Patent Description:
<CIT> discloses that the internal environment is controlled by inferring the amount of activated gas generated in time for the desired shipment timing and generating the activated gas based on the storage knowledge base, which is made by ruling of the optimum storage conditions based on the past storage experience and bringing thereof into knowledge, and environmental measurement information regarding gas metabolism amount. It is also described that software processing using regression analysis, which is a system of machine learning, is used to infer the amount of generated activated gas. A storage container, an adjustment unit and an environmental information acquisition unit are known from <CIT> and <CIT>.

<CIT> relates to an artificial intelligence device mounted on a wine refrigerator including one or more divided spaces including an input unit, a processor, and an output unit. The input unit is configured to recognize a wine label of each space and recognize an image for determining opening or non-opening of a wine. The processor is configured to acquire wine information by using an artificial intelligence model that receives image data acquired from the input unit as an input value, create a wine list table of each space by using the acquired information, and group wines having the same storage condition into at least one group according to the wine list table, and perform a control such that a temperature of each space is set based on the storage condition of the group. The output unit is configured to output a signal received from the processor.

<CIT> refers to an electronic device for food management and a control method thereof. The control method of an electronic device for food management includes measuring intensity of light, which is irradiated with food and reflected, by wavelengths using an infrared sensor; obtaining state information of the food based on the measured light intensity information; and adjusting at least one of temperature and humidity of an area where the food is disposed in the electronic device based on at least one of the obtained state information and preset usage plan information of the food.

The freshness control of perishable products is, for example, different by each perishable product, or changed in accordance with initial freshness at the time when a perishable product is contained in the container. For this reason, though the environment is controlled under the same uniform control conditions, it is difficult to perform proper freshness control.

An object of the present disclosure is to perform reinforcement learning on control conditions of the perishable product environment by using information regarding freshness of perishable products obtained by a freshness sensor to automatically control the perishable product environment.

A containing device of the present disclosure is a perishable product containing device according to independent claim <NUM>.

This makes it possible to perform reinforcement learning on control conditions of the inside environment by using the information regarding the freshness of the perishable product obtained from the storage container to automatically control the perishable product environment.

Here, the learning unit may learn the inside environment for the freshness of the perishable product measured by the freshness measurement unit to maximize the reward decided by the reward decision unit, and the adjustment unit may adjust the inside environment of the storage container to cause the environmental information acquired by the environmental information acquisition unit to serve as the inside environment for the freshness learned by the learning unit.

This makes it possible to learn the control of the inside environment optimized with respect to the decrease in the freshness of the perishable product.

In addition, when inside environments before and after the certain period of time are different, the reward decision unit may decide the reward based on an inside environment at one or more points during the certain period of time.

This makes it possible to learn the environmental control in response to the change in the inside environment over a certain period of time.

In addition, the inside environment at one or more points during the certain period of time may include the inside environment at an end point of the certain period of time.

This makes it possible to learn the environmental control assuming the inside environment when the certain period of time has elapsed.

A method of operating the perishable product containing device of the invention is defined in claim <NUM>. Moreover, the computer program of the present disclosure is defined in claim <NUM>.

Accordingly, the reinforcement learning can be performed on the control conditions of the perishable product environment by using the information regarding the freshness of the perishable product to automatically control the perishable product environment.

Hereinafter, an embodiment will be described in detail with reference to attached drawings.

<FIG> is a diagram showing an overall configuration of a perishable product control system to which the present invention is applied. The perishable product control system of the present embodiment includes a storage container <NUM>, an environment detection device <NUM>, an environment adjustment device <NUM>, a freshness detection device <NUM>, and an information processing device <NUM>.

The storage container <NUM> is a device that contains and stores perishable products. The storage container <NUM> has a delivery entrance (not shown) for carrying in the perishable products to be contained. By closing the delivery entrance of the storage container <NUM>, inside space is sealed and separated from the external environment. The inside of the storage container <NUM> may be divided into multiple rooms. The storage container <NUM> is a container used for, for example, transporting the perishable products. As the storage container <NUM>, those containing the perishable products in a refrigerated environment, those containing the perishable products in a frozen environment, those containing the perishable products in an ordinary-temperature environment, and so on can be assumed. The storage container <NUM> is an example of a containing device.

