阅读说明：本技术 冷藏货物监控 (Refrigerated cargo monitoring ) 是由 兰·梅纳赫姆·格林斯坦 于 2020-01-23 设计创作，主要内容包括：一种冷藏货柜实时监控方法和系统,包括：从多个传感器接收传感器信号,所述多个传感器包括温度传感器、湿度传感器及压力传感器当中的一者或多者；以及处理所述传感器信号,以识别与预定义模式对应的一个或多个信号特征,其中,所述预定义模式与一个或多个货柜状况相关联,所述货柜状况包括柜门打开、货柜载货物位及货物类型当中至少一者。(A real-time monitoring method and a system for a refrigeration container comprise the following steps: receiving sensor signals from a plurality of sensors, the plurality of sensors including one or more among a temperature sensor, a humidity sensor, and a pressure sensor; and processing the sensor signals to identify one or more signal features corresponding to a predefined pattern, wherein the predefined pattern is associated with one or more container conditions including at least one of a door opening, a container cargo level, and a cargo type.)

1. A method of real-time monitoring of a refrigeration container, the method being performed by a controller having one or more processors and having a storage device, the storage device including instructions that when executed by the one or more processors perform steps comprising:

receiving sensor signals from a plurality of sensors, the plurality of sensors including one or more among a temperature sensor, a humidity sensor, and a pressure sensor; and

the sensor signals are processed to identify one or more signal features corresponding to a predefined pattern associated with one or more container conditions including at least one of a cabinet door opening, a container cargo level, and a cargo type.

2. The method of claim 1, further comprising: providing a status notification indicative of the one or more container conditions.

3. The method of claim 2, wherein the status notification is provided at regular intervals.

4. The method of claim 2, wherein the status notification is an alarm provided when one of the container conditions is not within a predefined suitable range.

5. The method of claim 1, wherein the sensor signal comprises a signal indicative of refrigerated return air temperature, and the predefined pattern comprises a return air temperature signal pattern associated with the one or more container conditions.

6. The method of claim 1, wherein the sensor signal comprises a signal indicative of a refrigerated supply air temperature, and the predefined pattern comprises a supply air temperature signal pattern associated with the one or more container conditions.

7. The method of claim 1, wherein the sensor signal comprises a signal representative of an ambient temperature, and the predefined pattern comprises an ambient temperature signal pattern associated with the one or more container conditions.

8. The method of claim 1, wherein the sensor signal comprises a signal indicative of a refrigeration unit inlet air pressure, and the predefined pattern comprises a refrigeration unit inlet air pressure signal pattern associated with the one or more container conditions.

9. The method of claim 1, wherein the sensor signal comprises a signal indicative of a refrigeration unit discharge pressure, and the predefined pattern comprises a refrigeration unit discharge pressure signal pattern associated with the one or more container conditions.

10. The method of claim 1, wherein receiving the sensor signal comprises: receiving the sensor data from the same refrigeration unit that provided the data used to generate the predefined pattern.

11. The method of claim 1, wherein the sensor signals include data from one or more of temperature and pressure sensors at evaporator, condenser and compressor nodes in a refrigerant gas cycle.

12. The method of claim 1, further comprising determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of corresponding refrigerated cabinets during training mode operation under the condition that the opening and closing conditions of the cabinet door are known; identifying patterns in the training sensor signal; and associating the identified pattern with the opening and closing condition of the cabinet door.

13. The method of claim 12, wherein said training sensor signals further comprise door sensor signals simultaneously from a plurality of respective door sensors mounted on said plurality of respective refrigerated cabinets, said door open and closed condition being determined by said door sensor signals.

14. The method of claim 1, further comprising determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of respective refrigerated cabinets during a training mode of operation with known cargo level conditions; identifying patterns in the training sensor signal; and associating the identified pattern with the cargo level condition.

15. The method of claim 14, wherein said cargo level condition is determined by cargo level sensors mounted within said plurality of respective refrigerated cabinets.

16. The method of claim 1, further comprising determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of respective refrigerated cabinets during training mode operation with a known cargo type; identifying patterns in the training sensor signal; and associating the identified pattern with the cargo type.

17. The method of claim 1, wherein receiving the sensor signals from the plurality of sensors comprises: receiving, by the auxiliary processor of the refrigeration container, the sensor signal from a refrigeration controller of the refrigeration container.

18. The method of claim 1, wherein the association of the predefined pattern with the container condition of the container cargo level is an association with either a container being empty or a container being full.

19. The method of claim 1, wherein the association of the predefined pattern with the container cargo level condition is an association with a utilized percentage of container capacity.

20. A real-time monitoring system for a refrigerated container, comprising:

a plurality of sensors including one or more of a temperature sensor, a humidity sensor, and a pressure sensor; and

a controller comprising one or more processors and comprising a storage device comprising instructions that when executed by the one or more processors perform the steps of:

receiving sensor signals from the plurality of sensors; and

processing the sensor signal to identify one or more container conditions including at least one of a cabinet door opening, a container cargo level, and a cargo type, wherein processing the sensor signal includes identifying a signal characteristic corresponding to a predefined pattern associated with at least one of the one or more container conditions.

