Monitoring equipment casting method and device, electronic equipment, storage medium, program product

Abstract

The embodiments of the present application disclose a method, device, equipment and storage medium for dropping monitoring equipment. The method includes: collecting at least one picture of a geological disaster area, cropping and splicing the at least one picture of a geological disaster area to obtain a geological disaster area map; determining the target drop point of the monitoring equipment on the geological disaster area map; using the digital elevation model data of the survey area to determine the drop guide of the monitoring equipment, so as to drop the monitoring equipment to the target drop point based on the drop guide. The embodiments of the present application can not only improve the measurement of the geological disaster area map, but also improve the drop accuracy of the monitoring equipment, which is conducive to collecting the address information of the geological disaster area and further improving the early warning of geological disasters.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to a method and device for casting a monitoring device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Geological disasters are geological actions or phenomena that are caused by natural or human factors and cause damage to the environment, seriously endangering human life and property. Although Beidou / GNSS has played an important role in geological disaster monitoring, the poor observation conditions, difficult power supply guarantee, harsh installation environment, unstable communication and other factors in complex mountainous areas make it impossible to accurately and continuously monitor. It is urgent to carry out the precise deployment of geological disaster monitoring equipment in complex mountainous areas to accurately monitor geological disaster areas. Summary of the invention

[0003] In order to solve the above technical problems, the embodiments of the present application provide a method and apparatus for casting a monitoring device, an electronic device, a computer-readable storage medium, and a computer program product.

[0004] According to one aspect of an embodiment of the present application, a method for dropping a monitoring device is provided, comprising: collecting at least one scene of a geological disaster area, cropping and splicing the at least one scene of the geological disaster area to obtain a geological disaster area map; determining a target drop point for the monitoring device on the geological disaster area map; determining a drop guide for the monitoring device using digital elevation model data of the survey area, and dropping the monitoring device to the target drop point based on the drop guide.

[0005] According to one aspect of an embodiment of the present application, determining a target deployment point of the monitoring equipment on the geological disaster area map includes:

[0006] Performing semantic segmentation on the geological disaster area map based on deep learning image semantic information extraction technology to obtain image semantic information corresponding to the geological disaster area map;

[0007] Extracting feature points from the geological disaster area map to obtain feature points corresponding to the geological disaster area map;

[0008] The target placement point of the monitoring equipment is determined based on the image semantic information and feature points of the geological disaster area map.

[0009] According to one aspect of an embodiment of the present application, the step of clipping and splicing the at least one geological disaster area scene to obtain a geological disaster area map includes:

[0010] The motion recovery structure algorithm and the multi-view stereo vision algorithm are used to crop and splice the at least one geological disaster area scene to obtain a geological disaster area map.

[0011] According to one aspect of the embodiment of the present application, the method further includes:

[0012] The monitoring device is dropped to the target drop point by a dropping drone based on the drop instructions.

[0013] According to one aspect of the embodiment of the present application, the method further includes:

[0014] The aerodynamic model of the throwing UAV is mathematically modeled by using the Lagrangian dynamics theorem to obtain the mathematical model of the throwing UAV;

[0015] The mathematical model is decoupled and calculated, and a stabilization system of the throwing drone is determined based on a proportional differential control technology, so that the throwing drone is controlled to hover above the target delivery point based on the stabilization system.

[0016] According to one aspect of an embodiment of the present application, before throwing the monitoring device to the target delivery point based on the delivery instruction, the method further includes:

[0017] Acquire the distance measurement information of the throwing drone, and determine the fixed throwing height of the throwing drone according to the distance measurement information;

[0018] The position information and environmental information of the throwing drone are obtained, and a delivery trajectory model of the throwing drone is determined based on the position information, environmental information and a fixed delivery height.

[0019] According to one aspect of an embodiment of the present application, throwing the monitoring device to the target delivery point based on the delivery instruction includes:

[0020] Determining a target delivery time of the drone based on the delivery trajectory model and delivery instructions;

[0021] The monitoring device is dropped to the target delivery point by a dropping drone based on the target delivery time.