The environment detection device <NUM> is provided inside the storage container <NUM> and detects the environment inside the storage container <NUM> (the inside environment). The environment detection device <NUM> acquires data indicating the state of the internal space of the storage container <NUM> (hereinafter referred to as "environmental data"). Specifically, data indicating temperature, humidity, air pressure, the component of gas filling the internal space, etc., is acquired. Consequently, as the environment detection device <NUM>, a temperature sensor, a humidity sensor, an air pressure sensor, sensors for various kinds of gas components, etc., can be used. In the case where the inside of the storage container <NUM> is divided into multiple rooms, the environmental data may be acquired individually from each room. The environment detection device <NUM> is an example of an environmental information acquisition unit.

The environment adjustment device <NUM> is provided in the storage container <NUM> and controls the inside environment of the storage container <NUM>. Factors of the inside environment to be controlled by the environment adjustment device <NUM> correspond to factors of the inside environment to be detected by the environment detection device <NUM>. Consequently. The environment adjustment device <NUM> controls the temperature, humidity, air pressure, components of gas filling the inside space, etc., to adjust the inside environment. In the case where the inside of the storage container <NUM> is divided into the multiple rooms, a different environment may be set for each room. The environment adjustment device <NUM> is an example of an adjustment unit.

The freshness detection device <NUM> detects the freshness of perishable products contained in the storage container <NUM>. The freshness of a perishable product can be evaluated with various indexes. Specifically, "appearance (color, gloss)," "scent," "taste," "physical properties (hardness)," "water content," "ingredients (sugar, acid)," etc. are used as the freshness indexes. In addition, for meat and fish, the index called "K value" regarding the ratio of ingredients contained in these perishable products is generally used as an index representing the freshness. Consequently, as the freshness detection device <NUM>, in accordance with the kinds of perishable products to be contained, a freshness sensor capable of acquiring data regarding these various indexes (hereinafter, referred to as "freshness data") is used.

The information processing device <NUM> processes data acquired by the environment detection device <NUM> and the freshness detection device <NUM>, and controls the operation of the environment adjustment device <NUM> based on the obtained results. The information processing device <NUM> is implemented by, for example, a personal computer, a smartphone, an embedded computer in the storage container <NUM>, etc. The information processing device <NUM> may be provided integrally with the storage container <NUM>, or may be provided separately from the storage container <NUM> to acquire data from the environment detection device <NUM> and the freshness detection device <NUM> using a communication line. The configuration example in <FIG> shows the information processing device <NUM> provided integrally with the storage container <NUM>. In the case where the information processing device <NUM> is provided separately from the storage container <NUM>, the communication line for sending and receiving data may be a wired line or a wireless network.

<FIG> is a diagram showing a functional configuration example of the information processing device <NUM>. The information processing device <NUM> includes a data acquisition section <NUM>, a freshness determination section <NUM>, an analysis section <NUM>, and a control section <NUM>.

The data acquisition section <NUM> acquires data from the environment detection device <NUM> and the freshness detection device <NUM>. Data is acquired, for example, at regular intervals. More specifically, environmental data is transmitted from the environment detection device <NUM> at regular intervals, which is acquired by the data acquisition section <NUM> of the information processing device <NUM>. Freshness data is transmitted from the freshness detection device <NUM> at regular intervals, which is acquired by the data acquisition section <NUM> of the information processing device <NUM>. The environmental data acquired from the environment detection device <NUM> is transmitted to the analysis section <NUM>. The freshness data acquired from the freshness detection device <NUM> is transmitted to the freshness determination section <NUM>.

