An unmanned aerial vehicle intelligent cargo box system

Abstract

The present application relates to a kind of unmanned vehicle intelligent cargo box systems, comprising: unmanned vehicle load-bearing module, for obtaining the interface strength between unmanned vehicle and cargo box, and the stress level of interface is calculated, when stress level meets stress condition, then obtain distribution task;Unmanned vehicle path planning module, for combining distribution task with the two-dimensional grid map of operating environment, determine flight planning path;Unmanned vehicle air avoidance module, for when unmanned vehicle flies according to flight planning path, when motion target appears, then obtain the air position of motion target, according to air position, establish motion target motion model, avoid motion target;Unmanned vehicle data analysis module, for obtaining the physical target parameter in cargo box, input physical target parameter into abnormality detection model, analyze physical target parameter and detect anomaly;Unmanned vehicle automatic unloading module, for when reaching distribution point in distribution task, then control the cargo box opening and closing time of unmanned vehicle, ensure that goods is released at preset speed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle transportation, in particular to an unmanned aerial vehicle intelligent cargo box system. BACKGROUND

[0002] In today's society, the logistics transportation industry is facing many challenges, especially in some special geographical environments, the logistics distribution problem is particularly prominent. The terrain in these areas is complex, the traffic is inconvenient, and the "last kilometer" problem of rural logistics is difficult to solve. Investigations show that local material distribution is facing challenges such as long transportation time, high cost, and limited coverage, especially in the aspects of emergency medical, educational material distribution, and characteristic agricultural product export, the demand is urgent. However, the traditional logistics mode is subject to backward road infrastructure and natural conditions, and it is difficult to meet the dual requirements of efficiency and cost.

[0003] At present, although the unmanned aerial vehicle technology has been applied in the logistics field to a certain extent, the existing technology still has many shortcomings. For example, the carrying capacity of the unmanned aerial vehicle is limited, and it is difficult to effectively judge whether the interface strength between the unmanned aerial vehicle and the cargo box meets the flight requirements, which may cause safety hazards such as cargo falling off during transportation. In addition, the unmanned aerial vehicle lacks effective path planning and air avoidance functions during flight, and is easily affected by complex environments and moving targets, increasing the transportation risk. At the same time, the monitoring of the state of the goods in the cargo box is relatively weak, and it is difficult to discover abnormal conditions of the goods in time, resulting in that the transportation quality cannot be guaranteed. Finally, in the cargo unloading link, the existing unmanned aerial vehicle cannot accurately control the cargo box opening and closing time and the goods release speed, which is easy to cause damage to the goods.

[0004] Therefore, it is urgent to develop an unmanned aerial vehicle intelligent cargo box system to solve the above problems, so as to improve the logistics transportation efficiency and safety of the unmanned aerial vehicle in complex environments, meet the logistics demand of special areas, and help the rural revitalization and intelligent logistics development. SUMMARY

[0005] In order to solve the problems existing in the prior art, the purpose of the present application is to provide an unmanned aerial vehicle intelligent cargo box system, a modular and intelligent logistics solution, which aims at the logistics problems in mountainous areas and helps the rural revitalization and intelligent logistics development.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] An unmanned aerial vehicle intelligent cargo box system, comprising:

[0008] The unmanned aerial vehicle load-bearing module is configured to obtain an interface strength between the unmanned aerial vehicle and the cargo box, calculate a stress level of the interface during flight of the unmanned aerial vehicle, and determine whether the stress level meets a stress condition. When the stress level meets the stress condition, a delivery task is obtained. The cargo box is loaded with lightweight goods that meet the requirements of logistics in mountainous areas.

[0009] The unmanned aerial vehicle path planning module is configured to combine the delivery task with a two-dimensional grid map of an operating environment to determine a flight planning path.

[0010] The unmanned aerial vehicle aerial avoidance module is configured to obtain an aerial position of a moving target when the unmanned aerial vehicle flies according to the flight planning path, establish a moving model of the moving target according to the aerial position, obtain a visual rotation angle of the moving target relative to the unmanned aerial vehicle, and avoid the moving target based on the visual rotation angle.

[0011] The unmanned aerial vehicle data analysis module is configured to obtain physical target parameters in the cargo box, input the physical target parameters into an anomaly detection model, analyze the physical target parameters, and detect anomalies. When the physical target parameters are abnormal, an alarm signal is sent to indicate that the goods in the cargo box are abnormal. The anomaly detection model is obtained by training a support vector machine model using a training set. The training set includes original physical target parameters.

[0012] The unmanned aerial vehicle automatic unloading module is configured to control opening and closing times of the cargo box of the unmanned aerial vehicle when the unmanned aerial vehicle reaches a delivery point in the delivery task, and ensure that the goods are released at a preset speed.

[0013] Optionally, the unmanned aerial vehicle load-bearing module includes:

[0014] The goods weight setting unit is configured to set a target weight of goods.

[0015] The stress level calculation unit is configured to obtain an interface strength between the unmanned aerial vehicle and the cargo box, and calculate a stress level of the interface during flight of the unmanned aerial vehicle.

