5G intelligent router power regulation method, system and electronic device

Abstract

This invention relates to the field of power regulation technology, specifically to a power regulation method, system, and electronic device for a 5G smart router. The method includes: acquiring RSSI intensity data; determining a change range and a comparison range; determining an intensity change correlation coefficient based on the differences in RSSI intensity data changes and range durations between the change range and the comparison range; determining the matching degree of the current router's movement to other router nodes based on the intensity change correlation coefficient between the currently accessed router node and other router nodes, and the changes in the router nodes accessed during the current movement process and historical movement processes; and combining the matching degrees of all other router nodes at the current moment to achieve power regulation of the Mesh router within the router node. This invention avoids the contradiction between delayed response and overcompensation in power adjustment, improving the energy efficiency of router networking.

Description

Technical Field

[0001] This invention relates to the field of power regulation technology, specifically to a 5G smart router power regulation method, system, and electronic device. Background Technology

[0002] Current network architectures have evolved from single routers to complex architectures integrating multiple devices and protocols. Mainstream solutions include Wi-Fi 6 / 7 routers, Mesh nodes, smart home gateways, and ubiquitous connectivity for IoT devices. With the surge in 4K streaming media and smart home devices, network load and coverage requirements have increased significantly, highlighting power consumption issues. Traditional routers often operate at consistently high power to ensure signal strength, with single devices consuming 10-20 watts. In Mesh networks, multiple nodes remaining idle for extended periods further increases energy consumption, with some mid-to-high-end solutions exceeding 50 watts for the entire network. Furthermore, the hardware design of multi-band concurrency and multi-antenna arrays exacerbates heat dissipation and electricity costs. With the widespread use of smart homes, the continuous communication between massive numbers of terminals and routers leads to a decline in overall network energy efficiency, making router power consumption a significant component of total electricity consumption.

[0003] Currently, RSSI strength is often used to measure the signal quality between mobile terminal devices and nodes. When the signal quality is relatively poor compared to other nodes, the signal transmission power of the node closest to the mobile terminal device is increased, thereby allowing access to that node and meeting the user's internet access needs. This power consumption adjustment method results in signal lag compensation, increases roaming latency, and has low router networking energy efficiency. Summary of the Invention

[0004] To address the technical problems of signal lag compensation, increased roaming latency, and low energy efficiency in router networking caused by power consumption adjustment methods in related technologies, this invention provides a 5G smart router power adjustment method, system, and electronic device. The specific technical solution adopted is as follows:

[0005] This invention proposes a 5G smart router power adjustment method, which configures Mesh routers at different routing nodes within a region. The method includes:

[0006] Periodically acquire RSSI strength data of the Mesh router for mobile terminals in each routing node; take any routing node as the analysis node, and take the interval between two adjacent extreme values ​​of RSSI strength data detected by the analysis node during the movement as the change interval; in other routing nodes, take the interval between two extreme points in the same time period as the comparison interval.

[0007] Based on the changes in RSSI intensity data between the analysis node and the comparison interval, and the difference in the duration between the analysis node and the comparison interval, determine the correlation coefficient between the intensity changes of the analysis node and each other routing node.

[0008] Based on the correlation coefficient between the current access routing node and other routing nodes, and the changes in the routing nodes accessed during the current movement process and the historical movement process, the matching degree of the current movement to other routing nodes is determined.

[0009] By combining the matching degree of all other routing nodes at the current moment, the power adjustment of the Mesh router within the routing node is realized.

[0010] Further, determining the correlation coefficient between the intensity change of the analysis node and each other routing node based on the changes in RSSI intensity data between the analysis node's change interval and the comparison interval, and the difference in the duration of the analysis node's change interval and the comparison interval, includes:

[0011] Based on the changes in RSSI intensity data between the analysis node and the comparison node, determine the correlation index between the changing trends of any routing node and other routing nodes.

[0012] Based on the difference in duration between the change intervals of the analyzed nodes and the comparison intervals, the time-series correlation indicators of change are determined;

[0013] By combining the trend correlation indicators and time-series correlation indicators of the analysis node and each other routing node during all migration processes, the intensity change correlation coefficient between the analysis node and each other routing node is determined.

