WO2024098415A1 - 一种基于5g网联无人机通信网络覆盖边缘检测方法、系统及平台 - Google Patents

一种基于5g网联无人机通信网络覆盖边缘检测方法、系统及平台 Download PDF

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WO2024098415A1
WO2024098415A1 PCT/CN2022/131520 CN2022131520W WO2024098415A1 WO 2024098415 A1 WO2024098415 A1 WO 2024098415A1 CN 2022131520 W CN2022131520 W CN 2022131520W WO 2024098415 A1 WO2024098415 A1 WO 2024098415A1
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network coverage
data
network
real time
communication network
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PCT/CN2022/131520
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English (en)
French (fr)
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袁飞
李昌
刘忆森
徐晨
周松斌
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广东省科学院智能制造研究所
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Priority to PCT/CN2022/131520 priority Critical patent/WO2024098415A1/zh
Publication of WO2024098415A1 publication Critical patent/WO2024098415A1/zh

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements

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  • the present invention belongs to the technical field of communication network coverage edge detection, and specifically relates to a method, system and platform for communication network coverage edge detection based on 5G networked unmanned aerial vehicle.
  • 5G networks mainly provide network coverage for ground users.
  • the existing solution is to carry out targeted blind spots in weak or no coverage areas, but it is necessary to optimize the existing 5G network deployment, which will cause a lot of network optimization work.
  • a traversal network quality test is performed on the flight area of the networked drone to form a network environment quality database in the flight area to guide the actual flight path planning of the networked drone.
  • this method has the prominent problem of heavy work during network quality testing.
  • the existing communication network detection technology has detection methods that will lead to technical problems such as long detection cycles and low detection accuracy.
  • the purpose of the present invention is to provide a method, system and platform for detecting the edge of the communication network coverage of 5G networked drones, and to shorten the detection cycle and reduce the detection cost by improving the communication network detection method.
  • the accuracy of the detection method is improved.
  • the first object of the present invention is to provide a method for detecting the edge of a 5G network-connected drone communication network.
  • the second object of the present invention is to provide a 5G network-connected drone communication network coverage edge detection system
  • the third object of the present invention is to provide a 5G network-connected drone communication network coverage edge detection platform
  • the first object of the present invention is achieved in this way: the method specifically comprises the following steps:
  • a detection model is constructed, and network coverage edge data is generated in real time through the detection model.
  • parameter data within the network coverage area specifically includes: length data, width data and altitude data within the flight area.
  • the real-time generation of the network coverage area grid according to the acquired parameter data also includes the following steps:
  • the network covers an area grid, specifically a cubic grid network.
  • the step of combining the network coverage area grid to construct a detection model and generating network coverage edge data in real time through the detection model also includes the following steps:
  • command data for controlling the flight status of the 5G networked drone is generated in real time.
  • the step of combining the network coverage area grid to construct a detection model and generating network coverage edge data in real time through the detection model also includes the following steps:
  • the generated network coverage edge data is compared with the weights in real time, and finally the network coverage edge data after weight processing is generated.
  • the second object of the present invention is achieved in that the system specifically comprises:
  • a data acquisition unit for acquiring parameter data within the network coverage area in real time; a regional grid generation unit for generating a network coverage area grid in real time based on the acquired parameter data; and an edge data detection unit for constructing a detection model in combination with the network coverage area grid, and generating network coverage edge data in real time through the detection model.
  • the data acquisition unit is also provided with: a data verification module for respectively verifying the operation performance data, flight effect data and power supply system data of the 5G network-connected UAV; a determination module for determining the 5G communication network connection status of the 5G network-connected UAV;
  • the parameter data within the network coverage area specifically includes: length data, width data and height data within the flight area;
  • the regional grid generation unit is further provided with: a first generation module for generating quality test point data in real time according to the grid connection point data;
  • the network coverage area grid is specifically: a cubic grid network
  • the edge data detection unit is also provided with: a first acquisition module for acquiring network coverage quality data of the grid network coverage area in real time; a second generation module for generating real-time command data for controlling the flight status of the 5G networked drone based on the network coverage quality data; and a weight comparison generation module for real-time weight comparison of the generated network coverage edge data to finally generate weighted network coverage edge data.
  • the third object of the present invention is achieved by: comprising: a processor, a memory, and a control program for a 5G network-connected drone communication network coverage edge detection platform;
  • the processor executes the control program of the platform for edge detection of 5G networked unmanned aerial vehicle communication network coverage, and the control program of the platform for edge detection of 5G networked unmanned aerial vehicle communication network coverage is stored in the memory, and the control program of the platform for edge detection of 5G networked unmanned aerial vehicle communication network coverage implements the steps of the method for edge detection of 5G networked unmanned aerial vehicle communication network coverage.
