Quality control system and quality control method

Abstract

To provide a quality control system for managing the quality of a cargo based on the smell of the cargo.SOLUTION: There is provided a quality control system for managing the quality of a cargo based on the smell within a container having a cargo stored therein, which comprises: a plurality of sensors including at least a smell sensor for measuring the smell within the container to generate smell data, for measuring a state within the container to generate measurement data; a calculation device for analyzing a state of the cargo based on the smell data; and a monitoring device for determining whether or not to execute an alarm operation providing notification of the state of the cargo based on an alarm level corresponding to the analysis result of the state of the cargo, analyzing an abnormality factor based on the measurement data and a specified threshold corresponding to the measurement data when the alarm operation is executed, and executing a specified action based on the alarm level and the abnormality factor.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a quality management system and a quality management method.

Background Art

[0002] Conventionally, for the prevention and countermeasures of quality troubles of articles during transportation, quality management of articles during transportation has been required. For example, Patent Document 1 discloses a transportation quality management system that grasps the stress received by an article during transportation and manages the stress. This transportation quality management system includes a sensor module having a stress sensor that detects the stress received by an article packed and transported in an actual package and a transmission unit that transmits the data detected by the stress sensor by weak radio waves, and a dummy package obtained by replacing part or all of the articles in the package with the sensor module. The transportation quality management system further includes a receiving module installed in a truck that receives the data transmitted from the sensor module, and an external communication module connected to the receiving module and connected to a transportation management center via a communication line. Then, the transportation quality management system monitors the stress applied to the articles being transported at the transportation management center.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, for the quality control of goods during transportation, various measures have been considered, such as transporting while controlling the temperature of the goods, or packing and transporting the goods so that they do not collapse. However, it is difficult to completely eliminate quality troubles of goods during transportation. When it is found that there is a risk of quality troubles in the goods during transportation, it may be considered to unpack the container storing the goods in order to check the specific state of the goods. However, if the container is unpacked during transportation, the unpacking may cause quality deterioration of the goods. In addition, it may not be realistic to check the state of the goods by unpacking the container when the number of containers to be checked is large.

[0005] The present disclosure has been devised in view of the above-described conventional situation, and aims to manage the quality of goods based on the odor of the goods.

Means for Solving the Problem

[0006] The present disclosure is a quality control system for managing the quality of the goods based on the odor in the container storing the goods, and includes at least an odor sensor that measures the odor in the container to generate odor data, a plurality of sensors that measure the state in the container to generate measurement data, a computing device that analyzes the state of the goods based on the odor data, and determines whether to execute an alarm operation for notifying the state of the goods based on an alarm level corresponding to the analysis result of the state of the goods. When the alarm operation is executed, an abnormal factor is analyzed based on the measurement data and a threshold value corresponding to the measurement data, and a monitoring device that executes an action based on the alarm level and the abnormal factor. A quality control system is provided.

[0007] In addition, the present disclosure provides a quality control method for controlling the quality of goods based on the odor inside a container storing the goods, the method including measuring the odor inside the container to generate odor data, measuring the state inside the container to generate measurement data, analyzing the state of the goods based on the odor data, determining whether to execute an alarm operation for notifying the state of the goods based on an alarm level corresponding to the analysis result of the state of the goods, analyzing an abnormal factor based on the measurement data and a threshold value corresponding to the measurement data when the alarm operation is executed, and executing an action based on the alarm level and the abnormal factor.

[0008] Note that any combination of the above components, and those obtained by converting the expression of the present disclosure among a method, an apparatus, a system, a storage medium, a computer program, etc., are also effective as aspects of the present disclosure.

Advantages of the Invention

[0009] According to the present disclosure, the quality of goods can be controlled based on the odor of the goods.

Brief Description of the Drawings

[0010]

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Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments specifically disclosing a quality management system and a quality management method according to the present disclosure will be described in detail with reference to the drawings as appropriate. However, a more detailed description than necessary may be omitted. For example, detailed descriptions of well-known matters and duplicate descriptions of substantially the same configurations may be omitted. This is to avoid making the following description unnecessarily redundant and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided for those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims thereto.

[0012] (Embodiment 1) [System Configuration] FIG. 1 is a block diagram showing a configuration example of a quality management system 1 according to Embodiment 1. The quality management system 1 is a system that manages the quality of the cargo 6 based on the atmosphere inside the container 10 in which the cargo 6 is stored. When the cargo 6 is transported by land, the container 10 is loaded on, for example, a trailer 17. Note that the trailer 17 may be read as a truck. When the cargo 6 is transported by sea, the container 10 is loaded on, for example, a container ship 18. In this specification, the “cargo” includes articles, goods, materials, parts, or combinations thereof.

[0013] The quality management system 1 includes a sensor module 2, a pump 3, a power supply device 4, a communication device 5, a computing device 8, and a monitoring device 9.

[0014] The sensor module 2, the pump 3, the power supply device 4, and the communication device 5 are installed in the container 10. Power can be supplied to the container 10. The container 10 may be, for example, a refrigerated container. Although not shown in FIG. 1, an air conditioner is installed in the container 10.

[0015] The sensor module 2 is composed of a plurality of sensors that measure the state inside the container 10 and generate measurement data. The sensor module 2 includes an odor sensor 11, a temperature sensor 12, a humidity sensor 13, an acceleration sensor 14, an illuminance sensor 15, and a Global Positioning System (hereinafter referred to as "GPS") sensor 16.

[0016] The odor sensor 11 measures the odor inside the container 10 and generates odor data as measurement data. More precisely, the odor sensor 11 measures the odor of the air sent in from the pump 3. The temperature sensor 12 measures the temperature inside the container 10 and generates temperature data as measurement data. The humidity sensor 13 measures the humidity inside the container 10 and generates humidity data as measurement data. The acceleration sensor 14 measures the acceleration of the container 10 and generates acceleration data as measurement data. The illuminance sensor 15 measures the illuminance inside the container 10 and generates illuminance data as measurement data. The GPS sensor 16 measures the position of the container 10 and generates the position information of the container 10 as measurement data. The odor data will be described later with reference to FIG. 6. The temperature data, humidity data, acceleration data, illuminance data, and position information will be described later with reference to FIG. 13.

[0017] The pump 3 sends air to the odor sensor 11. The pump 3 alternately sends the air in the container 10, that is, the air containing the odor of the cargo 6, and fresh air to the odor sensor 11 at a specified time interval. Here, fresh air refers to a gas that does not contain odor and is a term for distinguishing it from the air containing the odor of the cargo 6. Examples of fresh air include the air outside the container 10, that is, the outside air. In the example of FIG. 1, the pump 3 takes in fresh air from outside the container 10. However, for example, a container storing odorless air may be installed in the container 10 in advance. And the pump 3 may send the air stored in the container to the odor sensor 11 as fresh air.

[0018] The power supply device 4 supplies power to each sensor included in the sensor module 2, the pump 3, and the communication device 5. When the container 10 is loaded on the trailer 17, for example, power may be supplied from a power supply facility installed on the trailer 17 or a tractor towing the trailer 17 to the power supply device 4. Also, when the container 10 is loaded on the container ship 18, for example, power may be supplied from a power supply facility installed on the container ship 18 to the power supply device 4.

[0019] The communication device 5 acquires measurement data from each sensor included in the sensor module 2 and transmits it to the computing device 8 via the network 7. The network 7 is not particularly limited. The communication device 5 may communicate with the computing device 8 by satellite communication, for example, or may communicate with the computing device 8 using a wireless communication function provided in the trailer 17 or the container ship 18. It is desirable for the communication device 5 to transmit the acquired measurement data to the computing device 8 in real time. However, for example, when the cargo 6 is being transported by sea on the container ship 18, etc., there may be a case where communication with the computing device 8 cannot be performed. In such a case, the communication device 5 holds the acquired measurement data until communication with the computing device 8 becomes possible, and may transmit the held measurement data to the computing device 8 in a lump at the timing when communication with the computing device 8 becomes possible.

[0020] The computing device 8 is constituted by, for example, a personal computer or a server computer. Although details will be described later, it is preferable that the computing device 8 be capable of executing machine learning. The computing device 8 acquires measurement data from each sensor included in the sensor module 2. The computing device 8 analyzes the state of the cargo 6 based on the acquired odor data. Details will be described later with reference to FIGS. 4, 5, 6, and 7. The computing device 8 transmits the analysis result of the state of the cargo 6 to the monitoring device 9. Further, the computing device 8 transmits each measurement data such as temperature data and humidity data to the monitoring device 9. Although not shown in FIG. 1, the functions of the computing device 8 are realized by the cooperation of the processor and the memory included in the computing device 8. Further, the computing device 8 may be provided with an interface for communicating with the communication device 5, the monitoring device 9, and the like. In the example of FIG. 1, the computing device 8 acquires measurement data regarding one container 10, but the computing device 8 may be capable of acquiring measurement data regarding a plurality of containers.