[0014] Furthermore, the step of determining the correlation index between the changing trends of any routing node and other routing nodes based on the changes in RSSI intensity data within the analysis node's change interval and the comparison interval includes:

[0015] The range of RSSI intensity data within the variation interval is taken as the variation range; the range of RSSI intensity data within the comparison interval is taken as the comparison range.

[0016] Calculate the absolute value of the difference between the range of change and the range of comparison, and normalize the opposite of the absolute value of the difference to obtain the trend correlation index.

[0017] Furthermore, determining the time-series correlation index based on the difference in duration between the change interval of the analysis node and the comparison interval includes:

[0018] Calculate the absolute value of the difference between the duration of the change interval and the comparison interval of the analysis node, and normalize the negative of the absolute value of the difference to obtain the change time series correlation index.

[0019] Furthermore, by combining the trend correlation indicators and time-series correlation indicators of the analysis node and each other routing node during all migrations, the strength change correlation coefficient between the analysis node and each other routing node is determined, including:

[0020] Within any range of change during any movement process, the product of the corresponding trend-related indicator and the time-series-related indicator is used as the range-related indicator.

[0021] The mean of the interval correlation index between the analysis node and each other routing node in all changes during all migration processes is calculated and normalized to serve as the intensity change correlation coefficient.

[0022] Furthermore, based on the correlation coefficient between the current access routing node and other routing nodes, and the changes in the routing nodes accessed during the current movement process and the historical movement processes, the matching degree of movement to other routing nodes at the current time is determined, including:

[0023] The frequency index is obtained by counting the frequency of the next routing node of the accessed routing node at the current moment in all historical migration processes.

[0024] The product of the correlation coefficient between the current access routing node and other routing nodes and the corresponding frequency index is normalized and used as the matching degree for moving to other routing nodes at the current time.

[0025] Furthermore, by combining the matching degree of all other routing nodes at the current moment, the power adjustment of the Mesh router within the routing node is realized, including:

[0026] The matching nodes to be moved are obtained by filtering based on the matching degree value;

[0027] Power adjustments are made to all matching nodes according to the matching degree value, wherein the higher the matching degree value, the higher the power of the corresponding matching node.

[0028] Further, the matching nodes to be moved are selected based on the numerical value of the matching degree, including:

[0029] Other routing nodes with a matching degree greater than the preset matching threshold are used as matching nodes.

[0030] On the other hand, a 5G smart router power adjustment system is also provided, which configures Mesh routers at different routing nodes within the area, including:

[0031] The acquisition module is used to periodically acquire RSSI strength data of the Mesh router for mobile terminals in each routing node; taking any routing node as the analysis node, the interval between two adjacent extreme values ​​of RSSI strength data detected by the analysis node during the movement is taken as the change interval; in other routing nodes, the interval between two extreme points in the same time period of the change interval is taken as the comparison interval.

[0032] The strength correlation module is used to determine the strength change correlation coefficient between the analysis node and each other routing node based on the changes in RSSI strength data between the analysis node's change interval and the comparison interval, and the difference in the duration between the analysis node's change interval and the comparison interval.

[0033] The matching module is used to determine the matching degree of moving to other routing nodes at the current time based on the correlation coefficient between the current access routing node and other routing nodes, as well as the changes in the routing nodes accessed during the current movement process and the historical movement process.

[0034] The adjustment module is used to adjust the power of the Mesh router within the routing node by combining the matching degree of all other routing nodes at the current moment.

[0035] On the other hand, an electronic device is also provided, the electronic device including a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of a 5G smart router power adjustment method as described in any of the foregoing claims.