  • the present invention obtains parameter data in the network coverage area in real time through a method; generates a network coverage area grid in real time based on the obtained parameter data; constructs a detection model in combination with the network coverage area grid, and generates network coverage edge data in real time through the detection model; and a system and platform corresponding to the method; that is, by improving the communication network detection method, the detection cycle is shortened and the detection cost is reduced. Moreover, by establishing a detection model, the accuracy of the detection method is improved.
  • FIG1 is a schematic diagram of a flow chart of a method for detecting the edge of a 5G networked drone communication network according to the present invention
  • FIG2 is a schematic diagram of a 5G network-connected drone communication network coverage edge detection system architecture according to the present invention.
  • FIG3 is a schematic diagram of an architecture of a 5G network-connected drone communication network coverage edge detection platform according to the present invention.
  • FIG4 is a schematic diagram of a computer-readable storage medium architecture in one embodiment of the present invention.
  • FIG5 is a schematic diagram of test point planning according to an embodiment of a method for detecting edge coverage of a 5G networked drone communication network according to the present invention
  • FIG6 is a schematic cross-sectional view of a test point according to an embodiment of a method for detecting edge coverage of a 5G network-connected drone communication network of the present invention
  • the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.
  • directional indications such as up, down, left, right, front, back, etc.
  • the present invention is a method for detecting the edge of the coverage of a 5G network-connected drone communication network applied in one or more terminals or servers.
  • the terminal is a device that can automatically perform numerical calculations and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.
  • the terminal can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc.
  • the terminal can interact with the client through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.
  • the present invention provides a method, system, platform and storage medium for detecting the edge of a 5G networked drone communication network.
  • this is a flowchart of a method for detecting edge coverage of a 5G networked drone communication network provided in an embodiment of the present invention.
  • the 5G network-connected drone communication network coverage edge detection method can be applied to a terminal with a display function or a fixed terminal.
  • the terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.
  • the 5G network-connected drone communication network coverage edge detection method can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal through a network.
  • the network includes but is not limited to: a wide area network, a metropolitan area network or a local area network.
  • the 5G network-connected drone communication network coverage edge detection method of the embodiment of the present invention can be executed by a server, or by a terminal, or by both a server and a terminal.
  • the edge detection function based on 5G networked drone communication network coverage provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed.
  • the method provided by the present invention can also be run on a server or other device in the form of a software development kit (SDK), and an interface based on the edge detection function of 5G networked drone communication network coverage is provided in the form of SDK.
  • SDK software development kit
  • the terminal or other device can implement the edge detection function based on 5G networked drone communication network coverage through the provided interface.
  • the present invention provides a method for detecting the edge of a 5G network-connected drone communication network, wherein the method specifically comprises the following steps:
  • S02. Determine the 5G communication network connection status of the 5G networked drone.
  • the parameter data within the network coverage area specifically includes: length data, width data and height data within the flight area.
  • the real-time generation of a network coverage area grid based on the acquired parameter data also includes the following steps:
  • the network coverage area grid is specifically a cubic grid network.
  • the step of combining the network coverage area grid, constructing a detection model, and generating network coverage edge data in real time through the detection model further includes the following steps:
  • the step of combining the network coverage area grid, constructing a detection model, and generating network coverage edge data in real time through the detection model further includes the following steps:
  • a 5G networked drone communication network coverage edge detection method comprising the following steps:
  • Preparation Professionals select 5G networked drones as network detection equipment, where the selected drones must maintain the same parameter data and flight performance, and the battery usage times of the selected drones are no more than 3, and the battery health efficiency is 100%. At the same time, all selected drones are connected using the 5G network, and the 5G network used for network connection comes from the same local area network;
  • Professionals shall delineate the flight area and measure the delineated flight area.
  • the measurement data includes the length and width of the flight area.
  • the area is calculated based on the measured data.
  • the professionals shall conduct flight parameter experiments on the 5G networked drones used.
  • the professionals shall obtain the highest flight altitude data of the 5G networked drones through test flights, and use the obtained highest flight altitude data as the altitude data of the planned flight area.
  • Grid division Professionals calculate the flight area based on the acquired data, obtain the space of the flight area through calculation, and plan the flight area into a cubic grid network.
  • the volume of each grid must be kept the same, and the length, width and height data of the grid are set by professionals according to the maximum circle range of a 5G networked drone in one flight, and the divided grid connection points are used as quality test points;
  • Conduct detection select a vertical section in the divided grid, wherein random selection is adopted when selecting the vertical section, and the number of selections of the same vertical section in the same detection process does not exceed 1.
  • the 5G networked drone is located at the lowest edge position, and a professional starts the 5G networked drone to fly forward in the horizontal direction, and processes it according to whether the network quality meets the business requirements. If the network quality meets the business requirements, the professional continues to control the 5G networked drone to fly forward in the horizontal direction. If the network quality does not meet the business requirements, the drone stops flying forward. At the same time, the professional controls the drone that stops flying forward to fly upward along the section, and flies vertically for a certain distance to test the network quality. The test results are processed.
