Patent Description:
<CIT> discloses a controlling system for environmental comfort and controlling method of the controlling system, the controlling system being capable of automatically adjusting comfort of an indoor environment by automatically controlling indoor apparatuses when a person is detected indoor.

PTL <NUM>, according to its abstract, states that, to construct an environment of a living room space acceptable by each person while taking individual difference such as preference of each person into consideration with respect to the plurality of users, an environment control system manages and controls control conditions of a lighting system and an air conditioning system capable of individually controlling environments of a plurality of regions in the living room space. A terminal information collecting section collects terminal information including positional information of portable terminals respectively carried by each of the plurality of users utilizing the living room space, and comfort level information relating to comfort levels to the environment of the living room space, of the users carrying the portable terminals. An adjustment section adjusts the control conditions to increase the comfort level of the user having low comfort level, and to keep the comfort level of the user having high comfort level on the basis of the collected terminal information.

The controlling system for environmental comfort disclosed in <CIT>, however, does not take into account the presence of a plurality of users, and thus does not automatically adjust comfort to suit a plurality of different users. Further, comfort cannot be guaranteed when a plurality of users are present in the same room.

Further, only environment parameters are taken into account, so that comfort may be significantly reduced immediately after a person moves from the outside, for example.

An information processing device and an air conditioning system according to the present disclosure are provided to solve the above-described problems and achieve air conditioning control suitable even for a situation where there are a plurality of users such as an office.

According to the present invention an information processing device and an air conditioning system as defined in the independent claims are provided. Further embodiments of the claimed invention are defined in the dependent claims. Although the claimed invention is only defined by the claims, the below embodiments, examples, and aspects are present for aiding in understanding the background and advantages of the claimed invention.

The information processing device and the air conditioning system according to the present disclosure perform, even when a plurality of users are present, air conditioning control to set a temperature of the air conditioning target space appropriate for the users.

Embodiments of the present invention will be described in detail with reference to the drawings. Note that the same or corresponding parts in the drawings are denoted by the same reference numerals to avoid the description from being redundant. Note that, in the following drawings, a relation among the sizes of the components may be different from an actual relation.

<FIG> is a diagram illustrating a schematic configuration of an air conditioning system according to the present embodiment.

An air conditioning system <NUM> includes an air conditioning device <NUM> and an air conditioning management device <NUM>. Air conditioning device <NUM> includes an outdoor unit <NUM> and indoor units 40A, 40B.

Outdoor unit <NUM> includes a compressor <NUM> that compresses and discharges a refrigerant, a heat source-side heat exchanger <NUM> that exchanges heat between outside air and the refrigerant, and a four-way valve <NUM> that changes a circulation direction of the refrigerant in accordance with an operation mode. Outdoor unit <NUM> includes an outside-air temperature sensor <NUM> that detects an outside-air temperature and an outside-air humidity sensor <NUM> that detects an outside-air humidity.

Indoor unit 40A and indoor unit 40B are connected in parallel to outdoor unit <NUM> in a refrigerant circuit.

Indoor unit 40A includes a load-side heat exchanger <NUM> that exchanges heat between indoor air and the refrigerant, an expansion device <NUM> that decompresses the highly pressurized refrigerant to expand the refrigerant, an indoor temperature sensor <NUM> that detects an indoor temperature, and an indoor humidity sensor <NUM> that detects an indoor humidity. Indoor unit 40B is the same in configuration as indoor unit 40A, so that neither illustration nor description of the internal configuration will be given below.

Compressor <NUM> is, for example, an inverter compressor having a capacity variable in accordance with a change in operating frequency. Expansion device <NUM> is, for example, an electronic expansion valve.

In outdoor unit <NUM> and indoor units 40A, 40B, compressor <NUM>, heat source-side heat exchanger <NUM>, expansion device <NUM>, and load-side heat exchanger <NUM> are connected to constitute a refrigerant circuit <NUM> through which the refrigerant circulates. Accordingly, in a space having a plurality of indoor units provided, even when an indoor unit other than the nearest indoor unit is put into operation, the temperature and humidity in the space will change. Therefore, according to the present embodiment, for air conditioning of a space having a plurality of indoor units provided, reinforcement learning of control of a plurality of air conditioners is performed to explore an optimal value.

