Unmanned aerial vehicle intelligent container system

Abstract

The invention relates to an unmanned aerial vehicle intelligent container system, and the system comprises an unmanned aerial vehicle bearing module which is used for obtaining the strength of an interface between an unmanned aerial vehicle and a container, calculating the stress level of the interface, and obtaining a distribution task when the stress level meets a stress condition; the unmanned aerial vehicle path planning module is used for combining the distribution task with a two-dimensional grid map of the operation environment to determine a flight planning path; the unmanned aerial vehicle air avoidance module is used for obtaining the air position of a moving target when the unmanned aerial vehicle flies according to the flight planning path and the moving target appears, establishing a moving target motion model according to the air position and avoiding the moving target; the unmanned aerial vehicle data analysis module is used for acquiring physical target parameters in the container, inputting the physical target parameters into the anomaly detection model, analyzing the physical target parameters and detecting anomaly; and the unmanned aerial vehicle automatic unloading module is used for controlling the container opening and closing time of the unmanned aerial vehicle when the unmanned aerial vehicle arrives at the distribution point in the distribution task so as to ensure that the cargoes are released at a preset speed.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) transportation, and particularly to an intelligent cargo box system for UAVs. Background Art

[0002] In today's society, the logistics and transportation industry is facing numerous challenges. Especially in some special geographical environments, the problem of logistics distribution is particularly prominent. These areas have complex terrains and inconvenient transportation, making it difficult to solve the "last mile" problem of rural logistics. Research shows that local material distribution faces challenges such as long transportation time, high cost, and limited coverage. There is an urgent need, especially in aspects such as emergency medical, educational material distribution, and the export of special agricultural products. However, traditional logistics methods are restricted by backward road infrastructure and natural conditions and are difficult to meet the dual requirements of efficiency and cost.

[0003] Currently, although UAV technology has been applied to a certain extent in the logistics field, there are still many deficiencies in the existing technology. For example, the load-bearing capacity of UAVs is limited, and it is unable to effectively judge whether the interface strength with the cargo box meets the flight requirements, resulting in potential safety hazards such as the falling off of goods during transportation. In addition, UAVs lack effective path planning and aerial avoidance functions during flight, are easily affected by complex environments and moving targets, and increase the transportation risk. At the same time, the monitoring of the state of goods in the cargo box is also relatively weak, and it is unable to detect abnormal situations of goods in a timely manner, resulting in the inability to guarantee the transportation quality. Finally, in the process of unloading goods, existing UAVs are unable to accurately control the opening and closing time of the cargo box and the release speed of goods, easily causing damage to goods.

[0004] Therefore, there is an urgent need for an intelligent cargo box system for UAVs that can solve the above problems to improve the logistics transportation efficiency and safety of UAVs in complex environments, meet the logistics needs of special regions, and contribute to rural revitalization and the development of smart logistics. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide an intelligent cargo box system for UAVs, a modular and intelligent logistics solution, to address the logistics problems in mountainous areas and contribute to rural revitalization and the development of smart logistics.

[0006] To achieve the above purpose, the present invention provides the following solution:

[0007] An intelligent cargo box system for UAVs, comprising:

[0008] The UAV load-bearing module is used to obtain the interface strength between the UAV and the cargo box, calculate the stress level of the interface during the flight of the UAV, and determine whether the stress level meets the stress condition. When the stress level meets the stress condition, it obtains the delivery task; the cargo box is loaded with lightweight goods that meet the requirements of mountain logistics;

[0009] The UAV path planning module is used to combine the delivery task with the two-dimensional grid map of the operating environment to determine the flight planning path;

[0010] The UAV air avoidance module is used to obtain the air position of the moving target when a moving target appears during the flight of the UAV according to the flight planning path, establish a moving target motion model based on the air position, obtain the visual rotation angle of the moving target on the UAV, and avoid the moving target based on the visual rotation angle;

[0011] The UAV data analysis module is used to obtain the physical target parameters in the cargo box, input the physical target parameters into the 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 status in the cargo box is abnormal; the anomaly detection model is obtained by training a support vector machine model using a training set, and the training set includes: original physical target parameters;

[0012] The UAV automatic unloading module is used to control the opening and closing time of the cargo box of the UAV when the UAV arrives at the delivery point in the delivery task to ensure that the goods are released at a preset speed.

[0013] Optionally, the UAV load-bearing module includes:

[0014] The cargo weight setting unit is used to set the goods with the target weight;

[0015] The stress level calculation unit is used to obtain the interface strength between the UAV and the cargo box and calculate the stress level of the interface during the flight of the UAV:

[0016]

[0017] 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;

[0018] The UAV load-bearing unit is used to determine whether the stress level meets the stress condition. When the stress level meets the stress condition, it obtains the delivery task. When the stress level does not meet the stress condition, it resets the target weight until the stress condition is met;

[0019] The stress conditions are as follows:

[0020] σ v <σ yicld

[0021] where σyicld is the maximum stress level that the drone can withstand during flight.

