire the control data of the actual results of controlling the facility 20. The data acquisition unit 100 may acquire at least one of the past control data used for controlling the facility 20, the distribution of the past control data, or the range of the past control data as the control data of the actual results.

[0023] The margin acquisition unit 110 is connected to the data acquisition unit 100. The margin acquisition unit 110 acquires the margin of the production results of the facility 20. The margin acquisition unit 110 may acquire the margin of the production results of the facility 20 based on the state data acquired by the data acquisition unit 100. Here, the margin indicates an evaluation of, for example, the production volume, energy efficiency, or fuel consumption in the facility 20.

[0024] The determination unit 120 is connected to the data acquisition unit 100 and the margin acquisition unit 110. The determination unit 120 determines the trial amount in the control of the facility 20 according to the margin acquired by the margin acquisition unit 110. Here, the trial amount may indicate, for example, the constraint range (or allowable range) of the deviation from the control data of the actual results for the generated control data, and the larger the trial amount, the larger the constraint range of the deviation from the control data of the actual results may be.

[0025] The generation unit 130 is connected to the determination unit 120. The generation unit 130 generates control data for controlling the facility 20 according to the trial amount determined by the determination unit 120.

[0026] The output unit 140 is connected to the generation unit 130 and the facility 20. The output unit 140 outputs the control data of the facility 20 generated by the generation unit 130 to the facility 20.

[0027] Figure 2 shows an example of the operation of the control support device 10 in this embodiment. The control support device 10 outputs control data according to the state of the equipment 20.

[0028] In step S200, the data acquisition unit 100 acquires status data of the equipment 20. The data acquisition unit 100 may acquire actual control data for the equipment 20 and actual status data that includes past status data corresponding to the control performed by the actual control data (for example, status data obtained at the next time point in time after the control performed by the actual control data), in association with each other. The data acquisition unit 100 may acquire past actual control data and actual status data at a period longer than the period in which status data is measured for the control of the equipment 20.

[0029] In step S210, the margin acquisition unit 110 acquires the margin of production performance of the equipment 20. The margin acquisition unit 110 may acquire the margin from state data. The margin acquisition unit 110 may acquire the production volume of the production process in the equipment 20 from the acquired state data, and calculate a larger margin the larger the production volume. The margin acquisition unit 110 may acquire the production volume entered by a user such as an operator or measured by a sensor, etc. The margin acquisition unit 110 may also calculate the production volume from state data. The margin acquisition unit 110 may acquire the total production volume or the time average value, etc., for a predetermined period. The margin acquisition unit 110 may calculate the margin from the acquired production volume using a predetermined function or lookup table, etc., that shows the relationship between production volume and margin. As an example, the margin acquisition unit 110 calculates the margin as the ratio of the production volume to the production capacity (maximum production volume or design value) of the production process of the equipment 20 (margin = (acquired production volume * 100) / maximum production volume).

[0030] The margin acquisition unit 110 may acquire the storage amount of a storage unit (such as a tank) between multiple production processes in the equipment 20 from the acquired state data, and calculate a larger margin for the production process upstream of the storage unit as the storage amount is larger. The margin acquisition unit 110 may calculate the margin for each of the multiple production processes. The margin acquisition unit 110 may calculate the margin for a production process from the storage amount as the margin for the production equipment that executes the production process. The margin acquisition unit 110 may acquire the storage amount input by a user such as an operator or measured by a sensor from the state data. The margin acquisition unit 110 may also calculate the storage amount from the state data. The margin acquisition unit 110 may calculate the margin from the acquired storage amount using a predetermined function or lookup table that shows the relationship between the storage amount and the margin. As an example, the margin acquisition unit 110 calculates the margin as the ratio of the acquired storage amount to the maximum storage amount that the storage unit can store (margin = (acquired storage amount * 100) / maximum storage amount).

