A link bandwidth allocation method and allocation system for multi-link aggregation of multi-modal mobile networks

Abstract

The application discloses a link bandwidth allocation method and allocation system for multi-mode mobile network multi-link aggregation, comprising: obtaining mobile network base station, device position, real-time environment data; using intelligent power control algorithm, dynamically adjusting transmission power according to environment data and network load; obtaining real-time signal strength data and historical data of network device to base station; predicting minimum transmission point of signal transmission based on real-time and historical data; adjusting antenna direction and position according to base station position, and designing connection using beamforming antenna; setting threshold value according to real-time weather and minimum transmission point data, and deploying signal enhancer; the application discloses a link bandwidth allocation method and allocation system for multi-mode mobile network multi-link aggregation, through intelligent algorithm, real-time monitoring of link quality, dynamic adjustment of network, balancing of network load, and combination of real-time weather data for evaluating signal transmission environment, deploying signal enhancer, and improving signal coverage range.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of link bandwidth allocation of multi-link aggregation of mobile networks, and particularly relates to a link bandwidth allocation method and system for multi-link aggregation of multi-modal mobile networks. BACKGROUND

[0002] Multi-link aggregation of multi-modal mobile networks is a method for improving the speed and reliability of data transmission of mobile devices; its basic idea is to aggregate the bandwidth of multiple communication links by using them simultaneously, thereby achieving faster data transmission and more stable connections; multi-link aggregation is the combination of multiple communication links to increase the total bandwidth; these links can come from different network technologies, such as Wi-Fi, mobile data (such as 4G, 5G), Bluetooth, etc.; by aggregation, their total bandwidth is the sum of the bandwidth of each link; link bandwidth is the amount of data that a single communication link can transmit per unit time, usually expressed in bits per second (bps), for example, a 4G link may have a bandwidth of 10 Mbps, while a Wi-Fi link may have a bandwidth of 100 Mbps; the link bandwidth of multi-link aggregation is the total bandwidth of all links when multiple links are aggregated together, which is usually equal to the sum of all individual link bandwidths, for example, if a device is connected to two 10 Mbps 4G links and a 100 Mbps Wi-Fi link simultaneously, the total bandwidth after aggregation can theoretically reach 120 Mbps; multi-link aggregation can be used in various application scenarios, for example, on mobile devices, users may want to obtain faster download speeds and lower latency; in network congestion or poor signal environment, multi-link aggregation can provide more stable connections.

[0003] However, different types of weather conditions, such as rain, snow, wind, etc., can interfere with the signal transmission of mobile networks, and certain weather conditions can cause refraction in the atmosphere, causing the signal to be deflected during transmission, thereby affecting the transmission distance and strength of the signal. SUMMARY

[0004] In view of the deficiencies in the prior art, the purpose of the present application is to provide a link bandwidth allocation method and system for multi-link aggregation of multi-modal mobile networks, which develops more intelligent algorithms and strategies for real-time monitoring of link quality, dynamic selection of optimal links, balancing of network load, and real-time evaluation of signal transmission environment in combination with real-time weather data, deployment of signal enhancers, and improvement of signal coverage range and quality.

[0005] The technical solution adopted by the application to solve its technical problems is:

[0006] A link bandwidth allocation method for multi-link aggregation of multi-modal mobile networks, comprising:

[0007] Obtain the link connection base station location information, mobile network device location information and real-time environmental data of the current mobile network multi-link aggregation;

[0008] Adopt intelligent power control algorithm, dynamically adjust the transmission power of the link base station and mobile device of the mobile network multi-link aggregation according to real-time environmental data and network load condition;

[0009] Obtain the real-time signal transmission strength data and historical signal transmission strength data of the current location mobile network device to the link base station of the mobile network multi-link aggregation;

[0010] Based on the real-time signal transmission strength data and historical signal transmission strength data of the current location mobile network device to the link base station of the mobile network multi-link aggregation, predict the minimum transmission point of signal transmission strength;

[0011] Adjust the direction and position of the antenna according to the connected multi-link aggregation link base station location, and adopt beamforming antenna design to connect the mobile network base station to the mobile device;

[0012] According to the obtained real-time weather data and signal minimum transmission point data, set threshold value to deploy signal enhancer.

