Storage container for diversified logistics unmanned aerial vehicle and operation system of storage container

Abstract

The invention relates to the technical field of unmanned aerial vehicle transportation, discloses a storage container for a diversified logistics unmanned aerial vehicle and an operation system of the storage container, and aims to solve the problems that in the existing unmanned aerial vehicle distribution technology, the design of the storage container is lack of flexibility, an effective cargo tracking and information identification means is lack, and the operation system is lack of a comprehensive and intelligent logistics management function. The storage container adopts a modular design, can be flexibly combined by standard modules, is suitable for goods of different types and sizes, and is provided with an RFID or two-dimensional code tag to realize goods tracking and information identification. The container is made of a light high-strength material, is internally provided with a shock pad and an anti-collision corner to protect goods, and is stably connected with the unmanned aerial vehicle through an electromagnetic locking device. The integrated intelligent operation system comprises a sensing layer, a control layer, a data processing layer, a decision-making layer and an application layer, efficient and intelligent logistics management is achieved, and complex and changeable logistics requirements are met.

Description

Technical Field

[0001] The present invention relates to the field of drone transportation technology, and in particular to a storage cargo box for a diversified logistics drone and an operating system thereof. Background Art

[0002] With the rapid development of the logistics industry, drone delivery, as a new delivery method, has attracted much attention due to its high efficiency and speed. However, in the existing drone delivery technology, there are still many defects in the design of storage boxes and their operating systems, which limits the widespread application and further development of drone delivery.

[0003] Most existing storage box designs lack flexibility and cannot be flexibly adjusted according to the type and size of goods, resulting in many inconveniences when delivering goods of different types and sizes. At the same time, the boxes lack effective means of tracking and identifying goods, making it impossible to achieve full tracking and information management of goods.

[0004] In terms of operating systems, existing technologies lack comprehensive and intelligent logistics management functions. The collaborative work between the perception layer, control layer, data processing layer, decision layer, and application layer is not efficient enough to achieve real-time and accurate logistics management and distribution scheduling. In particular, the lack of intelligent task scheduling, exception handling, and user interaction functions cannot meet the complex and changing logistics needs. To this end, we propose a storage box for diversified logistics drones and its operating system. Summary of the invention

[0005] The purpose of the present invention is to provide a storage cargo box and an operating system for a diversified logistics drone to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a storage box for a diversified logistics drone, characterized in that: With modular design, the storage box is composed of standardized modules, which can be flexibly adjusted according to the type and size of goods; Each module is equipped with RFID or QR code tags for cargo tracking and information identification; The cargo box is made of lightweight, high-strength material, with built-in shock-absorbing pads and anti-collision corners to protect the cargo; An electromagnetic locking device is used between the cargo box and the drone to ensure that the cargo box is stable and does not fall off during flight; The cargo box design is compatible with the automated loading and unloading system, and supports the robotic arm to assist in loading and unloading the cargo box; The cargo box is integrated with an intelligent operating system to achieve efficient and intelligent logistics management.

[0007] Preferably, an operating system for a storage cargo box for a diversified logistics drone includes a perception layer, a control layer, a data processing layer, a decision layer, and an application layer; The perception layer includes a sensor module, a positioning module and a communication module; the sensor module is used to monitor the internal environmental parameters and cargo information of the cargo box in real time; the positioning module is used to ensure the accurate positioning of the drone and the cargo box; the communication module is used to realize information exchange between the cargo box, the drone and the control center; The control layer includes a flight control module, a cargo box management module and an autopilot module; the flight control module is responsible for the flight attitude control, route planning and execution of the UAV; the cargo box management module is responsible for controlling the opening and closing of the cargo box, cargo status monitoring and abnormal alarm; the autopilot module is responsible for integrating the autopilot algorithm to realize the autonomous flight of the UAV in complex environments; The data processing layer includes a data acquisition module, a data analysis module and a data storage module; the data acquisition module is used to collect various sensor data from the perception layer; the data analysis module is used to analyze the collected data using machine learning algorithms to extract useful information; the data storage module is responsible for data storage and management to ensure data security and traceability; The decision-making layer includes a task scheduling module, an exception handling module and a user interaction module; the task scheduling module is used to intelligently schedule drones and cargo boxes to perform delivery tasks according to order requirements, drone status and environmental factors; the exception handling module is used to monitor abnormal situations during system operation and automatically trigger corresponding emergency handling mechanisms; the user interaction module is used to provide a user interface to allow users to query delivery status and submit order operations; The application layer includes a logistics management system module, a data visualization module and a third-party interface module; the logistics management system module is used to integrate order management, inventory management and delivery tracking functions; the data visualization module is used to generate various reports and charts using data analysis results; the third-party interface module is used to provide an API interface to facilitate data exchange and integration with other logistics systems and e-commerce platforms.

