Apparatus for calculating temperature setting, system for low temperature treatment, method of calculating temperature setting, and program for calculating temperature setting

A set temperature calculating apparatus, a low temperature treatment system, a set temperature calculating method, and a set temperature calculating program are provided for realizing a low temperature treatment. The set temperature calculating apparatus includes: a first obtaining unit configured to obtain data correlating with a heat load in a container storage; a second obtaining unit configured to obtain a set temperature when performing temperature control in the container storage; and a learning unit configured to learn a cargo core temperature in the container storage according to a data set including a combination of the data correlating with the heat load and the set temperature.

TECHNICAL FIELD

The present disclosure relates to an apparatus for calculating temperature setting, a system for low temperature treatment, a method of calculating temperature setting, and a program for calculating temperature setting.

BACKGROUND ART

Conventionally, when fruits such as lemons and grapefruits are marine-transported, a low temperature treatment is performed to exterminate the eggs of pests (Mediterranean fruit flies, etc.) in the fruits are treated in accordance with quarantine regulations. Specifically, temperature control is performed so that the cargo core temperature (the center temperature of fruit that is a cargo) is equal to or less than a reference temperature for a predetermined period.

Patent Document

SUMMARY OF THE INVENTION

Problem to be Solved by the Invention

On the other hand, because the heat load of a container storage during marine transport changes greatly, it is impossible to realize a low temperature treatment in accordance with quarantine regulations unless the set temperature is properly changed in accordance with the change in the heat load.

The present disclosure provides a set temperature calculating apparatus, a low temperature treatment system, a set temperature calculating method, and a set temperature calculating program for realizing a low temperature treatment.

Means to Solve the Problem

According to a first aspect of the present disclosure, a set temperature calculating apparatus includes:a first obtaining unit configured to obtain data correlating with a heat load in a container storage;a second obtaining unit configured to obtain a set temperature when performing temperature control in the container storage; anda learning unit configured to learn a cargo core temperature in the container storage according to a data set including a combination of the data correlating with the heat load and the set temperature.

According to the first aspect of the present disclosure, it is possible to provide a set temperature calculating apparatus for realizing a low temperature treatment.

According to a second aspect, the set temperature calculating apparatus according to the first aspect further includes:a changing unit configured to change, based on a difference between a cargo core temperature in the container storage inferred by inputting data correlating with a heat load in the container storage and a predetermined set temperature to a result of learning by the learning unit and a target temperature of the cargo core temperature in the container storage based on a low temperature treatment condition, the predetermined set temperature.

According to a third aspect, in the set temperature calculating apparatus according to the second aspect,the data correlating with the heat load includes any of temperature data and humidity data in the container storage, a ventilation volume in the container storage, and outside air temperature data and outside air humidity data outside the container storage.

According to a fourth aspect, in the set temperature calculating apparatus according to the third aspect,the temperature data and the humidity data in the container storage are output by a suction temperature sensor, a discharge temperature sensor, and a humidity sensor installed on a refrigerator included by the container storage.

According to a fifth aspect, in the set temperature calculating apparatus according to the third aspect,the temperature data in the container storage is calculated based on temperature data and pressure data output by a temperature sensor and a pressure sensor installed on an evaporator of a refrigerator included by the container storage.

According to a sixth aspect, in the set temperature calculating apparatus according to the third aspect,the outside air temperature data and the outside air humidity data outside the container storage are output by an outside air temperature sensor and an outside air humidity sensor installed on a refrigerator included by the container storage or an outside air temperature sensor and an outside air humidity sensor installed separately from the refrigerator included by the container storage.

According to a seventh aspect, in the set temperature calculating apparatus according to the third aspect,the outside air temperature data outside the container storage is calculated based on temperature data and pressure data output by a temperature sensor and a pressure sensor installed on a condenser of a refrigerator included by the container storage.

According to an eighth aspect, a low temperature treatment system includes:the set temperature calculating apparatus according to any one of the second to seventh aspects; anda control apparatus configured to control a refrigerator included by the container storage so as to make a temperature in the container storage closer to the set temperature changed by the changing unit.

According to the eighth aspect of the present disclosure, it is possible to provide a low temperature treatment system for realizing a low temperature treatment.

According to a ninth aspect, the set temperature calculating system according to the eighth further includes:a reporting unit configured to report abnormality when the cargo core temperature in the container storage does not change, even after a predetermined time has elapsed since control was started by the control apparatus so as to make the temperature in the container storage closer to the set temperature changed by the changing unit.

According to a tenth aspect, in the set temperature calculating apparatus according to the eighth aspect,the refrigerator includes a refrigerant circuit including a compressor, a condenser, an expansion mechanism, and an evaporator; a suction temperature sensor; a discharge temperature sensor; a humidity sensor; an outside air temperature sensor; an outside air humidity sensor; and a cargo core temperature sensor.

According to an eleventh aspect of the present disclosure, a set temperature calculating method includes:a first obtaining step of obtaining data correlating with a heat load in a container storage;a second obtaining step of obtaining a set temperature when performing temperature control in the container storage; anda learning step of learning a cargo core temperature in the container storage according to a data set including a combination of the data correlating with the heat load and the set temperature.