[0016]

[0017] wherein σ v is an equivalent stress, σ x and σ y are normal stresses in x and y directions, respectively, and τ xy is a shear stress in an xy plane.

[0018] The unmanned aerial vehicle load-bearing unit is configured to determine whether the stress level meets a stress condition. When the stress level meets the stress condition, a delivery task is obtained. When the stress level does not meet the stress condition, the target weight is reset until the stress condition is met.

[0019] The stress condition is:

[0020] σ v <σ y icld

[0021] Wherein, σ yicld is the maximum stress level of the UAV in flight.

[0022] Optionally, the UAV path planning module comprises:

[0023] A two-dimensional grid map generation unit configured to acquire depth information of an original path in a delivery task, and generate the two-dimensional grid map according to the depth information;

[0024] A UAV path planning unit configured to set a shortest avoidance distance of the UAV and an obstacle based on a grid and a position of the obstacle in the two-dimensional grid map, update the original path through the shortest avoidance distance, and determine the flight planning path.

[0025] The shortest avoidance distance of the UAV and the obstacle is set as:

[0026] D safe =D min +R u +R o

[0027] Wherein, D safe is the shortest avoidance distance, D min is a minimum safety distance between the UAV and the obstacle, R u is a radius of the UAV, and R o is a radius of the obstacle.

[0028] Optionally, the UAV aerial avoidance module comprises:

[0029] A moving target position acquisition unit configured to acquire an aerial position of a moving target when the UAV flies according to the flight planning path and the moving target appears.

[0030] A first UAV aerial avoidance unit configured to establish a moving model of the moving target according to the aerial position, obtain distance information between the UAV and the moving target, and obtain an instantaneous value corresponding to the moving target, acquire a visual turning angle of the moving target on the UAV based on the distance information and the instantaneous value, determine a moving direction of the UAV through the visual turning angle, and avoid the moving target.

[0031] A second UAV aerial avoidance unit configured to fly to the flight planning path again to continue flying after the UAV avoids the aerial target.

[0032] Optionally, the establishing the motion model of the moving target comprises:

[0033]

[0034] wherein x(k) is the distance of the moving target, v(k) is the speed of the moving target, Δt is the sampling interval, a(k) is the acceleration of the moving target, and w(k) is the noise of the acceleration.

[0035] Optionally, the acquiring the gaze angle of the moving target on the UAV comprises:

[0036]

[0037] wherein w(t) is the gaze angle, r(t) is the position vector of the moving target relative to the UAV, v r (t) is the speed vector of the moving target relative to the UAV.

[0038] Optionally, the UAV data analysis module comprises:

[0039] a physical parameter acquisition unit, configured to acquire the physical target parameter in the cargo box;

[0040] D i ={T, H, W, GPS}

[0041] wherein D i is the monitoring data of the i-th batch of goods, T is the temperature, H is the humidity, W is the weight, and GPS is the geographic position information;

[0042] a UAV data analysis unit, configured to input the physical target parameter into an anomaly detection model, analyze the physical target parameter, and detect an anomaly, and when the physical target parameter is abnormal, an alarm signal is sent to prompt that the state of the goods in the cargo box is abnormal; the anomaly detection model is obtained by training a support vector machine model using a training set;

[0043] the expression of the support vector machine model is:

[0044]

[0045] wherein a i is the Lagrange multiplier in the support vector machine algorithm, y i is the class label of the training sample x i , K(x i , x) is a kernel function, used to map the input data to a high-dimensional space to facilitate linear classification and other operations in the high-dimensional space, and b is a bias term in the support vector machine model.

[0046] Optionally, the unmanned aerial vehicle automatic unloading module comprises:

[0047] An unmanned aerial vehicle automatic unloading unit is configured to control the opening and closing time of the cargo box of the unmanned aerial vehicle when the unmanned aerial vehicle reaches a delivery point in the delivery task, so as to ensure that the goods are released at a preset speed.

[0048] A dynamic adjustment unit is configured to control the tilt angle of the unmanned aerial vehicle while ensuring that the goods are released at a preset speed, and adjust the opening and closing action.

[0049] Optionally, ensuring that the goods are released at a preset speed comprises:

[0050]

[0051] wherein v release is the preset speed, g is the acceleration of gravity, and h is the height of the cargo box from the ground, so as to ensure that the goods are not damaged when falling to the ground.

[0052] The present application has the following advantages:

[0053] Improving logistics efficiency: the present application accurately judges the interface strength between the unmanned aerial vehicle and the cargo box through the unmanned aerial vehicle load-bearing module, ensures that the unmanned aerial vehicle can safely carry goods during flight, and avoids transportation interruption caused by insufficient load-bearing. At the same time, the unmanned aerial vehicle path planning module can quickly determine the flight planning path in combination with the two-dimensional grid map, avoid obstacles, reduce flight time, and improve logistics distribution efficiency.

[0054] Enhancing flight safety: the unmanned aerial vehicle air avoidance module can monitor moving targets in real time during flight, and quickly avoid air obstacles by establishing a moving target motion model and calculating a visual turning angle, effectively reducing the risk of collision and ensuring the safety of the unmanned aerial vehicle and the goods.

