Air film building snow removal method, intelligent control terminal and intelligent snow removal control system

Abstract

The embodiment of the invention relates to the technical field of air film building snow removal, in particular to an air film building snow removal method. The intelligent snow removal system corresponding to the method comprises an air film building simulation system, an intelligent route planning system and an intelligent snow removal control system. According to the air film building simulation system, an unmanned aerial vehicle unit carries scanning equipment to complete establishment of an air film model database, and the snow accumulation state is determined through model comparison. The intelligent route planning system plans a reasonable flight route of the unmanned aerial vehicle by means of the positioning system according to the snow accumulation state and climate conditions. The intelligent snow removal control system comprises an intelligent control terminal and an unmanned aerial vehicle unit carrying a snow removal device, and a scheme matched with the air film state information can be selected from multiple snow removal schemes and implemented. According to the embodiment of the invention, manual high-altitude operation can be replaced by snow removal through the unmanned aerial vehicle, intelligence and automation of snow removal operation of the air film building are realized through intelligent route planning and snow removal strategy optimization, the snow removal efficiency of the air film building is improved, and the high-altitude operation risk is reduced.

Description

Technical Field

[0001] This application relates to the field of snow removal technology for air-supported membrane structures, and in particular to a snow removal method for air-supported membrane structures, an intelligent control terminal, and an intelligent snow removal control system. Background Technology

[0002] Air-supported membrane structures are widely used in sports stadiums, industrial warehouses, and other fields due to their advantages such as large-span column-free spaces, rapid construction, and energy conservation and environmental protection. However, in cold and snowy regions, snow accumulation on the membrane roof seriously threatens the safety and operation of the building. Snow loads may cause the membrane surface to tear or the structure to become unstable. Traditional hot air snow melting solutions are energy-intensive; manual snow removal requires high-altitude operations, which pose safety risks and are inefficient, especially for large-span air-supported membrane structures. Summary of the Invention

[0003] One objective of this application is to provide a snow removal method, intelligent control terminal, and intelligent snow removal control system for air-supported membrane structures, in order to solve the technical problem of low snow removal efficiency of air-supported membrane structures in the prior art.

[0004] In a first aspect, embodiments of this application provide a snow removal method using an air-supported membrane structure, comprising: The scanning information of the drone unit is obtained. The scanning information is the information collected by the drone unit when scanning the top of the air-supported membrane structure. The drone unit is equipped with a variety of snow removal devices. The air-supported membrane state information is determined based on the scanning information, and the air-supported membrane state information is used to indicate the state of the snow accumulation and membrane surface on the top of the air-supported membrane structure; Among a variety of historical snow removal schemes, the historical snow removal scheme that matches the air film state information is selected as the target snow removal scheme, and the target snow removal scheme includes the target planned route and the target snow removal strategy; Among the various snow removal devices, the snow removal device that matches the target snow removal strategy is selected as the target snow removal device; Control commands are sent to the drone unit to make it fly along the target planned route and use the target snow removal device to remove the snow from the air-supported structure according to the target snow removal plan.

[0005] Optionally, the scanning information includes first scanning data and second scanning data, and determining the air film state information based on the scanning information includes: Acquire the first scan data of the UAV unit; A target three-dimensional model of the air-supported membrane structure is constructed based on the first scan data; Acquire information about the air-supported membrane structure, including a standard three-dimensional model of the air-supported membrane structure in a snowless state; The target 3D model is compared with the standard 3D model to determine the snow accumulation area and membrane collapse information; Second scan data is obtained based on the snow-covered area; Snow description information is determined based on the second scan data, including snow thickness and / or snow area and / or snow area shape and / or icing state. The air film state information is determined by combining the membrane collapse information and the snow accumulation description information.

[0006] Optionally, the air-supported membrane structure information also includes the area, height, and geographical location of the air-supported membrane structure, and the step of selecting a historical snow removal scheme that matches the air-supported membrane structure status information as the target snow removal scheme from multiple historical snow removal schemes includes: Obtain a snow removal plan database, which includes various historical snow removal plans; The system checks the snow removal plan database to see if there are any historical snow removal plans that match the area, height, geographical location, and status information of the air-supported structure. If it exists, then the historical snow removal scheme that matches the air film state information is determined as the target snow removal scheme; If it does not exist, the target snow removal plan will be regenerated based on the air film state information.

[0007] Optionally, the air-film state information includes film surface collapse information and snow accumulation description information, and the step of regenerating the target snow removal plan based on the air-film state information includes: Obtain first climate information and similar historical snow removal schemes. The first climate information includes the sunlight and / or wind force and / or air pressure and / or temperature and / or humidity information of the geographical location of the air-supported membrane structure during the snow removal operation. The similar historical snow removal schemes are historical snow removal schemes in the snow removal scheme database that have a similarity to the membrane area, height, geographical location and the air-supported membrane state information of the air-supported membrane structure that is greater than a second preset threshold and less than a first preset threshold. The first preset threshold is greater than the second preset threshold. A simulation model is generated based on the snow accumulation description information, the membrane collapse information, the first climate information, and the air-supported membrane structure information. A target snow removal plan is generated based on the simulation model and the similar historical snow removal plans.

[0008] Optionally, the drone unit includes multiple snow removal drones, each equipped with different snow removal devices. The target snow removal strategy includes at least one snow removal step information. Selecting the snow removal device matching the target snow removal strategy from among the multiple snow removal devices as the target snow removal device includes: The target snow removal strategy is analyzed to obtain information on at least one snow removal step. Among the various snow removal devices, the snow removal device that matches the snow removal step information is selected as the target snow removal device; Among them, all target snow removal devices are combined according to the execution order of all snow removal step information to obtain the snow removal operation sequence; Sending control commands to the drone unit to cause the drone unit to use the target snow removal device to remove snow from the air-supported structure according to the target snow removal plan includes: Control commands are sent to the drone unit so that the drone unit sequentially controls different target snow removal devices to remove snow from the air-supported structure in accordance with the snow removal operation sequence.

[0009] Alternatively, snow removal methods using air-supported membrane structures also include: The system acquires real-time distance and first air-film model information. The real-time distance includes the distance between the drone wing of the drone unit and the membrane surface. The real-time distance is collected by the distance sensor mounted on the drone unit. The first air-film model information includes a first air-film model constructed based on the real-time scanning information of the drone unit. Compare the real-time distance with the safe distance threshold; In response to the real-time distance being less than the safe distance threshold, the system combines the first air-supported membrane model and the target 3D model to determine whether the air-supported membrane structure has experienced membrane surface rebound. In response to the membrane rebound of the air-supported structure, a new target planning route is generated, which includes the flight speed, flight altitude, flight path of the UAV unit and the angle between the snow removal device and the membrane surface; Generate adjustment instructions, which include flight parameters for the new target planned route; An adjustment command is sent to the drone unit so that the drone unit flies along the planned route of the new target and uses the target snow removal device to clear the snow from the air-supported structure.

[0010] Optionally, the step of determining whether the air-supported structure has experienced surface rebound by combining the first air-supported membrane model and the target three-dimensional model includes: Calculate the displacement from the first spatial coordinate to the second spatial coordinate, where the first spatial coordinate is the spatial coordinate of the deepest membrane collapse in the snow area of ​​the target three-dimensional model, and the second spatial coordinate is the spatial coordinate in the first air-supported membrane model corresponding to the first spatial coordinate; The displacement is compared with a preset rebound threshold. If the displacement is greater than the preset rebound threshold, it is determined that the air-supported membrane structure has experienced membrane surface rebound. If the displacement is less than or equal to the preset rebound threshold, it is determined that the air-supported membrane structure has not experienced membrane surface rebound.

[0011] Optionally, in response to the membrane surface rebound of the air-supported structure, a new target planned flight path is generated, including: Acquire second air-supported membrane model information and second climate information. The second air-supported membrane model information includes a second air-supported membrane model constructed based on scanning information collected after the membrane surface of the air-supported membrane structure rebounds. The second climate information is the light and / or wind and / or air pressure and / or temperature and / or humidity information of the geographical location of the air-supported membrane structure collected after the membrane surface of the air-supported membrane structure rebounds. Obtain an air-supported membrane model database, which includes morphological information of air-supported membrane structures during multiple historical snow removal operations. The morphological information of the air-supported membrane structures includes the area, curvature, shape, and location of multiple curved surfaces of the air-supported membrane structures. Based on the second air film model information, the target morphology information is obtained by matching it in the air film state database. A new target planning route is generated by combining the target morphology information and the second climate information.

