Dewar flask temperature rise alarm monitoring system

Abstract

Systems and methods for monitoring an environmental variable associated with a temperature sensitive product during transportation may include a sensor attached to or within a transported container and a remote processor that receives data provided by the sensor. For example, temperature and orientation may be sensed over time, and a state associated with the temperature-sensitive product may be determined based on the temperature and / or orientation and based on changes in temperature and / or orientation over time. In this manner, potentially detrimental temperatures and / or orientation shifts may be predicted and monitored.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 440,025, filed on January 19, 2023, entitled "DEWAR WARMING ALARM MONITORING SYSTEM," the contents of which are incorporated herein by reference in their entirety. Technical Field

[0002] The present disclosure relates to systems, methods, and programs for monitoring environmental conditions associated with shipping containers, and more particularly, to systems, methods, and programs for providing electronic data corresponding to the monitored environmental conditions. Background Art

[0003] Frequently, materials need to be transported from one location to another while maintaining them under controlled environmental conditions. For example, the materials may need to be maintained within a specific temperature range. However, unexpected delays and other events during transportation may expose the materials to environmental conditions outside the desired range. For example, materials may be transported in cryogenic shipping containers containing a dewar containing one or more coolants, such as liquid nitrogen. Over time, the coolant may dissipate, causing the materials to begin to heat up and change in temperature, potentially exceeding a specific temperature range. Therefore, there remains a need to remotely monitor conditions associated with shipping containers and provide various alerts of these conditions to remote operators. Summary of the Invention

[0004] Disclosed are systems, methods, and articles of manufacture (collectively, the "system") for electronically monitoring temperature-sensitive materials within a shipping container during transport. The system can electronically monitor variables associated with a shipping container during transport and can determine whether the contents of the shipping container are at risk of damage due to temperature or other environmental changes. Because false alarms can be caused by transient conditions, the system can also compare the monitored variables with past data to determine whether the environmental change is brief and temporary, or persists for a sufficient period of time to potentially damage the contents of the shipping container. Furthermore, the system can predict what future values ​​of the variables are likely to be, allowing predictions to be made regarding how much time remains until the contents are likely to suffer damage. In this way, the system facilitates monitoring during transport and also facilitates intervention before damage occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The subject matter of the present disclosure is particularly pointed out and distinctly claimed in the concluding portion of the specification. However, a more complete understanding of the disclosure may be obtained by referring to the detailed description and claims when considered in conjunction with the accompanying drawings, wherein like reference numerals represent like elements.

[0006] A more complete understanding may be derived by referring to the detailed description and claims when considered in conjunction with the accompanying drawings, wherein like reference numerals refer to like elements throughout, and:

[0007] Figure 1 is a block diagram illustrating an electronic monitoring system according to various embodiments.

[0008] Figure 2 is a flow chart illustrating a method of electronic monitoring according to various embodiments.

[0009] Figure 3 is a flow chart illustrating logical aspects of a remote monitoring unit that may perform a method of electronic monitoring according to various embodiments.

[0010] Figure 4A-4B is a flow chart illustrating aspects of a method of electronically monitoring temperature-sensitive materials within a shipping container during transport, according to various embodiments.

[0011] Figure 5 is a diagram illustrating various embodiments Figure 3 The temperature rise alert engine and execution Figure 4A-4B A block diagram of various electronic components of various aspects of the method.

[0012] Figure 6 It is an icon Figure 2 A flow chart of further aspects of elements of the method is provided.

[0013] Figure 7 It is an icon Figure 2 A flow chart of further aspects of elements of the method is provided.

[0014] Figure 8 According to some embodiments Figure 2 and Figure 7 Autocorrelation plot of the prediction algorithm used in the method provided.

[0015] Figure 9 According to some embodiments Figure 2 and Figure 7 Partial autocorrelation plot of the forecasting algorithm used in the method provided.

[0016] Figure 10 According to some embodiments Figure 2 and Figure 7 Diagram of the neural network used in the method provided in. DETAILED DESCRIPTION

[0017] The specific implementation of each embodiment herein is with reference to the accompanying drawings and pictures, which illustrate each embodiment in a diagrammatic manner. Although these various embodiments are described in sufficient detail to enable those skilled in the art to practice the present disclosure, it should be understood that other embodiments can be implemented and, without departing from the spirit and scope of the present disclosure, logical and mechanical changes can be made. Thus, the specific implementation herein is presented for illustrative purposes only and not for limiting purposes. For example, the steps described in any method or process description in the method or process description can be performed in any order and are not limited to the order presented. In addition, any one of the functions or steps can be outsourced to one or more third parties or performed by one or more third parties. In addition, any reference to the singular includes multiple embodiments, and any reference to more than one component may include a single embodiment.

[0018] The progress of cryogenic storage technology has caused the method for allowing low temperature to maintain various cell types and molecules.Technology can be used for the culture of virus and bacterium, the tissue cell of separating in tissue culture, small multicellular organism, enzyme, human and animal DNA, the medicine comprising vaccine, diagnostic chemical substrate and the cryogenic storage of more complicated organism (such as embryo, unfertilized oocyte and sperm).These biological products must be transported or transported at low temperatures with frozen state, to keep activity.This requirement can keep low temperature environment up to 10 days, and meet the transportation shell of other transportation requirements (such as the influence that is not affected by mechanical shock and direction orientation influence relatively).

[0019] In addition to the existing difficulties in transporting heat-sensitive biological products, the International Air Transport Association (IATA) implemented new regulations for all shipments involving specimens containing infectious or potentially infectious agents, effective January 1995. Approved by the U.S. Department of Transportation (DOT), these regulations apply to all public and private air, ocean, and land carriers and impose significantly increased requirements on shipping units to withstand extensive physical damage (drop testing, puncture testing, pressure seal testing, vibration testing, thermal shock, and water damage) without rupturing the internal primary container (vial). The implementation of these regulations further complicated the transport of frozen biological products. Although biological transport boxes currently utilize liquid nitrogen as a refrigerant, there has been little innovation in packaging design for cryogenic transport. Current shipping boxes are often susceptible to physical damage and orientation changes encountered during routine shipping procedures. Furthermore, these boxes rarely comply with the IATA Dangerous Goods Regulations (effective January 1995 or later revisions). Commercial vendors have yet to develop or certify cost-effective, standardized shipping units with the necessary specimen capacity and holding time to meet user needs.

[0020] A fully charged shipping container in use and containing samples (e.g., while being processed and transported) can be used for a period of time, such as the static holding time. The static holding time is the amount of time a cryogenic container can be used and maintain the appropriate temperature. While the static holding time is typically advertised as 20 days, if the container is tilted or placed on its side, the holding time can be reduced to hours rather than days. This can occur because liquid nitrogen transitions to the gas (vapor) phase more quickly, leading to outgassing. Liquid nitrogen can also simply leak out of the container when placed on its side. Current cryogenic containers are advertised as durable because of their metal construction. However, rough handling frequently results in punctures in the shell or cracked necks, leading to loss of high-vacuum insulation. Therefore, a monitoring system is needed to ensure temperature maintenance and to properly record inappropriate temperatures using notifications or alarms. Furthermore, false alarms need to be monitored, where the temperature may increase but not at a rate or threshold that would trigger an alarm. One challenge is the ability to monitor the temperature of the dewar flask and determine whether the increase in temperature is the cause of the alarm. False alarms can occur when there is a spike in the dewar flask's temperature, which then quickly returns to normal. False alarms can have many causes, including incorrect temperature readings or a temporary increase in temperature.

[0021] When transporting cryogenic materials, it's crucial to ensure the contents maintain a consistent temperature. Rapid temperature rises or falls can damage the cryogenic contents. Accurate and efficient monitoring of cryogenic contents is crucial. Indicators can be set to flag rapid temperature changes. Monitoring systems are required to efficiently detect problems while also identifying false alarms.

[0022] The present system and method can address the problem of incorrect false alarms while also ensuring appropriate indication when an alarm is needed. The system and method can identify a sharp increase in temperature over a period of time to indicate an alarm. The system and method can identify tilting of contents and changes in temperature.

[0023] Monitoring systems can be used to monitor temperature-sensitive contents in shipping containers. When transporting temperature-sensitive materials, electrical temperature probes and monitoring systems can be used to detect rapid increases and decreases in temperature. The system can monitor temperature changes over time to determine if the contents of the shipping container are being properly stored. Rapid temperature changes can cause an alarm or other indication to be issued.

[0024] In various embodiments, the system and method utilizes a tilt monitor and a temperature probe to determine if there is sufficient change in temperature and tilt to indicate an alarm. The system and method may also monitor for indications of elevated temperatures. A benefit of the described electrical monitoring system is the reduction of false alarms.

[0025] In various embodiments, the tilt of the shipping container is monitored. By definition, a shipping package needs to function in almost any position, including sideways and inverted orientations. This is particularly useful for services that cover delivery via package delivery, including parcel post, These services are necessary if economical, reliable and timely transportation and delivery are desired. All currently available cryogenic shipping containers will spill some liquid refrigerant if laid on their side or upside down, as one would expect in a commercial shipping environment. Most of these shipping containers include an interior primary absorbent material which acts with varying degrees of efficiency to contain the amount of liquid refrigerant that will spill; however, none of them can completely eliminate the possibility of all spillage as they all rely on surface tension capillary forces to contain the liquid. In the drawings and in the more detailed description that follows, reference numerals indicate various features of the invention, with like reference numerals referring to like features throughout the drawings and description.

