Abstract: Provided is a control apparatus including an acquisition unit configured to acquire a measurement value measured regarding control target equipment, a first control unit configured to output an operation amount of the control target equipment according to the measurement value by at least one of feedback control or feed-forward control, a second control unit configured to output an operation amount of the control target equipment according to the measurement value using a model learnt by using learning data, and a switching unit configured to perform switching between the first control unit and the second control unit by which the control target equipment is controlled.

Abstract: There is provided a model selection apparatus including: an evaluation model storage unit configured to store each of a plurality of evaluation models capable of outputting an index for evaluating a state of a facility that is configured to manufacture a product from a raw material, in association with the raw material; a property data acquisition unit configured to acquire property data indicating a property of the raw material which is used in the facility; a model selection unit configured to select a target model for evaluating the state of the facility based on the property data, from among the plurality of evaluation models, when a target raw material in the facility is used; and a target model output unit configured to output the target model.

Abstract: An evaluation model generating apparatus, including: a property data obtaining unit which obtains property data representing a property of a raw material to be used in equipment that manufactures a product from the raw material; a determining unit which determines, based on the property data, whether to generate an evaluation model that outputs an indicator obtained by evaluating a state of the equipment; and an evaluation model generating unit which generates, according to a result of the determining, the evaluation model by machine learning, is provided. In the evaluation model generating apparatus, the determining unit may determine, if it is judged that a target raw material is not similar in property to a raw material that has been used in the past based on the property data, to generate the evaluation model.

Abstract: An estimation apparatus is provided, including an abnormality index acquisition unit for acquiring, as an abnormality index, an evaluation index output by an evaluation model according to state data being input when an abnormality occurs in a facility, among the state data indicating a state of the facility when an operation model is used to control a control target, the operation model being generated by reinforcement learning where an output of the evaluation model trained by machine learning to output the evaluation index in accordance with the state of the facility is set as at least a part of a reward, and outputting an action in accordance with the state of the facility; an estimation unit for estimating which of the evaluation model or the operation model is a main cause of the abnormality; and an output unit for executing an output in accordance with a result of the estimate.

Abstract: Provided is a model selection apparatus including a candidate model storage unit configured to store a plurality of candidate models each of which is generated by reinforcement learning using, as at least a part of a reward, an output of an evaluation model configured to output an indicator obtained by evaluating a state of equipment and is capable of output an action, a state data acquisition unit configured to acquire a plurality of pieces of state data representing the state of the equipment in a case where each of manipulated variables based on outputs of the plurality of candidate models is applied to a controlled object in the equipment, an indicator acquisition unit configured to acquire a plurality of indicators, a model selection unit configured to select an object model for controlling the controlled object, and an object model output unit configured to output the object model.

Abstract: There is provided a control apparatus including: a model output acquisition unit configured to acquire an operation model output which is output according to inputting state data indicating a state of a facility, to an operation model trained by machine learning to output an action in accordance with the state of the facility by using simulation data from a simulator that simulates an operation in the facility; an index acquisition unit configured to acquire an index which is calculated by using real data from the facility and which is for monitoring a difference between a behavior of the simulator and an actual operation in the facility; a correction unit configured to correct the operation model output based on the index; and a control unit configured to output a manipulated variable for controlling a control target provided in the facility, according to the corrected operation model output.

Abstract: Provided is a model verification apparatus including a model storage unit configured to store a machine learning model that has been subjected to machine learning, by using a learning sample including state data indicating a state of equipment in which a controlled object is provided and action data indicating an action for deciding a manipulated variable to be applied to the controlled object, so as to output an action according to the state of the equipment, a learning range identification unit configured to identify a learning range indicating a range of the learning sample, a verification data acquisition unit configured to acquire verification data indicating a plurality of states of the equipment outside the learning range, and a verification result output unit configured to output a verification result indicating a plurality of actions to be output by the machine learning model in response to an input of the verification data.

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 prediction apparatus including: a data acquisition unit configured to acquire setting value data indicating a setting value of a controlled object and physical quantity data indicating a physical quantity of a product obtained by controlling the controlled object; a prediction unit configured to calculate, using the setting value data and the physical quantity data, a plurality of prediction values obtained by predicting a plurality of physical quantities in the product on a basis of a setting value used for control of the controlled object; an evaluation unit configured to evaluate the plurality of prediction values on a basis of a predefined reference; and an output unit configured to output a setting value recommended according to a result of the evaluation.

Abstract: Provided is an evaluation apparatus comprising: an environmental data acquisition unit configured to acquire environmental data indicating an operation environment of equipment; a performance data acquisition unit configured to acquire performance data indicating an operation performance of the equipment; an estimation unit configured to estimate an operation performance on an operation basis in a learning target period under an operation environment in an evaluation target period, based on the environmental data and the performance data in the learning target period; an evaluation unit configured to calculate an indicator that relatively evaluates an actually measured value of an operation performance in the evaluation target period with respect to an estimated value of the estimated operation performance; and an output unit configured to output the indicator.

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: 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 control system comprising a control apparatus including a learning processing unit for generating a control model by learning, the control model being for calculating control data for controlling a facility according to state data detected by at least one sensor for measuring a state of the facility, and a model transmission unit for transmitting the generated control model to a controller for controlling the facility; and a controller including a model receiving unit for receiving the learned control model from the control apparatus, a state receiving unit for receiving the state data from the at least one sensor, a calculation unit for calculating control data for controlling the facility according to the state data, which is a processing target, by using the control model received by the model receiving unit, and a control unit for controlling the facility by using the calculated control data.

Abstract: Provided is a control apparatus including an acquisition unit configured to acquire a measurement value measured regarding control target equipment, a first control unit configured to output an operation amount of the control target equipment according to the measurement value by at least one of feedback control or feed-forward control, a second control unit configured to output an operation amount of the control target equipment according to the measurement value using a model learnt by using learning data, and a switching unit configured to perform switching between the first control unit and the second control unit by which the control target equipment is controlled.

Abstract: An information processing device according to one aspect of the present invention includes a first acquirer configured to acquire measured data of a sensor, a second acquirer configured to acquire maintenance information related to maintenance performed on the sensor, a learner configured to learn teacher data in which the acquired maintenance information as label information is associated with the acquired measured data to generate a determination model, and a storage storing the generated determination model.

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 first acquisition unit for acquiring measurement data measured by a sensor and a first learning processing unit for executing, by using learning data including the measurement data acquired by the first acquisition unit and a control parameter indicating a first type of control content of at least one device to be controlled, a learning processing of a first model configured to output a recommended control parameter indicating the first type of control content recommended for increasing a reward value determined by a preset reward function in response to input of the measurement data.