Wireless channel switching method and device, computer device and storage medium

Abstract

The application belongs to the technical field of wireless channels, and relates to a wireless channel switching method and device, a computer device and a storage medium, which comprises the following steps: acquiring network performance data of a mobile terminal; if the network performance data is within a preset range, acquiring scene data of the mobile terminal; determining a current location type of the mobile terminal according to the scene data, wherein the current location type is a complex location or a simple location; selecting an adaptive channel corresponding to the current location type, and switching a current channel in which the mobile terminal is located to the adaptive channel. The application effectively improves the stability and transmission performance of the network after switching, reduces problems such as disconnection and delay caused by poor channel adaptability, and finally optimizes the wireless use experience of users.

Description

Technical Field

[0001] This application relates to the field of wireless channel technology, and in particular to a wireless channel switching method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the increasing popularity of wireless devices and the diversification of mobile scenarios, the complexity of network environments continues to rise. Wireless channel switching technology has become one of the core technologies to ensure the stability of device network connectivity and has been widely used in various wireless terminal devices such as smartphones and smart routers.

[0003] Existing wireless channel switching technologies are mostly based on single-dimensional triggering. For example, they may determine whether to switch channels solely based on signal strength (such as Received Signal Strength Indication (RSSI)) or by selecting a new channel based solely on the interference level of the current channel. Some technologies periodically perform network performance checks to assist in the switching decision. However, single-dimensional triggering of wireless channel switching is prone to timing errors or incorrect selection of the optimal channel, leading to network disconnections, increased latency, and failing to meet users' needs for stable network connectivity in complex scenarios, thus affecting the user experience.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this application is to provide a wireless channel switching method, apparatus, computer device, and storage medium to solve the technical problem that it is difficult to meet users' needs for stable network connectivity in wireless channel switching triggered by a single dimension.

[0006] To address the aforementioned technical problems, this application provides a wireless channel switching method, employing the following technical solution:

[0007] Obtain network performance data from mobile devices;

[0008] If the network performance data is within a preset range, then the scene data of the mobile terminal is obtained;

[0009] Based on the scene data, the current location type of the mobile device is determined, which is either a complex location or a simple point.

[0010] Select the appropriate channel corresponding to the current location type, and switch the current channel of the mobile device to the appropriate channel.

[0011] Furthermore, selecting the appropriate channel corresponding to the current location type includes:

[0012] If the current location type is the complex location, then the scene data and the network performance data are input into the preset channel prediction model, and the first candidate channel is output through the channel prediction model;

[0013] Verify the first candidate channel;

[0014] If the first candidate channel passes the verification, then the first candidate channel will be used as the adapted channel.

[0015] Furthermore, the step of inputting the scene data and the network performance data into a preset channel prediction model, and outputting a first candidate channel through the channel prediction model, includes:

[0016] By using the channel prediction model, feature extraction is performed on the scene data and the network performance data to obtain comprehensive feature data;

[0017] Based on the comprehensive feature data, a channel prediction set is determined;

[0018] The first candidate channel is obtained by filtering the predicted channel set.

[0019] Furthermore, selecting the appropriate channel corresponding to the current location type includes:

[0020] If the current location type is the simple point, then obtain multiple wireless channels;

[0021] Detect real-time interference and channel usage frequencies of multiple wireless channels;

[0022] Based on the real-time interference situation and the channel usage frequency, a second candidate channel is determined;

[0023] Verify the second candidate channel;

[0024] If the second candidate channel passes the verification, then the second candidate channel will be used as the adapted channel.

[0025] Furthermore, the scene data includes signal characteristics, time, number of surrounding hotspots, and current location information. Determining the current location type of the mobile terminal based on the scene data includes:

[0026] Based on the signal characteristics, the time, the number of surrounding hotspots, and the current location information, an initial location type is selected from a preset location type library;

[0027] In response to a correction command triggered by the user based on the mobile device, the initial location type is corrected according to the correction command to obtain the current location type.

[0028] Furthermore, the scene data includes the current location information of the mobile device, and obtaining the scene data of the mobile device includes:

[0029] Detect whether the mobile device has a Global Positioning System (GPS) signal;

[0030] If the Global Positioning System (GPS) signal is present, the latitude and longitude coordinates of the mobile terminal are obtained based on the GPS signal.

[0031] The latitude and longitude coordinates are encoded to obtain the current location information of the mobile terminal;

[0032] If the GPS signal is not available, a base station signal or a wireless hotspot is obtained, and the current location information of the mobile device is determined based on the base station signal or the wireless hotspot.

[0033] Furthermore, switching the current channel of the mobile device to the adapted channel includes:

[0034] Obtain the preset authentication key for the adapted channel;

[0035] The mobile device is verified based on the authentication key;

[0036] If the mobile device passes the verification, the current channel of the mobile device will be switched to the adapted channel.

[0037] To address the aforementioned technical problems, this application also provides a wireless channel switching device, which employs the following technical solution:

[0038] A wireless channel switching device, comprising:

[0039] The first acquisition module is used to acquire network performance data of the mobile device;

[0040] The second acquisition module is used to acquire the scene data of the mobile terminal if the network performance data is within a preset range.

[0041] The determination module is used to determine the current location type of the mobile terminal based on the scene data, wherein the current location type is a complex location or a simple point;

[0042] The switching module is used to select the appropriate channel corresponding to the current location type and switch the current channel of the mobile device to the appropriate channel.

[0043] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0044] A computer device includes a memory and a processor, the memory storing computer-readable instructions, the processor executing the computer-readable instructions to implement the steps of the wireless channel switching method described above.

[0045] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0046] A computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the wireless channel switching method described above.

