Modular unmanned vehicle for desert road and surrounding environment management and management method thereof

Abstract

The application discloses a modular unmanned vehicle for treating desert highway and surrounding environment, comprising a moving chassis, a vehicle body module, a modular sand cleaning device and a positioning and sensing device; the moving chassis is connected with the modular sand cleaning device through a modular connecting interface, the vehicle body module is installed on the moving chassis, and the positioning and sensing device is embedded in a shell of the vehicle body module; a main body structure of the moving chassis is connected by a pipeline framework, suspension and spring shockproof mechanisms are arranged at both ends of the pipeline framework, sandproof wheels are connected, and a sandproof wheel cover is arranged outside the main body structure; the vehicle body module is provided with a control center and a material storage area, the modular sand cleaning device is composed of a front sand suction device, side sand discharge devices and a middle ventilator, and the positioning and sensing device comprises a camera, a radar, a GPS and a high-precision gyroscope; the application further discloses a treatment method applied to the unmanned vehicle, and the modular unmanned vehicle for automatically cleaning sand and inspection improves equipment flexibility, reduces maintenance cost and improves work efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicles for desert environment management, specifically to a modular unmanned vehicle and its management method for managing desert highways and their surrounding environment. Background Technology

[0002] In the Taklamakan Desert of Xinjiang, numerous "lifelines," commonly known as "desert highways," crisscross the landscape. Due to the mobility of wind and sand, these roads are frequently covered by sand. Consequently, the country invests significant human and financial resources annually in maintaining the vegetation along these highways. Each highway has a water well house every 3-4 kilometers, supplying water to the surrounding vegetation. Correspondingly, highway administrators are stationed at these well houses to manage the surrounding area, playing a crucial role in the management of desert roads and the protection of the surrounding vegetation.

[0003] Currently, the maintenance of desert roads suffers from low efficiency in clearing accumulated sand, limited vegetation protection, and a lack of intelligent and automated methods. Sandstorms burying roads and delayed cleanup increase the risk of accidents. The surrounding environment is uninhabitable, and the transportation of water, wells, and supplies is costly and time-consuming. Furthermore, in the harsh desert environment, repairing or replacing damaged or loosely constructed equipment is difficult and costly. Therefore, this invention proposes a modular unmanned vehicle and its management method for desert roads and their surrounding environment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings and deficiencies of the prior art and provide a modular unmanned vehicle and its management method for managing desert highways and their surrounding environment. The modular unmanned vehicle, which automatically clears sand and performs inspections, solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A modular unmanned vehicle for managing desert highways and their surrounding environment includes a battery for rear-wheel drive powered by the battery, a motion chassis, a body module, a modular sand-clearing device, and positioning and sensing equipment. The motion chassis is connected to the modular sand-clearing device through a modular connection interface, the body module is mounted and fixed on the motion chassis, and the positioning and sensing equipment is embedded in the shell of the body module.

[0007] The main structure of the sports chassis is connected by a pipe frame. Suspension and spring shock absorption mechanisms are provided at both ends of the pipe frame. The suspension and spring shock absorption mechanisms are connected to sand-proof wheels. The main structure is covered with sand-proof wheel sleeves.

[0008] The vehicle body module is equipped with a control center and a material storage area. The control center is controlled by a remote control system that sends commands via wireless communication to control the operation of the unmanned vehicle.

[0009] The modular sand removal device is installed under the moving chassis and consists of a front sand suction device, a side sand discharge device and a central ventilation fan. The modular sand removal device is scheduled by the control center.

[0010] The positioning and sensing devices include a camera, radar, GPS, and a high-precision gyroscope; the camera is located at both ends of the vehicle module to collect high-definition images and assist the radar in environmental perception; the radar is mounted at the front, sides, and rear of the vehicle module to detect the driving environment in front and behind.

[0011] Furthermore, the body module is equipped with a lighting module, including front lights, taillights and top lights, and an anemometer and solar panel are installed on the top of the body module.

[0012] Furthermore, the control center includes a central processing unit, a data storage device, a communication module, a navigation and positioning system, and a sensor interface. The control center is responsible for receiving and processing data from the vehicle system. The unmanned vehicle sends its location, speed, and desert environment data to the remote control system in real time via the network. The remote control system continuously monitors the status and operating environment of the unmanned vehicle to ensure timely response.

[0013] Furthermore, the modular sand-clearing device is scheduled by the control center, and its scheduling mechanism is as follows:

[0014] The control center determines the sand accumulation on the road based on real-time data. When sand accumulation is detected, the control center instructs the front sand suction device to start suctioning the sand, and uses the central ventilation fan to assist in adjusting the movement of the sand particles, discharging the sand particles from the side sand discharge device to outside the road.