[0012] Secondly, embodiments of this application provide an intelligent control terminal, including a memory and a processor. The memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the intelligent control terminal implements the method described above.

[0013] Thirdly, embodiments of this application provide an intelligent snow removal control system, comprising: The drone unit is equipped with a multimodal sensor and a snow removal device. The multimodal sensor is configured to collect environmental information around the drone unit, and the snow removal device is configured to perform snow removal operations on the air-supported membrane structure. The intelligent control terminal described above is communicatively connected to the UAV unit and is configured to integrate the environmental information, pre-collected navigation and positioning data, and three-dimensional simulation information about the air-supported membrane structure to construct a snow removal solution library. The snow removal solution library includes multiple optimal flight routes, multiple snow removal solutions, and multiple air-supported membrane models.

[0014] The embodiments of this application can achieve the following technical effects: In the snow removal method for air-supported membrane structures provided in this application embodiment, scanning information of a drone unit is obtained, and the drone unit is equipped with various snow removal devices. Air-supported membrane status information is determined based on the scanning information. A historical snow removal scheme matching the air-supported membrane status information is selected from multiple historical snow removal schemes as the target snow removal scheme, which includes a target planned flight path and a target snow removal strategy. A snow removal device matching the target snow removal strategy is selected from multiple snow removal devices as the target snow removal device. Control commands are sent to the drone unit to make it fly according to the target planned flight path and use the target snow removal device to remove snow from the air-supported membrane structure according to the target snow removal scheme. The embodiments of this application can obtain air-supported membrane status information through the drone unit, intelligently match snow removal schemes and snow removal devices, and perform snow removal, realizing intelligent and automated snow removal operations for air-supported membrane structures, improving snow removal efficiency, and enabling real-time detection of the distance between the drone unit and the air-supported membrane structure and determination of whether membrane surface rebound occurs, reducing the possibility of collision between the air-supported membrane structure and the drone, and ensuring the safety of the air-supported membrane structure and the drone. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the architecture of an intelligent snow removal control system provided in an embodiment of this application; Figure 2 A schematic flowchart of a snow removal method using an air-supported membrane structure provided in an embodiment of this application; Figure 3 A schematic diagram of a simulation model of an air-supported membrane structure provided in an embodiment of this application; Figure 4 A schematic diagram illustrating the snow removal principle of an air-supported membrane structure provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a snow removal device for an air-supported membrane structure provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an intelligent control terminal provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0018] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0019] Air-supported membrane structures are characterized by using internal air pressure to support the membrane material, forming a stable architectural space. The most common shapes include: dome-shaped, resembling a giant dome or hemisphere; arched / ellipsoidal, similar to a tunnel or half an ellipsoid, with a cross-section typically semi-circular or elliptical, extending along its length; semi-cylindrical, which can be seen as a special case of the arch, with a standard semi-circular cross-section; and also composite / complex curved surfaces, forming irregular architectural shapes.

[0020] Due to the large span of air-supported membrane structures, in areas prone to snowfall, winter snowfall accumulates at approximately one-third to one-half of the top surface. Factors such as wind, sunlight, and the structure's shape (e.g., arched tops are prone to snow accumulation, while sides are prone to slippage) result in highly uneven snow distribution on the membrane surface. Localized areas (such as leeward sides or low-lying areas) may accumulate very thick snow, generating concentrated loads far exceeding design values. If the actual snow load exceeds the design snow load of the air-supported membrane structure, or if the snow accumulation is excessively thick in some areas, it may cause the membrane material to tear, affecting overall stability, or even leading to structural displacement or collapse. To prevent serious accidents, it is essential to promptly remove snow from the top of the air-supported membrane structure.

[0021] This application provides a snow removal method for air-supported membrane structures. The corresponding intelligent snow removal system includes an air-supported membrane structure simulation system, an intelligent route planning system, and an intelligent snow removal control system. The air-supported membrane structure simulation system uses a drone unit equipped with scanning equipment to establish an air-supported membrane model database and determines the snow accumulation state through model comparison. The intelligent route planning system plans a reasonable flight route for the drone based on the snow accumulation state and weather conditions, using a positioning system. The intelligent snow removal control system includes an intelligent control terminal and a drone unit equipped with snow removal devices, capable of selecting and implementing a snow removal scheme that matches the air-supported membrane state information from various options. This application embodiment can replace manual high-altitude operations with drone snow removal. Through intelligent route planning and snow removal strategy optimization, it achieves intelligent and automated snow removal operations for air-supported membrane structures, improving snow removal efficiency and reducing the risks of high-altitude operations.

[0022] Please see Figure 1 , Figure 1 This is a schematic diagram of the architecture of an intelligent snow removal control system provided in an embodiment of this application. The intelligent snow removal control system 100 includes a drone unit 101 and an intelligent control terminal 102. The drone unit 101 and the intelligent control terminal 102 are communicatively connected.

[0023] The drone unit 101 includes a scanning drone and various snow removal drones. The scanning drone is a drone equipped with a scanning device. The scanning device is equipped with a laser scanner and millimeter-wave radar. The scanning drone scans the top of the air-supported structure using the laser scanner, enabling the intelligent control terminal 102 to construct a 3D model of the air-supported structure based on the laser point cloud data. By comparing this model with a standard 3D model of the air-supported structure in a snow-free state, snow-covered areas are detected. The millimeter-wave radar scans the snow-covered areas, allowing the intelligent control terminal 102 to analyze the radar echo signals and assess the snow accumulation in the snow-covered areas.

[0024] A snow removal drone is a drone equipped with a snow removal device. The snow removal device consists of a mounting assembly, a support assembly, and a cleaning assembly. The mounting assembly connects the snow removal device to the drone, and the cleaning assembly connects to the support assembly. The cleaning assembly includes a drive assembly and a blade assembly; the drive assembly rotates the blade assembly. The blades can be shaped, but are not limited to, spoon-shaped, flat, spiral, scoop-shaped, or have several bristles. The support assembly has an adjustment device for adjusting the angle between the cleaning assembly and the tangent of the air-supported membrane structure. The adjustment angle is set within the range of 0-15° to allow the cleaning assembly to remove snow without damaging the membrane structure. The blades of the snow removal device on the first snow removal drone are designed as lightweight brushes, which can directly sweep the powder snow layer by layer from the top of the air-supported membrane structure, allowing the powder snow to gradually approach the top edge of the structure or slide directly down the slope of the structure.

[0025] The cleaning component of the snow removal device carried by the second snow removal drone is set as a rigid roller. The rigid roller has a cylindrical rigid structure with anti-slip or propulsion texture on the surface to enhance the snow pushing effect. It can push the snow outward layer by layer until the snow is thrown off the top of the building. The cleaning component is equipped with a baffle or guide plate to prevent snow from being thrown onto the wings of the second snow removal drone and causing a safety accident.

[0026] The third snow removal drone is equipped with a cleaning component that features an oscillating ice-breaking function. This component can use mechanical or ultrasonic oscillation to break the ice layer on the snow surface into small, cleanable fragments. For example, the cleaning component includes a drive assembly and a fan blade assembly. The fan blade assembly is configured as a rigid rubber plate, with a rigid spherical hammer clamped on it. The drive assembly rotates the rigid rubber plate and the hammer to break the ice layer in the snow-covered area.

[0027] The fourth snow removal drone is equipped with a cleaning component that can spray environmentally friendly de-icing agent, which quickly melts the snow and allows it to slide down the building's slope.

[0028] The fifth snow removal drone is equipped with a snow removal device featuring a cleaning component. This component includes a bucket-shaped snowplow body, a blade, and a cutting structure. The snowplow body is made of lightweight, high-strength composite materials or aluminum alloy, with its leading edge serving as the blade for cutting into and lifting snow. The front or bottom of the snowplow body has a cutting structure, such as a set of high-speed rotating cutting discs or other mechanical cutting devices that can effectively divide and compact snow. For areas with thick snow accumulation, such as those exceeding 1 meter in thickness, the fifth snow removal drone can first use the cutting structure to cut the snow into sections, then adjust the cutting angle of the blade to shovel the snow into the bucket of the snowplow body, and fly to a designated open area or snow collection point to drop it off.

[0029] The sixth snow removal drone is equipped with a snow removal device featuring a cleaning component that blows snow. This component generates a directional airflow through its built-in power unit, applying high pressure and stable wind force to the snow accumulation on the surface of the air-supported membrane structure. The airflow effectively breaks the adhesion between the snow and the air-supported membrane surface, while simultaneously blowing the snow away from the structure, causing it to detach from the main body of the structure.