21. The system of claim 20, further comprising: providing a status notification indicative of the one or more container conditions.

22. The system of claim 21, wherein the status notification is provided at regular intervals.

23. The system of claim 21, wherein the status notification is an alarm provided when one of the container conditions is not within a predefined suitable range.

24. The system of claim 20, wherein the sensor signal comprises a signal indicative of refrigerated return air temperature, and the predefined pattern comprises a return air temperature signal pattern associated with the one or more container conditions.

25. The system of claim 20, wherein the sensor signal comprises a signal indicative of a refrigerated supply air temperature, and the predefined pattern comprises a supply air temperature signal pattern associated with the one or more container conditions.

26. The system of claim 20, wherein the sensor signal comprises a signal representative of an ambient temperature, and the predefined pattern comprises an ambient temperature signal pattern associated with the one or more container conditions.

27. The system of claim 20, wherein the sensor signal comprises a signal indicative of a refrigeration unit inlet air pressure, and the predefined pattern comprises a refrigeration unit inlet air pressure signal pattern associated with the one or more container conditions.

28. The system of claim 20, wherein the sensor signal comprises a signal indicative of a refrigeration unit discharge pressure, and the predefined pattern comprises a refrigeration unit discharge pressure signal pattern associated with the one or more container conditions.

29. The system of claim 20, wherein receiving the sensor signal comprises: receiving the sensor data from the same refrigeration unit that provided the data used to generate the predefined pattern.

30. The system of claim 20, wherein the sensor signals include data from one or more of temperature and pressure sensors at evaporator, condenser and compressor nodes in a refrigerant gas cycle.

31. The system of claim 20, further comprising determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of corresponding refrigerated cabinets during training mode operation under the condition that the opening and closing conditions of the cabinet door are known; identifying patterns in the training sensor signal; and associating the identified pattern with the opening and closing condition of the cabinet door.

32. The system of claim 31, wherein said training sensor signals further comprise door sensor signals simultaneously from a plurality of respective door sensors mounted on said plurality of respective refrigerated cabinets, said door open and closed condition being determined by said door sensor signals.

33. The system of claim 20, further comprising determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of respective refrigerated cabinets during a training mode of operation with known cargo level conditions; identifying patterns in the training sensor signal; and associating the identified pattern with the cargo level condition.

34. The system of claim 33, wherein said cargo level condition is determined by cargo level sensors mounted within said plurality of respective refrigerated cabinets.

35. The system of claim 20, further comprising determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of respective refrigerated cabinets during training mode operation with a known cargo type; identifying patterns in the training sensor signal; and associating the identified pattern with the cargo type.

36. The system of claim 20, wherein receiving the sensor signals from the plurality of sensors comprises: receiving, by the auxiliary processor of the refrigeration container, the sensor signal from a refrigeration controller of the refrigeration container.

37. The system of claim 20, wherein the association of the predefined pattern with the container condition of the container cargo level is an association with either a container being empty or a container being full.

38. The system of claim 20, wherein the association of the predefined pattern with the container cargo level condition is an association with a utilized percentage of container capacity.

Technical Field

The present invention relates generally to refrigeration systems and, more particularly, to monitoring the refrigeration status of cargo during transport.

Background

There are a variety of refrigerated bins that carry refrigerated goods around the world. Hereinafter, the term "refrigerated cabinet" (or "refrigerated container") refers to any cabinet used for transporting refrigerated goods, such as standard size intermodal containers, truck boxes, air cargo containers, and the like. The term "refrigeration unit" refers to the elements, including refrigeration compressors, valves and piping, used to perform the freezing function within a refrigerated cabinet or the like.

Nowadays, the transport situation of cargo (whether refrigerated or not) during transport is increasingly tracked by the transmission of identification data and GPS positioning information. Certain conditions may be measured, among other things, by standard sensors of the refrigerated cabinet, such as temperature and humidity sensors built into the "refrigeration unit" of such refrigerated cabinets. Other conditions may require custom mounting of non-standard sensors within the refrigerated cabinet. Solving this drawback of common refrigerated cabinets would benefit many parties involved in the transportation of goods.

Disclosure of Invention

Embodiments of the present invention provide apparatus, methods and systems for monitoring refrigerated containers, particularly for monitoring the condition of refrigerated containers, by: acquiring a data signal from a refrigeration controller; and processing these signals to predict indirectly measured container conditions such as cargo level (air space), cargo type and possible problems such as door opening. Some conditions that are not directly measured may be measured by sensors, but such sensors are cabinet door sensors and the like that are not typically mounted on refrigerated cabinets. By alerting drivers or crews to the problematic container condition, the problem can be corrected before it causes damage. Furthermore, early warning of such conditions may also be of great value to other stakeholders of the transportation process.

Accordingly, embodiments of the present invention provide a method for real-time monitoring of a refrigerated container, executed by a controller having one or more processors and having a storage device, the storage device including instructions which, when executed by the one or more processors, perform the steps of: receiving sensor signals from a plurality of sensors, the plurality of sensors including one or more among a temperature sensor, a humidity sensor, and a pressure sensor; and processing the sensor signals to identify one or more signal features corresponding to a predefined pattern, wherein the predefined pattern is associated with one or more container conditions including at least one of a door opening, a container cargo level, and a cargo type.

