Link bandwidth allocation method and allocation system for multi-link aggregation of multi-mode mobile network

Abstract

The invention discloses a link bandwidth allocation method and system for multilink aggregation of a multimodal mobile network. The method comprises the following steps: acquiring a mobile network base station, an equipment position and real-time environment data; an intelligent power control algorithm is adopted, and transmission power is dynamically adjusted according to environment data and network loads; acquiring real-time signal intensity data and historical data from the network equipment to the base station; predicting a minimum transmission point of signal transmission based on real-time and historical data; adjusting the direction and position of an antenna according to the position of the base station, and designing connection by adopting a beam forming antenna; setting a threshold value according to the real-time weather and the minimum transmission point data, and deploying a signal enhancer; according to the link bandwidth allocation method and system for multi-link aggregation of the multi-modal mobile network, the link quality is monitored in real time, the network is dynamically adjusted, the network load is balanced through an intelligent algorithm, the signal transmission environment is evaluated in combination with real-time weather data, the signal enhancer is deployed, and the signal coverage range is expanded.

Description

Technical Field

[0001] The present invention belongs to the field of link bandwidth allocation for multi-link aggregation of mobile networks, and in particular relates to a link bandwidth allocation method and allocation system for multi-link aggregation of multi-modal mobile networks. Background Art

[0002] Multi-link aggregation in multimodal mobile networks is a method for improving the speed and reliability of data transmission on mobile devices. Its basic idea is to achieve faster data transmission and more stable connections by aggregating the bandwidth of multiple communication links at the same time. Multi-link aggregation combines multiple communication links together 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. Through aggregation, their total bandwidth is the sum of the bandwidths 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, the bandwidth of a 4G link can be The bandwidth of a 4G link may be 10Mbps, while the bandwidth of a Wi-Fi link may be 100Mbps; 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 the bandwidths of all individual links. For example, if a device is connected to two 10Mbps 4G links and one 100Mbps Wi-Fi link at the same time, the total bandwidth after aggregation can theoretically reach 120Mbps; multi-link aggregation can be used in a variety of application scenarios. For example, on mobile devices, users may want faster download speeds and lower latency; in environments with network congestion or poor signals, multi-link aggregation can provide a more stable connection.

[0003] However, different types of weather conditions, such as rain, snow, and strong winds, may interfere with the signal transmission of mobile networks. Certain weather conditions may cause refraction in the atmosphere, causing the signal to be deflected during propagation, thereby affecting the transmission distance and strength of the signal. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a link bandwidth allocation method and allocation system for multi-link aggregation in a multimodal mobile network, which develops more intelligent algorithms and strategies for real-time monitoring of link quality, dynamic selection of the best link, and balancing of network loads. In combination with real-time weather data, the signal transmission environment is evaluated in real time, signal enhancers are deployed, and signal coverage and quality are improved.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A link bandwidth allocation method for multi-link aggregation in a multi-modal mobile network, 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 to dynamically adjust the transmission power of link base stations and mobile devices in mobile network multi-link aggregation according to real-time environmental data and network load conditions;

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

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

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

[0012] Signal boosters are deployed based on thresholds set based on the real-time weather data and signal minimum transmission point data.

[0013] Preferably, the method for obtaining the link connection base station location information of the current mobile network multi-link aggregation, the mobile network device location information and the real-time environment data is as follows:

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

[0015] Get real-time temperature, humidity, and air pressure environmental data through the weather API.

[0016] As a preferred method, an intelligent power control algorithm is used to dynamically adjust the transmission power of link base stations and mobile devices in mobile network multi-link aggregation according to real-time environmental data and network load conditions:

[0017] Connect users' real-time traffic data to the central control system to collect environmental data and network load data in real time. The network load data includes network traffic, number of users, and signal strength.

[0018] Set up a power control machine learning algorithm for mobile networks to dynamically adjust the transmission power of mobile network base stations and mobile devices based on environmental data and network load conditions.

[0019] Preferably, the method for obtaining the real-time signal transmission strength data and the historical signal transmission strength transmission data of the link base station from the current location mobile network device to the mobile network multi-link aggregation is:

[0020] Obtain information about the base station to which the mobile device is connected and data on signal transmission strength through the API provided by the mobile network operator;

[0021] 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 at different locations near the mobile device;

[0022] The signal transmission strength data of mobile network devices is collected and recorded through the interface of the mobile network operator to obtain historical signal transmission strength data or the network performance monitoring tool, and the query and analysis functions of historical data are obtained.