[0022] According to one aspect of an embodiment of the present application, a monitoring equipment casting device is provided, the device comprising: a collection module, used to collect at least one geological disaster area scene, and to crop and splice the at least one geological disaster area scene to obtain a geological disaster area map; a determination module, used to determine the target casting point of the monitoring equipment on the geological disaster area map; and a casting module, used to determine the casting guidance of the monitoring equipment by using the digital elevation model data of the survey area, so as to cast the monitoring equipment to the target casting point based on the casting guidance.

[0023] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the monitoring device casting method as described above.

[0024] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the monitoring device casting method as described above.

[0025] According to one aspect of an embodiment of the present application, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the steps in the monitoring device casting method as described above.

[0026] In the technical solution provided in the embodiments of the present application, a map of the geological disaster area is obtained by collecting at least one picture of the scene in the geological disaster area, and cropping and splicing the at least one picture of the scene in the geological disaster area, and determining the target placement point where the monitoring equipment needs to be placed on the map of the geological disaster area, and determining the placement guidance of the monitoring equipment through the digital elevation model data of the survey area, so that the monitoring equipment is thrown to the target placement point according to the placement guidance, thereby not only improving the measurement of the geological disaster area map, but also improving the placement accuracy of the monitoring equipment, which is conducive to collecting address information of the geological disaster area and further improving the early warning of geological disasters.

[0027] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0029] Figure 1 is a schematic diagram of a monitoring device shown in an exemplary embodiment of the present application;

[0030] Figure 2 is a flow chart of a monitoring device casting method shown in an exemplary embodiment of the present application;

[0031] Figure 3 is a flow chart of a monitoring device casting method shown in an exemplary embodiment of the present application;

[0032] Figure 4 is a flow chart of a method for casting a monitoring device shown in an exemplary embodiment of the present application;

[0033] Figure 5 is a flow chart of a method for casting a monitoring device shown in an exemplary embodiment of the present application;

[0034] Figure 6 is a flow chart of a method for casting a monitoring device shown in an exemplary embodiment of the present application;

[0035] Figure 7 is a block diagram of a monitoring device throwing device shown in an exemplary embodiment of the present application;

[0036] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0038] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0039] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0040] The term "multiple" as used in this application refers to two or more than two. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0041] First of all, please refer to Figure 1 , Figure 1 It is the monitoring equipment mentioned in this application, and this application focuses on the key scientific and technological issues of low-power and high-precision GNSS / MEMS chip module technology for geological disaster monitoring, the development of low-power adaptive multi-parameter modular terminals, GNSS ground / satellite-based collaborative precise positioning under complex mountain observation conditions, high integration and precise casting of GNSS monitoring and early warning equipment in high-risk environments, and the construction of end-cloud combined self-learning geological disaster early warning models and criteria. It develops Beidou intelligent perception fusion chip module software and hardware technology for weak signal scenarios, the development of low-power and high-precision adaptive multi-parameter Beidou / GNSS intelligent terminals under extreme working conditions, and drone-dropped GNSS monitoring equipment for high-risk environments to improve the quality of observation signals of monitoring equipment.

[0042] In the existing technology, in response to the demand for real-time high-precision positioning of terminals, real-time detection and processing methods for roughness and cycle slips that take into account the deformation characteristics of the monitored body, as well as detection, identification and elimination methods for abnormal GNSS observations in complex scenarios based on multi-fault assumptions are studied to provide "clean" observation data for real-time positioning solutions; colored noise estimation theory is introduced to study multipath suppression methods that combine random model compensation and function model correction to reduce the impact of multipath errors on the real-time positioning accuracy of terminals; a tropospheric delay parameter process noise adaptive processing method suitable for complex monitoring environments and weather changes is established to achieve real-time precision positioning in large altitude differences and extreme weather conditions.

