An unmanned aerial vehicle-based hoisting operation management and control method, device, equipment and medium

Abstract

The application discloses a kind of hoisting operation management and control method, device, equipment and medium based on unmanned aerial vehicle.Therein, the hoisting operation management and control method based on unmanned aerial vehicle includes: obtaining the operation parameter of target crane;According to operation parameter and the three-dimensional point cloud data corresponding to each equipment in target operation area, determine the target operation sub-area corresponding to target crane and display in man-machine three-dimensional model interactive interface;According to the feedback information on man-machine three-dimensional model interactive interface, determine the target parking point corresponding to target crane;According to the equipment association data of each equipment in target operation sub-area and target parking point, determine target control area;Based on target control area, determine the unmanned aerial vehicle hovering monitoring position of deployment, to deploy target unmanned aerial vehicle based on unmanned aerial vehicle hovering monitoring position.The technical scheme of the embodiment of the application realizes the automatic deployment and control of unmanned aerial vehicle to equipment by constructing the three-dimensional point cloud model of operation area, further improves the operation safety.

Description

Technical Field

[0001] This invention relates to the field of risk management technology, and in particular to a method, device, equipment and medium for controlling hoisting operations based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Safety management at power construction sites is of paramount importance. The effectiveness of on-site safety management directly impacts both the company's development and the safety of its workers.

[0003] Typically, operators and supervisors estimate the distance between large hoisting machinery and electrical equipment by visual estimation.

[0004] Due to angle and obstruction, visual estimation cannot accurately determine the distance between hoisting machinery and electrical equipment, posing a significant safety hazard. Summary of the Invention

[0005] This invention provides a method, device, equipment, and medium for controlling hoisting operations based on unmanned aerial vehicles (UAVs), so as to realize the automated control of risky equipment using UAVs and further ensure operational safety.

[0006] In a first aspect, embodiments of the present invention provide a method for controlling hoisting operations based on unmanned aerial vehicles (UAVs), the method comprising:

[0007] Obtain the operating parameters of the target crane; wherein, the operating parameters include at least one of the target crane's current docking point, maximum lifting height, and boom length;

[0008] Based on the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, the target operation sub-area corresponding to the target crane is determined and displayed in the human-machine three-dimensional model interaction interface;

[0009] Based on the feedback information on the human-machine 3D model interaction interface, determine the target docking point corresponding to the target crane;

[0010] The target control area is determined based on the equipment association data of each device in the target docking point and the target operation sub-area.

[0011] Based on the target control area, determine the hovering monitoring position of the deployed drones, and deploy the target drones based on the hovering monitoring position of the drones.

[0012] Secondly, embodiments of the present invention also provide a hoisting operation control device based on a drone, the device comprising:

[0013] The parameter acquisition module is used to acquire the operating parameters of the target crane; wherein, the operating parameters include at least one of the target crane's current docking point, maximum lifting height, and boom length;

[0014] The sub-region determination module is used to determine the target operation sub-region corresponding to the target crane based on the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, and display it in the human-machine three-dimensional model interaction interface.

[0015] The target docking point determination module is used to determine the target docking point corresponding to the target crane based on the feedback information on the human-machine three-dimensional model interaction interface;

[0016] The target control area determination module is used to determine the target control area based on the target docking point and the equipment association data of each device in the target operation sub-area;

[0017] The drone deployment module is used to determine the hovering monitoring position of the deployed drone based on the target control area, and to deploy the target drone based on the drone hovering monitoring position.

[0018] Thirdly, the present invention also provides an electronic device comprising:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the UAV-based hoisting operation control method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the UAV-based hoisting operation control method according to any embodiment of the present invention.

[0023] The technical solution of this invention involves acquiring the operating parameters of a target crane, including at least one of the target crane's current docking point, maximum lifting height, and boom length. Based on the operating parameters and the 3D point cloud data corresponding to each device within the target operating area, a target operating sub-region corresponding to the target crane is determined and displayed in a human-machine 3D model interaction interface. Based on feedback information on the human-machine 3D model interaction interface, a target docking point corresponding to the target crane is determined. Based on the target docking point and the device association data of each device within the target operating sub-region, a target control area is determined. Based on the target control area, the hovering monitoring position of a deployment drone is determined, and the target drone is deployed based on the drone's hovering monitoring position. This solves the problem that manual visual inspection methods cannot accurately determine the distance between lifting machinery and electrical equipment, posing significant safety hazards. It achieves automated and precise control of electrical equipment, improving operational safety.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a hoisting operation control method based on unmanned aerial vehicles (UAVs) according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a scene diagram illustrating a method for controlling hoisting operations based on unmanned aerial vehicles (UAVs) according to an embodiment of the present invention.