[0015] Furthermore, the positioning and sensing equipment also includes a surface temperature sensor, a hygrometer, a soil moisture sensor, and a dust sensor for detecting suspended particles in the atmosphere. The above devices collect desert environmental data in real time, including temperature, humidity, and air quality. All collected desert environmental data is transmitted to a remote control system in real time, and the desert environmental data is analyzed and maintenance strategies are formulated using machine learning algorithms.

[0016] Furthermore, the modular connection interface combines a quick-plug mechanism, electrical connection, and automatic locking mechanism. The quick-plug mechanism uses strong magnetic force to quickly and accurately attract the modular sand-removing device. The quick-plug mechanism integrates an intelligent electrical connection using spring-conductive magnetic material contacts. When the sand-removing device is attracted into place, the electrical interface automatically connects, completing the connection for power and data communication. After the magnetic connection is completed, the automatic locking mechanism is triggered. After the control center receives the connection signal, it automatically activates the electric bolt, which quickly extends and firmly locks the sand-removing device.

[0017] This invention proposes a modular unmanned vehicle management method for managing desert highways and their surrounding environment, comprising:

[0018] Once the unmanned vehicle is started, staff can remotely control the vehicle module's control center to deploy the unmanned vehicle cluster to carry out daily inspections and environmental maintenance on the desert highway; the central battery on the moving chassis powers the unmanned vehicle, while sand-proof wheel covers and wheels ensure stable driving in the desert environment.

[0019] During the road cleaning process, the modular sand removal device cleans the road surface through sand suction and sand discharge devices. When the unmanned vehicle encounters accumulated sand while driving, the front sand suction device is activated, and the central ventilation fan circulates the sand particles. Then, the side sand discharge device evenly discharges the sand particles on the outside of the road to maintain the road's passability. This process is scheduled and managed by the control center.

[0020] In terms of material transportation, the vehicle module has a material storage area inside for storing and transporting materials. When the unmanned vehicle is deployed, the staff loads the materials to be transported into it. During the transportation process, the unmanned vehicle delivers the materials to each work station accurately according to the preset route and timetable.

[0021] In terms of road inspection, the unmanned vehicle relies on camera algorithms for recognition and radar for precise positioning. If sand is detected on the road surface, the sand removal device will be activated immediately to remove the sand. In inspection mode, the unmanned vehicle is powered by solar energy. When the battery power is insufficient, the unmanned vehicle will automatically return to the nearest garage station for charging.

[0022] Furthermore, the deployment of the unmanned vehicle cluster includes:

[0023] Based on the length and environmental complexity of the desert highway, as well as the layout of the well houses, the area is divided into multiple management zones. Unmanned vehicles are deployed in each zone, and the number of unmanned vehicles is determined according to the size of the zone and maintenance needs. The control center assigns daily inspection and environmental maintenance tasks to the corresponding unmanned vehicle clusters, including environmental maintenance tasks such as road surface and vegetation monitoring.

[0024] When an area is detected to require additional maintenance, the remote control system will adjust task allocation and coordinate work according to the nature and urgency of the task. Specifically, if a severe sand accumulation is found in an area, requiring multiple unmanned vehicles to work together to clear it, the remote control system will automatically dispatch other nearby unmanned vehicles to assist. When an unmanned vehicle malfunctions, the remote control system will immediately reassign the task to other unmanned vehicles to ensure that the maintenance work in the area is not affected and allow the malfunctioning vehicle to return to the nearest station for repair.

[0025] Furthermore, during transportation, the unmanned vehicles accurately deliver supplies to various work stations according to preset routes and schedules, including:

[0026] Before the material transportation mission begins, the remote control system formulates transportation routes and schedules based on the desert highway layout and the needs of each work station. The transportation routes and schedules take into account the shortest path, time, and the urgency of materials at each work station. The transportation routes and schedules are transmitted wirelessly to the navigation system of each unmanned vehicle to ensure that each unmanned vehicle is clear about its mission and destination.

[0027] The unmanned vehicles return to the base station and load the supplies onto designated unmanned vehicles. The load capacity and type of supplies for each vehicle are determined by the pre-planned task. During transportation, the location and status of the unmanned vehicles are fed back to the remote control system in real time. The remote control system monitors the progress of each vehicle. When the unmanned vehicles arrive at the designated work station, the staff at the station receive the supplies. After each transportation task is completed, the unmanned vehicle's travel time, energy consumption, and supply delivery status are recorded and fed back to the remote control system.