In some embodiments, the steps further comprise: providing a status notification indicative of the one or more container conditions. The status notification may be provided at regular intervals or be an alarm provided when one of the container conditions is not within a predefined suitable range.

The sensor signals may include signals indicative of refrigerated return air temperature, and the predefined pattern may include a return air temperature signal pattern associated with the one or more container conditions. The sensor signal may comprise a signal indicative of a refrigerated supply air temperature, and the predefined pattern may comprise a supply air temperature signal pattern associated with the one or more container conditions. The sensor signals may comprise signals indicative of ambient air temperature outside the container, and the predefined pattern may comprise a pattern of ambient air temperature signals associated with the one or more container conditions.

The sensor signal may comprise a signal indicative of a refrigeration unit inlet air pressure, and the predefined pattern may comprise a refrigeration unit inlet air pressure signal pattern associated with the one or more container conditions. The sensor signal may comprise a signal indicative of a refrigeration unit discharge pressure, and the predefined pattern may comprise a refrigeration unit discharge pressure signal pattern associated with the one or more container conditions.

In some embodiments, receiving the sensor signal may include: receiving the sensor data from the same refrigeration unit that provided the data used to generate the predefined pattern. The sensor signals may include data from one or more of temperature and pressure sensors at evaporator, condenser and compressor nodes in the refrigerant gas cycle.

The method may further comprise: determining the predefined pattern from training sensor signals from a plurality of refrigeration controllers on a plurality of respective refrigerated cabinets during training mode operation with known cabinet door opening and closing conditions; identifying patterns in the training sensor signal; and associating the identified pattern with the opening and closing condition of the cabinet door. The training sensor signals may further comprise cabinet door sensor signals simultaneously from a plurality of respective cabinet door sensors mounted on the plurality of respective refrigerated cabinets, and the cabinet door opening and closing condition may be determined from the cabinet door sensor signals.

The method may further comprise: determining the predefined pattern by: receiving training sensor signals from a plurality of refrigeration controllers on a plurality of respective refrigerated cabinets during a training mode of operation with known cargo level conditions; identifying patterns in the training sensor signal; and associating the identified pattern with the cargo level condition. The cargo level condition may be determined by cargo level sensors mounted within the plurality of respective refrigerated cabinets.

Additionally or alternatively, the method may comprise: in the training mode operation, the identified pattern is associated with the cargo type.

Receiving the sensor signals from the plurality of sensors may include: receiving, by the auxiliary processor of the refrigeration container, the sensor signal from a refrigeration controller of the refrigeration container.

The association of the predefined pattern with the container condition of the container cargo level may be an association with either a container being empty or a container being full. Additionally or alternatively, the association may be an association with a utilized percentage of the container capacity.

In other embodiments, there is provided a refrigeration container real-time monitoring system, the system comprising: a plurality of sensors, such as temperature sensors, humidity sensors, and pressure sensors; and a controller, which may include one or more processors and a storage device, the storage device including instructions that when executed by the one or more processors perform the monitoring step. These steps may include: receiving sensor signals from the plurality of sensors; and processing the sensor signal to identify one or more container conditions including at least one of a cabinet door opening, a container cargo level, and a cargo type, wherein processing the sensor signal includes identifying a signal characteristic corresponding to a predefined pattern associated with at least one of the one or more container conditions.

Drawings

For a better understanding of various embodiments of the present invention, and to show how the same may be carried into effect, reference will now be made, by way of example, to the accompanying drawings. There are shown in the drawings details of construction which will enable a basic understanding of the invention, and it will be apparent to those skilled in the art how the several forms of the invention may be embodied in practice, when the description is taken in conjunction with the accompanying drawings. In the drawings:

FIG. 1 is a schematic block diagram of a refrigerated cabinet monitoring system according to some embodiments of the present invention;

FIGS. 2-11 are graphs of refrigerator compartment sensor signals collected during monitoring while a refrigerator is in transit according to some embodiments of the present invention;

figure 12 is a flow chart of a method for remotely monitoring a refrigerated case according to some embodiments of the present invention.

Detailed Description

It is to be understood that the invention and its applications are not limited to the methods and systems described below, nor to the arrangements of the components set forth or illustrated in the drawings, but are applicable to other embodiments that may be practiced or carried out in various ways.

The embodiment of the invention provides a monitoring method and a monitoring system for a refrigerated cabinet. Some types of suitable refrigeration units are listed in the appendix hereof.

FIG. 1 is a schematic block diagram of a refrigerated cabinet 24 monitoring system 20 according to some embodiments of the present invention. The refrigerated cabinet 24 includes a refrigeration controller 30. The refrigeration controller 30 includes a control module 32 that controls the operation of the compressor, fans, and valves of the refrigeration unit 34. The control module controls the refrigeration unit 34 according to preset parameters, such as a preset target temperature range, and according to input signals received by the data acquisition module 36 from a plurality of standard refrigeration compartment sensors 40 (hereinafter "standard sensors"). The standard sensor 40 is a sensor that provides an input signal for controlling the refrigeration unit, typically by being mounted within the refrigerated cabinet. Such sensors include sensors for measuring container air temperature (e.g., supply and return air temperature) and the temperature and pressure of the refrigerant gas at various nodes of the refrigeration cycle (e.g., evaporator, condenser, and compressor nodes). Wherein pressure readings (i.e., signals) may be received from an intake pressure sensor (upstream of the compressor) and a discharge pressure sensor (compressor output) of the refrigeration unit. The standard sensor signal may include humidity readings from one or more humidity sensors. Other standard sensors are also listed in the appendix below. Most standard sensors provide signals in a continuous manner or at regular time intervals, which can be down to seconds, up to minutes. The signals from the standard sensors may be analog signals that may be converted to digital signals by data acquisition module 36.

