Intelligent network signal optimizing and switching method, system and equipment based on Android and medium

Abstract

The invention discloses a network signal intelligent optimization and switching method, system and device based on Android and a medium, belongs to the technical field of network signal optimization, and aims to solve the technical problem of how to realize more intelligent and automatic network signal optimization and switching according to various factors such as specific network conditions, user requirements and network quality. The technical scheme adopted by the invention is as follows: signal quality monitoring: monitoring the signal quality of each network connected with equipment in real time, and monitoring the signal quality of the current network through a hardware interface of Android equipment; user behavior analysis: analyzing user behavior characteristics and network requirements by collecting behavior data of the user so as to speculate the requirements of the user on the network; intelligent optimization: dynamically judging and optimizing network connection by utilizing a machine learning or rule engine technology according to the signal monitoring data and a user behavior analysis result, and selecting the most suitable network; and network switching: according to an optimization result, automatically selecting the most suitable network for switching.

Description

Technical Field

[0001] The present invention relates to the technical field of network signal optimization, and specifically to an intelligent network signal optimization and switching method, system, device, and medium based on Android. Background Art

[0002] With the popularization of smart phones, the usage frequency of mobile communication networks is getting higher and higher. As one of the most widely used smart phone operating systems, the Android operating system supports various mobile communication technologies, including but not limited to networks such as 2G, 3G, 4G, and 5G. However, there are certain deficiencies in the current Android system in terms of network signal management. Especially in a multi-network environment, how to intelligently switch network signals and optimize their quality has become an urgent problem to be solved.

[0003] Most of the existing network signal switching and optimization technologies rely on users to manually select networks or default switching rules preset by the system. The traditional network switching strategy mainly switches based on signal strength. This method does not take into account various factors such as the actual needs of users, data traffic usage, and network latency. As a result, in some cases, the device cannot maintain the best connection state in the most suitable network environment.

[0004] Therefore, how to achieve more intelligent and automated network signal optimization and switching according to various factors such as specific network conditions, user needs, and network quality is an urgent technical problem to be solved at present. Summary of the Invention

[0005] The technical task of the present invention is to provide an intelligent network signal optimization and switching method, system, device, and medium based on Android to solve the problem of how to achieve more intelligent and automated network signal optimization and switching according to various factors such as specific network conditions, user needs, and network quality.

[0006] The technical task of the present invention is realized in the following way. An intelligent network signal optimization and switching method based on Android is as follows:

[0007] Signal quality monitoring: Real-time monitor the signal quality of each network connected to the device, and monitor the signal quality of the current network through the hardware interface of the Android device;

[0008] User behavior analysis: By collecting the behavior data of users, analyze the user behavior characteristics and network requirements, so as to infer the network requirements of users; among them, the user behavior characteristics include the usage habits of users, the network requirements of application programs, and the current network load;

[0009] Intelligent Optimization: Based on signal monitoring data and user behavior analysis results, use machine learning or rule engine technology to dynamically judge and optimize network connections and select the most suitable network;

[0010] Network Switching: Automatically select the most suitable network for switching according to the optimization results; among them, the switching decision includes network type, network quality, and data requirements.

[0011] Preferably, the hardware interfaces of Android devices include Wi-Fi and mobile data interfaces;

[0012] The signal quality of the network includes signal strength, network latency, and data transmission rate;

[0013] Among them, the signal strength is used to judge the network quality by measuring the received signal strength;

[0014] The network latency is used to evaluate the network's response speed by measuring the latency time of data transmission;

[0015] The data transmission rate is used to evaluate the actual performance of the network by measuring the data upload and download rates.

[0016] Preferably, the user behavior analysis includes the following:

[0017] Whether the user is performing large-flow data transmission; among them, large-flow data transmission includes watching high-definition videos and downloading large files;

[0018] Whether the user is in specific application scenarios such as a call or a video conference.

[0019] Preferably, the intelligent optimization is as follows:

[0020] If the Wi-Fi network signal is lower than the set threshold and the user is making a high-definition video call, then select to switch to the 4G / 5G network;

[0021] If the user is using a low-bandwidth application, preferentially select the low-power 2G network.

[0022] Preferably, the network switching is specifically: automatically switch from the 3G network to the 4G or 5G network, or switch from mobile data to the Wi-Fi network; and during the switching process, avoid disconnection and data interruption.

[0023] More preferably, the method further includes user feedback, specifically: collecting the user's feedback data and adjusting the intelligent optimization strategy through the setting interface; the intelligent optimization strategy includes setting the preferentially selected network type and controlling the use of data traffic.