[0030] In other embodiments, the drone unit is a drone equipped with a scanning device and various snow removal devices. The scanning device can scan the top of the air-supported structure to obtain scanning information. Various snow removal devices perform different snow removal operations to complete the snow removal work on the air-supported structure.

[0031] In other embodiments, the drone unit is equipped with a multimodal sensor and a snow removal device. The multimodal sensor is configured to collect environmental information around the drone unit, such as location information, obstacle information, and climate information. The snow removal device is configured to perform snow removal operations on the air-supported structure.

[0032] The form of the intelligent control terminal 102 shown in the figure is only illustrative and does not constitute a unique limitation on its form. The form of the intelligent control terminal 102 may also include handheld devices, such as mobile phones, tablets, and drone remote controllers. The intelligent control terminal 102 establishes a data interaction link with the drone unit through a wireless communication module, thereby driving the drone unit to achieve automated snow removal operations. The intelligent control terminal integrates environmental information, pre-collected navigation and positioning data, and three-dimensional simulation information about the air-supported membrane structure to construct a snow removal solution library. The snow removal solution library includes multiple optimal flight routes, multiple snow removal solutions, and multiple air-supported membrane models. Specifically, the intelligent control terminal can intelligently match the target snow removal solution and the target snow removal device based on the air-supported membrane status information, generate a collaborative control command set, and control the drone unit to fly according to the target planned route and use the target snow removal device to clear the snow accumulated on the air-supported membrane structure according to the target snow removal solution.

[0033] In some embodiments, the intelligent control terminal 102 is also equipped with a human-machine interface, allowing operators to manually take over control of the drone. This ensures that in the event of an emergency (such as detected equipment failure, sudden changes in environmental parameters, or anticipated collision risk), control commands can be sent through the human-machine interface, prioritizing the automatic control logic and ensuring the safety of the drone, the air-supported structure, and on-site personnel. The manual takeover mechanism supports hierarchical access control, dynamically adjusting control priority based on the severity of the emergency.

[0034] Please see Figure 2 , Figure 2 This is a schematic flowchart of a snow removal method for an air-supported membrane structure provided in an embodiment of this application. The snow removal method for an air-supported membrane structure provided in this embodiment of the application includes the following steps: S21: Obtain scan information from the UAV unit.

[0035] In step S21, the scanning information is the information collected by the UAV unit scanning the air-supported membrane structure. The UAV unit includes a scanning UAV, which is equipped with a laser scanner and a millimeter-wave radar. The scanning UAV scans the air-supported membrane structure using the laser scanner to obtain laser point cloud data of the structure. Based on the point cloud data, a three-dimensional model of the air-supported membrane structure is constructed, and the snow-covered areas of the structure are detected. The scanning UAV then scans the snow-covered areas using millimeter-wave radar to obtain radar echo signals. The scanning information is obtained by combining the laser point cloud data and the radar echo signals.

[0036] S22: Determine the air film status information based on the scanning information.

[0037] In step S22, the air-supported membrane state information is used to represent the state of the snow accumulation and membrane surface above the air-supported membrane structure. A three-dimensional model of the air-supported membrane structure is constructed using point cloud data. The membrane surface of the three-dimensional model is compared with the membrane surface of the three-dimensional model of the top of the air-supported membrane structure in a snow-free state to obtain membrane surface collapse information. By analyzing the Doppler frequency shift and attenuation characteristics of the radar echo signal, it is analyzed whether the snow-covered area is icy and the thickness of the snow is detected to obtain snow accumulation description information. The air-supported membrane state information is determined by combining the membrane surface collapse information and the snow accumulation description information.

[0038] S23: Select the historical snow removal scheme that matches the air film status information from a variety of historical snow removal schemes as the target snow removal scheme.

[0039] In step S23, the intelligent control terminal has a snow removal plan database containing various historical snow removal plans. The intelligent control terminal can intelligently match applicable snow removal plans based on air film status information to determine the target snow removal plan. Specifically, the intelligent control terminal acquires air film status information, such as snow thickness, icing status, and film collapse depth parameters, and uses a preset matching algorithm to filter through the snow removal plan database to determine the optimal target snow removal plan based on the air film status information.

[0040] S24: Select the snow removal device that matches the target snow removal strategy from among various snow removal devices as the target snow removal device.

[0041] In step S24, the drone unit includes multiple snow removal drones, each equipped with different snow removal devices. The target snow removal plan includes a snow removal strategy, which contains snow removal step information of the snow removal operations that the snow removal drones need to perform during snow removal operations. The target snow removal device is determined by semantically parsing the snow removal step information and selecting the target snow removal drone based on preset matching rules.

[0042] S25: Send control commands to the UAV unit so that the UAV unit flies along the target planned route and uses the target snow removal device to remove the snow from the air-supported membrane structure in accordance with the target snow removal plan.

[0043] In step S25, the UAV unit includes a target snow removal UAV, which is equipped with a target snow removal device. Specifically, the intelligent control terminal generates control commands for the target snow removal UAV, which at least include target planned flight path parameters for the target snow removal UAV. Specifically, by sending the control commands to the automatic flight control module of the target snow removal UAV, the target snow removal UAV is driven to perform flight operations based on the target planned flight path parameters, which at least include waypoint coordinate sequences, flight altitude, turning angular velocity, and operating speed range. The control commands also include snow removal operation timing control parameters that match the target planned flight path to achieve coordinated control of the flight trajectory and snow removal operation.

[0044] This application embodiment acquires air-supported membrane structure status information through a drone unit, intelligently matching snow removal plans and devices to avoid the subjectivity and inefficiency of manual judgment, thus achieving intelligent and automated snow removal operations for air-supported membrane structures. Simultaneously, a multi-drone collaborative operation mode allows for parallel snow removal operations, shortening the overall operation time and improving the snow removal efficiency of air-supported membrane structures.

[0045] In some embodiments, the scanning information includes first scanning data and second scanning data. Determining the air film state information based on the scanning information includes the following steps: S221: Acquire the first scan data of the UAV unit, which is the data of the UAV unit scanning the top of the air-supported structure using the laser scanner in the scanning device.

[0046] S222: Construct a three-dimensional model of the top of the air-supported structure based on the first scan data.

[0047] S223: Obtain information about the air-supported membrane structure, which includes a standard three-dimensional model of the air-supported membrane structure in a snowless state.

[0048] S224: Compare the target 3D model with the standard 3D model to determine the snow accumulation area and membrane collapse information.

[0049] S225: Acquire second scan data based on the snow-covered area. The second scan data is the data obtained by the UAV unit scanning the snow-covered area using the millimeter-wave radar in the scanning device.

[0050] S226: Determine snow cover description information based on the second scan data.

[0051] S227: Determine the air film status information by combining the information on membrane collapse and snow accumulation description.

[0052] In S221, the unmanned aerial vehicle (UAV) unit includes a scanning UAV, which is equipped with a scanning device including a laser scanner and millimeter-wave radar. The first scan data is laser point cloud data obtained by scanning the air-supported membrane structure using the scanning UAV. The scanning UAV scans the air-supported membrane structure at a frequency of at least 100Hz, achieving a point cloud density of at least 50 points per square meter. It emits a laser beam and receives the reflected echo through the laser scanner, collecting laser point cloud data from the membrane surface to obtain the first scan data. This first scan data contains the three-dimensional spatial coordinates and reflection intensity information of the membrane surface, which can be used to construct a three-dimensional model of the top of the air-supported membrane structure.

[0053] In step S222, a point cloud registration algorithm is used to unify the coordinates of the discrete point clouds in the first scan data, generating an initial point cloud dataset. A Laplacian smoothing-based surface reconstruction algorithm is then used to convert the initial point cloud dataset into a triangular mesh model. Geometric constraints are then optimized using a pre-defined CAD model of the air-supported membrane structure to generate a 3D model that includes the membrane surface topology and snow distribution characteristics.

[0054] In S223, the air-supported membrane structure information includes a standard 3D model of the air-supported membrane structure in a snow-free state. This standard 3D model is constructed by scanning the air-supported membrane structure in a snow-free state using a laser 3D scanner, utilizing laser point cloud data, and stored in the storage device of the intelligent control terminal. The air-supported membrane structure information can be retrieved directly from the memory of the intelligent control terminal.