[0026] Now refer to Figure 1 An electronic monitoring system 2 is provided. The electronic monitoring system can be operable to electronically monitor temperature-sensitive materials within a shipping container during transport and provide alerts corresponding to measurements taken by sensors during the electronic monitoring. For example, a remote monitoring unit 4 can communicate with various aspects of a sensing shipping container 8 via a network 6. The remote monitoring unit 4 can store data associated with measurements taken by temperature sensors 22, wherein the sensors evaluate temperature or other measurements. Various methods discussed further herein can facilitate the identification of alarm conditions (such as when a temperature or other measurement exceeds a safety threshold for a product being transported in the shipping container) and can further facilitate the identification of false alarm conditions (such as when a temperature or other measurement may initially indicate that a safety threshold for a product has been exceeded, but further analysis reveals that the measurement is not associated with an environmental condition that has become unsafe for the transport of the product. As used herein, a "safe" condition refers to a condition that facilitates the transport of a product without damage or degradation, while an "unsafe" condition refers to a condition that would result in damage or degradation of the product and / or indicates that damage or degradation has occurred.

[0027] Continue to refer Figure 1 , the electronic monitoring system may include a remote monitoring unit 4. In various embodiments, the remote monitoring unit 4 may be a computerized monitoring unit located outside or inside the shipping container 8. The remote monitoring unit 4 may be used to monitor the tilt and / or temperature of the shipping container and its contents.

[0028] In addition, the electronic monitoring system may include a network 6. The network 6 may provide two-way data communication, which may include a local area network, a wide area network, or some combination of the two. For example, an integrated services digital network (ISDN) may be used in conjunction with a local area network (LAN). In another example, a LAN may include a wireless link. A network link typically provides data communication to other data devices through one or more networks. For example, a network link may provide a connection to a host computer or to a wide area network (such as the Internet) through a local network. Both the local network and the Internet may use electrical, electromagnetic, or optical signals that carry digital data streams. A computing system may use one or more networks to send messages and data, including program code and other information.

[0029] Finally, the electronic monitoring system may include a sensing shipping container 8. The sensing shipping container 8 may be a sensing shipping container 8 designed to store contents at low temperatures. In various embodiments, the sensing shipping container may contain multiple components designed to perform various electronic monitoring steps.

[0030] Continue to refer Figure 1, the sensing shipping container 8 may include various associated components. For example, the sensing shipping container may include a sensor array 10. The sensor array 10 may include a temperature sensor 22, a tilt sensor 24, an altitude sensor (not shown), a position sensor (not shown), a humidity sensor (not shown), a light sensor (not shown), and / or a battery sensor (not shown). The temperature sensor 22 may include a thermocouple wire configured to measure temperature, or an optical temperature sensor, or any other component configured to measure temperature. The sensor array 10 may further include a tilt sensor 24. The tilt sensor 24 may include an accelerometer or a gyroscope or any other sensor configured to measure orientation or movement. The tilt sensor 24 may be configured to measure movement of the shipping container, including rotational speed. The tilt sensor 24 may measure rotation about one or more axes. The altitude sensor may be configured to measure air pressure (e.g., an air pressure sensor). The position sensor may be configured to identify the position of the sensing shipping container 8. Many different types of position sensors may be used alone or in combination with other position sensors. For example, the position sensor may include one or more of a global positioning system (GPS) sensor, a Wi-Fi positioning system (WPS), a cellular positioning system (CPS), a Bluetooth positioning system (BPS), a radio frequency identification (RFID) positioning system, or another suitable positioning system. The humidity sensor may be configured to detect and / or measure changes in local humidity. Many different types of humidity sensors may be used. For example, the humidity sensor may include a capacitive humidity sensor, a resistive humidity sensor, a thermal humidity sensor, or another suitable humidity sensor. The light sensor may be configured to detect and / or measure light intensity. Many different types of light sensors may be used. For example, the light sensor may include a photovoltaic light sensor, a photoresistor light sensor, a photoconductive light sensor, or another suitable light sensor. The battery sensor may be configured to detect and / or measure one or more of battery capacity (e.g., battery charge percentage), battery voltage, and / or battery cycles.

[0031] The sensing transport container 8 may include a controller 12. The controller 12 may communicate with the sensor array 10 and a memory 14, wherein the controller 12 may receive temperature measurements and tilt measurements. The controller 12 may transmit the temperature measurements and tilt measurements to the memory 14. The controller 12 may include a processor or other data processing device.

[0032] The sensing transport container 8 may include a memory 14. The memory may include one or more of random access memory ("RAM"), static memory, cache, flash memory, and any other suitable type of storage device that may be coupled to a bus or other communication mechanism. In various embodiments, the memory 14 and the one or more controllers 12 may be manufactured in a common device and / or co-located in a common package. The memory 14 may be used to store instructions and data that enable the one or more controllers 12 to perform a desired process. The memory 14 may be used to store transient and / or temporary data, such as variables and intermediate information generated and / or used during the execution of instructions by the processor. The memory may include one or more separate non-volatile storage devices, such as read-only memory ("ROM"), flash memory, memory cards, etc. The memory 14 may be connected to a data transceiver 16 or other communication mechanism. The memory 14 may be used to store configuration and other information, including instructions executed by the controller 12.

[0033] The sensing transport container 8 may include a data transceiver 16. The data transceiver 16 may be a data communication device, a wireless transceiver, a radio transmitter, or any other form of data transceiver. In various embodiments, the data transceiver 16 may be configured to send and / or receive data between the sensing transport container 8 and the remote monitoring unit 4. In various embodiments, the data transceiver 16 may be configured to send and / or receive data via the network 6. In various embodiments, the data transceiver 16 may be configured to both send and receive data. In various embodiments, the data transceiver 16 may be configured to only send data or only receive data. In various embodiments, the data transceiver 16 may be a high-speed universal serial bus (USB), FireWire, or other such communication mechanism.

[0034] The sensor transport container 8 may include a dewar 18 containing a product 26. The dewar 18 may be a cryogenic storage container, such as a vacuum flask for storing cryogenic materials. In some embodiments, the dewar may have multiple vacuum-tight walls. The dewar 18 may be configured to hold the product 26.

[0035] The sensing shipping container 8 may include a power source 20. For example, the power source 20 may include a battery, a solar charging device, a thermal or other energy harvesting charging device, and / or any other source of electrical power as desired.

[0036] Having discussed various aspects of the electronic monitoring system 2, attention is now directed to Figure 1 and Figure 2to discuss the method 200 of electronic monitoring. The method 200 of electronic monitoring can have various steps. For example, the remote monitoring unit 4 can query the controller 12 of the sensing shipping container 8 to return data corresponding to whether a temperature increase alarm should be triggered for the sensing shipping container 8, so that the operator is alerted to the condition of the product 26 in the dewar 18. In further cases, the controller 12 of the sensing shipping container 8 can transmit an alarm to the remote monitoring unit 4 instead of responding to the query. Brief Reference Figure 1 、 Figure 2 and Figure 3 It will be appreciated that the remote monitoring unit 4 may therefore have logic aspects that perform different logic steps. Figure 3 These logical aspects are shown separately in FIG, but it is understood that the logical aspects may be combined or arranged differently. For example, the temperature alarm engine 34 includes a combination of machine instructions that cause the remote monitoring unit 4 to obtain a temperature alarm state (block 210). In response to the temperature alarm state not being set, the process stops (block 240), while in response to the temperature alarm state being set to any different state (discussed further herein), the temperature state engine 36 of the remote monitoring unit 4 operates to perform further analysis, such as setting a time-to-failure calculation and a temperature rise rate calculation, so that an operator can determine how quickly the condition of the product 26 in the dewar 18 may become unsafe (block 220). Finally, the temperature data module 38 of the remote monitoring unit can obtain temperature data, meaning that the temperature data module 28 can evaluate historical data associated with the dewar 18 and the product 26 and implement machine learning methods to further assess whether the product is in a safe or unsafe condition based on the data collected by the sensor array 10 (block 230).

[0037] Continuing the discussion of the electronic monitoring system 2, more specifically, attention is directed to the temperature alarm engine 34 of the remote monitoring unit 4. The temperature alarm engine 34 implements a set of data filters and sets a failure status flag in response to the data filters. The failure status flag can be set to a state corresponding to the current condition of the product 26 in the dewar. Therefore, with reference to Figure 1 and Figure 4A-4B , provides a method for electronically monitoring temperature sensitive materials within a shipping container during transport 400. The method may include the sequential application of data filters. Also refer to Figure 1 、 Figure 4A-4B and Figure 5 It will also be appreciated that the elevated temperature alert engine 34 may include various electronic components that operate in conjunction to perform the method 400 .

[0038] For example, the remote monitoring unit 4, and specifically the temperature alarm engine 34 of the remote monitoring unit, may include a remote monitoring unit processor 51. The remote monitoring unit processor 51 may be configured to provide instructions to and receive data from other aspects of the temperature alarm engine 34 to execute the method 400.

