A logistics unmanned aerial vehicle logistics cabin door intelligent lock control and state monitoring method and system

Abstract

The application relates to the technical field of unmanned aerial vehicles, and provides a logistics unmanned aerial vehicle logistics cabin door intelligent lock control and state monitoring method and system. The method continuously collects lock position sensor data, pressure sensor data and visual image data corresponding to the logistics cabin door, and performs fusion processing; the closed-in-place state, the locked state and the sealed state of the logistics cabin door are determined according to the fusion data; real-time flight state data of the logistics unmanned aerial vehicle is acquired; when it is determined that the logistics cabin door has an unlocked or abnormally opened state, if the real-time flight state data shows that the logistics unmanned aerial vehicle is in an air flight state, an emergency locking protection mechanism is started; if the real-time flight state data shows that the logistics unmanned aerial vehicle is in a ground stationary state, automatic locking or automatic unlocking of the logistics cabin door is completed. The method can accurately determine the closed-in-place state, the locked state and the sealed state of the cabin door, comprehensively cover the cabin door safety hazards, and improve the accuracy and reliability of state monitoring.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and system for intelligent locking and status monitoring of the logistics cabin door of a logistics UAV. Background Technology

[0002] Currently, logistics drones are widely used in short-distance cargo transportation scenarios. However, existing door status monitoring technology for logistics drones has significant shortcomings, including: 1. The monitoring dimensions are limited, with most systems using only a single position sensor to detect the position of the latch, making it impossible to accurately determine both the closed and sealed status of the hatch at the same time. This can easily lead to problems such as false locking and sealing failure. 2. The anomaly handling logic does not differentiate between flight states, and the same handling strategy is used in the air and on the ground. When the cabin door is abnormal in the air, targeted safety protection measures cannot be activated in time, which poses a significant risk of cargo falling and loss of flight control. 3. Ground door operations rely on manual, step-by-step execution, resulting in low automation and impacting logistics efficiency. Summary of the Invention

[0003] This application provides a method and system for intelligent locking and status monitoring of the cargo door of a logistics drone, aiming to solve many obvious defects in the existing cargo door status monitoring technology of logistics drones.

[0004] In a first aspect, embodiments of this application provide a method for intelligent locking and status monitoring of the logistics cabin door of a logistics drone, the method comprising: Continuously collect data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door; Data from locking position sensors, pressure sensors, and visual images are fused to obtain fused data; based on the fused data, the closed position, locking state, and sealing state of the logistics compartment door are determined respectively. Acquire real-time flight status data of the logistics drone; when the logistics cabin door is determined to be unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, activate the emergency locking protection mechanism and send a cabin door abnormality alarm signal to the flight control system; if the real-time flight status data shows that the logistics drone is stationary on the ground, respond to the externally input one-click control command to complete the automatic locking or unlocking operation of the logistics cabin door; train a cabin door abnormality prediction model based on historical cabin door status data and corresponding sensor data; and integrate real-time collected locking position sensor data, pressure sensor data, and visual images. The system inputs data into the hatch status anomaly prediction model; predicts the hatch status change trend within a preset time period; if an unlocked or abnormally opened state is predicted, an early warning signal is sent to the flight control system and external terminals in advance; the system trains the hatch status anomaly prediction model based on historical hatch status data and corresponding sensor data; real-time collected locking position sensor data, pressure sensor data, and visual image data are input into the hatch status anomaly prediction model; predicts the hatch status change trend within a preset time period; if an unlocked or abnormally opened state is predicted, an early warning signal is sent to the flight control system and external terminals in advance.

[0005] In some embodiments, the continuous acquisition of locking position sensor data, pressure sensor data, and visual image data corresponding to the logistics hatch includes: acquiring real-time displacement data of the latch through the locking position sensor; acquiring multi-point pressure data on the contact surface between the hatch and the cabin body through the pressure sensor; acquiring real-time image data of the mating area between the hatch and the cabin body through the visual sensor; increasing the sampling frequency of the three types of data during hatch movement; and decreasing the sampling frequency of the three types of data when the hatch is stationary.

[0006] In some embodiments, the process of fusing the locking position sensor data, pressure sensor data, and visual image data to obtain fused data includes: performing filtering preprocessing on the locking position sensor data, pressure sensor data, and visual image data respectively; removing outliers from the preprocessed data; extracting corresponding state features from the three types of preprocessed data respectively; weighting and fusing all extracted state features; and generating fused data containing multi-dimensional state information.

[0007] In some embodiments, determining the closed position, locked position, and sealed position of the logistics hatch based on the fused data includes: extracting gap features and contact pressure features from the fused data; determining the closed position of the hatch based on the gap features and contact pressure features; extracting latch position features and latch engagement features from the fused data; determining the locked position of the hatch based on the latch position features and latch engagement features; extracting pressure distribution features and circumferential gap features from the fused data; and determining the sealed position of the hatch based on the pressure distribution features and circumferential gap features.

[0008] In some embodiments, acquiring real-time flight status data of the logistics drone includes: acquiring real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data of the drone from the flight control system; and determining whether the logistics drone is in flight or stationary on the ground based on the real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data.

[0009] In some embodiments, when it is determined that the logistics cabin door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, an emergency locking protection mechanism is activated, and a cabin door abnormality alarm signal is sent to the flight control system. This includes: sending a maximum output force locking command to the electric push rod; continuously monitoring the locking status of the cabin door; if the locking status returns to normal within a preset time, the emergency locking protection mechanism is deactivated; if the locking status does not return to normal within a preset time, an emergency landing command is sent to the flight control system.

[0010] In some embodiments, if the real-time flight status data shows that the logistics drone is stationary on the ground, the automatic locking or unlocking operation of the logistics cabin door in response to an externally input one-key control command includes: when responding to an externally input one-key locking command, sequentially performing a cabin door closing operation, a latch extension operation, and a sealing status verification operation; when responding to an externally input one-key unlocking command, sequentially performing a latch retraction operation and a cabin door opening operation; and after all operations are completed, sending an operation completion feedback signal to an external terminal.

[0011] In some embodiments, the method further includes: continuously collecting hatch opening and closing angle data and electric push rod operating current data during hatch opening and closing; adjusting the operating speed of the electric push rod in real time according to the opening and closing angle data; immediately stopping the electric push rod if the operating current of the electric push rod exceeds a preset threshold; and sending a hatch jamming alarm signal to an external terminal.

[0012] In some embodiments, the logistics hatch includes a weight sensor and a vision sensor. The method further includes: acquiring real-time weight distribution data of the cargo inside the hatch using the weight sensor; acquiring real-time image data of the cargo inside the hatch using the vision sensor; determining the displacement status of the cargo inside the hatch based on the real-time weight distribution data and the real-time image data; jointly analyzing the displacement status and the hatch status; if it is determined that the cargo has shifted and there is a risk of impacting the hatch, sending a flight attitude adjustment command to the flight control system; if it is determined that the cargo has impacted the hatch and caused the hatch to malfunction, executing the emergency handling procedure corresponding to the flight status.