[0055] Improving the monitoring capability of goods: the unmanned aerial vehicle data analysis module can obtain physical target parameters in the cargo box in real time, and analyze and monitor the state of the goods using an anomaly detection model. When the state of the goods is abnormal, an alarm signal is sent in time to remind relevant personnel to take measures to ensure that the quality of the goods is not affected during transportation.

[0056] Optimizing the unloading process of goods: the unmanned aerial vehicle automatic unloading module can accurately control the opening and closing time of the cargo box and the release speed of the goods when reaching the delivery point, ensure that the goods are safely landed at a preset speed, avoid damage caused by too fast or too slow release of the goods, and improve the success rate and safety of the unloading of the goods.

[0057] Adapt to complex environment: The invention is designed for the complex geographical environment of mountainous areas, which can effectively solve the transportation problems faced by traditional logistics in these areas, and provide efficient and reliable logistics solutions for emergency medical, educational material distribution and export of specialty agricultural products, etc., helping to promote rural revitalization and intelligent logistics development.

[0058] Modular design: The invention adopts modular design, each module has clear function, mutual cooperation, easy to expand and upgrade. According to different logistics demand and environmental conditions, the function and parameter of each module can be adjusted flexibly, improving the adaptability and flexibility of the system. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0060] Figure 1 A schematic diagram of a UAV intelligent cargo box system according to an embodiment of the present application;

[0061] Figure 2 A schematic diagram of a UAV intelligent cargo box according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0064] The present embodiment improves the mountain logistics system and improves the efficiency of material distribution. The mountain logistics has long been faced with the problem of "last kilometer" distribution, especially in the mountainous areas with poor transportation conditions. The complex terrain and imperfect infrastructure result in low efficiency and high cost of traditional transportation methods, and the acquisition of materials by many villagers has been significantly restricted. The core mission of the present embodiment is to develop a UAV intelligent cargo box system to optimize the mountain logistics system and provide technical support to improve these problems.

[0065] Intelligent logistics technology plays a crucial role in rural revitalization, significantly boosting the circulation of goods and economic development in mountainous areas. This implementation, centered on the unmanned aerial vehicle intelligent cargo box system, applies intelligent logistics technology to the logistics scenarios in mountainous areas, filling the gaps in efficiency, coverage, and adaptability of traditional logistics methods. This implementation provides innovative demonstrations and technical support for rural revitalization.

[0066] In the circulation of educational resources, the system can quickly and efficiently distribute teaching materials, stationery, and teaching equipment, shortening the transportation time from the county to rural schools and providing a guarantee for improving the fairness of education in mountainous areas.

[0067] In terms of agricultural economy, the flexibility and efficiency of the modular cargo box system can help farmers transport specialty agricultural products to towns or county cities for sale more quickly, reducing economic losses caused by unsold agricultural products. This implementation also optimizes logistics efficiency to provide a new channel for the export of agricultural products, enhancing the vitality of the rural economy.

[0068] In addition, this implementation can provide support for emergency rescue. For example, during an epidemic or natural disaster, the system can quickly transport medical supplies and emergency supplies, providing an efficient solution for emergency situations in mountainous areas. This technology not only directly improves the living conditions of villagers but also demonstrates the potential of intelligent logistics technology in the field of social public welfare, injecting technological power into rural revitalization.

[0069] Providing sustainable logistics solutions to promote social and economic development The sustainable development of the logistics system is an important foundation for social and economic development. The mission of this implementation is not only to solve the practical problems in mountainous area logistics but also to provide a set of sustainable, low-cost, and efficient logistics solutions with broad social and economic value.

[0070] This implementation adopts lightweight and modular design, reducing the manufacturing cost of the cargo box and also reducing the operating energy consumption of the unmanned aerial vehicle, making it suitable for popularization and application in resource-limited mountainous areas. The intelligent monitoring system reduces the risk of damage to goods during transportation through real-time collection of sensor data, thereby reducing logistics costs.

[0071] The automatic unloading technology optimizes the multi-point distribution process through precise delivery and rapid distribution functions, reducing the dependence on manual intervention. These technical features not only improve logistics efficiency but also make the system economically sustainable.

[0072] From the perspective of popularization, the technical solution of the embodiment has strong applicability, not only can be applied to mountainous areas, but also can be extended to disaster emergency rescue, express delivery in remote urban areas and other scenarios, with broad market prospects. In terms of economic benefits, the unmanned aerial vehicle intelligent cargo box system significantly reduces the operating cost of logistics in mountainous areas, creating more profit opportunities for rural logistics enterprises. In addition, with large-scale application of technology, the embodiment will also drive the development of upstream and downstream industries such as unmanned aerial vehicle manufacturing, intelligent cargo box production and logistics management services, creating more employment opportunities for the society.

[0073] By providing a sustainable logistics solution, the embodiment not only meets the logistics needs of mountainous areas, but also provides a reference model for other areas with poor transportation around the world. The embodiment will further promote the optimal allocation of urban and rural logistics resources and inject strong impetus into social and economic development.

