Networking method, device, storage medium and electronic equipment of network node

Abstract

The application discloses a networking method and device of a network node, a storage medium and an electronic device, and relates to the technical field of communication. The method comprises the following steps: receiving a target operation performed by a target person on a target page, wherein the target page is used for displaying the connection state between N network nodes in a target range environment, and the target operation comprises a drag operation on a target node in the N network nodes; triggering a target event according to the target operation, updating the position information of the target node by executing the target event, and broadcasting the position change information of the target node in the target range environment; and based on the updated position information of the target node, adjusting the connection between the N network nodes through a feature matching engine to obtain the updated networking of the N network nodes. The application solves the technical problem that the traditional networking method is inefficient and difficult to quickly adapt to environmental changes in a dynamically changing target range environment.

Description

Technical Field

[0001] This application relates to the field of communication technology, and more specifically, to a network node networking method, apparatus, storage medium, and electronic device. Background Technology

[0002] In the dynamically changing environment of test ranges, traditional networking technologies are facing unprecedented challenges. Test ranges, as venues for aerospace testing and complex industrial experiments, have network requirements far exceeding those of conventional systems. They demand not only high-bandwidth, low-latency communication but also the maintenance of network connectivity and stability under conditions of frequent node movement and rapidly changing environmental parameters. However, the limitations of traditional statically configured networking methods in test range environments are becoming increasingly apparent, posing a key bottleneck to operational efficiency and safety.

[0003] First, the dynamic nature of the test range environment requires the network to respond quickly to changes in node locations. Traditional networking relies on a pre-defined network architecture and manual configuration. Once node locations change, the network reconstruction process is time-consuming and error-prone, severely impacting the continuity and response speed of test range missions. Second, the complex terrain and variable weather conditions within the test range place higher demands on network communication quality. Traditional networking methods lack real-time monitoring and adaptive adjustment mechanisms, making it difficult to maintain stable link quality and network performance in the test range environment, resulting in low data transmission efficiency and frequent communication interruptions.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a network node networking method, apparatus, storage medium, and electronic device to at least solve the technical problems of low efficiency and difficulty in quickly adapting to environmental changes in traditional networking methods in dynamically changing target range environments.

[0006] According to one aspect of the embodiments of this application, a method for networking network nodes is provided, comprising: receiving a target operation performed by a target person on a target page, wherein the target page is used to display the connection status between N network nodes in a target range environment, the target operation includes dragging a target node among the N network nodes, wherein the target operation on the target page synchronously affects the network nodes in the target range environment, and each of the N network nodes includes the initial position information of the network node; triggering a target event according to the target operation, updating the position information of the target node by executing the target event, and broadcasting the position change information of the target node in the target range environment; adjusting the connection between the N network nodes based on the updated position information of the target node through a feature matching engine to obtain an updated network of N network nodes, wherein the feature matching engine is used to determine the matching degree of the N network nodes based on a matching degree strategy and the position information of the target node.

[0007] Optionally, based on the updated target node location information, the connection between N network nodes is adjusted using a feature matching engine, including: obtaining target information of the current N network nodes using the feature matching engine based on the updated target node location information, wherein the target information includes at least the network node location information, signal strength, and security level; determining the target distance between adjacent network nodes among the N network nodes based on the location information of the N network nodes, obtaining S target distances, where S is an integer greater than or equal to 1; determining the channel quality between adjacent network nodes among the N network nodes based on the signal strength of the N network nodes, obtaining S channel quality values; determining a target factor based on the security level of the N network nodes, wherein the target factor is used to characterize the security level of the N network nodes and its impact on the connection matching degree between network nodes; and adjusting the connection between the N network nodes based on the target factor, the S target distances, and the S channel quality values.

[0008] Optionally, adjusting the connections between N network nodes based on the target factor, S target distances, and S channel quality parameters includes: obtaining a first coefficient, a second coefficient, and a third coefficient, wherein the first coefficient is used to characterize the weight of the target distance, the second coefficient is used to characterize the weight of the channel quality, and the third coefficient is used to characterize the weight of the target factor; determining S matching degrees based on the first coefficient, the second coefficient, the third coefficient, the target factor, the S target distances, and the S channel quality parameters; when the matching degree is greater than or equal to a first preset threshold, determining to establish a connection between the two adjacent network nodes corresponding to the matching degree; when the matching degree is less than the first preset threshold, disconnecting the two adjacent network nodes corresponding to the matching degree.

[0009] Optionally, after adjusting the connections between N network nodes based on the updated target node location information using a feature matching engine, the method further includes: obtaining state information of the target environment, wherein the state information includes at least the location information, link load information, and channel quality of the N network nodes; based on the state information, using a target algorithm to determine a target strategy for each network node according to the state information and global network environment information, wherein the target strategy is used to adjust the communication parameters of the network nodes; and adjusting the N network nodes according to the target strategy.

[0010] Optionally, after adjusting the N network nodes according to the target strategy, the process includes: collecting feedback information, wherein the feedback information is used to characterize the performance parameters of the N network nodes after adjustment according to the target strategy in the test range environment, wherein the performance parameters include at least a first value, a second value, and a third value, wherein the first value is used to characterize the amount of data transmitted per unit time in the test range environment, the second value is used to characterize the transmission time delay between data network nodes, and the third value is used to characterize the energy consumption of each network node in data transmission; determining a reward value based on the first value, the second value, and the third value, wherein the reward value is used to quantitatively evaluate the performance of the N network nodes after adjustment in the test range environment; and updating the target algorithm based on the reward value.

