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CN116321536A - Method, device and medium for wireless transmission of lightning arrester data based on heterogeneous network - Google Patents

Method, device and medium for wireless transmission of lightning arrester data based on heterogeneous network Download PDF

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CN116321536A
CN116321536A CN202310197927.8A CN202310197927A CN116321536A CN 116321536 A CN116321536 A CN 116321536A CN 202310197927 A CN202310197927 A CN 202310197927A CN 116321536 A CN116321536 A CN 116321536A
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path
network
wireless transmission
data
lightning arrester
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马晖
孙峰伟
范渤
李成
刘俊田
张广新
娄展豪
齐恩铁
思军武
王敏珍
李志轩
赵立英
李坤银
张琦
刘博文
宋志杰
汪鑫
王军
任浩
高巍
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Changchun Institute of Applied Chemistry of CAS
Fushun Power Supply Co of State Grid Liaoning Electric Power Co Ltd
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Changchun Institute of Applied Chemistry of CAS
Fushun Power Supply Co of State Grid Liaoning Electric Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W80/00—Wireless network protocols or protocol adaptations to wireless operation
    • H04W80/06—Transport layer protocols, e.g. TCP [Transport Control Protocol] over wireless
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/10—Small scale networks; Flat hierarchical networks
    • H04W84/12—WLAN [Wireless Local Area Networks]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

本发明涉及无线传输技术领域，尤其为基于异构网络的避雷器数据无线传输方法、装置及介质，包括如下步骤：基于杆塔的检测节点建立自组网信号传输线性拓扑结构模型；构建线路后端云服务平台和前端杆塔的监测传感器的边缘计算层，完成小区域数据的离线处理及分析；根据监测端信号强度自主选择选取无线通信协议进行通讯进行数据传送。本发明实现无信号区域采集数据的低时延、高速率、高可靠传输，大大降低巡检的难度和强度。根据监测传感器的信号强度通过边缘计算层在云服务平台中自主选择无线多跳网络或公共网络，以构建自组网信号传输线性拓扑结构模型，将数据传送至动态汇聚节点或直接接入移动通信网络，实现输电线路无信号区域无盲点监测。

Figure 202310197927

The present invention relates to the technical field of wireless transmission, in particular to a heterogeneous network-based lightning arrester data wireless transmission method, device and medium, comprising the following steps: establishing a linear topology structure model of ad hoc network signal transmission based on a detection node of a pole tower; constructing a line back-end cloud The edge computing layer of the service platform and the monitoring sensor of the front-end tower completes the off-line processing and analysis of small area data; according to the signal strength of the monitoring terminal, the wireless communication protocol is independently selected for communication and data transmission. The invention realizes low time delay, high speed and high reliability transmission of data collected in a no-signal area, and greatly reduces the difficulty and intensity of inspection. According to the signal strength of the monitoring sensor, the wireless multi-hop network or public network is independently selected in the cloud service platform through the edge computing layer to build a linear topology model of ad hoc network signal transmission, and transmit data to dynamic aggregation nodes or directly access mobile communication Network, to achieve no blind spot monitoring in areas with no signal on transmission lines.

Figure 202310197927

Description

基于异构网络的避雷器数据无线传输方法、装置及介质Method, device and medium for wireless transmission of lightning arrester data based on heterogeneous network

技术领域technical field

本发明涉及无线传输技术领域，尤其是基于异构网络的避雷器数据无线传输方法、装置及介质。The invention relates to the technical field of wireless transmission, in particular to a heterogeneous network-based lightning arrester data wireless transmission method, device and medium.

背景技术Background technique

物联网技术是以无线传感网络技术、射频识别RFID技术等作为实物智能识别、监测手段,结合无线通信网络,为输变电设备状态监测提供了新的智能化手段。基于物联网的无线传感网技术可有效解决有线通信方式的弊端，窄带物联网（Narrow Band Internet ofThings，NB-IoT）是一种构建于蜂窝网络的新型低功耗广域网技术，适用于远距离、小数据量、低速率、低频次、高时延、非移动性的物联网通信业务。 LoRa是创建长距离通讯连接的物理层或无线调制，基于CSS调制技术（Chirp Spread Spectrum）的LoRa技术相较于传统的FSK技术，能极大地增加通讯范围，且CSS技术数十年已经广受军事和空间通讯所采用，具有传输距离远、抗干扰性强等特点。The Internet of Things technology uses wireless sensor network technology and radio frequency identification (RFID) technology as physical intelligent identification and monitoring means, combined with wireless communication network, to provide a new intelligent means for the state monitoring of power transmission and transformation equipment. The wireless sensor network technology based on the Internet of Things can effectively solve the disadvantages of wired communication methods. Narrow Band Internet of Things (NB-IoT) is a new type of low-power wide area network technology built on a cellular network, suitable for long-distance , Small data volume, low rate, low frequency, high delay, non-mobile IoT communication services. LoRa is a physical layer or wireless modulation that creates long-distance communication connections. Compared with traditional FSK technology, LoRa technology based on CSS modulation technology (Chirp Spread Spectrum) can greatly increase the communication range, and CSS technology has been widely used for decades. Used in military and space communications, it has the characteristics of long transmission distance and strong anti-interference.