The freshness determination section <NUM> determines the freshness of the perishable products contained in the storage container <NUM> based on the freshness data obtained by the freshness detection device <NUM>. This provides information regarding the freshness of the perishable products. As described above, the freshness of the perishable products is determined based on the various freshness indexes. Therefore, the freshness determination section <NUM> determines the freshness by a method specified in accordance with the kind of perishable products contained in the storage container <NUM> and the type of freshness data obtained by the freshness detection device <NUM>. The freshness may be determined using existing determination methods, for example, a method using the K values performed for fish and shellfish, or meat. The freshness determination section <NUM> is an example of a freshness information acquisition unit. In addition, the freshness detection device <NUM> and the freshness determination section <NUM> of the information processing device <NUM> are an example of a freshness measurement unit.

The analysis section <NUM> analyzes the information regarding the inside environment (the environmental data) acquired by the data acquisition section <NUM> and the freshness information of the perishable products determined by the freshness determination section <NUM>, to thereby find the proper inside environment. The proper inside environment differs depending on the kinds and states of the perishable products contained in the storage container <NUM>. It is needless to say that the proper inside environment differs depending on the kind of perishable product, but the proper inside environment is sometimes different for even the same kind of perishable product depending on the state thereof. For example, regarding tomatoes, the proper ranges of temperature and humidity are different between the fully ripened tomatoes and mature-green tomatoes. In addition, the fully ripened tomatoes produce a large amount of ethylene and have low ethylene sensitivity, whereas the mature-green tomatoes produce a small amount of ethylene and have high ethylene sensitivity. Therefore, the proper gas components are also different. In addition, the range of proper temperature for potatoes differs between the unripened state and the fully ripened state. Further, different freshness of the same kind of perishable product corresponds to different proper inside environment. For example, the state of high freshness, as compared to the state of low freshness, makes it possible to select the inside environment with the assumption of long-term storage. As described above, the analysis section <NUM> is required to identify the inside environment in consideration of various factors of the target perishable product.

In the present invention, the analysis section <NUM> uses a learning model obtained by the reinforcement learning to select the inside environment to be adjusted by the environment adjustment device <NUM>. The learning model used by the analysis section <NUM> is obtained by performing reinforcement learning with settings of "state s" for the information regarding the inside environment and the freshness information, "action a" for the control condition of the environment adjustment device <NUM>, and "reward r" for the degree of decrease in the freshness after elapse of a certain period of time. The "reward r" is set so that the smaller the decrease in the freshness, the larger the value of the "reward r. " Thus, according to the learning model, based on the "state s" obtained by the environment detection device <NUM>, the freshness detection device <NUM>, and the freshness determination section <NUM>, the "action a" is optimized to minimize the decrease in the freshness (to maximize the "reward r") after elapse of a certain period of time in the inside environment controlled by the "action a.

In addition, the analysis section <NUM> may actually control the environment adjustment device <NUM> to perform freshness control of the perishable products while adjusting the inside environment by the information processing device <NUM> provided in the storage container <NUM>, to thereby proceed with the reinforcement learning using the results of the freshness control. Specifically, the above-described learning model may be updated (setting of the "reward r" may be changed) based on the information regarding the inside environment actually adjusted, and the state of decrease in the freshness of the perishable product after elapse of a certain period of time from the start of control by the environment adjustment device <NUM>. The analysis section <NUM> is an example of a learning unit and a reward decision unit.

The control section <NUM> controls the operation of the environment adjustment device <NUM> by generating a control instruction for the environment adjustment device <NUM>, and transmitting the generated control instruction to the environment adjustment device <NUM>. The control section <NUM> controls the environment adjustment device <NUM> to have the inside environment selected by the analysis section <NUM>. In other words, the control section <NUM> controls the environment adjustment device <NUM> so that the environmental information acquired by the environment detection device <NUM> is that of the inside environment for the freshness of the perishable product learned at the analysis section <NUM> (the inside environment selected by the learning model). The control of the inside environment is individually performed for each of environmental factors, such as temperature, humidity, air pressure, and gas component, by comparing the current inside environment with the inside environment selected by the analysis section <NUM>. Focusing on the inside temperature as an example, if the current inside temperature is the same as the inside temperature selected by the analysis section <NUM>, the control section <NUM> controls the environment adjustment device <NUM> to maintain the current inside temperature. In addition, in the case where the current inside temperature is different from the inside temperature selected by the analysis section <NUM>, the control section <NUM> controls the environment adjustment device <NUM> so that the inside temperature becomes the latter temperature.