[0011] Optionally, before receiving the target operation performed by the target personnel on the target page, the process includes: deploying N network nodes in the target range environment, wherein each network node includes at least attribute information, a positioning module, a communication module, and an environmental sensor, wherein the attribute information includes at least a unique identifier, type label, and security level of the network node; performing an initialization operation on the N network nodes, wherein the initialization operation is used to initially network the N network nodes through a self-organizing network protocol; and uploading the attribute information of the N network nodes after the initialization operation to the central server.

[0012] Optionally, the security levels of N network nodes are detected; when the difference between the security levels of the i-th network node and the j-th network node is less than a second preset threshold, the connection between the i-th network node and the j-th network node is maintained, where i and j are both integers greater than or equal to 1 and less than or equal to N; when the difference between the security levels of the i-th network node and the j-th network node is greater than or equal to the second preset threshold, the connection between the i-th network node and the j-th network node is disconnected, and an early warning is issued.

[0013] According to another aspect of this application, a network node networking device is also provided, comprising: a receiving unit for receiving target operations performed by a target person on a target page, wherein the target page is used to display the connection status between N network nodes in a target range environment, the target operation includes dragging a target node among the N network nodes, wherein the target operation on the target page synchronously affects the network nodes in the target range environment, and each of the N network nodes includes the initial position information of the network node; an updating unit for triggering a target event according to the target operation, updating the position information of the target node by executing the target event, and broadcasting the position change information of the target node in the target range environment; and a connection unit for adjusting the connection between the N network nodes based on the updated position information of the target node through a feature matching engine to obtain an updated network of the N network nodes, wherein the feature matching engine is used to determine the matching degree of the N network nodes based on a matching degree strategy and the position information of the target node.

[0014] According to another aspect of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein when the computer program is executed, the device in which the computer-readable storage medium is located performs the above-described network node networking method.

[0015] According to another aspect of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the networking method of the network nodes described above.

[0016] In this application, the target operation performed by the target personnel on the target page is first received. The target page displays the connection status between N network nodes in the target range environment. The target operation includes dragging a target node among the N network nodes. The target operation on the target page synchronously affects the network nodes in the target range environment. Each of the N network nodes includes its initial position information. Then, a target event is triggered according to the target operation. By executing the target event, the position information of the target node is updated, and the position change information of the target node is broadcast in the target range environment. Then, based on the updated position information of the target node, the connection between the N network nodes is adjusted through a feature matching engine to obtain the updated network of the N network nodes. The feature matching engine is used to determine the matching degree of the N network nodes based on the matching degree strategy and the position information of the target node. That is, through interactive drag and drop and real-time dynamic networking based on the feature matching engine, the purpose of quickly responding to changes in node position and automatically updating the network configuration is achieved. This achieves the technical effect of efficient and adaptive network reconstruction, thereby solving the technical problem that traditional networking methods are inefficient and difficult to adapt to environmental changes in a dynamically changing target range environment. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a flowchart of an optional network node networking method according to an embodiment of this application. Figure 1 ;

[0019] Figure 2 This is a flowchart of an optional feature matching engine according to an embodiment of this application;

[0020] Figure 3 This is a flowchart of an optional network node networking method according to an embodiment of this application. Figure 2 ;

[0021] Figure 4 This is a schematic diagram of a networking device for an optional network node according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0025] According to an embodiment of this application, a method embodiment for networking network nodes is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] It should be noted that an intelligent networking system can serve as the execution subject of the networking method for network nodes in the embodiments of this application. It is understood that the networking method for network nodes provided in the embodiments of this application can also be executed by other systems or devices, and the embodiments of this application do not specifically limit this.

[0027] Figure 1 This is a flowchart of an optional network node networking method according to an embodiment of this application. Figure 1 ,like Figure 1 As shown, the method includes the following steps:

[0028] Step S101: Receive the target operation performed by the target user on the target page.

[0029] In step S101, the target page is used to display the connection status between N network nodes in the target environment, and the target operation includes dragging the target node among the N network nodes.

[0030] In step S101, the target operation of the target page synchronously affects the network nodes in the target range environment, and each of the N network nodes includes the initial position information of that network node.

[0031] Optionally, the target page refers to a 3D visualization interface built using WebGL technology (used to render interactive 2D and 3D graphics in a web browser without relying on any additional plugins), used to intuitively display the connection status between N network nodes in the target range environment. Operators can use this page to perform intuitive operations on the network nodes, such as node dragging, drawing connections, and editing the topology.

[0032] Optionally, target operation refers to a drag-and-drop operation performed by a target person on one or more of the N network nodes, with the purpose of changing the physical location of the node in the target environment and the resulting update of the network connection status.

[0033] Optionally, by receiving operation instructions through the target page, the intelligent networking system converts physical operations (such as dragging) into signals that the system can recognize, ensuring that network nodes in the test range environment can respond to the operator's intentions in real time and achieve dynamic adjustment of the network topology.

[0034] Step S102: Trigger a target event based on the target operation, update the target node's position information by executing the target event, and broadcast the target node's position change information in the target range environment.

[0035] Optionally, a target event is an internal system event triggered by a drag-and-drop operation by a target user, designed to notify the system to update the location information of the target node. For example, if a user drags node A to coordinates (x, y) on the interface, the event EVENT_DRAG(A, x, y) is triggered, and the system updates the position of node A and broadcasts it to the entire network.

[0036] Optionally, position change information refers to the updated position data calculated by the system after the target node moves from its initial position to a new position, including the node's new coordinates, direction, and other information; broadcasting refers to sending the target node's position change information to each of the N network nodes through a communication protocol in the target range environment to ensure information synchronization among all network nodes.

[0037] Optionally, this step transforms user operation into system response, ensuring real-time updates and broadcasting of network node location information, and providing accurate location data for subsequent adjustments to network connection status. Through broadcasting, all network nodes can promptly obtain the target node's new location, thus preparing to respond to possible network topology changes.