[0021] The monitoring device 9 is constituted by, for example, a personal computer or a server computer. The monitoring device 9 monitors the quality of the cargo 6. Specifically, the monitoring device 9 acquires the analysis result of the state of the cargo 6 by the computing device 8. Although details will be described later, the monitoring device 9 determines whether or not to execute an alarm operation based on the analysis result of the state of the cargo 6. The alarm operation is, for example, notification of the state of the cargo 6 to a user such as the person in charge of transporting the cargo 6 or the shipper of the cargo 6. When the alarm operation by the monitoring device 9 is executed, it is considered that some abnormality has occurred in the quality of the cargo 6. When the monitoring device 9 executes the alarm operation, it performs factor analysis of the abnormality in the quality of the cargo 6 based on measurement data such as temperature data and humidity data. The monitoring device 9 further executes an action such as cancellation of the delivery of the cargo 6. Although not shown in FIG. 1, the functions of the monitoring device 9 are realized by the cooperation of the processor and the memory included in the monitoring device 9. Further, the monitoring device 9 may be provided with an interface for communicating with the computing device 8 or the like. Also, in the example of FIG. 1, the monitoring device 9 monitors the quality of the cargo 6 stored in one container 10, but the monitoring device 9 may be able to monitor the cargo stored in a plurality of containers.

[0022] In this way, the quality management system 1 analyzes the state of the cargo 6 by sensing the odor of the cargo 6. Then, the quality management system 1 determines whether or not an abnormality has occurred in the quality of the cargo 6 based on the analyzed state of the cargo 6, and when an abnormality has occurred, it performs factor analysis of the abnormality and some action. Therefore, it is premised that the cargo 6 stored in the container 10 and transported is a cargo that emits an odor. Examples of the cargo 6 include fresh foods such as fruits or liquids having an odor. Hereinafter, for convenience of explanation, it is assumed that the cargo 6 is a banana.

[0023] The smell of a banana changes as the banana ripens. The color of the banana peel also changes as the banana ripens. Hereinafter, the color of the banana peel may be simply referred to as the color of the banana. FIG. 2 is a schematic diagram for explaining the color change of a banana as it ripens. As shown in FIG. 2, the ripeness of a banana increases as the number of days passes. The color of the banana is all green at the time when the ripeness is the lowest. Then, as the ripeness increases, the color of the banana changes from all green to light green, from light green to half green, from half green to half yellow, from half yellow to green tip, from green tip to full yellow, from full yellow to star, and from star to dapple. The smell of a banana is different at each of the times when the color of the banana is any one of the above eight colors. That is, based on the smell of the banana, it can be determined which of the above eight colors the color of the banana is. Further, based on the change in the smell of the banana, the change in the color of the banana, that is, the progress of the ripening of the banana can be determined. The quality control system 1 senses the smell of the banana and analyzes the state, and if the banana is ripening, it detects the change in the ripeness. In this way, the quality control system 1 can monitor the quality of the banana.

[0024] The smell of the banana is measured by the smell sensor 11. The smell sensor 11 has a plurality of types of molecular recognition materials that react to different smell components, more precisely, specific smell molecules. The electrical characteristics of each molecular recognition material change when a smell molecule is adsorbed. Briefly speaking, an electric current is generated when each molecular recognition material reacts to a smell component. The generated electric current is detected by a probe corresponding to each molecular recognition material. The smell sensor 11 is provided with at least eight types of molecular recognition materials for measuring the smell of the banana. The smell sensor 11 may be provided with, for example, 16 types of molecular recognition materials in order to measure the smell of the banana more accurately.

[0025] When the molecular recognition material included in the odor sensor 11 continuously adsorbs odor molecules, the odor molecules may adhere to the molecular recognition material, resulting in a deterioration in the accuracy of odor measurement. Therefore, as described in the explanation of FIG. 1, the pump 3 alternately feeds fresh air and air containing banana odor into the odor sensor 11 at a specified time interval. As a result, when the odor sensor 11 measures banana odor, the odor molecules adhering to the molecular recognition material are removed from the molecular recognition material by the fresh air. That is, the odor attached to the odor sensor 11 is cleaned. FIG. 3 is a graph for explaining the cleaning of the odor attached to the odor sensor according to Embodiment 1.

[0026] In the graph shown in FIG. 3, the vertical axis represents the current value, and the horizontal axis represents time. Characteristics 20, 21, and 22 respectively show the changes in the current value when three different types of molecular recognition materials of the odor sensor 11 adsorb odor molecules. For the sake of convenience of explanation, only three characteristics 20, 21, and 22 are shown. However, for example, when the odor sensor 11 includes 16 types of molecular recognition materials, 16 patterns of characteristics indicating changes in the current value also occur.

[0027] As shown by characteristics 20, 21, and 22, in the odor measurement by the odor sensor 11, period T1 and period T2 are alternately repeated. The odor sensor 11 measures the odor of fresh air in period T1. At this time, the current value hardly changes. This is because fresh air is odorless. In other words, it is because fresh air does not contain odor molecules that react with the molecular recognition material included in the odor sensor 11. The odor sensor 11 measures the air in the container 10, that is, the banana odor, in period T2. At this time, the current value increases. By repeating the measurement in period T1 and the measurement in period T2, the odor sensor 11 can maintain the accuracy of odor measurement. Note that the length of each of period T1 and period T2 may be preset by the user, for example.

[0028] [Odor Analysis] Next, the process of analyzing the state of bananas based on odor data by the computing device 8 will be described. The computing device 8 analyzes the state of the cargo 6 using a learned model based on the learning data for each type of the cargo 6. The types of the cargo 6 are, for example, bananas, apples, fish, and the like. In Embodiment 1, the computing device 8 analyzes the state of bananas using the learned model.

[0029] The computing device 8 may generate the learned model in advance. The learned model may be generated by a computer other than the computing device 8. The learned model analyzes, based on the odor data of bananas, which of the odors of the bananas to be analyzed is the odor of the bananas in any of the eight states of the bananas shown in FIG. 2. That is, the learned model analyzes in which of the eight states of all green, light green, half green, half yellow, full yellow, star, and dimple is the state of the bananas to be analyzed, that is, the state of the bananas from which the odor data is derived. An example of the analysis result will be described later with reference to FIG. 6.

[0030] The learned model analyzes the odor data based on the feature amount data of the odor. The feature amount data represents the features of the odor data. The feature amount data differs depending on the state of the bananas. For example, the feature amount data extracted from the odor data of bananas when the state of the bananas is all green is different from the feature amount data extracted from the odor data of bananas when the state of the bananas is dimple.

[0031] Before entering the description of a series of processes for odor analysis in the computing device 8, the generation of a learned model will be briefly described. For the sake of convenience in the description, it is assumed that the generation of the learned model is performed by the computing device 8. The computing device 8 acquires, for example, odor data of a banana in a state where the color, that is, the maturity, is all green. The computing device 8 extracts feature amount data from the odor data. For example, when the odor sensor 11 is provided with 16 types of molecular recognition materials, one by one, the computing device 8 obtains 16 types of characteristics indicating changes in current values over time as shown in FIG. 3. In this case, the computing device 8 extracts, for example, a specific value of the current values from a certain time to a certain time from each of the 16 types of characteristics as the feature amount data. The specific value is, for example, the maximum value of the current, etc. Thereby, the computing device 8 acquires the feature amount data when the state of the banana is all green. The computing device 8 acquires the feature amount data not only when the state of the banana is all green but also when the banana is in each state.

[0032] The computing device 8 uses the odor data stored together with a label indicating a state such as all green as learning data. The computing device 8 performs model learning using the learning data, the feature amount data, and an arbitrary algorithm. The model learns the classification of each state based on the feature amounts of the odor data. Thereby, a learned model is generated. The algorithm used for the generation of the learned model is not particularly limited, and for example, logistic regression, decision tree, random forest, etc. may be used.

[0033] The computing device 8 executes each process of the flowchart shown in FIG. 4 using the generated learned model. Note that the learned model may be stored in the memory of the computing device 8 or may be stored in an external device or the like that can communicate with the computing device 8. FIG. 4 is a flowchart of the processing of the computing device 8 according to the first embodiment.

[0034] The computing device 8 acquires the odor data of the banana generated by the odor sensor 11 (step St30). An example of the odor data will be described with reference to FIG. 5.

[0035] FIG. 5 is a schematic diagram for explaining an example of odor data 40 according to Embodiment 1. The odor data 40 includes a device ID 41, target item data 42, date data 43, and an odor data body 44. The device ID 41 is an ID for identifying the odor sensor 11. The target item data 42 is data indicating the target item from which the odor data originated, that is, the cargo 6, and is pre-registered in the odor sensor 11. In the example of FIG. 5, the target item data 42 indicates that the target item is a banana. The date data 43 is data indicating the date and time when the odor data 40 started to be generated, in other words, the date and time when the measurement of the odor started. In the example of FIG. 5, the date data 43 indicates that the measurement of the odor started at 9:00:00 on September 23, 2023. The odor data body 44 is digitized data of the measured odor. For example, when the odor sensor 11 includes 16 types of molecular recognition materials, one each, the odor data body 44 includes the current values detected by the probes corresponding to each molecular recognition material. The digitized data of the odor is recorded as the odor data body 44, for example, at regular time intervals. Specifically, the odor sensor 11 may record, every 1 / 100 of a second, the digitized data of the odor, for example, the current value, as the odor data body 44. Thereby, for example, when the measurement of the odor starts at 9:00:00 on September 23, 2023, the measurement results are recorded in the odor data body 44 every 1 / 100 of a second from 9:00:00 on September 23, 2023.