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

[0024] Analyze the signal change trend and transmission strength in the past time by collecting real-time signal transmission strength data from mobile network devices to surrounding base stations and historical signal transmission strength data;

[0025] The machine learning model adopts a supervised learning algorithm, uses historical data to train the model, and inputs the data collected in real time into the model for prediction to predict the minimum transmission point of signal transmission strength.

[0026] Preferably, the direction and position of the antenna are adjusted according to the position of the link base station of the connected multi-link aggregation, and the method of connecting the mobile network base station to the mobile device using a beamforming antenna design is:

[0027] 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 location, the beamforming antenna is designed and configured based on the signal measurement and analysis results;

[0028] Adjust the antenna of the mobile device toward the base station according to the acquired latitude and longitude coordinates of the mobile network base station and the signal coverage range location information;

[0029] Place antennas at different locations and directions based on environmental data, test the signal strength at each location, and install fixed antennas using wall mounts and angle adjusters based on signal strength.

[0030] As a preferred method, the method of deploying the signal booster by setting a threshold value according to the acquired real-time weather data and the signal minimum transmission point data is:

[0031] Get real-time environmental data through the weather API and set the minimum signal transmission strength threshold from the obtained mobile network device to the base station;

[0032] Deploy signal repeaters around mobile network devices according to the minimum threshold of mobile network signal transmission strength. When environmental factors cause the mobile network signal transmission to fall below the threshold, trigger the signal repeater threshold and start the repeater to improve the signal quality and coverage of the mobile network device at the current location.

[0033] After deployment, real-time monitoring tests are conducted to dynamically adjust the signal booster based on real-time weather and signal quality data.

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

[0035] The data acquisition and processing module is used to obtain the current mobile network base station location information, mobile network device location information and real-time environmental data, obtain the real-time signal transmission strength data from the current mobile network device to the mobile network base station, and the historical signal transmission strength data from the base station to the mobile network device;

[0036] The central control system module is used to monitor the real-time network traffic data, track the number of users in the network, the utilization of network bandwidth, the delay and packet loss rate of data transmission, and make dynamic adjustments to the real-time network transmission usage by monitoring and analyzing the above indicators and data;

[0037] The signal transmission strength prediction module is used to compare the real-time signal transmission strength data of the current location with the historical data and establish a prediction model to predict the minimum transmission point of the signal transmission strength;

[0038] Antenna adjustment and beamforming design module, which is used to adjust the antenna direction and position of mobile network base stations, design and deploy beamforming antennas to achieve the connection between mobile network base stations and mobile devices, and dynamically adjust the antenna radiation pattern according to real-time signal conditions;

[0039] The signal booster deployment module is used to set the threshold and deploy the signal booster according to the acquired real-time weather data and the signal minimum transmission point data. When the signal transmission strength is lower than the threshold, the deployed signal booster improves the signal quality and coverage.

[0040] The system integration and optimization module integrates the above steps into a complete system to achieve real-time data collection, processing and control, and improve signal transmission efficiency, reduce interference and optimize antenna adjustment algorithms according to weather and environmental factors.

[0041] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a link bandwidth allocation method and allocation system for multi-link aggregation for a multimodal mobile network as described above are implemented.

[0042] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a link bandwidth allocation method and allocation system for multi-link aggregation in a multimodal mobile network.

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

[0044] By adopting intelligent power control algorithms and real-time environmental data, the system can dynamically adjust the transmission power of link base stations and mobile devices 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, the signal transmission path is optimized and the direction and position of the antenna are adjusted to maximize the signal transmission quality and coverage; by adopting beamforming antenna design connections and deploying signal boosters based on thresholds set according to real-time weather data and signal transmission quality data, the network capacity and coverage can be effectively improved, thereby improving the user's network experience; by optimizing signal transmission paths and adjusting power control, existing network resources can be more effectively utilized, network construction and operation costs can be reduced, and the sustainability and economy of the network can be improved; by improving signal transmission quality and coverage, optimizing network capacity and performance, improving users' network experience, reducing communication interruptions and data transmission delays, thereby enhancing user satisfaction and loyalty to the mobile network. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of a link bandwidth allocation method and allocation system for multi-link aggregation in a multi-modal mobile network. DETAILED DESCRIPTION

[0046] The principles and features of the present invention are described below, and the examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0048] Example

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

[0050] 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;

[0051] Adopt intelligent power control algorithm to dynamically adjust the transmission power of link base stations and mobile devices in mobile network multi-link aggregation according to real-time environmental data and network load conditions;

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

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

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

[0055] Signal boosters are deployed based on thresholds set based on the real-time weather data and signal minimum transmission point data.