According to the eleventh aspect of the present disclosure, it is possible to provide a set temperature calculating method for realizing a low temperature treatment.

According to a twelfth aspect, the set temperature calculating method according to the eleventh aspect further includes:a changing step of changing, based on a difference between a cargo core temperature in the container storage inferred by inputting data correlating with a heat load in the container storage and a predetermined set temperature to a result of learning by the learning unit and a target temperature of the cargo core temperature in the container storage based on a low temperature treatment condition, the predetermined set temperature.

According to a thirteenth aspect of the present disclosure, a set temperature calculating program causes a compute to execute:a first obtaining step of obtaining data correlating with a heat load in a container storage;a second obtaining step of obtaining a set temperature when performing temperature control in the container storage; anda learning step of learning a cargo core temperature in the container storage according to a data set including a combination of the data correlating with the heat load and the set temperature.

According to the thirteenth aspect of the present disclosure, it is possible to provide a set temperature calculating program for realizing a low temperature treatment.

According to a fourteenth aspect, the set temperature calculating program according to the thirteenth aspect further includes:a changing step of changing, based on a difference between a cargo core temperature in the container storage inferred by inputting data correlating with a heat load in the container storage and a predetermined set temperature to a result of learning by the learning unit and a target temperature of the cargo core temperature in the container storage based on a low temperature treatment condition, the predetermined set temperature.

MODE FOR CARRYING OUT THE INVENTION

In the following, embodiments will be described with reference to the accompanying drawings. In the specification and drawings, elements having substantially the same functional configurations are referred to by the same numerals, and a duplicate description thereof will be omitted.

First Embodiment

<Outline of Low Temperature Treatment>

First, an outline of a low temperature treatment that is realized by a low temperature treatment system according to a first embodiment will be described.FIG. 1is a diagram for explaining an outline of a low temperature treatment.

Generally, fruits such as lemons and grapefruits are marine-transported by a container ship110as illustrated inFIG. 1. A plurality of container storages are loaded on the container ship110. For each of the container storages (for example, container storages120), the temperature in the container storage (for example, the container storage120) is controlled by a refrigerator.

Here, when fruits are stored in a container storage and transported by sea, a low temperature treatment is performed to exterminate the eggs of pests (for example, Mediterranean fruit flies) in the fruits in accordance with quarantine regulations.

The example ofFIG. 1indicates, as defined low temperature treatment conditions130when the low temperature treatment is performed, that:Cargo reference temperature: −0.3° or less;Period of low temperature treatment: 24 days; andAction when exceeding the cargo reference temperature: extend the time of the low temperature treatment by eight hours.

On the other hand, inFIG. 1, a graph140indicates the temperature in the container actually controlled so as to satisfy the low temperature treatment conditions130. In the graph140, the horizontal axis represents time and the vertical axis represents temperature. Also, a line141indicates the transition of cargo core temperature data, and a line142indicates the change history of the set temperature.

In the example ofFIG. 1, with the time point at which the cargo core temperature data (line141) decreases by a predetermined temperature or more with respect to a reference temperature (time t1) as a start point, measurement of the period of the low temperature treatment is started. In the example illustrated inFIG. 1, after the measurement of the period of the low temperature treatment was started, the set temperature was changed a plurality of times (refer to the history of the change of the set temperature (line142)). Specifically, the set temperature has been changed at times t2, t3, t4, t5, t6, and t7.

This is because in the case of the container storage120loaded on the container ship110, the cargo core temperature data is not constant even if temperature control in the container storage is performed by fixing the set temperature because the heat load changes significantly during marine transport. For this reason, conventionally, for example, a crew member monitors the cargo core temperature data and perform an operation such as lowering the set temperature when the reference temperature is about to be exceeded. However, if the crew member mistakenly operates the set temperature, the reference temperature may be exceeded and a low temperature treatment in accordance with quarantine regulations may not be realized.

In the example ofFIG. 1, a reference numeral143indicates that the cargo core temperature data has exceeded the reference temperature. In such a case, the low temperature treatment conditions130increase the period of the low temperature treatment by 8 hours.

Here, the low temperature treatment system according to the first embodiment is configured to replace and automate an operation by a crew member, avoid a situation as described above, and be able to set an appropriate set temperature for a refrigerator in accordance with the change in heat load. Hereinafter, details of the low temperature treatment system according to the first embodiment will be described.

<Configuration Example of Container Storage>

First, a configuration of the container storage120, which is included in the low temperature treatment system according to the first embodiment, will be described.FIG. 2is a diagram illustrating a configuration example of the container storage. Here,2aofFIG. 2is a schematic view illustrating the container storage120when viewed from the front.

As illustrated in2aofFIG. 2, a refrigerator200is disposed on the front side of the container storage120. Although various devices are attached to the front surface of the refrigerator200, in the example of2aofFIG. 2, a refrigerator display apparatus270and a ventilation window260are clearly illustrated.

Here,2bofFIG. 2is a cross-sectional view of the container storage120. As illustrated in2bofFIG. 2, a suction temperature sensor201, a humidity sensor202, and a discharge temperature sensor203are attached to the refrigerator200disposed in the container storage120. Also, the refrigerator200includes an evaporator210, and an evaporator temperature sensor211and an evaporator pressure sensor212are attached to the evaporator210.