<FIG> is a diagram showing a hardware configuration example of the information processing device <NUM>. The information processing device <NUM> is implemented by a computer. The computer that implements the information processing device <NUM> includes a CPU (Central Processing Unit) <NUM>, which is an arithmetic unit, a RAM (Random Access Memory) <NUM>, which is a storage unit, a ROM (Read Only Memory) <NUM>, and a storage device <NUM>. The RAM <NUM> is a main storage device (main memory), and used as a working memory when the CPU <NUM> performs arithmetic processing. The ROM <NUM> holds programs and data of setting values prepared in advance, etc., and the CPU <NUM> can execute processing by directly reading the programs and data from the ROM <NUM>. The storage device <NUM> is a storing unit for programs and data. The storage device <NUM> stores the programs, and the CPU <NUM> reads the programs stored in the storage device <NUM> into the main storage device to execute thereof. In addition, the storage device <NUM> stores the processing results by the CPU <NUM> to preserve thereof. Moreover, the storage device <NUM> stores the learning model by the above-described reinforcement learning, which is used to select the inside environment. As the storage device <NUM>, for example, a magnetic disk device, an SSD (Solid State Drive), etc. can be used.

In the case where the information processing device <NUM> is implemented by the computer shown in <FIG>, each of the functions of the data acquisition section <NUM>, the freshness determination section <NUM>, the analysis section <NUM>, and the control section <NUM>, which have been described with reference to <FIG>, is implemented by the CPU <NUM> executing the programs. The information processing device <NUM> implementing each of the above-described functions by execution of the programs by the CPU <NUM> is an example of a learning device.

As described above, the analysis section <NUM> of the information processing device <NUM> uses a learning model obtained by the reinforcement learning to select the inside environment to be adjusted by the environment adjustment device <NUM>. It has been described that, as the learning model, the model obtained by performing reinforcement learning with settings of "state s" for the information regarding the inside environment and the freshness information, "action a" for the control condition of the inside environment, and "reward r" for the degree of decrease in the freshness after elapse of a certain period of time is used; the further description will be given of the "state s," the "action a," and the "reward r.

The inside environment is adjusted by the environment adjustment device <NUM> over a certain period of time (for example, <NUM> hours, <NUM> hours, <NUM> day, <NUM> days, <NUM> week, etc.). Consequently, for the inside environment as the "state s," the inside environment during the certain period of time is considered. The operation of the environment adjustment device <NUM> during the certain period of time is the target of the "action a. " The "reward r" is determined based on the difference between the freshness at the start point and the freshness at the end point of the certain period of time.

The inside environment during the certain period of time will be considered further. If the inside environment is adjusted by the environment adjustment device <NUM> over a certain period of time, depending on the adjustment, the inside environment sometimes differs between the start point and the end point of the certain period of time. In addition, the operation of the environment adjustment device <NUM> during the certain period of time dynamically changes in some cases. In this case, even though the inside environment at the start point is the same, it is assumed that the degree of decrease in the freshness of the perishable product is different in response to the inside environment at the end point or midway through the period of time. Consequently, the "state s" is identified by considering the inside environment, not only at the start point, but also at the end point of and midway through the certain period of time. In addition, with regard to the control of the environment adjustment device <NUM> as the "action a," even though the inside environment at the start point is the same, multiple "action a" can be set with the inside environments at the end point of and midway through the certain period of time that are different. Hereinafter, specific description will be given of some examples. In the following examples, the inside temperature will be focused on as a specific example of the inside environment.