[0056] By acquiring the real-time signal transmission strength data and historical data from the current mobile network device to the multi-link aggregation base station, the system can analyze and predict the minimum transmission point of the signal transmission strength. This can optimize the signal transmission path, ensure the optimization of signal transmission quality, and reduce signal attenuation and loss; using the intelligent power control algorithm, the system can dynamically adjust the transmission power of the multi-link aggregation link base station and mobile device according to the real-time environmental data and network load conditions. 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 location 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 to connect. This design can make the signal more concentrated and directional, improve the signal coverage and transmission efficiency; according to the real-time weather data and signal minimum transmission point data obtained, set the threshold and deploy the signal booster. This can timely adjust the signal transmission parameters under different weather conditions to ensure signal quality and network stability.

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

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

[0059] Get real-time temperature, humidity, and air pressure environmental data through the weather API.

[0060] Through the API provided by the mobile network operator, you can obtain the location information and signal coverage of the current mobile network base station, as well as the location information of the mobile device. These data are updated in real time and can reflect the current status of the network and the device; obtain real-time temperature, humidity, air pressure and other environmental data through the weather API. These data can reflect the current weather conditions, including the impact of temperature, humidity and other factors on signal transmission, and help adjust signal transmission parameters; by obtaining the location information of base stations and devices, more accurate link allocation and signal transmission optimization can be performed to ensure that the device is connected to the best base station, and dynamically adjust transmission parameters according to environmental data to optimize network performance and coverage.

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

[0062] Connect users' real-time traffic data to the central control system to collect environmental data and network load data in real time. The network load data includes network traffic, number of users, and signal strength.

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

[0064] By connecting users' real-time traffic data to the central control system and collecting environmental data and network load data in real time, real-time monitoring and analysis of network status can be achieved. This can timely detect network load conditions and environmental changes, providing real-time data support for power adjustment; machine learning algorithms are used for power control to dynamically adjust the transmission power of mobile network base stations and mobile devices based on environmental data and network load conditions. These algorithms can learn network status and environmental characteristics based on historical data and real-time data to achieve more accurate power adjustment, thereby improving network performance and efficiency; dynamically adjusting transmission power according to network load conditions can effectively allocate network resources, avoid network congestion and resource waste, and thus improve the overall performance and stability of the network.

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

[0066] Obtain information about the base station to which the mobile device is connected and data on signal transmission strength through the API provided by the mobile network operator;

[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 at different locations near the mobile device;

[0068] The signal transmission strength data of mobile network devices is collected and recorded through the interface of the mobile network operator to obtain historical signal transmission strength data or the network performance monitoring tool, and the query and analysis functions of historical data are obtained.

[0069] Through the API provided by the mobile network operator, you can obtain the base station information and real-time signal transmission strength data of the mobile device. These data can reflect the signal quality of the base station currently connected to the mobile device and provide a real-time reference for network optimization; using the API provided by the Android and iOS operating systems, you can obtain the signal transmission strength data of the base station currently connected to the mobile device at different locations. Through this data, you can understand the changes in the signal quality of the mobile device at different locations and provide users with a better network experience; through the interface or network performance monitoring tool provided by the mobile network operator, you can collect and record the historical signal transmission strength data of mobile network devices. These data can be used for historical data query and analysis, help understand the long-term trend of network performance, and provide a reference for network planning and optimization.