[0043] The problems pointed out above are generally applicable in general geological disaster monitoring scenarios. It can be seen that how to accurately cast geological monitoring equipment in geological disaster areas. In order to solve this technical problem, the embodiments of the present application respectively propose a monitoring equipment casting method, a monitoring equipment casting device, an electronic device, a computer-readable storage medium and a computer program product. These embodiments will be described in detail below.

[0044] Please refer to Figure 2 , Figure 2 It is a flow chart of a monitoring device casting method shown in an exemplary embodiment of the present application.

[0045] like Figure 2 As shown, in an exemplary embodiment, the monitoring device casting method includes at least steps S210 to S230, which are described in detail as follows:

[0046] Step S210, collecting at least one picture of the geological disaster area, and cutting and splicing the at least one picture of the geological disaster area to obtain a geological disaster area map;

[0047] Step S220, determining a target deployment point of the monitoring equipment on the geological disaster area map;

[0048] Step S230: Determine the placement guide of the monitoring device by using the digital elevation model data of the survey area, so as to drop the monitoring device to the target drop point based on the placement guide.

[0049] First of all, it should be explained that, based on the national geological monitoring and early warning information system and with long-term continuous observation of multi-source monitoring data as the basis, a complex mountain landslide monitoring and early warning sample system is established from the two major perspectives of geological elements and monitoring elements; landslides with obvious deformation are screened, and monitoring data cleaning such as GNSS, rainfall, and cracks is carried out, and cleaning rules for different types of monitoring equipment are established; sample data table construction rules and data acquisition processes are established, and a complex mountain landslide monitoring and early warning sample library is constructed; typical landslide geological models in complex mountainous areas are analyzed and constructed, and correlation studies on different monitoring data are carried out to determine the optimal sampling time for monitoring data such as rainfall, GNSS, and crack meters, so as to achieve the optimal selection and standardized expression of sample library monitoring data, and support monitoring program design and early warning model indicator selection and threshold setting.

[0050] Specifically, in step S210, in some feasible embodiments, a small survey drone with integrated Beidou and vision modules can be used to conduct preliminary survey, framing, cropping and splicing of geological disaster areas, and a map of geological disaster areas can be constructed by combining the motion recovery structure algorithm SFM and the multi-view stereo vision algorithm PMVS.

[0051] In step S220, based on the stitched geological disaster area map, the InSAR image digital elevation model and other information are used to detect key deformation areas with large deformation magnitudes in the monitoring area; at the same time, based on the constructed disaster area map, a series of algorithms such as the Laplace operator (LoG), the Canny operator and the edge detection method based on mathematical morphology are used in the deformation area to accurately identify the key deformation features such as cracks, depressions, steps on the surface of the disaster body in the monitoring area, and select the monitoring target deployment points.

[0052] It should be noted that regarding how to deploy the monitoring equipment to the monitoring target deployment points in the address disaster area, in this embodiment, the monitoring equipment is deployed to the monitoring target deployment points in the address disaster area based on the high-precision navigation and positioning technology of the throwing drone with a multi-sensor combination and the high-reliability regional communication technology.

[0053] In step S230, the digital elevation model (DEM) data of the survey area is used to design indicators such as the relative altitude and lateral overlap of the survey area, and an intelligent algorithm is used to achieve optimal flight path planning for the UAV, providing guidance information for the UAV to quickly release the GNSS monitoring equipment.

[0054] In this embodiment, a map of the geological disaster area is obtained by collecting at least one picture of the scene in the geological disaster area, and cropping and splicing the at least one picture of the scene in the geological disaster area, and determining the target placement point where the monitoring equipment needs to be placed on the map of the geological disaster area, and determining the placement guidance of the monitoring equipment through the digital elevation model data of the survey area, so that the monitoring equipment is thrown to the target placement point according to the placement guidance, thereby not only improving the measurement of the geological disaster area map, but also improving the placement accuracy of the monitoring equipment, which is conducive to collecting the address information of the geological disaster area and further improving the early warning of geological disasters.

