An airborne device for detecting the distribution of aggregate steel slag

Abstract

The utility model provides a kind of airborne device for aggregate steel slag distribution detection, it includes the detection part for obtaining aggregate steel slag distribution data;For driving detection part movement unmanned aerial vehicle;Wherein: detection part includes the box body detachably connected with unmanned aerial vehicle, controller, communication system, task load cabin body and analysis storage system are equipped on box body;Task load cabin body includes the three-axis holder set in box body, multispectral camera and infrared range finder set in the bottom of box body, annular light source around multispectral camera and level installed in the side of box body.The utility model is combined by unmanned aerial vehicle platform+infrared ranging+multispectral image, can effectively distinguish the physical characteristic difference (such as texture, light reflectivity) of steel slag and aggregate, realize the stereoscopic rapid detection (non-contact type) of steel slag distribution, solve the problem that traditional manual sampling detection efficiency is low, coverage is narrow, efficiency is improved 5~10 times.

Description

Technical Field

[0001] This utility model belongs to the field of steel slag detection technology, and in particular relates to an airborne device for detecting the distribution of aggregate steel slag. Background Technology

[0002] In the construction aggregate production process, the inclusion of steel slag has a significant negative impact on the strength and durability of concrete. Due to its high iron content, steel slag particles are dark in color. Furthermore, the release of internal gases during the high-temperature cooling process results in a typically rough and porous surface. These physical differences provide a theoretical basis for vision- or spectral-based detection methods, but they still face many challenges in practical industrial testing. Currently, conventional detection methods mainly rely on manual sample collection followed by chemical analysis in the laboratory. This approach has many drawbacks, such as low efficiency, high cost, and limited coverage. In recent years, alternative solutions based on ground-based visual inspection equipment have gradually emerged. However, these devices exhibit problems such as fixed viewing angles and difficulty adapting to complex terrains. They also require manual deployment and cannot achieve dynamic real-time monitoring.

[0003] Most drone-based inspection devices on the market are concentrated in agriculture or geological exploration, exhibiting significant shortcomings in construction aggregate inspection applications. Firstly, image acquisition quality is poor; conventional cameras are easily affected by ambient light, performing poorly in distinguishing subtle color differences and surface texture features between aggregates and steel slag, and failing to effectively utilize differences in surface roughness, porosity, etc., for accurate classification. Secondly, system integration is low; sensors, communication, and computing units are designed separately, making it difficult to adapt to harsh field environments. Finally, real-time performance is difficult to guarantee; data transmission and processing are cumbersome, making it impossible to quickly generate immediate inspection reports. Utility Model Content

[0004] To address the problems existing in the prior art, this utility model aims to propose an airborne device for detecting the distribution of aggregate steel slag. This detection device solves the problems of insufficient accuracy and low efficiency in traditional detection methods, and achieves three-dimensional rapid detection through multi-system collaborative innovation.

[0005] To achieve the above objectives, the technical solution of this utility model is implemented as follows:

[0006] An airborne device for detecting aggregate slag distribution includes:

[0007] A detection unit used to acquire data on the distribution of aggregate and steel slag;

[0008] The drone used to drive the movement of the inspection unit; among which:

[0009] The detection unit includes a housing that is detachably connected to the UAV, and the housing is equipped with a controller, a communication system, a mission payload compartment, and an analysis and storage system.

[0010] The mission payload cabin includes a three-axis gimbal inside the cabin, a multispectral camera and an infrared rangefinder at the bottom of the cabin, a ring light source surrounding the multispectral camera, and a level installed on the side of the cabin. The three-axis gimbal, infrared rangefinder, and level are respectively connected to a controller, and the three-axis gimbal is connected to the multispectral camera.

[0011] Furthermore, the drone includes a drone body, with multiple arms symmetrically arranged on the upper part of the drone body, propellers installed at the ends of the arms, and landing gear installed in the middle of the arms.

[0012] Furthermore, the drone body includes a magnesium-aluminum alloy frame, which is covered with a Kevlar anti-collision layer with a thickness of 0.8mm.