[0008] Preferably, the positioning module includes a GPS receiver, an inertial navigation system and a positioning algorithm processing unit; The GPS receiver is used to receive satellite signals from the global positioning system and parse the longitude, latitude and altitude information of the drone; The inertial navigation system is used to measure the acceleration, angular velocity and direction of the drone through accelerometer, gyroscope and magnetometer sensors, so as to calculate the attitude, speed and position information of the drone; The positioning algorithm processing unit is used to fuse the data of the GPS receiver and the inertial navigation system, and adopts the Kalman filter algorithm or the particle filter algorithm to perform data processing; In the positioning algorithm processing unit, the specific implementation includes the following steps: Use a GPS receiver to obtain preliminary location information of the drone; Use the inertial navigation system to obtain the attitude, speed and acceleration information of the drone; The location information of the GPS receiver is integrated with the location information of the inertial navigation system, and the Kalman filter algorithm or particle filter algorithm is used for data processing to obtain the final drone location information.

[0009] Preferably, the communication module includes a wireless communication unit, a satellite communication unit and a communication protocol processing unit; The wireless communication unit is used to realize short-range wireless communication between the UAV and the ground control station, other UAVs or logistics centers, using Wi-Fi, Bluetooth or Zigbee wireless communication technology; The satellite communication unit is used to achieve remote communication through a satellite communication link when the UAV is far away from the ground control station or in an area not covered by wireless communication signals; The communication protocol processing unit is used to process the data received and sent by the wireless communication unit and the satellite communication unit, and perform data packet encapsulation, analysis, verification and error processing according to a predetermined communication protocol; The wireless communication unit and the satellite communication unit respectively receive data signals from the ground control station, other drones or logistics centers; the communication protocol processing unit parses the received data signals, extracts valid data, and performs verification and error processing; the processed data is passed to other modules of the operating system for further processing and use; at the same time, the communication protocol processing unit is also responsible for encapsulating the data of other modules of the operating system according to the predetermined communication protocol, and sending it to the ground control station, other drones or logistics centers through the wireless communication unit or the satellite communication unit.

[0010] Preferably, the flight control module includes a sensor interface, a flight control algorithm unit and an actuator interface; the flight control module receives data from various sensors on the UAV in real time through the sensor interface; the flight control algorithm unit processes the received data and extracts the current flight status information of the UAV; according to the preset flight mission and target, as well as the current flight status information, the flight control algorithm unit calculates and generates corresponding control instructions; the control instructions are transmitted to the actuator of the UAV through the actuator interface to control the flight attitude and speed of the UAV to achieve stable flight and route tracking.

[0011] Preferably, the data analysis module uses a linear regression algorithm to train a cargo status prediction model, and the specific steps are: Step 1: Collect and preprocess a data set for training a cargo status prediction model, where the data set contains historical cargo status records and various characteristic factors related thereto, including temperature, humidity, and vibration of the cargo box; Step 2: Use linear regression algorithm to build a cargo status prediction model. The algorithm formula of the model is:

[0012] Among them, y is the target variable, is the input variable, representing the multi-scale feature vector extracted from the image, is the intercept term, is the regression coefficient, is the error term; Step 3: Initialize model parameters for each feature in the linear regression model Corresponding parameters Assign an initial value, which is set to a random number close to 0 and is also the intercept term Also assign an initial value; Step 4: Use the least squares method as the optimization algorithm to train the linear regression model and solve the model parameters by minimizing the sum of squared errors between the predicted value and the actual value; Step 5: Use the trained linear regression model to predict the new cargo status, input new feature factor data, and output the prediction results based on the model.

[0013] Preferably, the task scheduling module includes a task receiving unit, a task analyzing unit, a resource allocation unit and a task execution unit; The task receiving unit is used to receive a task request from a logistics center or other drones, where the task request includes a task type, priority, and execution time; The task analysis unit is used to analyze the received task request and determine the execution order and resource requirements of the tasks according to the task type, priority and execution time; The resource allocation unit is used to allocate necessary resources for the task, including drones, storage boxes and energy, according to the output of the task analysis unit; The task execution unit is used to schedule the allocated resources to execute the tasks according to the execution order determined by the task analysis unit, and monitor the execution status of the tasks to ensure that the tasks are completed on time; In the task scheduling module, the specific implementation includes the following steps: Receive mission requests from logistics centers or other UAVs through mission receiving units; The task analysis unit analyzes the task request, extracts the task type, priority and execution time, and determines the execution order and resource requirements of the tasks; The resource allocation unit allocates necessary resources to the task according to the output of the task analysis unit; The task execution unit schedules the allocated resources to execute tasks according to the execution order determined by the task analysis unit, and monitors the execution status of the tasks in real time.

[0014] Preferably, the exception handling module includes an exception monitoring unit, an exception analysis unit, an exception response unit and a log recording unit; The abnormality monitoring unit is used to monitor the system operation status in real time, including the flight status of the drone, the storage container status and the communication status, and to detect possible abnormal events; The abnormality analysis unit is used to analyze the abnormal events detected by the abnormality monitoring unit to determine the abnormality type, severity and impact range; The abnormal response unit is used to take corresponding abnormality handling measures according to the analysis results of the abnormality analysis unit, including sending alarms, adjusting system parameters and executing preset emergency plans to eliminate or reduce the impact of abnormal events on system operation; The log recording unit is used to record the occurrence time, type, severity, impact scope of abnormal events and the execution status of abnormal handling measures.