Air conditioning management device <NUM> includes a CPU <NUM>, a memory <NUM>, a temperature sensor (not illustrated), an input device, and a communication device. Air conditioning management device <NUM> transmits a control signal from the communication device to each of indoor units 40A, 40B.

Memory <NUM> includes, for example, a read only memory (ROM), a random access memory (RAM), and a flash memory. Note that the flash memory stores an operating system, an application program, and various types of data.

CPU <NUM> controls the overall operation of air conditioning device <NUM>. Note that air conditioning management device <NUM> illustrated in <FIG> is implemented by the operating system and the application program executed by CPU <NUM>, the operating system and the application program being stored in memory <NUM>. Note that, during the execution of the application program, the various types of data stored in memory <NUM> are accessed. A receiver that receives the control signal from the communication device of air conditioning management device <NUM> is provided in each of indoor units 40A, 40B.

<FIG> is a functional block diagram of air conditioning management device <NUM>. Air conditioning management device <NUM> includes a control unit 101A and a model storage unit 102A. CPU <NUM> illustrated in <FIG> operates as control unit 101A, and memory <NUM> operates as model storage unit 102A.

Control unit 101A controls indoor units 40A, 40B and outdoor unit <NUM> on the basis of outputs of various sensors and setting information. Control unit 101A receives, from indoor units 40A, 40B, a temperature detected by indoor temperature sensor <NUM>, a humidity detected by indoor humidity sensor <NUM>, a solar radiation amount detected by a solar radiation sensor <NUM>, thermal information detected by a radiant heat sensor <NUM>, and a detection signal of a motion sensor <NUM> as the outputs of the various sensors. Control unit 101A further receives, from outdoor unit <NUM>, a temperature detected by outside-air temperature sensor <NUM> and a humidity detected by outside-air humidity sensor <NUM> as the outputs of the various sensors.

Control unit 101A further receives, as the setting information, various types of information including a target temperature, a target humidity, an airflow rate, and an airflow direction set for indoor units 40A, 40B.

Control unit 101A changes a flow path of four-way valve <NUM> in accordance with the operation mode of air conditioning device <NUM>, either a cooling operation mode or a heating operation mode.

Control unit 101A controls additional learning for a learned model stored in model storage unit 102A. Control unit 101A controls air conditioning system <NUM> using the learned model stored in model storage unit 102A in the inference phase.

Air conditioning management device <NUM> manages air conditioning device <NUM> to enable automatic control of air conditioning device <NUM> using action information on a person.

<FIG> is a block diagram illustrating blocks of a personal terminal and the air conditioning management device linked with the personal terminal.

As illustrated in <FIG>, air conditioning management device <NUM> includes a communication management unit <NUM>, a personal comfort data learning unit <NUM>, a control learning unit <NUM>, an air conditioning data holding unit <NUM>, an environment data holding unit <NUM>, a learning data holding unit <NUM>, and an air conditioning control device <NUM>. Air conditioning control device <NUM> includes an air conditioner communication management unit <NUM> and an air conditioner management unit <NUM>.

Air conditioning management device <NUM> is connected to a personal terminal <NUM> by radio. Communication management unit <NUM> manages communications with personal terminal <NUM>.

Personal comfort data learning unit <NUM> groups individuals who possess personal terminals <NUM> on the basis of information held by personal terminals <NUM>. Personal comfort data learning unit <NUM> groups the possessors of personal terminals <NUM> using unsupervised learning of comfort data of each individual held by comfort data holding unit <NUM> of a corresponding personal terminal <NUM>.

Control learning unit <NUM> uses data in air conditioning data holding unit <NUM>, environment data holding unit <NUM>, and learning data holding unit <NUM> to learn and infer control optimal for each condition using reinforcement learning.