[0038] Step S103: Based on the updated location information of the target node, adjust the connection between N network nodes through the feature matching engine to obtain the updated network of N network nodes.

[0039] In step S103, the feature matching engine is used to determine the matching degree of N network nodes based on the matching degree strategy and the location information of the target node.

[0040] Optionally, the feature matching engine is an algorithm engine that can calculate the matching degree between nodes based on the new location information of the target node and the characteristics of N network nodes (such as type label, security level, channel quality, etc.).

[0041] Optionally, the matching strategy refers to the algorithm or rules used by the feature matching engine to evaluate the suitability of connections between N network nodes.

[0042] Optionally, the suitability of connections between network nodes can be automatically evaluated through the calculation of a feature matching engine, and the network connections can be dynamically adjusted based on the calculation results. This step ensures that the network topology can quickly adapt to changes in the test range environment, such as node movement, while taking into account factors such as signal quality and security level, thereby improving the stability and security of the network.

[0043] As can be seen from steps S101 to S103, in this application, the target operation performed by the target personnel on the target page is first received. The target page displays the connection status between N network nodes in the target range environment. The target operation includes dragging a target node among the N network nodes. The target operation on the target page synchronously affects the network nodes in the target range environment. Each of the N network nodes includes its initial position information. Then, a target event is triggered based on the target operation. By executing the target event, the position information of the target node is updated, and the position change information of the target node is broadcast in the target range environment. Based on the updated target node location information, the connections between N network nodes are adjusted through a feature matching engine to obtain an updated network of N network nodes. The feature matching engine is used to determine the matching degree of the N network nodes based on a matching degree strategy and the target node location information. That is, through interactive drag-and-drop and real-time dynamic networking based on the feature matching engine, the goal of quickly responding to changes in node location and automatically updating the network configuration is achieved. This achieves the technical effect of efficient and adaptive network reconstruction, and solves the technical problem that traditional networking methods are inefficient and unable to quickly adapt to environmental changes in a dynamically changing target environment.

[0044] In one optional embodiment, the intelligent networking system obtains target information of the current N network nodes through a feature matching engine based on the updated target node location information. The target information includes at least the location information, signal strength, and security level of the network nodes. Then, based on the location information of the N network nodes, the system determines the target distances between adjacent network nodes, obtaining S target distances, where S is an integer greater than or equal to 1. Based on the signal strength of the N network nodes, the system determines the channel quality between adjacent network nodes, obtaining S channel quality parameters. Based on the security level of the N network nodes, the system determines a target factor, which characterizes the impact of the security level of the N network nodes on the connection matching degree between network nodes. Finally, the system adjusts the connection between the N network nodes based on the target factor, the S target distances, and the S channel quality parameters.

[0045] Optionally, the intelligent networking system receives a drag-and-drop operation performed by the target user on the target page. This operation immediately triggers a target event within the system. The execution of the target event causes the location information of the target node to be updated, and this updated location information is immediately synchronized to all N network nodes in the target range environment through a broadcast mechanism, ensuring that the entire network has an immediate understanding of the new location of the target node. Subsequently, the feature matching engine extracts the updated location information, signal strength detection data, and node security level information as the basis for calculating the matching degree. The target information mentioned here includes three key dimensions: location information, signal strength, and security level. These information reflect the key characteristics of the current state of the network node.

[0046] Optionally, the feature matching engine then calculates the target distances between the N network nodes precisely based on their location information, resulting in a total of S target distances. The calculation of these target distances takes into account the three-dimensional spatial distribution of the network nodes, ensuring the assessment of the feasibility of physical connections (e.g., whether the actual distance between two nodes is close enough to establish an effective connection). The S value reflects the number of connections that may exist in the actual network, and it varies with network size and topology. Simultaneously, the feature matching engine determines the channel quality of adjacent network nodes based on the signal strength data of the N network nodes, obtaining S channel quality values. This process considers the effectiveness and stability of signal transmission; higher channel quality indicates better communication quality between the two nodes, making them more suitable for establishing a direct connection. Next, the feature matching engine evaluates the security level information of the N network nodes and calculates a target factor. The target factor aims to quantify the impact of differences in security levels between network nodes on connection matching. When the security levels of two nodes differ significantly, the target factor reduces their matching degree, thereby reducing potential security risks. Finally, the feature matching engine uses the target factor, S target distances, and S channel quality to calculate the matching degree of all possible connections according to a predetermined matching degree strategy, and automatically adjusts the connection status between N network nodes based on the matching degree.

[0047] As can be seen from the above, by implementing the steps described, the intelligent networking system can quickly respond to the physical movement of network nodes in a constantly changing target environment, automatically adjust the connection status between nodes, and ensure that the network connection is both efficient and secure. Specifically, the overall effect of the system is reflected in the following aspects: the system can quickly capture the movement of nodes and adjust the connection relationship accordingly, so that the network is always in an optimal or suboptimal state, reducing the need for manual intervention; with the computing power of the feature matching engine, the system can intelligently evaluate the matching degree between network nodes, and automatically adjust the connection between N network nodes based on a comprehensive consideration of location, signal strength, and security level, avoiding invalid connections caused by weak signals or mismatched security levels; by introducing a target factor to consider differences in security levels, the system can effectively prevent the establishment of inappropriate connections between network nodes with different security levels, thereby reducing potential security risks to the network and improving overall network security.