[0055] Further, based on the above embodiment, please refer to Figure 3 In one of the exemplary embodiments provided in the present application, the specific implementation process of determining the target deployment point of the monitoring equipment on the geological disaster area map may also include steps S310 to S330, which are described in detail as follows:

[0056] Step S310, performing semantic segmentation on the geological disaster area map based on deep learning image semantic information extraction technology to obtain image semantic information corresponding to the geological disaster area map;

[0057] Step S320, extracting feature points from the geological disaster area map to obtain feature points corresponding to the geological disaster area map;

[0058] Step S330, determining the target deployment point of the monitoring equipment based on the image semantic information and feature points of the geological disaster area map.

[0059] Specifically, in this embodiment, the image semantic information extraction technology based on deep learning is studied, and the image semantic segmentation algorithm based on Mask-RCNN and the segmentation refinement algorithm based on three-dimensional geometric information are used to realize the fine segmentation of image semantics; the Oriented FAST and BRIEF method is used to extract feature points, and the feature points are screened in combination with semantic information; the feature matching algorithm based on semantic information is studied to find the correspondence between each feature of the preset image and the on-site image, so as to realize fast and accurate matching with the preset casting target point image.

[0060] It should be noted that ORB (Oriented FAST and BRIEF) feature is a very common feature in SLAM. Due to its binary characteristics, it can be extracted and calculated very quickly. ORB feature points take into account scale invariance and rotation invariance in the previous faster algorithm, further improving the algorithm effect and taking into account both real-time and algorithm performance.

[0061] In this embodiment, the image semantic information corresponding to the geological disaster area map is obtained by cropping and splicing the collected pictures of the geological disaster area, and the disaster area map is quickly constructed by extracting the feature points of the geological disaster area map. Then, the disaster area map and the intelligent algorithm are used to select the key landslide monitoring target deployment points, and the relative altitude of the survey area, lateral overlap and other indicators are designed. The intelligent algorithm is used to realize the selection of the optimal flight path for the throwing drone, so as to provide guidance information for the subsequent rapid deployment of high-precision GNSS monitoring equipment for geological disasters in complex mountainous areas by the throwing drone.

[0062] Further, based on the above embodiment, in one of the exemplary embodiments provided in the present application, the specific implementation process of clipping and splicing the at least one geological disaster area scene to obtain a geological disaster area map may also include:

[0063] The motion recovery structure algorithm and the multi-view stereo vision algorithm are used to crop and splice the at least one geological disaster area scene to obtain a geological disaster area map.

[0064] Specifically, in this embodiment, the Structure from Motion (SfM) algorithm is a 3D reconstruction algorithm that can restore the camera pose through two or more scenes (pictures) and reconstruct 3D coordinate points. This includes extracting and matching the feature points of the pictures, selecting two pictures from a group of pictures of geological disaster areas, and estimating the camera pose based on the two pictures, thereby reconstructing the 3D coordinate points.

[0065] In addition, it should be noted that stereoscopic vision literally means perceiving three-dimensional structure with one or two eyes. Generally speaking, it refers to obtaining two or more images from different viewpoints to reconstruct the 3D structure or depth information of the target object. Depth perception visual cues can be divided into Monocular cues and Binocular cues (binocular parallax). Currently, stereoscopic vision 3D can be achieved through monocular vision, binocular vision, multi-(eye) vision, and light field 3D imaging (electronic compound eyes or array cameras).

[0066] Therefore, in this embodiment, based on the collected multiple photos of geological disaster areas, the motion recovery structure algorithm and the multi-view stereo vision algorithm are used to crop and splice the geological disaster area map, so that the constructed geological disaster area map is more real and reliable, laying a solid map foundation for the further deployment of monitoring equipment.

[0067] Further, based on the above embodiment, in one of the exemplary embodiments provided in the present application, the monitoring device casting method further includes:

[0068] The monitoring device is dropped to the target drop point by a dropping drone based on the drop instructions.