[0028] Figure 3 This is a flowchart of a method for establishing a three-dimensional spatial model and determining device-related data provided in an embodiment of the present invention;

[0029] Figure 4 This is a flowchart of a hoisting operation control method based on unmanned aerial vehicles (UAVs) according to Embodiment 2 of the present invention;

[0030] Figure 5 This is a structural schematic diagram of a hoisting operation control device based on an unmanned aerial vehicle (UAV) according to Embodiment 3 of the present invention;

[0031] Figure 6This is a schematic diagram of the structure of an electronic device that implements the drone-based hoisting operation control method of this invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Before introducing the technical solution of the embodiments of the present invention, the application scenario of the unmanned aerial vehicle (UAV)-based hoisting operation control method is first explained: When large machinery operates near electrical equipment (mainly referring to overhead live wires, leads, high-altitude busbars, etc.), due to the safety risks within a certain range of the electrical equipment, control is required in the vicinity of the electrical equipment. The orthogonal lidar monitoring device carried by the UAV uses a rotating mechanism to make the laser emitter rotate at high speed, thereby constructing two orthogonal planes. By using the orthogonal vertical combination of two rotating lidars, a two-dimensional work space can be monitored, and a three-dimensional work area can be divided for monitoring. The range within a certain distance around the electrical equipment is the work control area. The UAV carrying the orthogonal lidar monitoring device is deployed at the safe working critical distance of the electrical equipment according to the pre-set deployment rules such as the number of UAVs, deployment position, and deployment direction, forming a virtual fence monitoring boundary. See [link to relevant documentation]. Figure 2 If any equipment enters the controlled area, the lidar receiver will receive abnormal feedback, issue an audible and visual alarm, and send an alarm message to the ground system to alert command personnel and the driver.

[0035] Example 1

[0036] Figure 1This is a flowchart of a drone-based hoisting operation control method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where control is needed for areas near high-risk equipment. The method can be executed by a drone-based hoisting operation control device, which can be implemented in hardware and / or software. The drone-based hoisting operation control device can be configured in a computer.

[0037] like Figure 1 As shown, the method includes:

[0038] S110. Obtain the operating parameters of the target crane; wherein the operating parameters include at least one of the target crane's current docking point, maximum lifting height, and boom length.

[0039] The target crane refers to large-scale mechanical equipment operating in the work area, such as cranes or excavators. Work parameters refer to the relevant parameters of the large-scale mechanical equipment operating at the work site, including the target crane's current docking point, maximum lifting height, and boom length. Furthermore, the current docking point refers to the positional parameter determined based on the pre-established construction operation plan information for the target crane. The maximum lifting height refers to the maximum lifting height of the crane when its boom forms the maximum angle with the horizontal plane.

[0040] Specifically, by structuring the information of the construction operation plan for large machinery, the operating parameters of the target crane are extracted to obtain the current docking point, maximum lifting height, and boom length parameters of the target crane.

[0041] For example, the target crane is crane A. The pre-filled work ticket of crane A is structured to obtain the geographical location parameters of the work stop of crane A, the maximum lifting height is 20 meters, and the boom length is 15 meters.

[0042] S120. Based on the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, determine the target operation sub-area corresponding to the target crane and display it in the human-machine three-dimensional model interaction interface.

[0043] The target work area refers to the entire area of ​​the work site where the target crane is located. Equipment refers to transformers, wires, overhead lines, etc., within the work area. 3D point cloud data refers to a collection of massive point position data on the surface of an object under the same spatial reference frame; for example, Pi = (x, y, z) represents a point in space, and Point Cloud = (P1, P2, P3, P4, ... Pi) represents i points in space. The target work sub-region refers to the sub-region where the target crane's current docking point is located during operation. The human-machine 3D model is a 3D spatial model created using modeling software by collecting 3D point cloud data of various equipment and the environment within the target work area.

[0044] Specifically, based on the geographical location information parameters of the current docking point of the target crane, combined with the three-dimensional point cloud data of each device in the target work area, the target work sub-area of ​​the target crane is displayed on the human-machine three-dimensional model interaction interface.

[0045] Optionally, based on the current docking point in the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, the operation area closest to the current docking point is determined, and the operation area is taken as the target operation sub-area.