  • the professional controls the drone to fly forward in the horizontal direction. Processing is carried out according to whether the network quality meets the business requirements. If the test result is that the network quality does not meet the business requirements, the professional controls the drone to fly in the horizontal direction, and the horizontal flight is a horizontal flight in the direction of the starting point;
  • Establishing a model Professionals establish a detection model and train the established detection model. During the training, the network quality of each quality test point in the grid is changed by changing the network quality. At the same time, the network quality at each quality test point is detected by a network signal detector, and the detection result is compared with the detection result of the drone. The accuracy of the detection method is calculated by comparing the results. Professionals make a judgment based on the calculated accuracy, and process the judgment results. If the calculated accuracy is greater than 98%, the model is determined to be mature, and if the calculated accuracy is not greater than 98%, the model is determined to be immature. If the model is determined to be mature, the detection method is applied. If the model is determined to be immature, the length data, width data and height data of the grid are reduced, and training is continued after the data is reduced until the training is stopped when the judgment result is that the model is mature.
  • the present invention also provides a 5G network-connected drone communication network coverage edge detection system, as shown in FIG2 , the system specifically includes:
  • a data acquisition unit for acquiring parameter data within the network coverage area in real time; a regional grid generation unit for generating a network coverage area grid in real time based on the acquired parameter data; and an edge data detection unit for constructing a detection model in combination with the network coverage area grid, and generating network coverage edge data in real time through the detection model.
  • the data acquisition unit is further provided with: a data verification module for respectively verifying the operation performance data, flight effect data and power supply system data of the 5G networked drone; a determination module for determining the 5G communication network connection status of the 5G networked drone;
  • the parameter data within the network coverage area specifically includes: length data, width data and height data within the flight area;
  • the regional grid generation unit is further provided with: a first generation module for generating quality test point data in real time according to the grid connection point data;
  • the network coverage area grid is specifically: a cubic grid network
  • the edge data detection unit is also provided with: a first acquisition module for acquiring network coverage quality data of the grid network coverage area in real time; a second generation module for generating real-time command data for controlling the flight status of the 5G networked drone based on the network coverage quality data; and a weight comparison generation module for real-time weight comparison of the generated network coverage edge data to finally generate weighted network coverage edge data.
  • the present invention also provides a 5G network-connected unmanned aerial vehicle communication network coverage edge detection platform, as shown in FIG3 , including: a processor, a memory, and a 5G network-connected unmanned aerial vehicle communication network coverage edge detection platform control program;
  • the processor executes the control program of the 5G networked unmanned aerial vehicle communication network coverage edge detection platform
  • the control program of the 5G networked unmanned aerial vehicle communication network coverage edge detection platform is stored in the memory
  • the control program of the 5G networked unmanned aerial vehicle communication network coverage edge detection platform implements the steps of the 5G networked unmanned aerial vehicle communication network coverage edge detection method, for example:
  • the built-in processor of the 5G network-connected drone communication network coverage edge detection platform can be composed of integrated circuits, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and various control chips.
  • the processor uses various interfaces and lines to connect various components, and executes or executes programs or units stored in the memory, and calls data stored in the memory to perform various functions and process data based on the 5G network-connected drone communication network coverage edge detection;
  • the memory is used to store program codes and various data. It is installed in the edge detection platform based on the 5G networked drone communication network coverage, and realizes high-speed and automatic access to programs or data during operation.
  • the memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.
  • ROM read-only memory
  • RAM random access memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read-only memory
  • OTPROM one-time programmable read-only memory
  • EEPROM electronically erasable programmable read-only memory
  • CD-ROM compact disc read-only memory
  • CD-ROM compact disc read-only memory
  • the present invention also provides a computer-readable storage medium, as shown in FIG4, wherein the computer-readable storage medium stores a control program for a platform for detecting edge coverage of a 5G networked unmanned aerial vehicle communication network, and the control program for a platform for detecting edge coverage of a 5G networked unmanned aerial vehicle communication network implements the steps of the method for detecting edge coverage of a 5G networked unmanned aerial vehicle communication network, for example:
  • any process or method description in the flowchart or otherwise described herein may be understood as representing a module, fragment or portion of a code comprising one or more executable instructions for implementing steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations, in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.
  • a "computer-readable medium” can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses.
  • Computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM).
  • wires electronic devices
  • portable computer disk box magnetic device
  • RAM random access memory
  • ROM read-only memory
  • EPROM or flash memory erasable and programmable read-only memory
  • CDROM portable compact disk read-only memory
  • the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.
  • the present invention further provides a chip system, wherein the chip system includes at least one processor.
  • the chip system executes the steps of the method for detecting the coverage edge of the 5G network-connected drone communication network, for example:
  • the present invention obtains parameter data in the network coverage area in real time through a method; generates a network coverage area grid in real time based on the obtained parameter data; constructs a detection model in combination with the network coverage area grid, and generates network coverage edge data in real time through the detection model; and a system and platform corresponding to the method; that is, by improving the communication network detection method, the detection cycle is shortened and the detection cost is reduced. Moreover, by establishing a detection model, the accuracy of the detection method is improved.

