Control method and device for air container iot module, and air container

Abstract

The application relates to the technical field of aviation container control, and discloses a control method and device for an aviation container internet-of-things module and an aviation container. The control method is applied to the internet-of-things module arranged on the aviation container. The control method comprises the following steps: collecting air pressure information of an environment in which the aviation container internet-of-things module is located, so as to determine an air pressure change state of an aircraft; collecting acceleration information of the aviation container in a motion process, so as to determine a running state of the aircraft; determining a flight stage of the aircraft based on the air pressure change state and the running state; and controlling a wireless radio frequency unit of the aviation container internet-of-things module according to the flight stage. By introducing multi-dimensional running state information for joint judgment, the probability of misjudgment caused by vibration, attitude change or environmental meteorological factors can be effectively reduced, so that the accuracy and reliability of the judgment result of the flight stage are improved.

Description

Technical Field

[0001] This application relates to the field of aviation container control technology, such as a control method, device, and aviation container for an aviation container IoT module. Background Technology

[0002] Airline containers are widely used in air freight, cold chain transportation, and the air transport of high-value goods. To achieve real-time monitoring of the transport status of air containers, IoT modules are typically installed on them to collect environmental parameters and operational status information, and transmit the relevant data to a remote monitoring system via wireless communication. These IoT modules usually integrate cellular communication and wireless data transmission radio frequency units, and are considered portable electronic devices. During aircraft operation, the radio frequency signals generated by these IoT modules may cause electromagnetic interference to the aircraft's avionics systems. For flight safety reasons, current aviation regulations typically require limiting or disabling the radio frequency transmission functions of portable electronic devices during specific phases of aircraft takeoff and flight. Therefore, in the application of IoT in air containers, how to automatically control the radio frequency operation status of IoT modules at appropriate times has become a key technical problem that needs to be solved.

[0003] In existing technologies, to achieve automatic control of the radio frequency unit (RFU), it is usually necessary to determine the flight phase of the aircraft. A common technical solution is to make this determination based on the acceleration information of the aircraft during operation. For example, by collecting acceleration data of the IoT module during transportation and performing statistical analysis or model matching on the acceleration data, it is possible to identify whether the aircraft is in flight and control the RF unit to turn on or off accordingly.

[0004] In the process of realizing this invention, at least the following problems were found in the prior art: The aforementioned scheme based solely on acceleration information has certain limitations in practical applications. Because air containers may be affected by vibration, impact, or changes in installation attitude during transportation, loading, unloading, or ground taxiing, relying solely on acceleration information can easily misinterpret ground transportation or equipment vibration as flight conditions, thus affecting the accuracy and reliability of radio frequency control. Furthermore, it is difficult to simultaneously meet both flight safety requirements and the practical needs of IoT applications.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0007] This disclosure provides a control method and apparatus for an IoT module in an aviation container, and an aviation container, to improve the accuracy of the on / off control of the wireless radio frequency unit of the IoT module.

[0008] In some embodiments, the control method for an aviation container IoT module is applied to an IoT module installed on an aviation container; the control method includes: collecting air pressure information of the environment where the aviation container IoT module is located to determine the air pressure change state of the aircraft; collecting acceleration information of the aviation container during movement to determine the operating state of the aircraft; determining the flight stage of the aircraft based on the air pressure change state and the operating state; and controlling the radio frequency unit of the aviation container IoT module according to the flight stage.

[0009] In some embodiments, the control device for the aviation container IoT module includes: a pressure acquisition unit configured to acquire pressure information of the environment in which the aviation container IoT module is located, to determine the pressure change state of the aircraft; an acceleration acquisition unit configured to acquire acceleration information of the aviation container during movement, to determine the operating state of the aircraft; a processing unit configured to determine the flight stage of the aircraft based on the pressure change state and the operating state; and a radio frequency control unit configured to control the radio frequency unit of the aviation container IoT module according to the flight stage.

[0010] In some embodiments, the control device for an air container IoT module includes a processor and a memory storing program instructions, the processor being configured to execute the control method for the air container IoT module as described above when running the program instructions.

[0011] In some embodiments, the air container includes: Air containers; The IoT module is installed in the aviation container; The control device for the IoT module of the aviation container, as described above, is installed in the IoT module.

[0012] The control method and apparatus for the IoT module of an aviation container and the aviation container provided in this disclosure can achieve the following technical effects: This invention determines the aircraft's air pressure change status by collecting air pressure information of the environment where the aviation container IoT module is located, and determines the aircraft's operating status by collecting acceleration information of the aviation container during movement. Then, based on the air pressure change status and the operating status, the aircraft's flight phase is determined. Compared to existing technologies that rely solely on single air pressure or single acceleration information for judgment, this invention introduces multi-dimensional operating status information for joint judgment, effectively reducing the probability of misjudgment caused by vibration, attitude changes, or environmental meteorological factors, thereby improving the accuracy and reliability of flight phase determination results. After determining the aircraft's flight phase, this invention controls the wireless radio frequency unit (RF unit) of the aviation container IoT module according to the flight phase, causing the RF unit to enter a restricted operating state during flight-related phases and a normal operating state during non-flight-related phases. Through this method, this invention can achieve automatic control of the RF unit without human intervention, reducing the risk of potential electromagnetic interference from RF signals to the aircraft's avionics system during flight-related phases, thereby improving safety during flight.

[0013] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0014] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a flowchart illustrating a control method for an IoT module for an aviation container provided in an embodiment of this disclosure; Figure 2 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment of the present disclosure; Figure 3 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment of the present disclosure; Figure 4 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment of the present disclosure; Figure 5 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment of the present disclosure; Figure 6 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment of the present disclosure; Figure 7 This is a schematic diagram of a control device for an IoT module for an aviation container provided in an embodiment of the present disclosure; Figure 8 This is a schematic diagram of another control device for an aviation container IoT module provided in an embodiment of the present disclosure. Detailed Implementation

[0015] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0016] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0017] Unless otherwise stated, the term "multiple" means two or more.

[0018] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0019] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0020] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0021] In the field of flight phase identification and radio frequency control technology for aviation container IoT modules, those skilled in the art typically tend to rely on a single operational parameter for judgment when solving the problem of automatic aircraft flight phase identification. For example, they might determine whether an aircraft has taken off or entered a flight phase by detecting acceleration information during aircraft operation or by detecting changes in ambient air pressure with altitude. Those skilled in the art generally believe that acceleration changes or air pressure changes are direct physical quantities reflecting the aircraft's flight status, and that automatic flight phase identification can only be achieved by detecting and thresholding these single parameters. Therefore, for a long time, related research and engineering implementations have largely revolved around data from a single sensor.

[0022] However, the inventors discovered limitations in their research. While both acceleration and air pressure information can reflect an aircraft's operational status to some extent, their physical meanings and mechanisms of change differ across different operational phases, and both are susceptible to misjudgment due to specific operating conditions. For example, acceleration information may fluctuate significantly during ground taxiing or equipment vibration, while air pressure changes are not yet significant during takeoff preparation. Relying solely on either parameter makes it difficult to ensure both timeliness and reliability of the assessment.

