Abstract: Provided is an operation system including: an evaluation model generation apparatus configured to generate, by machine learning, an evaluation model configured to output an indicator indicating a result of evaluating a state in a piece of equipment with respect to an intended target based on an operation target in the piece of equipment and a state in the piece of equipment; an operation model generation apparatus configured to generate an operation model configured to output an action corresponding to the state in the piece of equipment, by reinforcement learning in which an output of the evaluation model is set as at least a part of a reward; and a control apparatus configured to apply, to a controlled object in the piece of equipment, a manipulated variable based on the action that is output by the operation model according to the state in the piece of equipment.

Abstract: Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

Abstract: Provided is an apparatus including a supply unit suppling a value of a state parameter to an operation model outputting a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit acquiring a recommendation value of a control parameter output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit acquiring a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit evaluating the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through manipulation by a human.

Abstract: Provided is a learning device including: a data acquisition unit configured to acquire, before control of a control target provided in equipment by a machine learning model that outputs an action corresponding to a state of the equipment, initialization data including state data indicating the state of the equipment and action data indicating an action on the control target; and a preliminary learning unit configured to initialize the machine learning model by performing preliminary learning on the basis of the initialization data before start of reinforcement learning corresponding to the control of the control target by the machine learning model.

Abstract: There is provided a learning processing apparatus comprising: a learning processing unit configured to generate a control model that outputs a manipulated variable corresponding to an indicated variable and a process variable of a predetermined system by means of machine learning; a generation unit configured to generate controlling data that indicates a correspondence relation of a combination of the indicated variable and the process variable to the manipulated variable corresponding to the combination by using the control model; and a supply unit configured to supply the controlling data to a predetermined control apparatus.

Abstract: A controller includes: an acquisition unit configured to acquire process data for an actually used controlled object; and a calculation unit configured to, based on at least one of a target set value and parameters for the actually used controlled object, convert the process data acquired by the acquisition unit, and calculate a manipulated variable for the actually used controlled object by use of the converted process data and a trained model. Upon receiving input of process data for a specific controlled object, the trained model outputs a manipulated variable for approximating process data for the specific controlled object to a specific target set value. The parameters include parameters for specifying the relation between manipulated variables for the actually used controlled object and process data obtained by use of the manipulated variables.

Abstract: Provided is a control apparatus comprising a control unit configured to control a control target by a control model machine-learned so as to output an operation amount of the control target according to a state of equipment provided with the control target; a simulation unit configured to simulate, by using a simulation model, the state of the equipment in a case where the operation amount, which is output by the control model, is given to the control target; and a stop unit configured to stop control of the control target by the control model, based on a simulation result.

Abstract: Provided is a control apparatus, including a control unit, configured to control a control target by a machine learned control model to output an operation amount of the control target according to a state of equipment having the control target provided therein; a prediction unit, configured to predict a future state of the equipment; an adjusting unit, configured to adjust the operation amount based on a predicted result; and an output unit, configured to output an adjusted operation amount to the control target.

Abstract: A plant control supporting apparatus includes a segment selector configured to select, from among a plurality of segments defined in a plant, a segment for which learning for acquiring an optimal value of at least one parameter representing an operation state is executed, a reward function definer configured to define a reward function used for the learning, a parameter extractor configured to extract at least one parameter that is a target for the learning in the selected segment on the basis of input and output information of a device used in the plant and segment information representing a configuration of a device included in the selected segment, and a learner configured to perform the learning for acquiring the optimal value for each segment on the basis of the reward function and the at least one parameter.

Abstract: Provided is a learning apparatus comprising: a correspondence receiving unit for receiving a correspondence between each produced object of a target process and a quality evaluation of each downstream produced object produced by using each produced object of the target process; a learning processing unit for generating, through learning, an estimation model for estimating the quality evaluation of the downstream produced object, by using the at least one production parameter about the production of each produced object of the target process and the quality evaluation of each downstream produced object; a calculating unit for calculating a model evaluation based on at least one of certainty or complexity of the estimation model; and a model evaluation sending unit for sending the calculated model evaluation, to an evaluation apparatus for evaluating at least one upstream process by using a model evaluation about each of the at least one upstream process.

Abstract: Provided is a determination apparatus comprising: a state data acquisition unit configured to acquire state data indicative of a state of equipment provided with a control target; an operation amount data acquisition unit configured to acquire operation amount data indicative of an operation amount of the control target; a control model generation unit configured to generate a control model, which outputs the operation amount corresponding to the state of the equipment, by machine learning by using the state data and the operation amount data; a simulation unit configured to simulate, by using a simulation model, the state of the equipment in a case where the operation amount, which is output by the control model, is given to the control target; and a determination unit configured to determine whether control of the control target by the control model is possible, based on a simulation result.