From the above-described data, the control learning unit determines to perform control so as to maximize energy saving while maintaining the comfort of a person present in an air conditioning area as much as possible.

Air conditioning data holding unit <NUM> holds control data (target temperature, target humidity, airflow rate, airflow direction, etc.) of air conditioning device <NUM> used for learning.

Environment data holding unit <NUM> holds, in time series, an outside-air temperature, and a temperature, a humidity, a solar radiation amount, and an object surface temperature (radiant heat) in each air conditioning area.

When the plurality of indoor units 40A, 40B are provided, motion sensor <NUM> is provided for each indoor unit. A range that motion sensor <NUM> can cover is the air conditioning area of the air conditioner. Air conditioning system <NUM> can change a temperature set for each air conditioning area. Movement of a person in the area can be detected by motion sensor <NUM> connected to each of indoor units 40A, 40B.

Learning data holding unit <NUM> holds data to be used by control learning unit <NUM> and personal comfort data learning unit <NUM>. Specifically, learning data holding unit <NUM> holds a degree of dissatisfaction necessary for evaluation of learning and power consumption of air conditioning device <NUM>.

Air conditioner communication management unit <NUM> of air conditioning control device <NUM> manages communications with air conditioning device <NUM>. Air conditioner management unit <NUM> manages control of air conditioning device <NUM>.

Personal terminal <NUM> is a terminal possessed by each individual. Personal terminal <NUM> includes a display unit <NUM>, a communication management unit <NUM>, an input unit <NUM>, an action information holding unit <NUM>, a comfort data holding unit <NUM>, a computation unit <NUM>, and a sensor unit <NUM>. Communication management unit <NUM> manages communications with air conditioning management device <NUM>.

Sensor unit <NUM> is capable of detecting a location and movement distance of personal terminal <NUM>, and a temperature and humidity in the vicinity of personal terminal <NUM>. For example, sensor unit <NUM> includes an acceleration sensor, a GPS, a temperature sensor, and a humidity sensor. Computation unit <NUM> can compute the movement distance by integrating acceleration detected by the acceleration sensor and combining the integration result with location information detected by the GPS. It is thought that the smaller a temperature change, the smaller the influence on comfort. Therefore, in the present embodiment, movement of a person from the outside of the air conditioning area (outside of a room) to the air conditioning area that causes a large temperature change is mainly detected.

Action information holding unit <NUM> holds a movement path of an individual carrying personal terminal <NUM>. The movement path includes a movement distance, a movement time, a movement speed, and the like.

Comfort data holding unit <NUM> holds, in time series, comfort data such as hot or cold input by an individual and location information at the time of the input.

Note that action information holding unit <NUM> and comfort data holding unit <NUM> may be associated with each other in time series.

In <FIG>, personal comfort data learning unit <NUM> is provided in air conditioning management device <NUM>, but personal comfort data learning unit <NUM> may be provided in personal terminal <NUM>, so that computational resources required for air conditioning management device <NUM> can be reduced.

Further, not all the data detected by sensor unit <NUM> but some of the data may be used for learning. This allows a reduction in the computational resources.

Further, in <FIG>, communication management unit <NUM> is described as if to directly communicate with personal terminal <NUM>, but communication management unit <NUM> may communicate with personal terminal <NUM> via a cloud or a relay device.

<FIG> is a diagram illustrating an example of individual comfort data used for learning held by comfort data holding unit <NUM>. Reference numerals <NUM>-<NUM> to <NUM>-<NUM> in <FIG> denote codes for identifying the personal terminals. Comfort data holding unit <NUM> holds a range of a comfort index in which an individual feels comfortable (for example, predicted mean vote (PMV) that is a thermal environment evaluation index). Computation unit <NUM> computes the comfort index such as PMV from an indoor temperature, an indoor humidity, an airflow rate, and the like when sensory data such as "hot" or "cold" is input from input unit <NUM> of the personal terminal, and accumulates the comfort index thus computed into comfort data holding unit <NUM> as data. Computation unit <NUM> computes boundary values BL, BR of "cold", "comfortable", and "hot" from such pieces of data, and stores boundary values BL, BR into comfort data holding unit <NUM>.

