Abstract: An information processing device acquires a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant. Based on the prediction result, the information processing device specifies a relevant variable dependent on the premise from the plurality of variables. Thereafter, with respect to the relevant variable, the information processing device displays information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant.

Abstract: A prediction apparatus (10) of the present disclosure includes a memory (12) storing a prediction model for calculating a predicted value of a harvest result including harvest weight and information on physiological disorders of plants in a plant factory (80), and a value of an environmental parameter indicating an environment of the plant factory (80), a controller (11) configured to input the value of the environmental parameter retrieved from the memory (12) into the prediction model to calculate the predicted value of the harvest result, and a communication interface (13) configured to output the predicted value of the harvest result calculated by the controller (11).

Abstract: A support system (1) of the present disclosure includes a first memory (12) storing data indicating an environment of a cultivation facility (60) and data indicating an occurrence status of disease and pests, and a first controller (11) that performs machine learning using the data indicating the environment and the data indicating the occurrence status of disease and pests acquired from the first memory (12) to generate a prediction model for predicting a probability of occurrence of disease and pests in the cultivation facility (60).

Abstract: A learning apparatus is provided, which comprises: a characteristic acquisition unit for acquiring one of a voltage and a capacity of a storage battery and a characteristic corresponding to a capacity change and a voltage change of the storage battery under a plurality of conditions during at least one of charging and discharging of the storage battery; and a learning unit for learning a relationship between one of the voltage and the capacity of the storage battery and the characteristic under each of the plurality of conditions to generate an estimation model for estimating at least one of a maximum capacity and a remaining capacity of the storage battery from one of the voltage and the capacity of the storage battery and the characteristic.

Abstract: A secondary battery capacity measuring system that estimates maximum capacities of other cells using characteristics of a deteriorated cell even when a charging process in the deteriorated cell having a smaller maximum capacity than the other cells in cells constituting a battery module ends and data for measuring the maximum capacities of the other cells cannot be acquired is provided.

Abstract: A secondary battery capacity measurement system includes a data convertor, an SOC computer, and a maximum capacity computer. The data convertor determines a partial derivative characteristic curve of a capacity-to-voltage derivative over voltage, the partial derivative characteristic curve indicating a characteristic of a capacity-to-voltage derivative, from a set of historical data of time-sequentially-measured values of voltage and current. The SOC computer computes a difference between the partial derivative characteristic curve and a reference derivative curve indicating a reference characteristic of the capacity-to-voltage derivative, and fits the partial derivative characteristic curve to the reference derivative curve by reducing the difference to estimate an SOC. The maximum capacity computer estimates a maximum value of capacity, from the partial derivative characteristic curve and the reference derivative curve.

Abstract: A secondary battery capacity measuring system that estimates maximum capacities of other cells using characteristics of a deteriorated cell even when a charging process in the deteriorated cell having a smaller maximum capacity than the other cells in cells constituting a battery module ends and data for measuring the maximum capacities of the other cells cannot be acquired is provided.

Abstract: A secondary battery capacity measurement system includes a data convertor, an SOC computer, and a maximum capacity computer. The data convertor determines a partial derivative characteristic curve of a capacity-to-voltage derivative over voltage, the partial derivative characteristic curve indicating a characteristic of a capacity-to-voltage derivative, from a set of historical data of time-sequentially-measured values of voltage and current. The SOC computer computes a difference between the partial derivative characteristic curve and a reference derivative curve indicating a reference characteristic of the capacity-to-voltage derivative, and fits the partial derivative characteristic curve to the reference derivative curve by reducing the difference to estimate an SOC. The maximum capacity computer estimates a maximum value of capacity, from the partial derivative characteristic curve and the reference derivative curve.