A wireless relay node deployment method and device for complex environment and electronic equipment

Abstract

The application discloses a wireless relay node deployment method and device for complex environment and electronic equipment, and aims to solve the deployment problem of relay nodes in the case of complex environment of wireless Mesh network. In the case of unknown or time-varying of wireless Mesh network, the signal strength is estimated and corrected according to the dynamic calculation of process noise and observation noise covariance of measurement error variation on the basis of Kalman filtering. And the deployment of relay nodes adopts an event accumulation-based decision process to carry out relay deployment. Through the relay node deployment method, the signal processing in the complex case is fully considered in the deployment process, so that a more stable and efficient wireless Mesh network is established.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless relay network, in particular to a wireless relay node deployment method and device for complex environment and electronic equipment. BACKGROUND

[0002] In today's society, with the frequent occurrence of natural disasters and the increasing number of man-made accidents, emergency communication systems as a key component of disaster response and crisis management, its importance is increasingly prominent. Emergency communication not only concerns the timely delivery of information, but also relates to the improvement of rescue efficiency and the protection of people's life and property. However, disasters often lead to damage or failure of traditional communication infrastructure, which requires an emergency communication system that can be quickly deployed in harsh environments. Emergency communication system is the information hub in the rescue action, it not only ensures the real-time communication between the rescue team, but also ensures the connection between the disaster area personnel and the outside world. A powerful emergency communication system can provide accurate disaster information, coordinate rescue resources, guide rescue routes, and even help prevent further spread of disasters to some extent.

[0003] Wireless Mesh network is a self-organizing and self-healing network system, aiming to provide high reliability and flexible network coverage. This network does not rely on centralized infrastructure, and can establish a stable communication network in the disaster area in a very short time. Its nodes can communicate directly or through other nodes to relay information, greatly enhancing the robustness and flexibility of the network. Wireless Mesh network is developed from wireless self-organizing network, is an important technology to establish a connection between the Internet and wireless terminals, which can effectively solve the bottleneck problem of "the last mile" of wireless access, and has broad application prospects in homes, enterprises and public places and many other environments. In the case of disaster, wireless Mesh network can quickly adapt to environmental changes, even if some nodes are damaged, the network can still maintain operation. In addition, the deployment cost of wireless Mesh network is relatively low, which is an economical and effective long-term solution for areas where natural disasters often occur.

[0004] Relay nodes play a crucial role in wireless Mesh networks, as they are responsible for expanding network coverage, ensuring signal continuity and stability. In the event of a disaster, especially in vast or complex terrain disaster areas, traditional communication signals often fail to cover the entire affected area, which requires relay nodes to fill in the gaps in communication. These nodes effectively extend the communication range to remote areas or hard-to-reach locations, such as mountainous areas or underground environments, by receiving, amplifying, and retransmitting signals. In addition, relay nodes can improve communication quality. In complex disaster environments, communication signals are often disturbed or attenuated, and relay nodes can enhance signal strength, reduce errors and delays in data transmission, and ensure that rescue teams can receive clear and reliable information. Therefore, the rational deployment of relay nodes directly affects the effectiveness of emergency communication networks and influences the real-time response and accuracy of decision-making in rescue operations. SUMMARY

[0005] The present application is aimed at signal attenuation in complex emergency communication environments, taking into full consideration network indicators such as received signal strength, connectivity, and node location information. A wireless relay node deployment method, device, and electronic equipment for complex environments are proposed, which specifically includes three parts: an information collection module, a network analysis module, and a node deployment module. The information collection module collects network state information from the entire network at regular intervals, the network analysis module analyzes the received signal strength of task nodes based on an adaptive Kalman filtering method, and the node deployment module deploys relay nodes based on an event accumulation decision-making process. Through the continuous iteration of these three modules, the method can gradually deploy relay nodes for the wireless Mesh network, thereby constructing a reliable and stable wireless Mesh network.