[0039] The remote monitoring unit processor 51 may receive sensor array data 52. The sensor array data 52 may include data from the sensor array 10. The sensor array data 52 may be data from the temperature sensor 22 and / or the tilt sensor 24. Thus, the sensor array data may include both temperature data and tilt data. The sensor array data 52 may include sampled values ​​of the tilt of the monitored transport container over a period of time. Furthermore, in various embodiments, the sensor array data 52 may include a maximum tilt value within a period corresponding to a maximum sample value and an intermediate tilt value within a period corresponding to a minimum sample value. In various embodiments, the remote monitoring unit processor 51 may store the sensor array data 52 in a sensor memory 53 for future retrieval and processing. The remote monitoring unit processor 51 may be configured to process the sensor array data 52 to subdivide the temperature sample array into subarrays associated with corresponding sub-durations of the first duration. Furthermore, the remote monitoring unit processor 51 may calculate a temperature change rate for each subarray associated with each corresponding sub-duration, calculate a temperature change rate for the temperature sample array over the first duration, calculate an amount of temperature change for each subarray associated with each corresponding sub-duration, and calculate an amount of temperature change for the temperature sample array over the first duration. The remote monitoring unit processor 51 may calculate the rate of change of temperature at various time increments.

[0040] The remote monitoring unit processor 51 can receive filters from the filter repository 58 to apply to the sensor array data 52, and can apply the filters to various data. Applying filters may be referred to as "filtering" elsewhere in this document. For example, in various embodiments, the filter repository 58 can apply multiple filters to the maximum tilt value, the minimum tilt value, the current temperature value, the temperature change of at least one subarray associated with the corresponding sub-duration, the temperature change of the temperature sample array within the first duration, the temperature change rate of at least one subarray associated with the corresponding sub-duration, and the temperature change rate of the temperature sample array within the first duration. The filters implemented by the remote monitoring unit processor 51 can be implemented in parallel. In further examples, the filters can be implemented sequentially. In still further examples, the filters can be implemented in different combinations and both in parallel and sequentially. Figure 4A-4B The application of the filter sequence described in can be obtained Figure 2Aspects of the temperature rise alarm (box 210) in.

[0041] The remote monitoring unit processor 51 may set a failure status flag in response to applying the filter and store the failure status flag in the status flag memory 54. In various embodiments, in response to filtering, the status flag is set to a state and stored in the status flag memory, where the state includes one of: not set, alarm, no alarm, rising temperature, and false alarm.

[0042] Finally, the remote monitoring unit processor 51 may include a human interface device 56 configured to display a human-readable indication of the failure status indicator. The human interface device 56 may be a visual display, a data port, or other device for interfacing with data. The human interface device 56 may be a data communication device that connects to additional devices via a network.

[0043] In various embodiments, and primarily with reference to Figure 4A-4B and periodically refer to Figure 5 A method 400 for electronically monitoring temperature sensitive materials within a shipping container during transport may include the following aspects. Such a method may be Figure 2 In various embodiments, the elevated temperature alert engine 34 may include various electronic components that operate in conjunction to perform the method 400. The method 400 for electronically monitoring a transport may include one or more filtering steps 450, 460, 470, 480, and 490. For example, one or more filtering steps may be used to monitor the transport 200 and determine whether a status indicator needs to be signaled. In various embodiments, the method 400 may include a first filter 450, a second filter 460, a third filter 470, a fourth filter 480, and a fifth filter 490.

[0044] In various embodiments, in response to each condition in the first set of conditions (402, 404, 406, and 408) constituting the first filter 450 being true, the state of the failure status flag can be set to false alarm 440. In various embodiments, the first filter 450 can include one or more of the first set of conditions 402, 404, 406, and 408. In various embodiments, the first set of conditions (402, 404, 406, and 408) can include the amount of temperature change in the temperature sample array over a first duration being greater than a first temperature rate threshold (block 402), the maximum tilt value during the first duration being greater than or equal to a maximum tilt limit (block 404), the minimum tilt value during the first duration being less than or equal to a minimum tilt limit (block 406), and the current temperature value being less than a first upper temperature threshold (block 408). For example, the maximum tilt and minimum tilt are measures of the amount that a component of the shipping container has rotated about an axis. Tilt can be measured using the tilt sensor 24.

[0045] In various embodiments of the first filter 450, a maximum tilt and a minimum tilt can be measured over a period of time. The maximum tilt during the period can exceed a maximum tilt limit, while the measured minimum tilt can remain below a minimum tilt limit. Additionally, the temperature can not exceed a temperature threshold. In response to this set of conditions, a false alarm can be set.

[0046] Furthermore, in various embodiments of the first filter, the first temperature threshold may be 4 degrees, the first duration may be 5 hours, the maximum tilt limit may be 20 degrees, and the minimum tilt limit may be 20 degrees. In various embodiments, the duration may be greater than or less than five hours. Temperatures may be recorded. For example, the temperatures for the previous five hours may be recorded and stored. Tilt measurements may be recorded. For example, the tilt measurements for the previous five hours may be recorded and stored. The recorded temperatures may include a first upper temperature threshold. As described above, the first temperature threshold may be measured using a temperature probe or sensor. The sensor may communicate with a memory and store the temperature measurements. The memory may store multiple temperature measurements at various time increments. The sensor may monitor the temperature continuously or sample the temperature at various time increments. For example, the temperature sensor or probe may sample the temperature at five-minute increments. Furthermore, the first upper temperature threshold may be -150 degrees Celsius. The first filter 450 may be used to determine whether the tilt and temperature changes in the transport container are sufficient to set the status indicator to a false alarm. The first filter 450 can be placed at any point in the monitoring process, such as after the second filter 460, the third filter 470, the fourth filter 480, or the fifth filter 490. The first filter 450 can be placed first in the monitoring process to identify false alarms before an alarm is set. For example, in the event that the tilt and temperature may be sufficient to satisfy an alarm but actually correspond to a false alarm rather than an actual alarm condition, the first filter 450 will set the status indicator to a false alarm 440 rather than having the second filter 460 review the second set of conditions.

[0047] In various embodiments, in response to at least one of the first set of conditions (402, 404, 406, and 408) being false, the second filter 460 may be applied. In various embodiments, the second filter 460 may include a second set of conditions (410, 412, 414, 416, 418, 420), and the state of the failure status flag may be set to alarm 422 in response to all of the second set of conditions (410, 412, 414, 416, 418, 420) being true, and set to unset in response to at least one of the second set of conditions (410, 412, 414, 416, 418, 420) being false. The second set of conditions (410, 412, 414, 416, 418, 420) may include the amount of temperature change for each subarray being greater than or equal to a minimum temperature increase threshold (blocks 410, 412, 414, 416, and 418), and the current temperature value being between a first upper temperature threshold and a first lower temperature threshold (block 420). The minimum temperature increase threshold may be 0.8 degrees Celsius. Each subarray may be associated with a subduration equal to one hour. The first duration may be equal to five hours. The first upper temperature threshold may be -150 degrees Celsius. The first lower temperature threshold may be -190 degrees Celsius.

[0048] In various embodiments, in response to at least one of the second set of conditions (410, 412, 414, 416, 418, 420) being false, a third filter 470 may be applied. The third filter 470 may include a third set of conditions (422, 424), and the state of the failure status flag may be set to alarm 442 in response to all of the third set of conditions being true, and set to unset in response to at least one of the third set of conditions (422, 424) being false. The third set of conditions (422, 424) may include: the current temperature value being greater than a first upper temperature threshold (block 422), and the temperature sample array containing temperatures that are each greater than the first upper temperature threshold for a plurality of samples corresponding to less than a second duration (block 424). The second duration may be four hours. The first duration may be five hours. The first upper temperature threshold may be -150 degrees Celsius.

[0049] In various embodiments, in response to at least one of the third set of conditions (422, 424) being false, a fourth filter 480 may be applied. The fourth filter 480 may include a fourth set of conditions (426), and wherein the state of the failure status flag may be set to warm 444 in response to all of the fourth set of conditions (426) being true, and set to unset in response to at least one of the fourth set of conditions (426) being false. The fourth set of conditions (426) may include: the temperature sample array includes temperatures that are each above a first upper temperature threshold for a plurality of samples corresponding to a second duration greater than or equal to a second duration (block 426). The second duration may be four hours, and the first upper temperature threshold may be -150 degrees Celsius. In some embodiments, the status flag is set to warm to indicate that the contents of the shipping container are warm. For example, the temperature of the shipping container is above the upper threshold for more than a period of time. For example, if the temperature exceeds -150 for more than four hours, the status may be set to warm. Setting the status to warm indicates that conditions in the dewar of the container have become irreversibly unsuitable for the contents of the dewar, such that it can be assumed that the contents have deteriorated. In contrast, an alarm state indicates that unless corrective action is taken, the contents can be assumed to have spoiled, but have not yet deteriorated.

[0050] In various embodiments, in response to at least one condition in the fourth set of conditions (426) being false, a fifth filter 490 may be applied. The fifth filter 490 may include a fifth set of conditions (428, 430, 432, 434, 436, 438), and wherein the state of the failure status flag is set to false alarm 446 in response to all of the fifth set of conditions (428, 430, 432, 434, 436, 438) being true, and the state of the failure status flag is set to no alarm 448 in response to at least one condition in the fifth set of conditions (428, 430, 432, 434, 436, 438) being false. The fifth set of conditions (428, 430, 432, 434, 436, 438) may include: the temperature change amount of at least one of the subarrays exceeds or is equal to the subarray amount limit (blocks 428, 430, 432, 434, 436). The fifth set of conditions (428, 430, 432, 434, 436, and 438) may also include the current temperature value being less than or equal to a sampled value of the monitored temperature within the shipping container collected within the past first duration (block 438). Each sub-duration may be one hour, the first duration may be five hours, and the sub-array limit may be 20 degrees Celsius. The remote monitoring controller may read the state of the failure status flag and display a human-readable alert corresponding to the state of the failure status flag. For example, the fifth filter 490 may be used to determine whether there has been a sharp increase in the temperature of the shipping container within a period of time (such as the previous five hours). When some or all of the conditions are met, the fifth filter 490 sets the failure status flag to a false alarm 446, indicating that there was a sharp increase, but the increase was not sufficient to cause deterioration or potential deterioration of the contents of the shipping container, and therefore an alert is not necessary. In various embodiments, the fifth filter 490 may include measuring the temperature increase over various time increments in which a sharp increase in temperature has occurred. For example, each of the fifth set of conditions (428, 430, 432, 434, 436) can be used to determine whether the temperature rise in the hour is greater than 20 degrees. In response to the presence of a rise greater than 20 degrees, the temperature is then compared to the temperature at some time in the past (e.g., the past five hours). If the current temperature is less than or equal to the past temperature, the rise can be considered to be associated with a false alarm, and the failure status flag is set to false alarm 446.