[0013] As a preferred, the method for obtaining the link connection base station location information, mobile network device location information and real-time environmental data of the current mobile network multi-link aggregation is:

[0014] Through the API interface provided by the mobile network operator, obtain the latitude and longitude coordinates and signal coverage range location information of the current mobile network base station, and obtain the location information of the mobile network device by using the GPS function on the mobile device or through the location service provided by the mobile network operator;

[0015] Obtain real-time temperature, humidity, and atmospheric pressure environmental data through weather API.

[0016] As a preferred, the method for adopting intelligent power control algorithm, dynamically adjusting the transmission power of the link base station and mobile device of the mobile network multi-link aggregation according to real-time environmental data and network load condition is:

[0017] Connect the user real-time usage flow data to the central control system, real-time collect environmental data and network load data, and the network load data includes network flow, user quantity, and signal strength;

[0018] Set the power control machine learning algorithm for the mobile network, dynamically adjust the transmission power of the mobile network base station and mobile device according to the environmental data and network load condition.

[0019] As a preferred embodiment, the method for obtaining real-time signal transmission strength data of the link base station of the mobile network multi-link aggregation of the current location mobile network device and historical signal transmission strength data is as follows:

[0020] Through the API provided by the mobile network operator, the base station information and signal transmission strength data connected by the mobile device are obtained.

[0021] According to the location of the mobile device, the signal transmission strength data of the base station currently connected in different locations near the mobile device is obtained by using the API provided by the Android and iOS operating systems.

[0022] Through the interface or network performance monitoring tool provided by the mobile network operator, the signal transmission strength data of the mobile network device is collected and recorded, and the query and analysis functions of the historical data are obtained.

[0023] As a preferred embodiment, the method for predicting the minimum transmission point of signal transmission strength based on the real-time signal transmission strength data of the link base station of the mobile network multi-link aggregation of the current location mobile network device and the historical signal transmission strength data is as follows:

[0024] According to the real-time collection of signal transmission strength data of the mobile network device to the surrounding base station and the historical signal transmission strength data, the signal change trend and transmission strength in the past time are analyzed.

[0025] Using a machine learning model with a supervised learning algorithm, the model is trained with historical data, and the real-time collected data is input into the model for prediction to predict the minimum transmission point of signal transmission strength.

[0026] As a preferred embodiment, the method for adjusting the direction and position of the antenna according to the position of the connected multi-link aggregation link base station and designing the connection between the mobile network base station and the mobile device with beamforming antenna is as follows:

[0027] By measuring and analyzing the signal strength, signal-to-noise ratio, and multipath fading parameters of the current mobile device position connected to the mobile base station, according to the results of signal measurement and analysis, the beamforming antenna is designed and configured.

[0028] According to the latitude and longitude coordinates and signal coverage range position information of the obtained mobile network base station, the antenna of the mobile device is adjusted to face the base station.

[0029] According to the environmental data, the antenna is placed in different positions and directions, and the signal strength of each position is tested, and the antenna is installed and fixed by using a wall-mounted bracket and an angle adjuster according to the signal strength.

[0030] As a preferred embodiment, the method for setting a threshold to deploy a signal booster according to the obtained real-time weather data and signal minimum transmission point data is as follows:

[0031] Obtain real-time environmental data through weather API and set signal transmission strength minimum threshold value for mobile network device to base station signal strength minimum transmission point;

[0032] Deploy signal repeater around mobile network device according to mobile network signal transmission strength minimum threshold value, trigger signal repeater threshold value and start repeater to improve current location mobile network device signal quality and coverage when environmental factors cause mobile network signal transmission to be lower than threshold value;

[0033] Real-time monitoring test is conducted after deployment, and signal enhancer is dynamically adjusted according to real-time weather and signal quality data.