[0013] Secondly, this application provides an intelligent lock control and status monitoring system for the logistics cabin door of a logistics drone, the system comprising: The data acquisition unit is used to continuously collect data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door. The status determination unit is used to fuse data from the locking position sensor, pressure sensor, and visual image to obtain fused data; and to determine the closed position, locking position, and sealing position of the logistics door based on the fused data. The operation completion unit is used to acquire real-time flight status data of the logistics drone. When it is determined that the logistics door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, the emergency locking protection mechanism is activated, and a door abnormality alarm signal is sent to the flight control system. If the real-time flight status data shows that the logistics drone is stationary on the ground, it responds to the one-key control command input by the external input and completes the automatic locking or unlocking operation of the logistics door. The unit trains a door abnormality prediction model based on historical door status data and corresponding sensor data. The real-time collected locking position sensor data, pressure sensor data, and visual image data are input into the door abnormality prediction model to predict the status change trend of the door within a preset time period. If it is predicted that the door will be unlocked or abnormally opened, an early warning signal is sent to the flight control system and external terminals in advance.

[0014] This application employs a multi-source fusion monitoring method combining locking position sensors, pressure sensors, and visual image data to simultaneously and accurately determine the door's closed, locked, and sealed status, comprehensively covering potential door safety hazards and significantly improving the accuracy and reliability of status monitoring. By establishing a graded response mechanism based on flight status, emergency locking protection is immediately activated and an alarm is sent to the flight control system when an abnormality occurs in the air, effectively ensuring flight safety. In ground mode, one-click automatic locking and unlocking are supported, significantly improving the automation level and transfer efficiency of logistics operations. The overall solution does not require significant modifications to the door's mechanical structure and is easily promoted and applied on existing logistics drone platforms.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart illustrating the steps of a method for intelligent locking and status monitoring of the logistics cabin door of a logistics drone, provided in one embodiment of this application. Figure 2 This is a schematic diagram illustrating the principle of an intelligent lock control and status monitoring method for the logistics cabin door of a logistics drone provided in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a logistics drone cargo door provided in one embodiment of this application; Figure 4 This is a schematic block diagram of the structure of an intelligent lock control and status monitoring system for the logistics cabin door of a logistics drone, provided in one embodiment of this application; Figure 5 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0021] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0022] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] Currently, logistics drones are widely used in short-distance cargo transportation. However, existing door status monitoring technology for logistics drones has significant shortcomings: 1. The monitoring dimensions are limited, with most systems using only a single position sensor to detect the position of the latch, making it impossible to accurately determine both the closed and sealed status of the hatch at the same time. This can easily lead to problems such as false locking and sealing failure. 2. The anomaly handling logic does not differentiate between flight states, and the same handling strategy is used in the air and on the ground. When the cabin door is abnormal in the air, targeted safety protection measures cannot be activated in time, which poses a significant risk of cargo falling and loss of flight control. 3. Ground door operations rely on manual, step-by-step execution, resulting in low automation and impacting logistics efficiency.

[0025] like Figures 1 to 3 As shown in the illustration, this invention provides a method for intelligent locking and status monitoring of the cargo door of a logistics drone, which is applied to computer equipment. The computer equipment can be deployed on a single server or server cluster, or on a handheld terminal, laptop, wearable device, or robot. All information collection and processing involved in this application are conducted with the authorization of relevant users and in compliance with relevant laws and regulations, and will not infringe upon the legitimate privacy rights of any user.

[0026] The overall process of this invention is as follows: Figure 1 As shown, the method mainly includes three core steps: continuous acquisition of multi-source sensor data, multi-source data fusion and status determination, and hierarchical response processing based on flight status. The provided intelligent locking and status monitoring method for the logistics drone's cargo door includes steps S101 to S103. Details are as follows: Step S101. Continuously collect data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door.

[0027] Specifically, this step uses three different types of sensors to collect raw data related to the status of the logistics hatch from multiple dimensions. The locking position sensor is installed inside the latch mechanism of the logistics hatch to collect real-time position and displacement information of the latch; the pressure sensor is installed on the sealing surface where the hatch contacts the hatch body, evenly distributed circumferentially along the sealing surface, to collect pressure distribution information on the sealing surface after the hatch is closed; and the vision sensor is installed inside the hatch body directly opposite the edge of the hatch mating area to collect real-time image information of the area where the hatch and hatch body meet.

[0028] The data acquisition process is continuous and unaffected by the hatch status. After system startup, the three types of sensors begin working simultaneously, sending the collected raw data to the computer at a preset basic sampling frequency. The computer buffers the received raw data, providing a data foundation for subsequent fusion processing and status determination.

[0029] Step S102. Perform fusion processing on the locking position sensor data, pressure sensor data, and visual image data to obtain fused data; determine the closed position, locking state, and sealing state of the logistics compartment door based on the fused data.

[0030] Specifically, this step is the core processing step of the invention. By fusing multi-source data, it achieves accurate determination of the three core states of the hatch. First, the three types of raw data are preprocessed to remove noise and interference. Then, key features reflecting the hatch state are extracted from the preprocessed data. Finally, the extracted multi-dimensional features are fused to generate unified fused data, and the hatch's closed position, locked position, and sealed position are determined based on the fused data.

[0031] The purpose of data fusion processing is to comprehensively utilize the advantages of different sensors and compensate for the shortcomings of a single sensor. Locking position sensors can accurately reflect the mechanical position of the latch, but cannot detect the closing gap and sealing condition of the hatch; pressure sensors can accurately reflect the contact pressure of the sealing surface, but cannot distinguish whether abnormal pressure is caused by an open hatch or locking failure; vision sensors can visually reflect the overall fit of the hatch, but are easily affected by environmental factors such as light and dust. Through multi-source data fusion, the accuracy and reliability of status determination can be effectively improved.

[0032] Step S103. Acquire real-time flight status data of the logistics drone; when it is determined that the logistics door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, activate the emergency locking protection mechanism and send a door abnormality alarm signal to the flight control system; if the real-time flight status data shows that the logistics drone is stationary on the ground, respond to the one-click control command input by the external system to complete the automatic locking or unlocking operation of the logistics door; train the door status abnormality prediction model based on historical door status data and corresponding sensor data; input the real-time collected locking position sensor data, pressure sensor data and visual image data into the door status abnormality prediction model; predict the status change trend of the door within a preset time period in the future; if it is predicted that the door will be unlocked or abnormally opened, send a warning signal to the flight control system and external terminal in advance.