[0055] In S224, the snow-covered area is the region on the top of the air-supported membrane structure where snow has accumulated. Membrane collapse information includes the depth of the collapse. Understandably, the accumulated snow in the snow-covered area will change the shape of the membrane, causing it to collapse. The 3D model of the air-supported membrane structure's top constructed in step S222 is spatially aligned and its coordinate system unified with the standard 3D model of the air-supported membrane structure in a snow-free state. Boolean difference operations are used to extract the geometrically different regions between the target 3D model and the standard 3D model, and depth analysis is performed on the discrete points within these regions. Specifically, the depth of the collapse is obtained by calculating the coordinate difference of each point relative to its corresponding position in the standard 3D model in the vertical direction. Through point cloud height difference analysis and clustering algorithms, areas higher than the normal membrane top surface and conforming to the distribution characteristics of snow point clouds are identified as snow-covered areas. The snow-covered areas and their boundary contours are identified, and a snow distribution heatmap is generated, thus obtaining the snow-covered areas.

[0056] In step S225, based on the snow-covered area identified in step S223, the scanning drone performs a detailed scan of the snow-covered area using millimeter-wave radar along a preset spiral or grid-like scanning path. The millimeter-wave radar emits millimeter-wave signals in a specific frequency band and receives radar echo signals to acquire second scan data containing distance detection data of the snow-covered area, reflected wave spectral characteristic data, and signal attenuation parameters.

[0057] In S226, the snow cover description information includes snow thickness, snow cover area, snow cover shape, and icing state. By analyzing the radar echo signal in the second scan data through echo intensity analysis and time-frequency domain processing, and utilizing the differences in signal reflection characteristics in different media (snow, ice, and membrane), the interfaces between the snow and ice layers and between the snow and membrane are identified. Snow thickness data is obtained by calculating the distances between each interface. The laser point cloud is segmented into adjacent snow points and membrane points based on differences in reflection intensity. These segmented snow points are projected onto a horizontal plane, and the geometric outer edge of the projected point group is detected using the Alpha Shape algorithm. Connecting the boundary points of the projected point group forms a closed polygonal outline, yielding the shape of the snow cover area. The projected area is divided into a fine grid (e.g., 1cm x 1cm grids), and grids containing snow points are marked as valid grids. The area of ​​the snow cover area is obtained by multiplying the total number of valid grids by the grid area. Meanwhile, the spectral characteristics, attenuation rate, and phase change of the millimeter-wave reflected signal are analyzed. When the spectral characteristics of the millimeter-wave reflected signal match the characteristics of ice layer reflection and the signal attenuation rate exceeds the snow reflection threshold, it is determined that there is icing in the snow-covered area, and the location of icing in the snow-covered area is determined.

[0058] In S227, the air-supported membrane state information is determined by combining the membrane collapse information and the snow accumulation description information. Specifically, the membrane collapse information, which includes the membrane collapse depth, obtained in step S224, is fused with the snow accumulation description information, which includes the snow accumulation thickness distribution, snow accumulation area, snow accumulation area shape, and icing state, obtained in step S226, to comprehensively characterize the current state of the air-supported membrane structure.

[0059] In some embodiments, the scanning information includes first scanning data. Determining the air-supported membrane state information based on the scanning information includes the following steps: acquiring first scanning data from a drone unit, wherein the first scanning data is data obtained by the drone unit scanning the top of the air-supported membrane structure using a laser scanner in a scanning device; constructing a three-dimensional model of the top of the air-supported membrane structure based on the first scanning data; acquiring air-supported membrane structure information, including a standard three-dimensional model of the air-supported membrane structure in a snow-free state; comparing the target three-dimensional model with the standard three-dimensional model to determine the snow accumulation area and membrane collapse information. Specific implementation methods are as described above. The intelligent control terminal then acquires the third scan data from the ultrasonic snow gauge. The ultrasonic snow gauge is fixed above the air-supported structure using a mounting device. It supports a measurement range of 0-1000mm and the probe height (h) can be adjusted according to the height of the air-supported structure. The probe height (h) is the distance from the probe to the ground. For example, the probe height can be set between 300mm and 3000mm to allow the ultrasonic snow gauge to perform a global scan of the snow-covered area of ​​the air-supported structure, obtaining the third scan data. Analyzing this third scan data yields the snow thickness distribution. (See [link to relevant documentation]). Figure 3 The scanning device of the drone also includes a video monitor, which can capture images of the air-supported structure during the scanning process, obtaining images of the snow-covered areas. Staff can then determine whether icing has occurred by comparing the different reflectivities of the snow-covered areas and the images, and mark the images of areas with icing, thus obtaining the icing status of the snow-covered areas. The air-supported structure's status information is determined by combining the target's 3D model, snow thickness distribution, and icing status.

[0060] In some embodiments, the air-supported membrane structure information also includes the membrane surface area, height, and geographical location of the air-supported membrane structure. Selecting a historical snow removal scheme that matches the air-supported membrane structure status information from a variety of historical snow removal schemes as the target snow removal scheme includes the following steps: The system retrieves a snow removal plan database, which includes various historical snow removal plans. It checks this database to see if a historical snow removal plan matches the membrane area, height, geographical location, and air-supported structure's status information. If a plan exists, it is selected as the target snow removal plan. If not, a new target snow removal plan is generated based on the air-supported structure's status information.

[0061] Specifically, the intelligent control terminal accesses a pre-built snow removal plan database. This database uses an associative database structure to store historical operation and maintenance data, including the membrane area, height, geographical location, snow thickness, snow area area, snow area shape, icing status, membrane collapse depth, and historical snow removal plans (snow removal strategies, target planned routes, and timing control parameters) of the air-supported membrane structure. The database uses machine learning algorithms to perform data clustering and rule extraction, forming a standardized operating condition model library and a set of associated rules. By fusing the air-supported membrane structure information (including its membrane area, height, and geographical location), snow description information (including snow thickness and / or snow area area and / or snow area shape and / or icing status), and membrane collapse information, a multi-dimensional state vector is constructed. Based on this multi-dimensional state vector, a K-nearest neighbor (KNN) search algorithm is executed in the database to calculate the cosine similarity with historical operating conditions, and candidate snow removal plans with a cosine similarity greater than or equal to a preset threshold (e.g., 0.9) are selected. Candidate snow removal plans include at least one of several historical snow removal plans.

[0062] If a candidate snow removal scheme with a cosine similarity greater than or equal to a preset threshold exists, a match is successful. Based on an intelligent sorting algorithm, the matched candidate snow removal schemes are sorted according to constraints such as membrane safety priority, snow removal efficiency, and resource consumption. The optimal candidate snow removal scheme that meets the constraints is then output and determined as the target snow removal scheme. If no candidate snow removal scheme with a cosine similarity greater than or equal to the preset threshold exists, a simulation model is constructed using the acquired snow accumulation description information and membrane collapse information. This model is then combined with historical snow removal schemes from the snow removal scheme database to regenerate the target snow removal scheme.

[0063] In some embodiments, the air-supported membrane state information includes membrane collapse information and snow accumulation description information. Based on the air-supported membrane state information, a target snow removal plan is regenerated, including the following steps: S41: Obtain primary climate information and similar historical snow removal plans.

[0064] S42: Generate a simulation model based on snow cover description information, membrane collapse information, primary climate information, and air-supported membrane structure information.

[0065] S43: Generate the target snow removal plan based on the simulation model and similar historical snow removal plans.

[0066] In S41: The first climate information includes sunlight and / or wind force and / or air pressure and / or temperature and / or humidity information of the geographical location of the air-supported membrane structure during snow removal operations. Similar historical snow removal schemes are those in the snow removal scheme database whose area, height, geographical location, and air-supported membrane structure status information is greater than a second preset threshold but less than a first preset threshold. The first preset threshold is greater than the second preset threshold; specifically, the first preset threshold is set to 0.9, and the second preset threshold is set to 0.8. Both preset thresholds are empirical values ​​obtained through long-term experimental testing. The first climate information is obtained from a meteorological website using GNSS navigation information from the UAV unit. Similar historical snow removal schemes are obtained by filtering historical snow removal schemes in the snow removal scheme database whose cosine similarity is greater than the second preset threshold but less than the first preset threshold.