[0036] The present invention has the following beneficial effects:

[0037] This invention acquires RSSI strength data of the Mesh routers in the routing node for mobile terminals, and divides the data into intervals based on the temporal distribution characteristics of the RSSI strength data to obtain the unidirectional change interval of the RSSI strength data. Then, based on the difference in the change and duration of RSSI strength data between the change area and the comparison interval, strength analysis is performed to obtain the strength change correlation coefficient. Subsequently, combining the strength change correlation coefficient with the changes in the routing nodes accessed during the historical movement process, the matching degree of moving to other routing nodes at the current moment is determined. This matching degree can effectively characterize the actual matching situation. By realizing the power adjustment of the Mesh routers within the routing node through the matching degree, the power loss caused by multiple Mesh routers using high power at the same time can be avoided. While meeting the data transmission efficiency, the contradiction between power adjustment exhibiting lag response and overcompensation is avoided, thereby improving the energy efficiency of router networking. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a 5G smart router power adjustment method provided in one embodiment of the present invention. Detailed Implementation

[0040] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a 5G smart router power adjustment method, system, and electronic device proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] The following description, in conjunction with the accompanying drawings, details a specific scheme for a 5G smart router power adjustment method provided by the present invention.

[0043] Please see Figure 1 The diagram illustrates a flowchart of a 5G smart router power adjustment method according to an embodiment of the present invention, the method comprising:

[0044] S101: Periodically acquire RSSI strength data of the Mesh router for the mobile terminal in each routing node; take any routing node as the analysis node, and take the interval between two adjacent extreme values ​​of RSSI strength data detected by the analysis node during the movement as the change interval; in other routing nodes, take the interval between two extreme points in the same time period as the comparison interval.

[0045] Mesh router nodes often use the 5GHz band for inter-node communication backhaul. The 5GHz band offers higher transmission rates, wider channel bandwidth, and less signal interference, providing a stable, low-latency channel for data exchange between nodes. Compared to 2.4GHz, 5GHz theoretically achieves gigabit-level speeds and has abundant channel resources, significantly reducing the risk of co-channel interference. This ensures efficient collaboration within the mesh network. The short-range characteristics of 5GHz do not significantly impact coverage when nodes are deployed appropriately, while the high-speed backhaul better supports the high bandwidth demands of multiple concurrent devices, thus achieving a seamless roaming experience throughout the home.

[0046] In a network environment, several 5G smart routers are deployed in a 5G band mesh network. The RSSI value can be used to reflect the signal quality from the mobile terminal device to the wireless access point, and can also indirectly reflect the distance from the mobile terminal device to the wireless access point. Since the location of the mesh routing nodes generally does not change, and the layout of the user's residence also remains unchanged, the user's real-time movement path at home has a high degree of similarity to the historical path. This movement pattern reflects the user's movement habits, and by analyzing the user's movement habits, power consumption can be optimized.

[0047] Since the value of RSSI received by a mobile device terminal is related to the type and attributes of the mobile device terminal, and the change of RSSI value is related to the user's walking habits, the mobile device terminal can be identified by its MAC address and analyzed individually.

[0048] Because user habits need to be learned in the early stages of device deployment, the RSSI values ​​collected by the mesh routing nodes are used to locate the device, thereby analyzing and learning the user's habitual movement routes. However, a single mesh routing node cannot locate the user's movement path, so the movement path of the user's mobile terminal is analyzed based on the wireless signals emitted by multiple mesh routing nodes.

[0049] When a mesh routing node (analysis node) connects to a mobile terminal device for the first time, it calls up the power of all mesh routing nodes to the maximum power, obtains the RSSI values ​​of all mesh routing nodes to the mobile terminal device, performs k-means clustering on all RSSI values ​​with K=2, and records the mesh routing node corresponding to the largest RSSI value as the other routing nodes of the analysis node.

[0050] When the difference between the maximum and minimum signal strength values ​​of a mobile terminal connected to a mesh routing node in the last 2 seconds is greater than 6dBm (preset value), all other routing nodes of the connected mesh routing node are called to obtain the RSSI value of the mobile terminal from each mesh routing node. When the mobile terminal connects to a new mesh routing node, the other routing nodes of the new analysis node continue to monitor until the difference between the maximum and minimum signal strength values ​​of the mobile terminal in the last 2 seconds of all other routing nodes of the connected mesh routing node is less than or equal to 6dBm (preset value). At this point, the monitoring of the mobile terminal device by the other routing nodes of the connected mesh routing node is stopped.

[0051] In this embodiment of the invention, the RSSI intensity data of a single routing node will change numerically due to the distance of the mobile terminal. The change in numerical value represents the corresponding movement process. Therefore, in this embodiment of the invention, the interval between two adjacent extreme values ​​of the detected RSSI intensity data in time sequence is taken as the change interval.