[0024] An Android-based intelligent network signal optimization and switching system, the system includes:

[0025] A signal monitoring module for real-time monitoring of the signal quality of each network connected to the device; the signal quality of the network includes signal strength, network latency, and data transmission rate;

[0026] A user behavior analysis module for analyzing user behavior characteristics and network requirements by collecting user behavior data, thereby inferring the user's network requirements; among them, user behavior characteristics include the user's usage habits, the network requirements of application programs, and the current network load;

[0027] An intelligent optimization module for dynamically judging and optimizing network connections according to signal monitoring data and user behavior analysis results, and selecting the most suitable network using machine learning or rule engine technology;

[0028] A network switching module for automatically selecting the most suitable network for switching according to the optimization result; among them, the switching decision includes network type, network quality, and data requirements;

[0029] A user feedback module for collecting user feedback data and adjusting the intelligent optimization strategy through the setting interface; among them, the intelligent optimization strategy includes setting the preferred network type and controlling the use of data traffic.

[0030] Preferably, the content analyzed by the user behavior analysis module is as follows:

[0031] ① Whether the user is performing large-flow data transmission; among them, large-flow data transmission includes watching high-definition videos and downloading large files;

[0032] ② Whether the user is in a specific application scenario such as a call or a video conference;

[0033] The optimization situation of the intelligent optimization module is as follows:

[0034] ① If the Wi-Fi network signal is lower than the set threshold and the user is making a high-definition video call, then select to switch to the 4G / 5G network;

[0035] ② If the user is using a low-bandwidth application, preferentially select the low-power 2G network;

[0036] The network switching module automatically switches from the 3G network to the 4G or 5G network, or from mobile data to the Wi-Fi network; and during the switching process, avoid disconnection and data interruption.

[0037] An electronic device, including: a memory and at least one processor;

[0038] Wherein, a computer program is stored on the memory;

[0039] The at least one processor executes the computer program stored in the memory, such that the at least one processor executes the Android-based intelligent optimization and switching method of network signal as described above.

[0040] A computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to implement the Android-based intelligent optimization and switching method of network signal as described above.

[0041] The Android-based intelligent optimization and switching method, system, device and medium of the present invention have the following advantages:

[0042] (1) The present invention can intelligently optimize network connections according to the actual needs of user devices and the quality of current network signals, and automatically select the best network signal for switching, thereby improving the network performance of the device and the user experience.

[0043] (2) By dynamically monitoring information such as network signal quality, network type, and user behavior, the present invention intelligently optimizes network connections and automatically switches to the best network state, thereby enhancing the user's network experience, solving the problems of non-intelligent and inflexible network signal switching in the prior art, and having a wide range of application prospects.

[0044] (3) By analyzing factors such as user behavior habits and network conditions of devices, the system of the present invention can intelligently optimize network connections, provide the most suitable network services, and avoid the trouble of users manually selecting networks.

[0045] (4) The present invention can automatically perform network switching according to real-time network conditions and user needs, avoiding network quality fluctuations caused by manual network switching.

[0046] (5) By optimizing network connections, the present invention improves the stability of the network and the data transmission rate, thereby enhancing the overall experience of users.

[0047] (6) Through intelligent optimization, the present invention can avoid using high-traffic networks when the network quality is poor, reduce unnecessary battery consumption, and extend the usage time of the device. Description of the Drawings

[0048] The present invention will be further described below with reference to the drawings.

[0049] Attached Figure 1 is a flowchart of the Android-based intelligent optimization and switching method of network signal. Detailed Embodiments

[0050] The following provides a detailed description of the Android-based intelligent network signal optimization and switching method, system, device, and medium of the present invention with reference to the accompanying drawings of the specification and specific embodiments.

[0051] Embodiment 1:

[0052] As shown in the Figure 1 accompanying drawings, this embodiment provides an Android-based intelligent network signal optimization and switching method, which is specifically as follows:

[0053] S1. Signal quality monitoring: Real-time monitor the signal quality of each network connected to the device, and monitor the signal quality of the current network through the hardware interface of the Android device;

[0054] S2. User behavior analysis: By collecting the user's behavior data, analyze the user's behavior characteristics and network requirements, so as to infer the user's network requirements; among them, the user's behavior characteristics include the user's usage habits, the network requirements of application programs, and the current network load;

[0055] S3. Intelligent optimization: According to the signal monitoring data and the results of user behavior analysis, use machine learning or rule engine technology to dynamically judge and optimize the network connection, and select the most suitable network;

[0056] S4. Network switching: Automatically select the most suitable network for switching according to the optimization results; among them, the switching decision includes network type, network quality, and data requirements.

[0057] The hardware interface of the Android device in step S1 of this embodiment includes Wi-Fi and mobile data interfaces.