[0023] Based on the above understanding, the inventors broke through the technological inertia of "relying on a single operational parameter to determine the flight stage" in existing technologies, and proposed a technical approach to determine the aircraft's flight stage based on a comprehensive analysis of air pressure changes and operational status. The inventors recognized that air pressure changes reflect the overall trend of aircraft altitude changes, while operational status characterizes whether the aircraft is in actual motion; both reflect the aircraft's operational characteristics from different physical dimensions. In actual operation, when air pressure changes and operational status are matched, their combined result can more accurately reflect the aircraft's flight stage.

[0024] Based on the aforementioned technical concept, this application determines the air pressure change state by collecting the air pressure information of the environment where the aviation container IoT module is located, and determines the operating state by collecting the acceleration information of the aviation container during its movement. Then, based on a comprehensive analysis of the air pressure change state and the operating state, the flight stage of the aircraft is determined, and the wireless radio frequency unit of the IoT module is controlled according to the flight stage. Through experiments and engineering verification, the inventors have found that this method does not rely on a single operating parameter, and can achieve more accurate and reliable stage identification during the aircraft's takeoff preparation and flight stages, effectively avoiding the problems of judgment lag or misjudgment in existing technologies.

[0025] It is important to note that during actual aircraft operation, pressure changes and operational status are not independent entities, but rather jointly influenced by the aircraft's overall operating conditions. Specifically, pressure changes are not only related to changes in aircraft altitude but may also be affected by factors such as changes in weather conditions; operational status is not only related to the aircraft's actual motion but may also be affected by external factors such as ground vibrations and loading / unloading operations. Therefore, a single operational parameter cannot fully reflect the aircraft's actual flight phase. The combined result of pressure changes and operational status allows for cross-verification of the aircraft's operational conditions from different dimensions, thus providing a more accurate reflection of the aircraft's flight phase, rather than simply the change in a single physical quantity.

[0026] Figure 1This is a flowchart illustrating a control method for an IoT module in an aviation container, provided by an embodiment of this disclosure. The control method is applied to an IoT module installed on an aviation container. The IoT module includes at least a barometric pressure acquisition unit, an acceleration acquisition unit, a processing unit, and a radio frequency control unit, used to achieve automatic control of the radio frequency unit during air transport.

[0027] like Figure 1 As shown, the control method includes: Step S101: Collect the air pressure information of the environment where the aviation container IoT module is located to determine the air pressure change status of the aircraft.

[0028] Air pressure change state is used to describe the characteristics of environmental air pressure change over time, reflecting the altitude or environmental change trend corresponding to the operational phase of the aircraft. In this application, air pressure change state includes at least one of the following types: air pressure rising state, air pressure falling state, and air pressure stable state.

[0029] Here, we avoid relying directly on instantaneous air pressure values ​​or absolute altitude, and instead reflect the aircraft's operational phase through trends in change.

[0030] For example, multiple air pressure measurements can be collected within a preset time range, the air pressure measurements can be processed to obtain characteristic information reflecting the change of air pressure over time, and the air pressure change state can be determined based on the characteristic information.

[0031] In another example, the current air pressure status can be determined by comparing the currently collected air pressure information with pre-acquired or stored reference air pressure information and analyzing the differences between the two. This reference air pressure information can be historical air pressure information, initial air pressure information, or statistical air pressure information.

[0032] In this way, the raw air pressure measurement value is transformed into state information that can characterize the trend of changes in the operating environment, which can reduce the impact of instantaneous air pressure fluctuations on the judgment results and provide input conditions for the environmental change dimension for subsequent flight phase judgments.

[0033] Step S102: Collect acceleration information of the air container during its movement to determine the aircraft's operating status.

[0034] Operational status is used to characterize whether an aircraft is in motion and the comprehensive state of motion intensity and duration, so as to summarize the motion characteristics of the aircraft under different operating conditions.

[0035] For example, the acceleration information of air containers can be collected within a preset time range, the collected acceleration information can be processed, and the operating status of the aircraft can be determined based on the changes in acceleration.

[0036] In another example, statistical features that reflect the intensity or duration of motion can be obtained by statistically processing the collected acceleration information, and the operating status of the aircraft can be determined based on these statistical features.

[0037] In this way, the raw acceleration data is transformed into operational status information that reflects the dynamic behavior of the aircraft, providing a basis for determining whether the aircraft is in motion during the flight phase and improving the ability to identify the true operational status of the aircraft.

[0038] Step S103: Determine the flight phase of the aircraft based on the air pressure change status and operating status.

[0039] Flight phases are used to describe the stage an aircraft is in during operation. Their definition serves the needs of subsequent radio frequency control, rather than referring to a complete flight phase division in the sense of aviation standards.

[0040] For example, by using air pressure changes and operational status as input conditions, the flight phase of an aircraft can be determined based on the combination of these two conditions. This can be a rule-based judgment, a logical judgment, or a mapping relationship.

[0041] In another example, stage judgment can be achieved by comprehensively analyzing the air pressure change state and the operational state, such as weighting analysis, priority analysis or other comprehensive methods, to obtain an assessment result that reflects the overall operational stage of the aircraft, and the flight stage of the aircraft can be determined based on the assessment result.

[0042] In this way, by integrating information from different physical dimensions, a more comprehensive judgment result on the operational phase can be obtained, avoiding misjudgments or judgment delays caused by relying on a single operational parameter, and improving the stability and reliability of flight phase identification.

[0043] Step S104: Control the wireless radio frequency unit of the aviation container IoT module according to the flight phase.

[0044] The wireless radio frequency unit is a functional module used to realize wireless communication functions, and its working state can change according to control commands.

[0045] For example, the radio frequency unit can be controlled to switch between different operating states according to the determined flight phase, in order to adapt to the needs of different operating phases.

[0046] In another example, the operating parameters of the radio frequency unit can be adjusted according to the flight phase, so that it exhibits different operating behaviors at different flight phases.

[0047] In this way, the working status of the radio frequency unit is linked to the flight phase of the aircraft, enabling the radio frequency unit to automatically adjust its working behavior according to the operational phase.

[0048] In summary, the control method for IoT modules in air containers provided in this disclosure can accurately determine the flight stage of the aircraft based on a comprehensive analysis of the environmental air pressure change and the aircraft's operating status during the operation of the air container with the aircraft. It can also automatically control the wireless radio frequency unit of the IoT module during the flight-related stages, thereby reducing the risk of interference from wireless radio frequency signals to the aircraft's avionics system without relying on manual intervention.

[0049] For example, in step S103, the flight phase can be determined in the following way: By treating air pressure change and operational status as two independent judgment dimensions, the flight phase of the aircraft is determined by judging the relationship between the combination of these two states.