[0074] Mountainous areas usually have complex terrain and poor transportation infrastructure, resulting in high logistics distribution costs and low efficiency, which limits the supply of daily necessities, especially in emergency situations. For example, medical supplies and urgently needed goods cannot be delivered in time, posing a great challenge to residents' lives. The embodiment provides an efficient and low-cost logistics solution for these groups through the unmanned aerial vehicle intelligent cargo box system, ensuring that medical supplies and daily necessities can be quickly and safely delivered to villagers.

[0075] In terms of education resources, the system can be used to deliver teaching materials, teaching aids and other materials to remote rural schools, reducing the transportation time of materials and supporting education equity. At the same time, by delivering characteristic agricultural products through unmanned aerial vehicles, the economic income of villagers will also be improved. For these villagers, the embodiment not only meets their living needs, but also provides more possibilities for production development.

[0076] For example, Figure 1As shown, the embodiment discloses a kind of unmanned aerial vehicle intelligent cargo box systems, including: unmanned aerial vehicle load-bearing module, for obtaining the interface strength between unmanned aerial vehicle and cargo box, the stress level of interface in the process of unmanned aerial vehicle flight is calculated, and whether stress level meets stress condition is judged, when stress level meets stress condition, then obtain distribution task;Cargo box is loaded with light weight goods meeting mountainous area logistics;Unmanned aerial vehicle path planning module, for combining distribution task with the two-dimensional grid map of operating environment, determine flight planning path;Unmanned aerial vehicle air avoidance module, for when unmanned aerial vehicle flies according to flight planning path, when motion target appears, then obtain the air position of motion target, according to air position, establish motion target motion model, obtain the visual rotation angle of motion target on unmanned aerial vehicle, based on visual rotation angle, avoid motion target;Unmanned aerial vehicle data analysis module, for obtaining the physical target parameter in cargo box, physical target parameter is input into abnormality detection model, analyze physical target parameter and detect abnormality, when physical target parameter appears abnormal, then send alarm signal, for prompting that the goods state in cargo box is abnormal;Abnormality detection model is obtained using training set to train support vector machine model, training set includes: original physical target parameter;Unmanned aerial vehicle automatic unloading module, for when unmanned aerial vehicle reaches distribution point in distribution task, then control the opening and closing time of cargo box of unmanned aerial vehicle, ensure that goods is released at preset speed.

[0077] Further, unmanned aerial vehicle load-bearing module includes: goods weight setting unit, for setting target weight goods;Stress level calculation unit, for obtaining the interface strength between unmanned aerial vehicle and cargo box, calculate the stress level of interface in the process of unmanned aerial vehicle flight:

[0078]

[0079] Wherein, σ v Equivalent stress, σ x , σ y X direction and y direction normal stress respectively, τ xy The shear stress in xy plane;

[0080] Unmanned aerial vehicle load-bearing unit, for judging whether stress level meets stress condition, when stress level meets stress condition, then obtain distribution task, when stress level does not meet stress condition, then re-set target weight, until stress condition is met;

[0081] Stress condition is:

[0082] σ v <σ y icld

[0083] Wherein, σ yicld Maximum stress level in the process of unmanned aerial vehicle flight.

[0084] Specifically:

[0085] This embodiment realizes flexible cargo loading function through modular design, which can classify and store different types of goods (such as medical supplies, daily necessities and agricultural products) and efficiently load them, avoiding damage risks caused by mixed loading. In addition, the system also integrates intelligent sensor technology, which can monitor the weight, temperature and humidity of the goods in real time, and transmit the data to the ground control center through the wireless communication module, realizing the whole process visualization of the transportation process. This precise monitoring function not only improves the delivery efficiency, but also greatly reduces the risk of goods loss and damage.

[0086] As shown in Figure 2 , the modular cargo box design is one of the core technologies of the system, aiming to solve the loading adaptation and cargo protection problems in the diversified logistics demand in mountainous areas. The cargo box realizes flexible adjustment through modular structure, and each module is made of lightweight composite materials (such as carbon fiber and high-strength polymer), which meets the strength and weight reduction requirements.

[0087] The cargo box modules can be freely combined according to the size, weight and characteristics of the goods to meet the transportation needs of different types of materials. For example, for medical supplies, the cargo box can set up independent isolation areas to ensure the safety of medicines;

[0088] For agricultural products, the module can be configured with a breathable structure to prevent the goods from deteriorating due to high humidity. The main technical parameters of the modular design include:

[0089] Maximum bearing weight of single module W: set to 5kg to meet the characteristics of lightweight goods in mountainous area logistics.

[0090] Cargo box modular interface strength: Von Mises criterion is used to calculate whether the stress condition is met during flight:

[0091]

[0092] Where σ v is the equivalent stress, which needs to meet the design condition of σ v <σ yicld .

