Method and system for adjusting antenna angle of intelligent router

Abstract

The invention relates to a method and a system for adjusting an antenna angle of an intelligent router. The method for adjusting the antenna angle of the intelligent router comprises the steps of obtaining environment network data of an environment where the intelligent router is located, wherein the environment network data comprises signal strength, user distribution and obstacle positions; preprocessing the environment network data; analyzing the preprocessed environment network data so as to analyze a signal to-be-enhanced area; and adjusting the angle of the intelligent router antenna based on the analyzed signal to-be-enhanced area. According to the invention, the angle of the antenna of the intelligent router can be automatically adjusted according to actual needs, so that the environment where the intelligent router is located can obtain stable network signals, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the technical field of router antenna angle adjustment, and in particular to a method and system for adjusting the angle of an intelligent router antenna. Background Art

[0002] In today's wireless networking landscape, routers play a crucial role in network communications. Their location and antenna angles determine network coverage and quality. Traditional router designs typically employ fixed antenna angles. This design has significant practical limitations and struggles to adapt to changing environments.

[0003] For example, a large office has a complex layout with multiple work areas and numerous obstructions such as walls and furniture. In such an environment, a router with a fixed antenna angle can easily lead to uneven signal coverage, with weak signal strength in certain areas, resulting in unstable or even complete loss of network connection. The same is true in complex residential structures, where factors such as the layout of different rooms and building materials can affect signal propagation. Fixed antenna angles cannot be adjusted according to actual conditions, resulting in poor network signal in some rooms and poor network service in some areas, seriously affecting the user experience.

[0004] Existing solutions mostly rely on manually adjusting the router's antenna angle. This approach is not only inefficient but also difficult to respond to real-time environmental changes. For example, in an office, personnel turnover is frequent, and device usage fluctuates constantly. Manual adjustment cannot keep up with these dynamic changes. Summary of the Invention

[0005] Based on this, it is necessary to provide a method and system for adjusting the antenna angle of an intelligent router to address the problems in the related technology.

[0006] To achieve the above objectives, in a first aspect, the present application provides a method for adjusting the antenna angle of a smart router, the method comprising: Obtaining environmental network data of the environment in which the smart router is located, wherein the environmental network data includes signal strength, user distribution, and obstacle locations; Preprocessing the environmental network data; Analyze the pre-processed environmental network data to identify the area where the signal needs to be enhanced; Based on the analyzed signal enhancement area, the angle of the smart router antenna is adjusted.

[0007] In some embodiments, obtaining environmental network data of the environment in which the intelligent router is located includes: Arrange sensors at different locations in the environment where the smart router is located; Receive the wireless signal transmitted by the smart router based on the sensor to obtain the signal strength at different locations; Detecting the number and location of user devices connected to the smart router based on sensors to obtain the user distribution; The position of the obstacle is obtained based on the time delay and strength of the reflected signal received by the sensor.

[0008] In some embodiments, analyzing the pre-processed environmental network data to identify areas where signals are to be enhanced includes: The DBSCAN algorithm based on density clustering is used to identify signal-weak areas. The algorithm includes the following steps: calculating the number of points in the ϵ neighborhood of each data point, where ϵ is the neighborhood radius; marking a data point as a core point if the number of points in the ϵ neighborhood of a data point is greater than or equal to MinPts; grouping all core points and the points in their ϵ neighborhood into a cluster; and marking data points that are not grouped into any cluster as noise points. The areas corresponding to these noise points are the signal-weak areas. The K-means clustering algorithm is used to identify user-dense areas, including: randomly generating K initial cluster centers, where K is an integer greater than 1; assigning each data point to the nearest cluster center to obtain K clusters; recalculating the cluster center of each cluster; repeating the above steps until the cluster centers of each cluster no longer change, then the cluster centers of each cluster are the user-dense areas.

[0009] In some embodiments, analyzing the pre-processed environmental network data to identify areas where signals are to be enhanced further includes: The long short-term memory network is used to predict the network load change trend of the environment in which the intelligent router is located, including: Obtaining historical network load data of the environment in which the intelligent router is located to construct a time series data set; Training a long short-term memory network based on the time series dataset; The trained long short-term memory network is used to predict the network load change trend of the environment where the intelligent router is located in the future.