[0036] Next, return to the description of the flowchart in FIG. 4. The computing device 8 extracts and acquires the odor data body for a specified period from the reference time among the odor data bodies included in the odor data (step St31). The reference time is the date and time indicated by the date data included in the odor data. Extracting the odor data body for a specified period means, for example, extracting the odor data body for one hour from 9:00:00 on September 23, 2023 to 10:00:00 on September 23, 2023, etc. This is because the data volume of the odor data body included in the odor data may be enormous. The specified period may be, for example, preset by the user in advance.

[0037] If the computing device 8 fails to extract the odor data body (step St32; NO), it ends the processing flow. The case where the computing device 8 cannot extract the odor data body means that all the odor data bodies included in the odor data have already been extracted by repeating the process of step St31. Therefore, in this case, the computing device 8 ends the processing flow.

[0038] If the computing device 8 can extract the odor data body (step St32; YES), it inputs the odor data acquired in step St30 into the learned model and causes the learned model to analyze the state of the banana (step St33). More precisely, the computing device 8 extracts feature quantity data from the odor data and causes the learned model to analyze the state of the banana based on the feature quantity data.

[0039] The computing device 8 acquires the analysis result by the learned model (step St34). Examples of the analysis results will be described with reference to FIG. 6.

[0040] FIG. 6 is a table diagram for explaining an example of the odor analysis result by the computing device 8 according to Embodiment 1. The learned model outputs a matching rate for each state of the banana. The matching rate indicates the probability that the banana is in a certain state. In the example of FIG. 6, the probability that the state of the banana from which the analyzed odor data is derived is all green is 30%. Also, the probability that the state of the banana is light green is 63%, the probability that it is half green is 5%, the probability that it is half yellow is 2%, the probability that it is green tip is 0%, the probability that it is fully yellow is 0%, the probability that it is star is 0%, and the probability that it is dimple is 0%.

[0041] Next, returning to the description of the flowchart of FIG. 4, the computing device 8 creates an odor analysis telegram based on the odor data acquired in step St30 and the analysis result acquired in step St34 (step St35). An example of the odor analysis telegram will be described later with reference to FIG. 7.

[0042] The computing device 8 updates the reference time (step St36), returns to step St31, and repeats the process. For example, if the reference time, that is, the date and time when the odor measurement started is 9:00:00 on September 23, 2023, and the specified period for cutting out the odor data main body is 30 minutes, the reference time is updated to 9:30:00 on September 23, 2023. Also, as will be described later with reference to FIG. 7, by repeating the process of step St35, analysis result elements are added to the odor analysis telegram.

[0043] FIG. 7 is a schematic diagram for explaining an example of the odor analysis telegram 50 according to Embodiment 1. The odor analysis telegram 50 includes date data 51, at least analysis result element 52 and analysis result element 53 as analysis result elements. The date data 51 is the same data as the date data included in the odor data, and indicates the year, month, day, hour, minute, and second when the measurement of the odor started. In the example of FIG. 7, the date data 51 indicates that the measurement of the odor started at 9:00:00 on September 23, 2023. The analysis result elements include the device ID included in the odor data, the target item data, and the analysis result in a specified period. The analysis result element 52 indicates the matching rate for each state of the banana in the period from 9:00:00 on September 23, 2023 to 9:30:00 on September 23, 2023. The analysis result element 53 indicates the matching rate for each state of the banana in the period from 9:30:00 on September 23, 2023 to 10:00:00 on September 23, 2023. In the example of FIG. 7, in step St31 of the flowchart shown in FIG. 4, the main body of the odor data for 30 minutes from the reference time is cut out. One analysis result element is added to the odor analysis telegram each time the process of step St35 of the flowchart shown in FIG. 4 is performed once.

[0044] [Alarm operation] Next, the alarm operation executed by the monitoring device 9 that has received the odor analysis telegram from the computing device 8 will be described based on the odor analysis telegram and the like. The monitoring device 9 executes the alarm operation when necessary with reference to the alarm level management table shown in FIG. 8 and the cargo management table shown in FIG. 9 in addition to the odor analysis telegram.

[0045] FIG. 8 is a table diagram for explaining an alarm level management table corresponding to the banana according to Embodiment 1. The alarm level management table may be stored, for example, in the memory of the monitoring device 9. The alarm level management table indicates the alarm level corresponding to each state of the cargo 6. In the example of the alarm level management table corresponding to the banana shown in FIG. 8, the alarm level corresponding to each state of the banana is shown. The maturity level of the banana represents the maturity of the banana, which is 1 when the color of the banana is all green, and increases by 1 as the color of the banana changes from all green to dapple. When the color of the banana is dapple, the maturity level is 8. The alarm level represents the strength of the warning regarding the quality of the cargo 6, and the higher the level, the stronger the warning. In the example of the banana, when the color of the banana is all green and the maturity level is 1, the alarm level is 0. As the banana ripens, when the color of the banana changes from light green to half green and the maturity level becomes 3, the alarm level becomes 2. Further, as the banana ripens and the maturity level of the banana becomes 4 or more, the alarm level becomes 3. Note that the alarm level is represented by "1", "2", and "3", but is not limited thereto. For example, the alarm level may be represented by a character string or the like.

[0046] FIG. 9 is a table diagram for explaining a cargo management table according to Embodiment 1. The cargo management table may be stored, for example, in the memory of the monitoring device 9. The cargo management table includes, as items, a container ID for identifying the container 10, the loading capacity of the container 10 to which the container ID is assigned (for example, 33.1 m 3 ), the type of the container 10, the company name that owns the container 10, the item of the cargo 6, the loading amount of the cargo 6 (for example, 1000 pieces), a device ID for identifying the odor sensor 11 installed in the container 10, and at least the alarm level of the cargo 6. The cargo management table may further include items other than the above items.

[0047] Next, the processing of the monitoring device 9 will be described. FIGS. 10 and 11 are flowcharts of the processing of the monitoring device 9 according to Embodiment 1. "A", "B", and "C" shown in each of the flowcharts of FIGS. 10 and 11 represent the connection between the flowchart shown in FIG. 10 and the flowchart shown in FIG. 11.

[0048] The monitoring device 9 receives an odor analysis telegram from the computing device 8 (step St60).

[0049] The monitoring device 9 identifies the target item from the target item data of the odor analysis telegram received in step St60 (step St61). In the description of this flowchart, the target item is a banana.

[0050] The monitoring device 9 reads an alarm level management table corresponding to the target item identified in step St61 (step St62). The monitoring device 9 reads the alarm level management table corresponding to the banana shown in FIG. 8.

[0051] The monitoring device 9 focuses on the first analysis result element among the analysis result elements included in the odor analysis telegram received in step St60 (step St63). In this specification, to focus on a certain data or information means to extract and acquire it so that some processing can be performed on the data or information. The first analysis result element is the analysis result element that was added earliest to the odor analysis telegram. In the example of FIG. 7, among the analysis result elements included in the odor analysis telegram 50, the first analysis result element is the analysis result element 52, and the next analysis result element after the analysis result element 52 is the analysis result element 53.

[0052] When the monitoring device 9 can focus on the analysis result element (step St64; YES), it acquires the matching rate for each state of the banana from the focused analysis result element (step St65).

[0053] When the monitoring device 9 cannot focus on the analysis result element (step St64; NO), the processing flow ends. The case where the monitoring device 9 cannot focus on the analysis result element means that, by repeating the processing of step St73 described later, all the analysis result elements included in the odor analysis telegram received in step St60 have been focused on and finished.

[0054] Based on the matching rate for each state of the banana obtained by the monitoring device 9 in step St65, the monitoring device 9 determines whether the matching rate for at least one of half yellow, green tip, full yellow, star, and dimple is 1% or more (step St66).

[0055] First, the case where the matching rate for at least one of half yellow, green tip, full yellow, star, and dimple is 1% or more (step St66; YES) will be described. In this case, the monitoring device 9 determines that the alarm level of the banana that is the analysis target is 3 (step St67).

[0056] The monitoring device 9 refers to the cargo management table and identifies the alarm level of the banana that is the analysis target (step St68). The monitoring device 9 identifies, for example, the alarm level of the banana described in the cargo management table based on the device ID included in the odor analysis telegram.

[0057] The monitoring device 9 determines whether the alarm level recognized in step St67, step St75 described later, or step St77 described later is higher than the alarm level identified in step St68 (step St69).

[0058] When the monitoring device 9 determines that the alarm level recognized in step St67, step St75 described later, or step St77 described later is not higher than the alarm level identified in step St68 (step St69; NO), the process proceeds to step St73 described later.