Also, an outside air temperature sensor221and an outside air humidity sensor222are attached to the refrigerator200. In addition, the refrigerator200includes a condenser230, and a condenser temperature sensor231and a condenser pressure sensor232are attached to the condenser230.

On the other hand, cargo core temperature sensors251to253are disposed within the container storage120. Further, an external temperature sensor281and an external humidity sensor282are attached outside the container storage120.

<System Configuration of Low Temperature Treatment System (Learning Phase)>

Next, a system configuration of the low temperature treatment system in a learning phase will be described. In the learning phase, a crew member operates the set temperature while monitoring the cargo core temperature data as conventionally, and the low temperature treatment system collects information for learning.

FIG. 3is a diagram illustrating an example of the system configuration of the low temperature treatment system in the learning phase. As illustrated inFIG. 3, a low temperature treatment system300includes the container storage120, a set temperature calculating apparatus310, and an external sensor group350. Since the respective sensors included in the external sensor group350have been described with reference toFIG. 2, the description thereof is omitted here.

The refrigerator200of the container storage120includes a container storage sensor group340, a refrigerator input apparatus331, a refrigerator control apparatus332, a refrigerant circuit333, and a refrigerator display apparatus270. Among these, the respective sensors included in the container storage sensor group340have been described above with reference toFIG. 2, and thus the description thereof is omitted here.

The refrigerator input apparatus331receives the set temperature of the refrigerator200input by a crew member of the container ship110. The refrigerator input apparatus331transmits the received set temperature to the set temperature calculating apparatus310and to set the received set temperature in the refrigerator control apparatus332.

The refrigerator control apparatus332controls the refrigerant circuit333so as to make the suction temperature data output from the suction temperature sensor201closer to the set temperature.

The refrigerant circuit333includes a compressor, a condenser, an expansion mechanism, and an evaporator to control the temperature within the container storage120by circulating a refrigerant.

The refrigerator display apparatus270displays cargo core temperature data output from the cargo core temperature sensors251to253.

A set temperature calculating program is installed in the set temperature calculating apparatus310. The set temperature calculating apparatus310functions as an internal temperature obtaining unit311, an internal humidity obtaining unit312, an outside air temperature obtaining unit313, and an outside air humidity obtaining unit314by executing the program in the learning phase. Also, the set temperature calculating apparatus310functions as an internal ventilation volume obtaining unit315, a cargo core temperature obtaining unit316, and an input information obtaining unit317. Further, the set temperature calculating apparatus310functions as a learning unit320.

The internal temperature obtaining unit311obtains data correlating with the temperature in the container storage120. Specifically, the internal temperature obtaining unit311obtains either suction temperature data output from the suction temperature sensor201or discharge temperature data output from the discharge temperature sensor203as the data correlating with the temperature inside the container storage120.

Alternatively, the internal temperature obtaining unit311may obtain the data correlating with the temperature in the container storage120based on evaporator temperature data output from the evaporator temperature sensor211and evaporator pressure data output from the evaporator pressure sensor212.

Also, the internal temperature obtaining unit311stores, as information for learning, the obtained data correlating with the temperature in the container storage120in the learning information storage unit318in association with the time information.

The internal humidity obtaining unit312obtains data correlating to the humidity in the container storage120. Specifically, the internal humidity obtaining unit312obtains humidity data output from the humidity sensor202as the data correlating with the humidity in the container storage120.

Also, the internal humidity obtaining unit312stores, as information for learning, the obtained data correlating with the humidity in the container storage120in the learning information storage unit318in association with the time information.

The outside air temperature obtaining unit313obtains data correlating with the temperature of the outside air of the container storage (for example, outside of the container storage120). Specifically, the outside air temperature obtaining unit313obtains outside air temperature data output from the outside air temperature sensor221. Alternatively, the outside air temperature obtaining unit313may obtain external temperature data output from the external temperature sensor281. Alternatively, the outside air temperature obtaining unit313may obtain data correlating with the temperature of the outside air of the container storage120based on the condenser temperature data output from the condenser temperature sensor231and the condenser pressure data output from the condenser pressure sensor232.

The outside air temperature obtaining unit313stores, as information for learning, the obtained data correlating with the temperature of the outside air of the container storage120in the learning information storage unit318in association with the time information.

The outside air humidity obtaining unit314obtains data correlating with the humidity of the outside air of the container storage120. Specifically, the outside air humidity obtaining unit314obtains the data correlating with the humidity of the outside air of the container storage120based on the outside air humidity data output from the outside air humidity sensor222or the external humidity data output from the external humidity sensor282.

The outside air humidity obtaining unit314stores, as information for learning, the obtained data correlating with the humidity of the outside air of the container storage120in the learning information storage unit318in association with the time information.