[0010] In some embodiments, adjusting the angle of the smart router antenna based on the analyzed signal enhancement area includes: The optimal angle is obtained based on the following formula: in, is the optimal angle, N is the number of monitoring points, is the weight of the i-th monitoring point, is the signal strength of each monitoring point i, is the target signal strength; The angle of the smart router antenna is adjusted based on the optimal angle.

[0011] In some embodiments, adjusting the angle of the smart router antenna based on the optimal angle includes: generating a pulse width modulated signal based on the optimal angle; Based on the pulse width modulation signal, a stepper motor is driven to drive the smart router antenna to rotate, so as to adjust the angle of the smart router antenna.

[0012] In some embodiments, after adjusting the angle of the smart router antenna based on the analyzed signal enhancement area, the method further includes: evaluating the network performance of the environment in which the smart router is located, and optimizing the angle of the smart router antenna, including: Obtaining network performance data of the environment in which the smart router is located, wherein the network performance data includes signal strength, connection stability, and user satisfaction; Preprocessing the network performance data; The comprehensive performance index of the evaluation network is obtained based on the following formula: Among them, P is the comprehensive performance index of the network, is the weight coefficient of signal strength, is the weight coefficient of connection stability, is the weight coefficient of user satisfaction, is the weight of the i-th monitoring point, N is the number of monitoring points, is the signal strength of the i-th monitoring point, is the connection stability of the i-th monitoring point, is the user satisfaction of the i-th monitoring point; According to the comprehensive network performance index, an optimization method based on a genetic algorithm is used to optimize and adjust the angle of the smart router antenna.

[0013] In some embodiments, after evaluating the network performance of the environment in which the smart router is located and optimizing the angle of the smart router antenna, the method further includes: Monitor changes in the environment where the smart router is located in real time. When changes in signal strength or user distribution are detected, perform network performance evaluation on the environment where the smart router is located and optimize the angle of the smart router antenna.

[0014] In some embodiments, after adjusting the angle of the smart router antenna based on the analyzed signal enhancement area, the method further includes: Generate a real-time network coverage map based on the network performance data and display it on an interactive interface; Display the adjustment status of the smart router antenna on the interactive interface; storing the network performance data and changes in the network performance data for a user to query via voice or gestures based on an interactive interface; A network performance report is generated and sent based on the real-time network coverage map, the adjustment status of the smart router antenna and the network performance data.

[0015] In a second aspect, the present application further provides a system for adjusting the antenna angle of an intelligent router, the system comprising: An environment monitoring module is used to obtain environmental network data of the environment in which the smart router is located, wherein the environmental network data includes signal strength, user distribution and obstacle location; A preprocessing module, used for preprocessing the environmental network data; An analysis module is used to analyze the pre-processed environmental network data to identify areas where signals need to be enhanced; The angle adjustment module is used to adjust the angle of the smart router antenna based on the analyzed signal enhancement area.

[0016] In the above-mentioned method and system for adjusting the antenna angle of the smart router, environmental network data of the environment in which the smart router is located is obtained, and the environmental network data includes signal strength, user distribution and obstacle location; the environmental network data is preprocessed; the preprocessed environmental network data is analyzed to analyze the area where the signal is to be enhanced; based on the analyzed area where the signal is to be enhanced, the angle of the smart router antenna is adjusted; the angle of the smart router antenna can be automatically adjusted according to actual needs, so that the environment in which the smart router is located can obtain a stable network signal, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a flow chart of a method for adjusting the antenna angle of a smart router provided in one embodiment of the present application; Figure 2 This is a structural block diagram of the intelligent router antenna angle adjustment system provided in different embodiments of the present application.

[0019] Explanation of the accompanying drawings: 10, environmental monitoring module; 20, pre-processing module; 30, analysis module; 40, angle adjustment module. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0021] Routers play a crucial role in network communications, and their location and antenna angles determine network coverage and quality. Traditional router designs typically use fixed antenna angles. This design has significant practical limitations and struggles to meet the demands of dynamically changing environments.

[0022] For example, a large office has a complex layout with multiple work areas and numerous obstructions such as walls and furniture. In such an environment, a router with a fixed antenna angle can easily lead to uneven signal coverage, with weak signal strength in certain areas, resulting in unstable or even complete loss of network connection. The same is true in complex residential structures, where factors such as the layout of different rooms and building materials can affect signal propagation. Fixed antenna angles cannot be adjusted according to actual conditions, resulting in poor network signal in some rooms and poor network service in some areas, seriously affecting the user experience.