<FIG> is a diagram showing an example of the inside environment (inside temperature) over a certain period of time adjusted by the environment adjustment device <NUM>. In the example shown in <FIG>, the environment adjustment device <NUM> adjusts the inside temperature to be constant at the temperature (t0) during the period t(<NUM>-n) from the start point t0 to the end point tn. In this case, since the inside temperature is constant throughout the period t(<NUM>-n), the "state s" in the reinforcement learning is the temperature (t0). In addition, the operation of the environment adjustment device <NUM> as the "action a" is controlled so that the "state s" at the temperature (t0) continues for a certain period of time t(<NUM>-n). Then, the "reward r" is set based on the difference between the freshness of the perishable product at the point t0 and the freshness of the perishable product at the point tn.

<FIG> is a diagram showing another example of the inside environment (inside temperature) over a certain period of time adjusted by the environment adjustment device <NUM>. In the example shown in <FIG>, the environment adjustment device <NUM> adjusts the inside temperature to change from the temperature (t0) to the temperature (tn) during the period t(<NUM>-n) from the start point t0 to the end point tn. Note that, in the example, the temperature change is adjusted to be constant with the elapsed time. In this case, the temperature (t0) at the start point t0 of the period of time t(<NUM>-n) and the temperature (tn) at the end point tn are different; accordingly, the temperature (t0) at the start point cannot simply be the "state s. " Therefore, the temperature at a specific point within the period t(<NUM>-n) is set to the "state s. " For example, it can be considered that a temperature (t1) at a middle point t1 between the point t0 and the point tn is set to the "state s. " This means that the "action a" is considered as controlling the operation of the environment adjustment device <NUM> so that the inside temperature at the point t0 is the temperature (t0), and the inside temperature after elapse of the period of time t(<NUM>-n) reaches the temperature (tn), and the "state s" is considered as the inside temperature (t1) throughout the period of time t(<NUM>-n). Then, the "reward r" is set based on the difference in the freshness between the point t0 and the point tn. Here, in the example shown in <FIG>, since the degree of temperature change is constant, <MAT> and <MAT>.

<FIG> is a diagram showing still another example of the inside environment (inside temperature) over a certain period of time adjusted by the environment adjustment device <NUM>. In the example shown in <FIG>, the environment adjustment device <NUM> adjusts the inside temperature to change from the temperature (t0) to the temperature (tn) during the period t(<NUM>-n) from the start point t0 to the end point tn. Note that, in the example, the temperature change is adjusted to be constant with the elapsed time. In this case, the temperature (t0) at the start point t0 of the period of time t(<NUM>-n) and the temperature (tn) at the end point tn are different; accordingly, the temperature (t0) at the start point cannot simply be the "state s. " The above is similar to the example described with reference to <FIG>; however, in this example, both the temperature (t0) at the start point t0 and the temperature (tn) at the end point tn of the period t(<NUM>-n) are set as the "state s. " The "action a" is the operation control of the environment adjustment device <NUM>, which implements such temperature changes. Then, the "reward r" is set based on the difference in the freshness between the point t0 and the point tn.

<FIG> is a diagram showing still another example of the inside environment (inside temperature) over a certain period of time adjusted by the environment adjustment device <NUM>. In the example shown in <FIG>, the environment adjustment device <NUM> adjusts the inside temperature to change from the temperature (t0) to the temperature (tn) during the period t(<NUM>-n) from the start point t0 to the end point tn. Note that, in <FIG>, the temperature change is constant with the elapsed time, but this example does not specify how the temperature changes. For example, the temperature may be changed rapidly during the first half of the period t(<NUM>-n), and may be changed slowly during the second half. In addition, the temperature may be changed several times in a stepwise manner. In the example shown in <FIG>, one or more points are set within the period t(<NUM>-n), and the "state s" is set based on the temperature at each point. For example, a representative value resulting from the statistical treatment of the temperature at each point may be set to the "state s. " An average value, a median value, etc. can be selected as the representative value, but the representative value may be set in accordance with how the temperature changes during the period t(<NUM>-n).