In an embodiment of the present invention, the refrigeration controller 30 also includes a pattern classifier module 44 for identifying non-sensor condition related patterns as described below. The pattern classifier module 44 is generally used to capture and process data from the refrigeration controller input drive, such as the data acquisition module 36. In other embodiments, the pattern classifier module 44 may be executed by an auxiliary processor 46, which auxiliary processor 46 may be a processor mounted within the cabinet outside of the refrigeration controller (optionally, the auxiliary processor is separate from the refrigeration controller and is represented by a dashed line). The standard refrigeration controller may be a dedicated closed system, eliminating the need to retrofit the standard refrigeration controller by installing an auxiliary processor. When executed by the auxiliary processor, the pattern classifier may receive signal data provided by the refrigeration controller via wireless or wired means as known in the art, such as a bluetooth or RS-232 connection. In some embodiments, the auxiliary processor also functions as a telematics processor 48 to further provide telematics functionality, such as sending wireless communications via satellite and/or GSM connections in the manner described below.

The pattern classifier module 44 may operate in either of two modes, a training mode and an operating mode, and these two modes are typically performed by respective training and operating software and/or hardware-based programs (which may be executed in an auxiliary processor as described above). Before the operation mode begins, an initial training mode may be performed. In the training mode, the pattern classifier module 44 receives standard sensor signals from the data acquisition module 36 of the refrigeration controller 30. Hereinafter, the "standard sensor signal" received in the training mode may be referred to as a "training sensor signal".

The pattern classifier module 44 may also receive signals from "non-standard" sensors 50 that are not typically installed with refrigeration units. Such non-standard sensors 50 may be installed specifically for the training mode and may include cabinet door sensors that sense when the cabinet doors of the refrigerated cabinet are open or closed, and cargo level sensors, such as optical or ultrasonic level sensors, that sense the cargo container loading level. Alternatively or additionally, the non-standard signal may be input into the pattern classifier module 44 by an operator or other manual input method, wherein the operator may record the cabinet door open condition, cargo level, cargo type, and the like container condition in an accessible storage device such as a log file 52.

After collecting the standard and non-standard signals in the training mode, the mode classifier module 44 classifies the standard signals according to the non-standard sensors 50 or the container condition indicated by the manual input. Specifically, the patterns of standard sensor signals collected during training mode operation are classified according to a number of container conditions as follows: door open condition (whether the door is open or not); cargo level of the container (expressed in percentages, where zero indicates that the container is empty and 100% indicates that the container is full); and the type of cargo (e.g., frozen food, liquid or insulated boxes). The cargo level and cargo type may also be manually entered at the beginning of a delivery trip, such as when a manifest is recorded.

In training mode operation, the pattern classifier module 44 identifies patterns of recurring characteristics (or "features") of the standard sensor signal, such as patterns related to data values (in degrees or psi, corresponding units of measure), range, rate of change, frequency and variability of recurring patterns. Furthermore, these patterns (which may also be referred to as "signature features") are also associated with the container conditions described above to generate a rule base 55 consisting of pattern-based rules.

The rule base includes associations between standard sensor patterns and container conditions in the training process that coexist with these patterns. The pattern-based rules specify "predefined patterns" of standard sensor signals that predict container conditions measured while training, i.e., the state of the container door, the level of cargo, and the type of cargo.

The rule base may be generated by the pattern classifier module 44 in real time during the training process, or the pattern classifier module 44 may send the sensor data to an external computing system before it is generated by the external computing system. Alternatively, the data collected by the pattern classifier module 44 may be stored in a corresponding storage device of the log file 52 and may be processed by the training algorithm (e.g., by an external computing system) after the shipment of the cargo is completed. The log file 52 may be located within a storage device of the refrigeration controller 30 or the telematics unit 48, or within other accessible memory in the refrigerated case 24.

The sensor signals may differ due to differences between containers in hardware aging and wear that have an effect on the container housing and refrigeration unit. In some embodiments, as described below, the sensor signals used for training may be collected and processed separately for each container to generate a rule base for the particular container. Alternatively, the signals of multiple containers may be statistically combined, for example by a pattern recognition (e.g., machine learning) algorithm, to generate a rule base with a high enough versatility to be able to recognize patterns in the data of multiple containers.

Standard sensor signals that may be correlated to container conditions include refrigeration unit inlet pressure, outlet pressure, gas temperature, compressor on-off status, container power on-off status, supply return air and ambient temperature, and interior humidity. In addition, other data such as air temperature set points and humidity set points provided by the cold room controller can also be correlated to container conditions. In addition, other sensor signals listed in the appendix can also be utilized to improve prediction accuracy. In some embodiments, the signal pattern indicative of the condition of the container may include a pattern of multiple sensors, such as a pattern of temperature, pressure, and humidity sensors used in combination with one another.