[0059] When the monitoring device 9 determines that the alarm level certified in step St67, step St75 to be described later, or step St77 to be described later is higher than the alarm level specified in step St68 (step St69; YES), it creates an alarm message (step St70). The alarm message will be described later with reference to FIG. 12.

[0060] The monitoring device 9 executes an alarm operation (step St71). The alarm operation is, for example, to notify all at once by email or the like to the transport operator or the person in charge, etc. that the alarm level has increased, that is, there has been a change to the banana state, or to display an alarm message or the like on the display screen of a display device (not shown) provided in the monitoring device 9. Alternatively, the monitoring device 9 may notify another system (not shown) different from the quality management system 1 as an alarm operation. Thereby, the scheduling of the unloading work or the like can be performed by the relevant person or some system or the like.

[0061] The monitoring device 9 rewrites the alarm level of the banana targeted for analysis described in the cargo management table to the alarm level certified in step St67, step St75 to be described later, or step St77 to be described later (step St72).

[0062] The monitoring device 9 focuses on the next analysis result element of the analysis result element of interest among the analysis result elements included in the odor analysis message received in step St60 (step St73). Then, the monitoring device 9 returns to step St64 and repeats the process.

[0063] Next, the case where the matching rate for each of half yellow, green tip, full yellow, star, and double is less than 1% (step St66; NO) will be described. In this case, the monitoring device 9 determines whether the matching rate for half green is 10% or more (step St74).

[0064] When the matching rate for half green is 10% or more (step St74; YES), the monitoring device 9 determines that the alarm level of the banana being analyzed is 2 (step St75). Then, the monitoring device 9 proceeds with the process to step St68.

[0065] When the matching rate for half green is less than 10% (step St74; NO), the monitoring device 9 determines whether the matching rate for light green is 50% or more (step St76).

[0066] When the matching rate for light green is 50% or more (step St76; YES), the monitoring device 9 determines that the alarm level of the banana being analyzed is 1 (step St77). Then, the monitoring device 9 proceeds with the process to step St68.

[0067] When the matching rate for light green is less than 50% (step St76; NO), the monitoring device 9 determines that the banana being analyzed has no abnormality with respect to the analysis result element being focused on (step St78). Then, the monitoring device 9 proceeds with the process to step St73.

[0068] Note that the determination contents in each of step St66, step St74, and step St76 are an example when the cargo 6 is a banana. Also, the alarm levels recognized by the monitoring device 9 were in three levels of "1", "2", and "3", but it is not limited to this. Further, each state of the banana for determining the alarm level and the threshold value of the matching rate for each state shown in FIGS. 10 and 11 are an example and are not limited to this. For example, in step St74, when the matching rate for half green is 10% or more, the monitoring device 9 determined that the alarm level was 2, but it is not limited to this. For example, the monitoring device 9 may determine that the alarm level is 2 when the matching rate for at least one of half green and light green is 20% or more.

[0069] FIG. 12 is a schematic diagram for explaining an example of the alarm message 80 according to Embodiment 1. The alarm message 80 includes a container ID 81, target item data 82, date data 83, alarm level data 84, and match rate data 85. The container ID 81 is the container ID of the container 10 in which the bananas to be analyzed are stored. The target item data 82 indicates that the target item is a banana. The date data 83 indicates the date and time (year, month, day, hour, minute, second) advanced by the next hour from the date and time shown by the date data included in the odor analysis message. This will be described in detail below.

[0070] The calculation device 8 cut out the odor data main body for a specified period from the odor data in step St31 of the flowchart shown in FIG. 4. The date data 83 indicates the date and time (year, month, day, hour, minute, second) when the specified period is advanced by the number of times the process returns to step St64 of the flowchart shown in FIG. 10 from the date and time shown by the date data included in the odor analysis message. Taking the odor analysis message 50 shown in FIG. 7 as a specific example for explanation. In the example of FIG. 7, the specified period when cutting out the odor data main body from the odor data is 30 minutes. When the monitoring device 9 pays attention to the analysis result element 53 included in the odor analysis message 50, the number of times the process returns to step St64 of the flowchart shown in FIG. 10 is 1 time. When creating an alarm message while the monitoring device 9 is paying attention to the analysis result element 53 included in the odor analysis message 50, the date data included in the alarm message is as follows. That is, the date data included in the alarm message indicates 2023-09-23 09:30:00, where 30 minutes has been advanced only once from 2023-09-23 09:00:00 indicated by the date data 51 included in the odor analysis message 50. The analysis result element 53 indicates the match rate for each state of the bananas from 2023-09-23 09:30:00 to 2023-09-23 10:00:00. That is, the date data included in the alarm message indicates approximately the time when the alarm level increased, that is, when the state of the bananas changed.

[0071] The alarm level data 84 indicates the alarm level recognized by the monitoring device 9. The matching rate data 85 indicates the matching rate for each state of the banana indicated by the analysis result element being focused on.

[0072] In this way, the monitoring device 9 acquires the analysis result of the banana state, and determines whether to execute an alarm operation for notifying the user of the banana state based on the alarm level corresponding to the banana state.

[0073] [Cause Analysis] Next, an explanation will be given regarding the factor for the increase in the alarm level, that is, the analysis of the abnormal factor, which is performed when the monitoring device 9 executes the alarm operation. The monitoring device 9 analyzes the abnormal factor based on the measurement data shown in FIG. 13 and the sensing table shown in FIG. 14.

[0074] FIG. 13 is a table diagram for explaining an example of measurement data according to Embodiment 1. FIG. 13(a) is a table diagram showing temperature data and humidity data. FIG. 13(b) is a table diagram showing acceleration data. FIG. 13(c) is a table diagram showing illuminance data. FIG. 13(d) is a table diagram showing position information. Each measurement data is acquired by the computing device 8 from each sensor. Then, the computing device 8 transmits each measurement data to the monitoring device 9.

[0075] In Embodiment 1, the acceleration sensor 14 measures the acceleration along three mutually perpendicular axes, namely the X-axis, Y-axis, and Z-axis. Note that the unit of acceleration is m / s 2 Let's assume it is. Also, in Embodiment 1, the illuminance sensor 15 is composed of an element using cadmium sulfide (hereinafter referred to as "CdS"). When the illuminance is low, the resistance value of the illuminance sensor 15 becomes high. On the other hand, when the illuminance is high, the resistance value of the illuminance sensor 15 becomes low. Also, the position information includes longitude, latitude, and altitude.

[0076] For example, from the table diagram shown in Fig. 13(c), it can be seen that at 0:18 on August 19, 2023, the resistance value of the illuminance sensor is low. That is, it can be seen that the container 10 was unpacked at this time. Thereby, for example, it is possible to determine from the position information of the container 10 where the container 10 was near which transportation base at this time.

[0077] Fig. 14 is a table diagram for explaining an example of the sensing table according to Embodiment 1. The sensing table may be stored, for example, in the memory of the monitoring device 9. The sensing table shows the priority and threshold value of each of the four data types: temperature data, humidity data, illuminance data, and acceleration data. Note that the units of each data type are omitted from the sensing table. The monitoring device 9 analyzes the presence or absence of abnormalities by paying attention to the data types in descending order of priority. Note that a higher priority means a smaller priority number. That is, the temperature with a priority of 1 has the highest priority, and the acceleration with a priority of 4 has the lowest priority. Also, the range from the lower limit value to the upper limit value of the threshold value of each data type shown in the sensing table is the normal value range of each data type. When the upper limit value of the threshold value is not defined, such as for illuminance data, the range of values equal to or greater than the lower limit value is the normal value range. When the lower limit value of the threshold value is not defined, such as for acceleration data, the range of values equal to or less than the upper limit value is the normal value range. The monitoring device 9 determines that the temperature data is abnormal data when the temperature indicated by the temperature data is below 10°C or above 15°C. Also, the monitoring device 9 determines that the humidity data is abnormal data when the humidity indicated by the humidity data is below 85% or above 90%. Also, the monitoring device 9 determines that the illuminance data is abnormal data when the resistance value indicated by the illuminance data is below 100 kΩ. Also, the monitoring device 9 determines that the acceleration data is abnormal data when any of the accelerations of the X-axis, Y-axis, and Z-axis indicated by the acceleration data exceeds 20 m / s 2 ². Note that these threshold values are an example for explanation. Also, for example, the upper limit value of the threshold value of the illuminance data may be defined, or the lower limit value of the threshold value of the acceleration data may be defined.

[0078] Next, the process of cause analysis by the monitoring device 9 will be described. FIG. 15 is a flowchart of the process of the monitoring device 9 according to the first embodiment. At the start of the flowchart shown in FIG. 15, the monitoring device 9 is executing an alarm operation. In other words, the execution of the alarm operation becomes the trigger for the start of the flowchart shown in FIG. 15.

[0079] The monitoring device 9 reads the sensing table (step St90). The monitoring device 9 reads the sensing table shown in FIG. 14.

[0080] The monitoring device 9 focuses on the data type with the highest priority among the data types described in the sensing table read in step St90 (step St91). The monitoring device 9 focuses on the temperature data.