In the example shown in <FIG>, three points, from the point t1 to the point t3, are set between the start point t0 and the end point tn, to thereby obtain five temperatures, the temperature (t0), the temperature (t1), the temperature (t2), the temperature (t3), and the temperature (tn). These points and temperatures may be set in accordance with how the temperature changes during the period of time t(<NUM>-n). Then, the representative value (for example, the average value) of these five temperatures is given as the "state s. " In addition, the operation control of the environment adjustment device <NUM> to achieve the above-described temperature change during the period t(<NUM>-n) is given as the "action a. " Then, the "reward r" is set based on the difference in the freshness between the point t0 and the point tn. Note that, in this example, the environment adjustment device <NUM> starts the control from the current inside environment to obtain the inside environment at the point when the period t(<NUM>-n) has elapsed (the point tn); accordingly, the point to obtain the inside environment may surely include the point tn.

Note that, in the examples described with reference to <FIG>, the description has been given of the case in which the inside temperature is controlled as one of the inside environments, but it is possible to similarly analyze each factor of the inside environment, such as humidity or gas component, that can be detected by the environment detection device <NUM> and adjusted by the environment adjustment device <NUM>. In addition, each factor of the inside environment may be weighted, or conditions may be added in accordance with the kind and state of the perishable products, the freshness of the perishable products at the time of analysis, etc. For example, since the storable period in the storage container <NUM> differs depending on the types of perishable products, different periods can be set to the period t(<NUM>-n) shown in the above-described analysis example.

So far, the embodiment has been described, but the technical scope of the present disclosure is not limited to the above-described embodiment. For example, in the above-described embodiment, the information processing device <NUM> has been described as the device that implements each of the function to process the data acquired by the environment detection device <NUM> and the freshness detection device <NUM>, the function to control the operation of the environment adjustment device <NUM> based on the results of the processing, and the function to perform the reinforcement learning based on the acquired data; however, these functions may be implemented by individual pieces of hardware. The device for performing the learning function is also implemented as a learning device that learns by use of data acquired and collected by the environment detection devices <NUM> and the freshness detection devices <NUM> of the multiple storage containers <NUM>.

In addition, the above-described embodiment has shown that the analysis section <NUM> of the information processing device <NUM> learns to obtain a proper inside environment in accordance with the kinds and states of the perishable products. Consequently, when the freshness of the perishable products is controlled, the user identifies the perishable products to be controlled on the information processing device <NUM>, and uses the learning model corresponding to the identified perishable products to control the freshness. In contrast thereto, a means of identifying the kinds of perishable products subjected to the freshness control may be provided in the information processing device <NUM>. This makes it possible for the information processing device <NUM> to identify the perishable products to be controlled and perform the freshness control using the corresponding learning model when the freshness of the perishable products is controlled. The perishable products may be identified, for example, by image analysis using images obtained by photographing the perishable products.

Here, the above-described embodiment can be viewed as follows. The learning device of the present disclosure includes: a freshness determination section <NUM> acquiring information regarding freshness of a perishable product contained in a storage container; and an analysis section <NUM> learning an inside environment of the storage container for the freshness of the perishable product acquired by the freshness determination section <NUM> to decide a reward used in the learning. The analysis section <NUM> is a learning device of the inside environment for perishable products that decides the reward based on the decrease in the freshness over a certain period of time under the inside environment for the freshness determined based on the freshness acquired by the freshness determination section <NUM>, and learns the inside environment for the freshness based on the decided reward.

In this manner, the reinforcement learning can be performed on control conditions of the perishable product environment by using the information regarding the freshness of perishable products to automatically control the perishable product environment.

Here, in the case where the inside environments before and after the certain period of time are constant, the analysis section <NUM> may decide the reward based on the inside environment at the start point of the certain period of time.

In this manner, the environmental control to provide the constant inside environment over a certain period of time can be learned.

Alternatively, in the case where the inside environment before and after the certain period of time is different, the analysis section <NUM> may decide the reward based on the inside environment at a specific point during the certain period of time.

In this manner, in the case where the inside environment changes during the certain period of time, the environmental control based on the inside environment at the specific point can be learned.

Alternatively, in the case where the inside environment before and after the certain period of time is different, the analysis section <NUM> may decide the reward based on the inside environment at the start point and the inside environment at the end point of the certain period of time.