[0046] Specifically, the distance between the current docking point's location coordinates and the 3D point cloud data of the target work area is calculated, and the area corresponding to the nearest 3D point cloud data is taken as the target work sub-area.

[0047] For example, if the current docking point's coordinates are (x, y, z), since the processing method is the same for all devices, we will now explain the processing of device A. The 3D point cloud data of device A is (P1, P2, P3, P4, ... Pi). We calculate the distance from each point to the current docking point, sum and average the distances from each point to the current docking point to obtain the distance information from device A to the current docking point. Based on this, we compare the distances between each device and the current docking point, and take the work area corresponding to the device that is closest to the current docking point as the target work sub-area.

[0048] S130. Based on the feedback information on the human-machine three-dimensional model interaction interface, determine the target docking point corresponding to the target crane.

[0049] Feedback information refers to the user's specification or update of the target docking point on the human-computer 3D model interaction interface. The target docking point is the actual location where the target crane stops during operation.

[0050] Specifically, the user determines the actual parking position of the target crane and inputs the actual parking position into the human-machine 3D model interaction interface, thereby determining the target parking point corresponding to the target crane.

[0051] Optionally, based on the user's triggering operation on the 3D model interaction interface, the current docking point of the target crane is updated to obtain the target docking point.

[0052] Triggering an operation refers to the user's actions on the 3D model interaction interface, such as clicking a specified location to make settings or entering relevant information.

[0053] Specifically, since the actual working area of ​​the target crane may change temporarily, the user needs to update the current docking point information of the target crane in the 3D model interaction interface to obtain the target docking point of the target crane.

[0054] S140. Determine the target control area based on the equipment association data of the target docking point and each device in the target operation sub-area.

[0055] Among them, equipment-related data refers to information related to aerial electrical equipment associated with each piece of equipment, as well as information related to the operational sub-area associated with each piece of equipment. The target control area refers to the area near aerial electrical equipment where large machinery is prohibited from entering.

[0056] Specifically, based on the target operation sub-area corresponding to the target docking point, and the voltage level and distance information of the aerial electrical equipment associated with each device within the target operation sub-area, the area that needs to be controlled is determined.

[0057] For example, based on the location information of the target docking point P, the target operation sub-area where the target crane is operating can be determined. In the pre-established association data corresponding to the equipment A associated with the target operation sub-area, the live equipment L1 that needs to be controlled is determined, and the boundary and size of the control area are set according to the location and voltage level parameters of the live equipment L1.

[0058] Based on the equipment association data of each device in the target operation sub-area and the target docking point, at least one aerial device with a risk of electric shock is identified; based on the maximum lifting height, boom length and three-dimensional point cloud data of the at least one aerial device in the operation parameters, the target aerial device is identified; based on the target aerial device and the pre-set distance limit information, the target control area is determined.

[0059] In this context, "aerial equipment" refers to live conductors suspended in mid-air within the target work area, which can include crossbars, lead wires, and overhead busbars. Distance restriction information refers to the distance limits imposed on large machinery and aerial equipment based on their voltage levels. Furthermore, the higher the voltage level of the aerial equipment, the greater the distance restriction. Target aerial equipment refers to any aerial equipment that the target crane may come into contact with during operation.

[0060] For example, the target operation sub-area is determined based on the target crane's target docking point location, and then each piece of equipment within the target operation sub-area is identified. Since the processing method for each piece of equipment is the same, the processing method for one piece of equipment is explained below: For equipment A, based on the pre-established association information of equipment A, the aerial equipment L1, L2, and L3 associated with equipment A that may pose a risk of electric shock can be identified. Using the location information of the target docking point and the 3D point cloud data of aerial equipment L1, L2, and L3, the vertical distance between the target docking point and aerial equipment L1, L2, and L3 can be calculated. Since the processing method for each piece of aerial equipment is the same, the processing method for aerial equipment L1 is explained below: The distance D between aerial equipment L1 and the target docking point is obtained through the above calculation. Based on the known voltage level of aerial equipment L1, its pre-set distance limit information d1 can be determined. Using the maximum lifting height and boom length of the target crane, the farthest operating distance d2 of the target crane can be calculated. Aerial equipment that satisfies the condition: D < d1 + d2 is then designated as the target aerial equipment. The area at a distance d1 from the target airborne equipment is designated as the target control zone. (See [reference]) Figure 2 .