[0015] Preferably, the user interaction module includes a user input unit, an interface display unit, an interaction logic processing unit and a user feedback unit; The user input unit is used to receive operation instructions or data input by the user through an input device, and the input device includes a keyboard, a mouse and a touch screen; The interface display unit is used to display various information of the operating system to the user, including system status, task progress and abnormal alarms, and provide a graphical interface for the user to operate; The interactive logic processing unit is used to parse and process the operation instructions or data received by the user input unit, and perform corresponding operations or generate corresponding responses according to the current state of the operating system and user permissions; The user feedback unit is used to feed back operation results or system status changes to the user, and the feedback methods include interface prompts, sound prompts and light prompts.

[0016] Preferably, the third-party interface module includes an interface definition unit, a data exchange unit, a security authentication unit and an interface management unit; The interface definition unit is used to define the API interface specifications required for data exchange and integration with other logistics systems and e-commerce platforms, including interface addresses, request parameters and response formats; The data exchange unit is used to realize data exchange with other logistics systems and e-commerce platforms, receive requests from other systems according to the defined interface specifications, and send response data; The security authentication unit is used to perform security authentication on data exchange with other logistics systems and e-commerce platforms; The interface management unit is used to manage the defined API interface, including publishing, updating and offline operations of the interface, and monitoring the usage and performance of the interface.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The modular design allows the storage box to be flexibly adjusted and combined according to the type and size of the goods, greatly improving the applicability and flexibility of the box. The modular design also facilitates the maintenance and replacement of the box, reducing the cost of use.

[0018] Each module is equipped with RFID or QR code tags, which realizes the full tracking and information management of goods, and improves the transparency and efficiency of logistics. Through cargo tracking and information identification, the location and status of goods can be grasped in real time, effectively preventing the loss or damage of goods.

[0019] The storage box integrates an intelligent operating system, which realizes efficient collaboration among the perception layer, control layer, data processing layer, decision layer and application layer. The intelligent operating system has intelligent task scheduling, exception handling and user interaction functions, which can meet the complex and ever-changing logistics needs. Through the logistics management system module, data visualization module and third-party interface module, the integrated management of orders, effective monitoring of inventory and full tracking of distribution are realized, which improves the overall efficiency of logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the overall structure diagram of the present invention; Figure 2 A diagram showing the steps to achieve drone positioning for the positioning module; Figure 3 Diagram of the steps for building and training a cargo status prediction model. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1-3The present invention provides a technical solution: a storage cargo box for a diversified logistics drone, which fully considers the requirements of flexibility, cargo tracking and information management, cargo protection, stability, automated loading and unloading, and intelligent logistics management in design.

[0023] The storage box adopts a modular design and consists of a series of standardized modules. This design allows the box to be flexibly adjusted and combined according to the type and size of the goods, so as to adapt to different delivery needs. Whether it is a small package or a large cargo, the best delivery effect can be achieved by adjusting the combination of modules.

[0024] Each module is equipped with RFID or QR code tags for cargo tracking and information identification. These tags record detailed information about the cargo, including type, quantity, destination, etc., so that the cargo can be tracked and managed in an informationized manner throughout the distribution process. By scanning the tags, the location and status of the cargo can be obtained in real time, improving the accuracy and efficiency of logistics management.

[0025] In terms of cargo protection, the storage box is made of lightweight and high-strength materials, with built-in shock-absorbing pads and anti-collision corners. These designs effectively protect the cargo from damage caused by vibration and collision during transportation, ensuring the safe delivery of the cargo.

[0026] An electromagnetic locking device is used between the cargo box and the drone. This device can ensure that the cargo box is stable and does not fall off during flight, improving the safety and reliability of delivery. At the same time, the design of the cargo box is also compatible with the automated loading and unloading system, supporting the robotic arm to assist in the loading and unloading operations of the cargo box, further improving logistics efficiency.

[0027] The storage box also integrates an intelligent system to achieve efficient and intelligent logistics management. This intelligent system can be seamlessly connected with the drone's control system to achieve real-time data transmission and command execution. With the assistance of the intelligent system, intelligent task scheduling, exception handling and user interaction functions can be realized to meet complex and changing logistics needs.

[0028] The present invention provides an operating system for a storage box for a diversified logistics drone, which realizes efficient and intelligent logistics management through the collaborative work of a perception layer, a control layer, a data processing layer, a decision layer and an application layer.