[0067] In S42: Snow accumulation description information, membrane collapse information, and primary climate information are fused with the standard 3D model of the air-supported membrane structure. Based on finite element analysis (FEA) and computational fluid dynamics (CFD) algorithms, a simulation model incorporating snow and ice loads, membrane deformation, climate effects, and stress response is constructed. Specifically, the primary climate information includes wind parameters, which at least cover meteorological data such as wind speed and direction. These wind parameters are input as boundary conditions into the simulation model based on the coupling of computational fluid dynamics (CFD) and structural mechanics. Numerical simulation methods are used to calculate the dynamic impact of wind loads on the membrane surface and snow distribution of the air-supported membrane structure. During the simulation, a dynamic mesh is used to simulate the deformation process of the membrane surface under wind action. The interaction between wind force, snow and ice loads, and membrane stress is comprehensively analyzed to construct a simulation model incorporating multiphysics coupling effects. The simulation model is meshed to form a 3D finite element mesh with no fewer than 10,000 nodes to ensure the accuracy of the simulation calculations.

[0068] Specifically, in S43, the simulation model serves as the basis for environmental simulation. Simultaneously, historical snow removal schemes from the snow removal scheme database with a cosine similarity to the air film state information exceeding a second preset threshold (e.g., cosine similarity greater than 0.8) but less than a first preset threshold (cosine similarity less than 0.9) are retrieved. Using a sequence-to-sequence (Seq2Seq) model from deep learning, snow removal strategies, planned routes, and timing control parameters from historical snow removal schemes are used as training data. Simulation results, such as membrane stress distribution and snow removal efficiency, output from the simulation model are used as constraints. Strategy optimization is achieved through reinforcement learning algorithms. During optimization, the objective functions are: membrane stress not exceeding the allowable material stress, maximizing snow removal efficiency, and minimizing energy consumption. The operational parameters of the snow removal drone (e.g., drone flight altitude, flight speed, flight route, and single-operation snow removal thickness) are dynamically adjusted to generate a new snow removal scheme that satisfies the air film state information. This new snow removal scheme is designated as the target snow removal scheme, which includes the three-dimensional flight path coordinate sequence of the target snow removal drone (including waypoint positions, flight speed, and turning angle parameters), operational timing, and equipment operating parameter configuration.

[0069] In some embodiments, the drone unit includes multiple snow removal drones, each equipped with different snow removal devices. The target snow removal strategy includes at least one snow removal step information. Selecting a snow removal device matching the target snow removal strategy from among the multiple snow removal devices includes the following steps: parsing the target snow removal strategy to obtain at least one snow removal step information; selecting a snow removal device matching the snow removal step information from among the multiple snow removal devices as the target snow removal device. All target snow removal devices are combined according to the execution order of all snow removal step information to obtain a snow removal operation sequence. Sending control commands to the drone unit to cause the drone unit to use the target snow removal devices to clear snow from the air-supported structure according to the target snow removal plan includes the following steps: sending control commands to the drone unit to cause the drone unit to sequentially control different target snow removal devices to operate and clear snow from the air-supported structure according to the snow removal operation sequence.

[0070] The target snow removal plan includes a target snow removal strategy. The target snow removal strategy is a snow removal strategy that matches the current snow accumulation description information and the current membrane collapse information in the snow removal strategy table. The specific snow removal strategy table is shown in Table 1: Table 1

[0071] By analyzing the target snow removal strategy, the intelligent control terminal can obtain information on at least one snow removal step. Specifically, the target snow removal strategy includes information on at least one snow removal step, and different snow removal step information is broken down into different atomic operations. The intelligent control terminal performs semantic analysis on the target snow removal strategy, and through natural language processing or keyword extraction technology, it breaks it down into a sequence of atomic operations (such as "oscillating to break ice" and "hard rollers throwing layer by layer").

[0072] The system selects the snow removal device that matches the snow removal step information from a variety of snow removal devices as the target snow removal device. Specifically, the drone unit includes various snow removal drones, each equipped with different snow removal devices. Selecting the snow removal device that matches the snow removal step information from a variety of snow removal devices means selecting the snow removal drone that matches the target snow removal strategy from a variety of snow removal drones. The intelligent control terminal searches a pre-built snow removal drone functional attribute library. Each snow removal drone entry in the library includes: operation type, core performance parameters (operation coverage area, runtime, energy consumption), and safe operation thresholds (maximum membrane contact pressure, equipment load limit). Different operation types correspond to different atomic operations. If the atomic operation sequence obtained from the decomposition of the target snow removal strategy involves multiple different atomic operations (e.g., "first oscillate to break ice, then throw with hard rollers"), then for each atomic operation, an independent match is performed in the equipment functional attribute library to obtain the snow removal drone corresponding to each atomic operation. In this way, the target snow removal drone can be matched.

[0073] Understandably, if an atomic operation sequence contains two or more atomic operations, it will match multiple target snow removal drones, and the intelligent control terminal needs to determine the snow removal order of these drones. Based on the target snow removal drones and their corresponding atomic operations, an operation dependency graph G=(V,E) is constructed, where: the node set V represents atomic operations (e.g., "oscillating ice breaking," "hard roller throwing"); and the edge set E represents the temporal constraints between atomic operations (e.g., ice breaking must precede snow throwing). The operation dependency graph clarifies the order of each atomic operation and the conditions for parallel execution (areas without dependencies can operate synchronously). The temporal control parameters (job start / end time, single operation duration) in the target snow removal scheme are mapped to the operation dependency graph, forming time window constraints for each atomic operation node. Using a priority scheduling algorithm, with the optimization objective of minimizing the total job duration and maximizing equipment resource utilization, a snow removal operation sequence table containing equipment identifiers, operation start times, job path coordinates, and equipment coordination signal triggering conditions is generated, thus obtaining the snow removal operation order.

[0074] In this embodiment of the application, sending control commands to the drone unit to enable the drone unit to use the target snow removal device to remove snow from the air-supported membrane structure according to the target snow removal plan includes the following steps: sending control commands to the drone unit to enable the drone unit to sequentially control different target snow removal devices to work in accordance with the snow removal operation sequence to remove snow from the air-supported membrane structure.

[0075] Specifically, the drone unit includes a target snow removal drone, which is equipped with a target snow removal device. The intelligent control terminal generates control commands for multiple target snow removal drones based on a snow removal operation sequence list, and sends the control commands to the multiple snow removal drones so that they can perform snow removal operations according to the snow removal operation sequence list to clear the snow from the top of the air-supported structure.

[0076] In some embodiments, the snow removal method for air-supported membrane structures further includes the following steps: S51: Obtain real-time distance and first air-film model information. The real-time distance includes the distance between the drone wing of the drone unit and the membrane surface. The real-time distance is collected by the distance sensor carried by the drone unit. The first air-film model information includes a first air-film model constructed based on the real-time scanning information of the drone unit.

[0077] S52: Compare the real-time distance with the safe distance threshold.

[0078] S53: In response to the real-time distance being less than the safe distance threshold, determine whether the air-supported structure has experienced membrane surface rebound by combining the first air-supported membrane model and the target three-dimensional model.

[0079] S54: In response to the rebound of the membrane surface of the air-supported structure, a new target planning route is generated. The new target planning route includes the flight speed, flight altitude, flight path of the UAV unit and the angle between the snow removal device and the membrane surface.

[0080] S55: Generate adjustment instructions, which include flight parameters for the new target planned route.

[0081] S56: Send adjustment instructions to the UAV unit so that the UAV unit flies along the new target route and uses the target snow removal device to clear the snow from the air-supported structure.

[0082] In S51, the drone unit includes a target snow removal drone, which carries a target snow removal device. Real-time distance includes the distance between the drone's wings and the membrane surface, acquired by a distance sensor mounted on the target snow removal drone. The target snow removal drone, using its mounted distance sensor (such as a photoelectric proximity switch or infrared sensor), acquires real-time vertical distance data between the drone's wings and the membrane surface of the air-supported structure at a sampling frequency of not less than 10Hz. In some embodiments, the real-time distance also includes the distance between the snow removal device of the target snow removal drone and the air-supported structure; specifically, it includes the distance between the snow removal device and the membrane surface / steel structure. The snow removal device is equipped with a distance sensor for acquiring the distance between the snow removal device and the membrane surface or steel structure of the air-supported structure. The acquired real-time distance data is transmitted to a smart control terminal for processing via a wireless communication module. The first air-supported membrane model information includes a first air-supported membrane model constructed based on the real-time scanning information of the drone unit. The drone unit includes a scanning drone. During snow removal, the scanning drone scans the air-supported structure according to a preset planned route, obtains real-time scanning information, and sends the real-time scanning information to the intelligent control terminal. The intelligent control terminal constructs a three-dimensional model of the air-supported structure in real time, namely the first air-supported structure model, and the construction method is as described above.