[0013] Furthermore, the arm is equipped with wave-shaped reinforcing ribs inside.

[0014] Furthermore, the controller includes a receiver, a flight control module, a GPS positioning module, and an obstacle avoidance sensor. The receiver is connected to both a ground reference station and the flight control module, and transmits the commands sent by the ground reference station to the flight control module. The flight control module is connected to both the GPS positioning module and the obstacle avoidance sensor.

[0015] Furthermore, the drone also includes a power supply, an electronic speed controller, and a brushless motor. The power supply, electronic speed controller, brushless motor, and propeller are connected in sequence. The electronic speed controller is connected to the flight control module, and the brushless motor is installed at the end of the arm.

[0016] Furthermore, the communication system includes a data transmission module and an image transmission module, which are used to transmit image information acquired by the mission payload cabin to the analysis and storage system.

[0017] Furthermore, the multispectral camera is model MicaSense RedEdge-MX Dual, which includes an RGB channel and a near-infrared channel. The RGB channel is used to capture the surface texture features of the aggregate pile, and the near-infrared channel is used to detect the iron oxide reflection peak of the steel slag.

[0018] Furthermore, the edge computing module is an NVIDIA Jetson Xavier NX.

[0019] Compared with existing technologies, the airborne device for detecting aggregate steel slag distribution described in this utility model has the following advantages:

[0020] (1) The airborne device for detecting steel slag distribution in aggregates described in this utility model is used for rapid identification and monitoring of steel slag distribution in building aggregate stockpiles. It transmits images and data collected by the mission payload cabin to the analysis and storage system over long distances via a communication system. The analysis and storage system performs feature recognition analysis on the collected sample images to determine whether steel slag is present in the aggregates. The result output module generates image analysis results and analysis reports. The solid-state storage collects and saves the image analysis results and analysis reports generated by the result output module for future use. This detection device can not only effectively distinguish the differences in physical properties (such as texture and reflectivity) between steel slag and aggregates, but also achieve three-dimensional rapid detection of steel slag distribution (non-contact) through the combination of UAV platform + infrared ranging + multispectral images. This solves the problems of low efficiency and narrow coverage of traditional manual sampling detection, and improves efficiency by 5 to 10 times.

[0021] (2) This utility model uses a drone platform for rapid inspection, and a single flight can cover an area of ​​up to 5,000 square meters. By pre-setting a spiral or grid-shaped flight path and combining it with a GPS positioning module to achieve centimeter-level positioning, manual intervention is greatly reduced. The entire inspection process, including scanning, focusing detection and report generation, can be completed autonomously.

[0022] (3) This invention uses the RGB channel of a spectral camera to capture surface texture features, utilizes the near-infrared channel to detect the reflection peak of iron oxide in steel slag, and combines NDVI correction to eliminate vegetation interference. A dual-model collaborative analysis is employed, achieving a steel slag detection rate of up to 98.5%, while the false alarm rate is controlled within 2%. A ring light source provides dynamic illumination, ensuring clear imaging in low-light environments. Simultaneously, the three-axis gimbal is equipped with a brushless direct-drive motor and anti-shake algorithm, achieving a angular jitter suppression rate greater than 90%, effectively reducing image blur.

[0023] (4) The fuselage of this utility model adopts a combination of magnesium-aluminum alloy frame and Kevlar anti-collision layer, with an impact energy absorption rate of 72%. The overall protection level reaches IP65, which can adapt to harsh working conditions such as sandstorms and rain. The operating temperature range covers -20°C to 50°C, and the lithium battery has low range fluctuation in low temperature environment. The adjustable landing gear is equipped with polyurethane buffer pads to adapt to landing on rough terrain.

[0024] (5) This utility model is equipped with a Jetson Xavier NX edge computing module, and the processing time for a single frame image is less than 50 milliseconds, enabling the generation of a full-process report within 5 minutes. It can generate heat maps of steel slag distribution and three-dimensional aggregation models in real time. An infrared rangefinder monitors the flight altitude, and triggers flight control compensation when the fluctuation is ±0.2m to ensure the stability of data acquisition.