[0034] A link bandwidth allocation system for multi-modal mobile network multi-link aggregation, comprising:

[0035] A data acquisition and processing module for obtaining current mobile network base station location information, mobile network device location information and real-time environmental data, obtaining real-time signal transmission strength data of current location mobile network device to mobile network base station, and historical signal transmission strength data of base station to mobile network device;

[0036] A central control system module for monitoring real-time network traffic data, tracking number of users in network, utilization rate of network bandwidth, time delay and packet loss rate of data transmission, and dynamically adjusting real-time network transmission usage through monitoring and analyzing above indexes and data;

[0037] A signal transmission strength prediction module for comparing current location real-time signal transmission strength data with historical data, establishing prediction model to predict minimum transmission point of signal transmission strength;

[0038] An antenna adjustment and beamforming design module for adjusting antenna direction and position of mobile network base station, designing and deploying beamforming antenna, realizing connection between mobile network base station and mobile device, and dynamically adjusting antenna radiation mode according to real-time signal condition;

[0039] A signal enhancer deployment module for setting threshold value according to obtained real-time weather data and signal minimum transmission point data and deploying signal enhancer, which improves signal quality and coverage when signal transmission strength is lower than threshold value;

[0040] A system integration and optimization module for integrating above steps into a complete system, realizing real-time data acquisition, processing and control, and improving signal transmission efficiency, reducing interference and optimizing antenna adjustment algorithm according to weather environmental factors.

[0041] Another technical problem to be solved by the present application is to provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the link bandwidth allocation method and allocation system for multi-modal mobile network multi-link aggregation according to any one of the above.

[0042] Another technical problem to be solved by the present application is to provide a computer-readable storage medium having a computer program stored thereon, wherein the program is executable on a processor to implement the link bandwidth allocation method and allocation system for multi-modal mobile network multi-link aggregation.

[0043] The beneficial effects of the present application are:

[0044] By adopting intelligent power control algorithm and real-time environmental data, the system can dynamically adjust the transmission power of the link base station and mobile device to adapt to different environmental conditions and network load conditions, thereby improving the adaptability and performance of the network; by obtaining real-time signal transmission strength data and historical data, and based on the predicted minimum transmission point, optimizing the signal transmission path and adjusting the direction and position of the antenna to maximize the signal transmission quality and coverage; by adopting beamforming antenna design connection and setting threshold value deployment signal enhancer according to real-time weather data and signal transmission quality data, the capacity and coverage of the network can be effectively improved, thereby improving the network experience of users; by optimizing the signal transmission path and adjusting the power control, the existing network resources can be more effectively utilized, the network construction and operation cost can be reduced, and the sustainability and economy of the network can be improved; by improving the signal transmission quality and coverage, optimizing the network capacity and performance, and improving the network experience of users, the communication interruption and data transmission delay can be reduced, thereby enhancing the satisfaction and loyalty of users to the mobile network. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A flowchart of the link bandwidth allocation method and allocation system for multi-modal mobile network multi-link aggregation of the present application. DETAILED DESCRIPTION

[0046] The principles and features of the present application are described below, and the examples are only used to explain the present application and not to limit the scope of the present application. In the following paragraphs, the present application is described in more detail by way of example. The advantages and features of the present application will be more apparent from the following description and claims.

[0047] 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 application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0048] Embodiments

[0049] A link bandwidth allocation method for multi-mode mobile network multi-link aggregation, comprising:

[0050] Obtain the current mobile network multi-link aggregation link connection base station location information, mobile network device location information and real-time environment data;

[0051] Using intelligent power control algorithm, according to real-time environment data and network load, dynamically adjust the transmission power of mobile network multi-link aggregation link base station and mobile device;

[0052] Obtain the current location mobile network device to mobile network multi-link aggregation link base station real-time signal transmission strength data and historical signal transmission strength transmission data;

[0053] Based on the current location real-time mobile network device to mobile network multi-link aggregation link base station signal transmission strength data and historical signal transmission strength data, predict the minimum transmission point of signal transmission strength;

[0054] According to the connected multi-link aggregation link base station location, adjust the direction and position of the antenna, and connect the mobile network base station to the mobile device using beamforming antenna design;

[0055] According to the obtained real-time weather data and signal minimum transmission point data, set threshold value to deploy signal enhancer.