[0031] The margin acquisition unit 110 performs an improvement evaluation of the operation of the equipment 20, and may calculate a larger margin the higher the improvement evaluation. The margin acquisition unit 110 may acquire the improvement evaluation entered by a user, such as an operator, from the status data. Alternatively, the margin acquisition unit 110 may calculate the improvement evaluation from the acquired status data using a predetermined function or lookup table that shows the relationship between the improvement evaluation and the status data. For example, the margin acquisition unit 110 may calculate the improvement evaluation as the degree of improvement in the period after a predetermined point in time compared to the period before a predetermined point in time, with respect to the target parameters of the equipment 20, including at least one of production volume, energy efficiency, or cost. The margin acquisition unit 110 may also calculate the improvement evaluation as the ratio of the total value of the target parameters in the second period prior to a predetermined point in time (or the value obtained by dividing the total value by the length of the second period) to the total value of the target parameters in the first period from a predetermined point in time to the present time (or the value obtained by dividing the total value by the length of the first period).

[0032] Furthermore, the margin acquisition unit 110 may calculate a total margin by weighting and adding the margins calculated for each of the multiple types of parameters (at least one of production volume, storage volume, or improvement evaluation) as described above. In this case, the weights multiplied by each margin may be set in advance by the user.

[0033] In step S220, the determination unit 120 determines the trial amount according to the margin obtained by the margin acquisition unit 110. The determination unit 120 may set the constraint range based on actual control data for the control data to be output from the present time onward (after the determination of the trial amount) as the trial amount. The determination unit 120 may determine the trial amount from the acquired margin using a predetermined function or lookup table that shows the relationship between the trial amount and the margin. The determination unit 120 may determine the trial amount such that the control data becomes closer to the actual control data the smaller the margin. The determination unit 120 may determine a larger trial amount the larger the margin. For example, the determination unit 120 may determine the size of the constraint range (radius of the constraint range circle, etc.) centered on the current control data (latest actual control data or control data currently set for equipment 20, etc.) to be smaller the smaller the margin and larger the larger the margin.

[0034] Furthermore, the determination unit 120 may determine the constraint range as the trial quantity, which is the value at the peak of the distribution of control data for multiple performances, the median of the distribution, or the distance (constraint range) from the mean value of the control data for multiple performances. For example, the determination unit 120 may determine the size of the constraint range, which is the distance from the value of the control data at the peak of the distribution of control data for multiple performances, the median of the distribution, or the mean value of the control data for multiple performances, to be smaller as the margin is small and larger as the margin is large. Alternatively, the determination unit 120 may determine the constraint range as the trial quantity, which is based on the range between the upper and lower limits of the control data for multiple performances. For example, if the margin is greater than or equal to a predetermined threshold, the determination unit 120 may determine the constraint range that exceeds the range of the control data for performances as the trial quantity, and if the margin is less than a predetermined threshold, it may determine the constraint range that is within the range of the control data for performances as the trial quantity. The distance used by the determination unit 120 may be the Euclidean distance, which represents the sum of the squares of the differences between corresponding data, the Mahalanobis distance, or a distance that increases as the difference between data increases. Alternatively, the determination unit 120 may determine the trial quantity using a difference degree that indicates how much the data differ from each other, such as using distance.

[0035] Furthermore, the decision unit 120 may, for example, estimate the probability density function of the distribution of control data for multiple results and determine the range of occurrence probabilities (constraint range) in the probability density function as the trial quantity. For example, the decision unit 120 may determine the constraint range (trial quantity) from the peak of the occurrence probability as follows: the smaller the margin, the higher the occurrence probability range; and the larger the margin, the lower the occurrence probability range. This results in a larger trial quantity for events with lower occurrence probabilities.

[0036] The determination unit 120 may determine the trial quantity for each production process according to the corresponding margin. The determination unit 120 may determine the trial quantity from the margin obtained for each production process from the storage quantity, etc. The determination unit 120 may determine the trial quantity for each production process as the trial quantity for controlling the production equipment operating in the corresponding production process.

[0037] In step S230, the generation unit 130 generates control data according to the trial quantity. The generation unit 130 may generate candidate control data output according to the input of the current (latest) state data using PID calculation or a machine learning model that outputs control data according to state data. The generation unit 130 may use a neural network, a support vector machine (SVM), or other machine learning model as the machine learning model.

[0038] The generation unit 130 may modify the generated candidate control data using the determined trial quantity to generate control data. For example, if the candidate control data is within the constraint range of the trial quantity, the generation unit 130 may use the candidate control data as control data without modification. If the candidate control data is outside the constraint range of the trial quantity, the generation unit 130 may modify the candidate control data so that it is within the constraint range of the trial quantity to generate control data.