[0048] In one optional embodiment, the intelligent networking system obtains a first coefficient, a second coefficient, and a third coefficient, wherein the first coefficient is used to characterize the weight of the target distance, the second coefficient is used to characterize the weight of the channel quality, and the third coefficient is used to characterize the weight of the target factor. Then, based on the first coefficient, the second coefficient, the third coefficient, the target factor, S target distances, and S channel quality values, S matching degrees are determined. When the matching degree is greater than or equal to a first preset threshold, a connection is established between the two adjacent network nodes corresponding to the matching degree. When the matching degree is less than the first preset threshold, the connection between the two adjacent network nodes corresponding to the matching degree is disconnected.

[0049] Optionally, the intelligent networking system first reads the first, second, and third coefficients, which correspond to the weights of the target distance, channel quality, and target factor, respectively. During the system initialization phase, these weight coefficients can be set to default values ​​(e.g., 0.4, 0.3, 0.3), but in more advanced application scenarios, they can be dynamically adjusted according to specific needs to more accurately reflect the degree of influence of different factors on network connectivity matching.

[0050] Optionally, the feature matching engine will use the first, second, and third coefficients, combined with the target factor, S target distances, and S channel quality parameters, to determine S matching degrees using a matching degree calculation formula. Each matching degree value reflects the suitability of the connection between the corresponding two network nodes.

[0051] Optionally, for each calculated matching degree value, the feature matching engine compares it with a pre-set first preset threshold (e.g., θ, with an initial value of 0.7). If the matching degree is greater than or equal to the first preset threshold, the connection between the two adjacent network nodes is considered appropriate, and the networking system will automatically establish or maintain this connection; conversely, if the matching degree is lower than the first preset threshold, the connection between the two nodes is considered unsuitable, and the system will automatically disconnect this connection to avoid unnecessary communication burden or security risks.

[0052] Optionally, the matching degree between nodes i and j is calculated as shown in Formula 1:

[0053]

[0054] Among them, D ij This refers to the Euclidean distance (target distance) between nodes i and j; CQI ij ω1 refers to the channel quality indicator (channel quality is determined based on parameters such as signal strength and bit error rate); L1 and L2 refer to the security levels of nodes i and j; ω1, ω2, and ω3 refer to the weighting coefficients (default 0.4, 0.3, 0.3), which can be dynamically adjusted; λ refers to the security level difference attenuation factor (default 0.5).

[0055] Optionally, Figure 2 This is a flowchart of an optional feature matching engine according to an embodiment of this application, such as... Figure 2 As shown, the feature matching engine first collects current information from all network nodes, including the node's precise location (obtained via GPS or other positioning technologies), signal strength (signal quality based on communication technologies such as Wi-Fi 6 and LoRaWAN), and pre-set security levels (reflecting the network node's security status, such as sensitivity and access level). This data forms the basis for calculating the matching degree between nodes. Next, the Euclidean distance between adjacent network nodes is calculated; closer distances generally indicate better communication and are an important factor in the matching degree calculation. Simultaneously, channel quality and security level difference attenuation factors are calculated. Finally, the data from the previous steps are summed to calculate the matching degree between nodes, and based on this matching degree, corresponding decisions are made.

[0056] As can be seen from the above, by introducing the first, second, and third coefficients, the intelligent networking system can more meticulously balance the impact of target distance, channel quality, and target factors on network connectivity, achieving refined management of connection status. The system can dynamically adjust connections based on the actual status of network nodes, responding quickly to changes in the network environment and proactively optimizing network topology to improve overall network performance. The introduction of target factors, particularly by considering differences in security levels, effectively avoids potential risks caused by security level mismatches, strengthening the network's security control mechanism. Using the matching degree calculation formula, the system can autonomously determine the suitability of connections between network nodes, reducing the uncertainty caused by human decision-making and improving the intelligence level of network connection decisions.

[0057] In one optional embodiment, the intelligent networking system, based on the updated target node location information, adjusts the connections between N network nodes according to a feature matching engine, and then obtains the state information of the target environment. The state information includes at least the location information, link load information, and channel quality of the N network nodes. Based on the state information, a target algorithm is used to determine a target strategy for each network node according to the state information and the global network environment information. The target strategy is used to adjust the communication parameters of the network nodes. Finally, the N network nodes are adjusted according to the target strategy.

[0058] Optionally, after the feature matching engine completes the connection adjustments between network nodes, the intelligent networking system immediately collects real-time status information from the test range environment and network nodes, including but not limited to the current location information, link load information, and channel quality data of the N network nodes. This information forms the basis for subsequent optimization of network communication parameters, ensuring that network adjustments are based on the latest and most accurate information.

[0059] Optionally, the intelligent networking system initiates a target algorithm based on the acquired state information. This target algorithm can be a deep learning model, such as a deep deterministic policy gradient, or a rule-based algorithm, such as a genetic algorithm or simulated annealing, depending on the system design and optimization objectives. The algorithm calculates the target policy for each network node based on the state information and global network environment information, such as node distribution and environmental interference. The target policy may include adjusting transmission power, switching communication frequency bands, and modifying routing tables, with the aim of optimizing network communication efficiency, reducing energy consumption, and ensuring that communication quality is not compromised.

[0060] Optionally, the target algorithm module issues adjustment instructions to N network nodes based on the calculated target strategy. Upon receiving the instructions, each network node adjusts its communication parameters in real time according to its own situation, such as reducing the transmit power from 20dBm to 18dBm, to adapt to changes in the test range environment and optimize network performance. This process ensures the immediacy and effectiveness of network adjustments, enabling each node to quickly respond to the optimization strategy and improve its communication status.