[0056] By obtaining real-time signal transmission strength data and historical data of the current mobile network device to the multi-link aggregation base station, the system can analyze and predict the minimum transmission point of signal transmission strength. This can optimize the signal transmission path, ensure the optimization of signal transmission quality, reduce signal attenuation and loss; using intelligent power control algorithm, the system can dynamically adjust the transmission power of the link base station and mobile device of multi-link aggregation according to real-time environmental data and network load. This can ensure that the network can still maintain stable signal transmission under different environmental conditions, while saving energy and reducing interference; according to the position of the connected multi-link aggregation link base station, the system can adjust the direction and position of the antenna, and use beamforming antenna design connection. This design can make the signal more concentrated and directional transmission, improve the signal coverage range and transmission efficiency; according to the obtained real-time weather data and signal minimum transmission point data, set threshold and deploy signal enhancer. This can timely adjust the signal transmission parameters under different weather conditions, ensure signal quality and network stability.

[0057] The method for obtaining the position information of the current mobile network multi-link aggregation link connection base station, the position information of the mobile network device and the real-time environmental data is as follows:

[0058] Through the API interface provided by the mobile network operator, the latitude and longitude coordinates of the current mobile network base station, the signal coverage range position information, the position information of the mobile network device is obtained by using the GPS function on the mobile device or through the location service provided by the mobile network operator;

[0059] Through the weather API, real-time temperature, humidity, air pressure environmental data is obtained.

[0060] Through the API interface provided by the mobile network operator, the position information of the current mobile network base station and the signal coverage range, as well as the position information of the mobile device can be obtained. These data are updated in real time, which can reflect the current state of the network and the device; through the weather API, real-time temperature, humidity, air pressure and other environmental data is obtained. These data can reflect the current weather conditions, including temperature, humidity and other factors affecting signal transmission, which helps to adjust signal transmission parameters; by obtaining the position information of the base station and the device, more accurate link allocation and signal transmission optimization can be carried out, ensuring that the device is connected to the best base station, and the transmission parameters are dynamically adjusted according to the environmental data to optimize network performance and coverage.

[0061] The method for dynamically adjusting the transmission power of the link base station and the mobile device of the mobile network multi-link aggregation by using intelligent power control algorithm according to real-time environmental data and network load is as follows:

[0062] Connect user real-time usage traffic data to the central control system, real-time collection of environmental data and network load data, network load data including network traffic, user number, signal strength;

[0063] Set up a power control machine learning algorithm for mobile networks, dynamically adjust the transmission power of mobile network base stations and mobile devices according to environmental data and network load conditions.

[0064] By connecting user real-time usage traffic data to the central control system, and real-time collection of environmental data and network load data, real-time monitoring and analysis of network status can be achieved. This can timely discover network load conditions and environmental changes, and provide real-time data support for power adjustment; use machine learning algorithms for power control, dynamically adjust the transmission power of mobile network base stations and mobile devices according to environmental data and network load conditions. These algorithms can learn network status and environmental characteristics according to historical data and real-time data to achieve more accurate power adjustment, thereby improving network performance and efficiency; dynamically adjust the transmission power according to the network load conditions, which can effectively allocate network resources, avoid network congestion and resource waste, and improve the overall performance and stability of the network.

[0065] The method for obtaining real-time signal transmission strength data and historical signal transmission strength data of the current position mobile network device to the mobile network multi-link aggregation link base station is as follows:

[0066] Through the API provided by the mobile network operator, obtain the base station information connected by the mobile device, signal transmission strength data;

[0067] According to the location of the mobile device, use the API provided by the Android and iOS operating systems to obtain the signal transmission strength data of the base stations currently connected in different locations near the mobile device;

[0068] Through the interface or network performance monitoring tool provided by the mobile network operator, collect and record the signal transmission strength data of the mobile network device, and obtain the query and analysis function of the historical data.