[0025] The high integration of sensors (such as multispectral cameras and infrared rangefinders), communication (data transmission / image transmission), and computing units avoids the reliability issues faced by discrete systems. Solid-state storage files are named according to "time + latitude and longitude," and raw data is retained for no less than 30 days, supporting historical data analysis and traceability.

[0026] Fully automated detection eliminates the need for manual equipment deployment or on-site sampling, effectively reducing labor costs. Millimeter-wave radar combined with binocular vision can detect obstacles within 30 meters and generate 3D avoidance paths, reducing the risk of collisions. Attached Figure Description

[0027] The accompanying drawings, which form part of this utility model, are used to provide a further understanding of the utility model. The illustrative embodiments of the utility model and their descriptions are used to explain the utility model and do not constitute an undue limitation of the utility model. In the drawings:

[0028] Figure 1 A schematic diagram of the UAV body structure provided in this embodiment of the utility model;

[0029] Figure 2 A bottom view of the UAV body provided for an embodiment of this utility model;

[0030] Figure 3 A schematic diagram of the power system provided for an embodiment of this utility model;

[0031] Figure 4 A schematic diagram of the mission payload compartment provided for an embodiment of this utility model;

[0032] Figure 5 A schematic diagram of the analysis and storage system provided in an embodiment of this utility model.

[0033] Explanation of reference numerals in the attached figures:

[0034] 1. Drone body; 2. Arm; 3. Landing gear; 4. Propeller; 5. Landing gear; 6. Multispectral camera; 7. Infrared rangefinder; 8. Ring light source; 9. Level; 10. Brushless motor; 11. Power switch; 12. Type-C charging port. Detailed Implementation

[0035] It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0036] In the description of this utility model, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this utility model and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this utility model. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this utility model, unless otherwise stated, "a plurality of" means two or more.

[0037] In the description of this utility model, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this utility model based on the specific circumstances.

[0038] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0039] like Figures 1 to 5 As shown, an airborne device for detecting aggregate slag distribution includes:

[0040] A detection unit used to acquire data on the distribution of aggregate and steel slag;

[0041] The drone used to drive the movement of the inspection unit; among which:

[0042] The detection unit includes a housing that is detachably connected to the UAV, and the housing is equipped with a controller, a communication system, a mission payload compartment, and an analysis and storage system.

[0043] The mission payload cabin includes a three-axis gimbal inside the cabin, a multispectral camera 6 and an infrared rangefinder 7 at the bottom of the cabin, a ring light source 8 surrounding the multispectral camera 6, and a level 9 installed on the side of the cabin. The three-axis gimbal, infrared rangefinder 7 and level 9 are respectively connected to the flight control module of the controller, and the three-axis gimbal is connected to the multispectral camera 6.

[0044] The analysis and storage system includes an edge computing module, an image feature recognition module, a result output module, and a solid-state storage unit, which are installed inside the housing and connected in sequence. The result output module is connected to the flight control module of the controller. The multispectral camera 6 is used to acquire multispectral images of the aggregate stockpile and is connected to the edge computing module through a communication system. The edge computing module and the image feature recognition module are used to identify and analyze the location of steel slag.

[0045] In a preferred embodiment of this utility model, the models of the components in the mission payload compartment are as follows:

[0046] The three-axis gimbal is the DJI Zenmuse X7. Its performance includes angular vibration suppression of ±0.005° and support for a 3kg payload. The gimbal uses brushless direct-drive motors and integrates IMU data with visual optical flow information to compensate for drone attitude jitter in real time, reducing image blur and achieving an angular jitter suppression rate of over 90%.

[0047] The MicaSense RedEdge-MX Dual multispectral camera (model 6) includes a visible light module (using a 1-inch BSI CMOS sensor, supporting simultaneous acquisition of RGB three channels), a near-infrared module, and an optical lens; imaging frame rate: 30fps in visible light mode, 10fps in multispectral mode; spectral channels: 475nm / 560nm / 668nm / 717nm / 842nm; storage temperature: -20°C to +65°C; the multispectral camera uses the RGB channel to capture surface texture features and the near-infrared channel to highlight the reflection peaks of iron oxide components in steel slag to complete the image acquisition of terrain and material samples.