[0081] The monitoring device 9 reads the measurement data for a specific period before the date and time when the alarm operation was performed for the data type on which it is focusing (step St92). More precisely, the monitoring device 9 reads the measurement data for a specific period before the date and time shown by the date data included in the alarm telegram. The specific period may be, for example, one day or one hour. Taking the alarm telegram 80 shown in FIG. 12 as an example, for example, the monitoring device 9 reads the temperature data for one day before 9:30:00 on September 23, 2023, indicated by the date data 83 included in the alarm telegram 80. That is, the monitoring device 9 reads the temperature data for one day from 9:30:00 on September 22, 2023, to 9:30:00 on September 23, 2023.

[0082] The monitoring device 9 reads the threshold value for the data type on which it is focusing from the sensing table read in step St90 (step St93).

[0083] The monitoring device 9 focuses on the data at the earliest time among the measurement data for a specific period read in step St92 (step St94). Regarding the data at the earliest time, taking the temperature data as an example, it will be described with reference to the table diagram of temperature data and humidity data shown in FIG. 13(a). In the example of FIG. 13(a), the data at the earliest time among the temperature data is the data at 0:00 on August 19, 2023. Also, the data at the next time after the data at 0:00 on August 19, 2023 is the data at 0:02 on August 19, 2023.

[0084] The monitoring device 9 determines whether the data being focused on exceeds the upper limit value or is below the lower limit value of the threshold read in step St93 (step St95).

[0085] If the monitoring device 9 determines that the data being focused on does not exceed the upper limit value of the threshold and is not below the lower limit value (step St95; NO), it proceeds to the process of step St97 described later.

[0086] If the monitoring device 9 determines that the data being focused on exceeds the upper limit value or is below the lower limit value of the threshold (step St95; YES), it extracts the data being focused on as abnormal data (step St96).

[0087] The monitoring device 9 determines whether it has finished focusing on the data at all times in the specific period obtained in step St92 (step St97). In other words, the monitoring device 9 determines whether it has completed the determination of whether the data at all times in the specific period obtained in step St92 is abnormal based on the threshold.

[0088] If the monitoring device 9 determines that it has not finished focusing on the data at all times in the specific period obtained in step St92 (step St97; NO), it focuses on the data at the next time after the data it is focusing on (step St98). Then, the monitoring device 9 returns to step St95 and repeats the process.

[0089] When the monitoring device 9 determines that it has finished focusing on the data at all times during the specific period acquired in step St92 (step St97; YES), it determines whether it has focused on all the data types included in the sensing table (step St99).

[0090] When the monitoring device 9 determines that it has not focused on all the data types included in the sensing table (step St99; NO), it focuses on the data type with a priority one level lower than the priority of the currently focused data type (step St100). As shown in the sensing table shown in FIG. 14, for example, the data type with a priority one level lower than the temperature data is the humidity data. Then, the monitoring device 9 returns to step St92 and repeats the process.

[0091] When the monitoring device 9 determines that it has focused on all the data types included in the sensing table (step St99; YES), it creates a cause message and notifies the user (step St101). Then, the monitoring device 9 ends this processing flow. The method of notifying the user is not particularly limited, and for example, it may be sending an email or displaying on a display device. The cause message will be described later with reference to FIG. 16.

[0092] FIG. 16 is a schematic diagram for explaining an example of a cause message according to Embodiment 1. In FIG. 16, in addition to the cause message 110, a schematic diagram 120 for explaining the time series of the occurrence of the abnormal cause and the alarm operation is shown.

[0093] The causal message 110 includes the container ID 111, the target item data 112, and the alarm level data 113. These data are the same as the data included in the alarm message and are directly added to the causal message 110. The causal message 110 further includes the abnormal data extracted at step St96 of the flowchart shown in FIG. 15. In the example of FIG. 16, the causal message 110 includes the abnormal data 114, the abnormal data 115, and the abnormal data 116. The abnormal data 114 is the abnormal data regarding temperature. The abnormal data 115 is the abnormal data regarding acceleration. The abnormal data 116 is the abnormal data regarding illuminance. Note that if no abnormal data is extracted, the causal message does not include abnormal data.

[0094] The schematic diagram 120 includes a graph 121 representing humidity change, a graph 122 representing temperature change, a graph 123 representing acceleration change, and a graph 124 representing illuminance change. Also, the schematic diagram 120 is marked with dates for explaining the time series of the changes in the measurement data represented by each graph.

[0095] The color of the bananas being transported was all green until time P1 on August 19, but turned light green at time P1. This information is obtained through the measurement and analysis of smell. Also, as the ripening of the bananas progressed, the monitoring device 9 determined that the alarm level was 1 and created an alarm message. Then, the monitoring device 9 acquires various measurement data for a specific period before time P1, conducts a cause analysis, and extracts abnormal data 114, abnormal data 115, and abnormal data 116. It can also be seen from schematic diagram 120 that each of graph 122, graph 123, and graph 124 fluctuates greatly near the time indicated by each of abnormal data 114, abnormal data 115, and abnormal data 116. Based on cause message 110, the monitoring device 9 can make the following judgment. That is, the monitoring device 9 can judge that some impact was applied to container 10 around 20:00 on August 18, and the container 10 being transported was opened around 0:00 on the 19th for safety confirmation, thereby increasing the temperature inside container 10, and due to the influence of this temperature increase, the ripening of the bananas progressed. Note that the judgment by the monitoring device 9 that some impact was applied to container 10 is based on the abnormality of acceleration, the judgment that container 10 was opened is based on the abnormality of illuminance, and the judgment that the temperature increased is based on the abnormality of temperature.

[0096] [Execution of Action] After the cause analysis, based on the alarm level and the cause of the abnormality, the monitoring device 9 executes actions regarding the change in the state of the bananas, that is, the possibility of quality abnormality of the bananas. The monitoring device 9 executes countermeasure actions for quality control based on the cargo destination management table shown in FIG. 17, the allowable level management table by destination shown in FIG. 18, and the transport base management table shown in FIG. 19.

[0097] FIG. 17 is a table diagram for explaining an example of a cargo destination management table according to Embodiment 1. The cargo destination management table may be stored, for example, in the memory of the monitoring device 9. The cargo destination management table includes, as items, a container ID, the destination of the container 10 to which the container ID is assigned, the type of the destination, the address of the destination, and the predicted arrival date and time at the destination. Hereinafter, the predicted arrival date and time may be referred to as the scheduled arrival date and time.

[0098] FIG. 18 is a table diagram for explaining an example of a destination-specific allowable level management table according to Embodiment 1. The destination-specific allowable level management table may be stored, for example, in the memory of the monitoring device 9. The destination-specific allowable level management table indicates the allowable banana maturity level determined according to the type of the destination. In the example of FIG. 18, when the type of the destination is a supermarket, bananas with a maturity level up to 2 are allowed. That is, the supermarket accepts only bananas with a maturity level up to 2, that is, bananas that are all green or light green in color. Also, when the type of the destination is a confectionery manufacturer, bananas with a maturity level up to 5 are allowed. For simplicity of explanation, only examples of a supermarket and a confectionery manufacturer are shown in the destination-specific allowable level management table shown in FIG. 18. Note that the destination-specific allowable level management table shown in FIG. 18 is a table when the cargo 6 is bananas, and a table corresponding to the item of the cargo 6 may be prepared.

[0099] FIG. 19 is a table diagram for explaining an example of a transportation base management table according to Embodiment 1. The transportation base management table may be stored, for example, in the memory of the monitoring device 9. The transportation base management table includes, as items, a carrier in charge of transportation, that is, a carrier ID for identifying the transportation base, the carrier name, the latitude and longitude of the carrier, the address of the carrier, the contact information of the person in charge of the carrier, and the evaluation of the carrier. The evaluation of the carrier may be determined by numbers such as "8", "9", and "10" as in the example of FIG. 19. In Embodiment 1, it is assumed that the larger the number, the higher the evaluation. Also, it is assumed that the maximum value of the number used for evaluation is 10.

[0100] With reference to FIGS. 20 and 21, the countermeasure action execution process for quality control by the monitoring device 9 will be described. FIGS. 20 and 21 are flowcharts of the processing of the monitoring device according to Embodiment 1. "D" and "E" shown in the respective flowcharts of FIGS. 20 and 21 represent the connection between the flowchart shown in FIG. 20 and the flowchart shown in FIG. 21. At the start point of the flowchart shown in FIG. 20, a cause message has been created by the monitoring device 9. In other words, the creation of the cause message is the trigger for the start of this processing flow.

[0101] The monitoring device 9 reads out the created cause message (step St130).

[0102] The monitoring device 9 determines whether the cause message read out in step St130 contains abnormal data (step St131).