[0061] As can be seen from the above, the intelligent networking system can not only quickly respond to changes in node location and adjust connections, but also further optimize the communication parameters of each node through intelligent algorithms based on the state information of the test range environment, significantly improving the overall communication efficiency and throughput of the network. The implementation of the target strategy helps network nodes dynamically adjust communication parameters such as transmission power according to the current link load and channel quality, thereby effectively reducing network energy consumption and extending battery life, which is particularly suitable for mobile and energy-constrained test range environments. The dynamic generation and implementation of the target strategy enhances the system's adaptability, enabling it to automatically adjust the network's communication strategy and maintain stable network performance when test range environmental conditions change. By using the target algorithm for strategy calculation, the system realizes data-driven intelligent decision-making, replacing traditional manual intervention and improving the automation level of network management and optimization.

[0062] In one optional embodiment, after adjusting N network nodes according to the target strategy, the intelligent networking system collects feedback information. The feedback information is used to characterize the performance parameters of the N network nodes after adjustment according to the target strategy in the test range environment. The performance parameters include at least a first value, a second value, and a third value. The first value is used to characterize the amount of data transmitted per unit time in the test range environment, the second value is used to characterize the transmission time delay between data network nodes, and the third value is used to characterize the energy consumption of each network node in data transmission. Then, a reward value is determined based on the first value, the second value, and the third value. The reward value is used to quantitatively evaluate the performance of the N network nodes after adjustment in the test range environment. Finally, the target algorithm is updated based on the reward value.

[0063] Optionally, after the intelligent networking system adjusts the communication parameters of N network nodes according to the target strategy, the system immediately collects feedback information on the adjusted network performance. This information covers the actual operating status of the network after adjustment and serves as the basis for subsequent evaluation and optimization. Among them, the most important performance parameters in the feedback information include the first, second, and third values, which correspond to the amount of data transmitted per unit time (throughput), the transmission time delay between data network nodes (latency), and the energy consumption of each network node during data transmission, respectively. These values ​​are obtained by monitoring the communication activities of network nodes in real time, ensuring the accuracy and timeliness of the feedback information.

[0064] Optionally, the intelligent networking system calculates a reward value based on the collected first, second, and third values ​​using a preset reward function. The reward function comprehensively considers three key indicators: throughput, latency, and energy consumption, and uses a specific calculation formula to quantitatively evaluate the overall performance of network nodes after adjustment. The reward value reflects the degree of closeness between the current network state and the optimization goal; a higher value indicates a better match between network performance and the goal.

[0065] Optionally, the formula for calculating the reward value is as shown in formula (2):

[0066] R = η1·throughput - η2·end-to-end delay - η3·energy consumption (2)

[0067] Optionally, η1, η2, and η3 represent normalization coefficients used to balance and adjust the relative importance of different performance indicators in the reward function.

[0068] Optionally, based on the calculated reward value, the intelligent networking system will feed back to the target algorithm for self-correction and optimization. In this embodiment, the target algorithm may be a Deep Deterministic Policy Gradient (DDPG) or other reinforcement learning algorithms. These algorithms have the ability to learn and adjust themselves, continuously optimizing their decision-making strategies through accumulated reward values, thereby improving the accuracy and efficiency of network adjustments. The reward value feedback mechanism enables the system to adjust algorithm parameters, such as weight coefficients and security level difference decay factors, according to actual results, thereby achieving better results in subsequent network optimization decisions.

[0069] As can be seen from the above, the intelligent networking system, by introducing the concept of reward value, can transform network performance parameters (throughput, latency, energy consumption) into intuitive quantitative evaluation results, facilitating understanding and learning by the algorithm modules. The feedback mechanism of reward value enables the target algorithm to self-adjust and optimize its decision-making strategy based on the actual effect of network adjustments, improving the intelligence and adaptability of the algorithm. Based on reward value-based algorithm updates, the system can specifically optimize the communication parameters of network nodes, such as transmission power and communication frequency bands, thereby improving network performance in terms of throughput, latency, and energy consumption. The entire implementation process demonstrates the system's real-time monitoring and feedback of network status, ensuring the timeliness and accuracy of network adjustment strategies and improving the stability and reliability of network operation.

[0070] In one optional embodiment, before receiving the target operation performed by the target personnel on the target page, the intelligent networking system deploys N network nodes in the target range environment. Each network node includes at least attribute information, a positioning module, a communication module, and an environmental sensor. The attribute information includes at least a unique identifier, type label, and security level of the network node. Then, an initialization operation is performed on the N network nodes. The initialization operation is used to initially network the N network nodes through a self-organizing network protocol. Subsequently, the attribute information of the N network nodes after the initialization operation is uploaded to the central server.

[0071] Optionally, the intelligent networking system deploys N network nodes in the test range environment. Each network node is equipped with necessary hardware components, including a positioning module (such as a GPS (Global Positioning System) module), a communication module (supporting multiple frequency bands such as Wi-Fi 6, LoRaWAN, etc.), environmental sensors (for monitoring environmental parameters such as temperature, humidity, and signal strength), and a control unit storing attribute information. Attribute information is an important identifier for network nodes, and at least includes a unique identifier for the network node (such as a node ID), a type label (such as a relay node, terminal node, control node, etc.), and a security level (used to distinguish the security level of the node in the test range environment).

[0072] Optionally, after deployment, the intelligent networking system initializes the N network nodes. This process is achieved through an ad hoc networking protocol (such as Ad-hoc On-Demand Distance Vector Routing, AODV), which allows network nodes to spontaneously establish connections and form a preliminary network topology. In the test range environment, the ad hoc networking mechanism can quickly respond to the joining and leaving of nodes, providing a dynamically adjustable basic network structure for subsequent operations.

[0073] Optionally, after the initialization operation is completed, the intelligent networking system uploads the attribute information of N network nodes to the central server. This upload process ensures that the central server can obtain detailed information about each node, including unique identifiers, type tags, and security levels, providing comprehensive data support for subsequent network management and optimization. The central server not only stores this information but may also perform preliminary network status analysis based on the attribute information, providing data preparation for subsequent functions such as feature matching and network optimization.