[0028] Furthermore, the camera's algorithm recognition employs a CNN image recognition algorithm to detect desert road surface conditions, vegetation cover conditions, and wind and sand conditions, including the following steps:

[0029] S1, preprocesses the collected images, including resizing, standardizing pixel values, and enhancing image features to suit the subsequent image analysis process;

[0030] S2, annotate the images, indicating the environmental condition represented by each image, including: Good: The road surface is clean, without sand or obstacles, and road signs are clearly visible; Moderate: Slight sand accumulation or minor damage, not affecting normal vehicle passage; Poor: Significant sand accumulation or damage, affecting vehicle passage; Very Poor: The road is largely covered or severely damaged, making it impassable.

[0031] S3 uses a convolutional neural network (CNN) model in deep learning for image recognition, automatically and effectively learning spatial hierarchical features in images. The AlexNet architecture is selected, and the CNN model is trained using a labeled image dataset.

[0032] S4. During training, the model will learn to recognize different desert environmental states and apply data augmentation techniques, including random rotation, scaling, and flipping of images, to increase the model's generalization ability; the model's performance will be evaluated using a test set to ensure that the model can accurately identify desert environmental states even on unknown data.

[0033] S5 deploys the trained model onto the autonomous vehicle.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] In this invention, through modular design, the sand-clearing device and positioning and sensing equipment of the unmanned vehicle can be replaced according to actual needs, improving the flexibility and adaptability of the equipment and enabling the desert unmanned vehicle to adapt to various environmental changes. Secondly, the unmanned vehicle proposed in this invention can automatically complete sand-clearing and inspection work, which not only significantly reduces the input of manpower and material resources and lowers maintenance costs, but also has high work efficiency, can operate continuously for 24 hours, and is several times more efficient than manual work, and is not affected by factors such as severe weather. In addition, the unmanned vehicles proposed in this invention can work in clusters. Through the unified management and scheduling of unmanned vehicles by the remote control system, they can provide material transportation services to various stations in the desert, comprehensively perceive the surrounding environment along the way, automatically detect road conditions and provide feedback, reduce the difficulty of inspection work, realize efficient transportation of desert materials, and improve work efficiency. Attached Figure Description

[0036] Figure 1 This is an exploded view of the modular unmanned vehicle proposed in this invention.

[0037] Figure 2 This is a schematic diagram of the overall structure of the modular unmanned vehicle proposed in this invention;

[0038] Figure 3 This is a side view of the overall structure of the modular unmanned vehicle proposed in this invention;

[0039] Figure 4 This is a front view of the overall structure of the modular unmanned vehicle proposed in this invention;

[0040] Figure 5 This is a schematic diagram of the positioning and sensing device structure of the modular unmanned vehicle proposed in this invention;

[0041] Figure 6 This is a schematic diagram of the motion chassis structure of the modular unmanned vehicle proposed in this invention;

[0042] Figure 7 This is a schematic diagram of the modular sand-clearing device for the modular unmanned vehicle proposed in this invention.

[0043] Figure 8 This is a side view of the body module of the modular unmanned vehicle proposed in this invention and a schematic diagram of its internal structure.

[0044] In the diagram: 1. Sport chassis; 11. Pipeline frame; 12. Sand-proof wheel covers; 13. Battery; 14. Suspension and spring shock absorption mechanism; 15. Sand-proof wheels; 16. Modular connection interface; 2. Body module; 21. Front headlights; 22. Control center; 23. Material storage area; 24. Taillights; 25. Top headlights; 26. Anemometer; 27. Solar panel; 3. Modular sand removal device; 31. Front sand suction device; 32. Side sand discharge device; 33. Central ventilation fan; 4. Positioning and sensing equipment; 41. Radar; 42. Camera. Detailed Implementation

[0045] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0046] Example 1

[0047] Please see Figure 1 A modular unmanned vehicle for managing desert highways and their surrounding environment includes a battery 13 for rear-wheel drive, a motion chassis 1, a body module 2, a modular sand-clearing device 3, and a positioning and sensing device 4. The motion chassis 1 is connected to the modular sand-clearing device 3 via a modular connection interface 16. The body module 2 is mounted and fixed on the motion chassis 1, and the positioning and sensing device 4 is embedded in the outer shell of the body module 2. The main structure of the motion chassis 1 is connected by a pipe frame 11, with suspension and spring anti-vibration mechanisms 14 at both ends. The suspension and spring anti-vibration mechanisms 14 are connected to sand-resistant wheels 15, and the main structure is covered with sand-resistant wheel sleeves 12. The vehicle body module 2 is equipped with a control center 22 and a material storage area 23. The control center 22 controls the operation of the unmanned vehicle by issuing commands through wireless communication via a remote control system. The modular sand removal device 3 is installed under the moving chassis 1 and consists of a front sand suction device 31, a side sand discharge device 32, and a central ventilation fan 33. The modular sand removal device 3 is scheduled by the control center 22. The positioning and sensing device 4 includes a camera 42, a radar 41, a GPS, and a high-precision gyroscope. The camera 42 is located at both ends of the vehicle body module 2 to collect high-definition images and assist the radar 41 in environmental perception. The radar 41 is mounted at the front, sides, and rear of the vehicle body module 2 to detect the driving environment in front and behind.