[0052] It is understandable that a higher RSSI value indicates a better network. As mobile terminals move, an increase in the RSSI value of one routing node may indicate a decrease in the RSSI value of other routing nodes. In other words, the mobile terminal is moving towards the routing node with the increased RSSI value. In this case, the power of the routing node with the increased RSSI value can be increased according to the actual situation to improve the user experience.

[0053] Therefore, by analyzing the change range of the node, the comparison range of other routing nodes is determined, that is, the interval between two extreme points in the same time period of the change range is used as the comparison range, that is, the change range and the comparison range are in the same time period.

[0054] S102: Based on the changes in RSSI intensity data between the analysis node and the comparison interval, and the difference in the duration between the analysis node and the comparison interval, determine the correlation coefficient between the intensity changes of the analysis node and each other routing node.

[0055] Since mobile devices operate within largely unchanged environments—for example, the layout of a house remains constant—real-time movement paths exhibit a high degree of similarity to historical paths. The mobile access sequence represents the mesh routing nodes a user will connect to along their movement path. Furthermore, the number of possible paths from any given room is finite. When the current RSSI value trend highly overlaps with a particular path, and the signal changes of that path are clearly distinguishable from other paths, it can be predicted that the user will follow that route. The power of the mesh routing nodes along that path can be increased in advance, thereby reducing the power of other unnecessary mesh routing nodes and achieving the goal of power reduction.

[0056] In this embodiment of the invention, the correlation coefficient of intensity change is used to characterize the correlation of changes in different routing nodes.

[0057] The correlation of routing nodes along the user's movement path is determined by the change in the RSSI value of the routing nodes during the mobile terminal's movement. Correlation is mainly reflected in the adjacency between any two mesh routing nodes. If a node is adjacent to the mesh routing node to which the mobile terminal is connected, it indicates that less energy is needed when using multiple mesh routing nodes to locate the mobile terminal during movement, while also enhancing the accuracy of the location.

[0058] Currently, mesh routing nodes typically have one port in each room, ensuring optimal network experience for users in every room and reducing the impact of wireless network signal attenuation when passing through walls. Therefore, when a user moves from one room to another, the mobile device selects a suitable mesh routing node to connect to. This process often involves a gradual decrease in the RSSI value of the old mesh routing node and a gradual increase in the RSSI value of the new mesh routing node, thus establishing the correlation between any two mesh routing nodes.

[0059] Furthermore, in some embodiments of the present invention, the intensity change correlation coefficient between the analysis node and each other routing node is determined based on the RSSI intensity data changes between the analysis node's change interval and the comparison interval, and the difference in the duration of the analysis node's change interval and the comparison interval. This includes: determining the trend correlation index between any routing node and other routing nodes based on the RSSI intensity data changes between the analysis node's change interval and the comparison interval; determining the temporal correlation index based on the difference in the duration of the analysis node's change interval and the comparison interval; and determining the intensity change correlation coefficient between the analysis node and each other routing node by combining the trend correlation index and the temporal correlation index corresponding to the analysis node and each other routing node in all migration processes.

[0060] Among them, the trend correlation index is the correlation of RSSI intensity data changes within the same time period. It is mainly reflected in the correlation of overall numerical changes. The more similar the RSSI intensity data changes of two routing nodes are, the higher the similarity between the two routing nodes during the movement. For example, the mobile terminal moves on the line connecting the corresponding positions of the two routing nodes.

[0061] In this embodiment of the invention, based on the changes in RSSI intensity data between the analysis node and the comparison interval, a trend correlation index between any routing node and other routing nodes is determined, including: taking the range of RSSI intensity data within the change interval as the change range; taking the range of RSSI intensity data within the comparison interval as the comparison range; calculating the absolute value of the difference between the change range and the comparison range; and normalizing the negative of the absolute value of the difference to obtain the trend correlation index.

[0062] In other words, by directly analyzing the range difference between the change interval and the comparison interval in RSSI intensity data, the trend correlation index is obtained. The smaller the range difference, the more similar the changes in RSSI intensity data between the two routing nodes are, which means that they are more likely to be routing nodes with higher similarity for analysis of the mobility process. The better the effect of combining the two routing nodes for analysis.