[0074] As described above, the intelligent networking system equips each network node with a positioning module, a communication module, and environmental sensors, ensuring hardware compatibility and the versatility of network nodes in the test range environment, providing a solid foundation for subsequent network operations. Through the initialization operation of the self-organizing network protocol, the system can establish a preliminary network structure in a short time, reducing the complexity of manual configuration and improving the efficiency of network deployment and initial networking. Uploading node attribute information to the central server enables centralized management and analysis of network node data, facilitating subsequent feature matching, network optimization decisions, etc., and improving the intelligence and systematization level of network management. The introduction of the self-organizing network mechanism supports the dynamic joining and leaving of network nodes, enabling the network to quickly adjust when the test range environment changes, enhancing the network's flexibility and adaptability. The security level in the attribute information provides assurance for the security and compliance of subsequent network connections, ensuring that communication between network nodes follows the security requirements of the test range environment.

[0075] In one optional embodiment, the intelligent networking system detects the security levels of N network nodes; when the difference between the security levels of the i-th network node and the j-th network node is less than a second preset threshold, the connection between the i-th network node and the j-th network node is maintained, where i and j are both integers greater than or equal to 1 and less than or equal to N; when the difference between the security levels of the i-th network node and the j-th network node is greater than or equal to the second preset threshold, the connection between the i-th network node and the j-th network node is disconnected, and an early warning is issued.

[0076] Optionally, the intelligent networking system monitors the security levels of N network nodes in real time. The security level is one of the key attributes predefined for each network node in the system, used to distinguish the security characteristics and permissions of different nodes in the test range environment. It is usually represented by a scale of 1 to 5, where level 1 represents the lowest security level and level 5 represents the highest security level.

[0077] Optionally, the feature matching engine in the system periodically (e.g., every 500 milliseconds) calculates the security level difference between any two nodes (denoted as the i-th network node and the j-th network node) out of N network nodes. The security level difference is obtained by comparing the security level values ​​of the two nodes, reflecting the gap in their security attributes. The calculated security level difference is then compared with a second preset threshold set by the system. The second preset threshold (e.g., level 3) is a critical value defined by the network system to maintain network security, used to determine whether the difference in security levels between two network nodes allows them to maintain their connection. If the difference in security levels between the i-th and j-th network nodes is less than the second preset threshold, the system determines that the security difference between these two nodes is within an acceptable range, and therefore maintains their connection, ensuring normal network communication and data exchange. If the difference in security levels between the i-th and j-th network nodes is greater than or equal to the second preset threshold, the system determines that the security difference between these two nodes is too large, posing a potential communication security risk. In this situation, the intelligent networking system will automatically disconnect the connection between the two nodes and issue an alert to the network administrator or target personnel through the early warning system, prompting them to pay attention to the security status of the specific network node and the possible further security checks or network reconstruction.

[0078] Optionally, Figure 3 This is a flowchart of an optional network node networking method according to an embodiment of this application. Figure 2 ,like Figure 3As shown, the system includes: a visual drag-and-drop interface, node location management, a feature matching engine, network optimization control, security policy execution, and physical layer communication. The visual drag-and-drop interface allows users to intuitively adjust the position of network nodes on a virtual map, thereby controlling the actual network layout in the test range. The interface provides real-time feedback on the network status after dragging, ensuring operators can immediately see the impact of their actions on the network. Node location management maintains and updates the location information of all network nodes. When users drag nodes on the interface, the node location management system records these changes instantly, providing accurate location data for subsequent functional modules. Furthermore, this module may integrate with positioning technologies such as GPS to automatically update the actual location of nodes. The feature matching engine calculates the matching degree between nodes (based on features such as distance and channel quality) to determine which nodes should establish connections. This engine is the core component of this invention. Based on machine learning algorithms, it intelligently analyzes and adjusts the network topology to ensure efficient and secure network operation in the test range environment. Network optimization control utilizes deep learning models to dynamically optimize network parameters, including adjusting transmit power, communication frequency bands, and routing strategies, aiming to maximize network performance while ensuring efficient energy use and communication stability. Security policy execution monitors the network's security status and assesses connection security based on differences in node security levels. If a potential security risk is detected, such as excessive differences in security levels between nodes, the system automatically disconnects and triggers an alarm mechanism to ensure network security in the test range environment. Physical layer communication enables wireless communication between nodes, including the application of technologies such as Wi-Fi 6 and LoRaWAN. This layer is responsible for data transmission and reception and is the foundation of the entire network architecture. By intelligently adjusting transmit power and communication frequency bands, physical layer communication can adapt to changes in the test range environment, providing stable and efficient communication services.

[0079] As can be seen from the above, by real-time detection of the security level of network nodes and dynamic adjustment of the connection status between nodes based on the comparison between the security level difference and the preset threshold, the security of the network in the test range environment is significantly enhanced, and the potential risks caused by security level mismatch are reduced. The intelligent networking system can quickly respond to changes in the security level of network nodes and adjust the network connection status in a timely manner, improving the network's dynamic response capability and self-repair capability in the face of security threats. The mechanism of automatic disconnection and early warning provides network administrators with timely security information, helping them to quickly identify and deal with security risks in the network and avoid the spread and impact of security incidents. It ensures that communication between network nodes with different security levels in the network complies with the security policies and compliance requirements of the test range environment, maintaining the legality and standardization of network operations.

[0080] This application also provides a network node networking device. It should be noted that the network node networking device of this application can be used to execute the network node networking method provided in this application. The following describes the network node networking device provided in this application.

