A low-frequency, high-throughput, large-scale sensing information control method and device

Abstract

The present invention discloses a low-frequency, high-throughput, large-scale sensing information control method and device, comprising: utilizing LiDAR technology to continuously generate 3D point cloud information of the ground environment during drone flight detection and recording the drone's flight information; the drone extracting high-level semantic information from the 3D point cloud information and performing a first judgment on the 3D point cloud information based on the extracted high-level semantic information combined with an obstacle recognition and judgment mechanism; if an obstacle fails to trigger the judgment mechanism, the drone calculates the spatial change rate during the flight detection process based on the 3D point cloud information and its own flight information, and performs a second judgment on the 3D point cloud information based on the spatial change rate; while performing the first and second judgments on the 3D point cloud information, the drone communicates with a terminal via a wireless communication module and transmits data in real time. The present invention can ensure the accuracy and integrity of the drone during underground environment exploration, avoid information congestion during the transmission of point cloud information, and improve the drone's detection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of information control, and in particular to a low-frequency, high-throughput, large-scale sensing information control method and device. Background Art

[0002] At present, the method of using drones for underground environment exploration has become very common. By using the rapid laser pulses of LiDAR technology to draw point clouds of the surface, it can also collect and map detailed information and accurate 3D models of objects, such as obstacles, terrain bumps, terrain depressions and other common surface morphologies in underground environments.

[0003] However, LiDAR technology cannot be used continuously during the drone's underground environment exploration process. If LiDAR technology is continuously used in the drone's underground environment exploration, a large amount of sensor information will be generated in a short period of time. Due to the limitations of the underground environment's wireless signals, the drone's sensor information will be congested, hindering the exploration and mapping process. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a low-frequency, high-throughput, large-scale sensor information control method and device to solve the problem that the existing UAV environment exploration continuously uses LiDAR technology, resulting in sensor information congestion and hindering the exploration and mapping process.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a low-frequency, high-throughput, large-scale sensing information control method, comprising: based on a UAV flight detection process, continuously generating 3D point cloud information of the ground environment using LiDAR technology and recording the flight information of the UAV;

[0009] The drone extracts high-level semantic information from the 3D point cloud information, and performs a first judgment on the returned 3D point cloud information based on the extracted high-level semantic information in combination with an obstacle recognition and judgment mechanism;

[0010] If the obstacle fails to trigger the judgment mechanism, the drone calculates the spatial change rate during the flight detection process based on the 3D point cloud information and its own flight information, and performs a second judgment on the 3D point cloud information returned according to the spatial change rate;

[0011] When the 3D point cloud information is transmitted back for the first judgment and the second judgment, communication is performed with the terminal via the wireless communication module to transmit data in real time.

[0012] As a preferred embodiment of the low-frequency, high-throughput, large-scale sensing information control method of the present invention, the continuous recording of the UAV's flight information includes:

[0013] Get the recording period T, time sampling interval t, maximum flight speed v, unit speed increment u, maximum flight acceleration α and the current state configuration of the drone.

[0014] As a preferred embodiment of the low-frequency, high-throughput, large-scale sensing information control method of the present invention, it further includes:

[0015] According to the recording period T and the time sampling interval t, the sampling times t1, t2, t3...T are obtained;

[0016] Obtain a sampling speed set based on the maximum flight speed v and the unit speed increment u;

[0017] Selecting a speed value based on the sampled speed set, and determining the speed at each sampling moment in sequence, wherein the speed difference between adjacent sampling moments does not exceed the maximum flight acceleration α;

[0018] The position of the drone at each sampling moment is calculated based on the current state configuration of the drone and the speed at each sampling moment.

[0019] As a preferred embodiment of the low-frequency, high-throughput, large-scale sensing information control method of the present invention, the extracted high-level semantic information is combined with the obstacle recognition and judgment mechanism to perform a first judgment on the 3D point cloud information returned, including:

[0020] The point cloud information is the point cloud information T seconds before the event triggers the node. The extraction of high-level semantic features and identification of obstacles is to use a classification regression tree for decision making. The obstacle features are the obstacle height, the obstacle height from the ground, and the ground area of ​​the obstacle's vertical projection.