The internal ventilation volume obtaining unit315obtains data correlating with the ventilation volume in the container storage120. Specifically, the internal ventilation volume obtaining unit315receives the input of the opening or closing information of the ventilation window260attached to the front side of the container storage120to derive the ventilation volume. For example, the internal ventilation volume obtaining unit315obtains the ventilation volume=“zero” when the ventilation window260is in the closed state. Meanwhile, the internal ventilation volume obtaining unit315obtains the ventilation volume “predetermined value” when the ventilation window260is in the open state. The internal ventilation volume obtaining unit315may be configured to obtain the ventilation volume corresponding to the degree of opening or closing of the ventilation window260.

The internal ventilation volume obtaining unit315stores, as information for learning, the data correlating with the ventilation volume in the obtained container storage120in the learning information storage unit318in association with the time information.

The cargo core temperature obtaining unit316obtains data correlating with the cargo core temperature. Specifically, the cargo core temperature obtaining unit316obtains the cargo core temperature data output from the cargo core temperature sensors251to253.

The cargo core temperature obtaining unit316stores, as information for learning, the obtained data correlating with the cargo core temperature in the learning information storage unit318in association with the time information.

The input information obtaining unit317obtains the set temperature received by the refrigerator input apparatus331. As described above, in a case of the container storage120loaded on the container ship110, because the heat load changes significantly during marine transport, a crew member changes the set temperature frequently in the learning phase (see the line142of the graph140inFIG. 1). The input information obtaining unit317obtains the latest set temperature every time the crew member changes the set temperature.

The input information obtaining unit317stores, as information for learning, the obtained set temperature in the learning information storage unit318in association with the time information.

The learning unit320performs machine learning on a model (cargo core temperature model) for inferring the cargo core temperature based on information for learning stored in the learning information storage unit318. Thus, the learning unit320generates a learned cargo core temperature model for inferring the cargo core temperature. Details are given below.

<Data List Obtained in Low Temperature Treatment System>

Next, a list of data obtained in the low temperature treatment system300in the learning phase will be described.FIG. 4is a diagram illustrating an example of the list of data obtained in the low temperature treatment system. Here,4aofFIG. 4indicates a list400of data received by the set temperature calculating apparatus310.

As illustrated in the list400of data at4aofFIG. 4, the set temperature calculating apparatus310receives data output from the suction temperature sensor201, the humidity sensor202, the discharge temperature sensor203, the evaporator temperature sensor211, and the evaporator pressure sensor212in association with the time data.

Also, the set temperature calculating apparatus310receives data output from the outside air temperature sensor221, the outside air humidity sensor222, the condenser temperature sensor231, the condenser pressure sensor232, and the cargo core temperature sensors251to253in association with the time data.

Also, the set temperature calculating apparatus310receives data output from the external temperature sensor281and the external humidity sensor282, received opening or closing information, and the set temperature output from the compressor input apparatus331in association with the time data.

Meanwhile, as described above, each unit of the set temperature calculating apparatus310stores, as information for learning, data derived based on the received data or a part of the received data in the learning information storage unit318.

As the learning information410, the example of4bofFIG. 4indicates:data correlating with the temperature in the container storage120(referred to as “internal temperature data”, for example, discharge temperature data obtained by the internal temperature obtaining unit311);data correlating the humidity in the container storage120(referred to as “internal humidity data”, for example, humidity data obtained by the internal humidity obtaining unit312);data correlating with the temperature of the outside air outside the container storage120(referred to as “outside air temperature data”, for example, outside air temperature data obtained by the outside air temperature obtaining unit313);data correlating the humidity of the outside air outside the container storage120(referred to as “outside air humidity data”, for example, outside air humidity data obtained by the outside air humidity obtaining unit314);data correlating with the ventilation volume in the container storage120(referred to as “internal ventilation volume data”, for example, internal ventilation volume data obtained by the internal ventilation volume obtaining unit315);data correlating with the cargo core temperature (referred to as “cargo core temperature data”, for example, cargo core temperature data obtained by the cargo core temperature obtaining unit316); anda set temperature set in the refrigerator control apparatus332(referred to as “set temperature”, set temperature obtained by the input information obtaining unit317),are stored in the learning information storage. unit318in association with the time data. The internal temperature data, the internal humidity data, the outside air temperature data, the outside air humidity data, and the internal ventilation volume data are each data correlating with the heat load in the container storage120.

<Hardware Configuration of Refrigerator Control Apparatus and Set Temperature Calculating Apparatus>

Next, a hardware configuration of the set temperature calculating apparatus310and the refrigerator control apparatus332included by the refrigerator200disposed on the container storage120of the low temperature treatment system300will be described. Since the hardware configuration of the refrigerator control apparatus332and the hardware configuration of the set temperature calculating apparatus310are substantially the same, the hardware configuration of the set temperature calculating apparatus310will be described herein.

FIG. 5is a diagram illustrating an example of the hardware configuration of the set temperature calculating apparatus. As illustrated inFIG. 5, the set temperature calculating apparatus310includes a CPU (central processing unit)501, a ROM (read only memory)502, and a RAM (random access memory)503. The CPU501, the ROM502, and the RAM503together constitute a computer. The set temperature calculating apparatus310further includes an auxiliary storage device504, a display device505, an operating device506, an I/F (interface) device507, and a drive device508. The individual hardware parts of the set temperature calculating apparatus150are connected to one another through a bus509.