[0103] When the monitoring device 9 determines that the cause message does not contain abnormal data (step St131; NO), it sends an instruction to cancel the transportation to the user or the system for transportation (step St142), and sends an instruction to reorder to the user or the system for transportation (step St143). Then, the monitoring device 9 ends this processing flow. This is because although no abnormal data is contained, the alarm level has risen, so the monitoring device 9 determines that the bananas being transported are defective products. The system for transportation is, for example, a system that manages the receipt and order of the goods 6. When sending the reorder instruction, the monitoring device 9, for example, based on the container ID, acquires the loaded items and the loaded quantity of the goods 6 to be reordered from the goods management table, and acquires the transportation destination of the goods 6 for which the transportation has been canceled from the goods transportation destination management table. Then, the monitoring device 9 sends an instruction to re - transport the same item as the goods 6 for which the transportation has been canceled in the same quantity to the same transportation destination to the system that manages the receipt and order, etc.

[0104] When the monitoring device 9 determines that the cause message contains abnormal data (step St131; YES), it determines whether the cause of the abnormality is temperature or humidity (step St132). That is, the monitoring device 9 determines whether the abnormal data contains temperature data, humidity data, or both.

[0105] When the monitoring device 9 determines that the cause of the abnormality is temperature or humidity (step St132; YES), it controls the air conditioner in the container 10 (step St133). Then, the monitoring device 9 proceeds to step St134 described later.

[0106] When the monitoring device 9 determines that the cause of the abnormality is not temperature or humidity (step St132; NO), in other words, when it determines that the cause of the abnormality is acceleration or illuminance, it proceeds to step St135 described later.

[0107] The monitoring device 9 determines whether the cause of the abnormality contains acceleration or illuminance (step St134).

[0108] When the monitoring device 9 determines that the abnormal factor does not include acceleration or illuminance (step St134; NO), the process proceeds to step St137 described later.

[0109] When the monitoring device 9 determines that the abnormal factor includes acceleration or illuminance (step St134; YES), it acquires the position information of the time of the abnormal data (step St135).

[0110] The monitoring device 9 determines whether a transport base close to the position indicated by the latitude and longitude of the position information acquired in step St135 is included in the transport base management table (step St136).

[0111] When the monitoring device 9 determines that a transport base close to the position indicated by the latitude and longitude of the position information acquired in step St135 is not included in the transport base management table (step St136; NO), the process proceeds to step St139 described later.

[0112] When the monitoring device 9 determines that a transport base close to the position indicated by the latitude and longitude of the position information acquired in step St135 is included in the transport base management table (step St136; YES), it notifies the person in charge of the transport base of the date and time of the occurrence of the abnormality and its cause (step St137). Or, when the monitoring device 9 determines in step St134 that the abnormal factor does not include acceleration or illuminance, it notifies the person in charge of the transport base where the air conditioner of the container 10 was set of the date and time of the occurrence of the abnormality and its cause (step St137).

[0113] The monitoring device 9 lowers the evaluation of the transport base to which the date and time of the occurrence of the abnormality and its cause were notified in step St137 and reflects it in the transport base management table (step St138).

[0114] The monitoring device 9 determines which of level 1, level 2, or level 3 the alarm level indicated by the factor telegram read in step St130 is (step St139).

[0115] When the alarm level is 1 (step St139; level 1), the monitoring device 9 ends this processing flow. This is because when the alarm level is 1, the monitoring device 9 determines that there is no problem with the quality of the bananas.

[0116] When the alarm level is 2 (step St139; level 2), the monitoring device 9 executes the process of changing the destination of the bananas (step St140). Details will be described later with reference to FIG. 22.

[0117] When the monitoring device 9 succeeds in changing the destination of the bananas or when the change is unnecessary (step St141; YES), it ends this processing flow.

[0118] When the monitoring device 9 fails to change the destination of the bananas (step St141; NO), it sends an instruction to cancel the transportation to the user or the system for transportation (step St142), and sends an instruction to reorder to the user or the system for transportation (step St143). Then, the monitoring device 9 ends this processing flow.

[0119] When the alarm level is 3 (step St139; level 3), the monitoring device 9 sends an instruction to cancel the transportation to the user or the system for transportation (step St142), and sends an instruction to reorder to the user or the system for transportation (step St143). Then, the monitoring device 9 ends this processing flow.

[0120] With reference to FIG. 22, the process of changing the destination of the bananas by the monitoring device 9 will be described. FIG. 22 is a flowchart of the process of the monitoring device according to Embodiment 1.

[0121] Based on the container ID of the banana to be replaced, the monitoring device 9 acquires the destination type of the banana from the cargo destination management table (step St150). This will be described with a specific example with reference to FIG. 23. FIG. 23 is a schematic diagram for explaining an example of destination replacement according to the first embodiment. In the example of FIG. 23, the banana with the container ID "2847469" is the banana to be replaced, that is, the banana with an alarm level of 2. Also, the destination type of the banana to be replaced is a supermarket. In the example of FIG. 23, the destination of the banana with the container ID "2847469" and the destination of the banana with the container ID "837498233" are replaced.

[0122] Based on the destination type of the banana acquired in step St150, the monitoring device 9 acquires the maturity level of the banana allowed by the destination type from the allowable level management table for bananas by destination (step St151). The destination type of the banana to be replaced is a supermarket. As shown in the example of FIG. 18, the maturity level of the banana allowed by the supermarket is 2. Since the alarm level of the banana to be replaced is 2, the current maturity level of the banana is 3, and its color is half green. More precisely, the current maturity level is the maturity level at the alarm detection date and time, that is, the date and time indicated by the date data of the alarm telegram.

[0123] Based on the container ID, the monitoring device 9 acquires the predicted arrival date and time of the banana to be replaced from the cargo destination management table (step St152). In the example of FIG. 23, the predicted arrival date and time of the banana to be replaced is August 20, 2023, 15:00.

[0124] The monitoring device 9 predicts, by known techniques, the color of the banana to be replaced at the predicted arrival date and time, and the respective number of days for the current maturity level of the banana to rise to each maturity level higher than the current maturity level (step St153). In the example of FIG. 23, the monitoring device 9 predicts the color of the banana to be replaced at 15:00 on August 20, 2023. Further, the monitoring device 9 predicts the current color of the banana to be replaced, that is, half green, and the number of days until it changes to each of half yellow, green tip, full yellow, star, and double. The monitoring device 9 may, for example, generate in advance a learned model that predicts what color the banana will be on what day after harvest under specific temperature and humidity conditions using the odor data of the banana, the odor data acquisition date and time, the harvest date, the temperature data, the humidity data, and the color of the banana. By inputting the odor data into the learned model, the monitoring device 9 can predict the color of the banana from which the odor data originated and the number of days until the banana changes to the specified color.

[0125] For the sake of convenience of explanation, assume that the maturity level of the banana to be replaced at 15:00 on August 20, 2023 is predicted to be 3 and the color is predicted to be half green. Also assume that the current color of the banana to be replaced, half green, is predicted to change to green tip in 4 days. Since the alarm detection date is August 19, 2023, the color of the banana to be replaced will be green tip on August 23, 2023.

[0126] The monitoring device 9 determines whether the maturity level acceptable to the destination of the banana to be replaced is equal to or higher than the maturity level at the time when the banana arrives at the destination (step St154).

[0127] When the monitoring device 9 determines that the maturity level acceptable to the destination of the banana to be replaced is equal to or higher than the maturity level at the time when the banana arrives at the destination (step St154; YES), it determines that replacement is unnecessary and ends this processing flow.

[0128] When the monitoring device 9 determines that the maturity level acceptable to the destination of the banana to be replaced is lower than the maturity level at the time when the banana arrives at the destination (step St154; NO), the process proceeds to step St155. In the example of FIG. 23, the maturity level acceptable to the destination of the banana to be replaced, that is, the supermarket, is 2. That is, the color of the banana acceptable to the supermarket is light green. As predicted in step St153, the color of the banana to be replaced at the time when it arrives at the supermarket is half green, and the maturity level is 3. Therefore, the monitoring device 9 determines that the maturity level acceptable to the destination of the banana to be replaced is lower than the maturity level at the time when the banana arrives at the destination, and proceeds with the process to step St155.

[0129] The monitoring device 9 refers to the cargo management table and determines whether there is a container storing cargo of the same item and the same loading amount as the cargo 6 to be replaced (step St155).

[0130] When the monitoring device 9 determines, with reference to the cargo management table, that there is no container storing cargo of the same item and the same loading amount as the cargo 6 to be replaced (step St155; NO), it determines that the replacement has failed and ends this processing flow.

[0131] When the monitoring device 9 determines, with reference to the cargo management table, that there is a container storing cargo of the same item and the same loading amount as the cargo 6 to be replaced (step St155; YES), it selects the destination of the container as a candidate for the destination of the cargo 6, and proceeds with the process to step St156. For the sake of explanation, assume that the monitoring device 9 determines, with reference to the cargo management table, that the container with the container ID "837498233" is loaded with the same amount of bananas as the bananas to be replaced. At this time, the monitoring device 9 selects "CakeMaker", which is the destination of the container with the container ID "837498233", as a candidate for the destination.

[0132] The monitoring device 9 refers to the cargo destination management table and determines whether the candidate destination satisfies the replacement condition (step St156). That is, the monitoring device 9 determines whether "CakeMaker", which is the destination of the container with container ID "837498233", satisfies the replacement condition. The replacement conditions are, for example, the following four conditions (A1) to (A4).