[0061] S150. Based on the target control area, determine the hovering monitoring position of the deployed drone, and deploy the target drone based on the drone hovering monitoring position.

[0062] The hovering monitoring position of the drone refers to the position where the drone hovers during the operation of the target crane. The target drone refers to a drone equipped with an orthogonal lidar monitoring device.

[0063] Specifically, such as Figure 2 As shown, the lower boundary of the target control area is monitoring plane 2, which is at the corresponding distance limit from the aerial equipment. The boundary between the target control area and the target crane is monitoring plane 1, which is at the corresponding distance limit from the aerial equipment. The drone hovering monitoring position can be any point on the intersection of the two planes. A suitable position can be selected at any point on the intersection of the two planes for the deployment of the target drone.

[0064] Optionally, the hovering monitoring location of the deployed drone can be determined based on the target docking point and the device association data of the target aerial equipment.

[0065] Specifically, based on the device association data of the target aerial equipment, the distance limit information d1 of the target aerial equipment is obtained. The area with a distance less than d1 from the target aerial equipment is designated as the target control area. A drone hovering monitoring point is deployed between the target docking point and the target aerial equipment, where the drone hovering monitoring position is the boundary line intersection of the target control area. It should be noted that, in order not to interfere with the operation of the target crane, the user needs to determine a suitable point for drone deployment.

[0066] Furthermore, the target drone is deployed based on the drone's hovering monitoring location and pre-set deployment rules.

[0067] Deployment rules refer to pre-set rules regarding the number of drones deployed, deployment duration, deployment direction, drone replacement time, and other aspects.

[0068] Specifically, based on the number of target aerial devices, the drone hovering monitoring position, and the operating time of the target crane, parameters such as the number of drones to be deployed and the drone replacement time are preset, and the target drones are deployed at the drone hovering monitoring position.

[0069] The technical solution of this invention involves acquiring the operating parameters of a target crane, including at least one of the target crane's current docking point, maximum lifting height, and boom length. Based on the operating parameters and the 3D point cloud data corresponding to each device within the target operating area, a target operating sub-region corresponding to the target crane is determined and displayed in a human-machine 3D model interaction interface. Based on feedback information on the human-machine 3D model interaction interface, a target docking point corresponding to the target crane is determined. Based on the target docking point and the device association data of each device within the target operating sub-region, a target control area is determined. Based on the target control area, the hovering monitoring position of a deployment drone is determined, and the target drone is deployed based on the drone's hovering monitoring position. This solves the problem that manual visual inspection methods cannot accurately determine the distance between lifting machinery and electrical equipment, posing significant safety hazards. It achieves automated and precise control of electrical equipment, improving operational safety.

[0070] In this embodiment, the method for establishing a three-dimensional spatial model and determining the associated data of the device can be: S101-S105, see... Figure 3 :

[0071] S101. Perform lidar scanning on each device and the environment within the target work area to obtain three-dimensional point cloud data corresponding to each device.

[0072] Among them, lidar scanning uses a three-dimensional scanning system to scan the surface of the object being scanned by a line laser emitted by a line laser, thereby obtaining the spatial position information of each point on the surface of the object.

[0073] Specifically, a lidar scanning system is used to scan the equipment and environment in the target work area to obtain three-dimensional point cloud data of all equipment in the target work area.

[0074] For example, the target work area is substation A. A lidar scanning system is used to scan the ground, walls, capacitor equipment, wires, etc. in the substation to obtain the three-dimensional point cloud data corresponding to each device.

[0075] S102. Based on the three-dimensional point cloud data, determine a three-dimensional spatial model corresponding to the target work area.

[0076] Specifically, the obtained 3D point cloud data of the equipment and environment corresponding to the target work area are input into 3D modeling software, and finally the 3D spatial model of the target work area is output.

[0077] Based on the above example, the obtained 3D point cloud data of each device and environment in substation A is input into the modeling software to obtain a 3D spatial model of the interior of substation A. In the 3D spatial model, the distribution of each device can be clearly observed.

[0078] S103. Based on the device spacing and name information of each device in the three-dimensional spatial model, determine the first associated data corresponding to the device.

[0079] Among these, device spacing refers to the blank area between devices. Name information refers to the name information of the devices. First associated data refers to the device spacing and device name associated with the device.

[0080] For example, since the method for determining the first associated data of each device is the same, the processing method for the current device A will be described below: For the current device A, the device name is coupling capacitor, and the corresponding interval region is region P. Then the first associated data of device A is (coupling capacitor, region P).