由于输电线路分布区域环境复杂，远距离大宽度传输线路会穿过人迹罕至的无信号区域，而通信网络信号不均衡会造成大量监测设备无法稳定及时回传数据，极大的增加了巡检的难度和强度。Due to the complex environment of the distribution area of the transmission line, the long-distance and large-width transmission line will pass through the inaccessible no-signal area, and the unbalanced communication network signal will cause a large number of monitoring equipment to fail to return data in a stable and timely manner, which greatly increases the difficulty of inspection. and strength.

发明内容Contents of the invention

本发明的目的是通过提出基于异构网络的避雷器数据无线传输方法、装置及介质，以解决上述背景技术中提出的缺陷。The object of the present invention is to solve the defects in the above-mentioned background technology by proposing a method, device and medium for wireless transmission of lightning arrester data based on a heterogeneous network.

本发明采用的技术方案如下：The technical scheme that the present invention adopts is as follows:

提供基于异构网络的避雷器数据无线传输方法，包括如下步骤：A method for wireless transmission of arrester data based on a heterogeneous network is provided, including the following steps:

S1：基于杆塔的检测节点建立自组网信号传输线性拓扑结构；S1: Establish a linear topology structure for ad hoc network signal transmission based on the detection node of the tower;

S2：构建线路后端云服务平台和前端杆塔的监测传感器的边缘计算层；S2: Build the edge computing layer of the line back-end cloud service platform and front-end tower monitoring sensors;

S3：根据监测传感器的信号强度通过边缘计算层在云服务平台中自主选择选取无线通信协议以构建自组网信号传输线性拓扑结构模型进行通讯。S3: According to the signal strength of the monitoring sensor, the wireless communication protocol is independently selected in the cloud service platform through the edge computing layer to construct a linear topology model of ad hoc network signal transmission for communication.

作为本发明的一种优选技术方案：所述自组网信号传输线性拓扑结构利用集成NB-IoT和LoRa非授权频谱技术进行混合组网，搭建成广域网络，其中，同一终端公用NB-IoT与LoRa模块通讯技术，LoRa模块与上一级无网络信号LoRa模块采集终端传回数据进行交互存储，NB-IoT模块将存储数据发至下一级。As a preferred technical solution of the present invention: the ad hoc network signal transmission linear topology uses integrated NB-IoT and LoRa unlicensed spectrum technology for hybrid networking to build a wide-area network, wherein the same terminal shares NB-IoT and LoRa module communication technology, the LoRa module and the upper-level no-network signal LoRa module collect the data returned by the terminal for interactive storage, and the NB-IoT module sends the stored data to the next level.

作为本发明的一种优选技术方案：所述S1中，通过自主感知路径选择算法的无线传输技术构建基于杆塔的检测节点建立自组网信号传输线性拓扑结构模型。As a preferred technical solution of the present invention: in the above S1, the wireless transmission technology of the autonomous perception path selection algorithm is used to construct the detection node based on the tower to establish the linear topology structure model of the signal transmission of the ad hoc network.

作为本发明的一种优选技术方案：所述自主感知路径选择算法基于蚁群算法改进实现。As a preferred technical solution of the present invention: the autonomous perception path selection algorithm is implemented based on an improved ant colony algorithm.

作为本发明的一种优选技术方案：所述自主感知路径选择算法如下：As a preferred technical solution of the present invention: the autonomous perception path selection algorithm is as follows:

将环境抽象模型通过划分栅格构造三维环境模型，设杆塔的检测节点任意点

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的启发式函数/>
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如下：The abstract model of the environment is constructed by dividing the grid into a 3D environment model, and any point of the detection node of the tower is set
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as follows:

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其中，

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is the relevant impact factor; />
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is the heuristic factor; the grid is divided into a feasible area A and a forbidden area B, and the value of the safe area coefficient is:

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Figure SMS_7

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为路径点/>
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所消耗的能量，下一路径点出消耗的能量如下：
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is waypoint />
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The energy consumed, the energy consumed by the next path point is as follows:

Figure SMS_10
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其中，

Figure SMS_11
表示下一步将要选择的点与上一个路径点的距离；/>
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表示下一步将要选择的点与规划路径目标点的距离；/>
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和/>
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分别为路程影响因子和转弯半径影响因子；
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的转弯半径；/>
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表示考虑环境障碍时可行点/>
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的平滑程度：in,
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Indicates the distance between the point to be selected in the next step and the previous path point; />
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smoothness of:

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其中，

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为下一个路径点的坐标值，/>
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为路径平滑系数。in,
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is the path smoothing coefficient.