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Abstract

本申请公开了一种基于5G网联无人机的通信网络覆盖边缘检测方法、系统及平台,属于通信网络覆盖边缘检测技术领域。该方法包括:实时获取网络覆盖区域内的参数数据;根据获取到的参数数据,实时生成网络覆盖区域网格;结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。本申请的技术方案通过改进通信网络探测的方法,缩短了探测周期,降低了探测成本;同时,通过建立探测模型,提高了探测方法的准确性。

Description

一种基于5G网联无人机通信网络覆盖边缘检测方法、系统及平台 技术领域
本发明属于通信网络覆盖边缘检测技术领域,具体涉及一种基于5G网联无人机通信网络覆盖边缘检测方法、系统及平台。
背景技术
随着5G网络的发展,5G网络多针对地面用户进行网络覆盖,低空域内存在网络弱覆盖或无覆盖的问题,影响网联无人机信号实时传输。特别是当飞行高度升高时,弱覆盖或无覆盖问题更突出。现有解决方案为对弱覆盖或者无覆盖区域进行针对性的补盲,但需要优化现有5G网络部署,将会造成大量网络优化工作。现目前,为避免大量5G网络优化工作,采用对网联无人机飞行区域进行遍历式的网络质量测试,形成飞行区域内网络环境质量数据库,以指导网联无人机实际飞行路径规划。但该方法在网络质量测试时,存在工作繁重的突出问题。也就是说,目前现有的通信网络探测技术存在探测方法,会导致探测周期长、探测准确率较低的技术问题缺陷。
因此,针对以上目前现有的通信网络探测技术存在探测方法,会导致探测周期长、探测准确率较低的技术问题缺陷,急需设计和开发一种基于5G网联无人机通信网络覆盖边缘检测方法、系统及平台。
发明内容
为克服上述现有技术存在的不足及困难,本发明之目的在于提供一种基于5G网联无人机通信网络覆盖边缘检测方法、系统及平台,并通过改进通信网络探测的方法,缩短了探测周期,降低了探测成本。而且通过建立探测模型,提高了探测方法的准确性。
本发明的第一目的在于提供一种基于5G网联无人机通信网络覆盖边缘检测方法;
本发明的第二目的在于提供一种基于5G网联无人机通信网络覆盖边缘检测系统;
本发明的第三目的在于提供一种基于5G网联无人机通信网络覆盖边缘检测平台;
本发明的第一目的是这样实现的:所述方法具体包括如下步骤:
实时获取网络覆盖区域内的参数数据;
根据获取到的参数数据,实时生成网络覆盖区域网格;
结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。
进一步地,所述实时获取网络覆盖区域内的参数数据之前,还包括如下步骤:
分别检定5G网联无人机作业性能数据、飞行效果数据以及供电系统数据;
判定5G网联无人机的5G通信网络连接状态。
进一步地,所述网络覆盖区域内的参数数据,具体包括:飞行区域内的长度数据、宽度数据以及高度数据。
进一步地,所述根据获取到的参数数据,实时生成网络覆盖区域网格,还包括如下步骤:
根据网格连接点数据实时生成质量测试点数据。
进一步地,所述网络覆盖区域网格,具体为:立方格形式网络。
进一步地,所述结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据,还包括如下步骤:
实时获取网格网络覆盖区域的网络覆盖质量数据;
根据所述网络覆盖质量数据,实时生成控制5G网联无人机飞行状态指令数 据。
进一步地,所述结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据,还包括如下步骤:
实时权重比对所述生成的网络覆盖边缘数据,最终生成权重处理后的网络覆盖边缘数据。
本发明的第二目的是这样实现的:所述系统具体包括:
用于实时获取网络覆盖区域内的参数数据的数据获取单元;用于根据获取到的参数数据,实时生成网络覆盖区域网格的区域网格生成单元;以及用于结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据的边缘数据检测单元。
进一步地,所述数据获取单元中,还设置有:用于分别检定5G网联无人机作业性能数据、飞行效果数据以及供电系统数据的数据检定模块;用于判定5G网联无人机的5G通信网络连接状态的判定模块;
所述网络覆盖区域内的参数数据,具体包括:飞行区域内的长度数据、宽度数据以及高度数据;
所述区域网格生成单元中,还设置有:用于根据网格连接点数据实时生成质量测试点数据的第一生成模块;
所述网络覆盖区域网格,具体为:立方格形式网络;
所述边缘数据检测单元中,还设置有:用于实时获取网格网络覆盖区域的网络覆盖质量数据的第一获取模块;用于根据所述网络覆盖质量数据,实时生成控制5G网联无人机飞行状态指令数据的第二生成模块;以及用于实时权重比对所述生成的网络覆盖边缘数据,最终生成权重处理后的网络覆盖边缘数据的权重比对生成模块。
本发明的第三目的是这样实现的:包括:处理器、存储器以及基于5G网联无人机通信网络覆盖边缘检测平台控制程序;
其中在所述的处理器执行所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序被存储在所述存储器中,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,实现所述的基于5G网联无人机通信网络覆盖边缘检测方法步骤。