[0081] S104. Based on the crossbars, lead wires, and high-altitude busbars between the devices in the three-dimensional spatial model, determine the second associated data corresponding to each device.

[0082] Among them, crossbars, lead wires, and overhead busbars refer to live equipment suspended above the target work area. The second set of related data refers to the relevant data of the crossbars, lead wires, and overhead busbars corresponding to the equipment, such as the name, energization status, and distance limits of the crossbars, lead wires, and overhead busbars.

[0083] For example, since the method for determining the second association data of each device is the same, the processing method for the current device A will be described below: For device A, the energized conductor L1 and the overhead busbar L2 near device A are associated, where conductor L1 is energized at 10kV and has a distance limit of 3 meters. The overhead busbar L2 is energized at 35kV and has a distance limit of 4m. Then the second association data of device A is: (L1, L2), where L1 is (L1, 10kV, 3m) and L2 is (L2, 35kV, 4m).

[0084] S105. Based on the first and second association data of each device, determine the device association data of the corresponding device.

[0085] Among them, equipment association information refers to the information related to the interval area, name information, crossbar, lead wire, and high-altitude busbar associated with the equipment.

[0086] Based on the above example, the first associated data of device A is (coupled capacitor, region P), and the second associated data is (L1, L2), where L1 is (L1, 10kV, 3m) and L2 is (L2, 35kV, 4m). Therefore, the device associated information corresponding to device A is (coupled capacitor, region P, L1, L2).

[0087] In this embodiment, the method for establishing a three-dimensional model involves scanning each device and the environment within the target work area using LiDAR to obtain three-dimensional point cloud data corresponding to each device. Based on the three-dimensional point cloud data, a three-dimensional spatial model corresponding to the target work area is determined. According to the device spacing and name information of each device in the three-dimensional spatial model, first association data corresponding to each device is determined. Based on the crossbars, lead wires, and high-altitude busbars between devices in the three-dimensional spatial model, second association data corresponding to each device is determined. Based on the first and second association data of each device, device association data for the corresponding device is determined, thus establishing a three-dimensional model of the work area and determining the association relationship between each device and the spacing area and the conductor. This provides data support for determining the deployment position of the UAV and enables real-time monitoring of the work area, facilitating timely detection and resolution of problems.

[0088] Example 2

[0089] Figure 4 The flowchart below shows a method for controlling hoisting operations based on unmanned aerial vehicles (UAVs) according to Embodiment 2 of the present invention. Based on the foregoing embodiments, the method for controlling hoisting operations based on UAVs can be further optimized. For specific implementation details, please refer to the detailed description of the embodiments of the present invention. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0090] like Figure 4 As shown, the method includes:

[0091] S210. Perform lidar scanning on the equipment and environment within the target work area, establish a three-dimensional spatial model of the target work area, and display it on the human-machine three-dimensional model interaction interface.

[0092] For example, the target work area is substation A. A lidar scanning system is used to scan the ground, walls, capacitors, wires, etc. in the substation to obtain 3D point cloud data for each device. The obtained 3D point cloud data of each device and environment in substation A is input into modeling software to obtain a 3D spatial model of the interior of substation A, which is displayed on the human-machine 3D model interaction interface. In the human-machine 3D model interaction interface, the distribution of each device can be clearly observed.

[0093] S220. Within the three-dimensional spatial model, the target operation area is divided, its features are labeled and saved; the aerial equipment is labeled and saved; and the various large vehicles are labeled and saved.

[0094] Specifically, the blank areas between various ground devices are named, and the location, area, and adjacent devices of each area are labeled to obtain the characteristic information corresponding to each area; the voltage, length, location, and distance of the aerial devices in the target area are labeled to obtain the characteristic information of each aerial device.

[0095] For example, since the feature annotation method is the same for all regions, we will now explain the feature annotation of one region: The current region is named region P1, and the location information corresponding to region P1 is (p1, p2, p3…pn), where p is the coordinate information of a point on the surface of region P1. The devices near region P1 are device A, device B, and device C. The feature information corresponding to region P1 is then saved. The same operation is performed on each region to obtain the feature information (P1, P2, P3…Pn) for each region. n It is stored on the server.

[0096] Furthermore, feature annotation is performed on each air device within the target area. Since the feature annotation method is the same for each air device, the feature annotation of one air device will be explained below: The current air device is named L1. The voltage level of air device L1 is 220kV and the corresponding distance information is 6M. Then, the feature information (L1, 220kV, 6M) corresponding to air device L1 is saved. The same operation is performed on each air device to obtain the feature information (L1, L2, L3...Ln) corresponding to each air device and save it in the server.