[0069] Through the API provided by the mobile network operator, the base station information connected by the mobile device and the real-time signal transmission strength data can be obtained. These data can reflect the current mobile device connected to the base station signal quality, providing real-time reference for network optimization; using the API provided by Android and iOS operating systems, the signal transmission strength data of the base station currently connected in different positions near the mobile device can be obtained, through these data, the signal quality change of the mobile device in different positions can be understood, and better network experience can be provided for users; through the interface or network performance monitoring tool provided by the mobile network operator, the historical signal transmission strength data of the mobile network device can be collected and recorded. These data can be used for historical data query and analysis, helping to understand the long-term trend of network performance, providing reference for network planning and optimization.

[0070] The method for predicting the minimum transmission point of signal transmission strength based on real-time mobile network device to mobile network multi-link aggregation link base station signal transmission strength data and historical signal transmission strength data at current location is:

[0071] According to the real-time collected signal transmission strength data of the mobile network device to the surrounding base station and the historical signal transmission strength data, the signal change trend and transmission strength in the past time are analyzed;

[0072] Using machine learning model, supervised learning algorithm is used to train the model with historical data, and the real-time collected data is input into the model for prediction, to predict the minimum transmission point of signal transmission strength.

[0073] By analyzing the real-time collected signal transmission strength data of the mobile network device to the surrounding base station and the historical signal transmission strength data, the change trend of signal transmission strength can be understood, which includes identifying the pattern and periodic change of signal transmission strength fluctuation, and the possible seasonal or periodic change; using supervised learning algorithm, the signal transmission strength is modeled and trained through historical data, machine learning model can capture the complex relationship and pattern in data, so as to predict the future signal transmission strength. Common machine learning algorithms include linear regression, decision tree, random forest, neural network, etc.; after the machine learning model is trained, the real-time collected data can be input into the model for prediction, the model will predict the signal transmission strength at a certain time point in the future based on the real-time data at current location and the historical pattern learned, through this way, the minimum transmission point of signal transmission strength can be predicted, that is, the position where the signal may appear the worst situation at a certain time point in the future; by predicting the minimum transmission point of signal transmission strength, the network operator can take corresponding measures to optimize network deployment and resource allocation.

[0074] The method for adjusting the direction and position of the antenna of the mobile network base station connected to the mobile device according to the link base station position of the connected multi-link aggregation is as follows:

[0075] By measuring and analyzing the signal strength, signal-to-noise ratio, and multipath fading parameters of the current mobile device position connected to the mobile base station, the beamforming antenna is designed and configured according to the results of signal measurement and analysis.

[0076] According to the obtained latitude and longitude coordinates and signal coverage range position information of the mobile network base station, the antenna of the mobile device is adjusted to face the base station.

[0077] According to the environmental data, the antenna is placed at different positions and directions, and the signal strength at each position is tested, and the antenna is installed and fixed with a wall-mounted bracket and an angle adjuster according to the signal strength.

[0078] By measuring and analyzing the signal strength, signal-to-noise ratio, and multipath fading parameters of the current mobile device position connected to the mobile base station, the communication environment between the mobile device and the base station can be understood, and the beamforming antenna is designed and configured according to the analysis results to adjust the radiation direction and shape of the antenna, thereby optimizing the reception and transmission effect of the signal. The beamforming antenna can dynamically adjust the radiation direction and shape according to the communication environment, making the signal more concentrated and directional, thereby improving the communication quality and transmission rate; according to the obtained latitude and longitude coordinates and signal coverage range position information of the mobile network base station, the antenna of the mobile device is adjusted to face the base station, so that the signal transmitted by the base station can be received to the maximum extent, thereby improving the reception quality and transmission efficiency of the signal; according to the environmental data, the antenna is placed at different positions and directions, and the signal strength at each position is tested, and the antenna is installed and fixed with a wall-mounted bracket and an angle adjuster according to the test results, so as to ensure that the antenna is in the best position and direction, so that it can receive the signal transmitted by the base station to the maximum extent; through the design of the beamforming antenna and the adjustment of the antenna direction, as well as the reasonable installation position and direction of the antenna, the transmission effect of the signal is optimized, the communication quality and transmission rate are improved, the signal interference and packet loss rate are reduced, thereby improving the network experience and service quality of the user.