[0050] When the air pressure change status indicates that the air pressure in the environment where the aircraft is located is changing significantly, and the operation status indicates that the aircraft is in continuous motion, it is determined that the aircraft has entered or is about to enter the flight-related phase. When the air pressure change indicates that the environment in which the aircraft is located is basically stable, and the operational status indicates that the aircraft is stationary, the aircraft is determined to be in the ground phase.

[0051] In other embodiments, different judgment weights or priorities can be assigned to the air pressure change state and the operating state respectively, and the flight phase can be determined based on the comprehensive evaluation results.

[0052] When the operational status indicates that the aircraft is taxiing or accelerating, it is preliminarily determined that the aircraft is not in the ground phase. Based on this, the takeoff preparation phase and the flight phase are further distinguished according to the changes in air pressure.

[0053] This implementation method allows for flexible adjustments to the stage division through different weight configurations or judgment logic to adapt to different application scenarios.

[0054] In addition, a mapping table between air pressure change states, operating states, and flight stages can be pre-established, and the flight stage can be determined by looking up the table based on the current state combination during actual operation.

[0055] Figure 2 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment.

[0056] like Figure 2 As shown, the control method includes: Step S201: Collect the air pressure information of the environment where the aviation container IoT module is located to determine the air pressure change status of the aircraft.

[0057] Step S202: Collect acceleration information of the air container during its movement to determine the aircraft's operating status.

[0058] Step S203: Determine the flight phase of the aircraft based on the air pressure change status and operating status.

[0059] Step S204: When the flight phase is a flight-related phase, control the wireless radio frequency unit of the aviation container IoT module to enter a restricted working state.

[0060] Here, flight-related phases are used to characterize the set of phases during aircraft operation where restrictions on radio frequency transmissions are required.

[0061] Restricted operating state is a functional control state defined for the operating mode of a radio frequency unit. It describes the operating state in which the radio frequency unit's radio transmission capability is restricted during flight-related phases.

[0062] For example, after determining that the aircraft is in a flight-related phase, the radio frequency unit is switched from normal operation to restricted operation by control commands, so that the radio frequency unit stops or restricts its radio frequency transmission behavior.

[0063] In another example, after determining that the aircraft is in a flight-related phase, the operating parameters of the radio frequency unit are adjusted to limit its radio frequency transmission capability, thereby putting it into a restricted operating state. The adjusted operating parameters may include transmission power, transmission timing, data transmission permissions, etc.

[0064] In this way, the radio frequency unit automatically enters a controlled state during the flight-related phase, effectively reducing or avoiding the transmission of radio frequency signals during the flight-related phase, and providing a direct technical control means for subsequent flight safety requirements.

[0065] Step S205: When the flight phase is a non-flight-related phase, control the wireless radio frequency unit of the aviation container IoT module to enter the normal working state.

[0066] The restricted operating states include at least one of shutting down the wireless radio frequency unit, reducing the transmission power, or prohibiting wireless data transmission.

[0067] Non-flight-related phases are functional phases that correspond to flight-related phases and are used to characterize the phases during aircraft operation where there is no need to restrict radio frequency transmission.

[0068] Normal operating state refers to the operating state of a wireless radio frequency unit when it is not restricted. It describes the state in which the wireless radio frequency unit can perform wireless communication normally according to its design function.

[0069] For example, after determining that the aircraft is in a non-flight-related phase, the restrictions imposed on the radio frequency unit are lifted, allowing the radio frequency unit to resume its normal radio frequency transmission and data communication capabilities.

[0070] In another example, when the flight phase switches from a flight-related phase to a non-flight-related phase, the control logic triggers the radio frequency unit to switch to normal operating mode, thereby restoring its original wireless communication behavior.

[0071] In this way, the radio frequency unit can maintain normal communication capabilities during the phase that does not affect flight safety, and avoid the radio frequency unit being in a restricted state for a long time, which would affect the IoT application function. This achieves dynamic matching between radio frequency control and the aircraft operation phase.

[0072] The control method for an aviation container IoT module provided in this disclosure can control the radio frequency unit (RF unit) of the aviation container IoT module according to the flight phase after determining the flight phase of the aircraft. This allows the RF unit to enter a restricted operating state during flight-related phases and a normal operating state during non-flight-related phases. Through this method, the present invention can achieve automatic control of the RF unit without manual intervention, reducing the risk of potential electromagnetic interference from RF signals to the aircraft's avionics system during flight-related phases, thereby improving safety during flight.

[0073] Meanwhile, by differentiating between flight-related and non-flight-related phases, this invention implements differentiated control of the radio frequency unit, enabling the IoT module to maintain normal wireless communication capabilities during non-flight-related phases without compromising flight safety. Therefore, while meeting aviation safety requirements, this invention fully leverages the application value of the aviation container IoT module in transportation monitoring and status acquisition, enhancing the system's practicality in real-world aviation logistics scenarios.

[0074] The following describes how to determine the air pressure change status of an aircraft, using specific examples.

[0075] Figure 3 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment.

[0076] like Figure 3 As shown, the control method includes: Step S301: Collect air pressure information within a preset time period and determine the air pressure reference value.

[0077] The preset time period is a functional time parameter used to limit the range of air pressure information collection. It is used to characterize the sampling and statistical analysis of air pressure information within a certain time range.

[0078] The air pressure reference value is a numerical value used in this application to characterize the air pressure reference level within a preset time period. It is not a single instantaneous air pressure measurement value, but a reference value used for subsequent calculations of air pressure changes. This reference value is used to reflect the air pressure reference state of the environment in which the aircraft is located within a specific time period.

[0079] Here, multiple air pressure measurements can be collected within a preset time period, and statistical processing can be performed on the collected air pressure measurements to obtain a reference air pressure value that characterizes the air pressure level within that time period. Statistical processing may include averaging, median calculation, or other statistical methods.

[0080] In another example, at least one representative air pressure measurement value can be selected within a preset time period as the air pressure reference value. This representative air pressure measurement value can be the initial air pressure measurement value, the final air pressure measurement value, or a screened air pressure measurement value.

[0081] Step S302: Obtain the change between the current air pressure information and the air pressure reference value.

[0082] The change is used to characterize the degree of deviation of the current air pressure information from the air pressure reference value, reflecting the relative change in air pressure.

[0083] Here, the difference between the current air pressure information and the air pressure reference value can be calculated to obtain the change in air pressure that characterizes the degree of change.

[0084] In another example, the change in air pressure can be obtained by proportionally calculating or normalizing the current air pressure information with the air pressure reference value.

[0085] Step S303: Determine the aircraft's air pressure change state based on the change amount.

[0086] Air pressure change status is used to characterize the air pressure change characteristics of the environment in which an aircraft is located, and to describe the overall trend or category of air pressure change.

[0087] Here, the corresponding air pressure change state can be determined based on the magnitude of the change relative to a preset reference condition. Alternatively, the air pressure change state can be determined based on how the change occurs over time.

[0088] By converting continuous pressure changes into discrete state information, the availability of pressure change characteristics in subsequent processing can be improved, providing state-level input for judging the operational status based on pressure changes.