[0029] The perception layer includes sensor modules, positioning modules and communication modules. The sensor module is used to monitor the internal environmental parameters and cargo information of the cargo box in real time, such as temperature, humidity, cargo location, etc., to ensure the safety of the cargo during transportation. The positioning module is responsible for ensuring the accurate positioning of the drone and cargo box. By integrating a high-precision positioning system, the drone and cargo box can be accurately positioned in three-dimensional space. The communication module is used to realize information exchange between the cargo box, the drone and the control center, and transmit information such as cargo status and drone status in real time through wireless communication.

[0030] The control layer includes the flight control module, the cargo box management module and the autopilot module. The flight control module is responsible for the flight attitude control, route planning and execution of the drone. It ensures the stable flight of the drone in complex environments through advanced flight control algorithms. The cargo box management module is responsible for controlling the opening and closing of the cargo box, cargo status monitoring and abnormal alarms. It realizes remote control and status monitoring of the cargo box through the integration of smart locks and sensors. The autopilot module integrates advanced autopilot algorithms, allowing the drone to achieve autonomous flight in complex environments without human intervention.

[0031] The data processing layer includes data acquisition module, data analysis module and data storage module. The data acquisition module is used to collect various sensor data from the perception layer, such as environmental parameters, cargo status, drone status, etc. The data analysis module uses machine learning algorithms to analyze the collected data and extract useful information, such as abnormal cargo status, drone flight trajectory, etc. The data storage module is responsible for data storage and management, and ensures data security and traceability by adopting distributed storage and backup mechanisms.

[0032] The decision-making layer includes the task scheduling module, the exception handling module and the user interaction module. The task scheduling module is used to intelligently schedule drones and cargo boxes to perform delivery tasks according to order requirements, drone status and environmental factors, and to achieve efficient execution of delivery tasks through optimization algorithms. The exception handling module is used to monitor abnormal conditions during system operation and automatically trigger corresponding emergency response mechanisms, such as automatically adjusting the delivery route or dispatching backup drones in the event of sudden weather or drone failure. The user interaction module provides a user interface that allows users to query delivery status and submit order operations, and improves the user experience through friendly interactive design.

[0033] The application layer includes the logistics management system module, the data visualization module and the third-party interface module. The logistics management system module is used to integrate order management, inventory management and delivery tracking functions, and realizes the comprehensive informatization of logistics management through a unified platform. The data visualization module uses data analysis results to generate various reports and charts to provide intuitive decision support for logistics managers. The third-party interface module provides an API interface to facilitate data exchange and integration with other logistics systems and e-commerce platforms, realizing the sharing and collaborative operation of logistics information.

[0034] The present invention will be further described below in conjunction with Examples 1 to 3: Embodiment 1: The positioning module in the present invention is a highly integrated system that combines a GPS receiver, an inertial navigation system and a positioning algorithm processing unit to achieve accurate and reliable positioning of the drone.

[0035] The GPS receiver is one of the core components of the module, which is responsible for receiving satellite signals from the global positioning system. After these signals are analyzed, they can provide the longitude, latitude and altitude information of the drone, providing a preliminary location reference based on the global positioning system for the drone.

[0036] The inertial navigation system is an independent navigation solution that does not rely on external signals, but instead measures the acceleration, angular velocity and direction of the drone through sensors such as accelerometers, gyroscopes and magnetometers. These data are then used to calculate the attitude, speed and position information of the drone, providing a continuous and autonomous positioning information source for the drone.

[0037] The positioning algorithm processing unit is the bridge and core processor of these two systems. It first uses the GPS receiver to obtain the preliminary position information of the drone, and then uses the inertial navigation system to obtain the attitude, speed and acceleration information of the drone. Next, the unit fuses these two sets of data and uses advanced Kalman filter algorithm or particle filter algorithm for data processing. Both algorithms can effectively fuse data from different sources and take into account various error factors to obtain the final, more accurate and reliable drone position information.

[0038] In this way, the positioning module of the present invention can fully utilize the advantages of GPS receivers and inertial navigation systems while overcoming their respective limitations. Whether in an open environment with good signals or in a complex environment with limited signals, the module can provide stable and accurate positioning services for drones.

[0039] The communication module combines a wireless communication unit, a satellite communication unit and a communication protocol processing unit to ensure that the UAV can achieve stable and reliable communication in various environments.

[0040] The wireless communication unit is the basic component of the module, which is responsible for realizing short-range wireless communication between the drone and the ground control station, other drones or logistics centers. This unit uses advanced Wi-Fi, Bluetooth or Zigbee wireless communication technologies, which have the advantages of fast transmission speed, low power consumption and moderate communication distance, and are very suitable for short-range communication between drones and ground equipment.

[0041] The satellite communication unit is a key backup and remote communication solution. When the drone is far away from the ground control station or in an area where wireless communication signals cannot reach, this unit can achieve remote communication through satellite communication links, ensuring that the drone can maintain contact with the control center in any environment.

[0042] The communication protocol processing unit is the brain of the entire communication module. It is responsible for processing the data received and sent by the wireless communication unit and the satellite communication unit, and performs data packet encapsulation, parsing, verification and error handling according to the predetermined communication protocol. This unit can ensure the integrity, accuracy and reliability of communication data, and is the cornerstone of the stable operation of the drone communication system.