The above rule base is then applied to the operation mode (when non-standard sensors are not installed in the container) to predict the container condition in terms of the state of the doors, the level of cargo and the type of cargo. By correlating the container condition during training with patterns identified from the plurality of sensor signals, the accuracy of the prediction when container condition is subsequently predicted in the operational mode may be improved.

In some embodiments, the container is operated in an "operation mode" after the training data processing for a certain refrigerated cabinet is completed. Alternatively, a plurality of refrigerated cabinets may be run in a training mode and the results combined for use in the operating mode of other containers.

In the operational mode, the pattern classifier module 44 collects real-time data from standard refrigeration sensors and extracts patterns from the sensor signals corresponding to the patterns determined during the training process and defined within the rule base 55. The rules within the rule base are defined before the operation mode begins. That is, the rules are "predefined" rules. In particular, such predefined patterns in the present embodiments correspond to one or more of the following: 1) whether a cabinet door of the container is opened or not; 2) cargo level; 3) the type of cargo. The sensor signal acquisition and pattern recognition process may continue throughout the entire cargo shipment from loading to unloading. The process may also be extended indefinitely, incorporating freezer tracking between cargo allocations. Furthermore, the operation mode may also be used to improve the mode determined in the training mode, i.e. the predefined mode in the rule base may be improved by processing sensor signals collected at the same time when the container condition is determined to be an exact known condition in the operation mode.

In the operational mode, the mode classifier module 44 may send the real-time predicted container conditions and other data related to the container status to electronic addresses (e.g., e-mails or other forms of electronic messages) of multiple parties or "stakeholders" 60 that are staked to the successful benefit of the transport. These stakeholders may include:

transport company (land, air and/or sea company)

Container manufacturer

Import and export quotient

Owner of goods

Port and warehouse terminal operators

Transporter

Insurance company

Government authorities (e.g. FDA)

Inland transporters

Technical and maintenance personnel

The situation of the container in the transportation process can be tracked by the stakes with different interests due to various reasons. For example, by tracking the opening of the doors, not only can the cargo be prevented from spoiling, but it can also help assess the carrier's responsibility for spoiling. If the door is opened at an early stage of the transportation process, for example, when the difference between the inside and outside temperatures is not too large, the door can be closed to prevent the goods from being spoiled.

Tracking whether a container is loaded with cargo and the percentage of the load may be important for reasons including the discovery of theft and determining whether power needs to be supplied to the container. Tracking of cargo type may be important for theft and smuggling discovery. Tracking by identifying patterns in the standard sensor signal avoids the need for non-standard sensors for installation and corresponding maintenance costs and reduces the need for human monitoring of container conditions.

The transmission may be sent to the stakeholders 60 via existing transmission techniques such as the telematics unit 48. The telematics unit 48 can be configured to employ satellite communications when the cargo is being shipped by sea and GSM communications when shipped by land. In some embodiments, the telematics unit 48 can also function as an auxiliary processor as described above, such that the pattern classifier module 44 is executed by the telematics unit, rather than the refrigeration controller 30.

If no real-time communication means are available, the status data (i.e., the results of the prediction of the container condition) and the raw data (sensor signal data) may be stored in the corresponding log file 52 of the pattern classifier module 44. The log file may also be stored in the telematics device 48.

The refrigerated case may include the following items (all of the items shown below are trademarks of the respective companies).

·Carrier Transicold：EliteLINE，ThinLINE，PrimeLINE，NaturaLINE，PrimeLINE ONE

·THERMO KING：Magnum

·DAIKIN：LXE，ZESTIA

·MAERSK CONTAINER INDUSTRY-STAR COOL：STAR COOL

·KLINGE TEMPERATURE CONTROL：Military Air Conditioning Unit，Military Refrigerated A-Frame Container，Multi-Temperature&Single Temperature Zone Military Refrigerated Containers，Blast Freezer Container，Deep Freezer Container，Explosion-Proof Refrigerated Container，Dual Refrigerated&Integral Generated Set

Light Commercial Vehicles (LCVs), including trucks, vans and other vehicles, may have a fixed refrigerated cabinet and may include the following items (all of the products shown below are trademarks of the respective companies).

·Carrier Transicold：Neos，Pulsor，Vanta，Viento，Xarios，Zephyr，Iceland，Supra，Syberia

·THERMO KING：B-100，V-220，V-320，V-520，V-800，T-580，T-680，T-880，T-1080，T-1280

·TKT：TKT-20，TKT-40，TKT-50，TKT-60，TKT-200，TKT-300，TKT-400，TKT-600，TKT-800，TKT-900，TKT-1000，TKT-1200

HWA SUNG: HWA SUNG series

·GUCHEN THERMO：TR，TRF，C

A truck trailer with a refrigerated compartment for transporting perishable goods may include the following (all products shown below are trademarks of the respective companies).