[0063] When a mobile terminal moves, if the signal change trends between mesh routing nodes show a large negative correlation, it indicates that the mobile terminal is moving away from the connected routing node and moving closer to another mesh routing node. These two routing nodes can more accurately reflect the movement of the mobile terminal device and have a greater correlation.

[0064] Similarly, when performing the same RSSI intensity data changes, it is also necessary to consider the similarity of the change time. The closer the duration, the higher the corresponding representation similarity. Based on this, the change time series correlation index is calculated.

[0065] Furthermore, in some embodiments of the present invention, determining the change time series correlation index based on the difference in duration between the change interval of the analysis node and the comparison interval includes: calculating the absolute value of the difference between the duration of the change interval of the analysis node and the comparison interval, and normalizing the negative of the absolute value of the difference to obtain the change time series correlation index.

[0066] Among them, the smaller the absolute value of the difference between the duration of the change range of the analysis node and the duration of the comparison range, the closer the duration is, and the greater the correlation is.

[0067] In summary, by combining the trend correlation indicators and time-series correlation indicators of the analysis node and each other routing node in all migration processes, the intensity change correlation coefficient between the analysis node and each other routing node is determined. This includes: within any change interval in any migration process, the product of the corresponding trend correlation indicator and time-series correlation indicator is used as the interval correlation indicator; the mean of the interval correlation indicators of the analysis node and each other routing node in all change intervals in all migration processes is calculated, normalized, and used as the intensity change correlation coefficient.

[0068] Since larger values ​​for both the trend correlation index and the time-series correlation index indicate a stronger correlation between the two corresponding routing nodes during the movement process, the mean of the interval correlation index between the analyzed node and each other routing node across all change intervals in all movement processes is directly calculated and normalized to obtain the intensity change correlation coefficient. Analytical nodes with larger intensity change correlation coefficients have stronger analytical significance in the current movement process.

[0069] S103: Determine the matching degree of moving to other routing nodes at the current time based on the correlation coefficient of the strength change between the current access routing node and other routing nodes, and the changes in the access routing nodes between the current movement process and the historical movement process.

[0070] After obtaining the correlation coefficient between the current access routing node and other routing nodes, the path of the mobile terminal is predicted. This involves determining the mobile terminal's next movement path based on previously collected mobile signal data. In this embodiment, the prediction analysis is performed using a matching degree; a higher matching degree value indicates a greater likelihood of movement towards the corresponding routing node.

[0071] Furthermore, in some embodiments of the present invention, the matching degree of moving to other routing nodes at the current time is determined based on the correlation coefficient of the strength change between the current access routing node and other routing nodes, and the changes in the routing nodes accessed during the current movement process and the historical movement process. This includes: counting the frequency of the next routing node of the current access routing node in all historical movement processes to obtain a frequency index; and normalizing the product of the correlation coefficient of the strength change between the current access routing node and other routing nodes and the corresponding frequency index to obtain the matching degree of moving to other routing nodes at the current time.

[0072] Among them, the frequency index is the indicator data used in frequency analysis. For example, there are three routing nodes, A, B and C. At the current moment, we are at node A. In all historical records, the frequency of routing to node B is 30 times and the frequency of routing to node C is 3 times. At this time, the matching degree of routing node B is significantly higher than that of routing node C. Therefore, the frequency index can be used as an important data to measure the matching degree.

[0073] Since the intensity change correlation coefficient represents the correlation index for intensity change, the product of the intensity change correlation coefficient and the frequency index can be directly calculated, normalized, and used as the matching degree for moving to other routing nodes at the current time. Each other routing node corresponds to a matching degree data, thereby realizing the subsequent matching analysis.

[0074] S104: Combine the matching degree of all other routing nodes at the current moment to realize the power adjustment of the Mesh router within the routing node.

[0075] Furthermore, in some embodiments of the present invention, the power adjustment of the Mesh router within a routing node is achieved by combining the matching degree of all other routing nodes at the current moment, including: filtering out matching nodes to be moved based on the matching degree value; and adjusting the power of all matching nodes according to the matching degree value, wherein the larger the matching degree value, the higher the power of the corresponding matching node.