[0051] The above discussion involves Figure 2 aspects, such as by the remote monitoring unit 4 ( Figure 1 )'s temperature rise alert engine 34( Figure 3 ) is executed in block 210. Further discussion below relates to Figure 2 Additional aspects, such as by the remote monitoring unit 4 ( Figure 1 ) of the heating state engine 36 ( Figure 3) is executed in block 220. In various embodiments, and with reference primarily to Figure 6 , and continue to refer to Figure 1-Figure 5 , the method 220 of cold tracking may be performed. For example, the method 220 of cold tracking may be performed by the warming state engine 36. In some embodiments, the method 220 of cold tracking may be performed following a method of electronically monitoring temperature sensitive materials within a shipping container during transport 400. In some embodiments, various aspects may be performed in parallel. Figure 6 These logical aspects are shown separately in FIG, but it is understood that the logical aspects may be combined or arranged differently.

[0052] The method 220 for low temperature tracking may have various elements. The method 220 for low temperature tracking may include determining a temperature rise alarm engine 34 ( Figure 3 ) has set the failure status flag to the alarm state (block 602). For example, method 400 ( Figure 4A-4B ) may result in the failure status flag being set to alerts 442, 446 ( Figure 4A-4B ). If the status flag is not set to alarm, the method 220 of low temperature tracking can end (box 612). If the temperature rise alarm engine has set the failure status flag to alarm, the method will include calculating the temperature rise rate and setting the rate flag (box 608). In addition, if the temperature rise alarm engine has set the failure status flag to alarm, the method will include checking whether the current temperature is greater than a threshold ("tracking temperature threshold") (box 604). In response to the current temperature exceeding the tracking temperature threshold, the method includes predicting a failure time (box 610). The failure time can be a duration after which the temperature sensitive material within the shipping container can be expected to have deteriorated. Alternatively, in response to the current temperature not exceeding the tracking temperature threshold, the method can continue to set the temperature rise failure status flag to a false alarm (box 606). It can be understood, therefore, that the method 220 of low temperature tracking and the method 400 of electronically monitoring temperature sensitive materials inside a shipping container during transportation ( Figure 4A-4B ) Both can set the state of the invalid status flag.

[0053] Block 610 (predicting failure time) can include further aspects. For example, such predictions can include performing calculations to establish the predicted time. Such calculations can be performed using the difference in probe temperature over time. For example, the difference between the current probe temperature and the upper threshold temperature can be compared to the difference between the current probe temperature and the probe temperature at a previous time. The resulting value is then divided by the amount of time since the last time. The resulting value will determine the amount of time until the temperature probe will reach a temperature above the upper temperature threshold. For example, the difference between the current probe temperature and -150 degrees divided by the difference between the probe temperature and the probe temperature 14 hours ago, divided by 14 hours, will yield the amount of time until the probe temperature will rise above -150 degrees.

[0054] Additionally, in some embodiments, block 608 (calculating the heating rate and setting the rate flag) may involve further aspects. For example, if the temperature increases at a rate below a slow temperature rise threshold, the heating rate flag may be set to slow. If the temperature increases at a rate below a normal temperature rise threshold, the heating rate flag may be set to normal. If neither of these conditions is met, the heating rate flag may be set to fast. Thus, if the temperature of the refrigerated transport container increases at a rate higher than normal, the heating rate flag may be set to fast.

[0055] In various embodiments, the prediction algorithm is applied to historical temperature rise data ( Figure 2 230) and is performed by the warming data module 38 (e.g., Cryoportal). Figure 7 Further aspects of warming prediction associated with machine learning and / or artificial intelligence are illustrated in FIG, which are described below.

[0056] Turning to the subsequent figures, Figure 7 700 is shown as a flow chart of a method 700 according to an embodiment. The method 700 is exemplary only and is not limited to the embodiments presented herein. The method 700 can be used in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the activities of the method 700 can be performed in the order presented. In other embodiments, the activities of the method 700 can be performed in any suitable order. In still further embodiments, one or more activities of the method 700 can be combined or skipped. In many embodiments, the monitoring system 2 ( Figure 1) may be adapted to perform the method 700 and / or one or more activities of the method 700. In these or other embodiments, one or more activities of the method 700 may be implemented as one or more computer instructions configured to be executed at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules. Such a non-transitory memory storage module may be part of a computer system, such as a remote monitoring unit 4 ( Figure 1 ) and / or memory 14( Figure 1 ). The processing module(s) can be connected to the remote monitoring unit 4 ( Figure 1 ) and / or controller 12( Figure 1 ) are similar or identical to the processing modules described in . In some embodiments, method 700 may be similar to method 200 ( Figure 2 ), Method 400( Figure 4A-4B ) and / or method 220( Figure 6 ) are executed in parallel, in method 200 ( Figure 2 ), Method 400( Figure 4A-4B ) and / or method 220( Figure 6 ) before, after, or as method 200( Figure 2 ), Method 400( Figure 4A-4B ) and / or method 220( Figure 6 ) is performed as part of the method 700. In various embodiments, one or more activities of the method 700 may be inserted into the method 200 ( Figure 2 ), Method 400( Figure 4A-4B ) and / or method 220( Figure 6 ) in whole or in part and / or in conjunction with method 200 ( Figure 2 ), Method 400( Figure 4A-4B ) and / or method 220( Figure 6 ) in whole or in part.

[0057] In many embodiments, the method 700 may include an activity 701 of receiving sensor data. In various embodiments, the sensor array 10 ( Figure 1 ) receives sensor data from one or more sensors in the prediction system. In various embodiments, the sensor data can be received at periodic intervals and / or continuously streamed to the prediction system. For example, sensor data can be received every 5 minutes. In this way, the techniques described herein can beneficially and more accurately make determinations based on dynamic information describing current conditions and / or conditions analyzed in the context of conditions that have occurred in the time period before the prediction was made. Receiving the latest data from the sensors can also avoid the problem of stale and / or outdated prediction algorithms through continuous updating. In these or other embodiments, the sensor data points can be referred to as one or more features of the prediction algorithm.

[0058] In many embodiments, sensor data can be processed by real-time stream processing software (e.g., Apache In various embodiments, the real-time stream processing software may be configured to classify one or more streams of sensor data into various categories and subcategories based on their content (in the context of Apache In these or other embodiments, sensor data may be stored in one or more databases for further processing and / or subsequent retrieval. In many embodiments, the remote monitoring unit 4 ( Figure 1 ) and / or sensing transport container 8 ( Figure 1 ) can be configured to communicate with one or more databases storing sensor data. For example, one or more databases can store data from the sensor array 10 ( Figure 1 ) of past (e.g., historical) readings. These readings can be compared to a particular sensing shipping container (e.g., sensing shipping container 8 ( Figure 1 )) is bound or can be de-identified. In some embodiments, data can be deleted from the database when the data's retention time exceeds a maximum age. In many embodiments, the maximum age can be determined by the administrator of the system. In various embodiments, data collected in real time can be streamed to a database for storage.

[0059] In some embodiments, method 700 may optionally include an activity 702 of creating a training dataset using sensor data. Generally speaking, a training dataset may include a grouping of data points used to train a prediction (e.g., machine learning) algorithm. The training data may be in a variety of forms. For example, the training data may be labeled (e.g., annotated) or unlabeled. For example, data received from sensor array 10 may be analyzed using a sensor shipping container 8 ( Figure 1 ). In many embodiments, the status of a shipping container may include a variable indicating whether the shipping container is suitable for shipping or whether it requires repair. In various embodiments, the repair required tag may be broken down into multiple different groupings representing various types of repairs. For example, a sensing shipping container may need its refrigerant (e.g., liquid nitrogen) recharged, its batteries recharged, its structure repaired, etc. In various embodiments, the training dataset may include a mix of labeled and unlabeled data. In many embodiments, the sensor data may be converted into a vector format before being labeled. For example, sensor readings may be concatenated together to create a vector.