·Carrier Transicold：Vector

·THERMO KING：PRECEDENT S-600，PRECEDENT S-700，PRECEDENT C-600，PRECEDENT S-610M，PRECEDENT C-610M，PRECEDENT S-610DE，Heat King

The goods/goods packaging or box cabinet containing the active refrigeration compartment cooling or heating system may include the following items of merchandise (all shown below are trademarks of the respective companies).

·ENVITOTAINER：RAP，RKN

KOREAN AIR: all refrigerating series products

PHARMA PORT: all refrigerating series products

SAFE: all refrigerating series products

AIR carmo: all refrigerating series products

Fig. 2-11 are graphs of refrigerator compartment sensor signals collected during monitoring while a refrigerator is in transit according to some embodiments of the present invention. The figures show the types of standard sensor signal patterns that may be associated with container conditions not measured by standard sensors (i.e., door open and closed states, cargo level, and cargo type). As described above, the standard sensor signals are acquired in both training and operational modes. During the training process, the container condition can be determined either by non-standard sensors or manually by the operator.

Figure 2 is a graph of the discharge pressure of the refrigeration unit at different ambient temperatures (i.e. the outside temperature of the container as measured by a "standard" outside temperature sensor connected to the freezer controller). The graph shows three exhaust pressure signals, each measured at different ambient temperature conditions. The three environmental temperature conditions were 10 deg.C, 24 deg.C and 33 deg.C, respectively. The pattern of the exhaust pressure signal varies according to the ambient temperature. For example, the pressure level at 33 deg.C is approximately 50PSI, 100PSI at 24 deg.C, and 150PSI at 10 deg.C. The pressure rises as the refrigeration compartment compressor continues to operate, as indicated by the stepped intervals in the discharge pressure signal. In addition, the compressor transition frequency is also affected by the ambient temperature and, as shown, the frequency increases with increasing temperature. When extracting the mode corresponding to the container condition (cabinet door state, cargo state and cargo type), the influence of the ambient temperature in the signal must be filtered out first. The measurements in fig. 2 and other figures described below are for the case of frozen corn as the cargo. Measurements may also be made based on the type of other types of refrigerated goods being transported to improve the accuracy of the correlation.

Fig. 3A and 3B are graphs of the discharge pressure of the refrigeration unit in the presence and absence of cargo, respectively. The temperature set point at signal acquisition is shown as 20 c and the ambient temperature is similar. As shown, the compressor is operated with cargo for a longer interval than without cargo, and the pressure fluctuation rises. In addition, the pressure rises to about 150PSI in the presence of cargo and to about 190PSI in the absence of cargo. The frequency of the compression cycle (from the start of this compression to the start of the next compression) is about 1000 seconds in the case of cargo and about 900 seconds in the case of no cargo. These patterns may be converted into rules in a rule base to associate the pressure signal with the cargo level.

Fig. 4A and 4B are graphs of the discharge pressure of the refrigeration unit in the presence and absence of cargo, respectively. The temperature set point at which the exhaust pressure signal is collected is shown as-18 deg.C, and the ambient temperature is similar. As shown, the compressor was operated with cargo for a longer period of 400 seconds compared to 150 seconds without cargo. Furthermore, the pressure fluctuations during operation of the compressor are more pronounced (i.e., more variable) in the presence of cargo. The pressure rises to about 130PSI with cargo and to about 190PSI without cargo. The frequency of the compression cycle (from the start of this compression to the start of the next compression) is about 900 seconds in the case of cargo and about 1000 seconds in the case of no cargo. These patterns may be converted into rules in a rule base to associate the pressure signal with the cargo level.

Fig. 5A and 5B are graphs of the intake pressure of the refrigeration unit in the presence and absence of cargo, respectively. The temperature set point at which the intake pressure signal is collected is illustrated as-18 deg.C, and the ambient temperature is similar. As shown, the compressor has a longer operation interval with cargo and intake pressure fluctuation is reduced, compared to the case without cargo. The intake pressure drops from about 70PSI, both with and without cargo, and to about 20PSI with cargo. However, when the compressor is not operated, the rising speed of the intake air pressure in the case of cargo is much higher than that in the case of no cargo (the rising time in the case of cargo is about 300 seconds, and the rising time in the case of no cargo is about 700 seconds). The intake pressure compression cycle (from the start of this compression to the start of the next compression) has a frequency of about 900 seconds in the case of cargo and about 700 seconds in the case of cargo. These patterns may be converted into rules in a rule base to associate the pressure signal with the cargo level.

Fig. 6A and 6B are graphs of the intake pressure of the refrigeration unit in the presence and absence of cargo, respectively. The temperature set point at which the intake pressure signal is collected is illustrated as-18 deg.C, and the ambient temperature is similar. As shown, the compressor has a longer operation interval with cargo and intake pressure fluctuation is reduced, compared to the case without cargo. The intake pressure drops from about 70PSI, both with and without cargo, and to about 20PSI with cargo. However, when the compressor is not operated, the rising speed of the intake air pressure in the case of cargo is much higher than that in the case of no cargo (the rising time in the case of cargo is about 300 seconds, and the rising time in the case of no cargo is about 700 seconds). The intake pressure compression cycle (from the start of this compression to the start of the next compression) has a frequency of about 900 seconds in the case of cargo and about 700 seconds in the case of cargo. These patterns may be converted into rules in a rule base to associate the pressure signal with the cargo level.