In this manner, in the case where the inside environment changes during the certain period of time, the environmental control based on the inside environments at the start point and the end point of the certain period of time can be learned.

Alternatively, in the case where the inside environments before and after the certain period of time are different, the analysis section <NUM> may find the representative value of environmental information values indicating the inside environment at multiple points during the certain period of time, and may decide the reward based on the representative value.

In this manner, the environmental control in response to the change in the inside environment over a certain period of time can be learned.

In addition, the above-described embodiment can also be viewed as follows. The containing device of the present disclosure includes: the storage container <NUM> containing perishable products; the environment adjustment device <NUM> adjusting the inside environment including at least the temperature inside the storage container <NUM>; the environment detection device <NUM> acquiring the environmental information including at least the temperature inside the storage container <NUM>; the freshness detection device <NUM> and the freshness determination section <NUM> measuring the freshness of the perishable products contained in the storage container <NUM>; and the analysis section <NUM> learning the inside environment of the storage container <NUM> for the freshness of the perishable products measured by the freshness detection device <NUM> and the freshness determination section <NUM> to decide the reward. Based on the freshness measured by the freshness detection device <NUM> and the freshness determination section <NUM>, the environmental information acquired by the environment detection device <NUM>, and the learning results of the analysis section <NUM>, the environment adjustment device <NUM> operates to cause the inside environment of the storage container <NUM> to serve as the inside environment for the freshness. The analysis section <NUM> is the perishable product containing device that decides the reward based on the decrease in the freshness over a certain period of time under the inside environment adjusted by the environment adjustment device <NUM>, and learns the inside environment for the freshness based on the decided reward.

In this manner, the reinforcement learning can be performed on control conditions of the inside environment by using the information regarding the freshness of perishable products obtained from the storage container <NUM> to automatically control the perishable product environment.

Here, the analysis section <NUM> may learn the inside environment for the freshness of perishable products measured by the freshness detection device <NUM> and the freshness determination section <NUM> to maximize the reward decided by the analysis section <NUM>, and the environment adjustment device <NUM> may adjust the inside environment of the storage container <NUM> to cause the environmental information acquired by the environment detection device <NUM> to serve as the inside environment for the freshness learned by the analysis section <NUM>.

In this manner, it is possible to learn the control of the inside environment optimized with respect to decrease in the freshness of perishable products.

In addition, in the case where the inside environments before and after the certain period of time are different, the analysis section <NUM> may decide the reward based on the inside environment at one or more points during the certain period of time.

Moreover, the inside environments at one or more points during the certain period of time may include the inside environment at the end point of the certain period of time.

Claim 1:
A perishable product containing device comprising:
a storage container (<NUM>) containing a perishable product;
an adjustment unit (<NUM>) adjusting an inside environment of the storage container, the inside environment including at least a temperature;
an environmental information acquisition unit (<NUM>) acquiring environmental information inside the container, the environmental information including at least the temperature;
a freshness measurement unit (<NUM>, <NUM>) measuring freshness of the perishable product contained in the storage container;
characterized in that the perishable product containing device further comprises: a learning unit (<NUM>) learning by reinforcement learning the control of the inside environment of the storage container to minimize the decrease in the freshness of the perishable product, measured by the freshness measurement unit; and
a reward decision unit (<NUM>) deciding a reward (r) used by the learning unit, wherein,
based on the freshness measured by the freshness measurement unit and the environmental information acquired by the environmental information acquisition unit as state (s) of the reinforcement learning model, and a learning result of the learning unit, the adjustment unit operates to cause the inside environment of the storage container to minimize the decrease in the freshness of the perishable product, being a control condition of the adjustment unit used as action (a) of the reinforcement learning model,
the reward decision unit decides the reward (r) of the reinforcement learning model based on a decrease in the freshness over a certain period of time under the inside environment adjusted by the adjustment unit, and
the learning unit learns the inside environment for the freshness based on the reward (r) decided by the reward decision unit.