作为本发明的一种优选技术方案：所述蚁群算法具有随机性，采用线性回归模型进行迭代更新：As a preferred technical solution of the present invention: the ant colony algorithm has randomness, and a linear regression model is used for iterative update:

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其中，

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采用前后2次迭代后线性回归路径的长度来自动调节信息素挥发因子

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作为本发明的一种优选技术方案：所述自主感知路径选择算法流程如下：As a preferred technical solution of the present invention: the autonomous perception path selection algorithm flow is as follows:

S2.1：进行环境信息参数和蚂蚁算法本身参数的初始化；S2.1: Initialize the environmental information parameters and the parameters of the ant algorithm itself;

S2.2：利用关联在每个状态中的信息素和启发信息,采用状态转移规则选择移动的方向，迭代获得问题的可行解；S2.2: Utilize the pheromone and heuristic information associated in each state, adopt the state transition rules to select the direction of movement, and iteratively obtain a feasible solution to the problem;

S2.3：完成一次迭代后,根据优化准则在当代中选择一条最优路径，保留该路径直到下一次迭代产生更优的路径；S2.3: After completing an iteration, select an optimal path in the current generation according to the optimization criterion, and keep this path until a better path is generated in the next iteration;

S2.4：该迭代过程持续到完成规定的迭代次数或者连续多代迭代结果一致为止。S2.4: The iterative process continues until the specified number of iterations is completed or the results of successive generations of iterations are consistent.

作为本发明的一种优选技术方案：所述S3中，基于多层异构无线网络自主择优算法，根据监测端信号强度自主选择LoRa+NB-IoT无线通信协议，将数据传送至动态汇聚节点或直接接入移动通信网络，协调数据的采集、分配和传输，实现输电线路无信号区域无盲点监测。As a preferred technical solution of the present invention: in the S3, based on the multi-layer heterogeneous wireless network independent selection algorithm, the LoRa+NB-IoT wireless communication protocol is independently selected according to the signal strength of the monitoring terminal, and the data is transmitted to the dynamic aggregation node or Directly connected to the mobile communication network, coordinates the collection, distribution and transmission of data, and realizes no blind spot monitoring in areas with no signal on transmission lines.

作为本发明的一种优选技术方案：所述自主择优算法基于粒子群算法进行寻优判断，并设定阈值进行判断。As a preferred technical solution of the present invention: the autonomous optimization algorithm is based on the particle swarm optimization algorithm for optimal judgment, and a threshold is set for judgment.

作为本发明的一种优选技术方案：所述S3中，通过自主择优算法设定的阈值判断网络信号较好的地区以所述自组网信号传输线性拓扑结构中的NB-IoT技术实现数据的交互，并与现有公网基站进行通讯，构建自组网信号传输线性拓扑结构模型，实现杆塔倾斜终端数据的采集与传输。As a preferred technical solution of the present invention: in the S3, the threshold value set by the independent optimization algorithm is used to judge the areas with better network signals, and the NB-IoT technology in the linear topology structure of the ad hoc network signal transmission is used to realize data transmission. Interact and communicate with existing public network base stations, construct a linear topology model of ad hoc network signal transmission, and realize data collection and transmission of tower tilt terminals.

基于异构网络的避雷器数据无线传输装置，包括：The lightning arrester data wireless transmission device based on heterogeneous network, including:

存储器，用于存储非暂时性计算机可读指令；以及memory for storing non-transitory computer readable instructions; and

处理器，用于运行所述计算机可读指令，使得所述计算机可读指令被所述处理器执行时实现上述的基于异构网络的避雷器数据无线传输方法。The processor is configured to run the computer-readable instructions, so that when the computer-readable instructions are executed by the processor, the above-mentioned method for wireless transmission of lightning arrester data based on a heterogeneous network is realized.

一种计算机可读存储介质，用于存储非暂时性计算机可读指令，当所述非暂时性计算机可读指令由计算机执行时，使得所述计算机执行上述的基于异构网络的避雷器数据无线传输方法。A computer-readable storage medium for storing non-transitory computer-readable instructions, when the non-transitory computer-readable instructions are executed by a computer, causing the computer to perform the above-mentioned wireless transmission of lightning arrester data based on a heterogeneous network method.

本发明提供的基于异构网络的避雷器数据无线传输方法，与现有技术相比，其有益效果有：Compared with the prior art, the heterogeneous network-based lightning arrester data wireless transmission method provided by the present invention has the following beneficial effects:

本发明基于自主感知路径选择算法的无线传输技术，建立大规模、远距离自组网信号传输线性拓扑结构模型，实现无信号区域采集数据的低时延、高速率、高可靠传输，大大降低巡检的难度和强度。基于安装在杆塔上的检测节点呈现的线性拓扑结构，通过多层异构无线网络自主择优算法，根据监测传感器的信号强度通过边缘计算层在云服务平台中自主选择无线多跳网络或公共网络，以构建自组网信号传输线性拓扑结构模型，将数据传送至动态汇聚节点或直接接入移动通信网络，实现输电线路无信号区域无盲点监测。The present invention is based on the wireless transmission technology of the autonomous perception path selection algorithm, establishes a large-scale, long-distance ad hoc network signal transmission linear topology model, realizes low delay, high speed, and high reliability transmission of data collected in no-signal areas, and greatly reduces patrolling time. Difficulty and strength of the test. Based on the linear topology presented by the detection nodes installed on the tower, through the multi-layer heterogeneous wireless network independent selection algorithm, according to the signal strength of the monitoring sensor through the edge computing layer to independently select the wireless multi-hop network or public network in the cloud service platform, To build a linear topology model of ad hoc network signal transmission, the data is transmitted to the dynamic aggregation node or directly connected to the mobile communication network, so as to realize the monitoring of no blind spot in the transmission line without signal area.