[0093] Further, the unmanned aerial vehicle path planning module includes: a two-dimensional grid map generation unit for obtaining depth information of an original path in a delivery task, and generating a two-dimensional grid map according to the depth information; an unmanned aerial vehicle path planning unit for setting a shortest avoidance distance between the unmanned aerial vehicle and an obstacle based on the grid and the position of the obstacle in the two-dimensional grid map, updating the original path through the shortest avoidance distance, and determining a flight planning path; the shortest avoidance distance between the unmanned aerial vehicle and the obstacle is:

[0094] D safe =Dmin +R u +R o

[0095] wherein, D safe is the shortest avoidance distance, D min is the minimum safety distance between the UAV and the obstacle, R u is the radius of the UAV, R o is the radius of the obstacle.

[0096] Further, the UAV aerial avoidance module comprises: a moving target position acquisition unit, configured to acquire an aerial position of a moving target when the UAV flies according to a flight planning path and the moving target appears; a first UAV aerial avoidance unit, configured to establish a moving target motion model according to the aerial position, obtain distance information between the UAV and the moving target, and obtain an instantaneous value corresponding to the moving target, acquire a visual rotation angle of the moving target on the UAV based on the distance information and the instantaneous value, and determine a moving direction of the UAV through the visual rotation angle, so as to avoid the moving target; and a second UAV aerial avoidance unit, configured to fly to the flight planning path again to continue flying after the UAV avoids the aerial target.

[0097] Further, the establishment of the moving target motion model comprises:

[0098]

[0099] wherein, x(k) is the distance of the moving target, v(k) is the speed of the moving target, Δt is a sampling interval, a(k) is the acceleration of the moving target, and w(k) is noise received by the acceleration.

[0100] Further, the acquisition of the visual rotation angle of the moving target on the UAV comprises:

[0101]

[0102] wherein, w(t) is the visual rotation angle, r(t) is a position vector of the moving target relative to the UAV, v r (t) is a speed vector of the moving target relative to the UAV.

[0103] Further, the UAV data analysis module comprises: a physical parameter acquisition unit, configured to acquire physical target parameters in a cargo box:

[0104] D i ={T, H, W, GPS}

[0105] wherein, D i is monitoring data of the i-th batch of goods, T is temperature, H is humidity, W is weight, and GPS is geographic position information.

[0106] The unmanned aerial vehicle data analysis unit is configured to input the physical target parameter into an anomaly detection model, analyze the physical target parameter, and detect an anomaly. When the physical target parameter is abnormal, an alarm signal is sent to prompt that the goods in the container are in an abnormal state. The anomaly detection model is obtained by training a support vector machine model using a training set;

[0107] The expression of the support vector machine model is:

[0108]

[0109] where a i is a Lagrange multiplier in the support vector machine algorithm, y i is a class label of the training sample x i (typically taking values +1 or -1), K(x i , x) is a kernel function, which is used to map the input data to a high-dimensional space to facilitate linear classification and other operations in the high-dimensional space, and b is a bias term in the support vector machine model.

[0110] Specifically:

[0111] The data analysis module plays an important role in the entire transportation process, providing real-time data acquisition and feedback functions to ensure the safety of the goods during transportation. The system embeds various sensors in the container, including temperature sensors (accuracy: ±0.5C), humidity sensors (accuracy: ±1% RH), and weight sensors (error range: ±2%), to monitor the state parameters of the goods in real time.

[0112] These data are transmitted to the ground control station or cloud server through LoRa communication technology, and the following data processing model is used:

[0113] 1. Data acquisition model:

[0114] D i = {T, H, W, GPS}

[0115] where D i is the monitoring data of the ith batch of goods, T is the temperature, H is the humidity, W is the weight, and GPS is the geographic location information.

[0116] 2. Anomaly detection model: using a support vector machine (SVM) algorithm to analyze real-time data and detect anomalies:

[0117]

[0118] When f(x) < 0, the system sends an alarm signal to indicate that the material is in an abnormal state.

[0119] Further, the unmanned aerial vehicle automatic unloading module comprises: an unmanned aerial vehicle automatic unloading unit, configured to control the opening and closing time of the cargo box of the unmanned aerial vehicle when the unmanned aerial vehicle reaches a delivery point in a delivery task, so as to ensure that the goods are released at a preset speed; and a dynamic adjustment unit, configured to control the tilt angle of the unmanned aerial vehicle while ensuring that the goods are released at the preset speed, and adjust the opening and closing action.

[0120] Further, the ensuring of the release of the goods at the preset speed comprises:

[0121]

[0122] wherein, v release is the preset speed of release, g is the acceleration of gravity, and h is the height of the cargo box from the ground, so as to ensure that the goods are not damaged when falling to the ground.

[0123] Specifically:

[0124] The automatic unloading function is designed for the actual needs of logistics distribution in mountainous areas, and a technical solution of precise delivery and rapid unloading is designed, which is particularly suitable for multi-site distribution in scattered villages. The unmanned aerial vehicle can complete the precise delivery of goods without human intervention, significantly improving the distribution efficiency and reducing the time and economic cost caused by human participation.

[0125] The automatic unloading technology realizes the precise delivery of goods at multiple delivery points by integrating electrical automation devices. The system controls the opening and closing of the cargo box by using a servo motor, determines the delivery position by combining GPS and MU (inertial measurement unit) data, and the delivery accuracy can reach ±2 meters.