[0022] Optionally, the drone path planning module includes:

[0023] A two-dimensional grid map generation unit for obtaining the depth information of the original path in the delivery task and generating the two-dimensional grid map according to the depth information;

[0024] A drone path planning unit for setting the shortest avoidance distance between the drone and the obstacle based on the positions of the grids and obstacles in the two-dimensional grid map, and updating the original path through the shortest avoidance distance to determine the flight planning path;

[0025] The shortest avoidance distance between the drone and the obstacle is set as:

[0026] Dsafe = Dmin + Ru + Ro

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

[0028] Optionally, the drone aerial avoidance module includes:

[0029] A moving target position acquisition unit for acquiring the aerial position of the moving target when the drone flies according to the flight planning path and a moving target appears;

[0030] A first drone aerial avoidance unit for establishing a moving target motion model based on the aerial position, obtaining the distance information between the drone and the moving target, and obtaining the instantaneous value corresponding to the moving target. Based on the distance information and the instantaneous value, obtaining the visual rotation angle of the moving target on the drone, and determining the motion direction of the drone through the visual rotation angle to avoid the moving target;

[0031] A second drone aerial avoidance unit for, after the drone avoids the aerial target, flying back to the flight planning path and continuing to fly.

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

[0033]

[0034] Among them, 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, obtaining the viewing angle of the moving target on the drone includes:

[0036]

[0037] Among them, w(t) is the viewing angle, r(t) is the position vector of the moving target relative to the drone, and v r (t) is the velocity vector of the moving target relative to the drone.

[0038] Optionally, the drone data analysis module includes:

[0039] A physical parameter acquisition unit for acquiring physical target parameters in the cargo box:

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

[0041] Among them, 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 geographical location information;

[0042] A drone data analysis unit for inputting the physical target parameters into an anomaly detection model, analyzing the physical target parameters and detecting anomalies, and when the physical target parameters are abnormal, an alarm signal is sent to prompt that the goods status 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] Among them, 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 the kernel function for mapping the input data to a high-dimensional space to facilitate linear classification and other operations in the high-dimensional space, and b is the bias term in the support vector machine model.

[0046] Optionally, the drone automatic unloading module includes:

[0047] The UAV automatic unloading unit is used to control the opening and closing time of the cargo box of the UAV when the UAV arrives at the delivery point in the delivery task, so as to ensure that the goods are released at a preset speed;

[0048] The dynamic adjustment unit is used to control the tilt angle of the UAV and adjust the opening and closing action while ensuring that the goods are released at a preset speed.

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

[0050]

[0051] where v release is the preset release speed, g is the acceleration due to gravity, and h is the height of the cargo box from the ground, ensuring that the goods are not damaged when they land.

[0052] The beneficial effects of the present invention are:

[0053] Improve logistics efficiency: The present invention accurately judges the interface strength between the UAV and the cargo box through the UAV load-bearing module, ensuring that the UAV can safely carry the goods during flight and avoiding transportation interruption caused by insufficient load-bearing. At the same time, the UAV path planning module combines with the two-dimensional grid map to quickly determine the flight planning path, avoid obstacles, reduce flight time, and improve the logistics distribution efficiency.

[0054] Enhance flight safety: The UAV 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 the visual rotation angle, effectively reducing the collision risk and ensuring the safety of the UAV and the goods.

[0055] Improve the ability to monitor goods: The UAV data analysis module can obtain the physical target parameters in the cargo box in real time, and analyze and monitor the goods status using an anomaly detection model. When the goods status 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] Optimize the goods unloading process: The UAV automatic unloading module can accurately control the opening and closing time of the cargo box and the goods release speed when arriving at the delivery point, ensuring that the goods land safely at a preset speed, avoiding damage caused by too fast or too slow goods release speed, and improving the success rate and safety of goods unloading.

[0057] Adapt to complex environments: The present invention is designed for the complex geographical environment of mountainous areas, can effectively solve the transportation problems faced by traditional logistics methods in these areas, provide efficient and reliable logistics solutions for emergency medical, educational material distribution and the export of special agricultural products, and contribute to rural revitalization and the development of smart logistics.