[0089] Step S304: Collect acceleration information of the air container during its movement to determine the aircraft's operating status.

[0090] Step S305: Determine the flight phase of the aircraft based on the air pressure change status and operating status.

[0091] Step S306: Control the wireless radio frequency unit of the aviation container IoT module according to the flight phase.

[0092] Optionally, the pressure change state of the aircraft can be determined based on the amount of change, including: When the change in the quantity shows an increasing trend over time, it is determined that the aircraft is in a state of increasing air pressure. If the change in pressure shows a decreasing trend over time, it can be determined that the aircraft is in a state of depressurization. If the change remains stable over time, it is determined that the aircraft is in a state of stable air pressure.

[0093] In this embodiment, the air container IoT module is equipped with a pressure sensor and a processing unit to collect ambient air pressure information during the operation of the air container with the aircraft, and to determine the air pressure change status of the aircraft based on the collected air pressure information.

[0094] Specifically, after the air container is loaded and operates with the aircraft, the barometric pressure sensor continuously collects the barometric pressure information of the environment where the air container IoT module is located at a preset sampling period, and sends the collected barometric pressure information to the processing unit.

[0095] The processing unit stores and processes the collected multiple air pressure data within a preset time period, and determines an air pressure reference value based on the air pressure data within the preset time period. In this embodiment, the air pressure reference value is the average value of each air pressure sample value within the preset time period.

[0096] After obtaining the air pressure reference value, the processing unit acquires the air pressure information at the current moment and calculates the difference between the current air pressure information and the air pressure reference value as the air pressure change.

[0097] Subsequently, the processing unit analyzes the changes in air pressure over multiple consecutive sampling periods to determine the air pressure status of the aircraft.

[0098] In this embodiment, when the change in air pressure shows a continuous upward trend over time, the aircraft is determined to be in a state of increasing air pressure; when the change in air pressure shows a continuous downward trend over time, the aircraft is determined to be in a state of decreasing air pressure; when the change in air pressure remains stable over time, that is, fluctuates within a preset range and the overall change is not significant, the aircraft is determined to be in a state of stable air pressure.

[0099] In this way, the original air pressure information collected by the aviation container IoT module can be transformed into an air pressure change state that characterizes the changing trend of the aircraft's operating environment, thereby providing a reliable state input for subsequent processing based on the air pressure change state.

[0100] For example, firstly, the barometric pressure sensor samples at a preset sampling period, such as once every 1 second, and writes the sampling results to a circular buffer. The buffer is used to store barometric pressure data within a preset time period, such as storing barometric pressure samples from the most recent 60 seconds, for subsequent sliding window analysis. To reduce power consumption, the processor can enter a low-power sleep state between two samplings and wake up to perform data processing after a sampling interrupt is triggered.

[0101] Secondly, the air pressure data in the buffer is preprocessed by the processor. Preprocessing includes: A moving average filter is performed, and the air pressure value obtained from each sampling is averaged over N points to obtain the filtered air pressure value; in this embodiment, N=5.

[0102] Anomaly removal is performed. When the difference between the sampled value and the filtered value at the previous time exceeds the preset anomaly threshold, the sampled value is marked as an anomaly and discarded, or the value at the previous time is used as a substitute.

[0103] The above preprocessing is used to ensure the smoothness and continuity of the pressure sequence and to avoid misjudgment of pressure change status caused by instantaneous anomalies.

[0104] Next, the processor retrieves filtered air pressure data for a preset time period from the circular buffer and calculates the air pressure reference value. The air pressure reference value is used to characterize the air pressure reference level within the current window.

[0105] In this embodiment, the air pressure reference value can be obtained in one of the following ways: The average value of filtered air pressure data within a preset time period is used as the air pressure reference value; or The median of filtered air pressure data within a preset time period is used as the air pressure reference value to improve robustness to a small number of abnormal fluctuations.

[0106] The processor obtains the current filtered air pressure value and calculates its change ΔP relative to the air pressure reference value using the following formula: ΔP=P current P base ; Among them, P base P is the reference value for air pressure. current This represents the current filtered air pressure value.

[0107] Furthermore, the processor takes the most recent M ΔP values ​​and calculates their trend indicators to determine the aircraft's air pressure change status. In this embodiment, M=10, corresponding to a time window of 10 seconds.

[0108] Trend indicators can be obtained in one of the following ways: A linear fit is performed on the change of ΔP over time to obtain the slope as a trend indicator; or The difference between the first and last values ​​of ΔP is calculated as a trend indicator.

[0109] The processor determines the air pressure change state based on the change ΔP and its trend indicators.

[0110] In this embodiment, the following determination strategy can be adopted: When the trend indicator is greater than the preset rise judgment threshold, it is determined that the aircraft is in a state of rising air pressure. When the trend indicator is less than the preset descent judgment threshold, it is determined that the aircraft is in a state of depressurization. When the trend indicator is within the preset stable range, it is determined that the aircraft is in a state of stable air pressure.

[0111] The aforementioned thresholds can be set based on the barometric pressure sensor resolution, sampling period, and preset time interval, and can be adjusted according to different device models or application scenarios. To avoid frequent status fluctuations, the processor can also introduce a continuous judgment mechanism, updating the barometric pressure change status only when the same judgment result is true within K consecutive judgment periods.

[0112] Finally, once the air pressure change is determined, the processor stores this state as the current air pressure change state and uses it as a state input in subsequent control processes. The processor continuously repeats the above steps to achieve real-time updates of the air pressure change state.

[0113] Thus, by introducing a circular buffer, filtering, and outlier removal, and by calculating the air pressure reference value and its changing trend based on a sliding window, this embodiment can stably output the air pressure change status under low-power hardware conditions, reduce the impact of air pressure noise and instantaneous disturbances on status determination, and improve the continuity and reliability of status output.

[0114] The following describes how to determine the operational status of an aircraft, using specific examples.

[0115] Figure 4 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment.

[0116] like Figure 4 As shown, the control method includes: Step S401: Collect the air pressure information of the environment where the aviation container IoT module is located to determine the air pressure change status of the aircraft.

[0117] Step S402: Obtain the multi-axis acceleration information of the aviation container IoT module.

[0118] Multi-axis acceleration information refers to the set of acceleration data collected by accelerometers along multiple spatial axes, used to reflect the motion of air containers in three-dimensional space. It can be three-axis acceleration information or acceleration information obtained from combinations of more axes.

[0119] Here, a triaxial accelerometer can be used to collect acceleration components along three mutually perpendicular directions, forming multi-axis acceleration information. Alternatively, a combination of multiple single-axis or dual-axis accelerometers can be used to acquire acceleration information in multiple directions, which is then integrated in the processing unit to form multi-axis acceleration information. In this way, raw acceleration data reflecting the spatial motion characteristics of the air cargo container is obtained, providing a basic input for subsequent gravity direction determination and motion analysis, and improving the completeness of the air cargo container motion information acquisition.