[0126] The control logic of the unloading process is as follows:

[0127] 1. Path planning and positioning: according to the coordinates of the delivery point, the unmanned aerial vehicle stops above the target position.

[0128] 2. Delivery algorithm: the servo motor controls the opening and closing time of the cargo box to ensure that the goods are released at an appropriate speed. The release speed is determined by the following formula:

[0129]

[0130] wherein, v release is the preset speed of release, g is the acceleration of gravity, and h is the height of the cargo box from the ground,

[0131] 3. Dynamic adjustment: the system monitors the tilt angle of the unmanned aerial vehicle through MU data, and adjusts the opening and closing action in real time to avoid delivery errors.

[0132] This embodiment realizes the flexibility and adaptability of cargo transportation in mountain logistics through the modular design concept. The modular cargo box is made of lightweight composite materials, and each module can be independently adjusted in size and capacity to meet the storage needs of different types of materials. The cargo box design takes into account the diverse needs of mountain logistics, such as isolated storage of medical supplies, prevention of moisture for agricultural products, and safety protection for high-value goods. The modular design combines with quick-release interfaces to ensure that the cargo remains balanced during unmanned aerial vehicle flight, improving the safety of the transportation process.

[0133] The technical basis of the cargo box design uses mechanical analysis tools to optimize interface strength and module load capacity through the Von Mises stress criterion. The design also combines lightweight material technology to reduce the impact of the cargo box on the load capacity of the unmanned aerial vehicle, ensuring that energy consumption during flight is controlled within a reasonable range. Through the modular cargo box, the system can quickly adapt to various transportation scenarios, not only improving logistics efficiency but also reducing unit distribution costs, providing an efficient solution for the diverse material needs of mountain areas.

[0134] The intelligent monitoring technology of the system realizes real-time collection and feedback of cargo status through the integration of sensors and data communication modules. The sensor network includes temperature sensors, humidity sensors, and weight sensors, which can monitor the environmental conditions inside the cargo box in real time and upload monitoring data to the ground station or cloud platform through LoRa wireless communication technology. The monitoring system combines support vector machine (SVM) algorithm to detect abnormal conditions during transportation in real time, such as temperature exceeding, humidity being too high, or cargo weight being abnormal, ensuring the quality and safety of the cargo during transportation.

[0135] The automatic unloading technology uses servo motors and precise positioning algorithms to achieve fast release and precise delivery of goods. In actual application, the unmanned aerial vehicle can hover according to the GPS coordinates of the target location, adjust the flight attitude through inertial measurement unit (IMU) data to ensure that the goods can be accurately landed. The delivery speed is optimized through the gravitational potential energy formula to ensure that the goods will not be damaged when landing. This technology is particularly suitable for the multi-point distribution needs of scattered villages in mountain areas, significantly reducing the complexity of manual operation, while improving the distribution efficiency and accuracy, providing a reliable guarantee for the development of smart logistics.

[0136] In view of the complex terrain conditions and variable weather environment in mountain areas, the system uses an improved A* algorithm for flight path planning. On the basis of the traditional path planning algorithm, the influence weight of terrain slope and wind speed is added, which significantly improves the safety and stability of unmanned aerial vehicle flight in mountain areas. The optimized path cost function can dynamically adjust the cost estimation between nodes to avoid high-risk areas, thereby effectively reducing the energy consumption of unmanned aerial vehicle flight and prolonging the endurance time of single task.

[0137] The overall performance of the system performs well in field tests: single flight radius reaches 20-50 kilometers, cargo box load capacity is 1-5 kilograms, and automatic unloading error is controlled within ±2 meters. These performance indicators make the system highly practical and valuable for promotion in mountain logistics scenarios. At the same time, the system has strong scalability and can adapt to more scenario requirements, such as post-disaster emergency rescue, urban edge express delivery, and agricultural logistics services, through further upgrading of algorithms and hardware. In summary, the "Cloud Wing Intelligent Delivery UAV Intelligent Cargo Box System" not only has technological innovation, but also provides excellent performance support for practical applications, demonstrating the unlimited potential of technology to support mountain development.

[0138] In summary, the embodiment can effectively solve the transportation difficulties in mountain logistics, improve the efficiency of material distribution, and ensure that critical materials can be delivered in a timely manner, providing important support for improving the quality of life of residents in mountainous areas.

[0139] The embodiment also discloses a method for using a UAV intelligent cargo box system, comprising:

[0140] 1. Before using the UAV intelligent cargo box system, the UAV and the cargo box need to be in good working condition. First, check the hardware of the UAV, including battery capacity, propeller status, and whether the GPS module is working normally. Ensure that the flight control system has completed the pre-calibration to provide stability in flight. Second, the modular cargo box needs to be installed. According to the requirements of the transportation task, select the appropriate cargo box module, such as a medical transportation module, an agricultural product module, or a general transportation module. Install the cargo box on the fixed interface of the UAV, ensure that the connection is firm, and the interface strength meets the operation requirements (such as no looseness).