The CPU501is an arithmetic device that executes various types of programs (e.g., a set temperature calculating program (learning phase)) installed in the auxiliary storage device504. The ROM502is a nonvolatile memory. The ROM502, which functions as a main memory device, stores various types of program, data, and the like necessary for the CPU501to execute the various types of programs installed in the auxiliary storage device504. Specifically, the ROM502stores boot programs and the like such as BIOS (basic input/output system) and EFI (extensible firmware interface).

The RAM503is a volatile memory such as a DRAM (dynamic random access memory) or an SRAM (static random access memory). The RAM503, which functions as a main memory device, provides a work area to which the various types of program installed in the auxiliary storage device504are loaded when executed by the CPU501.

The auxiliary storage device504stores various types of program, and stores information used when the various types of program are executed.

The display device505is a display device that displays an internal state of the set temperature calculating apparatus310. The operating device506is an operating device that is used by an administrator of the set temperature calculating apparatus310to perform various types of operations with respect to the set temperature calculating apparatus310, for example. The I/F device507is a connection device for connecting to the container storage sensor group340, the refrigerator input apparatus331, the external sensor group350of the container storage120and the like and for receiving data.

The drive device508is a device for setting a recording medium510. Here, the recording medium510includes a medium for optically, electrically, or magnetically recording information, such as a CD-ROM, a flexible disk, a magneto-optical disk, or the like. The recording medium510may also include a semiconductor memory or the like that electrically records information, such as a ROM, a flash memory, or the like.

The various types of programs to be installed in the auxiliary storage device504are installed by the drive device508reading the various types of programs recorded in the recording medium510upon the recording medium510being set in the drive device508, for example. Alternatively, the various types of program to be installed in the auxiliary storage device504may be installed upon being downloaded from a network (not illustrated.

<Functional Configuration of Refrigerator Control Apparatus>

Next, a functional configuration (control blocks) of the refrigerator control apparatus332that is included in by the low temperature treatment system300will be described.FIG. 6is a diagram illustrating an example of the functional configuration of the refrigerator control apparatus. As described above, the refrigerator control apparatus332receives the current set temperature reported by the refrigerator input apparatus331.

As illustrated inFIG. 6, the refrigerator control apparatus332calculates the difference between the received set temperature and the discharge temperature data output from the discharge temperature sensor203(data correlating with the temperature in the container storage120(internal temperature data)), and inputs the difference value to the controller600. The controller600may be, for example, a PID controller that calculates a control amount corresponding to a difference value and outputs it to the refrigerant circuit333.

As illustrated inFIG. 6, the refrigerant circuit333operates based on the control amount output by the controller600of the refrigerator control apparatus332. Accordingly, the discharge temperature data of the refrigerator200is changed, and the discharge temperature sensor203detects the discharge temperature data after being changed and transmits the data to the set temperature calculating apparatus310.

Thus, the refrigerator control apparatus332controls the operation of the refrigerant circuit333so as to make the discharge temperature data (data correlating with the temperature in the container storage120(the internal temperature data)) of the refrigerator200closer to the set temperature reported by the refrigerator input apparatus331. This enables the refrigerator control apparatus332to control the discharge temperature data of the refrigerator200(data correlating with the temperature in the container storage120(internal temperature data)).

<Functional Configuration of Temperature Calculating Apparatus (Learning Phase)>

Next, a functional configuration of the set temperature calculating apparatus.310in the learning phase will be described.FIG. 7is a diagram illustrating an example of the functional configuration of the set temperature calculating apparatus in the learning phase. As illustrated inFIG. 7, the learning unit320includes a cargo core temperature model701and a comparing and changing unit702. The learning unit320reads out the information for learning from the learning information storage unit318and inputs a data set including a combination of data correlating with the heat load in the container storage120and the set temperature to the cargo core temperature model701among the read-out learning information. Specifically, the learning unit320inputs the internal temperature data, the internal humidity data, the outside air temperature data, the outside air humidity data, the internal ventilation volume data, and the set temperature to the cargo core temperature model701.

Thus, the learning unit320executes the cargo core temperature model701, and the cargo core temperature model701outputs the cargo core temperature data.

The cargo core temperature data output from the cargo core temperature model701is input to the comparing and changing unit702. The comparing and changing unit702comparesthe cargo core temperature data output from cargo core temperature model701; andthe cargo core temperature data (correct data) read out from the learning information storage unit318.

The comparing and changing unit702changes the model parameters of the cargo core temperature model701in accordance with the comparison result.

The cargo core temperature data (correct data) compared by the comparing and changing unit702is not the cargo core temperature data associated with the same time data as the data set input to the cargo core temperature model701, but is the cargo core temperature data after a predetermined time. This is because there is a time lag of the predetermined time from when the cargo core temperature data becomes a state specified by the data set (internal temperature data, internal humidity data, outside air temperature data, outside air humidity data, internal ventilation volume data, and set temperature) to when the cargo core temperature data becomes stable under the specified state.

Thus, the learning unit320performs machine learning on the cargo core temperature model701that specifies the corresponding relationship betweena data set that includes a combination of data correlating with the thermal load in the container storage120and the set temperature; andthe cargo core temperature data (after the predetermined time).