[0047] Compared with the prior art, this application has the following main advantages:

[0048] The wireless channel switching method disclosed in this application can obtain network performance data to perceive the current network quality status in real time and accurately, providing an objective and reliable trigger basis for subsequent judgment on whether channel switching is necessary. When the network performance data is within a preset range, it obtains scene data from the mobile terminal, which can accurately obtain environmental correlation data when needed and reduce unnecessary location resource consumption (such as device power and network traffic), thereby improving device operating efficiency. Determining the current location type based on environmental data can deeply correlate channel selection with specific environmental scenarios, avoiding the problem that traditional channel selection based solely on single network data cannot adapt to the differences in signal characteristics of different locations, making channel selection more scenario-specific. Selecting and switching the corresponding suitable channel based on the current location type can ensure that the switched channel is highly matched with the network environment of the current location, effectively improving the stability and transmission performance of the network after switching, reducing problems such as disconnection and latency caused by poor channel compatibility, and ultimately optimizing the user's wireless experience. Attached Figure Description

[0049] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0051] Figure 2 This is a flowchart of one embodiment of the wireless channel switching method according to this application;

[0052] Figure 3 This is a schematic diagram of one embodiment of the wireless channel switching device according to this application;

[0053] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0054] 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 pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, the system architecture 100 may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0058] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0059] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 (Moving Picture Experts Group Audio Layer Ⅲ) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptops, and desktop computers, etc.

[0060] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

[0061] It should be noted that the wireless channel switching method provided in this application embodiment is generally executed by the terminal device, and correspondingly, the wireless channel switching device is generally installed in the terminal device.

[0062] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0063] Continue to refer to Figure 2 A flowchart of an embodiment of the wireless channel switching method according to this application is shown. The wireless channel switching method includes the following steps:

[0064] Step S201: Obtain network performance data from the mobile device.

[0065] In this embodiment, the wireless channel switching method operates on an electronic device (e.g., Figure 1 The terminal device shown can send or receive data via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, Wi-Fi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wide band) connections, and other currently known or future wireless connection methods.

[0066] In this embodiment, network performance data reflects the transmission capability and stability of the wireless network, as well as the quantitative data that measures the strength of wireless signals (such as Wi-Fi and cellular network signals). This includes latency, throughput, jitter, and signal strength. Latency is obtained through a packet ping test and is the round-trip time for network data to be sent from the device to the receiver. Throughput is obtained through a speed test and is the amount of data the network can transmit per unit time. Jitter is the fluctuation difference in latency within different data transmission cycles, directly affecting network stability. The strength of the wireless signal currently received by the device can be reflected by the RSSI value. This can be collected in real time by the device's hardware sensors, such as using the device's built-in signal receiving sensors (such as the RSSI detection function built into Wi-Fi or cellular network modules) to continuously capture the strength of surrounding wireless signals and convert it into a quantifiable RSSI value.

[0067] Step S202: If the network performance data is within a preset range, then obtain the scene data of the mobile terminal.

[0068] In this embodiment, the preset range can be latency ≥100ms, throughput ≤5Mbps, jitter ≥20ms, and RSSI value ≤-70dBm. When the latency is greater than or equal to 100ms, real-time interactions (such as video calls and online games) will experience noticeable stuttering; when the throughput is less than or equal to 5Mbps, operations such as loading high-definition videos and downloading files will experience buffering or slowness; when the jitter is greater than or equal to 20ms, network stability decreases, leading to intermittent voice calls and inconsistent video playback speeds; when the RSSI value is less than or equal to -70dBm, the wireless signal strength is insufficient, causing frequent network connection drops for devices (such as mobile phones and computers), increased data transmission packet loss rate, and significantly exacerbating problems such as video playback stuttering and slow webpage loading. When any of the above four indicators falls within the specified range, it can be determined that the current network condition is poor, triggering a channel switching process.

[0069] For example, scene data includes current location information, signal characteristics, time, and the number of surrounding hotspots. Current location information is obtained through Global Positioning System (GPS) signals, Wi-Fi hotspots, or base station signals, which can be used to estimate the device's location. Signal characteristics are acquired by the device's hardware scanning of surrounding wireless signals. This involves scanning the signal strength (e.g., RSSI value), frequency band (2.4GHz / 5GHz), and specific channel (e.g., 2.4GHz channels 1 / 6 / 11) of nearby Wi-Fi hotspots, as well as the signal strength and frequency band type of mobile communication base stations (2G / 4G / 5G). Integrating these key attributes of wireless signals forms the signal characteristics. Time is automatically collected by the device's real-time clock module, including specific time periods (e.g., morning peak 8:00-10:00, off-peak 14:00-16:00), weekdays / holidays, and other time-related data. The number of surrounding hotspots is calculated by counting all detectable Wi-Fi hotspots in the current environment.

[0070] Step S203: Based on the scene data, determine the current location type of the mobile terminal, wherein the current location type is a complex location or a simple point.

[0071] In this embodiment, a complex location refers to a scenario with dense wireless signals, numerous interference sources, and large fluctuations in network load, such as a commercial scenario, a transportation hub scenario, or an office scenario; a simple location refers to a scenario with sparse wireless signals, few interference sources, and stable network load, such as an outdoor recreational scenario, a low-density residential scenario, or an open outdoor scenario. The type of the current location can be determined through machine learning classification models such as decision trees, or it can be determined directly based on feature matching.

[0072] For example, the current location information is input through a decision tree model, and the current location type is output. Specifically, the model judges the environmental data layer by layer according to a preset feature priority (from the number of surrounding hotspots to signal features, then time, and finally the current location information). When the environmental data has completed all levels of splitting and reaches the leaf node of the decision tree, the model directly outputs the current location type, which can be a simple point or a complex location, providing a basis for environmental type adaptation for subsequent intelligent channel switching.