[0043] In actual operation, the wireless communication unit and the satellite communication unit will receive data signals from the ground control station, other drones or logistics centers respectively. These signals are then passed to the communication protocol processing unit, which parses the received data signals, extracts valid data, and performs verification and error processing. The processed data will be passed to other modules of the operating system for further processing and use.

[0044] At the same time, the communication protocol processing unit is also responsible for encapsulating the data of other modules of the operating system according to the predetermined communication protocol. The encapsulated data will be sent to the ground control station, other drones or logistics centers through the wireless communication unit or satellite communication unit to achieve two-way communication between the drone and the ground equipment.

[0045] Embodiment 2: The data analysis module in the present invention uses a linear regression algorithm to train a cargo status prediction model, which can effectively predict the current status of cargo based on historical data and characteristic factors. The following are the specific implementation steps of this module: Step 1: Data preparation and preprocessing First, it is necessary to collect and preprocess the data set used to train the cargo status prediction model. This data set contains historical cargo status records and various characteristic factors related to them. Characteristic factors are those factors that may affect the cargo status, such as the temperature, humidity, and vibration of the cargo box. After collecting this data, it is necessary to preprocess it, including data cleaning, missing value processing, outlier processing, etc., to ensure the accuracy and consistency of the data.

[0046] Step 2: Model building Next, a linear regression algorithm is used to construct a cargo status prediction model. Linear regression is a widely used prediction model that assumes that there is a linear relationship between the target variable and the input variable. In the present invention, the algorithm formula of the model is:

[0047] Where y is the target variable, representing the status of the goods; is the input variable, representing the multi-scale feature vector extracted from the image, which contains information such as temperature, humidity, and vibration of the cargo box; is the intercept term; is the regression coefficient, which represents the influence of each feature on the target variable; is the error term, which represents the difference between the model's predicted value and the actual value.

[0048] Step 3: Model parameter initialization After building the model, the model parameters need to be initialized. Give an initial value to the parameter corresponding to each feature in the linear regression model. This initial value is usually set to a random number close to 0. At the same time, an initial value is also given to the intercept term. These initial values ​​will be optimized in the subsequent model training process.

[0049] Step 4: Model training The least squares method is used as an optimization algorithm to train the linear regression model. The least squares method is a commonly used optimization algorithm that solves the model parameters by minimizing the sum of squared errors between the predicted values ​​and the actual values. During the training process, the algorithm will continuously adjust the model parameters so that the difference between the predicted values ​​and the actual values ​​gradually decreases until it reaches an acceptable level.

[0050] Step 5: Model prediction and application Finally, the trained linear regression model is used to predict the new cargo status. New feature factor data is input and the prediction result is output according to the model. This prediction result can be used to determine whether the current status of the cargo is normal and whether corresponding measures need to be taken to ensure the safety of the cargo. In this way, the data analysis module of the present invention can provide data support for the operating system of the storage box of the logistics drone, thereby improving the efficiency and accuracy of logistics management.

[0051] The task scheduling module includes a task receiving unit, a task analysis unit, a resource allocation unit, and a task execution unit, which work together to ensure the smooth progress of the UAV logistics mission.

[0052] The task receiving unit is the front-end interface of the module, which is responsible for receiving task requests from logistics centers or other drones. These task requests contain key information such as task type, priority and execution time, which is the basis of task scheduling.

[0053] The task analysis unit is a unit that conducts in-depth analysis of the received task requests. It determines the execution order and resource requirements of the tasks based on parameters such as task type, priority, and execution time. This step is the core of task scheduling, which ensures that the execution of tasks can be carried out in the optimal way.

[0054] The resource allocation unit is the resource manager in the task scheduling module. It allocates necessary resources for the task, including drones, storage boxes, and energy, based on the output of the task analysis unit. This unit ensures that there are sufficient resources to support the task during execution, which is an important guarantee for the successful completion of the task.

[0055] The task execution unit is the executor of the task scheduling module. It schedules the allocated resources to execute tasks according to the execution order determined by the task analysis unit, and monitors the execution status of tasks in real time. This unit ensures that tasks can be carried out according to plan and can make timely adjustments when abnormal situations occur.

[0056] In the specific implementation process, the workflow of the task scheduling module is as follows: i) The mission receiving unit receives mission requests from the logistics center or other UAVs and passes these requests to the mission analysis unit.

[0057] ii) The task analysis unit analyzes the received task requests, extracts key information such as task type, priority and execution time, and determines the execution order and resource requirements of the tasks based on this information.

[0058] iii) The resource allocation unit allocates necessary resources for the task according to the output of the task analysis unit, including drones, storage boxes, and energy, etc. These resources are the basis for the smooth execution of the task.

[0059] iv) The task execution unit dispatches the allocated resources to execute the tasks according to the execution order determined by the task analysis unit, and monitors the execution status of the tasks in real time. During the task execution process, if there are any abnormal situations or the plan needs to be adjusted, the task execution unit will handle them in time to ensure that the tasks can be completed on time.