[0120] Step S403: Determine the direction of gravity based on the multi-axis acceleration information.

[0121] Gravity direction refers to the directional component of gravitational acceleration in a multi-axis acceleration coordinate system under the current attitude, and is used to characterize the attitude direction of an air container relative to the gravitational field.

[0122] Here, the gravity component in the acceleration signal can be extracted by low-pass processing or time averaging of the multi-axis acceleration information, and the direction of gravity can be determined based on the gravity component. Alternatively, the main direction of gravity can be determined by analyzing the amplitude distribution of the multi-axis acceleration information along different axes, and the direction of gravity can be determined accordingly. In this way, a gravity reference direction can be obtained without the need for additional attitude sensors, providing a directional reference for subsequent coordinate system calibration and reducing the impact of differences in the installation attitude of IoT modules.

[0123] Step S404: Perform coordinate system calibration based on the multi-axis acceleration information according to the direction of gravity.

[0124] Coordinate system calibration refers to adjusting or transforming the coordinate system corresponding to multi-axis acceleration information according to the direction of gravity, so that the acceleration information can be compared and analyzed under a unified reference frame.

[0125] Here, the coordinate axis transformation of multi-axis acceleration information can be performed by calculating the relationship between the direction of gravity and a preset reference direction, aligning one of the axes with the direction of gravity. Alternatively, the acceleration components can be reconstructed by projecting the multi-axis acceleration information onto the direction of gravity and its perpendicular direction, achieving equivalent coordinate system calibration. This eliminates the influence of differences in the installation posture of IoT modules on acceleration analysis, making acceleration data under different installation conditions comparable and improving the stability of subsequent operational status assessments.

[0126] Step S405: Determine the aircraft's operating status based on the calibrated acceleration information.

[0127] By analyzing the variation characteristics of calibrated acceleration information within a preset time range, the corresponding operational state of the aircraft can be determined. Alternatively, statistical processing of the calibrated acceleration information can yield statistical characteristics reflecting motion intensity or activity level, and the operational state can be determined based on these statistical characteristics. Transforming the calibrated raw acceleration data into state-level operational information can effectively distinguish between the actual motion state and incidental vibrations or shocks, providing reliable input for subsequent control or judgment based on the operational state.

[0128] Step S406: Determine the flight phase of the aircraft based on the air pressure change status and operating status.

[0129] Step S407: Control the wireless radio frequency unit of the aviation container IoT module according to the flight phase.

[0130] Further, in step S405, the aircraft's operating status is determined based on the calibrated acceleration information, including: Acquire the variation characteristics of the calibrated acceleration information within a preset time period; Determine the aircraft's operational status based on the characteristics of the changes; The operating states include stationary state, gliding state, and acceleration state.

[0131] Here, the change characteristics refer to the changes in the calibrated acceleration information over a preset time period, used to characterize the variation law or dynamic characteristics of acceleration over time. These can include the magnitude of change, the trend of change, the stability of change, or the persistence of change.

[0132] For example, by analyzing the calibrated acceleration information within a preset time period, the range of acceleration amplitude variation and its stability characteristics can be obtained, and the aircraft's operating state can be determined based on the variation characteristics. When the acceleration variation amplitude is small and generally stable, it is determined to be in a stationary state; when the acceleration exhibits continuous variation but the amplitude is limited, it is determined to be in a taxiing state; when the acceleration variation amplitude is significant or shows a continuous increasing trend, it is determined to be in an acceleration state.

[0133] In another example, the aircraft's operational status can be determined by analyzing the trend of calibrated acceleration information over a preset time period, based on the directionality and persistence of the trend. When the acceleration trend is essentially zero or shows no obvious trend, it is determined to be in a stationary state; when the acceleration trend fluctuates within a small range but persists, it is determined to be in a taxiing state; and when the acceleration trend shows a clear unidirectional change and persists for a certain period of time, it is determined to be in an accelerating state.

[0134] In addition, statistical characteristics reflecting motion intensity can be obtained by statistically processing the calibrated acceleration information within a preset time period, and the operating status can be determined based on the statistical characteristics.

[0135] In this way, the continuous and noise-sensitive acceleration data can be transformed into stable operating status information, which can effectively distinguish different operating conditions such as stationary, coasting and acceleration, reduce the impact of short-term vibration and occasional impact on the judgment of operating status, and provide reliable status input for subsequent operating status-based processing or control.

[0136] Specifically, in this embodiment, the air container IoT module includes a three-axis accelerometer, a processor, and a memory. The three-axis accelerometer is installed inside the IoT module to collect acceleration information of the air container during transportation, and the processor is used to process the acceleration information and determine the aircraft's operating status.

[0137] First, the triaxial accelerometer samples the acceleration of the air container at a preset sampling frequency, for example, at 50Hz, acquiring acceleration components along the X, Y, and Z axes. a x 、a y 、a z The processor writes the sampled data into a circular buffer to store acceleration data within the most recent preset time period, such as storing the sampling sequence of the most recent 10 seconds.

[0138] Since the installation posture of IoT modules on air containers may vary, in order to improve the stability of the operation status judgment, the processor performs gravity direction estimation on the collected triaxial acceleration information.

[0139] In this embodiment, the processor performs low-pass processing or time averaging on the triaxial acceleration within a sliding window to obtain the gravity component vector. g=(g x ,g y ,g z ) And normalize it to obtain the unit vector of gravity direction. .

[0140] The processor calibrates the triaxial acceleration information based on the direction of gravity, enabling the acceleration information to be analyzed under a unified reference.

[0141] In this embodiment, the processor will use the instantaneous acceleration vector a = a x 、a y、a z It is decomposed into components along the direction of gravity and components perpendicular to the direction of gravity, and the dynamic component perpendicular to the direction of gravity is extracted. a ⊥ This serves as a basis for motion analysis, reducing the impact of posture changes on the judgment.

[0142] The processor extracts variation features from the calibrated acceleration data within a preset time period to characterize the motion intensity and duration of the air container. These variation features may include, but are not limited to: The root mean square value of the dynamic components of acceleration after calibration; Peak-to-peak value of the dynamic components of acceleration after calibration; The stability index of the change of dynamic acceleration components within the window after calibration, such as variance or absolute mean deviation.

[0143] The processor determines the aircraft's operating state based on the changing characteristics. In this embodiment, the operating state includes at least a stationary state, a taxiing state, and an acceleration state.

[0144] In this embodiment, it can be determined as follows: When the change characteristic is less than the first threshold and continues to meet the preset judgment time (e.g., 2 consecutive seconds), the aircraft is determined to be in a stationary state. When the change characteristics are between the first threshold and the second threshold and the preset judgment time is continuously met, the aircraft is determined to be in a taxiing state. When the change characteristic exceeds the second threshold or the change characteristic shows a continuous increasing trend within a preset time period, it is determined that the aircraft is in an acceleration state.