[0081] According to an embodiment of this application, an apparatus for implementing the above-described network node networking method is also provided. Figure 4 This is a schematic diagram of a networking device for an optional network node according to an embodiment of this application, as shown below. Figure 4 As shown, the device includes: a receiving unit 401, an updating unit 402, and a connecting unit 403.

[0082] Optionally, the receiving unit 401 receives the target operation performed by the target personnel on the target page, wherein the target page is used to display the connection status between N network nodes in the target range environment, and the target operation includes dragging the target node among the N network nodes. The target operation on the target page synchronously affects the network nodes in the target range environment, and each of the N network nodes includes the initial position information of the network node. The updating unit 402 is used to trigger a target event according to the target operation, update the position information of the target node by executing the target event, and broadcast the position change information of the target node in the target range environment. The connecting unit 403 adjusts the connection between the N network nodes based on the updated position information of the target node through a feature matching engine to obtain the updated network of the N network nodes. The feature matching engine is used to determine the matching degree of the N network nodes based on the matching degree strategy and the position information of the target node.

[0083] Optionally, the connection unit 403 includes: a first acquisition subunit, a first determination subunit, a second determination subunit, a third determination subunit, and a first adjustment subunit. The first acquisition subunit is used to acquire target information of the current N network nodes through a feature matching engine based on the updated target node location information, wherein the target information includes at least the network node location information, signal strength, and security level; the first determination subunit is used to determine the target distances between adjacent network nodes among the N network nodes based on the location information of the N network nodes, obtaining S target distances, where S is an integer greater than or equal to 1; the second determination subunit is used to determine the channel quality between adjacent network nodes among the N network nodes based on the signal strength of the N network nodes, obtaining S channel quality values; the third determination subunit is used to determine a target factor based on the security level of the N network nodes, wherein the target factor characterizes the impact of the security level of the N network nodes on the connection matching degree between network nodes; and the first adjustment subunit is used to adjust the connection between the N network nodes based on the target factor, the S target distances, and the S channel quality values.

[0084] Optionally, the first adjustment subunit includes: a first acquisition module, a first determination module, a second determination module, and a first processing module. The first acquisition module is used to acquire a first coefficient, a second coefficient, and a third coefficient, wherein the first coefficient is used to characterize the weight of the target distance, the second coefficient is used to characterize the weight of the channel quality, and the third coefficient is used to characterize the weight of the target factor; the first determination module is used to determine S matching degrees based on the first coefficient, the second coefficient, the third coefficient, the target factor, S target distances, and S channel quality values; the second determination module is used to determine to establish a connection between two adjacent network nodes corresponding to the matching degree when the matching degree is greater than or equal to a first preset threshold; the first processing module is used to disconnect the connection between the two adjacent network nodes corresponding to the matching degree when the matching degree is less than the first preset threshold.

[0085] Optionally, the networking device for network nodes further includes: a first acquisition unit, a first determination unit, and a first adjustment unit. The first acquisition unit is used to acquire state information of the target environment, wherein the state information includes at least the location information, link load information, and channel quality of N network nodes; the first determination unit is used to determine a target strategy for each network node based on the state information and using a target algorithm, which is based on the state information and global network environment information, wherein the target strategy is used to adjust the communication parameters of the network nodes; and the first adjustment unit is used to adjust the N network nodes according to the target strategy.

[0086] Optionally, the networking device for network nodes further includes: a first collection unit, a second determination unit, and a first update unit. The first collection unit is used to collect feedback information, which characterizes the performance parameters of N network nodes adjusted according to the target strategy in the test range environment. The performance parameters include at least a first value, a second value, and a third value. The first value characterizes the amount of data transmitted per unit time in the test range environment, the second value characterizes the transmission time delay between data network nodes, and the third value characterizes the energy consumption of each network node in data transmission. The second determination unit is used to determine a reward value based on the first, second, and third values. The reward value is used to quantitatively evaluate the performance of the N network nodes after adjustment in the test range environment. The first update unit is used to update the target algorithm based on the reward value.

[0087] Optionally, the networking device for network nodes further includes: a first deployment unit, a first processing unit, and a first uploading unit. The first deployment unit is used to deploy N network nodes in the target range environment, wherein each network node includes at least attribute information, a positioning module, a communication module, and environmental sensors, and the attribute information includes at least a unique identifier, type label, and security level of the network node; the first processing unit is used to perform initialization operations on the N network nodes, wherein the initialization operation is used to initially network the N network nodes using a self-organizing network protocol; and the first uploading unit is used to upload the attribute information of the N network nodes after initialization to the central server.

[0088] Optionally, the networking device for network nodes further includes: a first detection unit, a second processing unit, and a third processing unit. The first detection unit is used to detect the security levels of N network nodes; the second processing unit is used to maintain the connection between the i-th and j-th network nodes when the difference between their security levels is less than a second preset threshold, where i and j are both integers greater than or equal to 1 and less than or equal to N; the third processing unit is used to disconnect the connection between the i-th and j-th network nodes and issue a warning when the difference between their security levels is greater than or equal to the second preset threshold.

[0089] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located performs the above-described network node networking method.

[0090] According to another aspect of the embodiments of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to execute the networking method of the network nodes described above.