[0060] In some embodiments, method 700 may optionally include activity 703 of training the prediction algorithm. In various embodiments, activity 703 may be performed concurrently with, before, after, or in response to one or more of activities 701 and / or 702. For example, activity 703 may be performed upon receiving sensor data and / or after creating a training dataset. As another example, activity 702 may be skipped, and the prediction algorithm may be trained on an already assembled training dataset. In some embodiments, training the prediction algorithm may include estimating internal parameters of a model configured to identify shipping containers that require or will soon require repair and / or replacement. For example, the weights of one or more features may be adjusted. In this manner, the influence of one or more features on the prediction may be increased or decreased. In various embodiments, the prediction algorithm may be trained using unlabeled and / or labeled training data (also referred to as a training dataset). In the same or different embodiments, a pretrained prediction algorithm may be used, and the pretrained algorithm may be retrained on the training data. In some embodiments, the prediction algorithm may also take into account the sensor information from the transport container 8 ( Figure 1 ) of both historical and dynamic inputs. In this way, when the sensor from the transport container 8 ( Figure 1 ) is added to the training dataset, the prediction algorithm can be iteratively trained. In many embodiments, the prediction algorithm can be iteratively trained in real time as data is added to the training dataset. In various embodiments, the prediction algorithm can be at least partially trained on a single shipping container (e.g., sensing shipping container 8 ( Figure 1 The prediction algorithm can be trained on sensor data from a variety of sources, or the sensor data for the shipping container can be weighted within the training dataset. In this manner, a prediction algorithm customized for a single shipping container can be generated. In the same or different embodiments, a prediction algorithm customized for a single shipping container can be used as a pre-trained algorithm for similar shipping containers. In some embodiments, due to the large amount of data required to create and maintain the training dataset, the prediction algorithm can use a wide range of data inputs to predict the state of a shipping container. Due to these wide range of data inputs, in many embodiments, creating, training, and / or using a prediction algorithm configured to predict the state of a shipping container is not practically performable in the human mind.

[0061] Generally speaking, a prediction algorithm may include a computerized set of steps configured to determine the future and / or current state of a shipping container. For example, a prediction algorithm may determine whether a shipping container currently requires repair and / or will require repair in the future. A variety of different types of prediction algorithms may be used in method 700.

[0062] In many embodiments, the prediction algorithm may include a stochastic model. Generally speaking, the stochastic model may be configured to generate a prediction result by allowing the prediction result to be obtained from the sensing shipping container 8 ( Figure 2 ) to estimate the probability of the transport container being in a particular state. In many embodiments, the transport container 8 ( Figure 2 The data received can be modeled as a time series of data points. In this manner, various time series forecasting techniques can be used to predict the state of the shipping container. For example, an autoregressive model, an ensemble model, and / or a moving average model can be used to predict the state of the shipping container.

[0063] In many embodiments, an autoregressive (AR) model and a moving average (MA) model may be used in combination to predict the state of a shipping container. In some embodiments, the combined model may be referred to as an autoregressive moving average (ARMA) model. Compared to pure AR and MA models, the ARMA model provides a more effective linear model for stationary time series because the ARMA model is able to model the state of the container with fewer inputs than the AR or MA model alone. In various embodiments, the AR portion of the ARMA model may be configured to predict the future value of a variable based on the past values ​​of the variable (also referred to as lags). The multiple lags used to predict future values ​​may be referred to as the order of the AR model. For example, a tenth-order AR portion of an ARMA model will use ten lags to predict future values. In many embodiments, the AR portion of the ARMA model may be represented by an equation comprising:

[0064] In these embodiments, X t This can include the predicted value of sensor data at time t, X t-i can include the lagged value at time t-1, p can include the order of the AR part of the ARMA model, The parameters of the AR part of the ARMA model can be included, and ε t Error terms (eg, simultaneous noise terms) may be included.

[0065] In various embodiments, the AR portion of the ARMA model can be configured to predict future values ​​of a variable based on past error terms (also known as error lags). The number of lags used to predict future values ​​can be referred to as the order of the MA model. For example, a tenth-order MA portion of an ARMA model will use ten error lags to predict future values. In many embodiments, the MA portion of the ARMA model can be represented by an equation comprising:

[0066] In these embodiments, X tcan include the predicted value of the sensor data at time t, μ can include the mean of past sensor readings, and ε t-i can include the lagged value of the error at time t-1, q can include the order of the MA part of the ARMA model, θ i The parameters of the MA part of the ARMA model can be included, and ε t Error terms (eg, simultaneous noise terms) may be included.

[0067] In many embodiments, the AR portion of the ARMA model and the MA portion of the ARMA model can be combined to create an ARMA model. In these embodiments, the ARMA model can be represented by an equation comprising:

[0068] Example 1

[0069] Now go to Figure 8 , shows an autocorrelation plot 800 of an example sensed shipping container dataset. In general, an autocorrelation plot can be used to determine the randomness of a dataset. A dataset with a high autocorrelation coefficient (shown on the Y-axis) indicates high correlation and therefore low randomness. Figure 8 As can be seen in the autocorrelation plot shown in Figure 1, the correlation between signals with lags of different lengths (displayed on the X-axis) decreases as the interval between lags increases. Therefore, using fresh and up-to-date data in the ARMA model described in this article can provide accurate forecasts.

[0070] Example 2

[0071] Now go to Figure 9 , shows a partial autocorrelation plot 900 for an example sensored shipping container dataset. In general, a partial autocorrelation plot can be used to determine the order of an ARMA model. Many lags (shown on the X-axis) with high partial autocorrelation coefficients (shown on the Y-axis) show high statistical significance and therefore can create accurate predictions when implemented in an ARMA model. Figure 9 As can be seen in the partial autocorrelation plot shown in , the statistical significance flattens out after 10 lags. Therefore, using a tenth-order ARMA model (i.e., using 10 lags) can provide accurate forecasts.

[0072] In many embodiments, the forecasting algorithm may include an autoregressive integrated moving average model (ARIMA). Generally speaking, an ARIMA model can be used when a data set has a non-stationary mean (e.g., the mean of past data points changes as the time series progresses). Although ARIMA models and ARMA models are similar, one difference that may exist between the two models is that the ARIMA model contains an integral part. In some embodiments, the integral part of the ARIMA model can be created by transforming the time series so that the mean of the time series is approximately stationary. One way to transform the mean of a time series is to subtract a lagged (e.g., previous) time point from a more recent time point. For example, one can transform the mean of a time series by subtracting a lagged (e.g., previous) time point from a more recent reading a. t+1 Subtract the previous reading a t To create the transformed sensor reading z t In many embodiments, the number of subtractions used to transform an ARIMA model can be referred to as the order of the model. For example, w t Set equal to z t+1 -z t The transformed time series is used to create a second-order model. In various embodiments, the ARIMA model can be represented by an equation including:

[0073] While the ARIMA model described above is useful in many applications, in many embodiments, it may be beneficial to use the ARIMA model to predict future sensor readings rather than the differences between sensor readings. This may be referred to as recovering X t In the ARIMA model, the restored X t It can be represented by equations including:

[0074] In many embodiments, the prediction algorithm may include a neural network. Generally speaking, a neural network can be understood as a collection of connected nodes (sometimes referred to as artificial neurons or neurons) that loosely model the cellular neurons in a biological brain. In some embodiments, much like biological neuron / synaptic systems, the nodes of a neural network can transmit signals to other nodes when activated. In these or other embodiments, the nodes of the neural network can be activated when the activation function of the nodes exceeds a predetermined threshold. For example, when the output of the activation function exceeds 0.75, the neuron can be activated and propagate its signal. Various types of activation functions can be used in neural networks. For example, activation functions can include linear functions, hyperbolic functions, sigmoid functions, piecewise functions (e.g., rectified linear units), tangential functions, etc. In various embodiments, the connections between the nodes of the neural network can be weighted upward or downward. In this way, the signal between the nodes can be increased or decreased based on the weight assigned to the connection. In various embodiments, the weights of the neural network can be determined during the training process (e.g., as described in activity 703).

[0075] The nodes in a neural network can be organized in a variety of ways. For example, a neural network can be organized as a feedforward neural network and / or a recursive neural network. In various embodiments, all or a portion of different types of neural networks can be connected in series and / or operated sequentially. For example, the first portion of a neural network can include a set of recursive nodes, followed by a set of convolutional nodes.

[0076] In many embodiments, a feedforward neural network (RNN) can include a collection and / or sequence of nodes that do not form a loop. In some embodiments, the nodes in a feedforward neural network can only propagate their signals forward. For example, a single-layer perceptron network is a feedforward neural network. In some embodiments, a recursive neural network can include a neural network in which nodes create loops. For example, a long short-term memory (LSTM) is a recursive neural network. In general, LSTM networks can be configured to process not only a single data input (e.g., one type of sensor data), but also data series / sequences and multivariate (e.g., multiple types of sensor data) data. Turning to the subsequent figures, Figure 10An exemplary recurrent neural network 1000 is shown that can be used to predict and / or manage alarms for sensing shipping containers. Many different types of units (or layers), inputs, outputs, and labels can be seen in the neural network 1000, such as ground truth inputs into the neural network. In many embodiments, the ground truth inputs can include inputs into the neural network that are labeled as ground truth. Generally speaking, the ground truth inputs can include sensor data collected from sensing shipping containers. Ground truth data can be distinguished from predicted data (e.g., 1-step predictions and / or multi-step predictions) because the ground truth data can be derived (sometimes directly) from real-world measurements. Predicted data, on the other hand, can be generated by a neural network or some other prediction algorithm. For example, the predicted output Generated by an RNN node comprising a hidden state h0 using at least a true value input x0. In various embodiments, the hidden state (also referred to as a hidden layer) may comprise a node located between an input layer and an output layer of a neural network. In some embodiments, the hidden state may vary according to a function of the neural network. Further, the hidden states may vary from one another according to their associated weights. In general, the hidden state may comprise a nonlinear mathematical function (e.g., an activation function).