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

[0071] Analyze the signal change trend and transmission strength in the past time by collecting real-time signal transmission strength data from mobile network devices to surrounding base stations and historical signal transmission strength data;

[0072] The machine learning model adopts a supervised learning algorithm, uses historical data to train the model, and inputs the data collected in real time 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 from mobile network devices to surrounding base stations and historical signal transmission strength data, we can understand the changing trend of signal transmission strength, including identifying the pattern and periodic changes of signal transmission strength fluctuations, as well as possible seasonal or periodic changes. By using supervised learning algorithms, the signal transmission strength is modeled and trained using historical data. The machine learning model can capture the complex relationships and patterns in the data, so that it can predict the future signal transmission strength. Common machine learning algorithms include linear regression, decision trees, random forests, neural networks, etc. After the machine learning model is trained, the real-time collected data can be used to input the model for prediction. The model will predict the signal transmission strength at a certain point in the future based on the real-time data at the current location and the learned historical patterns. In this way, the minimum transmission point of the signal transmission strength can be predicted, that is, the location of the worst possible signal transmission situation at a certain point in the future. By predicting the minimum transmission point of the signal transmission strength, network operators can take corresponding measures to optimize network deployment and resource allocation.

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

[0075] 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 location, the beamforming antenna is designed and configured based on the signal measurement and analysis results;

[0076] Adjust the antenna of the mobile device toward the base station according to the acquired latitude and longitude coordinates of the mobile network base station and the signal coverage range location information;

[0077] Place antennas at different locations and directions based on environmental data, test the signal strength at each location, and install fixed antennas using wall mounts and angle adjusters based on signal strength.

[0078] By measuring and analyzing the signal strength, signal-to-noise ratio and multipath fading parameters of the currently connected mobile base station, the communication environment between the mobile device and the base station can be understood. Based on these analysis results, a beamforming antenna is designed and configured to adjust the radiation direction and shape of the antenna to optimize the signal reception and transmission effect. The beamforming antenna can dynamically adjust the radiation direction and shape according to the communication environment to make the signal more concentrated and directional, thereby improving the communication quality and transmission rate; according to the acquired latitude and longitude coordinates and signal coverage range location information of the mobile network base station, the antenna of the mobile device is adjusted to face the base station. By facing the antenna to the base station, the signal sent by the base station can be received to the greatest extent, thereby improving the signal reception quality and transmission efficiency; according to the environmental data, the antenna is placed at different positions and directions, and the signal strength of each position is tested. According to the test results, the wall bracket and angle adjuster are used to install and fix the antenna to ensure that the antenna is in the best position and direction so that it can receive the signal sent by the base station to the greatest 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 signal transmission effect is optimized, the communication quality and transmission rate are improved, and the signal interference and packet loss rate are reduced, thereby improving the user's network experience and service quality.

[0079] The method of deploying signal boosters by setting thresholds based on the acquired real-time weather data and the signal minimum transmission point data is as follows:

[0080] Get real-time environmental data through the weather API and set the minimum signal transmission strength threshold from the obtained mobile network device to the base station;

[0081] Deploy signal repeaters around mobile network devices according to the minimum threshold of mobile network signal transmission strength. When environmental factors cause the mobile network signal transmission to fall below the threshold, trigger the signal repeater threshold and start the repeater to improve the signal quality and coverage of the mobile network device at the current location.

[0082] After deployment, real-time monitoring tests are conducted to dynamically adjust the signal booster based on real-time weather and signal quality data.

[0083] Through the real-time acquisition of weather data and mobile network signal minimum transmission point data, we can understand the impact of the current environment on signal transmission. Using these data as a reference, we can set the minimum threshold of signal transmission strength. When environmental factors cause the mobile network signal transmission to be lower than the threshold, the signal repeater can be triggered to improve the signal quality and coverage of the mobile network equipment at the current location. According to the set minimum threshold of signal transmission strength, signal repeaters are deployed around the mobile network equipment. These repeaters can automatically start when the signal transmission strength is lower than the threshold according to changes in environmental factors and signal quality. By enhancing the signal transmission strength and coverage, mobile devices can obtain stable signal connections in any environment. After deploying the signal enhancer, real-time monitoring and testing are carried out to obtain real-time weather and signal quality data. According to these data, the working mode and parameters of the signal enhancer are dynamically adjusted to adapt to the signal transmission requirements in different environments. In severe weather conditions, the power of the signal enhancer is increased to ensure stable signal transmission.