[0073] Step S204: Select the appropriate channel corresponding to the current location type and switch the current channel of the mobile device to the appropriate channel.

[0074] In this embodiment, if the current location is a complex location, environmental data (such as the latitude and longitude of the 3rd floor of the shopping mall, signal characteristics of 15 surrounding Wi-Fi hotspots) and network performance data (such as current latency of 120ms and bandwidth of 4Mbps) are input into a preset channel prediction model. The model determines the first candidate channel that meets the conditions based on historical data (such as the stability record of the 5GHz channel during the morning peak in the shopping mall). The first candidate channel is then verified to obtain the suitable channel. If the current location is a simple location, all available wireless channels in the current environment are first scanned. Then, the real-time interference of each channel and the channel usage frequency are detected. Based on this, the second candidate channel with the least interference and the lowest usage frequency is selected. The second candidate channel is then verified to obtain the suitable channel. Finally, a fast roaming protocol (such as 802.11r) or dual-band synchronization technology is used to complete the channel switching within milliseconds to ensure that user devices (such as mobile phones) do not experience any disconnection.

[0075] This application acquires network performance data to accurately and in real-time perceive the current network quality status, providing an objective and reliable trigger for subsequent channel switching decisions. When network performance data is within a preset range, it acquires mobile scenario data, enabling accurate acquisition of environmental data when needed while reducing unnecessary location resource consumption (such as device battery and network traffic), thus improving device operating efficiency. Determining the current location type based on environmental data allows for deep association between channel selection and specific environmental scenarios, avoiding the problem of traditional channel selection relying solely on single network data failing to adapt to different location signal characteristics, making channel selection more scenario-specific. Selecting and switching to the corresponding suitable channel based on the current location type ensures a high degree of match between the switched channel and the current location's network environment, effectively improving network stability and transmission performance after switching, reducing disconnections and latency caused by poor channel compatibility, and ultimately optimizing the user's wireless experience.

[0076] In some optional implementations of this embodiment, the step of selecting the adaptation channel corresponding to the current location type includes:

[0077] If the current location type is the complex location, then the scene data and the network performance data are input into the preset channel prediction model, and the first candidate channel is output through the channel prediction model;

[0078] Verify the first candidate channel;

[0079] If the first candidate channel passes the verification, then the first candidate channel will be used as the adapted channel.

[0080] In this embodiment, the channel prediction model is a pre-defined machine learning model, which can be constructed using a regression model. This model needs to learn the mapping relationship between scenarios, network states, and channel performance through historical training phases (for example, in a complex location during morning rush hour, when latency exceeds the standard, a specific 5GHz channel has better stability). It can predict the performance of each available channel in the input data and output a quantified prediction result. The first candidate channel is the channel with the best predicted performance output by the channel prediction model for complex location scenarios.

[0081] For example, scene data and network performance data are standardized. The current location information in the environmental data is converted into regional attribute numerical features, time is mapped to time period labels, and the number of surrounding hotspots is retained. Indicators such as latency and bandwidth in the network performance data are normalized into numerical features, and whether they are within a preset range is converted into binary features, ensuring the data format is compatible with the channel prediction model. The preprocessed data is input into the preset channel prediction model. The model calls historical channel performance data (including historical stability and handover success rate of each channel) from the cloud that matches the current complex location type, and combines this with the current data to perform performance prediction. Finally, the channel with the best overall predicted performance is output as the first candidate channel. Finally, to ensure channel availability, the selected first candidate channel is verified. Specifically, the model controls the device to briefly connect to the first candidate channel, and the signal stability (e.g., number of disconnections within 10 seconds), real-time interference (signal overlap between adjacent channels), and the number of currently connected devices are monitored in real time. If the test results meet the following conditions: no disconnection within 10 seconds, signal overlap between adjacent channels ≤ 20%, and number of access devices ≤ 60% of the current channel's maximum load, then the verification is considered successful, and the first candidate channel becomes the final adapted channel. If the test results do not meet the above conditions, the model is re-triggered to predict and filter the remaining available channels until a verified adapted channel is obtained.

[0082] This application outputs the optimal candidate channel through a channel prediction model that integrates historical scenarios and current data. The model is verified in real time to ensure stable signal, low interference, and reasonable load. This not only improves the accuracy of channel selection in complex locations but also avoids prediction bias, effectively ensuring the reliability of network connectivity and user experience in complex environments.

[0083] In some optional implementations of this embodiment, the step of inputting the scene data and the network performance data into a preset channel prediction model and outputting a first candidate channel through the channel prediction model includes:

[0084] By using the channel prediction model, feature extraction is performed on the scene data and the network performance data to obtain comprehensive feature data;

[0085] Based on the comprehensive feature data, a channel prediction set is determined;

[0086] The first candidate channel is obtained by filtering the predicted channel set.

[0087] In this embodiment, the input environmental data and network performance data are first standardized. Specifically, the current location information in the environmental data is geocoded into regional attribute labels (e.g., commercial areas, transportation hubs) and mapped to numerical features. Signal features such as the proportion of 5GHz band hotspots (number of scanned 5GHz hotspots / total number of hotspots) and average signal strength are extracted and normalized to a 0-1 range. The current time is mapped to a time period label (e.g., morning peak, evening peak, off-peak). The count of surrounding hotspots is directly retained to form an environmental feature group. For network performance data, operations such as latency and bandwidth are extracted. The latency (e.g., 120ms) is normalized to a 0-1 range. Whether the bandwidth is ≤5Mbps, the latency is ≥100ms, and the signal strength is ≤-70dBm are converted into binary features (set to 1 within a preset range, 0 outside the preset range). Simultaneously, the original RSSI value is retained and normalized to form a network performance feature group.