[0023] One solution relies on manually adjusting the router's antenna angle. This approach is not only inefficient but also difficult to respond to real-time environmental changes. For example, in an office, personnel turnover is frequent, and device usage fluctuates constantly. Manual adjustments cannot keep up with these dynamic changes.

[0024] From an antenna perspective, while omnidirectional antennas theoretically provide 360° signal coverage, in practice, signal strength is often unevenly distributed vertically. In multi-story buildings, signal coverage can differ significantly between upper and lower floors, resulting in poor signal quality on lower or upper floors. Directional antennas can focus signals in a specific direction, enhancing signal strength in specific areas in some cases. However, their fixed directionality prevents them from automatically adjusting to changing environments. For example, if the distribution of people in an office changes or obstacles are added, directional antennas cannot adapt to these changes, limiting their flexibility.

[0025] In one embodiment, see Figure 1 The present application provides a method for adjusting the antenna angle of an intelligent router, and the method for adjusting the antenna angle of an intelligent router includes the following steps: S10~S40.

[0026] S10: Acquire environmental network data of the environment in which the smart router is located, where the environmental network data includes signal strength, user distribution, and obstacle locations.

[0027] S20: Preprocessing the environmental network data.

[0028] S30: Analyze the pre-processed environmental network data to identify areas where signals are to be enhanced.

[0029] S40: Based on the analyzed signal enhancement area, the angle of the smart router antenna is adjusted.

[0030] In the method for adjusting the antenna angle of the smart router of the present application, the environmental network data of the environment in which the smart router is located is obtained, and the environmental network data includes signal strength, user distribution and obstacle location; the environmental network data is preprocessed; the preprocessed environmental network data is analyzed to analyze the area where the signal is to be enhanced; based on the analyzed area where the signal is to be enhanced, the angle of the smart router antenna is adjusted; the angle of the smart router antenna can be automatically adjusted according to actual needs, so that the environment in which the smart router is located can obtain a stable network signal, thereby improving the user experience. The method for adjusting the antenna angle of the smart router of the present application realizes dynamic optimization of network coverage by combining the environmental network data of environmental monitoring with the intelligent adjustment of the antenna angle of the smart router; this not only improves the stability of the network connection, allowing users to enjoy smooth network services in various environments, but also improves the efficiency of network coverage, fully utilizes the signal transmission capability of the smart router, reduces signal blind spots, and provides users with a better network experience.

[0031] In step S10, refer to step S10 in 1 to obtain environmental network data of the environment in which the smart router is located, where the environmental network data includes signal strength, user distribution, and obstacle locations.

[0032] For example, high-sensitivity sensors can be used to collect real-time network environment information such as signal strength, user distribution, and obstacle location in the smart router's environment. Sensors can be distributed in different locations in the smart router's environment. These sensors can use advanced wireless signal receiving technology to accurately measure signal attenuation and reflection, ensuring the accuracy and real-time nature of the data.

[0033] As an example, in step S10, obtaining environmental network data of the environment in which the smart router is located may include the following steps: S101 to S104.

[0034] S101: Arrange sensors at different locations in the environment where the smart router is located.

[0035] S102: Receive the wireless signal transmitted by the smart router based on the sensor to obtain signal strength at different locations.

[0036] As an example, signal strength monitoring can include the following: the sensor receives the wireless signal of the smart router and measures the signal strength at different locations. The signal strength calculation formula can be: in, is the received signal strength, is the transmission power, n is the path loss exponent from the sensor to the smart router, d is the distance between the sensor and the smart router, and L is the additional loss caused by obstacles. This formula can accurately evaluate the signal strength distribution at different locations.

[0037] S103: Detecting the number and locations of user devices connected to the smart router based on sensors to obtain the user distribution.

[0038] As an example, user distribution monitoring can include the following: sensors monitor the number and location of user devices connected to the smart router to monitor user distribution in real time. Specifically, a triangulation positioning algorithm can be used to calculate the specific location of the user device based on the signal strength of multiple sensors. The formula for the triangulation positioning algorithm is: in, is the location of the user's device, is the position of the i-th sensor, is the distance from the user device to the i-th sensor. By solving this set of equations, the specific location of the user device can be determined.

[0039] S104: Obtain the position of the obstacle based on the time delay and strength of the reflected signal received by the sensor.