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

[0085] The data acquisition and processing module is used to obtain the current mobile network base station location information, mobile network device location information and real-time environmental data, obtain the real-time signal transmission strength data from the current mobile network device to the mobile network base station, and the historical signal transmission strength data from the base station to the mobile network device;

[0086] The central control system module is used to monitor the real-time network traffic data, track the number of users in the network, the utilization of network bandwidth, the delay and packet loss rate of data transmission, and make dynamic adjustments to the real-time network transmission usage by monitoring and analyzing the above indicators and data;

[0087] The signal transmission strength prediction module is used to compare the real-time signal transmission strength data of the current location with the historical data and establish a prediction model to predict the minimum transmission point of the signal transmission strength;

[0088] Antenna adjustment and beamforming design module, which is used to adjust the antenna direction and position of mobile network base stations, design and deploy beamforming antennas to achieve the connection between mobile network base stations and mobile devices, and dynamically adjust the antenna radiation pattern according to real-time signal conditions;

[0089] The signal booster deployment module is used to set the threshold and deploy the signal booster according to the acquired real-time weather data and the signal minimum transmission point data. When the signal transmission strength is lower than the threshold, the deployed signal booster improves the signal quality and coverage.

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

[0091] By comprehensively utilizing multiple network links, including links between mobile network base stations and links between mobile devices and base stations, as well as possible other transmission media, effective allocation and utilization of bandwidth can be achieved. This approach can improve the overall bandwidth and stability of the network and reduce network congestion and latency. By obtaining the current location information of mobile network base stations and devices, real-time environmental data, and signal transmission strength data, the system can fully understand the network status and environmental conditions and provide data support for subsequent decision-making. By monitoring and analyzing real-time network traffic data, number of users, bandwidth utilization and other indicators, network resource allocation and usage can be dynamically adjusted according to network load and user demand to optimize network performance and user experience. By establishing a prediction model, the minimum transmission point of signal transmission strength can be predicted based on the comparison of real-time signal transmission strength data at the current location and historical data, so that measures can be taken in advance to enhance or adjust the signal. By adjusting the antenna direction and position of the mobile network base station and designing and deploying beamforming antennas, the signal transmission path can be optimized, the signal coverage and transmission efficiency can be improved, and the antenna radiation pattern can be dynamically adjusted according to the real-time signal conditions to further optimize signal transmission; thresholds are set according to real-time weather data and signal minimum transmission point data, and signal boosters are deployed to ensure stable transmission when the signal transmission strength is below the threshold, thereby improving signal quality and coverage; the above modules are integrated into a complete system to realize real-time data collection, processing and control, improve signal transmission efficiency and reduce interference according to weather and environmental factors, and optimize the antenna adjustment algorithm to achieve efficient network operation and optimize user experience.

[0092] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a link bandwidth allocation method and allocation system for multi-link aggregation in a multimodal mobile network as described above are implemented.

[0093] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method and system for allocating link bandwidth for multi-link aggregation in a multi-modal mobile network as described above are implemented.

[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 instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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 will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0096] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. A link bandwidth allocation method for multi-link aggregation in a multi-modal mobile network, characterized in that: Included are: 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; Adopt intelligent power control algorithm to dynamically adjust the transmission power of link base stations and mobile devices in mobile network multi-link aggregation according to real-time environmental data and network load conditions; Obtain real-time signal transmission strength data and historical signal transmission strength transmission data from 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 based on the real-time mobile network device at the current location to the link base station signal transmission strength data of the mobile network multi-link aggregation and the historical signal transmission strength data; Adjust the direction and position of the antenna according to the location of the link base station of the connected multi-link aggregation, and connect the mobile network base station to the mobile device using a beamforming antenna design; Signal boosters are deployed based on thresholds set based on the real-time weather data and signal minimum transmission point data.

2. The link bandwidth allocation method for multi-link aggregation of a multi-modal mobile network according to claim 1, characterized in that: The method for obtaining the link connection base station location information of the current mobile network multi-link aggregation, the mobile network device location information and the real-time environment data is as follows; Obtain the latitude and longitude coordinates of the current mobile network base station and the signal coverage location information through the API interface provided by the mobile network operator, and obtain the location information of the mobile network device using the GPS function on the mobile device or through the location service provided by the mobile network operator; Get real-time temperature, humidity, and air pressure environmental data through the weather API.