[0069] Specifically, use multi-source sensors to obtain high-precision posture and environmental information of the UAV, and automatically adjust the fixed throwing height of the UAV to the target point based on the ranging information; combine the speed, windward area, shape and air density of the UAV relative to the air to establish the throwing trajectory model of the GNSS monitoring equipment, and calculate the final landing position from the hovering position; select the best throwing time according to information such as wind speed to achieve autonomous and accurate throwing of GNSS monitoring equipment. According to the requirements of the UAV's load capacity and endurance, efficiently integrate Beidou\inertial navigation\vision\anemometer and other multi-source sensors on the UAV, embed the precise matching technology of the throwing target monitoring point, the UAV hovering control technology, and the UAV autonomous control throwing technology into the UAV control module, and develop a UAV with integrated multi-source sensors and autonomous control throwing function.

[0070] Further, based on the above embodiment, please refer to Figure 4 In one of the exemplary embodiments provided in the present application, the above monitoring device casting method may further specifically include step S410 and step S420, which are described in detail as follows:

[0071] Step S410, using Lagrangian dynamics theorem to mathematically model the aerodynamic model of the throwing drone to obtain the mathematical model of the throwing drone;

[0072] Step S420, performing decoupling calculation on the mathematical model, and determining the stabilization system of the throwing drone based on the proportional differential control technology, so that the throwing drone is controlled to hover above the target delivery point based on the stabilization system.

[0073] Specifically, the drone hovering control technology based on PD (proportional differential control) uses the Lagrangian dynamics theorem to mathematically model the aerodynamic model of the drone, and then uses the basic methods of modern control theory to decouple the mathematical model of the drone, introduces PD regulation, and can respond quickly under the action of proportional control differential control parameters, thereby ensuring that the drone is stably hovering above the determined drop point. The aerodynamic model of the drone is a typical multi-input and multi-output system with a strongly coupled underactuated system. According to the Lagrangian dynamics equation, the parametric equation of the mass dynamic field is established.

[0074]

[0075] In the above formula, the angles of rotation of the quadcopter along the three coordinate axes are respectively, representing the roll angle, pitch angle and yaw angle. These angles are used to realize the real-time operation posture control of the drone, and realize more complex action control through posture changes, such as rolling and diving. Through posture control, the drone can optimize the flight trajectory during flight, and can also adaptively compensate for meteorological interference (such as wind speed) through posture control. The simplified particle position coordinates during the flight of the drone are, and the radius of the drone is 1. The essence of the drone flight process is the change of the corresponding three coordinate axis parameter values. In order to make the drone hover stably, the control target of the corresponding coordinate axis is,.

[0076] Secondly, the position control law is designed. For the drone hovering control system, the adjustment process of position control is mainly a quick response, which is used as a local parameter adjustment of the system, so this topic uses a PD regulator. For the x-axis position subsystem, y-axis position subsystem, and z-axis position subsystem, the control law is designed using the compensation PD control method. The proportional control coefficient and the differential control coefficient are both positive real numbers, which can ensure that the three coordinate axis control subsystems are stable systems, thereby improving the stability of the drone's stable hovering.

[0077] Further, based on the above embodiment, please refer to Figure 5 In one of the exemplary embodiments provided in the present application, before the monitoring device is thrown to the target delivery point based on the delivery instruction, the monitoring device throwing method further specifically includes step S510 and step S520, which are described in detail as follows:

[0078] Step S510, obtaining the distance measurement information of the throwing drone, and determining the fixed throwing height of the throwing drone according to the distance measurement information;

[0079] Step S520, obtaining the position information and environmental information of the throwing drone, and determining the throwing trajectory model of the throwing drone based on the position information, environmental information and a fixed throwing height.

[0080] Specifically, the flight controller is combined with multi-source sensors such as RTK-GNSS mobile station, lidar, radio station airborne terminal, etc. to obtain data such as high-precision position, attitude and environmental information of the throwing drone; the lidar is used to measure the real-time distance information between the drone and the ground point, and the drone is automatically adjusted to the set fixed throwing height based on the distance measurement information.