[0040] As an example, obstacle location monitoring can include the following: the sensor can identify the location and size of the obstacle by analyzing signal reflection and attenuation. Specifically, the distance and direction of the obstacle can be calculated based on the delay and strength of the reflected signal. The calculation formula for the reflected signal can be: Where t is the time delay of the reflected signal, d is the distance between the sensor and the obstacle, and c is the speed of light. This formula can accurately identify the location of obstacles.

[0041] For example, the strength of the reflected signal can be used to help determine the size, material, and shape of an obstacle. When a signal is reflected, it will be attenuated to varying degrees depending on the size, surface roughness, and material of the obstacle. For example, a larger obstacle will reflect a stronger signal, while an obstacle made of an absorptive material will significantly reduce the strength of the reflected signal.

[0042] In a specific example, consider a large office environment where a smart router resides. Multiple highly sensitive sensors are deployed within the office, monitoring signal strength, user distribution, and obstacle locations in real time. For example, sensor A detects weak signal strength in the conference room, sensor B detects a large number of connected user devices in the office area, and sensor C detects a large obstacle in the hallway. Based on this data, the signal strength in the conference room is calculated to be -70dBm, the number of user devices in the office area is 20, and the distance to the obstacle in the hallway is 5 meters. This data can be used to subsequently adjust the antenna angle of the smart router. This approach enables real-time response to environmental changes, optimizing network coverage and quality.

[0043] In step S20, refer to Figure 1 In step S20, the environmental network data is preprocessed.

[0044] As an example, preprocessing the environmental network data may include cleaning and normalization steps. First, the collected environmental network data may be cleaned to eliminate noise and outliers; then, the cleaned environmental network data may be normalized. The normalization formula may be: in, is the normalized environmental network data, is the preprocessed environmental network data, is the mean of the preprocessed environmental network data, is the standard deviation of the preprocessed environmental network data. Through normalization, we can ensure that the environmental network data collected by different sensors are compared and analyzed at the same scale.

[0045] In step S30, refer to Figure 1 In step S30, the pre-processed environmental network data is analyzed to identify the area where the signal is to be enhanced.

[0046] As an example, in step S30 , the pre-processed environmental network data is analyzed to determine the area where the signal is to be enhanced, which may include the following steps: S301 - S302 .

[0047] S301: Using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to identify signal-weak areas, the algorithm includes: calculating the number of points within the ϵ neighborhood of each data point, where ϵ is the neighborhood radius; marking a data point (i.e., a monitoring point, which can be the sensor location) as a core point if the number of points within its ϵ neighborhood is greater than or equal to MinPts (Minimum Points, the minimum number of neighborhood points required for a core object); grouping all core points and the points within their ϵ neighborhood into a single cluster; and labeling data points not assigned to any cluster as noise points. The areas corresponding to these noise points are designated as signal-weak areas. In this step, the DBSCAN algorithm calculates the density distribution of data points and identifies areas with lower density as signal-weak areas.

[0048] S302: Using the K-means clustering algorithm to identify densely populated areas, this includes: randomly generating K initial cluster centers, where K is an integer greater than 1; assigning each data point to the nearest cluster center to obtain K clusters; recalculating the cluster center of each cluster; and repeating these steps until the cluster centers of all clusters remain unchanged. The cluster centers of each cluster are the densely populated areas. In this step, the K-means algorithm iteratively divides the user device location data into K clusters. The center of each cluster ultimately obtained through the iterative calculation is the densely populated area.

[0049] As an example, step S30 may also include the following steps: S303: Using a long short-term memory (LSTM) network to predict network load trends in the environment in which the smart router resides. Long short-term memory networks are a special type of recurrent neural network (RNN) that can capture long-term dependencies in time series data. Specifically, step S303 may include the following steps: S3031-S3033.

[0050] S3031: Acquire historical network load data of the environment in which the intelligent router is located to construct a time series data set.

[0051] S3032: Training a long short-term memory network based on the time series dataset.

[0052] S3033: Use the trained long short-term memory network to predict the network load change trend of the environment where the intelligent router is located over a period of time in the future.

[0053] In a specific example, consider a large office environment where a smart router operates. First, collected network data, including signal strength, user distribution, and obstacle locations, is normalized to eliminate noise and outliers. The DBSCAN algorithm is then used to identify conference rooms as areas with weak signals, and the K-means algorithm is used to identify office areas as areas with high user density. Finally, an LSTM network is used to predict a 20% increase in network load in the office area within the next hour. These analysis results are then used to adjust the router antenna angle to optimize network coverage and quality. This allows the system to respond to environmental changes in real time and provide stable network services.