[0021] When the height of the identified obstacle is not less than 1.2 meters or less than 1.2 meters, the height of the obstacle from the ground is identified to complete one identification judgment;

[0022] When the obstacle height is not less than 1.2 meters and the obstacle height is greater than 3.5 meters from the ground, the judgment mechanism is not triggered. When the obstacle height is not less than 1.2 meters and the obstacle height is not greater than 3.5 meters from the ground, the ground area of ​​the vertical projection of the obstacle is calculated and identified to complete the secondary recognition and judgment.

[0023] When the obstacle height is less than 1.2 meters and the obstacle height is greater than 2 meters from the ground, the judgment mechanism is not triggered. When the obstacle height is less than 1.2 meters and the obstacle height is not greater than 2 meters from the ground, the ground area of ​​the vertical projection of the obstacle is calculated and identified, completing three identification and judgments.

[0024] When the vertical projection of the obstacle identified and judged is less than 4 square meters, the judgment mechanism is not triggered; when the vertical projection of the obstacle identified and judged is not less than 4 square meters, the judgment mechanism is triggered and four recognition and judgments are completed;

[0025] If the obstacle triggers the judgment mechanism, the drone will transmit the point cloud information back; if the obstacle does not trigger the judgment mechanism, the drone will calculate the spatial change rate within the recording period based on the terrain changes and its own flight information to complete the first judgment.

[0026] As a preferred solution of the low-frequency, high-throughput, large-scale sensing information control method of the present invention, wherein: performing a second judgment on the 3D point cloud information returned according to the spatial change rate includes:

[0027] When the spatial change rate is greater than the threshold, the drone transmits the point cloud information back; when the spatial change rate is not greater than the threshold, the trigger judgment process ends, and the drone continues to detect and completes the second judgment.

[0028] As a preferred solution of the low-frequency, high-throughput, large-scale sensing information control method of the present invention, the spatial change rate is expressed as:

[0029]

[0030] Where k is the spatial change rate, η i is the horizontal terrain change index at a certain sampling moment, τ i is the vertical terrain change index at a certain sampling moment, v i is the speed of the UAV at a certain sampling moment, ξ is the preset influencing factor, n is the total number of sampling times in the sampling period, T is the recording period, and the function p(x,x r ) represents the coincidence between the orientation of the UAV at the end of the sampling period and the expected orientation. The closer the two are to coincidence, the larger the function value.

[0031] As a preferred embodiment of the low-frequency, high-throughput, large-scale sensing information control method of the present invention, it further includes:

[0032] The horizontal terrain change index is expressed as:

[0033]

[0034] Among them, ρ A is the horizontal terrain variation coefficient, q is the number of selected directions, d i is the detection depth in direction i;

[0035] The vertical terrain change index is expressed as:

[0036]

[0037] Where [SOD1] is the slope change of positive terrain, [SOD2] is the slope change of negative terrain, and ρ B is the vertical terrain variation coefficient;

[0038] The terrain aspect variability is expressed as:

[0039]

[0040] Among them, f x and f y They are the changes in the slope aspect of the current point in the east-west and north-south directions within the neighborhood, respectively.

[0041] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0042] memory and processor;

[0043] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the low-frequency, high-throughput, large-scale sensing information control method as described in any embodiment of the present invention.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the low-frequency, high-throughput, large-scale sensing information control method.

[0045] Compared with the existing technology, the beneficial effects of the present invention are as follows: while using LiDAR technology continuously and uninterruptedly, the present invention can ensure the accuracy and integrity of the UAV during the exploration of the underground environment. The low-frequency and high-throughput large-scale sensing information control algorithm can effectively avoid the information congestion caused by the transmission of 3D point cloud information, thereby improving the efficiency of underground environment exploration and mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them:

[0047] Figure 1 This is a flow chart of a low-frequency, high-throughput, large-scale sensing information control method and device according to one embodiment of the present invention;

[0048] Figure 2 This is a flowchart of an event trigger based on semantics and spatial change rate for a low-frequency, high-throughput, large-scale sensor information control method and device according to one embodiment of the present invention;

[0049] Figure 3 A classification regression tree decision diagram based on semantics and spatial change rate for a low-frequency, high-throughput, large-scale sensing information control method and device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0053] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0054] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0055] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0056] Example 1

[0057] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides a low-frequency, high-throughput, large-scale sensing information control method, comprising:

[0058] S1: Based on the UAV flight detection process, LiDAR technology is used to continuously generate 3D point cloud information of the ground environment and record the UAV flight information;

[0059] Furthermore, the drone's flight information is continuously recorded, including:

[0060] Get the recording period T, time sampling interval t, maximum flight speed v, unit speed increment u, maximum flight acceleration α and the current state configuration of the drone.