Fig. 7A and 7B are graphs of the supply and return air temperatures of the refrigeration unit with and without cargo, respectively. The compressor is not operated at a temperature in the range of about 20 c to about 21 c, both with and without cargo. When the compressor is in operation, the supply air temperature in the case of cargo drops almost to 15 ℃ and only to about 17 ℃ in the case of no cargo. The frequency of the compression cycle (from the start of this compression to the start of the next compression) is about 950 seconds in the case of cargo and about 800 seconds in the case of no cargo. These patterns may be converted into rules in a rule base to associate the pressure signal with the cargo level.

FIG. 8 is a diagram showing signals of the air intake and exhaust pressures of the refrigeration unit in the case where the cabinet door of the container is opened. The two signals are measured for a fully loaded condition. In the case of an open cabinet door, the compressor has a longer operating time per cycle and the pressure signal fluctuates more than in the case of a closed cabinet door (fig. 2 to 7). The fluctuations in the discharge pressure signal exhibit a peak-to-peak jitter of about 50PSI and a frequency of about 50 seconds per jitter period. The discharge pressure reaches 270PSI at the peak point of compressor operation and drops to less than 130PSI after compressor shutdown. The frequency of the compression period (from the start of this compression to the start of the next compression) is about 1500 seconds. These patterns may also be converted into rules in a rule base to correlate pressure signals with the open and closed states of the cabinet door.

FIG. 9 is a humidity diagram of the refrigeration unit with the cabinet door open. This figure is measured with the container powered off (i.e. the compressor is not automatically running) and the container loaded with cargo. After the cabinet door is opened, the humidity is maintained for about 600 seconds and then begins to decrease, from about 97% to about 92% within a period of 600 seconds. Likewise, these patterns may be formulated as rules to relate pressure signals to cabinet door open states.

FIGS. 10A and 10B are graphs of return (i.e., inside) and ambient (i.e., outside) air temperature for a container in a de-energized (i.e., compressor is not operating) condition. In both fig. 10A and 10B, the power supply is turned off at time zero. In fig. 10B, the cabinet door is open at the point of 100 seconds (grey line), but in fig. 10A the cabinet door remains closed all the time. In both figures, the return air temperature starts at 20 ℃. As shown in fig. 10A, the temperature inside the container continues to rise to the initial level of the ambient temperature, although the ambient temperature slowly drops. The return air temperature then levels off and begins to drop (not shown). The reason for the delay between the change in the external temperature and the change in the internal temperature is the insulation effect of the container (due to the closing of the door). In contrast, as shown in fig. 10B, in the case where the cabinet door is opened, the inside temperature is rapidly changed to match the outside temperature, or more precisely, to match the linear average of the outside temperature. That is, the fluctuation of the return air temperature is not as rapid as the fluctuation of the ambient temperature, and thus represents the average value of the ambient temperature in the fluctuation. These patterns may also be converted into rules in a rule base to correlate pressure signals with cabinet door status.

FIG. 11 is a diagram showing the exhaust pressure of the refrigeration unit with the door of the container opened and the power supply turned off. In the figure, the first vertical line mark indicates that the power supply is turned off, and the second vertical line mark indicates that the cabinet door of the container is opened. About 400 seconds after the door is opened, the exhaust pressure is reduced by about 10PSI, providing a pattern correlation between the additional exhaust pressure signal and the container door opening.

Figure 12 is a flow chart of a method 200 for remote monitoring of a refrigerated case according to some embodiments of the present invention. The first phase of the method comprises: the pattern recognition system is trained to recognize patterns from data of "standard" installed sensors associated with container conditions monitored in non-standard metrics (step 202). Such container conditions may include the status of the doors of the cabinet (open or closed), the level of the cargo (whether there is cargo, if any, the loading condition of the container), and the type of cargo (e.g., product, consumer, fuel, etc.) being loaded. In step 204, a standard sensor signal provided by a common refrigeration controller is obtained. Such sensor signals include refrigeration unit pressure, air temperature, humidity, compressor on-off status, and power on-off, and may include signals from other sensors listed in the appendix. Meanwhile, in step 206, non-standard indicators of the container condition are also obtained through manual input by an operator or automatic acquisition by a "non-standard" sensor. The training phase comprises the following two steps: extracting patterns from the sensor signals (step 208); and generating rules for subsequently classifying the signals based on the identified patterns (step 210). In addition to this, the rules may be annotated to indicate that an alarm should be issued when certain conditions are detected, such as when it is determined (i.e., predicted through the association) that the cabinet door is open.

The second phase of remote monitoring is the operational mode 212, which includes: the rules of step 210 are applied to detect patterns in the standard sensor signal (step 220). In step 222, in the operational mode, standard sensor signals are acquired. When certain container conditions are detected, an alarm may be issued. The alarm may include a determination that a cabinet door is open, that a container is empty, or that a change in cargo type or suspicion exists. In step 230, such an alert may be issued. Further, periodic status updates distributed at regular intervals (e.g., once every 5 minutes) can be sent to parties related to the predicted container condition. In step 226, alarms and status updates may also be logged as described above.