[0058] Modular design: The present invention adopts a modular design, where the functions of each module are clear, cooperate with each other, and are easy to expand and upgrade. It is possible to flexibly adjust the functions and parameters of each module according to different logistics requirements and environmental conditions, improving the adaptability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0060] Figure 1 Schematic diagram of an unmanned aerial vehicle intelligent cargo box system according to an embodiment of the present invention;

[0061] Figure 2 Schematic diagram of the unmanned aerial vehicle intelligent cargo box according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0063] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0064] This embodiment improves the mountainous area logistics system and enhances the material distribution efficiency. The mountainous area logistics has long faced the "last mile" distribution problem, especially in mountainous areas with backward transportation conditions. The complex terrain and imperfect infrastructure have led to low efficiency and high costs of traditional transportation methods, and the access to materials for many villagers has been significantly restricted. The core mission of this embodiment is to provide technical support for improving these problems by developing an unmanned aerial vehicle intelligent cargo box system and optimizing the mountainous area logistics system.

[0065] Smart logistics technology is of great significance in rural revitalization and can significantly promote the material circulation and economic development in mountainous areas. This embodiment takes the unmanned aerial vehicle intelligent cargo box system as the core, applies smart logistics technology to the logistics scenarios in mountainous areas, and fills the deficiencies of traditional logistics methods in terms of efficiency, coverage, and adaptability. This embodiment provides an innovative demonstration and technical support for rural revitalization.

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

[0067] In terms of agricultural economy, the flexibility and efficiency of the modular cargo box system can help farmers transport characteristic agricultural products to market towns or county seats for sale more quickly, reducing the economic losses caused by the backlog of agricultural products. This embodiment also provides a new channel for the export of agricultural products by optimizing logistics efficiency, enhancing the vitality of the rural economy.

[0068] In addition, this embodiment can provide support for emergency rescue. For example, during the epidemic or natural disasters, this system can quickly transport medical supplies and emergency materials, providing an efficient solution for emergencies 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 welfare, injecting scientific and technological impetus into rural revitalization.

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

[0070] This embodiment adopts lightweight and modular design, reducing the manufacturing cost of the cargo box and also reducing the operating energy consumption of the drone, making it suitable for popularization and application in mountainous areas with limited resources. The intelligent monitoring system reduces the risk of cargo damage 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 placement 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 promotion, the technical solution of this embodiment has strong applicability and can be applied not only to mountainous areas, but also extended to scenarios such as emergency rescue in disaster areas and express delivery in remote urban areas, with broad market prospects. In terms of economic benefits, the drone intelligent cargo box system significantly reduces the operating costs of mountainous area logistics, creating more profit opportunities for rural logistics enterprises. In addition, with the large-scale application of the technology, this embodiment will also drive the development of the upstream and downstream industrial chains, such as drone manufacturing, intelligent cargo box production, and logistics management services, creating more employment opportunities for society.

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

[0074] In recent years, the Chinese drone logistics market has developed rapidly, especially showing great potential in solving the "last mile" delivery problem in remote areas. According to data from the Forward Industry Research Institute, the demand scale for drones in the logistics field across the country in 2023 was approximately 14.397 billion yuan, and it is expected to reach 30 billion yuan by 2024, showing strong growth momentum.

[0075] Regional development has become more balanced. Compared with the same period last year, the proportions of express delivery business volumes in the central and western regions in the first three quarters of this year accounted for 1.3 and 0.9 percentage points more of the national total respectively, and the growth rates of express delivery business volumes in Inner Mongolia, Shaanxi, and Gansu all exceeded 40%.

[0076] The express delivery market continued to develop rapidly. The average monthly business volume in the first three quarters exceeded 13 billion pieces, driving the continuous expansion of the rural express delivery market scale. The effectiveness of the industry in connecting urban and rural areas was further highlighted, and it is playing an increasingly important role in boosting rural revitalization.

[0077] However, despite the overall rapid development trend of the market, there are still obvious shortcomings in logistics infrastructure. According to the national census bulletin, the proportion of administrative villages with hardened roads is 99.6%, but only 62.7% of administrative villages have e-commerce distribution sites, indicating that rural delivery and logistics infrastructure still needs to be improved.

[0078] Against this background, a drone intelligent cargo box system emerged, aiming to improve the logistics efficiency in mountainous areas and reduce transportation costs through modular design, intelligent sensor monitoring, and automatic unloading technology. This system is particularly suitable for the transportation of medical supplies, educational resources, and agricultural products, meeting the basic living and production needs of local residents.

[0079] Market analysis shows that with the expansion of the industrial drone market, the low-altitude logistics industry is also developing rapidly.

[0080] In 2023, the market scale of industrial drones was approximately 73.723 billion yuan, with a year-on-year growth of 15.24%; it is expected to exceed 97 billion yuan by the end of 2024.

[0081] However, the terrain in mountainous areas is complex and the population is scattered, making it difficult for traditional logistics methods to cover, resulting in high logistics costs and low efficiency. The introduction of the drone logistics system can effectively solve these problems and achieve fast and efficient material distribution. At the same time, with the rise of rural e-commerce, the demand for logistics distribution is increasing day by day, and the application of the drone logistics system will further promote the local economic development.