[0077] In many embodiments, the prediction algorithm may include a decision tree algorithm. Generally speaking, a decision tree may include one or more nodes connected by one or more branches. A decision tree may contain many different types of nodes. For example, a node may be a decision node or a leaf node. In some embodiments, a decision node in a decision tree algorithm may classify a data point into two or more categories. For example, a decision node may classify a data point as indicating that a shipping container requires repair or as indicating that the shipping container does not require repair. In these or other embodiments, a leaf node may include nodes where data points have the same or similar classifications. In this manner, data points may be classified by feeding them into the first decision node of a decision tree (referred to as the root node) and then passing them through branches to other decision nodes and leaf nodes. In various embodiments, the classification of a data point may be completed when the data point reaches a leaf node. In some embodiments, the decision tree model may include a random forest model. Generally speaking, a random forest model may include an algorithm that uses multiple decision trees in parallel to classify data points. In various embodiments, the decision tree algorithm may include a gradient boosting algorithm. Generally speaking, boosting is an ensemble learning method that constructs a series of small trees (some as small as a single node), where each successive tree focuses on correcting errors from the previous tree. In some embodiments, an optimization algorithm can be used in combination with a boosted decision tree to create a gradient boosting algorithm. In various embodiments, the optimization algorithm can be configured to minimize a cost function (e.g., error or loss). In this way, a decision tree algorithm can be made more accurate than other prediction algorithms.

[0078] In many embodiments, method 700 may include activity 704 of analyzing sensor data using a predictive algorithm. In various embodiments, the sensor data may be concatenated into a series or sequence before being analyzed by the predictive algorithm. In some embodiments, the sensor data may be converted into a vector before being analyzed by the predictive algorithm. In various embodiments, the sensor data may be fed into the predictive algorithm for analysis. For example, the vector created using the sensor data may be input into the first node of a neural network or the first node of a tree-based algorithm. In some embodiments, the sensor data may be streamed from the sensing shipping container to a network server or computer hosting the predictive algorithm. In these or other embodiments, the predictive algorithm may be stored in a storage module of the sensing shipping container, and the analysis may occur on the sensing shipping container itself. In some embodiments, activity 704 may be performed in real time and / or periodically. As defined herein, in some embodiments, "real time" may be defined relative to operations that are performed as quickly as possible upon the occurrence of a triggering event. A triggering event may include the receipt of data necessary to perform a task or otherwise process information (e.g., receiving sensor data). Due to inherent delays in transmission and / or computation speeds, the term "real time" encompasses operations that occur "near" real time or slightly delayed from the triggering event. In various embodiments, "real time" may mean real time minus a time delay for processing (e.g., determining) and / or transmitting data. The specific time delay may vary depending on the type and / or amount of data, the processing speed of the hardware, the transmission capabilities of the communication hardware, the transmission distance, etc. However, in many embodiments, the time delay may be less than approximately 1 second, 2 seconds, 5 seconds, or 10 seconds.

[0079] In many embodiments, method 700 may include activity 705 of initiating one or more alarms based on the analysis of the sensor data. In various embodiments, the alarm may be initiated in response to the output of a predictive algorithm. Some predictive algorithms output a number in a range of numbers related to the state of the sensored shipping container. For example, the predictive algorithm may output a number between 0 and 1. In some embodiments, an alarm may be triggered when the output of the algorithm is above a predetermined threshold. A variety of different types of alarms may be triggered in activity 705. For example, a Figures 1-6 The alert described in .

[0080] While a detailed discussion of the systems, methods, and apparatus has concluded, it is helpful to provide a brief overview in the following summary paragraphs. For example, in various embodiments, a method is provided. The method can be used to electronically monitor temperature-sensitive materials within a shipping container during transport. The method can include various steps provided below. For example, the method can include monitoring the tilt of the shipping container and the temperature within the shipping container over a first duration. The method can also include sampling various data. For example, the method can include sampling values ​​of the monitored tilt of the shipping container over a first duration and storing a maximum tilt value corresponding to the maximum sample over the first duration and a minimum tilt value corresponding to the minimum sample over the first duration. In various cases, further sampling aspects are contemplated. For example, the method can include sampling values ​​of the monitored temperature inside the shipping container over a first duration and storing an array of temperature samples.

[0081] In addition to the sampling aspects, the method may include subdividing the temperature sample array into subarrays associated with corresponding sub-durations of the first duration and calculating a rate of temperature change for each subarray associated with each corresponding sub-duration. Other computational aspects may be included. The method may include calculating a rate of temperature change for the temperature sample array over the first duration, calculating an amount of temperature change for each subarray associated with each corresponding sub-duration, and calculating an amount of temperature change for the temperature sample array over the first duration.

[0082] In addition, the method may include applying a plurality of filters. For example, the method may include applying a plurality of filters to certain variables. The variables include (i) a maximum tilt value, (ii) a minimum tilt value, (iii) a current temperature value, (iv) a temperature change of at least one subarray associated with a corresponding sub-duration, (v) a temperature change of the temperature sample array over the first duration, (vi) a temperature change rate of at least one subarray associated with the corresponding sub-duration, and (vii) a temperature change rate of the temperature sample array over the first duration.

[0083] Finally, the method may include setting a status of a failure status flag in response to the filtering. The status may be one of the following. For example, the status may be one of the following: (i) not set, (ii) alarm, (iii) no alarm, (iv) warm, and (v) false alarm.

[0084] The method may include other features that may be presented in various optional and non-limiting embodiments. In one non-limiting embodiment, the method includes setting the state of the failure status flag to a false alarm in response to each condition of a first filter having a first set of conditions being true, and setting the state of the failure status flag to not set in response to at least one condition in the first set of conditions being false. In such a scenario, the first set of conditions may include the following aspects. First, the temperature change amount of the temperature sample array within the first duration is greater than a first temperature rate threshold. Second, the maximum tilt value during the first duration is greater than or equal to the maximum tilt limit. Third, the minimum tilt value during the first duration is less than or equal to the minimum tilt limit. Finally, the current temperature value is less than a first temperature upper limit threshold.

[0085] In various embodiments, the first temperature rate threshold is 4 degrees, the first duration is 5 hours, the maximum tilt limit is 20 degrees, and the minimum tilt limit is 20 degrees. In addition, the first upper temperature threshold may be -150 degrees Celsius.

[0086] In response to at least one condition in the first set of conditions being false, a second filter may be applied. The second filter may include a second set of conditions. Furthermore, in response to all of the second set of conditions being true, the state of the failure status flag may be set to alarm. In response to at least one condition in the second set of conditions being false, the state of the failure status flag may be set to unset. In this case, the second set of conditions may include the following: First, the temperature change of each subarray may each be greater than or equal to a minimum temperature increase threshold. Second, the current temperature value may be between the first upper temperature threshold and the first lower temperature threshold.

[0087] In various associated examples, the minimum temperature rise threshold is 0.8 degrees Celsius. Additionally, each subarray is associated with a subduration equal to one hour. Furthermore, the first duration is equal to five hours, the first upper temperature threshold is -150 degrees Celsius, and the first lower temperature threshold is -190 degrees Celsius.

[0088] Furthermore, in some embodiments, and in response to at least one condition in the second set of conditions being false, a third filter can be applied. The third filter can include a further set of conditions. For example, the third filter can include a third set of conditions, wherein the state of the failure status flag is set to alarm in response to all of the third set of conditions being true, and is set to unset in response to at least one condition in the third set of conditions being false. The third set of conditions can include the following: First, the current temperature value is greater than a first upper temperature threshold. Second, the temperature sample array contains temperatures that are each greater than the first upper temperature threshold for a plurality of samples corresponding to a duration less than the second duration.

[0089] For this case, in various embodiments, the second duration is four hours. Similarly, the first duration is five hours, and the first upper temperature threshold is -150 degrees Celsius.

[0090] In various circumstances, and further in response to at least one condition in the third set of conditions being false, a fourth filter is applied. For example, the fourth filter may include a fourth set of conditions. In response to all of the fourth set of conditions being true, the state of the fail status flag may be set to warm. In response to at least one condition in the fourth set of conditions being false, the state of the fail status flag may be set to unset.

[0091] The fourth set of conditions may include the array of temperature samples containing temperatures each above a first upper temperature threshold for a plurality of samples corresponding to a second duration greater than or equal to the second duration. In various embodiments, the second duration is four hours, the first duration is five hours, and the first upper temperature threshold is -150 degrees Celsius.

[0092] In some embodiments, a fifth filter is applied in response to at least one condition in the fourth set of conditions being false. The fifth filter may include additional conditions. For example, the fifth filter may include a fifth set of conditions, and the state of the failure status flag may be set to false alarm in response to all of the fifth set of conditions being true, and may be set to no alarm in response to at least one condition in the fifth set of conditions being false.

[0093] The fifth set of conditions may include the following: First, the temperature change of at least one of the subarrays exceeds or equals the subarray limit. Second, the current temperature value is less than or equal to a sampled value of the monitored temperature within the transport container collected during a first duration. In this case, each sub-duration may be one hour. The first duration may be five hours. The subarray limit may be 20 degrees Celsius. Furthermore, the remote monitoring controller may read the status of a failure status flag and display a human-readable alert corresponding to the status of the failure status flag.