本发明通过方法实时获取网络覆盖区域内的参数数据;根据获取到的参数数据,实时生成网络覆盖区域网格;结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据;以及与方法相应的系统、平台;即通过改进通信网络探测的方法,缩短了探测周期,降低了探测成本。而且通过建立探测模型,提高了探测方法的准确性。
附图说明
为了更清楚地说明本发明实施例中的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1为本发明一种基于5G网联无人机通信网络覆盖边缘检测方法流程示意图;
图2为本发明一种基于5G网联无人机通信网络覆盖边缘检测系统架构示意图;
图3为本发明一种基于5G网联无人机通信网络覆盖边缘检测平台架构示意图;
图4为本发明一种实施例中计算机可读取存储介质架构示意图;
图5为本发明一种基于5G网联无人机通信网络覆盖边缘检测方法之实施例测试点规划示意图;
图6为本发明一种基于5G网联无人机通信网络覆盖边缘检测方法之实施例测 试点剖视面示意图;
图中:
1-起点;
本发明目的实现、功能特点及优点将结合实施例,参照附图做进一步说明。
具体实施方式
为便于更好的理解本发明的目的、技术方案和优点更加清楚,下面结合附图和具体的实施方式对本发明作进一步说明,本领域技术人员可由本说明书所揭示的内容轻易地了解本发明的其它优点与功效。
本发明亦可通过其它不同的具体实例加以施行或应用,本说明书中的各项细节亦可基于不同观点与应用,在不背离本发明的精神下进行各种修饰与变更。
需要说明,若本发明实施例中有涉及方向性指示(诸如上、下、左、右、前、后……),则该方向性指示仅用于解释在某一特定姿态(如附图所示)下各部件之间的相对位置关系、运动情况等,如果该特定姿态发生改变时,则该方向性指示也相应地随之改变。
另外,若本发明实施例中有涉及“第一”、“第二”等的描述,则该“第一”、“第二”等的描述仅用于描述目的,而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此,限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。其次,各个实施例之间的技术方案可以相互结合,但是必须是以本领域普通技术人员能够实现为基础,当技术方案的结合出现相互矛盾或无法实现时,应当认为这种技术方案的结合不存在,也不在本发明要求的保护范围之内。
优选地,本发明一种基于5G网联无人机通信网络覆盖边缘检测方法应用在一个或者多个终端或者服务器中。所述终端是一种能够按照事先设定或存储的指令,自动进行数值计算和/或信息处理的设备,其硬件包括但不限于微处理器、 专用集成电路(Application Specific Integrated Circuit,ASIC)、可编程门阵列(Field-Programmable Gate Array,FPGA)、数字处理器(Digital Signal Processor,DSP)、嵌入式设备等。
所述终端可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端可以与客户通过键盘、鼠标、遥控器、触摸板或声控设备等方式进行人机交互。
本发明为实现一种基于5G网联无人机通信网络覆盖边缘检测方法、系统、平台及存储介质。
如图1所示,是本发明实施例提供的基于5G网联无人机通信网络覆盖边缘检测方法的流程图。
在本实施例中,所述基于5G网联无人机通信网络覆盖边缘检测方法,可以应用于具备显示功能的终端或者固定终端中,所述终端并不限定于个人电脑、智能手机、平板电脑、安装有摄像头的台式机或一体机等。
所述基于5G网联无人机通信网络覆盖边缘检测方法也可以应用于由终端和通过网络与所述终端进行连接的服务器所构成的硬件环境中。网络包括但不限于:广域网、城域网或局域网。本发明实施例的基于5G网联无人机通信网络覆盖边缘检测方法可以由服务器来执行,也可以由终端来执行,还可以是由服务器和终端共同执行。
例如,对于需要进行基于5G网联无人机通信网络覆盖边缘检测终端,可以直接在终端上集成本发明的方法所提供的基于5G网联无人机通信网络覆盖边缘检测功能,或者安装用于实现本发明的方法的客户端。再如,本发明所提供的方法还可以软件开发工具包(Software Development Kit,SDK)的形式运行在服务器等设备上,以SDK的形式提供基于5G网联无人机通信网络覆盖边缘检测功能的接口,终端或其他设备通过所提供的接口即可实现基于5G网联无人机通信网络覆盖边缘检测功能。
以下结合附图对本发明作进一步阐述。
如图1-6所示,本发明提供了一种基于5G网联无人机通信网络覆盖边缘检测方法,所述的方法具体包括如下步骤:
S1、实时获取网络覆盖区域内的参数数据;
S2、根据获取到的参数数据,实时生成网络覆盖区域网格;
S3、结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。
所述实时获取网络覆盖区域内的参数数据之前,还包括如下步骤:
S01、分别检定5G网联无人机作业性能数据、飞行效果数据以及供电系统数据;
S02、判定5G网联无人机的5G通信网络连接状态。
所述网络覆盖区域内的参数数据,具体包括:飞行区域内的长度数据、宽度数据以及高度数据。
所述根据获取到的参数数据,实时生成网络覆盖区域网格,还包括如下步骤:
S21、根据网格连接点数据实时生成质量测试点数据。