<FIG> is a diagram illustrating an example of a machine learning model used by personal comfort data learning unit <NUM>. As data input to the machine learning model illustrated in <FIG>, the individual comfort data illustrated in <FIG> is used.

Circles plotted in <FIG> are each associated with a corresponding one of the personal terminals denoted as <NUM>-<NUM> to <NUM>-<NUM> in <FIG>. The vertical axis in <FIG> represents a position of a boundary between "comfortable" and "cold" in <FIG>, and the horizontal axis in <FIG> represents a position of a boundary between "comfortable" and "hot" in <FIG>. In <FIG>, points each indicating individual comfort in <FIG> are plotted. Clustering, belonging to unsupervised learning, is applied to the set of plotted points to classify users on the basis of comfortableness.

That is, the input to the machine learning model illustrated in <FIG> includes boundary value BL between "cold" and "comfortable" and boundary value BR between "comfortable" and "cold" when the individual comfort index (for example, PMV) described with reference to <FIG> is used as an index. When such values are input, the output from the machine learning model is a classification result (CA to CD).

<FIG> illustrates an example in which k-means clustering is used. As a result of the clustering, the personal terminals are classified into four classes CA, CB, CC, CD. A triangle located approximately at a center of each class indicates a centroid of the set of points indicated by the personal terminal belonging to the class. The centroid is a point indicating a mean of ordinate values of the set of points of each class and a mean of abscissa values.

The machine learning model illustrated in <FIG> groups the input data under unsupervised learning.

<FIG> is a diagram illustrating a comfort range of each class after classification. The point (median value of comfort) indicated by the triangle, which is the centroid obtained by k-means clustering, is used to indicate the comfort of each class.

The result of the clustering obtained in <FIG> is used for controlling the air conditioner as follows. When a plurality of people are present in an air conditioning target space and belong to a plurality of classes, control is performed on an area where the comfort ranges of the plurality of classes overlap. For example, when a person belonging to class CA and a person belonging to class CB in <FIG> are present, control is performed on an area between a boundary value BLA and a boundary value BRB as a comfort area.

Note that, when there is no overlapping comfort area such as between class CA and class CC, control is performed on an area where a distance to the comfort areas of the two classes is shortest, for example, an area between boundary value BLA and a boundary value BRC.

The policy of the above-described control is to enhance "comfort". Further, the other policy of the control is to enhance "energy saving".

In the present embodiment, specific values are learned to determine what kind of control is specifically performed in what state. Such learning is called reinforcement learning.

Positive control includes the enhancement of "comfort" for reducing user's dissatisfaction and the enhancement of "energy saving" for reducing power consumption.

When the control of the air conditioning for the air conditioning area cannot be applied to the comfort area of the user, for example, when a higher priority is given to the enhancement of "energy saving", recommendation control described in the second embodiment to be described later is performed.

Control learning unit <NUM> illustrated in <FIG> learns what kind of control should be performed in a certain state in order to reduce dissatisfaction and enhance energy saving to determine the control. Reinforcement learning is used as the determination method.

<FIG> is a diagram illustrating a structure of machine learning used by control learning unit <NUM> according to the first embodiment. Under reinforcement learning, an agent (action subject) in a certain environment observes a current state s (environment parameter) to determine an action a to be taken. The action taken by the agent causes the environment to dynamically change, and a reward r is given to the agent in accordance with the change in the environment. The agent repeats this process to learn an action policy under which reward r is maximized through a series of actions a. As representative algorithms of reinforcement learning, Q-learning and TD-learning are known.

Input and output parameters of reinforcement learning are as follows:.

Control learning unit <NUM> can select the enhancement of "energy saving" or the enhancement of "comfort" as policy π. As action a, four settings are listed above, which takes time for learning, so that the settings may be narrowed down to only the change in target temperature or only the change in target humidity. Further, other settings of the air conditioner such as the setting of vanes may be changed.