[0141] When loading goods, the goods need to be stored according to the module partition to avoid pollution or damage caused by mixed loading. For goods with strict temperature control requirements (such as vaccines or cold chain food), make sure to use modules with temperature and humidity adjustment functions, and pre-set the environmental parameters inside the cargo box. After loading is completed, the ground control station (GCS)

[0142] The weight of the goods, the stability of the loading, and the carrying capacity of the UAV are checked again to ensure that they do not exceed the maximum carrying capacity of the UAV. After all the checks are completed, start the self-checking program before the UAV flight to verify whether the GPS positioning, path planning, and communication modules of the flight system are working normally to ensure flight safety.

[0143] 2. Flight path planning and task setting Before the UAV takes off, the delivery task and flight path need to be set through the control terminal. This system integrates an improved A* algorithm for path optimization in complex mountain environments. By inputting the starting point and multiple target points (such as GPS coordinates of villages or transfer stations) of the delivery task, the system will automatically plan the best path to avoid dangerous terrain and high-risk areas (such as steep slopes and windy areas).

[0144] Users can adjust the priority of delivery tasks at the terminal interface, for example, setting emergency medical supplies as the highest priority, and the system will prioritize related tasks. After path planning is completed, the system will simulate the flight path and provide relevant parameters, including estimated flight time, energy consumption, and cargo drop time at each delivery point. Users need to confirm that all task settings are correct before uploading the task to the flight control system of the UAV.

[0145] After takeoff, the flight path will be adjusted in real time based on environmental data. The system monitors flight status through embedded sensors such as MU and anemometers, and re-plans the path when encountering weather changes or obstacles to ensure safety and efficiency. The entire path planning process is mainly automated, and users only need to monitor task progress and handle abnormal situations.

[0146] 3. Cargo monitoring and transportation process During flight, cargo status is monitored by an intelligent monitoring system throughout the entire process. The cargo box is embedded with multiple sensors, including temperature, humidity, and weight sensors, which can record environmental parameters of the cargo in real time. These data are uploaded to the ground station through the oRa communication module, and users can view the cargo status through the control terminal and respond to abnormal situations in a timely manner. For example, when the temperature exceeds the set range, the system will send an alarm message to prompt the user to adjust the task or return immediately.

[0147] The flight status of the UAV is also monitored. The flight control system assesses flight altitude, speed, and attitude in real time through GPS, MU, and barometer. If an emergency situation occurs (such as signal loss or low battery), the UAV will execute emergency measures according to the pre-set program, including returning or emergency landing. The entire transportation process is centered on intelligence, reducing manual operation and ensuring cargo safety.

[0148] 4. Automatic unloading and delivery operation After the UAV arrives at the target delivery point, the automatic unloading function will start. The unloading process is controlled by a servo motor to open and close the cargo box, and combined with the GPS coordinates of the target point and MU data to ensure that the cargo can be accurately dropped into the designated area. Users need to define the drop method (such as fixed-point drop or ground slow descent) at each delivery point in the task setting stage.

[0149] After delivery is completed, the system will upload the drop record, including drop location, cargo status, and task completion time, providing data support for subsequent task analysis. Users can also review the drop results through the ground station to verify the execution of the delivery task.

[0150] 5. After completing the delivery task, the UAV will return to the starting point or the designated transfer station for landing. The user needs to perform maintenance checks on the device, including battery level, propeller status, and the operational status of the flight control system, to ensure that the UAV can continue to perform the next task. The cargo box module needs to be cleaned and inspected after the task is completed, especially when transporting food or medical supplies, the interior of the cargo box should be thoroughly cleaned to avoid residual affecting the next transportation task.

[0151] In addition, all task data will be automatically saved to the system database, and users can optimize the task process by analyzing historical data. For example, based on the records of energy consumption and flight time, adjust the path planning strategy; based on the monitoring records of the cargo status, optimize the environmental control parameters of the cargo box module. Regular software upgrades and hardware maintenance are also very important to ensure that the system can run stably for a long time.

[0152] The system provides a complete and easy-to-operate logistics solution for users through intelligent operation process and highly integrated hardware design. By following the above usage instructions, users can maximize the technical advantages of the UAV intelligent cargo box system, improve the efficiency of mountain logistics, and help the rural revitalization.