[0006] The technical scheme adopted by the present application to achieve the above-mentioned purpose is:

[0007] In a first aspect, the present application discloses a wireless relay node deployment method for complex environments. This method is applied to a command center in a wireless Mesh network, which is used to command and monitor the deployment of relay nodes. The relay nodes are responsible for data forwarding, while the task nodes perform specific tasks and need to maintain communication connection with the command center during the execution process. The deployment method includes the following steps:

[0008] 1) The deployment of relay nodes is controlled and managed by a command center, which is responsible for formulating deployment strategies for relay nodes, issuing deployment instructions, and monitoring the entire deployment process in real time, including monitoring the installation status of relay nodes, communication conditions, and interactive responses with task nodes;

[0009] 2) The command center is equipped with an information collection module to collect network status information of the entire network at regular intervals. The entire network includes the wireless mesh network composed of deployed relay nodes, task nodes and the command center.

[0010] 3) The command center installs a network analysis module to analyze the network status information collected by the information acquisition module, and evaluates the overall network status and communication quality;

[0011] 4) The command center controls the deployment of relay nodes, generates relay node deployment plans, and directs task nodes to deploy relay nodes through the relay node deployment method installed on the node deployment module.

[0012] 5) During the deployment of relay nodes, the command center continuously monitors and evaluates the status of the entire wireless mesh network, including changes in the location and communication requirements of task nodes, as well as the network's communication quality and signal coverage, until the task nodes complete their exploration tasks or the relay nodes are fully deployed.

[0013] Optionally, the information acquisition module is used to collect network status information in real time, including: the location information of the task nodes, the received signal strength of the task nodes, and the number of remaining relay nodes.

[0014] The network status information of the command center is as follows:

[0015] S t ={C,RSSI,N}

[0016] Wherein, C represents the location information of the task node, and the received signal strength of the task node is represented by RSSI. Since multiple nodes may be directly connected to the task node, the received signal strength of the task node is represented as RSSI = {RSSI1, RSSI2, ..., RSSI...} L}, where L represents the number of nodes directly connected to the current task node, and N represents the number of remaining relay nodes for the task node.

[0017] Optionally, the wireless mesh network analysis module analyzes the network status information collected by the information acquisition module to find the maximum received signal strength of the current task node and feeds it back to the relay node deployment module. Specifically, the network analysis module performs adaptive Kalman filtering on the received signal strength of each link of the task node.

[0018] RSSI′={RSSI′1,RSSI′2,···,RSSI′ L}

[0019] Where RSSI′ L This represents the received signal strength of the Lth direct link after the task node has undergone adaptive Kalman filtering.

[0020] For the task node, the current maximum received signal strength is selected as the signal strength reference value of the task node, i.e. RSSI' max , and the calculation formula is:

[0021] RSSI' max = {RSSI'1, RSSI'2, ···, RSSI' L max

[0022] Optionally, the adaptive Kalman filtering method is used to process and analyze the fluctuations of the received signal strength. This filtering method is optimized for signal fading and fluctuations in complex environments to reduce misjudgment. The filtering method is specially designed to dynamically adjust its internal parameters, including process noise covariance and observation noise covariance, to reflect environmental changes and signal fluctuations in real time. This adaptive ability enables the filtering method to more accurately estimate the true signal state when facing sudden signal changes, thereby supporting the effective deployment of relay nodes. The adaptive Kalman filtering method includes prediction, update and optimization.

[0023] The prediction process satisfies the following formula:

[0024]

[0025] where represents the state prediction value at time step k, A is the state transition matrix, B is the control input matrix, u k-1 is the control input at time step k-1, is the error covariance of the predicted state, Q k represents the process noise covariance at time k.

[0026] The update process satisfies the following formula:

[0027]

[0028] K k is the Kalman gain, H is the observation matrix, is the state estimate considering new measurement data, Z k represents the actual measurement value at time step k, P k is the error covariance considering new measurement data, R k represents the observation noise covariance at time k.