所述网络覆盖区域网格,具体为:立方格形式网络。
所述结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据,还包括如下步骤:
S31、实时获取网格网络覆盖区域的网络覆盖质量数据;
S32、根据所述网络覆盖质量数据,实时生成控制5G网联无人机飞行状态指令数据。
所述结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据,还包括如下步骤:
S33、实时权重比对所述生成的网络覆盖边缘数据,最终生成权重处理后的 网络覆盖边缘数据。
具体地,在本发明实施例中,提供一种5G网联无人机通信网络覆盖边缘探测方法,包括以下步骤:
进行准备:由专业人员选取5G联网无人机作为网络探测设备,其中所述选取的无人机需保持相同参数数据和飞行效果,且选取的无人机的电池使用次数不大于3,电池健康效率为100%,同时将选取的所有无人机采用5G网络进行连接,其中进行网络连接时使用的5G网络来自同一局域网;
设备检查:由专业人员对选取的5G联网无人机进行检查,并通过检查结果进行判定,通过判定结果进行处理,其中检查结果为不存在无人机连接未成功则判定为连接完成,检查结果为存在无人机连接未成功则判定为连接未完成,且判定为连接完成则将无人机进行充电处理,判定为连接未完成则将连接未成功的无人机进行二次连接,同时将连接成功的无人机进行充电处理,且二次连接完成后由专业人员再次进行检查,并通过检查结果进行判定,通过判定结果进行处理,直至判定结果为所以无人机均连接成功则停止检查,并将连接成功的无人机进行充电处理;
数据获取:由专业人员对飞行区域进行划定,并对划定的飞行区域进行测量,其中进行测量时测量数据包括所述飞行区域的长度数据、宽度数据,通过测量出的数据进行面积计算,面积计算完成后由专业人员对使用的5G联网无人机进行飞行参数实验,其中进行参数实验时由专业人员通过试飞获得所述5G联网无人机的最高飞行高度数据,并将获得的最高飞行高度数据作为规划的飞行区域的高度数据;
网格划分:由专业人员根据获取的数据对飞行区域进行计算,通过计算获取飞行区域的空间,将飞行区域规划为立方格形式的网络,其中进行立方格形式网格划分时需保持每个网格的体积相同,且网格的长度数据、宽度数据和高度数据由专业人员根据5G联网无人机一次飞行的最大绕圈范围进行设置,并将 划分出的网格连接点作为质量测试点;
进行探测:在划分出的网格中选择一个垂直剖面,其中进行垂直剖面选择时采用随机选择,且在同一次探测过程中同一垂直剖面选择次数不超过1次,垂直剖面选择完成后将5G网联无人机位于最低的边缘位置,由专业人员启动5G网联无人机在水平方向上往前飞,通过网络质量是否满足业务需求进行处理,其中网络质量满足业务需求则由专业人员继续控制所述5G联网无人机在水平方向上往前飞,网络质量不满足业务需求则停止前飞,同时由专业人员控制停止前飞的无人机沿着剖面往上飞,并垂直飞行一定距离测试网络质量,通过测试结果进行处理,其中测试结果为网络质量满足业务需求则由专业人员控制无人机在水平方向上往前飞,通过网络质量是否满足业务需求进行处理,测试结果为网络质量不满足业务需求则由专业人员控制无人机沿着水平方向飞行,且所述水平方向飞行是往起点方向水平飞行;
建立模型:由专业人员建立探测模型,并对建立的探测模型进行训练,其中进行训练时通过改变网络质量改变网格内各质量测试点的网络质量,同时通过网络信号检测仪对各质量测试点处的网络质量进行检测,将检测结果与无人机探测结果进行对比,通过对比结果计算所述探测方法的准确率,由专业人员通过计算出的准确率进行判定,通过判定结果进行处理,其中计算出的准确率大于98%则判定为模型成熟,计算出的准确率不大于98%则判定为模型未成熟,且判定为模型成熟则将所述探测方法进行应用,判定为模型未成熟则缩小网格的长度数据、宽度数据和高度数据,并在数据缩小后继续进行训练,直至判定结果为模型成熟时停止训练。
为实现上述目的,本发明还提供一种基于5G网联无人机通信网络覆盖边缘检测系统,如图2所示,所述的系统具体包括:
用于实时获取网络覆盖区域内的参数数据的数据获取单元;用于根据获取到的参数数据,实时生成网络覆盖区域网格的区域网格生成单元;以及用于结 合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据的边缘数据检测单元。
所述数据获取单元中,还设置有:用于分别检定5G网联无人机作业性能数据、飞行效果数据以及供电系统数据的数据检定模块;用于判定5G网联无人机的5G通信网络连接状态的判定模块;
所述网络覆盖区域内的参数数据,具体包括:飞行区域内的长度数据、宽度数据以及高度数据;
所述区域网格生成单元中,还设置有:用于根据网格连接点数据实时生成质量测试点数据的第一生成模块;
所述网络覆盖区域网格,具体为:立方格形式网络;
所述边缘数据检测单元中,还设置有:用于实时获取网格网络覆盖区域的网络覆盖质量数据的第一获取模块;用于根据所述网络覆盖质量数据,实时生成控制5G网联无人机飞行状态指令数据的第二生成模块;以及用于实时权重比对所述生成的网络覆盖边缘数据,最终生成权重处理后的网络覆盖边缘数据的权重比对生成模块。
在本发明系统方案实施例中,所述的一种基于5G网联无人机通信网络覆盖边缘检测中涉及的方法步骤,具体细节已在上文阐述,此处不再赘述。
为实现上述目的,本发明还提供一种基于5G网联无人机通信网络覆盖边缘检测平台,如图3所示,包括:处理器、存储器以及基于5G网联无人机通信网络覆盖边缘检测平台控制程序;