This enables the learning unit320to generate a learned cargo core temperature model for inferring the cargo core temperature data.

The example illustrated byFIG. 7is a case in which the learning unit320inputs the internal temperature data, the internal humidity data, the outside air temperature data, the outside air humidity data, and the internal ventilation volume data to the cargo core temperature model701as data correlating with the heat load in the container storage120. However, the learning unit320may input only a portion of these information to the cargo core temperature model701.

<System Configuration of Low Temperature Treatment System (Inferring Phase)>

Next, a system configuration of the low temperature treatment system in the inferring phase will be described. In the inferring phase, the set temperature inferred by the set temperature calculating apparatus is automatically set to the refrigerator200.

FIG. 8is a diagram illustrating an example of the system configuration of the low temperature treatment system in the inferring phase. From the low temperature treatment system300(FIG. 3) in the learning phase, the low temperature treatment system800in the inferring phase has difference points in that a set temperature calculating apparatus810includes an input information obtaining unit817, an inferring unit820, a set temperature output unit830, and a reporting unit840. Also, the low temperature treatment system800in the inferring phase has also a difference point of including a management terminal850. Further, the low temperature treatment system800in the inferring phase has also difference points in that the low temperature treatment conditions130are input to the refrigerator input apparatus331, the set temperature after being changed is input to the refrigerator control apparatus332, and an abnormality report is input to the refrigerator display apparatus270.

The set temperature calculating apparatus810monitors the data correlating with the thermal load in the container storage120and changes the set temperature of the refrigerator200so that the inferred cargo core temperature data does not exceed the cargo reference temperature.

A set temperature calculating program (inferring phase) is installed in the set temperature calculating apparatus810. By the program being executed, the set temperature calculating apparatus810functions as an internal temperature obtaining unit311, an internal humidity obtaining unit312, an outside air temperature obtaining unit313, an outside air humidity obtaining unit314, an internal ventilation volume obtaining unit315, a cargo core temperature obtaining unit316, and an input information obtaining unit817. By the program being executed, the set temperature calculating apparatus810functions as an inferring unit820, a set temperature output unit830, and a reporting unit840.

Since the cargo core temperature obtaining unit316to the internal temperature obtaining unit311have been already described with reference toFIG. 3, the description thereof is omitted here.

The input information obtaining unit817obtains the cargo reference temperature among the low temperature treatment conditions130input to the refrigerator input apparatus331. Also, the input information obtaining unit817obtains the set temperature currently set in the refrigerator control apparatus e332from the refrigerator input apparatus331. Further, the input information obtaining unit817derives the cargo target temperature based on the obtained cargo reference temperature (for example, subtracting a predetermined temperature from the cargo reference temperature to derive the cargo target temperature) and reports the derived temperature to the inferring unit820together with the obtained current set temperature.

The inferring unit820has a learned cargo core temperature model. The inferring unit820obtains data correlating with the thermal load in the container storage120, the current set temperature, and the cargo target temperature from the respective units of the internal temperature obtaining unit311to the input information obtaining unit817.

The inferring unit820performs the learned cargo core temperature model by inputting a data set including a combination of the obtained data correlating with the thermal load in the container storage120and the current set temperature to the learned cargo core temperature model. Thus, the inferring unit820infers the cargo core temperature data after a predetermined time. Details are given below.

Also, the inferring unit820calculates the error between the inferred cargo core temperature data and the obtained cargo target temperature, and change the current, set temperature by backpropagation of the calculated error. Further, the inferring unit820reports the set changed set temperature to the set temperature output unit830.

The set temperature output unit830transmits the changed set temperature reported by the inferring unit820to the refrigerator control apparatus332. This enables the refrigerant control apparatus332to control the refrigerant circuit333based on the changed set temperature so that the cargo core temperature data can be made closer to the cargo target temperature after the predetermined time.

Upon the changed set temperature being reported by the set temperature output unit830, the reporting unit840monitors the cargo core temperature data output from the cargo core temperature sensors251to253for a predetermined time. The reporting unit840determines whether the cargo core temperature data has changed during the predetermined time after the changed set temperature is reported, and when determining that no change has occurred even after the predetermined time, the reporting unit840determines that abnormality has occurred in the refrigerator200. In this case, the reporting unit840reports, to the refrigerator display apparatus270of the container storage120, the abnormality. This enables a crew member to be informed of the abnormality in the refrigerator200. The reporting unit840also reports, to the management terminal850, the abnormality.

The management terminal850is a management terminal of the shipping company that owns the container ship110and displays when receiving an abnormality report by the reporting unit840. This enables to report abnormality of the refrigerator200to the shipping company owning the container ship110.

<Details of Functional Configuration of Inferring Unit>

Next, within the functional configuration of the set temperature calculating apparatus810in the inferring phase, a functional configuration of the inferring unit820of will be described in detail.FIG. 9is a diagram illustrating an example of the functional configuration of the set temperature calculating apparatus in the inferring phase.

As illustrated inFIG. 9, the inferring unit820includes a learned cargo core temperature model910and an error determining unit920.