The computational aspects of the method 200 and system 20 may be performed in digital electronic circuitry, computer hardware, embedded firmware, software, or a combination thereof. All or part of the method can be implemented as a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by, or to control the operation of, a data processing apparatus, e.g., a programmable processor or a computer, or to be deployed in a manner to be executed by the same site or a plurality of distributed sites, including cloud configurations. The storage device may further include a plurality of distributed storage units, which may include one or more storage media. A computing system for implementing the above-described system may have one or more processors and one or more network interface modules. Each processor may be configured as a multi-processing system or a distributed processing system. The network interface module may control the transmission and reception of data packets in the network.

It should be understood that the scope of the present invention includes variations and modifications of the present invention that may occur to persons skilled in the art upon reading the foregoing description and that are not disclosed in the prior art.

Appendix

Refrigerator controller data

The above-described refrigerator controller may provide the output content including sensor data listed below. Since each container may have different thermodynamic properties, as described above, data may be collected separately for each container to train the system to identify the container condition. In addition, the controller may maintain a unique identification number for the containers (as is common with multi-modal intermodal containers) so that each container can be individually monitored and tracked. The refrigeration controller typically provides a series of data:

1. the container identification number is as follows: the identification number of each container (e.g., a refrigerated case or "asset") is a unique number that can be used to track monitored characteristics

2. Temperature set point: target temperature in a container

3. Air supply temperature: temperature of air leaving a refrigeration system

4. Temperature of the auxiliary air supply detector: auxiliary measurement of supply air temperature

5. Air return temperature: temperature of air drawn into refrigeration vents in containers

6. Temperature of the auxiliary return air detector: auxiliary measurement

7. Humidity: current humidity in the container

8. Humidity set point

9. Ambient temperature: external temperature of container

10. Vent cubic meters per hour (CMH): a measure of the degree of opening of a ventilation opening of a container, which represents how much fresh air enters the ventilation opening per hour

11. In-range state: indicating whether the temperature of the cargo is within the target range

PTI (pre-processing check) state: indicates whether or not the check is performed

13. Defrosting state: if the refrigerator machinery is in the defrost state, the device disregards the data and does not use it for any calculation

14. Vent percentage: the percentage of opening of the vent has an effect on humidity

15. In-range state: indicating whether the temperature of the cargo is within the target range

PTI (pre-processing check) state: indicates whether or not the check is performed

17. Defrosting state: if the refrigerator machinery is in the defrost state, the device disregards the data and does not use it for any calculation

18. Saving mode

19. System mode

20. Control mode

21. Temperature sensing ball mode

22. The air inlet pressure: compressor upstream pressure

23. The inlet air temperature: compressor upstream temperature

24. Exhaust pressure: compressor downstream pressure

25. Exhaust temperature: compressor downstream temperature

26. Condenser pressure: condenser downstream pressure

27. Metering by a compressor: indicating the number of compressor operations

28. Last successful PTI: PTI time

29.USDA 1

30.USDA 2

31.USDA 3

32. Temperature of goods

33.O2

34.CO2

35. Target temperature range: preset of

36. Evaporator 1 coil temperature

37. Condenser coil temperature

38. Compressor coil temperature

39. Compressor 2 coil temperature

40. Ambient air temperature (. degree. C.)

41. Temperature of cargo 1

42. Temperature of cargo 2

43. Temperature of goods 3

44. Temperature of cargo 4

45. Relative humidity

46.AVL Fp1 CMH

47. Relative inlet pressure (bar)

48. Relative exhaust pressure (bar)

49. Line voltage (V)

50. Line frequency Fp1(Hz)

51. Current consumption (A)

52. Current consumption (A)

53. Current consumption (A)

Co2 reading (%)

O2 reading (%)

56. When alarming, the alarm device is used for: if there is an alarm, no analysis

57. Cargo capacity Fp1

58.DI-LPCO

59.DI-HPCO

DO-fuel saving valve

61. DO-digital valve

62. Power DO-heater:

63. power DO compressor

64. Power DO-evaporator high

65. Power DO-evaporator low

66. Power DO-condenser fan

67. The container identification number is as follows: character indicating container identification number

68. Setpoint 1

69. Setpoint 2

70. The current operating mode: can be used with PTI

71.O2 setpoint

CO2 setpoint

73.CA mode

74.USDA 1

75.USDA 2

76.USDA 3

77.USDA 4/cargo

78. High pressure of refrigerant:

79. low pressure of refrigerant:

80. mode of operation

81. Controller hardware version

82. Controller software version

83. Controller identification number

QUEST mode 84

85. The compressor is not normally operated: if not, no analysis

86. Return/supply air

87. An alarm sensor: if there is an alarm, no analysis

88. Exhaust gas sensor

89. Ambient temperature sensor

90. Evaporator output sensor

91. Humidity sensor

92. High-voltage sensor

93. Low-voltage sensor

94. Voltage of

95. Total current: if the range is exceeded, it indicates that there is a problem

96. Compressor current

97. Time and date

98.GPS time and date: time stamping applied to the location and time of all data

99. Ambient temperature: when the ambient temperature sensor is not operating, the temperature is used as another reference value

100. Power state-on/off

101. Accelerometer

102. The battery voltage.