[0094] In addition to the aspects presented above, the disclosure herein may include further methods. For example, a method of electronically monitoring temperature-sensitive materials within a shipping container during transport. The method may include various aspects. For example, the method may include receiving electronic data corresponding to a plurality of environmental variables collected by a sensor attached to the shipping container. The method may include storing electronic values ​​of the plurality of environmental variables over a first duration. Furthermore, the method may include calculating a rate of change of the electronic value of at least one of the environmental variables over the first duration, calculating a magnitude of change of the electronic value of at least one of the environmental variables over the first duration, and, in response to the rate of change of at least one of the environmental variables over the first duration and the magnitude of change of at least one of the environmental variables over the first duration satisfying one or more filters, setting a state of a failure status flag.

[0095] A computer-readable storage medium is also disclosed. The computer-readable storage medium may be used to store instructions that, when executed by a computer, cause the computer to perform a method for electronically monitoring temperature-sensitive materials within a shipping container during transport using a computer system. The method may include the following aspects. For example, the method may include monitoring the tilt of the shipping container and the temperature within the shipping container over a first duration. The method may include sampling values ​​of the monitored tilt of the shipping container over the first duration and storing a maximum tilt value corresponding to a maximum sample over the first duration and a minimum tilt value corresponding to a minimum sample over the first duration. The method may also include sampling values ​​of the monitored temperature within the shipping container over the first duration and storing an array of temperature samples. Furthermore, the method may include subdividing the array of temperature samples into sub-arrays associated with corresponding sub-durations of the first duration.

[0096] Various calculation steps can be envisioned. For example, the method can include calculating the temperature change rate of each subarray associated with each corresponding sub-duration. The method can include calculating the temperature change rate of the temperature sample array over the first duration. The method can include calculating the temperature change of each subarray associated with each corresponding sub-duration, and calculating the temperature change of the temperature sample array over the first duration. In addition, the method can include applying multiple filters. For example, the method can include applying multiple filters to certain variables. These variables include (i) a maximum tilt value, (ii) a minimum tilt value, (iii) a current temperature value, (iv) a temperature change of at least one subarray associated with the corresponding sub-duration, (v) a temperature change of the temperature sample array over the first duration, (vi) a temperature change rate of at least one subarray associated with the corresponding sub-duration, and (vii) a temperature change rate of the temperature sample array over the first duration.

[0097] Finally, the method may include setting a status of a failure status flag in response to the filtering. The status may be one of the following. For example, the status may be one of the following: (i) not set, (ii) alarm, (iii) no alarm, (iv) warm, and (v) false alarm.

[0098] In response to each condition of the first filter having the first set of conditions being true, the state of the failure status flag can be set to a false alarm. In response to at least one condition in the first set of conditions being false, the state of the failure status flag can be set to unset. The first set of conditions can include the following aspects. For example, the first set of conditions can include: (i) the temperature change amount of the temperature sample array during the first duration is greater than a first temperature rate threshold, (ii) the maximum tilt value during the first duration is greater than or equal to the maximum tilt limit, (iii) the minimum tilt value during the first duration is less than or equal to the minimum tilt limit, and (iv) the current temperature value is less than a first upper temperature limit threshold.

[0099] In various embodiments, and further in response to at least one condition in the first set of conditions being false, a second filter may be applied. The second filter may include a second set of conditions. The state of the failure status flag may be set to alarm in response to all of the second set of conditions being true, and set to unset in response to at least one condition in the second set of conditions being false.

[0100] The second set of conditions may include the following conditions: First, the temperature change of each sub-array may be greater than or equal to the minimum temperature increase threshold; Second, the current temperature value may be between the first upper temperature threshold and the first lower temperature threshold.

[0101] In various embodiments, the minimum temperature rise threshold is 0.8 degrees Celsius. Each subarray may be associated with a subduration equal to one hour, and the first duration may be equal to five hours. Furthermore, the first upper temperature threshold may be -150 degrees Celsius, and the first lower temperature threshold may be -190 degrees Celsius.

[0102] Additionally, and further in response to at least one condition in the second set of conditions being false, a third filter may be applied. The third filter may include a third set of conditions. Similarly, the state of the failure status flag may be set to alarm in response to all of the third set of conditions being true, and set to unset in response to at least one condition in the third set of conditions being false.

[0103] In various embodiments, the third set of conditions includes two conditions: first, the current temperature value is greater than the first upper temperature threshold; and second, the temperature sample array includes temperatures that are each greater than the first upper temperature threshold for a plurality of samples corresponding to a duration less than the second duration.

[0104] In yet further embodiments, and further in response to at least one condition in the third set of conditions being false, a fourth filter can be applied. In various cases, the fourth filter can include a fourth set of conditions. In various cases, the state of the fail status flag is set to warm in response to all of the fourth set of conditions being true, and is set to unset in response to at least one condition in the fourth set of conditions being false.

[0105] The fourth set of conditions may include the array of temperature samples containing temperatures that are each above the first upper temperature threshold for a plurality of samples corresponding to greater than or equal to the second duration.

[0106] Finally, further in response to at least one condition in the fourth set of conditions being false, a fifth filter can be applied. The fifth filter can include a fifth set of conditions. Similarly, the state of the failure status flag can be set to false alarm in response to all of the fifth set of conditions being true, and set to no alarm in response to at least one condition in the fifth set of conditions being false. The fifth set of conditions can include the amount of temperature change of at least one of the subarrays exceeding or equal to a subarray amount limit. The fifth set of conditions can also include the current temperature value being less than or equal to a sampled value of the monitored temperature within the transport container collected within the past first duration.

[0107] The specific implementation of the exemplary embodiments herein is with reference to the accompanying drawings and pictures, which illustrate various embodiments in a diagrammatic manner. Although these various embodiments are described in sufficient detail to enable those skilled in the art to practice the present disclosure, it should be understood that other embodiments can be implemented and, without departing from the spirit and scope of the present disclosure, logical and mechanical changes can be made. Thus, the specific implementation herein is presented for illustrative and non-restrictive purposes only. For example, the steps described in any method or process description in the method or process description can be performed in any order and are not limited to the order presented. In addition, any one of the functions or steps can be outsourced to one or more third parties or performed by one or more third parties. In addition, any reference to the singular includes multiple embodiments, and any reference to more than one component may include a single embodiment.

[0108] For the purposes of this specification and the appended claims, unless otherwise indicated, all numbers expressing amounts, percentages or ratios, as well as other numerical values ​​used in the specification and claims, should be understood as being modified in all cases by the term "about". Therefore, unless indicated to the contrary, the numerical parameters set forth in this specification and the appended claims are approximate values ​​that may vary depending on the desired properties sought to be obtained by the present invention. At the very least, and without attempting to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0109] Although the numerical ranges and parameters setting forth the broad scope of the present disclosure are approximate, the numerical values ​​set forth in the specific examples are reported as accurately as possible. However, any numerical value inherently contains certain errors, which are necessarily caused by the standard deviation found in their respective test measurements. In addition, all ranges disclosed herein are to be understood as encompassing any and all subranges contained therein. For example, a range of "less than 10" includes any and all subranges between (and including) a minimum of 0 and a maximum of 10, i.e., any and all subranges having a minimum equal to or greater than 0 and a maximum equal to or less than 10, such as 1 to 5.

[0110] Benefits, other advantages and solutions to problems have been described herein with respect to specific embodiments. However, benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage or solution to occur or become more apparent are not to be construed as key, required or essential features or elements of this disclosure. Unless expressly stated, reference to an element in the singular is not intended to mean "one and only one", but rather "one or more". In addition, where a phrase similar to "at least one of A, B and C" or "at least one of A, B or C" is used in a claim or specification, it is intended that the phrase be interpreted to mean that A alone may be present in an embodiment, B alone may be present in an embodiment, C alone may be present in an embodiment, or any combination of elements A, B and C (e.g., A and B, A and C, B and C, or A and B and C) may be present in a single embodiment.

[0111] The term "non-transitory" should be interpreted as excluding only those types of transitory computer-readable media that propagate transitory signals themselves from the scope of the claims and not as disclaiming all standard computer-readable media that propagate more than just transitory signals themselves. In other words, the terms "non-transitory computer-readable media" and "non-transitory computer-readable storage media" should be interpreted as excluding only those types of transitory computer-readable media found in In Re Nuijten that fall outside the scope of patentable subject matter under 35 U.S.C. § 101.

[0112] While the present disclosure includes a method, it is contemplated that it may be embodied as computer program instructions on a tangible computer-readable carrier, such as a magnetic or optical memory or a magnetic or optical disk. All structural, chemical, and functional equivalents to the elements of the exemplary embodiments described above that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be covered by the present claims. Moreover, it is not a requirement for an apparatus or method to address each and every problem sought to be solved by the present disclosure to be covered by the present claims. Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public, regardless of whether the element, component, or method step is explicitly recited in a claim. Unless the element is explicitly recited using the phrase “means for…,” no claim element herein should be construed under 35 U.S.C. §112(f). As used herein, the terms “comprises,” “comprising,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but may also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Claims

1. A method of electronically monitoring temperature sensitive materials within a shipping container during transport, the method comprising: monitoring a tilt of the transport container and a temperature within the transport container for a first duration; sampling values ​​of the monitored tilt of the transport container over the first duration and storing a maximum tilt value corresponding to a maximum sample over the first duration and a minimum tilt value corresponding to a minimum sample over the first duration; sampling values ​​of the monitored temperature within the transport container over the first duration and storing an array of temperature samples; subdividing the array of temperature samples into subarrays associated with corresponding subdurations of the first duration; calculating a rate of temperature change for each subarray associated with each corresponding subduration; calculating a temperature change rate of the temperature sample array during the first duration; calculating a temperature change for each subarray associated with each corresponding subduration; calculating a temperature variation of the temperature sample array within the first duration; applying a plurality of filters to the maximum tilt value, the minimum tilt value, a current temperature value, an amount of temperature change of at least one subarray associated with the corresponding subduration, an amount of temperature change of the temperature sample array over the first duration, a rate of temperature change of at least one subarray associated with the corresponding subduration, and a rate of temperature change of the temperature sample array over the first duration; as well as A status of a failure status flag is set in response to the filtering, wherein the status comprises one of: not set, alarm, no alarm, warm, and false alarm.