[0054] In step S40, refer to Figure 1 In step S40, the angle of the smart router antenna is adjusted based on the analyzed signal enhancement area.

[0055] As an example, in step S40, the optimal antenna angle is first calculated based on data from signal-weak areas and user-dense areas. Smart router antennas can include omnidirectional and directional antennas. For omnidirectional antennas, their vertical angle can be adjusted based on the optimal angle to achieve more even vertical signal distribution. For directional antennas, their pointing direction (for example, horizontal or elevation angle) can be adjusted based on the optimal angle to ensure concentrated signal coverage in areas requiring enhanced coverage.

[0056] As an example, for both omnidirectional and directional antennas, the optimal angle can be obtained based on the following formula: in, is the optimal angle, N is the number of monitoring points, is the weight of the i-th monitoring point, is the signal strength of each monitoring point i, is the target signal strength.

[0057] As an example, in step S40, adjusting the angle of the smart router antenna based on the optimal angle includes the following steps: generating a pulse width modulation (PWM) signal based on the optimal angle; and driving a stepper motor based on the pulse width modulation signal to rotate the smart router antenna to adjust the angle of the smart router antenna.

[0058] As an example, a high-precision stepper motor and servo system can be used to precisely adjust the angle of the smart router antenna. After a pulse width modulation signal is generated based on the optimal angle, the pulse width modulation signal drives the stepper motor to rotate to adjust the angle of the smart router antenna.

[0059] As an example, the actual angle of the smart router antenna may be fed back through an encoder or the like to ensure the accuracy of the angle adjustment.

[0060] It should be noted that when the signal-weak area and the user-dense area are in different directions (for example, in opposite directions or at a certain angle), a time-division multiplexing strategy or a beamforming-assisted strategy can be used to determine how to adjust the angle of the smart router antenna.

[0061] Specifically, the time-division reuse strategy is designed to avoid overlapping high-load periods in areas with weak signals and dense user populations. For example, weak signal areas might be residential areas at night, while dense user populations might be office areas during the day. Based on the predicted network load trends, the smart router antenna angle can be adjusted to point toward dense user populations during time period t1 (8:00 AM to 6:00 PM) and toward weak signal areas during time period t2 (7:00 PM to 12:00 AM). During the off-peak hours between t1 and t2, the smart router antenna angle can be adjusted to point between the weak signal area and the dense user population.

[0062] Specifically, the beamforming-assisted strategy is suitable for multi-beam coordinated coverage, that is, the directional antenna is a phased array antenna (which can generate multi-beam signals). If the weight of the user-dense area is greater than the weight of the signal-weak area (or the priority of the user-dense area is higher than the priority of the signal-weak area), the smart router antenna angle can be adjusted so that the main beam points to the user-dense area and the sidelobe beam points to the signal-weak area.

[0063] As an example, the method for adjusting the antenna angle of a smart router may also include a fault detection step. Specifically, the operating status of the stepper motor and servo system can be monitored in real time; when an anomaly is detected, an alarm message is automatically sent to notify maintenance personnel to perform an inspection.

[0064] In one example, the deviation between the actual angle and the target angle of the smart router antenna can be monitored in real time. When an abnormality is detected (for example, the deviation between the actual angle and the target angle exceeds the angle error threshold), an alarm message is automatically sent to notify maintenance personnel to perform maintenance. Specifically, the fault detection formula can be: in, is the actual angle of the smart router antenna, is the target angle, is the angle error threshold, 1 indicates a fault and 0 indicates normal. When the deviation between the actual angle and the target angle exceeds the angle error threshold, a fault is determined.

[0065] In another example, a support vector machine (SVM)-based fault detection algorithm can be used to monitor the operating status of a stepper motor and servo system in real time. The feature vectors for fault detection can include motor current I, temperature T, and vibration frequency F. The decision function for fault detection can be: Where, is the output of the support vector machine, 1 indicates a fault and 0 indicates normal. When a fault is detected, an alarm message can be automatically sent to notify maintenance personnel to perform repairs.

[0066] As an example, alarm information can be sent through various methods such as SMS, email, and system interface to ensure timely response.