[0039] For example, the generation unit 130 may modify each control parameter in the candidate control data to bring it closer to the current control data or actual control data, depending on the trial quantity. As an example, the generation unit 130 may modify the control data using the following equation 1. MVa-α×(MVa-MVb)=MVc...Equation 1

[0040] Here, in Equation 1, MVa represents candidate control data (vectors, etc.), MVb represents current control data or actual control data (vectors, etc.), and MVc represents modified control data (vectors, etc.). α may be a value determined according to the number of trials (constraint range), for example, 0 < α ≤ 1, and α is smaller as the number of trials increases.

[0041] The generation unit 130 may modify the candidate control data based on the control data of the actual performance that is closest to the candidate control data among the control data of the actual performance, or the current (latest) control data of the actual performance. For example, the generation unit 130 may calculate the distance between the candidate control data and each of the control data of the actual performance, and modify the candidate control data so that the distance between the control data of the actual performance with the shortest distance and the candidate control data is within the constraint range. Alternatively, the generation unit 130 may modify the candidate control data so that the distance between the current control data and the candidate control data is within the constraint range. As an example, the generation unit 130 generates control data by modifying the candidate control data to the value that is closer to the candidate control data among the upper and lower limits of the constraint range.

[0042] Furthermore, the generation unit 130 may directly consider the number of trials when generating control data. For example, when the generation unit 130 generates control data using a machine learning model, it may search (optimize) the input control data only within the range of the determined number of trials so that the output of the machine learning model is as close as possible to the control target. This allows the generation unit 130 to obtain control data that satisfies the constraints of the number of trials from the beginning.

[0043] In step S240, the output unit 140 may output the control data generated by the generation unit 130 to the equipment 20, causing the equipment 20 to be configured according to the control data. A user such as an operator of the equipment 20 may execute control on the equipment 20 according to the control data output by the output unit 140.

[0044] After step S240, the control support device 10 may proceed to step S200, acquire data, and repeat the transition step. The control support device 10 may also control the operation of the equipment 20 by repeating steps S200 and S230, and update the trial quantity by executing steps S210 and S220 at predetermined intervals.

[0045] In this embodiment, the control support device 10 can perform control actions not seen in past performance when there is sufficient margin in the operation of the equipment 20 because the target parameters are favorable, and collect new sets of control data and state data. This allows for the exploration of a more optimal control method using the new dataset. Furthermore, when there is insufficient margin in operation, the control support device 10 can safely control the equipment 20 within a range closer to actual performance by constraining the control data generated by a model or the like to the actual performance range.

[0046] The control support device 10 may determine the trial amount for each type (parameter) of control data. For example, in step S220, the determination unit may determine the trial amount for each of the multiple types of control data corresponding to the margin (i.e., in the equipment 20), and in step S230, the generation unit 130 may generate the control data according to the corresponding trial amount. Alternatively, the control support device 10 may adjust the control data according to the trial amount for at least some of the multiple types of control data in steps S210-S230, but may not adjust the other types of control data according to the trial amount.

[0047] Figure 3 shows an explanatory diagram of multiple production processes (continuous production) in equipment 20. Equipment 20 has production device A, production device B, storage unit C, production device D, storage unit E, and production device F from upstream. Production devices A, B, D, and F each operate to produce products in the production process. For example, production device B may execute the production process using intermediate products produced by production device A, production device D may execute the production process using intermediate products produced by production device B, and production device F may execute the production process using intermediate products produced by production device D. Storage unit C temporarily stores the intermediate products produced by production device B and supplies them to the next production process (production device D). Storage unit E temporarily stores the intermediate products produced by production device D and supplies them to the next production process (production device F).

[0048] The margin acquisition unit 110 acquires, as the margins of the production apparatuses A and B upstream of the storage unit C, the ratio of the current storage amount to the maximum storage amount of the storage unit C (for example, 95%). The margin acquisition unit 110 acquires, as the margin of the production apparatus D upstream of the storage unit E, the ratio of the current storage amount to the maximum storage amount of the storage unit E (for example, 55%).

[0049] As an example, the determination unit 120 may determine a larger trial amount for the control of the production apparatuses A and B with larger margins, and determine a smaller trial amount for the control of the production apparatus D with a smaller margin.