3. The link bandwidth allocation method for multi-link aggregation of a multi-modal mobile network according to claim 2, characterized in that: The method of dynamically adjusting the transmission power of the link base station and mobile device of the mobile network multi-link aggregation by using the intelligent power control algorithm according to the real-time environmental data and network load is as follows: Connect users' real-time traffic data to the central control system to collect environmental data and network load data in real time. The network load data includes network traffic, number of users, and signal strength. Set up a power control machine learning algorithm for mobile networks to dynamically adjust the transmission power of mobile network base stations and mobile devices based on environmental data and network load conditions.

4. The link bandwidth allocation method for multi-link aggregation in a multi-modal mobile network according to claim 3, characterized in that: The method for obtaining the real-time signal transmission strength data and the historical signal transmission strength transmission data of the link base station of the mobile network device at the current location to the mobile network multi-link aggregation is as follows: Obtain information about the base station to which the mobile device is connected and data on signal transmission strength through the API provided by the mobile network operator; 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 at different locations near the mobile device; The signal transmission strength data of mobile network devices is collected and recorded through the interface of the mobile network operator to obtain historical signal transmission strength data or the network performance monitoring tool, and the query and analysis functions of historical data are obtained.

5. The link bandwidth allocation method for multi-link aggregation in a multi-modal mobile network according to claim 4, characterized in that: The method for predicting the minimum transmission point of signal transmission strength based on the signal transmission strength data of the link base station from the current real-time mobile network device to the mobile network multi-link aggregation and the historical signal transmission strength data is: Analyze the signal change trend and transmission strength in the past time by collecting real-time signal transmission strength data from mobile network devices to surrounding base stations and historical signal transmission strength data; The machine learning model adopts a supervised learning algorithm, uses historical data to train the model, and inputs the data collected in real time into the model for prediction to predict the minimum transmission point of signal transmission strength.

6. The link bandwidth allocation method for multi-link aggregation in a multi-modal mobile network according to claim 5, characterized in that: The method of adjusting the direction and position of the antenna according to the location of the link base station of the connected multi-link aggregation 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 location, the beamforming antenna is designed and configured based on the signal measurement and analysis results; Adjust the antenna of the mobile device toward the base station according to the acquired latitude and longitude coordinates of the mobile network base station and the signal coverage range location information; Place antennas at different locations and directions based on environmental data, test the signal strength at each location, and install fixed antennas using wall mounts and angle adjusters based on signal strength.

7. The link bandwidth allocation method for multi-link aggregation in a multi-modal mobile network according to claim 6, characterized in that: The method of deploying signal boosters by setting thresholds based on the acquired real-time weather data and the signal minimum transmission point data is as follows: Get real-time environmental data through the weather API and set the minimum signal transmission strength threshold from the obtained mobile network device to the base station; Deploy signal repeaters around mobile network devices according to the minimum threshold of mobile network signal transmission strength. When environmental factors cause the mobile network signal transmission to fall below the threshold, trigger the signal repeater threshold and start the repeater to improve the signal quality and coverage of the mobile network device at the current location. After deployment, real-time monitoring tests are conducted to dynamically adjust the signal booster based on real-time weather and signal quality data.

8. A link bandwidth allocation system for multi-link aggregation in a multimodal mobile network, characterized in that: Included are: The data acquisition and processing module is used to obtain the current mobile network base station location information, mobile network device location information and real-time environmental data, obtain the real-time signal transmission strength data from the current mobile network device 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 the real-time network traffic data, track the number of users in the network, the utilization of network bandwidth, the delay and packet loss rate of data transmission, and make dynamic adjustments to the real-time network transmission usage by monitoring and analyzing the above indicators and data; The signal transmission strength prediction module is used to compare the real-time signal transmission strength data of the current location with the historical data and establish a prediction model to predict the minimum transmission point of the signal transmission strength; Antenna adjustment and beamforming design module, which is used to adjust the antenna direction and position of mobile network base stations, design and deploy beamforming antennas to achieve the connection between mobile network base stations and mobile devices, and dynamically adjust the antenna radiation pattern according to real-time signal conditions; The signal booster deployment module is used to set the threshold and deploy the signal booster according to the acquired real-time weather data and the signal minimum 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 steps into a complete system to achieve real-time data collection, processing and control, and improve signal transmission efficiency, reduce interference and optimize antenna adjustment algorithms according to weather and environmental factors.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the link bandwidth allocation method for multi-link aggregation of a multimodal mobile network as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the link bandwidth allocation method for multi-link aggregation in a multimodal mobile network as described in any one of claims 1 to 7 is implemented.

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