The inferring unit820obtains data correlating with the heat load in the container storage120from the respective units of the internal temperature obtaining unit311to the internal ventilation volume obtaining unit315. The data correlating with the heat load in the container storage120obtained by the inferring unit820is different from the data used when the learning unit320performs machine learning. Also, the inferring unit820obtains the current set temperature set to the refrigerator control apparatus332from the input information obtaining unit817.

By inputting into the learned cargo core temperature model910a data set including a combination of the obtained data correlating with the thermal load in the container storage120and the obtained current set temperature, the inferring unit820executes the learned cargo core temperature model910. Thus, the learned cargo core temperature model910infers the cargo core temperature data after the predetermined time.

The error determining unit920obtains the cargo target temperature from the input information obtaining unit817. Also, the error determining unit920calculates the error between the cargo core temperature data after the predetermined time inferred by the learned cargo core temperature model910and the cargo target temperature obtained by the input information obtaining unit817.

By backpropagation of the error calculated by the error determining unit920, the learned cargo core temperature model910changes the current set temperature input to the learned cargo core temperature model910and obtains the changed set temperature.

Thus, the low temperature treatment system800changes the set temperature to eliminate the error betweencargo core temperature data after a predetermined time inferred based on a data set containing a combination of data correlating with the thermal load in the container storage120and a current set temperature; anda cargo target temperature.

This enables the low temperature treatment system800to derive the current appropriate set temperature for achieving the appropriate cargo core temperature data after the predetermined time.

In the above description, the inferring unit820inputs, as data correlating with the heat load in the container storage120, the internal temperature data, the internal humidity data, the outside air temperature data, the outside air humidity data, and the internal ventilation volume data to the learned cargo core temperature model910. However, when the learned cargo core temperature model910is generated based on the data of a portion of the internal temperature data, the inferring unit820inputs the data of the portion to the learned cargo core temperature model910.

<Flow of Set Temperature Calculating Process>

Next, a flow of the set temperature calculating process by the set temperature calculating apparatus310in the learning phase and the set temperature calculating apparatus810in the inferring phase will be described.FIG. 10is a flowchart illustrating the flow of the set temperature calculating process performed by the set temperature calculating apparatus according to the first embodiment.

In step S1001, each of the units from the internal temperature obtaining unit311to the internal ventilation volume obtaining unit315obtains data correlating with the heat load in the container storage120. The input information obtaining unit317obtains the set temperature.

In step S1002, the cargo core temperature obtaining unit316obtains the cargo core temperature data.

In step S1003, by inputting a data set including a combination of the data correlating with the thermal load in the container storage120and the set temperature to the cargo core temperature model701, the learning unit320executes the cargo core temperature model701. The learning unit320then performs machine learning on the cargo core temperature model701so as to make the cargo core temperature data output by the cargo core temperature model701closer to the cargo core temperature data (correct data) obtained after a predetermined time by the cargo core temperature obtaining unit316. Thus, the learning unit320generates a learned cargo core temperature model. The generated learned cargo core temperature model is incorporated into the set temperature calculating apparatus810in the inferring phase.

In step S1004, the inferring unit820obtains the data correlating with the thermal load in the container storage120, the current set temperature, and the cargo target temperature.

In step S1005, by inputting a data set including a combination of the data correlating with the thermal load in the container storage120and the current set temperature to the learned cargo core temperature model910, the inferring unit820executes the learned cargo core temperature model910. Thus, the inferring unit820infers the cargo core temperature data after a predetermined time.

In step S1006, the inferring unit820calculates an error between the inferred cargo core temperature data after the predetermined time and the cargo target temperature and determines whether the calculated error is equal to or more than a predetermined threshold value. In a case of determining in step S1006that the calculated error is less than the predetermined threshold value (in a case of YES in step S1006), the process proceeds to step S1011.

Meanwhile, in a case of determining in step S1006that the calculated error is greater than or equal to the predetermined threshold value (in a case of NO in step S1006), the process proceeds to step S1007. In step S1007, the inferring unit820changes by backpropagation the current set temperature input to the learned cargo core temperature model910and obtains the changed set temperature.

In step S1008, the set temperature output unit830reports the changed set temperature to the refrigerator control apparatus332.

In step S1009, after the changed set temperature is reported by the set temperature output unit830, during a predetermined time, the reporting unit840monitors the cargo core temperature data and determines whether the cargo core temperature data has changed.

In a case of determining in step S1009that the cargo core temperature data has changed (in a case of YES in step S1009), the process proceeds to step S1011. Meanwhile, in a case of determining in step S1009that the cargo core temperature data has not changed even after the predetermined time (in a case of NO in step S1009), the process proceeds to step S1010.

In step S1010, the reporting unit840reports an abnormality to the refrigerator display apparatus270of the container storage120. The reporting unit840reports the abnormality to the management terminal850.

In step S1011, the inferring unit820determines whether to end the set temperature calculating process. In a case of determining in step S1011to continue the set temperature calculating process (in a case of NO in step S1011), the process returns to step S1004.

Meanwhile, in a case of determining in step S1011to end the set temperature calculating process (in a case of YES in step S1011), the set temperature calculating process ends.