其中在所述的处理器执行所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序被存储在所述存储器中,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,实现所述的基于5G网联无人机通信网络覆盖边缘检测方法步骤,例如:
S1、实时获取网络覆盖区域内的参数数据;
S2、根据获取到的参数数据,实时生成网络覆盖区域网格;
S3、结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。
步骤具体细节已在上文阐述,此处不再赘述。
本发明实施例中,所述的基于5G网联无人机通信网络覆盖边缘检测平台内置处理器,可以由集成电路组成,例如可以由单个封装的集成电路所组成,也可以是由多个相同功能或不同功能封装的集成电路所组成,包括一个或者多个中央处理器(Central Processing unit,CPU)、微处理器、数字处理芯片、图形处理器及各种控制芯片的组合等。处理器利用各种接口和线路连接取各个部件,通过运行或执行存储在存储器内的程序或者单元,以及调用存储在存储器内的数据,以执行基于5G网联无人机通信网络覆盖边缘检测各种功能和处理数据;
存储器用于存储程序代码和各种数据,安装在基于5G网联无人机通信网络覆盖边缘检测平台中,并在运行过程中实现高速、自动地完成程序或数据的存取。
所述存储器包括只读存储器(Read-Only Memory,ROM),随机存储器(Random Access Memory,RAM)、可编程只读存储器(Programmable Read-Only Memory,PROM)、可擦除可编程只读存储器(Erasable Programmable Read-Only Memory,EPROM)、一次可编程只读存储器(One-time Programmable Read-Only Memory,OTPROM)、电子擦除式可复写只读存储器(Electrically-Erasable Programmable Read-Only Memory,EEPROM)、只读光盘(Compact Disc Read-Only Memory,CD-ROM)或其他光盘存储器、磁盘存储器、磁带存储器、或者能够用于携带或存储数据的计算机可读的任何其他介质。
为实现上述目的,本发明还提供一种计算机可读取存储介质,如图4所示,所述计算机可读取存储介质存储有基于5G网联无人机通信网络覆盖边缘检测 平台控制程序,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,实现所述的基于5G网联无人机通信网络覆盖边缘检测方法步骤,例如:
S1、实时获取网络覆盖区域内的参数数据;
S2、根据获取到的参数数据,实时生成网络覆盖区域网格;
S3、结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。
步骤具体细节已在上文阐述,此处不再赘述。
在本发明的实施方式的描述中,需要说明的是,流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为,表示包括一个或更多个用于实现特定逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分,并且本发明的优选实施方式的范围包括另外的实现,其中可以不按所示出或讨论的顺序,包括根据所涉及的功能按基本同时的方式或按相反的顺序,来执行功能,这应被本发明的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤,例如,可以被认为是用于实现逻辑功能的可执行指令的定序列表,可以具体实现在任何计算机可读介质中,以供指令执行系统、装置或设备(如基于计算机的系统、包括处理模块的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用,或结合这些指令执行系统、装置或设备而使用。就本说明书而言,“计算机可读取介质”可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下:具有一个或多个布线的电连接部(电子装置),便携式计算机盘盒(磁装置),随机存取存储器(RAM),只读存储器(ROM),可擦除可编辑只读存储器(EPROM或闪速存储器),光纤装置,以及便携式光盘只读存储器(CDROM)。
另外,计算机可读取介质甚至可以是可在其上打印所述程序的纸或其他合 适的介质,因为可以例如通过对纸或其他介质进行光学扫描,接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序,然后将其存储在计算机存储器中。
在本发明实施例中,为实现上述目的,本发明还提供一种芯片系统,所述芯片系统包括至少一个处理器,当程序指令在所述至少一个处理器中执行时,使得所述芯片系统执行所述的基于5G网联无人机通信网络覆盖边缘检测方法步骤,例如:
S1、实时获取网络覆盖区域内的参数数据;
S2、根据获取到的参数数据,实时生成网络覆盖区域网格;
S3、结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。
步骤具体细节已在上文阐述,此处不再赘述。