[0097] Furthermore, feature annotation is performed on each vehicle. Since the feature annotation method is the same for all vehicles, we will now explain the feature annotation for one vehicle: The current vehicle is named C1, and the maximum operating distance of the current vehicle C1 is 35 meters. The corresponding feature information is (C1, 35M). The same operation is performed on each vehicle to obtain the corresponding feature information (C1, C2, C3…Cn), which is then stored in the server.

[0098] S230: Obtain the target vehicle's operating parameters and maximum operating distance, including the target vehicle's current docking point information, aerial equipment near the docking point, and operating time.

[0099] For example, by structuring the operation plan information of target vehicle C1, the operation parameters of target vehicle C1 are extracted to obtain the coordinate information of the current stopping point P of target vehicle C1, the aerial equipment L1, L2, L3 near the stopping point, and the operation time of the target vehicle as 9:00-10:00 am; through the feature annotation information of target vehicle C1, the maximum operation distance of target vehicle C1 can be obtained as 35M.

[0100] S240. Determine the target parking point of the target vehicle based on the feedback information on the human-machine three-dimensional model interaction interface.

[0101] For example, a user inputs the actual parking location information of the target vehicle into the human-computer 3D model interaction interface, and the location corresponding to this location information is used as the target parking point of the target vehicle.

[0102] S250. Determine the target work area based on the target parking point information corresponding to the target vehicle; determine at least one target aerial device based on the three-dimensional point cloud data of the target work area and the aerial equipment.

[0103] For example, the target stopping point for target vehicle C1 is P. x Given (x, y, z), the distance between each region and the target docking point is calculated based on the region location information in the region feature information. The region corresponding to the smallest distance value is taken as the target operation area. Furthermore, since the processing method is the same for each aerial device, the processing of one aerial device will be explained below: For aerial device L1, the three-dimensional point cloud data of L1 is acquired, and P is calculated. xThe distance value to each point on aerial equipment L1 is used, and the minimum distance value is taken as the distance information between the target vehicle and aerial equipment L1, denoted as d1. The limit distance of aerial equipment L1 is d2, and the maximum operating distance of target vehicle C1 is d3. d4 = d2 + d3. Compare the magnitudes of d1 and d4. If d1 > d4, it means that aerial equipment L1 does not pose a danger to target vehicle C1. If d1 ≤ d4, then aerial equipment L1 is identified as the target aerial equipment.

[0104] S260. Determine the target control area based on the characteristic markings of the target airborne equipment and the target docking point information.

[0105] For example, if the current target air device L1 has the characteristic information (L1, 220Kv, 6M), then the area between the target vehicle and the air device and at a distance of 6M from the target air device L1 will be the target control area.

[0106] S270. Based on the target control area, determine the drone deployment drone hovering monitoring position, and deploy the target drone according to the pre-set deployment rules.

[0107] For example, such as Figure 2 As shown, the lower boundary of the target control area is plane 2, which is at the corresponding distance limit from the aerial equipment. The boundary between the target control area and the target crane is plane 1, which is at the corresponding distance limit from the aerial equipment. The drone hovering monitoring position can be any point on the intersection of the two planes. A suitable location can be selected at any point on the intersection of the two planes for the deployment of the target drone. Further, before deploying the drone, it is necessary to set the number of drones deployed, the deployment duration, the deployment direction, and the drone replacement time.

[0108] Furthermore, when the target vehicle needs to change location for work purposes, the user can provide real-time feedback on the human-machine 3D model interaction interface and redeploy the drone according to the aforementioned process.

[0109] It should be noted that, in order to maintain drone air defense around the clock, drone power management needs to be introduced. Based on deployment requirements, the operating time T1 of a single drone should be greater than or equal to the operating time T2 of the target vehicle; or the number of drones N deployed in the target area should be greater than the number of drones required for deployment. When one drone runs out of power, it returns to the helipad for battery replacement, and a backup drone takes over monitoring the deployed area.