[0048] In this invention, through modular design, the sand-clearing device and positioning and sensing equipment 4 of the unmanned vehicle can be replaced according to actual needs, improving the flexibility and adaptability of the equipment and enabling the desert unmanned vehicle to adapt to various environmental changes. Secondly, the unmanned vehicle proposed in this invention can automatically complete sand-clearing and inspection work, which not only significantly reduces the input of manpower and material resources and lowers maintenance costs, but also has high work efficiency, can operate continuously for 24 hours, and is several times more efficient than manual work, and is not affected by factors such as severe weather. In addition, the unmanned vehicles proposed in this invention can work in clusters. Through the unified management and scheduling of the unmanned vehicles by the remote control system, they can provide material transportation services to various stations in the desert, comprehensively perceive the surrounding environment along the way, automatically detect road conditions and provide feedback, reduce the difficulty of inspection work, realize efficient transportation of desert materials, and improve work efficiency.

[0049] To further adapt to the harsh desert environment, such as Figure 3 and Figure 4 As shown, the vehicle body module 2 is equipped with a lighting module, including front lights 21, rear lights 24, and top lights 25. An anemometer 26 and a solar panel 27 are mounted on the top of the vehicle body module 2. The anemometer 26 measures the wind speed in the surrounding environment, providing data support for the autonomous vehicle's operation. The solar panel 27 is a foldable solar panel that provides power to the battery 13 under sufficient sunlight, extending the autonomous vehicle's operating time. Furthermore, a GPS module provides accurate geographic location information, which is crucial for the autonomous vehicle's navigation. The GPS module is installed in multiple locations on the vehicle, such as the front, rear, sides, and top, to achieve optimal signal reception and positioning accuracy. A high-precision gyroscope is used to continuously monitor the vehicle's attitude and directional stability. It is installed in the central area of ​​the vehicle body module 2 and is tightly integrated with the vehicle's internal control system to provide real-time dynamic feedback, i.e., the autonomous vehicle's motion changes in three-dimensional space, including tilting, rotation, and acceleration.

[0050] In this embodiment, the control center 22 includes a central processing unit, a data storage device, a communication module, a navigation and positioning system, and a sensor interface. The control center 22 is responsible for receiving and processing data from the vehicle system. The unmanned vehicle sends its position, speed, and desert environment data to the remote control system in real time via the network. The remote control system continuously monitors the status and operating environment of the unmanned vehicle to ensure timely response. The remote control system is the central control center 22 for all unmanned vehicles, responsible for the overall task planning and scheduling. The remote control system can send advanced instructions to the control center 22 of each unmanned vehicle, such as adjusting the sand clearing strategy or redirecting to a new work area. The control center 22 of each unmanned vehicle is a local control center 22, receiving information from the camera 42, radar 41, and other sensors, making real-time decisions based on this information, and the central processing unit is responsible for executing advanced instructions.

[0051] Deploying network base stations in desert areas and along the route ensures coverage of the entire unmanned vehicle operating area; these base stations provide stable 4G / 5G network connections, ensuring high-speed and low-latency wireless communication; setting communication channels and data transmission protocols ensures the security and reliability of data transmission.

[0052] In this embodiment, the modular sand-removing device 3 is scheduled by the control center 22, and its scheduling mechanism is as follows:

[0053] The control center 22 determines the sand accumulation on the road surface based on real-time data. When sand accumulation is detected, the control center 22 instructs the front sand suction device 31 to begin suctioning sand, and the central ventilation fan 33 assists in adjusting the movement of sand particles, discharging the sand particles off the road through the side sand discharge device 32. The control center 22 determines the sand accumulation based on road image analysis and radar 41 scanning results. The autonomous driving computer built into the unmanned vehicle is equipped with a deep learning model. This model quickly and accurately determines the current road conditions based on real-time desert environment data collected from sensors such as cameras 42, radar 41, and laser scanners, and updates the vehicle's driving decisions in real time. In addition to monitoring and promptly identifying potential obstacles and hazards to ensure road safety, it can also accurately identify and analyze complex road conditions, such as sand dune movement, road surface sand accumulation, and vegetation distribution, to determine sand accumulation areas. The autonomous driving computer plans an efficient route to ensure that the unmanned vehicle can cover and clear all identified sand accumulation areas.