附图说明Description of drawings

图1为本发明优选实施例的方法流程图；Fig. 1 is the method flowchart of preferred embodiment of the present invention;

图2为本发明优选实施例中无网络信号地区到网络信号较好地区的过渡图。Fig. 2 is a transition diagram from an area without a network signal to an area with a better network signal in a preferred embodiment of the present invention.

具体实施方式Detailed ways

需要说明的是，在不冲突的情况下，本实施例中的实施例及实施例中的特征可以相互组合，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。It should be noted that, in the case of no conflict, the embodiments in this embodiment and the features in the embodiments can be combined with each other. The technical solutions in the embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention. Clearly and completely described, it is obvious that the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

参照图1，本发明优选实施例提供了基于异构网络的避雷器数据无线传输方法，包括如下步骤：Referring to Fig. 1, the preferred embodiment of the present invention provides a heterogeneous network-based lightning arrester data wireless transmission method, including the following steps:

S1：基于杆塔的检测节点建立自组网信号传输线性拓扑结构；S1: Establish a linear topology structure for ad hoc network signal transmission based on the detection node of the tower;

S2：构建线路后端云服务平台和前端杆塔的监测传感器的边缘计算层；S2: Build the edge computing layer of the line back-end cloud service platform and front-end tower monitoring sensors;

S3：根据监测传感器的信号强度通过边缘计算层在云服务平台中自主选择选取无线通信协议以构建自组网信号传输线性拓扑结构模型进行通讯。S3: According to the signal strength of the monitoring sensor, the wireless communication protocol is independently selected in the cloud service platform through the edge computing layer to construct a linear topology model of ad hoc network signal transmission for communication.

所述自组网信号传输线性拓扑结构利用集成NB-IoT和LoRa非授权频谱技术进行混合组网，搭建成广域网络，其中，同一终端公用NB-IoT与LoRa模块通讯技术，LoRa模块与上一级无网络信号LoRa模块采集终端传回数据进行交互存储，NB-IoT模块将存储数据发至下一级。The ad hoc network signal transmission linear topology uses integrated NB-IoT and LoRa unlicensed spectrum technology for hybrid networking to build a wide area network, wherein the same terminal shares NB-IoT and LoRa module communication technology, and the LoRa module communicates with the previous There is no network signal in the first level LoRa module collects the data sent back by the terminal for interactive storage, and the NB-IoT module sends the stored data to the next level.

所述S1中，通过自主感知路径选择算法的无线传输技术构建基于杆塔的检测节点建立自组网信号传输线性拓扑结构模型。In the above S1, the wireless transmission technology based on the autonomous perception path selection algorithm is used to construct the detection node based on the tower to establish the linear topology structure model of the signal transmission of the ad hoc network.

所述自主感知路径选择算法基于蚁群算法改进实现。The autonomous perception path selection algorithm is implemented based on an improved ant colony algorithm.

所述自主感知路径选择算法如下：The autonomous perception path selection algorithm is as follows:

将环境抽象模型通过划分栅格构造三维环境模型，设杆塔的检测节点任意点

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的启发式函数/>
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如下：The abstract model of the environment is constructed by dividing the grid into a 3D environment model, and any point of the detection node of the tower is set
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heuristic function for />
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as follows:

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其中，

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和/>
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为相关影响因子；/>
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为启发因子；将栅格分为可行区域A和禁行区域B，安全区域系数的取值为：in,
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and />
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is the relevant impact factor; />
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is the heuristic factor; the grid is divided into a feasible area A and a forbidden area B, and the value of the safe area coefficient is:

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为/>
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路径点所消耗的能量，下一路径点出消耗的能量如下：
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for />
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The energy consumed by the waypoint and the energy consumed by the next waypoint are as follows:

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其中，

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表示下一步将要选择的点与上一个路径点的距离；/>
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表示下一步将要选择的点与规划路径目标点的距离；/>
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和/>
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分别为路程影响因子和转弯半径影响因子；
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为路径点/>
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的转弯半径；/>
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表示考虑环境障碍时可行点/>
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的平滑程度：in,
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Indicates the distance between the point to be selected in the next step and the previous path point; />
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Indicates the distance between the point to be selected in the next step and the target point of the planned path; />
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and />
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Respectively, the influence factor of distance and the influence factor of turning radius;
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is waypoint />
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turning radius; />
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Indicates feasible points when considering environmental obstacles />
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smoothness of:

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其中，

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为下一个路径点的坐标值，/>
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为路径平滑系数。in,
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is the coordinate value of the next path point, />
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is the path smoothing coefficient.

所述蚁群算法具有随机性，采用线性回归模型进行迭代更新：The ant colony algorithm has randomness, and a linear regression model is used for iterative update:

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其中，

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为第/>
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只蚂蚁所选择路径点的坐标；/>
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为线性回归模型；
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为线性回归模型中的权重参数向量；/>
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为线性回归模型中的权重参数向量的转置；/>
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为路径点的横坐标向量；/>
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为采用梯度下降法求解线性回归模型的目标函数；/>
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为学习率。in,
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for No. />
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The coordinates of the path point selected by the ant; />
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is a linear regression model;
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is the weight parameter vector in the linear regression model; />
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is the transpose of the weight parameter vector in the linear regression model; />
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is the abscissa vector of the path point; />
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To solve the objective function of the linear regression model using the gradient descent method; />
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is the learning rate.