[0133] (A1) The maturity level acceptable to the candidate destination is equal to or higher than the maturity level of the banana to be replaced when the banana to be replaced is transported to the candidate destination at the predicted arrival date and time.

[0134] (A2) When the banana scheduled to be transported to the candidate destination is transported to the destination of the banana to be replaced, the maturity level of the banana scheduled to be transported to the candidate destination at the predicted arrival date and time is equal to or lower than the maturity level acceptable to the destination of the banana to be replaced.

[0135] (A3) When the banana to be replaced is transported to the candidate destination, the predicted arrival date and time is earlier than the date and time obtained by adding the number of days until the color of the banana to be replaced changes to the color of the banana acceptable to the candidate destination to the alarm detection date and time.

[0136] (A4) Among the candidate destinations that satisfy the three conditions (A1) to (A3), it is the destination with the earliest predicted arrival date and time when the banana scheduled to be transported to the candidate destination is transported to the destination of the banana to be replaced.

[0137] (A1) The condition is, for example, to avoid the over-ripening of the banana to be replaced to the extent that "CakeMaker" cannot accept the banana when the banana to be replaced is transported to "CakeMaker".

[0138] (A2) The condition is, for example, a condition to avoid the ripening of the bananas scheduled to be transported to "CakeMaker" and instead transported to "SuperX" to the extent that "SuperX" cannot accept the bananas when they arrive at "SuperX".

[0139] (A3) The condition is, for example, a condition to avoid the following situations.

[0140] At step St153, it was predicted that the number of days until the current color of the bananas to be swapped, half green, changes to green tip, which is the color acceptable to "CakeMaker", is 4 days. That is, since the alarm detection date is August 19, 2023, the bananas to be swapped will change to green tip on August 23, 2023. Even if the bananas to be swapped are transported to "CakeMaker", if the bananas arrive at "CakeMaker" after August 23, the ripening of the bananas may progress and "CakeMaker" may not be able to accept the bananas. In order to avoid such situations, when the predicted arrival date and time when the bananas to be swapped are transported to the candidate destinations is earlier than the date and time obtained by adding the number of days until the color of the bananas to be swapped changes to the color of the bananas acceptable to the candidate destinations to the alarm detection date and time. For simplicity of explanation, the time of the date and time is omitted.

[0141] (A4) The condition is a condition for narrowing down the candidate destinations to one when the monitoring device 9 selects a plurality of candidate destinations at step St155.

[0142] Note that the monitoring device 9 may use a known technique, for example, a pre-generated trained model, used at step St153 to determine whether the candidate destination satisfies the replacement condition.

[0143] When the monitoring device 9 determines that the candidate destination does not satisfy the replacement condition (step St156; NO), it determines that the replacement has failed and ends this processing flow.

[0144] When the monitoring device 9 determines that the candidate for the destination of transportation satisfies the replacement condition (step St156; YES), it modifies the cargo destination management table (step St157). Then, the monitoring device 9 determines that the replacement is successful and ends this processing flow. In the example of FIG. 23, the monitoring device 9 modifies the cargo destination management table so that the container ID "2847469" and the container ID "837498233" are replaced. Also, in the example of FIG. 23, the predicted arrival date and time is not modified. That is, each cargo is transported as originally predicted. Thus, even if the destination of cargo 6 is replaced, it is preferable that the change in the predicted arrival date and time of each cargo does not occur. Further, by modifying the cargo destination management table by the monitoring device 9, an instruction to replace the destination of cargo 6 may be transmitted to the user or a system for transportation, etc. Thus, when the alarm operation is executed, the monitoring device 9 analyzes the cause of the abnormality of cargo 6 and executes an action based on the alarm level and the cause of the abnormality. As an action, the monitoring device 9 executes, for example, a change in the destination of cargo 6, a reorder of cargo 6, or a notification of the cause of the abnormality. Here, the change in the destination of cargo 6 includes the replacement of the destination of cargo 6 with the destination of another cargo, the cancellation of the transportation of cargo 6, and the return of cargo 6 to the shipper.

[0145] (Modification Example of Embodiment 1) In the above-described Embodiment 1, an example in which the cargo 6 stored in the container 10 is a banana has been described. However, it is not limited to this, and the cargo 6 may be a motorcycle. In this case, the alarm level management table and the allowable level management table by destination are, for example, as shown in FIGS. 24 and 25, respectively.

[0146] FIG. 24 is a table diagram for explaining an alarm level management table corresponding to oil according to a modification of Embodiment 1. When the cargo 6 is a motorcycle, the alarm level is defined based on the presence or absence of the smell of the motorcycle oil. When there is no smell of oil, that is, when the oil is not leaking, the smell level indicating the degree of smell is 0, and the alarm level is 0. On the other hand, when there is a smell of oil, that is, when the oil is leaking, the smell level is 1, and the alarm level is 1.

[0147] FIG. 25 is a table diagram for explaining an example of an allowable level management table by destination according to a modification of Embodiment 1. When the cargo 6 is a motorcycle, when the destination type is an individual importer, only a smell level of 0 is allowed. On the other hand, when the destination type is other than an individual importer such as a dealer or a manufacturer, a smell level of 1 is also allowed. This is because a dealer or a manufacturer can repair it by themselves even if the oil is leaking.

[0148] FIG. 26 is a schematic diagram for explaining the time series of alarm operation and occurrence of abnormality according to a modification of Embodiment 1. The schematic diagram 160 shown in FIG. 26 includes a graph 161 representing humidity change, a graph 162 representing temperature change, a graph 163 representing acceleration change, and a graph 164 representing illuminance change. In addition, the schematic diagram 160 is marked with a date for explaining the time series of changes in the measurement data represented by each graph. For convenience of explanation, only the date is shown and the time is omitted.

[0149] The oil of the motorcycle being transported leaks at the time of date and time P2, and the odor sensor 11 measures the odor of the oil. The computing device 8 analyzes that there is an odor of oil based on the odor data. Also, the monitoring device 9 determines the alarm level as 1 and creates an alarm message. Then, the monitoring device 9 acquires various measurement data for a specific period before date and time P2 and performs a cause analysis. Through the cause analysis, the monitoring device 9 can determine that some impact was applied to the container 10 on August 8, and due to that impact, the oil of the motorcycle leaked.

[0150] (Summary of Embodiment 1) According to the description of Embodiment 1 above, at least the following technologies are disclosed. In the parentheses, the corresponding components, etc. in Embodiment 1 are exemplified, but it is not limited thereto.

[0151] (Technology 1) A quality management system (for example, quality management system 1) is a quality management system that manages the quality of goods (for example, goods 6) based on the odor inside a container (for example, container 10) in which the goods are stored. It includes at least an odor sensor (for example, odor sensor 11) that measures the odor inside the container and generates odor data (for example, odor data 40), a plurality of sensors that measure the state inside the container and generate measurement data, a computing device (for example, computing device 8) that analyzes the state of the goods based on the odor data, and a monitoring device (for example, monitoring device 9) that determines whether to execute an alarm operation to notify the state of the goods based on an alarm level corresponding to the analysis result of the state of the goods. When the alarm operation is executed, it analyzes the abnormal cause based on the measurement data and the threshold value corresponding to the measurement data, and executes an action based on the alarm level and the abnormal cause.

[0152] As a result, the quality control system can analyze the state of the goods based on the smell of the goods. In addition, the quality control system can execute an alarm operation based on the analyzed state of the goods. Thereby, users such as transportation-related persons can confirm that there may be an abnormality in the quality of the goods during transportation. Further, the quality control system can analyze the possible causes of abnormalities occurring in the goods based on the measurement data regarding the state inside the container. Also, the quality control system can execute actions for quality control based on the analyzed cause of the abnormality and the alarm level of the goods. In this way, by analyzing the state of the goods based on the smell, the quality of the goods can be managed without unpacking the container. Also, since it is not necessary to unpack the container, even when the number of containers is large, the quality of the goods can be managed by the quality control system.

[0153] (Technology 2) In the quality control system described in Technology 1, the quality control system further includes a pump (for example, Pump 3) that sends air into the smell sensor, and the pump alternately sends the air inside the container and fresh air to the smell sensor at a specified time interval, and the smell sensor alternately measures the smell of the goods and the smell of fresh air at a specified time interval.

[0154] Thereby, the smell components of the goods adhering to the smell sensor can be removed periodically, so that it is possible to avoid the deterioration of the accuracy of the smell measurement by the smell sensor.

[0155] (Technology 3) In the quality control system described in Technology 1 or 2, the monitoring device predicts the state of the first goods at the scheduled arrival date and time at the first destination of the first goods based on the scheduled arrival date and time, and determines whether the first goods can be transported to the first destination based on the prediction result and the allowable range of the state of the goods specified for each destination of the goods.

[0156] As a result, the monitoring device can predict the state of the first cargo at the scheduled arrival date and time at the first destination. Further, the monitoring device can determine that the first cargo cannot be transported to the first destination based on the allowable range of the state of the cargo defined for each destination.