[0029] The optimization process includes adaptive process noise dynamic adjustment and adaptive observation noise dynamic adjustment.

[0030] The adaptive process noise dynamic adjustment satisfies the following formula: ​

[0031]

[0032] wherein is the innovation sequence; is the innovation covariance. β is a tuning factor, usually between 0 and 1, Q min , Q max are upper and lower bounds on the process noise covariance, respectively.

[0033] The adaptive observation noise dynamically adjusts to satisfy the following formula:

[0034]

[0035] wherein α is a tuning factor, usually between 0 and 1, R min , R max are upper and lower bounds on the observation noise covariance, respectively.

[0036] Optionally, the node deployment module generates the deployment position of the relay node using an event accumulation-based decision process according to the analysis result of the network analysis module, and instructs the task node to deploy the relay node.

[0037] Optionally, the complex environment wireless relay node deployment method iteration process continuously monitors the network state by the information collection module and the network analysis module of the command center during the wireless relay node deployment process, and responds to the position change and communication demand of the task node in real time. These modules evaluate the signal coverage and communication quality, dynamically guide the node deployment module to deploy the relay node, until the task node completes the exploration task or the relay node is completely deployed.

[0038] In a second aspect, the embodiments of the present application disclose a wireless relay node deployment device for a complex environment, applied to a command center, and the relay node deployment device comprises:

[0039] An information collection module is configured to collect network state information in real time, including position information of the task node and received signal strength of the task node.

[0040] A network analysis module is configured to analyze the information collected by the information collection module and feed back to a relay node deployment module.

[0041] The node deployment module generates the deployment position of the relay node using an event accumulation-based decision process according to the analysis result of the network analysis module, and instructs the task node to deploy the relay node.

[0042] In a third aspect, the embodiments of the present application disclose an electronic device, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus.

[0043] The memory is used for storing a computer program.

[0044] The processor is used for implementing the method steps of the complex environment-oriented wireless relay node deployment method when executing the program stored on the memory.

[0045] From the above technical solution, it can be seen that the embodiment of the present application provides a complex environment-oriented wireless relay node deployment method, device and electronic equipment. The state information of the entire network is obtained through the information acquisition module, the network state is analyzed by using the network analysis module to comprehensively analyze the signal receiving strength of the task node, it is judged whether the relay node needs to be deployed at the current time, if the relay node needs to be deployed at the current time, the deployment position of the relay node is output through the node deployment module, and the deployment of one relay node is realized. Through the continuous operation of the information acquisition module, the network analysis module and the node deployment module, the continuous deployment of the relay node is realized, and the deployment of the relay node of the entire wireless Mesh network is completed. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 is a flowchart of the relay node deployment method provided by the embodiment of the present application;

[0048] Figure 2 is a network topology diagram provided by the embodiment of the present application;

[0049] Figure 3 is another network topology diagram provided by the embodiment of the present application;

[0050] Figure 4 is a flowchart of an adaptive Kalman filtering method provided by the embodiment of the present application;

[0051] Figure 5 is a flowchart of an event accumulation-based decision process provided by the embodiment of the present application;

[0052] Figure 6 is a structure schematic diagram of a wireless relay node deployment device provided by the embodiment of the present application;

[0053] Figure 7 is a structure schematic diagram of an electronic equipment provided by the embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0055] In a first aspect, the embodiments of the present application disclose a wireless relay node deployment method for a complex environment, as shown in Figure 1 Figure 1 A flow chart of the relay node deployment method provided by the embodiments of the present application is applied to a command center node in a wireless Mesh network, and the method comprises the following steps.

[0056] S101, the deployment of the relay node is controlled and managed by a command center, which is responsible for formulating a deployment strategy of the relay node, issuing a deployment instruction, and monitoring the whole deployment process in real time, including monitoring the installation state of the relay node, the communication condition, and the interactive response with the task node.