[0061] Specifically, it also includes:

[0062] According to the recording period T and the time sampling interval t, the sampling times t1, t2, t3...T are obtained;

[0063] Obtain a sampling speed set based on the maximum flight speed v and the unit speed increment u;

[0064] Selecting a speed value based on the sampled speed set, and determining the speed at each sampling moment in sequence, wherein the speed difference between adjacent sampling moments does not exceed the maximum flight acceleration α;

[0065] The position of the drone at each sampling moment is calculated based on the current state configuration of the drone and the speed at each sampling moment.

[0066] S2: The drone extracts high-level semantic information from the 3D point cloud information, and performs a first judgment on the 3D point cloud information returned based on the extracted high-level semantic information and the obstacle recognition and judgment mechanism;

[0067] Furthermore, the extracted high-level semantic information is combined with the obstacle recognition and judgment mechanism to perform the first judgment on the returned 3D point cloud information, including:

[0068] The point cloud information is the point cloud information T seconds before the event triggers the node. Extracting high-level semantic features and identifying obstacles is done using a classification regression tree. The obstacle features are the obstacle height, the obstacle height from the ground, and the ground area of ​​the obstacle's vertical projection.

[0069] When the height of the identified obstacle is not less than 1.2 meters or less than 1.2 meters, the height of the obstacle from the ground is identified to complete one identification judgment;

[0070] When the obstacle height is not less than 1.2 meters and the obstacle height is greater than 3.5 meters from the ground, the judgment mechanism is not triggered. When the obstacle height is not less than 1.2 meters and the obstacle height is not greater than 3.5 meters from the ground, the ground area of ​​the vertical projection of the obstacle is calculated and identified to complete the secondary recognition and judgment.

[0071] When the obstacle height is less than 1.2 meters and the obstacle height is greater than 2 meters from the ground, the judgment mechanism is not triggered. When the obstacle height is less than 1.2 meters and the obstacle height is not greater than 2 meters from the ground, the ground area of ​​the vertical projection of the obstacle is calculated and identified, completing three identification and judgments.

[0072] When the vertical projection of the identified obstacle is less than 4 square meters, the judgment mechanism is not triggered; when the vertical projection of the identified obstacle is not less than 4 square meters, the judgment mechanism is triggered and four identification and judgments are completed;

[0073] If the obstacle triggers the judgment mechanism, the drone will transmit the point cloud information back; if the obstacle does not trigger the judgment mechanism, the drone will calculate the spatial change rate within the recording period based on the terrain changes and its own flight information to complete the first judgment.

[0074] S3: If the obstacle fails to trigger the judgment mechanism, the drone calculates the spatial change rate during the flight detection process based on the 3D point cloud information and its own flight information, and performs a second judgment on the 3D point cloud information returned based on the spatial change rate;

[0075] Furthermore, a second judgment is performed on the returned 3D point cloud information based on the spatial change rate, including:

[0076] When the spatial change rate is greater than the threshold, the drone will transmit the point cloud information back; when the spatial change rate is not greater than the threshold, the trigger judgment process ends, and the drone continues to detect and complete the second judgment.

[0077] In an optional embodiment, the threshold is set at 3.5×10 -5 to 7×10 -5 The more complex the terrain, the smaller the value.

[0078] Specifically, the spatial change rate is expressed as:

[0079]

[0080] Where k is the spatial change rate, η i is the horizontal terrain change index at a certain sampling moment, τ i is the vertical terrain change index at a certain sampling moment, v i is the speed of the UAV at a certain sampling moment, ξ is the preset influencing factor, n is the total number of sampling times in the sampling period, T is the recording period, and the function p(x,x r ) represents the coincidence between the orientation of the UAV at the end of the sampling period and the expected orientation. The closer the two are to coincidence, the larger the function value.

[0081] Furthermore, it also includes:

[0082] The horizontal terrain change index is expressed as:

[0083]

[0084] Among them, ρ A is the horizontal terrain variation coefficient, q is the number of selected directions, d i is the detection depth in direction i;

[0085] The vertical terrain variation index is expressed as:

[0086]

[0087] Where [SOD1] is the slope change of positive terrain, [SOD2] is the slope change of negative terrain, and ρ B is the vertical terrain variation coefficient;

[0088] The terrain aspect variability is expressed as:

[0089]

[0090] Among them, f x and f y They are the changes in the slope aspect of the current point in the east-west and north-south directions within the neighborhood, respectively.