[0067] In a specific example, consider a multi-story office environment where a smart router operates. Within a multi-story office building, the third floor is identified as a weak signal area, while the second floor is a densely populated area. Based on this data, the optimal vertical angle for the omnidirectional antenna is calculated to be 15 degrees, and the optimal pointing direction for the directional antenna is southeast. A corresponding PWM signal is generated to drive a stepper motor to adjust the antenna angle. After adjustment, the actual antenna angle is fed back via an encoder to ensure accuracy. If the signal strength change on the second floor exceeds a preset threshold, the dynamic adjustment mechanism is automatically triggered to recalculate and adjust the antenna angle. Simultaneously, the operating status of the stepper motor and servo system is monitored in real time. If the angle deviation exceeds the threshold, an alarm is automatically sent to notify maintenance personnel for repair. This approach enables real-time response to environmental changes and provides stable network services.

[0068] As an example, after step S40, that is, after adjusting the angle of the smart router antenna based on the analyzed signal enhancement area, the following steps may also be included: S50: Evaluate the network performance of the smart router's environment and optimize the angle of the smart router's antenna.

[0069] As an example, step S50 may include the following steps: S501 to S504.

[0070] S501: Acquire network performance data of the environment in which the smart router is located, where the network performance data includes signal strength, connection stability, and user satisfaction.

[0071] Specifically, sensors deployed at multiple locations can be used to collect real-time network performance data about the environment in which the smart router is located. The frequency of collecting network performance data can be set based on actual needs, for example, once per second, to ensure real-time network performance data.

[0072] S502: Preprocess the network performance data.

[0073] Specifically, preprocessing the network performance data can include denoising, normalization, and data alignment. Denoising uses a wavelet transform algorithm to remove high-frequency noise from the network performance data. Normalization maps the denoised network performance data to the [0, 1] interval for ease of subsequent processing. Data alignment ensures that data timestamps from different sensors are consistent.

[0074] S503: Obtain the comprehensive performance index of the evaluation network based on the following formula: Among them, P is the comprehensive performance index of the network, is the weight coefficient of signal strength, is the weight coefficient of connection stability, is the weight coefficient of user satisfaction, is the weight of the i-th monitoring point, N is the number of monitoring points, is the signal strength of the i-th monitoring point, is the connection stability of the i-th monitoring point, is the user satisfaction of the i-th monitoring point.

[0075] S504: According to the comprehensive network performance index, an optimization method based on a genetic algorithm is used to optimize and adjust the angle of the smart router antenna.

[0076] Specifically, the fitness function of the genetic algorithm can be: in, is the angle of the smart router antenna. Through selection, crossover and mutation operations, the genetic algorithm gradually optimizes the angle of the smart router antenna, and finds the angle that maximizes the network comprehensive performance index, which is the optimal angle. The maximum angle is the optimal angle. The optimal angle is the target angle for adjusting the angle of the smart router antenna.

[0077] As an example, after evaluating the network performance of the environment in which the smart router is located and optimizing the angle of the smart router antenna, that is, after step S50, the following steps are further included: S60: Monitor changes in the environment of the smart router in real time. When changes in signal strength or user distribution are detected, perform a step of evaluating network performance of the environment of the smart router and optimizing the angle of the smart router antenna.

[0078] Specifically, step S60 is a step of dynamically adjusting the angle of the smart router antenna. In this step, the triggering condition for the dynamic adjustment may be: Δ P > P threshold Among them, Δ P is the change in the comprehensive performance index of the network, P threshold The preset network comprehensive index threshold. When the change in the network comprehensive performance index exceeds the preset network comprehensive index threshold, the angle of the smart router antenna needs to be dynamically adjusted.

[0079] As an example, step S60 may be repeated several times according to actual needs to form a closed-loop control to ensure that the network coverage effect can be optimized.

[0080] As an example, after adjusting the angle of the smart router antenna based on the analyzed signal area to be enhanced, that is, after step S40, the following contents may also be included: generating a real-time network coverage map based on the network performance data and displaying it on the interactive interface; displaying the adjustment status of the smart router antenna on the interactive interface; storing the network performance data and changes in the network performance data for users to query through voice or gestures based on the interactive interface; generating and sending a network performance report based on the real-time network coverage map, the adjustment status of the smart router antenna and the network performance data.

[0081] For example, a graphical interface (i.e., interactive interface) can be provided to display real-time network coverage maps and the adjustment status of smart router antennas, support touch and voice control, and have historical data query and report generation functions. Specifically, this can include: graphical interface display, touch and voice control, historical data query, and report generation functions.