[0057] S102, network state information of the current wireless Mesh network is collected, including the position information of the task node, the received signal strength of the task node, and the remaining relay quantity of the task node.

[0058] Before the network state information of the wireless Mesh network is collected, the wireless Mesh network can be initialized first to obtain the topology structure of the wireless Mesh network.

[0059] Suppose that there is a wireless Mesh network with 2 nodes {A, B} in a 1000*50m 2 area at the beginning, node A is set as the command center of the current wireless Mesh network, and node B is a task node. For example, a network topology diagram provided by the embodiments of the present application is shown in Figure 2 As the task node moves, some relay nodes {C, D, E} are deployed in the wireless Mesh network, for example, another network topology diagram provided by the embodiments of the present application is shown in Figure 3

[0060] In this step, the network information state of the wireless Mesh network is collected by using the command center, and the network state information of the command center is:

[0061] S t ={C, RSSI, N}

[0062] ​​Where C represents the location information of the task node, the received signal strength of the task node is represented by RSSI, and since there can be multiple nodes directly connected to the task node, the received strength of the task node is represented as RSSI = {RSSI1, RSSI2, ···, RSSI L}, L represents the number of nodes directly connected to the task node, and N represents the number of remaining relay nodes of the task node.

[0063] S103, analyze the network state information, use the adaptive Kalman filtering method to find the maximum received signal strength of the current task node, and feed back to the relay node deployment module. The network analysis module respectively performs adaptive Kalman filtering on the received signal strength of each link of the task node collected:

[0064] RSSI' = {RSSI1', RSSI2', ···, RSSI' L}

[0065] Where RSSI L ' is the value of the received signal strength of the Lth direct connection link of the task node after adaptive Kalman filtering; for the task node, the current maximum received signal strength is selected as the signal strength reference value of the task node, i.e. RSSI' max , and its calculation formula is:

[0066] RSSI' max = {RSSI1', RSSI2', ···, RSSI' L} max

[0067] Optionally, in S103, the network state information is analyzed by using the adaptive Kalman filtering method, and the adaptive Kalman filtering method is applied to process and analyze the fluctuations of the received signal strength. This filtering method is optimized for signal fading and fluctuations in complex environments to reduce misjudgment. This filtering method is specially designed to dynamically adjust its internal parameters, including process noise covariance and observation noise covariance, to reflect environmental changes and signal fluctuations in real time. This adaptive ability enables the filtering method to more accurately estimate the true signal state when facing sudden signal changes, thereby supporting the effective deployment of relay nodes. Analyzing the network state using the adaptive Kalman filtering method includes:

[0068] Step A, RSSI i data reading, an adaptive Kalman filter is used for the received signal strength of each link;

[0069] Step B, determine whether the filter needs to be initialized, if it needs to be initialized, initialize the filter parameters;

[0070] Step C, if the filter does not need to be initialized, the RSSI of each link segment is filtered M,i Data is marked as measurement data;

[0071] Step D, the three processes of prediction, estimation, and optimization of parameters are performed;

[0072] Step E, data estimation is completed, and the optimized parameters of the filter are recorded;

[0073] Step F, the estimated value RSSI is output i ′.

[0074] For example Figure 4 An adaptive Kalman filtering method flow chart is an embodiment of the present application, and the adaptive Kalman filtering method includes three steps of prediction, update, and optimization.

[0075] The prediction process satisfies the following formula:

[0076]

[0077] Wherein represents the state prediction value at time step k, A is the state transition matrix, B is the control input matrix, u k-1 is the control input at time step k-1, is the error covariance of the predicted state, Q k represents the process noise covariance at k.

[0078] The update process satisfies the following formula:

[0079]

[0080] K k is the Kalman gain, H is the observation matrix, is the state estimation considering new measurement data, Z k represents the actual measurement value at time step k, P k is the error covariance considering new measurement data, R k represents the observation noise covariance at k.