[0058] The signal quality of the network in step S1 of this embodiment includes signal strength, network latency, and data transmission rate;

[0059] Among them, the signal strength judges the network quality by measuring the received signal strength;

[0060] The network latency evaluates the network response speed by measuring the data transmission latency;

[0061] The data transmission rate evaluates the actual performance of the network by measuring the data upload and download rates.

[0062] The user behavior analysis in step S2 of this embodiment includes the following contents:

[0063] ① Whether the user is performing large-flow data transmission; among them, large-flow data transmission includes watching high-definition videos and downloading large files;

[0064] ② Whether the user is in a specific application scenario such as a call or a video conference.

[0065] The intelligent optimization in step S3 of this embodiment is specifically as follows:

[0066] ① If the Wi-Fi network signal is lower than the set threshold and the user is making a high-definition video call, then select to switch to the 4G / 5G network;

[0067] ② If the user is using a low-bandwidth application, preferentially select the low-power 2G network.

[0068] The network switching in step S4 of this embodiment is specifically: automatically switch from the 3G network to the 4G or 5G network, or switch from mobile data to the Wi-Fi network; and during the switching process, avoid disconnection and data interruption.

[0069] This embodiment also includes user feedback, specifically: collect the feedback data of users, and adjust the intelligent optimization strategy through the setting interface; the intelligent optimization strategy includes setting the preferentially selected network type and controlling the use of data traffic.

[0070] Embodiment 2:

[0071] This embodiment provides an Android-based intelligent network signal optimization and switching system, which includes:

[0072] A signal monitoring module, which is used to monitor the signal quality of each network connected to the device in real time; the signal quality of the network includes signal strength, network latency, and data transmission rate;

[0073] A user behavior analysis module, which is used to analyze the user behavior characteristics and network requirements by collecting the user's behavior data, so as to infer the user's network requirements; among them, the user behavior characteristics include the user's usage habits, the network requirements of the application program, and the current network load;

[0074] An intelligent optimization module, which is used to dynamically judge and optimize the network connection according to the signal monitoring data and the user behavior analysis results, and select the most suitable network by using machine learning or rule engine technology;

[0075] A network switching module, which is used to automatically select the most suitable network for switching according to the optimization results; among them, the switching decision includes network type, network quality, and data requirements;

[0076] A user feedback module, which is used to collect the feedback data of users and adjust the intelligent optimization strategy through the setting interface; among them, the intelligent optimization strategy includes setting the preferentially selected network type and controlling the use of data traffic.

[0077] The content analyzed by the user behavior analysis module in this embodiment is as follows:

[0078] ① Whether the user is performing large - volume data transmission; where large - volume data transmission includes watching high - definition videos and downloading large files;

[0079] ② Whether the user is in a specific application scenario such as a call or a video conference.

[0080] The optimization situation of the intelligent optimization module in this embodiment is as follows:

[0081] ① If the Wi - Fi network signal is lower than the set threshold and the user is having a high - definition video call, then select to switch to the 4G / 5G network;

[0082] ② If the user is using a low - bandwidth application, preferentially select the low - power 2G network.

[0083] The network switching module in this embodiment automatically switches from the 3G network to the 4G or 5G network, or from mobile data to the Wi - Fi network; and during the switching process, avoid disconnection and data interruption.

[0084] Embodiment 3:

[0085] This embodiment also provides an electronic device, including: a memory and a processor;

[0086] Wherein, the memory stores computer - executable instructions;

[0087] The processor executes the computer - executable instructions stored in the memory, so that the processor executes the Android - based intelligent optimization and switching method of network signals in any embodiment of the present invention.

[0088] The processor can be a central processing unit (CPU), and can also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0089] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, the processor can implement various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include high-speed random access memory, and can also include non-volatile memory, such as hard disks, memory, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash memory cards, at least one magnetic disk storage period, flash memory devices, or other volatile solid-state storage devices.

[0090] Embodiment 4:

[0091] This embodiment also provides a computer-readable storage medium, which stores multiple instructions. The instructions are loaded by the processor to cause the processor to execute the Android-based network signal intelligent optimization and switching method in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, and software program codes for implementing the functions in any of the above embodiments are stored on the storage medium, and the computer (or CPU or MPU) of the system or device reads and executes the program codes stored in the storage medium.

[0092] In this case, the program code read from the storage medium itself can implement the functions in any of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0093] Examples of storage media for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0094] In addition, it should be clear that not only can the functions in any of the above embodiments be implemented by executing the program code read by the computer, but also by instructing the operating system or the like operating on the computer based on the program code to complete part or all of the actual operations.