[0048] Model 7 Infrared Rangefinder: Garmin LIDAR-Lite v3HP; Principle: 905nm VCSEL laser (Class 1 eye-safe); Accuracy: ±1cm@10m, sampling rate 100Hz. This infrared rangefinder employs phase-detection ranging, using a VCSEL laser emitter in conjunction with an APD avalanche photodiode to achieve high-precision ranging. Data is synchronized in real-time with the flight control module.

[0049] Ring Light Source Model 8: Neewer RL-300 RGBW (Custom High-Density Version); Brightness: Adjustable from 1000-2000 lux, Color Temperature: 3200K~5600K. The Ring Light Source 8 automatically turns on when the ambient light is low, and the projection density is adjustable.

[0050] The model of the level 9 is BOSCH GRL300HVK; its accuracy is ±0.2° (with built-in dual-axis compensation system).

[0051] In a preferred embodiment of this utility model, the operation flow of the analysis and storage system is as follows: after receiving image data, the edge computing module performs three-step processing: geometric correction, feature extraction, and result fusion; the result output module generates a heat map of steel slag distribution containing spatial coordinates (overlaid on the aerial base map of the UAV body 1), and marks the equivalent diameter, azimuth, and height from the detection surface of a single steel slag in the report; the solid-state storage files are named according to "time + latitude and longitude", and the original data retention period is ≥30 days.

[0052] Specifically, the model numbers of the components in the storage system are analyzed as follows:

[0053] Edge computing module model: NVIDIA Jetson Xavier NX 16GB; performance: 21 TOPS computing power, 15 WTDP. The edge computing module is equipped with Jetson Xavier NX, achieving ultra-low time consumption for single-frame image processing.

[0054] The image feature recognition module uses ResNet-18 and MobileNetV3 network models; performance: steel slag recognition accuracy ≥98.5%. The image feature recognition module can identify steel slag by utilizing the differences in color, surface roughness, etc. between steel slag particles and fine aggregates.

[0055] The results output module is used to generate a thermal map of the steel slag location and a statistical report on the dosage (including quantity, percentage, and confidence level).

[0056] Solid-state storage model: Samsung 870 EVO 1TB (industrial grade); Performance: Data retention period > 10,000 hours. The solid-state storage is used to collect and store the image analysis results and reports generated by the output module for later use.

[0057] In a preferred embodiment of this utility model, the UAV body 1 includes a magnesium-aluminum alloy frame and a Kevlar anti-collision layer covering the magnesium-aluminum alloy frame. The magnesium-aluminum alloy frame is made of A6061-T6 alloy, and the magnesium-aluminum alloy profiles form a honeycomb load-bearing frame to effectively disperse impact loads. The Kevlar anti-collision layer is made of DuPont™ Kevlar® 29 material. The magnesium-aluminum alloy frame is covered with a 0.8mm Kevlar anti-collision layer and fixed by epoxy resin bonding. Friction coefficient tests show that this structure can absorb up to 72% of the fall impact energy.

[0058] The drone body 1 weighs ≤4.2kg and has a maximum payload of 2.8kg. The housing is equipped with a power switch 11 and a Type-C charging port 12.

[0059] When the arm 2 is extended, the wheelbase is 680mm, and when folded, the wheelbase is 320mm. The arm 2 has a corrugated reinforcing rib structure inside, and a propeller mounting base is provided at the end of the arm 2. The propeller mounting base and the arm 2 are integrally molded, and the joint has a rounded corner radius to effectively avoid stress cracking.