[0082] In summary, under the current market environment and policy support, an intelligent drone cargo box system has broad application prospects. By improving the logistics efficiency in mountainous areas and enhancing infrastructure, this embodiment will have a positive impact on the lives and production of local residents, contributing to rural revitalization and regional economic development.

[0083] Mountainous areas usually have complex terrains and underdeveloped transportation infrastructure, resulting in high logistics distribution costs and low efficiency, which restricts the supply of daily necessities, especially in emergency situations. For example, medical supplies and urgently needed items cannot be delivered in a timely manner, posing a huge challenge to residents' lives. This embodiment provides an efficient and low-cost logistics solution for these groups through the intelligent drone cargo box system, ensuring that medical supplies and daily necessities can be quickly and safely delivered to the hands of villagers.

[0084] In terms of educational resources, the system can be used to deliver teaching materials, teaching aids, etc. to remote rural schools, shortening the material transportation time and providing support for educational equity. At the same time, by using drones to deliver special agricultural products, the economic income of villagers will also be increased. For these villagers, this embodiment not only meets their living needs but also provides more possibilities for production development.

[0085] As Figure 1 shown, this embodiment discloses an intelligent drone cargo box system, including: a drone load-bearing module, configured to obtain the interface strength between the drone and the cargo box, calculate the stress level of the interface during the flight of the drone, and determine whether the stress level meets the stress condition. When the stress level meets the stress condition, the delivery task is obtained; the cargo box is loaded with lightweight goods for mountainous area logistics; a drone path planning module, configured to combine the delivery task with a two-dimensional grid map of the operating environment to determine the flight planning path; a drone air avoidance module, configured to, when the drone flies according to the flight planning path and a moving target appears, obtain the air position of the moving target, establish a moving target motion model based on the air position, obtain the visual rotation angle of the moving target on the drone, and avoid the moving target based on the visual rotation angle; a drone data analysis module, configured to obtain the 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 anomalies occur in the physical target parameters, an alarm signal is sent to indicate that the goods status in the cargo box is abnormal; the anomaly detection model is obtained by training a support vector machine model using a training set, and the training set includes: original physical target parameters; a drone automatic unloading module, configured to, when the drone reaches the delivery point in the delivery task, control the opening and closing time of the cargo box of the drone to ensure that the goods are released at a preset speed.

[0086] Furthermore, the UAV load-bearing module includes: a cargo weight setting unit for setting the cargo of the target weight; a stress level calculation unit for obtaining the interface strength between the UAV and the cargo box and calculating the stress level of the interface during the UAV flight:

[0087]

[0088] wherein, σ v is the equivalent stress, σ x , σ y are the normal stresses in the x and y directions respectively, and τ xy is the shear stress in the xy plane;

[0089] The UAV load-bearing unit is used to determine whether the stress level meets the stress condition. When the stress level meets the stress condition, the 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;

[0090] The stress condition is:

[0091] σ v <σ yicld

[0092] wherein, σ yicld is the maximum stress level that the UAV can withstand during flight.

[0093] Specifically:

[0094] In this embodiment, the flexible cargo loading function is realized through modular design, and different types of goods (such as medical supplies, daily necessities and agricultural products) can be classified and stored and efficiently loaded, avoiding the damage risk caused by mixed loading. In addition, the system also integrates intelligent sensor technology, which can monitor parameters such as the weight, temperature, humidity of the goods in real time, and transmit the data to the ground control center through the wireless communication module to realize the whole process visualization of the transportation process. This precise monitoring function not only improves the distribution efficiency, but also greatly reduces the risk of goods loss and damage.

[0095] As Figure 2 shown, the modular cargo box design is one of the core technologies of the system, aiming to solve the problems of loading adaptation and cargo protection in the diversified logistics needs in mountainous areas. The cargo box is flexibly adjusted through a modular structure, and each module is made of lightweight composite materials (such as carbon fiber and high-strength polymer), taking into account both strength and weight reduction requirements.

[0096] 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 be set with an independent isolation area to ensure the safety of drugs;

[0097] For agricultural products, the module can be configured with a breathable structure to prevent the goods from deteriorating due to excessive humidity. The main technical parameters of the modular design include:

[0098] The maximum load weight W of a single module: It is set to 5 kg to meet the characteristics of mainly lightweight goods in mountain logistics.

[0099] The strength of the modular interface of the cargo box: Using the Von Mises criterion, calculate whether the interface meets the stress conditions during flight:

[0100]

[0101] Among them, σ v is the equivalent stress, and it is required to meet the design condition of σ v <σ yicld of.