[0054] The deep learning model dynamically adjusts the vehicle's speed based on the density and distribution of sand: in areas with thicker sand, the vehicle slows down to ensure the sand-clearing device can effectively target the sand; in areas with thinner sand, the vehicle accelerates to improve cleaning efficiency. During the sand-clearing process, the autonomous vehicle monitors its effectiveness in real time. If sand residue is detected after clearing, the system automatically adjusts the path for secondary or multiple cleanups. Simultaneously, using historical data and environmental maps, the deep learning model can predict which areas are likely to experience sand accumulation, thus planning the sand-clearing path in advance.

[0055] In this embodiment, the positioning and sensing device 4 also includes a surface temperature sensor, a hygrometer, a soil moisture sensor, and a dust sensor for detecting suspended particles in the atmosphere. The above devices collect desert environmental data in real time, including temperature, humidity and air quality. All collected desert environmental data is transmitted to the remote control system in real time, and the desert environmental data is analyzed and maintenance strategies are formulated using machine learning algorithms.

[0056] Among them, the machine learning algorithm adopts the Long Short-Term Memory (LSTM) machine learning algorithm, which specifically uses the time points and various environmental conditions in the data features as input;

[0057] The core formula of LSTM is as follows:

[0058] Forgotten Gate: f t =σ(W f ·[h t-1 ,x t ]+b f )

[0059] Input gate: i t =σ(W i ·[h t-1 ,x t ]+b i )

[0060] Cell status update:

[0061] Final cell state:

[0062] Output gate: o t =σ(W o ·[h t-1 ,x t ]+b o )

[0063] Output value: h t =o t *tanh(C t )

[0064] Where σ is the sigmoid activation function (compresses the input value to an output between 0 and 1). tanh is the hyperbolic tangent function, W and b are the weights and bias parameters, and h... t The output at time step t, x t It is the input at time step t.

[0065] Enter x t In the application of autonomous vehicles, x t It is data on the environment and vehicle status at time step t, including: temperature, humidity, wind speed, sandstorm level, and vegetation coverage.

[0066] Hidden state h t-1 : Represents the hidden state of the LSTM unit at time step t-1. This is the model's internal memory, which helps the model understand what has happened so far. It is represented as a summary of the vehicle's operating state or environmental conditions in the previous time step.

[0067] In the application of autonomous vehicles, the hidden state h t-1 It represents all the key information accumulated by the autonomous vehicle system up to the previous point in time, including: vehicle operation data, environmental monitoring data (temperature, humidity, wind speed, etc.), as well as historical wind and sand activity intensity and vegetation cover.

[0068] The hidden state acts as a dynamic data storage device, comprehensively reflecting the autonomous vehicle's operational history and environmental conditions encountered over time. It provides the model with a reference framework based on historical data, thereby assisting the decision-making process and enabling the model to more effectively adapt to and respond to current and future environmental changes.

[0069] Forgotten Gate f t Input gate i t and output gate o t These gates control how LSTM cells update their state, determining what information the model should forget, what new information it should store, and what information it should output. They help the model decide which previous information (such as past sandstorm levels) should be retained and which new information (such as the latest environmental readings) should be added under given environmental changes.

[0070] The above information includes environmental data and vehicle data, specifically: Environmental data: temperature, humidity, wind speed, sandstorm intensity, vegetation coverage, and road accessibility; Vehicle data: vehicle speed, vehicle location, route selection, sand removal operation status, and material transportation status.

[0071] In the application of autonomous vehicles, the forget gate f t The decision-making process will determine whether environmental and vehicle data is no longer relevant and should be discarded. This includes environmental monitoring data from some time ago, such as old temperature and wind speed readings or outdated vehicle operation data, which will be discarded if they no longer have an impact on the current situation.

[0072] Input gate i t It is responsible for evaluating newly received data, real-time environmental monitoring data, and current vehicle status information, and determining whether to add new sandstorm level readings and current vehicle location information to the internal state of the LSTM unit.

[0073] Output gate o t By combining the current internal state of the model (including new information selected by the input gate and historical information retained by the forget gate), the operational decision output for the current time step is generated to execute decisions such as adjusting the autonomous vehicle's travel route, activating the sand removal device, or changing the material transportation plan.

[0074] Cell state C t and the final output h t Cellular state C t It is the core of the LSTM network, storing long-term information. Output h t It refers to the model's prediction or decision at the current time step. In autonomous vehicles, C... t Contains important information obtained from vehicle inspection, h tIt concerns the decision regarding the next steps, specifically whether the route needs to be adjusted to carry out the task.