[0088] For example, the preprocessed environmental feature set and network performance feature set are fused with historical data features in the model (such as the average stability score of a channel during morning rush hour in complex locations) to construct a structured feature matrix. This matrix includes scene features (complex location type and time period label), historical performance features (stability / handover success rate), and current network features (latency / bandwidth / signal strength). Based on the mapping relationship between network status and channel performance in complex locations learned during the historical training phase, the model predicts the performance of each available channel in the feature matrix. Through linear regression calculation, the core performance prediction value of each channel for a specified time period in the future (e.g., within 3 minutes) is output, specifically including the predicted stability score, predicted latency value, and predicted uninterrupted connection duration, quantifying the subsequent performance of each channel. The model sorts the prediction results of all available channels according to the criterion of optimal prediction performance, prioritizing the channel with the highest predicted stability score, the lowest predicted latency value, and the longest predicted uninterrupted connection duration, and identifies this channel as the first candidate channel.

[0089] This application clarifies the mapping relationship between data and channels, generates a channel prediction set by combining multi-dimensional features, and accurately filters channels, making the selection of the first candidate channel more targeted and scientific. It not only fully explores the correlation between scenarios, network conditions, and channel performance, but also ensures optimal results through set filtering, further improving the accuracy of channel selection and network stability in complex environments.

[0090] In some optional implementations of this embodiment, the step of selecting the adaptation channel corresponding to the current location type includes:

[0091] If the current location type is the simple point, then obtain multiple wireless channels;

[0092] Detect real-time interference and channel usage frequencies of multiple wireless channels;

[0093] Based on the real-time interference situation and the channel usage frequency, a second candidate channel is determined;

[0094] Verify the second candidate channel;

[0095] If the second candidate channel passes the verification, then the second candidate channel will be used as the adapted channel.

[0096] In this embodiment, when the location is determined to be a simple point (such as a suburban park with sparse surrounding wireless signals and few interference sources) through the location identification process, the built-in wireless signal scanning module automatically scans all accessible wireless channels in the current environment, ultimately obtaining a list of multiple available channels, such as channels 1, 6, and 11 in the 2.4GHz band, and channels 149 and 153 in the 5GHz band. Real-time data detection is performed on the five available channels. Specifically, the signal overlap between adjacent channels is detected using a signal analysis algorithm (lower overlap means less interference); the number of currently accessed devices on each channel is also obtained (fewer devices mean lower usage frequency and lighter network load). Then, the detection results are sorted and filtered according to the requirements of low interference and low load. For example, the first filtering is based on the primary condition that the signal overlap between adjacent channels is ≤20%, and then the filtered channels are further sorted according to the fewest number of accessed devices. After comprehensive judgment, the channel with the highest ranking is selected as the second candidate channel. Finally, the device performs a brief access verification of 15 seconds on the second candidate channel to verify its actual compatibility. If there are zero disconnections within 15 seconds, the signal stability requirement is met. If the continuous change in the number of connected devices does not exceed the channel's maximum load, the load controllability requirement is met. When both verification indicators are met, the second candidate channel is deemed to have passed the verification and is ultimately determined as the suitable channel for the current simple point. If the verification fails (e.g., due to one disconnection), the remaining candidate channels are re-verified until a suitable channel that meets the standard is obtained.

[0097] This application directly acquires wireless channels and detects real-time interference and usage frequencies. Combined with verification and screening of suitable channels, it not only simplifies the process to suit the characteristics of simple point environments, but also quickly locks low-interference, low-load channels, ensuring network stability and improving connection efficiency and user experience.

[0098] In some optional implementations of this embodiment, the scene data includes signal characteristics, time, number of surrounding hotspots, and current location information. The step of determining the current location type of the mobile terminal based on the scene data includes:

[0099] Based on the signal characteristics, the time, the number of surrounding hotspots, and the current location information, an initial location type is selected from a preset location type library;

[0100] In response to a correction command triggered by the user based on the mobile device, the initial location type is corrected according to the correction command to obtain the current location type.

[0101] In this embodiment, filtering can be performed in the following order: number of surrounding hotspots, signal characteristics, time, and current location information. This order is based on the fact that the number of hotspots is the primary standard for environmental signal density, followed by signal frequency band distribution (a high proportion of 5GHz usually corresponds to complex scenarios), then considering the load differences during different time periods, and finally verifying the regional attributes through current location information, ensuring that the filtering dimensions progress from core to auxiliary. The preset location type library includes two main categories: complex locations and simple locations. For simple locations, the feature thresholds can be: number of surrounding hotspots less than 10, signal characteristics less than 60%, time thresholds of 10-17 am during off-peak hours and 22-6 am during off-peak hours, and current location information associated with areas such as suburban residences, parks, and rural green spaces. For complex locations, the feature thresholds can be: number of surrounding hotspots greater than or equal to 10, signal characteristics greater than or equal to 60%, time thresholds of 7-9 am during morning peak hours and 18-21 pm during evening peak hours, and current location information associated with areas such as commercial centers, subway stations, and office buildings.