[0145] To avoid frequent state transitions under critical conditions, processors can also introduce hysteresis judgment or debouncing mechanisms, such as using different entry and exit thresholds, or requiring that the running state is updated only after multiple consecutive window judgment results are consistent.

[0146] The processor outputs the determined operating state as the current operating state and can periodically update the operating state to adapt to the changes in the state of air containers under different transportation conditions.

[0147] Through the above methods, this embodiment can suppress attitude differences and instantaneous disturbances based on the multi-axis acceleration information collected by the aviation container IoT module, and stably determine the aircraft's operating status based on the change characteristics within a preset time period, thereby improving the continuity and reliability of operating status identification.

[0148] The following describes how to determine the flight phase based on air pressure changes and operational status, using specific examples.

[0149] Figure 5This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment.

[0150] like Figure 5 As shown, the control method includes: Step S501: Collect the air pressure information of the environment where the aviation container IoT module is located to determine the air pressure change status of the aircraft. Step S502: Collect acceleration information of the air container during its movement to determine the aircraft's operating status; Step S503: When the air pressure change state is an air pressure increase state and the operating state is a taxiing state or an acceleration state, determine that the aircraft is in the takeoff preparation stage. Step S504: When the air pressure change state is a decreasing air pressure state and the operating state is a taxiing state or an acceleration state, determine that the aircraft is in the flight phase.

[0151] Step S505: When the air pressure change state is a stable air pressure state and the operating state is a stationary state, determine that the aircraft is in the ground phase.

[0152] Step S506: When the flight phase is a flight-related phase, control the wireless radio frequency unit of the aviation container IoT module to enter a restricted working state.

[0153] Step S507: When the flight phase is a non-flight-related phase, control the wireless radio frequency unit of the aviation container IoT module to enter the normal working state.

[0154] Step S508: If the flight stage cannot be determined based on the air pressure change and operating status, maintain the flight stage determined at the previous moment.

[0155] The flight phase determination conditions shown in steps S503 to S505 are illustrative examples used to describe the flight phase determination method when the air pressure change state and the operating state present a typical combination relationship. In actual operation, when the current air pressure change state and the operating state do not meet any of the above combination conditions, the processing unit may not update the aircraft's flight phase, continuing to maintain the flight phase determined at the previous moment, or may treat the current state as an undetermined flight phase state until the preset phase determination conditions are met.

[0156] In this embodiment, the processing unit determines and updates the aircraft's flight phase status based on the combination of air pressure change and operational status. The flight phase includes at least the ground phase, the takeoff preparation phase, and the flight phase.

[0157] During the flight phase determination process, the processing unit sequentially checks whether preset phase determination conditions are met based on the currently acquired air pressure change status and operational status. When the corresponding conditions are met, the processing unit updates the aircraft's flight phase; when no phase determination conditions are met, the processing unit maintains the current flight phase unchanged.

[0158] Specifically, when the air pressure change is in a state of rising air pressure and the operating state is in a taxiing or acceleration state, the processing unit determines the aircraft's flight phase as the takeoff preparation phase; when the air pressure change is in a state of falling air pressure and the operating state is in a taxiing or acceleration state, the processing unit determines the aircraft's flight phase as the flight phase; when the air pressure change is in a state of stable air pressure and the operating state is in a stationary state, the processing unit determines the aircraft's flight phase as the ground phase.

[0159] In actual operation, if the current air pressure change and operating status do not meet any of the above-mentioned stage determination conditions, the processing unit will not update the aircraft's flight stage and will continue to maintain the flight stage determination result from the previous moment until the subsequent status meets the preset stage determination conditions. This method avoids frequent flight stage switching caused by state fluctuations or transitional operating conditions, thereby improving the stability and reliability of the flight stage determination results.

[0160] The flight-related phases include the takeoff preparation phase and the flight phase; the non-flight-related phases include the ground phase.

[0161] Thus, by simultaneously incorporating both air pressure changes and operational status, multi-dimensional identification of aircraft flight phases can be achieved, effectively avoiding misjudgments caused by relying solely on single air pressure or acceleration information for phase determination. Furthermore, it can distinguish between different phases of an aircraft—stationary on the ground, during takeoff preparation, and during flight—improving the accuracy and stability of phase identification and providing reliable phase determination results for subsequent flight phase-based processing or control.

[0162] Figure 6 This is a flowchart illustrating another control method for an aviation container IoT module provided in this embodiment.

[0163] like Figure 6 As shown, the control method includes: Step S601: Collect the air pressure information of the environment where the aviation container IoT module is located to determine the air pressure change status of the aircraft.

[0164] Step S602: Collect acceleration information of the air container during its movement to determine the aircraft's operating status.

[0165] Step S603: Determine the flight phase of the aircraft based on the air pressure change status and operating status.

[0166] Step S604: Receive flight status information broadcast by the aircraft through the ADS-B system.

[0167] Step S605: Based on the flight status information, perform auxiliary judgment on the determination result of the flight phase.

[0168] Step S606: Based on the flight stage determined by the auxiliary judgment, control the wireless radio frequency unit of the aviation container IoT module.

[0169] Flight status information refers to the set of data information that an aircraft broadcasts to the outside world through the ADS-B (Automatic Dependent Surveillance-Broadcast) system, reflecting the flight status of the aircraft.

[0170] Here, ADS-B flight status information is not the sole basis for determining flight phases, but is used to verify, correct, or confirm the flight phase determination results obtained based on air pressure changes and operational status.

[0171] "Correction" or "confirmation" are used to describe two different outcomes that may arise from the auxiliary judgment: Correction refers to adjusting the flight phase when there is a significant inconsistency between the ADS-B information and the current flight phase determination result. Confirmation means that when the ADS-B information is consistent with or matches the judgment result of the current flight phase, the original judgment result remains unchanged.

[0172] In one implementation, the processing unit obtains the aircraft's altitude and / or airspeed information from the flight status information broadcast by ADS-B, and compares the altitude and / or airspeed information with preset reference conditions.

[0173] When altitude or airspeed information indicates that the aircraft is clearly in flight, but the current flight phase determination is still the ground phase or takeoff preparation phase, the flight phase determination is corrected; when altitude or airspeed information is consistent with the current flight phase determination, the flight phase determination is confirmed.

[0174] In another implementation, the processing unit makes a consistency judgment between the aircraft status reflected by the ADS-B flight status information and the flight phase determined based on the air pressure change status and operational status.

[0175] When the two remain consistent within a preset time range, the current flight phase determination result is confirmed; when the two remain inconsistent within a preset time range, the flight phase determination result is corrected.

[0176] Thus, without changing the main decision logic, flight status broadcast information from the aircraft itself is introduced to verify and enhance the flight phase determination results obtained based on air pressure change and operational status, thereby improving the reliability of flight phase identification under complex operating conditions or boundary conditions; and reducing the risk of flight phase misjudgment caused by sensor noise and short-term state fluctuations.

[0177] Among these, the determination of the flight phase is aided by flight status information, including: Obtain the aircraft's altitude and / or airspeed information from the flight status information.