The enhancement of "comfort" as policy π is to perform control to bring the current state into a range in which an individual feels comfortable. The enhancement of "energy saving" is to perform control to reduce power consumption relative to the current state. For example, during the cooling period, the set temperature or the set humidity is increased, and during the heating period, the set temperature or the set humidity is decreased. Further, making the airflow rate lower also corresponds to the control for the enhancement of energy saving.

One of the features of the present embodiment is that comfort priority and energy saving priority are used as policy π of reinforcement learning illustrated in <FIG>. Reinforcement learning is performed with the comfort priority and the energy saving priority selectable as policy π for each air conditioning area. This allows the control of the air conditioner to be changed to control suitable for each air conditioning area.

The input to the machine learning model illustrated in <FIG> includes information listed in state s described above. The reinforcement learning according to the present embodiment is learning in which action a (output) is taken with respect to state s, and action a is corrected in accordance with how the results such as the degree of individual dissatisfaction and the power amount have changed. How to correct action a correspond to policy π. Policy π can be selected from the two types, that is, the enhancement of energy saving (reduction in power amount) and the enhancement of comfort (reduction in degree of dissatisfaction), and learning is advanced.

Policy π may be either of the two types, but policy π need not necessarily be either of the two types and may be determined as a probability of each policy. For example, when the learning is performed with the probability of the enhancement of energy saving set at <NUM>% and the probability of the enhancement of comfort set at <NUM>%, it is possible to learn to enhance energy saving while maintaining comfort.

<FIG> is a flowchart for describing control performed according to the present embodiment. The machine learning illustrated in <FIG> is performed in steps S6, S9, S11 in the flowchart of <FIG>.

First, environment data of the air conditioning target space is periodically acquired. Specifically, in step S1, air conditioner management unit <NUM> acquires the indoor temperature, the indoor humidity, the outside-air temperature, the solar radiation amount, and the radiant heat from the various sensors of air conditioning device <NUM> (indoor units 40A, 40B and outdoor unit <NUM>).

Subsequently, upon receipt input from the personal terminal, air conditioning control and learning are performed. The comfort data of the individual who has made the input is acquired, and when there is a change in the comfort data, learning of comfort is performed.

Specifically, when input is made to input unit <NUM> of personal terminal <NUM> in step S2, the input information is notified to air conditioning management device <NUM> via communication management unit <NUM>. With this notification as a trigger, air conditioning management device <NUM> makes the determination in step S2.

When input is made to personal terminal <NUM> (YES in S2), air conditioning management device <NUM> acquires the information held in comfort data holding unit <NUM> of personal terminal <NUM> via communication management unit <NUM> in step S3.

In step S4, individual comfort data in <FIG> is taken from the comfort data thus acquired, and when the boundary value between "cold " and "comfort" and the boundary value between "comfort" and "hot" have changed, it is determined that there is a change in comfort distribution (YES in S4).

In step S5, learning of classification is performed using the machine learning model illustrated in <FIG>. Subsequently, in step S6, reinforcement learning is performed using the machine learning model illustrated in <FIG>.

Next, when a person moves within the air conditioning area, data of individuals in the area is acquired, and air conditioning control and learning are performed.

First, in step S7, air conditioner management unit <NUM> determines that a person has moved when a change in motion information is detected from the information from motion sensor <NUM> connected to air conditioning device <NUM>.

In step S8, air conditioning management device <NUM> acquires the information held in action information holding unit <NUM> and the information held in comfort data holding unit <NUM> from personal terminal <NUM> via communication management unit <NUM>.

Subsequently, in step S9, reinforcement learning is performed using the machine learning model illustrated in <FIG>.

Air conditioning management device <NUM> further performs air conditioning control and learning at predetermined regular intervals to increase control accuracy.

Specifically, in order to perform control to enhance energy saving and comfort even when no person moves or no input is made from the personal terminal, it is determined whether the repetition at the regular intervals is enabled in step S10, and in step S11, and reinforcement learning is performed using the machine learning model illustrated in <FIG>. The length of the regular intervals may be, for example, <NUM> minutes, but may be a different length.