[0081] The optimization process includes: adaptive process noise dynamic adjustment and adaptive observation noise dynamic adjustment.

[0082] The adaptive process noise dynamic adjustment satisfies the following formula:

[0083]

[0084] Wherein y k is the innovation sequence; S k is the innovation covariance. Q kQ represents the process noise covariance at time k, β is an adjustment factor, usually between 0 and 1, and Q min Q max These are the upper and lower limits of the process noise covariance, respectively.

[0085] The adaptive observation noise dynamic adjustment satisfies the following formula:

[0086]

[0087] Where R k R represents the observation noise covariance at time k, α is an adjustment factor, usually between 0 and 1, and R min R max These are the upper and lower limits of the observation noise covariance, respectively.

[0088] S104, the node deployment module generates the deployment location of the relay node using an event accumulation-based decision-making process based on the analysis results of the network analysis module.

[0089] Optionally, according to S104, by using an event-accumulation-based decision-making process, the judgment method constitutes a comprehensive judgment method through multiple judgment conditions, aiming to improve the accuracy and reliability of signal quality assessment. For example... Figure 5 This is a flowchart of a decision-making process based on event accumulation according to an embodiment of the present invention.

[0090] S105, during the deployment of relay nodes in the wireless mesh network, the command center's information acquisition and network analysis modules continuously monitor the network status and respond in real time to changes in the location and communication needs of the task nodes. These modules assess signal coverage and communication quality, dynamically guiding the node deployment module to adjust the layout of the relay nodes. This process continues until the task nodes complete their exploration tasks or the relay nodes are fully deployed.

[0091] Secondly, embodiments of the present invention disclose a wireless relay node deployment device for complex environments, applied to a command center in a wireless mesh network, such as... Figure 6 As shown. Figure 6 This is a schematic diagram of a wireless relay node deployment device according to an embodiment of the present invention. The device includes:

[0092] The information acquisition module 601 is used to collect network status information in real time, including the location information of task nodes, the received signal strength of task nodes, and the remaining number of relays of task nodes.

[0093] The network analysis module 602 is used to analyze the information collected by the information acquisition module and evaluate the overall status and communication quality of the network.

[0094] The node deployment module 603 generates the deployment position of the relay node by using an event accumulation-based decision process according to the analysis result of the network information by the network analysis module, and instructs the task node to deploy the relay node.

[0095] In a third aspect, an electronic device is disclosed, comprising Figure 7 As shown in the figure. Figure 7 An electronic device structure diagram is shown in the figure, which comprises a processor 701, a communication interface 702, a memory 703 and a communication bus 704, wherein the processor 701, the communication interface 702 and the memory 703 complete mutual communication through the communication bus 704.

[0096] In the above embodiments, all or part of the embodiments can be realized by software, hardware, firmware or any combination thereof. When realized by software, all or part of the embodiments can be realized in the form of a computer program product. The computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as DVD) or semiconductor media (such as solid state disk (SSD)) and the like.

[0097] It should be noted that in this paper, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0098] The various embodiments in the specification are described in a related manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0099] The above only describes the preferred embodiments of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for complex environment oriented wireless relay node deployment, applied to a command center in a wireless mesh network, characterized in that, The method comprises the following steps: The command center controls and manages: the command center formulates the deployment strategy of the relay node, issues the deployment instruction, and monitors the deployment process in real time, including monitoring the installation state of the relay node, the communication connection state, and the interaction response with the task node; Information collection: the command center acquires the network state information of the wireless Mesh network through the information collection module in a timely manner, and the information includes the position information of the task node, the received signal strength of the direct link with the task node, the number of direct nodes, and the remaining relay number of the task node; Network analysis: the command center processes the received signal strength of each link in the network state information by using the adaptive Kalman filtering method respectively through the network analysis module, dynamically adjusts the process noise covariance and the observation noise covariance, optimizes the signal strength estimation, and selects the maximum received signal strength of the current task node as the deployment reference index; Node deployment control: the command center generates the deployment position of the relay node by using the decision process based on event accumulation based on the deployment reference index and the communication demand of the task node through the node deployment module, and commands the task node to deploy the relay node; Continuous network monitoring and evaluation: during the deployment process of the relay node, the information collection module and the network analysis module continuously monitor the network state, and dynamically update the deployment scheme according to the position change of the task node, the communication quality change, and the signal coverage, until the task node completes the exploration task or the relay node is completely deployed. The adaptive Kalman filtering method comprises three stages of prediction, update and optimization, and adaptive process noise dynamic adjustment and adaptive observation noise dynamic adjustment are respectively performed in the optimization stage to adapt to the changes of signal fading and fluctuation in a complex environment.