Abstract: Provided is a data recording apparatus including a key exchange unit for exchanging a first encryption key with a system in response to authentication by the system, a data collection unit for collecting measurement data obtained by measuring a physical quantity associated with a measurement target, a data recording unit for recording the measurement data, and a data transmission unit for transmitting the measurement data encrypted using the first encryption key to the system. In addition, provided is a system including a key management unit for exchanging a first encryption key with a data recording apparatus in response to authentication of the data recording apparatus, a data obtaining unit for obtaining measurement data obtained by measuring a physical quantity associated with the measurement target encrypted by the data recording apparatus using the first encryption key, and a data management unit for managing the measurement data using a distributed ledger technology.

Abstract: Provided is an apparatus including a plurality of agents that each set some devices among a plurality of devices provided in a facility to be target devices, wherein each of the plurality of agents includes a state acquiring section that acquires state data indicating a state of the facility; a control condition acquiring section that acquires control condition data indicating a control condition of each target device; and a learning processing section that uses learning data including the state data and the control condition data to perform learning processing of a model that outputs recommended control condition data indicating a control condition recommended for each target device in response to input of the state data.

Abstract: An apparatus is provided, which includes a setting unit for setting an operation content for a manufacturing system configured to manufacture an object to be manufactured, a first acquisition unit for acquiring a posterior state parameter set indicating a state of at least one of the manufacturing system or the object to be manufactured after the operation content is set, and a learning processing unit for executing, by using learning data including the operation content and the posterior state parameter set, a learning process of a control model of the manufacturing system configured to output the operation content that increases a reward value determined by a preset reward function in response to input of a state parameter set indicating a state of at least one of the manufacturing system or the object to be manufactured.

Abstract: A battery management device according to one aspect of the present invention for managing a plurality of field devices of a battery-driven type, the plurality of field devices being associated with a facility disposed in a plant and being defined as belonging to a same group includes a battery manager that, in a case receiving battery life information representing approach to a battery life of an installed battery from a first field device that is one field device currently operating among the plurality of field devices, switches the operating field device to a second field device that is another field device belonging to the same group, an operation of the second field device being stopped.

Abstract: Provided is an apparatus including a plurality of agents that each set some devices among a plurality of devices provided in a facility to be target devices, wherein each of the plurality of agents includes a state acquiring section that acquires state data indicating a state of the facility; a control condition acquiring section that acquires control condition data indicating a control condition of each target device; and a learning processing section that uses learning data including the state data and the control condition data to perform learning processing of a model that outputs recommended control condition data indicating a control condition recommended for each target device in response to input of the state data.

Abstract: A plant control device of the present invention includes a register configured to register a simulated dangerous condition which is a simulated representation of an operation condition under which a plant is dangerous, a first acquirer configured to acquire an operation condition of the plant, a learner configured to learn the operation condition acquired and the simulated dangerous condition registered and produce an operation model of the plant, a determiner configured to determine an operation parameter of the plant on the basis of the operation condition acquired and the operation model produced, and an instructor configured to instruct an operation of the plant on the basis of the operation parameter determined.

Abstract: An information processing system includes a state data acquisition device configured to acquire state data indicating an operation state of a plant, a first classification device configured to generate a determination line used to classify the state data, a mirror image data generation device configured to generate mirror image data obtained by mirroring the acquired state data using the generated determination line, and a second classification device configured to generate a determination model on the basis of the state data and the generated mirror image data.

Abstract: A plant control supporting apparatus includes a segment selector configured to select, from among a plurality of segments defined in a plant, a segment for which learning for acquiring an optimal value of at least one parameter representing an operation state is executed, a reward function definer configured to define a reward function used for the learning, a parameter extractor configured to extract at least one parameter that is a target for the learning in the selected segment on the basis of input and output information of a device used in the plant and segment information representing a configuration of a device included in the selected segment, and a learner configured to perform the learning for acquiring the optimal value for each segment on the basis of the reward function and the at least one parameter.

Abstract: Provided is a data management system including a plurality of nodes to be connected to and disconnected from a network. At least one of the plurality of nodes includes: a communicator that performs a communication via the network by using data having a data structure including a key; a database that accumulates the data; and a data analyzer that, when the node is newly connected to the network, provides another node already connected to the network with node information, the node information being used for enabling the node to serve as node for accumulating the data.