[0178] And based on altitude and / or airspeed information, correct or confirm the corresponding flight phase of the aircraft.

[0179] Here, altitude and airspeed information refer to key parameters extracted from the flight status information broadcast by the aircraft via the ADS-B system, reflecting the aircraft's flight status. Altitude information can be barometric altitude, geometric altitude, or equivalent altitude; airspeed information can be ground speed, airspeed, or other parameters reflecting the aircraft's speed. This information serves as external flight status parameters to assist in determining the aircraft's current flight phase, rather than for directly controlling the radio frequency unit.

[0180] In one implementation, the processing unit obtains the aircraft's altitude information from the flight status information and compares the altitude information with the current flight phase determination result. When the altitude information indicates that the aircraft has clearly taken off and is in a continuous ascent state, but the current flight phase determination result is still the ground phase or takeoff preparation phase, the processing unit corrects the flight phase determination result; when the altitude information indicates that the aircraft's altitude change is not significant or remains stable, the processing unit confirms the current flight phase determination result.

[0181] In another implementation, the processing unit obtains the aircraft's airspeed information from the flight status information and makes auxiliary judgments on the flight phase determination result based on changes in the airspeed information. When the airspeed information indicates that the aircraft's airspeed has exceeded the preset flight-related reference conditions, but the current flight phase determination result is still the ground phase, the processing unit corrects the flight phase determination result; when the airspeed information matches the current flight phase determination result, the processing unit confirms the flight phase determination result.

[0182] In a further implementation, the processing unit simultaneously acquires altitude and airspeed information, and uses their combined relationship to assist in determining the flight phase. When both altitude and airspeed information indicate that the aircraft has entered flight, the flight phase determination result is confirmed or corrected; when only one of them meets a preset condition, the processing unit may simply confirm without making a correction.

[0183] By correcting or confirming the flight phase determination results based on altitude and / or airspeed information, this application introduces flight status broadcast information from the aircraft itself while maintaining the stability of the main determination logic, thereby enhancing the flight phase determination results and improving the reliability of flight phase identification under complex operating conditions.

[0184] Thus, by introducing flight status information broadcast by the ADS-B system, auxiliary judgments can be made on flight phases determined based on air pressure changes and operational status. The auxiliary judgments support multiple implementation methods and do not replace the main judgment logic, thereby improving the reliability and robustness of flight phase judgment results while maintaining system stability.

[0185] In practical applications, the IoT module for air containers is installed on the air container and includes a processor, a barometric pressure sensor, an accelerometer, an ADS-B signal receiving unit, and a radio frequency unit. The processor is used to determine the aircraft's operating status and flight phase based on multi-source information during the air container's operation with the aircraft, and controls the radio frequency unit accordingly.

[0186] After the air container IoT module is powered on and completes initialization, the processor first enters the initialization complete state. Upon completion of initialization, the processor records the following baseline parameters: 1) Record the initial zero-point value of acceleration. When the air container is initially stationary, the processor collects the acceleration information output by the multi-axis accelerometer and records the corresponding acceleration values ​​as the zero-point acceleration values ​​X0, Y0, and Z0, which serve as the reference benchmark for subsequent acceleration change calculations.

[0187] 2) Record the air pressure reference value After initialization, the processor collects the ambient air pressure information output by the barometer and records the first collected air pressure value as the air pressure reference value P0.

[0188] During the operation of the air container with the aircraft, the processor periodically collects multi-axis acceleration information and determines the aircraft's operating status based on the acceleration information.

[0189] Specifically, the processor determines the direction of gravity based on the zero-point value of acceleration and performs calibration processing on the collected acceleration information; subsequently, the processor calculates the acceleration magnitude A at each moment according to a preset sampling period. i .

[0190] In this embodiment, the processor determines the acceleration change through continuous data statistics, for example: When at least 9 out of 15 consecutive sampling points satisfy the condition that the acceleration modulus is greater than the first threshold, the aircraft is considered to have entered a state of continuous motion; the first threshold is 0.15g. Furthermore, when at least 3 out of 5 consecutive sampling points satisfy the acceleration modulus value being greater than the first threshold, the aircraft is confirmed to be in a taxiing or accelerating state. When the acceleration magnitude remains below the first threshold for an extended period of time, the aircraft is determined to be stationary.

[0191] In this way, the processor can distinguish between the stationary state, taxiing state, and acceleration state of an aircraft based on acceleration information.

[0192] The processor periodically collects air pressure information of the environment where the aviation container IoT module is located, and determines the air pressure change status of the aircraft based on the air pressure information.

[0193] In this embodiment, the processor records the air pressure value Pi at a preset period and records the minimum air pressure value P within a preset time window. imin The processor compares the minimum air pressure values ​​within multiple consecutive time windows, for example, to obtain P. 1min P 2min ... P 5min The preset time window is 5 seconds.

[0194] Based on the above air pressure data, the processor makes the following judgment: When the difference between the current air pressure value and the air pressure reference value is greater than the preset air pressure threshold, and the air pressure change rate is greater than the preset air pressure change rate threshold, the aircraft is determined to be in a state of increasing air pressure; the preset air pressure threshold is 0.6 kPa, and the preset air pressure change rate threshold is 0.15 kPa / min; When the minimum air pressure value shows a continuous decreasing trend within multiple consecutive time windows, and the minimum air pressure difference exceeds the preset air pressure threshold, it is determined that the aircraft is in a state of air pressure decline. When the air pressure changes are small and remains generally stable, the aircraft is considered to be in a state of stable air pressure.

[0195] During aircraft operation, the processor also receives flight status information broadcast by the aircraft through the ADS-B signal receiving unit, and parses it to obtain the aircraft's airspeed information v and altitude-related information h.

[0196] In this embodiment, the processor makes the following judgments on the ADS-B information: When at least two aircraft are detected to have speeds below a preset speed threshold, the aircraft are considered to be in a ground-related state; the preset speed threshold is 100 km / h. When the altitude status of at least two aircraft indicates that they have taken off the ground, the aircraft are considered to be in a flight-related state.

[0197] ADS-B information serves as supplementary input to the local sensor's judgment results, and is used to assist in judgment during subsequent flight phases.

[0198] The processor first makes the main determination for the flight phase based on the air pressure change and operational status: When the air pressure change is in the state of rising air pressure and the operating state is in the state of taxiing or acceleration, it is determined that the aircraft is in the takeoff preparation stage. When the air pressure change is a decreasing air pressure state and the operating state is a taxiing state or an acceleration state, the aircraft is determined to be in the flight phase. When the air pressure change state is a stable air pressure state and the operating state is a stationary state, the aircraft is determined to be in the ground phase.

[0199] After completing the primary determination, the processor further verifies the flight phase determination result based on ADS-B flight status information. When the altitude or airspeed status reflected in the ADS-B information is inconsistent with the current flight phase determination result, the processor corrects the flight phase; when the two are consistent, the current flight phase determination result is confirmed.