[0060] The exception handling module integrates the exception monitoring unit, exception analysis unit, exception response unit and log recording unit to ensure the stable operation of the drone logistics system.

[0061] The abnormal monitoring unit is the outpost of this module. It is responsible for real-time monitoring of the system operation status, including the flight status of the drone, the storage container status and the communication status. Through advanced monitoring technology, it can detect possible abnormal events in a timely manner and provide basic data for subsequent abnormal processing.

[0062] The abnormal analysis unit is a unit that conducts in-depth analysis of abnormal events detected by the abnormal monitoring unit. It uses advanced algorithms and technologies to conduct detailed analysis of abnormal events to determine the type, severity and scope of impact of the abnormality. This step is the key to abnormal handling, which ensures that abnormal events can be properly handled.

[0063] The exception response unit is the executor of the exception handling module. It takes corresponding exception handling measures based on the analysis results of the exception analysis unit. These measures may include sending alarms to notify relevant personnel, adjusting system parameters to restore normal system operation, or executing preset emergency plans to deal with serious abnormal events. Through these measures, the exception response unit can eliminate or reduce the impact of abnormal events on system operation.

[0064] The logging unit is the recorder of the exception handling module. It is responsible for recording the time, type, severity, impact scope of the exception event, and the execution of the exception handling measures. These records are of great significance for the subsequent analysis and improvement of the system. They can help us understand the operation status of the system, discover potential problems, and optimize the exception handling strategy.

[0065] In the specific implementation process, the workflow of the exception handling module is as follows: i) The abnormality monitoring unit monitors the system operation status in real time and detects possible abnormal events in a timely manner.

[0066] ii) The anomaly analysis unit conducts in-depth analysis of detected abnormal events to determine the type, severity and impact of the anomaly.

[0067] iii) The abnormal response unit takes corresponding abnormal handling measures according to the analysis results to eliminate or reduce the impact of abnormal events on system operation.

[0068] iv) The log recording unit records the occurrence time, type, severity, impact scope and execution status of abnormal event handling measures, providing data support for subsequent analysis and improvement of the system.

[0069] Embodiment 3: The user interaction module integrates the user input unit, interface display unit, interaction logic processing unit and user feedback unit, providing users of the drone logistics system with a convenient and efficient interaction experience.

[0070] The user input unit is an important interface of the module, which is responsible for receiving the operation instructions or data input by the user through the input device. These input devices include but are not limited to keyboard, mouse and touch screen. The user can choose the appropriate input method to operate according to his own habits and preferences.

[0071] The interface display unit is responsible for displaying various information of the operating system to the user. It can not only display the system status, task progress and abnormal alarms in real time, but also provide a graphical interface for users to operate. These graphical interfaces are simple and intuitive, allowing users to easily get started and quickly complete the required operations.

[0072] The interactive logic processing unit is the core of the user interaction module. It is responsible for parsing and processing the operation instructions or data received by the user input unit. According to the current state of the operating system and user permissions, the interactive logic processing unit can perform corresponding operations or generate corresponding responses. This step ensures that the user's operation can be processed and executed correctly.

[0073] The user feedback unit is the feedback mechanism of the user interaction module. It is responsible for feeding back the operation results or system status changes to the user. There are various feedback methods, including interface prompts, sound prompts, and light prompts. These feedback methods can inform the user of the operation results or system status in a timely and accurate manner, allowing the user to understand the system's operating status at any time and take corresponding measures.

[0074] In the specific implementation process, the workflow of the user interaction module is as follows: i) The user inputs operation instructions or data to the user input unit through the input device, and these instructions or data are transmitted to the interactive logic processing unit for analysis and processing.

[0075] ii) The interactive logic processing unit performs corresponding operations or generates corresponding responses according to the current state of the operating system and user permissions. These operations or responses may include adjusting system parameters, executing tasks, displaying information, etc.

[0076] iii) The interface display unit displays various information of the operating system and the graphical interface that the user can operate to the user. The user can understand the operating status of the system through these interfaces and perform corresponding operations.

[0077] iv) The user feedback unit provides feedback to the user on the operation results or system status changes. Through interface prompts, sound prompts, and light prompts, the user can promptly understand the results of his or her operation or the operating status of the system and take corresponding actions.

[0078] The third-party interface module includes an interface definition unit, a data exchange unit, a security authentication unit and an interface management unit, aiming to achieve seamless data exchange and integration with other logistics systems and e-commerce platforms.

[0079] The interface definition unit is the foundation of this module. It is responsible for defining the API interface specifications required for data exchange and integration with other logistics systems and e-commerce platforms. These specifications include key information such as interface address, request parameters and response format, ensuring the accuracy and consistency of data exchange.

[0080] The data exchange unit is the core of this module, which is responsible for realizing the actual data exchange with other logistics systems and e-commerce platforms. According to the interface specifications defined by the interface definition unit, the data exchange unit can receive requests from other systems and send corresponding response data. This step ensures the real-time and accurate exchange of data, providing a guarantee for the smooth operation of the logistics system.