[0060] Landing Gear 3 is an enhanced adaptive landing gear version of the DJI Matrice 300 RTK UAV. The landing gear features a two-stage telescopic strut and polyurethane buffer pads (Shore A hardness 70±5). The polyurethane buffer pads have a Shore A hardness between 65 and 75, which significantly reduces landing impact and adapts to landing on rough terrain. Landing Gear 3 has an altitude-adaptive function, with an adjustable deployment height of 250–400 mm, and includes built-in pressure sensors for landing status monitoring.

[0061] In a preferred embodiment of this utility model, the controller includes a receiver, a flight control module, a GPS positioning module, and an obstacle avoidance sensor. The receiver is connected to both a ground reference station and the flight control module, and transmits the commands sent by the ground reference station to the flight control module. The flight control module is connected to both the GPS positioning module and the obstacle avoidance sensor.

[0062] Specifically, the flight control module is a Holybro Pixhawk 6C (customized PX4 firmware). It integrates data from a six-axis gyroscope and barometer to achieve flight attitude control. The six-axis gyroscope is a TDK InvenSense ICM-42688-P six-axis motion tracking IMU, and the barometer is an MS5611. The flight control module has a preset automatic inspection path mode, supporting both spiral and grid scanning trajectories with extremely high trajectory planning accuracy.

[0063] GPS positioning module model: Here+ RTK GNSS (dual frequency L1 / L2), accuracy: horizontal positioning ±1cm (with D-RTK2 ground station); The GPS positioning module adopts dual frequency reception, with a short cold start time. It can achieve centimeter-level positioning with ground reference station, eliminate ionospheric interference, reduce horizontal positioning error, and facilitate accurate path planning.

[0064] Obstacle avoidance sensor model: Texas Instruments AWR1642 (77GHz), performance: Intel RealSense D435i (TOF depth sensing). The obstacle avoidance sensor uses millimeter-wave radar combined with binocular vision, which can identify obstacles with a wire diameter of more than 5mm in low light conditions, detect obstacles within 30m, and generate a 3D avoidance path.

[0065] In a preferred embodiment of the present invention, the UAV further includes a power system, which includes a power supply, an electronic speed controller, and a brushless motor 10. The power supply, electronic speed controller, brushless motor 10, and propeller 4 are connected in sequence. The electronic speed controller is connected to the flight control module, and the brushless motor 10 is installed at the end of the arm 2.

[0066] Specifically, the power source is a high-density lithium battery pack, model: Tattu 48V 16000mAh quaternary lithium battery, with integrated intelligent BMS (overvoltage / undervoltage protection), continuous flight time ≥45 minutes, and low fluctuation rate of flight time in ambient temperature range of -10℃ to 50℃.

[0067] The model of the brushless motor 10 is T-Motor MN4010 400KV. The brushless motor 10 is equipped with ceramic bearings and active heat dissipation fins.

[0068] The model of the electronic speed controller is Hobbywing FlyFun V5 60A. The electronic speed controller has a waterproof housing, a built-in adaptive PID algorithm, a response frequency of 200Hz, and can maintain positioning accuracy under ±5° attitude disturbance.

[0069] Propeller Model 4: T-Motor CF9563 three-bladed folding propeller, made of carbon fiber, with an asymmetrical airfoil design, connected to the brushless motor 10 shaft via a quick-release structure, achieving high cruising speeds even at higher speeds.

[0070] The power supply is provided by the electronic speed controller, which receives DC power from the high-density lithium battery and converts it into three-phase AC power output. The brushless motor 10 receives the three-phase AC power output from the electronic speed controller and converts electrical energy into mechanical energy to drive the propeller 4 to rotate, enabling the UAV body 1 to take off, hover, cruise, and maneuver.

[0071] The electronic speed controller is used to receive the PWM control signal from the control module, adjust the speed of the brushless motor 10 according to the PWM control signal, ensure the torque balance of the multi-rotor system, maintain flight attitude stability, and reduce hovering stability error; and send real-time telemetry feedback information to the flight control module, so that the flight control module can dynamically adjust the control of the brushless motor 10 according to the flight status and needs of the UAV.

[0072] In a preferred embodiment of this utility model, the communication system includes a data transmission module and an image transmission module, which are used to transmit image information acquired by the mission payload cabin to the analysis and storage system.