[0081] The drone hovers over the target and releases the monitoring equipment. The falling process is considered as a falling motion affected by gravity and air resistance in the vertical direction and as a variable speed motion affected only by continuous wind in the horizontal direction. After the monitoring equipment is released from the hovering position by the drone, the trajectory function model of the motion trajectory x, y and the falling time t is established by combining the influencing factors such as the speed relative to the air, the windward area, the shape and the air density, which is used to calculate and improve the throwing accuracy of the thrown monitoring equipment.

[0082] Further, based on the above embodiment, please refer to Figure 6 In one of the exemplary embodiments provided in the present application, the specific implementation process of throwing the monitoring device to the target delivery point based on the delivery instruction may further specifically include step S610 and step S620, which are described in detail as follows:

[0083] Step S610, determining a target delivery time of the drone based on the delivery trajectory model and delivery instructions;

[0084] Step S620: Dropping the monitoring device to the target drop point via a dropping drone based on the target drop time.

[0085] Specifically, in this embodiment, the dropping time point of the drone used to drop the monitoring equipment is determined through the monitoring equipment dropping drone delivery trajectory model constructed in the above embodiments and the monitoring equipment delivery guidance, that is, it is determined at what time point the drone will drop the monitoring equipment to the target delivery point in the geological disaster area, wherein there are multiple target delivery points in the geological disaster area, wherein the target delivery time points for multiple target delivery points may be the same or different, and the specific target delivery time point may be determined based on the terrain characteristics of the target delivery point and the delivery trajectory of the dropping drone, and this application does not impose any restrictions on this.

[0086] In step S620, by utilizing a computer simulation algorithm and introducing parameters such as wind speed, air pressure, equipment shape and mass, and various postures of the drone and equipment, the casting trajectory function is calculated and posture modeling is performed for the various casting equipment developed, to ensure that the best casting posture and timing can be appropriately selected in actual use; finally, real casting trajectory data is collected for different landslides, and the empirical trajectory function and posture model are improved so that the monitoring equipment can be dropped by the casting drone to the target release point in the geological disaster area at the target release time.

[0087] In this embodiment, a computer simulation algorithm is used to introduce parameters such as wind speed, air pressure, equipment shape and mass, and various postures of the drone and equipment. The casting trajectory function and posture modeling are performed for the multiple types of casting equipment developed, ensuring that the best casting posture and timing can be appropriately selected in actual use, thereby improving the accuracy of the deployment of monitoring equipment in complex mountainous geological conditions.

[0088] Figure 7 It is a block diagram of a monitoring equipment casting device shown in an exemplary embodiment of the present application.

[0089] like Figure 7 As shown, the exemplary monitoring equipment throwing device includes:

[0090] The acquisition module 710 is used to acquire at least one geological disaster area scene, and to cut and splice the at least one geological disaster area scene to obtain a geological disaster area map;

[0091] A determination module 720 is used to determine a target deployment point of the monitoring equipment on the geological disaster area map;

[0092] The delivery module 730 is used to determine the delivery guidance of the monitoring device by using the digital elevation model data of the survey area, so as to drop the monitoring device to the target delivery point based on the delivery guidance.

[0093] According to another exemplary embodiment of the present application, the determination module 720 further includes:

[0094] An image semantic information extraction unit is used to perform semantic segmentation on the geological disaster area map based on a deep learning image semantic information extraction technology to obtain image semantic information corresponding to the geological disaster area map;

[0095] A feature point extraction unit is used to extract feature points from the geological disaster area map to obtain feature points corresponding to the geological disaster area map;

[0096] The determination unit is used to determine the target deployment point of the monitoring equipment based on the image semantic information and feature points of the geological disaster area map.

[0097] According to another exemplary embodiment of the present application, the device further includes:

[0098] The geological disaster area map acquisition unit is used to obtain a geological disaster area map by cutting and splicing based on the at least one geological disaster area scene through the motion recovery structure algorithm and the multi-view stereo vision algorithm.