[0081] The security authentication unit is an important guarantee for this module. It is responsible for the security authentication of data exchange with other logistics systems and e-commerce platforms. Through advanced security technology, the security authentication unit can ensure the security and confidentiality of data exchange and prevent data leakage and illegal access.

[0082] The interface management unit is the management center of the module, which is responsible for managing the defined API interfaces. This includes publishing, updating, and offline operations of the interfaces, as well as monitoring the usage and performance of the interfaces. Through the interface management unit, system administrators can easily maintain and manage the interfaces to ensure the stability and availability of the interfaces.

[0083] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A storage box for a diversified logistics drone, characterized by: With modular design, the storage box is composed of standardized modules, which can be flexibly adjusted according to the type and size of goods; Each module is equipped with RFID or QR code tags for cargo tracking and information identification; The cargo box is made of lightweight, high-strength material, with built-in shock-absorbing pads and anti-collision corners to protect the cargo; An electromagnetic locking device is used between the cargo box and the drone to ensure that the cargo box is stable and does not fall off during flight; The cargo box design is compatible with the automated loading and unloading system, and supports the robotic arm to assist in loading and unloading the cargo box; The cargo box is integrated with an intelligent operating system to achieve efficient and intelligent logistics management.

2. The operating system of the storage box for a diversified logistics drone according to claim 1, characterized in that: Includes perception layer, control layer, data processing layer, decision layer and application layer; The perception layer includes a sensor module, a positioning module and a communication module; the sensor module is used to monitor the internal environmental parameters and cargo information of the cargo box in real time; the positioning module is used to ensure the accurate positioning of the drone and the cargo box; the communication module is used to realize information exchange between the cargo box, the drone and the control center; The control layer includes a flight control module, a cargo box management module and an autopilot module; the flight control module is responsible for the flight attitude control, route planning and execution of the UAV; the cargo box management module is responsible for controlling the opening and closing of the cargo box, cargo status monitoring and abnormal alarm; the autopilot module is responsible for integrating the autopilot algorithm to realize the autonomous flight of the UAV in complex environments; The data processing layer includes a data acquisition module, a data analysis module and a data storage module; the data acquisition module is used to collect various sensor data from the perception layer; the data analysis module is used to analyze the collected data using machine learning algorithms to extract useful information; the data storage module is responsible for data storage and management to ensure data security and traceability; The decision-making layer includes a task scheduling module, an exception handling module and a user interaction module; the task scheduling module is used to intelligently schedule drones and cargo boxes to perform delivery tasks according to order requirements, drone status and environmental factors; the exception handling module is used to monitor abnormal situations during system operation and automatically trigger corresponding emergency handling mechanisms; the user interaction module is used to provide a user interface to allow users to query delivery status and submit order operations; The application layer includes a logistics management system module, a data visualization module and a third-party interface module; The logistics management system module is used to integrate order management, inventory management and delivery tracking functions; the data visualization module is used to generate various reports and charts using data analysis results; The third-party interface module is used to provide an API interface to facilitate data exchange and integration with other logistics systems and e-commerce platforms.

3. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The positioning module includes a GPS receiver, an inertial navigation system and a positioning algorithm processing unit; The GPS receiver is used to receive satellite signals from the global positioning system and parse the longitude, latitude and altitude information of the drone; The inertial navigation system is used to measure the acceleration, angular velocity and direction of the drone through accelerometer, gyroscope and magnetometer sensors, so as to calculate the attitude, speed and position information of the drone; The positioning algorithm processing unit is used to fuse the data of the GPS receiver and the inertial navigation system, and adopts the Kalman filter algorithm or the particle filter algorithm to perform data processing; In the positioning algorithm processing unit, the specific implementation includes the following steps: Use a GPS receiver to obtain preliminary location information of the drone; Use the inertial navigation system to obtain the attitude, speed and acceleration information of the drone; The location information of the GPS receiver is integrated with the location information of the inertial navigation system, and the Kalman filter algorithm or particle filter algorithm is used for data processing to obtain the final drone location information.

4. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The communication module includes a wireless communication unit, a satellite communication unit and a communication protocol processing unit; The wireless communication unit is used to realize short-range wireless communication between the UAV and the ground control station, other UAVs or logistics centers, using Wi-Fi, Bluetooth or Zigbee wireless communication technology; The satellite communication unit is used to achieve remote communication through a satellite communication link when the UAV is far away from the ground control station or in an area not covered by wireless communication signals; The communication protocol processing unit is used to process the data received and sent by the wireless communication unit and the satellite communication unit, and perform data packet encapsulation, analysis, verification and error processing according to a predetermined communication protocol; The wireless communication unit and the satellite communication unit receive data signals from the ground control station, other UAVs or logistics centers respectively; The communication protocol processing unit parses the received data signal, extracts valid data, and performs verification and error processing; the processed data is passed to other modules of the operating system for further processing and use; at the same time, the communication protocol processing unit is also responsible for encapsulating the data of other modules of the operating system according to the predetermined communication protocol, and sending it to the ground control station, other drones or logistics centers through the wireless communication unit or satellite communication unit.

5. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The flight control module includes a sensor interface, a flight control algorithm unit and an actuator interface; the flight control module receives data from various sensors on the UAV in real time through the sensor interface; the flight control algorithm unit processes the received data and extracts the current flight status information of the UAV; according to the preset flight mission and target, as well as the current flight status information, the flight control algorithm unit calculates and generates corresponding control instructions; the control instructions are transmitted to the actuator of the UAV through the actuator interface to control the flight attitude and speed of the UAV to achieve stable flight and route tracking.

6. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The data analysis module uses a linear regression algorithm to train a cargo status prediction model, and the specific steps are as follows: Step 1: Collect and preprocess a data set for training a cargo status prediction model, where the data set contains historical cargo status records and various characteristic factors related thereto, including temperature, humidity, and vibration of the cargo box; Step 2: Use linear regression algorithm to build a cargo status prediction model. The algorithm formula of the model is: Among them, y is the target variable, is the input variable, representing the multi-scale feature vector extracted from the image, is the intercept term, is the regression coefficient, is the error term; Step 3: Initialize model parameters for each feature in the linear regression model Corresponding parameters Assign an initial value, which is set to a random number close to 0 and is also the intercept term Also assign an initial value; Step 4: Use the least squares method as the optimization algorithm to train the linear regression model and solve the model parameters by minimizing the sum of squared errors between the predicted value and the actual value; Step 5: Use the trained linear regression model to predict the new cargo status, input new feature factor data, and output the prediction results based on the model.

7. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The task scheduling module includes a task receiving unit, a task analyzing unit, a resource allocation unit and a task execution unit; The task receiving unit is used to receive a task request from a logistics center or other drones, where the task request includes a task type, priority, and execution time; The task analysis unit is used to analyze the received task request and determine the execution order and resource requirements of the tasks according to the task type, priority and execution time; The resource allocation unit is used to allocate necessary resources for the task, including drones, storage boxes and energy, according to the output of the task analysis unit; The task execution unit is used to schedule the allocated resources to execute the tasks according to the execution order determined by the task analysis unit, and monitor the execution status of the tasks to ensure that the tasks are completed on time; In the task scheduling module, the specific implementation includes the following steps: Receive mission requests from logistics centers or other UAVs through mission receiving units; The task analysis unit analyzes the task request, extracts the task type, priority and execution time, and determines the execution order and resource requirements of the tasks; The resource allocation unit allocates necessary resources to the task according to the output of the task analysis unit; The task execution unit schedules the allocated resources to execute tasks according to the execution order determined by the task analysis unit, and monitors the execution status of the tasks in real time.

8. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The exception handling module includes an exception monitoring unit, an exception analysis unit, an exception response unit and a log recording unit; The abnormality monitoring unit is used to monitor the system operation status in real time, including the flight status of the drone, the storage container status and the communication status, and to detect possible abnormal events; The abnormality analysis unit is used to analyze the abnormal events detected by the abnormality monitoring unit to determine the abnormality type, severity and impact range; The abnormal response unit is used to take corresponding abnormality handling measures according to the analysis results of the abnormality analysis unit, including sending alarms, adjusting system parameters and executing preset emergency plans to eliminate or reduce the impact of abnormal events on system operation; The log recording unit is used to record the occurrence time, type, severity, impact scope of abnormal events and the execution status of abnormal handling measures.

9. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The user interaction module includes a user input unit, an interface display unit, an interaction logic processing unit and a user feedback unit; The user input unit is used to receive operation instructions or data input by the user through an input device, and the input device includes a keyboard, a mouse and a touch screen; The interface display unit is used to display various information of the operating system to the user, including system status, task progress and abnormal alarms, and provide a graphical interface for the user to operate; The interactive logic processing unit is used to parse and process the operation instructions or data received by the user input unit, and perform corresponding operations or generate corresponding responses according to the current state of the operating system and user permissions; The user feedback unit is used to feed back operation results or system status changes to the user, and the feedback methods include interface prompts, sound prompts and light prompts.

10. The operating system of the storage box for a diversified logistics drone according to claim 2, characterized in that: The third-party interface module includes an interface definition unit, a data exchange unit, a security authentication unit and an interface management unit; The interface definition unit is used to define the API interface specifications required for data exchange and integration with other logistics systems and e-commerce platforms, including interface addresses, request parameters and response formats; The data exchange unit is used to realize data exchange with other logistics systems and e-commerce platforms, receive requests from other systems according to the defined interface specifications, and send response data; The security authentication unit is used to perform security authentication on data exchange with other logistics systems and e-commerce platforms; The interface management unit is used to manage the defined API interface, including publishing, updating and offline operations of the interface, and monitoring the usage and performance of the interface.

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