[0091] S4: When the 3D point cloud information is transmitted back for the first judgment and the second judgment, the wireless communication module communicates with the terminal and transmits data in real time.

[0092] The above is a schematic diagram of a low-frequency, high-throughput, large-scale sensing information control method according to this embodiment. It should be noted that the technical solution of this low-frequency, high-throughput, large-scale sensing information control device and the technical solution of the aforementioned low-frequency, high-throughput, large-scale sensing information control method are based on the same concept. For details not described in detail in the technical solution of the low-frequency, high-throughput, large-scale sensing information control device according to this embodiment, please refer to the description of the technical solution of the aforementioned low-frequency, high-throughput, large-scale sensing information control method.

[0093] In this embodiment, a low-frequency, high-throughput, large-scale sensing information control device includes:

[0094] The information acquisition module is used to continuously generate 3D point cloud information of the ground environment and record the flight information of the drone based on the drone's flight detection process using LiDAR technology;

[0095] The first judgment module is used for the drone to extract high-level semantic information from 3D point cloud information, and perform a first judgment on the 3D point cloud information returned based on the extracted high-level semantic information and the obstacle recognition and judgment mechanism;

[0096] The second judgment module is used to calculate the spatial change rate during the flight detection process based on the 3D point cloud information and its own flight information if the obstacle fails to trigger the judgment mechanism, and perform a second judgment on the 3D point cloud information returned according to the spatial change rate;

[0097] The wireless communication module is used to communicate with the terminal through the wireless communication module and transmit data in real time when the 3D point cloud information is transmitted back for the first judgment and the second judgment.

[0098] In an optional embodiment, the wireless communication module and LiDAR are mounted on a multi-rotor drone. The multi-rotor drone generates point cloud information through LiDAR, obtains the drone status, detects terrain changes, and determines whether the trigger conditions are met based on the spatial change rate and semantic feature extraction. It also communicates with the terminal through the wireless communication module to realize data transmission.

[0099] This embodiment further provides a computing device applicable to the low-frequency, high-throughput, large-scale sensing information control method, including:

[0100] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the low-frequency, high-throughput, large-scale sensing information control method proposed in the above embodiment.

[0101] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0102] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the method for realizing low-frequency, high-throughput, large-scale sensing information control proposed in the above embodiment is implemented.

[0103] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0104] Example 2

[0105] Referring to Table 1, which is an embodiment of the present invention, based on the above method, a comparison with a traditional solution is provided to verify its beneficial effects.

[0106] Table 1 Comparison table

[0107]

[0108]

[0109] From the above description, we can clearly see the advantages of our solution over traditional methods in terms of obstacle trigger judgment mechanism, sensor information congestion, and operating environment restrictions.

[0110] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A low-frequency, high-throughput, large-scale sensing information control method, characterized in that: include: Based on the UAV flight detection process, LiDAR technology is used to continuously generate 3D point cloud information of the ground environment and record the flight information of the UAV; The drone extracts high-level semantic information from the 3D point cloud information, and performs a first judgment on the returned 3D point cloud information based on the extracted high-level semantic information in combination with an obstacle recognition and judgment mechanism; If the obstacle fails to trigger the judgment mechanism, the drone calculates the spatial change rate during the flight detection process based on the 3D point cloud information and its own flight information, and performs a second judgment on the 3D point cloud information returned based on the spatial change rate, including: When the spatial change rate is greater than the threshold, the drone transmits the point cloud information back; when the spatial change rate is not greater than the threshold, the trigger judgment process ends, and the drone continues to detect and complete the second judgment; The spatial variation rate is expressed as: Among them, κ is the spatial variation rate, η i is the horizontal terrain change index at a certain sampling moment, τ i is the vertical terrain change index at a certain sampling moment, v i is the speed of the UAV at a certain sampling moment, ξ is the preset influencing factor, n is the total number of sampling times in the sampling period, T is the recording period, and the function p(x,x r ) represents the coincidence between the orientation of the UAV at the end of the sampling period and the expected orientation. The closer the two are to coincidence, the larger the function value. Also included: the horizontal terrain change index, expressed as: Among them, ρ A is the horizontal terrain variation coefficient, q is the number of selected directions, d i is the detection depth in direction i; The vertical terrain change index is expressed as: Where [SOD1] is the slope change of positive terrain, [SOD2] is the slope change of negative terrain, and ρ B is the vertical terrain variation coefficient; The terrain slope change is expressed as: Among them, f x and f y They are respectively the changes in the slope direction of the current point in the east-west and north-south directions within the neighborhood; when the 3D point cloud information is transmitted back for the first judgment and the second judgment, communication is performed with the terminal through the wireless communication module to transmit data in real time.