[0050] FIG. 4 shows an explanatory diagram of the correction of control data based on the actual control data. In the graph of FIG. 4, the horizontal axis represents the control data MV1, and the vertical axis represents the control data MV2 of a type different from the control data MV1. In FIG.4, the actual control data MV1 and MV2 are indicated by black circles, and the candidate control data and the control data generated by the generation unit 130 are indicated by white circles.

[0051] In step S230, the generation unit 130 generates candidate control data using a machine learning model or the like. The generation unit 130 is based on the actual control data with the minimum distance from the candidate control data among the plurality of actual control data. When the trial amount determined by the determination unit 120 in step S220 is the trial amount M, since the candidate control data is within the constraint range (dashed line) of the trial amount M, the generation unit 130 outputs the candidate control data as the control data without correction. When the trial amount determined by the determination unit 120 is the trial amount N (N < M), since the candidate control data is outside the constraint range (dashed line) of the trial amount N, the generation unit 130 corrects the candidate control data and outputs it as the control data. The generation unit 130 corrects the candidate control data to the value at the position where the straight line connecting the candidate control data and the reference actual control data overlaps the boundary of the constraint range of the trial amount N, and outputs it as the control data.

[0052] Figure 5 shows an explanatory diagram of the generation of control data according to the number of trials. In the graph of Figure 5, the horizontal axis shows control data MV1, and the vertical axis shows control data MV2, which is a different type of control data from control data MV1. In Figure 5, the actual control data MV1 and MV2 are shown as black circles, and the contour lines of the probability density of the actual control data are shown as dashed lines.

[0053] In step S230, the generation unit 130 generates control data using a machine learning model. If the trial quantity determined by the decision unit 120 in step S220 is trial quantity M, the generation unit 130 searches for control data within the constraint range (dashed line) of trial quantity M. If the trial quantity determined by the decision unit 120 is trial quantity N, the generation unit 130 searches for control data within the constraint range (dashed line) of trial quantity N. Since the range of trial quantity N has a higher probability density than the range of trial quantity M, it is possible to generate control data that is closer to actual results.

[0054] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where a block may represent (1) a stage in a process in which an operation is performed or (2) a section of a device having the role of performing the operation. Specific stages and sections may be implemented by dedicated circuits, programmable circuits supplied with computer-readable instructions stored on a computer-readable medium, and / or processors supplied with computer-readable instructions stored on a computer-readable medium. Dedicated circuits may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuits may include reconfigurable hardware circuits, including logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logic operations, flip-flops, registers, memory elements such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc.

[0055] Computer-readable media may include any tangible device capable of storing instructions to be executed by a suitable device, and as a result, computer-readable media having instructions stored therein will comprise a product containing instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc, memory stick, integrated circuit card, etc.

[0056] Computer-readable instructions may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, Java®, C++, and traditional procedural programming languages ​​such as the C programming language or similar programming languages.

[0057] Computer-readable instructions may be provided locally or via a wide area network (WAN) such as a local area network (LAN) or the internet to the processor or programmable circuit of a programmable data processing device such as a general-purpose computer, a special-purpose computer, or another computer, and the computer-readable instructions may be executed to create means for performing operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0058] Figure 6 shows an example of a computer 2200 in which multiple aspects of the present invention may be embodied in whole or in part. A program installed on the computer 2200 can cause the computer 2200 to function as an operation or one or more sections of an apparatus according to an embodiment of the present invention, or to execute such operation or one or more sections, and / or to cause the computer 2200 to execute a process or a stage of such process according to an embodiment of the present invention. Such a program may be executed by the CPU 2212 to cause the computer 2200 to perform a particular operation associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0059] The computer 2200 according to this embodiment includes a CPU 2212, RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0060] The CPU 2212 operates according to programs stored in the ROM 2230 and RAM 2214, thereby controlling each unit. The graphics controller 2216 retrieves image data generated by the CPU 2212 from a frame buffer provided in RAM 2214 or from itself, and displays the image data on the display device 2218.

[0061] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides them to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from the IC card and / or writes programs and data to the IC card.

[0062] The ROM 2230 stores boot programs and / or programs that depend on the computer 2200's hardware, which are executed by the computer 2200 when activated. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via parallel ports, serial ports, keyboard ports, mouse ports, etc.