[0102] Furthermore, the UAV path planning module includes: a two-dimensional grid map generation unit, which is used to obtain the depth information of the original path in the distribution task and generate a two-dimensional grid map according to the depth information; a UAV path planning unit, which is used to set the shortest avoidance distance between the UAV and the obstacle based on the positions of the grids and obstacles in the two-dimensional grid map, and update the original path through the shortest avoidance distance to determine the flight planning path; the shortest avoidance distance between the UAV and the obstacle is set as:

[0103] Dsafe = Dmin + Ru + Ro

[0104] Among them, 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.

[0105] Furthermore, the UAV air avoidance module includes: a moving target position acquisition unit, which is used to acquire the air position of the moving target when the UAV is flying according to the flight planning path and a moving target appears; a first UAV air avoidance unit, which is used to establish a moving target motion model based on the air position, obtain the distance information between the UAV and the moving target, and obtain the instantaneous value corresponding to the moving target. Based on the distance information and the instantaneous value, obtain the visual rotation angle of the moving target on the UAV, and determine the motion direction of the UAV through the visual rotation angle to avoid the moving target; a second UAV air avoidance unit, which is used to fly back to the flight planning path to continue flying after the UAV has avoided the air target.

[0106] Furthermore, establishing a moving target motion model includes:

[0107]

[0108] Among them, \(x(k)\) is the distance of the moving target, \(v(k)\) is the speed of the moving target, \(\Delta t\) is the sampling interval, \(a(k)\) is the acceleration of the moving target, and \(w(k)\) is the noise of the acceleration.

[0109] Furthermore, obtaining the visual rotation angle of the moving target on the UAV includes:

[0110]

[0111] Among them, \(w(t)\) is the visual rotation angle, \(r(t)\) is the position vector of the moving target relative to the UAV, and \(v\) r (t) is the velocity vector of the moving target relative to the UAV.

[0112] Furthermore, the UAV data analysis module includes: a physical parameter acquisition unit for acquiring physical target parameters in the cargo box:

[0113] D i =\(\{T, H, W, GPS\}\)

[0114] Among them, \(D\) 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 geographical location information;

[0115] A UAV data analysis unit for inputting the physical target parameters into an anomaly detection model, analyzing the physical target parameters and detecting anomalies, and when the physical target parameters are abnormal, an alarm signal is sent to indicate that the goods status in the cargo box is abnormal; the anomaly detection model is obtained by training a support vector machine model using a training set;

[0116] The expression of the support vector machine model is:

[0117]

[0118] Among them, \(a\) i is the Lagrange multiplier in the support vector machine algorithm, \(y\) i is the class label of the training sample \(x\) i (usually taking values +1 or -1), \(K(x\) i , \(x)\) is the kernel function for mapping the input data to a high-dimensional space to facilitate linear classification and other operations in the high-dimensional space, and \(b\) is the bias term in the support vector machine model.

[0119] Specifically:

[0120] The data analysis module plays an important role throughout the transportation process, providing real-time data collection and feedback functions to ensure the safety of the cargo status during transportation. The system embeds a variety of sensors in the cargo box, including a temperature sensor (accuracy: ±0.5°C), a humidity sensor (accuracy: ±1%RH), and a weight sensor (error range: ±2%), to monitor the status parameters of the cargo in real time.

[0121] These data are transmitted to the ground control station or cloud server through LoRa communication technology, and the following data processing models are adopted:

[0122] 1. Data collection model:

[0123] D i ={T, H, W, GPS}

[0124] Among them, 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 geographical location information;

[0125] 2. Anomaly detection model: The support vector machine (SVM) algorithm is used to analyze the real-time data and detect anomalies:

[0126]

[0127] When f(x) < 0, the system issues an alarm signal to indicate that the material status is abnormal.

[0128] Furthermore, the unmanned aerial vehicle (UAV) automatic unloading module includes: a UAV automatic unloading unit, which is used to control the opening and closing time of the UAV's cargo box when the UAV reaches the delivery point in the delivery task, ensuring that the goods are released at a preset speed; a dynamic adjustment unit, which is used to control the tilt angle of the UAV and adjust the opening and closing action while ensuring that the goods are released at a preset speed.

[0129] Furthermore, ensuring that the goods are released at a preset speed includes:

[0130]

[0131] Among them, v release is the preset release speed, g is the acceleration due to gravity, and h is the height of the cargo box from the ground, ensuring that the goods are not damaged when they land.

[0132] Specifically:

[0133] The automatic unloading function designs a technical solution for precise delivery and rapid unloading according to the actual needs of mountain logistics distribution, which is especially suitable for multi-site distribution in scattered villages and stockades. The UAV can complete the precise delivery of goods without manual intervention, significantly improving the distribution efficiency and reducing the time and economic costs brought by manual participation.