[0073] Data transmission module model: Holybro SiK Radio V3, parameters: 2.4GHz frequency band, 10km transmission distance, achieving high transmission distance.

[0074] Image transmission module model: DJI O3 Air Unit Pro; parameters: 1080P@60fps, end-to-end latency <50ms. The image transmission module adopts 1080P@60fps real-time transmission to achieve low latency and establish a remote transmission channel for measurement data.

[0075] Example 1: Conventional detection mode of airborne device for aggregate and steel slag distribution detection

[0076] This embodiment targets a conventional aggregate stockpile scenario, employing an autonomous inspection mode to achieve three-dimensional detection of steel slag distribution. The specific steps are as follows:

[0077] S1: Takeoff Preparation

[0078] Adjust the landing gear height to 250mm (triggering pressure sensor feedback) to ensure the UAV body 1 adapts to the aggregate stockpile terrain. The ground base station sends inspection commands to the flight control module via the data transmission module, and the flight control module loads the preset grid scanning path (covering the stockpile boundary coordinates). The GPS positioning module completes centimeter-level positioning within 10 seconds after cold start, integrates point cloud data from obstacle avoidance sensors, and the flight control module generates a safe flight path.

[0079] S2: Data Acquisition

[0080] Specifically, the drone body 1 hovers at a height of 50m and initiates wide-area scanning mode: the multispectral camera 6 captures the surface texture of the aggregate stockpile at a frame rate of 30fps using RGB channels, while the infrared rangefinder 7 generates a digital elevation model at a sampling frequency of 100Hz. The edge computing module performs pre-screening in real time based on the data from the multispectral camera 6 and the infrared rangefinder 7: it identifies areas with abnormal reflectivity using the YOLOv5s model and marks the latitude and longitude coordinates of potential steel slag.

[0081] Fine-grained detection is initiated on the marked area: the drone descends to 10m, the three-axis gimbal activates image stabilization compensation mode, the multispectral camera 6 switches to four-channel mode (RGB channel + near-infrared channel), and the ring light source 8 automatically increases the projection density to 1200 lux. At the same time, the infrared rangefinder 6 dynamically monitors the relative altitude, triggering flight control altitude compensation when fluctuations exceed ±0.2m.

[0082] S3: Data Processing

[0083] The edge computing module and image feature recognition module perform a three-step analysis: geometric correction: establish a spatial mapping relationship based on GPS positioning module coordinates and point cloud data to remove image distortion; feature extraction: use an improved ResNet-18 model to analyze multispectral data and generate feature vectors through the reflectivity differences of steel slag iron oxide characteristic wavelengths (650-850nm); result fusion: combine visible light and near-infrared data and eliminate dust occlusion interference through Kalman filtering.

[0084] Step 4: Output Results

[0085] The output module generates a heat map of steel slag distribution (with confidence levels indicated by red, yellow, and green colors), overlaid on the aerial image taken by the UAV. It also generates a statistical report, including: total area of ​​the detected region; number of steel slag particles; steel slag percentage; and aggregation index. The solid-state storage automatically saves the raw data and analysis results to files named with "time + latitude and longitude".

[0086] In S2, the working logic of the multispectral camera 6 is as follows: during the global scanning stage, the RGB channel is used to acquire a large-scale image, and the potential steel slag area is pre-screened through the edge computing module; for the pre-selected area, the multispectral camera 6 switches to multispectral mode and simultaneously collects three band data: steel slag characteristic band, aggregate absorption valley, and background noise suppression; the multispectral image is NDVI corrected to eliminate the interference of vegetation on the detection.

[0087] Example 2: Emergency Detection Mode of Airborne Device for Aggregate and Steel Slag Distribution Detection

[0088] This embodiment is applicable to emergency scenarios requiring rapid verification of the steel slag diffusion range, and enables a high-sensitivity detection strategy:

[0089] S1: Quick Start

[0090] The drone body 1 automatically calibrates to the emergency detection coordinates via a one-click return mode, and the flight control module switches to a spiral scanning path (density increased by 30%).