本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。所属领域的技术人员可以清楚地了解到,为描述的方便和简洁,上述描述的系统、装置和单元的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。
本发明通过方法实时获取网络覆盖区域内的参数数据;根据获取到的参数数据,实时生成网络覆盖区域网格;结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据;以及与方法相应的系统、平台;即通过改进通信网络探测的方法,缩短了探测周期,降低了探测成本。而且通过建立探测模型,提高了探测方法的准确性。
以上所述实施例仅表达了本发明的几种实施方式,其描述较为具体和详细, 但并不能因此而理解为对本发明专利范围的限制。应当指出的是,对于本领域的普通技术人员来说,在不脱离本发明构思的前提下,还可以做出若干变形和改进,这些都属于本发明的保护范围。因此,本发明专利的保护范围应以所附权利要求为准。

Claims (10)

  1. 一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述方法具体包括如下步骤:
    实时获取网络覆盖区域内的参数数据;
    根据获取到的参数数据,实时生成网络覆盖区域网格;
    结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据。
  2. 根据权利要求1所述的一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述实时获取网络覆盖区域内的参数数据之前,还包括如下步骤:
    分别检定5G网联无人机作业性能数据、飞行效果数据以及供电系统数据;
    判定5G网联无人机的5G通信网络连接状态。
  3. 根据权利要求1或2所述的一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述网络覆盖区域内的参数数据,具体包括:
    飞行区域内的长度数据、宽度数据以及高度数据。
  4. 根据权利要求1所述的一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述根据获取到的参数数据,实时生成网络覆盖区域网格,还包括如下步骤:
    根据网格连接点数据实时生成质量测试点数据。
  5. 根据权利要求1或4所述的一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述网络覆盖区域网格,具体为:立方格形式网络。
  6. 根据权利要求1所述的一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据,还包括如下步骤:
    实时获取网格网络覆盖区域的网络覆盖质量数据;
    根据所述网络覆盖质量数据,实时生成控制5G网联无人机飞行状态指令数 据。
  7. 根据权利要求1或6所述的一种基于5G网联无人机通信网络覆盖边缘检测方法,其特征在于所述结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据,还包括如下步骤:
    实时权重比对所述生成的网络覆盖边缘数据,最终生成权重处理后的网络覆盖边缘数据。
  8. 一种基于5G网联无人机通信网络覆盖边缘检测系统,其特征在于所述系统具体包括:
    用于实时获取网络覆盖区域内的参数数据的数据获取单元;用于根据获取到的参数数据,实时生成网络覆盖区域网格的区域网格生成单元;以及用于结合所述网络覆盖区域网格,构建检测模型,通过所述检测模型实时生成网络覆盖边缘数据的边缘数据检测单元。
  9. 根据权利要求7所述的一种基于5G网联无人机通信网络覆盖边缘检测系统,其特征在于所述数据获取单元中,还设置有:用于分别检定5G网联无人机作业性能数据、飞行效果数据以及供电系统数据的数据检定模块;用于判定5G网联无人机的5G通信网络连接状态的判定模块;
    所述网络覆盖区域内的参数数据,具体包括:飞行区域内的长度数据、宽度数据以及高度数据;
    所述区域网格生成单元中,还设置有:用于根据网格连接点数据实时生成质量测试点数据的第一生成模块;
    所述网络覆盖区域网格,具体为:立方格形式网络;
    所述边缘数据检测单元中,还设置有:用于实时获取网格网络覆盖区域的网络覆盖质量数据的第一获取模块;用于根据所述网络覆盖质量数据,实时生成控制5G网联无人机飞行状态指令数据的第二生成模块;以及用于实时权重比对所述生成的网络覆盖边缘数据,最终生成权重处理后的网络覆盖边缘数据的 权重比对生成模块。
  10. 一种基于5G网联无人机通信网络覆盖边缘检测平台,其特征在于,包括:处理器、存储器以及基于5G网联无人机通信网络覆盖边缘检测平台控制程序;
    其中在所述的处理器执行所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序被存储在所述存储器中,所述的基于5G网联无人机通信网络覆盖边缘检测平台控制程序,实现如权利要求1至7中任一项所述的基于5G网联无人机通信网络覆盖边缘检测方法步骤。
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