[0033] Specifically, this step employs a tiered response strategy for abnormal cabin conditions based on the real-time flight status of the logistics drone. First, real-time flight status data, including altitude, speed, acceleration, and positioning information, is obtained from the drone's flight control system. Then, this data is used to determine whether the drone is currently in flight or stationary on the ground.

[0034] When step S102 determines that the hatch is unlocked or abnormally opened, different processing logic is executed according to the current state of the UAV: If the drone is in flight, an abnormal cabin door may cause serious safety accidents such as cargo falling or loss of flight control. Therefore, the highest level of emergency locking protection mechanism should be activated immediately, and a cabin door abnormality alarm signal should be sent to the flight control system to notify the flight control system to take corresponding safety measures. If the drone is stationary on the ground, abnormal cabin door conditions will not directly lead to flight safety accidents. Therefore, it can respond to one-click control commands input by external operators and automatically lock or unlock the cabin door, improving the automation efficiency of ground logistics transfer.

[0035] In some embodiments, the continuous acquisition of locking position sensor data, pressure sensor data, and visual image data corresponding to the logistics hatch includes: acquiring real-time displacement data of the latch through the locking position sensor; acquiring multi-point pressure data on the contact surface between the hatch and the cabin body through the pressure sensor; acquiring real-time image data of the mating area between the hatch and the cabin body through the visual sensor; increasing the sampling frequency of the three types of data during hatch movement; and decreasing the sampling frequency of the three types of data when the hatch is stationary.

[0036] Real-time displacement data of the latch is collected by a locking position sensor; multi-point pressure data on the contact surface between the hatch and the hull is collected by a pressure sensor; real-time image data of the mating area between the hatch and the hull is collected by a vision sensor; the sampling frequency of the three types of data is increased during hatch movement; and the sampling frequency of the three types of data is decreased when the hatch is stationary.

[0037] In practical implementation, two types of sampling frequencies are preset: a basic sampling frequency and a high sampling frequency. The basic sampling frequency is suitable for the static state of the hatch, which can reduce the power consumption and data processing pressure of the system while ensuring the effectiveness of state monitoring; the high sampling frequency is suitable for the moving process of the hatch, which can collect data more densely and improve the response speed and judgment accuracy of state changes.

[0038] The system monitors the hatch's movement in real time. When the hatch starts to move, it automatically switches the sampling frequency of the three types of sensors to a high sampling frequency. When the hatch stops moving and remains stationary for more than a preset time, it automatically switches the sampling frequency of the three types of sensors back to the basic sampling frequency. The frequency switching process is completed automatically without manual intervention.

[0039] The model predicts abnormal door status by training a prediction model based on historical door status data and corresponding sensor data; real-time collected data from locking position sensors, pressure sensors, and visual images are input into the prediction model to predict the door's status change trend within a preset time period; if it is predicted that the door will be unlocked or abnormally opened, an early warning signal is sent to the flight control system and external terminals in advance.

[0040] In practice, the first step is to collect a large amount of historical hatch status data and corresponding sensor data, including both normal and abnormal status data. The collected data is then divided into training and testing sets, and a machine learning algorithm is used to train a hatch status anomaly prediction model. After training, the model's prediction accuracy is validated using the testing set to ensure that the model's prediction accuracy meets the design requirements.

[0041] During system operation, real-time data from three types of sensors is input into a trained anomaly prediction model. The model outputs the trend of the hatch's state changes over a preset time period. If the model predicts that the hatch will be unlocked or abnormally opened within the preset time period, it immediately sends a warning signal to the flight control system and external terminals, notifying operators to take measures in advance to prevent the hatch from malfunctioning.

[0042] In some embodiments, the process of fusing the locking position sensor data, pressure sensor data, and visual image data to obtain fused data includes: performing filtering preprocessing on the locking position sensor data, pressure sensor data, and visual image data respectively; removing outliers from the preprocessed data; extracting corresponding state features from the three types of preprocessed data respectively; weighting and fusing all extracted state features; and generating fused data containing multi-dimensional state information.

[0043] The locking position sensor data, pressure sensor data, and visual image data are filtered and preprocessed respectively; outliers in the preprocessed data are removed; corresponding state features are extracted from the three types of preprocessed data respectively; all extracted state features are weighted and fused; and fused data containing multi-dimensional state information is generated.

[0044] In practice, the preprocessing of the filtering uses a moving average filtering method, which averages multiple continuously collected data points to remove random noise from the data. Outlier removal uses a statistical discrimination method, which calculates the mean and standard deviation of the data, and identifies data that exceed three times the standard deviation of the mean as outliers and removes them.

[0045] The state feature extraction process employs different extraction methods for different types of data: for locking position sensor data, features such as the current position of the latch, displacement change, and displacement change rate are extracted; for pressure sensor data, features such as the current pressure value, average pressure value, and pressure distribution uniformity of each pressure sensor are extracted; for visual image data, features such as the gap size between the hatch and the cabin, gap distribution uniformity, and the engagement position of the latch and the latch are extracted.

[0046] The weighted fusion process assigns a corresponding weight coefficient to each feature based on its importance in determining the hatch status. These weight coefficients can be predetermined through experimental testing or machine learning methods. The final fused data is obtained by multiplying each feature value by its corresponding weight coefficient and then summing the results.

[0047] In some embodiments, determining the closed position, locked position, and sealed position of the logistics hatch based on the fused data includes: extracting gap features and contact pressure features from the fused data; determining the closed position of the hatch based on the gap features and contact pressure features; extracting latch position features and latch engagement features from the fused data; determining the locked position of the hatch based on the latch position features and latch engagement features; extracting pressure distribution features and circumferential gap features from the fused data; and determining the sealed position of the hatch based on the pressure distribution features and circumferential gap features.

[0048] The door's closed-position state is determined by extracting gap and contact pressure features from the fused data; the latch position and latch engagement features are extracted from the fused data; the door's locking state is determined by the latch position and latch engagement features; and the door's sealing state is determined by extracting pressure distribution and circumferential gap features from the fused data.

[0049] In practice, a corresponding judgment threshold is pre-set for each state. For the closed-in state, if the gap characteristic value is less than the preset gap threshold and the contact pressure characteristic value is greater than the preset pressure threshold, the hatch is determined to be in the closed-in state; otherwise, the hatch is determined not to be closed in the closed state.

[0050] For the locked state, when the latch position feature value is within the preset locking position range and the latch engagement feature value is greater than the preset engagement threshold, the hatch is determined to be in the locked state; otherwise, the hatch is determined to be unlocked.

[0051] For a sealed state, when the pressure distribution characteristic value is greater than the preset distribution uniformity threshold and the circumferential clearance characteristic value is less than the preset circumferential clearance threshold, the hatch is determined to be in a good sealing state; otherwise, the hatch is determined to be in a sealing failure state.