采用前后2次迭代后线性回归路径的长度来自动调节信息素挥发因子

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：Using the length of the linear regression path after two iterations before and after to automatically adjust the pheromone volatilization factor
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:

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其中，

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、/>
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分别为信息素挥发因子最小值和最大值；/>
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为前后两次线性回归路径的长度差；/>
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为路径长度系数，标准路径长度对信息素的影响程度。in,
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, />
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are the minimum and maximum values of pheromone volatilization factors; />
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is the length difference between the two linear regression paths before and after; />
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is the path length coefficient, the influence degree of standard path length on pheromone.

所述自主感知路径选择算法流程如下：The autonomous perception path selection algorithm flow is as follows:

S2.1：进行环境信息参数和蚂蚁算法本身参数的初始化；S2.1: Initialize the environmental information parameters and the parameters of the ant algorithm itself;

S2.2：利用关联在每个状态中的信息素和启发信息,采用状态转移规则选择移动的方向，迭代获得问题的可行解；S2.2: Utilize the pheromone and heuristic information associated in each state, adopt the state transition rules to select the direction of movement, and iteratively obtain a feasible solution to the problem;

S2.3：完成一次迭代后,根据优化准则在当代中选择一条最优路径，保留该路径直到下一次迭代产生更优的路径；S2.3: After completing an iteration, select an optimal path in the current generation according to the optimization criterion, and keep this path until a better path is generated in the next iteration;

S2.4：该迭代过程持续到完成规定的迭代次数或者连续多代迭代结果一致为止。S2.4: The iterative process continues until the specified number of iterations is completed or the results of successive generations of iterations are consistent.

所述S3中，基于多层异构无线网络自主择优算法，根据监测端信号强度自主选择LoRa+NB-IoT无线通信协议，将数据传送至动态汇聚节点或直接接入移动通信网络，协调数据的采集、分配和传输，实现输电线路无信号区域无盲点监测。In the S3, based on the multi-layer heterogeneous wireless network independent selection algorithm, the LoRa+NB-IoT wireless communication protocol is independently selected according to the signal strength of the monitoring terminal, and the data is transmitted to the dynamic aggregation node or directly connected to the mobile communication network, and the data is coordinated. Acquisition, distribution and transmission, to achieve no blind spot monitoring in areas without signals on transmission lines.

所述自主择优算法基于粒子群算法进行寻优判断，并设定阈值进行判断。The autonomous optimization algorithm is based on the particle swarm optimization algorithm for optimal judgment, and a threshold is set for judgment.

所述S3中，通过自主择优算法设定的阈值判断网络信号较好的地区以所述自组网信号传输线性拓扑结构中的NB-IoT技术实现数据的交互，并与现有公网基站进行通讯，构建自组网信号传输线性拓扑结构模型，实现杆塔倾斜终端数据的采集与传输。In the S3, the threshold value set by the independent optimization algorithm is used to judge the areas with better network signals, and the NB-IoT technology in the linear topology structure of the ad hoc network signal transmission is used to realize the data interaction, and the existing public network base station For communication, construct a linear topology model of ad hoc network signal transmission, and realize the collection and transmission of tower tilt terminal data.

本实施例中，通过自主感知路径选择算法的无线传输技术，构建基于杆塔的检测节点建立自组网信号传输线性拓扑结构模型：In this embodiment, through the wireless transmission technology of the autonomous perception path selection algorithm, the detection node based on the tower is constructed to establish the linear topology model of the signal transmission of the ad hoc network:

设杆塔的检测节点任意点的启发式函数如下：The heuristic function of any point of the detection node of the tower is as follows:

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其中，

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和/>
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为相关影响因子；/>
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为启发因子；将栅格分为可行区域A和禁行区域B，安全区域系数的取值为：in,
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and />
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is the relevant impact factor; />
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is the heuristic factor; the grid is divided into a feasible area A and a forbidden area B, and the value of the safe area coefficient is:

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为路径点/>
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所消耗的能量，下一路径点出消耗的能量如下
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is waypoint />
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The energy consumed, the energy consumed by the next path point is as follows

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其中，

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表示下一步将要选择的点与上一个路径点的距离；/>
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表示下一步将要选择的点与规划路径目标点的距离；/>
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和/>
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分别为路程影响因子和转弯半径影响因子；
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为路径点/>
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的转弯半径；in,
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Indicates the distance between the point to be selected in the next step and the previous path point; />
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Indicates the distance between the point to be selected in the next step and the target point of the planned path; />
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and />
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Respectively, the influence factor of distance and the influence factor of turning radius;
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is waypoint />
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turning radius;

考虑路径平滑度因素

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，使得规划出来的航线尽可能地平滑Consider path smoothness factor
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, making the planned route as smooth as possible

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表示考虑环境障碍时可行点/>
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的平滑程度：
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Indicates feasible points when considering environmental obstacles />
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smoothness of:

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其中，

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为下一个路径点的坐标值，/>
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为路径平滑系数in,
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is the coordinate value of the next path point, />
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is the path smoothing coefficient

算法具有随机性，每一次迭代的m只蚂蚁选择的路径点都有可能距离最优路径点较远，甚至有些蚂蚁在搜索路径的过程中遇到思索，无法找到一条可行的路径，因此合理的路径更新方式是蚂蚁能够寻找到一条可达路径的保证；采用线性回归模型将本轮迭代最具有代表性的路径点的信息更新一次，强化下一轮蚂蚁搜索路径的方向性，从而提高算法的收敛性能，改善解的质量The algorithm is random, and the path points selected by m ants in each iteration may be far from the optimal path point, and some ants may even be unable to find a feasible path in the process of searching for the path, so it is reasonable The path update method is the guarantee that the ants can find a reachable path; the linear regression model is used to update the information of the most representative path point in the current round of iterations, and strengthen the directionality of the next round of ants' search paths, thereby improving the performance of the algorithm. Convergence performance, improving solution quality

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其中，

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为第/>
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只蚂蚁所选择路径点的坐标；/>
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为线性回归模型；/>
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为线性回归模型中的权重参数向量；/>
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为线性回归模型中的权重参数向量的转置；
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为路径点的横坐标向量；/>
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为采用梯度下降法求解线性回归模型的目标函数；/>
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为学习率。in,
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for No. />
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The coordinates of the path point selected by the ant; />
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is a linear regression model; />
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is the weight parameter vector in the linear regression model; />
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is the transpose of the weight parameter vector in the linear regression model;
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is the abscissa vector of the path point; />
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To solve the objective function of the linear regression model using the gradient descent method; />
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is the learning rate.

蚁群算法中的人工蚂蚁具有记忆功能，随着时间的推移，以前留下的信息素会逐渐消失,信息素挥发因子

Figure SMS_111
的大小直接影响到蚁群算法的全局搜索能力及其收敛速度。若求解问题的规模比较大时，会使得那些从未被搜索到的路径上的信息素减少到接近于零，降低了算法的全局搜索能力。采用前后2次迭代后线性回归路径的长度来自动调节信息素挥发因子，当回归路径长度差较大时，增强信息素的启发作用，适当调小，/>
Figure SMS_112
当回归路径长度差较小时，为提升蚂蚁寻找新路径的能力，增强全局搜索能力,适当增大/>
Figure SMS_113
。The artificial ants in the ant colony algorithm have a memory function. As time goes by, the pheromone left before will gradually disappear, and the pheromone volatilization factor
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The size of directly affects the global search ability of the ant colony algorithm and its convergence speed. If the scale of the solution problem is relatively large, the pheromones on the paths that have never been searched will be reduced to close to zero, which reduces the global search ability of the algorithm. The length of the linear regression path after two iterations before and after is used to automatically adjust the pheromone volatilization factor. When the difference in the length of the regression path is large, the heuristic effect of the pheromone is enhanced and adjusted appropriately. />
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When the return path length difference is small, in order to improve the ability of ants to find new paths and enhance the global search ability, appropriately increase
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.

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其中，

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、/>
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分别为信息素挥发因子最小值和最大值；/>
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为前后两次线性回归路径的长度差；/>
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为路径长度系数，标准路径长度对信息素的影响程度。in,
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, />
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are the minimum and maximum values of pheromone volatilization factors; />
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is the length difference between the two linear regression paths before and after; />
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is the path length coefficient, the influence degree of standard path length on pheromone.

自主感知路径选择算法中，首先对环境信息参数和蚂蚁算法本身参数的进行初始化；再进行数据的迭代更新，利用关联在每个状态中的信息素和启发信息,采用状态转移规则选择移动的方向，迭代获得问题的可行解；完成一次迭代后,根据优化准则在当代中选择一条最优路径，保留该路径直到下一次迭代产生更优的路径；该迭代过程持续到完成规定的迭代次数或者连续多代迭代结果一致为止。In the autonomous perception path selection algorithm, firstly initialize the environmental information parameters and the parameters of the ant algorithm itself; then iteratively update the data, use the pheromone and heuristic information associated in each state, and use the state transition rules to select the direction of movement , iteratively obtains a feasible solution to the problem; after completing an iteration, select an optimal path in the current generation according to the optimization criterion, and keep this path until a better path is generated in the next iteration; the iterative process continues until the specified number of iterations is completed or continuous Until the results of multiple iterations are consistent.

再构建线路后端云服务平台和前端杆塔的监测传感器的边缘计算层，完成小区域数据的离线处理及分析。参照图2，基于多层异构无线网络自主择优算法，根据监测端信号强度自主选择LoRa+NB-IoT无线通信协议，将数据传送至动态汇聚节点或直接接入移动通信网络，协调数据的采集、分配和传输，实现输电线路无信号区域无盲点监测。Then build the line back-end cloud service platform and the edge computing layer of the front-end tower monitoring sensor to complete the offline processing and analysis of small area data. Referring to Figure 2, based on the multi-layer heterogeneous wireless network independent optimization algorithm, the LoRa+NB-IoT wireless communication protocol is independently selected according to the signal strength of the monitoring terminal, and the data is transmitted to the dynamic aggregation node or directly connected to the mobile communication network to coordinate data collection. , distribution and transmission, to achieve no blind spot monitoring in areas with no signal on transmission lines.