[0110] The technical solution of this invention involves using LiDAR to scan the equipment and environment within a target work area, establishing a three-dimensional spatial model of the target work area, and displaying it on a human-machine three-dimensional model interaction interface. Within the three-dimensional spatial model, the target work area is divided, its features are labeled and saved; aerial equipment features are labeled and saved; and features of each large vehicle are labeled and saved. The operating parameters and maximum operating distance of the target vehicles are obtained, including the current stopping point information of the target vehicles, aerial equipment near the stopping point, and operating time. Based on the feedback information on the human-machine three-dimensional model interaction interface, the target vehicle is determined. The system identifies the target parking point for each vehicle; determines the target work area based on the target parking point information corresponding to the target vehicle; identifies at least one target aerial device based on the 3D point cloud data of the target work area and the aerial equipment; determines the target control area based on the feature annotations of the target aerial device and the target parking point information; and determines the drone deployment and hovering monitoring position based on the target control area, and deploys the target drone according to the pre-set deployment rules. This solves the problem that manual visual inspection methods cannot accurately judge the distance between hoisting machinery and live equipment, which poses a significant safety hazard. It achieves automated and precise control of live equipment and improves operational safety.

[0111] Example 3

[0112] Figure 5 This is a schematic diagram of a hoisting operation control device based on a drone, provided in Embodiment 3 of the present invention.

[0113] like Figure 5 As shown, the device includes:

[0114] The parameter acquisition module 310 is used to acquire the operating parameters of the target crane; wherein the operating parameters include at least one of the current docking point, maximum lifting height, and boom length of the target crane; the sub-region determination module 320 is used to determine the target operating sub-region corresponding to the target crane based on the operating parameters and the three-dimensional point cloud data corresponding to each device in the target operating region, and display it in the human-machine three-dimensional model interaction interface; the target docking point determination module 330 is used to determine the target docking point corresponding to the target crane based on the feedback information on the human-machine three-dimensional model interaction interface; the target control area determination module 340 is used to determine the target control area based on the target docking point and the device association data of each device in the target operating sub-region; the monitoring position determination module 350 is used to determine the hovering monitoring position of the deployed drone based on the target control area, so as to deploy the target drone based on the drone hovering monitoring position.

[0115] Based on the above technical solutions, the drone-based hoisting operation control device also includes:

[0116] The three-dimensional point cloud data acquisition module is used to perform lidar scanning on each device and the environment within the target work area to obtain three-dimensional point cloud data corresponding to each device.

[0117] The model building module is used to determine a three-dimensional spatial model corresponding to the target work area based on the three-dimensional point cloud data.

[0118] The first associated data determination module is used to determine the first associated data corresponding to the device based on the device spacing and name information of each device in the three-dimensional spatial model.

[0119] The second associated data determination module is used to determine the second associated data corresponding to each device based on the crossbars, lead wires and high-altitude busbars between each device in the three-dimensional space model.

[0120] The device association data determination module is used to determine the device association data of the corresponding device based on the first association data and the second association data of each device.

[0121] Based on the above technical solutions, the sub-region determination module is specifically used for:

[0122] Based on the current docking point in the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, the operation area closest to the current docking point is determined, and the operation area is taken as the target operation sub-area.

[0123] Based on the above technical solutions, the target docking point determination module is specifically used for:

[0124] Based on the user's triggering operation on the interactive interface of the 3D model, the current docking point of the target crane is updated to obtain the target docking point.

[0125] Based on the above technical solutions, the target control area determination module specifically includes:

[0126] The equipment determination unit is used to determine at least one aerial device that poses a risk of electric shock based on the equipment association data of each device in the target operation sub-area and the target docking point;

[0127] The target equipment determination unit is used to determine the target aerial equipment based on the maximum lifting height, boom length and three-dimensional point cloud data of the at least one aerial equipment in the operation parameters.

[0128] The target control area unit is used to determine the target control area based on the target airborne equipment and pre-set distance information.

[0129] Based on the above technical solutions, the monitoring location determination module is specifically used for:

[0130] Based on the target docking point and the device association data of the target aerial equipment, the location for deploying the drone for hovering monitoring is determined.

[0131] Based on the above technical solutions, the drone-based hoisting operation control device also includes:

[0132] The drone deployment module is used to deploy the target drone based on the drone's hovering monitoring location and pre-set deployment rules.

[0133] The technical solution of this invention involves acquiring the operating parameters of a target crane, including at least one of the target crane's current docking point, maximum lifting height, and boom length. Based on the operating parameters and the 3D point cloud data corresponding to each device within the target operating area, a target operating sub-region corresponding to the target crane is determined and displayed in a human-machine 3D model interaction interface. Based on feedback information on the human-machine 3D model interaction interface, a target docking point corresponding to the target crane is determined. Based on the target docking point and the device association data of each device within the target operating sub-region, a target control area is determined. Based on the target control area, the hovering monitoring position of a deployment drone is determined, and the target drone is deployed based on the drone's hovering monitoring position. This solves the problem that manual visual inspection methods cannot accurately determine the distance between lifting machinery and electrical equipment, posing significant safety hazards. It achieves automated and precise control of electrical equipment, improving operational safety.