[0052] All judgment results are cached and updated in real time to provide a basis for subsequent graded response processing.

[0053] In some embodiments, acquiring real-time flight status data of the logistics drone includes: acquiring real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data of the drone from the flight control system; and determining whether the logistics drone is in flight or stationary on the ground based on the real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data.

[0054] By acquiring real-time altitude, speed, acceleration, and positioning data of the drone from the flight control system, the system determines whether the logistics drone is in flight or stationary on the ground.

[0055] In practice, flight status determination thresholds are preset. When the drone's real-time altitude is greater than the preset altitude threshold and its real-time speed is greater than the preset speed threshold, the drone is determined to be in flight. When the drone's real-time altitude is less than the preset altitude threshold, its real-time speed is less than the preset speed threshold, its real-time acceleration change is less than the preset acceleration threshold, and its real-time positioning data remains stable for more than a preset duration, the drone is determined to be stationary on the ground.

[0056] The flight status determination process is ongoing, and is re-determined at preset intervals to ensure timely response to changes in the drone's flight status.

[0057] In some embodiments, when it is determined that the logistics cabin door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, an emergency locking protection mechanism is activated, and a cabin door abnormality alarm signal is sent to the flight control system. This includes: sending a maximum output force locking command to the electric push rod; continuously monitoring the locking status of the cabin door; if the locking status returns to normal within a preset time, the emergency locking protection mechanism is deactivated; if the locking status does not return to normal within a preset time, an emergency landing command is sent to the flight control system.

[0058] The system sends a maximum output force locking command to the electric actuator; continuously monitors the locking status of the cabin door; if the locking status returns to normal within a preset time, the emergency locking protection mechanism is released; if the locking status does not return to normal within a preset time, an emergency landing command is sent to the flight control system.

[0059] In practice, when an anomaly is detected in the cabin door, a maximum output force locking command is immediately sent to the electric push rod driving the door locking mechanism. This causes the electric push rod to extend the latch with maximum thrust, forcibly locking the cabin door. Simultaneously, the locking status of the cabin door is continuously monitored, and the locking status is reassessed at preset time intervals.

[0060] If the locking status returns to normal within the preset emergency locking time, the emergency locking protection mechanism will be released, the output force of the electric push rod will be restored to the normal working level, and an alarm release signal will be sent to the flight control system. If the locking status still does not return to normal within the preset emergency locking time, it indicates that the door locking mechanism has a serious malfunction and cannot be forcibly locked by the electric push rod. In this case, an emergency landing command will be immediately sent to the flight control system to notify the flight control system to control the UAV to make an emergency landing in the nearest safe area to avoid more serious safety accidents.

[0061] In some embodiments, if the real-time flight status data shows that the logistics drone is stationary on the ground, the automatic locking or unlocking operation of the logistics cabin door in response to an externally input one-key control command includes: when responding to an externally input one-key locking command, sequentially performing a cabin door closing operation, a latch extension operation, and a sealing status verification operation; when responding to an externally input one-key unlocking command, sequentially performing a latch retraction operation and a cabin door opening operation; and after all operations are completed, sending an operation completion feedback signal to an external terminal.

[0062] When responding to an externally inputted one-key locking command, the system sequentially performs the hatch closing operation, the latch extension operation, and the sealing status verification operation; when responding to an externally inputted one-key unlocking command, the system sequentially performs the latch retraction operation and the hatch opening operation; after all operations are completed, an operation completion feedback signal is sent to the external terminal.

[0063] In practice, when a one-button locking command is received from an external operator via a ground control terminal, the system first controls the hatch drive mechanism to close the hatch to its closed position. Then, it controls the electric push rod to extend the latch to the locking position. Finally, it performs a sealing status verification to determine if the hatch is properly sealed. If the sealing status verification passes, a locking completion feedback signal is sent to the external terminal; if the sealing status verification fails, a locking failure feedback signal is sent to the external terminal, prompting the operator to check for obstructions or other problems with the hatch.

[0064] When the system receives a one-click unlock command from an external operator, it first controls the electric push rod to retract the latch to the unlocked position; then it controls the door drive mechanism to open the door to the preset maximum opening angle. After all operations are completed, an unlocking completion feedback signal is sent to the external terminal.

[0065] In some embodiments, the method further includes: continuously collecting hatch opening and closing angle data and electric push rod operating current data during hatch opening and closing; adjusting the operating speed of the electric push rod in real time according to the opening and closing angle data; immediately stopping the electric push rod if the operating current of the electric push rod exceeds a preset threshold; and sending a hatch jamming alarm signal to an external terminal.

[0066] During the opening and closing of the hatch, the system continuously collects data on the opening and closing angle of the hatch and the operating current data of the electric push rod; it adjusts the operating speed of the electric push rod in real time based on the opening and closing angle data; if the operating current of the electric push rod exceeds a preset threshold, it immediately stops the operation of the electric push rod; and it sends a hatch jamming alarm signal to an external terminal.

[0067] In practice, an angle sensor is installed on the hatch drive mechanism to collect real-time opening and closing angle data of the hatch. A current sensor is connected in series in the drive circuit of the electric push rod to collect real-time operating current data of the electric push rod.

[0068] During the hatch opening and closing process, the operating speed of the electric push rod is adjusted in real time according to the real-time opening and closing angle of the hatch. At the initial and final stages of hatch opening or closing, the operating speed of the electric push rod is reduced to avoid large impacts on the hatch at the start and end positions of the movement; in the middle stage of hatch movement, the operating speed of the electric push rod is increased to shorten the hatch opening and closing time.

[0069] Meanwhile, the operating current of the electric actuator is continuously monitored. When the hatch encounters a foreign object during opening or closing, the load on the electric actuator will increase sharply, causing the operating current to rise rapidly. When the operating current of the electric actuator is detected to exceed the preset current threshold, the operation of the electric actuator will be stopped immediately to prevent damage due to overload. At the same time, a hatch jamming alarm signal will be sent to the external terminal to notify the operator to troubleshoot the jamming fault in time.

[0070] In some embodiments, a collaborative monitoring and linkage control function for door status in multi-UAV swarm operation scenarios is added. This is achieved by establishing a multi-UAV swarm communication network; each logistics UAV in the swarm sends its own door status data and flight status data to the swarm scheduling node in real time; the swarm scheduling node aggregates the status data of all UAVs and performs global monitoring; when the swarm scheduling node detects that a UAV's door is unlocked or abnormally opened, it sends a priority emergency handling command to that UAV; simultaneously, it sends formation adjustment commands to other UAVs in the swarm to adjust the formation flight distance and altitude; and it sends a swarm anomaly alarm signal to the ground dispatch center, synchronizing the location and status information of the abnormal UAV.