[0134] The automatic unloading technology realizes the precise delivery of goods at multiple distribution points by integrating electrical automation devices. The system uses a servo motor to control the opening and closing of the cargo box, and combines GPS and MU (Inertial Measurement Unit) data to determine the delivery position, with a delivery accuracy of up to ±2 meters.

[0135] The control logic of the unloading process is as follows:

[0136] 1. Route planning and positioning: According to the coordinates of the distribution point, the drone hovers above the target position.

[0137] 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:

[0138]

[0139] where v release is the preset release speed, g is the acceleration due to gravity, and h is the height of the cargo box from the ground.

[0140] 3. Dynamic adjustment: The system monitors the tilt angle of the drone through MU data and adjusts the opening and closing actions in real time to avoid delivery errors.

[0141] In this embodiment, through the modular design concept, the flexibility and adaptability of cargo transportation in mountainous areas are realized. The modular cargo box is made of lightweight composite materials, and each module can independently adjust its size and capacity to meet the storage needs of different types of materials. The design of the cargo box particularly considers the diverse needs of mountainous logistics, such as the isolated storage of medical supplies, the prevention of agricultural products from getting damp, and the safety protection of high-value goods. The modular design combined with the quick loading and unloading interface ensures that the goods can maintain balance during the flight of the drone, improving the safety of the transportation process.

[0142] The technical basis of the cargo box design uses mechanical analysis tools to optimize the 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-carrying performance of the drone, ensuring that the energy consumption during the flight process is controlled within a reasonable range. Through the modular cargo box, the system can quickly adapt to various transportation scenarios, not only improving the logistics efficiency but also reducing the unit distribution cost, providing an efficient solution for the diverse material needs in mountainous areas.

[0143] The intelligent monitoring technology of the system realizes the real-time collection and feedback of the goods status by integrating sensors and data communication modules. The sensor network includes temperature sensors, humidity sensors, and weight sensors, which can monitor the internal environmental conditions of the cargo box in real time and upload the monitoring data to the ground station or cloud platform through LoRa wireless communication technology. The monitoring system combines the support vector machine (SVM) algorithm to detect abnormal situations during transportation in real time, such as over-temperature, high humidity, or abnormal cargo weight, ensuring the quality and safety of the goods during transportation.

[0144] The automatic unloading technology uses a servo motor and a precise positioning algorithm to achieve the rapid release and precise delivery of goods. In practical applications, the drone can hover according to the GPS coordinates of the target location and adjust its flight attitude through the data of the inertial measurement unit (IMU) to ensure that the goods can land precisely. The delivery speed is optimized by the gravitational potential energy formula to ensure that the goods are not damaged when they land. This technology is particularly suitable for the multi-point distribution needs of scattered villages in mountainous areas, significantly reducing the complexity of manual operations, while improving the distribution efficiency and accuracy, providing a reliable guarantee for the development of intelligent logistics.

[0145] In response to the complex terrain conditions and changing weather environment in mountainous areas, the system adopts an improved A* algorithm for flight path planning. Based on the traditional path planning algorithm, the influence weights of terrain slope and wind speed are newly added, significantly improving the safety and stability of the drone's flight in mountainous areas. The optimized path cost function can dynamically adjust the cost estimation between nodes, avoiding high-risk areas, thereby effectively reducing the flight energy consumption of the drone and extending the endurance time of a single mission.

[0146] The overall performance of the system performs excellently in field tests: the single flight radius reaches 20 - 50 kilometers, the cargo box load capacity is 1 - 5 kilograms, and the automatic unloading error is controlled within ±2 meters. These performance indicators make the system highly practical and popularizable in the logistics scenarios of mountainous areas. At the same time, the system has strong scalability. By further upgrading the algorithm and hardware, it can adapt to more scenario requirements, such as post-disaster emergency rescue, urban fringe express delivery, and agricultural logistics services. In summary, the "Cloud Wing Intelligent Transportation - Drone Intelligent Cargo Box System" is not only innovative in technology but also provides excellent performance support for practical applications, demonstrating the infinite potential of technology to assist the development of mountainous areas.

[0147] In summary, this embodiment can effectively solve the transportation difficulties in mountainous area logistics, improve the material distribution efficiency, ensure that key materials can be delivered in a timely manner, and provide important support for improving the living quality of mountainous area residents.

[0148] This embodiment also discloses a usage method of the drone intelligent cargo box system, including:

[0149] 1. Before using the drone intelligent cargo box system, it is necessary to ensure that the drone and the cargo box are in good working condition. First, check the hardware of the drone, including the battery power, the condition of the propellers, and whether the GPS module is operating normally. Ensure that the flight control system has been pre-calibrated to provide stability during flight. Second, the modular cargo box needs to be installed. Select a suitable cargo box module according to the requirements of the transportation task, 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 drone, ensuring a firm connection and that the interface strength meets the operating requirements (e.g., no looseness).