[0082] Graphical interface display: The interactive interface can adopt a layered design. The main view displays a real-time network coverage map. The network coverage map presents the signal strength distribution in the form of a heat map. The color gradient of the heat map ranges from blue (weak signal) to red (strong signal). Users can intuitively identify areas with strong and weak signal coverage. The real-time network coverage map supports zooming and dragging operations. Users can view the signal strength details of a specific area through the touch screen or mouse interaction of the interactive interface. The adjustment status of the smart router antenna can be displayed in the form of a dynamic icon, including the current antenna angle, adjustment direction, and adjustment progress. For omnidirectional antennas, its vertical angle and horizontal rotation angle are displayed. For directional antennas, its pointing direction and signal coverage range are displayed.

[0083] Touch Operation and Voice Control: The interactive interface supports multi-touch, allowing users to adjust the view or operate function buttons through gestures (such as pinching and swiping). For example, users can pinch to zoom in and out of the network coverage map or swipe to view historical data. The voice control function integrates natural language processing (NLP) technology, allowing users to query network status or adjust the angle of the smart router antenna through voice commands. For example, users can say "Show signal strength in the southeast area" or "Adjust the antenna angle to 30 degrees." Voice control supports multi-language recognition, ensuring convenient use for users of different languages.

[0084] Historical Data Query: Users can query historical data, selecting a specific time period (such as the past 24 hours, the past week, or a custom time period) to view network performance data. Query results are displayed in charts, including signal strength trends, connection stability trends, and user satisfaction scores. Historical data supports multi-dimensional filtering, allowing users to filter data by region, antenna type, or network load. For example, users can query "signal strength changes in conference room areas over the past week." Query results can be exported to CSV or Excel formats for further analysis.

[0085] Report Generation: Automated report generation is available, allowing users to generate daily, weekly, or monthly network performance reports. Reports include network coverage analysis, antenna adjustment logs, user feedback summaries, and optimization recommendations. Reports can be templated, allowing users to customize the content and format. For example, users can add a signal strength comparison chart for a specific area or an antenna adjustment effectiveness assessment. Generated reports can be exported in PDF format, allowing users to share them via email or print.

[0086] In a specific example, taking the environment where the smart router is located as a large office environment, a real-time network coverage map is displayed in a large office environment through a graphical interface (i.e., an interactive interface). Users can zoom and drag on the touch screen to view the signal strength of different areas. The antenna adjustment status is displayed in the form of a dynamic icon, and the user can see the vertical angle of the omnidirectional antenna and the pointing direction of the directional antenna. The user uses the voice command "Show the signal strength of the conference room area", and the system immediately highlights the signal strength heat map of the area on the interface. The user chooses to query historical data for the past week, and the signal strength change curve and connection stability trend are displayed in the form of a chart. Finally, the user generates a weekly network performance report, which includes network coverage analysis, antenna adjustment records and optimization suggestions, and exports it to PDF format and sends it to the IT department for further analysis.

[0087] In another embodiment, see Figure 2The present application also provides a smart router antenna angle adjustment system, which may include: an environment monitoring module 10, a preprocessing module 20, an analysis module 30, and an angle adjustment module 40; the environment monitoring module 10 is used to obtain environmental network data of the environment in which the smart router is located, the environmental network data including signal strength, user distribution, and obstacle location; the preprocessing module 20 is used to preprocess the environmental network data; the analysis module 30 is used to analyze the preprocessed environmental network data to identify areas where signals need to be enhanced; and the angle adjustment module 40 is used to adjust the angle of the smart router antenna based on the analyzed areas where signals need to be enhanced.

[0088] As an example, the intelligent router antenna angle adjustment system in this embodiment can be used to perform the following operations: Figure 1 And the method for adjusting the antenna angle of the smart router described in the related embodiments.

[0089] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features of the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for adjusting the antenna angle of an intelligent router, characterized in that: include: Obtaining environmental network data of the environment in which the smart router is located, wherein the environmental network data includes signal strength, user distribution, and obstacle location; Preprocessing the environmental network data; Analyze the pre-processed environmental network data to identify the area where the signal needs to be enhanced; Based on the analyzed signal enhancement area, the angle of the smart router antenna is adjusted.

2. The method according to claim 1, characterized in that Obtain environmental network data about the smart router's environment, including: Arrange sensors at different locations in the environment where the smart router is located; Receive the wireless signal transmitted by the smart router based on the sensor to obtain the signal strength at different locations; Detecting the number and location of user devices connected to the smart router based on sensors to obtain the user distribution; The position of the obstacle is obtained based on the time delay and strength of the reflected signal received by the sensor.