[0071] In practice, the multi-UAV swarm communication network adopts a self-organizing network mode, with each UAV acting as a network node. Nodes transmit data wirelessly. Data transmission uses a combination of timed broadcasting and event triggering. Under normal conditions, the UAV broadcasts its own status data at preset intervals. When an abnormality is detected in the hatch, emergency data transmission is immediately triggered.

[0072] The cluster scheduling node can be either a master UAV within the cluster or a ground control center. The cluster scheduling node maintains a real-time status list of all UAVs within the cluster and independently monitors the door status of each UAV. When a door anomaly is detected in a UAV, the node first sends a high-priority emergency handling command to that UAV, forcing it to execute the emergency locking protection mechanism. Simultaneously, it sends formation adjustment commands to other UAVs in the cluster, keeping them away from the abnormal UAV to avoid collisions. Finally, it sends a cluster anomaly alarm signal to the ground control center, synchronizing the abnormal UAV's real-time location, flight status, and door status information, allowing the ground control center to proactively deploy emergency personnel and equipment.

[0073] In some embodiments, a dynamic adjustment function for the door status determination threshold based on adaptive environmental parameters is added. This is achieved by continuously collecting real-time temperature, air pressure, and wind speed data of the environment in which the logistics drone operates; calculating the elastic deformation coefficient of the sealing material based on the real-time temperature data; calculating the compensation value of air pressure on the pressure sensor readings based on the real-time air pressure data; calculating the influence coefficient of wind load on the door force based on the real-time wind speed data; and dynamically adjusting the determination thresholds corresponding to the door's closed, locked, and sealed states in real time based on the calculated elastic deformation coefficient, pressure compensation value, and wind load influence coefficient.

[0074] In practice, environmental sensor components are installed on the exterior of the logistics drone to collect data on ambient temperature, air pressure, and wind speed. A model is established beforehand through experimental testing to establish the correspondence between environmental parameters and judgment thresholds. This model uses ambient temperature, air pressure, and wind speed as input variables and judgment thresholds for the three states as output variables.

[0075] During system operation, environmental parameter data is collected at preset intervals. The collected environmental parameters are input into a pre-established correspondence model to calculate the optimal judgment threshold under the current environment. The calculated optimal judgment threshold is updated in real time to the state judgment module, replacing the original fixed judgment threshold. By dynamically adjusting the judgment threshold, the influence of environmental factors on the state judgment results can be effectively eliminated, improving the accuracy of state judgment under different environmental conditions.

[0076] In some embodiments, the logistics compartment door includes weight sensors and vision sensors. The method further includes: collecting real-time weight distribution data of the cargo inside the compartment using the weight sensors; collecting real-time image data of the cargo inside the compartment using the vision sensors; determining the displacement status of the cargo inside the compartment based on the real-time weight distribution data and the real-time image data; jointly analyzing the displacement status and the compartment door status; if it is determined that the cargo has shifted and there is a risk of impacting the compartment door, sending a flight attitude adjustment command to the flight control system; if it is determined that the cargo has impacted the compartment door and caused an abnormal state in the compartment, executing the emergency handling procedure corresponding to the flight status. In a specific implementation, multiple sets of weight sensors are installed at the bottom of the logistics compartment, evenly distributed along the bottom, to collect weight distribution information of the cargo inside the compartment. A compartment vision sensor is installed at the top inside the compartment to collect overall image information of the cargo inside the compartment.

[0077] The logic for determining cargo displacement is as follows: when the weight distribution data shows a sudden increase in the weight of a certain area, and the visual image data inside the hold shows a significant change in the cargo's position, it is determined that the cargo has been displaced. When the direction of cargo displacement is towards the hatch, and the distance between the cargo and the hatch is less than the preset safe distance, it is determined that the cargo is at risk of colliding with the hatch.

[0078] When it is determined that there is a risk of the cargo colliding with the hatch, a flight attitude adjustment command is sent to the flight control system to control the UAV to adjust its flight attitude and move the cargo away from the hatch, thus avoiding a collision. If it is determined that the cargo has already collided with the hatch and caused the hatch to be unlocked or abnormally opened, the emergency handling procedure for the corresponding flight status is immediately executed to ensure flight safety.

[0079] In some embodiments, sensor fault self-diagnosis and multi-source data redundancy reconstruction functions are added. This involves continuously monitoring the operating status of the locking position sensor, pressure sensor, and vision sensor; if a fault is detected in a certain type of sensor, the output data of that type of sensor is blocked; all usable state features are extracted from the remaining normally operating sensor data; fused data is reconstructed based on the remaining usable state features; the closed position, locking state, and sealing state of the hatch are further determined based on the reconstructed fused data; and a sensor fault alarm signal is sent to an external terminal simultaneously.

[0080] In practice, the monitoring of sensor operating status employs a combination of data validity assessment and communication status assessment. Data validity assessment determines whether the sensor output data is within a preset reasonable range. If the data from multiple consecutive sampling periods exceeds the reasonable range, the sensor is deemed to be faulty. Communication status assessment determines whether the communication between the sensor and the computer is normal. If no data is received from the sensor for multiple consecutive sampling periods, the sensor is deemed to be experiencing a communication failure.

[0081] When a fault is detected in a certain type of sensor, the system automatically blocks the output data of that sensor and excludes it from the data fusion process. Then, it extracts all features reflecting the hatch status from the remaining normally functioning sensor data and reconstructs the fused data based on these features. Although the reconstructed fused data has fewer dimensions, it still meets the requirements for determining the core status of the hatch.

[0082] The system simultaneously records the time and type of sensor failure, sends sensor failure alarm signals to external terminals, and notifies operators to replace the faulty sensor in a timely manner to restore the system's full monitoring capability.

[0083] In some embodiments, an emergency door locking control function based on dynamic flight attitude compensation is added. When it is determined that the door is unlocked or abnormally opened during flight, the system obtains real-time pitch angle data, real-time roll angle data, and real-time yaw angle data of the UAV from the flight control system; calculates the resultant inertial force and aerodynamic force on the door based on the real-time pitch angle data, roll angle data, and yaw angle data; dynamically adjusts the output force and direction of the electric actuator based on the calculated resultant force; and controls the electric actuator to perform an emergency locking operation according to the adjusted output force and direction.

[0084] In practice, a model is established beforehand through fluid dynamics simulation and mechanical experiments to establish the correspondence between the UAV's flight attitude and the forces acting on the hatch. This model uses the UAV's pitch angle, roll angle, yaw angle, and flight speed as input variables, and the magnitude and direction of the resultant force of inertial force and aerodynamic force acting on the hatch as output variables.