[0102] For example, the filtering is performed according to the above filtering order, gradually matching the type library. First, filtering is done by the number of surrounding hotspots. For example, if there are currently 8 surrounding hotspots, it meets the threshold of "simple points < 10", initially locking in simple point candidates. Second, signal characteristics are verified. Currently, 5GHz accounts for 25%, meeting the threshold of "simple points < 60%", indicating no complex scene signal characteristics, reinforcing the tendency towards simple points. Next, the time dimension is used to supplement the judgment. The current time is 10:30 am on a weekday (off-peak period), which meets the off-peak / non-peak time threshold for simple points, and no peak load characteristics of complex locations are found, maintaining the simple point judgment. Finally, the current location information is used for final verification. The current location information is a suburban residential area, which completely matches the suburban residential areas associated with simple points in the type library, with no contradictions. After the above four layers of filtering, the initial location type is finally obtained as "simple point". The device displays a message on the interface: "Initial location type is simple point, correct?". If the user is actually in a temporary community market in the suburban residential area (where 5 new temporary hotspots have been added but not initially scanned), upon receiving the "correct to complex location" instruction, the device corrects the initial type to complex location, thus determining the current location type. If the user does not respond, the initial location type is used as the current location type.

[0103] This application employs a layer-by-layer screening based on feature priority, combined with type library matching, to provide a clear and more accurate basis for initial location type determination, avoiding misjudgments caused by a single feature. Furthermore, the addition of user correction commands can compensate for type deviations caused by device scanning omissions (such as temporary hotspots). Ultimately, this ensures that the location type aligns with the actual scenario, providing an accurate basis for subsequent channel selection and effectively guaranteeing network stability and user experience.

[0104] In some optional implementations of this embodiment, the scene data includes the current location information of the mobile terminal, and the steps for obtaining the scene data of the mobile terminal include:

[0105] Detect whether the mobile device has a Global Positioning System (GPS) signal;

[0106] If the Global Positioning System (GPS) signal is present, the latitude and longitude coordinates of the mobile terminal are obtained based on the GPS signal.

[0107] The latitude and longitude coordinates are encoded to obtain the current location information of the mobile terminal;

[0108] If the GPS signal is not available, a base station signal or a wireless hotspot is obtained, and the current location information of the mobile device is determined based on the base station signal or the wireless hotspot.

[0109] In this embodiment, the presence of a GPS signal on the mobile device is detected. If the device is in an open outdoor environment (such as a city park lawn) without obstructions such as tall buildings or trees, and the GPS module can stably receive signals from multiple satellites (e.g., receiving signals from 6 GPS satellites with a signal strength ≥ -130dBm), a GPS signal is determined to be present. If the device is in an enclosed indoor environment (such as the second basement level of an underground shopping mall) or a densely obstructed area (such as a narrow alleyway with tall buildings), and the GPS module can only receive 1-2 satellite signals or has no signal at all (signal strength < -150dBm), a GPS signal is determined to be absent. When a GPS signal is determined to be present, the device obtains the precise latitude and longitude coordinates of its current location through the GPS module, calls a map application programming interface (API) (such as a geocoding interface) to parse the latitude and longitude, converts it into specific geographic area information, and finally obtains the current location information.

[0110] For example, when a GPS signal is determined to be absent, the device automatically switches to assisted positioning mode. The current location information can be determined based on base station signals. The device scans surrounding mobile communication base stations (such as 4G / 5G base stations) through its cellular network module, obtaining key information from three or more base stations, including base station ID and signal strength. The base station ID and signal strength data are then uploaded to the positioning service platform, which estimates the device's location using triangulation and outputs the current location information. Alternatively, the current location information can be determined based on wireless hotspots. The device scans for detectable wireless hotspots in the current environment (such as merchant Wi-Fi in a shopping mall or public Wi-Fi) through its Wi-Fi module, obtaining the Media Access Control (MAC) addresses and signal strengths of five or more hotspots. The positioning service platform matches the registered location corresponding to that MAC address in its database (such as the food court on the second basement floor of the Guomao Shopping Mall), and combines this with a weighted calculation based on the hotspot signal strength to finally determine the current location information.

[0111] This application prioritizes the use of GPS to obtain precise latitude and longitude positioning, ensuring accurate positioning in open outdoor scenarios. When there is no GPS signal, it automatically switches to base station or wireless hotspot positioning to solve positioning problems in indoor and densely obscured areas, avoiding positioning interruptions. The two methods complement each other, ensuring the reliability of the current location information and providing accurate location data for subsequent determination of location type and selection of appropriate channels, ensuring the stability of the overall channel switching process.

[0112] In some optional implementations of this embodiment, the step of switching the current channel of the mobile terminal to the adapted channel includes:

[0113] Obtain the preset authentication key for the adapted channel;

[0114] The mobile device is verified based on the authentication key;

[0115] If the mobile device passes the verification, the current channel of the mobile device will be switched to the adapted channel.

[0116] In this embodiment, the preset authentication key is pre-generated by the wireless access point corresponding to the current channel and stored in the local security module, and subsequently cached in the adapted channel. Specifically, when the mobile device has a stable connection on the current channel, it pre-authenticates with the wireless access point of the adapted channel, and caches the mobile device's authentication information as the authentication key in the wireless access point corresponding to the adapted channel. When the channel switching process is triggered, the preset authentication key is automatically obtained through the pre-communication link with the wireless access point of the adapted channel, without requiring manual input from the user. Then, based on the permission identifier in the key, the system verifies whether the mobile device is a legitimate device authorized to access this network, and verifies the integrity and timeliness of the key to ensure that the key has not been tampered with or expired. If the verification is successful, the wireless access point of the adapted channel will synchronize the mobile device's network configuration information (such as IP address and encryption context) in advance, and the mobile device will then terminate the connection with the current channel and quickly switch to the adapted channel.