3. The method according to claim 1, characterized in that Analyze the pre-processed environmental network data to identify areas where signals need to be enhanced, including: The DBSCAN algorithm based on density clustering is used to identify signal-weak areas. The algorithm includes the following steps: calculating the number of points in the ϵ neighborhood of each data point, where ϵ is the neighborhood radius; marking a data point as a core point if the number of points in the ϵ neighborhood of a data point is greater than or equal to MinPts; grouping all core points and the points in their ϵ neighborhood into a cluster; and marking data points that are not grouped into any cluster as noise points. The areas corresponding to these noise points are the signal-weak areas. The K-means clustering algorithm is used to identify user-dense areas, including: randomly generating K initial cluster centers, where K is an integer greater than 1; assigning each data point to the nearest cluster center to obtain K clusters; recalculating the cluster center of each cluster; repeating the above steps until the cluster centers of each cluster no longer change, then the cluster centers of each cluster are the user-dense areas.

4. The method according to claim 3, characterized in that Analyze the pre-processed environmental network data to identify areas where the signal needs to be enhanced, including: The long short-term memory network is used to predict the network load change trend of the environment in which the intelligent router is located, including: Obtaining historical network load data of the environment in which the intelligent router is located to construct a time series data set; Training a long short-term memory network based on the time series dataset; The trained long short-term memory network is used to predict the network load change trend of the environment where the intelligent router is located in the future.

5. The method according to claim 1, wherein Based on the analyzed signal enhancement area, the angle of the smart router antenna is adjusted, including: The optimal angle is obtained based on the following formula: in, is the optimal angle, N is the number of monitoring points, is the weight of the i-th monitoring point, is the signal strength of each monitoring point i, is the target signal strength; The angle of the smart router antenna is adjusted based on the optimal angle.

6. The method according to claim 5, characterized in that Adjusting the angle of the smart router antenna based on the optimal angle includes: generating a pulse width modulated signal based on the optimal angle; Based on the pulse width modulation signal, a stepper motor is driven to drive the smart router antenna to rotate, so as to adjust the angle of the smart router antenna.

7. The method according to any one of claims 1 to 6, characterized in that After adjusting the angle of the smart router antenna based on the analyzed signal enhancement area, the method further includes: evaluating the network performance of the environment in which the smart router is located, and optimizing the angle of the smart router antenna, including: Obtaining network performance data of the environment in which the smart router is located, wherein the network performance data includes signal strength, connection stability, and user satisfaction; Preprocessing the network performance data; The comprehensive performance index of the evaluation network is obtained based on the following formula: Among them, P is the comprehensive performance index of the network, is the weight coefficient of signal strength, is the weight coefficient of connection stability, is the weight coefficient of user satisfaction, is the weight of the i-th monitoring point, N is the number of monitoring points, is the signal strength of the i-th monitoring point, is the connection stability of the i-th monitoring point, is the user satisfaction of the i-th monitoring point; According to the comprehensive network performance index, an optimization method based on a genetic algorithm is used to optimize and adjust the angle of the smart router antenna.

8. The method according to claim 7, characterized in that After evaluating the network performance of the smart router's environment and optimizing the angle of the smart router's antenna, the following steps are also required: Monitor changes in the environment where the smart router is located in real time. When changes in signal strength or user distribution are detected, perform network performance evaluation on the environment where the smart router is located and optimize the angle of the smart router antenna.

9. The method according to claim 7, characterized in that After adjusting the angle of the smart router antenna based on the analyzed signal enhancement area, the method further includes: Generate a real-time network coverage map based on the network performance data and display it on an interactive interface; Display the adjustment status of the smart router antenna on the interactive interface; storing the network performance data and changes in the network performance data for a user to query via voice or gestures based on an interactive interface; A network performance report is generated and sent based on the real-time network coverage map, the adjustment status of the smart router antenna and the network performance data.

10. A system for adjusting the antenna angle of an intelligent router, characterized in that: include: An environment monitoring module is used to obtain environmental network data of the environment in which the smart router is located, wherein the environmental network data includes signal strength, user distribution and obstacle location; A preprocessing module, used for preprocessing the environmental network data; An analysis module is used to analyze the pre-processed environmental network data to identify areas where signals need to be enhanced; The angle adjustment module is used to adjust the angle of the smart router antenna based on the analyzed signal enhancement area.

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