[0085] Before performing the emergency locking operation, real-time flight attitude and speed data of the UAV are first obtained from the flight control system. This data is then input into a pre-established correspondence model to calculate the magnitude and direction of the resultant force acting on the hatch in the current state. Based on the magnitude and direction of the resultant force, the output parameters of the electric actuator are dynamically adjusted to ensure that the output force of the electric actuator can counteract the external force acting on the hatch, ensuring that the latch can smoothly engage the lock and achieve reliable locking.

[0086] In some embodiments, this embodiment combines Figure 2 The hierarchical response processing logic based on flight state of the present invention will be described in detail below. Figure 2 The system's working status and execution process are intuitively demonstrated in two core application scenarios.

[0087] like Figure 2 As shown, Figure 2 This is a schematic diagram of a smart lock control and status monitoring system for logistics cabin doors. The overall structure of the logistics drone and the attached logistics cabin and door structure are shown from a side view. A horizontal ground baseline divides the space into upper and lower areas: the area above the baseline is labeled "Airborne Flight Status," corresponding to the flight phase of the drone performing its transportation mission; the area below the baseline is labeled "Ground Stationary Status," corresponding to the ground phase where the drone completes loading and unloading of goods at the take-off and landing points. An emergency lock indicator is drawn in the left airborne area, representing emergency handling actions in abnormal airborne conditions; a wireless signal indicator is drawn in the right ground area, representing remote one-button control actions in ground mode.

[0088] By executing the multi-source data acquisition and status determination process from steps S101 to S102, the system continuously monitors the door's closed position, locking status, and sealing status. When step S102 determines that the door is unlocked or abnormally opened, the system immediately triggers a tiered response mechanism and jumps to step S103 to obtain the UAV's real-time flight status data.

[0089] If step S103 determines, based on the real-time altitude, speed, and acceleration data returned by the flight control system, that the drone is currently in a certain position... Figure 2 The flight status shown on the left: The system immediately sends a high-priority emergency locking command to the cabin door's electric locking mechanism, corresponding to... Figure 2 The system establishes a control signal link from the UAV fuselage to the door locking mechanism. Upon receiving the command, the electric locking mechanism extends its latch with maximum output force, forcibly locking the door in the closed position to prevent further opening and cargo loss. During the emergency locking process, the system continuously executes the status monitoring process from steps S101 to S102, re-evaluating the door's locking status every 100 milliseconds. If the door locking status returns to normal within 3 seconds, the emergency locking protection is released, the output force of the electric locking mechanism is restored to normal operating level, and an alarm release signal is sent to the flight control system. If the door locking status still does not return to normal within 3 seconds, an irreversible fault is determined in the locking mechanism, and an emergency landing command is immediately sent to the flight control system. The flight control system then controls the UAV to execute an emergency landing procedure in the nearest safe area within the planned flight path. If step S103 determines, based on the real-time data returned by the flight control system, that the UAV is currently in a state of emergency... Figure 2 The ground stationary state shown on the right indicates that the system has entered ground operation mode and is waiting to receive control commands from an external ground control terminal. Figure 2 The system establishes a wireless signal link from the ground terminal to the UAV. When it receives a one-click locking command from the operator, it automatically executes the following actions in sequence: door closing, latch extension, and seal verification. After all actions are completed, it sends a "locking complete" feedback signal to the ground terminal. When it receives a one-click unlock command from the operator, it automatically executes the following actions in sequence: latch retraction and door opening. After all actions are completed, it sends a "unlock complete" feedback signal to the ground terminal. If door jamming or seal verification failure is detected during one-click control, the system immediately stops the current action and sends a corresponding fault alarm signal to the ground terminal, prompting the operator to conduct manual troubleshooting.

[0090] Differentiated door anomaly handling strategies are adopted for two completely different application scenarios: air and ground. In the air scenario, flight safety is the highest priority, and a dual guarantee mechanism of mandatory emergency locking and emergency landing is used to minimize the safety risks caused by door anomalies. In the ground scenario, operational efficiency is the core focus, and a one-click automatic control process simplifies the work steps of operators and improves the overall efficiency of logistics transfer.

[0091] Please see Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of the intelligent lock control and status monitoring system 200 for the logistics drone's cargo compartment door provided in this application embodiment. The intelligent lock control and status monitoring system 200 for the logistics drone's cargo compartment door is used to execute the steps of the intelligent lock control and status monitoring method for the logistics drone's cargo compartment door shown in the above embodiments. The intelligent lock control and status monitoring system 200 for the logistics drone's cargo compartment door can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0092] like Figure 4 As shown, the intelligent lock control and status monitoring system 200 for the logistics drone's cargo compartment includes: The data acquisition unit 201 is used to continuously acquire data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door. The state determination unit 202 is used to perform fusion processing on the locking position sensor data, pressure sensor data and visual image data to obtain fused data; and to determine the closed position, locking state and sealing state of the logistics door based on the fused data. The operation completion unit 203 is used to acquire real-time flight status data of the logistics drone. When it is determined that the logistics door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, an emergency locking protection mechanism is activated, and a door abnormality alarm signal is sent to the flight control system. If the real-time flight status data shows that the logistics drone is stationary on the ground, it responds to the one-key control command input by the external system and completes the automatic locking or unlocking operation of the logistics door. The door abnormality prediction model is trained based on historical door status data and corresponding sensor data. The real-time collected locking position sensor data, pressure sensor data, and visual image data are input into the door abnormality prediction model to predict the state change trend of the door within a preset time period. If it is predicted that the door will be unlocked or abnormally opened, an early warning signal is sent to the flight control system and external terminals in advance.

[0093] In some embodiments, the continuous acquisition of locking position sensor data, pressure sensor data, and visual image data corresponding to the logistics hatch includes: acquiring real-time displacement data of the latch through the locking position sensor; acquiring multi-point pressure data on the contact surface between the hatch and the cabin body through the pressure sensor; acquiring real-time image data of the mating area between the hatch and the cabin body through the visual sensor; increasing the sampling frequency of the three types of data during hatch movement; and decreasing the sampling frequency of the three types of data when the hatch is stationary.

[0094] In some embodiments, the process of fusing the locking position sensor data, pressure sensor data, and visual image data to obtain fused data includes: performing filtering preprocessing on the locking position sensor data, pressure sensor data, and visual image data respectively; removing outliers from the preprocessed data; extracting corresponding state features from the three types of preprocessed data respectively; weighting and fusing all extracted state features; and generating fused data containing multi-dimensional state information.

[0095] In some embodiments, determining the closed position, locked position, and sealed position of the logistics hatch based on the fused data includes: extracting gap features and contact pressure features from the fused data; determining the closed position of the hatch based on the gap features and contact pressure features; extracting latch position features and latch engagement features from the fused data; determining the locked position of the hatch based on the latch position features and latch engagement features; extracting pressure distribution features and circumferential gap features from the fused data; and determining the sealed position of the hatch based on the pressure distribution features and circumferential gap features.