[0075] In this embodiment, the modular connection interface 16 combines a quick-plug mechanism, electrical connection, and automatic locking mechanism. The quick-plug mechanism uses strong magnetic force to quickly and accurately attract the modular sand-removing device 3. The quick-plug mechanism integrates an intelligent electrical connection using spring-conductive magnetic material contacts. When the sand-removing device is attracted into place, the electrical interface automatically connects, completing the connection for power and data communication. After the magnetic connection is completed, the automatic locking mechanism is triggered. After receiving the connection signal, the control center 22 automatically activates the electric bolt, which quickly extends and firmly locks the sand-removing device.

[0076] Based on the above technical solutions, this embodiment also proposes a method for using modular unmanned vehicles to manage desert highways and their surrounding environment, including the following steps:

[0077] Once the unmanned vehicle is started, staff can remotely control the control center 22 of the vehicle body module 2 to deploy the unmanned vehicle cluster to carry out daily inspections and environmental maintenance on the desert highway; the central battery 13 on the moving chassis 1 provides power to drive the unmanned vehicle, and the sand-proof wheel sleeves 12 and sand-proof wheels 15 ensure the stable driving of the unmanned vehicle in the desert environment.

[0078] During the road cleaning process, the modular sand removal device 3 cleans the road surface through sand suction and sand discharge devices. When the unmanned vehicle encounters accumulated sand during its driving, the front sand suction device 31 is activated, and the central ventilation fan 33 circulates and sucks in sand particles. Then, the side sand discharge device 32 evenly discharges the sucked sand particles on the outside of the road to maintain the road's passability. This process is scheduled and managed by the control center 22.

[0079] In terms of material transportation, the vehicle module 2 has a material storage area 23 inside, which is used to store and transport materials. When the unmanned vehicle is deployed, the staff loads the materials to be transported into it. During the transportation process, the unmanned vehicle delivers the materials to each work station accurately according to the preset route and timetable.

[0080] In terms of road inspection, the unmanned vehicle relies on the algorithm recognition of camera 42 and the precise positioning of radar 41. If sand is detected on the road surface, the sand removal device will be activated immediately to remove the sand. In inspection mode, the unmanned vehicle is powered by solar energy. When the battery 13 is low on power, the unmanned vehicle will automatically return to the nearest garage station for charging.

[0081] In this embodiment, the deployment of the unmanned vehicle cluster includes:

[0082] Based on the length and environmental complexity of the desert highway, as well as the layout of the well houses, the area is divided into multiple management zones. Unmanned vehicles are deployed in each zone, and the number of unmanned vehicles is determined according to the size of the zone and maintenance needs. The control center 22 assigns daily inspection and environmental maintenance tasks to the corresponding unmanned vehicle clusters, including environmental maintenance tasks such as road surface and vegetation monitoring.

[0083] When an area is detected to require additional maintenance, the remote control system will adjust task allocation and coordinate work according to the nature and urgency of the task. Specifically, if a severe sand accumulation is found in an area, requiring multiple unmanned vehicles to work together to clear it, the remote control system will automatically dispatch other nearby unmanned vehicles to assist. When an unmanned vehicle malfunctions, the remote control system will immediately reassign the task to other unmanned vehicles to ensure that the maintenance work in the area is not affected and allow the malfunctioning vehicle to return to the nearest station for repair.

[0084] In this embodiment, during transportation, the unmanned vehicle accurately delivers materials to various work stations according to a preset route and schedule, including:

[0085] Before the material transportation mission begins, the remote control system formulates transportation routes and schedules based on the desert highway layout and the needs of each work station. The transportation routes and schedules take into account the shortest path, time, and the urgency of materials at each work station. The transportation routes and schedules are transmitted wirelessly to the navigation system of each unmanned vehicle to ensure that each unmanned vehicle is clear about its mission and destination.

[0086] The unmanned vehicles return to the base station and load the supplies onto designated unmanned vehicles. The load capacity and type of supplies for each vehicle are determined by the pre-planned task. During transportation, the location and status of the unmanned vehicles are fed back to the remote control system in real time. The remote control system monitors the progress of each vehicle. When the unmanned vehicles arrive at the designated work station, the staff at the station receive the supplies. After each transportation task is completed, the unmanned vehicle's travel time, energy consumption, and supply delivery status are recorded and fed back to the remote control system.

[0087] In this embodiment, the camera 42 uses a CNN image recognition algorithm to detect the desert road surface condition, vegetation cover condition, and sandstorm condition, including the following steps:

[0088] S1, preprocessing the collected images, including resizing, standardizing pixel values, and enhancing image features to suit the subsequent image analysis process;

[0089] S2, annotate the images, indicating the environmental condition represented by each image, including: Good: The road surface is clean, without sand or obstacles, and road signs are clearly visible; Moderate: Slight sand accumulation or minor damage, not affecting normal vehicle passage; Poor: Significant sand accumulation or damage, affecting vehicle passage; Very Poor: The road is largely covered or severely damaged, making it impassable.