[0117] This application utilizes pre-authenticated cached keys, eliminating the need for manual input by users and improving convenience. Furthermore, it ensures that only legitimate devices can access the network through key integrity, timeliness verification, and authorization checks, thus enhancing security. Simultaneously, pre-synchronized configuration information enables rapid switching, reducing connection interruptions and guaranteeing a seamless network experience, balancing security and efficiency.

[0118] In some optional implementations of this embodiment, after the step of switching the current channel of the mobile terminal to the adapted channel, the method further includes:

[0119] The network performance data, signal strength data, and current location type are converted into visual charts.

[0120] The visualization charts and channel switching information are pushed to the user's device.

[0121] In this embodiment, the visualization charts can include a line graph of latency, a two-dimensional bar chart of signal strength and bandwidth, and a scene dashboard. The latency line graph uses the X-axis to represent the time from 1 minute before the switch to 2 minutes after the switch, and the Y-axis to represent latency (ms). A blue line shows the change in latency, intuitively demonstrating the optimization effect of the switch. The two-dimensional bar chart of signal strength and bandwidth uses the X-axis to represent the points before and after the switch, and the Y-axis to represent the left and right sides (signal strength (dBm) on the left and bandwidth (Mbps) on the right). Green bars compare signal strength, and orange bars compare bandwidth. The scene dashboard is based on a ring chart, with a 100% red sector. The center is labeled with a location type such as "Complex Location (XX Shopping Mall)," and the outer ring includes text such as "Adapted Channel: 5GHz-149," quickly conveying environmental and channel information. Simultaneously, channel switching information, such as "[Network Channel Optimization Complete] You have switched to the adapted 5GHz-149 channel, adapting to the current complex location (XX Shopping Mall) scenario," is pushed to the user's device.

[0122] This application transforms network performance, signal strength, and location type into intuitive charts, allowing users to clearly see the optimization effects of reduced latency and improved signal and bandwidth after switching. Coupled with prompts explaining the suitable scenarios and target channels, it eliminates information gaps. This not only allows users to intuitively perceive the value of switching but also improves process transparency, enhances trust in automatic channel switching, and optimizes the wireless user experience.

[0123] In some optional implementations of this embodiment, after the step of switching the current channel of the mobile terminal to the adapted channel, the method further includes:

[0124] Obtain the user identity information from the mobile device, and anonymize the user identity information and the scene data to obtain an anonymous dataset;

[0125] The anonymous dataset is then classified to obtain the classified anonymous dataset;

[0126] The classified anonymous dataset and the channel switching records of the mobile device are encrypted to obtain an encrypted dataset, which is then uploaded to the cloud.

[0127] In this embodiment, after the mobile device completes a channel switch, the system automatically obtains user identity information (such as a unique device identifier and associated account identifier) ​​and scene data (including current location information, signal characteristics, time, number of nearby hotspots, and other multi-dimensional information). The original user identifier is replaced with a randomly generated anonymous ID, and sensitive data such as precise location is obfuscated to remove information directly associated with the individual, forming an anonymous dataset. Subsequently, the anonymous dataset is categorized and organized according to preset classification rules (such as categorizing locations into complex locations and simple points, or categorizing switching trigger reasons into weak signal, high interference, and insufficient performance), resulting in a clearly structured categorized anonymous dataset. Finally, this categorized dataset is integrated with the mobile device's channel switching records (such as channels before and after the switch, switching time, and switching success rate), and encrypted using the Advanced Encryption Standard (AES) algorithm to generate an encrypted dataset. Then, using a secure channel established by a transport layer security protocol, the encrypted dataset is uploaded to a cloud-based big data platform, ensuring data privacy and providing compliant data support for subsequent scenario-based channel optimization.

[0128] This application mitigates the risk of personal information leakage through anonymization, categorizes the data for a clearer structure, and employs encrypted transmission and storage to further strengthen security. The compliant dataset uploaded to the cloud not only protects user privacy but also provides reliable data support for subsequent scenario-based channel optimization and switching strategy iterations.

[0129] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0130] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0131] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0132] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0133] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a wireless channel switching device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0134] like Figure 3 As shown, the wireless channel switching device 300 described in this embodiment includes: a first acquisition module 301, a second acquisition module 302, a determination module 303, and a switching module 304. Wherein:

[0135] The first acquisition module is used to acquire network performance data of the mobile device;

[0136] The second acquisition module is used to acquire the scene data of the mobile terminal if the network performance data is within a preset range.

[0137] The determination module is used to determine the current location type of the mobile terminal based on the scene data, wherein the current location type is a complex location or a simple point;

[0138] The switching module is used to select the appropriate channel corresponding to the current location type and switch the current channel of the mobile device to the appropriate channel.

[0139] The wireless channel switching device provided in this application can obtain network performance data to perceive the current network quality status in real time and accurately, providing an objective and reliable trigger basis for subsequent judgment on whether channel switching is necessary. When the network performance data is within a preset range, it obtains scene data from the mobile terminal, which can accurately obtain environmental correlation data when needed, reduce unnecessary location resource consumption (such as device power and network traffic), and improve device operating efficiency. By determining the current location type based on environmental data, it can deeply associate channel selection with specific environmental scenarios, avoiding the problem that traditional channel selection based solely on single network data cannot adapt to the differences in signal characteristics of different locations, making channel selection more scenario-specific. By selecting and switching the corresponding suitable channel based on the current location type, it can ensure that the switched channel is highly matched with the network environment of the current location, effectively improving the stability and transmission performance of the network after switching, reducing problems such as disconnection and latency caused by poor channel compatibility, and ultimately optimizing the user's wireless experience.