[0096] In some embodiments, acquiring real-time flight status data of the logistics drone includes: acquiring real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data of the drone from the flight control system; and determining whether the logistics drone is in flight or stationary on the ground based on the real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data.

[0097] In some embodiments, when it is determined that the logistics cabin door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, an emergency locking protection mechanism is activated, and a cabin door abnormality alarm signal is sent to the flight control system. This includes: sending a maximum output force locking command to the electric push rod; continuously monitoring the locking status of the cabin door; if the locking status returns to normal within a preset time, the emergency locking protection mechanism is deactivated; if the locking status does not return to normal within a preset time, an emergency landing command is sent to the flight control system.

[0098] In some embodiments, if the real-time flight status data shows that the logistics drone is stationary on the ground, the automatic locking or unlocking operation of the logistics cabin door in response to an externally input one-key control command includes: when responding to an externally input one-key locking command, sequentially performing a cabin door closing operation, a latch extension operation, and a sealing status verification operation; when responding to an externally input one-key unlocking command, sequentially performing a latch retraction operation and a cabin door opening operation; and after all operations are completed, sending an operation completion feedback signal to an external terminal.

[0099] In some embodiments, the method further includes: continuously collecting hatch opening and closing angle data and electric push rod operating current data during hatch opening and closing; adjusting the operating speed of the electric push rod in real time according to the opening and closing angle data; immediately stopping the electric push rod if the operating current of the electric push rod exceeds a preset threshold; and sending a hatch jamming alarm signal to an external terminal.

[0100] In some embodiments, the logistics hatch includes a weight sensor and a vision sensor. The method further includes: acquiring real-time weight distribution data of the cargo inside the hatch using the weight sensor; acquiring real-time image data of the cargo inside the hatch using the vision sensor; determining the displacement status of the cargo inside the hatch based on the real-time weight distribution data and the real-time image data; jointly analyzing the displacement status and the hatch status; if it is determined that the cargo has shifted and there is a risk of impacting the hatch, sending a flight attitude adjustment command to the flight control system; if it is determined that the cargo has impacted the hatch and caused the hatch to malfunction, executing the emergency handling procedure corresponding to the flight status.

[0101] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the intelligent lock control and status monitoring system for the logistics drone's cargo compartment and its various modules described above can be referred to the corresponding content in the various embodiments of the intelligent lock control and status monitoring method for the logistics drone's cargo compartment, and will not be repeated here.

[0102] The aforementioned intelligent locking and status monitoring method for the logistics drone's cargo door can be implemented as a computer program, which can, for example... Figure 4 It runs on the system shown.

[0103] Please see Figure 5 , Figure 5 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0104] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any intelligent locking and status monitoring method for the logistics drone's cargo door.

[0105] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0106] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any intelligent locking and status monitoring method for the logistics drone's cargo door.

[0107] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0109] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Continuously collect data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door; Data from locking position sensors, pressure sensors, and visual images are fused to obtain fused data; based on the fused data, the closed position, locking state, and sealing state of the logistics compartment door are determined respectively. The system acquires real-time flight status data of the logistics drone. When the logistics cabin door is determined to be unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, an emergency locking protection mechanism is activated, and a cabin abnormality alarm signal is sent to the flight control system. If the real-time flight status data shows that the logistics drone is stationary on the ground, it responds to the one-click control command input from the outside and completes the automatic locking or unlocking operation of the logistics cabin door. The system trains a cabin abnormality prediction model based on historical cabin status data and corresponding sensor data. The system inputs real-time collected locking position sensor data, pressure sensor data, and visual image data into the cabin abnormality prediction model to predict the cabin's status change trend within a preset time period. If the system predicts that the cabin door will be unlocked or abnormally opened, it sends an early warning signal to the flight control system and external terminals in advance.

[0110] In some embodiments, the continuous acquisition of locking position sensor data, pressure sensor data, and visual image data corresponding to the logistics hatch includes: acquiring real-time displacement data of the latch through the locking position sensor; acquiring multi-point pressure data on the contact surface between the hatch and the cabin body through the pressure sensor; acquiring real-time image data of the mating area between the hatch and the cabin body through the visual sensor; increasing the sampling frequency of the three types of data during hatch movement; and decreasing the sampling frequency of the three types of data when the hatch is stationary.

[0111] In some embodiments, the process of fusing the locking position sensor data, pressure sensor data, and visual image data to obtain fused data includes: performing filtering preprocessing on the locking position sensor data, pressure sensor data, and visual image data respectively; removing outliers from the preprocessed data; extracting corresponding state features from the three types of preprocessed data respectively; weighting and fusing all extracted state features; and generating fused data containing multi-dimensional state information.

[0112] In some embodiments, determining the closed position, locked position, and sealed position of the logistics hatch based on the fused data includes: extracting gap features and contact pressure features from the fused data; determining the closed position of the hatch based on the gap features and contact pressure features; extracting latch position features and latch engagement features from the fused data; determining the locked position of the hatch based on the latch position features and latch engagement features; extracting pressure distribution features and circumferential gap features from the fused data; and determining the sealed position of the hatch based on the pressure distribution features and circumferential gap features.

[0113] In some embodiments, acquiring real-time flight status data of the logistics drone includes: acquiring real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data of the drone from the flight control system; and determining whether the logistics drone is in flight or stationary on the ground based on the real-time altitude data, real-time speed data, real-time acceleration data, and real-time positioning data.

[0114] In some embodiments, when it is determined that the logistics cabin door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, an emergency locking protection mechanism is activated, and a cabin door abnormality alarm signal is sent to the flight control system. This includes: sending a maximum output force locking command to the electric push rod; continuously monitoring the locking status of the cabin door; if the locking status returns to normal within a preset time, the emergency locking protection mechanism is deactivated; if the locking status does not return to normal within a preset time, an emergency landing command is sent to the flight control system.

[0115] In some embodiments, if the real-time flight status data shows that the logistics drone is stationary on the ground, the automatic locking or unlocking operation of the logistics cabin door in response to an externally input one-key control command includes: when responding to an externally input one-key locking command, sequentially performing a cabin door closing operation, a latch extension operation, and a sealing status verification operation; when responding to an externally input one-key unlocking command, sequentially performing a latch retraction operation and a cabin door opening operation; and after all operations are completed, sending an operation completion feedback signal to an external terminal.

[0116] In some embodiments, the method further includes: continuously collecting hatch opening and closing angle data and electric push rod operating current data during hatch opening and closing; adjusting the operating speed of the electric push rod in real time according to the opening and closing angle data; immediately stopping the electric push rod if the operating current of the electric push rod exceeds a preset threshold; and sending a hatch jamming alarm signal to an external terminal.