In the first embodiment described above, it is possible to learn a change in comfort immediately after movement using action information on a person. Further, automatic control of air conditioning achieved by trial and error using reinforcement learning as illustrated in <FIG> makes it possible to maximize energy saving within a range in which the user feels comfortable.

Further, the number of operations made by the user gradually decreases as the learning progresses, so that it is possible to increase the usefulness of the air conditioner.

Further, in a place where the same team of users is present like an office and a plurality of indoor units are provided, it is possible to achieve air conditioning control optimal for a person present in the air conditioning area of each indoor unit.

<FIG> is a diagram illustrating a structure of machine learning used by control learning unit <NUM> according to a second embodiment. When the reinforcement learning model (control learning unit <NUM>) illustrated in <FIG> is changed as illustrated in <FIG>, the reinforcement learning model is also applicable to space recommendation control.

First, under the space recommendation control, temperature distribution in a space is controlled in accordance with a proportion of people belonging to the comfort clusters illustrated in <FIG> and <FIG>.

Specifically, under the space recommendation control, temperature distribution in the entire air conditioning space is controlled in accordance with the proportion of people belonging to classes CA to CD.

Parameters applied to the reinforcement learning model illustrated in <FIG> are as follows.

Actor-critic is a representative method for a reinforcement learning policy, and is a method of performing the policy basically as learned, but advancing learning by performing unlearned control with a certain probability.

As illustrated in <FIG>, the temperature distribution is brought closer to temperature distribution based on the proportion of people by adding the current radiation temperature distribution to state s to change the reward to the radiation temperature distribution in the space.

Then, after the temperature distribution is controlled, a space that falls within the comfort range of each user is displayed on display unit <NUM> or the like of personal terminal <NUM>, thereby recommending a comfortable air conditioning area to the possessor of personal terminal <NUM>. As described above, it is possible to prompt the possessor of the personal terminal to move by indicating which space is comfortable to the possessor of the personal terminal.

Furthermore, adding information such as a future temperature change prediction (computation of a comfort change when the current indoor temperature is ±α°C) to state s allows space recommendation to be made in advance. Further, even when there is no future temperature prediction information, a similar function can be realized by clearly indicating a future temperature change such as displaying "it is recommended to move to area <NUM> when feeling hot, and move to area <NUM> when feeling cold. " on the display unit.

Further, although the recommendation is made in accordance with a change in environment or a change in feeling as described above, it is also possible to analyze a movement history of personal terminal <NUM> and make a space recommendation on the basis of the action of a person, such as area <NUM> after exercise or area <NUM> when the action time is short.

The present disclosure relates to air conditioning management device <NUM> that is an information processing device capable of communicating with the plurality of personal terminals <NUM> possessed by a plurality of different possessors. Each of the plurality of personal terminals <NUM> is configured to acquire first data indicating a result of inputting whether a corresponding one of the possessors is comfortable, second data indicating a terminal location, and third data indicating a temperature and humidity at the terminal location. Air conditioning management device <NUM> includes personal comfort data learning unit <NUM> (first learning unit), air conditioning data holding unit <NUM>, and air conditioning control device <NUM>. Personal comfort data learning unit <NUM> (first learning unit) classifies the plurality of personal terminals <NUM> into the plurality of classes CA to CD illustrated in <FIG> and <FIG> based on the first to third data transmitted from the plurality of personal terminals <NUM>. Air conditioning data holding unit <NUM> is a storage unit that stores a plurality of control details each associated with a corresponding one of the plurality of classes into which personal comfort data learning unit <NUM> (first learning unit) classifies the plurality of personal terminals <NUM>. Air conditioning control device <NUM> is a control unit that reads, from the storage unit, a control detail associated with a class into which personal terminal <NUM> detected in an air conditioning target space is classified among the plurality of classes and controls an air conditioning device.

Controlling the air conditioning device as described above achieves air conditioning suitable for an individual who possesses the terminal.