[0063] The program is provided on a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from the computer-readable medium and installed on a hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of computer-readable medium, and executed by the CPU 2212. The information processing described within these programs is read by the computer 2200, resulting in coordination between the program and the various types of hardware resources described above. The apparatus or method may be configured to realize the manipulation or processing of information in accordance with the use of the computer 2200.

[0064] For example, when communication is performed between a computer 2200 and an external device, the CPU 2212 may execute a communication program loaded into RAM 2214 and, based on the processing described in the communication program, instruct the communication interface 2222 to perform communication processing. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in a recording medium such as RAM 2214, a hard disk drive 2224, a DVD-ROM 2201, or an IC card, transmits the read transmission data to the network, or writes received data received from the network to a reception buffer processing area provided on the recording medium.

[0065] Furthermore, the CPU 2212 may read all or necessary parts of files or databases stored on external storage media such as the hard disk drive 2224, DVD-ROM drive 2226 (DVD-ROM 2201), or IC card into the RAM 2214, and perform various types of processing on the data in the RAM 2214. The CPU 2212 then writes the processed data back to the external storage media.

[0066] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 2212 may perform various types of processing on the data read from RAM 2214, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and write the results back to RAM 2214. The CPU 2212 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 2212 may search among the multiple entries for an entry that matches the condition for which the attribute value of the first attribute is specified, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0067] The programs or software modules described above may be stored on or near computer 2200 on a computer-readable medium. Alternatively, recording media such as hard disks or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as computer-readable media, thereby providing programs to computer 2200 via the network.

[0068] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0069] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before," "prior to," etc., and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," "next," etc. for convenience, it does not mean that it is essential to perform the operations in that order.

[0070] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0071] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before," "prior to," etc., and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," "next," etc. for convenience, it does not mean that it is essential to perform the operations in that order. [Explanation of symbols]

[0072] 10 Control support device 20 Equipment 100 Data acquisition unit 110. Saturation acquisition unit 120 Decision Section 130 Generation part 140 Output section 2200 Computers 2201 DVD-ROM 2210 Host Controller 2212 CPU 2214 RAM 2216 Graphics Controller 2218 Display Devices 2220 Input / Output Controller 2222 Communication Interface 2224 Hard Disk Drive 2226 DVD-ROM drive 2230 ROM 2240 Input / Output Chip 2242 keyboard

Claims

1. A device equipped with a processor, The aforementioned processor, Obtain the margin of the equipment's production performance, The trial amount in the control of the equipment is determined according to the margin of safety. In accordance with the trial quantity, control data for controlling the equipment is generated. Output control data for the aforementioned equipment. Device.

2. The aforementioned processor, The control data obtained from the actual operation of the aforementioned equipment is acquired. The smaller the margin, the more the trial amount is determined to produce control data that is closer to the control data from the actual results. The apparatus according to claim 1.

3. The aforementioned processor, At least one of the following—past control data used to control the aforementioned equipment, the distribution of past control data, or the range of past control data—is acquired as actual control data. The apparatus according to claim 2.

4. The aforementioned processor, The larger the margin of error, the larger the trial quantity determined. The apparatus according to claim 1.

5. The aforementioned processor, The status data of the aforementioned equipment is acquired, Based on the acquired state data, the margin of error in the production performance of the equipment is obtained. The apparatus according to claim 1.

6. The aforementioned processor, From the acquired state data, the production volume of the production process in the equipment is obtained, and the larger the production volume, the larger the margin calculated. The apparatus according to claim 5.

7. The aforementioned processor, From the acquired state data, the amount of storage in the storage section between multiple production processes in the equipment is obtained. The larger the storage quantity, the greater the margin calculated for the production process upstream of the storage unit. The trial quantity for each production process is determined according to the corresponding margin. The apparatus according to claim 6.

8. The aforementioned processor, An improvement evaluation was conducted on the operation of the aforementioned equipment. The higher the improvement evaluation, the larger the margin calculated. The apparatus according to claim 1.

9. A method executed by a processor, Obtain the margin of the equipment's production performance, The trial amount in the control of the equipment is determined according to the margin of safety. In accordance with the trial quantity, control data for controlling the equipment is generated. Output control data for the aforementioned equipment. method.

10. When executed by the processor, the processor will Obtain the margin of the equipment's production performance, The trial amount in the control of the equipment is determined according to the margin of safety. In accordance with the trial quantity, control data for controlling the equipment is generated. The control data of the aforementioned equipment is output. program.

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