[0079] The method for setting a threshold value for deploying a signal booster according to the obtained real-time weather data and signal minimum transmission point data is as follows:

[0080] By obtaining real-time environmental data through a weather API and the minimum signal strength transmission point of the mobile network device to the base station, the minimum signal transmission strength threshold is set.

[0081] According to the minimum threshold of mobile network signal transmission intensity, signal repeaters are deployed around mobile network devices. When environmental factors cause mobile network signal transmission to be lower than the threshold, the signal repeater threshold is triggered and the repeater is started to improve the signal quality and coverage of the current location mobile network device.

[0082] After deployment, real-time monitoring tests are conducted, and the signal booster is dynamically adjusted according to real-time weather and signal quality data.

[0083] Through real-time acquisition of weather data and mobile network signal minimum transmission point data, the current environmental impact on signal transmission can be understood. These data are used as a reference to set the minimum threshold of signal transmission intensity. When environmental factors cause mobile network signal transmission to be lower than the threshold, the signal repeater is triggered and started to improve the signal quality and coverage of the current location mobile network device. According to the set minimum threshold of signal transmission intensity, signal repeaters are deployed around mobile network devices. These repeaters can automatically start when the signal transmission intensity is lower than the threshold according to environmental factors and signal quality changes, and through enhancing the transmission intensity and coverage of the signal, stable signal connection of mobile devices in any environment is ensured. After deployment of the signal booster, real-time monitoring and testing are conducted to obtain real-time weather and signal quality data. According to these data, the working mode and parameters of the signal booster are dynamically adjusted to adapt to signal transmission requirements in different environments, and the power of the signal booster is increased in severe weather conditions to ensure stable signal transmission.

[0084] A link bandwidth allocation system for multi-modal mobile network multi-link aggregation, comprising:

[0085] A data acquisition and processing module for acquiring current mobile network base station location information, mobile network device location information and real-time environmental data, acquiring real-time signal transmission intensity data of the current location mobile network device to the mobile network base station, and historical signal transmission intensity data of the base station to the mobile network device;

[0086] A central control system module for monitoring real-time network traffic data, tracking the number of users in the network, the utilization rate of network bandwidth, the delay and packet loss rate of data transmission, and dynamically adjusting real-time network transmission usage through monitoring and analyzing the above indicators and data;

[0087] A signal transmission intensity prediction module for comparing current location real-time signal transmission intensity data with historical data, establishing a prediction model to predict the minimum transmission point of signal transmission intensity;

[0088] An antenna adjustment and beamforming design module is used to adjust the direction and position of the antennas of the mobile network base station, design and deploy beamforming antennas, realize the connection between the mobile network base station and the mobile device, and dynamically adjust the antenna radiation mode according to the real-time signal situation;

[0089] A signal enhancer deployment module is used to set a threshold according to the acquired real-time weather data and signal minimum transmission point data and deploy a signal enhancer, so that when the signal transmission strength is lower than the threshold, the deployed signal enhancer improves the signal quality and coverage range;

[0090] A system integration and optimization module integrates the above steps into a complete system to realize real-time data acquisition, processing and control, and improve signal transmission efficiency, reduce interference and optimize antenna adjustment algorithm according to weather environmental factors.

[0091] By comprehensively utilizing multiple network links, including links between mobile network base stations and links between mobile devices and base stations, and possibly other transmission media, effective allocation and utilization of bandwidth is achieved. This way can improve the overall bandwidth and stability of the network, reduce network congestion and delay; by acquiring the current location information of the mobile network base station and the device, real-time environmental data and signal transmission strength data, the system can comprehensively understand the state and environmental conditions of the network, providing data support for subsequent decision-making; monitoring and analyzing real-time network traffic data, user quantity, bandwidth utilization and other indicators, network resource allocation and use can be dynamically adjusted according to network load and user demand to optimize network performance and user experience; by establishing a prediction model, comparing the current location real-time signal transmission strength data and historical data, the minimum transmission point of the signal transmission strength is predicted, so that measures for signal enhancement or adjustment are taken in advance; by adjusting the direction and position of the antennas of the mobile network base station, designing and deploying beamforming antennas, the signal transmission path can be optimized, the signal coverage range and transmission efficiency can be improved, and the antenna radiation mode can be dynamically adjusted according to the real-time signal situation to further optimize signal transmission; according to the real-time weather data and signal minimum transmission point data, a threshold is set and a signal enhancer is deployed to ensure stable transmission when the signal transmission strength is lower than the threshold, improve the signal quality and coverage range; the above modules are integrated into a complete system to realize real-time data acquisition, processing and control, improve signal transmission efficiency, reduce interference, and optimize antenna adjustment algorithm to realize efficient operation of the network and optimize user experience.