[0150] When loading the goods, the goods need to be stored according to the module partition to avoid contamination or damage caused by mixed loading. For goods with strict temperature control requirements (such as vaccines or cold chain food), it is necessary to use a module with temperature and humidity regulation functions and preset the environmental parameters inside the cargo box in advance. After loading is completed, through the Ground Control Station (GCS)

[0151] Perform a secondary verification of the goods weight, loading stability, and the load capacity of the drone to ensure that it does not exceed the maximum load capacity of the drone. After all inspections are completed, start the pre-flight self-check program of the drone to verify whether the GPS positioning, path planning, and communication modules of the flight system are operating normally to ensure flight safety.

[0152] 2. Flight Path Planning and Task Settings Before the drone takes off, it is necessary to set the delivery task and flight path through the control terminal. This system integrates an improved A* algorithm for path optimization in complex mountainous environments. By inputting the starting point of the delivery task and multiple target points (such as the GPS coordinates of villages or transfer stations), the system will automatically plan the best path, avoiding dangerous terrains and high-risk areas (such as steep slopes, high-wind areas).

[0153] Users can adjust the priority of the delivery task on the terminal interface. For example, setting emergency medical supplies as the highest priority, the system will give priority to completing related tasks. After the path planning is completed, the system will simulate the flight path and provide relevant parameters, including the estimated flight time, energy consumption, and the cargo delivery time at each delivery point. Users need to confirm that all task settings are correct and then upload the task to the flight control system of the drone.

[0154] After takeoff, the flight path will be adjusted in real time according to the environmental data. The system monitors the flight status through embedded sensors (such as MU and anemometers). When encountering weather changes or obstacles, it will re-plan the path to ensure safety and efficiency. The entire path planning process is mainly automated, and users only need to monitor the task progress and handle abnormal situations.

[0155] 3. Cargo Monitoring and Transportation Process During the flight, the status of the cargo is monitored throughout the process by an intelligent monitoring system. Multiple sensors, including temperature, humidity, and weight sensors, are embedded in the cargo container, which can record the environmental parameters of the cargo in real time. These data are uploaded to the ground station through the oRa communication module. Users can view the cargo status through the control terminal and respond promptly to abnormal situations. 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.

[0156] The flight status of the UAV is also monitored. The flight control system evaluates the flight altitude, speed, and attitude in real time through GPS, MU, and the barometer. In case of emergencies (such as signal loss or low battery), the UAV will execute emergency measures according to the preset program, including returning or making an emergency landing. The entire transportation process is centered around intelligence, which not only reduces manual operations but also ensures the safety of the cargo.

[0157] 4. Automatic Unloading and Delivery Operations After the UAV reaches the target delivery point, the automatic unloading function will be activated. The unloading process is controlled by a servo motor to open and close the cargo container, and combines the GPS coordinates and MU data of the target point to ensure that the cargo can be accurately delivered to the designated area. Users need to pre-define the delivery method for each delivery point (such as fixed-point delivery or ground slow descent) during the task setting stage.

[0158] After the delivery is completed, the system will upload the delivery record, including the delivery location, cargo status, and task completion time, providing data support for subsequent task analysis. Users can also review the delivery results through the ground station to verify the execution of the delivery task.

[0159] 5. Task Completion and Equipment Maintenance After completing the delivery task, the UAV will return to the starting point or a designated transfer station for landing. Users need to conduct maintenance inspections on the equipment, including battery power, propeller status, and the operating condition of the flight control system, to ensure that the UAV can continue to execute the next task. The cargo container module needs to be cleaned and inspected after the task is completed. Especially when transporting food or medical supplies, the inside of the cargo container should be thoroughly cleaned to avoid residues affecting the next transportation task.

[0160] In addition, all task data will be automatically saved to the system database. Users can optimize the task process by analyzing historical data. For example, adjust the path planning strategy based on the records of energy consumption and flight time; optimize the environmental control parameters of the cargo container module based on the monitoring records of the cargo status. Regular software upgrades and hardware maintenance are also very important to ensure the long-term stable operation of the system.

[0161] Through an intelligent operation process and a highly integrated hardware design, this system provides users with a complete and easy-to-operate logistics solution. By following the above usage instructions, users can maximize the technical advantages of the UAV intelligent cargo box system, improve mountainous area logistics efficiency, and contribute to rural revitalization.