对判断网络质量信号较好的地区以NB-IoT技术实现数据的交互，并与现有公网基站进行通讯，实现杆塔倾斜终端数据的采集与传输。For areas where the network quality signal is judged to be good, NB-IoT technology is used to realize data interaction and communicate with existing public network base stations to realize data collection and transmission of tower tilting terminals.

对判断网络质量较差的地区，利用集成NB-IoT和LoRa非授权频谱技术进行混合组网，将两张不同网络进行合一搭建成广域网络，通过多模合一终端技术，同一终端公用NB-IoT与LoRa模块通讯技术，LoRa模块与上一级无网络信号LoRa模块采集终端传回数据进行交互存储，NB-IoT模块将存储数据发至下一级。For areas where the network quality is judged to be poor, use the integrated NB-IoT and LoRa unlicensed spectrum technology for hybrid networking, and combine two different networks into one to build a wide area network. Through the multi-mode integration terminal technology, the same terminal shares NB -IoT and LoRa module communication technology, the LoRa module and the upper level no network signal LoRa module collects the data returned by the terminal for interactive storage, and the NB-IoT module sends the stored data to the next level.

基于异构网络的避雷器数据无线传输装置，包括：The lightning arrester data wireless transmission device based on heterogeneous network, including:

存储器，用于存储非暂时性计算机可读指令；以及memory for storing non-transitory computer readable instructions; and

处理器，用于运行所述计算机可读指令，使得所述计算机可读指令被所述处理器执行时实现上述的基于异构网络的避雷器数据无线传输方法。The processor is configured to run the computer-readable instructions, so that when the computer-readable instructions are executed by the processor, the above-mentioned method for wireless transmission of lightning arrester data based on a heterogeneous network is realized.

一种计算机可读存储介质，用于存储非暂时性计算机可读指令，当所述非暂时性计算机可读指令由计算机执行时，使得所述计算机执行上述的基于异构网络的避雷器数据无线传输方法。A computer-readable storage medium for storing non-transitory computer-readable instructions, when the non-transitory computer-readable instructions are executed by a computer, causing the computer to perform the above-mentioned wireless transmission of lightning arrester data based on a heterogeneous network method.

对于本领域技术人员而言，显然本发明不限于上述示范性实施例的细节，而且在不背离本发明的精神或基本特征的情况下，能够以其他的具体形式实现本发明。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化囊括在本发明内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。It will be apparent to those skilled in the art that the invention is not limited to the details of the above-described exemplary embodiments, but that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Accordingly, the embodiments should be regarded in all points of view as exemplary and not restrictive, the scope of the invention being defined by the appended claims rather than the foregoing description, and it is therefore intended that the scope of the invention be defined by the appended claims rather than by the foregoing description. All changes within the meaning and range of equivalents of the elements are embraced in the present invention. Any reference sign in a claim should not be construed as limiting the claim concerned.

此外，应当理解，虽然本说明书按照实施方式加以描述，但并非每个实施方式仅包含一个独立的技术方案，说明书的这种叙述方式仅仅是为清楚起见，本领域技术人员应当将说明书作为一个整体，各实施例中的技术方案也可以经适当组合，形成本领域技术人员可以理解的其他实施方式。In addition, it should be understood that although this specification is described according to implementation modes, not each implementation mode only includes an independent technical solution, and this description in the specification is only for clarity, and those skilled in the art should take the specification as a whole , the technical solutions in the various embodiments can also be properly combined to form other implementations that can be understood by those skilled in the art.

Claims (10)