[0092] The embodiment also provides an electronic device, including a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to realize the link bandwidth allocation method and allocation system for multi-modal mobile network multi-link aggregation as described above.

[0093] The embodiment also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the link bandwidth allocation method and the link bandwidth allocation system for multi-link aggregation of a multi-modal mobile network.

[0094] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0095] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the above-mentioned functions.

[0096] The above-mentioned embodiments of the present application are not a limitation on the protection scope of the present application, and the embodiments of the present application are not limited thereto. According to the above-mentioned content of the present application, other various forms of modifications, replacements or changes of the above-mentioned structure of the present application can be made according to ordinary technical knowledge and common means in the art without departing from the above-mentioned basic technical idea of the present application, and all of them should fall within the protection scope of the present application.

Claims

1. A link bandwidth allocation method for multi-link aggregation in multimodal mobile networks, characterized in that, Including: Obtain the location information of the base station, the location information of the mobile network device, and the real-time environmental data of the link connection in the current mobile network multi-link aggregation. The system employs an intelligent power control algorithm to dynamically adjust the transmission power of the base stations and mobile devices in the multi-link aggregation of the mobile network based on real-time environmental data and network load. Obtain real-time and historical signal transmission strength data of mobile network devices at the current location and link base stations of mobile network multi-link aggregation; Based on the current location, the minimum transmission point for predicting signal transmission strength is calculated using real-time mobile network device-to-mobile network multi-link aggregation link base station signal transmission strength data and historical signal transmission strength data. The direction and position of the antenna are adjusted according to the location of the base station of the multi-link aggregation link, and the mobile network base station to the mobile device is connected using a beamforming antenna design. Deploy signal boosters by setting thresholds based on the acquired real-time weather data and the minimum signal transmission point data; The method for obtaining the location information of the base station, the location information of the mobile network device, and the real-time environmental data of the link connection in the current mobile network multi-link aggregation is as follows: By using the API interface provided by the mobile network operator, the latitude and longitude coordinates and signal coverage location information of the current mobile network base station can be obtained, and the location information of the mobile network device can be obtained by using the GPS function on the mobile device or by using the location service provided by the mobile network operator. Obtain real-time temperature, humidity, and air pressure environmental data through the weather API; The method of dynamically adjusting the transmission power of base stations and mobile devices in multi-link aggregation of mobile networks using an intelligent power control algorithm based on real-time environmental data and network load conditions is as follows: Connect user real-time traffic data to the central control system to collect environmental data and network load data in real time. Network load data includes network traffic, number of users, and signal strength. A machine learning algorithm for power control in mobile networks is configured to dynamically adjust the transmission power of mobile network base stations and mobile devices based on environmental data and network load.

2. The link bandwidth allocation method for multi-link aggregation in a multimodal mobile network according to claim 1, characterized in that, The method for obtaining real-time signal transmission strength data and historical signal transmission strength data of the link base station from the current location mobile network device to the mobile network multi-link aggregation link is as follows: Obtain base station information and signal strength data of mobile devices by using APIs provided by mobile network operators; Based on the location of the mobile device, use the APIs provided by the Android and iOS operating systems to obtain the signal strength data of the currently connected base stations at different locations near the mobile device. Collect and record signal transmission strength data of mobile network devices through interfaces of mobile network operators or network performance monitoring tools, and obtain query and analysis functions for historical data.