In the example ofFIG. 10, a case is described in which the learning unit320performs batch learning such that model parameters are changed by inputting a data set including a combination of data correlating with the thermal load in the container storage120and a set temperature to the cargo core temperature model701. However, the learning unit320may perform sequential learning such that model parameters are changed by sequentially inputting a predetermined number of data sets including a combination of data correlating with the thermal load in the container storage120and a set temperature to the cargo core temperature model701

As is obvious from the above description, the set temperature calculating apparatus according to the first embodimentlearns cargo core temperature data after a predetermined time according to a data set including a combination of and data correlating with the heat load in the container storage and a set temperature; andinputs a newly obtained data set including a combination of data correlating with the heat load in the container storage and a set temperature to the learning results to infer the cargo core temperature data after the predetermined time; andby backpropagation an error between the inferred cargo core temperature data after the predetermined time and a cargo target temperature predetermined based a cargo reference temperature, changes the set temperature to calculate the changed set temperature.

Thus, the set temperature calculating apparatus according to the first embodiment can calculate the set temperature for controlling the cargo core temperature after the predetermined time to an appropriate temperature in accordance with the change in the heat load in the container storage. As a result, according to the first embodiment, a set temperature calculating apparatus, a low temperature treatment system, a set temperature calculating method, and a set temperature calculating program for realizing a low temperature treatment can be provided.

Second Embodiment

In the first embodiment described above, the location of the set temperature calculating apparatus is not specifically mentioned, but the set temperature calculating apparatus is located as desired. For example, the set temperature calculating apparatus310in the learning phase may be installed on or outside the container ship110. In a case of being located outside the container ship110, the set temperature calculating apparatus310may be configured to receive a list400of received data in real time via a network.

Alternatively, the learning information storage unit318may be disposed on the container ship110, the learning information410may be temporarily stored, and the set temperature calculating apparatus310may be configured to receive the stored learning information410via a network.

Alternatively, the set temperature calculating apparatus310may be configured to obtain the learning information410by connecting the learning information storage unit318to the set temperature calculating apparatus310after completion of marine transport by the container ship110.

Similarly, the set temperature calculating apparatus810in the inferring phase may be installed on the container ship110or installed outside the container ship110. In a case of being installed outside the container ship110, the set temperature calculating apparatus310may be configured to receive the list400of received data in real time via a network.

Alternatively, the respective units of the internal temperature obtaining unit311to the input information obtaining unit317may be disposed on the container ship110, and the respective units of the inferring unit820, the set temperature output unit830, and the reporting unit840may be installed outside the container ship110.

The set temperature calculating apparatus310in the learning phase and the set temperature calculating apparatus810in the inferring phase may be integrally configured or may be separately configured.

In the first embodiment described above, data at a specific time is input as the data correlating with the thermal load in the container storage120inputted to the cargo core temperature model701or the learned cargo core temperature model910.

However, the data correlating the thermal load in the container storage120input to the cargo core temperature model701or the learned cargo core temperature model910may be time series data having a predetermined time range. Alternatively, it may be a conversion value of time series data having a predetermined time range. Here, the conversion value of time series data means a value converted by performing some kind of operation on time series data, such as the average value, the maximum value, or the minimum value of time series data.

In the first embodiment described above, the external temperature sensor281and the external humidity sensor282are exemplified as the external sensor group350, but sensors that measure measurement items other than temperature or humidity may be mounted.

In the first embodiment described above, the details of the model (cargo core temperature model) that is used in machine learning are not specifically mentioned, but a suitable type of model is applied to a model that is used in machine learning. Specifically, a suitable type of model is applied, such as a NN (Neural Network) model, a random forest model, or a SVM (Support Vector Machine) model.

In the first embodiment, no details of the method of changing model parameters when changing model parameters based on the results of comparison by the comparing and changing are described. However, the method of changing model parameters by the comparing and changing is according to the model type.

Although a description has been given of the embodiments, it may be understood that various modifications may be made to the configurations and details thereof, without departing from the spirit and scope of claims.

This application is based on and claims priority to Japanese Patent Application No. 2019-052014 filed on Mar. 19, 2019, and Japanese Patent Application No. 2020-42822 filed on Mar. 12, 2020 and the entire contents of the Japanese Patent Applications are incorporated herein by reference.

DESCRIPTION OF REFERENCE SYMBOLS

110: Container ship120: Container storage130: Low temperature treatment conditions200: Refrigerator201: Suction temperature sensor202: Humidity sensor203: Discharge temperature sensor211: Evaporator temperature sensor212: Evaporator pressure sensor221: Outside air temperature sensor222: Outside air humidity sensor231: Condenser temperature sensor232: Condenser pressure sensor251to253: Cargo core temperature sensors300: Low temperature treatment system311: Internal temperature obtaining unit312: Internal humidity obtaining unit313: Outside air temperature obtaining unit314: Outside air humidity obtaining unit315: Internal ventilation volume obtaining unit316: Cargo core temperature obtaining unit317: Input information obtaining unit320: Learning unit331: Refrigerator input apparatus332: Refrigerator control apparatus333: Refrigerant circuit701: Cargo core temperature model800: Low temperature treatment system810: Set temperature calculating apparatus817: Input information obtaining unit820: Inferring unit830: Set temperature output840: Reporting unit910: Learned cargo core temperature model920: Error determining unit