Information processing device and method

1. An information processing device comprising:

a hardware processor configured to:

repeatedly acquire, from a plurality of sensors attached to a plurality of operators, state information of the plurality of operators, the plurality of operators include a target operator, and the state information include first state information indicative of a state of the target operator;

update operation history information to include the acquired state information, the operation history information indicative of a history of operations performed by the plurality of operators and a history of the state information of the plurality of operators;

generate, by using machine learning, a first learning model and a second learning model based on the operation history information, the first learning model outputting a period of time required for an operator to perform an operation by applying state information of the operator, the second learning model outputting degree of fatigue of an operator due to an operation by applying state information of the operator;

calculate a first period of time required for the target operator to perform a first target operation by applying the first state information to the first learning model, calculate first degree of fatigue of the target operator due to the first target operation by applying the first state information to the second learning model, obtain second state information of the target operator by reflecting the first degree of fatigue to the first state information, calculate a second period of time required to perform a second target operation by applying the second state information to the first learning model, and calculate second degree of fatigue of the target operator due to the second target operation by applying the second state information to the second learning model,

calculate a third period of time required to perform the second target operation by applying the first state information to the first learning model, calculate third degree of fatigue of the target operator due to the second target operation by applying the first state information to the second learning model, obtain third state information of the target operator by reflecting the third degree of fatigue to the first state information, calculate a fourth period of time required to perform the first target operation by applying the third state information to the first learning model, and calculate fourth degree of fatigue of the target operator due to the first target operation by applying the third state information to the second learning model, and

allocate the first target operation and the second target operation to the target operator in consideration of order of performing the first and second target operations by comparing a first total value of the first period of time and the second period of time with a second total value of the third period of time and the fourth period of time or by comparing the second degree of fatigue with the fourth degree of fatigue.

2. The information processing device according to claim 1 , wherein the hardware processor is further configured to:

generate a plurality of operation patterns for allocating the target operation to the target operator,

calculate a period of time required for the target operator to perform a target operation allocated to the target operator for each of the operation patterns,

determine one of the operation patterns based on a period of time calculated for each of the operation patterns, and

allocate the target operation to the target operator in accordance with the determined operation pattern.

3. The information processing device according to claim 1 , further comprising:

an attribute information storage configured to store attribute information indicative of an attribute relating to an operator; wherein

the hardware processor is further configured to correct the first learning model and the second learning model based on attribute information indicative of an attribute of the target operator.

4. The information processing device according to claim 1 , wherein the hardware processor is further configured to:

acquire environment information relating to an environment in which an operation is performed; and

correct the first learning model and the second learning model based on the environment information.

5. The information processing device according to claim 1 , wherein the sensor is attachable to the target operator.

6. The information processing device according to claim 1 , further comprising the storage.

7. The information processing device according to claim 1 , wherein the sensor is a wearable device configured to measure one or more of the target operator's:

myogenic potential,

blood sugar level,

ocular potential, or

blood alcohol concentration.

8. The information processing device according to claim 1 , wherein the sensor is a wearable device comprising one or more of:

a myogenic potential sensor,

a blood sugar level sensor,

a ocular potential sensor, or

a blood alcohol concentration sensor.

9. The information processing device according to claim 1 , wherein the hardware processor is further configured to:

display, on a display device, the allocated first and second target operations and the determined order of performing the first and second target operation.

10. A method executed by an information processing device, comprising:

repeatedly acquiring, from a plurality of sensors attached to a plurality of operators, state information of the plurality of operators, the plurality of operators include a target operator, and the state information include first state information indicative of a state of the target operator;

updating operation history information to include the acquired state information, the operation history information indicative of a history of operations performed by the plurality of operators and a history of the state information of the plurality of operators;

generating, by using machine learning, a first learning model and a second learning model based on the operation history information, the first learning model outputting a period of time required for an operator to perform an operation by applying state information of the operator, the second learning model outputting degree of fatigue of an operator due to an operation by applying state information of the operator;

calculating a first period of time required for the target operator to perform a first target operation by applying the first state information to the first learning model, calculating first degree of fatigue of the target operator due to the first target operation by applying the first state information to the second learning model, obtaining second state information of the target operator by reflecting the first degree of fatigue to the first state information, calculating a second period of time required to perform the second target operation by applying the second state information to the first learning model, calculating second degree of fatigue of the target operator due to the second target operation by applying the second state information to the second learning model, calculating a third period of time required to perform the second target operation by applying the first state information to the first learning model, calculating third degree of fatigue of the target operator due to the second target operation by applying the first state information to the second learning model, obtaining third state information of the target operator by reflecting the third degree of fatigue to the first state information, calculating a fourth period of time required to perform the first target operation by applying the third state information to the first learning model, and calculating fourth degree of fatigue of the target operator due to the first target operation by applying the third state information to the second learning model, and

allocating the first target operation and the second target operation to the target operator in consideration of order of performing the first and second target operations by comparing a first total value of the first period of time and the second period of time with a second total value of the third period of time and the fourth period of time or by comparing the second degree of fatigue with the fourth degree of fatigue.

11. The method according to claim 10 , wherein the sensor is attachable to the target operator.

12. The method according to claim 11 , wherein the sensor is a wearable device configured to measure one or more of the target operator's:

myogenic potential,

blood sugar level,

ocular potential, or

blood alcohol concentration.

13. The method according to claim 11 , wherein the sensor is a wearable device comprising one or more of:

a myogenic potential sensor,

a blood sugar level sensor,

a ocular potential sensor, or

a blood alcohol concentration sensor.

14. The method according to claim 10 , further comprising:

displaying, on a display device, the allocated first and second target operations and the determined order of performing the first and second target operation.