2. The low-frequency, high-throughput, large-scale sensing information control method according to claim 1, characterized in that: The continuous recording of drone flight information includes: Get the recording period T, time sampling interval t, maximum flight speed v, unit speed increment u, maximum flight acceleration α and the current state configuration of the drone.

3. The low-frequency, high-throughput, large-scale sensing information control method according to claim 1 or 2, characterized in that: Also includes: According to the recording period T and the time sampling interval t, the sampling times t1, t2, t3...T are obtained; Obtain a sampling speed set based on the maximum flight speed v and the unit speed increment u; Selecting a speed value based on the sampled speed set, and determining the speed at each sampling moment in sequence, wherein the speed difference between adjacent sampling moments does not exceed the maximum flight acceleration α; The position of the drone at each sampling moment is calculated based on the current state configuration of the drone and the speed at each sampling moment.

4. The low-frequency, high-throughput, large-scale sensing information control method according to claim 3, characterized in that: The extracted high-level semantic information is combined with the obstacle recognition and judgment mechanism to perform the first judgment on the 3D point cloud information returned, including: The point cloud information is the point cloud information T seconds before the event triggers the node. Extracting high-level semantic features and identifying obstacles is to use a classification regression tree for decision making. The obstacle features are the obstacle height, the obstacle height from the ground, and the ground area of ​​the obstacle's vertical projection. When the height of the identified obstacle is not less than 1.2 meters or less than 1.2 meters, the height of the obstacle from the ground is identified to complete one identification judgment; When the obstacle height is not less than 1.2 meters and the obstacle height is greater than 3.5 meters from the ground, the judgment mechanism is not triggered. When the obstacle height is not less than 1.2 meters and the obstacle height is not greater than 3.5 meters from the ground, the ground area of ​​the vertical projection of the obstacle is calculated and identified to complete the secondary recognition and judgment. When the obstacle height is less than 1.2 meters and the obstacle height is greater than 2 meters from the ground, the judgment mechanism is not triggered. When the obstacle height is less than 1.2 meters and the obstacle height is not greater than 2 meters from the ground, the ground area of ​​the vertical projection of the obstacle is calculated and identified, completing three identification and judgments. When the vertical projection of the obstacle identified and judged is less than 4 square meters, the judgment mechanism is not triggered; when the vertical projection of the obstacle identified and judged is not less than 4 square meters, the judgment mechanism is triggered and four recognition and judgments are completed; If the obstacle triggers the judgment mechanism, the drone will transmit the point cloud information back; if the obstacle does not trigger the judgment mechanism, the drone will calculate the spatial change rate within the recording period based on the terrain changes and its own flight information to complete the first judgment.

5. A low-frequency, high-throughput, large-scale sensing information control device, using a low-frequency, high-throughput, large-scale sensing information control method according to any one of claims 1 to 4, characterized in that: include: An information acquisition module is used to continuously generate 3D point cloud information of the ground environment and record the flight information of the drone based on the drone flight detection process using LiDAR technology; A first judgment module is configured for the drone to extract high-level semantic information from the 3D point cloud information, and perform a first judgment on the returned 3D point cloud information based on the extracted high-level semantic information in combination with an obstacle recognition and judgment mechanism; A second judgment module is configured to, if the obstacle fails to trigger the judgment mechanism, calculate the spatial change rate of the drone during the flight detection process based on the 3D point cloud information and its own flight information, and perform a second judgment on the 3D point cloud information returned according to the spatial change rate; The wireless communication module is used to communicate with the terminal through the wireless communication module and transmit data in real time when the 3D point cloud information is transmitted back for the first judgment and the second judgment.

6. A computing device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the low-frequency, high-throughput, large-scale sensing information control method described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the low-frequency, high-throughput, large-scale sensing information control method according to any one of claims 1 to 4.

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