1. The lightning arrester data wireless transmission method based on the heterogeneous network is characterized by comprising the following steps of: the method comprises the following steps:
s1: establishing an ad hoc network signal transmission linear topological structure based on detection nodes of the towers;
s2: constructing an edge computing layer of a monitoring sensor of a line rear-end cloud service platform and a front-end pole tower;
s3: and automatically selecting a wireless communication protocol in the cloud service platform through an edge computing layer according to the signal intensity of the monitoring sensor to construct an ad hoc network signal transmission linear topological structure model for communication.
2. The heterogeneous network-based lightning arrester data wireless transmission method according to claim 1, wherein: the Ad hoc network signal transmission linear topological structure utilizes an integrated NB-IoT and LoRa unlicensed spectrum technology to carry out hybrid networking, and a wide area network is built, wherein the same terminal uses an NB-IoT and LoRa module communication technology, the LoRa module and a last-level network-free signal LoRa module acquisition terminal transmit back data to carry out interactive storage, and the NB-IoT module transmits the stored data to the next level.
3. The heterogeneous network-based lightning arrester data wireless transmission method according to claim 2, wherein: in the S1, an ad hoc network signal transmission linear topological structure is established by constructing a detection node based on a pole tower through a wireless transmission technology of an autonomous perception path selection algorithm; the autonomous perception path selection algorithm is improved based on an ant colony algorithm.
4. A heterogeneous network-based lightning arrester data wireless transmission method according to claim 3, wherein: the autonomous perceptual path selection algorithm is as follows:
constructing a three-dimensional environment model by dividing the environment abstract model into grids, and setting any point of a detection node of the tower
Figure QLYQS_1
Heuristic function of->
Figure QLYQS_2
The following are provided:
Figure QLYQS_3
wherein (1)>
Figure QLYQS_4
And->
Figure QLYQS_5
Is a relevant influencing factor; />
Figure QLYQS_6
Is a heuristic factor; dividing the grid into a feasible region A and a forbidden region B, wherein the safety region coefficient has the following value:
Figure QLYQS_7
Figure QLYQS_8
is a waypoint->
Figure QLYQS_9
The energy consumed is as follows:
Figure QLYQS_11
wherein (1)>
Figure QLYQS_13
Representing the distance between the point to be selected in the next step and the last path point; />
Figure QLYQS_14
Representing the distance between the point to be selected in the next step and the target point of the planned path; />
Figure QLYQS_15
And->
Figure QLYQS_16
The distance influencing factors and the turning radius influencing factors are respectively; />
Figure QLYQS_17
Is a waypoint->
Figure QLYQS_18
Is provided; />
Figure QLYQS_10
Representing feasible points when considering environmental disorders +.>
Figure QLYQS_12
Is not limited by the smoothness of (a):
Figure QLYQS_19
wherein (1)>
Figure QLYQS_20
For the coordinate value of the next waypoint, +.>
Figure QLYQS_21
Is a path smoothing coefficient.
5. The heterogeneous network-based lightning arrester data wireless transmission method according to claim 4, wherein: the ant colony algorithm has randomness, and adopts a linear regression model to carry out iterative updating:
Figure QLYQS_22
Figure QLYQS_24
Figure QLYQS_27
Figure QLYQS_28
wherein (1)>
Figure QLYQS_30
Is->
Figure QLYQS_32
Only the coordinates of the path points selected by ants; />
Figure QLYQS_33
Is a linear regression model; />
Figure QLYQS_23
The weight parameter vector is in a linear regression model; />
Figure QLYQS_25
Transpose of weight parameter vectors in a linear regression model; />
Figure QLYQS_26
Is the abscissa vector of the path point; />
Figure QLYQS_29
Solving an objective function of a linear regression model by adopting a gradient descent method;
Figure QLYQS_31
is the learning rate;
the length of the linear regression path after 2 times of iteration is adopted to automatically adjust the pheromone volatilization factor
Figure QLYQS_34
：
Figure QLYQS_35
Wherein (1)>
Figure QLYQS_36
、/>
Figure QLYQS_37
Respectively a minimum value and a maximum value of the pheromone volatilization factors; />
Figure QLYQS_38
The length difference of the front linear regression path and the back linear regression path is obtained; />
Figure QLYQS_39
The degree of influence of the standard path length on the pheromone is a path length coefficient.
6. The heterogeneous network-based lightning arrester data wireless transmission method according to claim 5, wherein: the autonomous perception path selection algorithm flow is as follows:
s2.1: initializing environment information parameters and ant algorithm parameters;
s2.2: selecting a moving direction by using state transition rules by utilizing pheromone and heuristic information associated in each state, and iteratively obtaining a feasible solution of the problem;
s2.3: after one iteration is completed, selecting an optimal path in the current generation according to an optimization criterion, and reserving the path until the next iteration generates a better path;
s2.4: the iterative process continues until a prescribed number of iterations is completed or successive generations of iterative results agree.
7. The heterogeneous network-based lightning arrester data wireless transmission method according to claim 6, wherein: in the step S3, based on a multi-layer heterogeneous wireless network autonomous preferred algorithm, the LoRa+NB-IoT wireless communication protocol is autonomously selected according to the signal intensity of the monitoring end, data is transmitted to a dynamic sink node or directly connected to a mobile communication network, and the acquisition, distribution and transmission of the data are coordinated, so that no blind spot monitoring of a no-signal area of the power transmission line is realized; the autonomous preferred algorithm performs optimizing judgment based on a particle swarm algorithm, and sets a threshold value for judging.
8. The heterogeneous network-based lightning arrester data wireless transmission method according to claim 7, wherein: in the step S3, the region with better network signals is judged through the threshold value set by the autonomous preferred algorithm, the data interaction is realized by the NB-IoT technology in the ad hoc network signal transmission linear topological structure, the data interaction is communicated with the existing public network base station, the ad hoc network signal transmission linear topological structure model is built, and the acquisition and transmission of the data of the tower inclined terminal are realized.
9. Lightning arrester data wireless transmission device based on heterogeneous network includes:
a memory for storing non-transitory computer readable instructions; and
a processor for executing the computer readable instructions such that the computer readable instructions when executed by the processor implement the heterogeneous network based arrester data wireless transmission method according to any of claims 1 to 8.
10. A computer readable storage medium storing non-transitory computer readable instructions which, when executed by a computer, cause the computer to perform the heterogeneous network-based arrester data wireless transmission method of any of claims 1 to 8.
CN202310197927.8A 2023-03-03 2023-03-03 Method, device and medium for wireless transmission of lightning arrester data based on heterogeneous network Pending CN116321536A (en)

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