[0200] After obtaining final confirmation of the flight phase, the processor controls the radio frequency unit of the aviation container IoT module according to the flight phase: When the flight phase is a flight-related phase, the control radio frequency unit is turned off or enters a restricted operating state; the flight-related phase includes the takeoff preparation phase or the flight phase. When the flight phase is the ground phase, the control radio frequency unit enters normal operation.

[0201] Through the above methods, this embodiment can comprehensively utilize acceleration information, air pressure information, and ADS-B flight status information to accurately identify the flight phase of an aircraft without relying on a single sensor, and automatically shut down or limit the wireless radio frequency transmission of the aviation container IoT module during flight-related phases, thereby improving aviation operation safety.

[0202] Combination Figure 7 As shown, this disclosure provides a control device for an aviation container IoT module, comprising: The air pressure acquisition unit 701 is configured to collect air pressure information of the environment in which the aviation container IoT module is located in order to determine the air pressure change status of the aircraft. Acceleration acquisition unit 702 is configured to acquire acceleration information of air containers during movement in order to determine the operating status of the aircraft; Processing unit 703 is configured to determine the flight phase of the aircraft based on air pressure change status and operating status; The radio frequency control unit 704 is configured to control the radio frequency unit of the aviation container IoT module according to the flight phase.

[0203] Combination Figure 8 As shown, this disclosure provides a control device for an aviation container IoT module, including a processor 800 and a memory 801. Optionally, the device may further include a communication interface 802 and a bus 803. The processor 800, communication interface 802, and memory 801 can communicate with each other via the bus 803. The communication interface 802 can be used for information transmission. The processor 800 can call logical instructions in the memory 801 to execute the control method for the aviation container IoT module described in the above embodiment.

[0204] Furthermore, the logic instructions in the aforementioned memory 801 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0205] The memory 801, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 800 executes functional applications and data processing by running the program instructions / modules stored in the memory 801, thereby implementing the control method for the aviation container IoT module in the above embodiments.

[0206] The memory 801 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 801 may include high-speed random access memory and may also include non-volatile memory.

[0207] This disclosure also provides an aviation container, including: an aviation container; an IoT module installed in the aviation container; and the aforementioned control device for the aviation container IoT module. The control device for the aviation container IoT module is installed in the aforementioned IoT module. The installation relationship described herein is not limited to placement inside the aviation container IoT module, but also includes installation connections with other components of the IoT module, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the control device for the aviation container IoT module can be adapted to any feasible aviation container IoT module body, thereby realizing other feasible embodiments.

[0208] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to execute the control method described above for an aviation container IoT module.

[0209] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0210] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0211] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0212] The methods and products disclosed in the embodiments herein (including but not limited to devices and equipment) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0213] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A control method for an IoT module in an aviation container, characterized in that, The control method is applied to an IoT module installed on an aviation container; the control method includes: Collect air pressure information of the environment in which the aviation container IoT module is located to determine the air pressure change status of the aircraft; Acceleration information of air containers during their movement is collected to determine the aircraft's operational status; Based on the air pressure change and the operating status, the flight phase of the aircraft is determined; The wireless radio frequency unit of the aviation container IoT module is controlled according to the flight phase.

2. The control method according to claim 1, characterized in that, The wireless radio frequency unit that controls the aviation container IoT module according to the flight phase includes: When the flight phase is a flight-related phase, the wireless radio frequency unit of the control aviation container IoT module enters a restricted operating state; When the flight phase is a non-flight-related phase, the wireless radio frequency unit of the control aircraft container IoT module enters normal working state; The restricted operating state includes at least one of shutting down the wireless radio frequency unit, reducing the transmission power, or prohibiting wireless data transmission.

3. The control method according to claim 1, characterized in that, The process of collecting air pressure information of the environment in which the aviation container IoT module is located to determine the air pressure change status of the aircraft includes: Collect the air pressure information within a preset time period and determine the air pressure reference value; Obtain the change between the current air pressure information and the air pressure reference value; Based on the change, the pressure change state of the aircraft is determined.

4. The control method according to claim 3, characterized in that, Determining the aircraft's air pressure change state based on the change includes: When the change shows an increasing trend over time, it is determined that the aircraft is in a state of increasing air pressure. If the change shows a decreasing trend over time, it is determined that the aircraft is in a state of depressurization. If the change remains stable over time, the aircraft is determined to be in a state of stable air pressure.

5. The control method according to claim 1, characterized in that, The process of collecting acceleration information of the air container during its movement to determine the aircraft's operating status includes: Obtain the multi-axis acceleration information of the aviation container IoT module; The direction of gravity is determined based on the multi-axis acceleration information. The coordinate system is calibrated based on the direction of gravity for the multi-axis acceleration information. The aircraft's operational status is determined based on the calibrated acceleration information.

6. The control method according to claim 5, characterized in that, The process of determining the aircraft's operational status based on the calibrated acceleration information includes: Obtain the variation characteristics of the calibrated acceleration information within a preset time period; The aircraft's operational status is determined based on the aforementioned change characteristics; The operating states include a stationary state, a gliding state, and an acceleration state.

7. The control method according to claim 1, characterized in that, Determining the flight phase of the aircraft based on the air pressure change state and the operational state includes: When the air pressure change state is an upward air pressure state and the operating state is a taxiing state or an acceleration state, it is determined that the aircraft is in the takeoff preparation phase. When the air pressure change state is a decreasing air pressure state and the operating state is a taxiing state or an acceleration state, the aircraft is determined to be in the flight phase. When the air pressure change state is a stable air pressure state and the operating state is a stationary state, the aircraft is determined to be in the ground phase.

8. The control method according to claim 1, characterized in that, The process of determining the flight phase of the aircraft based on the air pressure change state and the operational state also includes: Receive flight status information broadcast by aircraft through the ADS-B system; Based on the flight status information, the determination result of the flight phase is used to make an auxiliary judgment.

9. The control method according to claim 8, characterized in that, The auxiliary judgment based on the flight status information to determine the flight phase includes: Obtain the aircraft's altitude and / or speed information from the flight status information; And based on the altitude information and / or airspeed information, the corresponding flight phase of the aircraft is corrected or confirmed.

10. A control device for an IoT module in an aviation container, characterized in that, include: The air pressure acquisition unit is configured to collect air pressure information of the environment in which the aviation container IoT module is located in order to determine the air pressure change status of the aircraft. The acceleration acquisition unit is configured to collect acceleration information of the air container during its movement in order to determine the aircraft's operating status; The processing unit is configured to determine the flight phase of the aircraft based on the air pressure change state and the operating state; A radio frequency control unit is configured to control the radio frequency unit of the aviation container IoT module according to the flight phase.

11. A control device for an aviation container IoT module, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running the program instructions, the control method for an aviation container IoT module as described in any one of claims 1 to 9.

12. An air container, characterized in that, include: Air containers; The IoT module is installed in the aviation container; The control device for an aviation container IoT module as described in claim 10 or 11 is installed in the IoT module.

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