[0140] In some optional implementations of this embodiment, the switching module 304 is further configured to:

[0141] If the current location type is the complex location, then the scene data and the network performance data are input into the preset channel prediction model, and the first candidate channel is output through the channel prediction model;

[0142] Verify the first candidate channel;

[0143] If the first candidate channel passes the verification, then the first candidate channel will be used as the adapted channel.

[0144] The wireless channel switching device provided in this application outputs the optimal candidate channel through a channel prediction model that integrates historical scenarios and current data. The model is verified in real time to ensure stable signal, low interference, and reasonable load. This not only improves the accuracy of channel selection in complex locations but also avoids prediction bias, effectively ensuring the reliability of network connections and user experience in complex environments.

[0145] In some optional implementations of this embodiment, the switching module 304 is further configured to:

[0146] By using the channel prediction model, feature extraction is performed on the scene data and the network performance data to obtain comprehensive feature data;

[0147] Based on the comprehensive feature data, a channel prediction set is determined;

[0148] The first candidate channel is obtained by filtering the predicted channel set.

[0149] The wireless channel switching device provided in this application, by clearly defining the mapping relationship between data and channels, and combining multi-dimensional features to generate a channel prediction set and accurately filter channels, makes the selection of the first candidate channel more targeted and scientific. It not only fully explores the correlation between scene, network status and channel performance, but also ensures the optimal result through set filtering, further improving the accuracy of channel selection and network stability in complex environments.

[0150] In some optional implementations of this embodiment, the switching module 304 is further configured to:

[0151] If the current location type is the simple point, then obtain multiple wireless channels;

[0152] Detect real-time interference and channel usage frequencies of multiple wireless channels;

[0153] Based on the real-time interference situation and the channel usage frequency, a second candidate channel is determined;

[0154] Verify the second candidate channel;

[0155] If the second candidate channel passes the verification, then the second candidate channel will be used as the adapted channel.

[0156] The wireless channel switching device provided in this application directly acquires wireless channels and detects real-time interference and usage frequencies. Combined with verification and screening of suitable channels, it not only simplifies the process by conforming to the characteristics of simple point environments, but also quickly locks low-interference, low-load channels, ensuring network stability and improving connection efficiency and user experience.

[0157] In some optional implementations of this embodiment, the determining module 303 is further configured to:

[0158] Based on the signal characteristics, the time, the number of surrounding hotspots, and the current location information, an initial location type is selected from a preset location type library;

[0159] In response to a correction command triggered by the user based on the mobile device, the initial location type is corrected according to the correction command to obtain the current location type.

[0160] The wireless channel switching device provided in this application, by filtering layer by layer according to feature priority and combining it with type library matching, provides a clear basis for the initial location type judgment and is more accurate, avoiding misjudgments caused by a single feature; by superimposing user correction commands, it can compensate for type deviations caused by device scanning omissions (such as temporary hotspots). Ultimately, it makes the location type fit the actual scenario, providing an accurate basis for subsequent channel selection and effectively ensuring network stability and user experience.

[0161] In some optional implementations of this embodiment, the second acquisition module 302 is further configured to:

[0162] Detect whether the mobile device has a Global Positioning System (GPS) signal;

[0163] If the Global Positioning System (GPS) signal is present, the latitude and longitude coordinates of the mobile terminal are obtained based on the GPS signal.

[0164] The latitude and longitude coordinates are encoded to obtain the current location information of the mobile terminal;

[0165] If the GPS signal is not available, a base station signal or a wireless hotspot is obtained, and the current location information of the mobile device is determined based on the base station signal or the wireless hotspot.

[0166] The wireless channel switching device provided in this application prioritizes GPS for precise latitude and longitude positioning, ensuring accurate positioning in open outdoor environments. When no GPS signal is available, it automatically switches to base station or wireless hotspot positioning, solving positioning problems in indoor and densely obstructed areas and preventing positioning interruptions. These two methods complement each other, ensuring reliable current location information and providing accurate location data for subsequent determination of location type and selection of suitable channels, thus ensuring the stability of the overall channel switching process.

[0167] In some optional implementations of this embodiment, the switching module 304 is further configured to:

[0168] Obtain the preset authentication key for the adapted channel;

[0169] The mobile device is verified based on the authentication key;

[0170] If the mobile device passes the verification, the current channel of the mobile device will be switched to the adapted channel.

[0171] The wireless channel switching device provided in this application, through pre-authenticated cached keys, not only eliminates the need for manual input by users, improving convenience, but also ensures that only legitimate devices can access the network through key integrity, timeliness verification, and authorization checks, enhancing security. Simultaneously, pre-synchronized configuration information enables rapid switching, reducing connection interruptions and guaranteeing a seamless network experience, balancing security and efficiency.

[0172] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0173] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41, 42, and 43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0174] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0175] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for wireless channel switching methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0176] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, such as executing computer-readable instructions for the wireless channel switching method.

[0177] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0178] The computer device provided in this application can acquire network performance data to perceive the current network quality status in real time and accurately, providing an objective and reliable trigger basis for subsequent judgment on whether channel switching is necessary. When the network performance data is within a preset range, it acquires scene data from the mobile terminal, which can accurately acquire environmental correlation data when needed, reduce unnecessary location resource consumption (such as device power and network traffic), and improve device operating efficiency. By determining the current location type based on environmental data, it can deeply associate channel selection with specific environmental scenarios, avoiding the problem that traditional channel selection based solely on single network data cannot adapt to the differences in signal characteristics of different locations, making channel selection more scenario-specific. By selecting and switching the corresponding suitable channel based on the current location type, it can ensure that the switched channel is highly matched with the network environment of the current location, effectively improving the stability and transmission performance of the network after switching, reducing problems such as disconnection and latency caused by poor channel compatibility, and ultimately optimizing the user's wireless experience.