[0090] S3 uses a convolutional neural network (CNN) model in deep learning for image recognition, automatically and effectively learning spatial hierarchical features in images. The AlexNet architecture is selected, and the CNN model is trained using a labeled image dataset.

[0091] S4. During training, the model will learn to recognize different desert environmental states and apply data augmentation techniques, including random rotation, scaling, and flipping of images, to increase the model's generalization ability; the model's performance will be evaluated using a test set to ensure that the model can accurately identify desert environmental states even on unknown data.

[0092] S5 deploys the trained model onto the autonomous vehicle.

[0093] In step S2 above, four dimensions are evaluated: road accessibility, vegetation coverage, wind and sand activity, and air quality. Specifically: Road accessibility: assess the cleanliness of the road surface, the amount of sand accumulation, and the overall integrity; Vegetation coverage: monitor the density and distribution of vegetation around the desert highway; Wind and sand activity: observe and record the intensity and frequency of wind and sand; Air quality: measure the concentration of dust and other pollutants in the air.

[0094] The above are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above content. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A modular unmanned vehicle for managing desert highways and their surrounding environment, comprising a battery, and powered by the battery for rear-wheel drive, characterized in that, It also includes a sports chassis, a body module, a modular sand removal device, and positioning and sensing equipment; the sports chassis is connected to the modular sand removal device through a modular connection interface, the body module is mounted and fixed on the sports chassis, and the positioning and sensing equipment is embedded in the shell of the body module; The main structure of the sports chassis is connected by a pipe frame, with suspension and spring shock absorption mechanisms at both ends. The suspension and spring shock absorption mechanisms are connected to sand-proof wheels, and the main structure is covered with sand-proof wheel covers. The vehicle body module has a control center and a material storage area. The control center is controlled by a remote control system that sends commands via wireless communication to control the operation of the unmanned vehicle. The modular sand-clearing device is installed under the sports chassis and consists of a front sand suction device, a side sand discharge device, and a central ventilation fan. The modular sand-clearing device is scheduled by the control center. The positioning and sensing equipment includes cameras, radar, GPS, and high-precision gyroscopes. The cameras are located at the front and rear ends of the vehicle body module to collect high-definition images to assist the radar in environmental perception. The radar is mounted at the front, sides, and rear of the vehicle body module to detect the driving environment.

2. The modular unmanned vehicle according to claim 1, characterized in that, The body module is equipped with a lighting module, including front lights, taillights and top lights. An anemometer and solar panels are installed on the top of the body module.

3. The modular unmanned vehicle according to claim 1, characterized in that, The control center includes a central processing unit, a data storage device, a communication module, a navigation and positioning system, and a sensor interface. The control center is responsible for receiving and processing data from the vehicle system. The unmanned vehicle sends its location, speed, and desert environment data to the remote control system in real time via the network. The remote control system continuously monitors the status and operating environment of the unmanned vehicle to ensure timely response.

4. The modular unmanned vehicle according to claim 1, characterized in that, The modular sand-removing device is scheduled by the control center, and its scheduling mechanism is as follows: The control center determines the sand accumulation on the road based on real-time data. When sand accumulation is detected, the control center instructs the front sand suction device to start suctioning the sand, and uses the central ventilation fan to assist in adjusting the movement of the sand particles, discharging the sand particles from the side sand discharge device to outside the road.

5. The modular unmanned vehicle according to claim 1, characterized in that, The positioning and sensing equipment also includes a surface temperature sensor, a hygrometer, a soil moisture sensor, and a dust sensor for detecting suspended particles in the atmosphere. The above devices collect desert environmental data in real time, including temperature, humidity, and air quality. All collected desert environmental data is transmitted to a remote control system in real time, and the desert environmental data is analyzed and maintenance strategies are formulated using machine learning algorithms.

6. The modular unmanned vehicle according to claim 1, characterized in that, The modular connection interface combines a quick-plug mechanism, electrical connection, and automatic locking mechanism. The quick-plug mechanism uses strong magnetic force to quickly and accurately attract the modular sand-removing device. The quick-plug mechanism integrates an intelligent electrical connection using spring-conductive magnetic material contacts. When the sand-removing device is attracted into place, the electrical interface automatically connects, completing the connection for power and data communication. After the magnetic connection is completed, the automatic locking mechanism is triggered. After the control center receives the connection signal, it automatically activates the electric bolt, which quickly extends and firmly locks the sand-removing device.