[0153] The above-described embodiments are only descriptions of the preferred modes of the present application and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. An unmanned aerial vehicle smart cargo box system, characterized in that, The unmanned aerial vehicle load-bearing module is configured to obtain an interface strength between the unmanned aerial vehicle and the cargo box, calculate a stress level of the interface during flight of the unmanned aerial vehicle, and determine whether the stress level meets a stress condition. The cargo box is configured to carry light cargo that meets a requirement of mountainous area logistics. The unmanned aerial vehicle load-bearing module includes: a cargo weight setting unit configured to set a target weight of cargo; a stress level calculation unit configured to obtain an interface strength between the unmanned aerial vehicle and the cargo box, and calculate a stress level of the interface during flight of the unmanned aerial vehicle; an unmanned aerial vehicle load-bearing unit configured to determine whether the stress level meets a stress condition, and obtain a delivery task when the stress level meets the stress condition, or reset the target weight until the stress condition is met when the stress level does not meet the stress condition. where σ v is the equivalent stress, σ x and σ y are the normal stresses in the x and y directions, respectively, and τ xy is the shear stress in the xy plane. The stress condition is: an unmanned aerial vehicle path planning module configured to determine a flight planning path by combining the delivery task with a two-dimensional grid map of an operating environment; σ v <σ y icld wherein σ y icld is the maximum stress level borne by the UAV during flight; an unmanned aerial vehicle aerial avoidance module configured to obtain an aerial position of a moving target when the unmanned aerial vehicle flies according to the flight planning path, establish a moving target motion model according to the aerial position, obtain a visual rotation angle of the moving target relative to the unmanned aerial vehicle based on the visual rotation angle, and avoid the moving target. The unmanned aerial vehicle aerial avoidance module includes: a moving target position acquisition unit configured to obtain an aerial position of a moving target when the unmanned aerial vehicle flies according to the flight planning path; a first unmanned aerial vehicle aerial avoidance unit configured to establish a moving target motion model according to the aerial position, obtain distance information between the unmanned aerial vehicle and the moving target, obtain an instantaneous value corresponding to the moving target, obtain a visual rotation angle of the moving target relative to the unmanned aerial vehicle based on the distance information and the instantaneous value, determine a moving direction of the unmanned aerial vehicle through the visual rotation angle, and thereby avoid the moving target; a second unmanned aerial vehicle aerial avoidance unit configured to continue flying on the flight planning path after the unmanned aerial vehicle avoids the aerial target. The moving target motion model includes: where x(k) is a distance of the moving target, v(k) is a speed of the moving target, Δt is a sampling interval, a(k) is an acceleration of the moving target, and w(k) is noise affecting the acceleration; The visual rotation angle of the moving target relative to the unmanned aerial vehicle includes: an unmanned aerial vehicle data analysis module configured to obtain physical target parameters in the cargo box, input the physical target parameters into an anomaly detection model, analyze the physical target parameters and detect anomalies, and issue an alarm signal to prompt a cargo state anomaly in the cargo box when the physical target parameters are abnormal. wherein w(t) is a viewing angle, r(t) is a position vector of the moving target relative to the UAV, v r (t) is a velocity vector of the moving target relative to the UAV; The unmanned aerial vehicle data analysis module includes: a physical parameter acquisition unit configured to obtain physical target parameters in the cargo box. ​ D i = {T, H, W, GPS} wherein D i is the monitoring data of the ith batch of goods, T is temperature, H is humidity, W is weight, and GPS is geographic position information. The unmanned aerial vehicle data analysis unit is configured to input the physical target parameter into an anomaly detection model, analyze the physical target parameter, and detect an anomaly. When the physical target parameter is abnormal, an alarm signal is sent to indicate that the goods in the cargo box are abnormal. The support vector machine model is expressed as: wherein a i is a Lagrange multiplier in the support vector machine algorithm, y i is a class label of a training sample x i , K(x i , x) is a kernel function for mapping input data into a high-dimensional space to facilitate linear classification operations in the high-dimensional space, and b is a bias term in the support vector machine model. The unmanned aerial vehicle automatic unloading module is configured to control the opening and closing time of the cargo box of the unmanned aerial vehicle when the unmanned aerial vehicle reaches a delivery point in the delivery task, to ensure that the goods are released at a preset speed.

2. The UAV smart cargo box system of claim 1, wherein, The unmanned aerial vehicle path planning module includes: A two-dimensional grid map generation unit is configured to obtain depth information of an original path in a delivery task, and generate a two-dimensional grid map according to the depth information. An unmanned aerial vehicle path planning unit is configured to set a shortest avoidance distance of the unmanned aerial vehicle from an obstacle based on the grid and the position of the obstacle in the two-dimensional grid map, update the original path by using the shortest avoidance distance, and determine a flight planning path. The shortest avoidance distance of the unmanned aerial vehicle from the obstacle is set as: D safe = D min + R u + R o wherein D safe is the shortest evasion distance, D min is the minimum safety distance between the UAV and the obstacle, R u is the radius of the UAV, R o is the radius of the obstacle.

3. The UAV smart cargo box system of claim 1, wherein, The unmanned aerial vehicle automatic unloading module includes: An unmanned aerial vehicle automatic unloading unit is configured to control the opening and closing time of the cargo box of the unmanned aerial vehicle when the unmanned aerial vehicle reaches a delivery point in the delivery task, to ensure that the goods are released at a preset speed. A dynamic adjustment unit is configured to control the tilt angle of the unmanned aerial vehicle while ensuring that the goods are released at a preset speed, and to adjust the opening and closing action.

4. The UAV smart cargo box system of claim 3, wherein, The ensuring that the goods are released at a preset speed includes: where v release is the preset speed release, g is the acceleration of gravity, and h is the height of the container from the ground, ensuring that the goods are not damaged when landing.

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