[0091] S2: Dynamic Altitude Data Acquisition

[0092] The three-axis gimbal activates super-resolution mode (multispectral camera 6 switches to 10x digital zoom); the ring light source 8 is forced to operate at full power, with a projection density of 2000 lux; the infrared rangefinder 7 is linked with the flight control module to achieve dynamic altitude adjustment (5m±0.1m hovering accuracy).

[0093] S3: Real-time Edge Analysis

[0094] The image feature recognition module uses a simplified model (processing time reduced to 50ms / frame); the steel slag discrimination threshold is lowered to 45% confidence level, and the three-dimensional coordinates of potentially contaminated areas are output first.

[0095] S4: Emergency Report Generation

[0096] The output module generates a simplified heat map (showing only high-risk areas) and automatically triggers an alarm signal to the ground reference station; the statistical report highlights key parameters such as the steel slag migration rate and the elevation difference of the maximum accumulation area.

[0097] The above description is only a preferred embodiment of the present utility model and is not intended to limit the present utility model. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present utility model should be included within the protection scope of the present utility model.

Claims

1. An airborne device for detecting the distribution of aggregate steel slag, characterized in that, include: A detection unit used to acquire data on the distribution of aggregate and steel slag; The drone used to drive the movement of the inspection unit; among which: The detection unit includes a housing that is detachably connected to the UAV, and the housing is equipped with a controller, a communication system, a mission payload compartment, and an analysis and storage system. The mission payload cabin includes a three-axis gimbal inside the cabin, a multispectral camera and an infrared rangefinder at the bottom of the cabin, a ring light source surrounding the multispectral camera, and a level installed on the side of the cabin. The three-axis gimbal, infrared rangefinder, and level are respectively connected to a controller, and the three-axis gimbal is connected to the multispectral camera.

2. The airborne device for detecting aggregate steel slag distribution according to claim 1, characterized in that: The drone includes a drone body, with multiple arms symmetrically arranged on the upper part of the drone body. Propellers are installed at the ends of the arms, and landing gear is installed in the middle of the arms.

3. The airborne device for detecting aggregate slag distribution according to claim 2, characterized in that: The drone body includes a magnesium-aluminum alloy frame, which is covered with a Kevlar anti-collision layer with a thickness of 0.8mm.

4. The airborne device for detecting aggregate steel slag distribution according to claim 2, characterized in that: The arm is equipped with wave-shaped reinforcing ribs.

5. The airborne device for detecting aggregate slag distribution according to claim 1, characterized in that: The controller includes a receiver, a flight control module, a GPS positioning module, and an obstacle avoidance sensor. The receiver is connected to both a ground base station and the flight control module, and transmits the commands sent by the ground base station to the flight control module. The flight control module is connected to both the GPS positioning module and the obstacle avoidance sensor.

6. The airborne device for detecting aggregate steel slag distribution according to claim 5, characterized in that: The drone also includes a power supply, an electronic speed controller, and a brushless motor. The power supply, electronic speed controller, brushless motor, and propeller are connected in sequence. The electronic speed controller is connected to the flight control module, and the brushless motor is installed at the end of the drone arm.

7. The airborne device for detecting aggregate steel slag distribution according to claim 1, characterized in that: The communication system includes a data transmission module and an image transmission module, which are used to transmit image information acquired by the mission payload cabin to the analysis and storage system.

8. The airborne device for detecting aggregate slag distribution according to claim 1, characterized in that: The multispectral camera is a MicaSense RedEdge-MX Dual, which includes an RGB channel and a near-infrared channel. The RGB channel is used to capture the surface texture features of the aggregate pile, and the near-infrared channel is used to detect the iron oxide reflection peak of the steel slag.

9. The airborne device for detecting aggregate slag distribution according to claim 1, characterized in that: The analysis and storage system includes an edge computing module located inside the enclosure, the edge computing module being an NVIDIA Jetson Xavier NX.

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