[0083] In S52, the safe distance threshold includes the minimum safe distance between the wing of the target snow removal drone and the membrane surface. The safe distance threshold is set dynamically based on parameters such as the drone model, operating environment, and membrane surface load-bearing capacity, and can be set to 500mm. In some embodiments, the safe distance threshold also includes the minimum safe distance between the snow removal device of the target snow removal drone and the air-supported membrane structure. Similarly, it can be dynamically set based on parameters such as the drone model, operating environment, and membrane surface load-bearing capacity, and can be set to 400mm. The real-time distance is compared with the safe distance threshold to determine whether the real-time distance is less than the safe distance threshold.

[0084] In S53, membrane rebound refers to the phenomenon where, during snow removal operations, when snow is rapidly cleared (e.g., a large amount of snow suddenly slides down a slope to the ground), the membrane surface quickly returns to its original shape due to the release of elastic potential energy and the action of the pre-tensioning system. When the real-time distance is less than the safe distance threshold, the membrane surface morphology of the first air-supported membrane model is compared with that of the target 3D model, such as by detecting membrane rebound through feature point displacement or surface changes.

[0085] In S54, when membrane rebound is detected, a new target planned flight path is generated. For example, the intelligent control terminal identifies the rebound area, rebound amplitude, and membrane change trend based on a comparison of the membrane morphology between the first air-supported membrane model and the target 3D model. Based on historical deformation data of the rebound area and the mechanical properties of the membrane material, a dynamic safety margin greater than the distance safety threshold is calculated. Based on the relevant data of the rebound area and the dynamic safety margin, the intelligent control terminal, combined with a path planning algorithm, collaboratively optimizes the flight parameters of the target planned flight path (such as flight route, flight altitude, flight speed, and the angle between the snow removal device and the membrane surface) to obtain a new target planned flight path. For example, the local or global flight path is replanned to allow the snow removal drone to avoid high-risk rebound areas, employing detour and segmented operation strategies to obtain the flight path. Based on the dynamic safety margin, the flight altitude of the target snow removal drone in the rebound area and adjacent areas is raised or adjusted to ensure that the real-time distance between the snow removal device and the membrane surface / steel structure remains within a safe range. Simultaneously, the flight speed of the target snow removal drone is reduced over the rebound area and in the predicted direction of travel, allowing more time for the intelligent control terminal to respond and adjust its attitude. The attitude angle of the target snow removal drone is calculated based on the flight path and the rebound amplitude to adjust the angle between the snow removal device and the membrane surface, enabling it to operate effectively even when slightly away from the membrane surface.

[0086] In S55, specifically, the intelligent control terminal deconstructs the planned flight path of the new target into an ordered sequence of control commands with timestamps or sequential identifiers. This sequence includes positioning and navigation commands, motion control commands, and equipment attitude commands. Different commands include corresponding flight parameters. For example, positioning and navigation commands include the three-dimensional coordinates of the target waypoint; motion control commands include preset speeds and accelerations for the next target waypoint; and equipment attitude commands include attitude angles during flight and the angle between the snow removal device and the membrane surface. The control command sequence is encoded and encapsulated according to the target snow removal UAV's communication protocol (such as MAVLink or DJIMSDK) to generate binary or specific format data packets, thus obtaining the adjustment commands to be sent.

[0087] In S56, an adjustment command is sent to the UAV unit to instruct it to fly along the new target-planned flight path and use the target snow removal device to clear snow from the air-supported structure. Specifically, the intelligent control terminal sends the adjustment command to the snow removal UAV via a wireless communication module. The snow removal UAV parses the adjustment command, loads the flight parameters of the new target-planned flight path into its automatic flight control module, replaces the original mission plan, and drives the target snow removal UAV to fly along the new target-planned flight path. This ensures that the target snow removal UAV can continuously use the target snow removal device to clear snow from the air-supported structure while ensuring its structural safety.

[0088] In some embodiments, please refer to Figure 4 , Figure 4 This is a schematic diagram of a snow removal principle for an air-supported membrane structure provided in this application embodiment. When performing snow removal operations, the snow removal drone 300 may encounter scenarios with thick snow accumulation and significant membrane collapse depth. To prevent collisions between the drone's wings and the membrane surface of the air-supported membrane structure during snow removal, a flexible, extendable robotic arm 401 is provided at the end of the snow removal device 400. The snow removal drone 300 removes snow layer by layer from the snow-covered area 200 towards the slope. In areas with significant membrane collapse depth, the flexible, extendable robotic arm 401 is extended to increase the working depth of the snow removal device 400, forming a snow-covered area 201 after snow removal. The snow in the snow-covered area 201 gradually slides to the ground in the direction indicated by the arrow.

[0089] In some embodiments, determining whether the air-supported structure has experienced membrane surface rebound by combining the first air-supported membrane model and the target three-dimensional model includes the following steps: S61: Calculate the displacement from the first spatial coordinate to the second spatial coordinate. The first spatial coordinate is the spatial coordinate of the deepest collapse of the membrane surface in the snow area of ​​the target three-dimensional model, and the second spatial coordinate is the spatial coordinate in the first air membrane model corresponding to the first spatial coordinate.

[0090] S62: Compare the displacement with the preset rebound threshold.

[0091] S63: If the displacement is greater than the preset rebound threshold, it is determined that the air-supported membrane structure has rebounded.

[0092] S64: If the displacement is less than or equal to the preset rebound threshold, it is determined that the air-supported membrane structure has not experienced membrane surface rebound.

[0093] Specifically, in S61, the intelligent control terminal automatically identifies and locates the first feature point with the deepest membrane collapse in the snow-covered area of ​​the target 3D model, and sets the spatial coordinates of the first feature point as the first spatial coordinates. Through point cloud registration or feature matching algorithms, a second feature point corresponding to the first feature point is determined in the first air-supported membrane model, and the current spatial coordinates of the second feature point are obtained. The Euclidean distance between the first and second spatial coordinates is calculated to obtain the displacement of the first feature point.

[0094] In S62, the displacement is compared with a preset rebound threshold. The rebound threshold is a dynamic parameter set based on the elastic modulus of the air-supported membrane material, the magnitude of the pretension, historical deformation data, and safety margin. It is used to distinguish between slow deformation caused by normal snow removal and rapid, dangerous membrane rebound.

[0095] In S63, if the displacement is greater than the preset rebound threshold, it is determined that the membrane surface of the snow accumulation area where the first feature point is located has rebounded.

[0096] In S64, if the displacement is less than or equal to the preset rebound threshold, it is determined that the membrane surface of the snow-covered area where the first feature point is located is in a safe or normal recovery state and no membrane surface rebound has occurred.

[0097] In some embodiments, if the real-time distance is less than a safe distance threshold and the displacement is less than or equal to a preset rebound threshold, the membrane surface of the snow-covered area where the first feature point is located does not rebound. At this time, the target snow removal drone only adjusts its own flight altitude, flies along the target planned route, and uses the target snow removal device to remove the snow from the air-supported membrane structure according to the target snow removal plan.

[0098] Understandably, the planned flight path is pre-set with a safety margin, meaning that an additional safety redundancy is reserved on top of the planned flight altitude. When the target snow removal drone flies along the planned flight path, if its onboard distance sensor detects that the real-time distance between the drone's wings / snow removal device and the air film is less than a safe distance threshold, the target snow removal drone's control strategy will prioritize ensuring that the real-time distance does not exceed the safe distance threshold. In this case, the target snow removal drone can autonomously adjust its flight altitude or other flight parameters to ensure that the air film structure is not damaged.

[0099] In some embodiments, in response to the rebound of the membrane surface of the air-supported structure, a new target planned flight path is generated, including the following steps: S71: Obtain information on the second air-film model and the second climate.

[0100] S72: Obtain the air-supported membrane model database. The air-supported membrane state database includes the morphological information of the air-supported membrane structure during multiple historical snow removal operations. The morphological information of the air-supported membrane structure includes the area, curvature, shape, and location of multiple curved surfaces of the air-supported membrane structure.

[0101] S73: Based on the information of the second air film model, match it in the air film state database to obtain the target morphology information.

[0102] S74: Combine target morphology information and second climate information to generate a new target planning route.

[0103] In S71, the second air-supported membrane model information includes a second air-supported membrane model constructed based on scan information collected after the membrane surface rebounds. The second climate information consists of sunlight and / or wind speed and / or air pressure and / or temperature and / or humidity information at the geographical location of the air-supported membrane structure, collected after the membrane surface rebounds. Upon determining that the membrane surface of the air-supported membrane structure has rebounded, scan information of the structure is immediately acquired, and a second air-supported membrane model is constructed based on this information, using the method described above. Simultaneously, sunlight and / or wind speed and / or air pressure and / or temperature and / or humidity information at the geographical location of the air-supported membrane structure after the membrane surface rebounds is obtained from a meteorological website based on the GNSS navigation information (such as latitude and longitude coordinates) of the target snow removal UAV.