[0076] Among them, the matching nodes to be moved are selected based on the matching degree value, including: other routing nodes with a matching degree greater than a preset matching threshold are selected as matching nodes.

[0077] Understandably, the matching degree characterizes the degree of matching during the current movement process. Routing nodes can be filtered based on the matching degree to obtain matching nodes. Since a higher matching degree indicates a higher correlation between the corresponding other routing nodes and the current movement process, a greater power increase is needed.

[0078] In the specific screening process, a preset matching threshold can be set, such as 0.75. Other routing nodes with a matching degree greater than 0.75 are used as matching nodes. Power is allocated to matching nodes, and the power of non-matching nodes is reduced, thereby reducing power loss while ensuring transmission strength.

[0079] This invention acquires RSSI strength data of the Mesh routers in the routing node for mobile terminals, and divides the data into intervals based on the temporal distribution characteristics of the RSSI strength data to obtain the unidirectional change interval of the RSSI strength data. Then, based on the difference in the change and duration of RSSI strength data between the change area and the comparison interval, strength analysis is performed to obtain the strength change correlation coefficient. Subsequently, combining the strength change correlation coefficient with the changes in the routing nodes accessed during the historical movement process, the matching degree of moving to other routing nodes at the current moment is determined. This matching degree can effectively characterize the actual matching situation. By realizing the power adjustment of the Mesh routers within the routing node through the matching degree, the power loss caused by multiple Mesh routers using high power at the same time can be avoided. While meeting the data transmission efficiency, the contradiction between power adjustment exhibiting lag response and overcompensation is avoided, thereby improving the energy efficiency of router networking.

[0080] In another embodiment of the present invention, a 5G smart router power adjustment system is also provided, comprising:

[0081] The acquisition module is used to periodically acquire RSSI strength data of the Mesh router for mobile terminals in each routing node; taking any routing node as the analysis node, the interval between two adjacent extreme values ​​of RSSI strength data detected by the analysis node during the movement is taken as the change interval; in other routing nodes, the interval between two extreme points in the same time period of the change interval is taken as the comparison interval.

[0082] The strength correlation module is used to determine the strength change correlation coefficient between the analysis node and each other routing node based on the changes in RSSI strength data between the analysis node's change interval and the comparison interval, and the difference in the duration between the analysis node's change interval and the comparison interval.

[0083] The matching module is used to determine the matching degree of moving to other routing nodes at the current time based on the correlation coefficient between the current access routing node and other routing nodes, as well as the changes in the routing nodes accessed during the current movement process and the historical movement process.

[0084] The adjustment module is used to adjust the power of the Mesh router within the routing node by combining the matching degree of all other routing nodes at the current moment.

[0085] In another embodiment of the present invention, an electronic device is also provided, the electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the foregoing 5G smart router power adjustment methods.

[0086] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A power adjustment method for a 5G smart router, characterized in that, The method for configuring Mesh routers at different routing nodes within a region includes: Periodically acquire RSSI strength data of the Mesh router for mobile terminals in each routing node; take any routing node as the analysis node, and take the interval between two adjacent extreme values ​​of RSSI strength data detected by the analysis node during the movement as the change interval; in other routing nodes, take the interval between two extreme points in the same time period as the comparison interval. Based on the changes in RSSI intensity data between the analysis node and the comparison interval, and the difference in the duration between the analysis node and the comparison interval, determine the correlation coefficient between the intensity changes of the analysis node and each other routing node. Based on the correlation coefficient between the current access routing node and other routing nodes, and the changes in the routing nodes accessed during the current movement process and the historical movement process, the matching degree of the current movement to other routing nodes is determined. By combining the matching degree of all other routing nodes at the current moment, the power of the Mesh router within the routing node can be adjusted; The method for determining the correlation coefficient between the strength change of the analysis node and each other routing node includes: Based on the changes in RSSI intensity data between the analysis node and the comparison node, determine the correlation index between the changing trends of any routing node and other routing nodes. Based on the difference in duration between the change intervals of the analyzed nodes and the comparison intervals, the time-series correlation indicators of change are determined; By combining the trend correlation indicators and time-series correlation indicators of the analysis node and each other routing node during all migration processes, the intensity change correlation coefficient between the analysis node and each other routing node is determined.