2. The method of claim 1, wherein, The command center is provided with an information collection module for collecting network state information in real time, including the position information of the task node, the received signal strength of the task node, and the number of remaining relay nodes; The network state information of the command center is: S t = {C, RSSI, N} Wherein, C represents the position information of the task node, the receiving signal strength of the task node is represented by RSSI, since there can be multiple nodes directly connected with the task node, the receiving strength of the task node is represented as RSSI={RSSI1, RSSI2, ···, RSSI L}, L represents the number of nodes directly connected with the task node, and N represents the number of remaining relay nodes of the task node.

3. The method of claim 1, wherein, The command center is provided with a network analysis module for analyzing the information collected by the information collection module, finding the maximum received signal strength of the task node, and feeding back to the node deployment module; wherein the network analysis module performs adaptive Kalman filtering on the received signal strength of each link of the task node collected: RSSI' = {RSSI1', RSSI2', ···, RSSI' N} L} where RSSI L is the value of the received signal strength of the Lth direct link after adaptive Kalman filtering for the task node; for the task node, the current maximum received signal strength is selected as the signal strength reference value of the task node, that is, RSSI′ max , and the calculation formula is: RSSI′ max = {RSSI1′, RSSI2′, ···, RSSI′ L} max .

4. The method of claim 1, wherein, The analysis of the received signal strength adopts the adaptive Kalman filtering method, wherein, The prediction stage satisfies: wherein, denotes the state prediction at time step k, A is a state transition matrix, B is a control input matrix, u k-1 denotes the control input at time step k-1, is the error covariance matrix of the predicted state, Q k denotes the process noise covariance at time step k; The update stage satisfies: where K k is the Kalman gain, H is the observation matrix, is the state estimate after considering the new measurement data, Z k denotes the actual measurement value at time step k, P k is the error covariance matrix after considering the new measurement data, R k denotes the observation noise covariance at time step k; The optimization stage comprises adaptive process noise dynamic adjustment and adaptive observation noise dynamic adjustment; (1) The adaptive process noise dynamic adjustment satisfies the following formula: wherein, is an innovation sequence; is an innovation covariance; β is a tuning factor with 0≤β≤1; Q min , Q max are lower and upper bounds for the process noise covariance, respectively. (2) The adaptive observation noise dynamic adjustment satisfies the following formula: wherein a is an adjustment factor, and a has a value in a range of 0≤a≤1; R min , R max are a lower bound and an upper bound of the observation noise covariance, respectively.

5. The method of claim 1, wherein: The node deployment module generates the deployment position of the relay node by using the decision process based on event accumulation according to the analysis result of the network analysis module, and commands the task node to deploy the relay node.

6. The method of claim 1, wherein: In the relay node deployment process of the wireless Mesh network, the information collection module and the network analysis module of the command center continuously monitor the network state, and respond to the position change and communication demand of the task node in real time, these modules evaluate the signal coverage and communication quality, dynamically guide the node deployment module to deploy the relay node, until the task node completes the exploration task or the relay node is completely deployed.

7. An electronic device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; The memory is used for storing a computer program; The processor is used for executing the program stored on the memory, and realizes the method steps in any one of claims 1-6.

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