[0117] In some embodiments, the logistics hatch includes a weight sensor and a vision sensor. The method further includes: acquiring real-time weight distribution data of the cargo inside the hatch using the weight sensor; acquiring real-time image data of the cargo inside the hatch using the vision sensor; determining the displacement status of the cargo inside the hatch based on the real-time weight distribution data and the real-time image data; jointly analyzing the displacement status and the hatch status; if it is determined that the cargo has shifted and there is a risk of impacting the hatch, sending a flight attitude adjustment command to the flight control system; if it is determined that the cargo has impacted the hatch and caused the hatch to malfunction, executing the emergency handling procedure corresponding to the flight status.

[0118] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the intelligent locking and status monitoring method for the logistics drone cargo door provided in any embodiment of this application.

[0119] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0120] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent locking and status monitoring of the cargo door of a logistics drone, characterized in that, include: Continuously collect data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door; Data from locking position sensors, pressure sensors, and visual images are fused to obtain fused data; based on the fused data, the closed position, locking state, and sealing state of the logistics compartment door are determined respectively. Acquire real-time flight status data of logistics drones; when it is determined that the logistics cabin door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, activate the emergency locking protection mechanism and send a cabin door abnormality alarm signal to the flight control system. If the real-time flight status data shows that the logistics drone is stationary on the ground, it will respond to the one-click control command input from the outside and complete the automatic locking or unlocking operation of the logistics cabin door. The process of training a door status anomaly prediction model based on historical door status data and corresponding sensor data includes: collecting historical door status data and corresponding locking position sensor data, pressure sensor data, and visual image data, whereby the historical door status data includes normal and abnormal status data; dividing the collected historical door status data, locking position sensor data, pressure sensor data, and visual image data into training and testing sets, and using machine learning algorithms to train the door status anomaly prediction model; inputting real-time collected locking position sensor data, pressure sensor data, and visual image data into the door status anomaly prediction model; predicting the door's status change trend within a preset time period; and sending an early warning signal to the flight control system and external terminals if it is predicted that the door will be unlocked or abnormally opened.

2. The method according to claim 1, characterized in that, The continuous acquisition of data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door includes: Real-time displacement data of the latch is collected by a locking position sensor; multi-point pressure data on the contact surface between the hatch and the hull is collected by a pressure sensor; real-time image data of the mating area between the hatch and the hull is collected by a vision sensor; the sampling frequency of the three types of data is increased during hatch movement; and the sampling frequency of the three types of data is decreased when the hatch is stationary.

3. The method according to claim 1, characterized in that, The process of fusing data from the locking position sensor, pressure sensor, and visual image to obtain fused data includes: The locking position sensor data, pressure sensor data, and visual image data are filtered and preprocessed respectively; outliers in the preprocessed data are removed; corresponding state features are extracted from the three types of preprocessed data respectively; all extracted state features are weighted and fused; and fused data containing multi-dimensional state information is generated.

4. The method according to claim 3, characterized in that, The determination of the closed position, locked position, and sealed position of the logistics door based on the fused data includes: Extract gap and contact pressure features from the fused data; determine the door's closed position based on the gap and contact pressure features; extract latch position and latch engagement features from the fused data; determine the door's locking status based on the latch position and latch engagement features; extract pressure distribution and circumferential gap features from the fused data; determine the door's sealing status based on the pressure distribution and circumferential gap features.

5. The method according to claim 1, characterized in that, The acquisition of real-time flight status data of the logistics drone includes: The system acquires real-time altitude, speed, acceleration, and positioning data of the drone from the flight control system; based on these data, it determines whether the logistics drone is in flight or stationary on the ground.

6. The method according to claim 1, characterized in that, When it is determined that the logistics cabin door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, the emergency locking protection mechanism is activated, and a cabin door abnormality alarm signal is sent to the flight control system, including: Send the maximum output force lock command to the electric actuator; continuously monitor the locking status of the cabin door; if the locking status returns to normal within a preset time, release the emergency lock protection mechanism; if the locking status does not return to normal within a preset time, send an emergency landing command to the flight control system.

7. The method according to claim 6, characterized in that, If real-time flight status data shows that the logistics drone is stationary on the ground, it will respond to an externally input one-button control command to automatically lock or unlock the logistics cabin door, including: When responding to an externally inputted one-key locking command, the system sequentially performs the hatch closing operation, the latch extension operation, and the sealing status verification operation; when responding to an externally inputted one-key unlocking command, the system sequentially performs the latch retraction operation and the hatch opening operation; after all operations are completed, an operation completion feedback signal is sent to the external terminal.

8. The method according to claim 1, characterized in that, The method further includes: During the opening and closing of the hatch, the system continuously collects data on the opening and closing angle of the hatch and the operating current data of the electric push rod; it adjusts the operating speed of the electric push rod in real time based on the opening and closing angle data; if the operating current of the electric push rod exceeds the preset threshold, it immediately stops the operation of the electric push rod; and it sends a hatch jamming alarm signal to the external terminal.

9. The method according to claim 1, characterized in that, The logistics compartment includes weight sensors and vision sensors, and the method further includes: Real-time weight distribution data of cargo inside the cabin is collected using weight sensors. Real-time image data of the cargo inside the cabin is collected using visual sensors; The displacement status of cargo inside the cabin is determined based on real-time weight distribution data and real-time image data. By jointly analyzing the displacement status and the hatch status, if it is determined that the cargo has shifted and there is a risk of impacting the hatch, a flight attitude adjustment command is sent to the flight control system; if it is determined that the cargo has already impacted the hatch and caused the hatch to malfunction, the emergency handling procedure corresponding to the flight status is executed.

10. A smart lock control and status monitoring system for the cargo door of a logistics drone, used to implement the method as described in any one of claims 1-9, characterized in that, include: The data acquisition unit is used to continuously collect data from the locking position sensor, pressure sensor, and visual image data corresponding to the logistics compartment door. The status determination unit is used to fuse data from the locking position sensor, pressure sensor, and visual image to obtain fused data; and to determine the closed position, locking position, and sealing position of the logistics door based on the fused data. The operation completion unit is used to acquire real-time flight status data of the logistics drone. When it is determined that the logistics door is unlocked or abnormally opened, if the real-time flight status data shows that the logistics drone is in flight, the emergency locking protection mechanism is activated, and a door abnormality alarm signal is sent to the flight control system. If real-time flight status data shows that the logistics drone is stationary on the ground, it responds to the one-click control command input from the outside to complete the automatic locking or unlocking of the logistics cabin door; it trains a cabin door status anomaly prediction model based on historical cabin door status data and corresponding sensor data; it inputs real-time collected locking position sensor data, pressure sensor data, and visual image data into the cabin door status anomaly prediction model; it predicts the cabin door status change trend within a preset time period; if it predicts that the cabin door will be unlocked or abnormally opened, it sends a warning signal to the flight control system and external terminals in advance.

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