Further, the plurality of terminals are classified into the classes, and the settings of the air conditioner associated with the class to which the detected terminal belongs are used, so that it is not necessary to prepare settings for each individual who possesses the terminal, and the control of the air conditioner becomes simple accordingly.

Preferably, personal comfort data learning unit <NUM> (first learning unit) classifies the plurality of personal terminals <NUM> on the basis of the index PMV indicating comfort computed from the first to third data. As illustrated in <FIG> and <FIG>, the comfort range of the index PMV indicating that the possessor is comfortable is defined for each of the plurality of classes CA to CD. When the plurality of personal terminals <NUM> each belonging to a corresponding one of the plurality of classes are detected in the target space, air conditioning control device <NUM> controls air conditioning device <NUM> to cause the index when the target space is air-conditioned to fall within a range common to the plurality of comfort ranges each associated with a corresponding one of the plurality of classes.

Preferably, the plurality of personal terminals <NUM> are each structured to store the movement history of the possessor. The movement history is transmitted from personal terminal <NUM> located in the target space to air conditioning management device <NUM>. Air conditioning control device <NUM> changes the control detail of air conditioning device <NUM> in accordance with the movement history thus received.

At the beginning, default air conditioning control settings suitable immediately after movement are used, and dissatisfaction as a result of changing the settings is learned. Therefore, with the default changed and optimized, when the possessor returns from an outing in the summer, for example, control of causing the possessor to feel comfortable immediately after movement such as automatic setting to strong cooling is performed.

Preferably, air conditioning management device <NUM> further includes control learning unit <NUM> (second learning unit) that performs reinforcement learning of control of air conditioning device <NUM>. Control learning unit <NUM> (second learning unit) is capable of changing the probability of selecting the enhancement of energy saving for reducing the power consumption of air conditioning device <NUM> and the probability of selecting the enhancement of comfort for increasing the comfort of the possessor of personal terminal <NUM> as the policy under reinforcement learning.

In the related art, a user sets a temperature to suit his/her preference, and then control is performed, which is inefficient air conditioning in terms of space, but it is possible to configure control to maximize energy saving in terms of space, and it is thus possible to reduce energy consumption.

Preferably, air conditioning control device <NUM> controls air conditioning device <NUM> so as to make temperature distribution different among a plurality of air conditioning areas, and causes personal terminal <NUM> to display an air conditioning area that is comfortable for a possessor of personal terminal <NUM> present in the target space.

Another aspect of the present embodiment discloses an air conditioning system including an air conditioning device and any one of the above-described information processing devices.

Claim 1:
An information processing device (<NUM>) to communicate with a plurality of personal terminals (<NUM>) possessed by a plurality of different possessors, each of the plurality of personal terminals (<NUM>) being configured to acquire first data indicating a result of inputting whether a corresponding one of the possessors is comfortable, second data indicating a terminal location, and third data indicating a temperature at the terminal location, the information processing (<NUM>) device comprising:
a first learning unit (<NUM>) configured to classify the plurality of personal terminals (<NUM>) into a plurality of classes based on the first to third data transmitted from the plurality of personal terminals (<NUM>);
a storage unit (<NUM>) configured to store a plurality of control details each associated with a corresponding one of the plurality of classes into which the first learning unit (<NUM>) classifies the plurality of personal terminals (<NUM>); and
a control unit (<NUM>) configured to read, from the storage unit (<NUM>), a control detail associated with a class into which a personal terminal (<NUM>) detected in an air conditioning target space is classified among the plurality of classes and control an air conditioning device (<NUM>), characterized in that
the first learning unit (<NUM>) is configured to classify the plurality of personal terminals (<NUM>) based on an index indicating comfort computed from the first to third data,
for each of the plurality of classes, a comfort range of the index indicating that the possessors are comfortable is defined, and
when the plurality of personal terminals (<NUM>) each belonging to a corresponding one of the plurality of classes are detected in the target space, the control unit (<NUM>) controls the air conditioning device (<NUM>) to cause, when the target space is air-conditioned, the index to fall within a range common to the plurality of comfort ranges each associated with a corresponding one of the plurality of classes.