[0104] In S72, the intelligent control terminal accesses the pre-built air-supported membrane model database. The air-supported membrane model database uses an association database structure to store the morphological information of the air-supported membrane structure under different working conditions during historical snow removal operations. The morphological information includes the geometric properties of multiple curved surfaces of the air-supported membrane structure (such as area, curvature, and spatial location).

[0105] In S73, the air-supported membrane model database also includes various historical air-supported membrane models. These historical models are air-supported membrane structures preserved during historical snow removal operations under different working conditions. Each historical air-supported membrane model corresponds to a specific morphological information, and these are stored in association. The intelligent control terminal, through feature comparison or similarity calculation among the various historical air-supported membrane models in the database, identifies the first historical air-supported membrane model that is closest to the second model, and determines the morphological information corresponding to this first historical air-supported membrane model as the target morphological information.

[0106] S74: The intelligent control terminal performs flight path planning under multiple constraints based on target morphology information and secondary climate information to obtain a new target planned flight path. Specifically, the intelligent control terminal identifies high-risk rebound areas and areas of concentrated membrane stress based on target morphology information and sets them at higher flight altitudes. It then dynamically adjusts these areas using secondary climate information; for example, it adjusts the flight speed, heading, and attitude angle of the target snow removal drone based on wind speed and direction to enhance wind resistance stability; and it assesses snow adhesion based on ambient temperature and adjusts the operating parameters of the snow removal device. Finally, it combines the dynamic performance of the target snow removal drone (such as maximum climb rate and minimum turning radius) to generate a new target planned flight path.

[0107] As another aspect of the embodiments of this application, this application provides a snow removal device for air-supported membrane structures. The snow removal device for air-supported membrane structures can be a software module, which includes several instructions stored in a memory. A processor can access the memory, call the instructions, and execute them to complete the snow removal methods for air-supported membrane structures described in the various embodiments above.

[0108] Please see Figure 5 , Figure 5 This is a schematic diagram of a snow removal device for an air-supported membrane structure provided in an embodiment of this application. The air-supported membrane structure snow removal device 500 includes a scanning data acquisition module 501, an air-supported membrane status information confirmation module 502, a snow removal plan confirmation module 503, a snow removal device confirmation module 504, and a command sending module 505.

[0109] The scanning data acquisition module 501 is used to acquire the scanning information of the UAV unit. The scanning information is the information collected by the UAV unit from scanning the top of the air-supported membrane structure. The UAV unit is equipped with a variety of snow removal devices.

[0110] The air-supported membrane status information confirmation module 502 determines the air-supported membrane status information based on the scanned information. This information indicates the state of snow accumulation and the membrane surface on the top of the air-supported membrane structure. The snow removal plan confirmation module 503 selects a target snow removal plan from a variety of historical snow removal plans that matches the air-supported membrane status information. This target snow removal plan includes a target planned flight path and a target snow removal strategy. The snow removal device confirmation module 504 selects a target snow removal device from a variety of snow removal devices that matches the target snow removal strategy. The command sending module 505 sends control commands to the UAV unit, causing it to fly along the target planned flight path and use the target snow removal device to clear the snow from the air-supported membrane structure according to the target snow removal plan.

[0111] The scanning data acquisition module 501 is further specifically used for: acquiring first scanning data from the UAV unit, which is equipped with a scanning device including a laser scanner and a millimeter-wave radar; the first scanning data being data obtained by the UAV unit scanning the top of the air-supported membrane structure using the laser scanner in the scanning device; constructing a three-dimensional model of the top of the air-supported membrane structure based on the first scanning data; determining snow accumulation areas and membrane collapse information based on the three-dimensional model; acquiring second scanning data based on the snow accumulation areas, which is data obtained by the UAV unit scanning the snow accumulation areas using the millimeter-wave radar in the scanning device; determining snow accumulation description information based on the second scanning data, including snow thickness and icing state; and determining the air-supported membrane state information by combining the membrane collapse information and the snow accumulation description information.

[0112] The snow removal plan confirmation module 503 is also specifically used for: acquiring a snow removal plan database, which includes various historical snow removal plans; checking whether a historical snow removal plan matching the air film status information exists in the snow removal plan database; if it exists, determining the historical snow removal plan matching the air film status information as the target snow removal plan; if it does not exist, regenerating the target snow removal plan based on the air film status information.

[0113] The snow removal plan confirmation module 503 is also specifically used for: acquiring information about the air-supported membrane structure, including its 3D model information under snowless conditions; generating a simulation model based on snow accumulation description information, membrane collapse information, and the air-supported membrane structure information; and generating a target snow removal plan based on the simulation model.

[0114] The drone unit includes various snow removal drones, each equipped with different snow removal devices. The snow removal device confirmation module 504 is specifically used for: analyzing the target snow removal strategy to obtain at least one snow removal step information; selecting the snow removal device matching the snow removal step information from among the various snow removal devices as the target snow removal device; combining all target snow removal devices according to the execution order of all snow removal step information to obtain the snow removal operation sequence; and sending control commands to the drone unit to cause the drone unit to sequentially control the different target snow removal devices to work in accordance with the snow removal operation sequence to clear the snow accumulated on the air-supported structure.

[0115] In some embodiments, the snow removal device 500 for air-supported membrane structures further includes a distance monitoring module 506, which is used to: acquire real-time distance and first air-supported membrane model information, wherein the real-time distance includes the distance between the drone wing of the drone unit and the membrane surface, the real-time distance is collected by a distance sensor mounted on the drone unit, and the first air-supported membrane model information includes a first air-supported membrane model constructed based on the real-time scanning information of the drone unit; compare the real-time distance with a safe distance threshold; in response to the real-time distance being less than the safe distance threshold, determine whether the air-supported membrane structure has experienced membrane surface rebound by combining the first air-supported membrane model and the target three-dimensional model; in response to the air-supported membrane structure experiencing membrane surface rebound, generate a new target planned flight path, wherein the new target planned flight path includes the flight speed, flight altitude, flight route of the drone unit and the angle between the snow removal device and the membrane surface; generate an adjustment command, wherein the adjustment command includes the flight parameters of the new target planned flight path; and send the adjustment command to the drone unit so that the drone unit flies according to the new target planned flight path and uses the target snow removal device to clear the snow accumulation on the air-supported membrane structure.

[0116] The distance monitoring module 506 is also specifically used for: calculating the displacement from the first spatial coordinate to the second spatial coordinate, where the first spatial coordinate is the spatial coordinate of the deepest membrane collapse in the snow area of ​​the target three-dimensional model, and the second spatial coordinate is the spatial coordinate in the first air-supported membrane model corresponding to the first spatial coordinate; comparing the displacement with a preset rebound threshold; if the displacement is greater than the preset rebound threshold, it is determined that the air-supported membrane structure has rebounded; if the displacement is less than or equal to the preset rebound threshold, it is determined that the air-supported membrane structure has not rebounded.

[0117] The distance monitoring module 506 is also specifically used for: acquiring second air-supported membrane model information and second climate information. The second air-supported membrane model information includes a second air-supported membrane model constructed based on scanning information collected after the membrane surface rebounds. The second climate information is the light and / or wind and / or air pressure and / or temperature and / or humidity information of the geographical location of the air-supported membrane structure collected after the membrane surface rebounds. The second air-supported membrane model database includes morphological information of the air-supported membrane structure during multiple historical snow removal operations. The morphological information of the air-supported membrane structure includes the area, curvature, shape, and location of multiple curved surfaces of the air-supported membrane structure. The second air-supported membrane model information is matched with the air-supported membrane state database to obtain target morphological information. The second air-supported membrane morphological information and the second climate information are combined to generate a new target planned flight route.

[0118] It should be noted that the above-mentioned air-supported membrane structure snow removal device can perform the air-supported membrane structure snow removal method provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the method. Technical details not described in detail in the embodiments of the air-supported membrane structure snow removal device can be found in the air-supported membrane structure snow removal method provided in the embodiments of this application.

[0119] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an intelligent control terminal 600 provided in an embodiment of this application. The intelligent control terminal 600 includes one or more processors 61 and a memory 62. The memory 62 is connected to one or more processors 61, for example, via a bus.