[0134] The UAV-based hoisting operation control device provided in this embodiment of the invention can execute the UAV-based hoisting operation control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0135] Example 4

[0136] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0137] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0138] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0139] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a drone-based hoisting operation control method.

[0140] In some embodiments, the drone-based hoisting operation control method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the drone-based hoisting operation control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the drone-based hoisting operation control method by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0146] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0147] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0148] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling hoisting operations based on unmanned aerial vehicles (UAVs), characterized in that, include: Obtain the operating parameters of the target crane; wherein, the operating parameters include at least one of the target crane's current docking point, maximum lifting height, and boom length; Based on the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, the target operation sub-area corresponding to the target crane is determined and displayed in the human-machine three-dimensional model interaction interface; Based on the feedback information on the human-machine 3D model interaction interface, determine the target docking point corresponding to the target crane; The target control area is determined based on the equipment association data of each device in the target docking point and the target operation sub-area. Based on the target control area, determine the hovering monitoring position of the deployed drones, and deploy the target drones based on the hovering monitoring position of the drones; The method further includes: LiDAR scanning is performed on each device and the environment within the target work area to obtain three-dimensional point cloud data corresponding to each device. Based on the three-dimensional point cloud data, a three-dimensional spatial model corresponding to the target work area is determined; Based on the equipment intervals and name information of each device in the three-dimensional spatial model, determine the first associated data corresponding to the device; Based on the crossbars, leads, and high-altitude busbars between the devices in the three-dimensional spatial model, the second associated data corresponding to each device is determined. Based on the first and second association data of each device, determine the device association data of the corresponding device. The step of determining the target control area based on the equipment association data of the target docking point and each piece of equipment within the target operating sub-area includes: Based on the equipment association data of each device in the target operation sub-area and the target docking point, at least one aerial device with a risk of electric shock is identified. The target aerial equipment is determined based on the maximum lifting height, boom length, and three-dimensional point cloud data of the at least one aerial equipment in the operation parameters. Based on the target airborne equipment and the pre-set distance limit information, the target control area is determined.

2. The method according to claim 1, characterized in that, The step of determining the target operation sub-region corresponding to the target crane based on the operation parameters and the three-dimensional point cloud data corresponding to each device within the target operation area includes: Based on the current docking point in the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, the operation area closest to the current docking point is determined, and the operation area is taken as the target operation sub-area.

3. The method according to claim 1, characterized in that, The step of determining the target docking point corresponding to the target crane based on the feedback information on the human-machine 3D model interaction interface includes: Based on the user's triggering operation on the interactive interface of the 3D model, the current docking point of the target crane is updated to obtain the target docking point.

4. The method according to claim 1, characterized in that, The step of determining the hovering monitoring position of the deployed drones based on the target control area includes: Based on the target docking point and the device association data of the target aerial equipment, the location for deploying the drone for hovering monitoring is determined.

5. The method according to claim 1, characterized in that, Also includes: The target drone is deployed based on the drone's hovering monitoring location and pre-set deployment rules.

6. A drone-based hoisting operation control device, capable of executing the drone-based hoisting operation control method according to any one of claims 1-5, characterized in that, include: The parameter acquisition module is used to acquire the operating parameters of the target crane; wherein, the operating parameters include at least one of the target crane's current docking point, maximum lifting height, and boom length; The sub-region determination module is used to determine the target operation sub-region corresponding to the target crane based on the operation parameters and the three-dimensional point cloud data corresponding to each device in the target operation area, and display it in the human-machine three-dimensional model interaction interface; The target docking point determination module is used to determine the target docking point corresponding to the target crane based on the feedback information on the human-machine three-dimensional model interaction interface; The target control area determination module is used to determine the target control area based on the target docking point and the equipment association data of each device in the target operation sub-area; The drone deployment module is used to determine the hovering monitoring position of the deployed drone based on the target control area, and to deploy the target drone based on the drone hovering monitoring position.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the UAV-based hoisting operation control method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the unmanned aerial vehicle (UAV)-based hoisting operation control method according to any one of claims 1-5.

Images