[0091] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0097] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for networking network nodes, characterized in that, include: The system receives target operations performed by target personnel on a target page, wherein the target page is used to display the connection status between N network nodes in the target range environment, and the target operation includes dragging a target node among the N network nodes. The target operation on the target page synchronously affects the network nodes in the target range environment, and each of the N network nodes includes the initial position information of that network node. The target operation triggers a target event, the target node's location information is updated by executing the target event, and the target node's location change information is broadcast in the target range environment. Based on the updated location information of the target node, the connection between N network nodes is adjusted by a feature matching engine to obtain an updated network of N network nodes. The feature matching engine is used to determine the matching degree of the N network nodes based on a matching degree strategy and the location information of the target node. The process of adjusting the connections between N network nodes based on the updated location information of the target node using a feature matching engine includes: obtaining target information of the current N network nodes using the feature matching engine based on the updated location information of the target node, wherein the target information includes at least the location information, signal strength, and security level of the network nodes; determining target distances between adjacent network nodes among the N network nodes based on the location information of the N network nodes, obtaining S target distances, where S is an integer greater than or equal to 1; determining channel quality between adjacent network nodes among the N network nodes based on the signal strength of the N network nodes, obtaining S channel quality values; determining a target factor based on the security level of the N network nodes, wherein the target factor characterizes the impact of the security level of the N network nodes on the matching degree of the connections between the network nodes, including: when the difference in security levels between two nodes is greater, the target factor is used to reduce the matching degree between the two nodes; and adjusting the connections between the N network nodes based on the target factor, the S target distances, and the S channel quality values.

2. The network node networking method according to claim 1, characterized in that, Adjusting the connections between N network nodes based on the target factor, S target distances, and S channel quality parameters includes: Obtain a first coefficient, a second coefficient, and a third coefficient, wherein the first coefficient is used to characterize the weight of the target distance, the second coefficient is used to characterize the weight of the channel quality, and the third coefficient is used to characterize the weight of the target factor; S matching degrees are determined based on the first coefficient, the second coefficient, the third coefficient, the target factor, S target distances, and S channel quality parameters; When the matching degree is greater than or equal to a first preset threshold, a connection is established between the two adjacent network nodes corresponding to the matching degree. When the matching degree is less than a first preset threshold, the two adjacent network nodes corresponding to the matching degree are disconnected.

3. The network node networking method according to claim 1, characterized in that, After adjusting the connections between N network nodes based on the updated location information of the target node using a feature matching engine, the method further includes: Obtain the status information of the target range environment, wherein the status information includes at least the location information, link load information, and channel quality of the N network nodes; Based on the state information, a target algorithm is used to determine a target strategy for each network node according to the state information and the global network environment information. The target strategy is used to adjust the communication parameters of the network node, and the global network environment information includes environmental interference information. According to the target strategy, adjust the N network nodes.

4. The network node networking method according to claim 3, characterized in that, After adjusting the N network nodes according to the target strategy, the process includes: Collect feedback information, wherein the feedback information is used to characterize the performance parameters of N network nodes in the target environment after adjustment according to the target strategy, wherein the performance parameters include at least a first value, a second value and a third value, wherein the first value is used to characterize the amount of data transmitted per unit time in the target environment, the second value is used to characterize the data transmission time delay between the network nodes, and the third value is used to characterize the energy consumption of each network node in data transmission. A reward value is determined based on the first value, the second value, and the third value, wherein the reward value is used to quantitatively evaluate the performance of the N network nodes after adjustment in the test range environment; The target algorithm is updated based on the reward value.

5. The network node networking method according to claim 1, characterized in that, Before receiving the target user's target actions on the target page, the following is included: N network nodes are deployed in the target range environment, wherein each network node includes at least attribute information, a positioning module, a communication module, and an environmental sensor, wherein the attribute information includes at least the unique identifier, type label, and security level of the network node; An initialization operation is performed on N network nodes, wherein the initialization operation is used to initially network the N network nodes using an ad hoc network protocol; The attribute information of the N network nodes after initialization is uploaded to the central server.

6. The network node networking method according to claim 1, characterized in that, The method further includes: Detect the security level of N network nodes; When the difference between the security levels of the i-th network node and the j-th network node among the N network nodes is less than a second preset threshold, the connection between the i-th network node and the j-th network node is maintained, where i and j are both integers greater than or equal to 1 and less than or equal to N; When the difference between the security levels of the i-th network node and the j-th network node in the N network nodes is greater than or equal to the second preset threshold, the connection between the i-th network node and the j-th network node is disconnected and an early warning is issued.

7. A networking device for network nodes, characterized in that, include: The receiving unit receives the target operation performed by the target personnel on the target page, wherein the target page is used to display the connection status between N network nodes in the target range environment, and the target operation includes dragging a target node among the N network nodes, wherein the target operation of the target page synchronously affects the network nodes in the target range environment, and each of the N network nodes includes the initial position information of the network node. The update unit is configured to trigger a target event based on the target operation, update the position information of the target node by executing the target event, and broadcast the position change information of the target node in the target range environment; The connection unit, based on the updated location information of the target node, adjusts the connection between N network nodes through a feature matching engine to obtain an updated network of N network nodes. The feature matching engine is used to determine the matching degree of the N network nodes based on a matching degree strategy and the location information of the target node. The connection unit includes: a first acquisition subunit, configured to acquire target information of the current N network nodes through the feature matching engine based on the updated location information of the target nodes, wherein the target information includes at least the location information, signal strength, and security level of the network nodes; a first determination subunit, configured to determine the target distance between adjacent network nodes among the N network nodes based on the location information of the N network nodes, obtaining S target distances, wherein S is an integer greater than or equal to 1; a second determination subunit, configured to determine the channel quality between adjacent network nodes among the N network nodes based on the signal strength of the N network nodes, obtaining S channel quality; a third determination subunit, configured to determine a target factor based on the security level of the N network nodes, wherein the target factor is used to characterize the impact of the security level of the N network nodes on the matching degree of the connection between the network nodes, including: when the difference in security level between two nodes is greater, the target factor is used to reduce the matching degree between the two nodes; and a first adjustment subunit, configured to adjust the connection between the N network nodes based on the target factor, the S target distances, and the S channel quality.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the networking method of network nodes according to any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the networking method of network nodes according to any one of claims 1 to 6.

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