2. The 5G smart router power adjustment method as described in claim 1, characterized in that, The step of determining the correlation index between the changing trends of any routing node and other routing nodes based on the changes in RSSI intensity data within the analysis node's change range and the comparison range includes: The range of RSSI intensity data within the variation interval is taken as the variation range; the range of RSSI intensity data within the comparison interval is taken as the comparison range. Calculate the absolute value of the difference between the range of change and the range of comparison, and normalize the opposite of the absolute value of the difference to obtain the trend correlation index.

3. The 5G smart router power adjustment method as described in claim 1, characterized in that, The step of determining the time-series correlation index based on the difference in duration between the change interval of the analysis node and the comparison interval includes: Calculate the absolute value of the difference between the duration of the change interval and the comparison interval of the analysis node, and normalize the negative of the absolute value of the difference to obtain the change time series correlation index.

4. The 5G smart router power adjustment method as described in claim 1, characterized in that, By combining the trend correlation indicators and time-series correlation indicators of the analysis node and each other routing node during all migrations, the strength change correlation coefficient between the analysis node and each other routing node is determined, including: Within any range of change during any movement process, the product of the corresponding trend-related indicator and the time-series-related indicator is used as the range-related indicator. The mean of the interval correlation index between the analysis node and each other routing node in all changes during all migration processes is calculated and normalized to serve as the intensity change correlation coefficient.

5. The 5G smart router power adjustment method as described in claim 1, characterized in that, Based on the correlation coefficient between the current access route node and other route nodes, and the changes in the access routes during the current movement process and the historical movement processes, the matching degree of movement to other route nodes at the current time is determined, including: The frequency index is obtained by counting the frequency of the next routing node of the accessed routing node at the current moment in all historical migration processes. The product of the correlation coefficient between the current access routing node and other routing nodes and the corresponding frequency index is normalized and used as the matching degree for moving to other routing nodes at the current time.

6. The 5G smart router power adjustment method as described in claim 1, characterized in that, By combining the matching degree of all other routing nodes at the current moment, the power adjustment of the Mesh router within the routing node is realized, including: The matching nodes to be moved are obtained by filtering based on the matching degree value; Power adjustments are made to all matching nodes according to the matching degree value, wherein the higher the matching degree value, the higher the power of the corresponding matching node.

7. A 5G smart router power adjustment method as described in claim 6, characterized in that, The matching nodes to be moved are obtained by filtering based on the matching degree value, including: Other routing nodes with a matching degree greater than the preset matching threshold are used as matching nodes.

8. A 5G smart router power regulation system, characterized in that, Configure Mesh routers at different routing nodes within the area, including: The acquisition module is used to periodically acquire RSSI strength data of the Mesh router for mobile terminals in each routing node; taking any routing node as the analysis node, the interval between two adjacent extreme values ​​of RSSI strength data detected by the analysis node during the movement is taken as the change interval; in other routing nodes, the interval between two extreme points in the same time period of the change interval is taken as the comparison interval. The strength correlation module is used to determine the strength change correlation coefficient between the analysis node and each other routing node based on the changes in RSSI strength data between the analysis node's change interval and the comparison interval, and the difference in the duration between the analysis node's change interval and the comparison interval. The matching module is used to determine the matching degree of moving to other routing nodes at the current time based on the correlation coefficient between the current access routing node and other routing nodes, as well as the changes in the routing nodes accessed during the current movement process and the historical movement process. The adjustment module is used to adjust the power of the Mesh router within the routing node by combining the matching degree of all other routing nodes at the current moment. The method for determining the correlation coefficient between the strength change of the analysis node and each other routing node includes: Based on the changes in RSSI intensity data between the analysis node and the comparison node, determine the correlation index between the changing trends of any routing node and other routing nodes. Based on the difference in duration between the change intervals of the analyzed nodes and the comparison intervals, the time-series correlation indicators of change are determined; By combining the trend correlation indicators and time-series correlation indicators of the analysis node and each other routing node during all migration processes, the intensity change correlation coefficient between the analysis node and each other routing node is determined.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the 5G smart router power adjustment method as described in any one of claims 1 to 7.

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