7. A method for managing a modular unmanned vehicle used in any one of claims 1 to 6 to manage desert highways and their surrounding environment, characterized in that, Includes the following steps: Once the unmanned vehicle is started, staff can remotely control the vehicle module's control center to deploy the unmanned vehicle cluster to carry out daily inspections and environmental maintenance on the desert highway; the central battery on the moving chassis powers the unmanned vehicle, while sand-proof wheel covers and wheels ensure stable driving in the desert environment. During the road cleaning process, the modular sand removal device cleans the road surface through sand suction and sand discharge devices. When the unmanned vehicle encounters accumulated sand while driving, the front sand suction device is activated, and the central ventilation fan circulates the sand particles. Then, the side sand discharge device evenly discharges the sand particles on the outside of the road to maintain the road's passability. This process is scheduled and managed by the control center. In terms of material transportation, the vehicle module has a material storage area inside for storing and transporting materials. When the unmanned vehicle is deployed, the staff loads the materials to be transported into it. During the transportation process, the unmanned vehicle delivers the materials to each work station accurately according to the preset route and timetable. In terms of road inspection, the unmanned vehicle relies on the algorithm recognition of cameras and the precise positioning of radar. If it detects sand accumulation on the road surface, the sand removal device will be activated immediately to remove the sand. In inspection mode, the unmanned vehicle is powered by solar energy. When the battery power is low, the unmanned vehicle will automatically return to the nearest garage station for charging.

8. The management method for modular unmanned vehicles according to claim 7, characterized in that, The deployment of the unmanned vehicle cluster includes: Based on the length and environmental complexity of the desert highway, as well as the layout of the well houses, the area is divided into multiple management zones. Unmanned vehicles are deployed in each zone, and the number of unmanned vehicles is determined according to the size of the zone and maintenance needs. The control center assigns daily inspection and environmental maintenance tasks to the corresponding unmanned vehicle clusters, including environmental maintenance tasks such as road surface and vegetation monitoring. When an area is detected to require additional maintenance, the remote control system will adjust task allocation and coordinate work according to the nature and urgency of the task. Specifically, if a severe sand accumulation is found in an area, requiring multiple unmanned vehicles to work together to clear it, the remote control system will automatically dispatch other nearby unmanned vehicles to assist. When an unmanned vehicle malfunctions, the remote control system will immediately reassign the task to other unmanned vehicles to ensure that the maintenance work in the area is not affected and allow the malfunctioning vehicle to return to the nearest station for repair.

9. The management method for modular unmanned vehicles according to claim 7, characterized in that, During transportation, the unmanned vehicles accurately deliver supplies to various work stations according to preset routes and schedules, including: Before the material transportation mission begins, the remote control system formulates transportation routes and schedules based on the desert highway layout and the needs of each work station. The transportation routes and schedules take into account the shortest path, time, and the urgency of materials at each work station. The transportation routes and schedules are transmitted wirelessly to the navigation system of each unmanned vehicle to ensure that each unmanned vehicle is clear about its mission and destination. The unmanned vehicles return to the base station and load the supplies onto designated unmanned vehicles. The load capacity and type of supplies for each vehicle are determined by the pre-planned task. During transportation, the location and status of the unmanned vehicles are fed back to the remote control system in real time. The remote control system monitors the progress of each vehicle. When the unmanned vehicles arrive at the designated work station, the staff at the station receive the supplies. After each transportation task is completed, the unmanned vehicle's travel time, energy consumption, and supply delivery status are recorded and fed back to the remote control system.

10. The management method for modular unmanned vehicles according to claim 7, characterized in that, The camera's recognition algorithm employs a CNN image recognition algorithm to detect desert road surface conditions, vegetation cover conditions, and sandstorm conditions, including the following steps: S1, preprocesses the collected images, including resizing, standardizing pixel values, and enhancing image features to suit the subsequent image analysis process; S2, annotate the images, indicating the environmental condition represented by each image, including: Good: The road surface is clean, without sand or obstacles, and road signs are clearly visible; Moderate: Slight sand accumulation or minor damage, not affecting normal vehicle passage; Poor: Significant sand accumulation or damage, affecting vehicle passage; Very Poor: The road is largely covered or severely damaged, making it impassable. S3 uses a convolutional neural network (CNN) model in deep learning for image recognition, automatically and effectively learning spatial hierarchical features in images. The AlexNet architecture is selected, and the CNN model is trained using a labeled image dataset. S4. During training, the model will learn to recognize different desert environmental states and apply data augmentation techniques, including random rotation, scaling, and flipping of images, to increase the model's generalization ability; the model's performance will be evaluated using a test set to ensure that the model can accurately identify desert environmental states even on unknown data. S5 deploys the trained model onto the autonomous vehicle.

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