2. The method according to claim 1, wherein The state of the fail status flag is set to a false alarm in response to each condition of a first filter having a first set of conditions being true, and wherein the state of the fail status flag is set to unset in response to at least one condition of the first set of conditions being false, the first set of conditions comprising: The temperature change amount of the temperature sample array during the first duration is greater than a first temperature rate threshold; a maximum tilt value during said first duration being greater than or equal to a maximum tilt limit; The minimum tilt value during the first duration is less than or equal to a minimum tilt limit; and The current temperature value is less than the first upper temperature threshold.

3. The method according to claim 2, in, The first temperature rate threshold is 4 degrees; The first duration is 5 hours; The maximum tilt limit is 20 degrees, and The minimum tilt limit is 20 degrees.

4. The method according to claim 3, wherein: The first temperature upper limit threshold is -150 degrees Celsius.

5. The method according to claim 2, wherein: Further in response to at least one condition in the first set of conditions being false, applying a second filter, wherein the second filter comprises a second set of conditions, and wherein in response to all of the second set of conditions being true, the state of the failure status flag is set to alarm, and in response to at least one of the second set of conditions being false, the state of the failure status flag is set to unset, the second set of conditions comprising: The temperature change of each sub-array is greater than or equal to a minimum temperature increase threshold; and The current temperature value is between the first temperature upper threshold and the first temperature lower threshold.

6. The method according to claim 5, wherein: The minimum temperature increase threshold is 0.8 degrees Celsius, wherein each subarray is associated with a sub-duration equal to one hour, and wherein the first duration is equal to 5 hours, and wherein the first upper temperature threshold is -150 degrees Celsius, and the first lower temperature threshold is -190 degrees Celsius.

7. The method according to claim 5, wherein: Further in response to at least one condition in the second set of conditions being false, applying a third filter, wherein the third filter comprises a third set of conditions, and wherein, in response to all of the third set of conditions being true, the state of the failure status flag is set to alarm, and in response to at least one of the third set of conditions being false, the state of the failure status flag is set to unset, the third set of conditions comprising: The current temperature value is greater than a first temperature upper limit threshold; and The temperature sample array includes temperatures that are each above a first upper temperature threshold for a number of samples corresponding to less than a second duration.

8. The method according to claim 7, wherein: The second duration is 4 hours, the first duration is 5 hours, and the first upper temperature threshold is -150 degrees Celsius.

9. The method according to claim 7, wherein: Further in response to at least one condition in the third set of conditions being false, applying a fourth filter, wherein the fourth filter comprises a fourth set of conditions, and wherein in response to all of the fourth set of conditions being true, the state of the fail status flag is set to warm, and in response to at least one of the fourth set of conditions being false, the state of the fail status flag is set to unset, the fourth set of conditions comprising: The temperature sample array includes temperatures that are each above a first upper temperature threshold for a plurality of samples corresponding to greater than or equal to a second duration.

10. The method according to claim 9, wherein: The second duration is 4 hours, the first duration is 5 hours, and the first upper temperature threshold is -150 degrees Celsius.

11. The method according to claim 9, wherein Further in response to at least one condition in the fourth set of conditions being false, applying a fifth filter, wherein the fifth filter includes a fifth set of conditions, and wherein, in response to all of the fifth set of conditions being true, the state of the failure status flag is set to false alarm, and in response to at least one condition in the fifth set of conditions being false, the state of the failure status flag is set to no alarm, the fifth set of conditions including: The temperature variation of at least one of the subarrays exceeds or is equal to the subarray quantity limit; and The current temperature value is less than or equal to a sampled value of the monitored temperature within the transport container collected within the first duration in the past.

12. The method according to claim 11, wherein Each sub-duration is 1 hour, the first duration is 5 hours, and the sub-array quantity limit is 20 degrees Celsius, and wherein a remote monitoring controller reads the status of the failure status flag and displays a human-readable alarm corresponding to the status of the failure status flag.

13. A method of electronically monitoring temperature sensitive materials within a shipping container during transport, the method comprising: receiving electronic data corresponding to a plurality of environmental variables collected by sensors attached to the transport container; storing electronic values ​​of the plurality of environmental variables for a first duration; calculating a rate of change of an electronic value of at least one of the environmental variables over the first duration; calculating a magnitude of change in an electronic value of at least one of the environmental variables during the first duration; and In response to at least one of the rate of change of at least one of the environmental variables within the first duration and the magnitude of change of at least one of the environmental variables within the first duration satisfying one or more filters, setting a state of a failure status flag.

14. A computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform a method for electronically monitoring temperature-sensitive materials within a shipping container during transport using a computer system, the method comprising: monitoring a tilt of the transport container and a temperature within the transport container for a first duration; sampling values ​​of the monitored tilt of the transport container over the first duration and storing a maximum tilt value corresponding to a maximum sample over the first duration and a minimum tilt value corresponding to a minimum sample over the first duration; sampling values ​​of the monitored temperature within the transport container over the first duration and storing an array of temperature samples; subdividing the array of temperature samples into subarrays associated with corresponding subdurations of the first duration; calculating a rate of temperature change for each subarray associated with each corresponding subduration; calculating a temperature change rate of the temperature sample array during the first duration; calculating a temperature change for each subarray associated with each corresponding subduration; calculating a temperature variation of the temperature sample array within the first duration; applying a plurality of filters to the maximum tilt value, the minimum tilt value, a current temperature value, an amount of temperature change of at least one subarray associated with the corresponding subduration, an amount of temperature change of the temperature sample array over the first duration, a rate of temperature change of at least one subarray associated with the corresponding subduration, and a rate of temperature change of the temperature sample array over the first duration; as well as A status of a failure status flag is set in response to the filtering, wherein the status comprises one of: not set, alarm, no alarm, warm, and false alarm.

15. The computer-readable storage medium of claim 14, wherein: The state of the fail status flag is set to a false alarm in response to each condition of a first filter having a first set of conditions being true, and wherein the state of the fail status flag is set to unset in response to at least one condition of the first set of conditions being false, the first set of conditions comprising: The temperature change amount of the temperature sample array during the first duration is greater than a first temperature rate threshold; a maximum tilt value during said first duration being greater than or equal to a maximum tilt limit; The minimum tilt value during the first duration is less than or equal to a minimum tilt limit; and The current temperature value is less than the first upper temperature threshold.

16. The computer-readable storage medium of claim 15, wherein: Further in response to at least one condition in the first set of conditions being false, applying a second filter, wherein the second filter comprises a second set of conditions, and wherein in response to all of the second set of conditions being true, the state of the failure status flag is set to alarm, and in response to at least one of the second set of conditions being false, the state of the failure status flag is set to unset, the second set of conditions comprising: The temperature change of each sub-array is greater than or equal to a minimum temperature increase threshold; and The current temperature value is between the first temperature upper threshold and the first temperature lower threshold.

17. The computer-readable storage medium of claim 16, wherein: The minimum temperature increase threshold is 0.8 degrees Celsius, wherein each subarray is associated with a sub-duration equal to one hour, and wherein the first duration is equal to 5 hours, and wherein the first upper temperature threshold is -150 degrees Celsius, and the first lower temperature threshold is -190 degrees Celsius.

18. The computer-readable storage medium of claim 16, wherein: Further in response to at least one condition in the second set of conditions being false, applying a third filter, wherein the third filter comprises a third set of conditions, and wherein, in response to all of the third set of conditions being true, the state of the failure status flag is set to alarm, and in response to at least one of the third set of conditions being false, the state of the failure status flag is set to unset, the third set of conditions comprising: The current temperature value is greater than a first temperature upper limit threshold; and The temperature sample array includes temperatures that are each above a first upper temperature threshold for a number of samples corresponding to less than a second duration.

19. The computer-readable storage medium of claim 18, wherein: Further in response to at least one condition in the third set of conditions being false, applying a fourth filter, wherein the fourth filter comprises a fourth set of conditions, and wherein in response to all of the fourth set of conditions being true, the state of the fail status flag is set to warm, and in response to at least one of the fourth set of conditions being false, the state of the fail status flag is set to unset, the fourth set of conditions comprising: The temperature sample array includes temperatures that are each above a first upper temperature threshold for a plurality of samples corresponding to greater than or equal to a second duration.

20. The computer-readable storage medium of claim 18, wherein: Further in response to at least one condition in the fourth set of conditions being false, applying a fifth filter, wherein the fifth filter includes a fifth set of conditions, and wherein, in response to all of the fifth set of conditions being true, the state of the failure status flag is set to false alarm, and in response to at least one condition in the fifth set of conditions being false, the state of the failure status flag is set to no alarm, the fifth set of conditions including: The temperature variation of at least one of the subarrays exceeds or is equal to the subarray quantity limit; and The current temperature value is less than or equal to a sampled value of the monitored temperature within the transport container collected within the first duration in the past.

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