[0120] Processor 61 is configured to support the intelligent control terminal in performing the corresponding functions in the methods described in the above method embodiments. Processor 61 may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The aforementioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0121] Memory 62 is used to store program code, etc. Memory 62 may include volatile memory (VM), such as random access memory (RAM); memory may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory 62 may also include combinations of the above types of memory.

[0122] The memory 62 can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the air-supported membrane structure snow removal method in the embodiments of this application. The processor 61 executes various functional applications and data processing of the air-supported membrane structure snow removal method and air-supported membrane structure snow removal device by running the non-volatile software programs, instructions, and modules stored in the memory, that is, it realizes the functions of each module or unit of the air-supported membrane structure snow removal method and air-supported membrane structure snow removal device provided in the above method embodiments.

[0123] Memory 62 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the air-supported structure snow removal device. In some embodiments, memory 72 may optionally include memory remotely located relative to the processor, which can be connected to the air-supported structure snow removal device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] One or more modules are stored in memory 62. When executed by one or more processors 61, they perform the air-supported membrane structure snow removal method in any of the above method embodiments. For example, they perform the method steps described in the above method embodiments to realize the functions of the modules described in the above device embodiments.

[0125] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the method as described in the foregoing embodiments.

[0126] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0127] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A snow removal method using an air-supported membrane structure, characterized in that, include: The scanning information of the drone unit is obtained. The scanning information is the information collected by the drone unit when scanning the top of the air-supported membrane structure. The drone unit is equipped with a variety of snow removal devices. The air-supported membrane state information is determined based on the scanning information, and the air-supported membrane state information is used to indicate the state of the snow accumulation and membrane surface on the top of the air-supported membrane structure; Among a variety of historical snow removal schemes, the historical snow removal scheme that matches the air film state information is selected as the target snow removal scheme, and the target snow removal scheme includes the target planned route and the target snow removal strategy; Among the various snow removal devices, the snow removal device that matches the target snow removal strategy is selected as the target snow removal device; Control commands are sent to the drone unit to make it fly along the target planned route and use the target snow removal device to remove the snow from the air-supported structure according to the target snow removal plan.

2. The method according to claim 1, characterized in that, The scanning information includes first scanning data and second scanning data. Determining the air film state information based on the scanning information includes: Acquire the first scan data of the UAV unit; A target three-dimensional model of the air-supported membrane structure is constructed based on the first scan data; Acquire information about the air-supported membrane structure, including a standard three-dimensional model of the air-supported membrane structure in a snowless state; The target 3D model is compared with the standard 3D model to determine the snow accumulation area and membrane collapse information; Second scan data is obtained based on the snow-covered area; Snow description information is determined based on the second scan data, including snow thickness and / or snow area and / or snow area shape and / or icing state. The membrane top state and air film state information are determined by combining the membrane surface collapse information and the snow accumulation description information.

3. The method according to claim 2, characterized in that, The air-supported membrane structure information also includes the area, height, and geographical location of the air-supported membrane structure. The step of selecting a historical snow removal scheme that matches the air-supported membrane structure status information from multiple historical snow removal schemes as the target snow removal scheme includes: Obtain a snow removal plan database, which includes various historical snow removal plans; The system checks the snow removal plan database to see if there are any historical snow removal plans that match the area, height, geographical location, and status information of the air-supported structure. If it exists, then the historical snow removal scheme that matches the air film state information is determined as the target snow removal scheme; If it does not exist, the target snow removal plan will be regenerated based on the air film state information.

4. The method according to claim 3, characterized in that, The air-supported membrane state information includes membrane collapse information and snow accumulation description information. The step of regenerating the target snow removal plan based on the air-supported membrane state information includes: Obtain first climate information and similar historical snow removal schemes. The first climate information includes the sunlight and / or wind force and / or air pressure and / or temperature and / or humidity information of the geographical location of the air-supported membrane structure during the snow removal operation. The similar historical snow removal schemes are historical snow removal schemes in the snow removal scheme database that have a similarity to the membrane area, height, geographical location and the air-supported membrane state information of the air-supported membrane structure that is greater than a second preset threshold and less than a first preset threshold. The first preset threshold is greater than the second preset threshold. A simulation model is generated based on the snow accumulation description information, the membrane collapse information, the first climate information, and the air-supported membrane structure information. A target snow removal plan is generated based on the simulation model and the similar historical snow removal plans.

5. The method according to claim 1, characterized in that, The unmanned aerial vehicle (UAV) unit includes multiple snow removal UAVs, each equipped with different snow removal devices. The target snow removal strategy includes at least one snow removal step information. Selecting a snow removal device matching the target snow removal strategy from among the multiple snow removal devices includes: The target snow removal strategy is analyzed to obtain information on at least one snow removal step. Among the various snow removal devices, the snow removal device that matches the snow removal step information is selected as the target snow removal device; Among them, all target snow removal devices are combined according to the execution order of all snow removal step information to obtain the snow removal operation sequence; Sending control commands to the drone unit to cause the drone unit to use the target snow removal device to remove snow from the air-supported structure according to the target snow removal plan includes: Control commands are sent to the drone unit so that the drone unit sequentially controls different target snow removal devices to remove snow from the air-supported structure in accordance with the snow removal operation sequence.

6. The method according to claim 1, characterized in that, Also includes: The system acquires real-time distance and first air-film model information. The real-time distance includes the distance between the drone wing of the drone unit and the membrane surface. The real-time distance is collected by the distance sensor mounted on the drone unit. The first air-film model information includes a first air-film model constructed based on the real-time scanning information of the drone unit. Compare the real-time distance with the safe distance threshold; In response to the real-time distance being less than the safe distance threshold, the system combines the first air-supported membrane model and the target 3D model to determine whether the air-supported membrane structure has experienced membrane surface rebound. In response to the membrane rebound of the air-supported structure, a new target planning route is generated, which includes the flight speed, flight altitude, flight path of the UAV unit and the angle between the snow removal device and the membrane surface; Generate adjustment instructions, which include flight parameters for the new target planned route; An adjustment command is sent to the drone unit so that the drone unit flies along the planned route of the new target and uses the target snow removal device to clear the snow from the air-supported structure.

7. The method according to claim 6, characterized in that, The step of determining whether the air-supported structure has experienced membrane surface rebound by combining the first air-supported membrane model and the target 3D model includes: Calculate the displacement from the first spatial coordinate to the second spatial coordinate, where the first spatial coordinate is the spatial coordinate of the deepest membrane collapse in the snow area of ​​the target three-dimensional model, and the second spatial coordinate is the spatial coordinate in the first air-supported membrane model corresponding to the first spatial coordinate; The displacement is compared with a preset rebound threshold. If the displacement is greater than the preset rebound threshold, it is determined that the air-supported membrane structure has experienced membrane surface rebound. If the displacement is less than or equal to the preset rebound threshold, it is determined that the air-supported membrane structure has not experienced membrane surface rebound.

8. The method according to claim 6, characterized in that, In response to the membrane surface rebound of the air-supported structure, a new target planned flight path is generated, including: Acquire second air-supported membrane model information and second climate information. The second air-supported membrane model information includes a second air-supported membrane model constructed based on scanning information collected after the membrane surface of the air-supported membrane structure rebounds. The second climate information includes sunlight and / or wind force and / or air pressure and / or temperature and / or humidity information of the geographical location of the air-supported membrane structure collected after the membrane surface of the air-supported membrane structure rebounds. Obtain an air-supported membrane model database, which includes morphological information of air-supported membrane structures during multiple historical snow removal operations. The morphological information of the air-supported membrane structures includes the area, curvature, shape, and location of multiple curved surfaces of the air-supported membrane structures. Based on the second air film model information, the target morphology information is obtained by matching it in the air film state database. A new target planning route is generated by combining the target morphology information and the second climate information.

9. An intelligent control terminal, characterized in that, The device includes a memory and a processor, the memory being connected to the processor, the processor being configured to execute one or more computer programs stored in the memory, and the processor, when executing the one or more computer programs, causing the intelligent control terminal to implement the method as described in any one of claims 1-8.

10. An intelligent snow removal control system, characterized in that, include: The drone unit is equipped with a multimodal sensor and a snow removal device. The multimodal sensor is configured to collect environmental information around the drone unit, and the snow removal device is configured to perform snow removal operations on the air-supported membrane structure. The intelligent control terminal as described in claim 9 is communicatively connected to the UAV unit and is configured to integrate the environmental information, pre-collected navigation and positioning data, and three-dimensional simulation information about the air-supported membrane structure to construct a snow removal solution library, which includes multiple optimal flight routes, multiple snow removal solutions, and multiple air-supported membrane models.

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