[0095] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer. Subsequently, based on the instructions of the program code, the CPU or the like installed on the expansion board or the expansion unit is made to execute part or all of the actual operations, thereby implementing the functions of any one of the above embodiments.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent optimization and switching method for network signals based on Android, characterized in that, The method is as follows: Signal quality monitoring: Real-time monitor the signal quality of each network connected to the device, and monitor the signal quality of the current network through the hardware interface of the Android device; User behavior analysis: By collecting the user's behavior data, analyze the user behavior characteristics and network requirements, so as to infer the user's network requirements; among them, the user behavior characteristics include the user's usage habits, the network requirements of the application program, and the current network load; Intelligent optimization: According to the signal monitoring data and the results of user behavior analysis, use machine learning or rule engine technology to dynamically judge and optimize the network connection, and select the most suitable network; Network switching: Automatically select the most suitable network for switching according to the optimization results; among them, the switching decision includes network type, network quality, and data requirements.

2. The Android-based intelligent network signal optimization and switching method according to claim 1, wherein The hardware interface of the Android device includes Wi-Fi and mobile data interfaces; The signal quality of the network includes signal strength, network latency, and data transmission rate; Among them, the signal strength is used to judge the network quality by measuring the received signal strength; The network latency is used to evaluate the network response speed by measuring the latency time of data transmission; The data transmission rate is used to evaluate the actual performance of the network by measuring the data upload and download rates.

3. The Android-based intelligent network signal optimization and switching method according to claim 1, wherein The user behavior analysis includes the following: Whether the user is performing large-flow data transmission; among them, large-flow data transmission includes watching high-definition videos and downloading large files; Whether the user is in a specific application scenario such as a call or a video conference.

4. The Android-based intelligent network signal optimization and switching method according to claim 1, wherein The intelligent optimization is as follows: If the Wi-Fi network signal is lower than the set threshold and the user is making a high-definition video call, then select to switch to the 4G / 5G network; If the user is using a low-bandwidth application, preferentially select the low-power 2G network.

5. The Android-based intelligent optimization and switching method for network signals according to claim 1, characterized in that The network switching is specifically: Automatically switch from the 3G network to the 4G or 5G network, or switch from mobile data to the Wi-Fi network; and during the switching process, avoid disconnection and data interruption.

6. The Android-based intelligent network signal optimization and switching method according to any one of claims 1-5, characterized in that, The method also includes user feedback, specifically: Collect the user's feedback data and adjust the intelligent optimization strategy through the setting interface; the intelligent optimization strategy includes setting the preferentially selected network type and controlling the use of data traffic.

7. An Android-based intelligent network signal optimization and switching system, characterized in that The system includes: A signal monitoring module, which is used to real-time monitor the signal quality of each network connected to the device; the signal quality of the network includes signal strength, network latency, and data transmission rate; A user behavior analysis module, which is used to analyze the user behavior characteristics and network requirements by collecting the user's behavior data, so as to infer the user's network requirements; among them, the user behavior characteristics include the user's usage habits, the network requirements of the application program, and the current network load; An intelligent optimization module, which is used to dynamically judge and optimize the network connection according to the signal monitoring data and the results of user behavior analysis, and select the most suitable network by using machine learning or rule engine technology; A network switching module, which is used to automatically select the most suitable network for switching according to the optimization results; among them, the switching decision includes network type, network quality, and data requirements; A user feedback module, which is used to collect user feedback data and adjust the intelligent optimization strategy through a setting interface; wherein, the intelligent optimization strategy includes setting the preferentially selected network type and controlling the use of data traffic.

8. The Android-based intelligent network signal optimization and switching system according to claim 7, characterized in that, The content analyzed by the user behavior analysis module is as follows: ① Whether the user is performing large-flow data transmission; wherein, large-flow data transmission includes watching high-definition videos and downloading large files; ② Whether the user is in a specific application scenario such as a call or a video conference; The optimization situation of the intelligent optimization module is as follows: ① If the Wi-Fi network signal is lower than the set threshold and the user is making a high-definition video call, then select to switch to the 4G / 5G network; ② If the user is using a low-bandwidth application, preferentially select the low-power 2G network; The network switching module automatically switches from the 3G network to the 4G or 5G network, or from mobile data to the Wi-Fi network; and during the switching process, avoid disconnection and data interruption.

9. An electronic device, characterized in that, Comprising: A memory and at least one processor; Wherein, a computer program is stored on the memory; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the Android-based intelligent optimization and switching method of network signals according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and the computer program can be executed by a processor to implement the Android-based intelligent optimization and switching method of network signals according to any one of claims 1 to 6.

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