3. The link bandwidth allocation method for multi-link aggregation in a multimodal mobile network according to claim 2, characterized in that, The method for predicting the minimum transmission point of signal transmission strength based on real-time mobile network device-to-mobile network multi-link aggregation link base station signal transmission strength data and historical signal transmission strength data is as follows: Based on real-time collection of signal transmission strength data from mobile network devices to surrounding base stations and historical signal transmission strength data, the signal change trend and transmission strength over a period of time are analyzed. The machine learning model employs a supervised learning algorithm, trains the model using historical data, and inputs real-time collected data into the model to predict the minimum transmission point of the signal transmission strength.

4. The link bandwidth allocation method for multi-link aggregation in a multimodal mobile network according to claim 3, characterized in that, The method for adjusting the antenna direction and position based on the location of the base station in the multi-link aggregation connection, and connecting the mobile network base station to the mobile device using a beamforming antenna design, is as follows: By measuring and analyzing the signal strength, signal-to-noise ratio, and multipath fading parameters of the mobile base station connected to the current mobile device, a beamforming antenna is designed and configured based on the results of the signal measurement and analysis. Adjust the orientation of the mobile device's antenna toward the base station based on the obtained latitude and longitude coordinates and signal coverage location information of the mobile network base station; Antennas were placed in different locations and directions based on environmental data, and the signal strength at each location was tested. The antennas were then fixed in place using wall-mounted brackets and angle adjusters based on the signal strength.

5. The link bandwidth allocation method for multi-link aggregation in a multimodal mobile network according to claim 4, characterized in that, The method for deploying signal boosters by setting thresholds based on acquired real-time weather data and minimum signal transmission point data is as follows: The system obtains real-time environmental data through the weather API and sets a minimum threshold for signal transmission strength at the point where the signal strength from the mobile network device to the base station is at its minimum. Based on the minimum threshold of mobile network signal transmission strength, signal repeaters are deployed around mobile network devices. When environmental factors cause the mobile network signal transmission to fall below the threshold, the signal repeater threshold is triggered and the repeater is activated to improve the signal quality and coverage of the mobile network device at the current location. After deployment, real-time monitoring and testing are conducted, and the signal booster is dynamically adjusted based on real-time weather and signal quality data.

6. A link bandwidth allocation system for multi-link aggregation in multimodal mobile networks, characterized in that, Including: The data acquisition and processing module is used to execute the method described in claim 1 for obtaining the location information of the link connection base station, the location information of the mobile network device, and the real-time environmental data of the current mobile network multi-link aggregation, and the method for dynamically adjusting the transmission power of the link base station and the mobile device of the mobile network multi-link aggregation according to the real-time environmental data and network load, so as to obtain the location information of the current mobile network base station, the location information of the mobile network device, and the real-time signal transmission strength data from the mobile network device at the current location to the mobile network base station, and the historical signal transmission strength data from the base station to the mobile network device; The central control system module is used to monitor real-time network traffic data, track the number of users in the network, network bandwidth utilization, data transmission latency and packet loss rate. By monitoring and analyzing real-time network traffic data, it tracks the number of users in the network, network bandwidth utilization, data transmission latency and packet loss rate, and makes dynamic adjustments to real-time network transmission usage. The signal transmission strength prediction module is used to compare the real-time signal transmission strength data of the current location with historical data and establish a prediction model to predict the minimum transmission point of the signal transmission strength. The antenna adjustment and beamforming design module is used to adjust the antenna direction and position of the mobile network base station, design and deploy beamforming antennas, realize the connection between the mobile network base station and the mobile device, and dynamically adjust the antenna radiation mode according to the real-time signal conditions. The signal booster deployment module is used to set a threshold and deploy signal boosters based on the acquired real-time weather data and the minimum signal transmission point data. When the signal transmission strength is lower than the threshold, the deployed signal booster improves the signal quality and coverage. The system integration and optimization module integrates the above modules into a complete system, enabling real-time data acquisition, processing, and control, and improving signal transmission efficiency, reducing interference, and optimizing antenna adjustment algorithms based on weather and environmental factors.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the link bandwidth allocation method for multi-link aggregation in a multimodal mobile network as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the link bandwidth allocation method for multi-link aggregation in a multimodal mobile network as described in any of claims 1-5.

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