[0157] (Technology 4) In the quality control system described in Technology 3, when the monitoring device determines that the first cargo cannot be transported to the first destination, it executes a change of the destination of the first cargo or a reorder.

[0158] As a result, when the monitoring device determines that the first cargo cannot be transported to the first destination based on the allowable range of the state of the cargo defined for each destination, for example, it can execute a replacement of the destination of the first cargo, a cancellation of the transport, a return, or a reorder.

[0159] (Technology 5) In the quality control system described in Technology 4, when the monitoring device changes the destination of the first cargo, it selects a candidate destination as the second destination, predicts the scheduled arrival date and time when the first cargo is transported to the second destination, predicts the state of the first cargo at the scheduled arrival date and time, determines that the first cargo can be transported to the second destination based on the prediction result and the allowable range, and predicts the scheduled arrival date and time when the second cargo scheduled to be transported to the second destination is transported to the first destination, predicts the state of the second cargo at the scheduled arrival date and time, and when it determines that the second cargo can be transported to the first destination based on the prediction result and the allowable range, it exchanges the first destination of the first cargo and the second destination of the second cargo.

[0160] As a result, the monitoring device can change the destination of the first cargo from the first destination to the second destination and exchange the destination of the second cargo from the second destination to the first destination.

[0161] (Technology 6) In the quality management system described in Technique 4, the monitoring device selects a plurality of candidate destinations, predicts the respective scheduled arrival dates and times when the goods scheduled for transportation are transported to the first destination for each of the plurality of candidates, and replaces the candidate with the earliest predicted scheduled arrival date and time among the plurality of candidates with the first destination.

[0162] As a result, the monitoring device can narrow down a plurality of candidate destinations for replacing the destination of the first goods to one.

[0163] (Technique 7) In the quality management system described in Technique 5, the item and the loading amount of the second goods are the same as those of the first goods, respectively.

[0164] As a result, the monitoring device can replace the destination of the container storing goods of the same item and the same loading amount.

[0165] (Technique 8) In the quality management system described in any one of Techniques 1 to 7, the action includes changing the destination of the goods.

[0166] As a result, the quality management system can change the destination of the goods for the quality control of the goods. That is, the quality management system can, for example, replace the destination of the goods, cancel the transportation, or return the goods.

[0167] (Technique 9) In the quality management system described in any one of Techniques 1 to 8, the action includes reordering the goods.

[0168] As a result, the quality management system can reorder the goods for the quality control of the goods.

[0169] (Technique 10) In the quality management system described in any one of Techniques 1 to 9, the action includes notifying the cause of the abnormality.

[0170] As a result, the quality control system can notify the persons involved in transportation and the like of the factors of abnormalities that may occur in the goods for the quality control of the goods.

[0171] (Technology 11) In the quality control system according to any one of Technologies 1 to 10, the plurality of sensors include a humidity sensor (for example, humidity sensor 13), a temperature sensor (temperature sensor 12), an acceleration sensor (acceleration sensor 14), an illuminance sensor (illuminance sensor 15), and a GPS sensor (GPS sensor 16), and the measurement data includes humidity data, temperature data, and illuminance data inside the container, and acceleration data and position information of the container.

[0172] As a result, the quality control system can analyze the factors of abnormalities that may occur in the goods based on the temperature data, humidity data, and illuminance data inside the container, and the acceleration data and position information of the container.

[0173] (Technology 12) In the quality control system according to any one of Technologies 1 to 11, the monitoring device acquires measurement data for a specific period based on the time when the alarm operation is executed.

[0174] As a result, the monitoring device can acquire the measurement data for the period in which an abnormality is considered to have occurred.

[0175] (Technology 13) A quality control method for controlling the quality of goods based on the odor inside the container in which the goods are stored measures the odor inside the container to generate odor data, measures the state inside the container to generate measurement data, analyzes the state of the goods based on the odor data, determines whether to execute an alarm operation for notifying the state of the goods based on the alarm level corresponding to the analysis result of the state of the goods, and when the alarm operation is executed, analyzes the abnormal factors based on the measurement data and the threshold value corresponding to the measurement data, and executes an action based on the alarm level and the abnormal factors.

[0176] As a result, the quality control method can achieve the same effect as that of Technique 1.

[0177] The functions of the above-described embodiments can also be realized by supplying a program and an application for realizing the functions of the above-described embodiments to a system or device using a network or a storage medium or the like, and having one or more processors in the computer of the system or device read and execute the program.

[0178] Also, the functions of the above-described embodiments may be realized by a circuit (for example, an Application Specific Integrated Circuit (hereinafter referred to as "ASIC") or an FPGA) that realizes one or more functions.

[0179] As described above, the embodiments according to the present disclosure have been described with reference to the drawings. Needless to say, the present disclosure is not limited to such examples. It is obvious that those skilled in the art can conceive of various modification examples, correction examples, substitution examples, addition examples, deletion examples, and equivalent examples within the scope described in the claims, and it is naturally understood that they also belong to the technical scope of the present disclosure. Further, within the scope not departing from the gist of the invention, the components in the above-described embodiments may be arbitrarily combined.

Industrial Applicability

[0180] The present disclosure is useful as a quality control system and a quality control method.

Explanation of Signs

[0181] 1 Quality control system 2 Sensor module 3 Pump 4 Power supply device 5 Communication device 6 Cargo 7 Network 8 Computing device 9 Monitoring device 10 Container Odor sensor Temperature sensor Humidity sensor Acceleration sensor Illuminance sensor GPS sensor Tractor Container ship Odor data Analysis telegram Alarm telegram Factor telegram

Claims

1. A quality management system for managing the quality of goods based on the odor inside a container storing the goods, comprising: at least an odor sensor that measures the odor inside the container to generate odor data, and a plurality of sensors that measure the state inside the container to generate measurement data; a computing device that analyzes the state of the goods based on the odor data; a monitoring device that determines whether to execute an alarm operation for notifying the state of the goods based on an alarm level corresponding to the analysis result of the state of the goods, and when the alarm operation is executed, analyzes an abnormal factor based on the measurement data and a threshold value corresponding to the measurement data, and executes an action based on the alarm level and the abnormal factor. A quality management system.

2. further comprising a pump that sends air into the odor sensor, wherein the pump alternately sends the air inside the container and fresh air to the odor sensor at a prescribed time interval, and the odor sensor alternately measures the odor of the goods and the odor of the fresh air at the prescribed time interval. The quality management system according to claim 1.

3. The monitoring device predicts the state of the first goods at the scheduled arrival date and time at the first destination of the first goods based on the scheduled arrival date and time of the first goods at the first destination, and determines whether the first goods can be transported to the first destination based on the prediction result and the allowable range of the state of the goods defined for each destination of the goods. The quality management system according to claim 1.

4. When the monitoring device determines that the first goods cannot be transported to the first destination, the monitoring device executes a change of the destination of the first goods or a reorder. The quality management system according to claim 3.

5. When the monitoring device changes the destination of the first goods, selects a candidate for the destination as a second destination, predicts the scheduled arrival date and time when the first goods are transported to the second destination, predicts the state of the first goods at the predicted arrival date and time, determines that the first goods can be transported to the second destination based on the prediction result and the allowable range, and predicts the scheduled arrival date and time when a second goods scheduled to be transported to the second destination is transported to the first destination, predicts the state of the second goods at the predicted arrival date and time, and determines that the second goods can be transported to the first destination based on the prediction result and the allowable range. Swapping the first destination of the first cargo and the second destination of the second cargo The quality management system according to claim 4

6. The monitoring device selects a plurality of candidate destinations, predicts the respective scheduled arrival dates and times when the cargo scheduled for transportation is transported to the first destination for each of the plurality of candidates, and among the plurality of candidates, the candidate with the earliest predicted scheduled arrival date and time is swapped with the first destination The quality management system according to claim 4

7. The item and the loading quantity of the second cargo are the same as the item and the loading quantity of the first cargo, respectively The quality management system according to claim 5

8. The action includes changing the destination of the cargo The quality management system according to claim 1

9. The action includes reordering the cargo The quality management system according to claim 1

10. The action includes notifying the abnormal factor The quality management system according to claim 1

11. The plurality of sensors include a humidity sensor, a temperature sensor, an acceleration sensor, an illuminance sensor, and a GPS sensor The measurement data includes humidity data, temperature data, and illuminance data inside the container, and acceleration data and position information of the container The quality management system according to claim 1

12. The monitoring device acquires the measurement data during a specific period based on the time when the alarm operation is executed The quality management system according to claim 1

13. A quality management method for managing the quality of cargo based on the odor inside the container in which the cargo is stored, comprising: Measuring the odor inside the container to generate odor data Measuring the state inside the container to generate measurement data Analyzing the state of the cargo based on the odor data Determining whether to execute an alarm operation for notifying the state of the cargo based on an alarm level corresponding to the analysis result of the state of the cargo. When the alarm operation is executed, analyzing the abnormal factor based on the measurement data and the threshold corresponding to the measurement data, and executing an action based on the alarm level and the abnormal factor Quality management method

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