[0162] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A drone intelligent cargo box system, characterized in that: include: The UAV load-bearing module is used to obtain the interface strength between the UAV and the cargo box, calculate the stress level of the interface during the flight of the UAV, and determine whether the stress level meets the stress condition. When the stress level meets the stress condition, the delivery task is obtained; The cargo box is loaded with light goods that meet the needs of mountain logistics; A drone path planning module is used to combine the delivery task with a two-dimensional grid map of the operating environment to determine the flight planning path; The UAV air avoidance module is used to obtain the air position of the moving target when the UAV flies according to the flight planning path and a moving target appears, establish a moving target motion model based on the air position, obtain the visual turning angle of the moving target on the UAV, and avoid the moving target based on the visual turning angle; The drone data analysis module is used to obtain the physical target parameters in the cargo box, input the physical target parameters into the anomaly detection model, analyze the physical target parameters and detect anomalies, and when the physical target parameters are abnormal, an alarm signal is issued to indicate that the state of the cargo in the cargo box is abnormal; The anomaly detection model is obtained by training a support vector machine model using a training set, wherein the training set includes: original physical target parameters; The automatic unloading module of the drone is used to control the opening and closing time of the cargo box of the drone when the drone arrives at the delivery point in the delivery task, so as to ensure that the cargo is released at a preset speed.

2. The UAV intelligent cargo box system according to claim 1, characterized in that: The UAV load-bearing module comprises: A cargo weight setting unit is used to set the target weight of cargo; The stress level calculation unit is used to obtain the interface strength between the UAV and the cargo box, and calculate the stress level of the interface during the flight of the UAV: Among them, σ v is the equivalent stress, σ x , σ y are the normal stresses in the x and y directions, τ xy is the shear stress in the xy plane; The UAV load-bearing unit is used to determine whether the stress level meets the stress condition. When the stress level meets the stress condition, the 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. The stress conditions are: s v <s yicld Among them, σ yicld It is the maximum stress level that the UAV can withstand during flight.

3. The UAV intelligent cargo box system according to claim 1, characterized in that: The UAV path planning module includes: A two-dimensional grid map generating unit, used to obtain the depth information of the original path in the delivery task, and generate the two-dimensional grid map according to the depth information; A UAV path planning unit, configured to set a shortest avoidance distance between the UAV and the obstacle based on the grid and obstacle positions in the two-dimensional grid map, and update the original path by the shortest avoidance distance, thereby determining the flight planning path; Set the shortest avoidance distance between the drone and the obstacle to: D safe =D min +R u +R o Among them, D safe is the shortest evasion distance, D min is the minimum safe distance between the drone and the obstacle, R u is the radius of the drone, R o is the radius of the obstacle.

4. The UAV intelligent cargo box system according to claim 1, characterized in that: The drone air avoidance module includes: A moving target position acquisition unit, used for acquiring the aerial position of a moving target when the UAV flies according to the flight planning path and a moving target appears; a first UAV air 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, obtain an apparent 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 by the apparent turning angle, and thus avoid the moving target; The second UAV air avoidance unit is used to, after the UAV avoids the aerial target, return to the flight planning path to continue flying.

5. The UAV intelligent cargo box system according to claim 4, characterized in that: Establishing the moving target motion model includes: Among them, 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.

6. The UAV intelligent cargo box system according to claim 4, characterized in that: Obtaining the visual angle of the moving target on the drone includes: Among them, w(t) is the visual angle, r(t) is the position vector of the moving target relative to the drone, and v r (t) is the velocity vector of the moving target relative to the UAV.

7. The UAV intelligent cargo box system according to claim 1, characterized in that: The drone data analysis module includes: A physical parameter acquisition unit is used to acquire physical target parameters in the cargo box: D i ={T,H,W,GPS} Among them, D i is the monitoring data of the i-th batch of goods, T is temperature, H is humidity, W is weight, and GPS is geographic location information; The drone data analysis unit is used to input the physical target parameters into an anomaly detection model, analyze the physical target parameters and detect anomalies, and when the physical target parameters are abnormal, an alarm signal is issued to indicate 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; The support vector machine model expression is: Among them, a i is the Lagrange multiplier in the support vector machine algorithm, y i is the training sample x i The category label, K(x i ,x) is the kernel function, which is used to map the input data to a high-dimensional space to facilitate linear classification operations in the high-dimensional space, and b is the bias term in the support vector machine model.

8. The UAV intelligent cargo box system according to claim 1, characterized in that: The automatic unloading module of the drone includes: The automatic unloading unit of the drone is used to control the opening and closing time of the cargo box of the drone when the drone arrives at the delivery point in the delivery task, so as to ensure that the cargo is released at a preset speed; The dynamic adjustment unit is used to control the tilt angle of the drone and adjust the opening and closing action while ensuring that the cargo is released at a preset speed.

9. The UAV intelligent cargo box system according to claim 8, characterized in that: Ensuring that the cargo is released at a predetermined rate includes: Among them, v release It is released at a preset speed, g is the acceleration of gravity, and h is the height of the cargo box from the ground to ensure that the cargo is not damaged when it falls to the ground.

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