[0099] According to another exemplary embodiment of the present application, the device further includes:

[0100] A throwing drone control device is used to throw the monitoring equipment to the target delivery point through the throwing drone based on the delivery instructions.

[0101] According to another exemplary embodiment of the present application, the device further includes:

[0102] A mathematical model building module is used to mathematically model the aerodynamic model of the throwing drone using the Lagrangian dynamics theorem to obtain the mathematical model of the throwing drone;

[0103] The decoupling calculation module is used to perform decoupling calculation on the mathematical model and determine the stabilization system of the throwing drone based on the proportional differential control technology, so that the throwing drone is controlled to hover above the target delivery point based on the stabilization system.

[0104] According to another exemplary embodiment of the present application, the device further includes:

[0105] A first acquisition module is used to acquire the distance measurement information of the throwing drone, and determine the fixed delivery height of the throwing drone according to the distance measurement information;

[0106] The second acquisition module is used to obtain the position information and environmental information of the throwing drone, and determine the delivery trajectory model of the throwing drone based on the position information, environmental information and a fixed delivery height.

[0107] According to another exemplary embodiment of the present application, the delivery module 730 further includes:

[0108] A target delivery time determination unit, configured to determine a target delivery time for the delivery drone based on the delivery trajectory model and delivery instructions;

[0109] A throwing unit is used to throw the monitoring device to the target delivery point based on the target delivery time by a throwing drone.

[0110] It should be noted that the monitoring equipment casting device provided in the above embodiment and the monitoring equipment casting method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In practical applications, the monitoring equipment casting device provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0111] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the monitoring device casting method provided in the above-mentioned embodiments.

[0112] Figure 8 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 8 The computer system 800 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0113] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 to the random access memory (RAM) 803, such as executing the method described in the above embodiment. In the RAM 803, various programs and data required for system operation are also stored. The CPU 801, ROM 802 and RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0114] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0115] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 809, and / or installed from a removable medium 811. When the computer program is executed by a central processing unit (CPU) 801, various functions defined in the system of the present application are executed.

[0116] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0117] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0118] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0119] Another aspect of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method for casting a monitoring device as described above is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0120] Another aspect of the present application also provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the monitoring device casting method provided in each of the above embodiments.

[0121] The above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. A person skilled in the art can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A method for casting a monitoring device, characterized in that: include: Collecting at least one picture of a geological disaster area, and cutting and splicing the at least one picture of a geological disaster area to obtain a geological disaster area map; The step of cutting and splicing the at least one geological disaster area scene to obtain a geological disaster area map includes: The at least one geological disaster area scene is cut and spliced ​​by using a motion recovery structure algorithm and a multi-view stereo vision algorithm to obtain a geological disaster area map; Determining a target deployment point of the monitoring equipment on the geological disaster area map; Determining the target placement point of the monitoring equipment on the geological disaster area map includes: Performing semantic segmentation on the geological disaster area map based on deep learning image semantic information extraction technology to obtain image semantic information corresponding to the geological disaster area map; Extracting feature points from the geological disaster area map to obtain feature points corresponding to the geological disaster area map; Determine the target placement point of the monitoring equipment based on the image semantic information and feature points of the geological disaster area map, wherein there are multiple target placement points in the geological disaster area, find the correspondence between the preset image and the on-site image, achieve fast and accurate matching with the preset throwing target point image, and select the key landslide monitoring target placement point; Determine the placement guide of the monitoring device by using the digital elevation model data of the survey area, and drop the monitoring device to the target drop point by using a dropping drone based on the drop guide; Before throwing the monitoring device to the target delivery point based on the delivery instruction, the method further includes: Acquire the distance measurement information of the throwing drone, and determine the fixed throwing height of the throwing drone according to the distance measurement information; Acquire the position information and environmental information of the throwing drone, and determine the delivery trajectory model of the throwing drone based on the position information, environmental information and a fixed delivery height; The method of dropping the monitoring device to the target drop point by using a dropping drone based on the drop guide includes: Determining the target delivery time of the drone based on the delivery trajectory model and the delivery guide, wherein each target delivery point has a corresponding target delivery time point, and the target delivery time point is determined according to the terrain characteristics of the target delivery point and the delivery trajectory of the drone; The monitoring equipment is dropped to the target delivery point based on the target delivery time by a dropping drone, wherein the parameters of wind speed, air pressure, equipment shape and mass, and various postures of the drone and the equipment are introduced by utilizing a computer simulation algorithm, and the dropping trajectory function calculation and posture modeling are performed for the multiple throwing equipment developed; finally, real throwing trajectory data are collected for different landslides, and the empirical trajectory function and posture model are improved, so that the monitoring equipment can be dropped to the target delivery point in the geological disaster area by the dropping drone at the target delivery time.