[0179] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the wireless channel switching method described above.

[0180] The computer-readable storage medium provided in this application can acquire network performance data and signal strength data to perceive the current network quality status in real time and accurately, providing an objective and reliable trigger basis for subsequent judgment on whether channel switching is necessary. Instead of continuous positioning, it acquires current location information when network performance data exceeds a first preset threshold and / or signal strength data exceeds a second preset threshold. This allows for accurate acquisition of environmental data when needed, while reducing unnecessary positioning resource consumption (such as device power and network traffic), thus improving device operating efficiency. Determining the current location type based on the current location information enables deep association between channel selection and specific environmental scenarios, avoiding the problem of traditional channel selection relying solely on single network data failing to adapt to different location signal characteristics, making channel selection more scenario-specific. Selecting and switching to the corresponding suitable channel based on the current location type ensures a high degree of matching between the switched channel and the current location's network environment, effectively improving network stability and transmission performance after switching, reducing problems such as disconnection and latency caused by poor channel compatibility, and ultimately optimizing the user's wireless experience.

[0181] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0182] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A wireless channel switching method, characterized in that, Includes the following steps: Obtain network performance data from the mobile device, including latency, throughput, jitter, and signal strength; If the network performance data is within a preset range, then the scene data of the mobile terminal is obtained, including current location information, signal characteristics, time, and number of surrounding hotspots; Based on the scene data, the current location type of the mobile device is determined, which is either a complex location or a simple point. Select the appropriate channel corresponding to the current location type, and switch the current channel of the mobile device to the appropriate channel; The step of selecting the appropriate channel corresponding to the current location type includes: If the current location type is the complex location, then the scene data and the network performance data are input into the preset channel prediction model, and the first candidate channel is output through the channel prediction model; Verify the first candidate channel; If the first candidate channel passes the verification, then the first candidate channel will be used as the adapted channel. The step of selecting the appropriate channel corresponding to the current location type includes: If the current location type is the simple point, then obtain multiple wireless channels; Detect real-time interference and channel usage frequencies of multiple wireless channels; Based on the real-time interference situation and the channel usage frequency, a second candidate channel is determined; Verify the second candidate channel; If the second candidate channel passes the verification, then the second candidate channel will be used as the adapted channel. The step of verifying the second candidate channel includes: Perform a brief access verification on the second candidate channel to verify its compatibility.

2. The wireless channel switching method according to claim 1, characterized in that, The step of inputting the scene data and the network performance data into a preset channel prediction model, and outputting a first candidate channel through the channel prediction model, includes: By using the channel prediction model, feature extraction is performed on the scene data and the network performance data to obtain comprehensive feature data; Based on the comprehensive feature data, a channel prediction set is determined; The first candidate channel is obtained by filtering the predicted channel set.

3. The wireless channel switching method according to claim 1, characterized in that, The scene data includes signal characteristics, time, number of nearby hotspots, and current location information. Determining the current location type of the mobile device based on the scene data includes: Based on the signal characteristics, the time, the number of surrounding hotspots, and the current location information, an initial location type is selected from a preset location type library; In response to a correction command triggered by the user based on the mobile device, the initial location type is corrected according to the correction command to obtain the current location type.

4. The wireless channel switching method according to claim 1, characterized in that, The scene data includes the current location information of the mobile device, and obtaining the scene data of the mobile device includes: Detect whether the mobile device has a Global Positioning System (GPS) signal; If the Global Positioning System (GPS) signal is present, the latitude and longitude coordinates of the mobile terminal are obtained based on the GPS signal. The latitude and longitude coordinates are encoded to obtain the current location information of the mobile terminal; If the GPS signal is not available, a base station signal or a wireless hotspot is obtained, and the current location information of the mobile device is determined based on the base station signal or the wireless hotspot.

5. The wireless channel switching method according to any one of claims 1 to 4, characterized in that, The step of switching the current channel of the mobile device to the adapted channel includes: Obtain the preset authentication key for the adapted channel; The mobile device is verified based on the authentication key; If the mobile device passes the verification, the current channel of the mobile device will be switched to the adapted channel.

6. A wireless channel switching device, characterized in that, include: The first acquisition module is used to acquire network performance data of the mobile device, including latency, throughput, jitter, and signal strength. The second acquisition module is used to acquire scene data of the mobile terminal if the network performance data is within a preset range. The scene data includes current location information, signal characteristics, time, and number of surrounding hotspots. The determination module is used to determine the current location type of the mobile terminal based on the scene data, wherein the current location type is a complex location or a simple point; The switching module is used to select the appropriate channel corresponding to the current location type and switch the current channel of the mobile terminal to the appropriate channel; The step of selecting the appropriate channel corresponding to the current location type includes: If the current location type is the complex location, then the scene data and the network performance data are input into the preset channel prediction model, and the first candidate channel is output through the channel prediction model; Verify the first candidate channel; If the first candidate channel passes the verification, then the first candidate channel will be used as the adapted channel. The step of selecting the appropriate channel corresponding to the current location type includes: If the current location type is the simple point, then obtain multiple wireless channels; Detect real-time interference and channel usage frequencies of multiple wireless channels; Based on the real-time interference situation and the channel usage frequency, a second candidate channel is determined; Verify the second candidate channel; If the second candidate channel passes the verification, then the second candidate channel will be used as the adapted channel. The step of verifying the second candidate channel includes: Perform a brief access verification on the second candidate channel to verify its compatibility.

7. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the wireless channel switching method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the wireless channel switching method as described in any one of claims 1 to 5.

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