2. The method according to claim 1, characterized in that The method further comprises: The aerodynamic model of the throwing UAV is mathematically modeled by using the Lagrangian dynamics theorem to obtain a mathematical model of the throwing UAV; The mathematical model is decoupled and calculated, and a stabilization system of the throwing drone is determined based on a proportional differential control technology, so that the throwing drone is controlled to hover above the target delivery point based on the stabilization system.

3. A monitoring equipment throwing device, characterized in that: The device comprises: A collection module, used for collecting at least one geological disaster area scene, cutting and splicing the at least one geological disaster area scene to obtain a geological disaster area map; The step of cutting and splicing the at least one geological disaster area scene to obtain a geological disaster area map includes: The at least one geological disaster area scene is cut and spliced ​​by using a motion recovery structure algorithm and a multi-view stereo vision algorithm to obtain a geological disaster area map; A determination module, used to determine the target deployment point of the monitoring equipment on the geological disaster area map; Determining the target placement point of the monitoring equipment on the geological disaster area map includes: Performing semantic segmentation on the geological disaster area map based on deep learning image semantic information extraction technology to obtain image semantic information corresponding to the geological disaster area map; Extracting feature points from the geological disaster area map to obtain feature points corresponding to the geological disaster area map; Determine the target placement point of the monitoring equipment based on the image semantic information and feature points of the geological disaster area map, wherein there are multiple target placement points in the geological disaster area, find the correspondence between the preset image and the on-site image, achieve fast and accurate matching with the preset throwing target point image, and select the key landslide monitoring target placement point; A delivery module is used to determine the delivery instructions of the monitoring device by using the digital elevation model data of the survey area, and to deliver the monitoring device to the target delivery point by using a delivery drone based on the delivery instructions; Before throwing the monitoring device to the target delivery point based on the delivery instruction, the method further includes: Acquire the distance measurement information of the throwing drone, and determine the fixed throwing height of the throwing drone according to the distance measurement information; Acquire the position information and environmental information of the throwing drone, and determine the delivery trajectory model of the throwing drone based on the position information, environmental information and a fixed delivery height; The method of dropping the monitoring device to the target drop point by using a dropping drone based on the drop guide includes: Determining the target delivery time of the drone based on the delivery trajectory model and the delivery guide, wherein each target delivery point has a corresponding target delivery time point, and the target delivery time point is determined according to the terrain characteristics of the target delivery point and the delivery trajectory of the drone; The monitoring equipment is dropped to the target delivery point based on the target delivery time by a dropping drone, wherein the parameters of wind speed, air pressure, equipment shape and mass, and various postures of the drone and the equipment are introduced by utilizing a computer simulation algorithm, and the dropping trajectory function calculation and posture modeling are performed for the multiple throwing equipment developed; finally, real throwing trajectory data are collected for different landslides, and the empirical trajectory function and posture model are improved, so that the monitoring equipment can be dropped to the target delivery point in the geological disaster area by the dropping drone at the target delivery time.

4. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the monitoring device casting method as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the monitoring device casting method described in any one of claims 1 to 2.

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